Bioenergy

Plant-pathogen interactions are complex and dynamic phenomena, relevant to fundamental, environmental and bioenergy biology. Imaging plays important roles in understanding the interactions at the atomic, molecular, subcellular, cellular, to tissue and whole plant scales. Each bioimaging method has its intrinsic limitations in spatial and/or temporal resolution, field of view and depth, and sensitivity. Fluorescence-based optical microscopes have huge advantages in monitoring dynamics of cellular and subcellular events using wide spectral ranges with super-resolution, light-sheet 3D, and wide-field imaging, but cannot go beyond 10 or 20 nanometers spatial resolution. X-ray imaging and tomography can penetrate deeper than light, and with high brilliance synchrotron sources one can reach 10 nm spatial resolution, but with a high risk of sample damage from radiation dose. Cryogenic electron tomography (cryo-ET) can offer tremendous insight into the subcellular organization of organelles and macromolecules down to several nm resolution, but information gained is largely static. Large efforts have gone into correlative imaging but, to date, there is no easy way to correlate more than two modalities directly. Correlating these different modalities in an unequivocal manner will substantially advance understanding of multiscale complex biological phenomena.

This talk presents updates on the development of the next generation correlative X-ray, light, and electron tomography systems (see figure). In particular, the team has modularized electro-optic fluorescence lifetime imaging microscopy (EO-FLIM) (Bowman et al. 2019; Bowman and Kasevich 2021) for use in correlative microscopies, in the deep ultraviolet, or at synchrotron beamlines, preparing for a first implementation alongside transmission X-ray microscopy (TXM), beamline 6-2c, with correlative X-ray/vis optics at SLAC and Stanford Synchrotron Radiation Lightsource. The team will utilize both the micro- and nano-computed tomography (CT) imaging capability of beamline 6-2 to gain hierarchical insights into the dynamics of fungal-pathogen interactions. Towards this end, the team has successfully collected preliminary tomography data using a laboratory-based micro-CT X-ray source on plant root and leaf samples. The team has also established a standing EO-FLIM microscope at the synchrotron for further uncorrelated data collection and to explore extremes of resonant drive frequencies.

Finally, the team presents advances towards fluorescent protein- and nanocrystal-containing cages as molecular tracers for X-ray imaging/microscopy and EO-FLIM and fiducial markers for cryo-ET, including proof-of-concept synchrotron TXM images of leaf samples bombarded with 400 nm nanogold particles. The presentation discusses further plans for the initial application examining fungal-plant pathogen interactions via chitin binding domains at the plant cell surface and subsequent applications in an ongoing study of extracellular vesicles.

The DOE Biology Knowledgebase (KBase) is a knowledge creation and discovery environment designed for both biologists and bioinformaticians. KBase integrates a large variety of data and analysis tools, from DOE and other public services, into an easy-to-use platform that leverages scalable computing infrastructure to perform sophisticated systems biology analyses. KBase is a publicly available and developer extensible platform that enables scientists to analyze their own data within the context of public data and share their findings across the system.

Many of the concerns researchers have around sharing data include knowledge barriers, reuse concerns, and disincentives (Gomes et al. 2022). KBase already addresses components of knowledge barriers through its outreach strategy (e.g., in-person and virtual training sessions, documentation, and robust hands-on activities) and reuse concerns through the basic functionality of the platform (e.g., provenance, interoperability, and reproducibility) (Arkin et al. 2018). KBase is now working to address disincentives: concerns around getting scooped, the time it takes to curate and share data, and lack of clarity around the rewards that come from embracing open science and good data management.

Behind much of this is the fundamental tenet that neither the current culture of science nor the publishing infrastructure value data outside of a publication. In partnership with several efforts across BER and the publishing world, KBase is establishing its platform as a change agent focused on “getting and giving credit to data” (Wood-Charlson et al. 2022). Leveraging Persistent Identifiers (PIDs), group members are developing linkages to/from and within KBase that support data management best practices and ensure credit is retained. An example user (PID: ORCID) research workflow:

• Collect samples and assign them International General Sample Numbers (PID: IGSN),

• Submit sample metadata (Standard: MIxS) to the National Microbiome Data Collaborative (NMDC) Sample Submission Portal,

• Send sample material to the DOE Joint Genome Institute ( JGI) for sequencing,

• Send sample material to the Environmental Molecular Sciences Laboratory (EMSL) for Fourier-transform ion cyclotron resonance (FTICR) mass spectrometry analysis, and

• Submit geochemistry measurements on those samples to Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE).

Leveraging the (in development) Data Transfer Service (DTS), the user could request sample metadata from NMDC, sequence data from JGI, and FTICR data from EMSL to do a global community analysis and build community models. When the user is ready to publish their data and reproducible analyses, the KBase “Credit Engine” provides the user’s workflow (“Narrative”) with a dataset DOI that captures (Ireland and Wood-Charlson 2023):

1. Important credit metadata [e.g., authors/contributors (PID: ORCID), funder information (PID: Research Organization Registry, ROR)]

2. Citations for the funded proposal/data management plan (PID: DOI), samples (PID: IGSN), public data (PID: DOI, when available), and tools (DOI, typically a publication) that contributed to the analysis.

The KBase DOI is registered by Office of Scientific and Technical Information and submitted to DataCite, which links the now FAIR (findable, accessible, interoperable, and reusable) Narrative to the broader publishing infrastructure, and shared back to the ESS-DIVE project. These connections enable KBase to start tracking and reporting the reuse of shared data.

Ongoing efforts among DOE’s user facilities offer their unique and large-scale capabilities collaboratively and interactively to users to significantly advance their science programs.

The goal of the Biological and Environmental Research program is to achieve a predictive understanding of complex biological, Earth, and environmental systems with the aim of advancing the nation’s energy and infrastructure security. To pursue this goal, team science—collaborations among experts in diverse research areas that lead to multidisciplinary projects— is indispensable. The roles of DOE user facilities, which offer unique and powerful resources for such research projects, are evolving, and expectations for the facilities are increasing. To respond to users’ needs, the DOE Joint Genome Institute (JGI) and Environmental Molecular Sciences Laboratory (EMSL) initiated the Facilities Integrating Collaborations for User Science (FICUS) program in 2014. This collaboration has grown into a successful program, advancing more than 100 multidisciplinary projects to date. Similarly, new interfacility collaborations are becoming essential for cutting-edge transdisciplinary science. These collaborations include not only JGI and EMSL but also user resources for (1) BER structural biology and imaging at synchrotron and neutron facilities supported by DOE’s Basic Energy Sciences program and for (2) computing capabilities at the Advanced Scientific Computing Research program’s high-performance computing facilities.

To explore the need for the BER research community to combine genomic, functional, systems, biosystems design, structural, and computation approaches to advance their research, researchers performed various pilot programs to test the feasibility of offering the integrative capabilities to users and hosted workshops to better understand users’ needs. This report describes ongoing efforts among DOE user facilities to enable user research.

Soils are highly heterogeneous and opaque and a challenging medium for spatial and in situ characterization. Yet spatial characterization of soils is essential to comprehend the pore scale biogeochemistry that contributes to ecosystem-scale events. Reduced complexity platforms, such as soil micromodels that emulate soil properties, have demonstrated the potential for in situ visualization of microbial and plant growth dynamics. Inspired by previous studies, this project developed “TerraForms”, a platform that replicates specific physical and chemical properties of soils, reducing the complexity of analysis for soil-driven processes. Terra- Forms support plant and microbial growth yet allow the combined use of mass spectrometry and spectroscopic imaging methods to characterize processes in a realistic soil-like environment.

TerraForms are manufactured from an ultraviolet curable resin with a conductive indium tin oxide–coated glass backing using a combination of Bosch etching and soft lithography techniques. The soil habitats can be custom built to reproduce the heterogeneous pore size distribution of soil properties, including soil pores and soil microaggregates. Moreover, research can amend the soil habitats with soil minerals such as potassium (K) feldspar, hematite, and kaolinite to study effects of mineralogy on microbial and plant growth. The study has demonstrated that these polymer glass-based platforms are compatible with mass spectrometry imaging techniques, X-ray photoelectron spectroscopy, scanning electron microscopy/energy dispersive X-ray analysis, and synchrotron-based techniques such as X-ray fluorescence (XRF) and X-ray absorption near edge structure spectroscopy (XANES). This presentation will highlight results from collaborations built over the past years that helped create the TerraForms capability and user program.

These collaborations include early adopters and advisors who contributed to the creation of several of the TerraForms platforms and helped design rigorous tests that ensured workflow development and deployment to the user community, including Pacific Northwest National Laboratory’s (PNNL) Soil Microbiome Science Focus Area (SFA) and the Trail Ecosystems for the Advancement of Microbiome Science (TEAMS) group. Deployment to the user community has included several outreach activities, such as Environmental Molecular Sciences Laboratory webinars, workshops, and TerraForms publications, to train users how to use these platforms and downstream sample preparation for multimodal analysis. This talk will demonstrate specific examples of user science that used TerraForms for their work as well as collaborations with Stanford Synchrotron Radiation Lightsource, Lawrence Livermore National Laboratory’s carbon negative Earthshot (TerraForming Soil Energy Earthshot Research Center), and PNNL’s Soil Microbiome SFA and TEAMS.

Specifically, this talk will demonstrate the use of mineral micromodels for visualizing mineral-derived K uptake and transformation by fungi under drought. This presentation will also discuss using mineral RhizoChips to study mineral transformations and inorganic nutrient uptake by arbuscular mycorrhizal fungi. Finally, some of the newer developments within the TerraForms capability will be discussed, such as creating workflows for developing TerraForms representative of soil cores collected from field sites and upscaling pore scale biogeochemistry.

Utilizing DOE user facilities, this team investigated plant-soil-microbe interactions that drive rhizosphere processes contributing to metabolite turnover and nutrient cycling. As climate warming increases water scarcity frequency and severity, understanding how plant-mediated processes like root exudation influence rhizosphere soil organic matter turnover is critical. Using 16S rRNA gene amplicon sequencing, rhizosphere metabolomics, and position-specific carbon-13 pyruvate labeling, researchers examined three tropical plant species (Piper auritum, Hibiscus rosa sinensis, and Clitoria fairchildiana) and their associated microbial communities’ effects on rhizosphere soil organic carbon turnover.

The findings indicate the rhizosphere metabolome is primarily shaped by the roots’ drought response rather than direct rhizosphere bacterial community composition shifts. Specifically, reduced root exudation notably affected the P. auritum rhizosphere metabolome, with less reliance on neighboring microbes. Contrary to P. auritum, H. rosa sinensis and C. fairchildiana experienced exudate composition changes during drought, altering bacterial communities and collectively impacting the rhizosphere metabolome. Furthermore, excluding phylogenetically distant microbes shifted the rhizosphere metabolome. Under drought, C. fairchildiana associated with only a subset of symbiotic bacteria.

These results indicate plant species-specific microbial interactions systematically change with the root metabolome. As roots respond to drought, associated microbial communities adapt, potentially reinforcing plant roots’ drought tolerance strategies. These findings have significant implications for maintaining plant health and performance during drought stress and improving plant performance under climate change.

Interactions among terrestrial free-living microorganisms, root symbiotic microbes, and plants play a crucial role in modulating biogeochemical cycling within ecosystems. However, the dynamic mechanisms of these interactions and their responses to changing environmental conditions, such as soil nutrient status and stresses are poorly understood. Consequently, terrestrial nutrient fluxes in ecosystem models often overlook plant–symbiont–microbe interactions and cannot accurately reflect the impacts of environmental changes on their functions. Using pine trees as a model, researchers employ advanced metaomic, NanoSIMS and X-ray fluorescence (XRF) tools to uncover the mechanisms by which plants, ectomycorrhizal fungi, and free-living soil microorganisms interact to shape nutrient exchange and decomposition processes.

This experiment was established in a phytotron-based synthetic ecosystem for pine and its soil microbiomes. Treatments include the combination of soil sources harboring diverse soil biomes, elevated carbon dioxide (CO₂) levels, soil organic matter (SOM), inorganic nitrogen (N), iron (Fe), and pine-associated ectomycorrhizal fungi (EMF).

Findings from the subset of these treatment combinations indicated that an enriched abundance of EMF facilitated soil molecular and extracellular enzyme activities, involved in soil carbon (C), N, and Fe processes. NanoSIMS images revealed the ability of EMF-extended hyphae to acquire ¹⁵N from ¹⁵N- labeled SOM. Metaomic studies demonstrated that elevated CO₂ levels enhanced EMF pathways involved in symbiosis- mediated SOM decomposition and N transfer from the soil to the host plants.

However, the treatments with inorganic N or Fe compromised symbiotic C/N processes; such negative impacts can be mitigated by enhancing the diversity of the EMF community. The ongoing study integrating data from metaomic analyses and XRF will discuss the interactive effects of SOM, N, and Fe on free-living microorganisms and EMF-mediated nutrient fluxes.

The team’s next goal is to integrate these data into an ecosystem model to represent plant–microbe interactions under global change scenarios and their feedback to ecosystem-level nutrient fluxes. The team anticipates that this refined model will better capture the impacts of plant–microbe interactions on biogeochemical cycling in changing environments and inform forest management practices.

The syntrophic anaerobic oxidation of methane (AOM) between methanotrophic archaea and sulfate-reducing bacteria is a critical microbially controlled process in the global methane cycle. These metabolic partnerships are diverse, representing several genera and species-level clades that co-occur in the same environment, segregated into individual two-member consortia believed to consist of a single archaeal and bacterial lineage. The high strain level diversity in sediments harboring AOM consortia frequently complicates the bioinformatic assembly of high-quality genomes from metagenomic datasets and the field currently lacks pure culture representatives for detailed physiological study.

Working with the Joint Genome Institute (JGI), Environmental Molecular Sciences Laboratory (EMSL), and the Stanford Synchrotron Radiation Lightsource (SSRL), this research team has been developing culture-independent strategies to investigate AOM consortia. Through support from the Facilities Integrating Collaboration for User Science program with JGI, the team developed flow cytometry sorting and sequencing protocols for individual AOM consortia from sediment in combination with click-chemistry enabled fluorescent tagging of translationally active microbes using a technique called BONCAT (BioOrthogonal Noncanonical Amino Acid Tagging). Application of the BONCAT-FACS method to these environmental methane-fueled microbial communities has enabled novel genomic insights into specific syntrophic partner pairings and hidden variation in metabolic potential (e.g., nitrogen fixation) among co-existing strains of methanotrophic archaea.

To complement these genome-guided analyses, researchers are working with SSRL and collaborators at National Center for Microscopy and Imaging Research to develop multimodal imaging for environmental AOM consortia that enables various combinations of fluorescence, electron, X-ray (µ-XRF, XANES), and secondary ion (NanoSIMS) imaging. As part of this effort, researchers have been testing SSRL’s upgraded 14-3 beamline for high resolution analysis of sulfur distribution and speciation in methane and sulfate-respiring consortia, enabling testing of hypotheses regarding the underlying syntrophic mechanism(s) of AOM. Collectively, these molecular, microscopy, and chemical/ isotopic imaging approaches are providing key insights into the physiology, ecology, and evolution of this globally important syntrophic partnership and importantly, offer a methodological roadmap that can be used with diverse microbial ecosystems.

The organization and function of microbial communities in the plant rhizosphere are influenced by interactions among organisms within a complex and dynamic physical and chemical environment. This environment, which includes growing plants, microbial communities, complex metabolites, and nutrient gradients, cannot be characterized in a noninvasive and nondestructive manner using current bioimaging technologies. Imaging with quantum light offers the opportunity to realize long-term nondestructive imaging investigations of new species in the plant–microbial system.

In this talk, the reports of experiments with quantum light based on entangled two-photon absorption for specific probes will be discussed. In addition, group members describe new work concerning theoretical predictions of important cross-sections for the probes as well as the construction of a pump pulse- shaping setup for entangled photon generation and dispersion compensation.

Root–soil interactions play pivotal roles in biogeochemical processes from local scales to the global scale. The movement and transformation of nutrients, water, and soil organic matter in the root-soil interface or the rhizosphere are commonly referred to as rhizosphere processes, which include nutrient cycling, water fluxes, and carbon sequestration. Because of these crucial roles of the rhizosphere, better understanding of rhizosphere dynamics is highly needed.

To augment scientists’ capabilities to study the rhizosphere, this research group is exploring the potential of utilizing positron emission tomography (PET) and computed tomography (CT) for dynamic 3D imaging of intact rhizospheres. The CT system can provide high-resolution structural information, such as soil aggregates and pore networks, and the PET system can provide functional information at a super high temporal resolution by using a carbon-11 labeled carbon dioxide tracer (¹¹CO₂). The group has designed and developed a ¹¹C labeling and tracing chamber inside the fume hood (130 × 60 × 80 cm³) located at the Cyclotron and Radiochemistry Facility at Stanford University.

In collaboration with Jefferson Laboratory, a PET system consisting of eight PhytoPET modules was built and tested using the ¹¹C labeling and tracing system (see figure, this page). Currently, this research team is performing image normalization and attenuation correction to understand the quantitative accuracy of the PET system. Once the PET system is ready for further experimenting, researchers will demonstrate its capability using a plant-soil system with a different soil matrix to analyze the 3D dynamic coordination between rhizospheric hotspots from PET imaging and the 3D soil matrix from CT imaging. These rhizospheric hotspots often represent a very small fraction of the soil volume but can be responsible for a substantial portion of the overall biogeochemical functions. Current methods do not have the capability to visualize these crucial hotspots inside intact soil matrix.

An amorphous selenium (a-Se) direct conversion detector on a scalable Complementary Metal-Oxide- Semiconductor (CMOS) readout for a large capability is under development at University of California–Santa Cruz for high spatial resolution CT imaging (<20 micrometer resolution). The potential capability of the proposed hybrid detector for imaging very fine soil structures at less than 20 μm resolution has been demonstrated based on a previously developed prototype [1k × 1k Readout Integrated Circuit (ROIC) by KA Imaging]. Successful development of accessible PET/CT systems for rhizosphere imaging and direct observation of belowground ecosystems can reveal the puzzling complexity and crucial Earth system functions of root–soil systems. This new capability also has important implications for other disciplines in Earth system science.

The use of the quantum properties of light offers a new opportunity for imaging, in that the use of quantum correlations of the (two-photon) system allows for image construction with minimal dose requirements. The method primarily consists of generating two photon beams; of these, only one beam interacts with the object being measured. The resulting image and other observable effects are then discerned by examining the spatial correlations between these two beams. This team’s most recent investigations have explored the use of spontaneous parametric down-conversion spontaneous parametric down-conversion of hard X-rays as a source of a biphoton X-ray state strongly correlated in position and energy.

In this work, the group shares the world record rates of generating such correlated X-ray pairs using the high brightness of the National Synchrotron Light Source II and cutting-edge Timepix detector technology. Furthermore, researchers demonstrate a proof-of-concept quantum correlation imaging technique to image several objects, including a biological object—an Elettaria cardamomum seed. This work is a significant milestone in the field of X-ray quantum ghost imaging and provides a pathway to demonstrating the “quantum advantage” in this energy regime.

Nitrogen (N) fertilizer synthesis for agriculture sustains about half of the human population. Recent studies show that N input from N-fertilizer synthesis and river runoff poses a serious and growing problem with intensifying climate change. To address these major societal challenges, improvements to today’s agricultural strategies are necessary; however, the scientific community’s knowledge of plant–soil–microbe interactions in unperturbed soil remains extremely limited because of lacking noninvasive technology to probe metabolism. Here, this team develops new, noninvasive quantum sensing to directly observe metabolic transformation in the rhizosphere to acquire currently inaccessible knowledge.

This group’s approach takes advantage of the quantum- entangled nuclear spin state in hydrogen gas (i.e., parahydrogen), which can readily be enriched to ~99.5% to enhance magnetic resonance imaging (MRI) signals by up to seven orders of magnitude, such that metabolites at low, physiological concentrations become detectable by MRI even at low magnetic fields. The figure (see p. 14; A) shows the target metabolites central to N assimilation and their metabolic pathways; (B) illustrates hyperpolarization chemistry, which transfers the nuclear spin hyperpolarization to the metabolites of interest; (C) shows images of the hyperpolarized metabolite at low concentrations and low field in a test tube; (D) shows an example of noninvasive low-field MRI; and (E) illustrates the goal of obtaining molecular imaging of metabolic transformations in the rhizosphere with portable devices that could be employed directly in the field.

This poster shows significant progress towards several goals:

• The team has significantly expanded the substrate scope of its parahydrogen-based hyperpolarization technique.
• The team has detailed the fundamental quantum chemistry and spin dynamics governing the polarization transfer processes.
• The team has demonstrated MRI of the hyperpolarized molecular sensors at low magnetic fields and low concentrations.
• The team has enabled the detection of long-lived carbon-13 hyperpolarized metabolites on existing low-field hydrogen MRI systems that are already deployed in crop fields.

The team has demonstrated the very first detection of metabolic conversions with this technique using cryogen-free MRI systems. In summary, exciting progress toward noninvasive imaging of nitrogen assimilation using quantum-entangled hyperpolarized spin states will be presented.

The near infrared (NIR) and mid-infrared (MIR) portions of the electromagnetic spectrum are sensitive to absorption features of specific molecular bonds and chemical species in a sample. For example, lignan and proteins in plants have specific absorption signatures in the NIR. However, because detectors are inefficient in the NIR and MIR, infrared spectroscopy requires high light levels to overcome detector limitations. Cameras in particular do not perform well in this spectral range, and microscopy methods such as Fourier transform infrared spectroscopy typically rely on scanning confocal arrangements with single-element detectors to spatially map chemical information.

To overcome these limitations, the team developed and exploited a new quantum ghost imaging microscope for obtaining absorption measurements in the NIR without the need of scanning or high light intensities. The team reports on the use of a novel detector—NCam—in quantum ghost imaging using non-degenerate photon pairs generated by spontaneous parametric down conversion (SPDC). NCam records single- photon arrival events with ~100 picosecond resolution, enhancing the correlation window of SPDC pairs over previous wide-field ghost imaging by 30-fold. This permits ghost imaging of living and intact plant samples at light levels lower than what the plants would experience from starlight. For photosynthesizing organisms, this low-light imaging method enables the study of plants without disturbing or eliciting responses from the plant due to the measurement itself. Following the development of quantum ghost imaging in the near IR to visualize binary test targets, the team has demonstrated imaging of water content in live sorghum plants (see figure).

In the study of biological systems, real-time 3D microscopy is an important tool in understanding how live cells move and interact with other cells, microbes, and other external elements. Although these dynamics can currently be studied with confocal and light sheet microscopy, for example, these approaches require scanning either the beam or the sample, exposing the sample to higher excitation energies and limiting time resolution of the imaging process.

An alternative approach that is ideal for dynamic information is to simultaneously capture the scene from two perspectives. This limits the time resolution only by the acquisition rate of each sensor. However, recreating the 3D scene from two views requires correlating features in both views, posing challenges at higher densities. This study proposes that quantum-entangled light can provide the 3D information using a new detection architecture while keeping peak excitation intensity low to preserve the integrity of the sample and avoiding biases that can be caused by scanning. Quantum-entangled light can be spatially separated while preserving the momenta and temporal relations of entangled photons.

Utilizing quantum-entangled light generated by a beta barium borate crystal and the concepts of quantum ghost imaging, the team presents a microscope that views microscopic specimens in 3D from a single snapshot. This is achieved by utilizing two event-based 2D sensors in which information is relayed, through correlations, to generate two perspectives from a single scene. The quantum-entangled light source allows researchers to correlate signal and idler, both spatially and temporally. Group members characterize the microscope by imaging resolution targets at various depths, using models to guide them in the optimization of the crystal orientation, and attempt to understand the achievable depth of field with the specific light source.

After characterization, researchers image gold nanoclusters with the ghost imaging microscope. Additionally, the team presents its first use of utilizing scattering correlations with the 3DQ microscope for the imaging of nanoparticles. This group foresees a large potential for the 3DQ microscope in various areas, including measuring the dynamics of microbial symbiosis in bioenergy algal ponds and plants. More broadly, the 3DQ concept can be applied to many biological systems and extended to longer wavelength and spectroscopy applications requiring more dimensions of information while retaining high resolution and sensitivity.

This project is developing new hybrid quantum- enabled imaging platforms that combine advances in adaptive optics, quantum entanglement, coincidence detection, ghost imaging, quantum phase-contrast microscopy, and multidimensional nonlinear coherent spectromicroscopy. The approach has three parallel aims. The first two aims focus on developing new quantum imaging approaches in which entangled photons are employed to investigate samples with lower flux or lower-energy photons but with increased spatial resolution (Aim 1) and detection sensitivity (Aim 2). Aim 3 focuses on using coherent (nonentangled) photons and four-wave mixing along with structured illumination for super-resolution nonlinear imaging.

During the current project period, the team has been developing ghost imaging, quantum Differential Interference Contras and super-resolution second harmonic generation capabilities. The group also published a paper, “Quantum-Enhanced Phase Imaging Without Coincidence Counting”, in Optica (Black et al. 2023), which demonstrated a 1.7-fold increase in resolution. The team has since expanded that capability from a proof-of- principle low magnification setup to a setup more in line with plant, algal, and fungal bioimaging specifications. The study has acquired preliminary images off each new instrument in all three aims and begun initial experiments with test biological samples to evaluate real-world resolution and sensitivity. This poster will highlight the overall goals of the project and showcase recent results from each imaging modality.

Genome-enabled interrogation and manipulation of biological systems is converging with technologies for measuring the phenome and with advanced computational methods for integrating related and disparate data. The scientific community is poised to capitalize on this to explain the bases for health and disease and to harness biological processes for the betterment of mankind and the environment through the toggling of system function.

Molecular components of the phenome include genes, transcripts, proteins, and small molecules, and their measurement is made possible by a spectrum of technologies with broad diversity in efficiency, robustness, and impact. Genomes and transcriptomes are now routinely determined and measured with near completeness using high-throughput sequencing technologies. The measurement of proteomes and metabolomes lags that of their genetic counterparts, although technologies for broadly measuring proteins in high throughput (i.e., proteomics) are much more mature, in large part due to the high correlation of the proteome to the genome. For example, proteins are direct readouts of the genetic code, and if the genome is known, then the proteome can be predicted. While proteins are chemically more complex than genes and transcripts, they are chemically less complex than metabolites. Molecular structures of the latter, having nearly the same chemical composition as proteins, are limited in their complexity only by thermodynamics, and the chemical space is estimated to be 1,060 molecules.

Technologies for the measurement of metabolites, other biomolecules, and anthropogenic compounds have not significantly evolved since the late 1960s, when Linus Pauling implemented the concept of “orthomolecular medicine” to identify biosignatures that were correlated to phenotype. New, paradigm-disrupting technologies and concepts are needed to drive the next revolution in multiple fields of strategic importance to the nation: biological sciences, medical sciences, national security, chemistry, and the bioeconomy. This presentation will critically review the state of the art in measuring molecules integral to the phenome in the context of the respective chemical space and will propose a multidimensional evolution of the analytical approach.

Biomass derived from plant feedstocks is a renewable and sustainable energy resource, but these resources are vulnerable to environmental stress such as water and nutrient limitations. Understanding how cells work independently and in concert to regulate plant responses to their environment, including their surrounding microbial community, as well as abiotic stress will be crucial to improving their performance.

This group has applied several cutting-edge, single-cell, and spatially resolved transcriptome sequencing approaches to several plant species and is constructing a comprehensive single-cell resource for plants to better understand the complexity behind environmental responses among diverse cell types. In particular, the group has leveraged both single-nuclei and spatial transcriptomics to profile interactions between plants and arbuscular mycorrhizal fungi (AMF) using the Medicago truncatula and Rhizophagus irregularis system. The team developed a cross-kingdom transcriptome map for this crucial symbiosis, profiling both plant and fungal expression patterns. Team members are also working toward establishing more precise spatial omics tools to profile tissues from bioenergy grasses (including sorghum, switchgrass, and Brachypodium) reacting to AMF colonization, as well as physical stresses including nutrient deprivation and osmotic stress. The team hopes to build a multi-species model of cell type–specific environmental responses.

The Center for Bioenergy Innovation (CBI) vision is to accelerate domestication of bioenergy- relevant, non-model plants and microbes to enable high-impact innovations along the bioenergy and bioproduct supply chain while focusing on sustainable aviation fuels (SAF). CBI has four overarching innovation targets: (1) develop sustainable, process-advantaged biomass feedstocks; (2) refine  consolidated bioprocessing with cotreatment to create fermentation intermediates; (3) advance lignin valorization for biobased products and aviation fuel feedstocks; and (4) improve catalytic upgrading for SAF blendstocks certification.

There is a growing need to develop sustainable perennial crops and beneficial microbes that thrive in suboptimal environments, are resilient to biotic and abiotic stresses, and aid conversion to advanced bioproducts (e.g., sustainable aviation fuel). These goals are facilitated by connecting gene functions to observable traits through high-throughput measurement of key plant and microbe characteristics. However, crucial bottlenecks include the availability of large, high-quality datasets and the burden of time-, cost-, and labor-intensive phenotyping. Deep learning has enabled fast and accurate image-based phenotyping but is similarly challenged by the lack of annotated image data.

This presentation discusses ongoing efforts to address these challenges through the development of deep learning methods (Lagergren et al. 2023) that (1) maximize predictive accuracy while minimizing the amount of annotated training data; (2) ensure biologically realistic morphological features (e.g., contiguity); (3) scale to high-throughput, population-scale datasets for genomic analysis; and (4) extend across kingdoms and species (e.g., Populus spp., Bacillus spp.) and image modalities (e.g., RGB, hyperspectral, confocal microscopy). This team also shows how the model outputs (e.g., segmentations) are used to extract multiple traits (e.g., area, perimeter) using opensource tools (Seethepalli et al. 2021) that are validated with real-world physical measurements and used in downstream scientific analyses like genome-wide association studies.

Finally, the team introduces preliminary results that demonstrate how typical narrow artificial intelligence (e.g., models trained on specific datasets for a specific purpose as described above) struggle to generalize outside of their training distribution (e.g., adapting to new species, plant characteristics), and how foundation models for computer vision have recently emerged as powerful tools for image-based phenotyping with the ability to perform multiple tasks without the need to fine-tune or train new models from scratch. These new research directions are given in the context of the Advanced Plant Phenotyping Laboratory, a state-of-the-art plant phenotyping facility at Oak Ridge National Laboratory, which produces multimodal image data for DOE mission–relevant bioenergy crops at a rate of up to one petabyte per year. Harnessing the power of artificial intelligence through intentional development for biological sciences will be imperative to achieve sustainable energy independence for the nation.

In October 2022, the BER Advisory Committee (BERAC) received a charge letter from the DOE Office of Science director requesting a review of existing capabilities in data management and infrastructure relevant to BER science. The charge also requested a recommended strategy for next-generation data management and analysis within a unified framework. Further goals included identifying new science opportunities that could be enabled by increased integration of BER’s facilities while considering advances in artificial intelligence and machine learning (AI/ML).

The charge asked BERAC to examine synergistic investments within DOE and at other agencies and the impact of a more unified data infrastructure on the scientific workforce. To address these goals, the appointed subcommittee established five working groups focusing on (1) environmental science; (2) biological science; (3) BER data infrastructure services; (4) workforce development, inclusion, and diversity; and (5) data infrastructure technologies. The subcommittee organized a two-day virtual community workshop that included discussions on new unified data infrastructure– enabled science opportunities, barriers to broader inclusion of minorities, support for early career scientists, and potential unified data infrastructure solutions for BER. The results of the workshop and a public request for information were evaluated by the subcommittee and its working groups and its findings summarized in a report. The talk will outline the initial science opportunities identified that could be enabled by a new unified data infrastructure for BER sciences. The talk will also review how a unified data infrastructure may increase the accessibility of BER science and support early career and minority scientists. Furthermore, it will discuss existing data infrastructure capabilities available at BER and elsewhere, and finally, outline the subcommittee recommendations.

The volume and variety of microbiome data being generated continues to grow substantially, creating a significant data resource for researchers addressing critical challenges in environmental science. The National Microbiome Data Collaborative’s (NMDC) guiding principles are to make microbiome data findable, accessible, interoperable, and reusable (FAIR), to embrace open science, and to democratize data access through community engagement.

The three core infrastructure elements of the NMDC framework are: (1) the Submission Portal to support collection of standardized study and biosample information; (2) NMDC Empowering the Development of Genomics Expertise (EDGE), an intuitive user interface to access standardized bioinformatics workflows; and (3) the Data Portal, a resource for consistently processed and integrated multiomics data enabling search, access, and download. These tools are built on a foundation of community standards and a robust data model designed to advance the creation, use, and reuse of microbiome data. This open-access framework lowers the barrier for capture of contextual information about data and facilitates the effective use of microbiome data for applications in energy, environment, and agriculture.

DOE Joint Genome Institute, National Microbiome Data Collaborative, and KBase are collaborating to make data discovery and transfers easier.

Data discoverability is a challenge. BER supports the generation of petabytes of high-quality environmental data leading to exciting discoveries. Once the data are archived in national repositories the return on the original investment is amplified through reuse. In order to create more opportunities for data to be identified and utilized by the scientific community, organizations have pursued implementation of the FAIR (findable, accessible, interoperable, reusable) principles. Data and metadata quality are improving, however biological data requires more structure to ensure interoperability across studies.

DOE JGI, NMDC, and KBase are collaborating on software infrastructure including common data models, application programming interfaces, and transfer protocols to improve data flow between resources. The group’s shared vision is a consistent data discovery experience across BER platforms. This talk will share details of the team’s efforts to work toward this vision, including JGI’s planned reuse of the NMDC Submission Portal, KBase and JGI’s co-development of the Data Transfer Service (DTS), and exploration of data reuse through JGI’s Data Citation Explorer.

The DOE Systems Biology Knowledgebase (KBase) is a knowledge creation and discovery environment designed for biologists and bioinformaticians. KBase integrates a large variety of data and analysis tools, from DOE and other public services, into a user-friendly platform that leverages scalable computing infrastructure to perform sophisticated systems biology analyses. KBase is a publicly available and developer extensible platform, enabling scientists to analyze their own data alongside public and collaborator data, then share their findings across the system and ultimately publish reproducible analyses.

KBase aims to empower its users to predict, control, and design the behavior of biological systems from subcellular to ecosystem processes. A critical capability for such research is the ability to find and integrate relevant data from the larger scientific community that can be used to strengthen and test the generality of user analyses, and to help identify gaps in both personal and collective knowledge that reduce the effectiveness of such analyses. To address the integration problem, this research group is leading two central efforts.

First, the group is working with partners at the DOE Joint Genome Institute (JGI), National Microbiome Data Collaborative (NMDC), and Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE), among others, to develop a Data Transfer Service that streamlines finding and transporting data easily among, initially, BER platforms, while ensuring that provenance and ownership are tracked and credited. Researchers are developing an integrated system for scalable inference generation from user data, comprised of a central data model (CDM) containing the knowledge representation and data organization schema for the team’s system; a relation engine (RE) that powers the population of the CDM with public reference data; and a knowledge engine (KE) that interfaces with the RE to create a wide range of inferences for data entities within the CDM and for user data. Within KBase, these three elements (CDM, RE, and KE) work together to ensure data from diverse sources are linked by common concepts and thereby become comparable for analytical purposes.

The CDM is iteratively designed to represent biological, physical, and experimental relationships among data that are brought together from various resources and instantiated during intake into KBase. It will also enable queries supported by artificial intelligence (AI) to find and organize data relevant to a user’s question suitable for downstream analysis. This group intends for the CDM to serve projects beyond KBase and are assembling community members to aid in its design, testing, and iterative revision. The RE maps user data to the CDM and enables the creation, maintenance, and query of relationships within the CDM. The KE provides predicted and inferred relationships among data referenced by the CDM and creates facilities for data-driven search that enhance relevant data retrieval. The relationships include calculated similarity of genomes or genes, and predictions, such as phenotype and environmental distribution. The data-driven searches include sequenced or functional abundance profile-based queries that might return similar genomes or metagenomes to the user. The KE will also exploit new innovations in large language models and their interface to systems like the CDM to create AI-based assistants that enable users to employ natural language to state the problems they are trying to solve, and then navigate retrieving relevant data, organizing it alongside their own for analysis, and designing and executing the analyses with KBase tools.

This talk outlines the rationale and principles driving the development of the CDM and emphasize the importance of iterative community engagement throughout the process. In particular, this presentation outlines how it supports integration across resources and advancing biological data science within KBase. The goal is to ensure the CDM, and the tools it enables, will help lower the bar to data integration across BER, expand the types of science questions researchers can ask, and advance the field of data science to better handle the complexity in and among biological datasets this team’s scientists and the broader community are creating. This group will motivate this vision with examples drawn from causal microbial ecology that interfaces with a number of the goals of collaborating DOE programs.

This presentation details a proof-ofconcept demonstration that applies state-of-the-art natural language processing (NLP) techniques to automatically extract biological entities and genetic tools from the literature for synthetic biology research. This work seeks to address important knowledge gaps in this field, while simultaneously providing a meaningful staging ground to expose new NLP tools to the KBase community and to gather feedback on their efficacy and use.

Genetic tool engineering in non-model organisms remains a major challenge in the field of synthetic biology and is typically throttled by the large literature searches that invariably accompany the project. Indeed, with decades of publications containing a vast corpus of non-standardized data formats and methods, synthesizing an adequate protocol to guide the study can be a daunting task. But as global issues like climate change, degradation of ecosystems, and increasing food scarcity continue to emerge and grow, the need to engineer these organisms and their relevant toolsets becomes ever greater. Clearly, there is a pressing need for fast and comprehensive searches that help inform and guide laboratory research, as incomplete, cursory searches may increase the time it takes to complete the project or may even preempt its success.

Advances in the field of natural language processing have resulted in the development of powerful large language models (LLMs) to provide solutions to such problems. Fine-tuning these models to identify genetic engineerability terms and to perform biological entity extraction can direct researchers towards useful and informative answers that are driven by existing literature. This team presents a workflow for automating this extraction and subsequently incorporating the data into model file-tuning. Working with a large number of publications extracted from bioRxiv, the team uses text-mining techniques guided by human experts to extract biologically relevant entities from a large publication corpus: organism names and genetic tools, including plasmids, promoters, reporters, and other entities of interest. Researchers test various publicly available LLMs, including Falcon (Almazrouei et al. 2023), LLaMA-2 (Touvron et al. 2023), MPT-Chat (MosaicML 2023), and others, to identify the best-performing model and augment it using state-of-the-art techniques to mitigate model hallucination.

As a proof of principle, this group presents a web application that interfaces a chatbot driven by this model with a visualization tool. User queries are highlighted on the National Center for Biotechnology Information (Schoch et al. 2020) taxonomy tree at the genus-level, highlighting the organisms of interest and displaying their nearest relatives. Datasets collected from various isolate reference databases, including BacDive (Reimer et al. 2022), as well as specific genetic tool databases like the Phage-Host Database (Albrycht et al. 2022), the Plasmid Database (Schmartz et al. 2022), and others, are searched for relevant matches to display to the user. In addition, matches from the literature, mined by the LLM, are also provided to the user. Integrating this tool into KBase infrastructure provides another bridge for DOE BER researchers to access this information, linking it not only with biologically relevant organisms for laboratory experiments, but also with KBase’s own ecosystem to allow subsequent analyses and publication of results.

The Persistence Control Science Focus Area (PerCon SFA) at Pacific Northwest National Laboratory seeks to understand plant-microbiome interactions in bioenergy crops to establish plant growth–promoting microbiomes that are contained to the rhizosphere of a target plant. This vision requires the discovery of exudate catabolism pathways from plant roots, the elimination of genes that support fitness in bulk soil environments without decreasing rhizosphere fitness, and the engineering of rhizosphere niche occupation traits in phylogenetically distant bacteria. The team anticipates the impacts of these efforts will be to increase understanding of plant-microbe interactions and to extend high-throughput systems and synthetic biology tools to non-model microbes.

Metabolic modeling in bacterial genomes or metagenomes depends on several critical steps. Assigning functions to predicted genes and populating known metabolic pathways and modules with annotated genes precede the assembly a draft model. Typically, the limitations of functional annotation reveal critical reaction steps, or gaps, in a draft model for which no gene was identified. Further, comparison of the draft model against experimentally derived phenotypic data, such as cell growth experiments, can identify additional gaps or inconsistencies in the model. Gaps are typically filled either by simply assuming the that the function exists with no specific gene assignment to the step or by relaxing thresholds and computationally or manually searching original annotations for lower confidence or less specific annotations. There are multiple significant impediments to the metabolic modeling and gap-filling process: (1) incomplete genome information, including for metagenome-assembled genomes (MAGs); (2) low-confidence annotations from bacteria that are evolutionarily distant from well-studied organisms; and (3) the large pool of protein families that have either no functional assignment or a non-specific function.

This team has developed two tools to overcome these deficiencies. MetaPathPredict (Geller-McGrath et al. 2022) is a deep learning framework for prediction of complete metabolic modules in genome-scale models from incomplete genome data. Employing a gradient-boosted trees [XGBoost (Chen and Guestrin 2016)] and neural network stacked ensemble classification framework, MetaPathPredict can accurately predict the presence of metabolic modules with as much as 60 to 70% of the genome missing by learning from complete genomes.

The team also developed Snekmer (Chang et al. 2023), a generalized computational framework to model protein function families using sequence recoding into reduced-complexity amino acid alphabets and a k-mer based approach. Snekmer can be used to rapidly develop models for novel protein families and to screen genomes or metagenomes to evaluate the distribution of the family across organisms or environments. Using these tools to fill gaps will improve researchers’ ability to develop metabolic models from limited genome data, including MAGs. Team members have successfully integrated Snekmer as a KBase app, and next plan to integrate MetaPathPredict. Both tools will function with the new OMics-Enabled Global Gapfilling (OMEGGA) app in KBase to allow iterative model building, gap filling, and model refinement.

Research Plan: The goal of this project is to create an integrative optical imaging platform capable of independently quantifying the growth of plant roots and their metabolic interactions with symbiotic mycorrhizal fungi. To accomplish this, this research team is integrating interferometric (or quantitative phase) imaging with light-sheet fluorescence and Raman microscopy. This team’s approach to enable deep imaging within root tissue also involves the strategic combination of specially designed optical beams with ultralow light detection. Researchers deploy this strategy in order to overcome the challenges of the spatiotemporal degradation experienced by light as it propagates through tissue. To support these methodological advancements, group members deploy a mix of theoretical analyses, deep learning techniques for image reconstruction, and the development of dedicated biomarkers.

Current and Anticipated Accomplishments: Since its beginning (2021), the project has concentrated on four areas. First, the team combined photon-sparse imaging with Airy light-sheet microscopy to demonstrate video Raman imaging rates at more than 1,000-fold lower irradiance than current approaches (Dunn et al. 2023; Dunn et al. 2024). This technique allowed researchers to quantify fungal metabolic using a deuterated biomarker, while the group is presently concluding its investigations on further improving these gains via alternative image reconstruction strategies (Sheneman et al. in review 2024).

Second, the team developed a quantitative-phase microscope capable of measuring the dry density and mass within root tissue. This method integrates asymmetric illumination interferometry with standard differential interference contrast microscopes and enables the visualization of distinct features in the meristem of roots up to ~500 μm in diameter (see figure; Zhang et al. 2023 in review; Zhang and Vasdekis 2023).

Third, the group constructed a light-sheet fluorescence microscope that improves imaging efficiency in tissue via specially designed optical beams that can self-heal after scattering, and demonstrated the benefits of this technique in root tissue imaging. Fourth, the group is expanding its palette of biomarkers to track cellular changes underlying host cell accommodation of symbionts. Next steps involve: (1) enhancing the imaging depth in root tissue by refining optical fields temporally and spatially; and (2) investigate plant growth in microfluidics prior to transitioning to imaging the fungal-root symbiosis.

Benefits and Applications: This optical imaging system provides quantitative insights into root-fungi interactions, supporting the DOE’s energy prosperity goals with innovative tools, while it relies on commercial hardware and open-source software, improving its availability to the wider scientific community.

Root biology is pivotal in addressing global challenges including sustainable agriculture and climate change. However, roots have been relatively understudied among plant organs, partly due to the difficulties in imaging root structures in their natural environment. This team aims to develop advanced optical microscopy techniques to reduce photodamage and improve imaging resolution and depth for live plant roots and microbes grown in microfabricated ecosystems (EcoFABs).

Researchers have succeeded in using microfabricated ecosystems (EcoFABs) to establish growing environments with optical access and employing nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution.

THG has enabled researchers to observe key plant root features in mature and meristem roots including laminar structures down to the vasculature, Casparian strips, dividing meristematic cells, root cap and border-like cells, as well as resolve subcellular features including nuclear envelopes, nucleoli, starch granules, and putative stress granules (see figure, p. 48). THG from the cell walls of bacteria and fungi also provides label-free contrast for visualizing these microbes in the root rhizosphere. With simultaneously recorded 3PF fluorescence signal, the team has achieved single-bacterium tracking and subcellular imaging of fungal spores and hyphae in the rhizosphere, indicating this method’s potential for studying plant–microbe interactions.

To improve image resolution, the team combined adaptive optics with THG microscopy. By measuring and correcting sample-induced optical aberrations on the excitation light, adaptive optics has led to a substantial increase in root THG signal.

The group is now working on optimizing homodyne-mixing setups for both THG and second harmonic generation (another coherent light scattering process that provides structural contrast without external labels) microscopy. By interfering a larger reference harmonic signal with the harmonic signal generated by the sample, researchers can enhance the detectability of the sample signal thus reduce the amount of light needed for harmonic generation and its associated photodamage.

Beneficial diazotrophic microbes promote plant growth and productivity by consuming sugars and other compounds exuded by roots and, in turn, provide fixed nitrogen to the plant. Although a large fraction of the carbon fixed by plants during photosynthesis is secreted through roots to sustain the root microbiome, this metabolic exchange is not well understood. Understanding the nitrogen-carbon nexus will help to develop transformative biofertilization technologies that require a smaller carbon commitment from plants to the nitrogen-fixing microbes. This research team is building a label-free microscope to image metabolic activity and chemical exchange between plants and bacteria deep within thick living plant roots and their associated rhizosphere microbial communities.

In year three, the team continued to develop quantitative phase and broadband coherent Raman imaging modalities for quantifying dynamic and chemical aspects of the carbon-nitrogen nexus in the rhizosphere. The team used quantitative oblique back-illumination microscopy (DqOBM) to image bacterial culture of three different diazotrophs (Sinorhizobium meliloti, Azotobacter vinelandii, and Rahnella aquatilis) cultured on media. Results from these imaging experiments were compared with DqOBM images from the root cap and elongation zone of Arabidopsis thaliana primary roots that had been inoculated with either A. vinelandii or R. aquatilis the primary root system. This study’s findings demonstrate that quantitative dynamic phase imaging can effectively characterize microbial dynamics and provide insights into plant–microbe interactions in situ.

Trends in dynamics observed with DqOBM were consistent with a dependence of energy level on carbon and nitrogen, and the team demonstrated strong linear correlations between the nitrogen fixation and the DqOBM signal energy. Since the signal energy level measured by DqOBM is a sum of all microbial activities, it is necessary to have orthogonal information to directly link dynamics observed by imaging with rates of growth or nitrogen fixation.

Researchers also used broadband coherent anti-Stokes Raman imaging to identify spectral changes in bacteria under the same conditions where DqOBM was used. Raman spectra of S. meliloti are obtained during lag, exponential, and stationary growth phases under varying media conditions designed to induce nitrogen fixation. Known Raman signatures of general metabolic activity and nitrogen fixation were correlated to these culture conditions.

Additionally, broadband coherent anti-Stokes Raman scattering (BCARS) was used to image Medicago truncatula nodules that host S. meliloti in the infection thread and nodule zones where bacteria exhibit characteristics of these different growth and media conditions. Researchers used SampleMap—a variant of uniform manifold approximation and projection the team adapted for Raman imaging—to map the Raman signatures obtained from the Medicago nodules to phenotypes expressed in the culture experiments. These SampleMaps will ultimately be compared to high-resolution spatial transcriptomic maps of sectioned root nodules whose sister serial slices have been imaged with BCARS.

The project goal is to realize a high-speed lifetime imaging platform for light-sheet microscopy of metabolic pathways and interactions between plants and soil bacteria using electro-optic fluorescence lifetime microscopy (EO-FLIM) (Bowman et al. 2019; Bowman and Kasevich 2021). Wide-field optical modulators allow efficient lifetime capture combined with low noise readout on standard scientific cameras. This group presents results from its fluorescence lifetime light-sheet microscopy platform. Images acquired in a selective plane illumination microscope are gated using a Pockels cell driven at 80 megahertz, enabling light-sheet FLIM with up to 800 micrometer (μm) field of view. Volumetric lifetime acquisitions are demonstrated on live Arabidopsis thaliana root samples using both genetically encoded fluorescent proteins and endogenous autofluorescence. The group also presents application of EO-FLIM to record neuron activity in vivo at kilohertz frame rates using a genetically encoded voltage indicator (Bowman et al. 2023).

This research team is developing new approaches for live cell imaging within the wood-forming tissues of trees enabling deeper understanding of developmental and physiological processes underlying wood formation and function directly in woody bioenergy feedstocks. A primary challenge is that the dividing and differentiating cells of interest are embedded under thick layers of light-scattering bark tissues and are well beyond the working distances of traditional light microscopes.

The group is developing two types of microendoscopic, implantable imaging probes to access and visualize wood-forming tissues: miniscopes and fiber optic cannulas. These microscopy approaches are being tested by applying them to the following problems relevant to bioenergy feedstock development in poplar: (1) analysis of lignification and impacts of the altered cell wall polysaccharides on lignin formation; (2) vessel element differentiation and the impact of abscisic acid levels on vessel cell properties; and (3) fiber development in both tension-wood-inducing and normal growth conditions.

Previously, the group reported progress in fabricating and using embedded optical probes to carry out live cell imaging in poplar stems. This poster reports improvements to epifluorescence miniscopes fitted with implantable Gradient Index (GRIN) lenses inserted to reach internal tissues with minimal tissue damage. This approach has the advantage of directly rendering images and video in real time. It can be used in combination with fluorescent probes and dyes compatible with a range of different excitation and emission filters. However, miniscope resolution is challenged by light scattering in woody stems with complex autofluorescence.

The group is using ring deconvolution microscopy (Kohli et al. 2023), a technique for computational aberration with a calibration image of randomly distributed fluorescent microspheres. The second approach uses computational cannula microscopy with an individual cannula or an optrode array (Guo et al. 2023), which can be modified to enable hyperspectral imaging. This approach has the advantages of smaller diameter probes (0.22 mm) and larger fields of view (0.20 mm). However, in contrast to the miniscope, machine learning–based image processing algorithms are necessary to convert spatially scrambled fluorescent signals into images. The group implemented a generative adversarial convolutional neural network that surpasses the capabilities of the prior U-Net architecture (Isola et al. 2018). This advancement enables reconstructed images of living poplar branches, which display low contrast and dense structures and capture of dynamics at a cellular level. Finally, researchers will discuss opportunities to extend live cell imaging capabilities for woody plant bioenergy applications.

The goal of this project is to develop bioimaging technologies that will significantly advance understanding of microbial metabolism and communication in real space. This is an outstanding challenge because existing approaches do not have the required spatial resolution. This project will combine nano-optics tools with multimodal (non)linear optical spectroscopy to identify and image small biomolecules with nanometer spatial resolution under physiological conditions. Specifically, the team plans to enhance the spatial resolution in optical extinction, Raman/fluorescence, and coherent Raman/ two-photon fluorescence/second-harmonic generation spectroscopy down to ~1 to 2 nanometers under ambient conditions to visualize metabolites involved in a wide range of microbial and plant processes.

Though linear nano-optical measurements have been demonstrated, nonlinear nano-optical measurements comprise a novel high risk, high reward aspect of this project. Nonlinear nano-optical measurements, while providing improved signal-to-noise ratios, are challenging when chemical imaging and identification of biomolecules is the goal as this requires spectrally resolved detection schemes, e.g., in coherent Raman-based vibrational nanoimaging and nanospectroscopy. These coherent nano-optical measurements, however, require long collection times that restrict their usefulness in a point scanning hyperspectral nanoimaging scheme where full-time traces must be recorded at every position. The team plans to overcome this difficulty by decreasing collection times by orders of magnitude using time-series analysis in combination with machine learning.

With these measurements, an entirely novel set of nanoscopic selection rules is expected. Team members are developing a theoretical framework to assign experimental observables—primarily the linear and nonlinear optical signatures of biomolecules—by coupling ab initio molecular dynamics computed optical spectra to classical finite difference time-domain simulations to reproduce experimental plasmon-enhanced spectral nanoimages.

As these simulations are time-consuming, the team will apply machine learning and time-series analysis to dramatically accelerate these simulations. To optimize and validate the performance of this technology for bioimaging applications, this research team will benchmark the system using environmental consortia of anaerobic methane-oxidizing archaea and syntrophic partner sulfate-reducing bacteria. Using these consortia, this technology will allow researchers to spatially resolve the electronic and vibrational signatures of large multiheme cytochromes embedded in the extracellular matrix, thereby providing the first direct evidence that these proteins predicted in the genomes are exported into the extracellular space.

While this current effort is devoted to optimization of this multimodal nanoimaging technology capable of in-liquid operation, the team is also developing novel nano-optical methods, including (1) ultralow frequency tip-enhanced Raman scattering (Wang et al. 2023a) and (2) broadband extinction nanoimaging and nanospectroscopy (Wang et al. 2023b). The first approach not only allows tracking the chemical identities of bioanalytes, but also enables tracking crystallinity on the nanoscale. Further developments would allow this method to mature into a nano-optical analogue of X-ray diffraction. The second approach is a novel measurement that boasts subnanometer spatial resolution under ambient laboratory conditions. Initial measurements tracked spatially varying plasmon resonances throughout the formation of a junction plasmon. This effort sheds light on the fundamental mechanisms behind optical nanospectroscopy and nanoimaging, which is important for multimodal spectral nanoimaging of biomolecules. More generally, the use of the probe as a nanoscopic broadband light source will allow measuring the nano-extinction spectra of yoctomolar concentrations of biomolecules.

Background. The development of resilient and sustainable bioenergy crops is an important part of the U.S. strategy to transition to a net-zero economy. An important consideration in developing the U.S. bioeconomy is the biosecurity of crops grown for bioenergy production. The most likely biosecurity threats to bioenergy crops are either known pests or pathogens that emerge in new areas, possibly driven by climate change; or new pests or pathogens that are genetically related to known ones. Here, researchers report on an effort to develop a roadmap for a National Virtual Biosecurity for Bioenergy Crops Center (NVBBCC). In FY23, community input was gathered through workshops. This talk summarizes the results from these workshops.

Approach and Activities. Six community workshops with participants drawn from the DOE laboratory complex, U. S. Department of Agriculture, Department of Human Services, academia, and the private sector were held. Four workshops focused on the following science topics: (1) detection of diseases using remote sensing based on unmanned aerial vehicles; (2) aerial dispersal of disease vectors; (3) biomolecular characterization of plant-pathogen-vector interactions; and (4) disease mitigation strategies. In each of these four workshops, researchers asked: (1) what are the key knowledge gaps; (2) which of these gaps is DOE uniquely positioned to address; and (3) what investments in research infrastructure are needed. A fifth crosscutting workshop focused on computing needs for analytics, modeling, and data distribution as well as workforce development. A sixth focused on a framework for preparedness to respond to the emergence of biothreats in bioenergy crops.

In parallel to the workshops, the team engaged in an experimental study on a known disease in sorghum. Furthermore, a focused ion beam for cryo-tomography, a computer platform to support data-intensive collaborative research, and drone-based sensors were acquired and commissioned.

The input from these workshops as well as insights stemming from research activities conducted during this pilot study are now being summarized in an NVBBCC roadmap document to be delivered to BER.

Recommendations. The overarching recommendation is for BER to stand up a long-term research program that aims to safeguard bioenergy crops and establish a response capability to detect and identify emerging diseases in bioenergy crops. Specific recommendations are:

• Apply a systems-level approach to bioenergy crop health that would augment and extend the smart farming approaches that the USDA has adopted to safeguard the nation’s agricultural crops. In particular, DOE and BER resources could be applied to advance basic understanding of pathogen-host plant interactions at the molecular level and then apply this knowledge to guide genetic engineering of bioenergy crop plants for increased resilience against disease.
• Develop a capability to predict the impact of climate change on bioenergy (and agricultural) crop security. Building on DOE’s long expertise in Earth system modeling, a capability to predict climate- induced changes in host-pathogen ecosystems could guide efforts to avoid or limit the spreading of crop diseases.
• DOE’s expertise in stand-off detection and biosensing also could be harnessed to augment the USDA’s efforts in crop surveillance.

Finally, the envisioned center (NVBBCC) would allow scientists from DOE, USDA, and academia to collaborate and share dedicated research and computing facilities.

The ability to counter biological threats is limited given the enormous lack of knowledge of host resilience mechanisms in the face of pervasive pathogens. The resilience of multicellular hosts (e.g., plants, animals, and humans) is predicated on the susceptibility of individual cells and effective defense mechanisms to halt the spread of infection for pathogen elimination. Recent work in the field of epigenomics suggests that epigenetics play a key role in host defense. Epigenetic mechanisms collectively function to open or close regions of chromosomes to control gene expression. The resulting dynamic structural changes in the genome underpin most biological functions, including responses to infection. Conversely, genome structure can be altered by pathogens to reorient host cellular function to enhance pathogen replication or to establish latent or persistent infections.

In response, hosts employ epigenetic modifications to counter infection, thereby altering the expression and 3D spatial configuration of their own genomes. These researchers hypothesize that epigenetic modifications vary between resilient vs. susceptible host cells and that underlying changes to the epigenome and genome are characteristic of pathogen classes. Although recent progress has been made, scientists and decision-makers currently lack methods to quickly compare and identify these pathogen-induced changes to host genomes to understand susceptibility and resiliency.

This team proposes to address this gap by taking a holistic, ‘one health’ approach to (1) develop an experimental workflow to characterize and survey early onset molecular signatures of infection found in the genome and epigenome; (2) leverage Advanced Scientific Computing Research user facilities to create an exascale computational and explainable artificial intelligence (XAI) workflow that integrates this data (and data from the user community) to enable interactive, comparative 4D (3D + time) (epi)genome exploration and predictive dynamic modeling; and (3) integrate 3D genomic structural maps, epigenetic modifications, and ultra-resolution physical images (using cryo-electron microscopy) to validate genome structure–function relationships. This platform (see figure) is agnostic by design and can be adapted for any pathogen (e.g., bacteria, fungi, viruses). The research group will first apply this platform to examine viral infection(s) in mammalian and plant systems to determine (for the first time) realistic 3D spatial architecture and dynamic reconfigurations of key host genomes induced by viral pathogens. Broad knowledge of epigenetic regulation of host-pathogen interactions would greatly advance the ability to predict pathogens that have high potential to cause the next global scale catastrophe or pandemic and will directly advance genomics capabilities in biopreparedness to transform the nation’s ability to prepare for, and respond to, future biological threats.

The science of biopreparedness to counter biological threats hinges on understanding the fundamental principles and molecular mechanisms that lead to pathogenesis and disease transmission. One vision to address this challenge is to create a powerful and user-friendly platform to elucidate the fundamental principles of how molecular interactions drive pathogen–host relationships and host shifts. Researchers will enable groundbreaking discoveries by integrating a wide range of structural, genomics, proteomics, and other advanced omics measurements, along with evolutionary and artificial intelligence predictions.

To make sure the system is applicable to real-world problems, it will be developed in the context of a tractable model system, the small, abundant, and accessible photosynthetic cyanobacteria and their constantly co-adapting viral pathogens, cyanophages. This model will maintain the system’s applicability to real-world problems and techniques, but the overall focus will be on elucidating general principles of detecting, assessing, and surveilling molecular interaction, adaptation, and coevolution that are system agnostic and therefore extensible to any other viral-host interaction.

The objectives are to (1) identify the molecular complexes that comprise the cyanobacteria redox macromolecular subsystem and how they dynamically change with bacteriophage infection in situ, using cryo-electron tomography; (2) profile regulatory changes during infection using proteomics, multiomics, and experimental validation, and integrate the data with in situ structures; (3) use genomics and metagenomics to determine environmental and population factors across time scales that impact the interactions between marine cyanobacteria and their cyanophage parasites, predicting the evolutionary origins of in situ structural and functional interactions, convergence and coevolution; and (4) develop a data integration and transformation platform that facilitates the integration of in situ, proteomic, and evolutionary measurements of molecular interactions to surveil diverse hosts and parasites in various environmental contexts.

This powerful and user-friendly platform will enhance connections between the often-siloed fields of structure, molecular phenotype, and evolutionary genomics that are key to biopreparedness, but in need of integration. The team will do this by building a navigation tool to facilitate the effective use of globally distributed experimental data for integrated analysis and predictive modeling. The impact of the project will be to develop, implement, and test a platform to assess host-pathogen molecular interactions, adaptation to hosts and host shifts, and coevolution between hosts and pathogens. A successful project outcome will transform researchers’ ability to study any host-pathogen interaction, encourage diverse community contributions, and gain fundamental insights into how proteins adapt to new contexts. This ability will be critical for designing early interventions to address future threats. Researchers will build surveillance training capability, aiming for a fair and equitable response to future pandemics and biothreats.

These researchers present results from two multi-pass microscopes that provide metrological advantages in stimulated Raman scattering (SRS) microscopy.

SRS is a nonlinear optical process that is quantitative, bond-specific, and label-free. However, for fragile specimens such as live cells, the required optical intensities result in specimen damage or death, and photon shot noise limits the absolute achievable sensitivity for these dynamic and dose-sensitive samples. A multi-pass microscope interrogates a sample sequentially with the same probe field in a programmable and deterministic fashion, increasing the sensitivity of measurements of weak scatterers ( Juffmann et al. 2016; Israel et al. 2023). In such cases, multi-pass measurements are competitive with or can outperform measurements using squeezed or other quantum states using purely classical resources (Giovannetti et al. 2006).

This research demonstrates multi-passed SRS microscopy using both infrared and visible light modalities. The team quantified the metrological advantage in comparison with a conventional measurement scheme. These quantum-optimal imaging protocols will advance microscopy and flow cytometry for studying the life cycles and interactions of soil microbes and plants and can be shared with the BER science community.

The overarching goal of this project is to develop multimodal quantum nonlinear optical imaging based on a squeezed light source for co-registered, steady-state two-photon excited fluorescence, two-photon-excited fluorescence lifetime imaging, and second harmonic generation microscopies. The capabilities and advantages of these quantum light modalities will be validated through imaging the growth and dynamics of bacterial strains with intrinsic fluorescence or strains expressing fluorescent proteins in a synthetic microbial community or during plant colonization.

The team’s initial efforts focused on whether phenazines produced by Pseudomonas sp. could be used as a biomarker for live cell imaging. To this end, extensive steady-state fluorescence spectral measurements on two commercially available phenazines, phenazine-1-carboxylic acid and pyocyanine, were performed independently at JILA and Oak Ridge National Laboratory (ORNL). Stable fluorescence emission necessary for both two-photon spectral and imaging acquisition could not be established under various reduced conditions using different concentrations of sodium dithionite and incubation times. The findings suggest that use of bacterial phenazines as intrinsic biomarkers for live cell imaging of Pseudomonas strains isolated from the rhizosphere is unlikely.

The team is now focused on determining the feasibility of using green fluorescent protein for these studies. In parallel, researchers have built and tested a stable squeezed light source capable of producing up to ~3 milliwatts of twin beam power at JILA. The team used an electro-optic modulator and a temperature-stabilized etalon to redshift the seed beam and filter the undesired frequencies respectively. This simplification resulted in a stable seed beam power (~1% standard deviation) comparable to that of the commonly used acousto-optic modulator and could allow for an easier implementation into instrumentation. A gain of up to ~8.5 has been observed in the amplified probe beam that is also comparable to past four-wave mixing squeezed light experiments.

With the range of powers and gains available and recently assembled fluorescence detection system, researchers can now move onto the next stage of determining and optimizing the quantum-enhanced two-photon excitation rates of common fluorescent dyes such as fluorescein and rhodamine b. This same design will be used for building a second light source at ORNL. In the meantime, a two-photon fluorescence spectral system based on signal photon detection was built at ORNL and will be used to validate the linear intensity dependence using a recently developed squeezed light source in the Materials Science and Technology Division at ORNL while the dedicated light source is being built for this project.  These instruments will be used to evaluate photoinduced stress and toxicity in microbial communities resulting from the squeezed light source versus classical light irradiation to assess whether the predicted quantum advantage is realized.

Membrane potential (MP) changes can provide a simple readout of bacterial functional and metabolic state or stress levels. While several optical methods exist for measuring fast changes in MP in excitable cells, there is a dearth of such methods for precise (and calibrated) measurements of steady-state MPs in bacterial cells. Conventional electrode-based methods for the measurement of MP are not suitable for small bacterial cells. Existing optical electrophysiological techniques based on fluorescent Nernstian probes have been successfully used in many studies, but they do not provide precision or absolute quantification of MP or their changes. This team presents a novel, calibrated MP recording approach to address this gap.

This group’s method uses (1) a unique optical transducer (a chromophore wire-donor construct), that utilizes intrinsic photoinduced electron transfer (PeT) mechanism to measure MP via its fluorescence lifetime and (2) a quantitative fluorescence lifetime imaging microscopy (FLIM) data analysis based on phasor analysis. In order to visualize individual bacterial cells’ MPs under different extracellular conditions, amplitude-averaged lifetime maps were computed from pixel-wise phasor fractions. This allows group members to accurately measure even small MP changes in single bacterial cells.

Calibration of membrane potential estimation via phasor-FLIM measurements has been achieved by modulating MP artificially through changing ionic (potassium +) concentration gradients across the membrane utilizing ionophores. Applying this technique to Bacillus subtilis, researchers estimated their normal MP at -86 millivolts and a chemically modulated depolarized state at +1 mV. This breakthrough work paves the way for deeper insights into bacterial electrophysiology and bioelectricity research.

The overall project goal is to develop single photon counting avalanche photodiodes (SPAD) operating in mid-infrared spectral range above 3 micrometers. Application of the novel devices as a bucket detector in mid-infrared quantum ghost imaging of biological tissue is envisioned.

The team designed and fabricated GaSb-based separate absorption, charge, multiplication (SACM) heterostructures optimized for hole- initiated impact ionization. The devices were grown onto tellurium- doped GaSb substrates and contained 1 µm– thick nominally undoped InAs_0.91Sb_0.09 absorber, ~100 nm–thick tellurium-doped Al_0.9Ga_0.1As_0.07Sb_0.93 and 300 nm–thick nominally undoped Al_0.9Ga_0.1As_0.07Sb_0.93 multiplier layer terminated with ~300 nm–thick phosphorous-doped contact layers. The epitaxial wafers were processed into 40 and 80 diameter shallow etched mesa devices with, correspondingly, 20 and 60 µm diameter windows in top contact metallization. The SACM APD were indium-soldered epi-side-up with onto gold-plated carriers and characterized in wide temperature range.

The punch-through voltage was about 10 volts at liquid nitrogen temperature; dark and photocurrent current increase due to avalanche breakdown was observed at voltages above 13 V, which was taken as a unity gain voltage reference. The linear regime multiplication gains exceeding 200 were observed at voltages near 17.5 V at liquid nitrogen temperature. The dark current values of several nanoamperes have been recorded for all devices before the breakdown. The analysis of the temperature dependence of the dark current above punch-through confirms diffusion limited absorber operation at temperatures above 150°K (activation energy ~370 megaelectron volts). At temperatures below ~150°K, the dark current became ~nA and its dependence on temperature was characterized by activation energy ~10 millivolts indicating other current controlling mechanisms.

The dark current values below punch-through voltage at temperatures below 200°K remained under 100 picoamperes and were virtually temperature independent for all devices. Independently characterized responsivities values above 5 amperes per watt at bias voltages corresponding to linear gain values below 10. The device cutoff wavelength at liquid nitrogen temperature was ~3.9 µm as determined at the half maximum level. Experiment confirmed efficiency of the proposed device architecture, which does not require etching through absorber section. The observed mismatch of about several volts between punch through and start of the avalanche breakdown indicates that thickness of the multiplier section and doping level of the charge control section will need to be optimized to further reduce dark current values.

Mitigation of sample photodamage allows for a longer observation window to trace biological processes. In particular, the natural photosynthesis systems that use light to drive chemical reactions are very sensitive to optical imaging, as the probing light itself inevitably perturbs photosynthetic reactions. This challenge has prevented the effective real-time monitoring of photosynthesis reactions in dark conditions, where the chemicals synthesized by light redistribute in the plant. Understanding the mechanism of this redistribution is important to close the circle of knowledge on plant photosynthesis. Direct monitoring of the distribution in dark conditions would provide straightforward evidence but has never been achieved ever due to perturbation from the probing light. To address the above challenge, the team applied quantum imaging with undetected photons (QIUP) to probe the photosynthesis processes using a very low dose of photons.

QIUP uses two beams consisting of entangled photons separated into infrared (IR) and visible wavelengths. The IR photons probe the sample and obtain sample information, but they are not detected by the detector due to the very low sensitivity in the IR range. The visible photons do not touch the sample but carry the sample information through entanglement, and they are detected at high sensitivity and produce sample images. This study demonstrated that QIUP can produce sample images by using only picowatts of illumination power on the sample. The power density is many orders of magnitude lower than that of classic light microscopy, for example, confocal fluorescence microscopy.

Researchers applied QIUP to image squalene in tobacco leaves by using squalene’s IR absorption bands. The illumination power density used in QIUP to achieve similar chemical imaging capability is even more drastically lower than that of stimulated Raman scattering microscopy, which uses a picosecond pulsed laser to induce a high illumination field. The imaging data has informed the metabolic engineering of more efficient squalene production in tobacco.

Group members are working to develop three complementary microscopy techniques that exploit quantum correlations in light: Hong-OuMandel interferometric tomography, g⁽²⁾ correlation function imaging, and passive and active transverse mode sorting. Researchers will subsequently incorporate these methods into a single platform for tracking and imaging individual and few fluorescently labeled biomolecules, including cellulases, in the context of nearby biological interfaces and surfaces in order to unravel the fundamental processes involved in the conversion of lignocellulosic biomass into renewable fuels.

Since the publication of Hooke’s Micrographia in 1665, the scientific disciplines of light microscopy and (sub) cellular biology have progressed in lockstep with one another. Advances in the spatial and temporal resolution, specificity, and sensitivity of optical methods have continually led to new capabilities and insights in biological imaging. The pace of this evolution has quickened in the past century, as a mastery of the physics of light according to Maxwell’s equations has been wielded to more fully exploit classical effects like interference and diffraction. As the classical limits of light microscopy near saturation, however, sustained improvement in bioimaging technology is ultimately untenable without a more fundamental shift in research direction. Just as the field of quantum computing has gained prominence in anticipation of the inevitable breakdown of Moore’s Law, quantum-enabled light microscopy will likely provide the path forward for (sub)cellular biological imaging.

This team aims to help lead this effort by developing three complementary quantum microscopy modalities that each address a different challenge inherent to (sub) cellular microscopy:

1. Hong-Ou-Mandel Interference Microscopy to enable loss- and noise-tolerant depth imaging with exquisite resolution;
2. g ⁽²⁾ Microscopy to facilitate orders-of-magnitude sensitivity improvement in focusing and tracking single quantum emitters atop oppressive classical backgrounds at reduced excitation powers; and
3. Transverse Mode Sorting Microscopy to enable super-resolution microscopy at low excitation powers and high temporal resolution.

This poster presented results demonstrating progress in developing these constituent techniques. The team will ultimately incorporate them into a common imaging platform that can provide access to the many scales of interest in energy-relevant plant and microbial biology. The combined technique, QuOMBIS (Quantum Optical Microscopy of Biomolecules near Interfaces and Surfaces), will be especially powerful for tracking and imaging individual and few fluorescently labeled biomolecules in the context of nearby biological interfaces and surfaces. Upon development of the methods, the team will apply the platform to unravel and harness the enzymatic conversion of biomass into renewable fuels.

Development of quantum sensing tools for chemical analytes of relevance to the rhizosphere.

This poster outlines the collaborative efforts between University of California–Berkeley (UCB) and Lawrence Berkeley National Laboratory (LBNL) towards innovating quantum sensing technologies for the rhizosphere. Work at UCB is bifurcated into two main streams: firstly, the development of novel quantum sensor probes utilizing hyperpolarized carbon-13 (¹³C) nuclei in nanodiamonds. These probes, capitalizing on the unique properties of ¹³C nuclear spins, act as sensitive magnetometers for time-varying fields under strong bias magnetic fields. Group members report on protocols for their application as nuclear magnetic resonance (NMR) sensors, providing unprecedented chemical and spatial resolution. Notably, these sensors exhibit exceptionally long coherence times, surpassing T2’=800s at 100 K, while inherently filtering out common-mode instrumental noise. Secondly, the group is advancing a new technology platform for detecting rhizosphere-specific analytes using nanodiamonds embedded in monodisperse, picoliter-volume microdroplets.

This approach aims to encapsulate and sense chemical analytes from the rhizosphere efficiently within a rapidly flowing system. This research shows chemical sensing for model target paramagnetic analytes with an excellent limit of detection (~100 nm). At LBNL, group members discovered that nitrogen vacancy relaxometry can provide a new contrast mechanism for plant tissue imaging. Researchers observed gradients in the relaxation variables at subcellular length-scales and are working to understand the underlying processes or species that couple to the quantum sensing centers. Currently, the team is working on enhancing the signal-to-noise ratio through surface preparation and various instrumentation improvements. Simultaneously, the team is developing complementary approaches to enable biologically relevant characterization under the existing quantum sensing microscope. Additionally, researchers are constructing novel imaging and data analysis capabilities, e.g., using hydrogen relaxometry, to study water movements within biological systems.

A key challenge of the modern genomics era is developing empirical data-driven representations of gene function. This study presents the first unbiased morphology- based genome-wide perturbation atlas in human cells, containing three genome-wide genotype-phenotype maps comprising CRISPR/Cas9-based knockouts of >20,000 genes in >30 million cells. The optical pooled cell profiling platform (PERISCOPE) combines a de-stainable high-dimensional phenotyping panel (based on cell painting) with optical sequencing of molecular barcodes and a scalable open-source analysis pipeline to facilitate massively parallel screening of pooled perturbation libraries. This perturbation atlas comprises high-dimensional phenotypic profiles of individual cells with sufficient resolution to cluster thousands of human genes, reconstruct known pathways and protein-protein interaction networks, interrogate subcellular processes, and identify culture media-specific responses. Using this atlas, researchers identified the poorly characterized disease-associated TMEM251/LYSET as a Golgi-resident transmembrane protein essential for mannose-6-phosphate-dependent trafficking of lysosomal enzymes. In summary, this perturbation atlas and screening platform represents a rich and accessible resource for connecting genes to cellular functions at scale.

The field of microbiome research is experiencing a transformative shift towards data science, propelled by the massive influx of microbiome data. This burgeoning volume of data presents both formidable challenges in terms of establishing standards and management frameworks, and simultaneously unlocks unprecedented opportunities for groundbreaking discoveries. Current exploration into computational analysis of microbiome samples, including those from previously uncultured organisms, is significantly enriching scientists’ understanding of microbial community structures and functions. This, in turn, is broadening scientists’ grasp of the genetic and functional diversity within individual microorganisms.

This talk will elucidate cutting-edge computational methodologies, underscoring the pivotal role of big data processing and integration in mining metagenomic datasets. Such approaches are instrumental in unveiling novel insights and fostering discoveries. This talk details the latest strategies for data analysis and share illustrative science vignettes that highlight the exploration of microbial, viral, and functional diversities. This talk aims to showcase the transformative potential of integrating big data with microbiome research, paving the way for scientific breakthroughs in understanding the complexity and dynamism of microbial ecosystems.

