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Ecotoxicology and Human Environmental Health

Antibiotic resistome alteration by different disinfection strategies in a fullscale drinking water treatment plant deciphered by metagenomic assembly Huaicheng Zhang, Fangyu Chang, PENG SHI, Lin Ye, Qing Zhou, Yang Pan, and Ai-Min Li Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b05907 • Publication Date (Web): 23 Jan 2019 Downloaded from http://pubs.acs.org on January 24, 2019

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Environmental Science & Technology

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Title: Antibiotic resistome alteration by different disinfection strategies in a full-scale

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drinking water treatment plant deciphered by metagenomic assembly

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Authors:

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Huaicheng Zhang, Fangyu Chang, Peng Shi,* Lin Ye, Qing Zhou, Yang Pan, Aimin Li

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Affiliations of authors:

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State Key Laboratory of Pollution Control and Resource Reuse, School of the

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Environment, Nanjing University, Nanjing 210023, China.

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*Corresponding

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Addresses:

author

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State Key Laboratory of Pollution Control and Resource Reuse, School of the

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Environment, Nanjing University, 163 Xianlin Avenue, Nanjing 210023, China.

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Phone: +86-25-89680507

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Fax: +86-25-89680507

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Email: [email protected]

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ACS Paragon Plus Environment


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Abstract

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Disinfection regimes are considered the most solid strategy to reduce microbial risks

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in drinking water, but their roles in shaping the antibiotic resistome are poorly

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understood. This study revealed the alteration of antibiotic resistance genes (ARGs)

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profiles, their co-occurrence with mobile genetic elements (MGEs) and potential hosts

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during drinking water disinfection based on metagenomic assembly. We found the

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ozone/chlorine (O3/Cl2) coupled disinfection significantly increased the relative

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abundance of ARGs and MGE–carrying antibiotic resistance contigs (ARCs) through

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the enrichment of ARGs within the resistance–nodulation–cell division and

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ATP-binding cassette antibiotic efflux families that are primarily carried by

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Pseudomonas, Acinetobacter, Mycobacterium and Methylocystis, whereas the

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antimicrobial resin/chlorine coupled disinfection posed unremarkable changes to the

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ARG and MGE abundances. Moreover, the co-occurrence patterns of antibiotic efflux

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and beta-lactam ARGs and MGEs were widely identified, ARCs carrying the recR

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and mexH genes were detected in all the samples, with the highest abundance of 2.25

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× 10-2 copies per cell after O3/Cl2 disinfection. Sequence-independent binning

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analysis successfully retrieved two draft ARG-carrying genomes of Acidovorax sp.

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MR-S7 and Hydrogenophaga sp. IBVHS2, further revealing the host-ARG

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relationship during O3/Cl2 disinfection. Overall, this study provides novel insights into

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the antibiotic resistome alteration during drinking water disinfection.

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Keywords: antibiotic resistance genes, mobile genetic elements, drinking water,

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disinfection strategy, high-throughput sequencing

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INTRODUCTION

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Antibiotic resistance has become a challenging problem in clinical medicine

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worldwide, whereas new antibiotic development and usage is always far behind the

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evolution of antibiotic-resistant bacteria (ARB) and the rapid spread of antibiotic

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resistance genes (ARGs).1 Drinking water has been regarded as an important medium

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to propagate ARB and ARGs between the environment and humans,2-4 demonstrating

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the necessity and urgency of blocking their transmission. Disinfection is the most

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solid barrier that provides reliable physico-chemical removal of microorganisms in

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drinking water.5 However, increasing work has recently documented the unintended

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effects of many disinfection approaches on the drinking water microbiome.6,7

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Disinfection processes for drinking water mainly include chlorination, ozonation,

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ultraviolet radiation, antimicrobial resin (AR) treatment and their combinations, and

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usually act as the last barrier for reducing potential pathogens in drinking water

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treatment plants (DWTPs). Among these disinfection treatment, chlorination is a

