Identifying potential bacterial pathogens in wastewater by expert reliability analysis of full-length 16S rRNA and...

Municipal wastewater may serve as a critical community-composite sample for monitoring bacteria excreted by the contributing population, providing insights into public health risks and microbial diversity. The present study emphasizes the integration of DNA (full-length 16S rRNA) methods, untargeted RNA methods, and different bioinformatic protocols to identify potential human bacterial pathogens in wastewater. Results revealed that, DNA surveillance identified roughly 50 % of the sequencing reads were associated with potentially pathogenic bacteria, as compared to RNA surveillance, which identified roughly 33 % of the reads as associated with potential bacterial pathogens. Taxonomic annotation was performed using four bioinformatic workflows, DNA-Kraken2, DNA-QIIME2, RNA-DIAMOND, and RNA-Kraken2, respectively detecting 70, 270, 118, and 319 human pathogenic bacterial species. A total of 889 potential pathogenic species belonging to 174 bacterial genera were detected in Detroit, Michigan, wastewater. Four bacterial genera of Acinetobacter, Klebsiella, Streptococcus, and Flavobacterium were detected by all four bioinformatic methods. Expert reliability ranking analysis demonstrated that the combination of the RNA-Kraken2 and DNA-Kraken2 results provided greater taxonomic resolution of human disease-associated bacterial species. Genera Acinetobacter, Aliabacter, Arcobacter, Aeromonas, Bacteroides, and Klebsiella were the most abundant potential pathogens in wastewater. Clinical cases of associated diseases were collected for the study area to confirm the presence of bacterial pathogens identified in wastewater. This study addresses critical gaps in wastewater-based epidemiology by employing a combined polyphasic approach, including full-length 16S rRNA, RNA sequencing, and expert reliability ranking for a robust framework for detecting and monitoring bacterial pathogens. The findings highlight the potential of integrating DNA and RNA surveillance for improved public health preparedness and pathogen tracking.