Precipitation influences pathogenic bacteria and antibiotic resistance gene abundance in storm drain outfalls in coastal sub-tropical waters

Abstract

Stormwater contamination can threaten the health of aquatic ecosystems and human exposed to runoff via nutrient and pathogen influxes. In this study, the concentrations of 11 bacterial pathogens and 47 antibiotic resistance genes (ARGs) were determined by using high-throughput microfluidic qPCR (MFQPCR) in several storm drain outfalls (SDOs) during dry and wet weather in Tampa Bay, Florida, USA. Data generated in this study were also compared with the levels of fecal indicator bacteria (FIB) and sewage-associated molecular markers (i.e., Bacteroides HF183 and crAssphage markers) in same SDOs collected in a recent study (Ahmed et al., 2018). Concentration of FIB, sewage-associated markers, bacterial pathogens and many ARGs in water samples were relatively high and SDOs may be potentially hotspots for microbial contamination in Tampa Bay. Mean concentrations of culturable E. coli and Enterococcus spp. were tenfold higher in wet compared to dry weather. The majority of microbiological contaminants followed this trend. E. coli eaeA, encoding the virulence factor intimin, was correlated with levels of 20 ARGs, and was more frequently detected in wet weather than dry weather samples. The blaKPC gene associated with carbapenem resistant Enterobacteriaceae and the beta-lactam resistant gene (blaNPS) were only detected in wet weather samples. Frequency of integron genes Intl2 and Intl3 detection increased by 42% in wet weather samples. Culturable E. coli and Enterococcus spp. significantly correlated with 19 of 47 (40%) ARG tested. Sewage-associated markers crAssphage and HF183 significantly correlated (p < 0.05) with the following ARGs: intl1, sul1, tet(M), ampC, mexB, and tet(W). The presence of sewage-associated marker genes along with ARGs associated with sewage suggested that aging sewage infrastructure contributed to contaminant loading in the Bay. Further research should focus on collecting spatial and temporal data on the microbiological contaminants especially viruses in SDOs.