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widely used disinfection approach in DWTPs worldwide, but mounting evidence has

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demonstrated its enrichment effects on several types of ARGs among drinking water

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microbiomes.8-10 The safety of ultraviolet (UV) disinfection has been well

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acknowledged due to fewer disinfection byproducts,11 but there are still doubts about

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its bacterial inactivation capacity in drinking water.12 Several studies have suggested

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that to damage ARGs requires much greater UV doses than to inactivate ARB,13,14

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and total ARG abundances in wastewater were elevated after UV disinfection.15 To

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date, the fates of ARGs after UV disinfection are rarely reported in full-scale DWTPs.

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Moreover, previous studies also observed increased levels of some ARGs after ozone

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disinfection in DWTPs,16 and that higher doses of ozone required for ARG

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elimination.17 AR with quaternary ammonium salt moieties such as trimethylamine

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hydrochloride is able to interact with bacterial membranes through electrostatic

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attractions and then physically damages the bacterial morphology and thus induces

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cell death.18 However, its effects on the antibiotic resistome in drinking water have

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not yet been deeply explored. Overall, there is growing evidence to suggest that the

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antibiotic resistome alteration occurs and is closely associated with survival of

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bacteria during disinfection treatments,19,20 which this study further explores.

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Disinfectants often share the same mechanism of resistance with many

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antibiotics,21,22 and different ARGs within the same resistance mechanism may

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consistently respond to disinfection, so the dynamic variation of whole ARG

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responses to disinfection cannot be completely revealed if they are categorized

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according to antibiotic type. Furthermore, mobile genetic elements (MGEs) are

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hypothesized to play vital roles in shaping the antibiotic resistome during drinking

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water disinfection.23 Host and co-occurring MGEs have been proven to influence the

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antibiotic resistome,24 and this probably results in different fates of the same ARGs

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during drinking water disinfection. Moreover, Resfams has been specifically

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developed for ARG identification according to antibiotic resistance mechanism and

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ontology, which can provide higher annotation sensitivity and resolution than

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previous ARG databases.25 Metagenomic assembly combined with Resfams

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annotation has the advantage of improving the annotation accuracy of ARGs, the

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resolution of host identification and the determination of ARG co-occurrence, which

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have been successfully applied in antibiotic resistome analysis of the gut microbes,26

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animal manure27 and sediment.28

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In the present study, we selected a steady-operation DWTP that simultaneously

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operates disinfection units of chlorine, ozone, UV and AR in order to investigate the

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antibiotic resistome alteration under different disinfection strategies. Metagenomic

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assembly and Resfams annotation were jointly utilized to uncover the co-occurrence

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patterns of ARGs and MGEs, as well as their bacterial hosts. To our knowledge, this

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study is the first to comprehensively explore the effects of multiple disinfection

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strategies on the antibiotic resistome in a full-scale DWTP based on same source

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water and may provide novel insights leading to the further reduction of human health

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risks induced by the antibiotic resistome in drinking water.

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MATERIALS AND METHODS

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Sampling Locations in DWTP. The full-scale DWTP is located at Yancheng City,

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Jiangsu Province, China, which is simultaneously equipped with three sets of

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disinfection strategies. As shown in Figure S1, the first strategy includes ozone and

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chlorine (O3/Cl2) disinfection units, and another two strategies couple AR with

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chlorine (AR/Cl2) and UV (AR/UV) disinfection units. Specifically, the AR used in

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this study is a typical type of magnetic polymer with polyacrylic acid matrixes and

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quaternary ammonium salt groups, and its physiochemical parameters and application

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were reported in a previous study.29 In brief, the AR treatment in drinking water has

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realized engineering application in China and other countries. Detailed information

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and operational parameters of the DWTP are provided in Table S1. In detail, 800 L of

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source

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chlorine-disinfected water (O-CW), AR-disinfected water (ARW), AR coupled with

