Spatial and temporal distribution of E. coli contamination on three inland lake and recreational beach systems in the upper Midwestern United States

Development of Multiplex RT qPCR Assays for Simultaneous Detection and Quantification of Faecal Indicator Bacteria in Bathing Recreational Waters

In this study, we designed and validated in silico and experimentally a rapid, sensitive, and specific multiplex RT qPCR for the detection and quantification of faecal indicator bacteria (FIB) used as microbiological references in marine bathing water regulations (Escherichia coli and intestinal enterococci). The 16S rRNA gene was used to quantify group-specific enterococci and Escherichia/Shigella and species-specific such as Enterococcus faecalis and E. faecium. Additionally, a ybbW gene encoding allantoin transporter protein was used to detect E. coli. An assessment of marine coastal systems (i.e., marine water and sediment) revealed that intestinal enterococci were the predominant group compared to Escherichia/Shigella. The low contribution of E. faecalis to the intestinal enterococci group was reported. As E. faecalis and E. faecium were reported at low concentrations, it is assumed that other enterococci of faecal origin are contributing to the high gene copy number of this group-specific enterococci. Moreover, low 16S rRNA gene copy numbers with respect to E. faecalis and E. faecium were reported in seawater compared to marine sediment. We conclude that marine sediments can affect the quantification of FIBs included in bathing water regulations. Valuing the quality of the marine coastal system through sediment monitoring is recommended.

Genetic Determinants of Escherichia coli Survival in Beach Sand

Escherichia coli contain a high level of genetic diversity and are generally associated with the guts of warm-blooded animals but have also been isolated from secondary habitats outside hosts. We used E. coli isolates from previous in situ microcosm experiments conducted under actual beach conditions and performed population-level genomic analysis to identify accessory genes associated with survival within the beach sand environment. E. coli strains capable of surviving had been selected for by seeding isolates originating from sand, sewage, and gull waste (n = 528; 176 from each source) into sand, which was sealed in microcosm chambers and buried for 45 days in the backshore beach of Lake Michigan. In the current work, survival-associated genes were identified by comparing the pangenome of viable E. coli populations at the end of the microcosm experiment with the original isolate collection and identifying loci enriched in the out put samples. We found that environmental survival was associated with a wide variety of genetic factors, with the majority corresponding to metabolism enzymes and transport proteins. Of the 414 unique functions identified, most were present across E. coli phylogroups, except B2 which is often associated with human pathogens. Gene modules that were enriched in surviving populations included a betaine biosynthesis pathway, which produces an osmoprotectant, and the GABA (gamma-aminobutyrate) biosynthesis pathway, which aids in pH homeostasis and nutrient use versatility. Overall, these results demonstrate that the genetic flexibility within this species allows for survival in the environment for extended periods. IMPORTANCE Escherichia coli is commonly used as an indicator of recent fecal pollution in recreational water despite its known ability to survive in secondary environments, such as beach sand. These long-term survivors from sand reservoirs can be introduced into the water column through wave action or runoff during precipitation events, thereby impacting the perception of local water quality. Current beach monitoring methods cannot differentiate long-term environmental survivors from E. coli derived from recent fecal input, resulting in inaccurate monitoring results and unnecessary beach closures. This work identified the genetic factors that are associated with long-term survivors, providing insight into the mechanistic basis for E. coli accumulation in beach sand. A greater understanding of the intrinsic ability of E. coli to survive long-term and conditions that promote such survival will provide evidence of the limitations of beach water quality assessments using this indicator.

Temporal variations in fecal indicator bacteria in bathing water and sediment in a coastal ecosystem (Aytré Bay, Charente-Maritime, France)

This study presents the fecal contamination dynamic at the two bathing sites of Aytré Bay (Charente Maritime, France). We quantified fecal indicator bacteria (FIB) abundances (Escherichia coli and enterococci) from water and sediment samples over one-year survey. Then we measured biological (bacterial abundance, chlorophyll-a), physico-chemical (dissolved nutrients and oxygen, salinity, pH …) and climatic (temperatures, rainfall and tidal coefficient) parameters. Results showed that FIB abundances were occasionally higher than the European regulatory threshold during winter, summer and fall. The "poor quality" of the bathing water was due to high enterococci abundance. We found negative significant correlations between FIB and water temperature and salinity, and positive significant correlations between FIB and rainfall, PO₄, NO₃, NO₂, and SiO₂ mainly in water. Relationships between parameters showed that during summer and spring the main environmental drivers were temperature and salinity, while in fall and winter they were rainfall and dissolved nutrients.

Antibiotic resistance patterns of Escherichia coli isolates from the clinic through the wastewater pathway

Antimicrobial resistance (AMR) remains one of the leading global health threats. This study compared antimicrobial resistance patterns among E. coli isolates from clinical uropathogenic Escherichia coli (UPEC) to hospital wastewater populations and throughout an urban wastewater treatment facility - influent, pre- and post-chlorinated effluents. Antibiotic susceptibility of 201 isolates were analyzed against eleven different antibiotics, and the presence of twelve antibiotic resistant genes and type 1 integrase were identified. AMR exhibited the following pattern: UPEC (46.8%) > hospital wastewater (37.8%) > urban post-chlorinated effluent (27.6%) > pre-chlorinated effluent (21.4%) > urban influent wastewater (13.3%). However, multi-drug resistance against three or more antimicrobial classes was more prevalent among hospital wastewater populations (29.7%) compared to other sources. E. coli from wastewaters disinfected with chlorine were significantly correlated with increased trimethoprim-sulfamethoxazole resistance in E. coli compared to raw and treated wastewater populations. bla_CTX-M-1 group was the most common extended spectrum beta-lactamase in E. coli from hospital wastewater (90%), although UPEC strains also encoded bla_CTX-M-1 group (50%) and bla_TEM (100%) genes. Among tetracycline-resistant populations, tetA and tetB were the only resistance genes identified throughout wastewater populations that were associated with increased phenotypic resistance. Further characterization of the E. coli populations identified phylogroup B2 predominating among clinical UPEC populations and correlated with the highest AMR, whereas the elevated rate of multi-drug resistance among hospital wastewater was mostly phylogroup A. Together, our findings highlight hospital wastewater as a rich source of AMR and multi-drug resistant bacterial populations.

Application of Laser-Induced, Deep UV Raman Spectroscopy and Artificial Intelligence in Real-Time Environmental Monitoring-Solutions and First Results

Environmental monitoring of aquatic systems is the key requirement for sustainable environmental protection and future drinking water supply. The quality of water resources depends on the effectiveness of water treatment plants to reduce chemical pollutants, such as nitrates, pharmaceuticals, or microplastics. Changes in water quality can vary rapidly and must be monitored in real-time, enabling immediate action. In this study, we test the feasibility of a deep UV Raman spectrometer for the detection of nitrate/nitrite, selected pharmaceuticals and the most widespread microplastic polymers. Software utilizing artificial intelligence, such as a convolutional neural network, is trained for recognizing typical spectral patterns of individual pollutants, once processed by mathematical filters and machine learning algorithms. The results of an initial experimental study show that nitrates and nitrites can be detected and quantified. The detection of nitrates poses some challenges due to the noise-to-signal ratio and background and related noise due to water or other materials. Selected pharmaceutical substances could be detected via Raman spectroscopy, but not at concentrations in the µg/l or ng/l range. Microplastic particles are non-soluble substances and can be detected and identified, but the measurements suffer from the heterogeneous distribution of the microparticles in flow experiments.