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• W4360992607 abstract "Metabolic phenotypes are pivotal for many areas, but disentangling how evolutionary history and environmental adaptation shape these phenotypes is an open problem. Especially for microbes, which are metabolically diverse and often interact in complex communities, few phenotypes can be determined directly. Instead, potential phenotypes are commonly inferred from genomic information, and rarely were model-predicted phenotypes employed beyond the species level. Here, we propose sensitivity correlations to quantify similarity of predicted metabolic network responses to perturbations, and thereby link genotype and environment to phenotype. We show that these correlations provide a consistent functional complement to genomic information by capturing how network context shapes gene function. This enables, for example, phylogenetic inference across all domains of life at the organism level. For 245 bacterial species, we identify conserved and variable metabolic functions, elucidate the quantitative impact of evolutionary history and ecological niche on these functions, and generate hypotheses on associated metabolic phenotypes. We expect our framework for the joint interpretation of metabolic phenotypes, evolution, and environment to help guide future empirical studies." @default.
• W4360992607 created "2023-03-30" @default.
• W4360992607 creator A5000826167 @default.
• W4360992607 creator A5085247534 @default.
• W4360992607 date "2023-03-27" @default.
• W4360992607 modified "2023-09-30" @default.
• W4360992607 title "Functional comparison of metabolic networks across species" @default.
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• W4360992607 doi "https://doi.org/10.1038/s41467-023-37429-5" @default.
• W4360992607 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36973280" @default.
• W4360992607 hasPublicationYear "2023" @default.
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