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Characterization of Natural and Affected Environments
Tracking the Sources of Antibiotic Resistance Genes in an Urban Stream during Wet Weather using Shotgun Metagenomic Analyses Darshan Baral, Bruce I. Dvorak, David Admiraal, Shangang Jia, Chi Zhang, and Xu Li Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01219 • Publication Date (Web): 18 Jul 2018 Downloaded from http://pubs.acs.org on July 19, 2018
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Tracking the Sources of Antibiotic Resistance Genes in an Urban Stream during Wet
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Weather using Shotgun Metagenomic Analyses
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Darshan Baral,a,# Bruce I. Dvorak,a David Admiraal,a Shangang Jia,b Chi Zhang,b and Xu Lia,*
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a
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b
Department of Civil Engineering, University of Nebraska, Lincoln, NE, 68588 School of Biological Sciences, University of Nebraska, Lincoln, NE 68588
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Running Head: Relative Contribution of Sources to Microbes in Stream
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*
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#
Address Correspondence to Xu Li,
[email protected] Current Address: HDR, Inc., Omaha, NE, 68114
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ABSTRACT
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Stormwater runoff has been known to cause increases in bacterial loadings in urban streams.
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However, little is known about its impacts on antibiotic resistance genes (ARGs) in urban
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watersheds. This study was performed to characterize the ARG composition of various
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environmental compartments of an urban watershed and to quantify their contributions of
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microbes and ARGs to an urban stream under wet weather conditions. Shotgun metagenomic
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results showed that the ARG abundance in wet weather flow was significantly higher than in
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base flow. Multidrug resistance genes were the most common ARG type across environmental
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samples. Vancomycin resistance genes were abundant in embankment soil and street sweeping
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samples. Analyses using SourceTracker estimated storm drain outfall water to be the biggest
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contributor of microbes (54-57%) and ARGs (82-88%) in the urban stream during wet weather
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flows. Furthermore, results on street sweepings showed that wash-off from streets was the
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biggest known contributor of microbes (41-45%) and ARGs (92-96%) in storm drain outfall
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water. Pantoea and Pseudomonas were associated with the highest numbers of ARGs and were
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most abundant in stormwater-related samples. Results from this study can advance our
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knowledge about ARGs in urban streams, an important medium linking environmental ARGs to
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the general public.
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KEYWORDS
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Antibiotic resistance genes, Urban stream, Wet weather events, Street sweepings, Microbial
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source tracking
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INTRODUCTION
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Widespread use of antibiotics in clinical and agricultural settings has been a cause of the
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increased occurrence of antibiotic resistance in the environment (1). Antibiotic resistance genes
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(ARGs) are the genetic determinants that allow a bacterium to withstand a higher antibiotic
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concentration (2). Soil, sediment, and sewage have been identified as some of the important
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environmental reservoirs of ARGs (1, 3). Because surface water connects these environmental
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reservoirs, serves as a source of drinking and irrigation water, and may also be used for
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recreational purposes (4-6), characterizing the ARGs in surface water is particularly important to
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better understand pathways through which environmental ARGs may affect the general public.
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Various environmental compartments in an urban watershed may affect the levels and
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composition of ARGs in an urban stream. Discharge of treated effluent from wastewater
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treatment plants has been regarded as a major source of ARGs in surface streams (7-9). Some
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urban streams may also receive raw sewage due to sanitary sewer pipeline leakage and illicit
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connections (10, 11). As raw sewage often contains high levels of ARGs (6, 12), it may also
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contribute to the ARGs in urban streams. Stormwater can carry bacteria from several
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environmental compartments to urban streams during wet weather events (13, 14). When runoff
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originates from pervious surfaces during large storms (15), it can transport soil bacteria directly
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to urban streams. Since many soil-dwelling bacteria are naturally antibiotic resistant (16), these
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soil bacteria could contribute to the ARGs in receiving waters when washed into the water by
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runoff. Soil particles carried in runoff may eventually settle in streams and become sediments.
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ARGs can persist in sediments for an extended period of time and enter the water column when
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sediment is re-suspended (17, 18).
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Runoff from impervious surfaces contributes significant flow volumes and bacterial loads
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to urban streams during wet weather events. Impervious surfaces such as streets and parking lots
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are often directly connected to surface water bodies via drainage structures (19). Particles from
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such surfaces can be quickly washed off into receiving waterbodies with runoff water during wet
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weather events. Street sweepings, the residues collected by motorized sweeping equipment from
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the streets (20), represent materials which have accumulated on impervious surfaces over time.
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While previous studies have highlighted the major role of residues on streets as a source of
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metals and suspended solids in urban stormwater (21, 22), little is known about their contribution
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to the ARGs in receiving water bodies during storm events.
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Compared to other molecular tools, shotgun metagenomics has the unique advantage of
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being able to reveal the comprehensive image of both the ARG and taxonomic compositions of a
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sample (23). In addition, data from shotgun metagenomics can be used in SourceTracker
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analyses. SourceTracker is a computational tool, which uses a Bayesian approach for estimating
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the relative contributions from potential sources to a sink environment (24) based on a
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probabilistic mixture of the composition of the sources (25). The tool has been used to attribute
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microbes in environmental sinks to various sources (26-28). Because abundances of both taxa
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and ARGs can be obtained from shotgun metagenomics, SourceTracker can be used for
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estimating the relative contributions of both taxa and ARGs in the sink environment. A third
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advantage of shotgun metagenomics is its ability to be used to explore the association between
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ARGs and microbial taxa through network analyses. In microbiological studies, networks can be
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constructed by having the nodes represent different microbial taxa or genes of interest and the
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edges represent meaningful interactions between them (29). In this study, robust correlations
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(Spearman’s ρ>0.8, p
