Microbial Source Tracking in Adjacent Karst Springs.

Microbial Source Tracking in Adjacent Karst Springs Shoshanit Ohad,a Dalit Vaizel-Ohayon,b Meir Rom,b Joseph Guttman,b Diego Berger,b Valeria Kravitz,a Shlomo Pilo,a Zohar Huberman,a Yechezkel Kashi,c Efrat Rormana National Public Health Laboratory Tel Aviv, Ministry of Health, Tel Aviv, Israela; Mekorot, Israel National Water Company, Tel Aviv, Israelb; Faculty of Biotechnology and Food Engineering, Technion-Israel Institute of Technology, Haifa, Israelc

K

arst springs are often susceptible to point or nonpoint fecal contamination, as the karst geological structure enables rapid interaction between surface pollutants and underground layers. Microbial source tracking (MST) methodology offers tools to diagnose and identify fecal contamination sources in the environment (1, 2). Although it is not a standard environmental monitoring method used for routine regulatory purposes, it has been extensively studied and applied worldwide, especially for deciphering fecal pollution in surface and recreational waters (3–14). An MST field study aims to attribute water contamination to fecal source hosts in a defined geographic region in which key water locations are periodically tested. Molecular targets of fecal bacteria and viruses dominate the current MST mode and are often tested using quantitative PCR (qPCR) technology (15–20). Site susceptibility to fecal host contamination is illustrated by MST profiles presented as percentages or absolute numbers of positive samples or as quantitative marker copies (21, 22). Often, MST study design includes multiple samplings throughout time intervals in order to achieve a comprehensive understanding of all possible contaminating factors to which the tested area is likely to be exposed. The yearly time frame of many MST studies is set to monitor seasonal fluctuations, as weather parameters have a direct effect on the microbial quality of surface water (23, 24). Samples are categorized according to wet and dry conditions and cumulative local precipitation data. This partition of environmental conditions contributes to delineate and statistically analyze fecal source contribution (25). MST field research is designed to customize the complexity and distinctiveness of geographic settings for studying specific queries. Daily sampling was designed for assessing the efficiency of corrective actions carried out to protect the environment (5). However, even cyclic interval sampling throughout the year might not fully reflect the fecal contamination fingerprint, and to

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bridge this gap, more comprehensive sampling at shorter time intervals was suggested (26). The spectrum changes in host variety and quantity of fecal contamination may possibly be overlooked or underestimated, as point samplings even during a wet event are not necessarily carried out at the peak of an episode. An MST profile generated throughout such an event can serve as a hydrological tool indicating the pattern of fecal contamination along the event. The present study aimed at dual and complemented MST surveillance, i.e., monthly samplings throughout a year and daily testing during a precipitation event at karst springs in the western Galilee. The springs are located in geographical proximity and are subject to intense human and agricultural activities. Molecular assays of human, ruminant, bovine, swine, and alternative fecal indicator bacteria markers were applied. Both a yearly MST profile and an event MST profile were completed and complemented MST profiles to more clearly define spring susceptibility to host fecal contamination.

Received 16 March 2015 Accepted 26 April 2015 Accepted manuscript posted online 22 May 2015 Citation Ohad S, Vaizel-Ohayon D, Rom M, Guttman J, Berger D, Kravitz V, Pilo S, Huberman Z, Kashi Y, Rorman E. 2015. Microbial source tracking in adjacent karst springs. Appl Environ Microbiol 81:5037–5047. doi:10.1128/AEM.00855-15. Editor: S.-J. Liu Address correspondence to Shoshanit Ohad, [email protected]. Copyright © 2015, American Society for Microbiology. All Rights Reserved. doi:10.1128/AEM.00855-15

