Genomic Science Program
U.S. Department of Energy | Office of Science | Biological and Environmental Research Program

2023 Abstracts

Accelerating Carbon-Negative Biomanufacturing Through Systems-Level Biology and Genome Optimization


Nicholas Fackler1* ([email protected]), Christine Brown1, Hannah Ranft1, Tadas Kolpakovas1, Alexander P. Mueller1, Heidi Schindel1, Vinicio Reynoso1, Shilpa Nagaraju1, Rasmus O. Jensen1, Ching Leang1, Michael Köpke1, and Michael C. Jewett2,3


1LanzaTech, Inc.; 2Northwestern University; and 3Stanford University


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 CO2 across the biosphere. By harnessing the capacity to leverage chemoautotrophic gas–fermenting microorganisms, the project can begin to take advantage of this abundance of available CO2 to transform the way the world creates and uses carbon-based materials. This interdisciplinary project will address existing challenges around genomic optimization of chemoautotrophic gas–fermenting microorganisms to establish versatile and efficient CO2-utilizing biosystems. The team will use in silico, in vitro, and in vivo methods to develop novel tools for genome engineering while advancing systems-level knowledgebases for industrially relevant CO2-fixing organisms. These new developments will be deployed towards generating streamlined genomes in Clostridium autoethanogenum under the contexts of bioproduction and biocontainment.


The project seeks to develop and integrate innovative cell-free tools, genome engineering techniques, and machine learning–based methods for predictive design of CO2-fixing biosystems that deliver new routes to solve energy security and environmental stewardship challenges. The key idea is to develop (1) genome-scale cell-free tools for proteome study and design, (2) genome engineering techniques for systems-level design, (3) high-throughput cell-free enzyme engineering approaches, (4) machine learning and molecular tools to guide enzyme and product selection and engineering, and (5) transcription factor–based biosensors for genetic regulation. The team then aims to apply these new tools across industrially relevant CO2-fixing organisms.

Anaerobic acetogens (specifically the model acetogen, C. autoethanogenum) have emerged as sustainable biomanufacturing platforms capable of producing valuable chemicals from flexible non-food and waste feedstocks that are already used at a commercial scale by LanzaTech today, building on their ability to natively ferment CO2 or CO and produce a valuable product (ethanol) with high selectivity. Advances over the past decades including genome engineering tools, cell-free prototyping, and metabolic models have enabled carbon-negative production of commodity chemicals such as acetone and isopropanol in engineered acetogens at industrially relevant productivities for prolonged periods (Liew et al. 2022).

LanzaTech has previously published a polished genome sequence along with metabolomic, proteomic, and transcriptomic profiles. These datasets have enabled synthesis of sophisticated genome-scale metabolic models and computational analysis for the genome of C. autoethanogenum (Brown et al. 2014; Simpson et al. 2019). From these datasets, ~19% of the genome has been identified as essential for autotrophic growth (Woods et al. 2022). Even for scientists armed with developed models, automated gene engineering capabilities, and gene essentiality information, generation of a genome-streamlined strain through iterative knockout is a daunting task that includes abundant possible permutations that would be resource and time intensive.

To facilitate prioritization of gene knockout targets for strain optimization, cell-free metabolic engineering (CFME) has been previously demonstrated (Liew et al. 2022). Building on this work, the team plans to further expand by identifying and removing protein effectors that have the greatest effects on CO2 to product metabolism. Additionally, using CFME, researchers can screen candidate effectors as combinations to prototype strains that have been iteratively reduced well before the engineered strain exists. This data will improve or validate gene ontology and develop genotype-phenotype linkages that inform in silico modeling of cellular processes.

While CFME has been demonstrated to be able to handle rapid, low-volume and high-throughput screens, genome engineering, in the context of gas fermentation, still relies on at least microliter volumes and generation times significantly longer than those of other model organisms. The team is able to leverage unique gas fermentation capabilities developed at LanzaTech, including the world’s first and only automated biofoundry capable of anaerobic gas fermentation at high throughputs. This integrated system, developed with support from the Biological and Environmental Research (BER) Program’s Genomic Science program DE-SC-0019090, enables genome engineering, colony picking, cultivation, screening, and strain repository at throughputs inaccessible to bench scientists. The large amount of generated data is managed in a custom-built laboratory information management system and provides the team with access to an ever-growing repository of host knockout genotypes for use as platforms for metabolic engineering.

In order to develop strains with increased genomic efficiency that are robust enough to tolerate industrial fermentation, the team, in partnership with the DOE Joint Genome Institute (JGI), has developed the largest dataset to date characterizing transcription factor binding sites (TFBS) across six clostridial genomes. Here, the initial analysis of that dataset and some conserved TFBS motifs are demonstrated. Further work is planned to build and report on regulatory networks and to use that output to inform in vitro experiments. Validation through novel cell-free methods will aid in refining the knowledgebase through elucidating principles of regulation, promoter sequence ‘leakiness,’ dynamic range of the transcription factor in the presence of inducer, and ligand sensitivity.

Ultimately, the team will deliver novel C. autoethanogenum strains that have been streamlined to metabolize CO2 into products through continuous fermentation. These strains will have improved genomic efficiencies with increased functionally stability in the industrial bioproduction specific settings.


Brown, S. D., et al. 2014. “Comparison of Single-Molecule Sequencing and Hybrid Approaches for Finishing the Genome of Clostridium autoethanogenum and Analysis of CRISPR Systems in Industrial Relevant Clostridia,” Biotechnology for Biofuels and Bioproducts 7, 40. DOI:10.1186/1754-6834-7-40.

Liew, F. E., et al. 2022. “Carbon-Negative Production of Acetone and Isopropanol by Gas Fermentation at Industrial Pilot Scale,” Nature Biotechnology 40, 335–44. DOI:10.1038/s41587-021-01195-w.

Simpson, S. D., et al. 2019. “Development of a Sustainable Green Chemistry Platform for Production of Acetone and Downstream Drop-In Fuel and Commodity Products Directly from Biomass Syngas via a Novel Energy Conserving Route in Engineered Acetogenic Bacteria.” DOI:10.2172/1543199.

Woods, C., et al. 2022. “Required Gene Set for Autotrophic Growth of Clostridium autoethanogenum,” Applied and Environmental Microbiology 88, e02479-21. DOI:10.1073/pnas.2216244120.

Funding Information

This research was supported by the DOE Office of Science, Biological and Environmental Research (BER) Program, grant no. DE-SC0023278. DAP-seq work was supported by the Joint Genome Institute Community Science Program under award no. CSP-507323.