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

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

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...

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Main Authors: 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, the EMERGE Biology Integration Institute Coordinators, Eoin L. Brodie
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-57386-5
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Summary: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.
ISSN:2041-1723

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