Article pubs.acs.org/est
Probabilistic Analysis Showing That a Combination of Bacteroides and Methanobrevibacter Source Tracking Markers Is Effective for Identifying Waters Contaminated by Human Fecal Pollution Christopher Johnston,†,‡ Muruleedhara N. Byappanahalli,§ Jacqueline MacDonald Gibson,¶ Jennifer A. Ufnar,⊥ Richard L. Whitman,§ and Jill R. Stewart*,‡,¶ †
Jardon and Howard Technologies Incorporated, Orlando, Florida 32826, United States Center for Coastal Environmental Health and Biomolecular Research and Hollings Marine Laboratory, U.S. National Oceanic and Atmospheric Administration, Charleston, South Carolina 29412, United States § U.S. Geological Survey, Great Lakes Science Center, Lake Michigan Ecological Research Station, Porter, Indiana 46304, United States ⊥ Division of Science and Technology, Southern Vermont College, Bennington, Vermont 05201, United States ¶ Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Campus Box 7431, Chapel Hill, North Carolina 27599, United States ‡
ABSTRACT: Microbial source tracking assays to identify sources of waterborne contamination typically target genetic markers of host-specific microorganisms. However, no bacterial marker has been shown to be 100% host-specific, and cross-reactivity has been noted in studies evaluating known source samples. Using 485 challenge samples from 20 different human and animal fecal sources, this study evaluated microbial source tracking markers including the Bacteroides HF183 16S rRNA, M. smithii nif H, and Enterococcus esp gene targets that have been proposed as potential indicators of human fecal contamination. Bayes’ Theorem was used to calculate the conditional probability that these markers or a combination of markers can correctly identify human sources of fecal pollution. All three human-associated markers were detected in 100% of the sewage samples analyzed. Bacteroides HF183 was the most effective marker for determining whether contamination was specifically from a human source, and greater than 98% certainty that contamination was from a human source was shown when both Bacteroides HF183 and M. smithii nif H markers were present. A high degree of certainty was attained even in cases where the prior probability of human fecal contamination was as low as 8.5%. The combination of Bacteroides HF183 and M. smithii nif H source tracking markers can help identify surface waters impacted by human fecal contamination, information useful for prioritizing restoration activities or assessing health risks from exposure to contaminated waters.
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INTRODUCTION Protection of water resources, including waters used for drinking, shellfish harvesting, or recreation, depends on our ability to detect and remediate fecal contamination and associated human pathogens. Current methods used to monitor water quality, however, cannot distinguish between sources of pollution. Microbial source tracking (MST), an approach used to discriminate among different sources of fecal pollution, may help identify waters most likely to pose risks to human health and could help ensure selection of appropriate and effective mitigation strategies to restore impaired waters. Microbial source tracking is an approach to identify microbes or genetic markers specifically associated with their host sources (e.g., human, dog, cow). Anaerobic bacteria common to gut microflora are among the most promising targets for MST assays because there is evidence that strains of these bacteria have coevolved with their hosts.1 Although detection of anaerobic bacteria to assess fecal contamination has tradition© 2013 American Chemical Society
ally been avoided due to difficulty associated with cultivation, modern molecular methods including the polymerase chain reaction (PCR) now make detection of these organisms rapid and practical, removing the need for cultivation.1,2 Source tracking methods in current use commonly target the anaerobic gut bacteria in the order Bacteroidales.3−5Bacteroides spp. make up approximately one-third of the human fecal microflora,6 well outnumbering E. coli and Enterococcus spp. Bacteroides spp. are also obligate anaerobes, so there is little concern over prolonged persistence or regrowth in the environment.7,8Studies evaluating human-specific Bacteroides markers in Australia reported that the HF183 marker was detected in 95% (n = 79) of tested sewage and individual Received: Revised: Accepted: Published: 13621
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and the German Collection of Microorganisms and Cell Cultures (DSM 861) (DSMZ, Braunschweig, Germany). DNA was extracted directly from these cultures using the DNeasy Tissue Kit following the manufacturer’s protocol for bacteria cells (Qiagen, Valencia, CA). Genomic DNA extracted from a culture of M. smithii was used as a positive control for the M. smithii nif H conventional PCR method and to create quantification standards for generating a standard curve for the M. smithii nif H qPCR method. DNA concentration was determined by spectrophotometry using a Nanodrop ND-1000 spectrophotometer (Thermo Scientific, Waltham, MA). Enterococcus faecium C68 served as a positive control for the Enterococcus esp PCR method.24 DNA extracts from human sewage samples served as positive controls for the Bacteroides HF183 method. Sample Collection and Processing. To test and compare the specificity and sensitivity of different microbial source tracking gene targets, PCR methods were applied to a variety of DNA extracts from different hosts. Two geographically distinct panels of DNA extracts from animal and human source fecal samples were created from locations in Northwestern Indiana and Mississippi. For the Indiana panel, fecal samples were collected between June 2006 and