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Updated: Apr 18, 2026

Testing the Role of Multicopy Plasmids in the Evolution of Antibiotic Resistance
Published on: May 2, 2018
[Cross-host transmission of bacterial antibiotic resistance: research progress on ecological pattern and mechanism]
1School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China NHC Key Laboratory of Food Safety Risk Assessment/Chinese Academy of Medical Science Research Unit (2019RU014)/China National Center for Food Safety Risk Assessment, Beijing 100022, China.
Abstract:
Antimicrobial resistance (AMR) constitutes a global public health crisis, its transmission networks deeply entrenched at the animal-environment-human health interface. This review systematically elucidates the mechanisms underlying the emergence and characteristics of dissemination of AMR within China's livestock and poultry farming sector. It reveals the core pathways by which structural contradictions between antibiotic use and regulation drive the cross-boundary migration of resistance genes via manure, soil, water bodies, and the food chain. Molecular mechanism studies demonstrate that the synergistic interplay between the evolution of resistance phenotypes and horizontal gene transfer accelerates the formation of multi-drug resistance phenotypes under the co-selective pressure exerted by antibiotic residues and environmental stressors. This review pioneers the integration of a collaborative application framework for multi-dimensional genomic technologies in AMR research, elucidating how this framework provides technical support for resistance source-tracing and risk assessment by deciphering the transmission trajectories of antibiotic resistance genes (ARGs), the evolution of resistance lineages, and host adaptation. Concurrently, it identifies barriers to cross-system data integration as a critical bottleneck for precise prevention and control. Grounded in the "One Health" concept, this review advocates for the construction of a comprehensive "animal-environment-human" analytical framework to uncover key nodes in cross-boundary transmission. It further proposes coupling multi-omics, artificial intelligence, and big data technologies to establish a novel, integrated "monitoring-prediction-intervention" prevention paradigm. Through the deep integration of science and technology with governance strategies, this approach aims to optimize interventions across the entire chain from farm to fork, thereby providing a scientific decision-making basis for curbing the global spread of AMR.
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