Metabolite correlation networks reveal complex phenotypes of adaptation-driving mutations

Metabolic adaptation is often driven by mutations in pleiotropic genes that affect metabolism globally. However, identifying which phenotypes produced by such mutations drive selection remains difficult. Here, we consider metabolite correlation networks as a systems-level phenotype whose emergent properties reflect the combined effects of biochemical reactions and regulation. We hypothesize that mutation-induced changes in the covariance structure of these networks constitute complex phenotypes linking genetic change to bacterial fitness, and that node-level shifts can identify metabolites and pathways mechanistically associated with adaptation. We test this hypothesis by applying adaptive laboratory evolution to a metabolically suboptimal Escherichia coli strain in which metK, encoding methionine adenosyltransferase (MAT), is replaced by an ortholog from Ureaplasma urealyticum. Untargeted metabolite correlation networks reveal that evolution shifts S-adenosylmethionine (SAM), the MAT product, from a peripheral node to a key connector between network clusters. None of the accumulated pleiotropic mutations causing the shift directly affect MAT activity or SAM metabolism, indicating that this transition emerges from system-level metabolic reorganization. Targeted metabolomics of nodes showing similar shifts identifies additional metabolites involved in SAM-related pathways. These findings establish metabolite correlation networks as a framework for mapping genotype–phenotype–fitness relationships and prioritizing metabolic nodes linked to adaptive mechanisms. Adaptive mutations often reshape metabolism in complex ways, making it difficult to identify which changes improve fitness. This study shows that metabolite correlation networks reveal system-level metabolic reorganization and pinpoint metabolites linked to adaptive mechanisms.