High-throughput profiling of antibiotic resistance gene dynamic in a drinking water river-reservoir system

Accepted Manuscript High-throughput profiling of antibiotic resistance gene dynamic in a drinking water river-reservoir system Yihan Chen, Jian-Qiang Su, Junya Zhang, Peng Li, Hongjie Chen, Bo Zhang, Karina Yew-Hoong Gin, Yiliang He PII:

S0043-1354(18)30928-X

DOI:

https://doi.org/10.1016/j.watres.2018.11.007

Reference:

WR 14214

To appear in:

Water Research

Received Date: 7 August 2018 Revised Date:

25 October 2018

Accepted Date: 3 November 2018

Please cite this article as: Chen, Y., Su, J.-Q., Zhang, J., Li, P., Chen, H., Zhang, B., Yew-Hoong Gin, K., He, Y., High-throughput profiling of antibiotic resistance gene dynamic in a drinking water riverreservoir system, Water Research, https://doi.org/10.1016/j.watres.2018.11.007. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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ACCEPTED MANUSCRIPT 1

High-throughput profiling of antibiotic resistance gene dynamic in a drinking

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water river-reservoir system

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Yihan Chen a,b, Jian-Qiang Su c, Junya Zhang d, Peng Li a, Hongjie Chen e,f, Bo Zhang a

, Karina Yew-Hoong Gin e,f, Yiliang He a,g,*

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a*

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800 Dongchuan Road, Shanghai 200240, China

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b

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Anhui Province 230601, China

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School of Environmental Science and Engineering, Shanghai Jiao Tong University,

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School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei,

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c

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Chinese Academy of Sciences, Xiamen 361021, China

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d

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Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences,

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Beijing, 100085, China

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e

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Engineering Drive 1, #02-01, Singapore 117411, Singapore

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f

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Singapore, 1 Engineering Drive 2, E1A 07-03, Singapore 117576, Singapore

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g

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Road, Shanghai 200240, China

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Correspondence: Yiliang He, PhD, School of Environmental Science & Engineering,

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Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China. Tel.

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86-21-54744008. Email: [email protected]

Key Laboratory of Urban Environment and Health, Institute of Urban Environment,

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State Key Joint Laboratory of Environmental Simulation and Pollution Control,

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NUS Environmental Research Institute, National University of Singapore, 5A

Department of Civil and Environmental Engineering, National University of

China-UK Low Carbon College, Shanghai Jiao Tong University, 800 Dongchuan

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Abstract: The rapid construction of reservoir in river basin generates a river-reservoir

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system containing an environmental gradient from river system to reservoir system in

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modern aquatic environment worldwide. Profiles of antibiotic resistance genes (ARGs)

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in river-reservoir system is essential to better understand their dynamic mechanisms

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in aquatic eco-environment. In this study, we investigated the diversity, abundance,

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distribution of ARGs and mobile genetic elements (MGEs) in a representative

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river-reservoir system using high-throughput quantitative PCR, as well as ranked the

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factors (e.g. antibiotics, bacterial mass, bacteria communities, and MGEs) influencing

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the patterns of ARGs based on structural equation models (SEMs). Seasonal

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variations in absolute abundance of ARGs and MGEs exhibited similar trends with

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local rainfall, suggesting that seasonal runoff induced by the rainfall potentially

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promote the the absolute abundance of ARGs and MGEs. In contrast, environmental

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gradient played more important roles in the detected number, relative abundance,

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distribution pattern of ARGs and MGEs in the river-reservoir system. Moreover,

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environmental gradient also made the co-occurrence patterns associated with ARGs

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subtypes, MGEs and bacteria genera in river system different from those in reservoir

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system. The SEMs revealed that MGEs contributed the most to shape the ARG

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profiles. Overall, our findings provide novel insights into the mechanisms of

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environmental gradient on ARGs dynamics in river-reservoir system, probably via

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influencing the MGEs, antibiotics, pathogenic bacteria community and nonpathogenic

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bacteria community.

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Key words: Antibiotic resistance genes; High-throughput qPCR; River-reservoir

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system; Structural equation model; Environmental gradient

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1. Introduction Nowadays, increasing emergence and prevalence of antibiotic resistance and

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antibiotic resistant genes (ARGs) have been recognized as a globally major health

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challenge of the 21st century (Qiao et al., 2018). It should be noted that antibiotic

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resistance itself is a natural and ancient phenomenon (D'Costa et al., 2011; Martínez,

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2008). However, the overuse and misuse of antibiotics in modern era have accelerated

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the evolution and dissemination of ARGs ever since the use of penicillin for medical

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therapy in 1942 (Qiao et al., 2018; Vikesland et al., 2017). Almost simultaneously

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with the introduction of penicillin, an increasing number of reservoirs have been built

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to benefit human society around the world over the past 70 years, with more than

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50,000 large dams located in river basins in the management and control of water

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resources (Lehner et al., 2011). However, this would increase water residence time

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and improve light conditions, nutrient retention and sediment aggradation in the

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reservoir (Maavara et al., 2017). Undoubtedly, these dams have disrupted original

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geochemical processes and ecological connectivity of traditional rivers worldwide

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(Lehner et al., 2011). Based on these, then river-reservoir system has been commonly

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adopted to characterize this widely distributed environmental gradient from river

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system to reservoir system associated with hybrid aquatic environmental properties

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(Chen et al., 2017b).

