Assessing the nonlinear association of environmental factors with antibiotic resistance genes (ARGs) in the Yangtze River Mouth, China.

Abstract

The emergence of antibacterial resistance (ABR) is an urgent and complex public health challenge worldwide. Antibiotic resistant genes (ARGs) are considered as a new pollutant by the WHO because of their wide distribution and emerging prevalence. The role of environmental factors in developing ARGs in bacterial populations is still poorly understood. Therefore, the relationship between environmental factors and bacteria should be explored to combat ABR and propose more tailored solutions in a specific region. Here, we collected and analyzed surface water samples from Yangtze Delta, China during 2021, and assessed the nonlinear association of environmental factors with ARGs through a sigmoid model. A high abundance of ARGs was detected. Amoxicillin, phosphorus (P), chromium (Cr), manganese (Mn), calcium (Ca), and strontium (Sr) were found to be strongly associated with ARGs and identified as potential key contributors to ARG detection. Our findings suggest that the suppression of ARGs may be achieved by decreasing the concentration of phosphorus in surface water. Additionally, Group 2A light metals (e.g., magnesium and calcium) may be candidates for the development of eco-friendly reagents for controlling antibiotic resistance in the future.

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Published Version (Please cite this version)

10.1038/s41598-023-45973-9

Publication Info

Miao, Jiazheng, Yikai Ling, Xiaoyuan Chen, Siyuan Wu, Xinyue Liu, Shixin Xu, Sajid Umar, Benjamin D Anderson, et al. (2023). Assessing the nonlinear association of environmental factors with antibiotic resistance genes (ARGs) in the Yangtze River Mouth, China. Scientific reports, 13(1). p. 20367. 10.1038/s41598-023-45973-9 Retrieved from https://hdl.handle.net/10161/29459.

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Xu

Shixin Xu

Assistant Professor of Mathematics at Duke Kunshan University

Shixin Xu is an Assistant Professor of Mathematics.   His research interests are machine learning and data-driven models for diseases,  multiscale modeling of complex fluids, Neurovascular coupling, homogenization theory, and numerical analysis.  The current projects he is working on are

  • image data-based for the prediction of hemorrhagic transformation in acute ischemic stroke,
  • electrodynamics modeling of saltatory conduction along a myelinated axon
  • electrochemical modeling
  • fluid-structure interaction with mass transportation and reaction

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