Brookhaven National Laboratory was awarded a pilot project in FY22 under the DOE Office of Science Biopreparedness Research Virtual Environment (BRaVE) initiative to define research priorities, needs, and requirements for a national virtual center devoted to the biosecurity of bioenergy crops. The proposed center’s mission, the National Virtual Biosecurity for Bioenergy Crop Center (NVBBCC), would be to provide the scientific basis and tools to detect, characterize, model, and mitigate biothreats to bioenergy crops. This function will ensure increased U.S. reliance on essential plant-based energy products, e.g., biojet fuel, over the next few decades The NVBBCC is envisioned as a distributed, virtual center with multiple national laboratories at its core to maximize the use of existing unique facilities and expertise across the DOE complex. To underpin this collaborative and distributed effort, a flexible computational platform that supports high-performance computing workflows and data management and allows for efficiently conducting modeling simulations is needed.

This presentation outlines the computing infrastructure capabilities toward these goals, derived from a community requirements workshop:

• Develop an integrated research infrastructure that enables meaningful integration of data, computing, instrumentation, and related resources to allow researchers access to needed computational/data resources from anywhere.

• Employ robust data management systems that can manage diverse data types to ensure quality while adhering to FAIR (findability, accessibility, interoperability, and reusability) principles and supporting better metadata.

• Explore and adopt advanced technologies, such as 5G/6G wireless communication for high-throughput transmission with low latency in poorly connected areas.

• Develop scalable and generalizable models that link model design to downstream decision-making while using tools and techniques to create a unified, integrated modeling approach.

Microbes perform numerous essential roles in terrestrial ecosystems including biogeochemical cycling of nutrients, soil structuring, and plant productivity. Despite the tremendous gains in knowledge of the metabolism of microbes, predicting microbial phenotypes from genomic information across the vast diversity of microbes remains a challenge. This presentation will discuss ongoing collaborative efforts between the Plant-Microbe Interfaces and the Ecosystems and Networks Integrated with Genes and Molecular Assemblies Science Focus Areas (SFAs), to establish a cross-SFA characterized strain collection with measured microbial phenotypes for improving genome-based predictions. These collections represent isolates of plant microbiomes and subsurface microbiomes, respectively.

Standardized methodologies and phenotyping datasets are being developed for expanding this dataset and for serving as a data standard for genotype- to-phenotype prediction tools and methodologies within Systems Biology Knowledgebase. This dataset and related tools lay the groundwork for broader community engagement and will be useful for developing more robust phenotype prediction classifiers that cover a broader array of carbon sources and a phylogenetically diverse set of microbes. With this integration and standardized approaches, the research team strives to enable users to predict microbial phenotypes from a wide array of genomes, thereby significantly advancing microbial research.

Protein families of unknown function are a significant challenge facing the DOE BER research community. While many tools in KBase and elsewhere today permit the discovery of entirely new protein families, very few tools exist to study the function of these families. The Enzyme Function Initiative (EFI; enzymefunction.org) offers tools to address this critical problem. This project aims to integrate the EFI toolset into KBase fully, with complete ties to DOE BER sequencing sources, including all sequence data in KBase and the DOE Joint Genome Institute Integrated Microbial Genomes database. Further, the team will ensure the interoperability of these tools with other functional genomics tools in KBase, particularly tools to integrate structural data from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB).

One of the most significant challenges currently inhibiting understanding of complex biological systems from genomic and multiomic data is the staggering number of proteins with unknown functions. Tools are needed to integrate multiple sources of evidence to decode the functions of uncharacterized protein families and understand the limits of annotation propagation. EFI toolkit supports protein function discovery through Sequence Similarity Networks (SSNs) (Zallot et al. 2019; Oberg et al. 2023). Here, researchers will demonstrate how the EFI toolkit (now partially in KBase) is combined with other tools, particularly tools for integrating structural insights from RCSB-PDB, to study the propagation of function through the members of a close protein family.

The team’s first demonstration of the protein function discovery pipeline in KBase focuses on the aconitase superfamily. In the quest to enable automated rapid reconstruction of high-quality fungal metabolic models, researchers detected essential functions often misannotated in fungal genomes. The team focused on three examples: aconitases AcnN and AcnD and
2-methylisocitrate lyase. First, researchers constructed a protein family around correctly annotated instances of each function; then, for each family, researchers constructed phylogenetic protein trees and SSNs (generated by the EFI tools). The team correctly annotated the problematic protein families across diverse fungal genomes using these constructs, improving annotation consistency and the corresponding metabolic models. Furthermore, group members investigated the structure of the aconitase proteins using KBase-RCSB tools, observing that yeast aconitase D’s structure is similar to that of bacterial proteins. In contrast, the bacterial aconitase A has only 6% identity with its fungal mitochondrial equivalent (AcnN).

The second demonstration explores enzymes involved in microbial degradation of pyridine, specifically recently discovered Group C mono-oxygenases pdbA and pyrA and an alternative route involving Vanillate O-demethylase oxygenase VanA. Protein families were constructed in KBase around these enzymes; protein members were then organized into a tree and compiled into an SSN. Researchers used these approaches to study the evolutionary patterns of variation within these protein families to explore the potential phylogenetic breadth of the pyridine degradation activity discovered or proposed for these genes. The team also applied AlphaFold to produce structures for representative genes, which were compared with related structures in PDB and applied to perform docking simulations in KBase. Together, these tools reveal insights into accurately propagating these relatively new annotations to new genomes, improving the representation of the new pyridine degradation pathway in metabolic models produced by KBase.

This group is working to validate the new monooxygenase annotations in the pyridine degradation pathway using the self-driven laboratory system at Argonne National Laboratory and the strain Acinetobacter sp. ADP1. Using these capabilities, researchers can track the capacity of ADP1 to grow on pyridine with complementation and knockout of the candidate proteins. This team is exploring using this platform as an automated testbed for validating new protein function discoveries emerging from KBase.

The DOE Systems Biology Knowledgebase (KBase) is a knowledge creation and discovery environment designed for both biologists and bioinformaticians. KBase integrates a large variety of data and analysis tools, from DOE and other public services, into an easy-to-use platform that leverages scalable computing infrastructure to perform sophisticated systems biology analyses. KBase is a publicly available and developer extensible platform that enables scientists to analyze their own data within the context of public data and share their findings across the system.

In the rapidly evolving landscape of biological data analysis, KBase is uniquely positioned with respect to its capabilities that combine analytic tools, large-scale data compendia, user data, and publishing and sharing. To leverage these capabilities, this team is starting two new initiatives that are driven by custom trained large language models (LLMs). The first initiative is the use of LLM-powered artificial intelligence (AI) agents that assist users in their analysis and the interpretation of those results. And second, LLMs can assist users with search and discovery of data within KBase to help formulate and evaluate hypotheses. This presentation shows the progress made in these areas. This includes the creation of an AI agent with a natural language interface that guides the user through the analysis of a microbial genome from the reads through to a genome paper. This agent is capable of invoking all the necessary tools and assisting the user in interpreting the output of those tools. In addition, this presentation will discuss the development of an LLM infrastructure to enhance the search and discovery of data within KBase. This includes creating a natural language query interface, personalizing the search experience, and employing intelligent data retrieval and reasoning to answer complex scientific questions. By implementing these AI-enhanced capabilities, KBase aims to offer a more intuitive and effective platform for scientific exploration through a collaborative environment.

There is an urgent need to develop effective antimicrobials to address the public health crisis arising from antimicrobial-resistant (AMR) bacteria. Phages represent a promising alternative to antibiotics as they tend to specifically target a few bacterial hosts and can therefore be applied as precise antimicrobials without collateral disruption of the microbiome. However, isolation of phages against a bacterial strain of interest currently relies on a tedious workflow and may not be achieved in a timely manner.

In this project, this team is building the largest collection of phages (“phage banks”) targeting a panel of ESKAPE pathogens AMR pathogens (Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) and a plant pathogen (Pseudomonas syringae). The phage bank consisting of natural phages may not necessarily represent the optimal genotype for therapeutic use. The group will overcome this limitation by directed evolution of natural phages towards broadening the host-range and by pre-adapting phages to common bacterial resistance mechanisms.

In addition, the group will also develop technologies to rapidly engineer effective phages in the absence of optimal phages in the phage bank. Specifically, researchers aim to learn phage engineering design principles by applying CRISPR-based genome-scale phage functional genomics, determining which phage genes are dispensable and which are useful for host-range engineering. The group will also use CRISPR-Cas tools to build synthetic phages and will apply “rebooting” technologies to facilitate in vitro phage engineering. Taken together, the new resources and capabilities developed as part of the Biopreparedness Research Virtual Environment (BRaVE) Phage Foundry should enable a broader and more efficient use of phages as therapeutics.

The development of resilient and sustainable bioenergy crops such as sorghum, poplar, and switchgrass is a focal point within BER. Bioenergy crops, like all crops, are susceptible to diseases that can vastly impact yield and quality. With the large-scale deployment of bioenergy crops, pathogen outbreaks will inevitably occur. With climate change and growth in marginal conditions without competition with food crops, bioenergy crops are facing biothreats and diseases. Plant pathogens (fungi, bacteria, and viruses) produce a stunning array of virulence effector proteins and other molecules that interact and hijack plant defense systems resulting in infection and disease. Conversely, all plants encode intracellular innate immune receptors called nucleotide-binding leucine-rich repeat proteins (NLRs) that recognize effectors to elicit successful immune responses. The co-evolution of plants and pathogens drives cycles of infection and immunity. Researchers, therefore, are integrating systems biology, biomolecular characterization, and synthetic biology with computation and artificial intelligence/machine learning to provide foundational insights into the dynamic plant–pathogen interactions.

The output of this project will contribute to the development of a resilient U.S. bioeconomy, which includes the bioengineering and breeding of broad pathogen-resistant bioenergy crops and biocontrol of disease through mutualistic plant-bacteria interactions. The technologies and resources developed in this proposal may be rapidly deployable for combating emerging biotreats. Sorghum is the second most common biofuel crop in the United States and is the primary source of biodiesel production worldwide. However, a devastating anthracnose disease, caused by a fungal pathogen Colletotrichum sublineola can lead to yield losses of up to 67%. The co-evolution and genetic diversity of both sorghum and C. sublineola make this a highly relevant model system for studying plant-pathogen interactions. This team’s primary objective is to advance a fundamental understanding of plant-pathosystem interactions by investigating the molecular interactions between sorghum, its anthracnose- disease causative fungal pathogen C. sublineola, and antifungal biocontrol bacteria to create disease-resilient bioenergy crops.

The proposed project is organized into four linked aims. Aim (1) Identify molecular interactions underlying the pathogenicity of C. sublineola and its inhibition by bacteria. Aim (2) Characterize the molecular basis of key interactions determining C. sublineola pathogenicity, anthracnose resistance, and its susceptibility to biocontrol. Aim (3) Create synthetic pathogen infections to study pathogenicity, resilience, and disease biocontrol. Aim (4) Develop innovative computational resources to study plant–pathogen interactions across biological scales.

Yarrowia lipolytica is an emerging microbial host for the bioconversion of low-value carbon into natural products, but its endogenous terpenoid metabolism has yet to be fully mapped. Here, this research group aimed to illuminate novel terpenoid biosynthetic pathways and quantify metabolic flux and free energy therein by employing metabolomics, isotope tracing, and genetic engineering. The group engineered a strain to push increased carbon flux through the mevalonate pathway and to farnesyl pyrophosphate (FPP) by overexpression of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) and FPP synthase (FPPS). Overexpression of HMGR and FPPS led to a 150-fold increase in mevalonate production and a 1.5-fold increase in isopentenyl diphosphate (IPP) production after one day of growth, indicative of increased metabolic activity through the mevalonate pathway and terpenoid metabolism. However, the group observed a lower amount of IPP during the second and third days, suggesting the activation of secondary metabolism and prompting an investigation how the isoprenoid backbone was being utilized.

Upon untargeted metabolomic analysis using liquid chromatography-mass spectrometry (LC-MS), researchers discovered several new metabolites being produced in the engineered strain but absent in the wild-type strain. Based on measured monoisotopic mass-to-charge ratios and proposed molecular formulas, the group hypothesized that these molecules were oxygenated terpenoids. After compound purification and nuclear magnetic resonance (NMR) spectroscopy, the group confirmed that these compounds were terpenoids, with a farnesyl scaffold and bifunctionalized with carboxylic acids. To the group’s knowledge, this is the first observed biosynthesis of such diacid compounds. To map the novel terpenoid biosynthetic pathway, the group reconstituted the putative enzymatic steps in Saccharomyces cerevisiae and successfully conferred full biosynthetic capabilities.

Furthermore, isotope tracing and direct farnesol feeding were utilized to elucidate biosynthetic intermediates. Notably, a P450 enzyme previously shown to be involved in alkane assimilation was responsible for the hydroxylation of the allylic carbon-hydrogen bond, demonstrating the substrate promiscuity and multifunctionality of involved enzymes. This work demonstrates the utility of increasing precursor availability to activate untapped metabolic pathways for the discovery of new natural products. Furthermore, the new compounds and their biosynthetic intermediates represent an exciting pool of organic building blocks that can be accessed for renewable fuel, polymer, and natural product synthesis.

Metabolic fluxes are a fundamental descriptor of cellular state, representing the rates at which organisms operate metabolic pathways. Mass spectrometry and isotope tracing have been instrumental in quantifying fluxes, as metabolic pathways imprint unique isotope labeling patterns on metabolites corresponding to their fluxes. Metabolic flux analysis (MFA) is a commonly used computational framework that identifies the set of fluxes that best simulate observed isotope labeling patterns. However, quantitative flux analysis remains an expert method, and the relationships between isotopic labeling patterns and fluxes remain elusive in complex metabolic environments.

Here, researchers aimed to quantify fluxes in dynamic and complex biological systems including microbial communities. Using multiple isotope tracers, the group elucidated the evolutionary benefit of the Entner-Doudoroff (ED) pathway, which is parallel to textbook (EMP) glycolysis. Tracing from two asymmetrically labeled glucose on a minutes timescale revealed that the ED pathway flux accelerates faster than the textbook glycolysis in response to nutrient upshift.

The rapid utilization of the ED pathway endows Escherichia coli cells with rapid adaptability and evolutionary benefits in a microbial community during intermittent nutrient supply. Additionally, to make flux quantitation tools more scalable and accessible, the group innovated a two-stage machine learning (ML) framework termed ML-Flux. ML-Flux is trained using data from five universal models of central carbon metabolism and 26 different carbon-13 (¹³C) and dihydrogen (²H) glucose and glutamine tracers to convert isotope labeling patterns into metabolic fluxes. Using ML-Flux with multi-isotope tracing, the group determined fluxes and free energies through central carbon metabolism at orders-of- magnitude faster speeds than traditional MFA. Taken together, dynamic multi-isotope tracing identified the role of parallel pathways in balancing metabolic stability and adaptability as a key design principle. ML-assisted multi-isotope tracing is a promising step toward making flux quantitation in complex biological systems increasingly accessible and expanding understanding and control of metabolism.

The National Microbiome Data Collaborative (NMDC) is a multinational laboratory initiative focused on advancing innovation and discovery in the field of microbiome science through the project’s development of products and tools for the environmental microbiome research community (Wood-Charlson et al. 2020). The NMDC provides the community with three products: (1) The Submission Portal, (2) NMDC Empowering the Development of Genomics Expertise (EDGE), and (3) The Data Portal (Eloe-Fadrosh et al. 2022), each aimed at making multiomics microbiome data findable, accessible, interoperable, and reusable (FAIR). The NMDC team utilizes a user-centered design approach through the implementation of insights gleaned from user research, usability testing, and community feedback to continuously improve its products. This team routinely engages with microbiome researchers to discuss how they want the NMDC products to look and operate, as well as understand what new functionality would benefit future research.

The NMDC communicates and engages with many types of stakeholders, including funding agencies, publishers, institutions, programs, projects, and individual scientists. As part of these collaborative efforts, NMDC hosts and co-hosts workshops (Vangay et al. 2021), webinars, presentations, panel discussions, and other events aimed at spreading awareness of and lowering barriers to adoption of FAIR principles in microbiome research and data generation.

The NMDC Ambassador Program (microbiomedata.org/ambassadors) allows early career researchers to host some of these events, thus expanding the overall reach of the content and training materials, while providing the Ambassadors with valuable experiences and career opportunities. The NMDC Champions Program (microbiomedata.org/community/championsprogram) brings together microbiome researchers from diverse backgrounds to contribute to the NMDC (e.g., by beta-testing the NMDC products, co-authoring publications with the NMDC team, providing feedback). The NMDC will continue to prioritize community engagement as its products and network grow. The NMDC engagement strategy focuses on promoting a collaborative ecosystem for diverse microbiome researchers and implementing community feedback in all of the NMDC efforts and products.

Continental scale research is important to understand global processes such as climate change and ecosystem dynamics and can be used to identify patterns and trends including spatial variations and temporal trends. The National Microbiome Data Collaborative (NMDC) Data Portal (Eloe-Fadrosh et al. 2022) has sample and processing information as well as standardized workflow results for several continental scale datasets of high value to the research community. To support a continental-scale understanding of terrestrial ecosystems, the NMDC hosts soil data from National Ecological Observatory Network (NEON) sites, Environmental Molecular Sciences Laboratory’s (EMSL) 1,000 Soils Research Campaign, and the Earth Microbiome Project 500 (EMP500). To enable continental- scale research of aquatic ecosystems, the NMDC hosts freshwater and benthic data from NEON sites and freshwater samples used to generate the Genome Resolved Open Watershed (GROW) database. These efforts all leverage standardized and documented sampling protocols, enabling comparisons of datasets across sites.

The Data Portal is focused on making multiomics datasets more findable to enable data reuse. It provides search tools to find information by principal investigator or study name, by sample information like geographic location, collection date, and depth, or by information about how the omics data was generated. Additionally, data can be searched by Kyoto Encyclopedia of Genes and Genomes (KEGG) terms to identify samples by molecular function. Once datasets have been identified, standardized workflow results can be downloaded in bulk via the website.

The DOE Systems Biology Knowledgebase (KBase) is a knowledge creation and discovery environment designed for biologists and bioinformaticians. KBase integrates a large variety of data and analysis tools, from DOE and other public services, into a user-friendly platform that leverages scalable computing infrastructure to perform sophisticated systems biology analyses. KBase is a publicly available and developer extensible platform, enabling scientists to analyze their own data alongside public and collaborator data, then share their findings across the system and ultimately publish reproducible analyses.

The DOE Systems Biology Knowledgebase (KBase) is a knowledge creation and discovery environment designed for biologists and bioinformaticians. KBase integrates many data and analysis tools from DOE and other public services into an easy-to-use platform that leverages scalable computing infrastructure to perform sophisticated systems biology analyses. KBase is a publicly available and developer- extensible platform that enables scientists to analyze their data within the context of public data and share their findings across the system. This presentation describes a new modeling pipeline developed by a collaborative project between KBase and the μBiosphere Science Focus Area (SFA) and Pacific Northwest National Laboratory (PNNL) Soil SFA.

Mechanistic understanding of a biological system begins with accurate functional annotation of the system’s proteins. Unfortunately, in most cases, protein annotations are uncertain and error-prone, while most analytical pipelines treat annotations as either present or absent. Genome-scale metabolic models (GEMs) permit the evaluation of metabolic annotations within the broader context of the living machines they characterize and, thus, are ideal tools for considering and resolving uncertainty to arrive at the optimal set of annotations that best describe all experimental observations about an organism.

Here, researchers describe an ecosystem of metabolic modeling tools collaboratively developed within KBase to accomplish this goal. The system begins by annotating protein sequences using Rapid Annotation using Subsystems Technology (RAST), Protein Data Bank (PDB), Distilled and Refined Annotation of Metabolism (DRAM), Prokka, and GLM4EC. The system also supports the upload of annotations produced outside of KBase. These tools provide a pool of probabilistic protein annotations that this modeling framework will draw upon to mechanistically explain organism phenotypes.

Next, the newly developed ModelSEED2 tool is used to build a draft GEM. This tool offers significant enhancements over the previous reconstruction apps in KBase, including (1) dramatically improved representation of energy metabolism; (2) improved archaea and cyanobacteria reconstruction; and (3) curation of all metabolic pathways with mappings to RAST subsystems annotations (Faria et al. 2023). The ModelSEED2 generates larger models with more reactions and genes and fewer gaps. Applying ModelSEED2 to thousands of diverse species, group members see conserved patterns in the adenosine triphosphate biosynthesis mechanism across phylogeny and identify clades where understanding of energy biosynthesis is still poor. Gaps in the draft GEMs also offer a metric to evaluate annotation quality at the systems level.

New ensemble modeling tools then sample from the probabilistic pool of hypothesized protein annotations to produce an ensemble of potential draft GEMs. GEM ensembles are evaluated based on: (1) adenosine triphosphate biosynthesis mechanisms, (2) gap filling needed to replicate observed phenotypes, and (3) agreement of simulated flux with multiomics data. A subset of best-performing models can then be extracted and retained for further analysis. Gap filling is essential to this ecosystem as it selects the most probable annotations that best fit experimental observations (e.g., observed growth phenotypes or multiomics data). The new OMEGGA gap-filling algorithm globally fits a GEM to available phenotype data using reactions associated with the highest probability annotations and genes with expression in multiomics datasets.

With KBase building the ModelSEED2, μBiosphere SFA building the probabilistic annotation system, and PNNL Soil SFA building OMEGGA, this has been a collaborative endeavor. This project demonstrates the efficacy of the tools by applying them to study isolates and omics-datasets from the μBiosphere and PNNL’s Soil Microbiome SFA (McClure et al. 2022). The group annotates each isolate, constructs and optimizes GEMs, and fits the GEMs to phenotype and expression data generated for the isolates. As a result, researchers greatly reduce gaps in GEM pathways and improve isolate annotations.

The DOE Systems Biology Knowledgebase (KBase) is a knowledge creation and discovery environment designed for both biologists and bioinformaticians. It is a powerful platform for biological research and analysis, providing a breadth of reference data, analysis applications, and resources to the scientific community. Usage of these apps and narrative descriptions of their results can be easily presented in the KBase Narrative Interface. This poster presents a novel set of artificial intelligence–driven tools to assist researchers in using KBase to analyze their data and publish their findings.

Rapid advances are being made in artificial intelligence using large language models (LLMs) as a natural language processing tool for various applications. This project takes advantage of the growth of this technology to produce a KBase Research Assistant that will serve as a guide, facilitating navigation and analysis within the KBase platform. The team envisions the Assistant as a tool that can converse with a KBase user, understand their data and its relationship with public data on the system, and leverage this to reach the user’s analysis goals. This Assistant will also aid in communicating results via static narratives and academic publications.

The initial target for the KBase Assistant will help a user start with a set of sequenced reads from a microbial isolate to produce an annotated genome that can be used in further analyses by the community. The general workflow here uses KBase apps to assemble and annotate the reads, accompanied by quality assurance and control at each step. The Assistant will help with interpretation of the output of each app and craft each subsequent step in the workflow, with variation where needed.

To build this first genome builder assistant, several LLM-related tools are being created to interact with each other and the user’s data in a user-driven workflow. After uploading their reads to a KBase Narrative, a user will be able to activate the Assistant, which will orchestrate different groups of artificial intelligence agents. The first agent will be a modular reasoning, knowledge, and language agent that will make use of retrieval augmented generation (RAG) tools to perform app recommendation. These RAG tools provide knowledge from KBase documentation and tutorials to ensure proper use of KBase apps. The second agents will manage running the apps and provide results to the assistant for analysis and interpretation, followed by suggestions of the next steps. A third set of agents will ensure that the narrative gets populated properly with the apps and summaries of results. Once analysis is complete and findings are gathered, an agent will assist with developing a publication.

The KBase Research Assistant will be a powerful step forward in enabling KBase users to take advantage of the full breadth of computational tools and public data that KBase provides. Although its initial focus will be on genome annotation, the Assistant will grow to provide insight and utility for other biological analyses.

Enable integrated scientific research across BER and beyond by facilitating the transfer of data and accompanying metadata between sample, data, and analysis platforms.

Researchers working in the DOE BER program have access to a wide range of data and analysis resources across scientific domains. Each platform provides unique capabilities, ranging from user facilities generating data and preliminary analyses (e.g., DOE Joint Genome Institute and Environmental Molecular Sciences Laboratory), linking sample metadata to standardized data analyses (National Microbiome Data Collaborative), enabling complex reproducible analyses for publication [DOE Systems Biology Knowledgebase (KBase)], and serving as a BER project data repository (Environmental System Science Data Infrastructure for a Virtual Ecosystem). However, researchers typically need to use more than one platform for data integration, processing, publication, and/or collaboration with others, and so need to move data from one platform to another.

The lack of cross-platform coordination poses significant challenges to researchers: data transfer is usually performed manually, by downloading from one platform and uploading at another, which tends to be time consuming, difficult to automate or perform at scale, and error prone; it also removes useful metadata, including citation information and data provenance. As established components of the FAIR (findable, accessible, interoperable, and reusable) data principles, it is important that credit information, provenance, and file metadata are preserved; keeping this metadata also enables the tracking and reporting of the impact of samples and data generated by BER researchers and BER-funded data platforms.

This team is embarking on a new effort to build a Data Transfer Service (DTS) (Wood-Charlson et al. 2023) designed to streamline cross-platform research by providing a simple way to search, access, and transfer data between platforms. The DTS will leverage persistent identifiers (e.g., ORCID, DOI) and community-defined standards (e.g., Frictionless; PROV-O) for capturing file information and provenance. The data package delivered by the DTS will include the data file alongside file-level metadata, data citation, and funder information. These will be captured using the KBase Citation Metadata Schema (Ireland and Wood-Charlson 2023), which seamlessly integrates with OSTI and DataCite publishing schemas. The aim is to streamline and incentivize the practice of citing datasets in the same way that one might cite a publication (Wood-Charlson et al. 2022). In the future, the DTS will support the movement not only of public data but also private datasets with secure authentication, and the design is extensible enough that it could be used to connect resources outside BER (e.g., National Center for Biotechnology Information, National Aeronautics and Space Administration), making it a versatile tool for a wide range of scientific investigations.

The ability to easily move data between BER platforms, without the hassle of manual transfers or the risk of losing valuable metadata and provenance information, will be a significant benefit to the many researchers who use more than one platform for data analysis, management, and publication. The DTS not only promises to improve the efficiency of data transfers and facilitate cross-platform collaboration, but also to enhance the integrity and usability of the data itself, paving the way for new insights and facilitating advances in scientific research.

The goals of this project are to elucidate fundamental principles of species interactions using hybrid machine learning/simulation models of fungal-bacterial interactions and dynamics. These hybrid data analytic/simulation models are used to carry out virtual experiments and develop fundamental understanding of the interactions between fungi and bacteria, specifically the mycorrhizal fungus Laccaria bicolor and Pseudomonas sp. helper. At the same time, researchers carry out experiments aimed at developing and testing quantitative assays to measure the same interactions, and whose data inform views of biology. Researchers are:

• Evaluating the fundamental physical operating principles of cells and using these principles to develop physics-informed machine learning models of metabolism.
• Evaluating how fungal lifestyles impact growth in sparse nutrient environments.
• Computationally and experimentally investigating pseudomonas chemotaxis in response to L. bicolor produced metabolites.

The exchange of metabolites between microbes is an emergent property that evolves because the exchanged metabolites allow for increased growth of both species by reducing the thermodynamic cost of growth. Instead of each species producing every metabolite needed, metabolite exchange allows each microbe to specialize and efficiently produce a metabolite, such as trehalose, in exchange for one that it cannot produce as cheaply, such as thiamine. In order to evaluate the benefits of such microbial trade, physics-based models are needed that are capable of modeling the thermodynamic costs and benefits. The long-term goal of this project is to understand fundamental principles of fungal-bacterial interactions through physics-based, simulation and machine learning models of metabolism, protein expression and gene expression, and to couple these models to the mycelial growth and bacterial chemotaxis.

In this regard, the maximization of entropy, such as is done through metabolic exchange discussed above, has been alluded to historically or directly stated as the goal or an emergent property of biological systems by both physicists and ecologists, e.g., Lotka 1922a; Lotka 1922b; Odum and Pinkerton 1955; Prigogine 1978; Vallino 2009. Yet, the concept has been underdeveloped regarding application to systems such as metabolism, and because of the abstract nature of the concept, it has gained insufficient recognition as an operational principle among microbiologists and cell biologists. There are several issues regarding the application of the physical principles to biological systems the team addresses in this project. This project first demonstrates how one can determine a species phenotype based on knowledge of the genome and the environmental conditions by using physical principals. Specifically, using statistical thermodynamics, this study shows how a cell’s most probable state (phenotype) can be determined (Cannon et al. 2023), and how physicochemical constraints can be used to predict the internal regulation of the cell (Britton et al. 2020). The team demonstrates these concepts using a sophisticated model of metabolism (King et al. 2023).

To scale these concepts beyond the cell to interactions between fungi and bacteria, this group has developed realistic 2D and 3D models of fungal and bacterial growth in which each cell can contain sophisticated metabolisms and exchange nutrients with other species. These models require sophisticated, large-scale computing. Researchers have teamed with the DOE Exascale Computing project to implement Adaptive Mesh Refinement (AMR) using high performance computing in these models. The AMReX (AMR for the Exascale) project at Lawrence Berkeley National Laboratory supports the development of block-structured AMR algorithms for solving systems of partial differential equations on exascale architectures. The team has now developed large-scale, hybrid central processing unit-graphics processing unit simulations for growth of bacterial colonies (Palmer et al. 2023), growth of fungal mycelia, and the integrated growth and metabolic exchange between fungi and bacteria. By the end of the year, these methods will enable sophisticated computer experiments that can be used to complement experimental field work and omics measurements.

Marine picocyanobacteria are a ubiquitous component of phytoplankton and play an outsized role in global primary production. As part of a larger project to use cyanobacteria and cyanophage as a model system to understand molecular mechanisms of pathogenesis and disease transmission, this team is performing sampling, metadata collection, and sequencing to understand their complex natural host-parasite relationships in relation to environmental factors. To achieve this, the group sampling coastal Salish Sea waters along the northern Olympic Peninsula at various times of day, times of year, depths, and tidal flow conditions. Here, the poster describes preliminary sampling, sequencing results, and metadata analysis results. Sampling involved collecting surface-level serial filtration samples from the Pacific Northwest National Laboratory Sequim floating dock, in-line filtration of a raw seawater line, and cultured specimens.

Filtering was performed to collect whole plankton, picocyanobacteria, and viral fractions, the last two to enrich sequencing efforts to focus on the target organisms. Preprocessed sequence reads were aligned to known cyanophage as well as the cyanobacteria Prochlorococcus marinus and Synechococcus elongatus genomes, demonstrating the presence of organismal targets but at low frequency, as expected during the cold, low light winter months. These initial survey results provide a promising foundation to establish standard sampling and metadata collection protocols to sample over the next three years to better understand cyanobacterial-cyanophage coevolution. Further steps will integrate genomic, multimodal structural and proteomic data with evolutionary models to better understand cyanobacteria-cyanophage ecotypes and coevolutionary interactions.

Gene regulation is governed by a multitude of proteins and RNAs, and especially transcription factors. Transcription factors control gene expression by binding proximal to genes in their promoter regions or at distal enhancers. The binding of transcription factors is modulated by the state of the DNA molecule, namely whether it is accessible or wrapped around histones, and by DNA and histones modifications. In recent years, several databases that provide vast amounts of plant genomics data from various types of assays have been curated from thousands of published studies. These include genome-wide expression, transcription factor binding, histone modifications, and DNA accessibility. Deep learning has demonstrated its value in modeling large and complex genomics compendia in mammals, providing insights into gene regulation in those systems. However, very little such work has been carried out in plants. This initiative proposes to leverage the wealth of data available in plants to create a deep learning framework called DEEP-PLANT to model plant chromatin state and its consequences for gene regulation. More specifically, the DEEP-PLANT model will predict transcription factor binding and chromatin state directly from sequence. These models will provide a detailed picture of gene regulation and will support downstream applications including the prediction of gene expression, enhancers, and the effects of genetic variation. This work will be carried out in Arabidopsis and rice and shed light on conserved aspects of gene regulation across dicots and monocots and provide plant biologists with tools to form hypotheses on the factors that drive gene expression. The poster will provide a summary of preliminary work towards these goals.

The goal of this project is to use a vast, standardized collection of metagenome-sequenced samples from diverse ecosystems, to uncover microbial traits predictive of different environmental niches. Through advanced machine learning techniques, this research team aims to identify signature metagenome features—spanning sequence domains, functions, and taxa—that correspond to traits characteristic of specific ecosystems and indicate underlying ecosystem relationships at the microbial biogeographic scale.

Earth’s biosphere is an interconnected, dynamically changing network of ecosystems, with microbes playing a significant environmental role. Advances in microbial metagenomics have recently provided extensive data on microbial communities across ecosystems, including biological sequences, taxonomy, and functional annotations. Previous research, focusing on 16S taxonomic data, has shown promising results and indicated vast uncharacterized biological diversity, but 16S barcoding-based methods are constrained by the weak connections to functional annotation data. This project hypothesized that using diverse metagenome features, including sequence domains, functions, and taxa, could reveal traits essential for survival in different ecosystems. Using the largest standardized metagenome sample collection across varied ecosystems, the group trained machine learning models to predict the source ecosystem for metagenome samples. Group members identified optimal metagenome feature types and model parameters, resulting in models that performed well in cross-validation, and training at different ecosystem classification levels improved performance for ecosystems with sparse training data. Model interpretation methods identified signature metagenome features for distinguishing 41 ecosystems, leading to insights about traits that are characteristic of specific ecosystems. This collection of traits, which may have adaptive significance, reveals examples of direct linkages between microbial functions and environmental properties, highlights important unknown functions, and implies ecosystem relationships that align well with established classifications but with ecosystems being more interlinked than is currently appreciated.

The DOE Systems Biology Knowledgebase (KBase) is a knowledge creation and discovery environment designed for biologists and bioinformaticians. KBase integrates many data and analysis tools from DOE and other public services into an easy-to-use platform that leverages scalable computing infrastructure to perform sophisticated systems biology analyses. KBase is a publicly available and developer-extensible platform that enables scientists to analyze their data within the context of public data and share their findings across the system. Here, the team describes a microbial carbon-utilization phenotype prediction pipeline developed as a collaboration between KBase, the Oak Ridge National Laboratory’s Plant-Microbe Interfaces (PMI) Science Focus Area (SFA), and the Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA) SFA.

Deciphering the mechanisms of microbial nutrient utilization phenotypes from genomic data is necessary for understanding microbial niches in ecosystems. However, incomplete gene annotations and limited, inconsistent training data hamper the accuracy of current predictive models. This study tackles these obstacles by integrating diverse annotation sources, including metabolic [e.g., Rapid Annotation using Subsystem Technology (RAST), KOfam), protein functional (e.g., UniProt)], and de novo protein clustering, to enrich feature representations. The group employed a comprehensive dataset comprising 626 diverse microbial genomes and their individual growth outcomes across 98 different carbon sources, facilitating the development of 98 phenotype-specific classifiers.

This study employed a range of feature preprocessing and selection strategies alongside a standardized evaluation framework, to facilitate the comparison of classifier performance and enable the effective integration of models that use different feature representations. Group members evaluated the accuracy and robustness of these classifiers utilizing various feature representations and observed that metabolic features, specifically RAST, exhibit the highest average accuracy across the different phenotypes. However, specific phenotype classifiers exhibit improved performance when utilizing protein function annotations or de novo protein clusters, suggesting that these genomes may possess incomplete metabolic annotations in pathways relevant to the given phenotype. These findings highlight the potential limitations of current genome annotation methods and the need for continued research to enhance understanding of metabolic pathways and their associated phenotypes.

Moreover, the group observed that while feature selection enhances classifier accuracy, methods like non-negative matrix factorization, which reduce feature dimensionality, detrimentally impact performance. This loss in accuracy indicates the critical role of smaller sets of specific enzymes or proteins in phenotype expression. Overall, the merger of feature sets notably boosts prediction accuracy for challenging phenotypes, underscoring this method’s effectiveness in addressing annotation inaccuracies. Furthermore, as a part of the PMI and the ENIGMA SFAs, group members are currently curating gold-standard phenotypic datasets under the same experimental protocols. These standardized datasets will form the foundation for further developing more robust phenotype prediction classifiers that cover a broader array of carbon sources and a phylogenetically diverse set of microbes. Ultimately, the group aims to integrate these classifiers into the KBase platform, making them available as applications and as part of the relation engine-driven pipelines. With this integration, the group strives to enable users to predict microbial phenotypes from a wide array of genomes, including all imported Reference Sequence genomes, thereby significantly advancing microbial research.

This study aims to improve microbial phenotype prediction by utilizing multifaceted feature representations, advanced machine-learning techniques, and standardized datasets to create accurate classifiers for specific phenotypes. These classifiers have the potential to advance scientists’ understanding of microbial growth phenotypes and serve as essential resources for improving annotations of metabolic pathways and understanding of microbial ecology.

For over a decade, the DOE Systems Biology Knowledgebase (KBase) platform has been an essential resource for DOE Biological researchers and the broader biological research community. It provides a comprehensive web-based system for analysis, collaboration, and publishing results. However, as the platform has evolved, it faces increased complexity and security challenges, including end-of-life dependencies and potential vulnerabilities. This project aims to enhance the platform’s security and maintenance capabilities to mitigate high-impact outages, data integrity risks, and security breaches.

This poster presents an overview of the KBase platform architecture, highlighting its wide array of services and underlying databases. This poster outlines the project’s objectives, emphasizing the team’s proactive approach to prioritize updates based on a thorough audit, focusing on critical services and dependencies. Leveraging automated scanning tools like Dependabot and Trivy, the team can efficiently identify and address vulnerabilities, ranging from straightforward dependency updates to complex migrations ensuring both security and operational integrity.

Additionally, the group delves into the specifics of the platform services, emphasizing their importance and role within the KBase ecosystem. This presents progress to date, showcasing milestones achieved in addressing critical security and end-of-life issues for the platform’s core services. This poster concludes with an outline of the next steps, including ongoing maintenance processes and automation, aimed at sustaining these improvements.

Plants produce an amazing diversity of specialized metabolites (SM) that offer many benefits to human society. SMs are essential for pharmaceutical products and non-medicinal applications in the chemical industry, food additives, dyes, perfumes, cosmetics, and nutraceuticals. These products offer the potential to increase the return on investment of current biofuel crops by providing high-value co-products. While many specialized metabolic enzymes have been characterized, their spatial and temporal regulation is less understood, creating a challenge for engineering and optimizing metabolite levels. Understanding how diverse plants differentially regulate the production of the products arising from the same SM pathway will enable researchers to engineer such plants with greater reliability. The goal of this project is to build a computational tool in DOE Systems Biology Knowledgebase (KBase) that would enable researchers to integrate transcriptome data with metabolic networks of general and specialized metabolism for different plant species. This tool will enable researchers to explore different combinations of SM precursors and identify key enzyme targets for engineering.

The research team aims to build a set of classifiers through the application of machine learning techniques that would enable this prediction. To do so, the team will focus on the glucosinolate (GSLs) class of SMs within the plant order Brassicales. This project’s objectives are to (1) experimentally design and benchmark the biosynthesis of multiple GSLs in eight phylogenetically distinct species from diverse families within the Brassicales using high resolution time series datasets; (2) reconstruct the general and specialized metabolic networks for GSL biosynthesis, enabling the integration of omics data; (3) train and test the model to predict GSL biosynthesis; and (4) use the KBase platform to encode this approach in a series of apps that will enable other researchers to apply this approach to their pathway of interest. To disseminate the utility of the tool for target identification for SM production, the team will host virtual and onsite training workshops. This will help to spur research into engineering plants as platforms for co-production of biofuel and co-products and also increase the plant user community on KBase.

Project Goals: This team is expanding scikit-bio, a popular and versatile bioinformatics Python library. The team is implementing functionality for large-scale multiomic data analysis to examine complex relationships between plants, microbes, and the environment. Scikit-bio is an open source Python library offering an extensive range of bioinformatics functions to support microbiome research and beyond. The team’s continuous efforts to optimize fundamental algorithms have enabled the analysis of extremely large communities. The GSP award has empowered the team to accelerate scikit-bio development since September 2023.

Team Building: A competitive developer team has been successfully reassembled. Software engineer Matthew Aton and bioinformatician Dr. Lars Hunger have been recruited and are effectively working on the project with the senior members. Three undergraduate students from Arizona State University and University of California–San Diego have been engaged in the project. The revived scikit-bio has also attracted multiple community contributors. The team has been meeting monthly to ensure a cohesive overview of progress and plans.

Overall Advancements: In the first six months of this project, a total of 40 pull requests have been merged into the codebase. A redesigned website (scikit.bio) is online, featuring reorganized documentation for users and contributors. The codebase has been rigorously refactored to match modern standards. For example, Ruff was adopted to standardize code style across the project. Support for the latest Python ecosystem, including Python 3.12 and SciPy 1.12, has been unblocked. A new release of scikit-bio is anticipated by the meeting time.

Sparse Matrix: The Biological Observation Matrix (BIOM) library has been integrated into scikit-bio, marking a significant enhancement in its ability to represent and manipulate sparse data matrices, which are characteristic of various omic data types. This integration not only streamlines the handling of large-scale omic data but also optimizes computational resources by focusing on the non-zero values. Efforts are ongoing to further optimize algorithms to take advantage of sparse matrices.

Metadata Object: The team adapted and augmented the metadata module from the popular QIIME 2 package. This improved module supports a wider range of metadata types, extending from numeric and categorical to also include Boolean, ordinal, temporal, and free text, among others. Additionally, it introduces standardization for sample identifiers, like specimen ID and host subject ID. Efforts are underway to develop a data dictionary object, which will provide essential context for metadata values, facilitating harmonization of data across omic layers and studies.

Diversity Metrics: Multiple phylogeny-aware diversity metrics such as balance weighted phylogenetic diversity (BWPD) have been implemented to facilitate modeling of complex communities in light of the evolutionary relationships among microbes. Meanwhile, the team refined the implementation and documentation of existing metrics.

Biological Sequences: The team has expanded the functionality of biological sequences. The sequence alignment function is being redesigned to improve efficiency and usability. Sequences can be converted into tokens to facilitate feature annotation using machine learning frameworks.

Workshop: The team’s proposal for hosting a fullday tutorial of scikit-bio at the Intelligent Systems for Molecular Biology 2024 conference in July has been accepted. The team anticipates enrolling up to 40 mentees, including researchers, educators, and developers. These efforts aim to make scikit-bio increasingly useful to the science community.

The use of image classification machine learning models has the potential for great impact on the speed and accuracy of medical diagnoses. The ability to accurately identify genetic perturbations based on cellular morphology would be crucial to the medical field. A large amount of research has been conducted on the effects of acute, high-dose radiation on the morphological profile of human cells. However, the effects of low-dose radiation on cellular morphology have yet to be investigated.

For the purposes of this study, researchers focused on the analysis of human umbilical vein endothelial cells (HUVEC) that have undergone low-dose radiation exposure. If phenotypic features of the HUVEC cell’s morphology can be identified using this model, it could lead to advanced and more efficient screening for low-dose radiation exposure. The team implemented a vision transformer pipeline as the image classification model for this study, specifically the mura vision transformer, which has shown reliable validation accuracy. In order to train this model, the team implemented a pipeline that utilizes the CellProfiler cell image analysis software to perform cell segmentation on HUVEC cell painting images. The CellProfiler pipeline the team developed allows researchers to stack the several channels of cell painting images and export the images of each individual cell into the vision transformer.

Radiation exposure has a wide spectrum of impacts on human health, notably in carcinogenesis, but also in neurological and cardiovascular disorders. While acute toxicity from high doses of radiation is well characterized, understanding the range of outcomes following exposure to low-dose radiation is more challenging. This research team is establishing new experimental workflows that will enable high throughput experiments across molecular and cellular scales to facilitate more comprehensive modeling. In the pilot study, a monolayer of human umbilical vessel endothelial cells (HUVECs) was exposed to a point source of ¹³⁷Cs at a low-dose rate (6 milligrays/hour). Cells were exposed for one week in culture (a total dose of 1,008 milligrays), and then harvested for RNA or replated for Cell Painting staining. Cell Painting is a streamlined multiparameter approach to fluorescence microscopy that provides rich feature data of cell structure and function.

A major advantage of Cell Painting is a robust, publicly available dataset spanning thousands of small molecular and genomic perturbations produced by a collaborative team (the JUMP Consortium). The scale of characterized phenotypes has facilitated development of predictive models that incorporate chemical structural information, biological mechanism of action, and gene expression, which this team will expand into the realm of radiation exposure. With Cell Painting, features can be extracted based on staining of nuclear, endoplasmic reticulum, plasma membrane and Golgi, actin, nucleoli, and mitochondria. Principle component analysis of control and irradiated cells provided a proof-of-principle demonstration that Cell Painting enables detection of features impacted by irradiation. The team’s transcriptome analysis revealed that in endothelial cells, radiation robustly induced cell response pathways integral to cytokine and chemokine pathways, such as the Tumor Necrosis Factor pathway. Underscoring the relevancy of HUVECs to cardiovascular disease, pathways associated with “lipid” and “atherosclerosis” were also activated. Two Kyoto Encyclopedia of Genes and Genomes terms shed light on the molecular mechanisms of these processes, namely the HIF-1 and NF-kappa B signaling pathways.

To compare these results to previously obtained studies of low-dose radiation exposure, the team compared its data with gene expression datasets from the RadBioBase, a publicly available comprehensive transcriptome repository of irradiated mammalian samples. Researchers selected datasets that used human cells and doses below 0.5 grays to identify 235 genes impacted by radiation across four published datasets. Of these, 35 genes were also seen in this study’s data, notably the inflammatory cytokines IL6 and IL1B, as well as the genes PTGS2 (COX2) and CXCL12, which are involved in inflammatory processes underlying cardiovascular disease.

To overcome the limitations (variable dose field, high activity) of the point radiation source in the pilot study, a major goal of the next phase of this project is to prototype and deploy new source geometries (96-well plate format) for high-throughput experimental exposures. New source geometries will require minimal activity, provide uniform dose fields, and allow for multiple dose rate exposures in parallel. Researchers will then assess the impact of low-dose radiation harnessing molecular (multiomic) and cellular (Cell Painting) assays that can be used to develop advanced multiscale models of the impacts of low-dose radiation.

The complex dynamics of root-microbe interactions in the rhizosphere drives recognizable spatial structures. However, knowledge of the specific factors that lead to their development and sustain them for plant health and productivity is sparse.This project aims to develop a unique functional imaging technique that exploits native sense-and respond circuits of plant growth–promoting rhizobacteria (PGPR) to monitor chemical exchange between the plant root and microbe during the different phases of colonization.

Several native PGPRs are turned into biosensor cells, and root colonization is evaluated with Arabidopsis, camelina, and poplar. Genetic variants of Arabidopsis with gain or loss of function provide drastically altered local environments, resulting in colonization patterns that differ from those observed previously. An orthogonal X-ray imaging approach provides high resolution elemental analysis of the local environment, and imaging throughput in general is accelerated by automation and analysis driven by artificial intelligence (AI). In addition, the team aims to advance the throughput of current bioimaging capabilities that leverage imaging chips developed with BER funding with automation, and an AI-guided image analysis strategy.

Updates from this project will be presented with specific focus on promoter library development, biosensor design and testing with Arabidopsis, phenotyping and genotyping of new PGPRs, AI-based image analysis, and automation.

Cryogenic electron tomography (cryo-ET) is a powerful approach to observe subcellular architecture and can even achieve near atomic resolution when specific complexes can be computationally identified, aligned, and averaged. Advances in this area have led to a situation where biological insight is often not limited by resolution, but instead by a lack of contextual information with which to interpret observed structures and by an inability to work with non-model systems, such as plant roots. This work aims to tackle both these issues through the development of biosensor cryogenic correlative light and electron microscopy (BioCryoCLEM) and advanced sample preparation techniques, including custom electron microscopy grids.

BioCryoCLEM correlates fluorescent biosensor data with electron tomography, providing essential physiological context alongside high-resolution structural information. This research group has calibrated biosensors for calcium, pH, and molecular crowding, demonstrating the workflow using the molecular crowding sensor. While broadly applicable, this project’s focus is on investigating the plant plasma membrane-cell wall interface and its response to biotic (microbes, pathogens) and abiotic (salinity, drought, nutrients) effectors.

Unfortunately, achieving high-quality cryogenic electron microscopy is challenging as soon as one deviates from model systems. Thick plant tissues pose specific difficulties due to their size and the presence of large vacuoles which both serve to slow freezing and makes sample preparation prone to crystalline ice formation. This presentation discusses the team’s work in employing the latest of sample preparation techniques, including high pressure freezing, cryogenic-lift-out, and custom grids to hold the roots and minimize sample volume as researchers work towards the goal of obtaining cryo-ET of the plasma membrane–cell wall interface.

It is challenging to visualize the dynamic metabolic processes of living plants, algae, and fungi as they are exposed to environmental stressors. This is especially accurate for tracking biomolecules that are difficult to label with fluorescent probes, such as lipids and carbohydrates. A prototype of an advanced stimulated Raman scattering (SRS) microscope for conducting innovative studies in this field is currently being designed and assembled.

This microscope utilizes squeezed light and structured illumination to enable prolonged examination and direct chemical analysis of biological processes without compromising the system’s structural integrity or dynamics. The term squeezed refers to the quantum uncertainty of the electromagnetic field strength of the light. Light in a squeezed state has an uncertainty of the field strength that is smaller than that of a coherent state. The squeezed light source will increase the signal-to-noise ratio of SRS by up to ten times. Consequently, the range of chemical imaging studies that are possible will increase, and the likelihood of photodamage will decrease to allow for the examination of extensive regions of interest and prolonged image capture.

Plant–pathogen interactions are complex and dynamic phenomena, relevant to fundamental, environmental and bioenergy biology. Imaging plays important roles in understanding the interactions at the atomic, molecular, subcellular, cellular, to tissue and whole plant scales. Each bioimaging method has its intrinsic limitations in spatial and/or temporal resolution, field of view and depth, and sensitivity. Fluorescence-based optical microscopes have huge advantages in monitoring dynamics of cellular and subcellular events using wide spectral ranges with super-resolution, light-sheet 3D, and wide-field imaging, but cannot go beyond 10 or 20 nanometer spatial resolution. X-ray imaging and tomography can penetrate deeper than light, and with high brilliance synchrotron sources one can reach 10 nm spatial resolution, but with a high risk of sample damage from radiation dose. Cryogenic electron tomography (cryo-ET) can offer tremendous insight into the subcellular organization of organelles and macromolecules down to several nm resolution, but information gained is largely static.

Experimental validation of the spatial position and size of molecules observed in these methods typically requires some sort of reference probe or fiducial marker in the case of tomography. These markers are typically exogenously added for correlation, and this project arm aims to improve upon this by developing molecular tracers with protein nanocages containing metal nanocrystals that also serve as intrinsic fiducials. The group presents advances towards fluorescent protein- and nanocrystal-containing cages as molecular tracers for X-ray imaging/microscopy and EO-FLIM and fiducial markers for cryo-ET, including proof-of-concept synchrotron TXM images of leaf samples bombarded with 400 nm nanogold particles. The group shares ongoing development and discuss further plans for the initial application examining fungal-plant pathogen interactions via chitin binding domains at the plant cell surface using split fluorescence complementation probes.

Root biology is pivotal in addressing global challenges, including sustainable agriculture and climate change. However, roots have been relatively understudied among plant organs, partly due to difficulties in imaging root structures in their natural environment. Here, researchers used microfabricated ecosystems (EcoFABs) to establish growing environments with optical access and employed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution.

THG enabled researchers to observe key plant root features in mature and meristem roots including laminar structures down to the vasculature, Casparian strips, dividing meristematic cells, and root cap cells, as well as resolving subcellular features including nuclear envelopes, nucleoli, starch granules, and putative stress granules. THG from the cell walls of bacteria and fungi also provide label-free contrast for visualizing these microbes in the root rhizosphere. With simultaneously recorded 3PF fluorescence signal, the team demonstrated its ability to investigate root-microbe interactions by achieving single- bacterium tracking and subcellular imaging of fungal spores and hyphae in the rhizosphere.

Nowadays, Raman imaging represents only a modest fraction of all research and clinical microscopy to date even though it exhibits great potential. This limited adoption is primarily attributed to the ultralow Raman scattering cross-sections of most biomolecules, resulting in low-light or photon-sparse conditions. Imaging biological samples under such conditions is suboptimal, leading to either extremely low frame rates or the need for higher levels of irradiance. In this study, researchers address this tradeoff by introducing Raman imaging capable of operating at both video rates and with irradiance levels 1,000 times lower than existing methods. To achieve this, the team utilized a carefully designed Airy light-sheet microscope, which efficiently images large specimen areas (Dunn et al. 2023). The Airy beam, known for its unique diffraction-free properties such as self-healing and refocusing, has been employed in lightsheet microscopy (LSI) (Subedi et al. 2020; Subedi et al. 2021) and selective plane illumination imaging schemes. The team investigated the diffraction-free behavior of Airy beams as a function of cubic phase modulation ‘α’, both theoretically and experimentally.

Additionally, group members implemented subphoton per pixel image acquisition and reconstruction techniques to address challenges arising from sparse photon availability during short integration times. This project demonstrates the versatility of this approach through successful imaging of various samples, including the three-dimensional metabolic activity of individual microbial cells and their associated cell-to-cell variability. Moreover, to visualize small-scale targets, the group leveraged photon sparsity and photon superlocalization to increase magnification without sacrificing field-of-view, thereby overcoming another significant limitation in modern light-sheet microscopy.

Cellulose and lignin, the primary constituents of plant biomass, are essential to the development of sustainable bioenergy solutions and the advancement of the bioeconomy. Their abundant availability and renewable nature make them ideal candidates for biofuel production, biocomposite materials, and as models in cutting-edge characterization. This team investigates the synthesis and deposition of guaiacyl lignin on cellulose nanofibrils, emulating the process of secondary cell wall formation in plants. Such a focus is crucial for enhancing understanding of plant biomass’s resilience and efficiency in bioenergy conversion processes. Utilizing coniferyl alcohol and employing biocatalysis with horseradish peroxidase and hydrogen peroxide, the team mimics the natural polymerization of lignin, offering a controlled environment to study its interaction with cellulose at the highest achievable nanoscale classical resolution.

By combining Micro-Infrared Spectroscopy, Confocal Raman Spectroscopy, Atomic Force Microscopy, and Nano-IR Spectroscopy, the team aims to provide a broad understanding of the bulk and nanoscale properties of these biopolymer composites. Such polymer-scale bioimaging and chemical characterization are indispensable for revealing the molecular-level interactions and structural arrangements, enabling bio-based material science. Moreover, this work serves as a foundational study for exploring the properties of tension wood, which exhibits unique characteristics in its mechanical, cell-level structure, and compositional behavior.

Using these cellulose and lignin samples as control systems, the team can achieve tension and compression in a controlled manner, establishing baseline data crucial for understanding the material response in grown tension wood measurements. This approach not only aids in studying the complex polymers within tension wood but also sets the stage for comparing these natural systems with these bioengineered samples, enhancing understanding of plant biomass mechanics. The group’s motivation extends beyond traditional studies, aiming to bridge the gap between classical and quantum bioimaging techniques. By establishing a solid understanding of the classical Raman spectroscopy limits and characteristics of cellulose-lignin interactions, this study paves the way for research in quantum bioimaging. This biosystem will allow researchers to explore the quantum features of Raman measurements, providing a biomolecular framework for addressing the limitations faced by current imaging methodologies. Furthermore, in preparation for quantum microscopy studies of enzyme-plant cell interactions, the group will introduce enzymes to these cellulose and lignin films. Such preliminary characterizations are vital for understanding how enzymatic actions modify the plant cell walls at the nanoscale, ultimately informing the quantum bioimaging of real enzyme-plant sample interactions.

This sequential approach, from classical imaging to quantum measurements, offers a comprehensive strategy for dissecting the complex dynamics of plant biomass at the forefront of bioenergy research and quantum science. This investigation contributes to the fundamental understanding of plant biomass structure and introduces a methodological approach towards utilizing quantum bioimaging for bioenergy applications. By elucidating the interactions between lignin and cellulose, this study unlocks potential avenues for optimizing biomass conversion into biofuels and developing sustainable materials, aligning with the goals of a circular economy and pushing the boundaries of material science into the quantum realm.

The ability to perform chemical nanoimaging of biosystems in liquid remains challenging despite recent experimental advances. Fluorescence-based super-resolution techniques such as stimulated emission depletion microscopy and stochastic optical reconstruction microscopy allow tracking of tagged analytes with nanoscale spatial resolution. However, the development of generic chemical nanoimaging techniques is needed to study systems or analytes for which fluorescent tags are unavailable or infeasible.

Towards this goal, this team designed and built a multimodal hyperspectral micro/nanoscope (MHNano), which combines optical spectroscopy with scanning probe microscopy to enable (non)linear optical measurements with scales from hundreds of micrometers to a few nanometers in a single platform. Compatible with nonlinear imaging, perform micro- to nanoscale resolution the linear and nonlinear optics, [e.g., tip- enhanced (two-photon) photoluminescence (TEPL/ TE2PPL)]. In this work, researchers demonstrate the spatial resolution of TEPL and TE2PPL with sub 5 nm from cadmium selenide and zinc sulfide semiconductor quantum dots (QDs) using sputtered plasmonic gold probe under ambient conditions. A custom liquid cell allows high numerical aperture split excitation and collection for in situ Raman and nonlinear measurements. The capability of TEPL/TE2PPL paves the way for (non)linear photoluminescence-based or Raman spectral nanoimaging of biosystems in their native environment.

Quantitative phase imaging (QPI) has recently emerged as a widespread optical imaging method for measuring the dry mass and density of individual cells, two key metabolic parameters in biological systems. Despite its potential, applying QPI techniques to specimens that are thicker than 500 wavelengths faces significant challenges. In such cases, optical scattering from thick specimens compromises image quality by increasing background noise and reducing contrast. To overcome these challenges, various strategies have been explored, including laser-based tomographic methods and asymmetric illumination/detection interferometry that uses incoherent light to avoid speckle-driven image degradation. However, these methods require expensive optical elements, such as spatial light modulators or polarization-sensitive cameras, that additionally are known to reduce imaging efficiency due to energy losses.

To address these shortcomings, this research team developed Gradient Retardance Optical Microscopy (GROM), a QPI technique that is compatible with 3D imaging and requires no computational image reconstruction. GROM operates by transforming asymmetrically illuminated intensity images into phase gradient images and enables fully automated 3D acquisition of interferometric images using custom-made routines in open access platforms. Further, GROM can transform any standard microscope into a QPI platform by placing only a liquid crystal retarder between the illumination condenser and the sample. Through this method, the group has successfully reconstructed a variety of imaging targets, including conducting 3D volume viewing of individual bacteria and fungi, as well as a 500 micrometer–diameter plant roots tissue of the model system Medicago truncatula, showcasing the depth and versatility of GROM’s capabilities.

Marine cyanobacteria are well-known for their role in fixing nearly 30% of organic matter on Earth. Up to 60% of the cyanobacterial cells, however, are infected by phages. During phage infection, cyanobacterial metabolisms are reprogrammed towards phage replication, and the carbon dioxide (CO2) fixation functional module is inhibited via expressing phage auxiliary metabolic genes (AMGs). Such phenotypic change in primary producers is especially concerning given the impact of climate change. Moreover, different phages induce different host responses and life cycle changes that are likely driven by metabolic and molecular interaction network reprogramming.

Here, group members describe a genome-scale metabolic model of cyanobacteria combined with two additional biomass objective functions driving replication of two cyanophage strains, P-HM2 and P-SSP7. Using the dynamic flux balance analysis, the group will compare host-optimal solutions and phage-optimal solutions in the diurnal cycles with changing light intensities. Then the group will use this functional information of AMGs to compare the metabolic fluxes infected by different phage strains by enforcing AMG-associated reactions.

These analyses will reveal interactions between host and phages as well as metabolic reprogramming by cyanophages. Furthermore, this model will provide a basis for integrating multiomics data with a whole-cell systems model of cyanophage infection to better understand host-virus interactions. This research team will use the metabolic network as functional coordinates for enzymes. The abundance and state changes (e.g., post-translational modifications, conformational and interactional changes) can be mapped to the metabolic network model and whole-cell model to study their subsequent effects on phenotypes of cyanobacteria and/or cyanophages.

Sorghum is the second most cultivated U.S. biofuel crop and is the primary source of biodiesel production worldwide. Due to its drought resistance and fast growth of biomass as a source for ethanol production, sorghum is one of DOE’s flagship bioenergy crops. However, sorghum diseases such as anthracnose, stalk rot, downy mildew, grain mold, and leaf blight reduce the yield of sorghum biomass production.

Anthracnose alone can lead to yield losses of up to 67% in susceptible sorghum cultivars. Therefore, improving anthracnose resistance and biocontrol in sorghum directly improves its biomass production and bioeconomy.

Sorghum anthracnose disease is caused by the hemibiotrophic fungal pathogen Colletotrichum sublineola. C. sublineola produces a stunning array of virulence effector proteins and other molecules that interact and hijack plant defense systems resulting in infection and disease in sorghum. Conversely, some sorghum cultivars encode intracellular innate immune receptors called nucleotide-binding leucine-rich repeat proteins (NLRs) that recognize effectors to elicit successful immune responses. The co-evolution of sorghum and C. sublineola drives cycles of infection and immunity. This project hypothesizes that the dynamic coevolution of effectors and NLR receptors forms the basis of sorghum immunity in response to C. sublineola and other pathogen infections. A key knowledge gap is that none of C. sublineola effectors are biochemically or structurally characterized.

Also, there is a lack of mechanistic data regarding the interactions between specific NLR proteins and specific effectors. This lack of molecular understanding of sorghum–C. sublineola interactions is the biggest impediment to the rational design of resistance to anthracnose in sorghum crops and is thus the focus of this project. Leveraging the available reference genome sequences for both sorghum and C. sublineola, group members are employing comparative genomics, transcriptomics, proteomics, and metabolomics to identify C. sublineola effectors and their respective NLRs. The group is using structural biology and bioimaging to characterize the temporal and spatial NLR-effector interactions across scales from atoms to cells and plants. Subsequently, engineering and biocontrol strategies targeting the characterized NLRs or C. sublineola are used to develop resilient and sustainable bioenergy crops. This team is developing computational resources to analyze molecular interactions and genetic co-evolution of the plant–pathosystem, and foundational large natural language models to integrate and train multimodal text and imaging data for predictive understanding of pathogenicity and biocontrol.

While this project focuses on the sorghum C. sublineola system, the results will lay down a groundwork for studying plant-pathogen interactions more broadly. The research strategies and techniques developed under this project will advance scientists’ ability to rapidly respond to emerging biothreats impacting bioenergy crops and plants in unmanaged ecosystems.

Increase in the incidence of antimicrobial-resistant (AMR) bacterial pathogens currently poses an immense threat to normal world order. In addition to the tragic impact on human health, AMR is estimated to have a worldwide economic cost running into trillions of U.S. dollars by severely debilitating agriculture, dairy, aquaculture, livestock, and poultry industries. Bacteriophages (phages) have been proposed as an alternative to antibiotics due to a dearth of new antimicrobial molecules in the discovery pipeline. There have already been some successes in treating AMR pathogenesis by using phages under compassionate use protocols; however, biological tools for broad-scale mechanistic characterization of phage-host interactions in clinically and agriculturally relevant bacteria are still limited, hampering development and application of phages as robust antimicrobial agents.

In this project, researchers are developing tools for high-throughput characterization of phage-host interactions on highest priority ESKAPE human pathogens: Klebsiella pneumoniae, Acinetobacter baumannii and Pseudomonas aeruginosa, as well as important crop pathogen Pseudomonas syringae. By using panels of clinically relevant and genomically diverse strains, the team is building a highly diverse collection of phages (“phage banks”) and conducting large-scale phage-susceptibility assays for genome-wide association studies (GWAS)like analyses to identify bacterial and phage genes that drive host range and specificity. To guide phage isolation efforts and build a baseline understanding of the population diversity, mobile genetic elements, and phages associated with pathogens, the team is using a multiomics approach on a large wastewater system. To generate systematic genotype-phenotype mapping, powerful high-throughput genetic technologies, such as genome-wide loss-of-function randomly barcoded transposon sequencing, CRISPR interference and overexpression dual barcoded shotgun expression library sequencing, are being applied to a set of select strains.

This systematic effort in phenotyping and high-throughput genetics will provide fitness landscapes in presence of phages, antibiotics, metals, and other stressors, as well as enable researchers to map the cross-resistance and collateral sensitivities between phages and antibiotics. The characterization workflows, resources and datasets generated at the Biopreparedness Research Virtual Environment Phage Foundry will provide crucial foundational knowledge necessary to develop machine-learning models and facilitate quick and effective prediction of therapeutic formulations for countering any emerging recalcitrant infections.

The ability to counter biological threats is limited given the lack of knowledge of host resilience mechanisms in the face of pervasive pathogens. Research suggests that epigenetic mechanisms and associated chromatin structure regulate the functionality of the genome and play profound roles in host-pathogen interactions. As such, this team hypothesizes that these processes vary between resilient versus susceptible hosts, potentially providing specific signatures (patterns) of infectivity. Further, these signatures may be attributed to classes of pathogens allowing for early detection and mitigation. Yet, the paucity of epigenomics and chromatin structural datasets from systematically tested pathogen exposures precludes identifying these proposed signatures for surveillance and diagnostics in novel species.

As such, this project’s goal is to develop an experimental workflow to generate large omics datasets to characterize and survey early onset molecular signatures of infection, with particular focus on viruses. The workflow is designed to utilize representative molecules for various classes of viruses in the same primary cell culture system (mammalian and plant) to generate single cell sequencing assessments for deep learning and exascale computing analysis. The team’s initial assessments demonstrate the relationship between specific local (epigenetic) and global (genomic) structures and their variability in response to infection, providing novel signatures.

The Structural Molecular Biology (SMB) resource at the Stanford Synchrotron Radiation Lightsource (SSRL) develops, operates, and supports state-of-the-art synchrotron radiation capabilities for enabling biological and environmental research using macromolecular crystallography (MC), small angle X-ray scattering (SAXS), X-ray absorption spectroscopy (XAS) and X-ray fluorescence (XRF) imaging techniques. As a user facility, the SMB resource provides the national scientific community access to these advanced capabilities primarily through general user proposals as well as a strong user support program, which includes outreach, training, and dissemination. The suite of instruments operated by the resource encompasses seven full time synchrotron beamlines at SSRL, four for MC, one for SAXS, two for XAS, and ~1.4 beamline equivalents spread out over six beamlines for μXRF imaging and advanced spectroscopy. Two state-of-the-art undulator microfocus beamlines are optimized for challenging micro-crystal and time-resolved MC measurements. The SAXS beamline is equipped with highly automated solution scattering robotics and features a state-of-the-art chromatography coupled SAXS setup as well as an exchangeable high flux multilayer monochromator for time-resolved experiments in the millisecond timescale. Three dedicated XRF imaging beamlines cover a range of spatial scales (micrometer to centimeter) and elements of biological importance (phosphorus, sulfur, potassium, calcium, and metals).

A powerful aspect of the XRF imaging beamlines is that they can perform µ-XAS to characterize the oxidation state, or chemical species, at a single point within a sample. Combining XRF with XAS is a tool for generating spatial distribution images of individual chemical species of an element within a sample. The synchrotron resource is managed and operated in a fully integrated and centrally coordinated manner, across all beamlines and techniques, facilitating cross technique structural investigations in biological and environmental research, covering length scales from Angstrom to centimeters. Recent results enabled by the SMB resource will be presented, highlighting the scientific potential of interlaboratory collaborations for a multi-technique approach to BER science made possible by a dedicated BER-supported outreach program at SSRL.

The SIBYLS beamline conducts small-angle X-ray scattering (SAXS) to reveal the structure of biological macromolecules in solution (proteins complexes, RNA, lipid nanoparticles) and is supported by BER Integrated Diffraction Analysis Technologies (IDAT). The beamline primarily operates in high throughput (HT-SAXS) and size exclusion chromatography (SECSAXS) modes. The mail-in SAXS program offers users timely, high-quality data. Between September 2022 and September 2023, this program has resulted in 29 publications supported by IDAT. This poster summarizes some high impact studies that were made possible using the SAXS tools developed at this beamline.

First, SEC-SAXS was used in a comparative structural study to investigate the diversity in assembly of the forms of ribulose-1,5-bisphosphate carboxylase/ oxygenase (rubisco), an enzyme that catalyzes the first step in carbon fixation in plants. Characterization of the oligomeric states of deep-branching form Iα and I” rubiscos revealed structural origins of form I, shedding light onto the evolutionary path of rubisco and its transition from a homo-oligomer to a hetero- oligomer (Liu et al. 2023). Another high impact plant study investigated synthesis of pectin, an essential polysaccharide required for plant cell wall expansion. SEC-SAXS of the enzyme galactan synthase (GalS1), which catalyzes side chains in the pectin rhamnogalacturonan, was found to take on an antiparallel dimer orientation in solution, which differed from an alternative crystal structure (Prabhakar et al. 2023), giving insight into the mechanics of pectin synthesis. SAXS is also essential in understanding microbe metabolism. SEC-SAXS was used to elucidate the conformational changes within each step of electron bifurcation in EtfABCX, a membrane-bound superdimer of supermonomers (Murray et al. 2024). Electron bifurcation is a process that generates a high energy electron at the expense of the loss of energy in a second electron and is key in modelling the metabolisms of microbes for the design of novel bioenergetic systems. Finally, the team presents the utilization of SAXS as a tool in protein engineering. HT-SAXS was used to verify the architectures of various helical heterotrimer assemblies designed for use in complex protein nanostructures (Bermeo et al. 2022), as well as tunable protein crystals in solution and dried states (Li et al. 2023) for potential applications in biological sensing, catalysis, separations, and drug delivery.

After the upcoming Advanced Light Source upgrade period, the team will further improve data quality, efficiency of data collection, and implement new sample environments. Additionally, analysis pipelines are in development that will provide precise protein structural conformations by submitting sequences along with samples. The further insights that these improved SAXS studies will provide on biological macromolecules is the foundation for their applications in biomass utilization, microbe engineering, and advanced biomaterials.

This project is focused on the operation of a Krios cryogenic transmission electron microscope (Krios G3i) at the Environmental Molecular Sciences Laboratory (EMSL) to advance DOE BER user research in protein and small molecule structural biology and whole cell ultrastructure. The operation of the Krios G3i instrument is a joint funding venture between EMSL and BER. The microscope is available to the general EMSL user community and BER researchers in a 50/50 split allocation.

EMSL users can access this instrument free of charge via the normal EMSL user proposal calls, which permit combining cryo-electron microscopy (cryo-EM) with other EMSL capabilities such as mass spectrometry or super-resolution fluorescence microscopy. Access is offered free of charge for BER users, with Pacific Northwest National Laboratory staff time funded by this current project. The BER access mechanism allows for an expedited submission and review process for “cryo-EM only” projects.

The Krios G3i is fully operational and has been applied to multiple EMSL and BER User projects. The microscope has complete screening, data collection, and image processing workflows for (1) microelectron diffraction of small molecule or protein crystals, (2) single-particle analysis of soluble and membrane protein complexes, and (3) electron tomography of whole cells or isolated organelles. It is equipped with a K3 direct electron detector, Ceta-D camera, phase plate, and BioQuantum energy filter. In addition to semiautomated data collection, the facility has installed automated image processing workflows for real-time monitoring feedback of session quality and full 3D reconstruction of all workflows. To date, the facility has demonstrated sub-Å resolution microelectron diffraction, sub-2 Å resolution from 3D single-particle protein structure determination, and subnanometer resolution for whole-cell tomography. While the facility provides rapid access for samples that arrive frozen on clipped and prescreened grids, users can also begin with samples that arrive in buffer and require all steps of the cryo-EM workflow. In a subset of cases, users can start from a provided gene of interest and employ the cell-free expression system to produce enough protein for structural characterization. The team will highlight several recent user results as well as an example of going from cell-free expression through cryo-EM structure determination in less than 24 hours. The team will also present an overview of EMSL’s 1,000 Fungal Proteins project, which will use the Krios as a core capability and is accepting user proposals for structural and functional characterization of conserved fungal proteins.

Soft X-ray tomography (SXT) visualizes and quantifies the structural organization of biological organisms up to 20 micrometers in diameter. Specimens are imaged in the near-native state—rapidly frozen in their normal growth conditions—at a resolution up to 35 nanometers. Large numbers of cells can be imaged since it takes only five to 10 minutes to go from the frozen specimen to a reconstructed tomogram. Imaging is based primarily on the absorption of carbon, a common element of all known life. At the same time, water (ice) is virtually invisible so that high-contrast images are obtained based solely on the inherent properties of the structures examined. This is accomplished by imaging with X-ray photons in the ‘water window’ (between 284 to 543 electron volts), where X-ray photons are absorbed an order of magnitude more strongly by carbon- and nitrogen-containing organic material than by water. The absorption of soft X-rays adheres to the Beer-Lambert Law and is, therefore, a function of the chemical composition and concentration of organic material, yielding unique quantitative Linear Absorption Coefficient measurements for specimen components. The team has used this label-free imaging technology to image and quantify a wide variety of structures, including bacteria, yeast, spores, algae, larger mammalian cells and isolated organic particles. This poster will present examples of SXT data that enabled biological findings that couldn’t be obtained with other technologies, including the simultaneous visualization and quantification of carbon, effects of altered environments on cell structures, and novel findings about N₂ cycling in an endosymbiotic organism.

The eBERlight program at Argonne National Laboratory’s Advanced Photon Source (APS) offers comprehensive support to research communities specializing in biological and environmental science. Utilizing synchrotron radiation, the program aids in the understanding of complex Earth systems and offers user support for project development, proposal design, workflow creation, and data analysis. Operating across multiple APS beamlines, eBERlight provides access to the state-of- the-art instrumentation for macromolecular crystallography, X-ray full field imaging (computed tomography), X-ray fluorescence microscopy, X-ray absorption spectroscopy, scattering and coherent diffractive imaging (including ptychography). To ensure an optimal infrastructure for demanding experiments, eBERlight leverages additional campus resources for sample preparation and computational data analysis. The ongoing upgrade of the APS facility will greatly improve X-ray capabilities, enabling imaging of larger samples at high resolution, enhancing spatial resolution and addressing dynamic processes. Through high-throughput, multidimensional data collection, unprecedented statistical analysis of complex, heterogenous systems will become attainable. Additionally, simultaneous recording of structural and kinetic data will enable tracking correlations between molecular motions and chemistry, providing valuable insights through serial and time-resolved macromolecular crystallography. These developments will enable researchers to address complex questions relevant to the biological, geological, geochemical, biogeochemical, and environmental sciences.

The Center for Structural Molecular Biology (CSMB) at Oak Ridge National Laboratory (ORNL) is funded to support and develop the user access and science research program of the Biological Small-Angle Neutron Scattering (Bio-SANS) instrument at the High Flux Isotope Reactor (HFIR). Bio-SANS is dedicated to the analysis of the structure, function, and dynamics of complex biological systems. The CSMB also operates a Bio-Deuteration Laboratory (BDL) for expression and purification of deuterium labeled biomacromolecules and for synthesis of small molecules and ligands in support of the biology neutron scattering program. The CSMB supports a vibrant biological research community from academia, industry, and government laboratories.

The Bio-SANS instrument is ideally suited for studies of biomacromolecules including proteins, DNA/RNA, lipid membranes and other hierarchical complexes. The Bio-SANS detector system is designed to allow simultaneous access to a wide spatial range that enables utilization of the full potential of the high neutron flux from the ORNL HFIR cold source. This team has recently completed the next development stage of the detector system by installation and commissioning of a mid-range detector to complement the existing main and wing detectors. This development will improve data quality for hierarchical systems, decrease Q-resolution mismatch, increase angular coverage, and enable sub-minute time resolution.

