Abstract

This dataset contains the data produced by UMA team in the framework of task 2.2 of MADFORWATER project. This task deal with a set of data generated regarding the detection of antibiotic resistance genes and enterovirus, particularly Hepatitis A virus from Treated Municipal Wastewater. To monitor the prevalence of antibiotic resistant bacteria in MWW before and after treatment, the qualitative and quantitative PCR methods, allowing the detection of resistance genes in all microorganisms including the non-culturable species, were performed. PCR products of target antibiotic genes (tet(O), tet(Q), tet(W), tet(G), amp(C) and bla TEM) were loaded and visualized on agarose gels. For qPCR, the analysis was performed in order to detect and quantify the antibiotic resistance genes copy number. The investigation of the Hepatitis A virus prevalence in MTWW was performed using two different extraction methods (virus extraction, concentration and concentrate decontamination method according to US Environmental Protection Agency (1992) from Mud (M) and Supernatant (S) of the influent and from the effluent and virus concentration from water with adsorption/elution on glass wool (XP T 90-451. March 1996) (Rodier et al., 2009)) followed by qPCR. A specific attention was dedicated to the detection of SARS-CoV-2 in wastewaters using the same protocols developed and optimized under MADFORWATER (RNA extraction/concentration, cDNA synthesis, Q-RT-PCR) by using specific DNA primers approved in the RT-PCR test. This could contribute to better understanding and studying the emerged SARS-CoV-2 and its propagation routes and epidemiology in a given population (i.e. Tunis city). Regarding Microarray, the generated data were produced and elaborated basing on a list of targeted genes downloaded from accessible databases (NCBI, IMG, KEEG). The effectiveness of MWWTP cannot be approved without accepted microbiological quality. In the literature, there is a huge number of publications and standards related fecal or pathogen bacteria removal in wastewater treatment plant (WWTP), many publications show inefficiency of current MWWTP in removing virus but no standards dealing with virus detection from MWW in Tunisia. Generated data allowed the design of the WWchip, able to monitor catabolic genes markers of fecal indicators, pathogens indicators, virus and antibiotic resistant bacteria. A total of 3744 genes and 12832 probes were designated. In silico validation and verification of all probes was performed using the BLASTN algorithm and custom-made databases. Two set of data are proposed. The first one summarizes the gene type, number of genes and number of probes considered in the design of the WWchip. The list and sequences of all the probes of target genes is presented a second set of data. The data format produced by the microarray consists of a list of genes and corresponding values that represent relative DNA levels of each targeted gene. The developed WWChip will constitute a new rapid tool for pathogen monitoring of different types of treated wastewaters.