PhD ThesisPinpointing environmental antibiotic resistance (AR) hotspots in rivers 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, a
comprehensive spatial and seasonal assessment of water quality and AR conditions
in a Malaysian river catchment was preformed to identify potential 'simple' surrogates
that mirror elevated AR. This included screening for β-lactam resistant coliforms, 22
antibiotics, 287 AR genes and integrons, and routine water quality parameters,
covering absolute concentrations and mass loadings. Novel approaches were
developed and applied to advance environmental microbiome and resistome
research. To investigate relationships, standardised 'effect sizes' (Cohen's D) were
introduced for AR monitoring to improve comparability of field studies. Quantitative
microbiome profiling (QMP) was applied to overcome biases caused by relative taxa
abundance data. In addition, Hill numbers were introduced as a unified diversity
framework for environmental microbiome research. Overall, water quality generally
declined, and environmental AR levels increased as one moved downstream 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). This is suspected to result 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, relatively lower DO levels reflect wastewater inputs, flagging possible AR hot
spots. Furthermore, DO is easy-to-measure and inexpensive, already monitored in
many catchments, and exists in many numerical water quality models (e.g., oxygen
sag curves). Therefore, combining DO data and prospective modelling (e.g., with the
watershed model HSPF) could guide local interventions, especially in LMIC rivers
with limited data