Sample environment (SE) capabilities that can accommodate sample types ranging from biomacromolecules in solution to biomass are critically important to realize the full potential of Bio-SANS. One recent SE development effort was to upgrade the robotic sample changer originally installed in 2019 with a Universal Robot (UR5), which has an expanded a temperature-controlled holding area for up to 66 sample cells. A Peltier heating block at the sample position allows rapid temperature change between 10 to 100°C. New science opportunities include in situ kinetic processes of complex biological systems using time-resolved SANS with simultaneous access to multiple length scales. Further development is underway to expand this capability to allow liquid handling at Bio-SANS for mixing samples directly before measurement. Another example is chromatography—SANS for in beam fractionation of biomacromolecules that can operate in continuous flow mode as well as fractionation of complex mixtures of biomacromolecules. The flow cell design accommodates four cells to minimize down time during sequential purifications of multiple proteins.

To broaden the impact of the CSMB and catalyze the synergy between BER program–funded structural biology resources, the team established collaborative programs with the National Synchrotron Light Source II for joint access to SANS and SAXS and with the BER Facilities Integrating Collaborations for User Science (FICUS) program between the DOE Joint Genome Institute at Lawrence Berkeley National Laboratory and the Environmental Molecular Sciences Laboratory at Pacific Northwest National Laboratory.

Research in the DOE-University of California–Los Angeles (UCLA) Institute for Genomics and Proteomics (IGP) includes major efforts in the area of imaging science, proteomics, structure prediction and atomic structure determination. These new capabilities help scientists better understand microbial biosystems, their genomics and molecular biology. This team is pioneering new enabling capabilities that facilitate the discovery of molecular structural features affecting protein function and specificity, to better understanding of bioenergy crops and microbes. These capabilities span the broad areas of X-ray diffraction, electron microscopy, and micro-electron diffraction (MicroED), along with computational structure and function prediction methods. This team is also enabling rapid access to robust public-facing tools for use by the BER community. This group’s efforts in imaging science and protein characterization bridge a number of technological areas to address pressing problems in protein structure and function.

Breakthroughs in cryo-electron microscopy (cryo-EM): Numerous technical advances have made cryo-EM an attractive method for atomic structure determination. Cryo-EM is ideally suited for very large structures; symmetrical structures like viruses are especially amenable. However, problems of low-signalto-noise in imaging small proteins makes it practically impossible to determine structures smaller than about 50 kilodaltons, leaving a great many cellular proteins and enzymes (and nucleic acid molecules) outside the reach of this important structural technique. The DOE-UCLA IGP team has broken through this barrier by engineering novel scaffolds with sufficient rigidity and modularity to achieve resolution useful for interpreting atomic structure. This team has applied this system to image a 19 kDa protein, obtaining multiple structures of its sequence variants unbound and bound to a small molecule. The findings highlight the promise of these novel scaffolds for advancing the design of drug molecules against small therapeutic protein targets in cancer and other human diseases as well as other important targets. Recent efforts have been aimed at imaging microbial and plant protein targets.

Enabling microcrystal electron diffraction (MicroED) methods: A broad array of atomic structures has now been determined by MicroED; they include naturally occurring peptides, synthetic protein fragments and peptide-based natural products. This team is further enhancing the capabilities of electron diffraction (ED) by improving understanding of electron counting detectors and their application to diffraction measurements. In addition, the group is broadening comprehension of electron beam-induced radiation damage and its consequences for molecular systems and their characterization at atomic resolution. Collectively, these efforts have yielded new insights into how ED data are impacted by electron beam-induced lattice reorientation and the impact of radiation damage on the ability to determine the chiral nature of handed molecules.

Tools for analysis of condensate or aggregate-forming proteins: The recent revolution in artificial intelligence (AI) and machine learning methods has dramatically improved scientists’ ability to predict protein structure and sequence characteristics. This team has exploited the growing capacity of AI models to train a fully connected neural network to emulate the predictive abilities of computationally time-consuming 3D profiling approaches. This method relies on the network to calculate the propensity of segments in a sequence to form amyloid-like contacts or structures. Whereas the previous approach required weeks or months of compute time to evaluate an entire proteome, the new approach can evaluate the entire yeast proteome in 15 minutes and is available as an online server for public use.

The institute’s enabling capabilities will broadly facilitate the determination and prediction of unknown macromolecular structures with importance for bioenergy.

BSISB Missions:
1. Increase synchrotron radiation-based Fourier- transform infrared (SR-FTIR) capabilities accessible to BER users through Berkeley Synchrotron Infrared Structural Biology (BSISB) for tracking the locations and concentrations of chemical events in biological samples.

2. Enhance chemical identification capabilities available to users through subsequent mass spectrometry characterization of SR-FTIR informed regions of interest.

3. Enable autonomous experimentation for improved temporal resolution and faster, more efficient experiments for scientific discoveries at infrared beamlines at Lawrence Berkeley National Laboratory’s Advanced Light Source (ALS).

4. Support the diverse and evolving research interests and adapt to the changing needs of the user community through user-stimulated technology development and refinement.

The Berkeley Synchrotron Infrared Structural Biology (BSISB) imaging program is a BER-funded national user resource at three infrared (IR) beamlines at the Advanced Light Source (ALS) in Berkeley, Calif. Synchrotron IR (SIR) radiation spans the far-, mid-, and near-IR. It is 100 to 1,000 times brighter than a conventional thermal source, enabling broadband spectroscopic imaging with high signal-to-noise ratios. Spatial resolution is diffraction-limited for SIR spectromicroscopy or microspectroscopy, and well beyond the diffraction limit for SIR nanospectroscopy. By probing molecular and lattice vibrations, low-energy electronic excitations, and related collective plasmon and phonon resonances, SIR spectroscopy enables high spatial resolution measurements of heterogeneity in biological, chemical, and physical properties. Current capabilities enable the imaging of engineered, natural/living samples at the micro- and nanoscale. This poster presentation offers an overview of SIR as a unique method for chemical imaging. The team then provides an overview of the following capabilities available through BSISB as well as example applications: SR-FTIR spectromicroscopy, SR-FTIR nanospectroscopy, Autonomous Adaptive Data Acquisition, Integrated SR-FTIR with ambient atmospheric infrared ablation mass spectrometry, time-resolved imaging of chemical events, and membrane microfluidics to circumvent water interference.

Cryo-electron microscopy (cryo-EM) is a powerful imaging technique used to visualize biological specimens; it has experienced exponential growth in the past decade, marking a ‘resolution revolution’. Currently, there are more than 32,000 entries of EM maps and other results in the Electron Microscopy Data Bank. In addition to the atomic structures of biological macromolecules, cryo-EM has been employed to study protein-protein and protein-cell interactions and offers insights into cellular and tissue organizations at a resolution unsurpassed by other imaging techniques. This technique plays a pivotal role in advancing scientists’ insight into biological processes at the molecular and cellular level.

With the establishment of the Laboratory for BioMolecular Structure (LBMS), Brookhaven National Laboratory provides peer-reviewed research access, support, and training for the use of cryo-EM. By allowing science-driven use of these instruments, LBMS meets the urgent need to advance the molecular understanding of biological processes, enabling deeper insight and opening the possibility to engineer biological functions in a predictable fashion. Last year, LBMS supported more than 100 sessions, and collected more than one million cryo-EM images, which resulted in 116 high-resolution (better than 4 angstroms) structures and 15 publications.

LBMS also offers three-tiered trainings to current and potential users: (1) annual four-day cryo-EM course to the public; (2) quarterly cryo-EM workshops for current and potential LBMS users; (3) on-demand five-day training for LBMS users, either in person on screening EMs or remote training on the high-end EM, as needed. The average rating of the workshops is 4.4 out of 5.0, with 91% of participants indicating they would recommend the workshop to others.

In recent years, cryo-electron tomography (cryo-ET) has garnered increasing attention due to its unique capability for direct visualization of interactions between complexes in their cellular environment. It offers unparalleled insights into molecular organization, cellular structure, and cell physiology, making it a powerful tool for probing intricate details at the nanoscale within a cellular context. To expand the cryo-ET capability at LBMS, the team will establish and operate a cryo-ET user program to support a broad range of projects funded by DOE. Three distinct routes will be offered based on the nature of the sample and the specific regions of interest. With the development of the cryo-ET program, researchers can study cells/ organelles and tissues. This bridges a critical imaging gap in the biomedical size spectrum, connecting studies of molecules at atomic resolution to cellular and tissue investigations.

Ectomycorrhizal fungi (EMF) play a crucial role in aiding plant nutrition, specifically by extracting nitrogen (N) from organic compounds in soil organic matter (SOM)—a process known as N-mining. In iron deficient soils, EMF can strengthen iron (Fe) acquisition at both hypha-minerals and fungal-plant cell interfaces. However, the effect of EMF induced N-mining and SOM decomposition on Fe processing in mycorrhizal plants remains unclear. Furthermore, researchers aim to explore the interactive effects of inorganic N fertilization and SOM on shaping EMF-mediated Fe processes and plant Fe uptake, a topic that remains largely unexplored.

To address these questions, the team performed a mesocosm study using the Pinus-Suillus model system. Specifically, researchers inoculated Pinus taeda with Suillus cothurnatus and grew them in conditions treated with +/- Fe-coated sand, +/- SOM, and a gradient of ammonium nitrate concentrations. Using the synchrotron pink beam X-ray microfluorescence imaging (PB-XRF) on cross-sections of ectomycorrhizal roots two months post-fungal inoculation, the team found that the effect of inorganic N availability on Fe acquisition in ectomycorrhiza largely depended on SOM supply. Among the combinations of SOM and inorganic N treatments, mycorrhization demonstrated a greatest preference for +SOM/-inorganic N conditions, while mostly exhibiting negative responses to +SOM/high inorganic N conditions. With the addition of SOM, the Fe concentration in mycorrhizae was significantly decreased with the rising levels of treated inorganic N. Conversely, in the absence of SOM, an opposite trend was observed. Spatial analysis of Fe across ectomycorrhizal compartments showed that Fe was primarily accumulated in the fungal mantle underlying the Fe-enriched condition, while Fe was transferred more to the inner compartments, specifically the cortex and vascular tissues, when less Fe was acquired. These findings imply that in EMF-predominant forests, EMF may possess the capacity to facilitate Fe-associated SOM processing and mycorrhizal N/Fe uptake, enhancing the formation of mycorrhization. However, this ability may be compromised under elevated inorganic N conditions. Further studies on the molecular and biochemical aspects of plant-EMF interactions are necessary to precisely evaluate this implication. The team’s ongoing studies are dedicated to conducting these aspects of the research. For example, group members are utilizing the NanoSIMS tool to visualize and quantify the flux of N transformed from N-labeled SOM to EMF hyphae. The team also employs metatranscriptomics and fluorescence in situ hybridization imaging to visualize the activity of fungal genes responsible for cellulose metabolism in mycorrhizae, influenced by the combinations of SOM and inorganic N addition.

Biomass derived from plant feedstocks is a renewable and sustainable energy resource, but these resources are vulnerable to environmental stress such as water and nutrient limitations. Understanding how cells work independently and in concert to regulate plant responses to their environment, including their surrounding microbial community, as well as abiotic stress will be crucial to improving their performance. This Early Career Research Project applies several cutting-edge single-cell and spatially resolved–transcriptome sequencing approaches to construct a comprehensive single-cell resource for plants and to better understand the complexity behind environmental responses among diverse cell types. To this end, researchers have profiled thousands of individual sorghum root cells grown under normal and phosphate-limited conditions. The team has also begun to profile Brachypodium root and leaf cells using single nuclei-RNA sequencing. Researchers are currently integrating this nascent data with additional single-cell data from other species, including maize. The project is also characterizing environmental stress using other advanced profiling methods, including spatial transcriptomics and spatial metabolomics, on plant-arbuscular mycorrhizae interactions. The team hopes to build a multispecies model of cell type–specific environmental responses.

The accelerating climate crisis combined with rapid population growth poses some of the most urgent challenges to humankind, all linked to the unabated release and accumulation of carbon dioxide (CO₂) across the biosphere. By harnessing the capacity to partner with biology, the abundance of available CO₂ can be leveraged to transform the way the world produces and uses carbon. Yet, designing, building, and optimizing non-model CO₂-fixing biosystems to achieve a broader range and more complex biofuels, bioproducts, and biomaterials remains a formidable challenge.

To address this challenge, the research team is developing a cell-free protein synthesis approach for high-throughput engineering of natural and novel enzymes for CO₂ assimilation and biosynthetic product pathways. In one example, the team uses cell-free systems to study natural enzymes like ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO), which is the key enzyme of the Calvin cycle. Various types of RuBisCO were successfully expressed, including form I and form II, via cell-free protein synthesis, and their activity was confirmed by NADH-linked assay and liquid chromatography-mass spectroscopy.

In another example, hydroxyacyl-CoA lyases (HACLs) are being engineered. HACLs have become increasingly relevant due to their ability to form carbon-carbon bonds between formyl-CoA (C₁ donor) and a larger carbonyl-containing molecule (C₁ acceptor). The research team has expressed, purified, and characterized over 60 homologs selected by sequence similarity, uncovering the high promiscuity of these enzymes.

Collectively, these efforts demonstrate how cell-free systems can be used as a screening tool to explore the wide scope of natural enzyme diversity. The newly characterized enzymes can contribute to the engineering of C₁ assimilation routes and expand branches of synthetic metabolism to enable a diverse set of enzymatic reactions for sustainable bioproduction.

Methoxylated aromatics, originating from lignin hydrolysis, are toxic substrates for many species when consumed at higher concentrations. This is due to their aromaticity and the formaldehyde that is generated internally from the cleavage of methoxy groups. Researchers have discovered novel genetic factors in Methylobacterium involved in methoxylated aromatic metabolism and formaldehyde tolerance. The project has focused on multiple aspects: (1) characterizing the native pathway of vanillate (VA) catabolism in a natural strain of Methylobacterium; (2) expressing the VA pathway into a genetically tractable Methylobacterium strain and using experimental evolution to improve VA utilization; (3) characterizing the aromatic and formaldehyde stress response to VA; and (4) understanding the phenotypic heterogeneity of polyhydroxybutyrate (PHB) production using synthetic biology and novel single‐cell microscopy. Results have shown that Methylobacterium is a robust system for the utilization of lignin hydrolysis byproducts and their conversion to value-added products.

Hydroxycinnamoyl-CoA:shikimate/quinate hydroxycinnamoyl transferases (HCT) are an important group of BAHD acyltransferase enzymes because of their key roles in the lignin biosynthetic pathway and the biosynthesis of plant specialized metabolites, such as chlorogenic acid (caffeoylquinic acid) and p-coumaroyl shikimate. The inherent promiscuity of the BAHD acyltransferases leads to a combinatorial array of plant natural products and offers interesting targets for both enzyme and plant engineering. Many plants have large HCT families, but the differences in substrate specificity are not well understood.

To address this knowledge gap, researchers screened all Populus trichocarpa BAHD acyltransferases for HCT activity and found a distinct clade of nine enzymes with various activities. Results show two distinct catalytic classes of HCTs in poplar residing in different branches of the clade; shikimate-specific enzymes (HSTs), likely involved in lignin biosynthesis based on expression data, and quinate-preferring enzymes (HQTs). The extent of substrate promiscuity and competitive preferences of the different enzymes were also determined. Both the HST and HQT enzymes were found to generate shikimate or quinate ester products and convert the products back into CoA thioester and acid substrates under appropriate reaction conditions. Active site residues potentially involved in switching the reaction specificity between HSTs and HQTs were identified through AlphaFold protein structure analysis and tested for their role in defining HCT substrate specificity and activity by site-directed mutagenesis. Insights from this work will be presented.

The overall objective of this project is to use systems biology to identify metabolic control points and bottlenecks that regulate flux to free fatty acids (FFAs) in cyanobacteria. The central hypothesis is that cyanobacterial lipid metabolism can be modularized into pathways upstream and downstream of the nodal metabolite acetyl-CoA, which can be separately studied and optimized to enhance overall FFA production. The research team plans to test the central hypothesis and accomplish the overall objective of this project by pursuing the following specific aims:

(1) Identify upstream metabolic control points regulating acetyl-CoA precursor availability. The working hypothesis is that engineering glycolytic pathways in Synechococcus sp. strain PCC 7002 will reveal rate-controlling steps that can be manipulated to maximize acetyl-CoA availability.

(2) Assess flux bottlenecks in the downstream fatty acid biosynthesis pathway. The working hypothesis is that multi-omics analyses of thioesterase-expressing strains will elucidate regulatory nodes that control FFA production and overall lipid metabolism in strain PCC 7002.

Cyanobacteria, such as the halotolerant Synechococcus sp. strain PCC 7002, are attractive hosts for FFA biofuel precursor production because they produce renewable chemicals directly from photosynthesis, grow in nutrient-poor environments, and readily incorporate genetic modifications. Despite the advantages of cyanobacterial FFA synthesis, production rates are currently too low to support its adoption as a source of renewable fuel.

The overall objective of this project is to enhance cyanobacterial FFA production by identifying and eliminating metabolic bottlenecks upstream and downstream of the FFA building-block acetyl-CoA. Upstream of acetyl-CoA, the approach uses a lactate-producing pyruvate sink in an engineered PCC 7002 strain as a model to study the metabolic changes associated with increased flux toward the acetyl-CoA precursor, pyruvate. Applying ¹³C metabolic flux analysis has revealed that enhanced pyruvate flux in the L-lactate-producing strain is associated with increased flux through the malic enzyme shunt but no change in flux through pyruvate kinase. These data suggest that pyruvate kinase may constitute a metabolic bottleneck limiting overall pyruvate-generating flux.

Downstream of acetyl-CoA, work has centered around the ketosynthase FabH, a putative bottleneck in cyanobacterial FFA biosynthesis previously identified in vitro. Heterologous ketosynthase expression was found to enhance cyanobacterial FFA production, but this effect heavily depends upon the level and timing of ketosynthase induction.

This project leverages a suite of systems biology approaches and novel analytical methodologies (e.g., Fast-Pass DESI-MSI for high-throughput screening and AEXpurif for acyl carrier protein analysis) to identify and investigate distinctive FFA production phenotypes. Ultimately, this work will enable integrated strategies that simultaneously address both upstream and downstream metabolic bottlenecks to enhance FFA production in cyanobacteria.

The Center for Bioenergy Innovation (CBI) vision is to accelerate domestication of bioenergy-relevant, non-model plants and microbes to enable high-impact innovations along the bioenergy and bioproduct supply chain while focusing on sustainable aviation fuels (SAF). CBI has four overarching innovation targets: (1) develop sustainable, process-advantaged biomass feedstocks; (2) refine consolidated bioprocessing with cotreatment to create fermentation intermediates; (3) advance lignin valorization for bio-based products and aviation fuel feedstocks; and (4) improve catalytic upgrading for SAF blendstocks certification.

The degradation of plant material via bacterial digestion to be converted into bioproducts such as ethanol is a main CBI focus. Plant cell walls are comprised of cellulose, hemicellulose, and lignin, which combine to make the cell wall recalcitrant to total digestion. Historically, cryo-electron microscopy (cryo-EM) was essential to confirming the structure of the cellulose synthase rosette. Solving these problems requires deeper understanding of both the biosynthesis pathways to make recalcitrant biomass polymers and the conversion pathways to break down biomass. Here, the research team utilized cryo-EM to further delve into key enzymes and complexes, with the aim of understanding biomass biosynthesis and bioconversion at a molecular level.

The first target of this work is to investigate the poorly explored proteins necessary for hemicellulose formation, which has a complex branching pattern. Most hemicellulose synthesis occurs in the Golgi apparatus in a non-templated manner, meaning that the branching sugar chains are added to the main xylan backbone presumably due to local protein interaction networks (Chou et al. 2015). By examining the near-atomic structures of glycosyltransferase proteins as determined by cryo-EM, both alone and in complex, the molecular mechanisms of hemicellulose synthesis and branching can be determined. The aim is to modulate the pathways to create less recalcitrant plants that remain robust and grow rapidly (Smith et al. 2022).

Another target for plant recalcitrance is to examine the digesting bacterium. Clostridium thermocellum is one of the best bioprocessing organisms identified to date. However, it is hampered in its ability to produce industrially relevant titers of bioproducts, such as ethanol. In past directed evolution studies regarding ethanol tolerance in C. thermocellum, one of the most frequently mutated proteins was AdhE, an alcohol-aldehyde dehydrogenase that produces ethanol. AdhE forms fascinating, spring-like ultrastructures that contain up to one hundred AdhE monomers. Using cryo-EM, the research team solved the highest resolution structure of the AdhE ultrastructure to date, providing insight into the protein’s catalytic pockets, as well as furthering understanding of intermediate aldehyde channeling (Ziegler et al. 2024). The results from the cryo-EM structure are feeding directly into mutagenesis studies to increase C. thermocellum ethanol production and tolerance.

The overall goal of this project is to elucidate the relationship between the cellular metabolic network and the metabolic reactions in outer membrane vesicles secreted by soil Pseudomonas species. In particular, the research team aims to evaluate the catabolism of lignin-derived aromatics in Pseudomonas strains toward maximizing aromatic catabolic activity via engineered or synthetic cellular and vesicle systems. The results from this work will enhance understanding of carbon cycling by soil bacteria and have implications in the use of engineered pseudomonads for lignin valorization to value-added compounds to support the bioeconomy.

Valorization of lignin is an important component of a sustainable bioeconomy. Soil Pseudomonas strains, which natively catabolize lignin-derived aromatics (LDAs), are commonly engineered for the conversion of LDAs to value-added compounds. It was shown that Pseudomonas putida secretes outer membrane vesicles (OMVs) enriched with enzymes that catalyze LDA turnover (Salvachúa et al. 2020). However, the metabolic reaction networks of pseudomonad OMVs and their relationships to intracellular metabolism remain uncharacterized.

To reveal how OMVs potentially facilitate P. putida utilizing LDAs, potential bottlenecks of cells catabolizing different LDAs were identified by measuring the intracellular metabolite levels in cells fed with ferulate (FER), p-coumarate (COU), vanillate (VAN), or 4-hydroxybenzoate (4HB) as the sole carbon source. When P. putida was fed with FER and COU, intermediates accumulated in the peripheral pathways involved in the conversion of FER to VAN and COU to 4HB, suggesting the presence of bottlenecks in these pathways. Specifically, in FER-fed P. putida, vanillin was 20-fold higher than its upstream metabolite feruloyl-CoA and 4-fold higher than its downstream metabolite VAN; in COU-fed cells, 4HB accumulated 3-fold higher than its upstream metabolite 4-hydroxybenzoaldehyde and its downstream metabolite protochatechuate (PCA) was undetectable. When VAN was the carbon source, the PCA level was 25-fold smaller than in P. putida fed with 4HB. Based on quantification of intracellular metabolite levels, bottlenecks were identified at four metabolic nodes in the peripheral pathways for different LDA catabolism. The research team aims to overcome these bottlenecks by (1) overexpressing key enzymes involved in the bottlenecks and (2) synthesizing vesicles encapsulating key metabolites and delivering them directly to cells.

Evaluation of the metabolic capabilities of OMVs versus cells can provide insights into the spatial organization of catabolic pathways, providing further insights into potential bottlenecks in the LDA catabolic pathways. To overcome these bottlenecks, genetic tools for the manipulation of OMV biogenesis and enzyme packaging are needed. The current work aims to develop genetic tools in P. putida both to induce vesiculation and to target specific enzymes into the OMVs, thus providing additional approaches for engineering LDA bioconversion. To identify genetic targets that influence vesiculation, nine knockout mutants were screened for a hypervesiculation phenotype. Out of these mutants, only two knockouts, both involved in establishing linkages between the outer membrane and peptidoglycan layers, were found to induce biogenesis. Interestingly, high production of OMVs (i.e., 4-fold greater than wildtype) was found to coincide with higher cell membrane permeability and increased cell stress, whereas a moderate increase in OMVs (i.e., 1.5-fold greater than wildtype) did not impact cell performance.

Additionally, a SpyCatcher-SpyTag system was utilized to selectively target specific protein cargo into OMVs, which was demonstrated by an increase in extracellular enzyme activity. These advancements represent strides toward harnessing OMVs as a valuable synthetic biology tool.

This study delves into the plastic-degrading prowess of yellow mealworm (Tenebrio molitor) gut microbiomes, surpassing known microbial isolates in breaking down plastics like polyethylene and polystyrene without pre-treatment. Identified bacterial contributors play a role, but the enhancement of degradation rates—potentially up to 200% through co-feeding with alternative diets—points to unexplored biodegradation mechanisms. To bridge these gaps, the research team utilized metabolomics and computational analyses to dissect the metabolic pathways involved in plastic degradation. Both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) were employed for untargeted metabolomic analysis of mealworm gut samples (e.g., C18 reverse phase LC and Orbitrap MS analysis). Given the complexity of the data, advanced data processing was applied with Compound Discoverer for multivariable statistical analysis. Python was also used for principal component analysis (PCA), complemented by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaboAnalyst for pathway enrichment, providing deep insights into the enzymatic and metabolic underpinnings of this process.

To enhance differentiation between standard (i.e., oat) and plastic diets during sample preparation, microbial samples were separated from large pieces of insect tissue using a 900μm pore size filter paper. LC-high resolution mass spectrometry matching results from samples of three diets (i.e., oat, polystyrene, and polyethylene) reveal 217 matched compounds with 31 compounds achieving match scores exceeding 90. Subsequent metabolite identification highlights that 35% of identified metabolites exhibit significantly smaller normalized peak areas in mealworms consuming polystyrene compared to those on a standard diet (p<0.05; fold change >10). Meanwhile, 2% of identified metabolites exhibited significantly larger normalized peak areas. The differential analysis indicates reduced metabolic activity in polystyrene-fed mealworms.

Pathway enrichment analysis, using the KEGG database and MetaboAnalyst 6.0, assesses the impact of metabolites with significant differences. The top-most relevant pathways that may be involved in the mealworm’s response to the plastic diet are starch and sucrose metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and histidine metabolism. Computational algorithm hierarchical cluster analysis and PCA serve as statistical clustering methods for grouping detected metabolites. While these methods provide statistical insights, the biological implications of the clustering results remain unclear. Currently, the team is working on connecting these clusters with pathway databases and biological network methodologies. This metabolomic research collaborates with Lincoln University, one of the oldest historically black colleges in the United States and provides modern analytical technology and data science training to students from underprivileged communities.

Future work will focus on three areas: analyzing microbial metabolism changes with different feeding strategies for deeper insights into plastic biodegradation, enhancing data analysis with the reactome pathway database and biological networks, and integrating machine learning with metabolic interaction network analysis to better understand plastic degradation by insect microbial consortia. The research team aims to develop a predictive model for identifying plastic-degrading enzymes in the yellow mealworm gut microbiome using machine learning algorithms and a context-aware enzyme sequence representation. This strategy, inspired by termite gut microbiota research, aims to discover new enzymatic candidates and metabolic pathways crucial for biodegradation, advancing microbial engineering and environmental remediation while illuminating the complex interactions involved in the role of insect microbial consortia in plastic degradation.

Volatile organic compounds (VOCs) are ubiquitous carbon (C) pools in the Earth system, but often remain uncharacterized as vectors of soil organic C (SOC) transformations. Roots, litter, aboveground vegetation, and microbial metabolism are all sources of VOCs. However, little is known about how these omnipresent metabolites can contribute to C cycling in soils. This project aims to verify and quantify the direct contributions of VOCs to soil C pools and determine their underpinning ecological and metabolic mechanisms. Moreover, it aims to understand how VOCs connect distant metabolic and biochemical regions through their high mobility in soil. The long-term motivation for this project is to transform the current conceptual understanding and predictive capacity of microbial systems and soil C stabilization to include the important roles of volatile compounds.

Volatile organic compounds (VOCs) are diverse and prevalent metabolites exchanged in microbial systems but are often overlooked as vectors of SOC transformations (Honeker et al. 2021; Meredith et al. 2022; Meredith et al. 2023). Roots, litter, aboveground vegetation, and microbial metabolism (Honeker et al. 2023) are all sources of VOCs to soil; however, little is known about how they can contribute to soil C cycling. Microbial uptake of VOCs by soil is increasingly recognized as a ubiquitous process, largely unconstrained by observations. VOCs can contribute to key soil C pools including microbial biomass, dissolved organic matter, particulate organic matter, and mineral-associated organic matter (MAOM), suggesting that they can participate in critical soil C stabilization pathways such as the microbial necromass conduits to MAOM. Yet, understanding is still lacking regarding the fate of VOCs entering the soil system and the specific VOC-induced transformations they may elicit in SOC, hindering the characterization of this process across soil and volatile compounds. To address this research gap, the research team designed complementary studies to evaluate (1) the fate of VOCs added to soil and (2) the contributions of these VOCs to SOC pools in soil from a semi-arid agroecosystem.

In the first experiment, commonly observed VOCs were added into the subsurface of soil columns (100 cm depth) and the concentrations of added VOCs and their gas-phase degradation products were monitored using subsurface gas sampling probes (Roscioli et al. 2021; Gil-Loaiza et al. 2022) at different distances (7 points) from the source. Results indicated that all VOCs were consumed by soil, with the net consumption rates of many increasing over time, indicating microbial acclimation to increased substrate availability or sorption interactions. Interestingly, certain VOCs exhibited greater mobility in the soil compared to others, evidenced by their ability to diffuse over longer distances. This discrepancy in mobility highlights the diverse potential of VOCs to influence SOC levels in adjacent regions, potentially establishing VOC teleconnections within the soil environment.

Finally, partially oxidized volatile products of microbial VOC consumption pathways were observed, revealing the presence of microbes capable of oxidizing isopropanol and acetone.

The second experiment involved a soil incubation study to evaluate the contributions of VOCs to SOC pools using a subset of the compounds tested above. The research team evaluated whether the diversity and quality of SOC changed in response to weekly additions of five individual VOCs over a 3-month period: methanol, acetone, acetaldehyde, isoprene, and ɑ-pinene. Carbon dioxide concentrations were monitored regularly as a proxy for microbial activity. High-resolution SOC analysis by Fourier-transform ion cyclotron resonance mass spectrometry (FTICRMS) revealed that the different VOCs facilitated unique SOC transformations through microbial as well as potentially abiotic processes. Specifically, pinene, methanol, and acetaldehyde drove changes in lipid-like compounds, which represent SOM composition, possibly due to microbial biomass or metabolic pathway activation. External VOC exposure is presumed to have had a priming effect that trained the indigenous microbes to assimilate subsequent VOCs. This study aims to grow understanding of the role of VOCs in soil C cycling and their contributions to soil ecological and metabolic interactions related to C stabilization.

Switchgrass is a perennial warm season C₄ grass native to much of North America and a promising biofuel feedstock candidate. It is common in most prairies and exhibits extensive variability and adaptation across its range, especially related to latitude and precipitation gradients. Much of this variability is associated with evolved southern lowland and northern upland ecotypes.

This study utilized switchgrass biomass data from a structured mapping population and a diversity panel collected across 10 common gardens over multiple years. These field trial datasets were analyzed using the Finlay-Wilkinson regression (FW) approach to explore the genetic architecture of general vigor and environmental sensitivity. The FW method involves regressing the performance of each genotype against environmental means in a two-step procedure. The first step computes average plant performance at each site–year combination as a metric of environmental quality. The second step estimates the intercept and slope of each genotype regressed against the ordered environmental mean. The slope of the regression is a measure for adaptability and the intercept is a measure for general performance. The research team obtained an intercept, slope, and posterior standard deviation associated with the two parameters for each genotype of the two populations.

For the genetic mapping population, R/qtl2 software was used for quantitative trait locus (QTL) mapping while taking into consideration relatedness and individual weights. Individual weights in this case were calculated as n/(SD²), where n is the number of occurrences of that genotype across all the environments and SD is the posterior standard deviation for that genotype obtained from the FW regression. The team identified 23 QTLs associated with the intercept with the most significant QTL on chromosome 5N at marker position 84.03623 centimorgans (cM), and 11 QTLs associated with slope with the most significant QTL on chromosome 3N at marker position 77.91787 cM. For the Atlantic diversity panel, ASRgwas software was used to fit a linear mixed genome-wide association study model including a single nucleotide polymorphism (SNP)-based kinship matrix to control for population structure. A total of 4,128 SNPs were identified (p<5e-04) for the intercept with most significant SNPs showing signal on chromosomes 1N and 7K. For the slope, 2,725 SNPs were identified with the significant SNP primarily localized on chromosomes 5K and 7K. The extensive field trial dataset and analyses reveal several genomic regions and candidate genes impacting general vigor and environmental sensitivity. These data indicate direct strategies for improving high performing switchgrass cultivars across continental scale environmental variation.

This project combines CRISPR-Cas9-based genome-editing and network analysis for large-scale phenotyping in a model wood decomposer fungus relevant to the DOE mission area. The overall goal is to develop a high-throughput genetic platform that enables discovery of distinctive genes and genetic features that speed wood degradation by brown rot fungal species. The research endeavors to provide stand-alone tools and resources for discovering novel fungal genetic mechanisms that can be used together to advance relevant plant biomass conversion research in the post-genomic era.

This research focuses on a group of unique wood decomposer basidiomycete fungi—brown rot fungi—that harbor industrially relevant pathways for extracting carbohydrates from lignocellulose and have broad relevance to global carbon cycling. Distinct from other fungi, brown rot species use non-enzymatic reactive oxygen species mechanisms to modify lignin and selectively extract sugars. Their degradative mechanisms, from a process efficiency standpoint, represent a pathway upgrade relative to the ancestral approaches in white rot species (Hibbett and Donoghue 2001; Eastwood et al. 2011). Fungi obtained this capacity evolutionarily by shedding rather than gaining carbohydrate-active enzymes repertoire genes (Martinez et al. 2009; Floudas et al. 2012; Riley et al. 2014). This paradox therefore makes brown rot fungi a promising candidate for discovering unknown genetic mechanisms governing plant biomass degradation. Although DOE mission relevance is clear and major genomically informed advances in brown rot have been achieved, progress is limited by an inability to manipulate genes in any brown rot fungal strain.

The potential key roles of fungal genome reshuffling and gene regulation in determining brown rot efficacy are widely recognized (Zhang et al. 2016; Zhang et al. 2017; Zhang et al. 2019). Using functional genomic tools, a staggered two-step (i.e., oxidation-then-hydrolysis) gene regulation model for brown rot was elucidated in the Proceedings of the National Academy of Sciences (Zhang et al. 2016) and mBio (Zhang et al. 2019). Although these genomic studies have greatly advanced understanding of brown rot, its genetic basis remains uncharacterized and unharnessed. For example, (1) gene function in the two-step model remain unverified and ambiguous; (2) the gene regulatory mechanism used to control and consolidate the two steps is unclear; and (3) the functions of most genes identified by multi-omics are either hypothetical or unknown. The existence of these gaps is primarily due to the lack of a robust genome-editing tools for validating and discovering brown rot genetic features.

This project will integrate systems biology, genome-editing, and network modeling to address these key gaps. Three project objectives include:

Objective 1: Create a CRISPR-Cas9-mediated gene-editing system and use it to target genes. To genetically manipulate brown rot fungal species, the research team first created a DNA transformation procedure in a model species—Gloeophyllum trabeum. A series of genetic tools were then developed to test and control gene expression in the fungus, including a collective of promoters, a laccase reporter system for reporting extracellular protein function (Li et al. 2023), and a GFP reporter system for testing intracellular protein function and nuclear localization signals and for localizing the cellular loci of lignocellulolytic enzymes. The strain’s dikaryotic genome was resolved using long-read PacBio sequencing to enable gene editing on both alleles. A pre-assembled Cas9-single guide RNA (sgRNA) ribonucleoprotein method was attempted to target benzoquinone reductase, a key Fenton gene. Mutants with successful disruptions of one or two alleles were obtained. Mutation mechanisms involved in the editing process were studied. Although editing efficacy is low (2% to 5%), the method is acceptable to test brown rot gene functions at a singular gene level (e.g., by targeting crucial candidate genes pinpointed by omics and network analysis).

Objective 2: Model a carbon-utilizing network governing brown rot and use it to mine decay genes. To build a carbon-utilizing gene network for discovering novel brown rot genetic features, transcriptome response to a broad spectrum of lignocellulose derivative carbon sources was measured in two brown rot species, G. trabeum and Rhodonia placenta. This species comparison enabled identification of shared or distinct mechanisms. Different network analyzing tools were tested and compared, and key modules and their “hub” genes associated with lignocellulose polymers or monomers were identified. DNA affinity purification (DAP-seq) was then used to identify the cis- and trans-regulatory elements involved in the carbon signaling pathway, and the key regulatory machinery unique to brown rot was revealed (Zhang et al. 2022). Networks derived from gene co-expression and DAP-seq were overlapped. In the context of the full project, this objective will complement the gene targets for large-scale phenotypic screening.

Objective 3: Develop multiplexed genome editing for large-scale phenotypic screens. This objective aims to develop a pipeline to use the multiplexing sgRNA library for genome-editing and mutant library construction for large-scale phenotypic screens, followed by next-generation sequencing to discover key functional genes. An all-in-one Cas9 and sgRNA expression construct was built and used to target genes. Several candidate genes were selected for disruption experiments to test the method’s editing efficiency. Insertion frequency of the gene constructs was studied. Moving forward, the multiplexed sgRNA library will be expressed in G. trabeum to specifically study the pathways associated with lignin utilization revealed by network analysis as a step toward large-scale phenotypic screening.

This project aims to provide stand-alone tools and resources to elucidate fundamental microbial processes relevant to the DOE mission area, advancing new engineering designs for lignocellulose bioconversion.

This project will leverage a synthetic rumen consortium composed of anaerobic fungi and chain-elongating bacteria to study which metabolites are shared and exchanged between microbes and identify strategies to bolster lignocellulose conversion to value-added products. This approach will develop high-throughput systems and synthetic biology approaches to realize stable synthetic consortia that route lignocellulosic carbon into short- and medium-chain fatty acids (SCFAs/MCFAs) rather than methane. Key research objectives are to: (1) design and predict anaerobic fungal and bacterial consortia that efficiently convert lignocellulosic biomass into MCFAs; (2) understand how fermentation parameters and microbe–microbe interactions regulate and drive microbiome metabolic fluxes; and (3) use genomic editing to alter the fermentation byproducts of anaerobic fungi and bolster MCFA titers and yields.

Lignocellulose deconstruction and conversion in nature is driven by mixed microbial partnerships. For example, microbes are particularly well optimized to recycle organic matter in anaerobic habitats, ranging from landfills to intestinal tracts, via interspecies hydrogen transfer and methane release. Compared to aerobic processes, anaerobic digestion can far more efficiently convert substrate to chemical products. This is largely because much less carbon is funneled to cell growth, resulting in higher yields, and far fewer energy inputs are required because pretreatment, aeration, mixing, and heat removal are greatly reduced. Compartmentalizing difficult biomass deconstruction and production steps among specialist anaerobes is an exciting new route to converting biomass into value-added products, especially if consortia can be built predictively and engineered for stability.

Previously, the research team established model bacterial consortia, enriched from the rumen, which convert lignocellulose into high titers of butyrate, a four-carbon (C₄) volatile fatty acid (VFA). Metagenomic and metatranscriptomic analyses identified key chain-elongating bacteria in these consortia that maintain high expression of the reverse β-oxidation pathway responsible for production of C₄ through C₈ VFAs. In parallel, the team demonstrated that anaerobic rumen fungi within the Neocallimastix genus are superior biomass degraders that produce optimal substrates for chain elongators including lactate, acetate, and ethanol. Accordingly, partnering anaerobic fungi and chain-elongating bacteria in synthetic consortia represents a novel strategy for maximizing lignocellulose conversion to C₄ through C₈ VFAs.

Multiple chain-elongating bacteria were screened, and candidates identified that produce VFAs and grow robustly in culture with known fungal metabolites. The research team paired Pseudoramibacter alactolyticus, a top MCFA producer, with the anaerobic fungus Neocallimastix sp., observing lactate depletion and butyrate and hexanoate production. These strains were paired for several passages and produced consistent metabolic output each time, thus indicating a stable consortium.

Current work involves semi-quantitatively evaluating abundances of consortia members via quantitative polymerase chain reaction (qPCR). The team will also employ RNA sequencing to evaluate differences in gene expression when anaerobic fungi and chain elongators are grown together compared to monoculture, and under different conditions that might increase MCFA production or shift products to longer MCFAs (e.g., such as adding formate into fungal cultures to increase lactate production). These synthetic communities have potential to stably drive conversion of lignocellulose to value-added products.

Anaerobic fungi depend on hydrogenosomes to generate ATP and hydrogen. However, enzymes involved in carbon metabolism and redox balance in hydrogenosomes are not well understood. This accounts for a primary source of uncertainty in genome-scale metabolic models (GSMs) of anaerobic fungi. To address this, the research team isolated hydrogenosomes from Caecomyces churrovis via OptiPrep density gradient centrifugation and confirmed expression of an enzyme complex (i.e., NuoEF and HydA) involved in hydrogen production and redox balance in hydrogenosomes, as well as enzymes for pyruvate metabolism (i.e., PFL and PFOR) using NanoPOTS proteomic analysis and enzyme assays. The function of the heterologous generated NuoEF-HydA complex will be explored with enzyme assays to reveal the role of hydrogenosomal PFL and PFOR by inhibiting PFL with a specific synthesized PFL inhibitor. This approach will enhance understanding of anaerobic fungal metabolism and provide essential data for refining the metabolic modeling of consortia.

This work aims to develop a quantitative and mechanistic framework for understanding how bacterial–fungal interactions (BFIs) influence carbon (C) stabilization and mineralization within soil niches and across soil mineralogies. Leveraging principles of community systems biology and ecology, this experimental strategy combines stable isotope probing (SIP), SIP-assisted meta-omics, field mesocosms, soil process rate monitoring, and microbe-informed ecosystem modeling. Objectives include: (1) investigating the influence of various C sources (e.g., rhizodeposits, hyphal deposits, and litter) on grassland BFIs and their subsequent effects on the fates of these photosynthates; (2) examining the role of BFIs in promoting C stability within soil aggregates and on mineral surfaces, and their impact on C destabilization in soils across mineralogies; and (3) assessing how drought conditions, in conjunction with C source and soil mineralogy, shape BFIs and the soil processes they govern.

Bacteria and fungi are dominant soil microbes that play crucial roles in biogeochemical cycling. While cross-domain interactions in soil are well-documented, a mechanistic understanding of BFIs and their influence on biogeochemical cycling of essential soil nutrients under differing soil mineralogy is still lacking. This study comprises a field experiment at the University of California Hopland Research and Extension Center with ingrowth cores of different mesh sizes to separate soil niches. These cores were incubated in a randomized block design plot under rain-out shelters and subject to either 90% or 50% of ambient precipitation. A subset of hyphosphere cores (i.e., 44µm mesh allowing fungal hyphae and bacteria to permeate but excluding roots) and negative controls (i.e., 0.45µm mesh preventing hyphae from crossing) were excluded from receiving rhizodeposits and litter for two growth seasons. These treatments represented C depleted conditions. The research team monitored carbon dioxide (CO₂) efflux from the cores and soil moisture levels within the plots. Preliminary findings from a mixed-effects model indicated a significant effect of both precipitation level and core type on CO₂ efflux. Efflux was, on average, 10% lower under 50% precipitation from September 2023 to January 2024. This was consistent with 14% lower soil moisture under 50% precipitation from October to December 2023.

Furthermore, a significant interaction between mesh size and soil moisture was observed to influence CO₂ flux rates. Efflux from the 44µm mesh cores was 15% higher than that from the 0.45µm cores, likely linked to fungal hyphae activity within the 44µm mesh cores. Also being monitored is CO₂ efflux from 830µm mesh cores representing the rhizosphere (i.e., allowing roots, fungal hyphae, and bacteria to cross) to gain further insights into the role of BFIs on CO₂ flux dynamics across soil niches. In spring 2024, the research team aims to label the grasses growing adjacent to the cores with ¹³CO₂ followed by soil sampling for chemical and microbial analyses. The objective is to quantify the transportation of photosynthetic C into the cores via hyphae, identify the soil C pools that the photosynthetic C is transformed into, and characterize the bacterial and fungal taxa involved in these processes.

To characterize BFIs across soil mineralogy, this project aims to conduct a parallel field-based mesocosm experiment in which the same ingrowth cores will be deployed into intact megaliths of five soil types with distinct clay mineralogies from Hawai’i’s O’ahu Island. A similar ¹³CO₂ labeling event will be carried out to measure how soil mineralogy interplays with BFI-mediated C dynamics.

Lastly, the research team tested model frameworks to represent the diversified interactions between bacteria and fungi in soils. Sensitivity analysis demonstrated that the initial fungi to bacteria ratio and fungi/bacteria enzyme production rates are key parameters regulating competition between bacteria and fungi. After developing a suitable model framework, the team aims to integrate CO₂ effluxes, C pool sizes, ¹³C enrichment, and SIP-derived metagenomic data from the above experiments into a new generation of omics-informed, niche-identified Microbial ENzyme Decomposition model.

To develop robust and optimized anaerobic ethylene pathways in photosynthetic and lignocellulosic bacteria for high-yield conversion of renewable carbon dioxide (CO₂) and lignocellulose into bioethylene. This will be accomplished by:

(1) Bioinformatically mining and experimentally screening methylthioalkane reductase homologs, S-adenosyl-L-methionine hydrolase homologs, and alcohol dehydrogenase homologs from cultivated and uncultivated organisms to identify functional enzymes that enhance ethylene yields.

(2) Constructing and employing predictive systems-level models of ethylene production. This project will use a physics-based Rhodospirillum rubrum model to predict enzymes that participate in competing or supporting pathways and are thus targets for selection studies to increase ethylene yields.

(3) Metabolically engineering bacteria for enhanced, sustained ethylene production from CO₂ and lignocellulose. The project will assemble the best-performing genes under control of optimized active transcription elements on a modular DNA fragment in a combinatorial manner with guidance from predictive models (see goal 2).

Previously, the research team detailed a pathway in the phototrophic bacterium R. rubrum that produces ethylene in the absence of oxygen from methionine and ATP (North et al. 2020). Traditional ethylene production involves energy-intensive cracking of petroleum fossil fuels to meet the 300 million metric ton annual demand. Thus, a sustainable microbial platform for the renewable production of ethylene is urgently needed. The goal of this project is to optimize this anaerobic ethylene production pathway.

Enzyme Screens:
Methylthio-alkane reductase genes and other nitrogenase-like genes identified from microbial genomes were synthesized and assembled by the DOE Joint Genome Institute DNA synthesis program such that each set contained a NifB, NifH, NifD, and NifK homolog. Sequences were screened for activity in an R. rubrum deletion strain devoid of native methylthio-alkane reductase (North et al. 2020). Sequences from the methylthio-alkane reductase phylogenetic clade were active for reduction of volatile organic sulfur compounds of the form R-S-CH₃ if R. rubrum could couple electron transfer from cell redox carriers to the NifH homolog. Strikingly, proteins encoded by nitrogenase-like sequences from other phylogenetic clades of unknown function showed no methylthio-alkane reductase activity and showed no activity toward other methylated compounds like methylated amines, sulfates, or phosphates. Thus, the functions of many nitrogenase-like clades remain unknown.

Physics-based Modeling:
During photoheterotrophic growth on organic substrates, purple non-sulfur bacteria like R. rubrum acquire electrons by multiple means and store them as reduced electron carriers. The ratio of oxidized to reduced electron carriers [e.g., ratio of NAD(P)+:NAD(P)H)] is difficult to predict but essential for thermokinetic modeling and predicting the targeted metabolic engineering needed to increase ethylene synthesis. Using physics-based models that capture mass action kinetics consistent with the thermodynamics of reactions and pathways, a range of redox conditions for photoheterotrophic growth was evaluated (King et al. 2023; Cannon et al. 2024). Modeling results and experimental measurements of macromolecule levels (DNA, RNA, proteins, and fatty acids; North et al. 2020) indicate that cellular redox poise results in large-scale changes in biosynthetic pathway activity. The model, which agrees with experimental measurements of macromolecule ratios of cells growing on different carbon substrates, indicates that the dynamics of nucleotide versus lipid and protein production is likely a significant mechanism balancing cellular oxidation and reduction.

Metabolic Engineering:
In R. rubrum, ethylene production involves 12 genes and 9 primary reactions, plus pathways supporting ATP, Acetyl-CoA, and NAD(P)H synthesis. Previously, the research team identified high performing S-adenosyl-L-methionine (SAM) hydrolases, isomerases, aldolases, and methylthio-alkane reductases that individually increase ethylene production. Combining these elements on a mobile plasmid under modest to high levels of constitutive expression results in a 30,000-fold increase in ethylene yields from 0.01 to 300 µmol/g dry cell weight. When antibiotics are included to retain plasmid selection, yields further increase to 1,000 µmol/g dry cell weight. Additional genetic tools are needed to integrate multiple pathway elements onto the chromosome, as 4 to 5 pathway elements on a plasmid represents the largest plasmid size R. rubrum can stably incorporate.

This project examines genomic and functional variation among ectomycorrhizal fungi along a natural climate gradient and consequences for host adaptation and ecosystem function.

Dispersal limitations and geographic barriers can influence microbial population structure and gene flow at the landscape level, resulting in divergent genotypes and regional endemism. However, the drivers and genetic basis of population differentiation across large spatial scales, particularly among mycorrhizal fungi, remains poorly understood. This study investigates the population structure of Suillus pungens, an ectomycorrhizal fungus endemic to the California coast and a host-specialist to Pinus muricata and Pinus radiata. The research team performed whole-genome sequencing on 70 individuals collected across a latitudinal and 4-fold precipitation gradient. The team used a combination of paired-end sequencing on the Illumina NextSeq 500 System (with a 2 x 150 base pair read length) and MiSeq (2 x 250 base pair read length) and produced a total average read depth of 6. Using an annotated reference genome, gene variant bioinformatic approaches (GATK) were employed, resulting in identification of 541,091 SNPs across these 70 individuals. Significant population genetic structure was found among Northern and Southern populations as well as highly differentiated host-associated genotypes. Of these SNPs, a strong functional signature of adaptation was identified, with southern populations enriched in genes involved in cell signaling and membrane fluidity, a potential adaptation to drought stress. These results provide some of the first genomic evidence for local adaptation within ectomycorrhizal species and show that barriers to gene flow can develop over relatively small spatial scales. Future work will explore how this degree of local adaptation by ectomycorrhizal fungi contributes to host stress tolerance or affects ecosystem function.

This project will phenotypically and molecularly characterize the 33 existing CRISPR-Cas9-edited N-responsive genes. Subsequently, the research team will conduct gene editing for the three glutamate-like receptor (GLR) genes in a cluster and GLR-related genes in the N network and generate a population-scale RNA-seq dataset to cross-validate edited genes and identify new gene candidates for further characterization.

The inefficient use of inorganic nitrogen (N) fertilizer in crop production increases ecological burdens including biodiversity loss, N leaching into groundwater, and greenhouse gas emissions (e.g., nitrous oxide) that contribute to global warming. Moreover, inorganic N fertilizer stands out as one of the most expensive and energy-intensive agricultural inputs, particularly for sorghum cultivation.

Enhancing sorghum’s nitrogen use efficiency (NUE) will not only boost its profitability as an energy crop but also alleviate the environmental burdens associated with its cultivation. To understand the biological basis of this essential macronutrient and ultimately enhance NUE, extensive research has been conducted to reveal processes for N assimilation, transport, and reallocation. Studies in model plant species have identified specialized nitrate transporters, enabling N mobilization processes to be well characterized. However, N sensing, signaling, and downstream regulatory pathways in crop species remain largely unclear.

Previously, this research team has generated resources and accumulated extensive experiences in N-related research on sorghum. The team conducted transcriptomic analysis using data collected from sorghum genotype Tx430 grown in different N levels and edited 33 N-responsive genes using CRISPR-Cas9. The current project has developed a high-throughput phenotyping pipeline to extract N-related phenotypes from the state-of-the-art LemnaTec Greenhouse.

During the summer of 2023, the team collected a variety of manually measured and imagery-based data on the Sorghum Association Panel (SAP; n=330) under both high N and low N field conditions. Data included traits related to root morphology and root-associated microbial characteristics. Additionally, the team conducted RNA-seq on three-week-old seedlings from SAP grown in high N and low N greenhouse conditions. Currently, the research team is developing statistical models and conducting empirical analyses to integrate multi-omics data and provide valuable biological insights. Notably, population genetics and comparative genomics analyses have suggested that a cluster of glutamate-like receptor (GLR) genes may function as cellular N sensors, activating Ca2+-dependent N signaling—an essential step in the N pathway.

The Quantitative Plant Science Initiative (QPSI) is a capability that aims to bridge the knowledge gap between genes and their functions. A central strategy is combining genome-wide experimentation and comparative genomics with molecular-level experimentation. In this way, the project team leverages the scalability of omics data and bioinformatic approaches to capture system-level information, while generating sequence-specific understanding of gene and protein function. Incorporating molecular-level experimentation in the workflow addresses the question of how proteins function and establishes mechanistic insight into how sequence variation impacts phenotype. This knowledge serves as a touchstone for accurate genome-based computational propagation across sequenced genomes and forms a foundation for robust predictive modeling of plant productivity in diverse environments.

To understand how the bioenergy crops poplar and sorghum respond to metal bioavailability, with a view toward improving bioenergy crop resilience, the research team performed integrated, large-scale, multi-genotype omics experiments, computational simulation, and gene/protein-focused molecular-level experimentation. The project has two objectives. Objective 1 is to determine the genome-wide responses to zinc (Zn) and iron (Fe) availability in sorghum and poplar and identify the major genes involved in leaf-level acclimation to metal ions. The team performed time-series and genotype-specific multi-omics experiments and obtained datasets useful for the identification of key functional genes.

Objective 2 is to identify the molecular-level functions of key proteins and validate them by overexpression and loss-of-function phenotyping. Following the team’s recent discovery of previously unknown Zn chaperones in eukaryotes (Pasquini et al. 2022), a structure-function study of these novel proteins was completed and a plant-specific Zn-homeostatic mechanism that involves intracellular Zn transferases was identified (Zhang et al. 2023). The team also discovered a new heme sensor involved in cofactor-dependent post-translational regulation at the intersection of photosynthesis and respiration (Grosjean et al. 2024). The structure of a Zn transporter dimer was determined, revealing a flexible loop for sensing cellular Zn content and regulating Zn uptake from the environment (Pang et al. 2023).

In addition to molecular-level discoveries in micronutrient homeostasis, a protoplast-based experimental system was used to discover a key gene regulatory network that controls sorghum flowering time and biomass production (Tadesse et al. 2024). While working with Zn and Fe micronutrient stresses in the current project phase, there will be subsequent opportunities to incorporate other real-world conditions, through the addition of field experiments, which address the impacts of soil geochemistry, microbiome, and rhizosphere and study bioenergy crops and environment interactions.

This early career research project is dedicated to achieving transformative molecular-level insights into microbial lignocellulose deconstruction through the comprehensive and informative view of underlying biological pathways provided by the integration of spatiotemporal multi-omic measurements (i.e., proteomics, metabolomics, and lipidomics). A focus of this project is to uncover the mechanisms that drive cooperative fungal–bacterial interactions resulting in degradation of lignocellulosic plant material in the leafcutter ant fungal garden ecosystem. This approach will provide the knowledge needed for a predictive systems-level understanding of fungal–bacterial metabolic and signaling interactions that occur during cellulose deconstruction in an efficient, natural ecosystem.

Understanding inter-kingdom interactions is critical for predicting the metabolic outcomes of environmental perturbations to microbial processes. Biological samples, however, are often complex and heterogeneous. Thus, it is challenging to detect spatial and temporal variation in microbial interactions and activities.

In this study, the research team used six independent naturally evolved leafcutter ant fungal garden consortia that are biologically complex and known to achieve active lignocellulose degradation primarily mediated by Leucoagaricus (Khadempour et al. 2021) as the model system. A pathogenic fungus, Escovopsis, was introduced to one side of each consortium and proliferated toward its middle section. This introduction created an infection gradient as well as contrasting microbial compositions on the two sides of the consortium. To elucidate the active inter-kingdom interactions and their metabolic outcomes within this dynamic system, the team applied multi-omics (i.e., metaproteomics, lipidomics, and metabolomics) integrated with microscale imaging to capture shifts in microbial community members and map their detected activities (Veličković et al. 2024).

Deep metaproteomics reveal microbial population and functional dynamics along the infection gradient. High selectivity and sensitivity in peptide identifications was achieved with a Thermo Fisher Orbitrap Eclipse Tribrid mass spectrometer equipped with a front-end High Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) interface. The research leveraged a reference database containing 50 million proteins of known consortium members, which were grouped into >24 million clusters based on sequence similarity, to annotate the high-resolution tandem mass spectrometry spectra with stringent matching criteria. A total of 263,404 clusters were detected in the metaproteome data with relatively high representations of fungal, bacterial, and plant proteins followed by archaeal and insect proteins.

To identify patterns in the omics data, the team developed a method that leverages unsupervised machine learning algorithms to automatically recognize regularities across the infection gradient. Over 400 metabolites and 500 lipids exhibited significant trends (adjusted p-value<0.05). Combining these trends with the metaproteome data provided pathway-level species-specific interaction patterns. These methods recognized antagonistic interaction patterns between native and pathogenic fungal species. Leucoagaricus metabolic activities associated with carbohydrate metabolism and secondary metabolite biosynthesis decreased with increasing Escovopsis infection. Bacterial metabolic activities increased with fungal infections in some of the consortia, suggesting an active community response and potential inter-kingdom interactions under the impact of fungal infection. Interactions unique to the infection interface mapped complex activities underpinning both attack and defense metabolic strategies utilized by consortium members.

These spatiotemporal multi-omics measurements provided an integrated road map to efficiently harness microbiome data for a better understanding of microbial interactions and community response to a perturbation. In addition, the measurements provided a predictive systems-level understanding of how symbiotic fungal-bacterial metabolic and signaling interactions enable the fungal garden ecosystem to thrive and degrade lignocellulose in dynamic environments.

(1) Develop a mechanistic understanding of anaerobic oxidation of methane (AOM) syntrophic interactions; (2) define and functionally characterize the microbial community, including viruses, associated with methanotrophic consortia under changing environmental conditions; and (3) create an integrative modeling framework to explore the ecophysiology of AOM consortia and their community interactions in an environmental context.

AOM is a geologically important process that impacts methane utilization in marine sediments. AOM is mediated by anaerobic methanotrophic archaea (ANME) and is made energetically feasible by coupling methane oxidation with the reduction of electron acceptors such as nitrate, metals, or sulfate. In sulfate-coupled AOM, ANME are found in an obligate syntrophic partnership with sulfate reducing bacteria (SRB). This syntrophic association is driven by direct interspecies electron transfer between partners co-localized in a multicellular consortium.

Using comparative phylogenomics, the research team and others have shown that ANME archaea are polyphyletic, evolving multiple times from methanogenic ancestors (Evans et al. 2019; Chadwick et al. 2022). Analysis of gene phylogenies, locus organization, and sequence alignments suggests that during this process, each ANME clade has convergently evolved to encode distinctive genes often involved in central metabolic pathways. In particular, studies of the most recently evolved ANME-3 clade have produced a step-by-step view of this process. Results suggests that evolution of convergent modifications to proteins involved in carbon and energy metabolism precedes later optimization through horizontal acquisition of multi-heme cytochromes and genes involved in nutrient acquisition and cell-cell interaction. Sulfate-reducing syntrophic bacterial partners have also convergently evolved from free-living sulfate reducers to syntrophic SRB by adapting their energy metabolism and acquiring genes by lateral gene transfer that promote interspecies interaction and biofilm formation (Murali et al. 2023).

In this work, environmental metaproteomics and metabolic modeling were used to test phylogenomics-derived inferences. Model results highlight differences in electron transport pathways that are critical to the differences between ANME and methanogens. Metaproteomics analyses of environmental ANME–SRB consortia demonstrated that previously identified pathways associated with ANME and SRB energy metabolism (e.g., Mcr, Dsr, Apr, Rnf) and interspecies interactions (e.g., eCIS, adhesins) are actively expressed.

Additionally, the research team identified several highly expressed proteins that currently have no characterized function, highlighting additional unexplored aspects of AOM physiology. Targets were identified from highly expressed proteins for further heterologous expression (e.g., putative fibronectin binding matrix proteins, Rnf).

Further physiological insights of these slow-growing microbes will be gained by stable isotope probing metaproteomics (e.g., 13C-CH₄, 13C-NaHCO₃, 15N-NH₄Cl). Preliminary analysis shows uptake of labeled substrates by ANME-SRB and upcoming analysis will provide insight into protein turnover, growth rates, and carbon uptake.

The Intrinsic Control for Genome and Transcriptome Editing in Communities (InCoGenTEC) Science Focus Area aims to develop strategies for biocontainment, enable safe transformation of non-model prokaryotes using phage vectors, and understand gene mobility in microbial communities. The overall project goals are to: (1) mechanistically understand gene mobility events through comprehensive computational mapping of integrase- and transposon-driven mobility; (2) perform functional genomics studies to identify genes and pathways responsible for mobility and identify novel genes for use in biocontainment mechanisms; and (3) utilize prophages from a genomic island database to transform non-model microbes toward the goal of safe microbial community transformation.

DNA integrases catalyze recombination between specific attachment (att) sites on two circular DNAs: the attB site on a bacterial chromosome and the attP site on a pre-island circle. This results in a single circle with the genomic island precisely integrated into the chromosome. Integrases retain all DNA strands until after recombination is complete, forming covalent intermediates at either a catalytic tyrosine or serine residue depending on the protein family. If an att site is available for use in a locus, this reaction mechanism can provide an inherently more efficient and safer approach to genome editing than CRISPR methods which first introduce a double-stranded break in the chromosome. Additional safety and control come from the property of directionality: integrases require an additional partner protein (e.g., excisionase or recombination directionality factor) to catalyze the reverse excision reaction but not the forward integration reaction.

Serine integrases have been favored for genome editing applications because tyrosine integrases are perceived to require additional protein factors from their bacterial hosts. This dependence on host factors, particularly the integration host factor (IHF), occurs for certain classical tyrosine integrases (e.g., phage lambda) but may not apply to integrases from the many bacterial phyla that do not harbor IHF genes, nor to all integrases from IHF+ species. Using project software to precisely map hundreds of thousands of tyrosine integrase att sites, the research team assembled a panel of diverse tyrosine integrases which were assayed using E. coli-based in vivo and cell-free assays. Many integrases from phyla not known to bear IHF genes were functional, even in IHF-deficient genetic backgrounds, and in cell-free assays where IHF was diluted ~10-fold.

This work demonstrates that bias against tyrosine integrases has resulted from a misperception; most are not dependent on host factors. Tyrosine integrases are ~8-fold more abundant than serine integrases, offering far more site-specificity. Vetting numerous tyrosine integrases by assay, with diverse site-specifities, is expected to expand safe gene editing biotechnology.

The Center for Bioenergy Innovation (CBI) vision is to accelerate domestication of bioenergy-relevant, non-model plants and microbes to enable high-impact innovations along the bioenergy and bioproduct supply chain while focusing on sustainable aviation fuels (SAF). CBI has four overarching innovation targets: (1) develop sustainable, process-advantaged biomass feedstocks; (2) refine consolidated bioprocessing with cotreatment to create fermentation intermediates; (3) advance lignin valorization for bio-based products and aviation fuel feedstocks; and (4) improve catalytic upgrading for SAF blendstocks certification.

Lignin is a complex aromatic polymer found in plant cell walls and accordingly represents an underutilized, but also recalcitrant, carbon-rich stream in lignocellulosic biorefineries. Processes to valorize lignin to high-value products are therefore of interest. Biological funneling of the heterogeneous lignin-related compounds (LRCs) generated by chemical or enzymatic deconstruction into performance-advantaged products is a promising strategy toward this goal (Rinaldi et al. 2016; Sun et al. 2018). This project compares the catabolic capacities of several microbial platforms for biological funneling and identifies metabolic inefficiencies in the production of muconic acid from LRCs.

To characterize catabolic capabilities of several promising microbial strains as hosts for the valorization of lignin streams, growth, and substrate utilization of six bacterial strains and one yeast were directly compared on representative LRCs. Pseudomonas putida bacteria exhibited the fastest growth rate, highest tolerance, and broadest substrate range when grown on a lignin-rich stream, a model aromatic LRC mixture, guaiacyl (G)-type compounds, p-coumaryl (H)-type compounds, and aliphatic acids. Sphingobium lignivorans bacterial strains utilized the highest concentration of syringyl (S)-type compounds. This work provides a foundational comparison of microbial platforms for LRC catabolism as well as genetic reserves to tap for unique metabolic capabilities.

Systems-level characterization of muconic acid production from aromatic LRCs and biomass production from glucose was conducted in P. putida to identify metabolic inefficiencies and bottlenecks. After rewiring native cellular metabolism of 4-hydroxybenzoate to muconic acid in the production strain P. putida CJ781 (CJ781; Kuatsjah et al. 2022), a bottleneck was identified at the catechol 1,2-dioxygenase. Proteomics, exometabolomics, and fluxomics analyses of glucose conversion to biomass growth and energy revealed that, relative to the wildtype strain, CJ781 exhibited greater secretions of intracellular metabolites, higher periplasmic flux, and increased ATP production. Notably, CJ781 secreted pyruvate and acetate, indicating a potential bottleneck in carbon flux entering the tricarboxylic acid (TCA) cycle. Together, this work improves understanding of divided cellular metabolism between product formation and biomass production and identifies nonintuitive genetic targets for optimization of biological funneling.

The overriding goal of the Oak Ridge National Laboratory Plant-Microbe Interfaces (PMI) Science Focus Area is to predictively understand the productive relationship between a plant host and its microbiome based on molecular and environmentally defined information. Populus and its associated microbial community serve as the experimental system for understanding this dynamic, complex multi-organism system. To achieve this goal, the team focuses on: (1) defining the bidirectional progression of molecular and cellular events involved in selecting and maintaining specific, mutualistic Populus-microbe interfaces; (2) defining the chemical environment and molecular signals that influence community structure and function; and (3) understanding the dynamic relationship and extrinsic stressors that shape microbiome composition and affect host performance.

Recent studies have shown that microbes from extreme environments can confer plant stress tolerance. Such studies have led to the hypothesis that microbiomes adapted to harsh environmental conditions can benefit host plants in similar environments. However, the magnitude of these benefits, underlying microbial dynamics, and driving genetic mechanisms remain unclear. The current study employs synthetic community (SynCom) approaches, paired with high-throughput phenotyping and physiological assays, to dissect the specific roles of microbial strains and communities in plant thermotolerance. To quantify microbial benefits on plant growth and physiology across temperatures, SynComs were constructed with selected bacteria and applied to axenic tissue culture Populus trichocarpa x deltoides within a calcined clay medium. Bacteria were selected and prioritized based on phylotyping data from PMI field sites. The SynCom–host systems were then exposed to a range of temperatures (9°C to 28°C). These systems were enclosed for 3 weeks to ensure community establishment, and then containers were opened for an additional 4 weeks and subjected to automated phenotyping. The addition of a single Variovorax bacterial strain significantly enhanced plant growth and photosynthetic efficiency. Further investigation using a heterologous quantitative trait loci (QTL) study identified a seven-gene interval associated with microbially conferred thermotolerance. Interestingly, genetic analysis revealed a proteosome interacting protein (PIP) essential for the plant to benefit from the Variovorax strain. Future studies are integrating this newly found microbially mediated abiotic stress response pathway with known induced system resistance (ISR) and systemic acquired resistance (SAR) pathways. This knowledge paves the way for developing climate-resilient plants by harnessing the power of beneficial microbes and genetics.

Decarboxylation, the loss of carbon dioxide (CO₂) from a compound, is used in metabolism to commit carbon flux to a specific pathway. However, decarboxylation also limits the product carbon yield, with acetyl-CoA (two C₂) achieving only 66% carbon recovery when routing glucose (C₆) via glycolysis and oxidative decarboxylation. Carbon-conservation networks (CCNs) that circumvent CO₂ release can theoretically lead to carbon and product yields beyond those seen with endogenous metabolism (Westenberg and Peralta-Yahya 2023). Engineering metabolic pathways and developing technologies to improve carbon yield has the potential to increase the economic viability of large-volume low-cost chemicals. Toward this goal, the effects of overlaying CCNs have been mathematically modeled onto the endogenous metabolism of non-model organisms, such as Pseudomonas putida and Rhodobacter sphaeroides. The model predictions and prototyping combinations of existing and de novo CCNs predicted to improve carbon and product yields are now being implemented. As a proof-of-concept, the research team is measuring the effects of CCNs on the production of industrially relevant diacids: malic and itaconic. Going forward, the generality of CCNs will enable their implementation toward production of other large-scale chemicals that suffer from metabolic carbon loss.

The biological impact of low-dose radiation exposure remains an important open question in radiation biology research, with significant implications for human health risk assessment, policy, and regulations. This project is leveraging advances in AI, high throughput experimental technologies, and multiscale modeling and simulation to advance scientific understanding of the molecular and cellular processes involved in low-dose radiation and cancer risk, accelerate discovery, and connect insights across scales.

Radiation exposure has a wide spectrum of impacts on human health, notably in carcinogenesis but also in neurological and cardiovascular disorders. While acute toxicity from high doses of radiation is well-characterized, understanding the range of outcomes following exposure to low-dose radiation is more challenging. This project is establishing new experimental workflows that will enable high throughput experiments across molecular and cellular scales to facilitate more comprehensive modeling.

In a pilot study, a monolayer of Human Umbilical Vessel Endothelial Cells (HUVECs) was exposed to a point source of ¹³⁷Cs at a low-dose rate of 6 milligrays (mGy) per hour. Cells were exposed for one week in culture (i.e., 1,008 mGy total dose) and then harvested for RNA or replated for Cell Painting staining.

Cell Painting is a streamlined multi-parameter approach to fluorescence microscopy that provides rich feature data of cell structure and function. A major advantage of Cell Painting is a robust publicly available dataset spanning thousands of small molecular and genomic perturbations produced by the collaborative JUMP Consortium. The scale of characterized phenotypes has facilitated development of predictive models that incorporate chemical structural information, biological mechanism of action, and gene expression, which will be expanded into the realm of radiation exposure.

With Cell Painting, features can be extracted based on staining of the nuclear and endoplasmic reticulum plasma membranes and cellular Golgi, actin, nucleoli, and mitochondria. Principle component analysis of control and irradiated cells provided a proof-of-principle demonstration that Cell Painting enables detection of features impacted by irradiation. Transcriptome analysis revealed that in endothelial cells, radiation robustly induced cell response pathways integral to cytokine and chemokine pathways, such as the Tumor Necrosis Factor (TNF) pathway.

Underscoring the relevancy of HUVECs to cardiovascular disease, pathways associated with “lipid” and “atherosclerosis” were also activated. Two Kyoto Encyclopedia of Genes and Genomes terms shed light on the molecular mechanisms of these processes, namely the HIF-1 and NF-kappa B signaling pathways.

To compare these results to previous studies of low-dose radiation exposure, data were compared with gene expression datasets from the RadBioBase, a publicly available comprehensive transcriptome repository of irradiated mammalian samples. Datasets that used human cells and doses below 0.5 Gy were selected to identify 235 genes impacted by radiation across four published datasets. Of these, 35 genes were also seen in the data, notably the inflammatory cytokines IL6 and IL1B, as well as the genes PTGS2 (COX2) and CXCL12, which are involved in inflammatory processes underlying cardiovascular disease.

To overcome the limitations (e.g, variable dose field, high activity) of the point radiation source in the pilot study, a major goal of the next project phase is to prototype and deploy new source geometries in a 96-well plate format for high-throughput experimental exposures. New source geometries will require minimal activity, provide uniform dose fields, and enable multiple dose rate exposures in parallel. The impact of low-dose radiation will then be assessed with molecular (e.g., multi-omic) and cellular (e.g., Cell Painting) assays to develop advanced multi-scale models of low-dose radiation impacts.

Algal and plant systems have the unrivaled advantage of converting solar energy and CO₂ into useful organic molecules. Their growth and efficiency are largely shaped by the microbial communities in and around them. The μBiospheres Science Focus Area seeks to understand phototroph-heterotroph interactions that shape productivity, robustness, the balance of resource fluxes, and the functionality of the surrounding microbiome. Researchers hypothesize that different microbial associates not only have differential effects on host productivity but can change an entire system’s resource economy. This approach encompasses single-cell analyses, quantitative isotope tracing of elemental exchanges, omics measurements, and multiscale modeling to characterize microscale impacts on system-scale processes. Researchers aim to uncover cross-cutting principles that regulate these interactions and their resource allocation consequences to develop a general predictive framework for system-level impacts of microbial partnerships.

Multipartite mutualisms between plants and microbiota can enhance plant productivity, stress resilience, and carbon (C) allocation belowground. Researchers are investigating context-dependent mutualisms between Panicum virgatum (switchgrass, a cellulosic bioenergy grass), Panicum hallii (a model for bioenergy grasses), and mycorrhizal and endophytic fungi. The team is interested in how C flows are mediated by plant-associated fungi and altered by environmental stress (e.g., drought). In return, it is thought that hyphosphere microbes surrounding fungal hyphae enable root-associated fungi to obtain resources (N, P, H2O) that they provide to their hosts, but the mechanisms that enable this crosskingdom cooperation are unknown. Researchers are investigating these questions using (1) ¹³CO₂ stable isotope probing (SIP) and metabolomics; and (2) live imaging and spatial metabolomics coupled to metabolic modeling.

Fungal root endophytes can alleviate plant drought stress, but their effects on soil microbial activity and C flows during drought are poorly understood. The team used ¹³CO₂ labeling chambers, root exclusion cores, quantitative SIP (qSIP), and metabolomics to investigate how two functionally distinct root endophytes influenced rhizosphere and hyphosphere C dynamics in moisture-limited soils planted with P. hallii. Researchers compared the arbuscular mycorrhizal fungus (AMF) Rhizophagus irregularis, with a Sebacinales endophytic fungus Serendipita bescii. CO₂ efflux and ¹³CO₂ efflux were greater from fungal inoculated versus uninoculated soils, indicating that these fungi facilitated faster turnover of both native soil organic matter and ¹³C photosynthates. However, the team did not measure a net reduction in total soil C. Microbial ¹³C assimilation was greater in fungal-inoculated soil, and a distinct microbial consortia assimilated ¹³C in each treatment. The hyphosphere exometabolome was primarily structured by time and was distinct between well-watered and drought conditions; a subset of metabolites differed by the specific fungal partner inoculation. These results provide a putative mechanism to explain the previous observation that fungal-root endophytes help maintain bacterial growth potential, growth efficiency, and diversity following moisture limitation (Hestrin et al. 2022).

Fungal exudates are a key form of C in the hyphosphere and may mediate a metabolomic conversation between fungi and their microbiome. To relate fungal network development and exudation to microbiome nutrient acquisition, researchers are coupling spatially resolved analyses to a metabolic modeling simulation platform called “Toadstool.” These experiments start with automated live imaging and network identification to generate baseline structural data for Toadstool. Toadstool is built on a stochastic network representation of R. irregularis growth and resource allocation in soil, coupled to differential equations that represent light- and nutrient-dependent growth of P. virgatum. Currently, researchers are developing a method to spatially map AMF metabolites using matrix-assisted laser desorption and ionization (MALDI) that can pair with live-imaging data. Toadstool was designed to interact within a diffusive and advective grid, allowing a direct interface with spatially resolved metabolic models of R. irregularis hyphae and their bacterial partners. This approach enables a comparison of predicted metabolite exchanges with MALDI metabolite imaging. The model is intended to predict feedbacks between growth, plant-AMF-bacterial community resource exchange, and the soil matrix. This work will shed light on how multipartite biological interactions impact the soil resource economy.

This project couples novel laboratory and field studies to develop the first predictive model of grass-microbiomes based on new mechanistic insights into dynamic plant-microbe interactions in the grasses Sorghum bicolor and Brachypodium distachyon that improve plant nitrogen (N)-use efficiency (NUE). The results will be used to predict plant mutants and microbial amendments that improve low-input biomass production for laboratory and field studies validation. To achieve this goal, researchers will determine the mechanistic basis of dynamic exudate exchange in the grass rhizosphere with a specific focus on the identification of plant transporters and proteins that regulate root exudate composition. Researchers will also focus on how specific exudates select for beneficial microbes that increase plant biomass and NUE. The team will further develop a predictive plant-microbe model for advancing sustainable bioenergy crops and will predictively shift plant-microbe interactions to enhance plant biomass production and N acquisition from varied N forms.