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chlorine-disinfected water (AR-CW) and AR coupled with UV-disinfected water

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(AR-UVW) were collected from the different disinfection strategies in August 2016

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according to our previously established method.8 Sampling campaigns were

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performed in triplicate in each location using water filters and lasted for 24 h to

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control for the temporal variation of influent water. A total of 18 water filters were

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transported to the laboratory at 4 °C within 4 h for further experiments. The water

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quality of each sample is presented in Table S2, which were measured following the

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standard methods.30

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DNA Extraction and Illumina High-throughput Sequencing (HTS). Bacterial

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suspension of each water sample was isolated from the water filter according to our

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previous method,8 and then subjected to DNA extraction using the FastDNA SPIN Kit

water

(SW),

ozone-disinfected

water

(OW),

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ozone

coupled

with

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for Soil (MP Biomedicals, CA) according to the manufacturer’s instructions. The

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concentration and purity of DNA were quantified using microspectrophotometry

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(NanoDrop ND−2000, NanoDrop Technologies, Wilmington, DE), and the quality

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was determined by 1% agarose gel electrophoresis. The quantified DNA

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(approximately 6 μg) for each sample was subjected to the 350-bp library construction

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(Nextera DNA library Preparation Kit), and Illumina HTS with paired-end sequencing

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strategy (2 × 150 bp) was performed on an Illumina HiSeq 4000 platform (Illumina

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Inc., San Diego, CA) in the Novogene Sequencing Company (Beijing, China). The

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metagenomic data were deposited into the Sequence Read Archive (SRA) database of

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the National Center for Biotechnology Information (NCBI) under accession number

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SUB2036733. Low-quality reads were filtered to achieve better results in the

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downstream analysis by using Trimmomatic (Version 0.36) following a previous

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study.24 Finally, approximately 8.70−12.50 Gb high-quality paired-end reads were

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obtained for each sample with an average length of 150 bp (Table S3).

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Metagenomic Assembly and Gene Prediction. Clean reads of each sample were de

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novo assembled into contigs using the CLC Genomics Workbench (Version 9.0.1,

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CLC Bio., Aarhus, Denmark) with the k−mer size of 63.24 A total of 151,624–219,429

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contigs for each sample were generated with N50 values of 1,063–2,033 (Table S4),

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and then open reading frames (ORFs) on the assembled contigs of each sample were

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predicted using metagenome version of Prodigal (version 2.6.3). Finally, 271,619–

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410,328 ORFs were obtained for each sample (Table S4).

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ARGs and MGEs Identification and Quantification. The protein sequences of

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predicted ORFs were aligned against the Resfams core genes database

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(http://dantaslab.wustl.edu/resfams) using HMMSCAN (version 3.0) with the

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recommended parameters to identify the ARG-like ORFs,25 which are defined as the

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ARGs in this study. A contig was annotated as an antibiotic resistance contig (ARC)

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if at least one ARG was located on it. Then, the protein sequences of all ORFs on

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each ARC were searched against the nonredundant (NR) protein sequence database

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(retrieved on December, 2016) of NCBI using BLASTP with e-value cutoff ≤ 10-5.24

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One ORF was regarded as an MGE if one of the following keywords was in its best

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BLAST hit description: transposase, transposon, conjugative, conjugal, integrase,

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integron,

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co-occurrence of ARGs and MGEs was identified if they were both located on the

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same contig.

