Article pubs.acs.org/est

Bacterial Pathogen Gene Abundance and Relation to Recreational Water Quality at Seven Great Lakes Beaches Ryan J. Oster,*,† Rasanthi U. Wijesinghe,† Sheridan K. Haack,† Lisa R. Fogarty,† Taaja R. Tucker,‡ and Stephen C. Riley§ †

U.S. Geological Survey, Michigan Water Science Center, Lansing, Michigan 48911, United States CSS-Dynamac, 10301 Democracy Lane, Suite 300, Fairfax, Virginia 22030, United States § U.S. Geological Survey, Great Lakes Science Center, Ann Arbor, Michigan 48105, United States ‡

S Supporting Information *

ABSTRACT: Quantitative assessment of bacterial pathogens, their geographic variability, and distribution in various matrices at Great Lakes beaches are limited. Quantitative PCR (qPCR) was used to test for genes from E. coli O157:H7 (eaeO157), shiga-toxin producing E. coli (stx2), Campylobacter jejuni (mapA), Shigella spp. (ipaH), and a Salmonella enterica-specific (SE) DNA sequence at seven Great Lakes beaches, in algae, water, and sediment. Overall, detection frequencies were mapA>stx2>ipaH>SE>eaeO157. Results were highly variable among beaches and matrices; some correlations with environmental conditions were observed for mapA, stx2, and ipaH detections. Beach seasonal mean mapA abundance in water was correlated with beach seasonal mean log10 E. coli concentration. At one beach, stx2 gene abundance was positively correlated with concurrent daily E. coli concentrations. Concentration distributions for stx2, ipaH, and mapA within algae, sediment, and water were statistically different (Non-Detect and Data Analysis in R). Assuming 10, 50, or 100% of gene copies represented viable and presumably infective cells, a quantitative microbial risk assessment tool developed by Michigan State University indicated a moderate probability of illness for Campylobacter jejuni at the study beaches, especially where recreational water quality criteria were exceeded. Pathogen gene quantification may be useful for beach water quality management.

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INTRODUCTION Fecal indicator bacteria (FIB) and pathogenic microorganisms affect water quality at recreational beaches throughout the Great Lakes.1−9 To prevent pathogen exposure, determining the influence of matrices like Cladophora glomerata (a filamentous green alga) and sediment is critical for understanding bacterial pollution in water at recreational beaches. The U.S. Environmental Protection Agency’s (USEPA) recreational water quality criteria (RWQC) rely on culturing FIB10 and concerns over culturing time and whether FIB accurately reflect contamination by specific pathogenic microbes are often discussed.1,4,11,12 It is likely FIB-based beach advisories are posted after the highest risk for exposure has occurred. Rapid molecular methods like quantitative polymerase chain reaction (qPCR) can significantly reduce time for processing and posting advisories.1 Quantifying specific genes from pathogenic microorganisms is an advantage to using qPCR over presence/absence (P/A) PCR, as P/A PCR can only estimate relative abundance. Comparing quantifiable abundances of genes with land use, beach characteristics, and environmental variables is another benefit to using qPCR. Nevertheless, uncertainties also need to be considered with © 2014 American Chemical Society

qPCR, such as the inability to distinguish DNA from live as opposed to dead, or viable but unculturable, cells.13 For example, a qPCR method can be applied to monitor enterococci at recreational beaches according to USEPA RWQC.10 Using this method, criteria based on qPCR-based calibrated cell equivalents of enterococci are 1−2 orders of magnitude greater than enterococci abundances based on culture.10 Determining qPCR-based abundances of bacterial pathogen genes is an important first step for understanding beach water quality, interpreting pathogens relationships to FIB, and evaluating how to minimize the health risk for beachgoers by using tools such as quantitative microbial risk assessment (QMRA), and for designing future studies. QMRA estimates infection probability using dose−response models14 and may be useful for interpreting qPCR data to estimate health risk from exposure to specific pathogens. The goals of QMRA include investigating areas of concern, Received: Revised: Accepted: Published: 14148

January 14, 2014 November 13, 2014 November 14, 2014 November 25, 2014 dx.doi.org/10.1021/es5038657 | Environ. Sci. Technol. 2014, 48, 14148−14157

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Table 1. Beach Names, IDs, Locations, and Transect Lengths of Beaches in This Study beach

beach IDa

latitude, longitude

lake

state

transect length (m)

Deland Park Jeorse Park Portage Lakefront: Indiana Dunes National Lakeshore Sleeping Bear Dunes National Lakeshore: Esch Rd/Otter Creek Brimley State Park Bay City Recreation Area Maumee Bay State Park

WI949936 IN319633

43.758461 N, −87.702118 W 41.64936 N, −87.43324 W 41.630630 N, −87.181633 W 44.76207 N, −86.07634 W 46.417309 N, −84.555489 W 43.67138 N, −83.90676 W 41.6858 N, −83.3781 W

Michigan Michigan Michigan Michigan Superior Huron Erie

WI IN IN MI MI MI OH

829.3 788.1 375.1 777.3 625.5 550.0 238.6

a

MI001811 MI001552 MI000290 OH182884

Beach IDs are associated with state Beach Guard data.