Microbial amendments are a powerful approach for promoting plant (N) acquisition, uptake, and cycling using less inputs. Yet, the performance of microbial amendments is highly variable due to the dynamic and complex nature of soil abiotic and biotic interactions. Understanding the factors driving rhizosphere assembly and dynamics, especially when combined with plants with tailored exudates, has the potential to greatly improve the reliable performance of beneficial microbial amendments at lower N levels. The team assessed the potential of a grass rhizosphere synthetic microbiome in promoting Brachypodium distachyon growth in soils under replete and limited N levels and observed enhanced ability to extract N from soil organic N pools when subjected to limited N conditions. For a more detailed analysis of N cycling in the rhizosphere, including the potential role of root exudates in mediating beneficial microbial interactions, the team grew B. distachyon hydroponically in novel fabricated ecosystem devices (EcoFAB 2.0) under three inorganic nitrogen forms (nitrate, ammonium, or ammonium nitrate), followed by nitrogen starvation. EcoFAB 2.0 achieved low intratreatment data variability and reproducible plant phenotypes. Analyses of exudates with LC-MS/MS revealed that the three inorganic nitrogen forms caused differential exudation, generalized by an increase in amino acids/peptides and alkaloids. Comparatively, N-deficiency decreased N-containing compounds but increased carbon-rich shikimates/phenylpropanoids. Subsequent bioassays with two shikimates-phenylpropanoids (shikimic and p-coumaric acids) revealed their distinct capacity to regulate bacterial and plant growth. Given the importance of root exudates in structuring rhizosphere communities, researchers are also investigating transport mechanisms for root exudation, particularly nitrogen by using B. distachyon plant mutants. Hydroponic growth of these mutants with knockout N transporters resulted in significant phenotypic and exometabolic changes. Concurrently, researchers are analyzing the microbiome communities of these mutants in calcined clay treated with a field-soil extract to explore the role of root exudation in plant-microbe interactions. Sequencing of rhizosphere and root microbiomes has shown significant changes in bacterial species, indicating that membrane-transport engineering can alter plant-root exudates and microbiome composition. Together, these findings advance the understanding of the mechanisms that drive plant microbe interactions to inform the development of more robust microbial amendments for sustainable bioenergy.

This Science Focus Area is developing fundamental knowledge about the ways that solvents change the structures of plant cell walls and microbial membranes. The team’s overarching hypothesis is that the partitioning or binding of solvent molecules from the bulk phase to biomass or biomembranes will predict maximal or minimal disruption. Disruption of biological structures comprised of amphiphilic molecules and polymers (e.g., membranes and biomass) is a key step in biomass pretreatments and engineering the ultimate microbial limits in tolerating specific solvents. Researchers integrate the power of world-class neutron scattering capabilities and leadership-class supercomputing facilities available at Oak Ridge National Laboratory (ORNL). These capabilities are complemented by expertise in biodeuteration and biomembranes at ORNL, plant cell wall chemistry at the University of Tennessee, and neutron scattering and membrane biophysics at the University of Cincinnati.

Fuels and value-added chemicals derived from sustainable lignocellulosics are an important part of realizing the future circular bioeconomy. This requires efficient production of biointermediates, including ethanol or n-butanol, which have chaotropic effects on biomolecules leading to their toxicity to fermentative microbes. While these toxic effects can impact many macromolecules, the cellular membrane is especially vulnerable to disruption due to the partitioning of amphiphilic cosolvents into the lipid bilayer. This leads to well understood impacts on the transverse membrane structure at high cosolvent titers—membrane thinning, destabilization, loss of membrane potential, and eventually, cell death. The effects on lateral biomembrane structure have been less well studied, however. Lateral membrane organization can be understood in analogy to an in-plane phase separation of high and low melting point lipid species, as well as sterols (or their microbial analogs). This results in local membrane regions with different membrane compositions and physical properties. In the biological context, these structures are known as functional membrane microdomains or lipid rafts. Rafts play an important role in many cellular processes due to their role as platforms to sort, colocalize and assemble membrane proteins.

Researchers propose the hypothesis that amphiphilic cosolvents, such as ethanol and n-butanol, alter or disrupt functional membrane microdomains, leading to an unrecognized mode of cosolvent toxicity and cellular stress at cosolvent concentrations, far lower than those which induce membrane disruptions. To support the investigations, researchers developed novel neutron scattering and MD simulation strategies to experimentally observe the nanoscale changes in membrane structure in living cells.

Researchers demonstrated the direct disruption of a model lipid raft due to the presence of the amphiphilic cosolvent ethanol and elucidated the physical mechanism for the lipid domain disruption (Tan et al. 2023). Subsequently, n-butanol was observed to be more potent in disrupting membrane organization. Researchers have measured an unequal partitioning of n-butanol between the coexisting phases, leading to an increased mismatch in the hydrophobic thickness of these phases, confirmed by neutron-scattering observations. This leads to an increase in the domain line tension, which is a driver to minimize the domain interface-to-domain area ratio. These observations are complemented with large scale simulations replica lipid domain which molecular details about n-butanol partitioning and induced changes to the lipid domain interface.

Researchers also performed SANS measurements and extensive all-atom simulations on the partitioning of amphiphilic cosolvents (ethanol/THF/n-butanol) in living cell membranes and cell membrane extracts. The team showed direct measurements of cell membrane thinning in living Bacillus subtilis in the presence of these cosolvents using neutron scattering, estimated the true partition coefficient of these cosolvents, and interrogated the role of fatty acid supplementation in modulating the cellular response to these solvents. This work represents the first systematic approach of the nanoscale membrane phenotype in fermentative microorganisms. Further validation of this hypothesis will provide a more holistic understanding of solvent–membrane interactions and will inform strategies to improve cosolvent tolerance.

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA) use a systems biology approach to understand the interaction between microbial communities and the ecosystems that they inhabit. To link genetic, ecological, and environmental factors to the structure and function of microbial communities, ENIGMA integrates and develops laboratory, field, and computational methods.

The subsurface environment is one of the major sources of global nitrous oxide (N₂O) emissions. However, the estimation of N₂O from biotic/abiotic pathways in subsurface systems is still poorly understood. Researchers estimated N₂O production by building a field-scale reactive transport model via PFLOTRAN that integrates potential pathways of N-cycling at Area 3, which exhibits high concentrations of nitrate and low pH levels. The Subsurface Observatory (SSO) is located at Area 3 and is an intensive sampling site (5 x 5 x 8 m³). The SSO was designed and established by the ENIGMA Science Focus Area to provide high-temporal resolution datasets of groundwater flow, chemistry, and microbial communities with a highly instrumented set of continuously monitored nine multiport groundwater wells. The heterogeneous permeability field of the SSO site is reconstructed on the basis of the high-resolution data of soil types from Cone Penetration Testing (CPT), with fine grid blocks (0.3 x 0.3 x 0.004 m³ for a variably saturated zone (302.5 to 304.5 m depth) and or 0.3 x 0.3 x 0.1 m³ for the rest of the domain). The flow model has been initially calibrated and validated from the dataset of rainfall events and groundwater table elevation, collected for a dry season from September to December 2023.

Regarding reactive transport, the reaction network of the model includes many biogeochemical

reactions for and sulfate reduction. Based on this wide range of biogeochemical reactions, N₂O emission can be estimated by various biotic/abiotic pathways and calibrated by the measurements (e.g,. pH, DO, and nitrate concentration) from the SSO wells. As a result, researchers show the emergence of hot spots and hot moments of N₂O emission at the SSO site under a series of rainfall events.

This project seeks to expand the metabolic capabilities of a genetically malleable soil bacterium, Acinetobacter baylyi ADP1. This strain naturally degrades a wide variety of plant-derived aromatic compounds. Augmentation and alteration of these natural capabilities have the exciting potential to improve biotechnology applications ranging from lignin valorization to biomanufacturing. This team’s specific aims are to create novel pathways for the catabolism of syringol and pyrogallol and to develop new methods for large-scale genomic remodeling.

Advances in bacterial metabolic engineering and synthetic biology enable comprehensive genomic change. Altered aromatic compound metabolism in Acinetobacter baylyi ADP1 is facilitated by its exceptionally efficient natural transformation system. In this project, success was achieved by combining multiple approaches including enzyme design and the construction of modular synthetic pathways. Nevertheless, the biological consequences of such manipulation are often unpredictable. To achieve desired results, a growth-based adaptive evolution method was developed called Evolution by Amplification and Synthetic biology (EASy) (Tumen-Velasquez 2018). With this method, the targeted amplification of chromosomal regions serves as a rudimentary form of regulation to balance expression of different pathway segments.

Researchers focused on creating a pathway for syringol (2,6-dimethoxyphenol) degradation. This compound arises during lignin pyrolysis from the decomposition of sinapyl alcohol moieties. When converting lignin-derived mixtures to valuable products by microbes, syringol can be problematic both as an inhibitory compound and as an underutilized substrate. Since there is no characterized pathway for syringol consumption, researchers designed one to be expressed from the A. baylyi chromosome. In general, aromatic compound catabolism can be considered modular. The first module involves reactions to generate one of a limited number of aromatic substrates of ring-cleavage enzymes. The next key step is ring-cleavage itself, accomplished aerobically using ortho (intradiol) or meta (extradiol) dioxygenases. Finally, a multistep “lower” pathway typically feeds metabolites to central metabolism.

Researchers sought to convert syringol to pyrogallol, a potential ring-cleavage target. Pyrogallol cleavage can be mediated by some ortho– and meta-catechol dioxygenases, although specific protein sequences and responsible enzymes remain unknown. Strains were constructed to express combinations of different catechol dioxygenases and a guaiacol demethylase (GcoAB) variant, which converts syringol to pyrogallol (Machovina et al. 2019). The team’s initial attempts failed to express two separate enzymes capable of producing and cleaving pyrogallol from syringol. In contrast, colorometric assays suggested that the rational design of a novel chimeric enzyme successfully led to the in vivo metabolism of syringol and to the cleavage of pyrogallol by A. baylyi cells. The design of this chimeric enzyme was based on a similar enzyme that emerged from EASy experiments using guaiacol as a growth substrate (Tumen-Velasquez 2018). Key to success was the choice of sequence encoding an ortho-cleaving catechol dioxygenase with augmented activity on pyrogallol compared to the native version of this enzyme (CatA). Thus, these steps, mediated by a fabricated enzyme, represent two modules of a synthetic pathway for syringol degradation. As the final module to enable syringol to be used as growth substrate, the team incorporated a foreign pathway that is not native to A. baylyi for protocatechuate metabolism. Researchers mixed and matched these modules for functionality using a variety of different aromatic growth substrates. Growth on aromatic substrates using these non-native pathways involved targeted gene amplification and combinations of mutations selected during laboratory evolution. Collectively, these results highlight the feasibility of large-scale genomic remodeling for biotechnology. ADP1 offers exciting potential for further development as a synthetic biology chassis.

The program goal is to develop high-throughput biosystems design tools in carbon dioxide (CO₂)-fixing biosystems and apply these tools to engineer biosynthetic pathways for carbon-negative biomanufacturing of simple commodity chemicals. In this project, researchers aim to increase the CO₂ utilization efficiency of the CO₂-fixing microbe, Cupriavidus necator, by reconstituting the α-carboxysomal CO₂ concentrating mechanism native to Halothiobacillus neapolitanus.

The accelerating climate crisis combined with rapid population growth poses some of the most urgent challenges to humankind. A major contributing factor to this crisis is the unabated release and accumulation of CO₂ across the biosphere. Researchers can take advantage of this abundance of available CO₂ to transform the way the world produces and uses carbon (C) by engineering CO₂– fixing biosystems to produce commodity fuels and chemicals. A CO₂-fixing organism that is actively being studied as a C-negative biomanufacturing chassis is the bacterium, Cupriavidus necator. While proficient in producing high titers of metabolic products, wild-type C. necator does not grow optimally at ambient levels of CO₂ in comparison to at high CO₂ conditions on autotrophic metabolism. Researchers propose to optimize C. necator growth under atmospheric conditions by heterologously expressing the α-carboxysomal CO₂ concentrating mechanism (CCM) characterized in Halothiobacillus neapolitanus (Desmarais et al. 2019; Flamholz et al. 2020). To enable stable chromosomal expression of this 20kb H. neapolitanus CCM operon in C. necator, the team developed an inducible landing pad for integrase-mediated integration of synthetic cargo. In this project, the team demonstrates integration of the bacterial luminescence pathway and tunable expression of the pathway from the landing pad in C. necator. Another objective is to encapsulate the native C. necator Rubisco into the carboxysomal structures instead of heterologously expressing the H. neapolitanus Rubsico. C. necator Rubisco has been reported to retain optimal carboxylation rate in aerobic conditions with abundant competing O₂, which is an advantageous trait to maintain for aerobic cultivation (Satagopan and Tabita 2016). Researchers have utilized RFdiffusion, a protein design software, to de novo design C. necator Rubisco binding motifs to replace with the H. neapolitanus Rubsico for carboxysomal encapsulation (Watson et al. 2023). The next steps will entail synthesizing and testing these motifs in vitro for binding.

Bioenergy feedstocks need to be deployed on marginal soils with minimal inputs to be economically viable and have a low environmental impact. Currently, crop water supply is a key limitation to production. The yields of C₄ bioenergy crops such as Sorghum bicolor have increased through breeding and improved agronomy. Still, the amount of biomass produced for a given amount of water use (water-use efficiency, or WUE) remains unchanged. Therefore, this project aims to develop novel technologies and methodologies to redesign the bioenergy feedstock sorghum for optimal WUE. Within this broader context, this subproject is using Setaria viridis as a rapid-cycling model for gene discovery. Researchers aim to develop and demonstrate novel methods and resources to accelerate both the production of genetic variants and phenotyping of WUE traits as part of reverse and forward genetics approaches to discover genes regulating stomatal patterning and WUE.

Improving WUE in Sorghum requires the ability to manipulate endogenous genes and gene expression patterns. Researchers are implementing several technologies for the genetic improvement of sorghum as well as the model C₄ plant, Setaria. In both species, the delivery of DNA to cells is critical to alter the genetic code. Researchers currently have a robust pipeline for Setaria transformation; however, traditional methods of Sorghum transformation are laborious and time consuming. To expedite Sorghum transformation, researchers are using developmental regulators, including BABY BOOM and WUSCHEL2, to promote somatic embryogenesis from transformed sorghum leaf cells. Using traditional transformation methods in Setaria, researchers have succeeded in making specific nucleotide substitutions through prime editing. This technology enables precise small insertions (~30) or deletions (~100bp). Researchers are currently focused on introducing changes that would alter the kinetic activity of the key photosynthetic enzyme, PEP Carboxylase. To accelerate gene editing, researchers are using RNA viruses to deliver gene editing reagents through infection. Because the cargo capacity of most plant RNA viruses is limited to approximately 1 kb, researchers have made transgenic lines of sorghum and Seteria that express Cas9; sgRNAs are expressed from the virus, and gene editing occurs through infection. The current goal is to use viruses to edit the germline so that seeds can be harvested with heritable modifications to their genomes. To achieve precise control over gene expression, researchers are building synthetic genetic circuits to enable spatial and tissue-specific control over gene expression. Synthetic circuits offer a means to reprogram plant development and control growth. Finally, to expedite classical genetics, researchers have generated a male sterile line of Setaria using CRISPR/Cas9 by making targeted, inactivating mutations in a gene important for pollen development. These male sterile lines are currently being tested and promise to greatly accelerate genetic analyses in Setaria.

The goals of this project are to (1) develop a mechanistic understanding of the anaerobic oxidation of methane (AOM) syntrophic interactions; (2) define and functionally characterize the microbial community, including viruses, associated with methanotrophic consortia under changing environmental conditions; and (3) create an integrative modeling framework to explore the ecophysiology of AOM consortia and their community interactions in an environmental context.

Most elemental cycles in Earth’s surface environment are mediated by microbial reactions. To quantify these transformations and predict the distribution of nutrients and other chemicals of interest, biogeochemical models need to capture the transport and reaction rates of these processes, which depend on environmental conditions, such as the availability of substrates, metabolic pathway expression, and physiological constraints.

In this project, researchers explore environment-microbe interactions through reactive transport modeling of the microbially mediated AOM. First, researchers show simulations of spatially resolved microbial consortia composed of methanotrophic archaea (ANME) coupled metabolically to sulfate-reducing bacteria (SRB) via direct interspecies electron transfer (He et al. 2021). Growth efficiencies derived from estimates of catabolic energy yields, anabolic energy requirements and energy dissipation (Heijnen and Van Dijken 1992) resulted in growth-yields consistent with observations when using ammonium as the nitrogen (N) source.

The team’s model simulations showed that N demands can likely be fulfilled without causing significant ammonium drawdown within or surrounding the microbial aggregates. Nevertheless, some archaea and bacteria involved in AOM, a process with limited energy yield, have been shown to fix N₂ (Dekas et al. 2018; Metcalfe et al. 2021), which requires a significant amount of ATP and reducing equivalents. When extending this model to allow for N₂ as the N source, the predicted growth yields decreased but remained substantially higher than yields derived from measurements when N₂ fixation was active, suggesting that physiological controls are important.

To further investigate possible triggers for this energy-consuming process, researcherse studied growth and its dependence on N processing using a flux balance model of ANME (Faria et al. 2023). The simulations showed that even significant leakage of N-rich compounds is unlikely to induce N₂ fixation. Researchers therefore explored the potential of the use of N₂ fixation to maintain intracellular redox homeostasis as has recently been proposed for Geobacter sulfurreducens (Ortiz-Medina et al. 2023). However, researchers were unsuccessful at inducing N₂ fixation in the model under environmentally relevant conditions, which points to as yet poorly understood features of this energy-limited syntrophic partnership and the need for additional studies of the metabolic controls in methanotrophic ANME archaea.

Finally, environmental conditions within sediments, soils, and rock matrices may also vary on small spatial scales, depending on the pore connectivity, which could lead to conditions that trigger different metabolic activities. To explore the potential for the formation of distinct microenvironments within carbonate rocks that are formed through the process of sulfate-coupled anaerobic methane oxidation, the team developed a Lattice-Boltzmann porescale reactive transport model (CompLab3D). In these simulations, researchers established the model domain from CT scans of carbonate rocks, and then quantified the connectivity of their pore spaces by computing the distribution of water ages. This distinguishes well-connected regions from isolated pores, which may support different microbiological processes and levels of activity within the carbonate structure.

Facile gene targeting in the nuclear genome makes Auxenochlorella protothecoides, a unicellular, freshwater Trebouxiophyte, useful as a reference organism for discovery and a platform for synthetic biology. The team aims to expand the molecular genetic toolkit with additional neutral integration sites, transformation markers, regulatory sequences and reporter genes, along with improving transformation efficiency and developing RNP-mediated gene-editing methods for genome modification. Researchers are employing systems analyses and metabolic modeling approaches to inform engineering of the Calvin-Benson cycle for improved photosynthetic carbon (C) fixation, and to identify signaling pathways and regulators responsible for controlling fatty acid and triacylglycerol biosynthesis. Genome modifications predicted from these analyses to increase lipid productivity will be combined with strain engineering to produce cyclopropane fatty acids. Nonphotochemical quenching and a regulatory circuit for maintaining photosynthesis under Cu-limitation, both of which are absent in A. protothecoides, will be introduced to improve photosynthetic resilience.

Researchers used PacBio long-read sequencing to generate a gapless, telomere-to- telomere, phased diploid nuclear genome, and fully resolved the circular organelle genomes of Auxenochlorella protothecoides UTEX 250. This well-annotated, 45 Mb diploid nuclear genome resembles a genetic hybrid, with extensive inter- and intra-chromosomal recombination, and two instances of trisomy. Chromosome 3 trisomy was confirmed by knock-in of a Venus reporter at one allele of Ammonium Transporter 1B (AMT1B), and activation of the AMT1B promoter by nitrogen depletion in heterotrophic cells resulted in increased Venus fluorescence. The team will exploit this redundant chromosome as a landing pad for transgene integration, and putative centromere sequences will be tested for their ability to allow maintenance of stable centromeric plasmids.

Photosynthgreen algae can utilize sunlight to power photosystems for C fixation. In the daytime, algae are exposed to dynamic light conditions ranging from high-to-low or dark-changing light conditions have significant impacts on their growth, biomass and production. Researchers propose two strategies to improve C capture and growth under a field-like setting for A. protothecoides: (1) engineering a rate-limiting enzyme of the Calvin-Benson cycle (CBC), sedoheptulose 1,7-bisphosphatase (SBPase); and (2) introducing a photoprotective nonphotochemical quenching (NPQ) protein (LHCSR) to allow for robust growth under fluctuating light. Taking advantage of homologous recombination, researchers have generated strains to test both strategies. Preliminary data indicate that overexpression of SBPase leads to a growth benefit, and the introduction of a well characterized algal LHCSR improves NPQ kinetics. Molecular characterization of the engineered strains is in progress to understand the changes in the metabolic flux through CBC and the regulation of the newly introduced NPQ and to determine whether these modifications confer growth advantages under dynamic light conditions.

Transcription factors play critical roles in transcriptional regulation of fatty acid biosynthesis (FAS) genes and can be used in genetic engineering approaches to increase the expression of the FAS pathway. Researchers established a pipeline to extract transcription factors based on InterProScan IDs from the A. protothecoides UTEX 250 genome. In parallel, the team conducted a proteomics experiment under lipid accumulating conditions (nitrogen;N depletion and glucose addition). This analyses identified novel putative transcription factors that are upregulated in lipid accumulating cells and therefore are candidates for involvement in regulation of acclimation to N starvation, glycolysis and de novo fatty acid synthesis. Researchers are currently generating mutants to test the roles of these candidate transcription factors. Altogether, this work will inform the engineering of strains to increase total lipid accumulation in A. protothecoides.

To unravel the molecular mechanisms underlying winter maintenance in temperate deciduous tree species and their impact on woody biomass productivity, thereby advancing bioenergy crop improvement.

Dormant seasons constitute up to half the lifespan of woody perennial crops in temperate climates. While numerous studies have explored the seasonality of vegetative and floral buds, research investigating wood growth in these trees is primarily conducted in the greenhouse or during the summer season. Understanding maintenance and protection of wood-forming tissues during dormant seasons is crucial for improving stress resilience in the face of climate change. Researchers conducted seasonal transcriptome profiling of xylem tissues from mature Populus deltoides and young P. tremula x P. alba INRA 717-1B4 (717) trees. Self-organizing map (SOM) clustering analysis identified gene clusters that display season-specific expression patterns. Summer-based genes showed Gene Ontology (GO) enrichment associated with cell wall biogenesis as would be expected, whereas genes upregulated during the dormant season are associated with vital mechanisms for winter survival. These mechanisms encompass diverse aspects, including cold tolerance, cryoprotectant synthesis, cell membrane stability and integrity, metabolic adjustments, stress response, cell wall modification, growth regulation, various transport processes, and chromatin remodeling.

CRISPR-Cas9 is used to target winter-biased genes for knock-out in poplar 717 in order to characterize their functions. Candidate genes selected to date are mainly involved in the metabolism and transport of carbohydrates, which play a key role in antifreeze, dehydration protection, and spring regrowth. Other candidates are involved in nutrient transport and regulation, and protein modification and turnover. Knock-out mutants will be monitored for seasonal phenology during field trials. A subset of knockout lines will be subject to metabolomic and transcriptomic profiling. Network analysis will be used to investigate disrupted pathways and ultimately the molecular processes underlying winter maintenance and protection.

Wood vessel trait candidate genes coding for E3 ligase proteins will be functionally characterized through the creation of CRISPR-cas9 mutants, TurboID proximity labeling, and through drought and ABA treatments to study gene expression, protein ubiquitination, degradation, and abundance in poplar wood forming tissues.

Angiosperm wood contains highly lignified tube-like cells called vessel elements, which provide a pathway for the upward movement of water under tension. The dimensions and distribution of vessels in wood (i.e. wood anatomy) affect water transport and growth rates, as well as susceptibility to hydraulic failure during drought. Despite their crucial role in determining the hydraulic physiology of trees, the genetic regulation of vessel element anatomical traits is poorly understood. In a dosage-dependent genome-wide screen, researchers detected a significant correlation between the height-adjusted mean vessel diameter and frequency. A subsequent gene coexpression network analysis on poplar wood forming tissues found that height-corrected vessel frequency was significantly correlated to genes that code for E3 ubiquitin ligase, key components of the ubiquitin proteasome system (UPS). The team selected vessel trait-related E3 ligase candidates for further functional characterization of ubiquitin-proteasome regulation in poplar wood forming tissue. To achieve this goal, CRISPR-Cas9 mutants targeting poplar E3 ligase candidate genes have been generated to assess alterations in wood phenotype, gene expression, protein abundance, and ubiquitinomes. Additionally, TurboID transgenic lines are being generated to elucidate protein interacting partners for the candidate proteins through proximity labeling. In anticipation of poplar E3 ligase datasets, researchers examined the interactome of similar components in Arabidopsis transgenic lines subjected to drought and ABA treatments, revealing substantial treatment-induced alterations in the UPS compared to controls. These approaches aim to enhance the understanding of the role of the ubiquitin-proteasome system in wood formation, vessel trait variation, and tree responses to environmental stressors.

Researchers used deep mutational scanning to learn the molecular rules of specificity for a multidrug efflux pump. Next, the team will apply these rules to engineer transporters to efflux and confer resistance to toxins found in lignocellulosic hydrolysates, a major barrier to efficient biofuel production.

Transporter engineering offers the ability to precisely control which molecules remain in a cell. This could greatly improve the cost and efficiency of microbial biofuel production, for example by exporting biofuel end products for easier recovery, removing reaction byproducts to prevent toxic buildup, and conferring efflux-mediated resistance to lignocellulosic hydrolysate (LCH) inhibitors. LCH inhibitors can reduce the efficiency and yield of microbial biofuel production and are costly to remove from pretreated plant biomass. Researchers aim to engineer LCH inhibitor efflux pumps using bacterial multidrug resistance (MDR) transporters as a platform. In doing so, researchers will learn design rules for engineering of other biofuel-relevant transporters. To achieve this, Researchers must first understand the sequence determinants of transport specificity: how does each residue contribute to efflux of different types of substrates? To this end, researchers have used deep mutational scanning to characterize all single missense mutants of a bacterial MDR transporter in the context of several toxic molecules, including LCH inhibitors.

Exposure of a variant library to toxic transporter substrates results in enrichment of active variants when analyzed by deep sequencing. Researchers have identified general and substrate-specific functional hotspots and gain-of-function pathways using this method. Next, the team will use this data in combination with machine learning protein design techniques to design LCH inhibitor efflux pumps.

The goal of this project is to engineer a non-model bacterium, Novosphingobium aromaticivorans, for valorization of depolymerized lignin to value-added bioproducts. The project involves (1) discovery and optimization of pathways for assimilation of lignin-derived aromatic compounds; (2) engineering conversion pathways that match the stoichiometry of aromatic catabolism; and (3) development of genome-scale mapping techniques to identify new engineering targets in non-model bacteria.

The goal of this project is to engineer a non-model bacterium, Novosphingobium aromaticivorans, for valorization of depolymerized lignin to value-added bioproducts. The project involves (1) discovery and optimization of pathways for assimilation of lignin-derived aromatic compounds; (2) engineering conversion pathways that match the stoichiometry of aromatic catabolism; and (3) development of genome-scale mapping techniques to identify new engineering targets in non-model bacteria.

Lignin is one of the abundant renewable materials found in nature. This heterogeneous aromatic polymer is composed of a variety of p-hydroxyphenyl (H), guaiacyl (G), and syringyl

(S) monomers that are connected by diverse chemical linkages. Lignin valorization would improve biofuel economics, for example, through bacterial conversion of thermochemically depolymerized lignin into valuable bioproducts. N. aromaticivorans F199 is an Alphaproteobacterium capable of degrading G, S, and H monomers and, due to its genetic tractability and broad catabolic capabilities, is an emerging model organism for conversion of lignin-derived aromatic compounds. However, F199 cannot natively catabolize every component of depolymerized lignin, which limits conversion yields (Azubuike et al. 2022).

Researchers are identifying new aromatic degradation pathways to further increase the catabolic potential of F199 using a combination of barcoded transposon insertion sequencing, proteomics, experimental evolution, and in vitro biochemistry. The team demonstrated this approach with the aromatic monomer syringate, the β-1 linked dimer 1,2-diguaiacylpropane-1,3-diol (DGPD), and, more recently, the monomer guaiacol (Bleem et al. 2022; Cecil et al. 2018; Presley et al. 2021). However, there are multiple lignin-derived aromatic compounds that F199 catabolizes poorly or not at all. Researchers have evolved F199 to rapidly and completely catabolize the common β-O-4 dimer guaiacylglycerol-β-guaiacyl ether (GGE) and, in the process, identified an uncharacterized native catabolic pathway for the monomeric intermediate β-hydroxypropiovanillone. Researchers have also isolated a Novosphingobium strain that can assimilate the β-β linked dimer pinoresinol and fully characterized the pinoresinol catabolic pathway. Current efforts focus on transfer of heterologous catabolic pathways into F199.

In addition to optimizing lignin assimilation, researchers are converting the resulting intermediates into value-added products, such as building blocks for bioderived polymers. Using a combination of heterologous pathway expression, experimental evolution, and targeted chromosomal modification, researchers have enabled and improved conversion in F199 of the model lignin-derived aromatic substrate ferulate into 5-aminovaleric acid (5-AVA). Degradation pathways for 5-AVA have also been identified in F199 and are being removed. Additional optimization targets have been identified through metabolomic analysis of wild-type and engineered strains.

Finally, to better understand the effect of host genetic variation on pathway function, researchers are adapting a novel technique, bacterial quantitative trait locus (QTL) mapping, to F199. Researchers have demonstrated directional intraspecific recombination between strains of N. aromaticivorans driven by an Integrative and Conjugative Element (ICE) in the donor strain. The team is currently identifying the origin of transfer of this ICE to improve transfer. By combining novel pathway discovery, heterologous expression, and genome-scale optimization, researchers are engineering N. aromaticivorans F199 to efficiently valorize lignin-derived compounds.

Plants represent ideal chassis for metabolic engineering and light-driven synthetic biology. Their specific tissues and dedicated organelles give access to unique metabolite pools and allow insulation of newly installed traits. To fully harness the untapped potential and enable expression of complex traits, it is critical to develop a portfolio of tools to control gene expression and efficient testing and implementation technologies. The ultimate goal of this shared research objective is a shared standardized repository of validated biobricks, plant synthetic biology parts, and technologies for the improvement of bioenergy feed stocks. Researchers have initiated the cross-BRC repository and are populating it with regulatory elements, promoters that are tunable, organ, tissue, cell-type specific, and treatment responsive from sorghum and poplar. In this project, researchers showcase identification of genes specifically expressed in cell types of the sorghum stem, epidermis and root hairs, as well as a highly xylem specific gene in poplar. Expression analysis revealed complex coexpression networks, including so far uncharacterized genes.

Cloning, validation, and functional characterization of the respective regulatory elements enabled assembly into logic gates and genomic circuits. Tools for functional testing of promoters, delivery of multigene constructs and rapid transient expression technology are in development. Application of these tools can enable scalable biosustainable production of natural products and tuning of the plant’s capacity for adaptation to and interaction with their biotic and abiotic environment.

Pacific Northwest National Laboratory’s Phenotypic Response of Soil Microbiomes Science Focus Area aims to achieve a systems-level understanding of the soil microbiome’s phenotypic response to changing moisture. Researchers perform multiscale examinations of molecular and ecological interactions occurring within and between members of microbial consortia during organic carbon (C) decomposition, using chitin as a model compound. Integrated experiments address spatial and interkingdom interactions among bacteria, fungi, viruses, and plants that regulate community functions throughout the soil profile. Data are used to parametrize individual- and population-based models for predicting interspecies and interkingdom interactions. Lab and field experiments test predictions to reveal individual and community microbial phenotypes. Knowledge gained provides a fundamental understanding of how soil microbes interact to decompose and sequester organic C and enables prediction of how biochemical reaction networks shift in response to changing moisture regimes.

The fate of soil organic C depends in part on how efficiently bacteria and fungi incorporate C into biomass. Higher fungal: bacterial ratios in soil microbiomes have been associated with lower C-use efficiencies (CUE), and CUE is also sensitive to environmental factors, including C source (Soares 2019; Ullah et al. 2021). Therefore, understanding both CUE and interkingdom interactions, and how they affect one another, is key to a complete understanding of the soil ecosystem. However, as these systems are incredibly complex, direct analysis is often difficult. In this project, researchers use a model community of bacterial species, Model Soil Consortium – 2 (MSC-2) to explore how CUE is affected by different experimental growth conditions under four different C sources (N-acetylglucosamine, trehalose, chitin and carboxymethylcellulose; McClure et al. 2022). Researchers show that faster growth (determined via cell counting) does not always reflect high CUE, and that, in certain cases, CUE values are the highest in conditions causing slower or stagnant growth. The team also found that under growth conditions with shaking, vitamin and mineral deficiency led to slower growth and lower CUE that was dependent on the specific C sources. This suggests that factors that can limit growth (such as lack of key vitamins) are not always the rate-limiting step if another factor (complex C) is present. Identifying a rate-limiting factor can be difficult but the use of model communities helps by discovering paradigms that can be applied to more complex systems.

Researchers extended the successful model community analysis by increasing the complexity to the interkingdom level through the addition of a fungal partner, Fusarium oxysporum, and the structural complexity by using glass beads in a spatially structured soil habitat. The team found that the respiration of bacterial and fungal partners is greatly increased when cultured together vs. separately, revealing interkingdom interactions that can positively affect community metabolism. Researchers are expanding this work through the development of a Microbial Rhizosphere Community (MRC-1), a community of cocultured bacteria and fungi that will be key to future analysis of how CUE is affected by fungal: bacterial ratios and interactions.

In parallel, researchers are evaluating soils from the Tall Wheatgrass Irrigation Field Trials in Prosser, WA. The team generated 13 metagenome samples from fungal floats of field soil from which 333 metagenome-assembled genomes (MAGs) have been derived. These MAGs represent microorganisms, predominantly bacteria, associated with the fungal hyphosphere. Several novel genera were identified, some containing metabolic pathways for the degradation of complex C substances like chitin, cellulose, and starches, which may aid survival in the hyphosphere ecosystem.

The work presented here illuminates several potential crosskingdom interaction events across scales of complexity (soil analogous laboratory systems and field experiments). Future experiments in this area will explore how interactions between species, an approach made simpler with the defined MSC-2 community where species can be removed or added easily, drive CUE. Researchers also propose to use these findings to design and implement experiments that test hypotheses generated in controlled laboratory systems in native field environments so that it can be determined whether and to what degree CUE findings scale across systems.

Algal and plant systems have the unrivaled advantage of converting solar energy and CO₂ into useful organic molecules. Their growth and efficiency are largely shaped by the microbial communities in and around them. The μBiospheres Science Focus Area seeks to understand phototroph-heterotroph interactions that shape productivity, robustness, the balance of resource fluxes, and the functionality of the surrounding microbiome. Researchers hypothesize that different microbial associates not only have differential effects on host productivity but can change an entire system’s resource economy. This approach encompasses single-cell analyses, quantitative isotope tracing of elemental exchanges, omics measurements, and multiscale modeling to characterize microscale impacts on system-scale processes. Researchers aim to uncover crosscutting principles that regulate these interactions and their resource allocation consequences to develop a general predictive framework for system-level impacts of microbial partnerships.

Photosynthetic carbon (C) fixation by algae and cyanobacteria represents half of the global C fixation on Earth and holds promise as a strategy for nonfossil fuel-based generation of biofuels. Loss of fixed C as dissolved organic matter (DOM) through exudation, lysis, and as viral progeny after infection represent critical fluxes that become a source of biomass for the algal microbiome. Using four different experimental systems, researchers used stable isotope tracing with ¹³C and nitrogen-15 (¹⁵N) labeling combined with single-cell resolution isotope analysis by nanoSIMS to investigate mechanisms and fluxes of algal organic C into the algal microbiome.

The first two experimental systems examine the model biofuel-producing diatom Phaeodactylum tricornutum and bacterial isolates that grow using P. tricornutum fixed C. Using a porous microplate that cocultivates bacteia near a constant source of algal DOM, the team tested whether the isolates compete for the algal DOM. Researchers found that the presence of some bacterial strains inhibited the uptake of algal-derived C by Marinobacter, a common algal-associated bacterium, while others did not. This suggests that niche partitioning and competition directly influence C from algae to bacteria.

In the second experimental system, researchers are examining how oxidative stress, which is prevalent in high-biomass and high-light ecosystems, impacts the transfer of algal C and N into heterotrophic bacteria. The team aimed to compare bacteria that relieve oxidative stress versus those that do not, and thus used two photoheterotrophic bacteria (one mutualistic and the other commensal under oxidative stress). Researchers quantified the transfer of algal C and N into the bacteria with and without and addition of hydrogen peroxide, and found that the non-mutualist incorporated more C and N under oxidative stress, whereas the mutualist did not change its uptake.

The third experimental system used the alga Chlamydomonas reinhardtii and the vitamin producing soil bacterium Mesorhizobium japonicum, previously used as a model for vitamin exchange for fixed C. The team unexpectedly found little algal-derived C being exchanged from alga to bacterium, and the modest amounts could be explained by a low level of algal cell lysis, suggesting this bacterium grows using algal lysate rather than exudate.

For the fourth experimental system, the team aimed to examine the flux of algal derived C into bacteria using algal viruses as a mechanism. Researchers isotope-labeled a giant virus infecting the alga Emiliania huxleyii and added this isotope-labeled viral fraction to a complex aquatic microbial community. Viral particles in the presence of the microbial community decreased faster than without cells, and this corresponded to increased isotope labeling into bacteria and eukaryotic protist cells, demonstrating another mechanism for algal-derived C feeding the microbial loop.

The direct isotope measurements in these four experimental algal systems have demonstrated the capabilities to measure fluxes from algae to bacteria, and the team found they are impacted by mechanisms (cell lysis, exudation, and viral particles) and environmental factors (oxidative stress, competition by other bacteria).

The goals of this project are to (1) develop a mechanistic understanding of anaerobic oxidation of methane (AOM) syntrophic interactions; (2) define and functionally characterize the microbial community, including viruses, associated with methanotrophic consortia under changing environmental conditions; and (3) create an integrative modeling framework to explore the ecophysiology of AOM consortia and their community interactions in environmental context.

Beyond identifying the microorganisms present in the environment, characterizing their interactions and impact on other biological communities is becoming increasingly necessary to understand the functioning of ecosystems. One of the major regulators of biological communities are viruses, capable of infecting and killing a broad range of organisms across the tree of life. In recent years, metagenomics has significantly expanded the knowledge about the virosphere and its diversity (Laso-Pérez et al. 2023). In this project, researchers present the development of viral BONCAT-FACS coupled with metaviromic sequencing, where free viruses that have recently infected an active cell are specifically labeled with BONCAT and sequenced from complex environmental communities. This newly developed approach for sorting, quantifying, and sequencing active viral-like particles offers a new lens in which to track viral host dynamics and to characterize the selective pressure of distinct viral populations on microbial communities within diverse ecosystems.

Viral BONCAT-FACS is based on the BONCA methodology (Bioorthogonal noncanonical amino acid tagging), which marks newly application to the virosphere was demonstrated in a laboratory at Caltech, where free viruses were visualized using epifluorescence microscopy after incorporating newly synthesized peptides or amino acids from their active hosts (Pasulka et al. 2018). This optimization of viral BONCAT includes an enhancement in the fluorescence signal associated with active viruses, enabling detection by flow cytometry. Viral BONCAT-FACS, like the previously developed high-throughput FACS (HT-FACS) in the Martinez-Garcia group enables genomic analysis of flow sorted viral populations, allowing the differentiation of active, newly produced BONCAT labeled viruses, and the nonactive viral particles concurrently with active and inactive cell fractions (Martinez-Hernandez et al. 2017). The team successfully amplified and sequenced complete genomes using the WGA-X reaction, initially demonstrating the viability of this technique by sequencing more than 1,000 contigs from the active viral fraction in coastal waters followed by further optimization for sediment/rock associated microbial communities catalyzing the AOM (Stepanauskas et al. 2017; Garcia-Heredia et al. 2021).

Viral BONCAT-FACS was used on laboratory-maintained methane-fed sediment and AOM microbial mat incubations. After a 3-day BONCAT incubation in the presence of methane (CH₄), active viruses and microorganisms were fluorescently labeled with the optimized click reaction and sorted, yielding ~3,500 active viral-like particles (VLPs) and ~500 active microbial cells. Sequencing and bioinformatic analysis of the amplified genomes from both viral fractions confirmed the recovery of diverse viral genomes. In a second BONCAT experiment, AOM mat samples enriched in ANME archaea and sulfate-reducing bacteria were incubated with either CH4 and SO^-2 or CH4 and AQDS (e.g., decoupling ANME archaea from their sulfate-reducing partners; Scheller et al. 2016). After 4 weeks of incubation, higher cellular and viral BONCAT activity was observed in the AQDS treatment. Genomes of 75,000 active and 75,000 nonactive VLPs, along with more than 100,000 active and nonactive microbial cells were sorted and sequenced from the different treatment conditions. Sequencing is now underway and will help illuminate how viruses regulate the dynamics of these methane-fueled communities and how viral pressure is affected by cellular stress conditions.

Identify potential alternative routes in bacterial methylerythritol phosphate (MEP) metabolic pathway, which is the pathway used to generate high-value terpenoid products.

Terpenoids are a diverse class of compounds with wide-ranging uses such as industrial solvents, fragrances, and more. Industrial production of most terpenoids relies on nonrenewable feedstocks making alternative production methods desirable. Fermentation of engineered microbes using renewable feedstocks like lignocellulose is an attractive strategy for large-scale production of key terpenoids because it has the potential to be sustainable and relatively inexpensive. To achieve large-scale production of terpenoids, there are widespread efforts to engineer the metabolic pathway that generates terpenoids. All terpenoids are made from the final products of the methyl erythritol phosphate (MEP) pathway, which is composed of seven enzymatic steps. Efforts in engineering the MEP pathway have identified some of these enzymes as having unfavorable characteristics, so researchers are interested in identifying alternative enzymatic routes, which may have evolved that are functionally redundant to the canonical MEP pathway. The team used comparative genomics to search for alternative enzymes to the canonical MEP enzymes and found that enzymes early in the pathway likely evolved alternatives as supported by literature. In contrast, researchers found enzymes late in the pathway appear to have no alternatives in the database of 4,400 genomes in this study. Early pathway flexibility suggests that researchers may be able to identify the genes responsible for an incomplete canonical pathway and implement these alternatives enzymes in metabolic engineering should they have more favorable qualities. For the late pathway steps, if alternative enzymes have evolved at all, they are rare or their host organisms have not been sequenced. The ever-growing repository of sequenced bacterial genomes has great potential to provide metabolic engineers with alternative metabolic pathway solutions. The finding that late MEP pathway enzymes are evolutionarily indispensable informs both metabolic engineering efforts and the understanding of the evolution of terpenoid biosynthesis pathways.

The intrinsic control for genome and transcriptome editing in communities (InCoGenTEC) Science Focus Area aims to develop strategies for biocontainment, enable safe transformation of non-model prokaryotes using phage vectors, and understand gene mobility in microbial communities. This project’s overall goals are to mechanistically understand gene mobility events through comprehensive computational mapping of integrase and transposon driven mobility, perform functional genomics studies to identify genes and pathways responsible for mobility, identify novel genes for use in biocontainment mechanisms, and utilize the prophages from the genomic island (GI) database to transform non-model microbes towards the goal of safe microbial community transformation.

Development of phage vectors requires closing two major knowledge gaps: (1) idenifying viable phages that infect non-model bacterial species and (2) functionally understanding these phages.

Researchers have three strategies to obtain viable phages for non-model bacteria: environmental isolation, prophage induction, and synthetic phage rebooting. The team used environmental isolation in low- or high-throughput to isolate phages from environmental reservoirs. Two new environmental phages, each with unique features, were isolated from soil using traditional low-throughput methods. Pseudomonas pudita phage MiCath contains an entire queuosine biosynthesis cassette to produce modified nucleotides protecting the phage genome from nuclease activity, and Rhodococcus phage Perlina has three tRNA genes and a split lysin gene.

To speed up the discovery of novel phages, researchers have developed a high-throughput phage isolation method (HtPIP) that enables a single-soil sample to be used for screening of up to 96 strains. Researchers are currently using HtPIP to isolate a number of phages for a diverse range of bacterial isolates from soil microbial communities.

Experimental phage hunting (even in high throughput) is slower than discovery of prophages from bacterial genomic sequence data. The team has developed a genomic island database, which includes precisely defined prophage genomes that can be “mined” for any bacterial species of interest (Mageeney et al. 2020). This database contains ~20x more phages that currently found in public phage sequence repositories. Practically, however, many of the strains that harbor these prophages are inaccessible. Researchers have developed a system to synthetically rebuild and reboot these prophages from sequence alone using DNA synthesis, yeast assisted assembly, and cell-based phage production.

To understand the phage biology and engineering constraints, and to harvest useful gene products for delivery and transformation of synthetic genetic elements, researcher must understand the functions of genes contained within the phages and genomic islands. The team plans to use CRISPRi, CRISPRi-ART, and DART technologies to enable functional genomics for the non-model bacteria and their mobile genetic elements. Using these CRISPR tools, researchers will perform whole-MGE loss-of-function screens to identify candidates genes for removal or reuse. Overall, this work provides a foundation for understanding genomic islands, allows informed design of phages for vectors, and greatly increases the ability to mine prophages.

Understanding the intricate regulatory mechanisms governing gene expression in plants is important for precise and efficient crop engineering. In this project, researchers present a comprehensive study aiming to construct a cell atlas elucidating the cisregulatory elements (CREs) and the spatial-gene expression network in poplar (Populus sp.)

Leveraging single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), researchers dissect chromatin accessibility at the single-cell level to identify CREs related to important poplar traits. The team also applied spatial transcriptomics (spRNA-seq) on various poplar tissues to unravel gene expression patterns in a spatial and temporal context. The resulting single-cell and spatial regulatory atlas will provide essential knowledge for re-engineering poplar as a multipurpose crop that can be used for bioenergy, biomaterial, and bioproduct production.

Researchers will determine the factors within a complex natural microbial community that dictate how much carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) are released in thawing permafrost, including the precise mechanisms of soil organic carbon (C) degradation by specific microbial community members in permafrost from Svalbard. This research focuses on a critical area of the Arctic, with high rates of storm intensification, temperature increase, and permafrost thaw. The results from this study will therefore be predictors for the future of permafrost thaw in the rest of the Arctic.

Arctic soil communities are on the frontline of the global response to climate change. This multi-institutional and multi-international collaboration has tackled the complexity of microbial organic matter remineralization across permafrost and active layers, as well as sediments from both of these locations that have been redeposited onto the fjord floor by seasonal glacial melt. Researchers completed two field seasons in Svalbard, Norway, in spite of the COVID-19 pandemic, a new war affecting the participation of some of the collaborators, and the passing of a member of the leadership team who was also a dear friend. Researchers have leveraged this project with international collaborators who have greatly expanded the breadth of the original project to mutually enhance this work. This projected is reporting: (1) multiomics approach for examining in situ microbial communities combining metagenomics, metabolomics, metatranscriptomics, and metaproteomics with geochemical measurements to recreate depth gradients across the soils and fjord sediments; (2) incubation studies examining gas fluxes and uptake of labeled substrates into the active fraction of microbial biomass; (3) development of molecular dynamics models to understand the interactions of C-degrading enzymes with mineral surfaces; (4) multiseasonal metabolic flux measurements to yield system-wide net process rates. Across all these different metrics, researchers find that the activity and diversity of microbial communities, as well as their ability to breakdown different sources of organic matter and the amount of gases that result from that, changes with depth in unexpected ways. Most notably, the well-established steep drop-in microbial activity with depth that is present in most temperate ecosystems does not occur in the Svalbard active layer soils or in fjord sediments. By partnering with a long-term monitoring station in Bayelva, researchers have seasonal variation measurements of temperature and liquid water content from over 20 years, showing that soils buried tens of centimeters have higher volumetric water content due to insulation from surficial soils (Sipes et al. 2024). Fjord sediments, in turn, have access to deep reach of oxidized sulfur and C compounds, stimulating a burst of transcriptional activity in a range of fermentative and respiratory microorganisms. The pathways and activities for C degradation differ greatly by depth in both systems, as well as by location across the permafrost and fjord system. This implies that the organic matter in freshly thawed permafrost may be immediately available to microbial degradation, since the work demonstrates that the deeply buried communities are already active due to the higher liquid water availability. The presence of a new set of degradational capabilities in the fjord sediments suggests that even if organic matter from freshly thawed permafrost is not degraded in situ, it has a secondary chance to be degraded after being swept into the fjords from glacial run off. In total, a picture is emerging of the permafrost and fjord system as being a larger scale “factory” for processing thawing permafrost, with the subsurface playing a key role, possibly amplifying the rate of CO₂ production beyond what occurs in surficial terrestrial soils alone.

Establishing a digital anatomical atlas for roots of 11 members of the Brassicaceae family to inform the understanding of gene function and connection between genotype and phenotype. The long-term goal is to develop stress tolerant oil-seed crops to advance a sustainable production of biofuel.

Comparative anatomical studies of diverse plant species are vital for the understanding of changes in gene functions such as those involved in solute transport and hormone signaling in plant roots. Through the extraction of quantitative phenotypic data of root cells, researchers can further characterize their response to environmental stimuli, facilitating an in-depth characterization of how genes control root cell development. As the first step for comparative anatomical analysis of root cells, accurate segmentation of individual cells is essential to the analysis of whole root traits. Existing software such as PlantSeg and MorphographX utilized neural networks called U-Net for cell wall segmentation. U-Net was a last generation neural network model, which requires training with large amount of manually labeled confocal images. It is time consuming to retrain the model in order to adapt to new images. Foundational models like the Segment Anything Model (SAM) hold promise across various domains due to its zero-shot learning capability alongside prompt engineering can reduce the effort and time traditionally consumed in dataset annotation, facilitating a semiautomated training process. In this research, the team evaluated SAM’s segmentation capabilities against PlantSeg, a state-of-the-art model for plant cell segmentation. The team found that PlantSeg were able to segment 2,332 plant cells from 20 confocal images of Arabidopsis roots. However, 792 such segmentations (34.0% of total segmented cells) were incorrect based on a manual inspection. In contrast, SAM model without finetuning (Vanilla SAM, or V-SAM) was able to segment 1,052 cells, with only 7.8% of cells were incorrectly segmented. Although V-SAM can only find 68.3% of correct cells found by PlantSeg, this is a surprisingly good performance because V-SAM was never trained on root confocal images. Researchers further fine-tuned V-SAM with human prompt of ~1,000 cells, by drawing rectangle bounding boxes around cells that were not segmented by V-SAM. Note this is a substantially simpler annotation as compared to the required labeling by U-Net, which is to label every pixel of the cell wall from each training image. With the finetuned SAM (f-SAM), researchers were able to segment 2,885 cells correctly from the 20 confocal images, which is 187% of that obtained by PlantSeg. These findings demonstrate the efficiency of SAM in confocal image segmentation, showcasing its adaptability and performance compared to existing tools. By addressing challenges specific to confocal images, this approach offers a robust solution for studying plant structure and dynamics. Overall, this research highlights the potential of foundational models like SAM in specialized domains and underscores the importance of tailored approaches for achieving accurate semantic segmentation in confocal imaging.

The overall goal of this project is to design, construct, analyze and optimize a synthetic microbial consortium system consisting of three closely interacting members—a carbon dioxide (CO₂)-fixing photosynthetic specialist, a nitrogen (N₂)-fixing specialist, and a third specialist that can convert organic carbon (C) and N generated by the first two specialists to synthesize a desired product. By integrating complimentary expertise from multiple research laboratories at three institutions, researchers are pursuing three specific objectives: (1) develop tripartite microbial consortia for C or N fixation and production of biomolecules with various N or C ratios; (2) investigate molecular and cellular mechanisms governing the tripartite consortia via omics study and predictive modeling; and (3) explore alternative spatial configurations and develop scalable design principles.

Microbial communities are ubiquitous in nature, exhibiting incredibly versatile metabolic capabilities and remarkable robustness. Inspired by these synergistic microbial ecosystems, rationally designed synthetic microbial consortia is emerging as a new paradigm for bioprocessing and offers tremendous potential for solving some of the biggest challenges the society faces. In this project, researchers focus on a tripartite consortium consisting of a CO₂-fixing photosynthetic specialist, a N₂-fixing specialist, and a third specialist that can convert organic C and N generated by the first two specialists to synthesize a desired product. In addition to CO₂ fixation, a noteworthy feature of this design is the elimination of the requirement for N fertilizer, which has been produced through ammonia synthesis using the Haber-Bosch process and accounts for an estimated 2% of global energy expenditure. Researchers aim to develop a modular and flexible model system capable of producing diverse biomolecules (varying C:N ratio) as advanced biofuel or platform chemicals, to dissect this complex ecosystem using a spectrum of cutting-edge systems approaches, and to ultimately derive scalable and broadly applicable design principles for maximizing the system performance.

The team’s first prototype tripartite consortium employs genetically modified strains of photosynthetic cyanobacterium Synechococcus elongatus that secretes sucrose and N-fixing bacterium Azotobacter vinelandii that secretes ammonia respectively, to form a symbiotic foundation for supporting a third producer member (Abramson et al. 2016; Barney et al. 2015). Utilizing a customized bioreactor system consisting of multiple chambers separated with permeable membranes and allowing control of growth rates of individual consortium members, researchers demonstrate this platform technology with selected representative production specialists, including a sucrose-metabolizing Escherichia coli K-12 derivative strain, Corynebacterium glutamicum, and Bacillus subtilis (Carruthers et al. 2020; Carruthers et al. 2024).

The team’s ongoing work aims to develop new methods for creating novel spatial configurations that provide individualized environmental niches for each consortium member and thereby maximize their performance on intended functionalities. One initial focus is to dissect spatially separated and spatially consolidated cultivations of Azotobacter vinelandii and Synechococcus elongatus. Omics studies are conducted to unravel regulatory mechanisms. This allows the ability to gain fundamental insights on species interactions and their contributions to the robustness of the biculture system, which will guide future efforts in optimization of the whole system.

Other ongoing work includes: (1) development of predictive mathematical models to systematically explore the parameter space to understand how different biological parameters and operating strategies impact the system performance such as yield and productivity; and (2) investigation of spatially organized cocultures using 3D-printed communities in hydrogel matrices, which render high-resolution control and analysis capabilities.

The project addresses three goals: (1) systems-level analysis of camelina, pennycress and Cuphea for increased lipid content and predictable production of fatty acids with tailored chain lengths; (2) integration of a redesigned plastid biofactory with extra plastidial metabolism for enhanced oils within an engineered biocontainment strategy; and (3) controlled environment- and field-tested engineered germplasm.

Bigger, Better, Brassicaceae, Biofuels, and Bioproducts (B5) is providing fundamental knowledge to guide biodesigns of Brassicaceae nonfood oilseeds, camelina, and pennycress for sustainable biofuels and bioproducts. One target is the tailoring of fatty acid biosynthesis and storage to generate camelina and pennycress oils rich in medium-chain fatty acids (C8–C14) as feedstocks for sustainable aviation fuel. Researchers have undertaken a systems biology approach to understand the metabolic specialization that enables plants such as Cuphea species to accumulate oils highly enriched with medium-chain fatty acids versus typical oilseeds such as camelina and pennycress that accumulate C16- and C18-rich oils. Researchers are also conducting a systems-level analysis of existing camelina and pennycress lines engineered for C10 oil production to identify metabolic constraints that limit biosynthesis of these redesigned oils.

Lines engineered with genes for further work through a design-build-test-learn strategy. Early transcriptomics results have been incorporated into 3D omics and CCMT tools for comparative and cross- species analytics. Results to date from measurements of biomass, metabolic intermediates, omics studies, and isotope tracer investigations were presented.

A primary goal of the Lawrence Livermore National Laboratory BioSecure Science Focus Area (SFA) is to establish gene overlaps—in which two genes are encoded within the same DNA sequence through use of alternative reading frames—as a generalizable biocontainment strategy to protect engineered functions against mutational inactivation and to mitigate the horizontal transfer of invasive genes. This project is focused on determining the biophysical and molecular principles governing expression of overlapping genes. The project seeks to (1) identify the biophysical mechanisms and constraints underlying expression of overlapping genes and (2) predict and engineer future overlapping gene used in microbes for deployment.

In synthetic biology, methods for stabilizing genetically engineered functions in intended hosts are necessary to cope with mutation accumulation. One generalizable strategy to preserve genetic information is through gene overlaps, translating two distinct proteins from the same mRNA in different open reading frames. Overlapping a sequence with an essential gene can alter its fitness landscape and produce a constrained evolutionary path. While ongoing work is focused on large-scale redesign of the entangled proteins to satisfy sequence constraints required by sequence overlap, little is known about how expression is affected for entangled genes and its ramifications on the success of developing successful gene entanglements. To dissect the role of entanglement on gene expression, researchers have devised a methodological pipeline of genetic entanglement by inserting a protein encoded in an alternate reading frame with an external gene, minimizing amino acid changes to both genes, permitting functionality of both overlapped genes. Researchers demonstrate the creation and evaluation of multiple overlapping, out-of-frame insertion designs in flexible loops of inner membrane anchored antibiotic resistance alleles encoding efflux pumps. The team shows that inserted genes (toxins and fluorescent reporters) can function despite their location inside another coding sequence in an alternate frame and that function of both genes can be retained with minimal redesign. Interestingly, the team finds that the nested genes exhibit significant variability in the expression based on the location of the insertion of the external gene. This variability is not due to differences in mRNA levels but manifests at the level of protein abundance. To further generate a broad understanding of expression alterations during gene-nesting, researchers have performed insertional profiling to generate libraries of additional external genes (encoding for cytoplasmic globular proteins) with a nested gfp gene located throughout the sequence of the external gene. After identifying nested entanglements with full functionality of the external gene, the team will perform a series of mechanistic studies (Structure-Seq and Ribo-Seq) to identify whether and how expression may be altered for both entangled genes. These studies will provide general principles that underlie the expression of engineered entangled and nested genes with the goal of creating entangled genes capable of expressing at levels needed to stabilize function of both genes. Ultimately, this work will establish general guidelines for designing gene entanglements for improved stability of engineered genetics and circuits in microbes deployed in situ.

To develop high-throughput biosystems design tools that are applied to multiple testbeds for carbon-negative biomanufacturing.

In the face of escalating climate change, there is a pressing need for innovative strategies to mitigate carbon (C) emissions. LanzaTech stands at the forefront of this initiative, employing chemoautotrophic gas fermenting microorganisms to convert carbon dioxide (CO₂) into valuable C-based materials. This multidisciplinary project aims to enhance the metabolic efficiency of CO₂-utilizing biosystems through genome optimization and the integration of machine learning (ML) techniques. This approach employs cutting-edge genomic tools to iteratively knockout gene clusters towards engineering strains streamlined for the rigorous conditions of industrial fermentation. Given the abundant possible permutations, researchers leverage ML to inform the experimental strategies and strategically guide the knockout efforts. To this end, researchers have trained ML models using transcriptomic datasets to discern intricate patterns and relationships between genes and their functions. These models will be leveraged towards identifying contiguous genetic regions for targeted reduction. Strains generated from this process will not only enhance the understanding of gene functionality, they also enable the construction of genotype-phenotype associations through downstream screening (Sastry et al. 2019; Sastry et al. 2021). These efforts will significantly advance in silico models and streamline the development of microbial strains for industrial-scale applications.

The future bioeconomy requires engineered microbes that behave predictably, robustly, and safely in natural environments. Most engineered microbes, however, fail to function outside of the laboratory and carry uncontrolled risks of genetic pollution into natural gene pools. The BioSecure Science Focus Area (SFA) at Lawrence Livermore National Lab is developing new approaches to enhance biocontainment of engineered bacteria.

Introducing overlapping genes into engineered bacteria can enhance stability and productivity, while reducing the risk of uncontrolled genetic spread. Overlapping genes share a single coding sequence of DNA and RNA but are translated in alternate reading frames. By designing overlapping genes, researchers align their evolutionary trajectories towards desirable traits. For example, overlapping an essential gene can prolong an engineered function and overlapping a toxic gene can reduce horizontal gene transfer. However, creating functional overlapping sequences requires extensive protein redesign and remains technically and computationally challenging.

The team performed a large-scale computational screen between 118 genes by generating over 7-million overlapping sequences in silico. Researchers compared these overlapping sequences and their predicted function (scores) between gene pairs to identify genes and gene pairs more amenable to overlap. Genes vary in their malleability, and the resulting scores are influenced by several factors including gene length, number of orthologs, and amino acid content. Several small genes, such as hicA, infA, and purE, appear amenable to overlap with multiple partner genes. Several larger genes, such as lacZ and aroB, also score well when overlapped with multiple smaller partners. These results suggest designed overlaps are feasible for many genes. However, these predictions are based on unproven computational models of protein function derived from evolutionary sequence data and must be experimentally validated.

To validate the protein models, researchers have begun experimentally testing redesigned protein variants for individual proteins identified in the screen. Given that most genes in the screen are conditionally essential in E. coli, the team tested protein function by complementing growth in auxotroph strains that lack these genes. The team has implemented a pipeline to test pooled variants at scale by sequencing and then use these fitness data to interpret model predictions.

In small-scale trials, researchers have identified functional genes that have undergone significant redesign but retain function. For example, purE variants exhibited wild-type function despite over 40% of residues being altered, while hisI variants maintained wild-type function with ~50% residues changed. In a recent trial, all tested hicA sequences (6/6) were functional, with each variant featuring 30% to 50% changed residues. Overall, researchers have experimentally validated that the protein models for multiple genes can create sequence-diverse yet functional variants, with successful validation observed in 10 out of the14 genes tested.

In the next phase, researchers will expand the experimental throughput by testing thousands of variants for select genes while also testing the function of overlapping sequences for both genes. These results demonstrate the feasibility of computational redesign of entire proteins in support of designed gene overlap. This ability to create novel overlapping genes will foster the next generation of dependable and secure engineered microbes.

The Secure Ecosystem Engineering and Design (SEED) Science Focus Area (SFA) led by Oak Ridge National Laboratory combines unique resources and expertise in the biochemistry, genetics, and ecology of plant-microbe interactions with new approaches for analysis and manipulation of complex biological systems. The long-term objective is to develop a foundational understanding of how non-native microorganisms establish, spread, and impact ecosystems critical to DOE missions. This knowledge will guide biosystems design for ecosystem engineering while providing the baseline understanding needed for risk assessment and decision-making.

The introduction of microorganisms into new environments can have profound effects on resident communities (e.g., plants, associated microbiome) and local ecosystem services (e.g., soil stabilization, carbon sequestration). Depending on the organism and environmental context, these impacts can be positive, negative, or neutral. Over the last decade, the commercialization of several unique strains of beneficial fungi have begun improving agricultural yields at a lower cost-in-comparison to chemical fertilizers, while mitigating negative environmental impacts from agrochemicals. However, there are natural barriers limiting the use and reliability of beneficial fungi beyond the existing range of applications (i.e., host-specific benefits). Understanding these barriers will not only improve the ability to safely and reliably engineer ecosystems using fungi to reach specific goals (e.g., sustainable biofeedstocks) but also help predict and prevent economically and ecologically costly disease outbreaks. Evolutionary and ecological principles hindering targeted beneficial microbial inoculants frequently overlap with those overcome by invasive pathogens, thus learning about the first will enable better understanding of pathogen-mediated invasions.

Historically, there has been more research on the anthropogenic introduction and movement of fungal pathogens. In fact, recent population genomics analyses show human translocation of Populus across North America resulted in the spread of Sphaerulina musiva (formerly Septoria musiva) that now threatens natural forests and managed plantations. This pathogen has recently expanded to a new host, Populus balsamifera, and causes fatal stem cankers in the DOE-flagship species Populus trichocarpa.

Knowing the causal genetic factors associated with establishment and functional impact of S. musiva in the genus Populus will contribute to innovations in biodesign tools for early detection or altered outcomes of plant–fungal interactions. For the invader-centric research, researchers have assembled a pangenome from 146 S. musiva isolates collected from regions across North America spanning the range of several Populus species. This population genomics resource is being used to characterize the gene space of S. musiva and has identified more than 6-million single nucleotide polymorphisms, of which 50% were not found in the reference genome. Researchers have performed genome-wide association studies for rapid genotype-phenotype discovery. Using the recently developed protoplast-mediated transformation system with CRISPR-Cas9, the team tested several useful biodesign targets for manipulating S. musiva virulence.

Understanding the establishment and spread of S. musiva must also consider the host genes that regulate plant-fungal symbiosis. In several instances, the team has demonstrated the role of G-type receptor-like kinases (LecRLKs) in the susceptibility of a plant host to fungal colonization in both beneficial and pathogenic fungi. Building on this work, researchers hypothesized that advancing the understanding of G-type lecRLKs will inform biodesign strategies to detect and control plant- fungal interactions. To this end, researchers are working to determine how structural features of fungal cell walls are selectively recognized by G-type lecRLKs and this information is being used to design synthetic protein receptors systems that selectively detect fungal-derived ligands to report (biosensor) or permit (biocontrol) plant-fungal interactions.

Collectively, these studies will provide knowledge and tools to detect and control fungal invasions.

The Center for Bioenergy Innovation (CBI) vision is to accelerate domestication of bioenergy-relevant, non-model plants and microbes to enable high-impact innovations along the bioenergy and bioproduct supply chain while focusing on sustainable aviation fuels (SAF). CBI has four overarching innovation targets: (1) develop sustainable, process-advantaged biomass feedstocks; (2) refine consolidated bioprocessing with cotreatment to create fermentation intermediates; (3) advance lignin valorization for biobased products and aviation fuel feedstocks; and (4) improve catalytic upgrading for SAF blendstocks certification.

Overview. Linking genes of unknown function to relevant phenotypes in microbial systems is challenging. This necessitates the development of novel bacterial quantitative trait loci (QTL) mapping techniques enabled by genome shuffling. Bacillus subtilis is used as a proof-of-concept model for phenotyping and mapping causal genetic variants. Novel hardware and software were developed for high-throughput phenotyping, including an XY-robot for automated imaging and mathematical and statistical methods for image processing and feature extraction. Genome-wide association studies (GWAS) based on methods used in plant populations were used to identify predictive loci and validate causative genetic variants.

Approach. The approach is broken into five steps: (1) genome shuffling by protoplast fusion mimics sexual recombination in bacteria to enable QTL mapping (Vasileva et al. 2022); (2) a custom-built XY-robot with camera modularity and automated sample tracking enables high-throughput imaging; (3) convolutional neural networks (Lagergren et al. 2023) and mathematical models are used to predict morphological features of swarm assays and spore gemination dynamics; (4) genomic analysis is used to map multiple swarming and germination phenotypes using the QTL population; and (5) new strains are created with targeted modifications to confirm that genes in the QTL are causal.

Results. This work demonstrated B. subtilis as a proof-of-concept model to generate 386 recombined strains with 15,906 variants. The custom XY-robot captured high-resolution images of micro-organisms in six-well plates at 20 to 30 images per minute. Mathematical and statistical models extracted multiple phenotypes relating to swarming motility and germination. Novel software was developed to extract gene regions for alignment-based sample binning across the population. Genomic analysis revealed highly significant SNPs associated with colony area and germination. Causal regions were validated through the creation of new B. subtilis strains with targeted modifications that exhibited phenotypic differences in, for example, spore germination.

Impact. After successful demonstration in B. subtilis, researchers are now applying the same approach in Clostridium thermocellum, a model system relevant to bioprocessing and enzyme engineering. A similar approach could be widely used to connect phenotype to genotype in other bacteria important for the bioeconomy.

This project couples novel laboratory and field studies to develop the first predictive model of grass microbiomes based on new mechanistic insights into dynamic plant-microbe interactions in the grasses Sorghum bicolor and Brachypodium distachyon that improve plant nitrogen (N)-use efficiency (NUE). The results will be used to predict plant mutants and microbial amendments, which improve low-input biomass production for validation in laboratory and field studies. To achieve this goal, researchers will determine the mechanistic basis of dynamic exudate exchange in the grass rhizosphere with a specific focus on the identification of plant transporters and proteins that regulate root-exudate composition, and how specific exudates select for beneficial microbes that increase plant biomass and NUE. Researchers will further develop a predictive plant-microbe model for advancing sustainable bioenergy crops and will predictively shift plant-microbe interactions to enhance plant biomass production and N acquisition from varied N forms.

This research delves into the rich microbial diversity present in soils, particularly in the rhizosphere, where a myriad of bacteria influences soil properties through nutrient transformations, including carbon (C) and N pools that are directly linked to plant growth. To unravel the intricate web of metabolite exchanges among soil microbes and their dynamic interactions with the host plant, the team adopted a computational approach and utilized multiomics data (Kumar et al. 2019). Specifically, researchers focused on a synthetic microbial community (SynCom) composed of 16 rhizosphere bacteria isolated from switchgrass (Coker et al. 2022).

First, researchers constructed and manually curated genome-scale metabolic models for each of these rhizosphere bacteria, representing from various genera such as Arthrobacter, Bacillus, Bosea, Bradyrhizobium, Brevibacillus, Burkholderia, Chitinophaga, Lysobacter, Methylobacterium, Mucilaginibacter, Mycobacterium, Niastella, Paenibacillus, Rhizobium, Rhodococcus, Sphingomonas, and Variovorax. Integrating individual models with metatranscriptomic (RNA-Seq) and metatranslatomic (Ribo-Seq) data, researcherse constructed condition-specific community metabolic models (CM-models). Throughout this investigation, the team systematically evaluated the impact of removing individual microbes from the SynCom, shedding light on the specific contributions of each member. These CM-models predict the response of the SynCom to perturbation with very high accuracy. Furthermore, the CM-models played a crucial role in predicting metabolic exchanges between community members, unveiling the intricate nature of interactions, including competition and cooperation, among rhizosphere microbes.

These models have predicted substantial interactions involving the exchange of short-chain organic acids, carbohydrates, amino acids, and purine and pyrimidine derivatives among rhizosphere bacteria. Furthermore, the predictions suggest shifts in the nature of these metabolic exchanges when specific community members are removed. These model-driven hypotheses propose that such metabolic shifts confer nutritional advantages to select members, while concurrently suppressing the growth of others. Illuminating the intricate mechanisms of interaction among plant-associated microorganisms offers invaluable insights into the development of strategies for engineering microbial communities capable of enhancing plant growth and bolstering resilience against diseases.

Arctic lakes are important sources of methane (CH₄) into the atmosphere. CH₄ emissions from lakes are expected to increase as the Arctic warms due to increasing microbial activity and greater availability of more labile organic matter substrates. However, the effect of temperature on the sensitivity of different CH₄ production pathways and on the chemical composition of lake sediments remains understudied. In this project, researchers used incubations to measure the temperature sensitivity of sediment CH₄ production and sediment organic matter composition and diversity. CH₄ production increased exponentially with temperature across sediments from the edge and center of two lakes. Stable carbon (C) isotopic signatures of CH₄ and CO₂ suggest evidence of greater contribution of acetoclastic methanogenesis as opposed to hydrogenotrophic methanogenesis with increasing temperature, but the influence of anaerobic CH₄ oxidation on observed isotope signatures cannot be ruled out. CH₄ production was positively correlated with organic compounds that contained C, hydrogen, oxygen, nitrogen, and sulfure elemental compounds (r2 = 0.32, P <0.01). Further, the functional diversity (Rao’s quadratic entropy) of elemental composition of sediment porewater was negatively correlated with activation energy derived from incubations, suggesting less elementally diverse sediments are more sensitive to temperature changes, despite the more elementally diverse sediments having higher production rates overall. The preliminary results highlight the complex interactions between organic matter diversity and CH₄ cycling and pending microbial data (metaG and metaT analysis) will provide greater insights.

Motivated by the achievements in biocatalytic PolyEthylene Terephthalate (PET) recycling, there is a growing interest in investigating the enzymatic recycling of various other man-made polymers. Polyamides (nylons) have emerged as a logical focus due to the extensive range of naturally occurring amide-active enzymes. In this pursuit, researchers aimed to assess a selection of biocatalysts for their propensity for nylon-6 hydrolysis. The team assessed 40 potential nylon-deconstructing enzymes (nylonases) for their ability to depolymerize solid nylon-6 films. Initially considering enzymes with various catalytic mechanisms, such as nylon oligomer hydrolases, amidases, serine hydrolases, and proteases, researchers also strategically thermostabilized some of the most promising candidates to enhance nylon-6 hydrolysis at high temperatures. The testing of such a range of enzymes should allow researchers to select the best candidate biocatalysts for further interrogation.

Approach and Activities: A high-throughput LC-MS/MS method was devised to analyze the products of nylon-6 hydrolysis reactions, simultaneously identifying, and quantifying eight potential polymer deconstruction products from a single sample. The 40 potential nylonases were then assessed in time-course reactions spanning 40 to 70°C using nylon-6 film as the substrate, and the described LC-MS/MS based analytical method to quantify the extent of nylon deconstruction. These activities were coupled with rigorous assessments of the nylon substrate pre-and post-enzymatic deconstruction using a range of materials characterization techniques such as DSC, TGA, GPC and SEM. The best candidate enzymes were then examined more rigorously by altering reaction pH, substrate loadings, and enzyme loadings.

Results and Lessons Learned: Analysis of the products following enzymatic hydrolysis of solid nylon-6 unveiled notable differences in product selectivity among the studied enzyme types. Despite extensive testing, all the nylonases showed low depolymerization extents, hinting at the rarity of robust nylon deconstruction activity amongst natural enzymes. Within the examined enzyme set, a rationally thermostabilized N-terminal nucleophile (Ntn) hydrolase, NylCK-TS, exhibited the highest activity, suitable for depolymerization reactions up to 80°C. However, this enzyme only deconstructed 0.7 wt% of a nylon-6 film with reactions levelling off after 7 days. Inability to restart reactions after adding fresh enzyme led the team to hypothesize a substrate-based limitation in further nylon-6 deconstruction, possibly due to the lack of remaining enzyme- accessible amide bonds. In conclusion, this research expands the knowledge of nylonase activity distribution among diverse enzyme types, highlights the promise of Ntn-hydrolases for deeper exploration, and identifies crucial pathways for advancing the enzymatic depolymerization of nylon-6. These pathways include further enzyme engineering, refining product selectivity, and augmenting polymer accessibility.

The overall goal of this Early Career Award project is to test the hypothesis that white-rot fungi (WRF) can simultaneously depolymerize lignin extracellularly and catabolize depolymerization products intracellularly as carbon (C) and energy sources. The results from this project will lead to improved understanding of lignin utilization by WRF in nature and enable identification of promising fungal strains and enzymes for lignin catabolism and valorization.

White-rot fungi (WRF) are the most effective decomposers of lignin in nature. However, their ability to utilize aromatic compounds intermediate that enters central C metabolism. Additionally, researchers have solved the crystal structure of four of these enzymes and have analyzed their catalytic activity and structural features in comparison to homologous enzymes found in bacteria. Overall, this research significantly advances the understanding of the intracellular pathways involved in the breakdown of aromatic compounds within WRF and pinpoints key mechanistic disparities between fungal and bacterial systems. These insights are particularly valuable for the development of enzymatic or microbial biocatalysts aimed at producing high-value chemicals from aromatic compounds, whether in the context of lignin valorization or the utilization of aromatic waste products.

