Work

Tracing Zoonotic Pathogens Through Surface Water Monitoring: A Case Study in China

Year: 2025
Type: article
Abstract: Intensive aquaculture and animal farming along riverbanks have emerged as significant drivers of downstream public health risks, facilitating the transmission of zoonotic pathogens and antimicrobial resistance (AMR) genes from farm effluents into natural water systems. In this study, we conducted a comprehensive 12-week water monitoring program at the Wei River in Shandong, China, using a combination of rapid detection techniques (RPA-LFD) and whole-genome sequencing to trace the origins of detected pathogens. RPA-LFD screening revealed the sequential appearance of Vibrio parahaemolyticus, Aeromonas veronii, norovirus GII, and Brucella spp. in surface water from March onward, coinciding with documented wastewater discharge events from upstream shrimp and fox farms. Subsequent isolation efforts confirmed the presence of V. parahaemolyticus and A. veronii in both river water and shrimp samples, while Brucella abortus was isolated from fox feces and water samples. Whole-genome sequencing of bacterial isolates revealed that V. parahaemolyticus strains from water and shrimp shared identical sequence types (ST150 and ST809) and resistance gene profiles, indicating a clonal relationship. Similarly, B. abortus isolates from water and fox feces differed by fewer than five SNPs, confirming farm-to-water transmission. Norovirus GII.3 and GII.6 sequences from water and fecal samples clustered phylogenetically with regional clinical strains, suggesting local circulation and environmental dissemination. Our findings highlight the critical role of river water monitoring as an early warning system for pathogen spread, emphasizing the need for integrated surveillance systems that monitor both water quality and the health of upstream farms and wildlife populations. The combined use of RPA-LFD and whole-genome sequencing provides a robust framework for real-time detection and source tracing of zoonotic pathogens, offering valuable insights for future environmental monitoring and public health interventions.
Language: English
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4416996331
FWCI: 0.255
Cites: 45
Cited by: 1
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