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Noteworthily, river system is easily impacted by anthropogenic activities

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including livestock and poultry breeding, agricultural and municipal drainage, which

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together release diverse chemical and biologic pollutants into river system (Liao et al.,

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2018). Antibiotic residues, ARGs and their bacterial hosts could pass through artificial

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environmental systems, and are ultimately released into river system via various waste

ACCEPTED MANUSCRIPT streams (Vikesland et al., 2017). For instance, recent surveys estimated a total of

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24,748 tons of antibiotics and 9.47 × 1013 copies/person/day of the ARGs loads were

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released into rivers and related waterways in China (Su et al., 2017; Zhang et al.,

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2015). In contrast, most of the reservoir systems are located in some relatively pristine

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areas with little human activities other than riverine inputs, often acting as important

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drinking water sources. Compared with river system, reservoir system may be less

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susceptible to be polluted with the antibiotics and ARGs (Su et al., 2014). However,

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antibiotics can accumulate and persist in natural aquatic ecosystems, in which they

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would continue to exert selective pressures on bacterial communities, and increase the

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likelihood of spontaneous mutation creating resistance and horizontal gene transfer

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(HGT) among bacteria community via mobile genetic elements (MGEs) for the

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emergence and spread of ARGs (Grenni et al., 2018). As a result, the river-reservoir

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system could provide ideal settings for the physical transport, acquisition and

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dissemination of ARGs, which are closely related to the combined effects between

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natural processes and anthropogenic activities (Lupo et al., 2012; Marti et al., 2014).

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Previous studies mainly focused on the occurrence and distribution of ARGs in

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individual rivers, reservoirs, estuaries and artificial waters (Chen et al., 2015; Jiang et

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al., 2013; Su et al., 2017; Zhu et al., 2017b). However, the occurrence and distribution

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of ARGs in the river-reservoir system were not yet well addressed, even though the

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importance of river-reservoir system has been established. Most importantly, the

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dominant factors that may affect the dynamics of ARGs in different environments are

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conflicting to some extent based on the summary of previous literature (Wu et al.,

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2017; Zhang et al., 2016; Zhao et al., 2017). It is notable that the current volume of

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studies on the identification of dominant factors affecting the ARGs profiles is still

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lacking ， especially in the field studies. Taken together, more comprehensive

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investigations about the ARGs profiles and dominant factors affecting their profiles in

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river-reservoir system will be of great significance for a better understanding of the

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ARGs in actual aquatic environment. To accomplish this goal, a representative river-reservoir system was chosen to

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uncover broader profiles of ARGs in the South China, which is also a key drinking

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water source for several metropolitan cities of Guangzhou, Shenzhen and Hong Kong

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(Chen et al., 2017b). Technically, compared to the lower throughput conventional

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qPCR, high-throughput quantitative PCR (HT-qPCR) that includes 296 primer sets

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covering almost all major classes of ARGs and MGEs is more applicable to obtain a

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comprehensive result for ARGs (Zhao et al., 2017; Zheng et al., 2017). In addition,

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bacterial community compositions and antibiotic residues were also considered in this

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study. By combining these aspects, this study aims to (1) comprehensively investigate

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the temporal (season) and spatial (environmental gradient) characteristics of diversity

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and abundance of ARGs and MGEs in the river-reservoir system; (2) delineate the

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major factors (e.g. antibiotics, bacterial mass, bacteria communities, and MGEs), as

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well as to address the key driver in shaping the ARGs profiles along the

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environmental gradient.

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2. Materials and methods

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2.1. Site selection and sampling.

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The subtropical river-reservoir system (N24º21′-25º06′, E115º00′-115º47′) and

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sampling sites, covering a watershed area of 5150 km2, have been described

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elsewhere and showed in Fig. 1 (Chen et al., 2018; Chen et al., 2017b). Briefly, the

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river system (S1−S8) situated in the anthropic zone consists of two primary tributaries

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(Beiling River with 140 km length, 2363 km2 of catchment area (S1−S4) and Xunwu

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heavy nonpoint source pollution, such as untreated wastewater discharge, dispersed

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livestock wastewater and agricultural runoff. Then, it finally discharges into a huge

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and deep reservoir (Reservoir Fengshuba) located in a relatively pristine area. Notably,

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apart from pollutant inputs from the river system, we hypothesize that the reservoir

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system (S9−S13) in this natural environment was less directly contaminated due to

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reservoir resettlement and forestry projects. In general, environmental gradient in the

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study includes the differences in the hydrologic condition and degree of human

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activities between the river system and reservoir system (Table S1 and S2), which is

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also similar to other watersheds (Zhang et al., 2011; Zhou et al., 2016).

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The surveys were performed separately in the three seasons: July 2015 (summer

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season), November 2015 (autumn season) and March 2016 (spring season) (Table S2),

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respectively. In the river system, surface water samples (9 L) were collected from 8

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sampling locations (S1−S8) (Fig. S1). Specifically, at each site in the reservoir system

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(S9−S13) (Fig. S1), three water samples (3 L) collected separately from the surface

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water (0.5 m below surface), the middle water (half of the depth) and the bottom

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water (about 2−4 m above bottom) were combined to form a composite sample (9 L).

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All the samples were immediately transported to the laboratory and stored at 4 ºC

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before analysis (Chen et al., 2018).

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2.2. Chemical analysis

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The dissolved organic carbon (DOC), dissolved total nitrogen (DTN) and total

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phosphorus (DTP) contents in the water samples were measured according to the

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standard methods (State Environmental Protection Administration of China, 2002).

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Based on the survey of antibiotic consumptions in China (Zhang et al., 2015), 17

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specific antibiotics were selected as target compounds. These included: sulfonamides

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(including sulfadiazine (SDZ), sulfamonomethoxine (SMM) and sulfaquinoxaline

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(SQX)), fluoroquinolones (including norfloxacin (NOR), ciprofloxacin (CIP) and

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ofloxacin (OFC)), beta-lactams (including amoxicillin (AMX), cefalexin (CLX),

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penicillin G (PENG) and penicillin V (PENV)), tetracyclines

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oxytetracycline (OTC), tetracycline (TC) and doxycycline (DC)) and others

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(including tylosin (TYL), erythromycin-H2O (ETM-H2O), lincomycin (LIN) and

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vancomycin (VAN)). Detailed descriptions of these antibiotics (Table S3) and

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corresponding analysis in the water samples were presented in SI Text 1.

