Co-Occurrence of crAssphage with Antibiotic Resistance Genes in an

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Ecotoxicology and Human Environmental Health

Co-occurrence of crAssphage with antibiotic resistance genes in an impacted urban watershed Elyse Stachler, Katherine Crank, and Kyle Bibby Environ. Sci. Technol. Lett., Just Accepted Manuscript • DOI: 10.1021/acs.estlett.9b00130 • Publication Date (Web): 19 Mar 2019 Downloaded from http://pubs.acs.org on March 22, 2019

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Environmental Science & Technology Letters

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Co-occurrence of crAssphage with antibiotic

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resistance genes in an impacted urban watershed

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Elyse Stachlera,b, Katherine Crankc, Kyle Bibbyc*

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a Department

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Pittsburgh, PA, 15261, USA;

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b

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Switzerland;

of Civil and Environmental Engineering, University of Pittsburgh,

Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, 8600,

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c Department

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Notre Dame, Notre Dame, IN, 46556, USA

of Civil and Environmental Engineering and Earth Sciences, University of

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* Corresponding Author: Kyle Bibby, [email protected], +1-574-631-1130

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Keywords

crAssphage, antibiotic resistance, ddPCR, bacteriophage, water quality

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Abstract

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Environments that receive fecal pollution are reservoirs of antibiotic resistance. Recent

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metagenomic observations suggest that the fecal pollution indicator crAssphage

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correlates with antibiotic resistance gene (ARG) occurrence in the environment.

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Expanding the utility of the fecal pollution indicator crAssphage to represent

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environmental occurrence of ARGs would potentially facilitate ARG management in

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fecal-pollution contaminated environments. In the current study we analyzed a suite of

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molecular indicators for ARGs and crAssphage over a 30-day sampling period in an

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urban stream that receives combined sewer overflows. The sampled stream showed

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high levels of ARGs and crAssphage with statistically significant elevated levels during

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wet weather events. The observed correlation between crAssphage and ARG molecular

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detections was high when all were measured using digital droplet PCR. Quantitative

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PCR and digital droplet PCR quantifications of crAssphage showed only moderate

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agreement, emphasizing the importance of detection technology when making

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quantitative comparisons. Overall, this study demonstrates the potential of a crAssphage

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fecal indicator to correlate with ARG occurrence when employing a ‘toolbox’ approach to

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fecal pollution management.

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TOC Art

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Introduction

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Bacterial resistance to antibiotics results in billions of dollars in direct healthcare

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costs per year, while deaths due to antibiotic resistance are expected to increase in the

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future, causing a predicted 10 million deaths per year globally by 20501. Genetic

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determinants of antibiotic resistance, i.e., antibiotic resistance genes (ARGs), are

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abundant in human fecal material and wastewater.2-6 Untreated wastewater can enter

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natural bodies of water from leaking sanitary sewer lines, leaking septage tanks, and

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during large rain events which cause combined sewers to overflow into natural systems.

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Environmental ARG presence has also become a concern for the introduction of ARGs

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to health-relevant microorganisms through horizontal gene transfer.7, 8

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Improved monitoring of ARGs in natural water systems can contribute to

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strategies to reduce ARG proliferation through the environment. A recent metagenomics-

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based study found correlation between total ARGs and the human gut bacteriophage

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crAssphage employed as a human-specific fecal indicator.9 CrAssphage is a proposed

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bacteriophage-based fecal pollution indicator that is highly human associated,10, 11

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abundant in sewage and sewage-impacted environments,10-12 and globally distributed13;

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however, the correlation of crAssphage abundance with ARGs has not yet been directly 3 ACS Paragon Plus Environment


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assessed. Co-occurrence of crAssphage with ARGs would strengthen the application of

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this indicator for fecal pollution management.

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The ARGs surveyed in this study were chosen to cover resistance to a wide

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variety of antibiotics (macrolides, sulfonamides, and tetracyclines), including how long

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they have been in use, their source, and application. Macrolides and tetracyclines were

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both originally isolated from Streptomyces, while sulfonamides were the first synthetic

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antibiotics in use6, 14. Macrolides (active against Gram-positive bacteria) and

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tetracyclines (broad spectrum activity) both inhibit bacterial growth by interfering with

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protein synthesis.6, 14 Sulfonamides are competitive inhibitors, preventing bacterial cells

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to replicate.15 In addition, macrolides have a high frequency of use in human medicine,

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while tetracyclines are used widely in agriculture as livestock growth promoters in

