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Microbial ecology of antibiotic resistance genes: a one health perspective

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This review pulls together existing research showing that antibiotic-resistant genes are spreading through soil, water, landfills, and even melting permafrost, not just from hospitals, but from everyday environments like farm soil and plastic pollution in waterways, where bacteria trade resistance traits with each other. This matters because it means fighting antibiotic resistance requires looking beyond doctors' offices to how we manage farming, waste, and pollution worldwide, especially in lower-income countries where tracking this problem is harder due to limited resources.

The environmental resistome is a dynamic collection of antibiotic resistance genes (ARGs) that exist in both untouched and human-altered ecosystems, with important implications for worldwide antimicrobial resistance. This review traces the evolutionary development of the resistome, highlighting its origins from ancient microbial defences in permafrost and remote areas detected through metagenomics of β-lactamases and efflux systems that predate clinical antibiotic use. It also discusses how horizontal gene transfer (HGT) drives its spread today and mobile genetic elements (MGEs) during ecological competition and natural antibiotic production. Major reservoirs include soil microbiomes (e.g., fungicide-enriched rhizospheres increasing tetW and sul1 by 2—5 times through HGT), aquatic environments (e.g., microplastic biofilms containing bla TEM and mcr-1 in Pseudomonas groups), extreme habitats (e.g., thawing permafrost releasing blaNDM precursors), and human-related sites (e.g., landfills with 324 ARG subtypes), all contributing to the transfer of ARGs to pathogenic microbes. Mechanistic details show how specific taxa adapt, including RND efflux pumps in Proteobacteria, enzymes like metallo-β-lactamases that inactivate β-lactam antibiotics, mutations in ribosomal targets, and CRISPR-Cas systems that act as gatekeepers (e.g., 20–30% plasmid targeting in Lactobacillus, 90–100% conjugation block in Staphylococcus). Advances in long-read sequencing, artificial intelligence (AI) tools such as DeepARG, and qPCR/ddPCR now enable detailed mapping of ARG dynamics while identifying gaps in data from low- and middle-income countries (LMICs), the need for standardisation, and integration with microbial ecology beyond previous pollution-focused or roadmap-based reviews. Using a One Health approach, this review advocates for predictive modelling and resilient community strategies to limit ARG spread across food, water, and healthcare environments, opening new opportunities for fair and effective global AMR control.

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