4.8 Article

Principles of metabolome conservation in animals

Publisher

NATL ACAD SCIENCES
DOI: 10.1073/pnas.2302147120

Keywords

molecular evolution; metabolic networks; systems biology; phylogenetic comparative method; neutral evolution

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This study introduces a measure of conservation of individual metabolite levels among related species and reveals the association between conservation and functional properties of metabolites. It shows that metabolite abundance, essentiality, and association with human diseases predict conservation, suggesting a parallel between metabolome and protein sequence conservation. Additionally, the study demonstrates that biomarkers of metabolic diseases can be distinguished based on evolutionary conservation alone, without prior clinical knowledge.
Metabolite levels shape cellular physiology and disease susceptibility, yet the general principles governing metabolome evolution are largely unknown. Here, we introduce a measure of conservation of individual metabolite levels among related species. By analyzing multispecies tissue metabolome datasets in phylogenetically diverse mammals and fruit flies, we show that conservation varies extensively across metabolites. Three major functional properties, metabolite abundance, essentiality, and association with human diseases predict conservation, highlighting a striking parallel between the evolutionary forces driving metabolome and protein sequence conservation. Metabolic network simulations recapitulated these general patterns and revealed that abundant metabolites are highly conserved due to their strong coupling to key metabolic fluxes in the network. Finally, we show that biomarkers of metabolic diseases can be distinguished from other metabolites simply based on evolutionary conservation, without requiring any prior clinical knowledge. Overall, this study uncovers simple rules that govern metabolic evolution in animals and implies that most tissue metabolome differences between species are permitted, rather than favored by natural selection. More broadly, our work paves the way toward using evolutionary information to identify biomarkers, as well as to detect pathogenic metabolome alterations in individual patients.

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