Alabama A & M University (AAMU) has an excellent record in the training of underrepresented individuals in the various disciplines of STEM. However, the university lacks the resources to initiate new programs or enhance current ones to address topics of national needs in some rapidly developing disciplines including renewable energy. Therefore, through a combination of education, research, and partnerships with the DOE Joint Genome Institute (JGI) and Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), the assembled team will execute a set of objectives to (1) build foundational capacity for bioenergy research by establishing and maintaining field trials of bioenergy crops as well as the genomics and phenomics tools to study them; (2) train underrepresented students at AAMU and provide networking opportunities to promote recruitment into the bioenergy workforce and/or graduate training programs; (3) establish and sustain interactions with JGI and CABBI partners (HudsonAlpha Institute for Biotechnology; HA and University of Illinois Urbana Champaign; UIUC) for consistent training opportunities; and (4) foster an inclusive and equitable role for underrepresented student trainees through mentorship to create a well-trained workforce as the nation transitioned to bioenergy economy.

In the initial phase of this educational project, efforts have been made in several areas:

1. Student recruitment and engagement: Students have been recruited, both from within the university and from external institutions, to participate in various areas of bioenergy science. Two underrepresented undergraduate (female) students were recruited for microbiomics in partnership with JGI. Two underrepresented graduate students were recruited for plant genomics and biotechnology in partnership with CABBI and HA. Two trainees were recruited for high-throughput phenotyping/phenomics in partnership with CABBI and UIUC. These students are starting to engage with real-world projects and are being prepared for summer internships in 2024 at partner institutions.
2. Partnership development and expansion: Initial efforts have been made to establish and expand interactions with JGI and CABBI Specifically, regular meetings with the JGI team have been established. A formal joint training program between HA and AAMU is being developed and plans are underway for student engagement with UIUC.
3. Training module development: Based on identified needs and gaps in student knowledge and skills, training modules are being designed. Modules cover literature review, data collection, and basic data exploration with more to be developed for progressive learning.

The team will continue efforts in recruiting students for this project and other opportunities, enhancing their educational experience and preparing them for future careers in bioenergy science.

Switchgrass (Panicum virgatum) is a model perennial crop that can be grown on marginal land to sequester carbon (C) into stable soil organic matter and produce sustainable biofuels. Nitrogen (N) fertilizer maintains Switchgrass biomass productivity on marginal land but excessive fertilization offsets potential C sequestration gains by increasing both carbon dioxide (CO₂) and nitrous oxide (N₂O) emissions. To ameliorate excessive fertilizer, a combination of inoculated and native microbiota can be leveraged to improve Switchgrass N management. Free-living diazotrophs are ubiquitous, nonsymbiotic bacteria that fix atmospheric N into plant-available ammonium. Free-living diazotrophs are estimated to fix ~47 kg N ha^-1 in the Switchgrass feedstock system, and their activity is stimulated by Switchgrass root exudation in the rhizosphere. However, free-living N fixation is difficult to predict because it is controlled by soil edaphic conditions. To limit confounding effects from soil native communities on N fixation, researchers quantified the effects of diazotroph inoculation on Switchgrass N using sterile microcosms during a one-week pulse-labeling experiment. Switchgrass seedlings grew in a sterilized, sand-turface (1:1) mixture for 10 weeks under high or low N fertilizer conditions. Subsequently, seedlings were inoculated with either Azotobacter vinelandii DJ (0.6 OD) or sterile, nitrogen-free medium. Directly after diazotroph inoculation, the microcosms were placed into an airtight labeling chamber; the team pulsed 1.0L of ¹⁵N₂ for seven days before harvesting above ground biomass to quantify ¹⁵N enrichment. There was a significant, interactive effect between N fertilization and diazotroph inoculation: diazotroph inoculation increased Switchgrass total N (%) only under low-N conditions, suggesting that inorganic N significantly impacts N fixation activity (p < 0.05). δ¹⁵N enrichment (δ¹⁵N > 500 ‰) was only identified in Switchgrass inoculated with diazotrophs under low-N conditions (p < 0.05).

In addition to free-living diazotrophs, Switchgrass associate with arbuscular mycorrhizal fungi (AMF). The AMF-symbiont forages for inorganic nutrients beyond the plant’s root zone by extending extraradical hyphae deep into the bulk soil, AMF provide up to 55% of inorganic N requirements under low-N conditions. However, the plant-AMF mutualism does not occur in a vacuum. Bacteria live along the fungus’s hyphae, including free-living diazotrophs. Interactions with N-fixing bacteria could boost the plant’s benefits from AMF symbiosis. Yet, little work has been done to evaluate synergies between N-fixing bacteria residing on AMF. Building on previous GLBRC work on free-living N fixation in bioenergy crops, the team developed split-pot microcosm systems that enable spatially explicit sampling of AMF hyphal bacterial communities in the greenhouse and in the field.

In addition, the team identified methodological considerations for characterizing free-living diazotroph establishment and N-fixation activity on AMF. Finally, the team will share future research endeavors to quantify nutrient benefits from these interactions on the Bioenergy Cropping System Experiment (BCSE) at the Kellogg Biological Station. Ultimately, this research quantifies the contributions of AMF-diazotroph interactions to Switchgrass N health, determining if this alternative source of N can ameliorate excessive fertilization on marginal land.

The goal of this project is to elucidate the fundamental principles driving physiology and metabolic exchange within microbial interaction networks that regulate the rate-limiting steps in soil organic matter (SOM) degradation, specifically the oxidation of phenolic compounds derived from lignocellulose and lignin-like polymers in carbon-rich peatlands and their role in the preservation of organic matter under anaerobic, water-saturated conditions. The project combines multiomics with advanced analytical chemistry to test the “enzyme latch” hypothesis and its response to climate change drivers. Field and laboratory investigations will be integrated to construct and calibrate a predictive framework that links specific microbial processes and interactions to the mechanisms driving the rate-limiting steps of enzymatic SOM decomposition (phenolic compound oxidation and hydrolysis), SOM persistence, and greenhouse gas production in peatland soils. To investigate the response of microbial communities to climate change drivers, researchers leverage DOE’s Spruce and Peatland Responses under Changing Environments (SPRUCE) experiment where air and peat warming are combined in a whole ecosystem warming treatment.

Peatlands represent climate critical regions that cover only 3% of the Earth’s land surface but store approximately 1/3 of all soil carbon (C). The future role of peatlands in C sequestration remains uncertain and depends on the impact of global change-related perturbations on their C balance. In this project, researchers defined the microbial networks that regulate belowground C turnover by combining a genomic-centric metagenomics approach with biogeochemistry and metabolomics. The team analyzed 131 metagenomes (totaling 2.4 Tbp of sequences) obtained from soil samples collected to 2 meters depth in the peat column between 2015 and 2018, reconstructing 697 metagenome-assembled genomes (MAGs). Surprisingly, researchers found that only 2% of the MAGs retrieved from the SPRUCE site were shared with those identified in well-studied European peatlands where soils experience similar environmental conditions. Microbial community composition and functional potential are strongly depth-stratified and closely parallel changes in activity, redox, and organic matter quality. Overall, the metabolic pathways identified within the MAGs reveal a high-metabolic potential for sulfate/sulfite reduction, denitrification, methanogenesis, and homoacetogenesis, implicating, which are important terminal electron accepting processes. The dominant methanogens detected (Methanoflorens) demonstrate the potential to carry out acetoclastic as well as hydrogenotrophic methanogenesis, which has been only described previously for the genus Methanosarcina. In addition, the team uncovered a large diversity of sulfate/sulfite reducers and acetogens that were not previously associated with peatlands. The results indicate that C degradation is electron acceptor-limited and mediated by a much broader repertoire of anaerobic respiration processes than previously thought, likely supplied by electron acceptors derived from the soil organic matter itself. Despite the dramatic increase in gaseous emissions (e.g., carbon dioxide, methane) with warming over the same period, microbial diversity and composition remained stable, indicating slow growth and a resistant soil ecosystem. However, the genomic potential for methylotrophic methanogenesis was stimulated while homoacetogenesis was hampered by warming.

Reseachers took advantage of a generational drought that occurred in 2021 at the SPRUCE site to investigate the combined impacts of warming and drought on the belowground C cycle. The team hypothesized that the warmed, dried peatland will be released from the “enzyme latch,” thereby accelerating soil organic matter decomposition by enhancing the oxidation of phenolic compounds. During and postdrought, phenolic degradation and C-activated gene expression as well as enzyme activity increased, likely driven by heightened fungal activity.

Conversely, climate change-induced water-table drawdown reduced the activity of versatile polyphenol-degraders as well as the expression of anaerobic phenolic compound transforming genes. Temperature influenced the microbial community’s recovery postdrought, with warmer treatments exhibiting gene-expression patterns more divergent from the predrought profile compared to ambient conditions. This research indicates that phenolic compound degradation is more complex than the “enzyme latch” suggests, emphasizing the need for a deeper understanding of microbial processes to accurately predict the impact of climate change on peatland C storage.

To determine whether warming-induced shifts in plant species composition may act to bolster the “enzyme latch” through the accumulation of plant-derived phenolic compounds that inhibit microbial SOM decomposition, the team conducted a seasonal study of soluble phenolic compound concentrations across the SPRUCE temperature treatments. Phenolic compounds are highly sensitive to temperature and exhibit the greatest concentrations (by a factor of four) in the warmest treatments where shrubs, coincidentally, have significantly increased in biomass relative to other types of vegetation. Phenolic compounds, normalized to total dissolved organic matter (DOM) concentration, show a 50% increase across seasons in all plots. This data indicate that both phenolic compounds and DOM increase with growing season and temperature, but that phenolic compounds are either more recalcitrant over the annual cycle or they are produced and retained at a higher rate. In addition, researchers performed a comparison of peatland sites that vary in plant species composition, temperature, and pH (3.5 to 6.5) across a latitudinal gradient, and the team observed a significant negative correlation between soil pH and soluble phenolics with low pH sites showing up to 5-fold higher phenolic concentrations. Researchers are currently quantifying decomposition rates in soils from all peatlands sampled to explore the controls of C turnover across peatland types.

The goals of this project are to (1) identify the genes required for sporopollenin synthesis and deposition and (2) introduce their expression in Populus to engineer sporopollenin production in roots as a stable carbon (C) sink.

This recently funded project is part of the DOE Carbon Negative Shot™ Program, which calls for research on atmospheric carbon dioxide (CO₂) removal and storage. To increase C storage, researchers propose to engineer the production of sporopollenin—the most recalcitrant plant polymer known (biostable for centuries or more vs. decades for other biopolymers)—in roots of bioenergy crops. Researchers will target Populus species since these trees are among DOE’s most important crops to be used for bioenergy.

In the first part of this project, the team will attempt to synthesize sporopollenin in Populus root epidermal cells, based on current knowledge of sporopollenin synthesis genes and epidermis- specific regulation of gene expression. More specifically, researchers will induce the expression of the presently known core set of enzymes needed to synthesize sporopollenin precursors—the Populus orthologs of ACOS5, PKSA, PKSB, TKPR1, and MS2 and the proposed master transcription factor regulators of sporopollenin synthesis AMS and MS188. Gene constructs driven by a root epidermis-specific promoter will be introduced into Populus via hairy root transformation of in vitro-grown shoots with Agrobacterium rhizogenes.

The second component of this project will use single-cell genomics to uncover genes that are activated during tapetum development and sporopollenin synthesis in Arabidopsis and Populus. Researchers’ aim is to identify regulatory genes involved in this process that have not yet been characterized and define evolutionarily conserved genetic mechanisms of sporopollenin synthesis, which is likely to be essential for the transfer of this cellular role to other cell types or species. The team will validate the function of these genes in Arabidopsis before adding them to the Populus genetic toolkit developed in the first part of this project.

The proposed strategy, deployed at scale, has the potential to strip substantial amounts of C from the atmosphere. Based on typical Populus biomass yields and allocation belowground, engineering roots to contain 5% by weight sporopollenin could permanently store 120 to 300 kg CO₂-equivalents per hectare per year or 96 to 2,400 kg per hectare in an 8-year cycle. Researchers estimate that engineering the 36-million-hectare U.S. maize crop to accumulate 5% sporopollenin in roots and stover could sequester ~50 megatons of CO₂-equivalents per year, or ~0.5 gigatons per decade.

Engineering plants with polysaccharides that can be easily convertible to bioproducts and specialty biofuels.

Plants leverage sugars for various essential functions, including energy production, building cells and organs, and transmitting signals. Notably, a significant portion of sugars is channeled towards building the cell wall, a predominant component of plant biomass for bioenergy applications. Mixed-linkage glucan (MLG), a vital cell wall constituent in grasses, has emerged as a promising polysaccharide in bioenergy-related applications due to its abundance in easily fermentable glucans and positive impact on plant biomass digestibility. The team’s work demonstrated that the abundance of MLG is controlled through development along with cell-type specificity by the action of both MLG synthase and hydrolases. This unique modulation of sugar levels in cells through cell wall polysaccharides appears distinctive to MLG and sets it apart from other cell wall polysaccharides in vegetative tissues. Therefore, researchers have been working to improve the plant biomass by storing more MLG in the cell wall that can be easily converted into readily available forms of sugar and increase cell wall digestibility.

Due to its favorable attributes as a biofuel feedstock, such as high biomass yield, resilience to adverse environmental conditions, and low resource requirements, researchers focused on sorghum for the manipulation of MLG biosynthesis. The team demonstrated that overexpression of the major MLG synthase, CSLF6, resulted in significant MLG production and accumulation in transgenic sorghum (CSLF6OX), both in the greenhouse and field conditions. However, the team also observed a developmental degradation of MLG in CSLF6OX sorghum. To mitigate this degradation phenomenon, researchers embarked on the manipulation of the MLG degradation pathway. This strategy involved the identification and characterization of MLG hydrolases, also known as lichenases, with a particular focus on the major lichenase enzymes in sorghum. Using bioinformatics tools, researchers identified three sorghum lichenase candidates that possess a signal peptide for cell wall secretion and a GH17 domain indicative of hydrolase activity as well as high sequence similarity to known lichenases in diverse species. Using synthetic substrates and natural flours, the team optimized the experimental conditions and established the activities of three putative sorghum lichenases. Subsequently, researchers performed a multifaceted approach, including qRT-PCR, RNAseq, in situ hybridization, and concurrent MLG and starch quantification throughout different stages of sorghum leaf development in diurnal conditions. The results revealed cell-type specific, organ- specific, and development-dependent regulation of MLG levels. This regulation is achieved through precise control of CSLF6 and lichenase enzyme levels in different cell types. As a result, the team identified SbLCH1 as the predominant lichenase enzyme in sorghum. Currently, researchers are in the process of generating a knockout line of the major SbLCH1 that researchers identified, using the CRISPER-Cas9 gene-editing technology. The team’s ultimate goal is to integrate a SbLCH1 knockout line with the CSLF6OX hybrid energy sorghum, which researchers have recently developed. By employing this integrated approach, the team anticipates facilitating MLG accumulation by preventing lichenase-dependent degradation, thereby enhancing the accumulation of easily extractable polysaccharides suitable for biofuel production.

Evaluate bioreactor conditions to improve the production of 2-pyrone-4, 6-dicarboxylic acid (PDC) from plant-derived aromatics using Novosphingobium aromaticivorans.

Researchers found that the accumulation of intermediate compounds such as protocatechuic acid (PCA) and 3-O-methylgallate (3-MGA) is the major factor for the system failure. As such, the team determined that operational conditions that prevented protocatechuic acid (PCA) accumulation during aromatic metabolism improved bioreactor performance. In addition, researchers found that the accumulation of sodium (Na⁺) by the addition of sodium hydroxide (NaOH) for maintaining pH-base inhibits the growth and PDC production of Novosphingobium aromaticivorans and found that ammonium (NH₄₊) has less inhibitory effect than Na⁺. PDC productivity increased when using ammonium hydroxide (NH₄OH) instead of NaOH for pH control. Productivity was also increased when the hydraulic retention time in the reactor was reduced to 4 hours. At the best operational condition, a stable PDC production of 1.8 gPDC/L/hr was obtained, which is higher than the highest PDC productivity that has been reported, albeit with a lower product titer as 42 mM (7.9 g/L). Overall, the team’s findings demonstrate that the use of a membrane bioreactor with optimizing strategies can significantly enhance the productivity of PDC from plant-derived aromatics. This approach can be applied for production of other valuable chemicals from lignin and additional feedstocks to reduce the selling price of products, thereby contributing to the commercialization of lignocellulose and other renewal materials.

This project develops genetic and epigenetic tools for emerging model anaerobic fungi to identify the genomic determinants of their powerful biomass-degrading capabilities, facilitate their study, and enable direct fungal conversion of untreated lignocellulose-to-bioproducts.

Anaerobic fungi (Neocallimastigomycota) from the digestive tracts of large herbivores are emerging model species for the efficient deconstruction of untreated renewable plant biomass due to their integration of hydrolytic strategies from the bacterial and fungal kingdoms (Solomon et al. 2016). Anaerobic fungi secrete the largest known diversity of lignocellulolytic carbohydrate active enzymes (CAZymes) in the fungal kingdom, which unaided can degrade up to 60% of the ingested plant material within the animal digestive tract (Seppälä et al. 2017; Youssef et al. 2013). Unlike many other fungal systems, these CAZymes are tightly regulated and assembled in fungal cellulosomes to synergistically degrade plant material, including untreated agricultural residues, bioenergy crops, and woody biomass, with comparable efficiency regardless of composition (Haitjema et al. 2017; Hooker et al. 2018; Solomon et al. 2016; Solomon et al. 2018). Past efforts have characterized remarkable resistance to lignin composition (Hooker et al. 2018), revealed more efficient enzymes for biosynthesis (Hillman et al. 2021a), and created bioprocesses for efficient conversion of agricultural residues to high value products (Hillman et al. 2021b). More recently, researchers have created the first tools for transient and compartmentalized heterologous gene expression in these species (Hooker et al. 2023).

Engineering efforts exploit the natural competency of anaerobic fungi to uptake exogenous nucleic acids. While daily dosing with exogenous DNA enables transient transformation, this requires significant DNA input. To evaluate whether genomic integration is possible, the team created linear codon-optimized hph cassettes that confer hygromycin resistance. A single dose and selection on hygromycin produced several resistant colonies of which 30% had a stable resistance phenotype. Subsequent molecular characterization confirmed integration via non- homologous end joining in various loci including LTR retrotransposons serving as the first example of such in this family.

Researcheres have also sought to identify an autonomous replicating sequence (ARS) for anaerobic fungal plasmid replication and develop self-replicating plasmids. The team is pursuing a number of strategies, including screening shotgun genomic libraries and plasmids from other species for broad host activity. One plasmid that has shown promise is the yeast 2 μm plasmid, a relatively small multi-copy selfish DNA element that resides in the yeast nucleus at a copy number of 40 to 60. Combining AGF-optimized expression cassettes producing eGFP and hygromycin resistance marker, researchers were able to see a steady level of eGFP expression above background levels even approximately 12 days after a single plasmid dose. However, researchers are still unable to maintain a selectable phenotype for long periods with this, implying that the steady state copy number and/or promoter expression is too low. To address this, researchers are currently expanding the promoter library and evaluating the impact of plasmid features (e.g. GC content, size, etc.) on stability.

Leveraging natural competency to introduce exogenous DNA, researchers have achieved the first simple methods for targeted heterologous expression in anaerobic fungi and are optimizing approaches for stable phenotypes. This growing toolbox for anaerobic fungi forms foundational tools to generate a deeper systems-level understanding of anaerobic fungal physiology while establishing fundamental knowledge about regulation of gut fungal CAZymes. Ultimately, this research enables predictive biology in anaerobic fungi and derive insight into microbial plant deconstruction to advance the development of economical biofuels and bioproducts.

Microorganisms play key roles in soil carbon (C) turnover and stabilization of persistent organic matter via their metabolic activities, cellular biochemistry, and extracellular products. Microbial residues are the primary ingredients in soil organic matter (SOM), a pool critical to Earth’s soil health and climate. The team hypothesizes that microbial cellular-chemistry, functional potential, and ecophysiology fundamentally shape soil C persistence, and the researchers are characterizing this via stable isotope probing (SIP) of genome-resolved metagenomes and viromes. This study focuses on soil moisture as a “master controller’” of microbial activity and mortality since altered precipitation regimes are predicted across the temperate United States. The Science Focus Area’s (SFA’s) ultimate goal is to determine how microbial soil ecophysiology, population dynamics, and microbe-mineral-organic matter interactions regulate the persistence of microbial residues under changing moisture regimes

Researchers have developed a genomes-to-traits workflow (microTrait) and a compatible dynamic energy budget trait-based model (DEBmicroTrait) to (1) infer ecologically relevant traits from microbial genomes; (2) systematically reduce the high-dimensionality of genome-level microbial trait data by inferring functional guilds (sets of organisms performing the same ecological function irrespective of their phylogenetic origin); (3) quantify within-guild trait variance and capture trait linkages in trait-based models; and (4) explore trait-based simulations under different scenarios with varying levels of microbial community and environmental complexity (Karaoz and Brodie 2022; Marschmann et al. 2024). This computational workflow allows the team to predict trade-offs involving metabolic, biophysical, and thermodynamic traits of microorganisms. This includes the capability to predict substrate uptake kinetics for broad substrate classes. Researchers are working to integrate this tool within KBase, which will support ongoing efforts to integrate genome-centric knowledge into biogeochemical models.

In addition, the SFA team has formalized and optimized the code for quantitative stable isotope probing (qSIP) into an R package (qSIP2) and documentation website (Hungate et al. 2015; Koch et al. 2018; Simpson et al. 2023). The qSIP2 workflow (and forthcoming KBase applications) allow for the identification of enriched taxa in isotope addition experiments given density fractionation of DNA, sequence counts, and a quantitative measure of abundance in each fraction (e.g. 16S rRNA). The qSIP2 workflow can accept input for both amplicon (e.g. 16S rRNA) and metagenomic (e.g., MAG coverage) data and produce an excess atom fraction enrichment value quantifying the extent of “heavy” isotope incorporation. Results from qSIP can help experimentally identify microbial traits via quantifying the use of a given substrate.

Ongoing work to combine both the qSIP and (DEB)microTrait tools within KBase will provide a strong foundation for researchers who wish to use quantitative in situ measurements of microbial ecophysiology and population dynamics to benchmark models and build a predictive understanding of biological processes controlling material fluxes in complex environments.

The Center for Bioenergy Innovation (CBI) vision is to accelerate domestication of bioenergy-relevant, non-model plants and microbes to enable high-impact innovations along the bioenergy and bioproduct supply chain, while focusing on sustainable aviation fuels (SAF). CBI has four overarching innovation targets: (1) develop sustainable, process-advantaged biomass feedstocks; (2) refine consolidated bioprocessing with cotreatment to create fermentation intermediates; (3) advance lignin valorization for bio-based products and aviation fuel feedstocks; and (4) improve catalytic upgrading for SAF blendstocks certification.

Gaining a predictive understanding of above- and belowground plant system performance is critical to developing, climate smart and sustainable bioenergy crops for SAF. Plant and soil (including microbial) interactions are known to influence nutrient and water uptake, microbial association, biomass productivity, climate adaptability, and soil carbon storage. Current understanding of variability and correlations among aboveground properties of bioenergy crops and their underlying genetics is far greater in comparison to that of the belowground properties (root, soil, and microbes), which is explained by data asymmetries between above- and belowground datasets. The challenges of laborious sampling, lack of standardized methods, and sparse measurements are further confounding to available belowground empirical data. To achieve a holistic understanding of bioenergy crop performance, researchers are undertaking laboratory- and field-based belowground performance evaluations of natural genotypes of the perennial woody bioenergy crop species, Populus trichocarpa. This project presents the results from two such studies, one at greenhouse-scale and another at field-scale.

The utility of genome-wide association study (GWAS) analysis in identifying single nucleotide polymorphisms (SNPs) associated with variation in traits of interest in Populus has been previously demonstrated with remarkable genomic resolution for aboveground traits such as biomass yield, phenology, and lignin content (Evans et al. 2014; Xie et al. 2018). There has been limited progress, however, in defining the genetic components of belowground root-related traits. Researchers undertook a GWAS of greenhouse root chemistry and coassessment of below- and aboveground traits. The results showed that above- and belowground chemistry is largely independently controlled and generated the first comprehensive collection of genes and SNPs associated with variation in root chemistry of a bioenergy crop species. Furthermore, the team identified a subset of SNPs and genotypes that are coevolved in trait combinations potentially favorable to aboveground valorization of biomass and belowground storage of carbon (C). The discovery of both colocated or independent SNPs and naturally superior genotypes from the analyses serve as high potential springboard levers in advanced synthetic biology and breeding strategies for desirable below- and aboveground traits.

Researchers undertook a pilot empirical evaluation study to inform a planned population-wide sampling effort and iterative modeling designed to understand plant-soil interactions in a mature, common garden stand of the same GWAS population in the Pacific Northwest. This study, using four different P. trichocarpa genotypes (population extremes in lignin content), demonstrates the value of leveraging mature GWAS common garden sites and the potential of focal tree soil sampling for detecting genotype-specific associations with soil biogeochemistry (e.g., C; nitrogen; N, phosphorus; P, and a range of soil micro- and macronutrient elements) and soil-bulk density, as well as the extent of correlations between above- and belowground traits. This analyses revealed positive relationships between root C and soil C; root C: N and soil calcium (Ca), and a negative relationship of soil C with soil pH, K, and P. In the future, expansion of such species-, age- and region-specific efforts will enhance predictability of plant-microbe-soil interactions in bioenergy crop systems. This will ultimately accelerate the identification of genetic and edaphic factors that can enhance bioproduct yield, C storage, and sustainability.

To resolve key unknowns in the grand challenge of understanding the fate of carbon (C) in thawing permafrost, researchers focus on C cycle climate feedbacks to warming in high-methane (CH₄) emitting landscapes in an Arctic mire ecosystem. The team’s aims are to (1) identify and resolve key gaps in the understanding of microbial C processes consequential to C storage and CH₄ emission; (2) identify and resolve a mystery of microbial oxidation rates and controls consequential to emission mitigation; and (3) integrate resolved consequential unknowns into next-generation ecosystem models.

As the climate warms, permafrost thaw is fueling high CH₄ emissions, particularly in permafrost peatlands. Aerobic methane- oxidizing bacteria (i.e., methanotrophs) could dampen these emissions, but how these microbes will respond to rising temperatures remains unknown. Researchers conducted laboratory incubations to investigate how methanotroph communities respond to warming temperatures at different peat depths in a recently thawed fen in a thawing permafrost peatland located in Stordalen Mire, Sweden. The team used 16S rRNA to characterize microbial community composition at 20°C and 25°C. Oxidation rates did not differ across peat depths (10 cm increments from 0 to 40 cm). Isotopic analysis of ¹³C-CH₄ and ¹³C-CO₂ will reveal sources of CH₄. Future work will investigate the metabolomic and genomic controls on CH₄ oxidation. These results will inform a trait-based ecosystem model to improve emission predictions under climate change.

This project aims to understand mass accumulation processes in alluvial ridge (AR) basins in river deltas across scales by combining field observed and remotely sensed flow data from the Rio Grande Delta (RGD), Texas, with two numerical models, ANUGA (hydrodynamic) and dorado (particle transport). AR basins are topographic depressions bounded by abandoned deltaic channels that are natural depo centers for water, sediment, and nutrients. However, more is needed to know about such processes, as these areas comprise most of the broader populated deltas. Yet, previous studies have focused on understanding the infilling of channelized portions of a delta. Such a project on the RGD is much needed to not only fill in the knowledge gap for basic research but also provide the under-served community with such data to understand better how delta inundation patterns change to inform mitigation policies and engineering practices.

River deltas are net depositional landscapes that form at the coast, hosting populated socioeconomic hubs and providing essential ecosystem services, yet are facing stresses like land loss due to ongoing climate change and anthropogenic impacts. Channel avulsions, an abrupt shift in river course, is one of the main processes in that the delta distributes sediment to build land in coastal regions. Previous works have focused on assessing water, sediment, and nutrient transport and deposition in channelized portions of the delta. Through multiple avulsions, however, deltas contain several generations of abandoned channels with high relict levees that bound topographic degressions, known as AR basins, accounting for most of the broader delta region. How mass accumulates in AR basins between the relict deltaic channels still needs to be understood. More importantly, climate change and natural disasters are shown to disproportionately affect underserved communities, especially those of the RGD in south Texas. Hosting ~1 million people, the RGD region has the lowest median household incomes and the highest health risks in the United States. The RGD region contains seven main abandoned channels of the Rio Grande, known locally as Resacas. Despite frequent inundation due to extreme rainfall and storm surges, these AR basins’ water and sediment transport patterns remain elusive.

To fill this knowledge gap, researchers plan to deploy pressure sensors to measure water, nutrients, and sediment transport rates in three alluvial basins: Bahia Grande, Laguna Larga, and Vadia Ancha, spanning a range of potential sediment sources and tidal and human intervention conditions. Researchers will also collect sediment core samples throughout the alluvial basins and conduct radiocarbon age dating analysis to determine sediment and nutrient accumulation rates over multiple millennia. Researchers hypothesize that the accumulation rate is highest near the tidal inlet channels and decreases non-linearly towards the basin interior. In addition, given the shallow flow depth, modern transport conditions in the AR basin are strongly correlated with spring and neap cycles, storm surges, and extreme wind events. The results of this study will be the first systematic field measurements of mass accumulation rates and patterns in AR basins of an extensive delta system, the RGD, i.e., the second largest river delta in the United States. Researchers also plan to use these data to develop a mechanical explanation of how river deltas fill AR basins and provide the underserved community with such data to understand better how delta inundation patterns change to inform mitigation policies and engineering practices.

The Center for Bioenergy Innovation’s (CBI) vision is to accelerate domestication of bioenergy-relevant, nonmodel plants and microbes to enable high-impact innovations along the bioenergy and bioproduct supply chain while focusing on sustainable aviation fuels (SAF). CBI has four overarching innovation targets: (1) develop sustainable, process-advantaged biomass feedstocks; (2) refine consolidated bioprocessing with co-treatment to create fermentation intermediates; (3) advance lignin valorization for biobased products and aviation fuel feedstocks; and (4) improve catalytic upgrading for SAF blendstocks certification.

Switchgrass, Panicum virgatum, a grass native to North America, is of intense interest as a dedicated feedstock for the production of SAF. Switchgrass ecotypes differ by a number of characteristics, including the presence of wax on leaves and stems. Lowland ecotypes generally contain high levels of C33 β-diketones and hydroxy-β-diketones, which are associated with the formation of crystalline wax tubes on the abaxial leaf side and a blueish plant color (Bragg et al. 2020; Weaver et al. 2018). In contrast, β-diketones are largely lacking from upland accessions, which therefore have glossy green leaves. Researchers previously identified a cluster of genes as strong candidates for the quantitative trait locus that was identified for wax variation in an F₂ population from a cross between the lowland genotype AP13 and the upland genotype VS16 (Qi et al. 2021). One of the candidate genes, a likely 3-ketoacyl-CoA synthase 5 (KCS-5), was knocked out in Performer7, a transformable lowland accession, using CRISPR-Cas9. Interestingly, while edited plants had the expected glossy green color, they were also shorter in stature and had more tillers compared to the controls. To determine the effect of KCS-5 knockout on transcription, an RNA-seq analysis was conducted on two independent KCS-5 knockout plants and two nonedited control plants. A total of 1,781 and 415 genes were differentially expressed (DE) in leaves and stems, respectively, between the KCS-5 knockout lines and nonedited controls (p-value≤0.05, log2-fold difference≥1). 64% of the genes DE in stems were also DE in leaves. Work is ongoing to determine the affected pathways as well as the effect of the KCS-5 knockout on sustainability.

(1) Uncovering the natural principles that control greenhouse gas–capturing capacities of native arid soils; (2) Defining key players of the C₁B community and evaluating their impact on soil-atmosphere exchange fluxes, soil chemistry, and water retention; (3) Validating the applicability of C₁B supplements to benefit crop growth in nutrient-limited arid environments; and (4) Evaluating impacts and constraints of the C₁B-based technology implementation at midscale farm levels for energy crop production.

Sustainable use of arid soil will critically depend on the ability to foster natural principles that preserve soils and support the structure and functions of native ecosystem microbiomes in the long term. Native arid ecosystems represent an untapped reservoir of microbial functions essential for promoting plant growth and survival under water-limited and nutrient-poor conditions. The main goal of this study was to interrogate metagenomic data and functional capabilities of natural arid land microbiomes to design critical solutions for threatened agricultural land.

Here, the team presents a holistic study that started with thorough investigation of in situ methane (CH₄) fluxes and microbial communities inhabiting soil in the Anza Borrego Desert, a model arid ecosystem. Researchers found that in situ CH₄ fluxes indicate differences in the consumption of CH₄ between vegetated and unvegetated soil patches, reaching their peak on vegetated sites at the highest daylight times around noon with up to 12 micromoles per square meter x h. The metagenomic and enrichment studies revealed a ubiquitous presence of methanotrophic bacteria in the Anza Borrego Desert soil. The network analysis highlights the co-occurrence of CH₄-consuming bacteria (Methylocaldum, Methylobacter, and Methylomicrobium) and several members of Rhizobia and nitrifying bacteria. Sixty-one metagenome assembled genomes (MAGs) were generated, and eight MAGs were identified as methanotrophs, including four Methylocaldum spp. and Methylobacter luteus. Additional high-resolution sampling efforts revealed the co-occurrence of several methanotroph genera, of which the highest proportion corresponded to Methylocaldum, in both vegetated and unvegetated patches.

Several methanotrophic bacteria, most belonging to the genus Methylocaldum, were isolated and sequenced. Comparative genomic studies were carried out. The examination of the genome inventory of these strains found significant redundancy in primary metabolic pathways, including numerous copies of a key gene for methane oxidation and several genes for methanol oxidation, in addition to three pathways for one-carbon assimilation, and two strategies of carbon storage (glycogen and polyhydroxyalkanoates).

Furthermore, the interaction of native methanotrophic species with the California-native plant Boechera depauperata (Brassicaceae) were examined. When supplemented with methanotrophic traits, B. depauperata displays drought tolerance and increased growth as well as quantitative measures of plant resilience, including photosystem activity and increased leaf area. Metabolomic and transcriptomic analysis revealed promoting flavonoids in the plants and a decrease in various amino acids including tryptophan, followed by an upregulation in genes involved in the tryptophan mediated–indole-3-acetic acid (IAA) biosynthesis pathway.

These results suggest a mutualistic relationship between B. depauperata and Methylocaldum sp. leading to higher plant drought tolerance. Data suggest that in this arid ecosystem, methanotrophs are associated with vegetation and the association might enhance native plant drought tolerance. This work provides essential evidence that the association between plants and methanotrophs in (semi)arid ecosystems plays a major role in supporting the vegetation diversity of those ecosystems that subsequently might be key for methane cycling, having a significant impact on the global levels of this potent greenhouse gas and ultimately influencing the global climate.

The Secure Ecosystem Engineering and Design (SEED) Science Focus Area (SFA), led by Oak Ridge National Laboratory, combines unique resources and expertise in the biochemistry, genetics, and ecology of plant-microbe interactions with new approaches for analysis and manipulation of complex biological systems. The long-term objective is to develop a foundational understanding of how non-native microorganisms establish, spread, and impact ecosystems critical to U.S. DOE missions. This knowledge will guide biosystems design for ecosystem engineering while providing the baseline understanding needed for risk assessment and decision-making.

Precise manipulation of natural or managed ecosystems can improve ecosystem resilience and productivity benefiting biosecurity and the bioeconomy. Successful, targeted ecosystem alterations are increasingly feasible by deliberately introducing non-native or genetically modified plants and microbes. However, scientists currently lack the knowledge to successfully predict and introduce beneficial alterations, prevent undesired modifications, or predict the risks of proposed ecosystem biodesign efforts.

Today, microbes are routinely used as active ingredients in commercial biofertilizers and biopesticides to improve plant sustainability and productivity. However, these non-native microbes often fail to establish and spread, requiring frequent reapplication. Successful establishment, dispersal, and beneficial impact of these microbes relies on the interaction of multiple phenotypic traits with the environment and resident microbial community. Identifying the genetic determinants of these complex traits requires a genome-wide interrogation of gene function. Moreover, the ability to monitor the movement, activity, and persistence of microbes in the environment is limited primarily to destructive approaches such as meta-sequencing technologies. Therefore, new techniques for in situ or nondestructive measurements and imaging of environmental microbial activities are needed to interrogate the dynamics of microbial invasions.

To this end, the project has developed a workflow to rapidly enhance transformation efficiency and genetic part characterization in nonmodel bacteria to engineer genome-wide libraries for high-throughput CRISPR interference (CRISPRi) screens. Researchers are developing biodesign tools for real-time in situ detection and quantification of microbial activity in ecosystems. These tools are currently being deployed in the plant growth–promoting bacteria Bacillus velezensis, a strong candidate for improving Populus sp. resistance to the pathogenic fungus Sphaerulina musiva.

First, the team is using CRISPRi to study how perturbation of gene expression impacts microbial establishment in the soil, rhizosphere, and in planta. Researchers built a 40,000-gRNA library targeting 10 gRNAs per annotated coding region in the genome and are performing growth assays to measure gRNA enrichment/depletion using next-generation sequencing under different selective conditions, such as during growth with root exudates and in different soil types. These functional assays will allow researchers to identify genetic perturbations affecting microbial establishment and inform engineering targets for future rhizosphere microbiome manipulation.

Second, the team is engineering a gas-based biosensor into B. velezensis and S. musiva to monitor the dispersal and activity of engineered strains belowground. These genetically engineered microorganisms will use an enzyme called methyl halide transferase to continuously produce methyl halide gas in vegetative cells. This indicator gas can be easily detected using gas chromatography–mass spectrometry without sample disruption from laboratory-to-field scales. Monitoring the location and activity of B. velezensis and S. musiva will aid in tracking and controlling the spread of microbes within and between select environments.

Collectively, these studies will provide new tools to study, engineer, and optimize targeted beneficial alterations to microbial communities in managed ecosystems.

The goal of ENIGMA (Ecosystems and Networks Integrated with Genes and Molecular Assemblies) is to develop theoretical, technological, and scientific approaches to gain a predictive and mechanistic understanding of the biotic and abiotic factors that constrain microbial communities’ assembly and activity in dynamic environments. To link genetic, ecological, and environmental factors to the structure and function of microbial communities, ENIGMA uses a systems biology approach to integrate and develop laboratory, field, and computational methods.

To achieve its project goals, ENIGMA has been organized into several campaigns involving multiple institutes with varying expertise. The overarching goal of the Environmental Simulations and Modeling campaign is to simulate, model, and predict the mechanistic foundations of phenomena observed at a field site, the Oak Ridge Reservation Field Research Center.

Through field surveys and the recently installed SubSurface Observatory, the team collects high temporal–resolution datasets of environmental parameters [e.g., pH, dioxygen (O₂), nitrate, metabolites] and has generated an insightful view of the dynamic nature of the field site subsurface. By monitoring rainfall events, researchers observed that such events can be followed by a sudden decline in both pH and dissolved O₂, and this transition from a neutral to an acidic pH increases the emissions of nitrous oxide (N₂O). To investigate this phenomenon in the laboratory, the team uses an established synthetic community of two field denitrifiers, Rhodanobacter sp. R12 and Acidovorax sp. 3H11, which together can perform complete denitrification but cannot independently. Through laboratory simulations utilizing time course experiments, researchers established that a change in pH from neutral to pH 6 can decouple the denitrification process within the synthetic community, leading to significant increases in N₂O emissions. Additional abiotic controls have shown similar decoupling of denitrification partitioning at varying carbon/nitrogen ratios, oxygen levels, and increased metal concentrations such as nickel. These studies have generated a compendium of 306 transcriptomes that have been used to construct an R12/3H11 gene regulatory network that may help explain and predict how environmental fluctuations at the field site will impact emissions of N₂O.

Given the observed field dynamics, the team has constructed customized drip-flow reactors to mimic ecologically relevant subsurface parameters. The reactors systems generate a vertical O₂ gradient (aerobic to anoxic), mirroring observations from field wells. Additionally, reactors can contain sediment particles to allow for microbial surface attachment or to remain in suspension, reflecting different regimes in subsurface habitats. A five-member synthetic community comprising facultative anaerobes of varying physiological capabilities that respire nitrate is used to understand the interplay between attached and planktonic communities across the O₂ gradient. Initial results show distinct stratification of microbial communities along the attached phase of the gradient suggesting structure-function relationships at the community level. Long-term experiments are underway to probe community stability and the effects of environmental perturbations that simulate field observations.

To further explore the abiotic factors that determine community composition and biogeochemistry at the field site, the project is constructing anaerobic microbiological enrichments under varying nitrate concentrations, carbon sources, and pH conditions. Long-read metagenomic analyses of these enrichments are used to construct community networks relating taxonomy, biogeochemistry, and functional abilities. This information will guide the development of next-generation synthetic communities that recapitulate the natural community assembly process for continued discovery of genetic mechanisms underlying observations from the field.

The overall goal of this project is to enhance the robustness of biofuel-producing microbes in adverse and fluctuating environments, such as media containing toxic hydrolysates or elevated temperatures, by introducing cellular noise in gene expression. The project’s approach involves the identification of factors in the transcription process that increase cellular noise and the deployment of such factors to generate cells exhibiting increased cellular noise. The project uses modeling and single-cell analysis workflow to engineer Yarrowia lipolytica variants that can tolerate, grow, and efficiently synthesize biofuel precursors under steady state, albeit dynamically stressful, conditions. Overall, the team anticipates that strains with optimal levels of cellular noise will also exhibit robustness that maintains production under time-varying stresses.

Robustness represents a system-level trait that allows cell populations to maintain function under adverse and fluctuating environments. When observed at the cellular or subcellular level, an isogenic cell population exhibits increased cell-to-cell variability, or noise, even under steady-state conditions. In this context, isogenic cells can undergo division of labor, with some expressing the pathways that enable them to continue functioning in a new environment. This concept guides the project in developing workflows for introducing and manipulating cellular noise to enhance cellular tolerance to environmental stressors. Focus has been placed on the construction of Y. lipolytica strains with the double phenotype of tolerance and high lipid productivity. In its first steps on cellular noise engineering, the project refined gene editing toolboxes that can deterministically vary the level of cellular noise in protein expression levels. Accordingly, the team introduced two to eight tandem upstream activating sequences to the pTEF promoter. The synthetic hybrid promoters were placed upstream of a green fluorescent protein, fused into plasmids, and stably integrated into the genome of Y. lipolytica. Each transformant was screened separately by flow cytometry to categorize them into expression and noise levels. The impact of activators or repressors on the promoters was likewise investigated. As a next step, researchers introduced key genes that play a significant role in viability at varying inhibitor levels. To this end, the team applied rational design to develop a cellulosic oil Y. lipolytica strain that is tolerant to the primary lignocellulosic inhibitor furfural. To enable tolerance to furfural, researchers constructed Y. lipolytica overexpressing an endogenous aldehyde dehydrogenase that converts furfural to the less toxic furoic acid. The project finally evaluated front‐runner Y. lipolytica strains under both stressful and non‐stressful conditions to quantify the effects of noise and expression levels on furfural tolerance. The team has also identified the mechanisms and related gene targets that could enable Y. lipolytica to withstand elevated temperatures, which form the next cellular noise engineering goal in this project.

The microbiology-based projects within the UCLA-DOE Institute employ molecular, biochemical, genome sequencing, and in silico approaches to better understand biological processes that drive carbon recycling in nature. These findings impact multiple areas of BER interest, including bioconversion of model substrates in natural and manmade environments, the associated biochemistry of key degradative enzymes, and the design of plant-based biomass deconstruction strategies for biobased chemicals production. Genomics programs that instruct metabolic pathways for key substrates are being elucidated in both model and novel microbe systems. Next-generation omics methods are being applied to interrogate environmentally relevant pathways, as well as their interactions with defined microbial communities. In related work, the project seeks to define the pathways used by cellulolytic microbes to degrade lignocellulose and other polymers. Using a combination of experimental and bioinformatics approaches, researchers seek to learn how anaerobic microbes sense environmental changes that induce the synthesis and assembly of extracellular cellulosome-like structures that degrade different types of plant biomass. Collectively, the results of these basic science studies provide fundamental insight into processes that drive carbon recycling and will facilitate the development of new microbial-based methods to produce renewable chemicals and materials from abundant biomass.

Elucidation of microbial pathways for metabolism and degradation of model polymeric substrates. Genomic, proteomic, and informatic studies were performed on model and newly identified microbes to elucidate how representative plant-derived substrates are efficiently metabolized. Here, core pathway enzymes for polymers, sugars, and fatty acids are being investigated and further characterized in a spectrum of cellulolytic microbial species. Recombinant, structural, and informatic studies of key enzymes in these pathways were performed to explore the thermodynamic rate-limiting steps during anaerobic cell growth. Associated electron transfer pathways needed for hydrogen and formate production by polymer degrading microbes were also examined.

In complementary studies, proteomic and mass spectrometry methods were performed to further characterize metabolic pathways, protein post-translational modifications (PTMs), and cellular envelope components of model polymer-degrading microbes. Characterizing enzyme-disrupting PTMs will help decipher their relationship with the metabolism of biomass by microbial strains. The team has discovered that acyl-lysine modifications arising from reactive metabolites are strikingly abundant in model beta-oxidation bacteria. Acetyl, butyryl, 3-hydroxybutyryl, and crotonyl modifications were observed in a range of species including Syntrophomonas wolfei and Syntrophus aciditrophicus. Interestingly, the types of modifications that occur are correlated with the complexity of the carbon substrate, and the relative abundance of these modifications significantly change in response to different carbon sources. For example, S. wolfei subsp. methylbutyratica is capable of metabolizing longer carbon substrates displaying diverse methylbutyrylation, valerylation, and hexanoylation modifications.

Probing how bacteria produce cellulosome-like structures for efficient plant polymer degradation. The project’s efforts are focused on elucidating how these bacteria sense different types of biomass to optimize the enzyme composition of the cellulosome and gaining broad insight into how cellulosomes are assembled through comparative genome analyses. Here, the team reports recent results that suggest biomass sensing membrane receptors undergo autoproteolysis via a succinimide intermediate, thereby predisposing them to biomass-induced dissolution triggering gene expression changes in cellulosomal genes. Ongoing efforts are focused on using a transcriptomics-based approach to map the full set of genes controlled by each receptor and to determine the biomass signals which they detect. Finally, the team presents the initial results of a comprehensive analysis of published sequenced genomes from which novel cellulosome displaying bacteria have been identified. The results of this work will shed light onto the diversity of cellulosome architectures present in biology and could facilitate engineering efforts for designer recombinant cellulolytic bacteria.

To explore pathways for plant-derived polymer deconstruction, PacBio long-read sequencing approaches were used to sequence, assemble, and annotate genomes of previously isolated bacterial strains that utilize such substrates when grown in pure or in co-culture with suitable microbial partners. The project is also extending gene annotation methods beyond the standard homology-based interferences of these microbial genomes based on co-evolution such as phylogenetic profiling. To this end, researchers are generating maps of domain interactions by leveraging the data found in UniProt, allowing the mapping of interactions between thousands of domains based on their conservation across 10,000 prokaryotic genomes. The team is developing web-based tools to visualize these interactions and navigate relationships between bacteria.

This project aims to understand how the microorganisms in the Populus tree microbiome affect the host plant health and stress tolerance. The team combines plant physiology experiments under normal, nutrient-limited, and water-limited conditions with field and greenhouse data for crop modeling. The process-based model will guide further examination of microbiology, metabolomic, and transcriptomic data, resulting in a system-level understanding of the plant-endophyte interactions from the molecular to the canopy level.

Endophyte inoculation has a potential to improve biofuel crop sustainability by increasing resource use efficiency and stress tolerance, possibly reducing fresh water and fertilizer needs. Previous research demonstrated that Salicaceae endophytes improved water use efficiency in poplar plants under drought conditions (Banan et al. 2024). These endophytes are also known to fix atmospheric nitrogen and alter metabolite biosynthesis, processes that can boost crop productivity. In this study, researchers investigated the morphological changes and resource allocations in poplar trees inoculated with Salicaceae endophytes under nitrogen deficiency at multiple scales ranging from tissue and organ to whole plant. Nisqually-1 Populus trichocarpa were mock-inoculated or inoculated with the endophyte consortia as described by Banan et al. (2024). Plants were irrigated daily with either 10 millimeter (low nitrogen; LN) or 40 mM ammonium nitrate [NH₄NO₃; high nitrogen (HN)] for 150 days. The length of plant shoots and the number of fully expanded leaves were monitored weekly. Upon detecting differences in these data, additional measurements were taken of leaf size and leaf chlorophyll content by a SPAD meter, and their ratio were assessed for entire leaves weekly. Around 60 days after cultivation (DAC), the endophyte-inoculated plants under LN exhibited taller growth and more leaves than non-inoculated plants. The inoculated plants maintained higher shoot length up to harvest, but leaf numbers equalized by 150 DAC. Under HN, endophyte inoculation had no effect on the shoot length and leaf number. Total leaf area per plant was higher in inoculated plants around 60 DAC, regardless of NH₄NO₃ levels. This difference became larger up to harvest under LN but diminished after 100 DAC under HN. Chlorophyll content followed a similar trend.

Across treatments, the ratio of chlorophyll content to leaf area decreased over time. Under LN, inoculated plants initially had a higher ratio, but it declined rapidly after 100 DAC, reaching levels similar to non-inoculated plants. Under HN, inoculation did not affect the ratio. Inoculated plants exhibited greater total dry biomass than non-inoculated plants, regardless of NH₄NO₃ levels. Under LN, inoculation increased stem dry mass with no effect on root or leaf biomass.

Under HN, stem and leaf biomass trends were similar to LN, but inoculated plants also showed higher root biomass, resulting in a higher root-to-shoot ratio. These results suggest that endophyte inoculation aids plant adaptation to stress, enhancing host productivity and resource use efficiency; larger leaves and increased chlorophyll content suggest this adaptation promotes growth under nitrogen-deficient conditions. These physiological data will be used for process-based modeling to quantify the potential of endophytes to enhance biofuel feedstock production.

Microorganisms play key roles in soil carbon (C) turnover and stabilization of persistent organic matter via their metabolic activities, cellular biochemistry, and extracellular products. Microbial residues are the primary ingredients in soil organic matter, a pool critical to Earth’s soil health and climate. The project hypothesizes that microbial cellular chemistry, functional potential, and ecophysiology fundamentally shape soil C persistence, and the team is characterizing this via stable isotope probing of genome-resolved metagenomes and viromes. The project focuses on soil moisture as a “master controller” of microbial activity and mortality, since altered precipitation regimes are predicted across the temperate United States. The Science Focus Area’s (SFA) ultimate goal is to determine how microbial soil ecophysiology, population dynamics, and microbe-mineral-organic matter interactions regulate the persistence of microbial residues under changing moisture regimes.

Ecophysiology and community ecology are natural stops on the road from genes to ecosystems, are central to understanding how microbes function, respond to substrate availability and environmental changes, release carbon dioxide, consume and produce soil organic matter, and are essential to develop a molecular and mechanistic understanding of microbes’ roles in soil C cycling.

Metatranscriptomics is a powerful and relatively new tool to study soil communities. However, the vast number of genes and transcripts and the complexity of their functions and regulation often limit a straightforward interpretation, making it difficult to draw clear conclusions from experiments conducted in natural soils. Instead of a whole transcriptome analysis, the project advocates a modular approach to study ecophysiology and community ecology of wild soil microbial communities. Each module consists of sets of genes related to specific ecophysiological (e.g., starvation responses) or community ecological (e.g., trophic dynamics) functions, founded on expert knowledge, applied, criticized, improved, and expanded by researchers using cross-ecosystem comparisons.

As an illustration, the team has analyzed four metatranscriptome datasets to answer the following research questions.

1. It is often assumed that soil microbes spend most of their existence in a state of starvation with high-maintenance energy demand, where substrate is used only for energy production (Hagerty et al. 2018). Is there evidence that transcription of energy production and biosynthesis varies?
2. Although microbes are mostly C-limited, low availability of inorganic nutrients may restrict microbial activity when excess organic C is available. In response to nutrient limitations, microbial cells increase transcription of transporters for the most limiting nutrient (Ishige et al. 2003; Silberbach et al. 2005). Do researchers see signs of inorganic nutrient limitations in metatranscriptome datasets?
3. Bacteria form biofilms, consisting of extracellular polysaccharides mixed with lipids, proteins, and DNA. These biofilms protect the encapsulated cells, facilitate their survival, and may have an important role in soil organic matter formation. Can researchers use metatranscriptomes to study when and where biofilms are being produced?
4. Using metatranscriptomes, can researchers detect the influence of carbohydrates on microbial metabolism by looking at gene expression associated with glycolysis versus gluconeogenesis?

In addition to being waystations towards a mechanistic perspective of ecosystems, ecophysiology can be a research target by itself.

5. Can researchers use metatranscriptomes to study the molecular mechanisms of regulation of growth and starvation in soil microbial communities?

The experiments compared consist of a glucose addition; a water addition after a seasonal drought; a warming by time-since-deglaciation experiment in Antarctica; and a comparison between bulk, detritosphere, and rhizosphere communities.

Results show that transcript abundances for biosynthesis are mostly proportional to transcript abundances for energy production; high transcript abundances for nitrogen and phosphate transporters indicate short periods of nutrient limitations; and temporally and spatially explicit patterns of growth and biofilm production suggest that progress can be made to resolve questions relating soil organic matter formation and microbial activities.

Overall, the project signals a growing need for biochemical and cell physiological expertise within soil ecology.

The program’s goal is to understand the interactions, localization, and dynamics of grass rhizosphere microbial communities at the molecular level (e.g., genes, proteins, metabolites) to enable accurate predictions and interventions to effectively manage and harness microbes to achieve DOE missions in sustainable energy and carbon cycling.

Synthetic communities are excellent tools in microbial ecology research to decipher the complexity in microbe-microbe and plant-microbe interactions. However, this approach often constructs these communities by pooling together individual isolates that are not known to interact or even inhabit the same environment, making the system less biologically relevant. By contrast, reduced complexity communities created using enrichment strategies from native environments can produce less complex mixtures of naturally occurring and interacting organisms. Using a combination of these natural enrichment communities, genome-resolved metagenomics and networking microbiome sequencing data, these projects have developed reduced complexity communities from both field soil and the plant rhizosphere. Communities were enriched on multiple carbon compounds in minimal media conditions to generate different taxa composition from the same soil inoculum and then subcultured over multiple months to generate a highly stable microbiome. These communities were further tested for freezing tolerance and reproducibility over multiple freeze-growth cycles to confirm community stability under cryogenic conditions and allow for higher predictability of community structure. This high predictability will enable the modeling and precision community editing of native but elusive members of the soil environment, expanding knowledge of biologically relevant interactions in this complex ecosystem. These reduced complexity communities will be tested in field simulated conditions to allow for testing microbiome editing capabilities across scales.

The project engineered an ultra-safe strain of Escherichia coli for controlled growth and function, including production of peptides and proteins containing nonstandard amino acids.

Engineered cells can address unmet needs for planetary health. To develop safe cell-based technologies, researchers need engineering strategies that allow control of cellular proliferation and function. Currently, bacteria used as platform technologies rely on a wild-type genetic code, which can result in horizontal gene transfer, escape, and loss of control of the designed programs. Genetic code engineering emerges as a promising alternative since it removes a set of codons and tRNAs from the genome, which should prevent translation of incoming DNA. However, the team discovered a new mechanism of escape in bacteria with an engineered genetic code and characterized it with multiomics and protein language models, resulting in the development of an ultra-safe 61-codon E. coli strain. In this strain, researchers engineered a tRNA/aminoacyl-tRNA synthetase pair for the incorporation of a nonstandard amino acid and kill switches. This is the first organism that enables the production of proteins containing user-defined nonstandard amino acids while remaining tightly biocontained and bioisolated. This work is a headstart to develop ultra-safe living technologies and will allow researchers to decode and expand genome and protein designs.

The goal of ENIGMA (Ecosystems and Networks Integrated with Genes and Molecular Assemblies) is to develop theoretical, technological, and scientific approaches to gain a predictive and mechanistic understanding of the biotic and abiotic factors that constrain microbial communities’ assembly and activity in dynamic environments. To link genetic, ecological, and environmental factors to the structure and function of microbial communities, ENIGMA uses a systems biology approach to integrate and develop laboratory, field, and computational methods.

The project has made significant progress towards developing an “ENIGMA Environmental Atlas” and mapping genotype to phenotype for a significant number of diverse subsurface microbes from a field site, the Oak Ridge Field Research Center (ORFRC). This Atlas includes a growing collection of close to 3000 isolates across diverse phyla. Enrichment and isolation efforts reveal that microbial necromass is a major nutrient source for the community, and one recent success includes isolation of novel nitrous oxide reducers from the field site. High-resolution electron microscopy images have revealed unique morphotypes and features of ENIGMA isolates based on growth and nutrient conditions. Researchers are also isolating novel phages and phage tail–like bacteriocins (tailocins) from the field site. The systematic study of bacteria-phage and tailocin interactions will provide novel insights into microbial community dynamics and functional genomics. Genome sequencing of over 1,100 bacterial isolates to date has revealed both macro and microdiversity, such as in Sediminibacterium sp., with regards to denitrification genes. To facilitate analyses of these genomes, the team has developed scalable, web-based portals for rapid comparative genomics (https://fast.genomics.lbl.gov/cgi/search.cgi), including those that can readily incorporate newly sequenced genomes (https://iseq.lbl.gov/genomes). Applying diverse high-throughput phenotypic and genome-wide mutant libraries, researchers have investigated the physiology of strains under in situ conditions, and results indicate differential phenotypes in outer membrane genes under transient and chronic metal exposure in Pantoea sp. In addition, the team has discovered a novel origin of replication in this strain that allows transformation and expression of non-native genes. Such capability may allow a similar exploration of the metabolism and gene functions in other strains. Here, the team highlights several instances where the ENIGMA Atlas is used to better understand the complexities that govern microbial function in the environment and presents progress on the development of such a unique community-usable platform.

Determine how bioenergy crop engineering affects interactions with soil microbes.

Engineering bioenergy crops for reduced cell wall recalcitrance and increased production of high-value chemicals represents a promising approach to sustainably produce biofuels from both economic and environmental standpoints. Yet, the impact of modifying bioenergy traits in feedstocks on the indigenous soil microbiome of agricultural lands remains largely uncharacterized. The project profiled the rhizosphere bacterial communities associated with transgenic sorghum lines engineered for reduced cell wall recalcitrance (AT10) and increased protocatechuate production (Qsub) via 16s amplicon sequencing. The team found that the rhizosphere bacterial community composition was different between the transgenic sorghum lines and wildtype. In the rhizosphere of transgenic lines, researchers detected an enrichment of the phyla (Actinobacteriota, Bacteroidota, and Proteobacteria) that are generally considered as copiotrophs, which thrive on labile carbons and grow rapidly under nutrient-rich conditions. Conversely, the team observed a depletion in the abundance of oligotrophic phyla (Planctomycetota, Acidobacteriota) known for adapting to nutrient-poor conditions and having a slower growth rate. These findings suggest that reducing cell wall integrity and increasing secretion of bioproducts may favor and induce the proliferation of copiotrophic bacteria in soil. Such alterations in soil microbial communities could impact soil health and various soil processes, including nutrient turnover and greenhouse gas emissions, highlighting the importance for further investigation.

The Bacterial-Fungal Interactions (BFI) Science Focus Area (SFA) is focused on elucidating the mechanisms and implications of BFI on larger ecosystem functioning. Part of this work includes characterizing the interactions that occur within and immediately surrounding fungal hyphae (the fungal microbiome) between these fungal hosts, bacterial partners, and several other microorganisms.

Fungi are important members of larger, diverse microbiomes such as those in human guts, plant rhizospheres, or soil microbiomes. However, it has been demonstrated that fungi themselves can harbor complex microbiomes both within and directly surrounding the outer surface of their hyphae (Robinson et al. 2021). Endobacterial associates have been observed in both higher and basal lineages of fungi, but major questions remain regarding how these bacterial symbionts are acquired, how they persist within a fungal host, and how they impact fungal evolution and behavior. Using model interactions between Mollicutes-related endobacteria (MRE) and members of the Mortierellaceae, the group has discovered new insights into interaction persistence, potential co-evolution, physical properties (e.g., distribution of MRE throughout hyphae), and functional impacts of MRE on fungal hosts (Longley et al. 2023). This work has led to the acceptance of a DOE Joint Genome Institute (JGI) New Investigator Community Science Program (CSP) proposal that will investigate the impact of warming conditions on interactions between endobacteria and Mucoromycota taxa. This work will provide new insights into the mechanisms governing fungal-endobacterial interactions and will continue to help elucidate larger roles and impacts of endobacteria in complex natural microbiomes.

In addition to bacteria, fungi can harbor viruses, cyanobacteria, and algae as members of their microbiome (Kelliher et al. 2023). The group’s investigations have identified previously undescribed constituents of the fungal microbiome, including diverse bacterial taxa, archaea, and plant- and algal-derived plastids (Robinson et al. 2021). This discovery of plastids within fungi has led to a JGI Annual CSP award to investigate the mechanistic details underlying their internalization. Researchers have further examined diverse endohyphal microbiome components using multiomics, sequence-based enrichment sequencing techniques, fluorescence in situ hybridization imaging, large screens of publicly available sequencing data, and in vitro internalization experiments. In conjunction with these efforts, the team has been developing novel bioinformatic pipelines and genomic databases to facilitate screening of fungal sequencing projects for signatures of endohyphal microbiome inhabitants, thus enabling the group and other researchers to expand collective knowledge on the diversity of the fungal microbiome. The group also created a web portal where researchers can run these pipelines, report the relationships that have been found, and compare with existing endohyphal microbiome data (Robinson et al. 2023). Not only is the fungal microbiome much more diverse and complex than previously thought, but there is growing interest in examining its role in fungal physiology, fungal evolution, fungal interactions, and fungi in larger communities.

Establish the scientific knowledge and new technologies to transform the maximum amount of carbon available in bioenergy crops into biofuels and bioproducts.

Automation technologies expedite Design-Build-Test-Learn (DBTL) cycles while reducing time and resources. Here, the project developed an automated conversion pipeline that, coupled with machine learning, omics studies, and microfluidics, elevates the capacity to engineer microbes. Pseudomonas putida is a promising microbial host owing to its genetic tractability and capacity to grow on many carbon substrates (Wang et al. 2022). Recently, P. putida was engineered for isoprenoid production with subsequent work improving isoprenol titer. Isoprenol is a biological precursor to 1,4-dimethylcyclooctane (DMCO), a sustainable aviation fuel (Baral et al. 2021). Building upon that work, researchers applied CRISPR interference (CRISPRi), which uses a deactivated Cas9 enzyme (dCas9) and customizable gRNAs to selectively downregulate orthogonal metabolic pathways. The team developed an automated pipeline to rapidly screen CRISPRi targets and test strains for isoprenol production. The pipeline harnessed microfluidic liquid handlers (ECHO, Mantis, and Biomek) for nanoliter-scale dispensing of molecular cloning reagents then employed a customized high-throughput electroporation device for rapid transformation of 384 strains in parallel. Growth and production studies were completed in BioLector microfermentors with proteomics data collected to verify dCas9 expression and confirm gene of interest downregulation. Finally, researchers trained a machine learning model, the Automated Recommendation Tool (ART), with isoprenol titers and associated downregulated genes to iteratively generate recommendations for further downregulation (Radivojević et al. 2020). When coupled with automation, CRISPRi enabled researchers to screen 130 genes associated with isoprenoid precursors or utilization pathways in parallel to identify genes that improve isoprenol titer. Using ART, genes were then iteratively paired in 2- and 3-gRNA arrays for further investigation. CRISPRi successfully downregulated selected genes to increase metabolic flux towards isoprenol in P. putida and, by iteratively screening different target combinations in gRNA arrays, demonstrably improved titers. Following this successful application, the team plans to use ART to further explore and exploit gRNA combinations to maximize isoprenol titer. The pipeline demonstrates a successful application of machine learning tools to systematically and predictably improve isoprenol titers, rates, and yields.

The overriding goal of the Oak Ridge National Laboratory (ORNL) Plant-Microbe Interfaces (PMI) Science Focus Area (SFA) is to predictively understand the productive relationship between a plant host and its microbiome based on molecular and environmentally defined information. Populus and its associated microbial community serve as the experimental system for understanding this dynamic, complex multiorganism system. To achieve this goal, the project focuses on: (1) defining the bidirectional progression of molecular and cellular events involved in selecting and maintaining specific, mutualistic Populus-microbe interfaces; (2) defining the chemical environment and molecular signals that influence community structure and function; and (3) understanding the dynamic relationship and extrinsic stressors that shape microbiome composition and affect host performance.

Plants are colonized by beneficial microbes that may enhance their resistance to both abiotic and biotic stress, yet the mechanisms underlying these benefits remain largely unexplored. Coupling field observations to laboratory experiments is essential for understanding these microbe-mediated benefits, yet these cross-scale linkages remain a critical knowledge gap. In this study, researchers sampled Populus trichocarpa microbiomes across gradients in temperature and precipitation in Oregon and Washington. These sites revealed distinct microbial communities correlated with varying levels of temperature and moisture. To assess the potential benefits conferred by these microbial communities to host plants under thermal stress, whole soil microbiomes from the sites experiencing the coldest and hottest temperatures were transferred to axenic Populus tissue culture plants in a greenhouse environment. Notably, poplars receiving the microbiome from the highest temperature site demonstrated enhanced growth when placed in a high-temperature chamber, suggesting the microbiomes from warmer sites confer greater thermal tolerance to the host plant. Next, the team employed direct plating and flow sorting microbiome isolation approaches to obtain individual isolates from field sites at the thermal extremes. The project will next compare the isolates with taxa enriched in the microbiome of plants demonstrating thermal tolerance to identify potentially beneficial strains. Building on these findings, the research’s subsequent phase will utilize individual isolates assembled into Populus synthetic communities (SynCom) to dissect the molecular mechanisms underpinning the observed benefits, bridging the gap between field-based microbial ecology and controlled laboratory experiments. This research contributes to an understanding of the complex interactions between plants and their associated microbial communities, and findings have important implications for leveraging these relationships to enhance plant resilience in the face of changing environmental conditions.

The goal of ENIGMA (Ecosystems and Networks Integrated with Genes and Molecular Assemblies) is to develop theoretical, technological, and scientific approaches to gain a predictive and mechanistic understanding of the biotic and abiotic factors that constrain microbial communities’ assembly and activity in dynamic environments. To link genetic, ecological, and environmental factors to the structure and function of microbial communities, ENIGMA uses a systems biology approach to integrate and develop laboratory, field, and computational methods.

Over decades of measurements, Rhodanobacter species are consistently dominant in the most contaminated groundwater at the Oak Ridge Field Research Center (ORFRC). Scientists know that Rhodanobacter tend to be low pH and high metal tolerant relative to other bacteria but know less of the precise genetic and physiological mechanisms that enable them to survive and persist in the contaminated subsurface. Here, the team highlights work that exemplifies the ENIGMA Environmental Atlas experimental strategy with the aim of understanding the mechanisms behind Rhodanobacter survival at the ORFRC. In particular, high-throughput culturing and biofilm assays reveal phenotypic variability in metal stress resistance within the Rhodanobacter genus and between Rhodanobacter and other bacteria from the field site. Researchers have also generated random-barcode transposon sequencing (RB-TNSeq) mutant libraries in multiple ORFRC Rhodanobacter strains. Using these high-throughput genetics resources, the team has identified genes important for resistance to the key selective inorganic ion stressors at the ORFRC, including 33 efflux genes important for tolerance to 22 different inorganic ions. In addition, researchers have also implemented the DOE Joint Genome Institute DNA affinity purification sequencing (DAP-seq) approach to examine transcriptionally acting response regulators of signaling systems for two different Rhodanobacter strains. Additionally, the project has also developed CRISPR-based tools for precision genetics in ORFRC Rhodanobacter strains. Despite these advances, cultivation and analysis of the most highly abundant Rhodanobacter strain present in groundwater has remained a challenge. The project has made advances on this front by leveraging long-read metagenomics to identify key traits such as its unusual genomic capacity for carbon fixation.

The team has also identified an unusually high number of toxin-antitoxin systems in this genome, which may suggest rampant phage infection in the contaminated groundwater. As the project gains more insights into functional genomics and physiology of cultured Rhodanobacter, researchers will improve the ability to predict traits in uncultured strains.

• Develop comparative systems datasets for typical (pennycress) vs. extreme (Cuphea) seed fatty acid synthase (FAS).
• Develop kinetic model for pennycress vs. Cuphea FAS.
• Reconstitute and ratiometric-optimize FAS for increased fatty acid and C10 fatty acid production and kinetic model refinement.
• Generate synthetic biology–based FAS and acetyl-CoA carboxylase (ACCase) pennycress and camelina biodesigns for increased oil and C10 fatty acid production.

B5: Bigger Better Brassicaceae Biofuels and Bioproducts aims to dissect the biochemical underpinnings of fatty acid biosynthesis in plant plastids. This work will enable the predictable design of the nonfood Brassicaceae oilseeds Camelina and pennycress for increased seed oil and tailored fatty acid chain lengths to support U.S. bioenergy and oleochemical sectors. In oilseeds, energy-dense fatty acids are produced in plastids by type II FASs, which consist of discrete enzymes that function in concert to carry out the iterative elongation of fatty acid chains, two carbons at a time. FAS is fueled by malonyl-CoA generated by acetyl-CoA carboxylase (ACCase), the rate-limiting enzyme in fatty acid synthesis. Studies explore the hypothesis that the changes in the stoichiometry of both FAS enzymes and multimeric complexes of ACCase and associated regulatory proteins can enable fine-tuning of total fatty acid production and fatty acid chain lengths. The project’s investigation of FAS composition and stoichiometry is informed by comparative analyses of seeds with a “typical” FAS (pennycress) that produces C16 and C18 fatty acids vs. seeds with an “extreme” FAS (Cuphea viscosissima) that produces ~75% 10:0. As a first step, the team generated PacBio transcriptomes of developing pennycress and C. viscosissima seeds representing ~14,000 and ~21,000 gene families, respectively, which will be used for all integrative multiomics studies on data generated in this project. Through mining of these transcriptomes, researchers have generated a comprehensive set of cDNAs for FAS enzymes, ACCase enzymes, and regulatory proteins from seeds of both species. In pennycress seeds, researchers identified 37 genes, orthologous to Arabidopsis, involved in de novo fatty acid synthesis in the plastid. The team confirmed 31 key FAS and ACCase genes through global proteomic analysis of developing pennycress seeds. For absolute quantitation, 25 new AQUA peptides were synthesized alongside nine AQUA peptides shared with Arabidopsis. Researchers also expressed and purified recombinant pennycress FAS polypeptides, which will be used as standards in absolute quantitative proteomic studies and as constituents of in vitro FASs to support mathematical modeling of fatty acid synthesis. Early modeling efforts have focused on the integration of regulatory interactions associated with ACCase and FAS enzymes. To support the analysis of FAS compositions in developing seeds, the team also generated polyclonal antibodies against several recombinant FAS polypeptides that have yielded high-resolution western blots. Guided by prior research of the type II FAS of Escherichia coli, researchers are exploring the impact of changes in the expression of β-ketoacyl-acyl carrier protein synthase I (KASI), which acts as the initial condensing enzyme in fatty acid elongation, on yields of decanoic acid (C10) in pennycress and Camelina seeds engineered for the overproduction of this product. Overall, these collaborative efforts are generating fundamental knowledge that will be combined with B5 synthetic biology tool development for predictive design of optimized pennycress and Camelina feedstocks for biofuels and bioproducts.

This project seeks to discover and validate gene functions and pathways in Populus trichocarpa that lead to generation of biological nitrification inhibitors and to understand their role affecting nitrogen use efficiency.

Plants have evolved to mediate nitrification processes by exuding biological nitrification inhibitors (BNIs) in soil to alter the functions of the plant-associated microbiome. BNIs have emerged as one route to mitigate loss of nitrogen (N) and improve nitrogen use efficiency (NUE). Little is known of the genes, gene families, and associated pathways related to BNI production and function. Thus, experimental data that comprehensively link host traits, genetics, and exudation of BNI compounds are needed. Such data are critical to creating systems network models that can deconvolute complex host-microbiome interactions, system feedbacks on the microbiome, and impact on biogeochemical cycles. This project seeks to discover and validate gene functions and pathways in P. trichocarpa that lead to generation of BNIs and understand their role affecting NUE. To begin, methods and protocols are needed to measure nitrification traits in soil obtained from a P. trichocarpa genome-wide association study (GWAS) population. To accurately characterize such processes, measure of gross nitrification rate (GNR) using ¹⁵N-isotope dilution techniques is needed. Unfortunately, current assays can be costly and difficult to implement and are relatively low throughput. Linking plant genomic function to nitrification inhibition activity requires measure of GNR phenotypes across a diverse population comprising hundreds and thousands of samples, hence the need for alternate methods. Here, chemical derivatization protocols are adapted to enable isotope dilution measurements with high throughput and sensitivity.

Methods were developed to capitalize on the high-throughput capabilities of an immediate drop-on-demand (I.DOT) system coupled with open port sampling interface–mass spectrometry (OPSI-MS). Derivatization products were analyzed by dispensing 10 nanoliters of diluted (water) samples into a flow of solvent which is subsequently characterized by mass spectrometry. Reactions were optimized for reaction time, derivative concentration, and other variables. Soil samples were kept refrigerated until supplemented with ¹⁵N-rich ammonium sulfate or potassium nitrate, incubated for 24 hours, and extracted with 1 molar potassium chloride. Measure of nitrate was achieved by modifying derivatization protocols of 2,3-diaminonaphthalene. Using the I.DOT/OPSI-MS system, the reaction could be monitored over time using various 2,3-diaminonaphthalene concentrations. Without a concentration step, a limit of detection (LOD) of low micrometer was achieved for ¹⁴N-2,3-naphthotriazole (NAT) and even lower for ¹⁵N-NAT. O-phthaldialdehyde (OPA), dansyl chloride (DAN), and 9-fluorenylmethoxycarbonyl chloride (FMOC-Cl) derivatization reactions were explored for detection of ammonia. Of these, OPA resulted in the highest sensitivity with the simplest derivatization procedure. An LOD of low µM was achieved using OPA. Both DAN and OPA methods had <10% coefficient of variation. DAN and OPA methods were used to demonstrate measures of GNR and gross mineralization rate in soil samples collected near to a P. trichocarpa with varying concentrations of N (milligram ¹⁵N per kilogram of soil). Notably the DAN and OPA methods were able to quantitively relate ¹⁵N/¹⁴N isotope ratios using high-resolution mass spectrometry or selected multiple reaction monitoring with 3 s/sample throughput and no sample cleanup. Together, these methods represent a simple, flexible, and fast approach for measure of nitrate and ammonia in soil extracts.

This project aims to engineer a pH-tolerant strain, Issatchenkia orientalis, capable of using sustainable feedstocks to produce citramalate while achieving successful scale-up. Specifically, the team plans to:

• Increase citramalate titer and yield from glucose, feedstock hydrolysate, and oil through substrate optimization.
• Expand the knowledge base and tool repertoire for I. orientalis by implementing advanced toolkits, such as optogenetics and piggyBac transposon technology, and conducting genetic studies by constructing a comprehensive knockout library.
• Conduct thorough investigations on scale-up conditions for the production process.

Methyl methacrylate (MMA) is a building block of poly MMA (PMMA), a material commonly recognized as acrylic glass or plexiglass. The prevailing method to manufacture PMMA utilizes petroleum and the acetone cyanohydrin process, which is considered unsustainable and raises concerns regarding the use of toxic chemicals. An alternative route using semisynthesis, combining converting microbially produced citramalate to methacrylic acid (MA) using a catalyst and final esterification of MA to MMA, could present a viable solution.

Previous studies have shown large titers of citramalate in engineered Escherichia coli. However, the increased production cost and carbon footprint from having a neutralization and reacidification step makes this route less economically attractive. A pH-tolerant strain such as I. orientalis would be an attractive alternative. Through extensive collaborative efforts, the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI) team has previously demonstrated that I. orientalis can withstand citramalate of up to 80 g per liter at pH 3. Additionally, the team engineered a strain that produces 2 g/L citramalate by integrating the citramalate synthase gene (cimA) from Methanocaldococcus jannaschii into the I. orientalis genome using piggyBac.

