Developing surrogate markers for predicting antibiotic resistance "hot spots" in rivers where limited data are available

• Main Authors: Ott, Amelie, O’Donnell, Greg, Tran, Ngoc Han, Mohd. Haniffah, Mohd. Ridza, Su, Jian Qiang, Zealand, Andrew M., Gin, Karina Yew Hoong, Goodson, Michaela L., Zhu, Yong Guan, Graham, David W.
• Format: Article
• Language: English
• Published: ACS Publications 2021
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.utm.my/id/eprint/94214/1/MohdRidzaMohd2021_DevelopingSurrogateMarkersforPredicting.pdf; http://eprints.utm.my/id/eprint/94214/; http://dx.doi.org/10.1021/acs.est.1c00939

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 ρ 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.