4.8 Article

Developing Surrogate Markers for Predicting Antibiotic Resistance Hot Spots in Rivers Where Limited Data Are Available

期刊

ENVIRONMENTAL SCIENCE & TECHNOLOGY
卷 55, 期 11, 页码 7466-7478

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.est.1c00939

关键词

antibiotic resistance; LMICs; SE Asian rivers; water quality; environmental monitoring; high-throughput qPCR; modeling

资金

  1. Newcastle University SAgE Singapore Scholarships programme
  2. British Council Newton Fund Institutional Links grant [331945729]
  3. UK EPSRC Impact Acceleration Award [EP/K503885/1]
  4. UK Research and Innovation's Global Challenges Research Fund [ES/S008179/1]
  5. Key Collaborative Research Program of the Alliance of International Science Organizations [ANSO-CR-KP-2020-03]

向作者/读者索取更多资源

This study conducted a comprehensive assessment of water quality and antibiotic resistance in a Malaysian river catchment, finding a general decline in water quality and higher antibiotic concentrations in the dry season as one moved downstream. Dissolved oxygen (DO) showed a strong inverse correlation with total AR gene concentrations, indicating its potential use as a simple surrogate for identifying AR hotspots.
Pinpointing environmental antibiotic resistance (AR) hot spots in low-and middle-income countries (LMICs) is hindered by a lack of available and comparable AR monitoring data relevant to such settings. Addressing this problem, we performed a comprehensive spatial and seasonal assessment of water quality and AR conditions in a Malaysian river catchment to identify potential simple surrogates that mirror elevated AR. We screened for resistant coliforms, 22 antibiotics, 287 AR genes and integrons, and routine water quality parameters, covering absolute concentrations and mass loadings. To understand relationships, we introduced standardized effect sizes (Cohen's D) for AR monitoring to improve comparability of field studies. Overall, water quality generally declined and environmental AR levels increased as one moved down the catchment without major seasonal variations, except total antibiotic concentrations that were higher in the dry season (Cohen's D > 0.8, P < 0.05). Among simple surrogates, dissolved oxygen (DO) most strongly correlated (inversely) with total AR gene concentrations (Spearman's. 0.81, P < 0.05). We suspect this results from minimally treated sewage inputs, which also contain AR bacteria and genes, depleting DO in the most impacted reaches. Thus, although DO is not a measure of AR, lower DO levels reflect wastewater inputs, flagging possible AR hot spots. DO measurement is inexpensive, already monitored in many catchments, and exists in many numerical water quality models (e.g., oxygen sag curves). Therefore, we propose combining DO data and prospective modeling to guide local interventions, especially in LMIC rivers with limited data.

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