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Environmental drivers of high-risk antibiotic resistance genes propagation in the plastisphere unveiled by meta-analysis and interpretable machine learning
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Tiny plastic bits floating in water aren't just pollution—they act like rafts that collect and boost antibiotic-resistant bacteria genes, making them up to twice as concentrated as in the surrounding water. Using data analysis and machine learning, researchers found that the type of plastic and water conditions strongly predict how much these resistance genes build up, which could help scientists monitor and flag risky waters before resistant bacteria spread further. Since antibiotic resistance is already a growing threat to human health, understanding how plastic pollution fuels it is an important step toward protecting the effectiveness of antibiotics we rely
The plastisphere serves as an expanding reservoir and dissemination vector for antibiotic resistance genes (ARGs), yet the environmental driving factors on high-risk ARG dynamics within this niche remain poorly understood. Herein, a multi-effect meta-analysis was conducted to quantify the influence of environmental factors on high-risk ARGs within the plastisphere. Relative to ambient waters, significant enrichment was observed for ARGs targeting macrolide (ermC, +114.47%), quinolone (aac(6')-Ib, + 89.62%), sulfonamide (sul1, +57.16%), quinolone (qnrS, +33.01%), and class I integrons (intI1, +48.61%). Among microplastics, polyethylene and polypropylene exhibited selective ARGs enrichment, exceeding concentration levels of ambient waters by 80.63% and 71.89%, respectively. Mantel and binning analyses quantified contributions of 13 environmental factors to 9 ARG genotypes and intI1. Additionally, molecular fingerprints (n = 92) obtained via RDKit revealed the contributions of microplastics' physicochemical properties. Independent explainable machine learning (ML) models developed using the HO-AutoML platform for sul1 and intI1 achieved high predictive accuracy. Shapley Additive Explanations (SHAP) analysis identified near-equal associations between intI1 risk and aquatic parameters (49.3%) versus microplastics (MPs) characteristics (50.3%), whereas sul1 risk was predominantly influenced by MPs (91.2%). This study enhances the understanding of the dynamics of ARGs in the plastic cycle and provides a methodological reference for the subsequent development of a predictive framework for rapid, large-scale monitoring of high-risk ARGs in aquatic environments.
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