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2.3. DNA extraction and high-throughput quantitative PCR

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DNA was extracted from the sediment, soil, and water samples with EZNATM

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Soil and EZNATM Water DNA Kits (OMEGA, USA) according to the manufacturer’s

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protocols. The concentration and quality of the extracted DNA were checked using

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agarose gel electrophoresis and microspectrophotometry NanoDrop ND-2000

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(Thermo, USA). Then, qualified DNA was adjusted to 50 ng/µL and stored at −80 ºC

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until molecular analysis.

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All high-throughput qPCR reactions were performed using the Wafergen

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SmartChip Real-time PCR system as described previously (Wang et al., 2014). A total

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of 296 primer sets were quantify to the gene targets in the present study, including 285

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ARGs for major classes of antibiotics, nine target genes for MGEs (eight transposase

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genes and one universal class I integron-integrase gene (cintI-1), one clinical class I

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integron-integrase gene (intI-1) and the 16S rRNA gene (Table S4) (Xie et al., 2016).

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Detailed description on the PCR reaction conditions and raw data processing were

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conducted as previously described in other studies (Chen et al., 2017a; Wang et al.,

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ACCEPTED MANUSCRIPT 2014). The gene copy number was calculated according to the follow equation: Gene

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Copy Number = 10(31−Ct)/(10/3), where threshold cycle (Ct) referred to quantitative PCR

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result and 31 was identified as the detection limit (Looft et al., 2012). The relative

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abundance of each gene was calculated by normalizing each gene’s copy number to

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the 16S rRNA’s copy number (Zheng et al., 2018). The absolute abundance (absolute

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copy number) of ARGs were calculated by multiplying the value of relative gene copy

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number of ARGs by the absolute copy number of 16S rRNA gene (Xie et al., 2016).

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For all samples, all qPCRs were conducted in three technical replicates with negative

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controls.

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2.4. Bacterial 16S rRNA gene sequencing

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The V4-V5 region of 16S rRNA gene was used to characterize bacterial

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communities (Chen et al., 2017a). Each PCR reaction was carried out in 20 µL

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reaction mixtures and PCR amplification conditions were in accordance with previous

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study (Sun et al., 2014). To minimize potential PCR bias, triplicate PCR reactions

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were performed and purified for each sample as described above. After quantifying

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the purified PCR products, all the purified products were adjusted at the same

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concentration and then evenly mixed. Subsequently, the mixtures were sequenced on

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Miseq platform (Illumina, USA). Sequencing analysis was processed using QIIME

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toolkit for 16S rRNA data sets. Briefly, sequences were used to pick operational

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taxonomic units (OTUs) at a similarity of 97% using Usearch method. The taxonomic

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information was annotated with the SILVA database, bacterial communities (at the

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level of genus) were classfied into pathogenic bacteria community (PBC) and

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nonpathogenic bacteria community (NPBC) following the previous methodology

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(Han et al., 2017). All the sequence raw datasets have been deposited in the NCBI

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Sequence Read Archive with the BioProject accession number PRJNA386241.

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2.5. Data analysis The data were organized in Microsoft Excel 2016, and diagrams were plotted

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using OriginPro 2016. The differences of grouped data complying with the parametric

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assumption (Shapiro-Wilk test) were analyzed with a one-way ANOVA, followed by

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Tukey's post-hoc tests. Otherwise, nonparametric data were analyzed using one-way

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Kruskal-Wallis ANOVA tests, followed by Dunn’s multiple comparison tests (Chen et

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al., 2017b).

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Pearson’s and Spearman’s correlations were performed using SPSS 21.0 software.

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Profiles of ARGs, MGEs, PBC and NPBC assemblages were analyzed using principal

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coordinates analysis (PCoA) and permutational multivariate analysis of variance

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(PERMANOVA) (9999 permutations) implemented in PRIMER version 7.0 and

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Canoco 4.5. All statistical tests were considered significant when the P-value was

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below 0.05. Co-occurrence patterns of ARG subtypes with MGEs (relative copy

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number) and bacterial community (genus) at the level of relative abundance based on

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Spearman’s analysis (ρ ≥ 0.8 and P-value < 0.01) were explored by a network

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visualization using Gephi platform (0.9.2). To reduce the chances of false-positive

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results, P-values were adjusted using the Benjamini-Hochberg method (Li et al.,

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2015). Structural equation model (SEM) was adopted to evaluate the direct or indirect

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effects of antibiotics, basic properties, MGEs, bacterial abundances and community

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compositions on the ARGs patterns. The matrices of these variables were imported

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into AMOS 21 software for the SEMs construction based on the maximum likelihood

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estimation method (Hu et al., 2016). Furthermore, the indirect effect of latent

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variables was calculated by multiplying the standardized effects of all pathways on

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one route from one latent variable to another and then to ARGs, while the

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standardized total effects on ARGs were calculated by summing standardized indirect

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and direct effects (Eisenhauer et al., 2015; Hu et al., 2017b).