December 2007 and processed as described by Whitman et al.24 Briefly, sewage influent samples were collected from sewage treatment plants at various locations, pit toilets were sampled from camping and recreational areas, and samples were collected from different septic trucks at a local treatment facility. For domestic pets, livestock, or wildlife, samples were collected from animal boarding facilities, farms, or forested areas around the Indiana Dunes National Lakeshore in Porter County. For all samples, swabs of the fecal material (∼0.3−0.5 g) or pellets were transferred to sterile 15 mL centrifuge tubes containing 6 mL of phosphate-buffered water (PBW) and vortexed vigorously to create fecal slurry. For composite animal fecal samples, equal volumes of individual fecal slurries were pooled. The volume of fecal slurry used for the composite was dependent on the number of samples collected from individual animals (i.e., smaller volumes of fecal slurries were combined for a greater number of individual animal fecal samples collected). For the Mississippi panel, fecal samples were collected in 2005 and 2006 and processed as reported by Ufnar et al.12 Briefly, sewage samples (500 mL) were collected from sewers in Gulfport, MS. Individual human samples were collected and processed in Hattiesburg, MS. Individual animal fecal samples were collected from various farms, processing plants, or animal shelters in sterile 50 mL centrifuge tubes. Composite animal fecal samples consisted of bovine and swine waste lagoons and were collected in either 500 or 50 mL volumes. DNA Extractions. Aliquots (250 μL) of the fecal slurries collected in Indiana were transferred to 2 mL centrifuge tubes containing glass beads, and genomic DNA was extracted using the PowerSoil DNA Isolation Kit (MO BIO, Carlsbad, CA). The manufacturer’s protocol was followed with the following modifications: after adding solution C1 and vortexing, tubes were incubated at 60 °C for 4 min; tubes were put in a bead beater for 1 min rather than vortexing for 10 min; after adding solution C2 and C3, tubes were incubated at 4 °C for 30 min; C6 solution was added in increments of 30, 30, and 40 μL with centrifugation after each volume added. For the Mississippi DNA extract panel, individual fecal samples were processed using the UltraClean Soil DNA Extraction Kit (MO BIO) and the PowerSoil DNA Kit
human fecal samples and was absent in 94% (n = 201) of animal samples tested.9 Furthermore, a recent methods comparison study involving 27 participating laboratories demonstrated that HF183 is the most accurate MST target tested for discriminating human-source contamination.10 This latter study, however, was limited in the number and geographic diversity of challenge samples, and it only included challenge samples prepared by mixing one or two sources of fecal materials.11 Another promising anaerobic target proposed to track sewage pollution is nif H of Methanobrevibacter smithii.12,13 M. smithii is the dominant Achaean in the human gut, occurring in concentrations as high as 1010 per gram of dry weight.14−16 Moreover, M. smithii is the only species of Methanobrevibacter known to specifically colonize the human large intestine.17,18 Tests from the U.S. and U.K. using methane emissions from the breath found that approximately 33% of humans harbor methanogens.14 These data suggest that the marker is a more suitable indicator of sewage pollution rather than for identifying samples contaminated from individuals. Detection of M. smithii by PCR has been demonstrated in treated sewage and surface waters,19,20 and environmental persistence has been demonstrated for up to 21 days.12 A recent validation study reported that the nif H marker was 96% specific and 81% sensitive for human contamination when tested against 272 fecal and wastewater samples in Southeast Queensland, Australia.21 The authors concluded the nif H marker is sewage-specific, but that it may not be sensitive enough to detect sewage pollution in the environment unless it is combined with an additional marker. Using a combination of host-specific markers could help increase confidence in source identifications. No proposed MST markers have proven to be 100% host-specific.10 Also, more rigorous statistical tests are needed to calculate probabilities that a positive sample is indeed contaminated with the indicated host source, as well as to interpret negative results. Kildare et al.22 used Bayes’ Theorem to calculate conditional probabilities that four different Bacteroidales assays, targeting BacUni, BacHum, BacCow, and BacCan, could correctly identify host-specific fecal pollution. Wang et al.23 built on that work by evaluating ratios of these host-specific markers to general Bacteroidales to allocate sources of fecal contamination. No tests were conducted to evaluate whether combinations of markers targeting the same host species (e.g., humans) could increase confidence in source identifications. The goal of this study was to evaluate the sensitivity and specificity of promising human-specific source tracking markers using a large panel of fecal wastes from human and animal sources. A panel of DNA extracts consisting of 485 individual samples from 20 different host fecal sources was employed. To our knowledge, this study represents one of the largest panels of reference and known-source samples against which these markers have been tested. Using Bayes’ Theorem, we then calculated the conditional probability that these markers or combination of markers could correctly identify human sources of fecal pollution. Calculating these probabilities could make MST data more useful in resolving legal disputes over water contamination and could make MST more amenable for adoption in regulations aimed at protecting water resources.