Applied and Environmental Microbiology

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Modern man-made environments, including urban, agricultural, and industrial environments, have complex ecological interactions among themselves and with the natural surroundings. Microbial source tracking (MST) offers advanced tools to resolve the host source of fecal contamination beyond indicator monitoring. This study was intended to assess karst spring susceptibilities to different fecal sources using MST quantitative PCR (qPCR) assays targeting human, bovine, and swine markers. It involved a dual-time monitoring frame: (i) monthly throughout the calendar year and (ii) daily during a rainfall event. Data integration was taken from both monthly and daily MST profile monitoring and improved identification of spring susceptibility to host fecal contamination; three springs located in close geographic proximity revealed different MST profiles. The Giach spring showed moderate fluctuations of MST marker quantities amid wet and dry samplings, while the Zuf spring had the highest rise of the GenBac3 marker during the wet event, which was mirrored in other markers as well. The revelation of human fecal contamination during the dry season not connected to incidents of raining leachates suggests a continuous and direct exposure to septic systems. Pigpens were identified in the watersheds of Zuf, Shefa, and Giach springs and on the border of the Gaaton spring watershed. Their impact was correlated with partial detection of the Pig-2-Bac marker in Gaaton spring, which was lower than detection levels in all three of the other springs. Ruminant and swine markers were detected intermittently, and their contamination potential during the wet samplings was exposed. These results emphasized the importance of sampling design to utilize the MST approach to delineate subtleties of fecal contamination in the environment.

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MATERIALS AND METHODS Hydrological and geographical characterization. The study was conducted in the western Galilee in northern Israel, where karst systems are prominent. It focused on 4 springs, of which 3 are 1 km apart; these are named Zuf (33°0=52⬙N, 35°9=0.5⬙E), Giach (33°0=34⬙N, 35°8=28⬙E), and Shefa (33°0=29⬙N, 35°8=15⬙E) (Fig. 1). The three springs emerged in the vicinity of the Kabri Fault, close to the foothills at elevations of 75, 55, and 45 m above sea level, respectively, from the limestone dolomite layers of the upper subaquifer. Nearby, there are a few wells, some pumping from the upper subaquifer and others from a lower subaquifer. The volume flow of the springs was on average 0.25 m3/s. The fourth spring, Gaaton (33°0=53⬙N 35°12=7⬙E), is located 5 kilometers east of Zuf spring and emerges at an elevation of 200 m above sea level. In contrast to the other three springs, Gaaton spring is affected rapidly by the amount and distribution of rainfall and is characterized by large fluctuations in the spring’s discharge. This behavior indicates that this spring is fed from limited storage in a very developed karst system that responds immediately to the rainfall. In November 2006 a notable contamination event appeared in those springs as expressed by fresh sewage: a heavy load of organic matter, high concentration of microbes, foam, oil, and high turbidity. The wells located near the springs remained clean. This regional contamination acted as a regional tracer and gave a new insight into the interconnections between the subaquifers and the flow directions. A regional tracer test was conducted in

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February 2008 (27). The results showed that the Kabri and Gaaton springs are connected to one well-developed karst conduit that is separated from the entire aquifer in which the wells are pumping. The springs are outlets of this spring karst conduit at different elevations. The flow velocity in this spring karst conduit is between 0.029 and 0.035 m/s. The western Galilee is characterized by dense rural communities and intense agricultural activities. Potential risk sources of fecal contamination within the springs’ watershed are listed in Table 1.

TABLE 1 Potential risk sources of fecal contamination within spring watershed as defined by hydrology criteria, including both rainfall leachates and underground water influence Parameter

Gaaton

Zuf

Shefa

Giach

No. of sewer pumping stations No. of villages or rural communities Avian feces land applied Bovine grazing No. of dairy barns Pigpens No. of deer pens No. of chicken coops

6 6 No Yes 0 No 0 3

20 21 Yes Yes 1 Yes 1 6

20 21 Yes Yes 1 Yes 1 6

20 21 Yes Yes 1 Yes 1 6




FIG 1 Map of western Galilee showing locations of sampled karst springs: Gaaton, Zuf, Giach, and Shefa.