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3. Results

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3.1 Diversity of ARGs and MGEs In total, 251 (242 ARGs and 9 MGEs) out of the 295 targeted genes were

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detected in this study. Two different variations including season and environmental

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gradient were considered to evaluate the effect on the number of detected ARGs and

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MGEs assays. Along with the season variation, the number of detected ARGs in the

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waters did not exhibit significant difference among the summer, autumn and spring

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(One-way ANOVA, F = 1.10, P > 0.05). At the same time, the number of detected

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MGEs (8−9 genes) in the waters in the spring was significantly higher than that in the

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summer (6−9 genes) (Kruskal-Wallis (KW) test, χ2 = 6.81, P < 0.05) (Fig. S2A and C).

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However, the number of detected ARGs and MGEs in the waters of river system

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(116−213 genes and 7−9 genes) were significantly higher than those in the reservoir

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system (87−178 genes and 6−9 genes) (one-way ANOVA, F = 12.69, P < 0.01; KW

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test, χ2 = 17.97, P < 0.01) (Fig. 2A, Fig. S2B and D). Moreover, Venn diagram

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analysis demonstrated that 204 ARGs, which accounted for 84.3% of the total ARGs,

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were shared between the river system and reservoir system (Fig. 2B). The unique

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subtypes in the waters of river system and reservoir system included 34 ARGs and 4

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ARGs, respectively. More detailed information for the shared and unique ARGs can

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be found in the bipartite network analysis (Fig. S3).

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3.2. Abundances of ARGs and MGEs

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The absolute abundance of 16S rRNA, ARGs and MGEs in the waters of

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river-reservoir system ranged from 1.9 × 109 to 2.6 × 1011 copies/L, 6.6 × 107 to 2.1 ×

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1011 copies/L and 6.7 × 107 to 6.7 × 1010 copies/L, respectively (Fig. S4). A consistent

ACCEPTED MANUSCRIPT phenomenon was observed that the absolute abundance of 16S rRNA, ARGs and

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MGEs in the spring were significantly higher than that in the summer and autumn

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(KW tests, P < 0.05 or P < 0.01) (Fig. 3A−C). In contrast, there was no obvious

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difference in the absolute abundance of 16S rRNA and ARGs between the river

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system and reservoir system (KW tests, P > 0.05), whereas the absolute abundance of

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MGEs in the river system was significantly lower than that in the reservoir system

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(KW test, P < 0.05) (Fig. 3D−F). Notably, both the absolute abundance of ARGs and

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MGEs correlated significantly with that of the 16S rRNA (Pearson's r = 0.77, P < 0.01;

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Pearson's r = 0.78; P < 0.01) in the river-reservoir system, respectively (Table S5).

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In order to avoid potential influence caused by bacterial community size, we also

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used the relative abundance of ARGs and MGEs for the further exploration (Fig. S5).

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The relative abundance of ARGs and MGEs did not change along the seasonal

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variations (KW test, P > 0.05) (Fig. S6A and C). In contrast, both the relative

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abundance of ARGs and MGEs in the river system were significantly higher than

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those in the reservoir system (KW tests, P < 0.05 or P < 0.01) (Fig. S6B and D),

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respectively. Furthermore, the effects of season and environmental gradient on the

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relative abundance of ARGs and MGEs were investgated using the PCoA with

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One-way PERMANOVA (Fig. 4). Seasonal factor had a significant effect on the

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distribution patterns of ARGs (PERMANOVA, Pseudo-F = 2.91, P = 0.003) (Fig. 4A),

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while distribution patterns of MGEs exhibited no distinct according the seasonal

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factor (PERMANOVA, Pseudo-F = 2.04, P = 0.051) (Fig. 4C). In contrast, ARGs and

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MGEs distributions revealed significant differences between the river system and

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reservoir system according the environmental gradient factor (PERMANOVA,

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Pseudo-F = 3.97, P = 0.001; PERMANOVA, Pseudo-F = 5.10, P = 0.002) (Fig. 4B

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and D), respectively.

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ACCEPTED MANUSCRIPT Further, the co-occurrence patterns among ARG subtypes and MGEs in the river

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system and reservoir system were investigated using the network analysis, which

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showed significant clusters within the networks. The most frequently connected node

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in each network was defined as the “hub”. For instance, the tet(32) was the hub gene

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for network in the river system (Fig. S7), while the mpha-01 was the hub gene for

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network in the reservoir system (Fig. S8). Additionally, the network analyses also

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showed that many ARGs were co-occurred with MGEs. For instance, the genes

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encoding transposase (e.g., tnpA-02, tnpA-07, and tnpA-05) among the MGEs showed

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the most remarkable co-occurrence patterns with related genes in the river system,

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with the corresponding degrees of 53, 45, and 39 (Fig. S7). In comparison, the genes

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(e.g., tnpA-05, tp614, and cintI-1) among the MGEs showed the most remarkable

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co-occurrence patterns in the reservoir system, with the corresponding degrees of 15,

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13, and 12 (Fig. S8).

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3.3. Correlation between antibiotic residues and ARGs , MGEs and intI-1

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Antibiotic residues in the waters of river-reservoir system were analysed and

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presented in details in Fig. S9. The total concentrations (ng/L) of five major antibiotic

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classes (sulfonamides, fluoroquinolones, beta-lactams, tetracyclines and others) in the

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river system were 17.11−108.63, 78.60−597.21, 25.59−193.17, 9.10−218.25 and

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0.50−15.38, respectively. For the reservoir system, the corresponding values (ng/L)

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were 11.61−38.74, 26.80−239.55, 7.61−131.79, 37.77−300.72 and 0.34−3.58. In

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addition, the concentrations of fluoroquinolones and ∑antibiotics were positively

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correlated with the absolute abundance of related ARG types, ∑ARGs and MGEs in

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the river system (Pearson's r = 0.46−0.67, P < 0.05), while these correlations could

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not be found in the reservoir system (Fig. 5B). Nevertheless, the concentration of

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∑antibiotics showed a strong correlation with the absolute abundance of intI-1 in both

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the river system and reservoir system (P < 0.01) (Fig. 5B).