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addition to being used to treat human infections.14, 16 In addition, while integrons are not

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ARGs, they facilitate integration of plasmids or transposons that may contain ARGs into

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the bacterial genome4, 17, 18 and are thus included in this study. Class 1 integrons have

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been proposed as a marker for estimating anthropogenic impact in water systems.19

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Previously, we reported on culturable and molecular indicators of fecal pollution

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in an urban stream that receives human wastewater pollution through combined sewer

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overflows in a 30 day survey.12 In the present study, a suite of ARGs and crAssphage

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abundance were quantified by digital droplet PCR (ddPCR) on the samples collected in

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this previous study. Correlations between rain events, ARG levels, and crAssphage

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levels were analyzed to gain insight into the suitability of crAssphage as a indicator of

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ARG presence, as well as the contribution of combined sewer overflows to ARG

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abundance, in an environmental system. In addition, target quantifications using


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emerging ddPCR technology versus quantitative PCR (qPCR) were compared to provide

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insight into cross-platform comparisons.

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Materials and Methods

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Study Site

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Sampling was conducted at a small urban stream (Nine Mile Run) located in Frick Park

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in Pittsburgh, PA, USA. The site and sampling details of the present study have been

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described previously in a paper reporting chemical water quality parameters, culturable

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indicators, and qPCR assays for bacterial and viral markers of human fecal pollution.12

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Briefly, the stream was sampled daily for 30 days from September 6, 2016 through

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October 5, 2016. During sampling, a grab sample (three liters) was collected and

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processed daily. The surveyed stream has previously been shown to receive sewage

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pollution both through combined sewer overflow events during wet weather and through

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seepage of sewer lines during dry weather.12, 20 The sampling location used in this study

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was directly downstream of a combined sewer outfall to capture changes in stream

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quality due to this point source of pollution. Rainfall and CSO status were collected for

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each sample day (Supporting Information).

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ddPCR Assays

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A previously described protocol was used for the simultaneous concentration of viruses

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and bacteria from the stream water.12, 21, 22 Briefly, 500 mL of stream water was adjusted

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to pH=3.5 and filtered through a 47-mm 0.45-µm mixed cellulose HAWG filter (Millipore,

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Billerica, MA, USA). Duplicate filters for each sample were collected and frozen at -80°C.

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For bulk DNA extraction, filters were thawed to room temperature and DNA was 5 ACS Paragon Plus Environment


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extracted using a DNeasy PowerWater Kit (Qiagen, Valencia, CA, USA), following

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manufacturer’s protocol. Extracted DNA was stored at -20°C prior to analysis. As this

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study used legacy samples, care was taken to ensure optimal DNA integrity including

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preparation of fresh DNA dilutions before quantification.

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Previously published primers and probes for assays targeting sul123, sul223,

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tetO24, tetW24, ermF25, intI126, and crAssphage10 were used and adapted to the ddPCR

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platform for this study (see SI). Replicate filter samples were analyzed for each assay.

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ddPCR assay set up, primers and probes (Table S1), and controls are described in the

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supporting information. ddPCR performance statistics are shown in Table S2. We note

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that no inhibition was observed in samples previously using a qPCR internal

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amplification control.12 No amplification was observed in sample NTCs.

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Statistical Analyses

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Spearman’s rank correlation coefficients (r) were calculated on the means of duplicate

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values of each gene in GraphPad Prism 7 (La Jolla, CA, USA), using two-tailed 95%

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confidence intervals. Correlations were also generated between the data generated in

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this study and already published data of culturable and qPCR indicators.12 Multiple

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comparisons were corrected for using the Holm-Bonferroni method to adjust the p-value

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for each comparison. Coefficients are characterized by the following scale for

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comparison purposes: 0.2-0.39 (weak correlation), 0.4-0.59 (moderate correlation), 0.6-

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0.79 (strong correlation), and 0.8-1 (very strong correlation). Graphs were drawn in

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GraphPad Prism 7 using averages and standard deviations of data sets. Additionally,

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gene copy concentrations were compared between dry weather days and wet weather

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days by fitting to a general linear model ANOVA in Minitab 18 (State College, PA, USA). 6 ACS Paragon Plus Environment

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Multiple pairwise comparisons were corrected for using Bonferroni 95% confidence

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intervals. For comparisons between data generated with ddPCR and qPCR, individual

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filter values for ddPCR were plotted against the corresponding average filter value as

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measured with qPCR. Both ddPCR and qPCR were plotted with the error bars

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representing the 95% confidence intervals. The 95% confidence intervals for ddPCR

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data came from the Poisson distributions reported by QuantaSoft version 1.7. Linear

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regression was performed in GraphPad Prism 7 to calculate best fit lines of the data

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along with root mean square error (RMSE) values. This analysis is similar to previous

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comparisons between qPCR and ddPCR.27, 28 Rainfall is reported as the total amount of

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precipitation recorded in the 24 hrs before sampling. A sampling day was considered a

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wet weather sampling event if precipitation totaled at least 0.25 mm within the past 24

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hrs.