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plasmid,

recombinase,

or

mobilization.31,32

The

The abundance was defined as the copies of ARG/MGE/ARC per cell (hereafter abbreviated as cpc) and calculated by the following equation:33 𝑛

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recombination

Abundance =

∑ 𝑖=1

𝑁𝑖mapped reads × 𝐿𝑖read/𝐿𝑖ORF 𝑁16S sequence × 𝐿𝑖read/𝐿16S sequence

× 𝑁16𝑆 𝑐𝑜𝑝𝑦 𝑛𝑢𝑚𝑏𝑒𝑟

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where n is the number of ARG- or MGE-like ORFs or ARCs belonging to the same

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category; Nimapped reads is the number of reads mapped to ARG- or MGE-like ORFs or

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ARCs; 𝐿𝑖ORF is the sequence length of the corresponding target ARG- or MGE-like

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ORF or ARC sequences; Nimapped

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Genomics Workbench; 𝐿𝑖read is the read length (150 bp); 𝑁16S sequence is the total

reads

and 𝐿𝑖ORF are both calculated by the CLC

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number of the 16S rRNA sequences in each metagenomic dataset compared to the

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Greengenes database;34 L16S sequence is 1432 bp in this study and stands for the average

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length of 16S rRNA gene in Greengenes;35 N16S copy number is the average copy number

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of 16S rRNA genes per cell number in each metagenomic dataset, which is calculated

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by DIAMOND (v1.09) with 30 sets of universal essential single copy marker genes

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according to a previous study.33 Moreover, the abundance of each ARG category was

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the sum of the ARG abundance within the same resistance mechanism. The

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percentage (%) of each ARG/MGE/ARC category was calculated as the ratio of the

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abundance of the target ARG/MGE/ARC family in the total ARG/MGE/ARC

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abundance in each sample. Core ARGs are defined as being detected with the

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percentage of over 20% in all the samples.

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Taxonomic Classification of the Bacterial Hosts of Core ARGs. To identify

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potential hosts of ARGs, all the ORFs on each ARC were compared against the NCBI

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NR database using BLASTP with e-value cutoff ≤ 10−5.24 The output results of the

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BLASTP comparison were comprehensively parsed and taxonomically classified

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using MEGAN (Version 6, MEtaGenome Analyzer).24 The potential host of an ARC

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was confirmed based on a majority vote method meaning that if more than half of the

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ORFs on each ARC were assigned to the same taxonomy rank (the genus level), then

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the ARC was assigned to that taxon.36

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Sequence-independent

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composition-independent genome binning was performed to explore the fate and

Binning

of

Metagenome.

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Sequence


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distribution of ARGs and to retrieve genomes carrying ARGs from samples in the

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O3/Cl2 disinfection strategy. Thus, the trimmed reads from paired samples (SW/OW

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and OW/O-CW) were merged to form new datasets for metagenomic assembly, and

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then ORF prediction and antibiotic resistance annotation of the coassembled contigs

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were performed as described above. Next, two coverage values were obtained by

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mapping the trimmed reads from each paired sample to the coassembled contigs by

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using CLC Genomics Workbench and plotting against each other using R.37 The

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contigs were assigned to different colors based on taxonomic classification and

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guanine-cytosine (GC) content. The draft genomes were retrieved by using

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differential coverage binning of multiple metagenomes recommended by Albertsen et

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al. (2013),37 and the quality of the genome bins (completeness and potential

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contamination level) was determined by searching a set of 107 HMMs of essential

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single-copy genes, which represented 107 proteins conserved in 95% of all sequenced

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bacteria,38 against the predicted ORFs using HMMER3.37

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Statistical Analysis. The ARG/MGE/ARC abundance was expressed as the mean ±

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standard deviation, and statistical significance between different groups was assessed

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by one-way analysis of variance (ANOVA) with Tukey post hoc tests using IBM

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SPSS Statistics 22.0 (IBM Inc., USA) with a significance cutoff of 0.05. To evaluate

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the significant difference in the antibiotic resistomes of different samples, principal

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coordinate analysis (PCoA) and Adonis test were simultaneously conducted based on

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the Bray−Curtis distance of ARG abundances by R software (version 3.3.2) with the

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Vegan package. Additionally, cluster analysis using UPGMA algorithm based on

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Bray–Curtis similarity index was also performed to group samples with different

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co-occurrence patterns of ARGs and MGEs using PAST (PAleontological STatistics)

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software (University of Oslo, Norway).

