Back

Characterisation of Microbial Community Dynamics and Antibiotic Resistance Gene Dissemination in Malaysian Wastewater during the COVID-19 Pandemic

Shahid, U.; Hooi, S. L.; Lim, S. Y.; Mohd Aris, A.; Khor, B. C.; Ayub, Q.; Tan, H. S.

2024-07-27 infectious diseases
10.1101/2024.07.25.24311021 medRxiv
Show abstract

Wastewater is a well-known hotspot for pathogens and spread of antibiotic resistance across species. Surveillance of wastewater microbial community can help draw clearer representation of actively culturing taxonomic groups and resistance-inducing mobile genetic elements before and after treatment. Studies have suggested that COVID-19 pandemic may also have caused increased dissemination of antibiotic-resistance genes (ARGs) and antibiotic-resistant bacteria in wastewater. Although immensely significant, no research has yet been performed on Malaysian wastewater microbial community and ARGs or their correlation with COVID-19 infections. This study utilised 16S metagenomics approach to characterise microbial community in Malaysian wastewater during high and low-case phases of pandemic. Among 20 most prevalent genera around Kuala Lumpur, Malaysia, those belonging to Bacteriodales, Bacillales, Actinomycetales and opportunistic pathogens-Arcobacters, Flavobacteria, and Campylobacterales, Neisseriales, were enriched during high-case periods of the COVID-19 pandemic. Copy number profiling of ARGs in water samples showed prevalence of elements conferring resistance to antibiotics like sulphonamides, cephalosporins, and colistin. High prevalence of intI1 and other ion-based transporters in samples highlight an extensive risk of horizontal gene transfer to previously susceptible species. Our study emphasises the importance of wastewater surveillance in understanding microbial community dynamics and ARG dissemination, particularly during public health crises like the COVID-19 pandemic.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.