Metabolite correlation networks reveal complex phenotypes of adaptation-driving mutations

Living organisms are organized into hierarchical levels of increasing complexity. As mutational effects propagate through these levels, multiple phenotypes are produced. However, identifying which phenotypes influence fitness and drive selection remains a key challenge in understanding genotype-phenotype-fitness relationships. Here, we demonstrate that mutation-induced structural changes in metabolite correlation networks—an organizational level with emergent properties shaped by the cumulative effects of multiple biochemical reactions and regulatory mechanisms—constitute complex phenotypes linking mutations to bacterial fitness. We engineered a metabolically suboptimal E. coli strain by replacing the metK gene encoding methionine adenosyltransferase (MAT) with an ortholog from U. urealyticum and subjected it to laboratory evolution. Analysis of correlation networks constructed from 2,118 untargeted metabolites revealed that adaptive mutations enhanced the strain’s fitness by reducing network density, removing node hubs, and extensively rewiring connectivity. These changes yielded smaller, more cohesive, and better-interconnected network clusters. Moreover, evolution shifted the node representing S-adenosylmethionine (SAM), the product of the MAT-catalyzed reaction, from a peripheral role to a key connector between network clusters with a significantly increased betweenness. None of the accumulated mutations potentially driving this transition directly influenced MAT activity or SAM metabolism, indicating that the shift in SAM’s role is an adaptive phenotype emerging from the metabolic system’s complexity. Targeted metabolomics of key nodes displaying similar network transitions unveiled additional metabolites involved in SAM-related pathways. We propose the construction and analysis of metabolite correlation networks as an experimental and analytical framework for mapping genotype-phenotype-fitness relationships and exploring the mechanisms of metabolic adaptation.