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Biology subjects

Dagot, C.

Publications and source records attributed to Dagot, C..

2 recordsLinked to original sources

Seasonal resistome diversity and dissemination of WHO priority antibiotic-resistant pathogens in Lebanese estuaries

Anthropogenic activities are demonstrated to be the key drivers of antimicrobial resistance (AMR) dissemination in the environment. Lebanese rivers that lead to the Mediterranean Sea were sampled at estuaries sites, under high anthropogenic pressure, in spring 2017 and winter 2018 to study seasonal variation of antimicrobial-resistant bacteria (ARBs) and antimicrobial resistance genes (ARGs). Methods: A combined approach using culture techniques and high throughput qPCR identified hotspots for antimicrobial resistance and anthropogenic pressure in particular locations along the Lebanese coast. Results: Multi-resistant Gram-negative (Enterobacterales and Pseudomonas spp) and Gram-positive bacterial pathogens were isolated. A high abundance of certain ARGs (vanB, blaBIC-1, blaGES, tetM, and mcr-1) was detected in 5 Lebanese estuaries. The relative abundance of ARGs was highest in winter and areas with high anthropogenic activities and population growth with an influx of refugees. Conclusion: Qualitative analysis of ARB and the analysis of the Lebanese estuaries resistome revealed critical levels of contamination with pathogenic bacteria and provided significant information about the spread of ARGs in anthropogenically impacted estuaries.

microbiology↗

Classification of hospital and urban wastewater resistome and microbiota over time and their relationship to the eco-exposome.

Wastewaters (WW) are important sources for the dissemination of antimicrobial resistance (AMR) into the environment. Hospital WW (HWW) contain higher loads of micro-pollutants and AMR markers than urban WW (UWW). Little is known about the long-term dynamics of H and U WW and the impact of their joined treatment on the general burden of AMR. Here, we characterized the resistome, microbiota and eco-exposome signature of 126 H and U WW samples treated separately for three years, and then mixed, over one year. Multi-variate analysis and machine learning revealed a robust signature for each WW with no significant variation over time before mixing, and once mixed, both WW closely resembled U signatures. We demonstrated a significant impact of pharmaceuticals and surfactants on the resistome and microbiota of H and U WW. Our results present considerable targets for AMR related risk assessment of WW.

microbiology↗