ena-STUDY-INRA-26-11-2018-12:44:05:245-1133
Robust discrimination of resistome and microbiome signatures in hospital and urban effluents and implications for their management; 16s data of bacterial species.
Effluents from wastewater treatment plants (WWTPs) act as point sources of metals, pharmaceutical residues and other micro pollutants, as well as antimicrobial resistance genes (ARGs) and antimicrobial resistant bacteria (ARB) into the environment. Hospital wastewater (HWW) is conventionally mixed with urban wastewater (UWW) systems and treated jointly, which raises concerns due to the incapacity to provide targeted mechanisms to reduce the loads of micro pollutants and ARGs in HWW and their ultimate release into the environment. Here we characterize and compare the resistome (ARGs, mobile genetic elements (MGEs) and integrons), microbiota (bacterial population and community structures), and determine the levels of some pharmaceuticals (mainly antibiotics), surfactants and heavy metals (“the exposome”) of UWWs and HWWs -and a mix of both (with the ratio of 1/3 of HWW and 2/3 of UWW)- over a total period of four years. We identify a distinct signature of the resistome and microbiome for each HWW and UWW, with no significant variation over a period of three years. Mixed hospital and urban wastewater (MWW) assimilate urban signatures for resistome, microbiome and exposome. A machine learning (ML) technique known as the Random Forest Algorithm (RFA) and multivariate analyses applied to the resistome and microbiome datasets of all samples distinctly classified urban from hospital wastewaters and aided to classify putative key players of the respective resistomes and microbiomes and their relationship with the measured exposome.UWW significantly alters HWW composition when mixing them at a ratio of 2 to 1, indicating that in the context of secondary WWT by means of activated sludge, separate treatment of HWW is not necessary in order to reduce the emission of ARGs into the environment. The distinct UWW and HWW resistome and microbiome signature identified here may present considerable targets when establishing environmental policies with respect to wastewater treatment and the associated risks of wastewater effluents with the spread of ARGs into the environment.
Effluents from wastewater treatment plants (WWTPs) act as point sources of metals, pharmaceutical residues and other micro pollutants, as well as antimicrobial resistance genes (ARGs) and antimicrobial resistant bacteria (ARB) into the environment. Hospital wastewater (HWW) is conventionally mixed with urban wastewater (UWW) systems and treated jointly, which raises concerns due to the incapacity to provide targeted mechanisms to reduce the loads of micro pollutants and ARGs in HWW and their ultimate release into the environment. Here we characterize and compare the resistome (ARGs, mobile genetic elements (MGEs) and integrons), microbiota (bacterial population and community structures), and determine the levels of some pharmaceuticals (mainly antibiotics), surfactants and heavy metals (“the exposome”) of UWWs and HWWs -and a mix of both (with the ratio of 1/3 of HWW and 2/3 of UWW)- over a total period of four years. We identify a distinct signature of the resistome and microbiome for each HWW and UWW, with no significant variation over a period of three years. Mixed hospital and urban wastewater (MWW) assimilate urban signatures for resistome, microbiome and exposome. A machine learning (ML) technique known as the Random Forest Algorithm (RFA) and multivariate analyses applied to the resistome and microbiome datasets of all samples distinctly classified urban from hospital wastewaters and aided to classify putative key players of the respective resistomes and microbiomes and their relationship with the measured exposome.UWW significantly alters HWW composition when mixing them at a ratio of 2 to 1, indicating that in the context of secondary WWT by means of activated sludge, separate treatment of HWW is not necessary in order to reduce the emission of ARGs into the environment. The distinct UWW and HWW resistome and microbiome signature identified here may present considerable targets when establishing environmental policies with respect to wastewater treatment and the associated risks of wastewater effluents with the spread of ARGs into the environment.