Metagenomic insights into resistome coalescence in an urban sewage treatment plant-river system

Abstract

The effluents of sewage treatment plants (eSTP) are one of the critical contributors of antibiotic resistiome in rivers. Recently, community coalescence has been focused as the entire microbiome interchanges with one another. While works have reported the prevalence of antibiotic resistance genes (ARGs) in eSTP and their effects on river resistome, little research has investigated the extent of resistome coalescence in the environment. In the study, we have addressed the issue and focused on the resistome coalescence of eSTP in an urban river with a typical effluent/river coalescence model, by utilizing high-throughput sequencing (HTS)-based metagenomic assembly analysis. In all, a total of 609 ARGs were found in the eSTP-river system, conferring resistance to 30 antibiotic classes and including some emerging ARGs such as mcr-type, tetX and carbapenemase genes. Statistical analyses including linear discriminant analysis effect size (LEfSe) showed the coalescence of STP effluents increased the diversity and abundance of river resistome, indicating its low resistance to disturb the invasion of resistome community in eSTP. After coalescence in the river, the imprints of STP-derived ARGs presented a temporary increase and gradually decreased trend along the flow path. Further, an innovative fast expectation-maximization microbial source tracking (FEAST) method was used to quantitatively apportion the coalescence event, and demonstrated the contribution of eSTP on river resistome and its attenuation dynamics in the downstream. Notably, correlation-based network analysis and contig-based co-occurrence analysis showed the coalesced resistome in the downstream river co-occurred with human bacterial pathogens, mobile genetic elements and virulence factor genes, indicating potential resistome dissemination risk in the environment. This study provides more profound understanding of resistome coalescence between engineered and natural contexts, which is helpful for optimizing strategies to prevent and control resistome risk in aquatic environment.

Keywords: Antibiotic resistance genes; Attenuation dynamics; Fast expectation-maximization microbial source tracking; Metagenomic analysis; Resistome coalescence.