MST Monitoring in Karst Springs

TABLE 2 Primers and probes used in the study Primer or probe

Sequence (5=¡3=)

Reference

GenBac3

Forward primer Reverse primer Probe

GGGGTTCTGAGAGGAAGGT CCGTCATCCTTCACGCTACT FAM-CAATATTCCTCACTGCTGCCTCCCGTA-BHQ1

41

BacR


GCGTATCCAACCTTCCCG CATCCCCATCCGTTACCG FAM-CTTCCGAAAGGGAGATT-NFQ–MGB

31

CowM2


CGGCCAAATACTCCTGATCGT GCTTGTTGCGTTCCTTGAGATAAT FAM-AGGCACCTATGTCCTTTACCTCATCAACTACAGACA-BHQ1

32

CowM3


CCTCTAATGGAAAATGGATGGTATCT CCATACTTCGCCTGCTAATACCTT FAM-TTATGCATTGAGCATCGAGGCC-BHQ1

32

Pig-2-Bac


GCATGAATTTAGCTTGCTAAATTTGAT ACCTCATACGGTATTAATCCGC FAM-TCCACGGGATAGCC(NFQ-MGB)

10

BacH(I)


CTTGGCCAGCCTTCTGAAAG CCCCATCGTCTACCGAAAATAC FAM’-TCATGATCCCATCCTG-NFQ–MGB

34

BacH(II)


CTTGGCCAGCCTTCTGAAAG CCCCATCGTCTACCGAAAATAC FAM-TCATGATGCCATCTTG-NFQ–MGB

34

HumM3


GTAATTCGCGTTCTTCCTCACAT GGAGGAAACAAGTATGAAGATAGAAGAATTAA FAM-AGGTCTGTCCTTCGAAATAGCGGT-BHQ1

33

uidA-E.coli


CGGAAGCAACGCGTAAACTC GAGCGTCGCAGAACATTACATT FAM-CCGACGCGTCCGATCACCTG-BHQ1

This study

Amplification control


GACCACTACCAGCAGAACAC GAACTCCAGCAGGACCATG Rox-AGCACCCAGTCCGCCCTGAGCA-BHQ1

35

Water collection and sample processing. A total of 46 water samples were collected at monthly intervals from January 2010 to March 2011 from four karst springs in the western Galilee: Gaaton, Zuf, Shefa, and Giach. The number of samples from each spring was as follows: Gaaton, 10; Zuf, 13; Shefa,13; and Giach, 10. A daily sampling was performed in Zuf, Shefa, and Giach springs throughout 12, 8, and 9 days, respectively; these samplings were prior to, during, and after a wet event. Water samples of 250 ml were collected in sterile containers and transported to the laboratory on ice within 2 h. Samples of 250 ml were filtered through a 0.2-␮m Supor funnel filter (Pall, Ann Arbor, MI). The filters were kept at ⫺80°C until DNA extraction was carried out using a MoBio PowerWater kit (MoBio, Carlsbad, CA, USA) according to the manufacturer’s protocol. The final volume elution from DNA extraction was 100 ␮l, and it was kept at ⫺20°C until qPCR was performed. Escherichia coli qPCR assay design and evaluation. Forty-five uidA gene sequences were retrieved from GenBank (ABHU00000000, ABKY00000000, FM180568, CU928145, CU928160, CU928161, CU928162, CU928163, CU928164, CP001396, AP010958, AP010960, U00096, BA000007, AE005174, AE014075, CP000802, CP000800, AAJT00000000, AAJU00000000, AAJV00000000, AAJW00000000, AAKB00000000, AAMK00000000, CP000247, CP000243, AP009048, CP000468, CP000946, AP009240, CP000819, CP000970, CP000948,


CP001509, ABHM00000000, ABHL00000000, ABHQ00000000, ABHP00000000, CP001164, ABHO00000000, ABHK00000000, ABHR00000000, ABHS00000000, ABHT00000000, and ABHW00000000) (28) and aligned together with Clustal X version 2.0 software (29). Based on this alignment, a uidA-specific probe and primer sequences were designed

TABLE 3 Limit of detection and range of quantification of qPCR assays Assay

Experimental LODa

Statistical LOQ (107)b

GenBac3 BacR CowM2 CowM3 Pig-2-Bac BacH(I) BacH(II) HumM3 uidA-E.coli

3.2 3.2 3.9 2.6 6.7 20 6.4 10 1

9.5–9.5 11.9–11.9 7.5–7.5 8.4–8.5 9.3–9.3 5.9–5.9 8.3–8.3 4.5–4.5 6.1–6.1

a The limit of detection was experimentally set and identified as the copy number at which 95% of qPCRs were positive. b The limit of quantification was determined by a statistical module.