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Results and Discussion

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CrAssphage and ARG occurrence during the study period and correlation with pollution

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events

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The primary goal of the present study was to assess the correlation of crAssphage and

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ARG abundance in a sewage-impacted urban waterway to determine the utility of a

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crAssphage-based marker to indicate ARG abundance. The sampling location was

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immediately downstream of a combined sewer overflow (CSO) outfall. CSO events

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occur during wet weather when combined sanitary and storm water sewers receive an

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excess flow that must be discharged. In order to discern the effect of CSO events, the

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rainfall during the 30-day study period was tracked. In total, it rained 12 days, with 8 of



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those days reporting active CSOs. Total rainfall ranged from 0.51 mm to 25.65 mm of

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rain in a 24-hr period.

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Selected ARG concentrations were evaluated for each of 30 sampling days

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throughout the study to understand how rain events impacted their concentrations at the

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study location. Observed ranges of gene concentrations as quantified by ddPCR (per

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100 mL of sampled stream water) were 3.3-5.7 log10 copies sul1, 3.9-6.6 log10 copies

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sul2, 2.5-4.7 log10 copies tetO, 2.9-4.9 log10 copies tetW, 3.0-5.2 log10 copies ermF, 3.8-

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6.5 log10 copies intI1, and 3.0-5.2 log10 copies CPQ_056. Results for ARG copy number

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per volume are shown in Figure 1 along with plots of the daily rainfall throughout the

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study.


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Figure 1: Copy number (as measured by ddPCR) of ARGs normalized by volume along with daily rainfall during the sampling period. Rainfall is presented as the amount of rain that fell within the 24-hr period before that day’s sampling. Reported CSO events are distinguished by red asterisks. An open symbol on each graph represents an additional sampling time point (day 25) during an active CSO event. Data points represent averages of duplicate filters for each sampling time point and error bars represent standard deviations.

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The data were fit to an ANOVA general linear model analysis correcting for

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multiple comparisons with Bonferroni confidence intervals to compare total ARG and

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crAssphage copy number per volume of sampled water between dry and wet weather

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days. All molecular targets (ARGs and crAssphage) had significantly (p < 0.05) higher

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concentrations on wet weather days compared to dry weather days. No significant

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difference was observed between wet weather days with or without a correlating CSO

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event.

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This study evaluated levels of ARGs in an urban stream that receives human

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fecal pollution through combined sewer overflows during heavy rainfall. It has been

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reported previously that the sampled stream receives persistent human fecal

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contamination through leaking sanitary sewer lines in addition to CSOs.12, 20, 29 The study

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site was highly contaminated, exhibited by the observed concentrations of ARG genes

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on the same order of magnitude as previously observed in municipal wastewater.30 Also,

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no significant difference was observed between rain events associated with a reported

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CSO versus no reported CSO. It is possible that any amount of rain causes introduction

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of fecal contamination from nearby leaking sanitary sewers to enter the stream. Also, a

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CSO event may not have been reported even though a CSO event may have occurred

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at our sampling location. The absolute abundance of sulfonamide resistance genes was

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an order of magnitude higher than macrolide and tetracycline genes, agreeing with

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published studies demonstrating the predominance of sulfonamide resistance genes in

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wastewater.5, 30

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Correlation of antibiotic resistance genes and fecal indicators


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Spearman’s rank correlation coefficients were calculated between each pair of ARGs

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and crAssphage as measured by ddPCR (Figure 2, Table S3). In addition, correlation

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coefficients were calculated using previously published data of culturable indicators (E.