Wisconsin. E. coli samples were collected at the same beach locations and time range (8 am-12 pm), but not always on the same dates, as the pathogen samples we collected. RWQC [seasonal geometric mean (GM) and statistical threshold value (STV)] were calculated using all available E. coli data for each beach, and were evaluated against the seasonal mean gene copies (GC) or total number of detections for each gene. We used available E. coli data that matched our sampling dates for assessment of relationships between pathogen gene occurrence and RWQC, except at Sleeping Bear-Esch Road (no matching E. coli samples) and Portage Lakefront (only 2 matching samples). Sample Collection and Processing. Samples were collected one or more times per week from May to September 2012, along transects (Table 1) starting at the middle of the beach with samples taken equidistant (100−400 m) from the middle transect. Pathogen gene analysis was typically from the middle transect, unless it did not offer all three matrices. Using a sterile tongue depressor, algae (predominantly Cladophora) was scooped into a sterile Whirl-pak bag, until 3/4 of the bag was filled. Water was retrieved at waist depth halfway between the water surface and the lake bottom in sterile Whirl-pak bags. Lake bottom sediment was collected in knee depth water by forcing a sterile 50 mL tube 3/4 of the way down into the sediment. Samples were stored on ice in the field and frozen (−20 °C) the same day upon return to the laboratory. Water was mixed thoroughly, 100 mL was filtered through a 0.45 μm Millipore Isopore filter using Sterile Millipore Microfil disposable filter cups (EMD Millipore, Billerica, MA), then the filters were frozen (−20 °C) immediately. Dry weights were obtained from each sediment sample and from 10% of algae samples (representing a range of different beaches and dates, chosen randomly). DNA Extraction. DNA from water filters was extracted by using an UltraClean Soil DNA Isolation Kit (MoBio Inc., Carlsbad, CA). The filters were placed into the bead tubes and the tubes were placed in a mini bead beater for 1 min 30 s, in place of the horizontal shaker recommended in the kit instructions. DNA from sediment and Cladophora was extracted using PowerMax Soil DNA Isolation Kits (MoBio Inc., Carslbad, CA). For both sediment and Cladophora samples, the horizontal bead beating time was increased to 15 min. Sediment was first homogenized in a plastic weigh dish, and 9.5−10.0 g of material was weighed directly in a bead tube provided with the kit. The total amount of sample extracted was recorded and used for determining the number of gene copies per gram of dry weight (copies gdw−1) from wet:dry ratios. Cladophora was processed by transferring an ∼5.0 g (wet weight) subsample to a 50 mL plastic tube. A Cladophora slurry was made by adding 5 mL of liquid from the Cladophora sample bag, or sterile PBS, to the 50 mL tube and the sample

remediation, making regulatory decisions, or confirming whether regulations are being met.14 Limitations to using this method exist. Few studies have applied qPCR gene abundance data to QMRA to characterize risk to bathers in recreational areas, and as qPCR-generated gene abundances often exceed cultured cell counts,10 it remains unclear how qPCR-based gene abundances might be used in QMRA at beaches. Temporal and spatial variability demonstrated in FIB studies at beaches, environmental factors, and data gaps in hydrodynamics also can confound QMRA interpretation.15 An online QMRA tool developed by Michigan State University’s (MSU) Center for Advancing Microbial Risk Assessment16 could potentially be used by beach managers to evaluate bacterial pathogen gene abundances from Great Lakes beach water, as an aid to beach management and remediation.15 The occurrence of FIB in beach matrices throughout the Great Lakes has been extensively studied.1,3,6,7,11,17−20 However, quantification of bacterial pathogens has been limited in comparison.4,6,18,21 This study is one of the first extensive geographic comparisons of bacterial pathogen gene abundances in a variety of beach matrices in the Great Lakes. At seven Great Lakes beaches during the 2012 swimming season, and in three environmental matrices (water, sediment, and algae), our study tracked the occurrence of five genes: stx2, eaeO157, ipaH, mapA that are commonly associated with shiga-toxin 2 producing E. coli, E. coli O157:H7, Shigella spp., Campylobacter jejuni, respectively, and an uncharacterized DNA sequence specific for Salmonella enterica (hereafter SE). All of these microorganisms are known to cause water-borne gastrointestinal (GI) illness in humans.6,22 Gene abundances were related to RWQC at individual beaches and were evaluated against a range of environmental parameters that might influence results. Finally, we used qPCR gene abundance data from our analyses in a previously developed QMRA tool16 to estimate illness risk at beaches categorized by meeting or failing RWQC. Our study provides important information regarding the geographic distribution of pathogen genes in the beach environment among various matrices, with respect to RWQC, and shows how gene abundances generated from qPCR can potentially be applied to tools such as QMRA.

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MATERIALS AND METHODS Sampling Locations and Environmental Data. Seven Great Lakes beaches on federal land, or in USEPA-designated Areas of Concern (AOCs) with beach water quality impairments, were analyzed (Table 1). Land cover within the beach catchment and other beach characteristics are reported in the SI. A total of 40 seasonal and 27 temporal parameters were analyzed with relation to pathogen gene abundance (SI Table S1). E. coli monitoring data were obtained from public beachnotification web sites for Indiana, Michigan, Ohio, and 14149

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template. A melt curve was produced in each SYBR Green assay to verify the specificity of the primer set, and 2.2% agarose gels (Lonza Group Ltd., Switzerland) were run on random samples to determine that the correct product size was produced for the respective assays. Assay master mix for the TaqMan assays was comprised of the following: 12.5 μL of TaqMan Environmental Master Mix (Life Technologies, Foster City, CA), 0.5 μL each of 10 μM forward and reverse primer, 0.5 μL of 10 μg/μL BSA, 5 μL of DNA template, and nuclease-free water (5.75 μL for mapA; 5.0 μL for ipaH). The final probe concentration for mapA was 100 nM (0.25 μL of 10 μM probe) and 160 nM (1.0 μL of 4 μM probe) for the ipaH assay. To verify expected amplicon base pair (bp) product size (SI Table S2), 2.2% agarose gels (Lonza Group Ltd., Switzerland) were run using random amplified DNA from qPCR reactions. Limits of detection (LOD) and limits of quantification (LOQ) were calculated for each assay. The limit of detection (LOD) was calculated based on the Ct values for the compiled nontemplate controls (NTC). The 95% confidence value for the NTC average when used in the compiled standard curve equation, generated LODs of less than the theoretical limit of detection11 of 3 copies μL−1. Thus, the LOD was conservatively set at the theoretical limit, resulting in LODs of 15 copies per 5 μL reaction. The lowest standard concentration where 95% of the compiled values were detectable was considered the LOQ95. The LOQ95 for each of the assays in this study was 50 copies per 5 μL reaction. Individual standard curves were used to calculate GC. Four to 10 copies of the ipaH gene occur on chromosomal and plasmid DNA,27 thus, for comparison with results for other genes, and for use in QMRA, all ipaH copy numbers were adjusted assuming 5 GC per cell.28 For all water samples the LOD and LOQ were, respectively, 150 and 500 GC 100 mL−1 for algae, 3 × 103 and 1 × 104 GC mL−1 and for sediment, 1231 GC and 4105 GC gdw−1. Data were classified as quantifiable (Q), detectable but not quantifiable (DNQ), and nondetectable (ND). A sample was classified as Q if two or more replicates were above the LOQ, DNQ if two or more replicates were between the LOD and LOQ, and ND if two or more replicates were below the LOD. Acceptable individual standard curves had efficiencies from 85% to 115% and R2 above 0.95. Samples were rerun if standard curves did not meet these criteria. Data Reporting. We report GC per 100 mL beach water because this is the convention for reporting RWQC based on cultured FIB or as numbers of calibrated cell equivalents for enterococci (as derived by qPCR using EPA Method 1611).10 FIB, pathogen, and gene densities in Great Lakes Cladophora samples have been reported per mL, or per g wet or dry weight. Lacking a convention, we chose “per ml” so that Cladophora results could be compared to water results more directly. The average dry weight of the algal slurry was 3.8 gdw 100 mL−1 thus our results can also be compared to those of prior studies using a dry weight convention. Statistical Analyses. Due to the high percentage of left censored data (data below the LOD and LOQ) the NonDetects and Data Analysis (NADA) statistical package in R29 was used to determine differences in gene distribution among the three matrices. This analysis uses nonparametric Kaplan− Meier methods to generate empirical cumulative distribution functions (ECDFs) and estimate summary statistics.30,31 The censtats function estimated means and standard deviations for the matrices of each gene using three models: Kaplan−Meier,

