Metagenomics for antimicrobial resistance: from resistome surveillance to mechanistic inference
TL;DR
An evidence ladder for mechanistic inference that integrates metagenomics with targeted assays and culture-dependent experiments, a decision-gated workflow that ties metagenomic outputs to allowable claim language, and a minimum reporting checklist aligned to evidence strength are proposed to reduce over-interpretation.
Abstract
ABSTRACT Antimicrobial resistance (AMR) is a global health crisis shaped by complex ecological and evolutionary processes that often occur in polymicrobial communities. Metagenomics enables culture-independent profiling of microbial DNA directly from clinical or environmental samples, providing an unparalleled view of community composition, resistome content, and the mobile genetic elements that drive horizontal gene transfer (HGT). Yet, a recurring challenge is that metagenomic detection of antibiotic-resistance genes does not automatically translate into a mechanistic understanding of resistance phenotypes, nor does it replace culture-based functional validation. Here, we synthesize how modern metagenomics supports AMR research across three linked questions: (i) what resistance determinants are present and how do they change across time and space, (ii) which hosts and mobile genetic elements carry these determinants, and how gene flow can be inferred, and (iii) what evidence is required to move from “resistance potential” to robust mechanistic claims. We emphasize practical design principles (sampling, controls, and contamination management), analytical choices (database and parameter effects), and recent advances, including long-read sequencing for resolving antibiotic-resistance genes context, and rapid clinical metagenomic sequencing for time-sensitive decision support. We propose an evidence ladder for mechanistic inference that integrates metagenomics with targeted assays and culture-dependent experiments. Beyond synthesizing recent advances, this review provides operational tools for critical appraisal and study design: an evidence ladder for mechanistic inference, a decision-gated workflow that ties metagenomic outputs to allowable claim language, a minimum reporting checklist aligned to evidence strength, and a “pitfall → consequence → fix” guide to reduce over-interpretation. To support a more comprehensive, forward-looking view, we also summarize emerging directions that are rapidly reshaping AMR metagenomics—multi-omics integration, single-cell, and epigenetic linkage strategies, CRISPR-enabled enrichment/depletion, and AI-assisted discovery/mining—and clarify where these advances strengthen (or do not strengthen) mechanistic claims within the same evidence ladder.