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Assay

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TABLE 4 Bacterial strains tested for specificity and sensitivity of the uidA-E. coli qPCR assay Source

uidA-E. coli qPCR result

Cronobacter muytjensii ATCC 51329 Enterobacter aerogenes ATCC 13048 Enterobacter cloacae ATCC 10005 Enterobacter hormaechei ATCC 49163 Escherichia coli ATCC 25922 Salmonella enteritidis ATCC 13076 Citrobacter koseri Shigella dysenteriae Shigella flexneri Shigella sonnei Shigella boydii Serratia liquefaciens Serratia rubidaea Proteus vulgaris Stenotrophomonas maltophilia 326 Enterobacter amnigenus 330 Enterobacter cloacae 308 Enterobacter aerogenes 307 Aeromonas hydrophila 329 Aeromonas hydrophila 331 Shewanella putrefaciens 327 Vibrio fluvialis 332 Bacillus cereus 620 Bacillus cereus 617 Klebsiella pneumoniae Listeria monocytogenes group 3b Listeria monocytogenes group 4b Salmonella antum group E1 Salmonella infantis group C1 Staphylococcus aureus 691 Staphylococcus aureus 710 Pseudomonas aeruginosa 65 Pseudomonas aeruginosa 81 Legionella pneumophila serogroup 1 Legionella pneumophila serogroup 4 Legionella pneumophila serogroup 6 Escherichia coli 335 Escherichia coli 336 Escherichia coli 337 Escherichia coli 338 Escherichia coli 339 Escherichia coli 340 Escherichia coli 341 Escherichia coli 342 Escherichia coli 343 Escherichia coli 344 Escherichia coli 345 Escherichia coli 346 Escherichia coli 347 Escherichia coli 348 Escherichia coli 349 Escherichia coli 350 Escherichia coli 351 Escherichia coli 352 Escherichia coli 353 Escherichia coli 354 Escherichia coli 355 Escherichia coli 356 Escherichia coli 357 Escherichia coli 358 Escherichia coli 359 Escherichia coli 360 Escherichia coli 361 Escherichia coli 362 Escherichia coli 363 Escherichia coli 364 Escherichia coli 365

ATCC ATCC ATCC ATCC ATCC ATCC Central Public Health Laboratory of Jerusalem, clinical Central Public Health Laboratory of Jerusalem, clinical Central Public Health Laboratory of Jerusalem, clinical Central Public Health Laboratory of Jerusalem, clinical Central Public Health Laboratory of Jerusalem, clinical Central Public Health Laboratory of Jerusalem, clinical Central Public Health Laboratory of Jerusalem, clinical Central Public Health Laboratory of Jerusalem, clinical NPHLTAa, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental NPHLTA, environmental

Negative Negative Negative Negative Positive Negative Negative Positive Positive Positive Positive Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Negative Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive

a

NPHLTA, National Public Health Laboratory, Tel Aviv.

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Organism


TABLE 5 Positive detection of MST markers during monthly monitoring Detection (%) of: Spring

BacR

CowM3

CowM2

BacH(I)

BacH(II)