was placed on a horizontal vortex mixer for 30 min. Three milliliters of the homogenized Cladophora slurry was used for DNA extraction, and dry weight was determined from 10% of these aliquots. Extraction blanks for water were prepared by extracting a 0.45 μm membrane filter with 100 mL of sterile PBS buffer using the UltraClean Soil DNA isolation kit (MoBio Inc., Carlsbad, CA). Cladophora and sediment extraction blanks were prepared using a PowerMax Soil DNA isolation kit (MoBio Inc., Carlsbad, CA) with nothing extra added to the extraction. All DNA concentrations were read on a NanoDrop 2000 UV−Vis Spectrophotometer (Thermo Scientific, Wilmington, DE). Quantitative Polymerase Chain Reaction (qPCR). QPCR was performed using a StepOne Plus qPCR thermal cycler (Life Technologies, Grand Island, NY). Gene fragments were amplified from E. coli (ATCC 35150), Shigella sonnei (ATCC 9290), Campylobacter jejuni subsp. jejuni (ATCC 29428), and Salmonella enterica subsp. enterica (ATCC 14028) genomic DNA. Plasmids used for standard curves were generated using a TOPO TA Cloning Kit (Life Technologies, Grand Island, NY) followed by a Promega PureYield MiniPrep (Promega, Madison, WI), both according to the manufacturer’s instructions. Plasmid DNA concentration was measured with a NanoDrop ND-1000 UV spectrophotometer, and the gene copy numbers were determined using the formula: gene copy no. = 6.02 × 1023 × (plasmid DNA concentration) × (ng/μL) × template DNA (μL)/(molecular weight of plasmid DNA+ molecular weight of gene insert) × (g/mol). Plasmid molecular weight was from the TOPO 2.1 cloning vector product manual. A 5-point standard curve was included on each qPCR plate. All samples, standards, and controls were run in triplicate in a 25 μL reaction volume. Inhibition tests were performed on 10% of the extracts from each matrix by running undiluted, 1:10, and 1:25 diluted extracts and comparing Ct (threshold cycle) values between the dilution factors. To assess matrix inhibition, in most runs, a 1000 copy plasmid standard (2.5 μL) was spiked into 2.5 μL of extract that had previously tested negative for the gene of interest (all matrix types). To test the efficiency and accuracy of the standard curve for each assay, a control containing the 1000 copy standard was run as an unknown sample. A control DNA extraction blank was also tested on several qPCR runs. Three qPCR assays (stx2, eaeO157, SE) used SYBR Green chemistry and were based on previously described methods.23−25 Two assays (ipaH and mapA) used TaqMan chemistry and were based on previously described methods.25,26 Slight modifications to the original assays included reduced cycle number for ipaH and SE (40 cycles), and reduction (ipaH) or increase (SE) by 2 degrees in annealing temperature, as determined to be optimal using the VeriFlex block option on the StepOne Plus qPCR thermal cycler. Additionally, an extra final step at 81 °C for the SE assay was used as the data acquisition step to limit the influence of a minor second peak in the final fluorescence. The second peak was not present as a DNA product on a 2.2% agarose gel, indicating proper amplification of the desired 261 bp product. Reaction conditions and primer sequences are in Supporting Information (SI) Table S2. All assay master mixes using SYBR Green chemistry were comprised (per reaction) of the following: 12.5 μL of SYBR Green PCR Master Mix (Life Technologies, Foster City, CA), 0.5 μL each of 10 μM forward and reverse primer, 0.5 μL 10 μg/μL BSA, 6.0 μL nuclease free water, and 5 μL of DNA 14150

