A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry

Zhen Li, William J. Riley (), Gianna L. Marschmann, Ulas Karaoz, Ian A. Shirley, Qiong Wu, Nicholas J. Bouskill, Kuang-Yu Chang, Patrick M. Crill, Robert F. Grant, Eric King, Scott R. Saleska, Matthew B. Sullivan, Jinyun Tang, Ruth K. Varner, Ben J. Woodcroft, Kelly C. Wrighton and Eoin L. Brodie
Additional contact information
Zhen Li: Lawrence Berkeley National Laboratory
William J. Riley: Lawrence Berkeley National Laboratory
Gianna L. Marschmann: Lawrence Berkeley National Laboratory
Ulas Karaoz: Lawrence Berkeley National Laboratory
Ian A. Shirley: Lawrence Berkeley National Laboratory
Qiong Wu: Lawrence Berkeley National Laboratory
Nicholas J. Bouskill: Lawrence Berkeley National Laboratory
Kuang-Yu Chang: Lawrence Berkeley National Laboratory
Patrick M. Crill: Stockholm University
Robert F. Grant: University of Alberta
Eric King: Consumnes River College
Scott R. Saleska: University of Arizona
Matthew B. Sullivan: The Ohio State University
Jinyun Tang: Lawrence Berkeley National Laboratory
Ruth K. Varner: University of New Hampshire
Ben J. Woodcroft: Translational Research Institute
Kelly C. Wrighton: Colorado State University
Eoin L. Brodie: Lawrence Berkeley National Laboratory

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract Microbes drive the biogeochemical cycles of earth systems, yet the long-standing goal of linking emerging genomic information, microbial traits, mechanistic ecosystem models, and projections under climate change has remained elusive despite a wealth of emerging genomic information. Here we developed a general genome-to-ecosystem (G2E) framework for integrating genome-inferred microbial kinetic traits into mechanistic models of terrestrial ecosystems and applied it at a well-studied Arctic wetland by benchmarking predictions against observed greenhouse gas emissions. We found variation in genome-inferred microbial kinetic traits resulted in large differences in simulated annual methane emissions, quantitatively demonstrating that the genomically observable variations in microbial capacity are consequential for ecosystem functioning. Applying microbial community-aggregated traits via genome relative-abundance-weighting gave better methane emissions predictions (i.e., up to 54% decrease in bias) compared to ignoring the observed abundances, highlighting the value of combined trait inferences and abundances. This work provides an example of integrating microbial functional trait-based genomics, mechanistic and pragmatic trait parameterizations of diverse microbial metabolisms, and mechanistic ecosystem modeling. The generalizable G2E framework will enable the use of abundant microbial metagenomics data to improve predictions of microbial interactions in many complex systems, including oceanic microbiomes.

Date: 2025
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DOI: 10.1038/s41467-025-57386-5

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