HumM3

Pig-2-Bac

GenBac3

uidA-E. coli

Gaaton Zuf Shefa Giach

80 46 53 60

20 23 16 20

30 0 0 0

100 100 100 100

90 92 92 100

60 69 69 60

20 53 53 50

100 100 100 100

100 100 100 100


Quality controls. Quality controls (NTC and method blank) were performed routinely. An amplification control was performed in parallel to other MST qPCRs targeting the GFP gene as previously described (35). Fifty plasmid copies were included in a qPCR assay in which GFP gene primer and probe concentrations were 500 nM and 250 nM, respectively. An inhibition effect was defined as a delay of at least one threshold cycle (CT) compared to the reaction without filterextracted DNA samples. Statistical analysis. Our calibration process utilized advanced statistical properties to produce accurate estimations of the (log) marker copies from the associated CT values. Using basic statistical methodology of linear regression, we improved the way the detection limit was produced and quantified the uncertainty involved with the process. We fit a polynomial regression model to explain marker copies by the corresponding CT mean values. We started with a simple linear model and expanded it by additional quadratic and cubic terms until the best polynomial fit was obtained. Each new term was tested for its significance using the standard t test with a significance level of 0.05. In practice, most of the fitted models stayed in the linear form, and only four needed a second quadratic term. Those models related to BacR, GenBac3, CowM2, and CowM3. Each fitted calibrated model can produce a prediction interval for any given CT value. This interval, defined by its left and right values, indicates that for the given CT, any marker copy value can be predicted within that interval with high probability (e.g., confidence level). The prediction interval utilizes the variability inherited in both the CT marker copy data and model residual outcomes in order to quantify the length of the corresponding interval. Thus, the prediction interval produces additional and more accurate information than just simple point prediction. Here we used a 99% prediction interval for each model and defined a detection rule based upon the left interval (LI) value; i.e., detection is valid if LI exceeds 3 copies. Note that choosing a different detection rule that relies only on point prediction may result in unsatisfactory results for models with high variability (e.g., lower R2).

RESULTS

Sensitivity and specificity. The sensitivity and specificity of the selected markers were calculated by using local fecal and wastewater samples from the tested area. Briefly, the sensitivity values of BacH(I), BacH(II), and HumM3 were 91, 93, and 85%, respectively, while the specificity values were 73, 80, and 99%, respectively (raw data not shown). Ruminant (BacR) and bovine (CowM2 and CowM3) markers were evaluated using samples collected from both dairy cow and beef cattle (raw data not shown) and exhibited sensitivities of 100, 50, and 93% and specificities of 99, 89, and 99%, respectively. Pig-2-Bac, the swine marker, displayed 100% sensitivity and 100% specificity. Assay performance characterization. Assay performance characterization of MST markers included empirically ascertaining their limit of detection (LOD) values, defined as the lowest copy number positively recognized in 95% of qPCRs (Table 3). The other parameter, limit of quantitation (LOQ), was calculated


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using the Primer Express software (Applied Biosystems, Foster City, CA, USA). Environmental isolates (E. coli and non-E. coli) were obtained from routine water microbial monitoring performed according to standard methods using membrane filtration procedures (30). Briefly, membranes were transferred from m-Endo Agar LES-positive total coliform samples (Merck, Darmstadt, Germany), following 24 h of incubation at 35.5°C or from mFC agar fecal coliform samples (BBL, Sparks, MD, USA) following 24 h of incubation at 45.5°C to a nutrient agar substrate containing 4-methylumbelliferyl-␤-D-glucuronidase (MUG) (Difco, Sparks, MD, USA) and incubated for 4 h at 35.5°C. E. coli colonies were identified based on detection of fluorescence around them due to MUG hydrolysis and API 20E confirmation (bioMérieux, Marcy l’Etoile, France). Non-E. coli isolates were identified using API 20E (bioMérieux, Marcy l’Etoile, France) according to the manufacturer’s protocol. Thirty of the 67 environmental isolates, ATCC strains, and clinical isolates obtained from reference laboratories and included in assay sensitivity and specificity assessment were non-E. coli isolates. qPCR assays. qPCR assays were performed using the LightCycler 480 platform (Roche, Basel, Switzerland). The amplification program included an initial enzyme activation step at 95°C for 15 min followed by 45 cycles of 95°C for 15 s and 60°C for 1 min. qPCR settings were comparable for all assays, and qPCR was carried out in a 20-␮l final volume, of which 5 ␮l was the DNA sample. Reaction mixtures consisted of Absolute Blue QPCR No Rox mix (Thermo Scientific, Vilnius, Lithuania) with primers and probe (Table 2) at final concentrations 500 nM and 250 nM, respectively. Every batch of Absolute Blue QPCR was tested to be E. coli DNA free by performing 20 reactions with a nontemplate control (NTC), where only those that met the passing criterion of up to 1/20 positive reactions were used. The following host-associated assays were applied in the study: BacR for ruminant (31); CowM3 and CowM2 for bovine (32); Pig-2-Bac for swine (10); and HumM3 (33) and BacH(I) and BacH(II) (34) for human. BacH(I) and Bach(II) refer to the two probes in the original BacH assay. The other assays, GenBac3 and uidA-E. coli, target the Bacteroidales order and E. coli, respectively. In addition, an inhibition amplification control using the green fluorescent protein (GFP) gene was conducted to assess the inhibition effect (35). Sensitivity values for the qPCR assays are presented as percentages and were calculated as the fraction of true-positive (TP) host samples divided by all expected positive hosts, including both false negative (FN) and true positive, as follows: sensitivity ⫽ TP/(FN ⫹ TP). Specificity values are presented as percentages and were calculated as the fraction of true-negative (TN) host samples divided by all expected negative hosts, including both false positive (FP) and true negative, as follows: specificity ⫽ TN/(FP ⫹TN). Marker copy quantification. Plasmid constructs of pGEM (Promega, Mannheim, Germany) consisting of amplicons used for the BacR, CowM3, HumM3, BacH(I), BacH(II), and Pig-2-Bac assays were cloned to establish standard curves. Plasmids were quantified spectrophotometrically, from which the copies were calculated. Serial dilutions of cloned pGEM plasmid were carried out independently in triplicates. For the uidA-E. coli marker, a calculated CFU standard curve was conducted using a purified genomic DNA from an overnight E. coli culture.

