Microbial carbon use efficiency predicted from genome-scale metabolic models

Saifuddin, Mustafa; Bhatnagar, Jennifer M.; Segrè, Daniel; Finzi, Adrien C.

doi:10.1038/s41467-019-11488-z

Title: Microbial carbon use efficiency predicted from genome-scale metabolic models

Journal Article · 08 August 2019 · Nature Communications

DOI: https://doi.org/10.1038/s41467-019-11488-z · OSTI ID:1619681

Saifuddin, Mustafa;

;

; Finzi, Adrien C.

Abstract Respiration by soil bacteria and fungi is one of the largest fluxes of carbon (C) from the land surface. Although this flux is a direct product of microbial metabolism, controls over metabolism and their responses to global change are a major uncertainty in the global C cycle. Here, we explore an in silico approach to predict bacterial C-use efficiency (CUE) for over 200 species using genome-specific constraint-based metabolic modeling. We find that potential CUE averages 0.62 ± 0.17 with a range of 0.22 to 0.98 across taxa and phylogenetic structuring at the subphylum levels. Potential CUE is negatively correlated with genome size, while taxa with larger genomes are able to access a wider variety of C substrates. Incorporating the range of CUE values reported here into a next-generation model of soil biogeochemistry suggests that these differences in physiology across microbial taxa can feed back on soil-C cycling.

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Research Organization:: Univ. of Illinois, Champaign, IL (United States). Center for Advanced Bioenergy and Bioproducts Innovation

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: 502206221; SC0018420

OSTI ID:: 1619681

Alternate ID(s):: OSTI ID: 1558097

Journal Information:: Nature Communications, Journal Name: Nature Communications Vol. 10 Journal Issue: 1; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Cited by: 59 works

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Title: Microbial carbon use efficiency predicted from genome-scale metabolic models

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