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Table 2. Detection Frequencies (%) of Pathogen Genes in Algae, Water, and Sediment at Individual Beachesa matrix

n

stx2

eaeO157

ipaH

mapA

Salmonella enterica

Deland Park

algaeb water sediment

9 14 16

11.1 57.1 12.5

0.0 0.0 0.0

0.0 0.0 0.0

11.1 14.3 18.8

0.0 0.0 0.0

Jeorse Park

algae water sediment

11 13 15/14ac

45.5 0.0 6.7

36.4 15.4 26.7

0.0 0.0 0.0

18.2 100.0 93.3

0.0 0.0 7.1a

Portage Lakefront

algae water sediment

7 10 10

0.0 0.0 0.0

0.0 0.0 50.0

14.3 90.0 20.0

100.0 100.0 100.0

0.0 10.0 0.0

Sleeping Bear Dunes-Esch Rd./Otter Creek

algae water sediment

7/6b 5 10

0.0 100.0 0.0

0.0 20.0 0.0

42.9 0.0 0.0

50.0b 40.0 50.0

0.0 0.0 0.0

Brimley State Park

algae water sediment

3 12 14

0.0 100.0 14.3

0.0 25.0 21.4

0.0 0.0 0.0

0.0 91.7 0.0

0.0 0.0 7.1

Bay City Recreation Area

algae water sediment

11/10c 8 9

90.9 100.0 0.0

0.0c 0.0 0.0

36.4 87.5 66.7

50.0c 50.0 88.9

63.6 62.5 0.0

Maumee Bay State Park

algae water sediment

11 13/12d 12

0.0 0.0 0.0

0.0 8.3d 8.3

36.4 92.3 41.7

90.9 83.3d 16.7

0.0 0.0 8.3

total

algae water sediment

59/58e/57f 75/74g 86/85h

27.1 44.0 5.8

6.9e 9.5g 15.1

20.3 37.3 15.1

49.1f 70.3g 48.8

11.9 8.0 35.h

beach location

a

For some genes in matrices from specific beaches, samples may not have been run due to the lack of DNA extract. bAlgae = Unidentified algae or Cladophora. cSuperscript letters correspond to total samples (n) for matrices at beaches where total samples analyzed varied from others.

best fit model with an LD50/ID50 of 8.9 × 102 required for infection and the optimized parameters were α = 1.44 × 10−1 and N50 = 8.9 × 102,32 for Salmonella enterica a beta-Poisson model based on Salmonella Typhi with parameters α = 1.75 × 10−1 and LD50/ID50 = 1.11 × 106 was selected,33 a betaPoisson model with parameters α = 2.65 × 10−1 and N50=1.48 × 103 was selected for Shigella.34 Humans were the host for infection in all three models. An assessment was not completed for E. coli (stx2) or E. coli O157:H7 because the tool did not have models for enterohemorrhagic E. coli specific to human hosts. Assessments were based on the seasonal average GC mL−1 (using 1/2 LOD or LOQ for ND or DNQ samples) for water samples at each beach location. Doses were calculated by multiplying the GC ml−1 assuming 16 mL consumed by adults and 37 mL by children in 45 min.35 We assumed that each gene copy represented one cell except for ipaH where 5 GC or more can be present; however, as QPCR does not distinguish between viable and nonviable organisms or infectious organisms, dose response models were tested with dose ranges assuming 10%, 50%, or 100% of the mean GCs were from viable and infectious organisms. After obtaining infection probability from the QMRA tool, an illness probability was calculated by multiplying the infection risk by a morbidity factor (% of infections resulting in illness) for each organism.36 Shigella has an approximate morbidity of 15%,37 Campylobacter jejuni 28%,36 and Salmonella enterica

regression on order statistics (ROS), and maximum likelihood. Boxplots were generated in NADA, using the default ROS, considered better at estimating summary statistics and modeling distributions of censored data.30 The cendiff function tested the null hypothesis that the ECDF distribution among the three matrices was identical and uses the Peto−Peto test, which is appropriate for left-censored log-normal data sets often observed in environmental data.30 Statistical analyses were also performed using SigmaPlot 12.0 (Systat Software Inc., San Jose, CA), using log10 transformed gene abundances. The Mann−Whitney U statistic was used to test for gene abundance differences among matrices in SigmaPlot. Pearson correlation and linear regression were used to determine the relationship between seasonal mean or daily pathogen gene abundances, seasonal mean E. coli abundance, and relation to seasonal or daily environmental characteristics. The seasonal and daily environmental parameters evaluated are described in SI Table S1. In all cases, results were considered statistically significant when p < 0.05. Quantitative Microbial Risk Assessment (QMRA). A QMRA tool developed by MSU’s Center for Advancing Microbial Risk Assessment16 was used to further assess qPCR gene abundance data to estimate infection risk based on established dose−response models for Shigella, Salmonella, and Campylobacter jejuni. The following model parameters were described in the tool:16 Campylobacter jejuni was a beta-Poisson 14151

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Figure 1. Comparison of log10 pathogen gene copy data distributions using NADA statistical analysis in R between Cladophora, sediment, and water matrices. Water is expressed as log10 GC 100 mL−1, Cladophora as log10 GC mL−1, and sediment as log10 GC per gram dry weight (gdw−1). Panel A shows stx2 abundance, panel B eaeO157 abundance, panel C ipaH abundance, and panel D mapA abundance. The horizontal black line in the box is the median value. ALG = algae (predominantly Cladophora), SED = sediment, WAT = water.

20%.36 An illness risk threshold of 0.03 was used to assess the significance of our results from the QMRA tool.15,36 Beaches were categorized into three groups based on the RWQC: in group A both the statistical threshold value (STV) and geometric mean (GM) RWQC were exceeded; in group B beaches either the STV or GM were exceeded; and in group C beaches the RWQC were not exceeded.

detection frequencies, as could the amounts of material extracted, and methods used. Stx2 was not as frequently detected within the sediments as in Cladophora, and lake water had the greatest detection frequency (Table 1). EaeO157 was the least commonly detected gene among all of the beaches studied. IpaH was detected at four of seven beaches, was only detected in the water at beaches where it was also detected in the sediment and Cladophora, and detection frequency of ipaH in water and sediment was positively correlated (R = 0.89; p = 0.007; n = 7; df = 6). Of the five pathogen genes, mapA occurred most frequently in all matrices. While there was no statistical correlation between sediment and water concentrations for the mapA gene, the gene was often detected in sediment and water samples at Jeorse Park, Portage Lakefront, and Bay City at the same time (SI Tables S4−S7). Quantification of Pathogen Genes in Cladophora, Sediment and Water. Figure 1 shows NADA-determined gene copy number distributions across all beaches for each gene and matrix. Except in a few cases, NADA-predicted means were similar to the means generated using only quantifiable samples (provided for reference in SI Figure S2). NADA analysis based on ECDFs estimated the mean stx2 abundance to be 5.4 log10 GC ml−1 in algae, 4.7 log10 GC ml−1 in sediment, and 4.3 log10 GC 100 mL−1 in water. A Chi-square test comparing the ECDF data distributions between the matrices showed a significant difference (p < 0.001; Figure 1A). NADA statistics based on ECDFs estimated the mean eaeO157 abundance to be 4.7 log10 GC ml−1 algae, 4.1 log10 GC gdw−1 sediment, and 3.7 log10 GC 100 mL−1 of water. However, only one water sample had quantifiable GC of eaeO157 and no significant difference in the data distribution among the matrices was observed (Chisquare; p = 0.4; Figure 1B). The NADA-predicted ipaH mean based on the ECDFs was 3.9 log10 GC ml−1 for algae, 4.0 log10 GC gdw−1 sediment, and 3.2 log10 GC 100 mL−1 water, and the