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statistically from calibration curve analysis. Applying the statistical detection model to the data and comparing it to the limits produced by a simple point prediction showed that those limits ranged from 4.5 to 11.9 and that most of them were ⬍10 copies (Table 3). uidA-E. coli qPCR. The uidA-E. coli qPCR assay design was based on in silico analysis of representative sequences of E. coli. The assay primers and probe are located at conserved genetic determinants identified in the sequence assembly of the uidA gene, encoding the beta-glucuronidase protein, and target both E. coli and Shigella species. As traces of E. coli DNA are often present in enzyme preparations, we used only qPCR mix batches that were tested to show up to one positive out of 20 NTC reactions. Assay efficiency was calculated from calibration curves to be 1.8. Sensitivity and specificity of the qPCR assay were evaluated using both clinical and environmental isolates, as specified in Table 4. Thirtysix E. coli and Shigella strains were all identified positive by the qPCR, and non-E. coli strains were not detected; therefore, assay sensitivity and specificity values were both calculated to be 100%. MST monthly monitoring. The GenBac3 marker was identified in all 4 springs at high percentages, ranging from 92% to 100% (Table 5). Aligning precipitation records with GenBac3 marker copies data illustrates that the fluctuations in GenBac3 profiles were more noticeable during the winter and that profiles were relatively stable during the summer (Fig. 2). In agreement, the E. coli marker was identified in all samples throughout the monthly sampling period. Human MST-associated markers demonstrated a comparable pattern; BacH(I) and BacH(II) were identified in almost all monthly tested samples, while HumM3 was detected in 60 to 70% of the samples (Table 5). The amplification control assays identified no qPCR inhibition effects in the processed water samples; the mean and standard deviation of the CT difference between water samples and the control were 0.33 ⫾ 0.22, while the maximum was 0.69. Wet and dry samplings. Data from daily and monthly monitoring were combined into a unified database and partitioned into dry and wet categories at a threshold of 40 mm precipitation, accumulated 6 days previous to sampling. This cutoff divided positive detected samplings of most markers into 66% dry and 33% wet samplings. The box plot of positive detection data illustrated the markers’ dynamic profiles and revealed differences not only between the two categories but also among the three springs (Fig. 3). When the sample size was ⬍5 positive observations, actual values are presented without a box plot analysis (36). This was often observed for the CowM2 and CowM3 markers. GenBac3 had similar distributions in dry samplings of Zuf and Shefa, as they were spanned over comparable quantities, whereas the wet sampling data varied; the lower limit of the Zuf wet category was above the 75th percentile of its dry sampling and was noticeably higher than the Shefa and Giach patterns. Moreover, the Giach pattern illustrated a comparable median value of GenBac3 during the dry and wet samplings (Fig. 3). The Zuf difference between dry and wet samplings was demonstrated also by human marker patterns BacH(I), BacH(II), and HumM3, which showed equivalent increases of the maximum values. Comparing human marker profiles in the other springs showed that the Shefa values were lower than the Zuf values, and a distinctly tighter range for the wet samplings was observed. The Giach values showed a moderate increase of human markers as well. Event monitoring. Daily surveillance of the wet event ex-

FIG 2 Daily precipitation data throughout sampling period in alignment with GenBac3 copies calculated per 100 ml and log transformed in Zuf (A), Shefa (B), Giach (C), and Gaaton (D).



