Background/Objectives: Glyphosate-based formulations are globally pervasive pollutants increasingly recognized as potential contributors to antimicrobial resistance (AMR) in environmental microbiomes. Although glyphosate is designed to inhibit plant 5-enolpyruvylshikimate-3-phosphate synthase, it also affects microbial metabolism, stress response, and genetic exchange. This review synthesizes the pathways through which glyphosate, its metabolite aminomethylphosphonic acid (AMPA), and commercial mixtures influence resistance-associated phenotypes and the dissemination of antibiotic resistance (ABR). Methods: A critical synthesis of the literature was conducted to evaluate the mechanistic and ecological interactions between glyphosate exposure and bacterial resistance in soil, aquatic, and host-associated microbiomes. Results: Experimental evidence showed that sublethal glyphosate exposure induced oxidative stress, altered membrane permeability, activated multidrug efflux pumps, and promoted tolerance phenotypes that could modify antibiotic susceptibility. It also enhances mutation rates and horizontal gene transfer processes associated with the emergence of resistance under controlled conditions. At the community level, glyphosate exposure is associated with microbiome restructuring and enrichment of resistance determinants, often without major shifts in overall diversity of the microbiome. These effects have been reported at environmentally relevant concentrations, although the evidence remains largely derived from laboratory and mesocosm studies. Conclusions: Glyphosate acts as both a biochemical modulator of resistance-related phenotypes and an environmental selective pressure that shapes microbial communities. Its widespread use and environmental persistence position it as a context-dependent contributor to the emergence and dissemination of AMR through interacting mechanistic and ecological pathways. Integrating AMR endpoints into pesticide risk assessments and surveillance frameworks is warranted, in addition to expanded field-based validation.
Malinoski et al. (Tue,) studied this question.