Microbial resistance from genotype to metabolic phenotype.

Genome-scale metabolic models (GEMs) can generate quantitative, context-dependent resistance predictions that surpass sequence-based tools in understanding antimicrobial resistance.

• Why it matters: Current resistance surveillance focuses on individual resistance genes, which fails to capture the ecological, genomic, and metabolic complexity of resistance emergence and spread, hindering effective intervention.
• What they did: The authors advocate for using strain-resolved GEMs that embed resistance determinants within metabolic networks, incorporating mobile genetic elements, horizontal gene transfer, and community interactions to improve resistance profiling and therapeutic targeting.
• The result: Integrating GEMs with multiomics and data-driven approaches may enable a mechanism-aware platform for resistance surveillance, diagnosis, and drug discovery, advancing understanding and management of antimicrobial resistance.