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

Abstract

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.

Determination of the Antibiotic and Antibiotic Resistance Footprint in Surface Water Environments of a Metropolitan Area: Effects of Anthropogenic Activities

“…Antibiotic concentrations, ARG concentrations, and ARG relative abundances were log 10 transformed with the nondetects (defined as no signal or below MDLs) replaced with 1/2 of MDLs to allow statistical analysis of left-censored data . To avoid biasing the analysis results due to substitution, the target compounds or genes with frequencies <60% were excluded to ensure low degree of censoring (≤40%) unless otherwise specified. , Missing environmental parameters at certain sampling sites were imputed using the mean value of each parameter across the samples . Spearman correlation analysis was performed to assess the association of antibiotic concentrations, ARG concentrations/relative abundances, or the number of detected antibiotic/ARG types with environmental (water physicochemical properties and sediment LOI information) and anthropogenic factors.…”

“…To date, antibiotics and ARGs have been widely considered as classes of emerging environmental contaminants. − An increasing number of studies have demonstrated spatial variability in distributions of emerging contaminants including antibiotics and ARGs in aquatic systems. − Occurrences and concentrations of these chemical/biological emerging contaminants were clearly affected by anthropogenic pressures, with higher numbers and concentrations of the targeted contaminants typically found in water environments more impacted by human activities. ,,− More recent works have also highlighted that patterns of these contaminants in aquatic systems could be distinguished according to their origins from mixed versus wastewater sources. , A large majority of these investigations, however, have focused on water systems dominated by domestic wastewater effluents ,, or directly affected by known nonpoint sources (e.g., agricultural activities), , and lacked information across a wider gradient of potential anthropogenic impacts. Moreover, a limited number of such studies have targeted specifically on antibiotics and ARGs (or on both of them) and investigated their association with human activities, ,− leading to insufficient knowledge to identify drivers and estimate the potential for antibiotic resistance dissemination.…”

Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics: A Holistic Modeling Framework in a Tropical Reservoir

“…The first step to mitigate these antibiotics and related AMR issues is to understand their source, transport, fate, and final destination in aquatic environments . Widespread reports of AMR occurrence in many diverse aquatic environments have already provided early warnings to scientists and policymakers . However, the analysis of AMR highly depends on skilled expertise and state-of-the-art equipment, which makes the collection of AMR data limited and expensive.…”

Metagenomic and Machine Learning Meta-Analyses Characterize Airborne Resistome Features and Their Hosts in China Megacities

“…Thus, the specific features of source-influenced airborne resistomes remain largely unknown. Apart from the limited data inventory, the primary reason is that conventional statistical methods (e.g., linear discriminant effect size analysis) are not robust enough to identify the representative components from the thousand species of ARGs via the list-wise comparisons among the emission sources. Notably, machine learning classification is an emerging and efficient tool for application of metagenomics-based methods in real ARG monitoring by training models (e.g., Random Forest) to distinguish source influences, thereby allowing us to assess each ARG’s representativeness of its belonging resistome .…”