Downloaded from http://aem.asm.org/ on July 29, 2015 by NYU MEDICAL CENTER LIBRARY FIG 3 MST combined data monitoring, showing box plot diagrams of MST marker copy numbers (log10/100 ml) of BacR, BacH(I), BacH(II), Pig-2-Bac, and two general markers, GenBac3 and E. coli, detected in Zuf (A), Shefa (B), and Giach (C) karst springs. Data were attributed to wet and dry clusters upon a threshold of 40 mm precipitation accumulation 6 days prior to the sampling. The lower part of the diagram presents counts for wet and dry sets for each marker and is not equal among the markers, as the diagram illustrates only positive detection. Box plots are drawn only for samples sizes of ⬎4. Individual data points are shown for smaller samples. The edges of each box indicate the sample first and the third quartiles, while the (red) line inside the box indicates the median; thus, the length of each box is the interquartile range (IQR) (Q3 ⫺ Q1). Whiskers are extended to the most extreme point that is no more than 1.5 ⫻ IQR from the edge of the box. Outliers beyond the whiskers are indicated by a ⫹ sign.



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Downloaded from http://aem.asm.org/ on July 29, 2015 by NYU MEDICAL CENTER LIBRARY FIG 4 MST monitoring during a rainfall event in Zuf (A), Shefa (B), and Giach (C) springs, presented as copy number of MST markers per 100 ml and log transformed, along with precipitation data (mm).

tended over 8 to 12 days. The peaks of all markers were notably detected on the fifth day of precipitation at all three springs simultaneously (Fig. 4). Among the screened battery of markers, GenBac3 had the highest quantity, reaching up to 6.5 ⫻ 106 copies per 100 ml.

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The BacH(I) and BacH(II) markers displayed overlapping patterns during both rising and declining phases in each spring. HumM3 had a discontinuous detection appearance at Giach, whereas at Zuf and Shefa it was identified continuously from the fourth day of the rain event in association with the marker peak (Fig. 4).




While the monthly Pig-2-Bac identification rate was 50%, it reached up to 100% and 70% of the samples in Zuf and in the other two springs, respectively, during the rainfall event. In contrast to this similarity between the three springs, Gaaton showed lower vulnerability to swine source contamination. DISCUSSION



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Accurate and fit-for-purpose environmental monitoring methodologies are crucial for reliable assessment of the influence of various components on water microbial quality. They enable early identification of trends of change in contamination and allow for corrective action as needed. We used the MST approach to interpret fecal contamination of closely situated karst springs which are under extensive use for human and agricultural activities (Table 1). Water sampling at various time intervals was shown to be a key aspect in assessing source pollution in alpine karst systems. Reischer et al. extended their previously reported method monthly and a strong summer flood monitoring to a nested sampling design encompassing three higher sampling frequencies, especially during hydrological events (26, 37). Therefore, we used dual scales of time monitoring: one on a monthly basis and the second one on a daily basis during a rainfall event. A continuous contamination from human sewage sources was revealed by positive detection of the BacH(I) and BacH(II) markers in all monthly collected spring water samples. The Mediterranean climate, characterized by relatively low precipitation quantities during the winter and a prolonged dry season, points to fecal contamination in the springs that was not conveyed solely by raining leachates but also diffused directly from sewer and septic systems. The Pig-2-Bac marker had 50% identification during the monthly screening (except in Gaaton spring), whereas during the wet event it showed not only a continuous appearance but also higher marker copy quantities. The monthly monitoring schedule may miss the surge of rainfall events, and thus the swine fecal input to the environment might be underestimated. The tested set of MST markers aimed to pinpoint fecal contamination from human, ruminant/bovine, and swine sources, correlated with activities of these major host species within the catchment (Fig. 1). All markers targeted subpopulations of the Bacteroidales group which are prone to exhibit limited survival outside their hosts and therefore may indicate recent contamination (38). Human and bovine fecal monitoring included more than one qPCR assay, while swine pollution was tested by a single one. All three hosts had one assay aiming to identify the 16S rRNA gene, which is typically preferred as it presents at high copy numbers in the Bacteroidales genome. BacH(I), BacR, and Pig-2-Bac all target the 16S rRNA gene, and therefore their quantity comparison omitted the gene copy normalization process. The other MST markers, CowM2, CowM3, and HumM3 are of a single gene copy each. Recently, it was reported that the CowM2 and CowM3 markers showed high specificity values but failed to meet the benchmark criterion of a median value greater than 50 copies/ enterococcal CFU (15). Our results correlate well with these finding, as the markers were identified only infrequently and in low quantities (Fig. 3). The cooccurrence of all ruminant/bovine markers confirmed our positive identification of a bovine host, but the quantitative performance gap between BacR and the other cow markers may explain the generally low cooccurrence of the ruminant and bovine markers. The E. coli marker was identified in most samples and served as a representative of bacteria used in