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3.4. Characterization of bacterial community and co-occurred with ARGs

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subtypes, MGEs A total of 1,536,600 high quality sequences were obtained from the water

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samples with 39400 sequences per sample on average, which were clustered into 4348

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OTUs at 3% dissimilarity level. The main genus in the waters of river-reservoir

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system were showed in detail in Fig. S10. It should be noted that the composition of

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bacterial community in this study mainly includes 7 relative abundance of pathogenic

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bacteria community (PBC) and 94 nonpathogenic bacteria community (NPBC) at the

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genus level (Fig. S10). According to the environmental gradient, the community

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composition of NPBC showed a distinct difference between the river system and

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reservoir system (PERMANOVA, Pseudo-F = 12.05, P = 0.0001) (Fig. 6A). Likewise,

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the PBC also displayed a significant difference along the environmental gradient

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(PERMANOVA, Pseudo-F = 2.58, P = 0.049) (Fig. 6B), with a result that the relative

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abundance of total pathogenic bacteria in the river system was obviously higher than

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that in the reservoir system (One-way ANOVA, P < 0.05) (Fig. S11).

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The co-occurrence patterns among ARG subtypes, MGEs, and bacterial genera

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were also explored using the network analysis. Moreover, we hypothesized that the

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co-occurrence patterns between ARGs and bacterial taxa could be used to provide

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possible host information for ARGs if the ARGs and coexisting bacterial taxa had a

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strong and significantly positive correlation (Spearman’s correlation ρ ≥ 0.8, adjusted

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P

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aminoglycoside-resistance

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MLSB-resistance genes (ermB and ermF) and tetracycline-resistance gene (tetX) in

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<

0.01).

For

instance, genes

Dechloromonas (e.g.,

was

aac(6')-II,

the

possible

aadA-1-02,

and

host

of

aadA1),

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multidrug-resistance gene (adeA), tetracycline-resistance gene (tetD-02), and

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MLSB-resistance gene (matA/mel), Cronobacter was also found to be the possible

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host of multidrug-resistance gene (adeA) and vancomycin-resistance gene (vanRB) in

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the reservoir system (Fig. S13).

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3.5. Factors influencing the dynamics of ARGs

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Structural equation model (SEM) has been commonly applied in the complex

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eco-environmental relationships to develop causal understanding from observational

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data. In this study, the SEM was useful to explore the direct, indirect and total effects

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of antibiotics (Ants), nutrients (Nuts), bacterial biomass (BB), MGEs, NPBC and

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PBC on the ARG patterns (Fig. 7). In addition, the nutrition composition including

330

DOC, DTN and DTP in the water samples showed no distinct difference between the

331

river system and reservoir system (PERMANOVA, P > 0.05) (Table S6). Generally,

332

the factors influencing the dynamics of ARGs in terms of standardized total effect in

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the river system followed the order: MGEs (λ = 0.450) > NPBC (λ = 0.279) > PBC (λ

334

= 0.210) > Ants (λ = 0.178) > BB (λ = 0.162) > Nuts (λ = 0.103) (Fig. 7A and C),

335

whereas the factors influencing the dynamics of ARGs in the reservoir system

336

followed the order: MGEs (λ = 0.415) > NPBC (λ = 0.231) > PBC (λ = − 0.014) > BB

337

(− 0.030) = Nuts (λ = − 0.030) > Ants (− 0.093) (Fig. 7B and D). Strikingly, it could

338

be found that the MGEs and NPBC positively influenced the ARGs dynamics both in

339

the river system and reservoir system, while the other factors influencing the ARGs

340

dynamics between the river system and reservoir system were conflicting to some

341

extent.

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4. Discussion

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ACCEPTED MANUSCRIPT In the emerging world, as more and more dams are frequently constructed, the

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river-reservoir system is the most representative form of surface water and universally

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exists in the modern aquatic environment, not just an independent river or reservoir

346

system. However, little attention is afforded toward the ARGs contents in this system.

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To our best knowledge, this study provided the most comprehensive profiles of ARGs

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in river-reservoir system by using high throughput qPCR for the first time.

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4.1 Variations of ARGs along the season and environmental gradient

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The present study was performed at the watershed scale according to the

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variations of season and environmental gradient. The detected number and relative

352

abundance of ARGs and MGEs did not changed seasonally, suggesting that season

353

factor might exert a less role in influencing their diversities and relative abundances.

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However, the absolute abundance of 16S rRNA, ARGs and MGEs varied with the

355

seasonal variations and their higher abundance were all in the spring (Fig. 3A−C).

356

Considering the fact that rainfall in the spring (rainfall = 316.0 mm) was far greater

357

than summer (rainfall = 217.8 mm) and autumn (rainfall = 28.5 mm) (Table S2), these

358

suggested that more rainfall could increase the absolute abundance of ARGs and

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MGEs in the river-reservoir system attributed to storm-driven transport, which

360

probably contributed to the entry of ARGs and MGEs from domestic wastewater and

361

agricultural runoff into aquatic environment (Marti et al., 2014). Also, this finding

362

was consistent with previous studies in other catchments (Di Cesare et al., 2017;

363

Garner et al., 2017). Furthermore, the significant correlations between the absolute

364

abundance of 16S rRNA (an indicator of bacterial biomass) and ARGs/MGEs (P <

365

0.01) (Table S5), indicated that bacterial biomass played an important role in

366

impacting the absolute abundance of ARGs and MGEs (Yang et al., 2018). In other

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words, more bacterial biomass had the opportunity to harbor more ARGs and MGEs.

