Abstract:Antimicrobial resistance (AMR) has become a critical threat to human health globally. Since the COVID-19 pandemic, there has been much speculation about how COVID-19 and AMR may be interconnected. Considering that poor sanitation can be a factor that links COVID-19 and AMR threats, focusing on the prevalence of antibiotic resistance genes (ARGs) in untreated hospital wastewater may be needed. In this study, untreated wastewater was sampled from a hospital (hospital A) designated to treat COVID-19 patients during the first wave of COVID-19 pandemic in contrast to another hospital (hospital B) that did not receive any COVID-19 patients. Metagenomics investigated the relative abundance and mobile potential of ARGs and determined the correlation of ARGs with time/incidence of COVID-19. Metagenome-assembled genomes (MAGs) associated with ARGs were also assessed. Metagenomic analysis revealed the larger antibiotic resistome in hospital A (3.79 ‰) in contrast to hospital B (0.164 ‰). ARGs resistant to macrolides, sulfonamides and beta-lactams were found to be predominant and positively correlated with time in hospital A. Specifically, beta-lactamases of class B and part of class D (carbapenem-resistant) positively correlated with time, suggesting the prevalence of carbapenem-resistant ARGs in hospital A. Besides, non-carbapenemase blaVEB were found to be co-resistant to carbapenems across carbapenem-resistant MAGs. Furthermore, carbapenem-resistant pathogens (i.e., Shigella flexneri and Arcobacter butzleri) were found in hospital A revealed the risk of co-infection. This highlighted specific concerns related to AMR dissemination during the COVID-19 pandemic that may arise from untreated hospital wastewater discharge.

Bio: Changzhi Wang is a Ph.D. student in Prof. Pei-Ying Hong’s group. He received his bachelor’s in Bioinformatics from the Southern University of Science and Technology (SUSTech), Shenzhen, China and received his master’s in Environmental Science and Engineering from KAUST, Jeddah, Saudi Arabia. His research involves handling large data sets obtained from multi-omics and applying various bioinformatic pipelines to elucidate the presence of biological contaminants. He is keen to develop bioinformatics that would enable multi-omics in routine monitoring of water treatment technologies and water quality. He has been focused on this area for the past 4 years.