standard methods. Its presence confirms the fecal nature of contamination sources but cannot help identify specific host species responsible for such contamination. Fecal fingerprints of the three tested hosts were identified to different extents in water samples. These differences can be attributed to heterogeneity in the number of gene copies, in bacterial population sizes, or in their persistence in the environment. Nevertheless, the panel module monitoring was still able to demonstrate the effects of various precipitation levels on oscillations of marker levels, which supports the reliability of ruminant and human source identification. Gaaton spring is relatively separated from the other 3 springs and was characterized by less exposure to swine contamination as observed in the monthly monitoring. The pigpens in the tested watershed were confined to a definite location, and hydrology data were not included within the Gaaton watershed boundary but were within the other three (Table 1). The MST profile strengthens these hydrological boundaries, as the percentage of positive swine detection in Gaaton was lower but still present and there were similar swine marker profiles in the three springs. Moreover, analysis of BacR presence/absence in monthly monitoring showed that the Gaaton spring was exposed to bovine feces more than the other springs. Not only did it have a higher incidence of the ruminant marker, but it was the only spring in which CowM2 was detected (Table 5). The Zuf spring was identified among the tested springs as the most vulnerable to the rainfall effect, because it had not only the highest marker intensification in wet versus dry categories but also the maximum marker copies. Among the tested springs, Giach was identified as relatively less prone to contamination fluctuations, as its MST profiles showed a slight alteration between dry and wet samples and its daily monitoring showed marker quantities lessened by at least an order compared to those in the other springs. This may indicate that this spring was less affected by the rainfall impact. The similarity between Shefa and Zuf was demonstrated also in the ruminant and bovine contamination signature, which was observed not only at the value markers but also by frequencies of CowM3 and CowM2 during the dry and wet categories, whereas the Giach fluctuation between wet and dry was restrained. The overlap profiles of peak markers on the fifth day of the rainfall event in all springs highlighted their similarities. These peaks correlated with the analysis of a tracer test conducted in February 2008 (27). A mixture of uranine and LiCl was introduced into a karst cave located 14 km from the 4 springs. The first arrival of uranine appeared in the Zuf spring after 88 h and in the Giach and the Shefa springs after 90 h. All springs except Gaaton showed a stable discharge, a minor fluctuation, and a long recession curve, which indicated that the spring recharge areas are far from their outlets. This can shed light on the fecal source contamination diversity that is located far from the springs’ outlets and was still detected by MST markers. Human and ruminant markers had different decay profiles during the rain event. While the human markers had delayed appearance and relatively fast removal, the ruminant markers demonstrated a slower removal in Zuf and Shefa, which can be attributed to increasing ruminant influence from nonpoint sources. The different pattern in Giach can be coincidental to a drastic change which can be correlated to a point source. The MST profiles in the tested area revealed a complex fecal pollution arena, comprised of various combined host origins

Ohad et al.

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