Examining bottlenecks and increasing flux towards citramalate through metabolomic study and genome-scale modeling was essential in increasing citramalate production. The metabolomics studies found that there is pyruvate overflow, accumulation of intracellular citramalate, and excess ethanol byproduct. Using the genome-scale model, pathways of interest were identified to address these problems. The team simultaneously decreased pyruvate overflow and increased lacking acetyl-CoA by utilizing an aldehyde dehydrogenase gene (ALD6) from Saccharomyces cerevisiae and a mutated acetyl-CoA synthase gene (ACS_SE^L641P) from Salmonella enterica. Researchers also incorporated a multidrug transporter (QDR3) from S. cerevisiae to increase excretion of citramalate into the growing broth. Using piggyBac, researchers integrated QDR3–ALD6–ACS_SE^L641P– and a more active cimA (cimA3.7) to create a library of variants. The team selected this library’s top producer (Q42) for further engineering. To address excess ethanol production, the project employed CRISPR to delete the pyruvate decarboxylase gene (PDC). The resulting strain, Q42 Δpdc, produced 18 g/L of citramalate in shake flasks and was scaled up to a 3 L bioreactor to produce 30 g/L using synthetic complete medium with 6% ammonium sulfate, 5% glucose, and a trace metal supplement without needing pH or dissolved oxygen control.

In addition, the CABBI team successfully generated a xylose-utilizing strain, Q42X Δpdc, for future work involving potential feedstocks such as sorghum hydrolysate and sugarcane juice. Currently, the team is actively characterizing the metabolism and genomics of I. orientalis by developing a comprehensive knockout library and updating the genome-scale model. The team is also exploring new metabolic engineering strategies such as eliminating glycerol production, exploring alternative routes for acetyl-CoA production, identifying additional target genes for up- or down-regulation, and developing a light-controlled circuit.

This project will yield poplar chassis with multipurpose uses including bioenergy, biomaterials, and bioproduct production. The generation of a robust cell type–specific set of transcription factors and cis-regulatory elements and the ability to modulate gene expression in a high-resolution manner (i.e., that of specific cell types) will enable precision genome engineering of metabolism, a significant advancement in capabilities in modulating plant biochemistry. The change in architecture will be exploited to permit production of bioproducts (drop-in fuel precursors in leaves), biomaterials (modified wood composition) in wood, and changes in agronomic production practices such as increased stand density leading to increased yield. Collectively, these engineered chassis and tools provide the platform of a new era for poplar biology, agronomy, and processing.

Domestication and breeding efforts have shown that selection of specific plant architecture traits across a wide array of plant species, both annuals and perennials, results in improved traits for human use, either for food, feed, or fuel. Similarly, selective breeding can yield distinct chemotypes of crops with desired chemical profiles or compositions. Today, researchers can generate precision knowledge of gene regulation and function through high-resolution omics technologies and construct a synthetic biology toolkit to engineer plant genomes at the DNA sequence, chromatin accessibility, and expression levels. Thus, science has entered an era where scientists can model, design, and then engineer precise changes in plant genomes that will lead to predictive, modified traits.

This project will re-engineer poplar as a multipurpose crop that can be used for bioenergy, biomaterial, and bioproduct production. The team will generate a cell atlas that encompasses gene expression, gene regulatory networks, and cis-regulatory elements responsible for gene expression at the cell-type level, providing the requisite knowledge base and tools for precision biobased design and fabrication of multipurpose poplar. Researchers will couple single-cell datasets with new genome and epigenome editing tools to develop new morphotypes of poplar that have altered tree and leaf architecture. These morphotypes will substantially improve biomass potential via increased stand density and tree integrity, photosynthetic capture, and trichome density and serve as the foundational chassis. These chassis will have altered ratios of leaves to stems and/or trichome density in which researchers can further engineer cell wall composition and/or novel molecules such as precursors for drop-in fuels, thus making chemotypes of poplar that are “customized” to their biomaterial or bioproduct applications and simultaneously “maximized” in optimal morphotypes. The team will employ an iterative design process in which metabolic pathways are optimized to create unique chemotypes with tailored biomaterial and bioproduct composition.

The goal of this Early Career Program project is to engineer the structure and properties of cell membranes to improve the performance of industrially relevant microbes. The project’s first objective is to enhance the rate and efficiency of the respiratory metabolism by engineering the organization of the electron transport chain. Engineering efforts will define the limits of respiratory metabolism and seek to increase the production of energy-intensive next-generation biofuels. The second objective is to apply the emerging biochemistry of intracellular lipid trafficking pathways to develop new transporters for the capture of valuable biochemicals produced by the engineered yeast.

The project will present advancements in three areas of lipid membrane biology relevant for the performance of yeast microbial cell factories. In the first direction, the team has elucidated key factors that allow for mitochondrial function in fermentation conditions that are characterized by low oxygen availability. Paradoxically, mitochondria proliferate under these conditions, and the inner mitochondrial membrane increases its surface area and complexity. Researchers have found that synthesis and remodeling of the tetra-acyl lipid cardiolipin is essential under microaerobic fermentation due to lipidomic changes resulting from the loss of oxygen-dependent desaturase activity. In the second direction, researchers have characterized a putative lipid transfer protein (LTP) that is predicted to bind squalene, a biolubricant and intermediate in ergosterol biosynthesis. Researchers have found that loss of this LTP, Sfh2, results in accumulation of squalene under microaerobic conditions. The team is currently testing if this LTP traffics squalene from its site of synthesis in the endoplasmic reticulum to storage sites in lipid droplets in vivo. The project is also testing its in vitro activity and proposes that it could be harnessed to better extract squalene from cell factories. In the third direction, researchers have engineered sterol metabolism in yeast to develop cells that better tolerate high temperature and low oxygen fermentations. The project envisions these strains as allowing for new bioproduction capacities outside of standard conditions.

As climate change progresses, bioenergy crops will need to withstand increasingly formidable water, nutrient, and temperature stresses. Though yields of C₄ grasses, such as Sorghum bicolor, have increased through breeding and improved agronomy, annual yield gains will be hard hit by impending abiotic stressors (Prasad et al. 2021). Thus, new germplasm must be developed to maintain, or continue to enhance, yields. Unfortunately, relatively little is known about the traits that contribute to abiotic stress tolerance in sorghum or other related next-generation C₄ feedstock species. The project aims to develop a novel synthetic biology–based approach to determine the contribution of individual root features to abiotic stress tolerance. Synthetic genetic circuits will be employed to generate continuous variation in root depth and root branch density, so their contribution(s) to stress tolerance can be studied with the ultimate goal of identifying optimums and generating more resilient plants.

Trait variation is key to understanding the contribution of specific plant features to environmental stress resilience. By measuring the fitness of plants with changes in a specific trait under different stress conditions, researchers can identify traits that are associated with greater resilience (Upadhyaya et al. 2016). The project is engineering sorghum plants to have variation in two key root traits—depth and branch density. The goal is to develop lines that dramatically reduce the number of plants that need to be phenotyped in gene-environment experiments in order to increase the number of abiotic stress conditions that can be tested simultaneously. The project’s approach utilizes synthetic genetic circuits to tissue—specifically titrate—the expression of genes that control root development. Researchers have engineered Buffer gate components that can be used to vary gene expression over several orders of magnitude in C₄ grass protoplasts and are in the process of validating them in stably transformed lines. To tune root branch density, the team plans to express mutant Auxin/Indole-3-Acetic Acids (Aux/IAAs) at varying levels in specific root layers. The mutant Aux/IAAs should inhibit auxin response and prevent the development of lateral roots in a concentration dependent manner (Brophy et al. 2022). Researchers designed a library of mutant SbAux/IAAs with disruptions to their “degron” regions and have begun testing them in sorghum protoplasts (Moss et al. 2015). Initial results suggest that these mutant proteins are resistant to auxin-mediated degradation and can constitutively suppress the auxin transcriptional reporter DR5 (Yang et al. 2017). To modify root depth, the team is using CRISPR-Cas9 to knock out homologs of DEEPER ROOTING 1 (DRO1)—a gene identified in rice that alters root growth angles (Uga et al. 2013). Researchers are testing CRISPR guide RNA activity in sorghum protoplasts and plan to use the most efficacious guides for stable transformation. Once knocked out, DRO1 will be reintroduced at a variety of expression levels using Buffer gates. This work is building toward a new approach for understanding the contribution of root architecture features to plant fitness.

This research aims to identify and investigate the transcription factors involved in the regulation of photosynthesis in energy sorghum. The major goal of this project is to model and validate gene regulatory networks and integrate with physiological data to reveal transcription factors that can alleviate the loss of photosynthetic efficiency in lower canopy leaves. This information will allow ranking of transcription factors by importance and thus, will guide future design strategies for developing energy sorghum cultivars with improved photosynthetic light-use efficiency and overall productivity.

C₄ grasses such as energy sorghum (Sorghum bicolor) have great potential for both carbon sequestration and as feedstocks for biofuels and building materials. However, in contrast to what is typically observed for other plants, sorghum belongs to a clade of C₄ species that has undergone a maladaptive loss of photosynthetic efficiency in self-shaded leaves within the canopy. Current models predict that this loss results in a 15 to 20% reduction in potential productivity (Pignon et al. 2017). Specifically, most plants have evolved to dynamically tune their photosynthetic machinery by shifting the stoichiometry of proteins involved in the light reactions of photosynthesis to maintain a high maximum absolute quantum efficiency of carbon dioxide (CO₂) assimilation (ΦCO_2,max) in the shade. Work has shown that the lower self-shaded leaves from C₄ bioenergy crops (bioenergy sorghum, Miscanthus, and maize) do not retain a high ΦCO_2,max compared to their upper sun-exposed leaves; this change is due to the light environment rather than leaf age (Collison et al. 2020; Pignon et al. 2017). Variation in the severity of this ΦCO_2,max loss between sorghum cultivars suggests that this maladaptive trait may be the result of difference in the expression of one or more genes (Jaikumar et al. 2021). This is supported by recent greenhouse experiments where researchers showed that the phenotype is reversible by moving plants from shaded to light environments. The team hypothesizes that genes influencing ΦCO_2,max will have expression patterns that correspond to measurable changes in photosynthetic traits and that researchers will be able to identify these genes by comparing changes in expression in response to the light environment across energy sorghum cultivars and canopy positions.

Therefore, the team will have collected gene expression and physiological data on photosynthetic traits such as ΦCO_2,max across light conditions from different sorghum cultivars in the field and greenhouse. Since transcription factors (TFs) are key regulators of gene expression in response to environmental stimuli such as changes in light intensity and quality, the project expects that TF expression is important to the maladaptive loss of photosynthetic efficiency. To identify key TFs, researchers are building gene regulatory networks that integrate the gene expression and photosynthetic trait data. The project will improve the accuracy of these networks by including TF gene targets identified using a new in planta method. Identifying the cause of photosynthetic inefficiency in shaded energy sorghum canopies and engineering solutions to restore the 15 to 20% loss in productivity and enhance yield will improve the overall potential of this bioenergy crop to meet the growing needs for energy security.

Arbuscular mycorrhizal fungi (AMF) are ancient symbionts that form root associations with most plants. AMF play an important role in global nutrient and carbon (C) cycles, and understanding their biology is crucial to predict how C is stored and released from soil. This Early Career research investigates the mechanisms that underpin synergistic interactions between AMF and microbes that drive nitrogen (N) and C cycling, addressing DOE’s mission to understand and predict the roles of microbes in Earth’s nutrient cycles. By coupling isotope-enabled technologies with next-generation DNA sequencing techniques, the project investigates soil microbial interactions in situ using natural levels of soil complexity. This work will provide a greater mechanistic understanding needed to determine how mycorrhizal fungi influence organic matter decomposition and will shed light on nutrient cycling processes in terrestrial ecosystems.

The arbuscular mycorrhizal association between Glomeromycota fungi and land plants is ancient and widespread; 72% of all land plants form symbiotic associations with AMF. While AMF are obligate symbionts that depend on host plants for C and cannot decompose soil organic matter (SOM), AMF can stimulate the decomposition of SOM and dead plant tissue. Prior Early Career Program research strongly suggests that AMF partner with their microbiome in the zone surrounding hyphae (or “hyphosphere”) to encourage decomposition (Nuccio et al. 2022). The team examined AMF-microbial interactions in reduced-complexity microcosms and the field to assess the impact of AMF on terrestrial C and N cycling processes. In the laboratory, researchers are assessing how AMF and their microbiome impact litter decomposition, while in the field researchers assess how a deeply rooted perennial grass alters the “zone of influence” of AMF in soil depth profiles.

The molecular mechanisms that underpin interactions between AMF and the microbial community during SOM decomposition is a key knowledge gap. To facilitate metabolomics and mechanistic studies of the hyphosphere, the team has developed the MycoChip, a sterile plant-mycorrhizal microcosm for interrogating hyphal-microbial interactions in situ. The MycoChip allows both destructive and nondestructive resampling of hyphosphere communities over time and is optically clear to permit microscopy. This system has two chambers separated by a “raised airgap” containing a dam to prevent solute exchange between chambers and flanked by mesh barriers to block root entry and create a hyphosphere chamber isolated from the rhizosphere. Researchers used the MycoChip to examine how a living microbiome alters AMF exudation and the exometabolome during SOM decomposition. AMF were either allowed or denied access to nutrients (plant litter and bone meal) in the hyphal chamber by using different mesh sizes (31 micrometers and 0.45 µm respectively). To investigate how fungal-microbial interactions impact decomposition, AMF were exposed to “live” vs. “dead” soil in the hyphal chamber. Data analysis is ongoing to investigate how AMF impacts the detritusphere metabolome, microbiome, and plant photosynthesis and growth.

Most knowledge about physiology and ecology of AMF (and most soil organisms) has been learned from surface soils that are less than 20 cm deep. In a national field study, researchers assessed how the rhizosphere of a deep-rooted perennial bioenergy grass—switchgrass (Panicum virgatum)—alters the “zone of influence” of AMF in depth profiles and impacts soil C stocks. Rhizosphere and bulk samples from paired switchgrass and shallow-rooted fields were collected from 2.5 m deep soil cores across nine field sites in the eastern United States. The team characterized the impact of switchgrass on AMF communities, soil organic C, radiocarbon (¹⁴C), root abundance, and a range of soil physical and chemical properties. AMF diversity decreases linearly below 40 cm depth. At most sites, deeply rooted switchgrass extended the habitat of AMF down the soil profile compared to the shallow-rooted controls (maximum AMF depths under switchgrass ranged from ~25 cm to 175 cm). By moving AMF down the soil profile, deep root systems can potentially extend the influence of AMF to impact subsoil C cycling and weathering processes.

This project is designed to identify sorghum genetic factors that drive the formation and function of microbial communities to increase sorghum biomass yield under different environmental conditions. The team will find and characterize sorghum genes/genotypes that can determine optimal crop productivity and durability by creating microbial communities that minimize the need for fertilizer, water, and other inputs.

Plant-microbial interactions play an important role in the success of a plant through processes shaping survival, fitness, and crop yield. The extent to which plants benefit from or are negatively affected by their microbial associations is driven by host and microbial genetics, environmental context, and the interaction between these three contributing factors. As part of a larger research project directed at maximizing the biomass yield of Sorghum bicolor through understanding arbuscular mycorrhizal fungal (AMF) interactions with their host plant, researchers examined in situ responses in a diverse sorghum Bioenergy Association Panel to factorially manipulated nitrogen (N) and phosphorus (P) treatments (Brenton et al. 2016). In 2022, 337 sorghum genotypes were grown across 12 blocks (four fertilizer treatments, three replicate blocks) in a previously fallow field in Watkinsville, GA. Roots were harvested for microbial community analysis after 8 weeks of in-field growth, and aboveground biomass, lodging, and tiller traits were recorded after 5 months.

As expected, strong effects of genotype were found on biomass. N and P treatments, alone, were associated with a minor yet significant reduction in biomass relative to unfertilized controls whereas the combined N and P treatment did not affect yields. Using a subset of three sorghum genotypes with contrasting yield responses across the unfertilized and combined N and P fertilized plots, root-colonizing bacterial and fungal communities were assessed through whole-genome shotgun and targeted amplicon sequencing. As with the aboveground biomass response, there was no observable shift in microbial community composition associated with combined N and P fertilization. The three chosen genotypes also exhibited low intergenotype variability and high intragenotype variability, with individual replicated blocks and therefore field location being the major contributing factor to community composition. Post-harvest N and P soil contents were similar between treatments, though this may be a function of plant nutrient uptake that will be further elucidated by plant nutrient content analysis.

Auxenochlorella protothecoides, a Trebouxiophyte oleaginous alga, is a reference for discovery and a platform for photosynthesis-driven synthetic biology and sustainable bioproduction. The project will expand transformation markers, regulatory sequences, and reporter genes; improve transformation efficiency; and develop ribonucleoprotein-mediated gene-editing methods for genome modification. Systems analyses and metabolic modeling approaches will inform genome modifications for rational improvement of photosynthetic carbon fixation and strain engineering to produce cyclopropane fatty acids. Regulatory factors and signaling pathways responsible for activating fatty acid and triacylglycerol biosynthesis will be identified, and the team will manipulate them to increase lipid productivity. Nonphotochemical quenching and a regulatory circuit for maintaining photosynthesis under copper limitation, both of which are absent in A. protothecoides, will be introduced to improve photosynthetic resilience, and the performance of engineered strains will be modeled.

One approach to inform the design of production strains is the use of metabolic models to identify novel gene targets and reduce the potential solution space to maximize productivity. Using a high-quality genome sequence and highly accurate annotations, researchers have generated a complete metabolic network of A. protothecoides. To have representative biomass formation equations for a variety of growth conditions, the team has measured the macromolecule content and composition of A. protothecoides in autotrophic, mixotrophic, and heterotrophic growth regimes. This data, coupled with experimentally determined uptake and excretion rates, was used to constrain the model. Researchers simulated growth in the different growth regimes and performed a gene knockout analysis to determine gene essentiality and the impact of knockouts on fatty acid production. The complete genome scale model and the simulation results will be presented.

Despite their vital roles in transforming nutrients and controlling greenhouse gas fluxes in wetlands, microbial knowledge is often limited to taxonomic identity alone and rarely includes cross-site comparisons. The team proposed to address this knowledge gap using coordinated, reproducible field measurements collected across a wetland-methane continuum spanning geographical locations. This project tests the overarching hypothesis that microbial genomic attributes are conserved across high methane-emitting wetlands. First, researchers will use a cross-wetland approach to define the microbial membership, physiology, and interactions directly contributing to wetland methane production. Next, the team will uncover the microbial decomposition network features that classify high methane emitting wetlands. Using this information, researchers will test the genomic resolution needed to make robust predictions of regional and global methane fluxes. These integrated field, laboratory, and modeling approaches will identify the unifying microbial properties governing soil carbon decomposition and methane fluxes, such that some level of biological representation in models will enhance predictions of soil methane fluxes.

To illuminate the metabolic features of carbon decomposition in high methane-emitting wetlands, the project created the Multiomics for Understanding Climate Change (MUCC) database. This resource contains the identity, distribution, and functional information of 26,000 microbial genomes from wetlands, including over 500 methanogen and methanotroph genomes. The team coupled MUCC to 133 spatially and temporally resolved metatranscriptomes, along with paired amplicon, geochemical, and greenhouse gas data from over 700 samples collected from one of the most prolific methane-emitting wetlands in the United States. From this site, researchers unveiled previously unrecognized roles of archaea in carbon decomposition, resistance to redox changes exhibited by methane-cycling community members, and the delineation of metabolic networks contributing to methane production. This standardized sampling and sequencing design set the stage for future cross-site comparisons, enhancing the ability to generalize findings across wetlands.

Extending the sampling from a single wetland, coordinated field sampling in year 1 added paired greenhouse gas measurements, soil chemistry, and multiomics from 12 additional wetlands (three bogs, four fens, five marshes). Cross-wetland analysis of initial findings showed that in contrast to current paradigms, where acetolactic and hydrogenotrophic are the predominant forms of methanogenesis, methylotrophic methanogenesis is prevalent and active. Across sites genome-resolved metatranscriptomes showed that members of the Methanomassiliicoccales, Methanosarcinales, Methanomethyliales, and Methanobacteriales were expressing methylotrophic genes, while metabolomics revealed new methylotrophic substrates (e.g., syringate, trimethyllysine). Further corroborating these findings, the team established enrichments of wetland soils dosed with either carbon-13 (¹³C) dimethylsulfide or ¹³C acetate to demonstrate that field-relevant methylotrophic lineages were enriched in reactor ¹³C-labeled proteomes. Likewise, methylated substrates considerably contributed to methane production (on average 53%), demonstrating that methylotrophic methanogenesis is a likely contributor to methane across global freshwater wetlands and should be included in process-based models.

To integrate this new microbial knowledge into ecosystem scale models, researchers built genome-scale metabolic models (GEMs) from MUCC genomes. MUCC GEMs combine field-derived multiomics data, enrichment-derived isotopic evidence, reference genomes, and fundamental physical and thermodynamic principles to produce the most accurate metabolic representation of each strain observed in the MUCC samples. These GEMs utilize a novel probabilistic framework that captures the metabolic diversity of each metagenome assembled genome (MAG) by consolidating phylogenetically close references into a single GEM for simulation, allowing for the preferential simulation of common, conserved pathways while permitting niche pathways if conditions and constraints require it.

GEMs undergo initial testing for internal consistency, aligning with available experimental data. Then GEMs are merged into sample-level community models where GEMs are further refined to maximally recapitulate experimental data. This process tailors each sample to have a unique MAG model for each strain, whose metabolic behavior is described as how each strain transforms nutrients into biomass and byproducts. These whole-cell reactions constrained by relative abundance from multiomics data will ultimately be integrated into ecosystem-scale simulations. MUCC and the corresponding GEMs are publicly available on the DOE Systems Biology Knowledgebase (KBase), engendering collaborative enterprises with the goal to advance wetland climate-driven research. Ultimately, this research illuminates the metabolisms influencing the methane cycle, offering a direction for increased realism in predictive models of greenhouse gas emissions.

Microorganisms play key roles in soil carbon (C) turnover and stabilization of persistent organic matter via their metabolic activities, cellular biochemistry, and extracellular products. Microbial residues are the primary ingredients in soil organic matter, a pool critical to Earth’s soil health and climate. The team hypothesizes that microbial cellular chemistry, functional potential, and ecophysiology fundamentally shape soil C persistence, and researchers are characterizing this via stable isotope probing (SIP) of genome-resolved metagenomes and viromes. The project focuses on soil moisture as a “master controller” of microbial activity and mortality, since altered precipitation regimes are predicted across the temperate United States. The Science Focus Area’s (SFA) ultimate goal is to determine how microbial soil ecophysiology, population dynamics, and microbe-mineral-organic matter interactions regulate the persistence of microbial residues under changing moisture regimes.

The intensity and timing of precipitation not only affects soil microbial community composition and microbial ecological strategies, but also microbial-controlled decomposition and soil carbon dioxide efflux. However, scientists do not have a mechanistic understanding of how altered soil moisture regimes will affect soil C persistence or microbial population dynamics. Conducting a wet-up experiment on soils previously subjected to either historical average precipitation (100%) or a 50% water reduction, researchers employed a multiomics approach (16S-quantitative stable-isotope probing, metagenomics, viromics, metatranscriptomics, metabolomics, and lipidomics) to elucidate the mechanisms governing microbial response following wet-up.

Determining the relationship between bacterial traits and life strategies is a crucial step in linking soil microbial communities to ecosystem function. Genomic traits, such as genome size, codon usage, and nucleotide selection, may be particularly useful trait dimensions as they are relatively easy to measure and provide insight into the evolutionary forces which shape bacteria. In response to rewetting, researchers found that metagenome assembled genomes (MAGs) with high levels of codon bias and cheaper nucleotides in their ribosomal protein genes had high growth rates shortly after rewetting. The team also found that MAGs with smaller genomes had higher growth rates than larger genomes one week post rewetting. Together, these results point towards a set of genomic characteristics that are potentially important for bacteria in highly dynamic soil environments.

The project found that legacy precipitation (50 vs. 100% mean annual precipitation; MAP) had the largest impact on microbial population dynamics, where 100% MAP was associated with higher overall growth and mortality rates and greater changes in gene expression across multiple metabolic pathways. Similarly, metabolomics data showed a greater magnitude of changes in metabolite composition after rewetting at 100% MAP. Results suggest that decreased legacy moisture leads to decreased metabolic and growth potential for microbes following dry down, impacting which taxa are able to rapidly respond to rewetting.

The team posits that interactions between the soil water cycle, nutrient availability, and microbial predators (bacterivores, viruses) may also control a significant proportion of soil C flux, but this nascent research area is another major knowledge gap. Researchers detected 172 protist taxa and found that average population growth at 80% field capacity was 4.4 times greater than at 20% field capacity (3.2% vs. 0.15% per day, respectively), suggesting that protists’ growth activity is highly sensitive to soil moisture. Further, researchers found soil viruses exhibited sensitivity to nutrient availability upon rewetting. The introduction of phosphorus diminished the overall richness of RNA viruses, yet it concurrently triggered an upsurge in specific bacterial viruses. The increase of bacterial viruses may be attributed to the alleviation of stress experienced by bacterial hosts due to phosphorus limitation. Conversely, fungal viruses exhibited no significant change, potentially indicating the resilience of fungal mycelial networks in nutrient movement.

In summary, the project identified important genome-level traits predictive of microbial growth response to rewetting. The team also found that differences in legacy precipitation can influence microbial activities long after changes in soil moisture are no longer detectable, and microbial predators are sensitive to changes in soil moisture and nutrient changes.

Bioenergy with carbon capture and storage (BECCS) is explicitly being put forth as a cost-effective strategy to reconcile negative emissions targets with a sustainable energy supply. The deployment of BECCS at scale, however, raises concerns over land displacement, compensatory agricultural expansion (indirect land use change; ILUC), and the derived toll on emissions savings. ILUC can be minimized by targeting energy feedstock production on set-aside land. However, whether energy feedstocks can be sourced without incurring self-defeating emissions from land conversion remains unclear. The first goal of this study was to evaluate the emissions cost of sidestepping the drawbacks of ILUC. In addition, at present there is an important mismatch between low-carbon (C) scenarios that rely on sustained negative emissions and the emissions reduction pledges that lead international climate action. Further, uncertainty in effective C removal rates and the political appeal of energy independence may make energy targets more marketable and prioritize ethanol yields in climate portfolios. Therefore, the second goal was to examine the tradeoffs of the strategic deployment of BECCS targeting these intimately linked yet inherently different priorities.

BECCS sits at the nexus of climate and energy security. The project evaluated tradeoffs between scenarios that support climate stabilization (e.g., negative emissions and net climate benefit) or energy security (e.g., ethanol production). Further, central to the sustainable deployment of BECCS, the team estimated the cost of sidestepping ILUC, including disturbance emissions (C debt) and foregone greenhouse gas emission savings from the displaced system (opportunity cost). The project’s spatially explicit biogeochemical-life-cycle model indicates that the opportunity cost increased C emissions per unit of energy produced by 14 to 36%, roughly doubling breakeven times for the initial C debt and making geologic C capture and storage necessary to achieve negative emissions from any given energy crop. The toll of opportunity costs on the climate benefit of BECCS from set-aside land was offset through spatial allocation of crops based on their individual biophysical constraints. Dedicated energy crops consistently outperformed mixed grasslands. Researchers estimate that BECCS allocation to land enrolled in the Conservation Reserve Program (CRP) could capture up to 9 teragrams of C per year from the atmosphere, deliver up to 16 TgCE per year in emissions savings, and meet up to 10% of the U.S. energy statutory targets, but contributions varied substantially as the priority shifted from climate stabilization to energy provision. An energetically optimal deployment would generate 13.3 billion liters of ethanol annually but would reduce negative emissions by 21% and the net climate benefit of BECCS by 15% relative to alternative optimization strategies. Results indicate a significant potential to integrate energy security targets into sustainable pathways to climate stabilization but highlight the tradeoffs between divergent policy-driven agendas.

The overall goal of this project is to develop viral vectors for delivering gene editing reagents and creating somatic and heritable mutations through infection in sorghum. Specifically, this work will:

• Engineer and optimize novel plant RNA viruses to express heterologous proteins and CRISPR-Cas gene editing reagents.
• Test systemic infections by tracking fluorescent protein “AmCyan” and determine the most effective plant RNA virus.
• Develop viral vectors with a large genome cargo size and the ability to infect meristem or germline cells. Determine the viral cargo capacities and the stability of viral cargo at mRNA and protein level.
• Develop a nontransgenic genome editing platform based on the engineered RNA virus vectors. Optimize novel viral vectors to express Cas9 endonucleases and gRNAs simultaneously from the viral genome.
• Test different transformation techniques (particle bombardment, agro-injection, viral sap rub inoculation) to co-inoculate several viruses to determine their synergistic interaction to increase meristematic viral infection and targeted mutagenesis frequencies.

Transformation is an important step in genome editing. The requirement for in vitro tissue culture and regeneration limits the technology’s application to commercially relevant varieties of many crop species. To overcome this issue, plant viruses have recently been used as vectors for foreign gene expression, endogenous gene silencing, and delivering gene editing reagents to induce mutations mostly in Cas9-expressing transgenic plants, especially in dicots. However, a limited number of viruses have been developed into viral vectors for the purposes of gene editing in monocotyledonous plants. The team engineered a set of (monopartite) Foxtail Mosaic Virus (FoMV) and (tripartite) Barley Stripe Mosaic Virus (BSMV) vectors to deliver the fluorescent protein “AmCyan” to track viral infection and movement in Sorghum bicolor. Researchers further used these viruses to express and deliver single guide RNAs (sgRNAs) to Cas9-expressing transgenic sorghum lines, targeting S. bicolor Phytoene Desaturase (PDS), Magnesium-Chelatase (MgCh) and Lemon White (Lw) genes. BSMV was unable to infect sorghum and express “AmCyan” or deliver sgRNAs. In contrast, FoMV systemically infected the sorghum lines and induced somatic mutations at frequencies up to 60%, which produced phenotypes that were visibly distinguishable from the wild type, indicating the potential applications of this virus for in planta gene editing and functional genomics studies in sorghum. Initial research indicates that FoMV vectors can be further engineered to gain the ability to induce mutations in the germline as well.

The information and tools generated from this project will address the need for the exploration of interkingdom interactions, specifically viruses, which has been highlighted as a priority by the Biological Systems Science Division strategic plan. The project seeks to develop a dynamic visualization tool that will rapidly allow for the identification of plant-microbe linkages. Through inter- and intracellular interrogation of viral-mediated signaling, regulation, and communication within plant-microbe interactions, the team aims to determine and predict how the soil virome affects ecological functions in soil and modulates global nutrient cycling.

Plant phenotypes are influenced by their microbiome, which consists of a dynamic consortium of bacteria that can provide benefits to the plant such as increased nutrient availability and stress resilience. There are well-studied examples of bacteria that have positive and negative effects on plants. There is also circumstantial evidence that viruses infecting these bacteria (bacteriophages) can alter their metabolism, as observed where phages are integrated into bacterial genomes (prophages). However, while viruses are ubiquitous, diverse, and the most abundant biological entities on the planet, their role in modulating plant-associated microbiomes remains poorly understood. The potential of viruses to impact global elemental cycles at massive scale is exemplified by the discovery of a “viral shunt” mechanism in marine ecosystems, where viral activity redirects nutrients and causes the release of up to three gigatons of carbon annually (Breitbart et al. 2018). A similar phenomenon has been described in soil systems, where viruses can influence nutrient availability and plant productivity (Wang et al. 2022).

Despite the clear importance of viruses in soil microbiomes, their role in regulating microbe-microbe and plant-microbe interactions in the rhizosphere (the microenvironment at the interface of roots and soil) is unknown. To understand these processes, it is necessary to study the properties of plants, bacteria, and viruses within the context of a multipartite functional system, establishing links between viruses and the ability of infected bacteria to colonize plant roots and influence plant phenotypes.

A recent Laboratory Directed Research and Development (LDRD) Lawrence Berkeley National Laboratory award has enabled the team to identify a viral-bacterial-plant tripartite system where prophages of the root-colonizing bacterium Pseudomonas simiae WCS417 were detected and experimentally determined to be active. The project also leveraged high-throughput mutagenesis (randomly barcoded transposon insertion sequencing; RB-TnSeq) to evaluate the potential for viral genes to modulate the colonization efficiency of their bacterial host. Two of these genes that cause reduced fitness in the rhizosphere when mutated are components of a latent bacteriophage and are present among two phage loci ranging in size from 15 to 65 kilobase pairs.

Using a loss of function approach, researchers generated green fluorescent protein–labeled phage gene deletion mutants to conduct experimental characterization studies such as competition tests, root colonization assays, and phenotypic comparative assessments. The team identified clear changes in metabolic profile between no bacteria controls and bacteria treatments from pilot in vivo targeted metabolomics experiments conducted in liquid growth media. These findings suggest the possibility that bacteriophages are involved in modulating the ability of bacteria to colonize plants. This approach therefore supports understanding and predicting how viruses may impact a given microbiome. However, extending understanding of these relationships more broadly across the rhizosphere is limited by the ability to connect the diversity of bacteria and prophages, as it relates to plants.

The project proposes to further understand the role of soil viruses in modulating plant-associated bacteria and to shed light on interkingdom signaling, resource sharing, and global nutrient cycling. By using an integrated computational and experimental design, the team seeks to understand how viruses modulate plant-microbe interactions, contribute to nutrient cycling, and work in response to dramatically altered water availability wrought by a changing climate.

Enhance understanding of the economic viability of noncombustion lignin utilization routes, and determine the minimum conversion threshold from lignin stream to product that must be achieved.

Efficient lignin conversion is vital to the production of affordable, low-carbon fuels and chemicals from lignocellulosic biomass. However, lignin conversion remains challenging, and the alternative (i.e., combustion) can emit harmful air pollutants. This study explores the economic and environmental tradeoffs between lignin combustion and microbial utilization for producing bisabolene as a representative biobased fuel or chemical. Considering three different biomass feedstocks—biomass sorghum, switchgrass, and clean pine—the project primarily addressed two important questions: (1) what quantity of lignin must be utilized by the host microbe to render the strategy of co-utilizing sugars and lignin-derived bioavailable intermediates economically feasible and (2) what proportion of lignin can be utilized while still achieving the Renewable Fuel Standard life-cycle greenhouse gas (GHG) emissions reduction goal of a 60% reduction relative to petroleum equivalent.

Results for switchgrass and clean pine–based biorefineries show that using lignin to increase fuel yields rather than combusting it reduces the capital expenditures for the boiler and turbogenerator if the facilities process more than 1,100 bone-dry tons (bdt) feedstock per day and 560 bdt/day, respectively (Baral et al. 2023). No comparable advantage was observed for lower-lignin sorghum feedstock. Deconstructing lignin to bioavailable intermediates and utilizing those small molecules alongside sugars to boost product yields is economicallyattractive if the overall lignin-to-product conversion yield exceeds 11 to 20% by mass (Baral et al. 2023). Although lignin-to-fuel/chemical conversion can increase GHG emissions, most of the lignin can be diverted tofuel/chemical production while maintaining a >60% life-cycle GHG footprint reduction relative to diesel fuel(Baral et al. 2023). The results underscore that lignin utilization can be economically advantageous relative to combustion for higher-lignin feedstocks, but efficient depolymerization and high yields during conversion are both crucial to achieving viability.

The interactions between plants and their microbiomes, specifically arbuscular mycorrhizal fungi (AMF) and nitrogen-fixing bacteria (NFB), play a crucial role in supporting host nutrition, immunity, and development. The project aim is to uncover the genetic factors in sorghum that impact the development and effectiveness of microbial communities in different environmental disturbances. Large-scale farming practices commonly depend on water and chemical fertilizers, neglecting the potential advantages of microbiomes in enhancing plant ability to absorb soil water and nutrients and the current implications of irrigation and the utilization of chemical fertilizers on both the economy and the environment. Through a comprehensive analysis, the project’s main goal is to identify and characterize sorghum genotypes that can enhance crop productivity and resilience by establishing microbial communities to reduce farmers reliance on water and chemical fertilizers.

To address the knowledge gap regarding the influence of nutrient availability on microbiomes in multiple compartment niches (rhizosphere, soil, and root), researchers collected samples from an existing genome-wide association study (GWAS) field experiment to examine the differential response of bacteria versus fungi associated with biofuel sorghum genotypes to nitrogen (N) and/or phosphorus (P) inputs. Further, the team will investigate the mechanisms by which sorghum plants maintain a stable presence of AMF and phosphate solubilizing bacteria (PSB) in their root structures, even when the benefits of this symbiotic relationship for nutrient uptake may be compromised. The project will test three hypotheses.

1. The N or P inputs will have distinct impacts on the development of soil microbiome, particularly resulting in a decrease in the abundance of NFB (diazotrophs) and P uptake fungi (AMF, edaphophilic), but the root microbiomes will deviate from these trends. The team postulates that these outcomes may result from biotic filtering of the host, where roots separate the assemblage of microbiome communities associated with sorghum roots from the overall soil community.
2. The co-occurrence networks of AMF and PSB will show higher modularity when P is in limited supply compared to when in luxury supply.
3. The responses to dual N and P input will be less resistant across different compartments, but the strongest resistance will be observed in the root microbiomes. These findings will provide valuable insights for optimizing microbial communities to improve plant health and productivity in both agricultural and ecological contexts.

Algal and plant systems have the unrivaled advantage of converting solar energy and CO2 into useful organic molecules. Their growth and efficiency are largely shaped by the microbial communities in and around them. The μBiospheres Science Focus Area seeks to understand phototroph-heterotroph interactions that shape productivity, robustness, the balance of resource fluxes, and the functionality of the surrounding microbiome. We hypothesize that different microbial associates not only have differential effects on host productivity but can change an entire system’s resource economy. Our approach encompasses single cell analyses, quantitative isotope tracing of elemental exchanges, omics measurements, and multi-scale modeling to characterize micro-scale impacts on system-scale processes. We aim to uncover cross-cutting principles that regulate these interactions and their resource allocation consequences to develop a general predictive framework for system-level impacts of microbial partnerships.

In 2002, Lawrence Livermore National Laboratory (LLNL) installed a NanoSIMS 50 for the study of microbial ecology. NanoSIMS was unproven technology at the time, but it was rapidly adopted for microbial ecology because of its ability to trace isotopically labeled substrates into microbial communities. One limitation, however, has been that samples have to be prepared for high vacuum, which results in the loss of soluble species and can cause significant sample alteration. To overcome this limitation, LLNL worked with the manufacturer, CAMECA Instruments, to develop the next-generation NanoSIMS with a cryogenic stage. This winter at LLNL, CAMECA began the process of installing the prototype product of that collaboration: a first-of-its-kind cryo-NanoSIMS. This unique instrument has an analysis stage that can be cooled to liquid nitrogen temperatures and a cryogenic system for sample handling and introduction. With this cryogenic capability, we will be able to analyze frozen hydrated samples, which will capture soluble molecules that were previously lost during room temperature sample preparation methods. Sample freezing can also maintain spatial relationships among organisms without the addition of embedding resins that remove soluble molecules and mask the organic molecules that we seek to detect.

Another remarkable feature of the new cryo-NanoSIMS is its new 10 nm cesium ion source, which is used to image and analyze samples. This new cesium ion source generates an ion beam with unprecedented intensity, allowing the LLNL prototype cryo-NanoSIMS to achieve 10 nm spatial resolution (see Figure). This new cesium ion source allows us to resolve structures down to the size of a single phage.

In addition, the prototype cryo-NanoSIMS has been upgraded to improve useability and throughput, including redesigned electronics, sample stage, optical imaging, and sample introduction systems. The new electronics allow low energy sample analysis, improving the ability to resolve small structures by increasing depth resolution. The stage now has optical encoding and achieves better than 500 nm reproducibility, allowing 2 to 100 times faster automated analysis of selected targets, depending on the target size and analysis duration. The optical imaging system can now resolve micron-scale features and be used for automated sample registration, easing navigation. An automated sample introduction further increases ease of use and productivity.

We will be using our cryo-NanoSIMS for a wide range of applications, including host-microbe interactions in bioenergy relevant systems and soil carbon cycling. We will presen tperformance data, including initial analyses of microbial associations in perennial grasses and microalgae.

Investigate metabolic syntropy between anaerobic methane oxidation (AOM) archaea with sulfate-reducing bacteria (SRB). Design a coupled methane (archaea)/sulfate electron transport chain (ETC) model. Evaluate the interaction between diverse strains of AOM archaea and SRB.

Microbial communities of methanotrophic archaea (ANME) and sulfate-reducing bacteria (SRB) annually prevent the release of gigatons of methane into the environment and are therefore critical agents in climate regulation and geochemical cycling. The “reverse methanogenesis” of methane oxidation in ANME, which requires electron transfer to a syntrophic partner, is the proposed syntrophic mechanism that drives sulfate-coupled anaerobic oxidation of methane in these communities; however, their physiology, interactions, and ecology remain opaque.

To model this metabolic system and resolve mechanistic details, we improved our reconstruction pipeline for all archaea and bacteria to construct genome-scale metabolic models: the ModelSEED2. Our new archaea pipeline now captures unique pathways and reaction intermediates to archaea. We concurrently developed a suite of community modeling tools to mechanistically simulate syntrophic interactions within this community under native conditions, which is essential to contextualize the ecological roles of ANME and SRB. These community modeling tools permit the parameterization of omics data that represent metabolic phenotypes. Our method allows us to better recapitulate community dynamics with thermodynamic and uptake constraints. Further, we additionally developed new tools to leverage pangenome information from phylogenetically close strains to improve model reconstruction for metagenome-assembled- genomes (MAGs), which are often incomplete when analyzed on their own. These tools are critical for modeling ANME strains because they cannot be isolated in the laboratory and are thus all MAGs. Due to the limited biomass available in these systems, ANME MAGs are also often incomplete. To overcome this challenge, we applied a pangenome-based approach to enhance our ANME MAG models to include all core genes from the pangenome, boosting the size of our ANME models by hundreds of conserved reactions while still preserving the distinctive metabolic features that distinguish each ANME clade. We construct metabolic models of several ANME and SRB MAGs that were assembled and binned from metagenomic data from previous studies (Chadwick et al. 2022; Murali et al. 2023). We implemented an energy metabolism pathway that couples anaerobic methane oxidation with the sulfate reduction pathway.

The improved annotation accuracy of these models will empower community simulations towards resolving the “reverse methanogenesis” hypothesis, which may explain the natural stability and selectivity of these communities and would ultimately clarify anthropogenic influences on these keystone communities and biogeochemical cycles in marine environments. We perform a detailed accounting for the flow of nutrients and energy within our community model to mechanistically explain low yields and slow growth in these systems.

The Integrative Modeling and Genome-scale Engineering for Biosystems Security (IMAGINE BioSecurity) Science Focus Area seeks to establish an understanding of the behavior of engineered microbes in controlled versus environmental conditions to predictively devise new strategies for responding to biological escape. To this end, the IMAGINE Team has established a plant-soil mesocosm platform to track and quantify the fate of industrial microbes in environmental systems and assess the efficacy of biocontainment constraints upon genetically engineered microbe escape frequency and the impact of industrial microbes upon native ecological microbiomes.

Genetically modified industrial production microbes and their associated bioproducts have emerged as an integral component of a sustainable bioeconomy. However, the rapid development of these innovative technologies raises biosecurity concerns, namely, the risk of environmental escape. Thus, the realization of a bioeconomy hinges not only on the development and deployment of microbial production hosts, but also on the development of secure biosystems and biocontainment designs. Current laboratory-based biocontainment testing systems do not accurately reflect complexities found in natural environments, necessitating an environmentally relevant–analysis pipeline that allows for the detection of rare escapees, the effect of associated bioproducts, and the impact on native ecologies. To this end, the team has developed an approach that utilizes soil mesocosms and integrated systems analyses to evaluate the efficacy of novel biocontainment strategies and assess the impact of production systems upon terrestrial microbiome dynamics. The project demonstrates the utility of this approach by modeling a contamination with industrial microbial chasses versus their biocontained counterparts. Here, researchers demonstrate the broad utility of this system by highlighting findings from both strains of Saccharomyces cerevisiae that are contained with an inducible toxin anti-toxin system and strains of Escherichia coli that are contained via genomic recoding. The resultant data demonstrate that this system has broad utility across diverse microbial chassis and biocontainment strategies. The data also enable tracking the fate of the contaminating microbe with high sensitivity in the soil and monitoring broader impacts of the perturbation on the underlying soil system. The findings presented here support the use of this mesocosm-based approach to assess the environmental impact of industrial microbes and to validate biocontainment strategies.

This project is focused on understanding how encapsulin nanocompartment systems can be used to enhance the biosynthesis of next-generation biomaterials in Rhodococcus species. The project seeks (1) to probe the mechanistic basis for how these compartments are regulated, biosynthesized, and maintained and (2) to engineer these systems to achieve new biosynthetic functions.

With recent innovations in synthetic biology, engineered microbes now have the potential to produce a wide variety of bioproducts from renewable sources (e.g., biomass) to support the U.S. bioeconomy. Biosynthetic pathways leading to these products are often hindered by poor reaction efficiencies and toxicity, however, resulting in low yields and impure products. Compartmentalization of these pathways has the potential to overcome these challenges through co-localization, concentration, and sequestration. The goal of this early career research is to identify mechanisms for engineering compartmentalized biosynthesis in the emerging model bioproduction bacterium, Rhodococcus opacus PD630, using its native encapsulin nanocompartment system (herein called encapsulins). Toward this goal, we have integrated a fluorescent reporter into the R. opacus genome under the control of the native encapsulin promoter. With this, we are investigating the regulation, biosynthesis, and maintenance of the native encapsulin system using growth assays and transposon mutagenesis.

These studies will uncover the native pathways that govern encapsulin synthesis with the goal of ultimately harnessing these pathways for improved recombinant encapsulin formation and yield. We are also developing novel, high-throughput methods for the engineering of encapsulins to systematically identify optimal insertion locations and sequences, as well as easily modulate their properties. This will enable us to quickly tailor the properties of encapsulins to process requirements, greatly enhancing the utility of this system. As a case study, the R. opacus encapsulin system will be redirected to support and control the biosynthesis of cadmium sulfide nanoparticles, semi-conducting materials used in optical and electronic applications. Ultimately, this work will establish encapsulin compartmentalization systems as a means of improving yields and enabling new biosynthetic routes toward next generation bioproducts and biomaterials, in support of the DOE’s mission to build a strong bioeconomy.

The primary aim of this proposal is to deepen our comprehension of the molecular and cellular mechanisms governing associative nitrogen fixation traits within the mucilage of sorghum plants that form aerial roots. This will be achieved through a multifaceted approach encompassing genetics, synthetic bacterial communities, and systems biology. Our overarching hypotheses posit that both plant and bacterial gene networks play pivotal roles in regulating nitrogen fixation efficiency in sorghum, and unraveling these networks will facilitate the enhancement of nitrogen fixation in sorghum, thus advancing its potential for bioenergy production.

Current agricultural practices rely heavily on applying nitrogen-rich fertilizers, posing significant environmental risks, including pollution of ground and surface water, nitrous oxide emissions, and greenhouse gas emissions during the production of ammonia-based fertilizer. However, adopting biological nitrogen fixation presents a promising avenue for mitigating these risks. Sorghum (Sorghum bicolor L. Moench) is gaining recognition as a sustainable crop due to its resilience to drought and high temperatures, and certain types of sorghum produce high yields of lignocellulosic biomass that can be used to produce renewable fuels and chemicals. Select sorghum accessions exhibit prolific growth of thick aerial roots that secrete a dense, carbohydrate-rich mucilage following rainfall and in humid conditions (Venado et al. 2023). This mucilage is an optimal environment for diazotrophic microorganisms that provide the plant with ammonium. To improve sorghum’s ability to support diazotrophs, breeding programs require a detailed understanding of the genetic factors influencing aerial root development and mucilage production. In pursuit of this objective, we conducted a comprehensive genome-wide association study (GWAS) on the sorghum minicore that consists of 242 landraces and 30 accessions from the sorghum association panel at two locations (Florida and Wisconsin) and with a standard and reduced fertilizer treatment at each location. Through this GWAS, we identified loci associated with the phenotypes of aerial root diameter and the number of nodes with aerial roots. Sequence variations within genes responsible for encoding transcription factors governing phytohormone signaling and root system architecture were associated with these traits (Wolf et al. 2023). In parallel, several breeding populations were developed from crosses between accessions that produce aerial roots and regionally adapted sweet sorghums. Segregating F2 populations were used to validate some of the loci identified in the GWAS, whereas continued inbreeding and selection are expected to result in bioenergy sorghum cultivars capable of obtaining a portion of their nitrogen needs from biological nitrogen fixation. Furthermore, we conducted single-cell RNA sequencing (scRNAseq) on sorghum aerial roots, comparing those with and without mucilage. This analysis uncovered novel gene markers specific to different cell types. Leveraging our scRNAseq data, we constructed gene regulatory networks using various algorithms, including single-cell Multi-Task Network Inference (scMTNI). This approach will enable us to explore genes essential for mucilage production further. Together, these results offer opportunities for enhancing biological nitrogen fixation in cereal crops and reducing our reliance on synthetic fertilizers.

The Persistence Control Science Focus Area (PerCon SFA) at Pacific Northwest National Laboratory (PNNL) seeks to understand plant-microbiome interactions in bioenergy crops to establish plant growth–promoting microbiomes that are contained to the rhizosphere of a target plant. This vision requires the discovery of exudate catabolism pathways from plant roots, the elimination of genes that support fitness in bulk soil environments without decreasing rhizosphere fitness, and the engineering of rhizosphere-niche occupation traits in phylogenetically distant bacteria. The team anticipates the impacts of these efforts will be to increase understanding of plant-microbe interactions and to extend high-throughput systems and synthetic biology tools to nonmodel microbes.

The rhizosphere represents a critical zone of interaction between plant roots and soil microbiota, harboring complex biotic interactions that are essential for plant and soil health. The intricate nature of these interactions becomes particularly evident under environmental stress conditions such as drought. Though scientists know the soil microbiome changes as soil depth increases, previous research identifying drought-induced shifts in microbial abundance and root exudate composition used homogenized rhizosphere samples, losing spatial resolution.

With the increasing prevalence of drought conditions due to climate change, it is imperative to understand its impact on the rhizosphere. This study aims to elucidate the changes in microbe-metabolite interactions along the rhizosphere column of the bioenergy crop sorghum under drought stress.

The RhizoGrid, a spatial root cartography experimental system, was deployed to monitor variations in root physiology, microbial community assembly, and interactions with root exudates using planar and axial spatial sampling under drought and control conditions.

Investigation reveals a significant spatial organization within the healthy rhizosphere. Largely influenced by the enrichment of multiple microbial taxa at shallow depths, Flavisolibacter, Lysobacter, and Ramlibacter genera exhibited noticeable spatial variation, decreasing in abundance in the lower half of the rhizosphere soil column. Comprehensive analysis highlighted drought-induced shifts in rhizosphere community composition, with a marked decrease in taxa diversity, root exudate complexity, and an increase in intraplanar beta diversity across depth. Alterations in plant root physiology, characterized by reduced root mass and number along the RhizoGrid soil column, and machine learning network analysis of spatial microbe-metabolite patterns define the assembly of a drought-distinct microbiome state in the lower half of the soil column marked by a depletion of various Proteobacteria and a reduction in classes of benzenoid metabolites.

These findings underscore the importance of spatial resolution in assessing the rhizosphere’s response to drought, providing valuable insights into the resilience of soil ecosystems and recontextualizing previous work. The team believes this approach will be a model for high-resolution investigation of plant-microbe interactions subjected to environmental stress or the introduction of beneficial or pathogenic agents.

We aim to reduce the dependency of bioenergy production on synthetic nitrogen fertilizers by taking better advantage of biological nitrogen fixation. We specifically focus on nitrogen fixation in the mucilage produced by aerial sorghum roots (Sorghum bicolor). Alongside our collaborators exploring this plant trait, we investigate the sorghum-associated bacterial communities that contribute to biological nitrogen fixation. Our plan includes (1) isolating and characterizing bacterial strains, including diazotrophs, from aerial root mucilage; (2) assessing bacterial interspecies interactions that influence biological nitrogen fixation; and (3) developing and testing synthetic communities with robust biological nitrogen fixation capabilities.

Sorghum (S. bicolor) is a promising bioenergy crop due to its high biomass yield, resilience to harsh environmental conditions, and ability to grow in diverse geographical regions. However, sorghum production relies on synthetic nitrogen fertilizers, which have negative economic and ecological consequences such as leaching and greenhouse gas production. We identified sorghum accessions harboring high rates of biological nitrogen fixation in a carbohydrate-rich gel/mucilage produced by their aerial roots. We demonstrated that high rates of nitrogenase activity occur in the mucilage using acetylene reduction assays and the transfer of this fixed nitrogen to the plant using nitrogen (¹⁵N) gas enrichments. We also determined that these sorghum accessions obtain up to 43% of their nitrogen from the atmosphere using ¹⁵N isotope dilution experiments (Venado et al. 2023). In this part of this DOE-funded project, we aimed to (1) isolate bacteria, including diazotrophs, from the sorghum mucilage; (2) explore bacteria interspecies interactions using synthetic communities (SynComs); and (3) explore the potential of engineering diazotrophs from the mucilage to further enhance nitrogen fixation (Chakraborty et al. 2023;Venkataraman et al. 2023).

1. We isolated over 200 unique bacteria from sorghum mucilage. We assessed carbon utilization, plant growth-promoting activities, and nitrogenase activity of 34 promising diazotrophs along with Azospirillum brasilense FP2 and Klebsiella variicola A3 as reference strains. Among the 34 new strains tested, at least 23 showed robust nitrogenase activity, with 11 strains showing higher nitrogenase activity than our two reference Pairwise coculture assay indicated that several strains, including non-diazotrophs, enhanced the nitrogenase activity of A. brasilense and K. variicola, indicating that sorghum mucilage harbors “helper strains” in addition to diazotrophs.
2. We used a SynCom approach to investigate the bacterial interspecies interactions impacting biological nitrogen fixation. We assessed community nitrogenase activity and time-resolved composition from 93 different subcommunities of PComm1, leading to a dataset containing a range of diazotroph growth and fixation profiles. We found that nitrogenase activity is positively correlated with diazotroph abundance. However, there were several notable exceptions, suggesting the presence of interspecies competition impacting community fixation that is not growth-mediated. We observed pairwise cocultures containing one diazotrophic and one non-diazotrophic species can lead to improved nitrogenase activity compared to the monoculture, indicating again the presence of strains that can function as helpers for diazotrophs in a species-specific We are further exploring this dataset using a paired modeling approach.
3. We genetically engineered Klebsiella strains isolated from the mucilage to fix more nitrogen and release this fixed nitrogen as ammonium. Using biosensors, some of these “ammonium-excreting” diazotrophs were further engineered to release nitrogen only in the presence of arabinose, an abundant sugar in the sorghum mucilage, to improve the fitness of the engineered strains. Additional biosensors are currently being investigated.

Altogether, our project allowed us to identify efficient diazotrophs from the sorghum mucilage, better understand interactions between bacteria within this unique environment, and engineer these bacteria to increase further nitrogen fixation rates and the delivery of fixed nitrogen to sorghum and enhance the efficiency and sustainability of bioenergy production.

The goal of Center for Restoration of Soil Carbon by Precision Biological Strategies (RESTOR-C) is to harness plants and microbes to increase carbon flux into soil carbon storage pools to form persistent carbon that is stable for >100 years. This will address the Carbon Negative Shot goal to remove carbon dioxide (CO₂) from the atmosphere and durably store it at meaningful scales for less than $100 per net metric ton of CO₂-equivalent within a decade.

Improving root traits is an important research area for soil carbon sequestration. To accelerate this research, we have developed the EcoBOT, an innovative robotic system designed for plant imaging including growth and monitoring of plants in specialized devices called EcoFABs that enable detailed root scans. The EcoBOT allied to EcoFABs can generate many hundreds of root scans each week and so automated computer vision tools based on machine learning are needed to rapidly process the data. To address this challenge, we are building RhizoNet, a deep learning–based workflow tailored for precise semantic segmentation of plant root imagery, including modules for analysis and measurements of root biomass growth. RhizoNet overcomes many challenges by employing a sophisticated Residual U-Net architecture that significantly improves prediction accuracy. This is complemented by a convex hull operation aimed at precisely delineating the primary root component over time, thus facilitating a more accurate assessment of root biomass and its growth. Its robust root detection model has demonstrated generalization capabilities across a wide range of experimental conditions, underscoring its utility in standardizing and objectifying the analysis of thousands of root images. By integrating RhizoNet into EcoBOT’s operational framework, the process of acquiring and analyzing root scans can be significantly streamlined, reducing the need for manual intervention, and thereby increasing throughput and accuracy in root growth studies. This automation is crucial for real-time monitoring and autonomous decision- making. Furthermore, the application of RhizoNet to plant root analysis highlights the broader implications of semantic segmentation technologies fields to optimize plant growth, enhance crop yields, and contribute to sustainable agricultural practices.

Most of the Earth’s terrestrial carbon is stored in soil organic matter (SOM), and new SOM is derived largely from microbial necromass (Liang and Balser 2011). Necromass forms when microbes produce extracellular compounds, like extracellular polysaccharides and enzymes, or succumb to environmental stress and lyse. Because most soil microbes live attached to surfaces, necromass tends to remain associated with minerals, eventually persisting as SOM (Creamer et al. 2019). New necromass is the most sensitive to decomposition (Dong et al. 2021), so understanding new necromass decomposition directly informs long-term SOM stability and persistence.

Including microbial parameters in ecosystem models improves projections of soil carbon (C) stocks (Wieder et al. 2015). Soils with large C stores generally have large and active microbiomes (Liang et al. 2019), suggesting that yield or turnover drives necromass formation and persistence. However, growth is constrained by the acquisition of limiting resources. Soil microbes are primarily C limited, but soil minerals affect nutrient limitation, in turn affecting the ability to make new extracellular enzymes or dictating the size of cells and even genomes (Sorensen et al. 2019). Stress can also divert resources away from growth and nutrient acquisition, altering necromass deposition and microbial biomass composition (Fernandez and Kennedy 2018).

Mineral-organic associations are the most quantitatively important C storage mechanism in soils (Oldfield et al. 2018). Soil minerals modulate availability of energy and nutrients to microbes by restricting water and oxygen flow (Keiluweit et al. 2017). Clay minerals also slow microbial growth by sorbing added exogenous substrates (Finley et al. 2021). Both soil pore size and clay composition are intertwined with microbial traits to regulate growth and nutrient acquisition. By exploring traits associated with growth and resource acquisition from necromass biomolecules in soils and soil communities, researchers can better predict necromass persistence.

The goal is to better define the interactive roles of soil mineralogy and microbiomes that contribute to the persistence of necromass as SOM. The project’s overarching hypothesis is that necromass biomolecules are sensitive to decomposition depending on microbiome traits, nutrient bioavailability, and soil types. This work has four specific objectives:

• Define microbial populations capable of degrading necromass macromolecules.
• Define the mineral characteristics and microbial traits that drive necromass decomposition.
• Define the food webs and fates of necromass macromolecules in soils over time.
• Model the microbial or metabolic traits that best predict necromass formation.

Three experiments explore how microbes and minerals make persistent necromass, followed by statistical and mechanistic modeling to define conditions conducive to persistent necromass.

Some soil C persists, sometimes for a long time, but scientists don’t know enough about this process to predict the long-term storage of C. New organic matter enters soil through plant detritus, and similar amounts enter soil through the continuous recycling of nutrients by microbial turnover, which generates microbial necromass. This microbial necromass represents a steady stream of new organic matter to soil, but new necromass is very sensitive to decomposition and loss as carbon dioxide (CO₂). Surface attachment to minerals and other organic matter is the prevailing theory on the mechanism of soil C stabilization. However, reactive surfaces and soil pores reduce the bioavailability of nutrients that drive microbial turnover. How do microbes and minerals make necromass that persists?

Here, the project shows preliminary results from an ongoing experiment that assesses the impact of soil pore size and clay activity on the formation and cycling of necromass. The team has established nine artificial soils that consist of 90% acid-washed sand and 10% clay, which vary in both pore size (25 to 45, 75 to 105, and 150 to 250 micrometer particle sizes, using silica quartz sand) and clay activity (kaolinite and montmorillonite in 1:1, 1:9, and 9:1 mass ratios). These model soils were inoculated with soil microbes extracted from a temperate deciduous forest soil (Harvard Forest, Petersham, MA) and have been fed weekly since February 2023 with 0.5 milligram C g soil^-1 cellobiose and 0.05 mg nitrogen g soil^-1 ammonium nitrate and maintained at 45% soil moisture.

Preliminary results show that soils with smaller particle sizes and more active clays differ in microbial biomass (DNA yields), community composition (amplicon sequencing), and activity (CO₂ production) compared to coarser textured soils with less active clays. The team additionally presents an upcoming experiment that incubates the mature model soils with representative necromass biomolecules (carbohydrates, protein, nucleic acid, and lipids). Researchers will use oxygen-18 (¹⁸O) water to follow ¹⁸O incorporation into proteins in response to each necromass biomolecule. A novel combination of stable isotope probing–metaproteomics and metagenomics can be used to define the metabolic pathways and microbial populations active across soil and macromolecule types, as well as substrate-specific C use efficiency. Additionally, the team will track the fate of necromass C through continuous monitoring of CO₂ production and assessment necromass C stocks in microbial biomass (via chloroform fumigation) and particulate vs. mineral-associated pools (via density fractionation).

The long-term objective is to develop optimized experimental design schema for utilizing computational prediction and high-throughput functional validation to study plant processes at the systems level and efficiently translate knowledge gained to link genome sequence with gene function.

Duckweeds are fast-growing, aquatic energy crops that produce large amounts of biomass that is enriched in complex, non-cellulosic carbohydrates that are highly amenable to conversion into fuels and bioproducts. Enzymes called glycosyltransferases (GTs) participate in the biosynthesis of these carbohydrates that enable storage of carbon and energy as glycopolymers. To date, precise functional predictions are extremely difficult or completely unreliable for GTs, as many families are polyspecific. We are performing family-wide characterization of GT47 enzymes in duckweeds as a model for high-throughput (HTP) functional studies of enzymes involved in carbohydrate metabolism. This family is highly expanded in energy crops, and members display diverse substrate specificities, ultimately contributing to the synthesis of almost every class of polysaccharide within biomass (Zhang et al. 2023). Gene functional validation efforts are being performed using a multi-disciplinary approach involving enzyme and substrate library construction, HTP biochemical assays, and computational biology. The combined data are being used to populate a machine-learning framework to better enable prediction of plant GT function. We will also present preliminary work on how regression models, trained on GT sequences can be used to predict donor specificity, highlighting the need for robust curated data for interpretive machine learning frameworks. Functional validation achieved through this research project will be used to assign plant gene function and study processes at the systems level to efficiently link genome sequence with function in a feedstock-agnostic manner.

To investigate bacterial microcompartments in Clostridium autoethanogenum and engineer them to compartmentalize synthetic metabolic pathways.

One promising route to sustainable bioproduction of fuels and chemicals is the engineering of organisms such as acetogens to efficiently convert abundant and low-cost gases containing carbon monoxide or carbon dioxide and hydrogen to desirable, value-added products at high efficiency and low cost. This approach not only provides an avenue for repurposing greenhouse gases (GHG), but also minimizes the use of harsh chemicals and hazardous byproducts common in petroleum- based processes. However, many biochemicals are not yet produced biologically due to roadblocks in the cellular biosynthesis process. These roadblocks can include intermediate toxicity, redox imbalances, and loss of product to off-pathway reactions. In nature, these issues are often alleviated using spatial organization strategies, such as sequestration in organelles. In bacteria, such organization often occurs in protein-based organelles known as bacterial microcompartments (MCPs).

We will investigate the native regulation, assembly, and function of MCPs in the industrially relevant non-model host C. autoethanogenum. In the C. autoethanogenum genome, two unique gene clusters have been identified as putative operons encoding sets of proteins required for MCP formation. These putative operons express a variety of possible MCP shell proteins and encapsulation peptides that target enzymes into the MCP. We tested potential inducers of these operons and found that some of these small molecules were consumed by C. autoethanogenum; RNA sequencing data showed that these same small molecules transcriptionally activate the MCP operons. MCP formation in these conditions was corroborated by electron microscopy of C. autoethanogenum, which shows distinctive polyhedral shapes within the cells, indicative of MCP formation. We also used cell-free protein synthesis to produce putative MCP shell proteins from C. autoethanogenum and observed self-assembly of large structures, visible under light microscopy.

Beyond understanding the native function of these putative MCP operons, our engineering goal is to sequester key biosynthesis enzymes from two distinct metabolic pathways into MCPs to make compounds involved in rubber production. Specifically, we aim to showcase the power of enzyme encapsulation in an MCP for reducing toxicity and product losses to side reactions for these pathways. Towards enabling heterologous enzyme encapsulation in these new MCP systems, 16 C. autoethanogenum reporter strains were generated with different putative encapsulation peptides fused to Superfolder green fluorescent protein (sfGFP). Fluorescence microscopy shows that 11 of these 16 sfGFP-encapsulation peptide fusions exhibit punctate fluorescence upon MCP induction, indicating successful encapsulation of the fluorescent reporter within MCPs. These results demonstrate the potential for encapsulating biosynthesis enzymes and enable the cost-efficient production of chemicals that are currently derived from petroleum.

To fundamentally understand and redirect metabolism and regulation of thermophilic Bacillus coagulans for the efficient conversion of undetoxified lignocellulosic biomass hydrolysates into designer bioesters.

Bacillus coagulans (now recognized as Heyndrickxia coagulans), a gram-positive facultative thermophile, thrives across a broad temperature range, utilizes undetoxified biomass hydrolysates, and synthesizes valuable chemicals like acetoin, butanediol, and lactate. This project exploits the robustness of B. coagulans for converting biomass hydrolysates into designer bioesters—such as acetate and lactate esters—widely used in fragrances, flavors, pharmaceuticals, and advanced biofuels. Through comprehensive screening of a library of diverse B. coagulans strains for desirable traits, we identified B. coagulans B-768 as an optimal host for metabolic engineering, given its capability to ferment C5-C12 sugars, withstand undetoxified biomass hydrolysates, and produce significant lactate levels. Genome and proteome analyses highlighted B-768’s expanded genome, coding for enhanced sugar utilization and lactate synthesis. Successful DNA transformation in B-768 has led to the creation of production strains harboring the exchangeable ester production modules to produce acetate and lactate esters at elevated temperatures, facilitated by our engineered thermostable alcohol acetyltransferases. Notably, we uncovered B. coagulans strains capable of producing valerate esters. Current efforts focus on elucidating B. coagulans’ robust metabolism for complex hydrolysate utilization, improving synthetic biology tools (including transformation efficiency, plasmid stability, genome integration, and promoter and ribosome binding site optimization), and enabling modular cell engineering for selective biosynthesis of designer esters. Overall, B. coagulans is a promising microbial manufacturing platform that will be advanced by a fundamental understanding of its robustness, genetic engineering tool development, and the ability to harness it for production of designer bioesters from lignocellulosic hydrolysates.

The goal of the work is to advance understanding of the contributions of low-dose (LD) radiation to cancer by identifying and modeling the key molecular and cellular mechanisms involved, developing optimal strategies for interrogating these mechanisms experimentally, acquiring and integrating diverse datasets to formulate and test hypotheses, and validating predictive multiscale models of radiation risk. The work will be performed through three broad goals: (1) identifying and modeling molecular and cellular mechanisms of LD radiation damage and repair; (2) identifying and characterizing signatures of LD exposure and LD-induced tumorigenesis; and (3) developing a multiscale modeling and simulation framework for estimating cancer risk from radiation exposures.

This work describes the development and implementation of a multiscale computational digital twin framework to assess radiation exposure effects on humans. Using high-performance computing methods, the team assess radiation exposure effects at the whole-body, multicellular, and subcellular scales, seamlessly integrating between the three. The project’s framework includes a population of human digital phantoms along with computing environments to model radiation transport and associated radiation damage. The project includes a multiscale perfusion model for radioisotope delivery and uptake, modules for DNA damage and DNA repair, and multicellular growth models to determine radiation effects on cells and organs. An explanation of the integration of these scales into the framework is provided below.

At the subcellular level, using experimental Hi-C data (Sanders et al. 2020), researchers create 3D chromosome models imported into TOPAS-nbio (a Monte Carlo simulation platform) to estimate DNA double-strand breaks (DSB; Chatzipapas et al. 2020). Mechanistic repair models from the Mechanistic DNA Repair and Survival Model (MEDRAS) predict post-radiation aberrations over time (McMahon and Prise 2021). Validation against in vitro studies and cell experiments with a mouse breast cancer cell line confirms accuracy.

At the multicellular and whole-body scales, the project integrates a tumor growth model in Compucell3D with eXtended CArdiac Torso (XCAT) phantoms for whole-body simulations (Segars et al. 2013). Geant4, a Monte Carlo simulation software, calculates absorbed dose in radiation therapy protocols, feeding back into Compucell3D to assess cell survivability (Allison et al. 2006, 2016; Swat et al. 2012). Validation against recent multicellular models shows promising results, with ongoing efforts to validate against spheroid-based experiments.

For multiscale perfusion modeling, the team has implemented a physiology-based pharmacokinetic (PBPK) model simulating radioisotope distribution at the organ level, feeding into a computational fluid dynamics model for tissue-level perfusion. Work is underway to demonstrate subcellular and multicellular scale radiopharmaceutical perfusion. Preliminary results involve growing spherical tumors with multiple cell types in a controlled nutrient environment, exposing them to Actinium 225 in Geant4 to estimate cell radiation dose and survivability.

In conclusion, the project’s framework offers a robust tool for modeling radiation effects across various radiation dose–exposure scenarios. Importantly, parts of this pipeline are fully automated and optimized for graphics processing unit computation. With potential applications in environmental radiation exposures, occupational exposures, and medical exposures including radiation treatments, the team envisions the multiscale in silico digital twin framework providing a robust method to evaluate outcomes from unwanted exposures as well as the ability to optimize desired exposures (such as radiation treatments) for the benefit of the individual. This framework facilitates predicting cell survival and treatment outcomes across different cancer types, integrating absorbed dose, biodistribution, cell toxicity, and repair mechanisms to determine overall outcomes in the human body.

The goal of the Center for Restoration of Soil Carbon by Precision Biological Strategies (RESTOR-C) is to harness plants and microbes to increase carbon flux into soil carbon storage pools to form persistent carbon that is stable for >100 years. This will address the Carbon Negative Shot goal to remove carbon dioxide (CO₂) from the atmosphere and durably store it at meaningful scales for less than $100 per net metric ton of CO₂-equivalent within a decade.

Soil carbon represents a vast global carbon reservoir that has become depleted through human activities. Hence, soil carbon restoration can be used to sequester carbon at massive scales while improving soil fertility. To exploit this natural carbon sink and advance toward the cost and scale goals of the DOE Carbon Negative Shot, RESTOR-C will develop plant- and microbe-based strategies to increase accumulation of persistent carbon in soil. These strategies are designed to increase the amount of atmospheric carbon fixed by plants and increase the amount of the fixed carbon that is channeled belowground as soil persistent carbon. To accomplish this goal, the Center will apply cutting-edge molecular and computational methods to overcome key obstacles to persistent carbon storage in four key domains. The Soil Division will explore the chemical, biological and environmental factors that govern the persistence of carbon in soils, to enable the development of stable, long-term carbon storage solutions with a focus on arid and marginal lands. This work will combine soil carbon dating, advanced metabolomics methods, and artificial intelligence to determine the nature of the oldest carbon and features that influence its persistence. The Plant Division will design plant genotypes that efficiently capture and sequester carbon, through a combination of increased photosynthetic efficiency and optimized root phenotypes. These efforts will focus on sorghum, a stress-tolerant C₄ bioenergy crop that can grow in a range of soils and climates with minimal nutrient inputs, building on team members’ experience engineering improved photosynthetic efficiency and altered root phenotypes in plants. The Microbial Division will identify and optimize microbial communities to promote carbon retention in soil. Methods to achieve this include chemical analysis to identify microbes that produce persistent carbon, omics-based analyses to determine microbial niche preferences, enrichment and selection methods to obtain carbon storage promoting microbes, and artificial intelligence–guided high-throughput experiments to test and improve microbial strategies for soil carbon deposition. Finally, the Scaling and Impact Division will model, predict, evaluate, and optimize cost and scale of soil carbon sequestration approaches. This work will build and connect field-scale reactive transport and agroecosystem-scale models of carbon dynamics with national-scale models of economic feasibility to predict the impact of carbon sequestration approaches, evaluate implementation strategies, and test promising approaches at the field level.

This research will break new ground in multidisciplinary research, leveraging unique expertise at two national laboratories and four university partners, including two minority-serving institutions, to integrate recent developments and breakthroughs spanning the biological, ecological, chemical, and computing sciences. At the end of the 4-year period, the Center will have validated plant-microbe strategies to increase carbon at target field sites in California and New Mexico, as well as a dramatically expanded knowledge base and set of capabilities to rapidly extend these approaches to other locations and crops. In the long term, these methods have the potential to restore carbon in U.S. agricultural lands, forging the way toward a carbon negative future.

BAHD acyltransferases represent a large family of enzymes typically found in plants. They use acyl-CoA donors (produced from acyl-CoA ligases) to form esters or amides with alcohol or amine acceptor molecules. The products of these reactions are incorporated into large polymers such as lignin and suberin or into small secondary metabolites including phenolic esters, antimicrobials, antifungals, or compounds that contribute to drought resistance. The goal is to elucidate the identities and functions of these enzymes and use them in conjunction with acyl-CoA ligases to precision engineer bioenergy crops (Chaudhury et al. 2023). Pairing specific acyl-CoAs and BAHD transferases can allow fine-tuning of lignin content for simple deconstruction (Zip-lignin) or by incorporation of useful aromatics that can then easily be “clipped-off” increasing the net value of the plant biomass.

BAHD acyltransferases have the ability to produce valuable molecules in bioenergy crops. The discovery and characterization of a specific BAHD acyltransferases led to the creation of ‘Zip-lignin,’ in which introduction of ester-linked monolignols allows hydrolysis under milder conditions, avoiding harsher chemical treatments needed to remove lignin during bioenergy processing (Wilkerson et al. 2014). Further investigation showed that specific aromatics could be incorporated into terminal lignin positions, such as p-hydroxybenzoate, that can easily be clipped off due to their attachment via an ester linkage (de Vries et al. 2022). Thus, the ability to tune lignin composition not only allows for improved deconstruction but also positions lignin as an attractive source of energy-rich molecules.

By taking advantage of continually improving genomic data and tools, the team curated lists of high-potential target genes focusing on two priority bioenergy crops and a model plant (poplar, sorghum, Arabidopsis). Selected genes were synthesized into cell-free expression vectors by the DOE Joint Genome Institute and were then screened using a wheatgerm cell-free system (Cell Free Sciences) by a team of high school–student laboratory members. The expressed proteins were screened for potential activity and categorized by their preferred substrates. Active enzymes catalyzing interesting reactions were then introduced into Populus sp. to assess in vivo impacts (de Vries et al. 2022; Gonzales-Vigil et al. 2021; Smith et al. 2022), and cell-based expression systems such as Escherichia coli have been used to facilitate structural and biochemical characterization. The work presented here has given further understanding into the breadth of molecules this large family of enzymes can synthesize and how these molecules may be useful in producing more energy-efficient plants or providing engineered plant sources for fine specialty chemicals.

Using integrative metaomics technologies, determine a role of natural microbial populations in pristine permafrost, seasonally thawed active layer, and hydrogeologically connected fjord sediments to degradation of organic matter and contribution to climate feedbacks in thawing Arctic.

