Metabolite correlation networks reveal complex phenotypes of adaptation-driving mutations.
6 affiliations
- 1Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
- 2Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel.
- 3School of Plant Sciences and Food Security, The George S. Wise Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel.
- 4Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel. [email protected].
- 5The Goldman Sonnenfeldt School of Sustainability and Climate Change, Ben-Gurion University of the Negev, Beer-Sheva, Israel. [email protected].
- 6Department of Life Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel. [email protected].
Abstract
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.
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BibTeX
@article{samuel2026metabolite,
title = {{Metabolite correlation networks reveal complex phenotypes of adaptation-driving mutations.}},
author = {Sharon Samuel and Weronika Jasinska and Ezra Sternlicht and Matvey Nikelshparg and Daniel Kleiner and Shai Pilosof and Shimon Bershtein},
journal = {Nature communications},
year = {2026},
month = sep,
doi = {10.1038/s41467-026-77850-0},
url = {https://europepmc.org/article/MED/42894022},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Metabolite correlation networks reveal complex phenotypes of adaptation-driving mutations.
AU - Sharon Samuel
AU - Weronika Jasinska
AU - Ezra Sternlicht
AU - Matvey Nikelshparg
AU - Daniel Kleiner
AU - Shai Pilosof
AU - Shimon Bershtein
PY - 2026
DA - 2026/09/16
JO - Nature communications
DO - 10.1038/s41467-026-77850-0
UR - https://europepmc.org/article/MED/42894022
AB - 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.
SN - 2041-1723
ER -