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ACCEPTED MANUSCRIPT However, this concept is inconsistent with a previous finding that temperature is a

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potential factor driving the absolute abundance of ARGs in the Zhangxi River

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(Zhejiang Province, China) (Zheng et al., 2018). This phenomenon may be attributed

371

to that the differences in the characteristics of rainfall and air temperature between our

372

river-reservoir system and the Zhangxi River. The change trends of the rainfall and air

373

temperature were very similar in the Zhangxi River, while these trends were

374

dissimilar with the fact that the local air temperature/rainfall of river-reservoir system

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in the summer, autumn and spring were 27.3−28.4 ºC/217.8 mm, 15.6−18.0 ºC/28.5

376

mm and 14.3−16.5 ºC/316.0 mm, respectively (Table S2). In view of the above, when

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the trends of the rainfall and air temperature are coincident, it is actually difficult to

378

ascertain the role of rainfall or temperature in influencing the absolute abundance of

379

ARGs and MGEs. Fortunately, our study provides an opportunity to overcome this

380

problem. Taken together, these further support the idea that seasonal runoff induced

381

by the rainfall, rather than the uncontrollable air temperature, could strongly affect the

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entry of exogenous bacteria potentially harboring antibiotic resistomes into the aquatic

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environment, which in turn affects the absolute abundance of ARGs and MGEs.

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As expected, significant decreasing trends were observed in the detected number

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of ARGs and MGEs according to the environmental gradient from the river system to

386

reservoir system (P < 0.05), indicating that environmental gradient could markedly

387

influence their diversities in the river-reservoir system. Specifically, the 204 shared

388

ARGs between the river system and reservoir system suggested riverine input might

389

be responsible for these ARGs for the reservoir system, and these genes were

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relatively persistent along the environmental gradient. The 34 unique ARGs (e.g., sulI,

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tetT, ermA and vanC1) in the river system highlighted that these genes might be more

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closely related to anthropogenic release. For example, blaPAO, ermA, sulI and tetT

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ACCEPTED MANUSCRIPT were detected frequently in wastewater treatment plants, sludges, manures, human

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and pig faeces (An et al., 2018; Li et al., 2015; Qian et al., 2018; Wei et al., 2018).

395

Meanwhile, the relative abundance of ARGs and MGEs also decreased significantly

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along the environmental gradient. These findings suggested reservoir system had the

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potential to attenuate their diversity and relative abundance in river system. Indeed, in

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most developing countries, river system is commonly considered as a long-term

399

polluted water environment receiving inadequate waste discharges in anthropic zone,

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since these anthropogenic effluents derived from multiple sources contain more

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diverse and high relative abundance of ARGs and MGEs (Tang et al., 2016; Xiong et

402

al., 2014; Zhou et al., 2017). On the other hand, compared to the river system, long

403

hydraulic retention time with slow outflow in the reservoir system could promote the

404

ARGs deposition of particle-associated bacteria or bacterial aggregates exported from

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the river system (Czekalski et al., 2014). Additionally, significant differences in the

406

distribution of ARGs and MGEs were observed between the river system and

407

reservoir system (Adonis test, P < 0.05). The hub genes for the networks among ARG

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subtypes and MGEs between the two systems were also different. These findings

409

suggested that environmental gradient could obviously affect the distribution of ARGs

410

and MGEs in aquatic environment. Also, this was similar to other studies focusing on

411

the other environmental gradients (e.g., river and estuary, livestock wastewater and

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receiving river, pristine environment and human-impacted environment) from

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different environmental compartments (Chen et al., 2015; Chen et al., 2013; Jia et al.,

414

2017).

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In general, it was notable that values of Chi-square (χ2) in the absolute abundance

416

of 16S rRNA, ARGs and MGEs according to the season factor were obviously lower

417

than those according to the environmental gradient factor, while opposite phenomenon

ACCEPTED MANUSCRIPT was found for the values of Chi-square (χ2)/Pseudo-F (F) in the detected number,

419

relative abundance, distribution pattern of ARGs and MGEs (Table S7). These results

420

suggested that season factor played more important roles in the absolute abundance of

421

16S rRNA, ARGs and MGEs, whereas the environmental gradient exerted more

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influences in the detected number, relative abundance, distribution pattern of ARGs

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and MGEs in the river-reservoir system. Based on these, compared with season factor,

424

we affirmed that environmental gradient between the river system and reservoir

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system has been a dominant factor in differentiating the complex characteristics of

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ARGs and MGEs among the modern aquatic environment.

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4.2 Factors influencing the distribution of ARGs

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It has been demonstrated that ARGs can be increased by MGEs via the HGT to

429

acquire resistance genes among various microorganism (Martínez et al., 2014). In this

430

study, we also found that the absolute abundance of all ARGs types and ∑ARGs

431

showed strong correlations with MGEs in both river system and reservoir system

432

(Pearson's r = 0.54−0.95, P < 0.05 or P < 0.01) (Table S5), indicating that the

433

widespread prevalence of MGEs played an important role in accelerating the

434

proliferation of ARGs in the aquatic environment (Zheng et al., 2017). However,

435

obvious discrepancies between the river system and reservoir system were found

436

when regarding the patterns of ARGs co-occurring with MGEs (Fig. S7 and S8). In

437

particular, the degrees of tnpA-02 (53), tnpA-07 (45), and tnpA-05 (39) for the

438

co-occurrence patterns in the river system were higher than those of tnpA-05 (15),

439

tp614 (13), and cintI-1 (12) in the reservoir system. This probably suggested that the

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specific promotion effect of MGEs on ARGs is different along the variation of

441

environmental gradient, and the effect in the river system was stronger than that of the

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reservoir system.