Permafrost and permafrost-affected areas cover approximately 24% of the global terrestrial surface and reserve ~50% of the total soil organic carbon. Global warming drives permafrost degradation, release of organic carbon to decomposition, and intensification of microbial activity that in its turn leads to the increase of greenhouse gases flux exacerbating climate-change feedbacks. Biodiversity of heterotrophic microbes and the metabolic pathways they use to convert newly available organic matter to carbon dioxide and methane are little studied. Increased availability of organic carbon from thawing permafrost threatens to create a positive feedback on climate change, and since thawing organic carbon is transported by subterranean groundwater flow into nearby rivers or fjords, these microbial feedbacks involve communities in thawing permafrost as well as those in hydrogeologically connected soils. Greenhouse gas emissions at the soil surface are an amalgamation of microbial activity in all the layers underneath, so knowing the vertical layering of microbial communities is key to understanding the mechanisms of these climate change feedbacks.

Svalbard, Norway (79°N) is experiencing faster warming than the rest of the high Arctic, making it a bellwether for Arctic permafrost. The samples of permafrost and active layer were collected during winter at the Bayelva field site, and fjord sediments were collected during spring in close proximity to Ny Ålesund, Svalbard. The team examined in situ microbial communities using metagenomics, culturing, and extracellular enzyme assays from the surface down to 141 cm. Researchers compared the vertical layering of these communities and their carbon-degrading genes to those found in the adjacent marine fjord sediments using metagenomics, metatranscriptomics, and metaproteomics. Using comparative metagenomics and metagenome assembled genomes (MAGs), the team showed the depth distribution of individual MAGs leads to layered activity. The higher abundance of genes and peptides for major carbohydrate active enzymes (CAZymes) and glycoside hydrolases in subsurface at depths around 30 cm and 80 to 90 cm suggests that subsurface microbial communities are more active due to insulation from harsh surface conditions and due to high liquid water content, even though the deeper soils are sustained by older deposits of organic matter. Importance of the phyla of Verrucomicrobia and Proteobacteria was shown in both fjord sediments and permafrost-affected soils. Even though some matches between organisms that are capable of degrading similar organic matter in soils and the fjords were shown, the activity profiles differed from taxonomic profiles based on genetic potential alone. Researchers found significant overlap in potential for organic matter degradation in the subsurface of both the permafrost active layer and the fjord. It is likely that these subsurface communities are supported by the higher liquid water contents in the soil subsurface as well as depth-related changes in terminal electron accepting processes in fjord sediments. This suggests a direct role of subsurface microbial communities in a potential feedback loop with climate change, where thawing permafrost releases organic matter to active microbes which, in turn, convert the organic matter to greenhouse gases that may further warm the climate.

The Secure Ecosystem Engineering and Design (SEED) Science Focus Area (SFA), led by Oak Ridge National Laboratory, combines unique resources and expertise in the biochemistry, genetics, and ecology of plant-microbe interactions with new approaches for analysis and manipulation of complex biological systems. The long-term objective is to develop a foundational understanding of how non-native microorganisms establish, spread, and impact ecosystems critical to U.S. DOE missions. This knowledge will guide biosystems design for ecosystem engineering while providing the baseline understanding needed for risk assessment and decision-making.

The deliberate introduction of plants or microbes into new environments will be necessary to address national and global energy and environmental challenges. However, scientists currently lack the knowledge and tools necessary to successfully predict and introduce beneficial alterations, prevent undesired modifications, or predict the risks of proposed ecosystem engineering efforts.

A promising strategy for ecosystem engineering is the deliberate introduction of microbes to produce a specific effect on ecosystem function. At the same time, the anthropogenic-assisted movement of microbes and changes in climate are accelerating the emergence of non-native pathogens in resident communities. Regardless of the source, biosystems design strategies must accommodate and encompass the dynamic ecological and evolutionary factors that determine the outcome of natural and engineered invasions into ecosystems. Accounting for these barriers and their dynamics will enable new engineering approaches to safely manipulate the introduction of genes, pathways, and microbes into ecosystems to solve critical environmental challenges while limiting undesired community perturbations.

For ecosystem engineering using plant growth-promoting bacteria, the project has identified several nonmodel strains of Bacillus as testbeds for secure biodesign and genome engineering. While Bacillus species are abundant in soils and plant tissues and are common components of commercially available biological control products, there is a growing concern that the deliberate introduction of microbes into the environment will have unintended consequences on ecosystem health. Additionally, the mechanisms underlying the establishment and impact of introduced microbes for ecosystem engineering on plant and ecosystem health are not well understood. Therefore, the team has assessed the establishment and persistence of several Bacillus spp. across a series of laboratory-to-field experiments. For these experiments, researchers have benchmarked the persistence of these nonmodel Bacillus spp. in the soil microbiomes of Populus and quantified their impact on the host and resident microbiomes.

Furthermore, there is a growing concern that the anthropogenic movement of plants and their associated microbes will accelerate the emergence of novel pathogens. Microbial functions are notoriously context dependent and, with increasing movement of microbes and changes in climate, organisms are likely to transition from mutualist to pathogen with increased frequency. One such example is the fungal pathogen Sphaerulina musiva which following the human translocation of Populus across North America, spread from its original host, Populus deltoides, to novel hosts including Populus balsamifera and the DOE-flagship species Populus trichocarpa. In its new hosts, S. musiva induces fatal stem cankers in natural and managed settings that can greatly inhibit plant production. As a result, researchers have also assessed the effects of S. musiva establishment on host plants and their associated microbial communities in laboratory-to-field experiments.

Given the commercial applications of Bacillus spp. as a biofungicide, the team has tested the interactions between nonmodel Bacillus strains against several natural isolates of S. musiva. This characterization has uncovered a range of inhibitory strengths and varying tolerances for Bacillus and S. musiva, respectively. Using these results, researchers are developing a high-throughput image-based method paired with metabolomics to understand the genetic and chemical diversity for biocontrol.

Collectively, obtaining information on the direct or indirect mechanisms that control microbial-based biocontrol targeting fungal pathogens can help improve biodesign strategies aiming to increase Populus productivity and sustainability.

The goal of this project is to increase the production of vegetative lipids in miscanthus via genetic engineering.

Miscanthus, a Center for Advanced Bioenergy and Bioproducts Innovation (CABBI) feedstock, is a tall perennial rhizomatous temperate grass. Because of its high biomass yield, high carbohydrate and low ash content, high calorific value, remarkable environmental adaptability, high water and land use efficiency, and low fertilizer and pesticide requirements, it has become one of the most promising sustainable crops for biomass, bioenergy, and bioproducts. One of CABBI’s major goals is to increase the value of its feedstocks by using the “plants as factories” paradigm to produce novel high value bioproducts. Previously, researchers have demonstrated the ability to transform and edit multiple lines in the two major species of Miscanthus, M. sinensis and M. sacchariflorus, and a high performing orthospecies M. x giganteus, which is a hybrid of the two species. Here, the team reports the progress of this research on transforming miscanthus with two constructs designed to increase vegetative triacylglycerol (TAG) production. These constructs, pPTN1569 and pPTN1586, were previously vetted in sorghum.

The overall goal of this project is to develop robust and accessible tools for measuring macromolecular dynamics at DOE X-ray lightsources. As a proof of concept, researchers are studying the catalytic mechanism of lipoxygenase enzymes, which operate via a rate-limiting hydrogen tunneling.

Conformational dynamics underlie many important biochemical functions, such as enzyme catalysis, but they remain difficult to study. Rising to this challenge, the current generation of X- ray lightsources, including DOE synchrotrons and X-ray free electron lasers (XFELs), offers new opportunities to study molecular motion using time-resolved crystallography. These instruments produce ultrafast, high-brilliance X-ray pulses that enable the observation of protein dynamics in real time following a rapid perturbation that synchronizes motion within the ensemble of crystallized molecules. To help make time-resolved crystallography a general tool that can be applied to study the dynamics of any protein of interest, the group is developing the use of infrared laser-induced temperature-jump (T-jump) as a rapid perturbation. This poster will describe the team’s collaborative effort to pilot the use of new hardware and software for T-jump X-ray crystallography across structural biology facilities at SLAC National Laboratory. Development of T-jump crystallography methods synergize with the project’s mechanistic studies of lipoxygenase enzymes, which catalyze carbon hydrogen (C-H) bond activation reactions. The rate-limiting step for lipoxygenase catalysis is the abstraction of hydrogen via a tunneling mechanism, which is hypothesized to be linked to conformational dynamics that modulate the donor-acceptor distance. Researchers are using T-jump crystallography to test this hypothesis by mapping the conformational dynamics of several lipoxygenase variants with different catalytic properties. This comparative analysis will lend insight into which of the observed motions are functional in catalysis, and which are not. Experiments performed at both synchrotron (SSRL) and XFEL (LCLS) facilities will allow scientists to access broad timescales ranging from nanoseconds to milliseconds. Lipoxygenases are model systems for enzymatic C-H bond activation and prototypes for studying the role of tunneling in enzymatic reactions. This research will help shed light on the mechanistic details of how conformational dynamics promote function in these enzymes.

This project aims to establish the foundation for bioproduction using multifaceted microbial communities. Researchers will build metabolic community models of increasing complexity by integrating multiomics datasets. These models will guide engineering designs for optimized production of biofuels from lignocellulosic biomass. Furthermore, the team will use innovative approaches to augment existing communities for improved bioproduction and complete conversion of different biomass feedstocks. Overall, these strategies will provide knowledge of the functional metabolic exchanges driving interspecies interactions in microbial communities, thus providing insights into fundamental biological processes. Lessons learned here will be crucial for researchers’ ability to design stable microbial communities for various biotechnology applications in the future.

The multiplicity of intertwined, interspecies interactions within microbial communities regulates their functional organization and assembly. This allows these communities to perform complex functional tasks unreachable by axenic cultures, such as the breakdown of recalcitrant lignocellulosic materials into high-energy volatile fatty acids (VFAs). Bioproduction of one such fatty acid, butyric acid (BA), from sustainable lignocellulosic sources has gained attention owing to BA’s versatile applications as a precursor for a range of products, including sustainable aviation fuel, polymers, fibers, and cosmetics. However, the current necessity for costly enzymatic pretreatment of the lignocellulosic substrate that is currently required undermines the economic advantages of a biosustainable process.

A mutualistic co-culture of the thermophilic strains Clostridium thermocellum and Clostridium thermobutyricum was recently shown to be effective in converting lignocellulose to BA without expensive enzymatic pretreatment (Chi et al. 2018). However, the process is still suboptimal, leaving ample room for improvement in substrate utilization and product formation. While the co-culture of C. thermocellum and C. thermobutyricum resulted in a >100% improvement in substrate utilization compared to a monoculture of C. thermocellum, notable amounts of carbohydrates−primarily constituted by xylans−and the fermentation end products ethanol and acetate, were left unutilized.

Here, researchers characterized the metabolic interactions and exchanges of this thermophilic co-culture using high-quality, manually curated genome-scale metabolic models (GEMs) for both species. Compartmentalized as a community metabolic model (CM-model) comprising 1,777 reactions, 1,679 metabolites and 1,569 genes, the constructed CM-model enabled researchers to identify predicted metabolic bottlenecks that account for the co-culture’s constrained BA production and incomplete substrate utilization. The group aimed to release these bottlenecks via targeted and untargeted augmentation of the community to generate a reproducible synthetic community (SynCom), thereby improving substrate utilization and BA production. Targeted augmentation involves conducting bibliographic research and using CM-model guidance to select characterized microbial strains compatible with the system. Conversely, untargeted expansion entails isolating and selecting microbes with desirable metabolic traits.

For the targeted augmentation, the researchers considered specific roles fulfilled by individual members of the co-culture (with C. thermocellum specializing in recalcitrant carbohydrate biomass hydrolysis and C. thermobutyricum in BA production from soluble sugars) and structured the expanded SynCom around functional modules. This organizational framework integrates Thermoanaerobacterium xylanolyticum along with C. thermocellum into a “lytic” module, specializing in complex carbohydrate oligomer breakdown. Moorella thermoacetica was chosen for the “scavenging” module, aimed at recapturing and redirecting residual byproducts. Together, these augmented modules combined with the “yield” module, focused on C. thermobutyricum-mediated BA production, completed the setup.

Simultaneously, researchers explored augmenting the community in an untargeted approach. Soil samples were collected and grown on lignocellulosic substrates of varying recalcitrance under thermophilic, anoxic conditions to enrich thermophilic bacteria capable of hydrolyzing the complex oligomers in lignocellulose. Additional selection pressures were applied by growing these enrichments on spent supernatants from the co-culture’s growth on deacetylated, and mechanically refined corn-stover (DMR). From these, the group isolated and characterized thermophilic strains capable of growing solely on DMR and other non-pretreated lignocellulosic substrates, indicating possible new metabolic capabilities. Next, the group constructed three-member communities from these strains by pairing them with C. thermocellum and C. thermobutyricum and observed improved BA titers from DMR compared to the co-culture. Hence, this study establishes the foundation for advanced bioproduction using multifaceted microbial communities.

The goal of this research is to determine the role of signaling mediated by the target of rapamycin complex 1 (TORC1) in nutrient sensing in poplar and elucidate the functional role of genes regulating nutritional responses using CRISPR gene editing, genomics, biochemical, and computational approaches.

Poplar (Populus spp.) is an important and sustainable bioenergy and bioproduct plant feedstock, yet scientists’ understanding of the pathways and networks governing resource use efficiency is poorly developed. The protein target of rapamycin (TOR) kinase is part of an evolutionally conserved central hub that integrates nutrient, energy, hormones, biotic, and abiotic stresses signals by regulating transcription, translation, and metabolism. Compared to other plants species, such as Arabidopsis and rice, poplar contains two TOR genes. Using genome editing and computation approaches, this project will elucidate the role of the two poplar TOR genes in nitrogen utilization and if the two TOR genes have diverged in function.

An analysis of a compendium (over 800) of publicly available poplar RNA sequencing (RNA-seq) datasets indicates that both TOR genes are expressed in a variety of tissues and conditions. TOR expression appears to be coordinately regulated with the interacting partners LST8 and regulatory-associated protein of TOR (RAPTOR). Studies with each of the two TOR promoter (2.5 kilobyte) sequences fused to a beta-glucuronidase, green fluorescent protein (GUS-GFP) reporter gene indicate overlapping patterns of expression for each TOR gene. Using CRISPR activation of each TOR gene individually or the simultaneous activation of both poplar TOR genes showed that TOR activation enhanced growth under suboptimal levels of nitrogen fertility. This enhanced growth included significantly greater height, leaf area, stem dry weight, leaf dry weight and above ground biomass. Growth analysis suggests that the increase in growth was related to internode initiation/production as opposed to internode elongation. CRISPR TOR activation also revealed phenotypic differences resulting from the activation of the two poplar TOR genes. Interestingly, simultaneous activation of both TOR genes did not result in increased growth or biomass production. The team hypothesizes that differences in the ratio of homo and hetero dimers of TOR may result in different outputs that affects growth, biomass production, and phenotype. This hypothesis is being tested using genome edited poplars with independent biallelic mutants for each TOR gene; using CRISPR-Combo gene edited poplars for the simultaneous production of biallelic knockout mutants of one TOR gene while the respective second TOR gene is activated; and production of an expression variants via independent promoter editing of the two poplar TOR genes. Additionally, both constitutive active and dominant negative versions of poplar rho of plants (ROP) genes corresponding to ROP2 and ROP4 have been generated and transformed into poplar and the effect on nutrient use efficiency and root growth will be presented.

Develop paradigms for understanding the role of viruses and MGEs in soil ecology via ecogenomic inference and experimental interrogation new soil-derived model systems, and to build tools—scalable new methods, new databases, and new model systems—to test these paradigms.

The activity of soil microbes affects global energy and nutrient cycles, but they do so under largely unconstrained but likely significant virus impacts. While viruses play pivotal roles in other ecosystems like the oceans, soil virus research is hindered by technical challenges. In this project—‘VirSoil’—the group focused on three aims to (1) detect, identify, and classify soil viruses and their potential roles; (2) mechanistically understand soil virus infections; and (3) develop community resources for studying soil viruses.

In Aim 1, the team elucidated soil virus ecology in a decadal bulk metagenomic dataset from a permafrost thaw gradient at Stordalen Mire. For DNA viruses, 5,051 virus populations were cataloged, documented to have high year-to-year turnover, and linked to carbon cycling through host prediction and gene content analysis that identified virus-encoded carbon degradation, methanotrophy, and methanogenesis genes (Sun and Pratama et al. in review). For RNA viruses, nearly 9,000 sequences were identified, representing 2,651 novel “species”, and then ecologically contextualized according to habitat, depth, and soil properties (Pratama and Dominguez-Huerta et al. in prep).

In Aim 2, the team sought to begin understanding soil virocells (i.e., virus-infected cells) by advancing virocell-specific analytics and developing new soil virus-host model systems. Towards this, researchers applied time-resolved, multiomics technologies to diverse virus genomes, infection efficiencies, and nutrient conditions to establish knowledge and protocol pillars using an established Cellulophaga virocell model system. This revealed that virus genome type and infection efficiency strongly shape bacterial biomolecule composition and dynamics, where virocell biomolecule specificity was highest in transcripts, lower in proteins, and lowest in metabolites (Howard-Varona et al. in prep) and post-translational modifications were uncovered (Peters et al. in prep). In parallel, multiomics of nutrient limited Pseudoalteromonas virocells revealed the interplay between environment and virus infection intracellularly versus extracellularly (Howard-Varona and Lindback et al. in revision). Finally, this aim also established random bar code transposon-site sequencing (RB-TnSeq) and CRISPR-Cas9 engineering approaches to assess virus components of the virus- host arms race and scalably characterize resistance mechanisms.

In Aim 3, the team focused on community empowerment. To this end, researchers established standard operating procedures for auxiliary metabolic gene (AMG) analysis (Pratama et al. 2021), developed ‘MetaPop’ to simplify population genetics analysis (Gregory et al. 2022), created an enhanced protocol for identifying and annotating metabolites through machine learning (Rajakaruna et al. in prep), curated an efam virus protein cluster database to improve virus protein annotation (Zayed et al. 2021), and worked with KBase to layer in basic iVirus functionality including virus identification (VirSorter, VirSorter2) and taxonomic classification (vConTACT2) tools. Finally, towards expanding model systems for soil viral ecology, the group screened hundreds of microbial strains to isolate viruses, triply plaque-purified subsets of these, and developed 60 viruses that were genome‐sequenced and host‐range-characterized as new virus-host model systems for virocell multiomics and other characterization (Gittrich et al. in prep).

Algal and plant systems have the unrivaled advantage of converting solar energy and carbon dioxide (CO₂) into useful organic molecules. Their growth and efficiency are largely shaped by the microbial communities in and around them. The μBiospheres Science Focus Area seeks to understand phototroph-heterotroph interactions that shape productivity, robustness, the balance of resource fluxes, and the functionality of the surrounding microbiome. This team hypothesizes that different microbial associates not only have differential effects on host productivity but can change an entire system’s resource economy. This approach encompasses single cell analyses, quantitative isotope tracing of elemental exchanges, omics measurements, and multi-scale modeling to characterize microscale impacts on system-scale processes. Researchers aim to uncover cross-cutting principles that regulate these interactions and their resource allocation consequences to develop a general predictive framework for system-level impacts of microbial partnerships.

Iron (Fe) is an essential micronutrient, and microbial Fe acquisition strategies are predictive of host health across an array of environments. Despite this, a molecular level understanding of microbial Fe acquisition influence on partnership outcomes and system-level carbon (C) flux is lacking. Here, the group examines both algal-bacterial and plant-fungal interactions in response to changes in Fe to elucidate exchange mechanisms governing these interactions.

Researchers first examined bacterial isolates that grow with model diatom Phaeodactylum tricornutum. With the Boiteau laboratory at University of Minnesota (UMN), the team grew P. tricornutum alone or in co-culture with isolates under different concentrations of Fe-dust. The group used global exometabolomic profiling to determine the diatom Fe limitation response and compounds consumed or exuded by bacteria. When P. tricornutum’s growth is limited by Fe, the algae decreases production of saturated fatty acids relative to Fe-replete conditions. Some bacteria aid in algal Fe acquisition under limitation, significantly increasing algal abundance (p<0.02). Other bacteria compete for scarce Fe resources, significantly inhibiting algal growth under low Fe dust (p<0.05). These shifts in algal growth with and without bacterial partners under low Fe were accompanied by metabolomic shifts, providing insight into molecular drivers of growth.

To understand the systems-level impacts of these Fe acquisition strategies, it is important to also examine the role of bacteria-bacteria interactions. Researchers therefore examined effects of Fe limitation in a simplified community (~30 bacterial taxa) grown with P. tricornutum as the sole organic C source. The group found that under conditions of low dissolved Fe, the bacteria:alga ratio decreases significantly and the community composition shifts. The team hypothesizes that Fe-limitation enriches for taxa that can grow under reduced Fe (e.g., siderophore secretion) and/or low organic C. To determine whether algal organic C quality as well as Fe-chelating compounds shift with Fe, the group profiled the exometabolites. Initial results suggest algal-bacterial exudation and consumption are distinct under different Fe regimes. This points to a tight coupling of Fe and C in the P. tricornutum microbiome and regulation by external factors (e.g., nutrient limitation) and host physiological state.

On the plant-fungal side, with the Hawkes laboratory at North Carolina State University, researchers isolated diverse Ascomycota fungi from switchgrass roots and grew plants with single fungal partners. The team found significant fungus-dependent variation in plant Fe content, suggesting the different fungi had distinct Fe acquisition strategies and/or host transfer mechanisms. To test this, the Boiteau laboratory conducted global metabolomic profiling of 19 fungal isolates under Fe limitation. The non-siderophore producer Trichoderma had the largest negative effect on plant Fe, while the fusarinine-producing strain Chaetomium resulted in the largest increase in plant root Fe uptake (1.8 and 2.4-fold in shoots and roots, respectively, relative to non-inoculated controls). Fungi with distinct Fe uptake and metabolic strategies differentially impact plant Fe acquisition and are likely to constrain switchgrass productivity.

In summary, the group finds that Fe has an important role in phototroph-host interactions and system productivity. By characterizing microbial Fe acquisition strategies and associated C flux, researchers aim to gain a predictive understanding of the role of Fe across a broad range of host-microbe interactions.

Development of a machine learning pipeline which identifies key morphological features in cells treated with low dose radiation.

The use of machine learning models in cellular biology has drastically increased with rapid advances in artificial intelligence. These models, trained on cellular images, often analyze thousands of different features of an image of cells, from the number of cells to nucleus diameter. However, there needs to be more research done in the use of vision transformers for cellular classification models.

Vision transformers use techniques different from other image classification models, such as convolutional neural networks (CNNs). Vision transformers, for example, do not run the source image through various data augmentation layers but instead segment the original image into multiple patches, which are then linearly passed through the transformer. Many vision transformers have been developed in recent years, demonstrating promising validation accuracy when classifying large numbers of images; for the purposes of this project, researchers have implemented the MURA vision transformer, an efficient version that has shown reliable validation accuracy.

While there are many applications for the use of vision transformers when analyzing cellular images, for the purposes of this study, researchers have focused on the analysis of human umbilical vein endothelial cells (HUVEC), which have undergone low-dose radiation exposure. A large amount of research has been done on the effects of acute, high doses of radiation on the morphological profile of human cells. However, the effects of low-dose radiation on cellular morphology have yet to be studied. If phenotypic features of the HUVEC cell’s morphology can be identified using a vision transformer, it can lead to advanced and more efficient screening for low-dose radiation exposure.

The image data from the JUMP-Cell Painting Consortium was used to develop and fine-tune the vision transformer pipeline. The JUMP-Cell Painting Consortium is a collaboration between multiple laboratories to create a large repository of publicly available cell painting images. More specifically, the JUMP-Cell Painting Consortium contains approximately 115 terabytes of images of human osteosarcoma (U2OS) cells that have undergone either a chemical treatment or genetic perturbation and then have had cell painting performed on them for imaging. The size, consistency, and variety of this dataset provide an excellent benchmark for the MURA model’s validity and the developed pipeline’s effectiveness.

To provide clean and accurate data for the vision transformer model, the group performed cell segmentation on the cell painting images before training the model. The process of cell segmentation involves identifying the borders of each individual cell within the source image and extracting it so that each cell is contained within its own image to be fed into the model. This ensures factors such as the number of cells or clustering are not considered when training the vision transformer. The CellProfiler application was integrated into the group’s pipeline to perform cell segmentation. CellProfiler is a widely used software designed for biological image processing. For the purposes of this project, researchers developed a pipeline that will stack each image channel of the cell painting images, identify the cellular components, segment, and export the cell images into the group’s vision transformer pipeline.

DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) combined with total Internal Reflection Fluorescence (TIRF) microscopy enables the highest localization precisions, down to single nanometers in thin biological samples. However, most cellular targets, including the nucleus, elude the accessible TIRF range close to the cover glass and thus require alternative imaging conditions, affecting resolution and image quality. Here, researchers address this limitation by applying ultrathin physical cryosectioning in combination with DNA-PAINT. With “tomographic and kinetically enhanced” DNA-PAINT (tokPAINT; Stein et al. 2024), the group demonstrates imaging of nuclear proteins with sub-3 nanometer localization precision, advancing the study of nuclear organization within fixed cells and mouse tissues at the level of single antibodies. This team believes ultrathin sectioning combined with the versatility and multiplexing capabilities of DNA-PAINT should be able to contribute to the study of proteins, RNA, and DNA in genome organization at the molecular level and in situ.

Discovery, optimization and enhancement of tolerance mechanisms in bacterial hosts to biomass-related inhibitors and final products to generate robust, scalable production platforms.

Isoprenol (3-methyl-3-buten-1-ol), a precursor for diverse commodity chemicals (Baral et al. 2021), can be converted into 1,4-dimethylcyclooctane (DMCO), which can further be used as a sustainable aviation fuel blendstock (Baral et al. 2021). Bioproduction of isoprenol has been reported from various engineered bacterial hosts such as Escherichia coli, Corynebacterium glutamicum, and Pseudomonas putida (Kang et al. 2016; Sasaki et al. 2019; Wang et al. 2022; Banerjee et al. 2024). A critical factor affecting scalability for high titer rate–yield production of isoprenol in these microbial hosts is its toxicity. Therefore, it is imperative to understand and improve the tolerance to isoprenol in these bacterial hosts that affect both growth and production.

In the current study, this team specifically focused on P. putida KT2440, an ideal production chassis due to its fast growth rate, capability to utilize various substrates including lignin aromatics, and high stress tolerance, which are critical factors in industrial bioprocesses (Nikel and de Lorenzo 2018). P. putida has endogenous isoprenol catabolism pathways and hence the native regulatory cascades driving the microbial physiology could be perturbed upon heterologous production of isoprenol. The group used a top-down approach harnessing the power of adaptive laboratory evolution (ALE) to generate isoprenol tolerant phenotypes and further characterized them (Lim et al. 2021). For this purpose, the team tolerized wild-type (WT) as well as mutant strains of P. putida lacking isoprenol catabolism in glucose minimal medium by gradually increasing the concentration of isoprenol.

The group successfully obtained evolved strains that could robustly grow in the presence of 8 grams per liter isoprenol compared to the basal strain that was unable to grow at this concentration. Furthermore, the team utilized whole-genome sequencing, gene expression profiling (RNA sequencing), and global proteomics to understand determinants of isoprenol tolerance in these novel evolved isolates compared to the parent strains. Researchers also study how these profiles change when they heterologously express isoprenol production pathways in these evolved isolates and its subsequent effect on production. Taken together, unraveling and understanding the effect of the evolution-driven isoprenol tolerization mechanism and its effect on production in this important bacterial chassis will help scientists in rationally engineering robust isoprenol production platforms.

The goal of this program is to understand the interactions, localization, and dynamics of grass rhizosphere microbial communities at the molecular level (genes, proteins, metabolites) to enable accurate predictions and interventions to effectively manage and harness microbes to achieve DOE missions in sustainable energy and carbon cycling.

Functional understanding of microbial gene functions is largely based on genetic interrogation of isolated organisms, providing limited insights into the importance of genes within microbial communities, including the rhizosphere, which is the focus of the program. To address these knowledge gaps, recently researchers have created a generalizable toolset for targeted genome editing of individual organisms within complex microbial communities that uses type I-F CRISPR-associated transposons (CASTs) to make targeted genetic edits to complex microbial communities (Rubin et al. 2022). CASTs are broadly dispersed across bacteria and capable of integrating large genomic payloads. However, little is known about the host molecular factors which regulate CAST integration, and their widespread utilization is limited by low editing efficiency across diverse, non-model bacteria. To expand the range and applicability of Type I-F CASTs as editing tools, the group employed a genome wide mutant screening approach to identify putative regulators of CAST transposition in established model systems in which CAST is known to integrate. Candidate regulator hits were individually validated, with a particular focus on well-characterized genes involved in known mechanisms. Next, the team conducted a bioinformatic survey for the conservation of the candidate regulator hits across broad bacterial phyla. Finally, the team leveraged its findings by constructing vectors that incorporate these key regulators to increase editing efficiency. These results will shed light on the molecular mechanisms underlying CAST integration and enable more efficient editing in diverse non-model microorganisms. This information will enable the team to extend the application of community editing to better understand the molecular mechanisms governing assembly and interactions in the rhizosphere.

The Terraforming Soil Energy Earthshot Research Center (EERC) will study biological and geological solutions to accelerate scalable, affordable carbon drawdown in the United States’ 166 million hectares of agricultural soils. Research objectives include gene-edited plants and microorganisms that accelerate carbon sequestration, strategies that encourage soil mineral-organic interactions, and models that predict carbon durability in small soil pores as well as regional-scale estimates of locations with opportunities for increased soil carbon removal.

If applied at scale on croplands, enhanced rock weathering (ERW) could feasibly remove 0.5 to 2 Pg C of atmospheric carbon dioxide (CO₂) each year through amendments of relatively fast reacting crushed alkaline minerals (e.g., crushed basalt rock). However, several critical unknowns limit the scalability of ERW as a carbon drawdown strategy. First, published data remain limited and existing field studies employ different approaches for measuring inorganic carbon (C) removal, making it difficult to compare the net C removal of ERW across agricultural regions. Second, the impacts of ERW on soil organic carbon (SOC) and soil microbial communities are poorly understood. Given the massive size of the SOC reservoir, small SOC gains or losses could either amplify or entirely negate the inorganic C drawdown benefits of ERW. The EERC will address this gap by assessing impacts of field trials across different major agricultural regions within the United States (California, the Midwest, and Southeast) using a standardized set of total C drawdown measurements. Here, the team presents some preliminary data from existing field trials.

At three field trials in the Central Valley of California, the team found that stocks of mineral- associated organic matter (MAOM) were 8 to 16% lower in the surface soil (0 to 10 cm) of plots with crushed basalt amendments versus unamended controls. At the sites where baseline data was available, crushed rock amendments did not lead to net losses of total soil organic matter relative to initial conditions; however, the accrual rate of surface soil MAOM over the two-year period was 60 to 97% lower in plots with crushed rock relative to control plots. At the field trial in Minnesota on a typical corn-soybean rotational field, researchers found increased porewater alkalinity and increased yield in acidic soils treated with steel slag, though minimal effect of basalt on inorganic C removal, SOC, or plant yield. At the Illinois site, after 3 years of basalt application, the group found a signal for elevated or negligible shifts in crop yields, with the largest shifts observed in oats and soy. Using a cation accounting method, which tracks calcium and magnesium concentrations in the silicate phases relative to detrital elements, researchers found evidence for significant amounts of basalt weathering (representing multiple tons of carbon dioxide removal per hectare per year). Despite high basalt application rates and a signal for extensive weathering, the soil pH in the plots stayed near neutral, suggesting limited agronomic risk from the practice in these soils.

In sum, this group found evidence of inorganic C removal at some sites, and contrasting effects on SOC at the sites where it was measured. The next phase of field trials will be to measure the effects of ERW on inorganic C and SOC using a similar experimental design, a standardized sampling protocol, and a common set of C measurements, in order to facilitate more accurate and comparable estimates of net C removal across regions. Researchers will also investigate how co-applying organic amendments with crushed rock may hold promise for optimizing microbial-mediated inorganic and organic C removal.

• Establish safe-harbor landing pads for predictable transgene
• Characterize seed-specific promoters with a range of expression
• Define the impact of compositional genetic context effects on transgene expression with respect to alternative chromosomal loci for gene integration.
• Develop and use Programmable Transcriptional Activators (PTAs) to regulate expression of multiple (endogenous) genes for seed oil manipulation.
• Targeted gene replacement at the native genomic locus in pennycress.

The random nature of Agrobacterium-mediated transgene insertion into plant genomes affects expression strength, resulting in unpredictable product accumulation and the need to characterize many independent transgenic lines. This greatly limits throughput of different gene combinations to efficiently explore the expression space needed for effective multi-gene pathway metabolic engineering. To overcome this limitation, researchers are creating a suite of tools for reliable engineering of multi-gene systems to provide predictable control of the level of transgene expression in camelina and pennycress seeds. Specifically, this team will (1) generate camelina and pennycress lines with safe harbor landing pads; (2) develop a new set of seed specific promoters with a range of expression strengths; and (3) leverage sequence-programmable transcription activators (PTAs).

1. Safe harbor landing pads: Researchers are developing camelina and pennycress lines with safe harbor landing pads that will use site-specific recombinase systems to enable the targeted insertion of transgenic cargos into predetermined genomic loci, allowing better control of transgene expression strength and reducing the number of independent transgenic lines to be characterized. Using a protoplast expression system, this group screened five recombinase systems for their effectiveness in pennycress. Two serine recombinases, Bxb1 and phiC31, possessed the highest recombination rate. Landing pads using the recombination sites of these two recombinases have been generated and transformed into both pennycress and camelina.
2. Novel seed specific promoters: The relative expression levels of genes involved in primary or secondary metabolism can dramatically affect product profile and titer. To augment the suite of very strong seed specific promoters (e.g., napin, glycinin) currently used for oil seed engineering, the researchers have mined existing gene expression databases to identify nine seed specific genes expressed at levels 1x, 0.1x and 0.01x relative to the strong seed specific promoters in these species (e.g., napin orthologs). Promoter and terminator sequences have been cloned for these genes to be compatible with the GoldenBraid cloning system and used to express two different lipid biosynthetic Transgenic lines have been generated for all promoter/gene combinations and T2 seeds analyzed for the accumulation of target lipid molecules.
3. Sequence-programmable transcription activators (PTAs): Catalytically deactivated Cas9 (dCas9 or ‘dead’ Cas9) can be used as an RNA-guided DNA binding domain to deliver transcriptional activation or repression domains to a promoter of interest. These give unprecedented ability to control and fine-tune the expression level of multiple endogenous genes in parallel. Researchers have recently improved the performance of dCas9-based PTAs for plant applications by altering the mechanism by which activation domains are recruited to the dCas9 (Casas-Mollano et al. 2023) and by swapping out transcriptional activation domains derived from human viruses for those evolved in plant cells (Zinselmeier et al. 2022). Researchers now have transgenic lines of pennycress and camelina that stably express the components of MoonTag PTAs and are in position to begin leveraging these tools for seed oil engineering.

The goal is to create genome-scale models of endogenous metabolic pathways and develop metabolic sensitivity maps to identify reactions that dominate the control of flux. Specifically, researchers aim to gain insight into the regulatory mechanisms within pathways and predict outcomes of metabolic interventions at the genome-scale.

Biomanufacturing poses a sustainable alternative to producing traditionally petrochemical-derived commodity chemicals. For these biomanufacturing efforts to be commercially viable, the design and engineering process must be targeted and quick. The advent of CRISPRa/i enables the targeted activation and repression of specific genes in organisms. Choosing which genes to target with CRISPRa/i for maximal yield through the metabolic pathway requires identifying which enzymes exert the most influence on the flux through the pathway (Kacser and Burns 1995). Bayesian Metabolic Control Analysis (BMCA) has been developed as a way to integrate omics data and genome-scale models to predict the flux control coefficients of a metabolic pathway (St. John et al. 2019). Yet, BMCA’s predictive accuracy has yet to be quantified. Here, the results show that BMCA reliably predicts elasticity values in the absence of allosteric regulation and that the most informative type of data for BMCA is fluxomics, followed by enzyme concentrations, external metabolite concentrations, and internal metabolic concentrations. These results also demonstrate the fidelity and limitations of BMCA’s predictions given the strength of CRISPRa/i perturbations in the dataset and thereby establish guidelines for maximizing the predictive power of the BMCA method. This method was successfully applied to estimate sensitivities in random model topologies. Researchers anticipate that the insights drawn from this benchmarking study can be extended to other metabolic pathways, such as Pseudomonas putida.

The overarching project goal is to verify and quantify volatile organic compounds (VOCs) as direct and indirect contributors to soil carbon (C) stabilization within the rhizosphere and beyond through teleconnections, and to determine their underpinning ecological and metabolic mechanisms. The long-term motivation for this project is to transform the current conceptual understanding and predictive capacity of microbial systems and soil C stabilization to include the important roles of volatile compounds. This presentation falls under the group’s objective to determine the contributions of root-released VOCs and VOC transformations by soil microbiomes to soil C cycling and stabilization. Specifically, in these tasks, researchers quantify subsurface root and soil VOC cycling to determine how deep soil warming influences soil C in a coniferous forest and how plant productivity, root biomass, and plant growth stages influence soil C in an agroecosystem.

Plants are recognized as the dominant source of biogenic VOCs to the atmosphere, where they play critical roles in air quality and climate, yet the parallel impact of plant-derived VOCs on the pedosphere (soil) remains poorly quantified. VOCs released by decomposing litter can contribute to soil C pools including those associated with soil C stabilization, and researchers hypothesize that root VOCs can also contribute to these soil C pools. Furthermore, researchers anticipate this pathway for soil C stabilization will depend on plant physiological traits (e.g., photosynthesis, growth rates, stomatal conductance), rhizosphere microbes and their activity, and soil environmental factors. Currently, rhizosphere VOC cycling remains poorly described, in part due to a lack of developed methods. In this project, the group integrated new in situ and non-destructive approaches for measuring root VOCs and tracking their fate in soil.

The group designed two separate rhizobox systems to measure VOCs from soil and rhizosphere from Ponderosa pine seedlings and soil from the temperate coniferous Blodgett Experimental Forest in the Sierra Foothills in California. Both systems passively collected soil gas using diffusive teflon samplers shaped either as a cylindrical soil gas probe, as researchers have described previously (Roscioli et al. 2021; Gil-Loaiza et al. 2022), or a diffusive sheet connected to a large artificial macropore. Subsurface VOCs and carbon dioxide (CO₂) in soil (soil only) or rhizosphere (soil and plant) treatments were measured using a suite of online gas analyzers including a PTR-MS, TD-GC-MS, and CRDS over a 6-day period with a diurnal light and temperature program (light 6:00–20:00, 6:00: 15℃, 14:00: 35℃). Alongside higher levels of CO₂ (rhizosphere respiration), the group found elevated soil gas concentrations of methanol, acetic acid, acetone, and acetaldehyde in the pine tree rhizosphere compared to soil alone, indicating a root source. Soil appears to have been a source of 1,2-butadiene and isoprene (fragment) that were elevated in both treatments. These results are consistent with the previous discovery of high concentrations of volatile compounds including methanol at Blodgett (nuclear magnetic resonance on soil extracts), suggesting that pine roots may be an important source of these compounds and that the rhizobox systems are a useful tool for capturing and partitioning VOC sources and sinks in the rhizosphere. Ongoing research is comparing the performance of the two rhizobox systems and performing experiments to evaluate the impact of soil warming and moisture availability on root and soil VOC cycling and their contributions to soil carbon under controlled conditions. These results will be compared to those from the group’s upcoming field campaign at the deep soil warming experiment in the Blodgett Research Forest.

Improving soil carbon sequestration in agricultural systems is critical for reaching net-zero carbon goals. It is estimated that 50% of soil carbon in agricultural lands has been lost, making them a natural sink for rapid carbon restoration. In these systems, plant carbon inputs to the soil such as root exudates and liter are rapidly metabolized by microbes back to carbon dioxide (CO₂). However, recent research shows that differences in soil microbial communities can produce three-fold difference in the amount of dissolved organic carbon (DOC) leading to a proportionate decrease in CO₂ release to the atmosphere. This combined with the fact that microorganisms can beneficially influence plant growth, carbon uptake, and root morphology, promoting deeper rooting, suggest that microbiome optimization either alone or combined with other plant treatments could be a solution for increasing soil carbon sequestration in agricultural systems without compromising crop yield. One of the main challenges for microbiome optimization is to develop and produce microbiomes that maintain their viability in natural environments.

To develop beneficial microbiomes that could maintain their viability in natural soils, researchers tested inoculating plant seeds with growth promoting endophytic bacteria that live within the plant cells. This team hypothesized that provided successful inoculation, this habitat would protect the beneficial microbes from the impacts of the complex, existing microbiome in natural soils and help them maintain their beneficial effects on plant growth and carbon uptake. After developing the endophytic inocula, researchers tested their impact on plant growth in the laboratory on Camelina sativa and sorghum and on greenhouse gas emissions during growth in natural soils for C. sativa utilizing a soil core-based testbed with arid agricultural soil collected from a C. sativa field in the greenhouse. The group also compared the effects of endophytes on greenhouse gas emissions to the effects of inoculating the soil with nitrogen fixing cyanobacteria.

The preliminary results show that, overall, inoculation with the endophytes slightly decreased CO₂ emissions from the plant-soil system for C. sativa but increased the nitrous oxide (N₂O) emissions or reduced N₂O soil sink at high water contents compared to uninoculated plants. Interestingly, the effect of inoculating the topsoil with nitrogen fixing cyanobacteria on CO₂ and N₂O emissions was similar to having plants with or without endophyte inoculation. In both cases, the CO₂ and N₂O emissions increased above bare soil at mid-range soil moisture contents (20 to 28%), and the natural soil N₂O sink at soil moisture contents close to saturation was removed. This suggests that plant growth promoting endophytes can positively influence carbon sequestration in soil-plant systems, but the effects and their magnitude will depend complex interactions between the plant-soil-microbiome systems and environmental conditions.

The Center for Bioenergy Innovation (CBI) vision is to accelerate domestication of bioenergy-relevant, non-model plants and microbes to enable high impact innovations along the bioenergy and bioproduct supply chain while focusing on sustainable aviation fuels (SAF). CBI has four overarching innovation targets: (1) develop sustainable, process-advantaged biomass feedstocks; (2) refine consolidated bioprocessing with cotreatment to create fermentation intermediates; (3) advance lignin valorization for bio-based products and aviation fuel feedstocks; and (4) improve catalytic upgrading for SAF blendstocks certification.

Consolidated bioprocessing (CBP) with Clostridium thermocellum is a promising route to produce renewable biochemicals (such as ethanol) from lignocellulosic biomass. A disadvantage of the organism is the relatively low thermodynamic driving force (TDF) through its glycolysis, which limits ethanol titer, as less favorable energy substrates (e.g., pyrophosphate instead of adenosine triphosphate) or metabolic pathways (such as the malate shunt) are used. Recently an improved genome scale metabolic model (GSMM) was developed to help highlight potential energetic solutions in C. thermocellum (Schroeder et al. 2023). This presentation highlights recent efforts to quantify the proteomic and metabolic cost of low TDF and proposes interventions minimizing proteomic cost while maximizing TDF. Absolute concentration of 18 proteins and relative protein concentration (iBAQ) were measured under cellobiose-limed chemostat growth conditions. The correlation between absolute and iBAQ values were used to estimate absolute concentration for each measured protein. Enolase was discovered to be the enzyme with the greatest number of copies per cell, with more than 3.3 times that of the next most abundant enzyme accounting for more than 14% of estimated total protein mass per cell. Enolase has been previously shown to occur at the end of a series of four near-equilibrium reactions, the enzyme of which accounts for nearly 20% of total protein mass per cell, suggesting C. thermocellum uses a “pull” approach through these steps and is a key target for both improving TDF and reducing protein burden. To evaluate potential interventions, a resource allocation model (RAM) of C. thermocellum was reconstructed from the proteomics data and the recent stoichiometric GSMM model. The RAM was used to quantify proteomic and metabolomic effects of several intervention strategies including improving TDF through these steps, creating a greater “push” effect, and creating a greater “pull” effect. This allows researchers to test possible metabolic solutions in silico, prior to experimental validation via genetic engineering.

Bioenergy feedstocks need to be deployed on marginal soils with minimal inputs to be economically viable and have a low environmental impact. Currently, crop water supply is a key limitation to production. The yields of C₄ bioenergy crops such as Sorghum bicolor have increased through breeding and improved agronomy. Still, the amount of biomass produced for a given amount of water use (water use efficiency; WUE) remains unchanged. Therefore, this project aims to develop novel technologies and methodologies to redesign the bioenergy feedstock sorghum for optimal WUE. Within this broader context, this subproject is using Setaria viridis as a rapid cycling model for gene discovery. The team’s goal is to devise novel methods and develop resources to create genetic variations and streamline the phenotyping of WUE traits. These advancements are crucial for their application in forward genetics approaches aimed at identifying genes that regulate the efficiency of the carbon-concentrating mechanism (CCM) and WUE.

At the whole-plant scale, water use efficiency (WUE) is defined as biomass production per unit of water loss through transpiration. Plant WUE is largely determined by leaf-level “intrinsic” WUE, which is defined as the ratio of photosynthetic carbon gain (A_net) to stomatal conductance to water vapor (g_s). The intrinsic WUE in C₄ plants such as Sorghum is generally high because they use a carbon-concentrating mechanism (CCM) to increase A_net while maintaining low g_s. However, under drought conditions A_net can be limited by insufficient supply of carbon dioxide (CO₂) to drive the CCM. The focus of the research presented here is to enhance intrinsic WUE in Sorghum by (1) increasing the conductance of CO₂ within the leaf to overcome reduced g_s and (2) to enhance the catalytic efficiency of the first committed reaction of the CCM catalyzed by phosphoenolpyruvate carboxylase (PEPC).

The conductance of CO₂ within the leaf’s mesophyll is partially determined by cell wall structural polymers that influence wall thickness and porosity. The group has demonstrated changes in cell wall mixed-linkage glucans and ferulic/coumaric acids influence leaf CO₂ and H₂O conductance that led to an increase in whole plant WUE. This research team is also using a forward genetic screen to identify other key genes that influence traits related to the internal conductance of CO₂ and leaf intrinsic WUE.

Additionally, researchers have determined that variation in the affinity of PEPC for bicarbonate (HCO₃; K_HCO3) across several C₄ species is sufficient to increase modelled A_net under low g_s that occurs during drought. Researchers have further demonstrated using a heterologous Escherichia coli expression system that they can engineer enhanced in vitro PEPC kinetic properties with specific modifications to key amino acid residues. These modifications are also predicted through models of C₄ photosynthesis to increase photosynthesis under low g_s and enhance WUE. To translate these findings into plant systems, the team is using prime editors to create heritable edits of these key amino acid residues. Researchers are currently phenotyping plants engineered with these modified PEPCs to determine the impact on intrinsic and whole-plant WUE.

Systems modeling is an integral part of the Integrative Modeling and Genome- scale Engineering for Biosystems Security (IMAGINE BioSecurity) Science Focus Area project that address biosafety concerns related to microbial biocontainment and performance stability. Researchers aim to develop integrative bioinformatics tools for the efficient modeling of metabolism and gene expression (ME-models) of bacterial systems and metabolic flux analysis (MFA). These ME-models and MFA analysis will be used for the predictive design of novel containment strategies by identifying critical metabolic reactions governing secure biosystems designs in engineered bacteria.

Genetically modified microorganisms (GMMs) are widely used in agriculture and bioenergy industries. Among current systems biology tools, computational methods can interrogate systems with unprecedented detail and high throughput. However, lagging behind is the application of these tools for secure biosystems designs. Here, the group presents coralME, FreeFlux, and EMUlator2ML, new computational tools aiming to design predictable and generalizable biocontainment and robustness stabilization strategies.

First, the team developed coralME to automatically reconstruct nearly finished ME-models from genome-scale metabolic models (M-models), which allowed researchers to complete four highly curated ME-models for bioeconomy relevant microorganisms Pseudomonas putida KT2440 (iPpu1686-ME), Synechocystis sp. PCC 6803 (iSyn1015-ME), Clostridium ljungdahlii DSM 13528 (iClj978-ME) and Mycoplasma mycoides JCVI-Syn3A (iMmy259-ME). As they include gene expression, the number of components and reactions grows to accommodate transcription and translation. For instance, iPpu1686-ME models 224 additional gene products (+15.32%) compared to its parental M-model, iJN1463, in a network of 14,426 reactions and 7,566 components, increasing in 392.86% and 251.42% the number of reactions and components, respectively. Additionally, coralME aided the development of 17 draft ME-models for diverse bacteria, covering eight phyla. Application of coralME to these 21 different organisms resulted in short reconstruction times, effectively reducing the reconstruction of ME-models from several months to minutes. The platform is ideally suited for reconstruction of feasible ME-models employing efficient troubleshooting and reporting methods that repair and guide the manual addition of reactions, respectively. Consequently, coralME has accelerated modeling and simulation, further allowing the prediction of hundreds of essential genes, microbe-microbe interactions, overflow metabolites, use and essentiality of enzyme cofactors, and proteome composition.

In parallel, the team have developed FreeFlux, an open-source Python package which offers comprehensive ¹³carbon-MFA analysis, boasting swift, reliable flux computation. EMUlator2ML, a machine learning framework, accelerates flux estimation, enhancing large-scale analysis and strain screening by “learning” intrinsic relationships between metabolite labeling patterns and metabolic flux, inferring fluxomic phenotypes from isotopically metabolomic datasets. It demonstrated the ability to use M-models to generate the training dataset with minimal reliance on experimental data. Finally, the group is introducing a biocontainment approach combining ensemble computational modeling with CRISPR interference (CRISPRi) to modulate GMM metabolism, targeting core robustness for growth instability. This approach enables identification of enzymatic targets sensitive to expression perturbations and establishing genetic circuits for enhanced performance and safety, with strains meeting the National Institutes of Health escape frequency standard, validated across various conditions.

In summary, researchers used ensemble modeling, FreeFlux, EMUlator2ML, and highly curated M-models and ME-models to elucidate microbial metabolism under variable conditions, metabolic interactions within microbiomes, and the productivity of GMMs under secure biosystems constraints in an iterative Design-Build-Test-Learn cycle. The resultant pipeline will enable rapid and predictive secure biosystems designs.

In nature, plant oils represent one of the most energy-rich sources of renewable hydrocarbons. They are stored as triacylglycerols (TAGs) or oils, which can be used as alternative feedstocks for biodiesel production. As an alternative feedstock, plant-based oils have several advantages over other fuels, including high energy content, no need for fermentation, compatibility with existing fuel technologies, and being environmentally friendly. Life cycle analyses have shown that the production and use of jet fuel from oilseeds can results in lower greenhouse gas emissions as compared to petroleum-derived fuels. However, supplies of these energy-rich oilseeds compounds are limited due to low crop yield and limited available arable land. This project aims to use a combination of genomic, molecular biological, and biochemical analyses to explore transcription factor regulatory networks that regulate TAG/oils production in plants, and how they can be manipulated to increase the carbon conversion to oils in oilseed plants.

Specifically, transcriptional regulators that have been found to increase oil content when over- expressed in Arabidopsis will be targeted to better understand the mechanism by which seed oil storage is enhanced. The central hypothesis of this project is that a transcription factor described in duckweed is also required to regulate TAG metabolism in Brassicaceae oilseed crops such as Camelina, canola, and pennycress. Three objectives will be pursued, which are (1) to determine requisite duckweed transcription factor functions in oil storage metabolism; (2) to alter oil content and agronomic performance in Camelina; and (3) to define an overall metabolic engineering strategy to leverage duckweed transcription factors for increasing TAG yield. Knowledge gained from this research will unlock new and creative avenues to use genetic engineering to enhance the TAG in oilseed crops, which will help fulfill the world’s growing fuel needs.

The project objectives are to (1) design enzymes capable of complete depolymerization of nylon 6 and nylon 66 and (2) engineer bacterial strains able to metabolize the degradation products to higher-value sustainable materials.

As of 2015, a total of 6.3 billion tons of plastic waste had been generated globally. It is estimated that only 9% of this total has been recycled, while 12% has been incinerated to recover energy values, and the remainder has entered landfills. New technologies are needed to address this ever-growing problem. An alternative approach, harnessing the power of biology to not just depolymerize plastics back to their monomer precursors but convert them into higher-value products, offers stronger economic incentives and in turn would be expected to drive more rapid and widespread adoption. Toward that end, this group’s work focuses on combining cutting-edge computational protein design and synthetic biology to address the challenge of complete biodegradation and upcycling of the recalcitrant polymers nylon 6 and nylon 66. Although natural enzymes have been shown to be able to degrade amorphous portions of polyamides such as nylon 6 and nylon 66, complete enzymatic degradation has not been demonstrated. Researchers hypothesize this to be due in large part to a lack of natural enzymes able to efficiently catalyze degradation of the crystalline portion of the polymer. To alleviate this limitation, the team is using a combination of physics-based and generative deep learning-based protein design methods to engineering new and improved enzymes, with optimized active sites for binding and hydrolysis of polyamides. In conjunction, researchers are screening and engineering bacterial strains able to metabolize nylon 6 and nylon 66 degradation byproducts directly into central metabolism. Such platform strains can be used to produce a wide variety of fermentation products from central metabolites. Integration of nylon 6 and nylon 66 depolymerizing enzymes into these engineered hosts will provide a novel, elegant, and cost- effective consolidated fermentation process for nylon upcycling to higher value sustainable materials.

The overarching goal of this project is to predict microbial performance in large-scale bioreactors by understanding cell population performance, metabolism, and cell-to-cell heterogeneity in simulated bioreactor microenvironments. Addressing the uncertainty gap in scaling between laboratory- and industrial-scale cultivations is key to accelerate innovation in the bioeconomy and this project will do so by developing and integrating computational and experimental tools as well acquiring fundamental knowledge in microbial systems.

The biological conversion of renewable and waste sources to fuels and chemicals is an integral component of a sustainable bioeconomy. While biomanufacturing has been successfully demonstrated at the laboratory scale for a wide range of products, only a few have successfully been produced at industrial scales. This transition between laboratory- and industrial-scale cultivations represents a ‘valley of death’ in biological processes, where uncertainties arise regarding the lack of predictability for microbial performance across scales. Mixing becomes one of the significant challenges encountered in large-scale bioreactors. In contrast to the well-mixed cultivations at the laboratory scale, large-scale cultivations are not uniformly mixed which results in uneven distribution of nutrients, pH, gas composition, and temperature. These heterogeneities impact microbial performance in an unpredictable manner, decreasing bioconversion efficiency and ultimately increasing manufacturing costs. Unless the capability to predict microbial performance in large-scale bioreactors can be realized, many biological conversion pathways will not come to fruition in the bioeconomy.

This multidisciplinary and multi-institutional project will develop and integrate experimental and computation tools and will acquire fundamental knowledge in microbial systems to address the uncertainty gap in scaling between laboratory- and industrial-scale cultivations. The group will establish a framework to predict and address the adequacy of a microbe at the beginning of the innovation cycle to mitigate risks during the development and scale-up of new bioprocesses. The team combines the strengths of metabolic engineering, fermentation science, systems biology, genome-scale modeling, automated high-throughput DNA sequencing, computational fluid dynamics, and machine learning. The knowledge gained through this work will serve as a foundation to address conversion issues at the microbial level and will extend to other biological disciplines that seek predictive understanding of multi-cellular system behavior, for example at an ecosystem level, which are relevant goals to DOE-BER.

This team’s goal is to develop computer vision software pipelines for efficient analysis of minirhizotron and hyperspectral images of switchgrass.

Both minirhizotrons and unmanned aerial vehicles (UAVs) can provide a massive amount of image data on plants like switchgrass (Panicum virgatum), a potential source of biofuel. However, manual analysis of these images is time-consuming. Researchers focus on developing computer vision software pipelines to segment the images, or classify pixels based on their respective imagery, and accurately quantify data for these segmentations and raw images.

Minirhizotrons allow researchers to track the growth of the same plant roots over time. For any automated analysis, it will be necessary to align the images so that calculations from pixel differences are accurate. Researchers use the Binary Robust Invariant Scalable Keypoints (BRISK) algorithm for feature detection (Leutenegger et al. 2011), and then random sample consensus (RANSAC) to calculate a homography between matched points, to align two images from different dates. Minirhizotrons provide eight images, in color, of the roots at different depths. Researchers experiment with aligning raw minirhizotron images versus segmented images, where roots have been identified, and with aligning each level separately versus all at once, when images have been stitched together. Additionally, the group demonstrates an analysis pipeline for UAV hyperspectral data of individual switchgrass plants. This pipeline produces both individual segmentations of switchgrass plants, and extractions of vegetation indices for the respective plants using the hyperspectral data and segmentations. The team first obtains segmentations of individual plants by using a combination of Sparsity Promoting Iterated Constrained Endmembers (SPICE) and manually inputting key-points for each plant (Zare and Gader 2007). Then, the team applies the watershed algorithm to assign boundary labels for each plant from the binary SPICE output. The team’s minirhizotron analysis pipeline is able to identify a change in biomass over time, and alignment results are promising. Researchers’ hyperspectral analysis pipeline calculates all vegetation indices after performing radiance, reflectance, and orthorectification processing and stitching together all data cubes. The group’s combined aim with this research is to expedite data collection and analysis for biologists.

The overriding goal of the Oak Ridge National Laboratory (ORNL) Plant-Microbe Interfaces (PMI) Science Focus Area is to predictively understand the productive relationship between a plant host and its microbiome based on molecular and environmentally defined information. Populus and its associated microbial community serve as the experimental system for understanding this dynamic, complex multi-organism system. To achieve this goal, researchers focus on: (1) defining the bidirectional progression of molecular and cellular events involved in selecting and maintaining specific, mutualistic Populus-microbe interfaces; (2) defining the chemical environment and molecular signals that influence community structure and function; and (3) understanding the dynamic relationship and extrinsic stressors that shape microbiome composition and affect host performance.

Ectomycorrhizal fungi (EMF) are beneficial fungi that colonize the root tissues of multiple host plants. The interaction between EMF and their host is likely governed by metabolites, which act as direct lines of communication between organisms in the rhizosphere. However, the metabolites or signals that are produced when a fungus is alone or with a host or with other microbes are understudied. This team’s goal is to identify and characterize the repertoire of metabolites produced when EMFs are in co-culture with each other or when colonizing different Populus genotypes in tissue culture assays. Firstly, to develop a functioning EMF synthetic community, the group conducted co-culture assays between five EMFs isolated from Populus roots or within a Populus plantation. The aim was to find a combination of EMF that produce a repertoire of metabolites that promote growth of other fungal members. Since it is known that nutrient availability influences metabolite production (Rush et al. 2022; Meena et al. 2023), researchers utilized four different substrate media, two nutrient-rich and two nutrient-deprived. The group’s preliminary results determined that Hebeloma brunneifolium promoted the growth of nearly all the co-occurring fungi in a nutrient-poor environment; however, this was not reciprocal, with no growth benefit of H. brunneifolium. Laccaria bicolor and Cenococcum geophilum, regardless of the media, had the most beneficial interactions with co-occurring fungi. Based on the above results, the team determined that C. geophilum, H. brunneifolium, and L. bicolor would benefit each other for fungal growth.

Next, researchers examined how these EMFs individually impact Populus root and leaf development over time. Researchers used tissue cultures of Populus tremula x alba genotype 717-1B4 and Populus trichocarpa x Populus deltoides hybrid 52-225. Group members measured plant physiological traits (root growth and leaf development) as well as omic data (volatile organic compounds (VOCs), metabolomics, and proteomics) to determine the effect of each EMF on Populus. After 5 weeks of colonization, the team’s preliminary results determined that C. geophilum effectively colonized the root, as shown by the development of a Hartig Net, and increased root length and branching. H. brunneifolium interestingly did not colonize the host plant but still had a positive growth effect on root and shoot tissue, possibly attributed to VOCs or specialized metabolites. Lastly, L. bicolor effectively colonizes Populus tissue culture plants but showed no beneficial tradeoff with its host. Altogether, this project’s results show that researchers can construct a synthetic EMF community that will be symbiotic with each other and likely have a positive phenotypic effect on the host plant.

The overarching goals of this project are to establish an analytical and experimental framework for comprehensive characterization of viral-driven alteration of microbial metabolisms in soil. The specific results presented here focus on the development of new tools and resources to help researchers “see” the viral signal in their data, and the benefit of pairing metagenomics and metatranscriptomics approaches to better characterize the potential impacts of viruses in microbiomes.

Metagenomics has emerged as a powerful approach to explore environmental viral diversity and identify the potential impacts of viruses in microbiomes, including in complex ecosystems such as soil. As the throughput, quality, and range of omics data expand, new methods and tools are needed to help researchers leverage the growing viromics toolkit and more thoroughly characterize uncultivated viruses beyond genome diversity. Here, researchers first outline how datasets including paired metagenomes and metatranscriptomes can help investigate viral activity in microbiomes. Specifically, in both longitudinal sampling of a mountainous soil and diurnal sampling of a Yellowstone hot spring microbial mat, only a limited fraction (~20 to 50%) of viruses identified via metagenomics were typically detected as transcriptionally active, and sample ordination based on metatransciptomic coverage provided a much stronger sample clustering consistent with ecological parameters compared to similar ordinations based on metagenomic coverage. This indicates that viral dynamics and potential impacts on a microbiome can be better understood by considering transcriptional activity in addition to detection in a metagenome. Next, the group presents Multi-choice Viromics Pipeline (MVP), an integrated workflow designed to enable researchers to run standard viromics analysis of metagenomes and/or metatranscriptomes in only a few easy steps. Integrating state-of-the-art tools, MVP enables nonexpert users to seamlessly process a set of metagenomes into heatmaps and ordinations based on viral signal, immediately providing a window into the viral diversity present in these data. MVP also automates a number of tasks, such as correcting quality estimation of provirus predictions, and provides summary statistics throughout the workflow to inform users on the overall viral content of their sample. Ultimately, the development of new viromics approaches such as community-wide analysis of viral diversity through paired metagenomics and metatranscriptomics, along with the expansion of the viromics toolkit with both new tools and integrated user-friendly pipelines, will pave the way toward widespread adoption of these analyses and robust consideration of the role(s) of viruses in all microbiome studies.

The Lawrence Livermore National Laboratory Secure Biosystems Design Science Focus Area (SFA) aims to develop robust biosecurity tools at the sequence, cellular, and population levels to safeguard the deployment of genetically engineered bacteria for environmental applications. This project aims to exploit the competition between toxin-antitoxin (TA) systems to model and ultimately regulate horizontal gene transfer (HGT) within microbial communities for biocontainment.

Bacteria rapidly disseminate genetic information through HGT, a fundamental driver of microbial evolution. While there is enormous potential in the development of engineered microbial products that are compatible with native microbiota in target environments (e.g, gut or rhizosphere microbiomes), the challenge lies in controlling HGT to and from deployable engineered bacteria. This raises important questions regarding the maintenance of genetic stability over time, and the ecological containment of genetically modified organisms as well as the recombinant or synthetic constructs they harbor. Rather than attempting to suppress natural HGT in situ, the team’s goal is to examine the forces and barriers that shape HGT networks in microbial populations, and to leverage the principles uncovered to develop genetic tools that promote genetic stability of deployable engineered microbes.

The ubiquitous and mobile nature of TA systems in prokaryotes makes them versatile effectors of biocontainment mediated through HGT network interactions. Researchers systematically identified and mapped 40,000 TA systems onto the global bacterial plasmidome, discovering how TA systems are organized through HGT communities, rather than traditional taxonomic classifications. Machine learning models trained on the most common 10% of TA systems alone were able to assign plasmids to HGT communities with 95% accuracy, suggesting each HGT community has its own unique and predictable TA signature. The results of this study imply that HGT networks are constrained, at least in part, by the compatibility between TA systems and provide a coherent explanation for the otherwise erratic distribution across microbial genomes. Understanding and leveraging the dynamics of this innate competition between TA systems could form the basis of a TA-based mechanism for custom community-level invasion or biocontainment. Initial models will inform experiments to test the potential and limitations of TA-based design for controlling the horizontal spread of engineered plasmids outward and natural plasmids inward, in both simple and complex microbial consortia.

1. Exploit domain knowledge on phosphorus action in roots to identify strong predictors for phosphorus data and employ an XGBoost model to predict phosphorus levels for ~2,000 georeferenced sorghum landraces distributed in Africa.
2. Perform an environmental genome-wide association study (GWAS) in those landraces that have already been genotyped using the predicted phosphorus data as the phenotypes for the GWAS analysis.
3. Characterize the genetic architecture of lipid content during the early stages of sorghum development using the Sorghum Association Panel (SAP). Researchers will perform a GWAS on lipid content under low temperature and low phosphorus.
4. Develop algorithms that incorporate all the different types of information the group collects (i.e., metabolite levels, GWAS candidate genes, selection signals) to improve researchers’ ability to detect signals of small effects and increase their confidence in the selection of candidate genes. The algorithms and pipelines developed here will be made available to the community as R packages.

Phosphorus (P) is one of the three primary nutrients in commercial fertilizers, essential for plant growth and development. Excessive use of P-rich fertilizers in agriculture leads to leaching and runoff to water bodies, harming aquatic life. The limited global reserves of rock P and water pollution make it necessary to find a sustainable solution that ensures the proper utilization of P, minimizing leaching and runoff. Landrace varieties adapted in soils with varying levels of P availability likely possess unique genetic mechanisms to cope with P scarcity.

Environmental GWAS using these genotypes with georeferenced accessions present a potential for identifying candidate genes. Employing GWAS, the group seeks to identify genes and pathways associated with P in plants that will help researchers overcome these obstacles. Central to GWAS’s success is accurate phenotype measurement. To this end, researchers have developed an XGBoost model predicting P availability in the soil using domain-based knowledge, surpassing current models in prediction accuracy and capability to discern lower-end values. Utilizing high-dimensional genetic datasets of georeferenced Sorghum bicolor in Africa, the team will conduct environmental GWAS using the team’s new P availability data. The team will use a linear regression-based p-value combination method (MAGMA) to aggregate multiple small effects on a gene-based level. The group’s previous study has identified lipid variations, specifically phosphatidylcholine, in maize adapted to low P conditions in the Mexican highlands.

Additionally, researchers have obtained lipid profiles for 400 SAP grown in normal and low P conditions. Analyzing the corresponding lipid dynamics will play a significant role in understanding P utilization. Subsequently, a GWAS will be conducted focusing on these identified candidate lipids. By employing the Cauchy Combination test to combine the findings from both the P GWAS and lipid GWAS, this team aims to reevaluate and redefine the order of gene importance. This integrative analysis will facilitate the identification of candidates that are linked to both lipids and P efficiency.

Pacific Northwest National Laboratory’s Phenotypic Response of Soil Microbiomes Science Focus Area aims to achieve a systems-level understanding of the soil microbiome’s phenotypic response to changing moisture. Researchers perform multi-scale examinations of molecular and ecological interactions occurring within and between members of microbial consortia during organic carbon decomposition, using chitin as a model compound. Integrated experiments address spatial and inter-kingdom interactions among bacteria, fungi, viruses, and plants that regulate community functions throughout the soil profile. Data are used to parametrize individual- and population-based models for predicting interspecies and inter-kingdom interactions. Laboratory and field experiments test predictions to reveal individual and community microbial phenotypes. Knowledge gained provides a fundamental understanding of how soil microbes interact to decompose and sequester organic carbon and enables prediction of how biochemical reaction networks shift in response to changing moisture regimes.

The complexity of the soil microbiome and its environment makes it difficult to understand the networks of interactions among community members, ranging from positive interactions, such as metabolite exchange, to negative interactions like competition. Moisture is a critical attribute of the soil environment that constrains access to resources and interactions within the community, impacting microbial metabolism and biogeochemical processes. Here, the group investigates microbial metabolic interactions and functions that govern organic matter decomposition under contrasting moisture conditions across three levels of biogeochemical complexity that use chitin as a model substrate. At the most reduced complexity, the team used a tractable Model Soil Consortium containing 8 members (MSC-2) to understand interspecies interactions governing degradation of chitin in a well-mixed system (McClure et al. 2022). The team expanded to an intermediate complexity consortium of 31 members (MSC-1) incubated in a spatially structured synthetic soil habitat to identify microbial interactions and metabolic pathways involved in chitin decomposition (McClure et al. 2020). At the highest level of biogeochemical complexity, researchers used laboratory incubations of soil with chitin amendments collected from the team’s Tall Wheatgrass Irrigation Field Trial in Prosser, WA, to understand how microbial interactions mediate the decomposition of organic substrates. By examining chitin decomposition in a series of experiments that scale in biological and chemical complexity, researchers test how outcomes from culturing experiments translate to soil environments.

Experiments from reduced complexity MSC-2 incubations demonstrate the importance of a subset of chitin degraders for promoting community function. Streptomyces was a key member responsible for most chitin degradation, while other organisms like Dyadobacter had small, realized niches, and low expression. Expanding on this complexity, MSC-1 was used to test impacts of structure and moisture on community interactions in synthetic soil habitats. Using genome-resolved metagenomics and metatranscriptomics, the study shows consistency across experimental scales, with members from MSC-2 retaining prevalent expression patterns in the unstructured broth incubations of MSC-1 (i.e., Streptomyces). In contrast, Dyadobacter was a highly active member of broth incubations in MSC-1. Introducing structure invoked significant treatment effects observed as a shift from Dyadobacter and Streptomyces dominated communities in broth to Ensifer in structured incubations. These patterns were likely a result of motility and transporter related gene expression present in Ensifer but not in Dyadobacter or Streptomyces. Microbial consortium responses to moisture and chitin amendment were tested in a soil-based experiment by screening for chitinolytic and carbon cycling enzymes. Chitin amendment increased chitinolytic response regardless of moisture level. For other carbon cycling enzymes, high moisture caused greater activity compared to low moisture soils. Activity-based probes (ABP) were used to identify organisms producing chitinolytic enzymes. Moisture status and chitin amendment impacted the recovery of chitinolytic genera, Chitinibacter, Cellvibrio, and Massilia, enriched using ABPs, leading to evidence for division of labor on carbon cycling and fitness variation for soil moisture.

These results highlight how genome-resolved multiomics and scaling experimental complexity aid researchers’ understanding of microbial communities and suggest a disconnect between broth-based incubations and native incubations of soil. Researchers aim to use this knowledge to move beyond lab-scale experiments and towards integrating in vivo experimentation to field-scale in- situ experiments.

The overarching goal of this project is to create sustainable, managed ecosystems where important biofeedstocks can be produced while simultaneously maximizing soil health and mitigating adverse impacts of climatic change.

Over the past 2 decades, it has become clear that symbiotic host–microbe interactions alter the way plants grow and respond to abiotic and biotic stress. Harnessing diversity within these plant–microbe associations provides an opportunity to create sustainable, multipurpose ecosystems. Within these managed ecosystems, science can produce energy necessary to meet global needs, while maximizing soil health and mitigating adverse impacts to climate. Therefore, to increase sustainability within DOE relevant biofeedstocks, researchers aim to develop plant-microbial pairings tailored for specific environmental conditions. First, researchers are identifying genetic variation within plant hosts to select for plants that are tolerant to abiotic stress. Next, scientists are complementing these plants with varied belowground microbial partners to alter plant performance and ecosystem carbon cycling.

First, this research group identified Populus genotypes that varied in their response to drought without associated microbial partners. Over the last 2 years, team members conducted a series of greenhouse experiments where they identified variation in Populus response to drought using both hyperspectral imaging and assessing changes in plant physiology across 39 genotypes of Populus trichocarpa, 39 of Populus deltoides, and 26 hybrid genotypes (P. trichocarpa x P. deltoides). Overall, researchers found differences in plant phenotype across genotypes and in response to drought. Interestingly, drought tolerant genotypes maintained higher levels of stomatal conductance during drought relative to drought susceptible genotypes. Genotypes in these experiments will be leveraged in manipulative studies with mycorrhizal isolates to examine if mycorrhizae can increase host stress tolerance in both drought susceptible and tolerant genotypes.

Next, this team characterized variation in mycorrhizal community dynamics across drought tolerant and susceptible P. trichocarpa genotypes planted in a common garden in Davis, CA. In February and July of 2022, researchers collected root and rhizosphere samples from drought tolerant and susceptible P. trichocarpa. Across these genotypes, the team found both arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) fungi colonized the roots, and drought tolerant genotypes had a greater percentage of hyphae, greater number of arbuscules, and a larger Hartig net compared to drought susceptible trees. Researchers found that mycorrhizal diversity and community composition varied between well-watered and drought treatments and across plant genotypes. Fungal isolations yielded potentially new ECM taxa that can be leveraged in future experiments.

Finally, in the fall of 2023, team members collected root and rhizosphere samples from P. trichocarpa and P. deltoides across natural precipitation gradients in Washington and Texas, respectively. Within these collections, researchers are characterizing variation in mycorrhizal community composition, colonization, and abundance. Culture collections are being developed for future experimentation.

Combined, these initial efforts highlight significant genetic variation in the response of Populus to drought and demonstrates variation in belowground mycorrhizal communities across drought tolerant and susceptible genotypes. Plant and fungal resources resulting from these experiments will be used to develop tailored plant-fungal partnerships to alter host abiotic stress tolerance and soil carbon cycling.

1. Developing an abscisic acid responsive element binding factor (ABF) plus gene regulatory network (GRN) to define the diversification of stress-associated gene regulation and train predictive models.
2. Utilizing evolutionary context and species phylogeny to discover novel gene
3. Connecting phenotype to genotype using a modeling framework and advances in the rapid deployment of CRISPR/Cas9 mediated mutagenesis.

Engineering crops for sustainable growth in a rapidly changing environment requires an understanding of how growth-modulating gene regulatory networks are integrated with the environment. By analyzing 10 phylogenetically related species in the Brassicaceae family that occupy diverse ecological niches, researchers have identified species where increasing salinity causes growth reduction as well as species in which salinity promotes growth. Environment-dependent developmental decisions that are critical for an organism’s survival often rely upon the dose dependent action of signaling molecules such as hormones. Differential growth regulation by one such hormone, abscisic acid (ABA), is the primary mechanism plants use to acclimate to changes in water availability and salinity. The group identified species in which ABA predominantly functions either as a growth repressor or a growth enhancer, suggesting that the differential growth responses observed in response to salinity are partly mediated through this hormone. Comparative anatomical analysis of the Brassicaceae species revealed that reduction in meristem cell number contributes to ABA’s growth repression while growth promotion involves increases in mature cell length. Further, the team’s genetic studies reveal that the growth inhibition by ABA is dependent on components of the well-established canonical ABA signaling pathway and is accomplished through the action of ABA-responsive elements Binding Factors (ABF) transcription factors (TFs). DNA Affinity Purification sequencing (DAP-seq) analysis of the ABF TFs revealed that changes in the ABF-auxin regulatory network can explain differences in the extent of growth repression observed in the different species. To further characterize the ABF regulatory network and understand its role in different cellular and environmental contexts, researchers are now exploring the cis regulatory binding landscape of ABF interacting transcription factors across the 10 species.

Growth promotion by ABA, on the other hand, is regulated by an independent pathway which involves non-canonical ABA receptors, suggesting that the dichotomy in physiological responses to this hormone can be explained by differences at the level of perception, as well. The components of the growth promoting and growth repressing pathways identified through these comparative growth analyses in evolutionarily related species will help identify hormonal regulators of different growth patterns and provide candidates for tuning growth in agriculturally relevant species.

The long-term objective is to develop optimized computational and experimental design schema to study plant processes at the systems level to enable precise and reliable prediction of plant gene function. Studies of substrate specificity across the GT47 family will be evaluated through modeling-based predictions and cryo-electron microscopy (Cryo-EM) to determine the molecular mechanisms that underlie duckweed cell wall synthesis.