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coli, enterococci, and somatic coliphage) and qPCR markers of water quality (CPQ_056,

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CPQ_064, HF183, and Human Polyomavirus).12 All Spearman correlation coefficients

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showed statistical significance based on corrected p-values using the Holm-Bonferroni

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method with an overall p-value of 0.05 (pairwise p-values shown in Table S4). All

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correlation coefficients were positive. ARGs (except for intI1) and crAssphage as

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measured by ddPCR were very strongly correlated with each other. In addition, the

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bacterial markers of water quality (E. coli, enterococci, and HF183) strongly correlated

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with the ARGs. Interestingly, the marker CPQ_056 correlated very strongly with ARGs

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when it was measured with the same platform as the ARGs (i.e., ddPCR); in contrast,

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lower correlations were observed when CPQ_056 was measured with qPCR.



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Figure 2: Heat map of Spearman’s rank correlation coefficients matrix for ARG and crAssphage (denoted as CPQ_056_dd) concentrations as measured by ddPCR. Previous sample analysis from culturable indicators and qPCR markers are also included: culturable E. coli, culturable enterococci, somatic coliphage (SC), CPQ_056, CPQ_064, HF183 (Bacteroides qPCR marker), and human polyomavirus (HPyV).12 The correlations between these markers were not included in this figure as these correlations have previously been published12. Coefficients are colored based on the following scale of the absolute value of the coefficients: weak correlation (0.2 to 0.39), moderate correlation (0.4 to 0.59), strong correlation (0.6 to 0.79), and very strong correlation (0.8 to 1). Values of correlation coefficients and pairwise p-values are provided in Tables S3 and S4, respectively.

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This study demonstrated the correlation of various ARGs with commonly used fecal

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pollution indicators. The bacterial based markers (culturable E. coli, culturable 12 ACS Paragon Plus Environment

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enterococci, and HF183 qPCR assay) correlated very strongly with all ARGs. Based on

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the results of this study, the bacterial based markers could be an indicator of ARG

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presence in environmental waters due to human fecal pollution. However, the study site

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is heavily polluted by human sewage, likely leading to these strong correlations. E. coli

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and enterococci can grow and persist in environmental waters and sediments and thus

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are not inherently correlated with recent human fecal contamination.31-34 Further

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research is necessary to investigate if culturable fecal indicators correlate with ARG

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presence due to human fecal pollution in more pristine waters, where fecal bacteria may

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originate from multiple different sources. Due to different properties of various fecal

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indicators (e.g. different abundances of indicators and varying specificities to human

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sources), it is suggested that environmental waters are monitored using a toolbox

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approach, where many indicators of water quality are measured concurrently. It is

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important to monitor for a variety of parameters, as both bacteria and viruses are

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important to different aspects of water quality. For example, bacteria harbor resistance

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genes that they can spread through horizontal gene transfer, while bacteriophages can

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also contribute to the spread of resistance genes through transduction. Therefore, an

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environmental water quality monitoring toolkit should include diverse targets, e.g., a

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bacterial and viral target, and potentially other relevant markers to encompass the

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diverse potential sources of fecal contamination and disease-causing microorganisms.

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Comparison of crAssphage quantification by ddPCR and qPCR

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CrAssphage was measured using ddPCR in the current study. ddPCR is an emerging

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water quality monitoring technology that is suggested to allow improved estimation of

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low copy number genes with less inhibition due to environmental sample matrix.35 13 ACS Paragon Plus Environment


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ddPCR also does not require standard curve generation, which is a potential source of

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inaccuracy or bias in studies employing qPCR-based approaches. However, ddPCR

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also has a lower dynamic range of quantification than qPCR, requiring high

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concentration samples to be diluted to be read accurately36. The same DNA samples

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were previously used to measure crAssphage abundance as qPCR assays.12

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Descriptions of the qPCR assay performance metrics have previously been published.10,

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12

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of crAssphage measured by qPCR (CPQ_056 and CPQ_064) was plotted against the

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concentration measured by ddPCR (as CPQ_056) for each time point along with the

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respective 95% confidence intervals (Figure 3). The data was fit to two linear regression

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models, which exhibited similar slopes and intercepts but with different goodness of fit.

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Based on the same scale used to measure Spearman correlation coefficients, CPQ_064

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exhibited a strong correlation with the ddPCR data (R2 = 0.57) while CPQ_056 exhibited

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a moderate correlation (R2 = 0.27). In addition, the root mean square error (RMSE) is

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0.30 log10 copies for CPQ_064 and 0.53 log10 copies for CPQ_056. The two trendlines

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have similar slopes (0.79 and 0.75 for CPQ_064 and CPQ_056, respectively), showing

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that for the same samples, a wider concentration range was observed using ddPCR than

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qPCR. Moreover, the crAssphage concentration measured by qPCR was higher than the

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concentration measured with ddPCR as interpreted from the y-intercept of the linear

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regression lines (yintercept = 1.7 log10 copies for both lines). On average, qPCR resulted in

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a six times higher concentration for the CPQ_056 assay and 8 times higher

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concentration for the CPQ_064 assay. We note that previous studies found a higher

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concordance between ddPCR and qPCR than observed here,27, 28 and that further

In order to understand the differences between these two platforms, the concentration


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research is necessary to elucidate the differences observed between ddPCR and qPCR

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quantification in environmental matrices.