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ACCEPTED MANUSCRIPT Antibiotic residue (∑antibiotics) and its positive correlations with each type of

444

ARGs, ∑ARGs and MGEs in the river system suggested subinhibitory concentrations

445

of antibiotic residues might had a high probability to directly exert selective and

446

co-selective pressure for ARGs induction and/or stimulate the propagation of MGEs,

447

which in turn promoted the ARGs levels via the HGT under certain conditions

448

(Beaber et al., 2004; Levin-Reisman et al., 2017; Wang et al., 2014). Likewise, a

449

similar result was also observed between the most dominant antibiotics among the

450

residues (fluoroquinolones) and its correlations (Fig. 5B, Fig. S9). Generally, these

451

findings were consistent with previous studies in the water, sediment and soil (Chen et

452

al., 2015; Chen et al., 2014; Zhu et al., 2017b). In contrast, the above correlations

453

were not found in the reservoir system (Fig. 5B), suggesting that antibiotic residues

454

here might play a less role in the spread of ARGs and MGEs. Probably, these

455

differences along the environmental gradient could be partly attributed to the

456

discrepancies in attenuation rates of antibiotics and ARGs/MGEs in aquatic

457

environment (Akiyama et al., 2010). Because, the concentrations of antibiotic residues

458

decreased significantly from the river system to reservoir system (P < 0.05) (Fig. 5A),

459

while the absolute abundance of ARGs or MGEs did not vary obviously along the

460

environmental gradient (P > 0.05). In addition, the strong correlations between the

461

absolute abundance of intI-1 and concentration of ∑antibiotics in both the river

462

system and reservoir system suggested that antibiotic residues could permanently

463

exert selective stress for the propagation of intI-1, and intI-1 could be considered as an

464

alternative indicator to rapidly evaluate the level of antibiotic residues in aquatic

465

environment. Previous studies had suggested intI-1 as an environmental marker of

466

anthropogenic pollution because of its unparalleled advantages (Gillings et al., 2015;

467

Ma et al., 2017), and our finding further consolidated this previous proposal.

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ACCEPTED MANUSCRIPT Previous studies have demonstrated that bacterial community could also

469

structure the ARGs profiles, and pathogen is more prone to acquire and propagate

470

ARGs than nonpathogenic bacteria (Forsberg et al., 2014; Wu et al., 2017; Zhou et al.,

471

2017). Thus, the community compositions of NPBC and PBC were separately

472

investigated based on the PCoA in this study (Fig. 6). The results suggested that the

473

environmental gradient could significantly influence their compositions. Although the

474

environmental gradient in this study is mainly composed of hydrological gradient and

475

anthropogenic contamination level, this finding can still be seen as partially consistent

476

with previous studies, which supported the view that the degree of anthropogenic

477

contamination strongly impacts aquatic microbial community structure (Hu et al.,

478

2017a; Liao et al., 2018). Meanwhile, we found that a decreasing trend in the relative

479

abundance of total pathogenic bacteria from river system to reservoir system (Fig.

480

S11), suggesting that the environmental gradient might affect the attenuation process

481

of pathogens in aquatic environment. As a result, the chance of pathogenic bacteria

482

transmitting ARGs would be inhibited to some extent. Furthermore, obvious

483

discrepancies between the river system and reservoir system were also observed in the

484

patterns of ARGs/MGEs co-occurred with bacteria genera (Fig. S12 and S13). These

485

analyses indicated that the characteristics of bacteria that harbor ARGs were different

486

along the environmental gradient. Moreover, the opportunistic pathogen of Bacillus

487

and Cronobacter possibly harboring related ARGs should be given more attention

488

considering the reservoir system providing drinking water source. Of particular,

489

Cronobacter possibly harboring vancomycin-resistance gene (vanRB) in the drinking

490

water reservoir was a terrible phenomenon. Because, vancomycin is the one of the last

491

line defense against Gram-positive bacteria, however the Cronobacter is a

492

Gram-negative bacterial (Zhu et al., 2017b). Bao et al. (2017) and Singh et al. (2015)

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ACCEPTED MANUSCRIPT suggested that the isolated Cronobacter could harbor the genes involving in

494

vancomycin resistance pathway. Thus, it is very necessary to study the changing

495

characteristics of the Cronobacter in the receiving aquatic environment during the

496

drainage of reservoir. Meanwhile, it should be noted that the network analysis is only

497

based on mathematical statistics and inevitably has a chance to influence its

498

robustness and reliability in predicting ARGs hosts. Therefore, substantial

499

investigations need to be performed to further validate the related findings (Zhang et

500

al., 2016; Zhu et al., 2017a).

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As above mentioned, several factors (e.g, MGEs, antibiotics, NPBC and PBC)

502

could influence the distribution of ARGs. Further, SEM results in the study showed

503

that MGEs contributed the most to the ARGs in term of standardized total effect

504

followed by NPBC in both the river system and reservoir system (Fig. 7), suggesting

505

the ARGs profiles in actual aquatic environment were more closely associated with

506

their inherently molecular microbiological mechanisms (Blair et al., 2015), which was

507

also consistent with previous studies (Wu et al., 2017; Zheng et al., 2018). Probably,

508

our finding is conflicting to the preconception that antibiotics could be the dominant

509

factor driving the ARG profiles. This noticeable discrepancy may be attributed to the

510

following explanations. First, antibiotics are not the only selective pressure in the

511

selection and spread of ARGs in actual field environment (Martínez, 2008; Zhao et al.,

512

2017). Thus, it is easy to understand that antibiotic pollutants may exert a fraction of

513

selection and impact in the ARGs profiles in the aquatic environment. Second, the

514

concentration of antibiotics is relatively low at the level of ng/L in this environment