Complex carbohydrates are essential molecules of life that are responsible for energy supply and diverse cellular functions in all species. Glycosyltransferases (GTs) facilitate the creation of glycosidic bonds, which are essential for synthesizing intricate carbohydrates that are the building blocks of the carbon stored in plant biomass. One of the team’s main goals is to use high throughput methods to determine sugar nucleotide donor and acceptor substrate specificities for genes encoding GTs to functionally assign them into glycopolymer-specific pathways, with the aim of harnessing these pathways to reengineer duckweed cell walls for optimized biofuel and feedstock production. These data are being used in a combinatorial approach involving machine learning models to understand and predict substrate specificity: AlphaFold to predict structures of both monomeric and oligomeric GT47 Carbohydrate-Active enZYme family proteins (Zhang et al. 2023), and cryo-EM to validate the predictions experimentally. The Facilities Integrating Collaborations for User Science (FICUS) program through Environmental Molecular Sciences Laboratory (EMSL) and DOE Joint Genome Institute (JGI) will help researchers broaden the enzyme library construction to identify GT47 complexes through a combination of plant engineering, mass spectrometry, solid state nuclear magnetic resonance spectroscopy, and structural biology to analyze the protein interaction networks of all GT family members and the integral architecture of the cell wall structure of duckweed. Ultimately, the data generated from this proposal will be used to inform functional studies in a species-agnostic manner to create designer-specified cell wall structures for bioproduction.

This project’s goals include (1) identification of genes that are specifically expressed during winter dormancy; (2) identifying their transcriptional regulators using expression quantitative trait loci (eQTL) mapping; and (3) transgenic validation of candidate genes and 4) incorporation of workflows into the KBase platform.

Xylem and bark samples were collected from 800 natural accessions of mature black cottonwood (Populus trichocarpa) planted in a common garden in Clatskanie, OR. To investigate the molecular mechanisms involved in cell survival during dormancy, samples were collected at two time points: in December 2022, when trees were dormant, and during the growing season in July 2023.

Initially, a small set of core samples was collected from approximately 5-year-old field-grown poplar trees established at the University of Tennessee experimental field site in Alcoa, TN. These samples were then used to refine the sampling procedure to ensure (1) that the most RNA-rich tissues are collected (bark, cambium, and most recent xylem) to capture transcriptomic responses and (2) that both the sampling and subsequent sample processing steps are scalable and suited to study a large population under field conditions.

After this initial optimization, 818 genotypes of the population of natural variants of P. trichocarpa were sampled, generating a total of over 2,400 samples across dormancy and growing seasons. To optimize the transcriptomic analysis, a subset of 40 genotypes was selected based on contrasted growth (highest or lowest trunk diameter) and wood properties (highest or lowest wood density). For these genotypes, samples collected in winter were grinded, RNA were isolated and then used for RNA sequencing (RNA-seq).

Based on transcriptome sequencing results, workflows were tested, optimized, and established to perform comprehensive analyses to meet the project goals. Future efforts include completion of RNA-seq and differential gene expression analyses, eQTL mapping, and incorporation of workflows into the KBase platform.

Recent work aimed at improving the conversion of biomass to advanced biofuels and bioproducts has highlighted the critical importance of solvent effects. These effects are important both in the efficient solvent-based deconstruction of biomass and in the product titer limitations of fermentations due to the solvent-based destabilization of microbial membranes. This Science Focus Area provides fundamental knowledge about how solvents alter the structures of plant cell walls and of microbial membranes. The overarching hypothesis is that knowledge of partitioning or binding of the solvent from the bulk phase to biomass or biomembranes can help predict maximal or minimal disruption. Solvents disrupt biological structures comprising amphiphilic molecules and polymers (e.g., membranes and biomass). Determining common biophysical principles of solvent disruption will lead to new understandings of how solvents affect the relevant structures. This information will help determine the ultimate microbial limits in tolerating specific solvents, as well as the eventual design of cosolvents best suited for pretreatment. Researchers integrate the power of world class neutron scattering capabilities and leadership class supercomputing facilities available at Oak Ridge National Laboratory (ORNL). These capabilities are complemented by expertise in biodeuteration and biomembranes at ORNL, plant cell wall chemistry at the University of Tennessee, and neutron scattering and membrane biophysics at the University of Cincinnati.

Effective conversion of biomass remains challenging. The three major components— cellulose, hemicellulose, and lignin—form a recalcitrant lignin-carbohydrate complex (LCC) that must be fractionated for valorization. The team’s studies on the molecular structural changes underline two approaches to improve biomass conversion: pretreatment improvement and feedstock genetic engineering. In the first case, researchers describe the mechanism of action of Cyrene in solubilization and fractionation of lignin during thermochemical pretreatment. In the second case, researchers investigated LLCs in pectin knockdown transgenic switchgrass and model composites to gain insight into the molecular details of lignin–carbohydrate interactions. Overall, this comprehensive analysis furthers the understanding of the solvent effect during biomass fractionation and critical polymer interactions in plant cell walls that impact biomass recalcitrance.

Cyrene is the trademark name of dihydrolevoglucosenone, a biodegradable, non-toxic green dipolar aprotic solvent the Circa Group produces on a scale of 50 tons per year. Cyrene effectively extracted a significant amount of lignin from hardwood, herbaceous species, and even softwood at an aqueous acidic mild temperature of 120℃ (Wang et al. 2023). Nuclear magnetic resonance (NMR) revealed that the structure of extracted lignin was modified, correlating to the composition of the Cyrene co-solvent system.

The interactions between Cyrene and lignin were studied by molecular dynamic simulation (MD), revealing that Cyrene facilitated lignin solubilization and disrupted lignin aggregation. Cyrene also modified the cellulose fraction of the biomass. Small-angle X-ray scattering (SAXS) showed no lignin aggregation on the surface of microfibrils after pretreatment. However, small angle neutron scattering (SANS) showed the distances between cellulose microfibrils increased after Cyrene pretreatment then decreased to a level similar to untreated biomass after incubation with a dilute alkaline solution, suggesting the presence of Cyrene between microfibrils and its removal after alkaline incubation. This comprehensive analysis demonstrated the high potential of Cyrene co-solvent fractionation in extracting lignin and enhancing fermentable sugar yield by revealing the molecular interactions between Cyrene and LCCs.

Engineered plants with reduced pectin exhibit lower recalcitrance towards conversion to biofuels (Biswal et al. 2018), but complexes of pectin and lignin have not been confirmed as playing a role in recalcitrance. Researchers utilized a model composite system to investigate the effect of pectins on lignin polymerization (Shah et al. 2023). The lignin monomer coniferyl alcohol, protiated or deuterated, was polymerized in vitro by the hydrogen peroxide-horseradish peroxidase method in the presence of homogalacturonan, a linear pectin found in grasses. These composites were characterized by Fourier-transform infrared spectroscopy, solid-state NMR, SAXS, and SANS experiments. The lignin-pectin composites were compared to lignin synthesized without pectin and a physical mixture of pectin and lignin. Lignin particle sizes were smaller in the composites, and interconnected networks were formed. A unique ester bond was detected, supporting the existence of covalent bonds as well as hydrophobic interactions between lignin and pectin. These insights into the role of pectin in lignin deposition in the cell wall may inform improving biomass and its deconstruction for biofuels and bioproducts.

The Terraforming Soil Energy Earthshot Research Center (EERC) will study biological and geological solutions to accelerate scalable, affordable carbon drawdown in the United States’ 166 million hectares of agricultural soils. Research objectives include gene-edited plants and microorganisms that accelerate carbon sequestration, strategies that encourage soil mineral-organic interactions, and models that predict carbon durability in small soil pores as well as regional-scale estimates of locations with opportunities for increased soil carbon removal.

To reduce the United States’ net carbon dioxide (CO₂) emissions to zero and limit the impacts of global warming, it is essential to actively remove CO₂ from the atmosphere. Soils store a vast amount of carbon (C) in organic and inorganic forms, on the order of 3,000 billion tons globally. This is more carbon than is found in the atmosphere and on land combined. While the United States’ 166 million hectares of agricultural soils have lost a vast amount of carbon in the past century due to cultivation and erosion, there is clear potential to reverse this trend and actively manage agricultural lands with strategies that capture CO₂ from the atmosphere. The Terraforming Soil Energy Earthshot Research Center (EERC) will research new bio- and geoengineered techniques to understand, predict, and accelerate scalable and affordable CO₂ drawdown in soils, via both organic and inorganic carbon cycle pathways. The Center’s overarching goal is to advance the fundamental understanding of CO₂ drawdown in soils through both organic and inorganic pathways by measuring soil C storage capacity, durability, and regional variations that have bearing on land management practices. In Objective 1, synthetic biology tools will be used to accelerate naturally occurring plant and microbial traits that shape CO₂ fixation processes, organic matter formation, and mineral dissolution. Combined genome sequencing and isotope tracing approaches will be used to quantify the fundamental mechanisms of how organic matter accrues over time and the traits of plants and microorganisms that need to be better reflected in process models. In Objective 2, the Center will focus on positive interactions that can occur during the weathering of primary minerals and the formation of organic matter-mineral complexes—together, these have dramatic potential to accelerate soil CO₂ drawdown via combined organic and inorganic pathways. But currently, the interactions between soil weathering, soil biology, and organic matter cycling are poorly understood. The Center’s field and laboratory-based studies will measure how soil management approaches can be ‘stacked’ together to optimize total CO₂ drawdown via co-deployment of novel engineered crops or microbes, silicate minerals, or organic amendments. Research for Objective 3 will integrate new modeling capabilities and data exploration to enable better predictions of soil CO₂ drawdown in both space and time. Novel micro- and macro-scale simulation tools will be combined with advanced modeling, machine learning, and data science approaches, allowing the Center to better forecast the potential impacts of new soil CO₂ drawdown approaches at multiple scales. The Terraforming Soil EERC team includes world-class experts in soil carbon cycling, photosynthesis biochemistry, plant/microbial gene engineering and genomics, mineral geochemistry, machine learning, exascale modeling and computing, additive manufacturing, and in situ isotope-based characterization. Throughout the research program, the Center will bridge cutting-edge analytical and computational studies with a commitment to engage with community stakeholders, exploring the technical, social, and economic implications of engineered soil CO₂ drawdown. The Center will emphasize diverse training opportunities for students and early career scientists and amplify equity and inclusion throughout the research pipeline.

The team’s goal is to genetically increase constitutive terpene defenses of loblolly and slash pine to enhance protection against pests and pathogens and simultaneously expand terpene supplies for renewable biofuels and chemicals.

The constitutive and inducible oleoresin defense network in loblolly (Pinus taeda) and slash (Pinus elliottii var elliottii) pine provides physical and chemical resistance to insects and pathogens and the chemical composition of oleoresin can be used as a renewable source of biofuels harvested directly from live tree stems. Increasing pine terpenes is well aligned with the needs of the developing bioeconomy, as the southeastern U.S. currently hosts the world’s largest biomass supply chain, annually delivering 17% of global wood products, and has the potential to expand the U.S. pine chemicals industry by increasing biofuels from pine terpenes, which is currently limited by relatively low average wood terpene content. The team’s focus is to increase constitutive terpene production to enhance loblolly and slash pine’s resistance to pests and pathogens and to simultaneously increase biofuel feedstocks in these commercial pine species.

Pine terpenes evolved as a primary chemical and physical defense system and are a main component of a durable, quantitative defense mechanism against pests and pathogens. In previous research researchers demonstrated that terpene defense traits are under genetic control and behave as quantitative traits and have used genetic engineering to validate 12 genes that can significantly increase wood terpene content. In Objective 1, researchers are integrating existing and new genome wide association (GWAS) genetic results with RNA expression, quantitative trait loci (QTL) mapping, and allele frequency information in known high oleoresin flow selections and researchers’ breeding populations to discover and validate loblolly and slash pine alleles/genes that are important for resistance.

GWAS analyses of constitutive oleoresin flow, wood diterpenoid content, and resin canal number with ~83,000 biallelic single nucleotide polymorphisms (SNPs) were completed for the project’s Comparing Clonal Lines ON Experimental Sites (CCLONES) population. Constitutive and inducible oleoresin flow along with mono- and diterpene content were completed and resin canal number is in progress for the team’s Allele Discovery of Economic Pine Traits 2 (ADEPT2) population. In the ADEPT2 population, researchers simultaneously measured constitutive and induced oleoresin flow after treating clones with methyl-jasmonate (MeJA). While the goal is to increase constitutive terpene defenses, the group used MeJA to induce defense responses to identify the genes and genetic architecture of resinosis. In the ADEPT2 population, researchers found the clonal repeatability of constitutive oleoresin flow and inducible oleoresin flow to be 0.31, suggesting these traits are under moderate genetic control. In the ADEPT2 population researchers observed a strong genetic correlation (0.82) between induced and constitutive oleoresin flow, suggesting the genetic architecture of these traits is strongly shared. Researchers conducted association analyses with constitutive and inducible oleoresin flow, wood monoterpene content and composition and diterpenoid content obtained in the ADEPT2 population using linear mixed models and multilocus linear mixed models in ASRgwas and Genome Association and Prediction Integrated Tool packages using two sets of SNP markers totaling ~2.28 million biallelic SNPs. After controlling for multiple testing, researchers identified 146 significant SNPs (p<0.05) for 10 oleoresin traits, including constitutive oleoresin flow, monoterpene composition and content. Two of the significant SNPs for wood limonene content are in an α-pinene synthase gene. To validate significant SNPs the team quantified oleoresin flow in a pseudo-backcross population between one F1 slash x loblolly hybrid genotype backcrossed to slash and loblolly genotypes and are now completing genotyping 982 individuals with the Pita50k chip for future QTL mapping.

To identify genes regulating resin duct differentiation and function, researchers induced new axial resin canal formation in the cambial meristem by applying MeJA, a known inducer of traumatic resin canal formation in the Pinaceae family. Researchers conducted a time course experiment where the team created 78 RNA sequencing (RNAseq) libraries from vascular cambial zone tissue collected from days 0, 1 to 14, 17, and 21 after MeJA treatment. Pooled libraries were sequenced to a 30x read depth with the NovaSeq Illumina next-generation sequencing platform and reads were mapped to an improved de novo loblolly pine transcriptome that includes 64,671 genes that researchers constructed with existing expressed sequence tags contigs, Pacific Biosciences reads, and predicted transcripts from loblolly pine reference genome v2.01. DESeq2 analysis identified significantly 1,890 up and 4,634 down differentially expressed genes across the time course compared with wild-type controls. With these 6,524 differentially expressed genes the team created a Predictive Expression Network (PEN) using iterative Random Forest Leave One Out Prediction to illustrate higher order interactions between genes and to determine the gene-to-gene relationships that are the most highly predictive of each other. To identify and prioritize genes across the PEN that are involved in axial resin canal formation, researchers applied random walk with restart (RWR) algorithms based on a set of literature-curated seed genes that included known orthologous regulators of xylem formation and development, which are suppressed while resin canal formation is increased. The RWR approaches allowed the team to identify mechanistically associated genes that did not appear in GWAS due to a lack of statistical power or genetic variation but are still important components of resinosis. This identified 119 transcripts in the top 200 based on lines of evidence. Researchers are continuing to annotate the network to identify genes whose expression supports involvement in resin canal formation and terpene synthesis.

In Objective 2, the group is using information from Objective 1 to accelerate breeding for increased resistance in loblolly and slash pine through marker assisted introgression and will develop and test genomic selection models to accelerate breeding of resistant slash pine.

The overriding goal of the Oak Ridge National Laboratory (ORNL) Plant-Microbe Interfaces (PMI) Science Focus Area is to predictively understand the productive relationship between a plant host and its microbiome based on molecular and environmentally defined information. Populus and its associated microbial community serve as the experimental system for understanding this dynamic, complex multi-organism system. To achieve this goal, research focuses on (1) defining the bidirectional progression of molecular and cellular events involved in selecting and maintaining specific, mutualistic Populus-microbe interfaces; (2) defining the chemical environment and molecular signals that influence community structure and function; and (3) understanding the dynamic relationship and extrinsic stressors that shape microbiome composition and affect host performance.

Microbial communities play an integral role in the health and survival of their plant hosts. Environmental and host factors drive microbial community structure in the rhizosphere, but microbe-microbe chemical signaling undoubtably has a role in structuring the microbial community. The impact of microbe-microbe interactions on multispecies community structure and dynamics is not well understood. Researchers previously established that acyl-homoserine lactone (AHL), quorum sensing (QS), and natural product biosynthesis genes are prevalent in the Populus microbiome. QS often regulates extracellular enzyme production, biofilm formation, competence, and secondary metabolite production contributing to microbe-microbe interactions. Here the group utilized a synthetic biology approach to explore the diversity of AHL signals and an approach based on synthetic communities (SynCom) to investigate the influence of QS on microbial community structure and dynamics.

Bioinformatic analyses identified a large amount of unexplored AHL signal synthase gene (LuxI) diversity in the Populus metagenome and bacterial isolate genomes. As part of a DOE Joint Genome Institute (JGI) DNA Synthesis Community Sequencing Project, researchers selected 140 representative undefined LuxI homologs from the Populus microbiome for DNA synthesis and expression in the heterologous host Escherichia. coli. The group identified AHL signals synthesized by these LuxI homologs in E. coli using the JGI non-polar metabolomics pipeline and mass query language analyses. Researchers detected AHL production in about half of the synthesized LuxI homologs, including the first well-described AHL signal structures for several genera (Bosea, Duganella, Janthinobacterium, Massilia, Novosphingobium, Rhodanobacter, Rhodoferax, Sphingobium, Sphingomonas, Sphingopyxis, and Variovorax). Interestingly, the predicted AHL signal inventory of the endosphere is distinct compared to that of the rhizosphere/soil and is dominated by LuxI homologs that synthesize atypical AHL signals.

Next, the team utilized a AiiA-lactonase QS-off method with a previously established SynCom to assess the effects of AHL inactivation on microbial community structure, pairwise interactions, biofilm formation, and secondary metabolite production. Preliminary results demonstrate disruption of AHL signaling leads to changes in the community structure. Current efforts are focused on elucidating the molecular mechanisms through which AHL signaling mediates microbial community assembly. Collectively, these diverse applications of metagenomics and cultured representatives of Populus’ microbial community are facilitating researchers’ understanding of how Populus selects microbial partners and how its microbiome is structured.

Camelina (Camelina sativa L.) is an oilseed crop being developed as a biofuel crop for dryland agriculture. This team is investigating the role of the root microbiome in plant health, especially in relation to nutrient uptake, disease, and drought. Researchers sampled soil from 33 locations in four precipitation/cropping zones in Eastern Washington and grew cultivar Suneson in each soil in the greenhouse. Amplicon sequencing was used to describe the bulk soil, rhizosphere and endosphere bacterial and fungal communities. Plant compartment, cropping system zone, and location had significant effects on microbial composition. The group identified the core rhizosphere bacterial (several Actinobacteria including Aeromicrobium and Marmoricola, as well as the genera Rhizobium, Clostridium, and Sphingomonas) and fungal community (Pseudogymnoascus, Fusarium and Mortierella). Researchers are currently sequencing metagenomes from rhizosphere communities of 10 camelina lines grown under high and low nitrogen (N) conditions, to identify how soil N shifts the communities on the root.

More than 400 camelina-associated bacterial isolates were screened for growth promotion on camelina under normal and low nitrogen conditions. Eleven bacterial isolates were shown to promote elongation of primary roots under low nitrogen levels. Two bacterial strains Paraburkholderia tropica (isolates FMD144 and FMD568), showed consistent root growth promotion. One bacterial strain, Pseudomonas mediterranea isolate FMD348, inhibited the growth of camelina under all nitrogen levels. Co-inoculation of camelina with FMD348 and FMD144 revealed that strain 348 dominated the interaction and caused root inhibition, suppressing the growth promotion effect of strain FMD144. Interestingly, in a more complex bacteria-bacteria interaction in which FMD348 was co-inoculated with a bacterial community of either 21 isolates or the 11 beneficial isolates, root growth promotion was observed and growth inhibition by FMD348 was suppressed. The bacterial effect on camelina growth can be positively or negatively influenced by other bacteria in the community.

A diverse set of 33 bacteria selected from a larger collection of over 3,000 camelina-associated isolates were profiled by exometabolomics to determine what these bacteria consume and produce. They were cultured on Northen Lab Defined Medium, a diverse, defined media with over 60 compounds including sugars, organic acids, amino acids, and diverse nitrogenous cofactors and vitamins. Spent media was collected in late exponential phase and analyzed using liquid chromatography-tandem mass spectrometry. Almost every compound in the media was significantly reduced by at least one isolate; however, growth rates and consumption profiles varied greatly across the collection. Production of potential secondary metabolites produced by the cultures also varied greatly across the collection. This data is being analyzed to understand how these microbes are recruited by camelina and interact with one another and will aid in the targeted isolation of future isolates.

This project asks how ecological interactions (cooperative and antagonistic) within the soil microbiome influence soil carbon (C) cycling and persistence. The researchers’ primary goals are to (1) test how 23 years of climate change alter microbial interactions and affect the fate of soil carbon; (2) quantify microbiome interactions that change the biochemical community-scale efficiency of carbon use and its fate; and (3) infer ecological interactions using machine learning and ecological models.

Soil ecosystems are critical in the global carbon budget, and climate change can disrupt their functioning. In a northern Arizona climate change experiment, long-term alterations in temperature and precipitation have changed plant composition and primary production, leading to increased ecosystem respiration and photosynthesis but reduced soil carbon levels. However, much remains unknown about how microbial trophic interactions influence soil nutrient dynamics and how climate change affects these interactions. The research team hypothesizes that warming initially triggers cooperative interactions for complex carbon source degradation and top-down control of microbial communities by protists. Over time, as available carbon is depleted, predatory bacteria and viruses become the dominant top-down forces, altering predation modes and the fate and persistence of soil carbon.

To test the team’s hypotheses, researchers are conducting parallel field and laboratory experiments. In the field, researchers added plant roots highly labeled with carbon-13 (¹³C) to mixed conifer forest soils under warmed and unwarmed conditions in field mesocosms to trace carbon flow through the microbial food web.

Preliminary findings suggest that while total ecosystem respiration remained relatively constant in both conditions, plant root respiration was approximately 1.5 times higher in warmed soil than in unwarmed soil. Ongoing metatranscriptomics, metagenomics, and quantitive stable-isotope probing (qSIP) amplicon sequencing will identify potential trophic interactions driving carbon utilization dynamics.

In the laboratory, researchers are conducting trophic manipulation experiments using mixed conifer forest soils to investigate how predatory protists and bacteria influence carbon fate. Initially, microbial enrichments derived from researchers’ field soils, including diverse populations across life domains, were established. Prevalent microbial communities in the enrichments, identified as high-quality metagenome-assembled genomes, include bacterial groups such as Bacteriovorax sp., Pseudobdellovibrio sp., Bdellovibrio sp., Rhodoferax sp., Pedobacter sp., and Burkholderia sp., protists like Spumella sp. and Acanthamoeba sp., and viruses such as Mimivirus sp. and Kisquinquevirus sp.

The reintroduction of enriched protists and their prevalence in soil microcosms, along with their impact on bacterial communities, were assessed by quantifying gene expression using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) of 18S rRNA and 16S rRNA. Expression of 18S rRNA genes in protist-treated soils (soil and protists in water to 60% water holding capacity) was 90-fold higher than in controls (soil and water only), indicating a higher protist prevalence. Anticipated lower 16S rRNA expression in protist-treated soils suggests antagonistic associations. Ongoing oxygen-18 qSIP amplicon sequencing analysis will confirm findings and allow the calculation of the growth rates of targeted communities.

Predatory bacteria isolated from microbial enrichments will be used in trophic manipulation experiments alongside sorted protist populations to track ¹³C’s fate from labeled plant roots and study ecological dynamics and microbial community interactions.

Understanding how plants adapt to extreme environments is pivotal for designing novel biofuel crops that avoid competition with conventional food crops and thrive in marginal lands. Researchers’ previous studies using extremophyte models in Brassicaceae have suggested a balance among the regulation of salt and water transport, uninterrupted nutrient acquisition, and the capacity to maintain antioxidant and osmolyte pools play critical roles in surviving environmental stresses, including high salinity, compared to their stress-sensitive sister species. The group expanded its search to include multiple emerging biofuel crops and extremophyte models to test for convergent genomic and transcriptomic features to identify evolutionary strategies preferentially found in plants that are adapted to multiple environmental stresses. Researchers found gene family expansion and positive selection for genes known for their salt responses.

Additionally, the team tested selected candidate genes that were expanded in extremophytes and found evidence for gene subfunctionalization in the extremophyte model, Schrenkiella parvula. The results of this research highlight repeated evolutionary innovations in diverse Brassicaceae species that may allow scientists to select genes and pathways that are optimal candidates for improving stress tolerance in diverse biofuel crops better adapted to extreme and changing climates.

Address basic research challenges in developing and advancing technologies for green fertilizer production.

Nitrogen is essential for life on Earth. Today, most of life’s nitrogen need is met by chemical conversion of atmospheric nitrogen into readily usable forms, but such conversion comes at a massive environmental cost. It is conducted under high temperature and pressure, generating a massive carbon footprint. An alternative approach is based on biological conversion of nitrogen at ambient temperature, a greener process restricted to only a few select groups of microbes. Of these, cyanobacteria are uniquely capable of driving the energetically expensive nitrogen fixation reaction solely with solar power while simultaneously capturing carbon, and thus reducing the carbon footprint (Liberton et al. 2019; Bandyopadhyay et al. 2021).

The use of cyanobacteria, non-model microbes, as chassis for the conversion of atmospheric nitrogen into valuable N-rich compounds, however, requires significant fundamental research, including development of robust growth conditions and systems level understanding of the biology of these photosynthetic autotrophs. This project addresses foundational research challenges that stand in the way of developing nitrogen-fixing cyanobacteria as cell factories for the production of nitrogen-rich compounds. This group focuses on the production of guanidine, ammonia and urea, three nitrogen-rich compounds that can serve as substitutes for synthetic fertilizers (Wang et al. 2019). Specifically, researchers are (1) designing and building functional modules for the production of guanidine, urea, and ammonia; (2) designing and building nitrogen-fixing chassis strains for optimal carbon and nitrogen capture and product formation; and (3) optimizing the production chassis.

This team is using two strains representing the two contrasting paradigms that cyanobacteria use to accommodate the mutually antagonistic processes of oxygenic photosynthesis and nitrogen fixation: temporal separation in a unicell (Cyanothece 51142) and spatial separation in a multicellular filament (Anabaena 33047). Researchers are working to engineer novel enzymes capable of catalyzing the conversion of atmospheric nitrogen into guanidine, ammonia and urea, and membrane transporters that will secrete the products out of the cell. Multiomics studies and machine learning tools will unravel the fundamental principles underlying the regulation of carbon and nitrogen fixation in cyanobacteria and their channelization towards the products of interest. The research team’s goal is to develop chassis strains that can produce sufficient quantities of fertilizer compounds for pilot scale-up geared towards commercialization of the concept. In the longer term, this group envisions future deployment of such strains in soil as a local source of nitrogen for bioenergy and other crops, and also in ocean fertilization for carbon dioxide (CO₂) removal. This technology, when fully developed, has the potential to replace the use of synthetic fertilizers. The fundamental knowledgebase this research generates will also have broader scientific impact on carbon neutral biomanufacturing of nitrogen-containing petrochemical replacement compounds.

This research team of seven investigators from Washington University, National Renewable Energy Laboratory, and Alabama State University brings together significant interdisciplinary expertise in cyanobacterial systems biology, metabolic modeling, machine learning and synthetic biology. An important mission of this project is to train a number of students from underprivileged communities, equipping a future workforce with modern biomanufacturing technologies.

This project aims to metabolically engineer two extreme thermophiles, Caldicellulosiruptor bescii (T_max 90°C) and Pyrococcus furiosus (T_max 103°C), for the renewable production of key industrial chemicals through the conversion of lignocellulosic biomass, with targets including acetone, 2,3-butanediol, 1-propanol, 3-hydroxypropionate, and ethanol. This work includes efforts of carbon and energy optimization through harnessing carbon dioxide (CO₂) and dihydrogen (H₂) produced from fermentation into desired products and energy, respectively. Select enzymes responsible for degradation of lignocellulose will be expressed in P. furiosus to allow growth on cellulose and xylan. System-wide metabolic and regulatory models for both organisms will be leveraged to optimize biomass degradation and product yield for the target chemicals.

Extremely thermophilic organisms present a valuable opportunity to convert lignocellulosic biomass to industrial chemicals, as conversion at high temperatures offers specific advantages, such as reduced contamination risk and temperature-dependent separation of volatile products. P. furiosus is a hyperthermophilic archaeon (T_opt 100°C) with a growth range from 70°C to 103°C. The research group seeks to harness this organism’s extreme thermophily and robust genetic system for the production of chemicals of interest. P. furiosus has been previously engineered to produce lactate, ethanol, 3-hydroxypropionate, acetoin and butanol. The P. furiosus alcohol dehydrogenase F (AdhF) was recently identified as the ethanol-forming enzyme, with AdhF overexpression resulting in increased ethanol yields at temperatures up to 95°C (Lipscomb et al. 2023). P. furiosus strains producing 1-propanol were recently developed by constructing a nine-enzyme pathway consisting of both heterologous and native enzymes, although the characterization of these strains is still underway. Additionally, efforts are underway to express hemicellulases and cellulases in P. furiosus, with the goal of enabling the organism to grow directly on xylan and cellulose, as C. bescii does natively.

The other subject of this work, C. bescii, has been metabolically engineered for the production of acetone, ethanol, and other various alcohols. Recent work engineered 2,3-butanediol production in C. bescii when grown on unpretreated biomass (Tanwee et al. 2023). To further the researchers’ understanding of C. bescii and related thermophiles, this team has sequenced the genomes of many species in the genera Caldicellulosiruptor, Thermoclostridium, and Thermoanaerobacter (Bing et al. 2023a; Bing et al. 2023b; Manesh et al. 2024), leading to a reassessment of the taxonomic classification for the genus Caldicellulosiruptor and the order Thermoanerobacterales (Bing et al. 2023c). To better understand the ability of C. bescii to degrade biomass, the presence of microorganisms indigenous to various types of biomass was explored, alongside work to better understand cell-substrate associations during biomass solubilization (Bing et al. 2023d; Laemthong 2023). Work is also ongoing to engineer the cytoplasmic hydrogenase from P. furiosus into C. bescii to provide redox balancing for pathways dependent on the production of nicotinamide adenine dinucleotide phosphate (NADPH). System-wide metabolic and regulatory models of both C. bescii and P. furiosus have been created; these models have been and are currently being harnessed to predict optimization approaches for biomass conversion and product formation (Rodionov et al. 2021; Zhang et al. 2021; Vailionis 2023).

This project aims to enable the efficient deconstruction of polyolefins and upcycling to itaconic acid, through novel genomic insights into nutrient enhanced polyolefin degradation by the yellow mealworm gut microbiome and genetic tool development for gut microbiome isolates and engineered microbial communities.

Annually more than 200 million tons of plastic waste in the form of polypropene, high density polyethylene, and low density polyethylene (LDPE) are generated and accumulate in the environment. No robust system exists to capture this carbon; however, in prior work, researchers identified myriad upregulated non-model species in plastic-fed mealworm guts. Moreover, team members previously showed that gut isolates from these genera grow on LDPE as their primary carbon source and chemically modify LDPE films upon inoculation. These taxa have been identified as upcycling chassis for development due to their prevalence in plastic enriched microbial communities.

As a prerequisite for targeted genetic engineering, the research team collected genome and methylome sequence data of gut eight isolates that can grow on LDPE as their primary carbon source. The research group successfully identified methylation motifs in each of the eight isolates and have located and annotated methyltransferases in the genome of each strain. Escherichia coli strains capable of producing plasmids with these tailored methylation patterns greatly facilitates transformation and genetic engineering for future community engineering efforts.

To further understand the role of microbial isolates in plastics deconstruction processes and holistic gut community degradation processes, a collection of metaomics datasets are being developed. Synergizing findings from transcriptomes, proteomes, and metabolomes, as well as hypothetical pathways for LDPE deconstruction, are iteratively being built. This community-wide systems biology approach allows for a complete picture of degradation processes by highlighting genes, proteins, and metabolites that coincide as upregulated in plastics-enriched gut communities.

Existing microbial isolates and those identified through metaomics approaches will ultimately be constructed into synthetic plastic-degrading communities capable of plastics waste valorization. To better recapitulate mealworm gut community behavior, researchers are investigating the ability of minimal synthetic microbial communities to deconstruct LDPE. Researchers identified co-cultures that are metabolically active in media with LDPE as the sole carbon source. Preliminary data suggest that certain microbial isolates have enhanced plastic degradation potential in co-culture conditions. The group is further investigating co-culture behaviors using confocal microscopy to image microbial spatial variances and plastic particle surface colonization. Identifying essential features in minimal co-cultures will inform the design of more complex synthetic communities capable of enhanced plastic degradation.

Beyond microbial work, researchers must consider polymer characteristics to develop improved deconstruction systems. Post-consumer waste plastics contain additive packages to improve processability, antioxidation, and flame retardancy. Given additive variability among polymer grades, researchers developed a standardized plastics preparation procedure wherein additives are stripped from polymers, leaving only the base plastic for deconstruction studies. The use of stripped plastics facilitates a more accurate comparison of deconstruction rates across plastic materials from various sources. Additionally, researchers’ current work leverages successive self-seeding and annealing (SSA), differential scanning calorimetry (DSC), and thermal fractionation techniques to assess polymer architecture (e.g., branching densities) pre- and post-deconstruction. Preliminary data indicate that deconstruction predominantly occurs at low branching densities, indicating that high branching density plastics such as LDPE are less bioavailable than low branch-density plastics. Future efforts will concentrate on validating chain architecture hypotheses and on elucidating the mechanism of polyethylene deconstruction from a branching perspective.

The overall goal of the project is to develop tools to improve the team’s systems-level understanding of metabolism in non-model organisms, such as Clostridium thermocellum, and use that understanding to increase product titer.

Genetic approaches have been traditionally used to understand microbial metabolism, but this process can be slow in non-model organisms with limited genetic tools. An alternative approach is to study metabolism directly in the cell lysate. This avoids the need for genetic tools, and is routinely used to study individual enzymatic reactions, but is not generally used to study systems-level properties of metabolism. Here the researchers demonstrate a new approach they call “cell-free systems biology” where they use well-characterized enzymes and multi-enzyme cascades to serve as sources or sinks of intermediate metabolites. This allows researchers to isolate subnetworks within metabolism and study their systems-level properties. To demonstrate this, the research team worked with a three-enzyme cascade reaction that converts pyruvate to 2,3-butanediol. Although it has been previously used in cell-free systems, its pH dependence was not well characterized, limiting its utility as a sink for pyruvate. The research team showed that improved proton accounting allowed better prediction of pH changes, and that active pH control allowed 2,3-butanediol titers of up to 1.1 M (189 grams per liter) from acetoin and 1.6 M (144 g/l) from pyruvate. The improved proton accounting provided a crucial insight that preventing the escape of carbon dioxide (CO₂) from the system largely eliminated the need for active pH control, dramatically simplifying the team’s experimental setup. Researchers then used this cascade reaction to understand limits to product formation in C. thermocellum, an organism with potential applications for cellulosic biofuel production. This team showed that the fate of pyruvate is largely controlled by electron availability, and that reactions upstream of pyruvate limit overall product formation.

The Persistence Control Science Focus Area (PerCon SFA) at Pacific Northwest National Laboratory seeks to understand plant-microbiome interactions in bioenergy crops to establish plant growth-promoting microbiomes that are contained to the rhizosphere of a target plant. This vision requires the discovery of exudate catabolism pathways from plant roots, the elimination of genes that support fitness in bulk soil environments without decreasing rhizosphere fitness, and the engineering of rhizosphere niche occupation traits in phylogenetically distant bacteria. Researchers anticipate the impacts of these efforts will be to increase understanding of plant-microbe interactions and to extend high-throughput systems and synthetic biology tools to non-model microbes.

Metabolite exchange between plant roots and their associated rhizosphere microbiomes underpins plant growth promotion by microbes. Root tips of the bioenergy crop Sorghum bicolor exude large amounts of a lipophilic benzoquinone called sorgoleone. This allelochemical suppresses the growth of competing plant seedlings and is slowly mineralized by microbes in soil. As an avenue to understanding how sorghum and its root microbiome may be connected through root exudates, the group identified the molecular determinants of microbial sorgoleone degradation and the distribution of this trait among microbes. The team isolated and studied three bacterial strains from sorghum-cultivated soils that were classified as Acinetobacter, Burkholderia, and Pseudomonas species able to grow with sorgoleone as a sole carbon and energy source. The genomes of these strains were sequenced and subjected to transcriptomic and gene fitness analyses to identify candidate sorgoleone degradation genes. Follow up mutational analysis showed that sorgoleone catabolism is dependent on four contiguous genes that are conserved among the strains the teams sequenced. Researchers refer to these four genes as the srg (sorgoleone degradation) cluster. Phylogenetic analysis of the srg cluster using Snekmer showed that sorgoleone catabolism is enriched in sorghum-associated Streptomyces strains over isolates from the Populus rhizosphere. The discovery of bacteria that grow on a compound like sorgoleone that is plant specific and not widely distributed in the environment provides an opportunity for the PerCon SFA to study how sorghum exudates can enforce the development of a rhizosphere specific microbiome for the mutual benefit of plant and microbe.

The research group is finalizing the construction of a fully recoded, 3.97 megabase pair Escherichia coli genome that relies on the use of only 57 genetic codons. For this aim, the genome was computationally designed, synthesized, and assembled into 88 segments. In the final steps of genome construction, researchers combine and optimize these segments in vivo to assemble the fully recoded, viable chromosome. In parallel with the construction of this 57-codon organism, the team is investigating how mobile genetic elements and environmental viruses overcome the genetic isolation of organisms bearing modified genetic codes.

Researchers present the construction of a recoded, 57-codon E. coli genome, in which seven codons are replaced with synonymous alternatives in all protein-coding genes. For this aim, the entirely synthetic recoded genome was assembled as 88 21 to 52 kilobase pair episomal segments, individually tested for functionality, and then integrated into the genome. Developing a specialized integration system and optimizing the team’s workflow enhanced integration efficiency to 100%, resulting in an order of magnitude increase in construction speed. The team is now combining recoded genomic clusters with a novel technology that builds on the group’s latest developments in recombineering and CRISPR-associated nucleases (Wannier et al. 2020; Wannier et al. 2021). In parallel with genome construction, researchers developed novel experimental methods to identify fitness-decreasing changes and troubleshoot these cases. Leveraging massively parallel genome editing and accelerated laboratory evolution allowed the group to correct partially recoded strains’ fitness within weeks (Nyerges et al 2018). As researchers approach the final assembly of this E. coli genome, they also implement dependency on non-standard amino acids.

The team’s previous experiments showed that rational genetic code engineering could isolate genetically modified organisms (GMOs) from natural ecosystems by providing resistance to viral infections and blocking horizontal gene transfer (HGT); however, how natural mobile genetic elements and viruses could cross this genetic code-based barrier remained unanswered. By systematically investigating HGT into E. coli Syn61∆3, an E. coli strain with a synthetic, 61-codon genetic code, the group discovered that transfer (t) RNAs expressed by viruses and other mobile genetic elements readily substitute cellular tRNAs and abolish genetic-code-based resistance to HGT (Nyerges et al. 2023). Researchers also discovered 12 new bacteriophages in environmental samples that can infect and lyse this 61- codon organism. These viruses express 10 to 27 tRNAs, including functional tRNAs needed to replace the host’s missing tRNA genes. The team also identified viruses with tRNAs that hold the potential to abolish the virus resistance of this 57-codon organism. These findings suggest that the selection pressure of organisms with compressed genetic codes can facilitate the rapid evolution of viruses and mobile genetic elements capable of crossing a genetic code-based barrier. Therefore, researchers developed additional genetic biocontainment technologies to simultaneously block GMOs’ unwanted proliferation, eliminate viral infections, and prevent transgene escape (Nyerges et al. 2023).

In sum, this research group’s genome synthesis work will soon (1) demonstrate the first 57-codon organism; (2) establish a tightly biocontained chassis for new-to-nature protein production; and (3) open a new avenue for the bottom-up synthesis and refactoring of microbial genomes, both computationally and experimentally. Furthermore, the researchers demonstrate that horizontally transferred tRNA genes of mobile genetic elements and viruses can substitute deleted cellular tRNAs and thus rapidly abolish compressed genetic codes’ resistance to viral infections and HGT.

To characterize bacterial-fungal interaction (BFI) mechanisms and the impacts of BFIs on their environments under conditions relevant to future climate scenarios.

Bacteria and fungi are often dominant constituents of environmental microbial communities, and interactions between these groups can impact microbial functions within their environments, such as nutrient cycling, and plant and soil health. While there have been important advancements in identifying bacterial-fungal interactions (BFIs) and their roles, there is still much to be discovered regarding the underlying interaction mechanisms. It is additionally not well understood how these interactions may shift under changing climate conditions, and how BFIs may contribute to the resilience of soil communities and plant hosts. Through expansive characterization of BFI across experimental scales, the BFI Science Focus Area team seeks to develop foundational knowledge regarding the drivers of BFI and to provide to the broader research community an integrated suite of publicly available resources as the field rapidly expands. Here, researchers focus on bacteria and fungi isolated from the rhizosphere of the highly stress tolerant grass, Bouteloua gracilis, from the arid grassland sites at the Sevilleta Long Term Ecological Research Station in New Mexico. Grasslands have been estimated to store up to 33% of soil carbon globally (Bai and Cotrufo 2022), and the grassland sites from which the team samples experience stressors relevant to future climate scenarios, such as drought and extreme heat. Researchers hypothesize that microbes associated with B. gracilis in the arid grasslands may leverage inter-microbial interactions to respond and adapt to these stressors, providing a useful model ecosystem to understand how BFI will respond as environments become hotter and drier. Using co-occurrence networks built from amplicon sequencing datasets from a large geographical survey, researchers have predicted bacterial and fungal partners that are likely to interact based on co-occurrence rates and which may have greater influence on their ecological contributions due to the prevalence of their interactions. The novel approach will evaluate the ability of co-occurrence models to predict interactions between bacteria and fungi and utilize laboratory-based investigations of interactions to help develop more accurate interaction models based on sequencing and interaction feature data. Several bacterial and fungal isolates were selected for initial laboratory investigations based on network analyses and abundance in sequencing and culture-based surveys. Researchers have conducted preliminary investigations of how environmental conditions such as nutrient availability and temperature impact these BFIs. Phenotyping data indicates that some interactions appear to be more strongly impacted by changing environmental conditions, while other interactions are more stable. Researchers have conducted preliminary comparative genomics analyses of genomic differences that may contribute to the distinct responses underlying BFI phenotypes such as pigmentation and growth. Researchers aim to further characterize the underlying molecular mechanisms using a multi-omics approach to identify relevant molecular markers for BFIs, and which may eventually be applied to understanding the relevance of functional features found in broader-scale datasets (e.g., metagenomics, metatranscriptomics). This data will be made publicly available through the Bacterial-Fungal Interactions Portal (https://:sfa- bfi.edgebioinformatics.org/about), which was developed to provide a centralized resource of BFI research, including known BFI and their associated studies (Robinson et al 2023).

Bioenergy feedstocks need to be deployed on marginal soils with minimal inputs to be economically viable and have a low environmental impact. Currently, crop water supply is a key limitation to production. The yields of C4 bioenergy crops such as Sorghum bicolor have increased through breeding and improved agronomy. Still, the amount of biomass produced for a given amount of water use (water use efficiency, or WUE) remains unchanged. Therefore, the project aims to develop novel technologies and methodologies to redesign the bioenergy feedstock sorghum for optimal WUE. Within this broader context, this subproject is using Setaria viridis as a rapid cycling model for gene discovery. The goal is to devise novel methods and develop resources to create genetic variations and streamline the phenotyping of WUE traits. These advancements are crucial for their application in forward genetics approaches aimed at identifying genes that regulate stomatal patterning and water use efficiency.

Stomata regulate the exchange of carbon dioxide (CO₂) and water vapor between the leaf and atmosphere, and therefore play a key role in determining WUE. Relatively little is known about the genes that regulate stomatal patterning and WUE in C4 grasses. To advance efforts to engineer improved WUE of bioenergy crops, researchers are developing novel methods to accelerate the use of forward genetics for gene discovery. The team conducted a forward genetic screen of 340 families of a NMU-mutagenized Setaria population, of which 185 lines were selected for having interesting visual phenotypes in a pre-screen. Researchers assessed whole-plant WUE by imaging and automated lysimeters and then collected leaf sections to screen for abnormalities in stomatal patterning. The high throughput optical tomography imaging was utilized to generate high resolution images of the leaf surface. Researchers utilize a machine learning model for identifying the size, shape, and number of stomata in Setaria. Seventy families of the first 155 families were identified to segregate for WUE and/or stomata mutant phenotypes and therefore selected for a second screen. Seeds harvested from 40 families were confirmed as fixed WUE and/or Stomata mutants. These families are part of a larger population which are being sequenced by DOE Joint Genome Institute (JGI) to create a sequence indexed mutant population. DNA isolated from representatives of each family, or the mutant segregants from a family, is being sequenced by JGI to identify disruptive SNPs in candidate genes. Preliminary analysis suggests that each line contains an average of 266 disruptive polymorphisms, with ~15 classified as high impact. The work demonstrates high-throughput phenotyping and genotyping strategies to quickly identify genes of interest, followed by verification of genes through transgenic approach for a role in water-use efficiency. Success in this effort could be leveraged to accelerate research on a wide range of other traits and species.

IPReP: This project employs evolutionary and computational genomic approaches to identify key genetic variants that have enabled Thlaspi arvense L. (Field Pennycress; pennycress) to locally adapt and colonize all temperate regions of the world. This, combined with knowledge of metabolic and cellular networks derived from first principles, guides precise laboratory efforts to create and select high-resilience lines, both from arrays of random mutagenesis and by employing cutting-edge CRISPR genome editing techniques. This project will deliver speed-breeding methods and high-resilience mutants inspired by natural adaptations and newly formulated biological principles into a wide range of commercial pennycress varieties to precisely adapt them to the desired local environments.

Pennycress (Thlaspi arvense), an emergent winter annual bioenergy oilseed cover crop, is under development to be grown in the Midwest during typical fallow periods. Pennycress varieties can yield over 1,680 kg ha-1 (1,500 lb ac-1) of seeds, producing 600 liters ha-1 (65 gal ac-1) of oil annually without competing with food crops. However, crucial work remains to domesticate and optimize pennycress for incorporation into present cropping systems and its resilience to climate change. For example, interseeding into standing fields in late fall leads to shade-induced responses in pennycress. Similarly, higher temperatures during fall planting cause seedlings to elongate, resulting in poor stand establishment. Researchers have established that pennycress is shade and heat-intolerant and elongates in response to these stresses. Excessive elongation creates a cyclical dilemma in which the elongated plants increasingly shade their neighbors, further inducing retaliatory elongation responses to outgrow neighboring plants. These adaptive morphogenic changes are undesirable in cropping systems as elongated plants establish poorly, are more prone to lodging, and reduce yields. Researchers are using the knowledge base from Arabidopsis thaliana to manipulate genes in the phytochrome signaling pathway to improve resilience to shade present during interseeding and increasing winter and spring temperatures. Evaluation of CRISPR and EMS alleles of PHYTOCHROME INTERACTING FACTOR 7 show that pif7 mutants have reduced organ elongation and retain a compact rosette when exposed to shade, elevated temperature, and combined stresses while maintaining yield and desirable phenotypes such as earlier flowering in stress conditions. By lowering elongation responses to shade and elevated temperature, researchers aim to increase pennycress ground cover when grown at high densities, reduce shade-induced responses when interseeded into standing crops, and elongation in response to higher temperatures. In addition to light responses, researchers are addressing the freezing tolerance of pennycress by examining the role of fatty acid modifications on winter survival and in response to chilling and freezing stress. Furthermore, researchers have used CRISPR and gene editing to target CAMTA and CBF family genes and RNAseq, metabolomics, and fatty acid analysis to examine global changes to low temperatures. Future work will determine if these changes to shade and temperature responses improve the performance, productivity, and resilience of pennycress in the field.

The goal of this project is to develop and implement a process for improving bioproduction under conditions that are appealing for industrial processes, such as high temperature and low pH. The approach addresses the failure of metabolic reactions due to inhibition, denaturation, misfolding or disorder of enzymes. Researchers have developed and implemented a framework for identifying these enzymes and selection of robust replacement enzymes, using high temperature and low pH as model stressors in Escherichia coli. The engineering strategy of replacing enzymes to improve bio-production is well-established, but rarely applied to system-wide stressors. This approach is complementary to improvement of microbial robustness by engineering the cell membrane and has advantages relative to evolutionary-based organism improvement by prioritizing bioproduction rather than growth.

Temperature sensitivity: There is a wealth of data available regarding enzyme structural integrity at high temperatures. Researchers are using an in vitro metabolomics approach for proteome-wide analysis of enzyme activity at high temperatures. Candidate bottleneck enzymes have been identified and investigated, including homoserine O-succinyltransferase (MetA), biotin synthase (BioB), ketol-acid reductoisomerase (IlvC), and 3-oxoacyl-ACP synthase 1 (FabB). For these candidate enzymes, sequences from thousands of various microorganisms with differing temperature ranges of growth have been collected and aligned. Experimental and predicted structures of these enzymes are used to query the enzyme dynamics, with the goal of identifying which sequence differences account for the ability of these critical enzymes to function at elevated temperatures.

Acid sensitivity: The pH tolerance efforts have prioritized modeling the effect of pH on the allocation of cellular resources. The metabolic model accounts for the effect of intracellular acidification on cellular energetics, the thermodynamic characteristics of metabolic reactions and on enzyme activity. Each of these three model adjustments contribute to the predicted flux distribution. A sensitivity analysis is in progress to identify the most critical enzymes for replacement. Borrowing from the abundant proteomic data of enzyme stability in the presence of increasing temperatures, researchers have developed a proteomic approach for enzymes with structural sensitivity to decreasing pH. This approach has identified several enzymes critical for central metabolism with poor acid tolerance. Researchers are also using models of enzyme temperature sensitivity as inspiration in the development of predictive sequence- and structure-based models of enzyme pH sensitivity.

The goal of this project is to develop and apply multichromatic optogenetic tools in bacteria and yeast to control co-culture populations. Researchers have developed ways to alter microbial growth rates of different strains using blue, dark, red, or near-IR light by controlling the expression of an essential gene or toxin/antitoxin systems in yeast or bacteria, respectively (Wegner et al. 2022; Lalwani et al. 2021). Testing different light duty cycles and wavelengths allow for the exploration of optimal microbial population ratios when combined with computational methods for real time feedback. This work demonstrates the first example of polychromatic control in microbial co-cultures to maximize the production of valuable commodity chemicals and biofuels.

Metabolic engineering enables the sustainable production of valuable chemicals, drugs, or biofuels from low-cost renewable substrates by re-wiring microbial metabolism. However, growth defects caused by excessive metabolic burden, suboptimal expression/activity of heterologous enzymes, and endogenous regulatory mechanisms often limit microbial productivities (Wegner et al. 2022). These challenges can be addressed by dividing the labor among different microbes in synthetic microbial communities (consortia or co-cultures). Fragmenting biosynthetic pathways among different strains of bacteria or yeast, each producing unique intermediates, significantly reduces the metabolic burden, while harnessing special capabilities of different microbial species. This strategy also helps optimize each metabolic module in separate strains, override endogenous regulatory mechanisms, and avoid competing pathways to maximize flux through the biosynthetic pathway of interest. However, stabilizing and controlling the composition of microbial consortia is a formidable challenge (Duncker et al. 2021).  While some strains grow quickly, others lag–allowing the fast-growing members to take over the culture. Researchers apply optogenetics, where cellular processes are optically controlled using photoswitchable proteins that change shape and function in response to light, to maintain population ratios in co-cultures.

Light as gene inducers is nontoxic, tunable, and inexpensive, unlike chemical inducers. However, optogenetic control of microbial populations has only been demonstrated with blue light and only to control the growth rate of one strain in a two-member consortium, in which the optically controlled member grows significantly faster than the uncontrolled (blind) strain. Thus, an optogenetic tool other than blue light is in need for metabolic engineering applications. Researchers established red/near-IR systems, which enables the control of more complex microbial communities, including ones containing members of comparable growth rates. Combining this system with blue light circuits provide a multichromatic control over bacteria and/or yeast consortia populations (Wegner et al. 2022). In principle, four strains (bacteria/yeast) under different optogenetic circuits (blue, darkness, red, near-IR) can be combined to afford complex, multichromatic microbial consortia. This allows the engineering of microbial community members to cooperatively produce various commodity chemicals and biofuels, such as isobutanol, possibly maximizing their titers with various light schedules. This work will significantly advance the use of optogenetic control of microbial communities, which is a new paradigm with enormous potential to not only improve the basic understanding of microbial community interactions, but also to overcome the obstacles that have stifled the use of synthetic microbial consortia for biotechnological applications. These multichromatic co-culture methods are generalizable and can easily be commercialized when significant yield of any important fine chemicals is achieved.

Researchers plan to alter the genetic regulation of the cellular developmental programs that generate the vegetative tissues of sorghum, with the aim to increase the proportion of cells that are less recalcitrant to biomass deconstruction.

Plant biomass is comprised of distinct cell types, which largely determine its physical and chemical properties, and hence, its recalcitrance to biomass processing aimed at generating fermentable sugars that microbes can convert to biofuels. The walls of parenchyma cells in the stalks of maize (Zea mays L.) and sorghum (Sorghum bicolor (L.) Moench) can be broken down using milder pretreatment conditions and with lower cellulase loadings than the lignified cells present in the outer rind of the stalk. The different cell types within the sorghum plant develop from undifferentiated meristem cells through variation in the spatio-temporal expression of regulatory genes that control structural genes. The understanding of the role of specific genes and their regulation in this developmental process is incomplete. Uncovering the function of the complete ensemble of genes involved in the differentiation and maturation of the cells that make up sorghum biomass creates the opportunity to manipulate its cellular composition, impacting its physical and chemical properties and, consequently, its value for bioenergy.

We are applying single-cell genome and transcriptome analysis of the sorghum shoot apex and stem to identify the function of genes involved in differentiating cells that determine biomass composition. Researchers have identified the internode in developing sorghum stems of 30-day-old plants that displays a developmental gradient, whereby the top of the internode is undifferentiated, and the bottom of the internode contains parenchyma, sclerenchyma, and proto-xylem cells. Nuclei isolated from dissected internodes have been subjected to single-nucleus RNA-sequencing using the 10×Genomics platform. Uniform Manifold Approximation and Projection (UMAP) was used to identify clusters of nuclei with similar expression profiles that are hypothesized to represent different stages of development of the different cell types. In the next phase, inferred cell lineage trajectories involved in the development of the cellular components of biomass will be explored to discover their regulators. Specific objectives are to: (1) define the function of each gene (including specific members within gene families) with respect to the development of the main cell types that determine sorghum biomass and its cell-wall composition; (2) construct the cellular lineages that give rise to each cell type that composes biomass (from the shoot apical and vascular cambium meristem cells to cells in the stem), and identify genes and cis-regulatory elements that contribute to the lineage progression; (3) categorize the function of gene and associated cis-regulatory components for their relevance in the control of cellular lineages that lead to each cell type; and (4) validate multiple targets in isolation and in parallel, to confirm their role in biomass development and their potential for enhancing biomass yield and its properties.

The four DOE Bioenergy Research Centers (BRCs) have recently launched extensive formal collaborative projects with the goal of accelerating the common science goals for the bioenergy enterprise. Each BRC has integrated process concepts and feedstocks that feature novel deconstruction technologies and conversion platforms that transform plants to biofuels and bioproducts. In particular, the Joint BioEnergy Institute has developed a Feedstocks-to-Fuels pipeline for screening the efficiency of deconstruction and microbial conversion of lignocellulosic biomass. Here researchers present results obtained from subjecting two types of bioenergy feedstocks (sugarcane and poplar) to this pipeline consisting of an ionic liquid (IL) pretreatment process, enzymatic saccharification, and microbial conversion to the jet-fuel precursor bisabolene.

In this study researchers explore the conversion potential of two engineered sugarcane varieties named oilcane, 1565 and 1566 that produce high levels of lipids and the CP88 wild type (WT), developed at the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI). The starting material for the conversion experiments was the bagasse obtained after pressing, drying, and milling the biomass. Researchers also evaluated the use of natural poplar variants with different wood density developed at the Center for Bioenergy Innovation (CBI). Poplar biomass with higher wood density can increase biomass yield per hectare while reducing transportation costs, but the impact of density on the degree of conversion to sugars and bioproducts remains unexplored. Pretreatment was performed using the ionic liquids cholinium lysinate and ethanolamine acetate at different concentrations (10 to 85 wt.%), with 15 wt.% solid loading in high-pressure and high-temperature glass tube reactors at 140 °C for 3 hours. The pretreated feedstocks underwent enzymatic hydrolysis using a cellulase: hemicellulase enzyme cocktail (9:1 vol/vol, 30 mg/g biomass) and incubated at 50 °C for 72 hours. Compositional analysis of the raw and pretreated samples was performed using a 2-step acid hydrolysis method and high-performance liquid chromatography was used to quantify the sugars released during this reaction and after enzymatic hydrolysis. The WT sugarcane contained 37% glucan, while engineered sugarcane contained 39%, with lignin content below 24% for both WT and engineered sugarcane. Pretreatment reduced lignin by 1.5-fold, increasing glucan by 1.3-fold. Ethanolamine acetate performed better than cholinium lysinate with engineered sugarcane, yielding 88% glucose and 57% xylose, while both solvents achieved over 94% glucose and 70% xylose yield for WT. Fermentation with Rhodosporidium toruloides showed near-complete glucose consumption and bisabolene production, underlining the effectiveness of the pipeline for conversion of different bioenergy feedstocks to bioproducts.

This project aims to reduce bioenergy production’s dependence on synthetic nitrogen fertilizers by promoting biological nitrogen fixation in bioenergy crops. Bacteria typically perform nitrogen fixation under low nitrogen and low oxygen conditions. Various genetic strategies have been developed to de-regulate the system and enable nitrogen-fixing bacteria (diazotrophs) to fix nitrogen constitutively. However, nitrogen fixation is an energy-intensive process, and these manipulations reduce the fitness of engineered diazotrophs, making them less competitive than native ones. To overcome this challenge, researchers are engineering an inducible nitrogen fixation system that activates when diazotrophs can sense molecular signals from plant roots. Using plant molecules found in root exudates as signals and various biosensors, researchers aim to trigger nitrogen fixation in an inducible manner and optimize the delivery of fixed nitrogen to bioenergy crops.

Microbes can provide many benefits to plants, including plant growth promotion, enhancement of plant immunity, and nutrient uptake. In particular, some bacteria possessing a nitrogenase enzyme (diazotrophs) can convert atmospheric nitrogen into ammonium in a process known as biological nitrogen fixation. However, providing these benefits has a fitness cost for the diazotrophs. Nitrogen fixation, for instance, is well known to be very energetically expensive, making it a highly regulated process. Consequently, diazotrophs mostly fix nitrogen only under low nitrogen and low oxygen conditions. In gamma-proteobacteria, NifL responds to environmental nitrogen and represses the expression of NifA, which is the master activator of the nitrogenase gene cluster. Disrupting nifL and activating nifA is a classical strategy to enhance nitrogen fixation and trigger ammonium excretion, but this manipulation reduces the fitness of engineered bacteria. The project aims to engineer diazotrophs associated with plants with inducible biosensors that would enhance nitrogen fixation and ammonium excretion only in the presence of the host plant. The team isolated Klebsiella variicola and Klebsiella michiganensis strains from sorghum and maize roots. They are excellent nitrogen fixers, non-pathogenic, and tractable, making them suitable candidates for genetic engineering.

We replaced nifL with an arabinose-inducible biosensor to drive the expression of nifA, thus activating the nitrogenase activity in the presence of arabinose in the medium. This proof-of-concept experiment assessed the viability of an inducible nitrogen-fixing system in Klebsiella variicola. The addition of 1.33mM arabinose to the medium successfully induced nitrogenase activity, and measuring the ammonium excreted in response to different arabinose concentrations not only confirmed efficient ammonium excretion outside the cells but also revealed a titrated response dependent on the inducer concentration.

We are currently exploring using bacterial biosensors capable of detecting plant metabolites such as flavonoids and phenolic acids found in plant root exudates to replace nifL and drive the expression of nifA. Klebsiella strains were genetically engineered with flavonoid biosensor plasmids to assess their operational range and to determine if cereal root exudates contain sufficient signal molecules to activate them. Ongoing work involves integrating these biosensors into the Klebsiella genome to disrupt nifL and express nifA, thus advancing the development of inducible ammonium-excreting diazotrophs.

ENIGMA-Ecosystems and Networks Integrated with Genes and Molecular Assemblies use a systems biology approach to understand the interaction between microbial communities and the ecosystems that they inhabit. To link genetic, ecological, and environmental factors to the structure and function of microbial communities, ENIGMA integrates and develops laboratory, field, and computational methods.

Increasing atmospheric nitrous oxide, a greenhouse gas with 300 times greater radiative trapping than carbon dioxide (CO₂), is primarily attributed to intensive agriculture and the impact of climate change on soil conditions, estimated to contribute 73% of all U.S. nitrous oxide emissions. Prior studies revealed extremely high fluxes of nitrous oxide from the saturated subsurface and groundwater within the Field Research Center (FRC) at Oak Ridge National Laboratory, but the virtual absence of surface emissions. Ongoing characterization of system microbiota suggested a source derived from the activities of a subsurface community dominated by Rhodanobacter (see Carlson et al poster) species active in the low pH, high nitrate groundwater whereas the microbiota expressing the nitrous oxide reductase in the less contaminated upper soil column functioned as a major sink. These features suggested the utility of this system to better resolve environmental factors controlling nitrous oxide flux, both of its production and consumption. Ongoing studies, using stable isotope analyses and geochemical monitoring, demonstrated active nitrous oxide reduction at pH of 4, well below the observed limit for described industrial and environmental systems. Isotopic compositional studies of nitrogen species suggested that denitrification and chemodenitrification were both major sources of nitrous oxide, with a minor contribution by nitrification in shallower regions supported by the recovery of 16S sequences affiliated with known nitrifiers. A depth-resolved metagenomic analysis of the soil column showed a strong correlation between nitrous oxide depletion in the upper soil and the enrichment of microorganisms encoding the Clade II nitrous oxide reductase, whereas Clade I populations were more abundant near the variably saturated zone in proximity to groundwater. Researchers are now developing instrumentation, isotopic methods, and sampling strategies to confirm the role of the Clade II nitrous oxide variant in suppressing surface nitrous oxide emissions, combining isotopic, geochemical, and molecular measures to identify environmental variables controlling this critical function. Researchers are also quantifying isotopic fractionation and affinity for nitrous oxide of isolates encoding Clade I and II NosZ to inform the ecological role of these variants at the site. These studies will be based at the newly installed ENIGMA SubSurface Observatory (SSO; see Newcomer et al poster) at the FRC, providing the opportunity for continuous monitoring of nitrous oxide flux from wells screened at different depths coupled with geochemical, isotopic, and biological characterization. The goal is to develop a predictive understanding of biological and environmental factors controlling the emission of this critical greenhouse gas.

This project aims to exploit the genetic and phenotypic diversity of Issatchenckia orientalis through population genomics and machine learning approaches to: (1) explore the potential molecular mechanisms behind its multi-stress tolerance; (2) develop predictive models for various stress tolerances; and (3) identify key genes associated with tolerance to industrial stresses and resistance to different fungicides.

The environmental yeast I. orientalis plays a dual role in human society, owing to its multi-stress tolerance. Its ability to withstand common industrial stressors, such as low pH and high temperatures, makes it an ideal candidate for engineered biosynthesis of bioproducts. However, it also poses significant health risks as a multidrug-resistant pathogen capable of causing invasive fungal diseases and is recognized by the World Health Organization as a priority fungal pathogen. Deciphering the molecular mechanisms and evolutionary paths of its stress tolerance is key for managing risks and exploiting the benefits of this species. Here, researchers report the potential mechanisms driving the adaptive evolution of I. orientalis to various stressors using population genomics and machine learning approaches.

We conducted whole-genome sequencing of 170 I. orientalis isolates and assessed the growth of 161 isolates under 57 different stressors, including heat, low pH, organic acids, lignocellulosic inhibitors, and fungicides from different families (e.g., Azoles, Polyenes, and Echinocandins). The team also developed a machine-learning-assisted analysis pipeline, Machine-Learning-Assisted Engineering of Stress-Tolerance Rational Optimization (MAESTRO), to streamline the analysis. MAESTRO revealed that pleiotropic effects of copy-number variations (CNVs) among a small set of genes (less than 3.5%) play a significant role in I. orientalis’ stress tolerance. Using CNVs as features, the team successfully correlated genetic variation with phenotypic variation of stress tolerance, demonstrating a median R² of 0.67 and a median Pearson’s correlation of 0.92 across 57 stress conditions when comparing actual fitness to predicted fitness values. Notably, many of these genes (17 to 23%) were tandem repeat paralogs (TRPs), a genomic configuration known as hot spots for gene amplification, reduction, or even shuffling to invent new activities. Additionally, TRPs were significantly enriched with transporters (52%, compared to the genome-wide average of 3%), likely composing the resistome network. Finally, as a proof of concept, the team engineered a strain with enhanced tolerance to the lignocellulosic inhibitor 5-hydroxymethyl furfural but lower resistance to the fungicide fluconazole by deleting four TRPs. This work demonstrates the potential of leveraging fungal genetic variation to predict their potential risks in society and designing more robust industrial strains to develop a sustainable bioeconomy.

This work aims to shed light on the impacts of long-term and short-term nitrogen (N) inputs and of Miscanthus x giganteus (miscanthus) on soil responses. Greenhouse gas emissions, net nitrogen mineralization, and microbial community nitrogen cycling genes were compared between miscanthus and maize soils with varying legacies of N fertilization and with contemporary N amendments.

Understanding the role of bioenergy crops in carbon and nitrogen dynamics is crucial for sustainable production of biofuels and bioproducts. Miscanthus x giganteus, a promising perennial biomass crop, is favored due to its high biomass yields compared to annual crops like maize. However, the effects of miscanthus on carbon sequestration and reducing nitrogen leaching and emissions compared to corn have been inconsistent. In this study, the team directly compared soils from miscanthus and maize fields for greenhouse gas emissions, net nitrogen mineralization, and the abundance of microbial nitrogen cycling genes over a 150-day soil incubation period. Soils were obtained from miscanthus and corn fields at the end of the growing season for incubation experiments. The team evaluated these incubation soil responses to compare the impacts of legacies of fertilization to the responses to contemporary amendments. The results revealed that cumulative soil nitrous oxide (N₂O) emissions increased during the incubation, with miscanthus producing significantly greater N₂O than corn. Additionally, higher fertilization levels resulted in greater N₂O production, with N amendment showing a larger effect than C amendment. Net N mineralization was significantly affected by crop type and amendment but not historical fertilization. Microbial processes play a crucial role in determining soil N availability. The team observed no significant differences in total copies of the 16S rRNA gene between crops, historical fertilization treatments, or amendments. However, the abundance of specific bacterial genes involved in N cycling varied, with higher copies of genes associated with nitrification in miscanthus soils and an increase with historical fertilization levels. Genes encoding nitric oxide reductases generally decreased with higher N fertilization levels. The team found that contemporary N addition increased N₂O production as expected, but the larger difference in N₂O production was explained by the legacy of fertilization. This trend was observed in both corn and miscanthus but significantly supported only in miscanthus, indicating a unique response of miscanthus to fertilization legacy. Overall, the results suggest that microbial processes in miscanthus soils differ significantly from maize and emphasize the importance of considering previous land management history when evaluating the contribution of miscanthus and bioenergy crops to N balances.

Microorganisms play key roles in soil carbon turnover and stabilization of persistent organic matter via their metabolic activities, cellular biochemistry, and extracellular products. Microbial residues are the primary ingredients in soil organic matter (SOM), a pool critical to Earth’s soil health and climate. Researchers hypothesize that microbial cellular-chemistry, functional potential, and ecophysiology fundamentally shape soil carbon persistence, and are characterizing this via stable isotope probing (SIP) of genome-resolved metagenomes and viromes. Researchers focus on soil moisture as a master controller of microbial activity and mortality, since altered precipitation regimes are predicted across the temperate U.S. The Science Focus Area’s (SFA) ultimate goal is to determine how microbial soil ecophysiology, population dynamics, and microbe-mineral-organic matter interactions regulate the persistence of microbial residues under changing moisture regimes.

Soil microorganisms shape the global carbon balance by transforming plant rhizodeposits and root detritus into soil organic matter (SOM). It remains unclear how drought influences transformations of these distinct root sources, particularly into the largest and slowest-cycling SOM pool mineral-associated organic matter (MAOM), and how researchers might predict these transformations with changing climate. Since living and decaying roots often exist in close proximity, researchers need to understand how their interaction affects the accrual of MAOM, e.g., through priming effects induced by enhanced microbial activity or the effects of specific microbial taxa. To investigate relationships between drought, microbial ecophysiology, and SOM accrual in a Mediterranean grassland soil, the team conducted a 12-week continuous ¹³CO₂ tracer study with the annual grass Avena barbata, tracking movement of rhizodeposits and root detritus into microbial communities and SOM pools under moisture replete (15 ± 4.2%) or water-limited (8 ± 2%) conditions. Upon harvest, the team measured formation of ¹³C-MAOM from either ¹³C-enriched rhizodeposition alone, decomposing ¹³C-enriched root detritus alone, or the two together. The team measured active microbial community composition (via ¹⁸O- and ¹³C-quantiative stable isotope probing; qSIP), microbial community-level growth rate and carbon-use efficiency, and the chemical composition of SOM using mass spectrometry. These data inform the modeling, which integrates dynamic plant growth models, microhabitats, and a trait-based dynamic energy budget model (DEBmicroTrait) to simulate how precipitation patterns impact both, the timing and magnitude of belowground carbon allocation in Avena barbata, rhizosphere community dynamics and ultimately MAOM accrual.

Overall, drought significantly reduced the accrual of ¹³C-MAOM, with contrasting interactions between habitat and time. In droughted rhizosphere soil, there was significantly less ¹³C-MAOM relative to moisture replete soil at week 12. But at that time, moisture replete rhizosphere soils had the greatest aboveground plant biomass, microbial community-level growth rate and carbon use efficiency. In the detritusphere, droughted soils had the greatest difference in ¹³C-MAOM at week 4–during early stages of root litter decomposition. At this same point, detritusphere microbial community-level growth rate and carbon-use efficiency was greatest under normal moisture conditions. The chemical composition of SOM was also significantly different between rhizosphere and detritusphere habitats, with greater abundance of diacylglycerophosphocholine lipids in the detritusphere, and triacylglycerol lipids in the rhizosphere. Metabolomics suggested more short chain saturated and hydroxy fatty acids in the rhizosphere and more di- and tri-saccharides, C-8 amino sugars and some nucleobases (thymine and cytosine) in the detritusphere.

When living and dead roots co-existed, the presence of living roots decreased the accrual of ¹³C-MAOM formed from detritus. However, the inverse was not true: root detritus did not affect ¹³C-MAOM derived from rhizodeposition. In the detritusphere, the effect of living roots on microbial growth rates depended on soil moisture. Under drought, living roots increased relative growth rates of fungal and bacterial taxa. When soils were moisture replete, living roots increased relative growth rates of detritusphere fungal taxa, with no effect on bacteria. In comparison, the presence of detritus increased relative growth rates of fungal and bacterial rhizosphere taxa regardless of soil moisture.

Rhizobacteria play significant roles in influencing plants through symbiosis and virulence. However, there is limited understanding of rhizobacterial activities in the face of temporary and spatially changing environments within the rhizosphere. In this study, researchers aim to develop novel experimental approaches to characterize temporal and spatial activities of root-colonizing Pseudomonas strains. In the first project, the team developed a massively parallel reporter assay (MPRA), which employs DNA barcode as a reporter of promoter activity, in Pseudomonas simiae WCS417. This approach allowed researchers to characterize in planta promoter activities of P. simiae without the needs to remove overwhelming amount of plant RNA. In the second project, researchers are working on generating an engineered Pseudomonas putida KT2440 that harbors a chemoreceptor library. Using the developed strain, researchers aim to screen chemoreceptors functionally important for plant root colonization. These engineering strategies will be valuable to investigate plant-microbial interactions and may provide new insights to manipulate the microbial systems and enhance plant productivity.

PNNL’s Phenotypic Response of Soil Microbiomes Science Focus Area (SFA) aims to achieve a systems-level understanding of the soil microbiome’s phenotypic response to changing moisture. Researchers perform multi-scale examinations of molecular and ecological interactions occurring within and between members of microbial consortia during organic carbon decomposition, using chitin as a model compound. Integrated experiments address spatial and inter-kingdom interactions among bacteria, fungi, viruses, and plants that regulate community functions throughout the soil profile. Data are used to parametrize individual- and population-based models for predicting interspecies and inter-kingdom interactions. Laboratory and field experiments test predictions to reveal individual and community microbial phenotypes. Knowledge gained provides a fundamental understanding of how soil microbes interact to decompose and sequester organic carbon and enables prediction of how biochemical reaction networks shift in response to changing moisture regimes.

The rhizosphere, where plant roots meet soil, is a hub of biogeochemical activity. The impact of the small molecule metabolites and lipids in root exudates on microbial community structure, gene expression, and processes that control cycling and long-term storage of carbon (C) are poorly understood. Here the goal was to discover the molecular chemodiversity of metabolites and lipids in root exudates and root-associated soils to advance the understanding of plant-microbial feedbacks that regulate C cycling. Researchers worked with mature, field-grown tall wheatgrass (Thinopyrum ponticum), a deep-rooted perennial plant from the Tall Wheatgrass Irrigation Field Trial, in Prosser, WA, which features marginal, low-carbon aridisols. Researchers optimized exudate collection protocols to enable the capture of non-polar lipids in addition to polar and semi-polar metabolites. Researchers found that rates of C input via hydrophobic exudates were approximately double that of aqueous exudates and C/N ratios were markedly higher in hydrophobic compared to aqueous exudates (459 ± 90 vs 14.40 ± 0.58), emphasizing the importance of lipids, due to their high carbon content. Researchers used liquid chromatography coupled tandem mass-spectrometry (LC-MS/MS) for paired untargeted metabolomics and lipidomics or metabo-lipidomics for maximizing molecular coverage. To address the challenge of metabolite annotation, a major bottleneck in metabolomics, the team employed both MS/MS spectral library searching and deep learning-based chemical class assignment. The tandem approach substantially increased the characterization of the chemodiversity of root exudates. Notably, in an unprecedented characterization of intact lipids in root exudates, the team discovered the presence of a diverse variety of lipids, including substantial levels of triacylglycerols (~19 μg/g fresh root per min), fatty acyls, sphingolipids, sterol lipids, and glycerophospholipids. To understand how the spatial gradient of rhizodeposition impacts microbial community composition and metabolism, researchers performed metabo-lipidomics, metagenomics, and metatranscriptomics on a gradient of soil fractions with varying proximity to the roots. Lipids in exudates and soils had lower nominal oxidation state of C (NOSC) compared to more polar metabolites, suggesting increased persistence and less susceptibility to microbial breakdown. The team observed that microbial expression in members of the Actinomycetota, Acidobacteriota, Bacteroidota, Methylomirabilota, Myxococcota, Thermoproteota increased close to the root. Nucleoside metabolites (structural components of RNA) were more abundant near the root, suggesting higher microbial activity. Community expression related to the biosynthesis of secondary metabolites and fatty acid degradation increased closest to the roots while nitrogen metabolism decreased. Focusing on the phyla responsible for these metabolisms, the Actinomycetota and Methylomirabilota had the most significant gradients in abundance as distance toward the root decreased. Triacyglycerols and microbial phospholipids were abundant in bare soil while most secondary metabolites and organic acids increased close to the root. Here researchers show that metabo-lipidomics enables direct measurements of the functional molecules that govern metabolism, signaling, and resource sharing among microbes and in microbe-plant interactions. This builds on recent work that demonstrated the value of intact lipids in soil ecosystems as sensitive indicators of environmental stress response and substrate availability and highlights their great potential for interrogating interkingdom interactions and soil C accrual (Couvillion et al 2023; Naasko et al 2023).