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Figure 3. CPQ_056 and CPQ_064 concentration measured by qPCR plotted against the CPQ_056 concentration measured by ddPCR. ddPCR concentrations are plotted separately for each of duplicate filters with 95% confidence intervals. The separate ddPCR filter concentrations are paired with the corresponding qPCR concentrations plotted as averages of triplicate values measured for each filter along with 95% confidence intervals.

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Interestingly, CPQ_056 abundance correlated very strongly with ARG abundance

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when both were measured using ddPCR (rSpearman = 0.84-0.89), in contrast with lower

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correlation when CPQ_056 was measured with qPCR (rSpearman = 0.40-0.53). It has 15 ACS Paragon Plus Environment


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previously been shown that qPCR can overestimate or underestimate gene copy

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numbers, possibly due to the reliance on a standard curve. Proper qPCR standard

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generation and monitoring of performance metrics reduces negative effects of the

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standard; however, the potential for error still exists, such as the assumption that the

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standard curve amplifies DNA with the same efficiency as environmental samples.27, 35

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Likewise, ddPCR can underestimate concentrations due to variance in droplet size,

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causing multiple gene copies to occupy the same droplet.36 These results emphasize the

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importance of standardized protocols to enable quantitative comparisons between study

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targets. In addition, these results highlight potential pitfalls around cross-study

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quantitative comparisons, especially when differing molecular quantification approaches

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are employed.

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Implications and future research

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The study goal was to assess the co-occurrence of crAssphage with ARG presence in a

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sewage-impacted urban stream. CrAssphage abundance significantly correlated with

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various ARGs representing a range of resistance mechanisms, natural versus synthetic

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origin, and time since antibiotic introduction into the medical field. These results suggest

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that crAssphage could potentially be used to predict ARG presence in waters impacted

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by human fecal pollution. Additional studies are necessary to establish the association of

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crAssphage with ARGs in more pristine waters as well as water bodies impacted by

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other fecal pollution sources (e.g., agriculture). While more research should be

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conducted in additional water systems to further elucidate the range of correlation

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between crAssphage and ARG abundance, the correlations explored in this paper

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broaden the range of potential applications for crAssphage-based assays. This study 16 ACS Paragon Plus Environment

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also demonstrates the importance of method standardization (i.e., using the same

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instrumentation and protocols) and cautions against quantitative comparison of

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molecular fecal pollution indicators measured by different technologies. Further research

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is necessary in order to discern if there is one platform that is superior. In addition,

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further validation of ddPCR is necessary for ARG detection, as the majority of studies to

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date have been conducted using qPCR, confounding comparisons between studies. The

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continuing rise of antibiotic resistance in the environment will require constant vigilance.

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This study suggests a single fecal pollution indicator organism, crAssphage, correlates

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with various diverse ARGs with the potential to inform and improve monitoring efforts.

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SUPPORTING INFORMATION

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Supporting Information. Additional methods, data values, and p-values of Spearman’s

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rank correlation coefficients.

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CONFLICT OF INTEREST

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The authors declare the following competing financial interest(s): The primers reported in

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the manuscript are the subject of a patent application entitled “Cross-Assembly Phage

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DNA Sequences, Primers and Probes for PCR-based Identification of Human Fecal

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Pollution Sources” (Application Number: 62/386,532). Universities and non-profit

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researchers interested in using this technology must obtain a research license from the

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USEPA.

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from [email protected]. The authors declare no other conflict of interest.

To apply for a research license, please request additional information

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ACKNOWLEDGEMENTS 17 ACS Paragon Plus Environment


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This material is based upon work supported by the National Science Foundation

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Graduate Research Fellowship Program under Grant No. 1747452 and NSF Grant No.

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1510925. Any opinions, findings, and conclusions or recommendations expressed in this

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material are those of the author(s) and do not necessarily reflect the views of the

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National Science Foundation.

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REFERENCES

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Co-Occurrence of crAssphage with Antibiotic Resistance Genes in an

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