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RESULTS Quality Assurance. Data for individual and compiled standard curves from all qPCR runs of acceptable quality are in SI Table S3. Quantitative data for all analyses can be found in SI Tables S4−S7. For all assays the compiled NTCs and extraction blanks had Ct values between 38 and 40. Samples were rerun if average Ct values for NTCs or blanks were 2 Ct difference from undiluted samples) and all subsequent calculations accounted for this dilution factor. Matrix spikes generally did not deviate more than 10% from the expected value (500 GC) in any of the assays and GCs were never adjusted. The deviation could be due to both pipetting error and slight inhibition. Detection of Pathogen Genes in Cladophora, Sediment, and Water. The detection of pathogen genes (quantifiable and DNQ results) among the three matrices was variable and highly beach specific (Table 2). Two to four genes were normally detected at each beach, but the gene type varied and no beach had all genes present. Overall, there was a tendency for greater detection frequencies of stx2, ipaH, and mapA in water samples than in Cladophora and sediment, but this pattern did not necessarily hold at individual beaches, and there was no trend for the eaeO157 or SE genes. The lower LOD for water samples than for sediment and algae could influence 14152

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Table 3. Correlations Among Gene Detection Frequencies or Concentrations and Environmental Variablesa gene stx2 seasonal detection frequency daily log10 GC daily log10 GC daily log10 GC daily log10 GC mapA seasonal detection frequency daily log10 GC daily log10 GC ipaH daily log10 GC daily log10 GC a

Pearson R

probability (p)

number (n)

no. point sources on beach mean air temp (24 h) depth averaged water velocity antecedent 24 h wind speed water level

0.78 −0.63 0.36 0.35 −0.33

0.038 0.001 0.002 0.01 0.01

7 74 72 51 57

sediment sediment Cladophora

seasonal mean wind speed at beach daily magnitude of water velocity water level

0.86 −0.37 0.30

0.014 0.001 0.02

7 73 56

sediment Cladophora

daily magnitude of water velocity antecedent 24 h wind speed

−0.26 0.37

0.03 0.008

74 51

matrix

variable

water water sediment Cladophora Cladophora

Definitions and sources of environmental data in SI Table S1.

Table 4. Probability of Illness for Adults Assuming 10%, 50%, and 100% Bacterial Cell Viability and Infectivity for Pathogen Genes Shigella spp. (ipaH)

Salmonella enterica

Compylobacter jejuni (mapA)

beach group type

10%a

50%a

100%a

10%

50%

100%

10%

50%

100%

Ad Be B Cf C C C

0.000b 0.000 0.006 0.016 0.030 0.000 0.000

0.002 0.002 0.023 0.044 0.062 0.002 0.002

0.004 0.004 0.036c 0.058 0.076 0.004 0.004

0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.001 0.000 0.000

0.163 0.055 0.059 0.060 0.014 0.010 0.010

0.187 0.097 0.100 0.102 0.038 0.028 0.037

0.196 0.114 0.116 0.118 0.055 0.043 0.052

a

Assuming 10%, 50%, and 100% of the gene copies are from viable and infectious cells. bIllness probability. cBold numbers exceed a risk concern threshold of 0.03, as recommended by Ashbolt et al. 2010.15 dBeach group type A; both seasonal GM and STV exceeded recreational water quality criteria. eBeach group type B; either seasonal GM or STV exceeded recreational water quality criteria. fBeach group type C; seasonal GM or STV did not exceed recreational water quality criteria. gMean doses were calculated by multiplying the GC ml−1 by 16 mL (estimated volume of water ingested by adults),35 multiplied by the assumed doses of viable and infectious cells (10%, 50%, 100%) and these doses were evaluated with their respective dose−response models to give the probability of infection. Infection probabilities were then multiplied by morbidity factors to estimate the probability of illness.

beach. Between 15 and 100% of the total detections were DNQ depending on the gene and matrix (SI Tables S4−S7). Associations with Environmental Conditions. Variable environmental conditions at these beaches likely play a role in gene detections or concentrations. We observed some correlations between environmental variables such as water level, wind speed, and temperature, and detection frequencies or concentrations of the mapA, stx2, and ipaH genes (Table 3). Notably, those are the genes that were detected most frequently, suggesting that the highly variable and relatively infrequent detection of genes in our study limited the ability to fully evaluate the influence of environmental variables, and that more intensive sampling regimes would be needed in future studies. Fecal Indicator Bacteria and Gene Relationships in Water. At Brimley State Park, daily log10 E. coli (stx2) abundance in water was correlated to the concurrent daily log10 E. coli most probable number (MPN) (R = 0.69; p = 0.059; n = 8; df = 7). No other beaches in this study showed this same temporal relationship with any other gene. At two of the beaches (Portage Lakefront and Sleeping Bear-Esch Road) too few daily E. coli values were collected on the same day as the pathogen samples to make this comparison. Seasonally, mapA occurred in higher abundance at beaches where E. coli abundance was greater. The seasonal mean log10

