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Anthropogenic pressures amplify high-risk antibiotic resistome via co-selection among biocide resistance, virulence, and antibiotic resistance genes in the Ganjiang River basin: Drivers diverge in densely versus sparsely populated reaches.

Dingchang Li,Huihui Dai,Huimin Zhang,Chuqiao Wang,Chenglong Zou,Shan Huang,Yanling Wang,Jinchi Yao

Published 2025 in Journal of Hazardous Materials

ABSTRACT

As the largest river in the Poyang Lake system, the Ganjiang River faces escalating anthropogenic pressures that amplify resistance gene dissemination. This study integrated antibiotic resistance genes (ARGs), biocide resistance genes (BRGs), and virulence factor genes (VFGs) to reveal their co-selection mechanisms and divergent environmental drivers between densely (DES) and sparsely populated (SPAR) regions of the Ganjiang River basin. The microbial and viral communities and structures differed significantly between the DES and SPAR regions (PERMANOVA, p < 0.001). Midstream DES areas were hotspots for ARGs/BRGs/VFGs enrichment, with peak enrichment multiples reaching 10.2, 5.7, and 5.9-fold respectively. Procrustes analysis revealed limited dependence of ARGs transmission on mobile genetic elements (MGEs) (p > 0.05). Separately, 74 % of dominant ARGs (top 1 %) showed strong correlations with BRGs (r2 = 0.973, p < 0.01) and VFGs (r2 = 0.966, p < 0.01) via co-selection. Pathogenic Pseudomonas spp. carrying multidrug-resistant ARGs, BRGs, and adhesion-VFGs were identified as high-risk vectors. In SPAR areas, anthropogenic pressure directly dominated ARGs risk (RC = 54.2 %, β = 0.39, p < 0.05), with biological factors as secondary contributors (RC = 45.8 %, β = 0.33, p < 0.05). In contrast, DES regions showed anthropogenic pressure exerting broader, enduring influences across microorganisms, physicochemical parameters, and biological factors, escalating ARGs risks through diverse pathways, with BRGs/VFGs acting as direct drivers. This study proposes establishing a risk prevention system using BRGs and pathogenic microorganisms as early-warning indicators.

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