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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Nature Portfolio
2025-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-57386-5 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Zhen Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c949aafbe8f949188ffd6e5755e01b9e |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-c949aafbe8f949188ffd6e5755e01b9e2025-08-20T02:47:06ZengNature PortfolioNature Communications2041-17232025-03-0116111110.1038/s41467-025-57386-5A framework for integrating genomics, microbial traits, and ecosystem biogeochemistryZhen Li0William J. Riley1Gianna L. Marschmann2Ulas Karaoz3Ian A. Shirley4Qiong Wu5Nicholas J. Bouskill6Kuang-Yu Chang7Patrick M. Crill8Robert F. Grant9Eric King10Scott R. Saleska11Matthew B. Sullivan12Jinyun Tang13Ruth K. Varner14Ben J. Woodcroft15Kelly C. Wrighton16the EMERGE Biology Integration Institute CoordinatorsEoin L. Brodie17Climate and Ecosystem Sciences Division, Lawrence Berkeley National LaboratoryClimate and Ecosystem Sciences Division, Lawrence Berkeley National LaboratoryClimate and Ecosystem Sciences Division, Lawrence Berkeley National LaboratoryClimate and Ecosystem Sciences Division, Lawrence Berkeley National LaboratoryClimate and Ecosystem Sciences Division, Lawrence Berkeley National LaboratoryClimate and Ecosystem Sciences Division, Lawrence Berkeley National LaboratoryClimate and Ecosystem Sciences Division, Lawrence Berkeley National LaboratoryClimate and Ecosystem Sciences Division, Lawrence Berkeley National LaboratoryDepartment of Geological Sciences and Bolin Centre for Climate Research, Stockholm UniversityDepartment of Renewable Resources, University of AlbertaDepartment of Biology, Consumnes River CollegeDepartment of Ecology and Evolutionary Biology, University of ArizonaDepartment of Microbiology, The Ohio State UniversityClimate and Ecosystem Sciences Division, Lawrence Berkeley National LaboratoryDepartment of Earth Sciences and Institute for the Study of Earth, Oceans and Space, University of New HampshireCentre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Translational Research InstituteDepartment of Soil and Crop Sciences, Colorado State UniversityClimate and Ecosystem Sciences Division, Lawrence Berkeley National LaboratoryAbstract 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.https://doi.org/10.1038/s41467-025-57386-5 |
| spellingShingle | 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 A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry Nature Communications |
| title | A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry |
| title_full | A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry |
| title_fullStr | A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry |
| title_full_unstemmed | A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry |
| title_short | A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry |
| title_sort | framework for integrating genomics microbial traits and ecosystem biogeochemistry |
| url | https://doi.org/10.1038/s41467-025-57386-5 |
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