Chi-square test showed a moderate significant difference between their data distributions (p = 0.07; Figure 1C). The mean mapA concentrations for each matrix determined by NADA were 5.4 log10 GC ml−1 algae, 4.6 log10 GC gdw−1 sediment, and 5.3 log10 GC 100 mL−1 water, and a significant difference between their data distributions was observed (Chisquare; p = 0.03; Figure 1D). Bay City Recreational Area was the only beach that had quantifiable concentrations of SE in any of the matrices; the detections at the other beaches were all DNQ. The NADA-predicted SE mean was 4.5 log10 GC mL−1 of algae and was 2.9 log10 GC 100 mL−1 in water. Mean SE abundance for sediment could not be determined because all values were censored data. Gene Concentrations and Temporal Variability at Individual Beaches. There was a high degree of beachspecific temporal variability within each environmental matrix, especially in Cladophora and sediment (SI Tables S4−S7). At beaches where genes were commonly detected throughout the sampling season, their concentrations varied from nondetectable to quantifiable on a weekly basis. At Jeorse Park, where samples were collected daily from July 30 to August 2, 2012 and on August 14 and 15, and 17, and 18, 2012, some gene abundances varied between ND and fully quantifiable over the course of 24 h. However, other genes were consistently detected or nondetected over multiple successive days at this 14153

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mapA GC 100 mL−1 in water was positively related to the seasonal mean E. coli log10 MPN 100 mL−1 (R = 0.88; p = 0.009; n = 7; df = 6) across the beaches. MapA abundance was also related to the RWQC; the concentration in water was positively correlated with the percentage of water samples that exceeded the BAV (R = 0.81; p = 0.028; n = 7; df = 6) and those that exceeded the STV (R = 0.78; p = 0.037; n = 7, df = 6). However, BAV and STV relationships were driven mostly by the Jeorse Park beach where seasonally, 69.8% of the samples exceeded the BAV and 61.9% exceeded the STV. No other pathogen gene in this study was related to the seasonal E. coli concentrations or RWQC in water. Application of qPCR Data to Risk Assessment Tools. Using the online QMRA tool, we analyzed illness probabilities assuming first adult (16 mL) and then child (37 mL) consumption of water for Shigella spp., Salmonella enterica, and Campylobacter jejuni in the context of RWQC. We calculated illness probabilities assuming 10, 50, and 100% of the mean GC were from infective cells and assumed Shigella had 5 ipaH GC per cell. Assuming adult water consumption, two beaches that had no exceedances of RWQC had illness probabilities for both Shigella and Campylobacter jejuni that exceeded the recommended threshold of 0.03.15,36 Campylobacter illness probabilities were often above the 0.03 benchmark, due to high GC and a low infectious dose for C. jejuni, even if only 10% of the GC were assumed to be from infective viable cells, but only at one beach where RWQC were met (Table 4). Surprisingly, the illness probability for Shigella was above the benchmark at two beaches that met RWQC when 10% or 50% of the GC were assumed to represent infective cells. QMRA results for water consumption by children (37 mL) were similar to those of adults. However, illness probabilities were slightly greater for children, and three beaches had illness probabilities for Shigella greater than 0.03, assuming 10% of the GC came from infectious cells (SI Table S8). SE was rarely detectable in this study and QMRA results indicated low probability of illness induced by this organism (Table 4). Cabral et al. (2010)22 showed a 50% concentration reduction for E. coli in 1.5−3 days, 0.1−0.67 days for Salmonella, and 1 day for Shigella in surface water, thus the doses calculated are plausible for potentially viable organisms detected at the beaches.

primarily wetland (34%) land use. All three of these beaches are located in close proximity to major rivers or ditches and could be influenced by land use in the beach catchment. With a relatively high percentage of urbanization and the variety of sources for stx2 in urban, agricultural or wetland environments, it may not be surprising that the water had greater concentrations at these locations. A previous study at Portage Lakefront, showed 100% detections of Campylobacter spp. in Cladophora in August and 60% detection in September of 2006 (using MPN-PCR with 16SrDNA gene for the genus), but none in August using qPCR of the same gene (attributed to inhibition of the assay).6 Our study showed 100% detection of mapA in all matrices tested at Portage Lakefront. The relatively widespread occurrence of mapA may suggest the sources of this gene are common among most beaches. The detection of SE was highly beach specific with most of the detections occurring at Bay City Recreational Area. Similarly, the ipaH gene, which is suggestive of a human source, was frequently detected in the water, sediment, and Cladophora at Maumee Bay, Bay City, and Portage Lakefront. All three of these beaches have a large percentage of urban and/ or agricultural land in their watersheds. Similar to the pathogen genes we studied, others have shown that FIB are significantly more abundant in Cladophora and sediment than water.9,17,41−49 The ultimate source(s) of FIB and pathogens in Cladophora, sediment, and water remains unknown, however, a dynamic relationship may exist between them. Cladophora and sediment may act as a sink and source for not only FIB, but bacteria containing pathogen-specific genes in the Great Lakes6,17,21,41,43,44,48,49 and contact with these materials may increase exposure risk.50 Because of the greater abundance of genes in Cladophora compared to the water, Cladophora or algae could be trapping,21 protecting,6 and/or supporting the growth of attached enteric bacteria.9,21,42,47 In a laboratory environment Salmonella persisted in Cladophora mats for up to 10 days, Shigella 2 days, and E. coli up to 45 days.42 A study at a harbor in Lake Superior showed that both sediment and sand can act as a genetic reservoir for FIB with the ability to load the surrounding water column.41 Environmental factors like wind speed, wave height, and runoff could be responsible for suspension of sediment or Cladophora and may cause temporary gene concentration increases in the water.41,51 Lower abundance in the water column could be due to dilution and/or water could be inoculating the algae and sediment. One might expect decreases in sediment gene concentrations and increases in the water column when wind and water velocities are greater. Wind and wave action can result in free floating algae, suspended sediment, and bacterial loading to surrounding water.9,42,44 Temperatures during the summer can result in higher concentrations of E. coli in algae.9,52 It is unclear from our study whether microbes containing these genes are growing in the sediment and Cladophora or if DNA from nonviable organisms is being detected. Either way, any detection indicates an organism carrying the gene was growing in or delivered to the system. We observed some correlations between environmental variables and gene detection frequencies or abundances. Although our results are constrained by relatively few detections of most genes, our data suggest that influences of environmental variables on pathogen gene occurrence and abundance warrants further study. More intensive studies on materials transport and the association of pathogens on

