Microbial resistance from genotype to metabolic phenotype.

Abstract

Antimicrobial resistance (AMR) poses an escalating global threat, yet surveillance and mechanistic understanding remain anchored in individual resistance genes, an approach that is inadequate for capturing the ecological, genomic, and metabolic complexity through which resistance emerges and spreads. In this opinion article, we argue that genome-scale metabolic models (GEMs) offer a principled framework for advancing AMR research beyond this gene-centric paradigm. Strain-resolved GEMs embed resistance determinants within metabolic networks, with the potential to generate quantitative, context-dependent phenotypic predictions, including minimum inhibitory concentrations and environment-conditional resistance, that sequence-based tools do not provide. Expanded GEM frameworks incorporating mobile genetic elements, horizontal gene transfer, and community-level interactions support ecology-aware resistance profiling. At the therapeutic level, whole-network modelling can identify metabolic vulnerabilities, synthetic lethal interactions, and microbiome-targeted strategies. Integrating GEMs with multiomics and data-driven approaches represents a critical step toward a mechanism-aware AMR platform connecting surveillance, diagnosis, and drug discovery.