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

2023 Abstracts

Microbes Persist: Towards Quantitative Theory-Based Predictions of Soil Microbial Fitness, Interaction and Function in KBase


Jeffrey Kimbrel1* ([email protected]), Ulas Karaoz3, Gianna Marschmann3, Ben Koch2, Steve Blazewicz1, Bruce Hungate2, Eoin Brodie3, and Jennifer Pett-Ridge1


1Lawrence Livermore National Laboratory; 2Northern Arizona University; and 3Lawrence Berkeley National Laboratory



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. The team hypothesizes that microbial cellular chemistry, functional potential, and ecophysiology fundamentally shape soil carbon persistence, and team members are characterizing this via stable isotope probing (SIP) of genome-resolved metagenomes and viromes. Researchers are focusing on soil moisture as a master controller of microbial activity and mortality, since altered precipitation regimes are predicted across the temperate United States. This 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.


This SFA has pioneered methods that quantify element fluxes with taxonomic resolution and has proposed to integrate these into KBase. In particular, quantitative stable isotope probing (qSIP) allows us to evaluate in situ activity of individual taxa in complex communities by adding isotope tracers such as 18O-enriched heavy water or 13C-enriched compounds. Researchers have refactored a computational workflow that accepts both amplicon or metagenomic sequence SIP input and calculates atom fraction excess (enrichment) as well as growth and mortality rates for individual amplicon sequence variants (ASVs) and genomes assembled from metagenomes (MAGs and viral OTUs). Experiments using 18O-H2O labeling and qSIP provide critical information on organism growth rates and mortality in situ. The analytical pipelines the team is developing within KBase establish a standard qSIP analytical workflow and a qSIP database suitable for robust cross-site comparisons and for model benchmarking. The workflow will enable uniform bioinformatics and calculations of qSIP data (e.g., a uniform approach to density shift calculations) within the existing quantitative insights into microbial ecology (QIIME) platform, and the database will facilitate robust comparisons across experiments. Integration within KBase will support analyses that compare traits of organisms with their performance in nature across environments.

The qSIP pipeline is fully integrated with a genomes-to-traits workflow (microTrait) and compatible with a dynamic energy budget–based trait-based model (DEBmicroTrait). With microTrait and DEBmicroTrait, team members have developed and tested a computational workflow 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, (4) explore trait-based simulations under different scenarios with varying levels of microbial community and environmental complexity, and (5) benchmark emergent model substrate utilization (digested as chemical abundance data) and qSIP-derived growth and mortality rates (from qSIP database).

Ongoing work to combine both the qSIP and DEBmicroTrait 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.

Funding Information

This research is based upon work of the LLNL Microbes Persist Soil Microbiome SFA supported by the U.S. Department of Energy Office of Science, Office of Biological and Environmental Research Genomic Science program under Award Number SCW1632 to the Lawrence Livermore National Laboratory, and subcontracts to Northern Arizona University and Lawrence Berkeley National Laboratory. Work at Lawrence Livermore National Laboratory was performed under U.S. Department of Energy Contract DE-AC52-07NA27344.