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DISCUSSION The detection frequency and abundance of the pathogen genes differed in the beaches studied and detection frequency in algae and sediment was typically less than in water, but higher abundances were often observed in the algae and sediment. The mapA gene was widespread throughout the Great Lakes, stx2 and ipaH were moderately detected, and eaeO157 and Salmonella were infrequently detected. While the sources of these genes are unknown, source likely plays a key role for pathogen detection in the beach environment. For example, there are several sources for stx2,23,38−40 and sources other than the beach matrices studied likely have an influence on the occurrence of this gene in water. While the average gene abundances indicated that water typically harbored fewer of the genes studied, there were three beaches where the average stx2 abundance in water was greater than in sediment (Bay City Recreation Area, Brimley State Park, and Deland Park Beach). The Deland Park Beach catchment was 98.1% urban, but the Bay City catchment was dominantly agricultural (36.4%) and forest (47%), and the Brimley State Park catchment was 14154

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sediment and Cladophora are needed to better understand the interconnectedness between algae, sediment, and water. We observed a high degree of temporal and geographic variability in pathogen gene detections. Variability has been demonstrated previously in water and Cladophora matrices.4,6,21 Temporal and spatial variability of stx2 was exhibited in beach and tributary water at Presque Isle State Park on Lake Erie where beach water was an unstable reservoir for stx2.4 Variability in gene detection could also be due to weather or environmental factors such as runoff, temperature fluctuations, and solar radiation.6 A recent study showed FIB concentrations, microbial source tracking (MST) markers, and bacterial pathogen genes varied regionally and certain beach catchment variables (e.g., urban land cover, impervious surface, nearby rivers, and the presence of drains or creeks, etc.) influenced their occurrence at local Great Lakes beaches.20 Environmental conditions at beaches vary widely by day over the summer and could affect the detection and abundance of FIB and pathogen genes within the matrices on individual days. Few relationships existed between FIB and seasonal or temporal pathogen gene abundances. While our results indicated a relationship between daily E. coli and stx2 abundances at one beach, another study on Lake Erie determined that the stx2 concentration was not correlated with the total E. coli abundance.4 We also determined that seasonal E. coli abundances were positively related to seasonal mapA abundances at the seven beaches. Average mapA GC were greater at locations where the GM, STV, and the BAV were exceeded more often. The source and mode of delivery of E. coli to a beach may explain why genes at individual beaches have a relationship to FIB. Previous studies have shown that storm drains, combined sewage overflows, and stormwater discharge contribute to the presence of FIB and enteric pathogens in water that influence beaches,7,20,53,54 and Brimley State Park had the greatest number of storm drains and creeks that flowed directly over the beach. Birds, specifically gulls, are likely an important ecological link for the relationship between Campylobacter and E. coli. Gulls are common among the beaches and are a significant source of FIB.55 FIB abundance and Campylobacter occurrence in beach waters increases when gulls are present.56−58 Microbial source tracking (MST) would be useful at beaches where the dominant sources are unknown to help determine where pathogen genes are originating. Our quantitative approach has shown that sediments and Cladophora likely have a negative effect on water quality at recreational beaches around the Great Lakes. Concern whether FIB monitoring is adequately protecting the public from pathogens1,4,11,12 is one reason why QMRA may be beneficial for beach management. C. jejuni is a leading cause of diarrhea in humans and C. jejuni infections can be 2−7 times more common than infections due to Shigella or E. coli O157:H7.59 Our analysis shows Campylobacter jejuni is more likely to negatively influence water quality and human health at our study beaches compared to the other modeled pathogens. Beach managers could monitor for both FIB and pathogens using qPCR to see if any correlations exist; this information would make managers more confident about what risk is posed when FIB are high. Without epidemiological information in this study, it is difficult to assess the accuracy of this QMRA tool. Regardless, this tool is useful from a management standpoint because pathogens such as Shigella and Campylobacter have specific sources: humans are the common source for Shigella6,22 and birds and ruminants for Campylobacter.6,56,60 Knowing this

information may lead to better beach management strategies that aim to reduce sources of bacterial pathogens at recreational beaches.15 Management strategies might include bird control, sewage infrastructure improvements, reduction of storm drain outfalls on beaches, understanding agricultural impacts from nonpoint sources, or classifying beaches as nonrecreational in cases where improvements cannot be feasibly made. QMRA tools are useful to make generalized assumptions about the risk microorganisms may pose to humans. However, large sources of variability and other confounding factors must be considered when interpreting the results. Temporal and spatial variation of pathogen genes we observed would be important to consider when using QMRA to make management decisions.14,15 Due to the variation in gene detection among matrices, risk to human health at specific beach locations and times is only an estimate.36 In addition, standardized methods for amounts of materials to be analyzed, extraction methods, and reporting conventions will be required to move ahead with qPCR data. QMRA suffers from the lack of epidemiological studies that describe the fraction of infectious pathogens in ambient microbial populations.15,36 QPCR also poses constraints, as analysis does not distinguish DNA from viable or nonviable cells.6,11,12,41,61 PMA DNA binding dye could be used in PMA-qPCR to better characterize the genes that are from live cells.13,18,41,62 Even if the cells are viable, they may not be infective,15 and PCR inhibition can reduce estimated GC. Our study used 10, 50, and 100% viability estimates, but it is difficult to know at this time if these are appropriate ranges. Ultimately, more needs to be learned about the types (genotypic and phenotypic), viability, and infectivity of bacterial pathogens in recreational water, and the relationships between qPCR-based pathogen gene assessments and risk from actual pathogens. Additionally, filling research gaps in the hydrodynamics of the beach system and the source and fate of fecal matter would assist in obtaining accurate risk assessments.15,36 As our study detected pathogen genes intermittently at most beaches, intensive sampling (collecting samples multiple times per week) throughout the recreational year at specific beaches may be needed to establish stronger associations between gene abundance and environmental parameters. Concurrently collecting FIB and pathogen-specific gene abundances would be an important step in fully assessing water quality degradation at beaches and the factors contributing to their occurrence. Quantitative bacterial pathogen data has been lacking in many beach studies, and our study only begins to address pathogen gene abundance at beaches. In future studies, assessing quantitative pathogen data along with physical and environmental factors will help researchers, beach managers, and public health officials better understand what influences human health in the beach environment.