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RESULTS

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Antibiotic Resistome Characterization and Alteration. In this study, 0.04–0.06%

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of the total predicted ORFs were identified as ARGs, and 0.08–0.12% of the total

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contigs were considered ARCs in different samples (Figure S2). In total, 0.65–1.25

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cpc ARGs belonging to the resistance–nodulation–cell division (RND) antibiotic

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efflux, ATP–binding cassette (ABC) antibiotic efflux, major facilitator superfamily

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(MFS) antibiotic efflux, beta-lactamase, antibiotic inactivation, phosphotransferase,

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acetyltransferase,

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modulating resistance families were detected in all the drinking water samples (Figure

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1). In detail, ARGs within RND, ABC and MFS antibiotic efflux families are

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affiliated to the major function of antibiotic efflux; ARGs within beta-lactamase and

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antibiotic inactivation families belong to the functional group of antibiotic

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degradation; the major function of antibiotic modification contains ARGs within

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phosphotransferase, acetyltransferase, nucleotidyltransferase families; ARGs within

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rRNA methyltransferase and gene modulating resistance families pertain to function

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of target protection and others, respectively (Figure 1). Notably, the abundance of

nucleotidyltransferase,

rRNA

methyltransferase

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and

gene


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ARGs within the RND and ABC antibiotic efflux and beta-lactamase families

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accounted for 85.34–95.54% of the abundance of total ARGs (Figure S3), and these

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were the dominant ARGs in all of the samples and were identified as the core

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antibiotic resistome in this study. Moreover, 2.45–6.08% of the total identified ARCs

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were found to carry multiple ARGs (≥ 2 ARGs) (Figure S4A), and 86.07–93.93% of

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the ARCs carried ARGs within the RND, ABC antibiotic efflux and beta-lactamase

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families in different samples (Figure S4B).

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The relative abundance of total ARGs was remarkably increased from 0.82 ± 0.05

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cpc in the SW sample to 1.24 ± 0.04 cpc after ozone disinfection (p < 0.05), and

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further increased to 1.25 ± 0.08 cpc after the subsequent chlorination (Figure 1). In

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detail, ozone disinfection mainly increased the abundances of RND antibiotic efflux,

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ABC antibiotic efflux and beta-lactam ARGs (p < 0.05), but the following

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chlorination decreased the abundance of beta-lactam ARGs (p < 0.05) (Figure 1).

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However, the relative abundance of total ARGs was significantly reduced to 0.65 ±

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0.01 cpc after AR disinfection mainly through the reduction of the RND antibiotic

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efflux ARGs (p < 0.05) (Figure 1). The subsequent chlorination showed no significant

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effect on the relative abundance of total ARGs but remarkably increased the

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abundance of RND antibiotic efflux ARGs from 0.13 ± 0.01 cpc in the ARW sample

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to 0.26 ± 0.01 cpc in the AR-CW sample (p < 0.05) (Figure 1). Conversely, the

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alternative UV disinfection after AR treatment significantly enhanced the relative

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abundance of total ARGs (1.01 ± 0.17 cpc), and the abundances of all detected types

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of ARGs except for those in the MFS antibiotic efflux, antibiotic inactivation and

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rRNA methyltransferase families (p < 0.05) (Figure 1). Compared to the SW, the

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relative abundances of total ARGs in the final effluents were significantly increased

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after the O3/Cl2 and AR/UV coupling disinfection strategies, respectively (p < 0.05)

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(Figure 1). However, no significant change in the relative abundance of total ARGs

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was observed after the AR/Cl2 coupling disinfection (Figure 1). PCoA based on the

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Bray–Curtis distance showed the significant shift of antibiotic resistome after

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different drinking water disinfection strategies (Figure S5, Adonis test, R2 = 0.91, p

Antibiotic resistome alteration by different ... - ACS Publications

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