515

(Wu et al., 2018). It is highly likely that the concentration of antibiotics in the actual

516

environment is rather low and cannot dominantly govern the ARGs profiles

517

(Bengtsson-Palme & Joakim Larsson, 2016), which is in accordance with previous

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laboratory research that some selected antibiotic concentrations maintaining crucial

519

roles in the emergence and spread of ARGs were often observed to be more higher

520

than the level of ng/L (Levin-Reisman et al., 2017; Rysz et al., 2013; Van den Bergh et

521

al., 2016). Although the most contributed factors were the same between the river system and

523

reservoir system, it should be noted that their values in terms of standardized total

524

effects were different, suggesting that specific factors affect the extent of ARGs

525

changed along the environmental gradient. For example, the standardized total effects

526

of PBC and antibiotics on the ARGs in the reservoir system were obviously lower

527

than those in the river system (Fig. 7). Interestingly, this phenomenon further implied

528

that the reservoir system could act as a safety barrier to attenuate the contributions of

529

pathogenic bacteria and antibiotics on the dissemination of ARGs during the process

530

of receiving and mixing the imported riverine waters. Overall, given the fact that no

531

obvious variations of nutrients (P > 0.05) and bacterial mass (P > 0.05) were found

532

along the environmental gradient, our study highlights that the environmental gradient

533

could drive the ARGs profiles probably via influencing the MGEs, antibiotics,

534

pathogenic bacteria community and nonpathogenic bacteria community.

535

5. Conclusion

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This study revealed the comprehensive profiles of ARGs in a river-reservoir system.

537

Main conclusions derived from the present work are as follows:

538




Seasonal runoff induced by the rainfall could strongly affect the absolute

539

abundance of ARGs and MGEs, while environmental gradient between river

540

system and reservoir system exerted more influences on the diversity, relative

541

abundance, distribution of ARGs and MGEs.

ACCEPTED MANUSCRIPT 542




Environmental gradient could obviously influence the distribution of pathogenic

543

bacteria community and nonpathogenic bacteria community, as well as the

544

co-occurred patterns among the ARGs subtypes, MGEs.


546 547

Structural equation models indicated that MGEs contributed the most to shape the ARGs profiles in river-reservoir system.




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Environmental gradient drove the ARGs profiles in river-reservoir system, probably via influencing the MGEs, antibiotics, pathogenic bacteria community

549

and nonpathogenic bacteria community.

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Acknowledgements: The authors are grateful for the financial support from the

551

National Science and Technology Major Projects of Water Pollution Control and

552

Management of China (2014ZX07206001), and Singapore under its Campus for

553

Research

554

(E2S2-CREATE

555

Environmental Sustainability in Megacities).

556

Conflicts of Interest: The authors declare no conflicts of interest.

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Fig. 1. Map showing the sampling sites in the river system and reservoir system.

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Fig. 2. (A). The number of detected ARGs and MGEs assays (at the subtype level) in the water samples of the river-reservoir system. ARGs were classified based on the antibiotics to which they conferred resistance: aminoglycosides, beta-lactamase, macrolide-lincosamide-streptogramin

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chloramphenicol,

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(MLSB),

multidrug,

sulfonamides, tetracycline, vancomycin or others. (B). Venn diagram showing the

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number of shared and unique ARGs between the river system and reservoir system.

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RiW, River water (n = 24); ReW, Reservoir water (n = 15).

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Fig. 3. Comparison of the absolute abundance of 16S rRNA (A and D), ARGs (B and E) and MGEs (C and F) in the variations of season and environmental gradient.

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Kruskal-Wallis tests were used to determine the significant effect on the variation

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tested (*: P < 0.05, **: P < 0.01).

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Fig. 4. Principal coordinate analysis (PCoA) based on the Bray−Curtis dissimilarity matrices showing the overall distribution patterns of ARG and MGEs in relative

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abundances of their corresponding subtypes along the variations of seasn (A and C)

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and environmental gradient (B and D).

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Fig. 5. (A) Antibiotic concentrations in the waters of river system and reservoir

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system. (B) Pearson’s correlation between antibiotic concentration and absolute abundance of each type of ARGs, MGEs and intI-1 in the waters of river system and reservoir system. Bottom right delineates the color band indicator of the correlation coefficient (*: P < 0.05, **: P < 0.01). White boxes represent no significant

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correlation for corresponding parameters (P > 0.05). ∑ARGs: defined as the sum of the

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ARGs. ∑Antibiotics: defined as the sum of these antibiotics.

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Fig. 6. Principal coordinate analysis (PCoA) based on the Bray-Curtis dissimilarity

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matrices showing the overall distribution patterns of and nonpathogenic bacteria community (NPBC) (A) and pathogenic bacteria community (PBC) (B) along the variation of environmental gradient.

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Fig. 7. Structural equation models (SEMs) showing the direct and indirect effects of MGEs, Ants, Nuts (SI Table S6), BB, NPBC and PBC (SI Figure S10) on the ARG

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patterns in the river-reservoir system. (A). Waters in the river system. (B). Waters in the reservoir system. Standardized total effects (direct plus indirect effects) were calculated from the structural equation models (C and D). Red and black arrows

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indicate positive and negative effects, respectively. Solid and dashed lines indicate significant (*: P < 0.05, **: P < 0.01) and non-significant (P > 0.05) relationships,

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respectively. Width of the arrows is proportional to the strength of path coefficients (numbers adjacent to arrows).

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Seasonal rainfall potentially promoted the absolute abundance of ARGs and MGEs.




Environmental gradient played important roles in the distribution pattern of

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MGEs contributed the most to shape the ARG profiles both in river and reservoir systems.

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ARGs and MGEs.