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ASSOCIATED CONTENT

S Supporting Information *

Additional text and tables as mentioned in the text. This material is available free of charge via the Internet at http:// pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: 218-290-0944; e-mail: [email protected]. 14155

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Notes

quantitative microbial risk assessment (QMRA). Water Res. 2010, 44, 4692−4703. (16) MSU. Michigan State University, Center for Advancing Microbial Risk Assessment, QMRAwiki, 2013. http://qmrawiki.msu. edu/index.php?title=Dose-Response_Calculator. (17) Ishii, S.; Hansen, D. L.; Hicks, R. E.; Sadowsky, M. J. Beach sand and sediments are temporal sinks and sources of Escherichia coli in Lake Superior. Environ. Sci. Technol. 2007, 41, 2203−2209. (18) Eichmiller, J. J. The distribution and persistence of genetic markers on fecal pollution on Lake Superior Beaches. Ph.D. Dissertation, University of Minnesota: Minneapolis, MN, 2013. (19) Bauer, L.; Alm, E. Escherichia coli toxin and attachment genes in sand at Great Lakes recreational beaches. J. Great Lakes Res. 2012, 38, 129−133. (20) Haack, S. K.; Fogarty, L. R.; Stelzer, E. A.; Fuller, L. M.; Brennan, A. K.; Isaacs, N. M.; Johnson, H. E. Geographic setting influences Great Lakes beach microbiological water quality. Environ. Sci. Technol. 2013, 47, 12054−12063. (21) Byappanahalli, M. N.; Sawdey, R.; Ishii, S.; Shively, D. A.; Ferguson, J. A.; Whitman, R. L.; Sadowsky, M. J. Seasonal stability of Cladophora-associated Salmonella in Lake Michigan watersheds. Water Res. 2009, 43, 806−814. (22) Cabral, J. P. S. Water microbiology. Bacterial pathogens and water. Int. J. Environ. Res. Public Health 2010, 7, 3657−3703. (23) Beutin, L.; Kruger, U.; Krause, G.; Miko, A.; Martin, A.; Strauch, E. Evaluation of major types of shiga toxin 2e-producing Escherichia coli bacteria present in food, pigs, and the environment as potential pathogens for humans. Appl. Environ. Microbiol. 2008, 74 (15), 4806− 4816. (24) Ibekwe, A. M.; Watt, P. M.; Grieve, C. M.; Sharma, V. K.; Lyons, S. R. Multiplex fluorogenic real-time PCR for detection and quantification of Escherichia coli O157:H7 in dairy wastewater wetlands. Appl. Environ. Microbiol. 2002, 68 (10), 4853−4862. (25) Wang, L.; Li, Y.; Mustapha, A. Rapid and simultaneous quantitation of Escherichia coli O157:H7, Salmonella, and Shigella in ground beef by multiplex real-time PCR and immunomagnetic separation. J. Food Protection 2007, 70 (6), 1366−1372. (26) Best, E. L.; Powell, E. J.; Swift, C.; Grant, K. A.; Frost, J. A. Applicability of a rapid duplex real-time PCR assay for speciation of Campylobacter jejuni and Campylobacter coli directly from culture plates. FEMS Microbiol. Letters 2003, 229, 237−241. (27) Thong, K. L.; Hoe, S. L. L.; Puthucheary, S. D.; Yasin, R. M. Detection of virulence genes in Malaysian Shigella species by multiplex PCR assay. BMC Infect. Dis. 2005, 5 (8), 1−7. (28) Hartman, A. B.; Venkatesan, M.; Oaks, E. V.; Buysse, J. M. Sequence and molecular characterization of a multicopy invasion plasmid antigen gene, ipaH, of Shigella f lexneri. J. Bacteriol. 1990, 172 (4), 1905−1915. (29) R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria, 2014. http//www.R-project.org. (30) Lee, L.; Helsel, D. Statistical analysis of water-quality data containing multiple detection limits II: S-language software for nonparametric distribution modeling and hypothesis testing. Comput. Geosci. 2007, 33, 696−704. (31) Helsel, D. R. More than obvious: Better methods for interpreting nondetect data. Environ. Sci. Technol. 2005, 419A−423A. (32) Black, R. E.; Levine, M. M.; Clements, M. L.; Hughes, T. P.; Blaser, M. J. Experimental Campylobacter jejuni infection in humans. J. Infect. Dis. 1988, 157 (3), 472−479. (33) Hornick, R. B.; Woodward, T. E.; McCrumb, F. R.; Snyder, M. J.; Dawkins, A. T.; Bulkeley, J. T.; De la Macorra, F.; Corozza, F. A. Study of induced typhoid fever in man. I. Evaluation of vaccine effectiveness. Trans. Assoc. Am. Physicians 1996, 79, 361−367. (34) Dupont, H. L.; Hornick, R. B.; Snyder, M. J.; Libonati, J. P.; Formal, S. B.; Gangarosa, E. J. Immunity in Shigellosis. I. Response of man to attenuated strains of Shigella. J. Infect. Diseases 1972, 125 (1), 5−11.

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was funded by the Great Lakes Restoration Initiative. The authors would like to acknowledge the beach monitors who collected samples. We thank Joe Duris and Erin Stelzer for data and manuscript reviews. We express gratitude to Lori Fuller for assisting with GIS and to Alex Totten and Heather Johnson for their assistance in the laboratory. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

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dx.doi.org/10.1021/es5038657 | Environ. Sci. Technol. 2014, 48, 14148−14157