Metagenomic assembly provides a deep insight into the antibiotic resistome alteration induced by drinking water chlorination and its correlations with bacterial host changes

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Highlights

  • Drinking water chlorination altered bacterial antibiotic resistome.

  • Metagenomic assembly revealed co-occurrence of ARGs and MGEs.

  • Host shift played a major role in shaping antibiotic resistome during chlorination.

  • Chlorination contributed to the occurrence of some bacterial pathogens.

Abstract

Chlorination can contribute to the enrichment of specific antibiotic resistance genes (ARGs) in drinking water, but the underlying molecular ecological mechanisms remain unknown, which may hinder the assessment and control of the resulting health risks. In this study, metagenomic assembly and Resfams annotation were used to profile the co-occurrence patterns of ARGs, mobile genetic elements (MGEs) and their bacterial hosts, as well as the correlations of potential pathogens with the antibiotic resistome, in a full-scale drinking water treatment and transportation system. Seven ARG types involved in different resistance mechanisms occurred in drinking water and chlorination enhanced the total abundance of the ARGs (p <  0.05). The ARGs encoding resistance-nodulation-cell division and ATP-binding cassette antibiotic efflux pumps predominated in all the samples and were primarily responsible for the ARG accumulation. After chlorination, the ARGs were primarily carried by predominant Sphingomonas, Polaromonas, Hyphomicrobium, Acidovorax, Pseudomonas and Fluviicola. Further, enrichment of the bacterial hosts and MGEs greatly contributed to alteration of the antibiotic resistome. Pseudomonas alcaligenes, carrying multiple ARGs, was identified as a potential pathogen in the chlorinated drinking water. These findings provide novel insights into the host-ARG relationship and the mechanism underlying the resistome alteration during drinking water chlorination.

Introduction

The overuse and misuse of antibiotics in medicine and agriculture has promoted the emergence, dissemination, and accumulation of antibiotic resistance genes (ARGs) in various environmental matrices, including drinking water [1]. ARGs may be acquired via mutations in chromosomal genes [2] and disseminated among microorganisms through horizontal gene transfer (HGT) by mobile genetic elements (MGEs) [3]. Furthermore, environmental factors, such as heavy metals [4], disinfectants [5], and nanomaterials [6], have been found to co-select for antibiotic resistance in microbial populations. For example, mercury, cadmium, copper, and zinc can exert selective pressure to enhance the proliferation and evolution of antibiotic-resistant bacteria in soil and aquatic environment [7].

Chlorination is a widely used and effective approach for eliminating microorganisms in waterworks [8]. Our previous studies, conducting using molecular methods, have indicated that chlorination contributes to the accumulation of ARGs in drinking water [9,10]. Other studies have also profiled the changes in the diversity and abundance of ARGs in drinking water treatment systems [[11], [12], [13]]. The antibiotic resistome is largely constrained by ecological and phylogenetic factors [14,15]; therefore, environmental perturbations may interfere with the redistribution, transfer, and evolution of the antibiotic resistome by altering the bacterial community structure and the mechanism of action [[16], [17], [18], [19]]. Moreover, due to the potential health risks arising from the transfer of ARGs to pathogenic bacteria [20], it is crucial to characterize the links between ARGs and their bacterial hosts, particularly for the pathogens present in drinking water. Statistical analyses have indicated that the resistome alterations are closely related to the microbial community shift in drinking water treatment systems [9,21] and are influenced by the MGEs present in various environments [22]. However, the extensive diversity and complexity of the microbial community and antibiotic resistome in drinking water hinder accurate identification of the ecological status and function of the environmental ARGs, so solid biological evidence is still unavailable for the correlation of the antibiotic resistome with MGEs and bacterial hosts in drinking water under chlorine stress.

Resfams is a manually curated database of protein families with confirmed antibiotic resistance functions, which is organized by ontology and determined by highly precise and accurate hidden Markov models (HMMs) profile [23]. This database provides higher annotation sensitivity, accuracy, and resolution than the traditional Antibiotic Resistance Genes Database (ARDB) and the Comprehensive Antibiotic Resistance Database (CARD) [23]. Accordingly, Resfams yields more sophisticated insights into the dynamics of the antibiotic resistome during drinking water treatment. Metagenomic assembly combined with Resfams annotation serves as an effective and reliable approach for the investigation of the co-occurrence of ARGs and MGEs and host identification of ARGs in human gut [18], animal manure [24], and sediment [25], enabling the elucidation of the mechanisms underlying resistome alterations driven by environmental perturbations.

This study aimed to investigate the effects of chlorination on the antibiotic resistome and bacterial community of drinking water, so as to profile the correlations within ARGs, MGEs and their pathogenic hosts. Metagenomic assembly and Resfams annotation were used to elucidate the molecular ecological mechanisms underlying the antibiotic resistome alterations induced by the drinking water chlorination. The results extend our knowledge regarding the dynamic composition of the antibiotic resistome in full-scale drinking water treatment and transportation systems, which may facilitate the evaluation and minimization of health risks associated with antibiotic-resistant bacteria in drinking water.

Section snippets

Sample collection, DNA extraction, and metagenome sequencing

A drinking water treatment plant (Nanjing, China) with daily water production of 1.2 million tons, and treatment processes of sedimentation, sand filtration, and chlorination disinfection using liquid chlorine was selected. We collected water samples along the treatment processes of this plant and distribution pipeline, including source water (SW), effluent water from the sedimentation tank (ES), filtered water (FW), and chlorine-disinfected water (DW) at the plant as well as tap water A (TWA)

Alteration of antibiotic resistome by drinking water chlorination

Metagenomic assembly revealed that 80,884,152 reads from each sampling location could be assembled into 40,870–60,485 contigs with 70,793–125,586 ORFs (Table S3). In general, 0.05–0.10% of the total ORFs in all the samples were identified as AR-ORFs, and 0.08–0.19% of the contigs in all the samples were considered to be ARCs based on the core Resfams of HMM profile annotation (Table S3). Notably, 2.00–6.03% of total ARCs in all the samples were found to carry two or more AR-ORFs (Table S3), and

Discussion

In this study, HTS-based metagenomic assembly was performed to profile the effect of chlorination on diversity and abundance of ARGs and their correlations with MGEs and bacterial hosts in drinking water. ORF annotation and genome binning both indicated that chlorination induced a significant increase in AR-ORF abundance; this finding is supported by previous studies in which HTS read annotation [9] and high-throughput quantitative PCR [13,37] have revealed that chlorination increases the total

Conclusions

The enrichment of chlorine-resistant bacteria primarily increases the relative abundance of RND and ABC antibiotic efflux genes, which mainly contributes to the accumulation of ARGs in post-chlorination drinking water. The co-occurrence of MGEs (plasmids and transposons) and ARGs may promote the antibiotic resistome alteration induced by drinking water chlorination. Chlorination induces the emergence of some potential pathogens in drinking water, such as Pseudomonas alcaligenes, which carries

Acknowledgments

This study was financially supported by the National Science and Technology Major Project of China (2017ZX07202003), the Fundamental Research Funds for the Central Universities of China (14380116), the Outstanding PhD Candidate of Nanjing University, the Postgraduates Research & Practice Innovation Program of Jiangsu Province, China (KYZZ16_0056) and China Postdoctoral Science Foundation (2018M640475). We would also like to thank the High-Performance Computing Center (HPCC) of Nanjing

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