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MATERIALS AND METHODS Bacterial Cultures, Standards, and Controls. Live cultures of M. smithii were obtained from the American Type Culture Collection (ATCC 35061) (ATCC, Manassas, VA) 13622
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PCR amplification conditions consisted of initial denaturation for 10 min at 95 °C and 35 cycles of denaturation for 1 min at 94 °C, annealing for 1 min at 58 °C, and elongation for 1 min at 72 °C. PCR products were separated on a 1.5% agarose gel stained with ethidium bromide and viewed under UV light. Bacteroides HF183. The Bacteroides HF183 assay, targeting the 16S rRNA gene, was performed using a modified protocol from Bernhard and Field.3,26 PCR reactions were carried out in 25 μL volumes containing 1× PCR buffer, 200 μM dNTPs, 1 U Taq polymerase, 0.5 μM each primer, and 2 μL of DNA template. Cycling conditions for the reaction consisted of initial denaturation for 5 min at 95 °C and 35 cycles of denaturation for 30 s at 95 °C, annealing for 30 s at 58 °C, and elongation for 1 min at 72 °C. A final elongation was performed for 6 min at 72 °C. PCR products were separated on a 1% agarose gel stained with ethidium bromide and viewed under UV light. Data Analysis. Sensitivity is the ability to detect a source when it is present and is calculated by dividing the number of true-positive results by the number of samples that should contain the target.27 Specificity is the ability to not detect a source when it is not present and is calculated by dividing the number of true-negative results by the number of samples that should not contain the target. For this study, sensitivity (r) and specificity (s) were calculated as r = a/(a + c) and s = d/(b + d), where a is the number of DNA samples positive for the PCR marker of its own species (true positive); b is the number of DNA samples positive for a PCR marker of another species (false positive); c is the number of DNA samples negative for a PCR marker of its own species (false negative); d is the number of DNA samples negative for a PCR marker of another species (true negative).27,28 To explore the potential use of the markers tested in this research for identifying water bodies affected by human fecal contamination, we employed Bayes’ Theorem29 to calculate the posterior probability that a human fecal source is present, given a prior assumption about the probability that human fecal contamination is present and detection of each marker alone or in combination with the others. For individual markers, the posterior probability that human fecal contamination is present, once the marker is detected, can be derived from Bayes’ Theorem, as follows:
following the manufacturer instructions. Sewer samples were prefiltered through a 3 μm cellulose acetate filter (Pall Corporation, West Chester, PA) and concentrated onto a 0.2 μm Supor-200 filter (Pall Corporation). Bound bacteria were dislodged from the filter by agitating with a magnetic stir bar for 5 min in 5 mL of sterile phosphate-buffered saline (PBS). Cells were pelleted by centrifugation for 15 min at 13 000g and resuspended in 2 mL, and DNA was extracted using the Ultraclean Soil DNA Extraction kit (MO BIO). The resulting DNA extracts were stored at −80 °C and shipped to the laboratory in Charleston, SC on dry ice. PCR Methods. M. smithii nifH qPCR. A TaqMan-based qPCR assay was developed to detect the nif H gene of M. smithii.13 Thermal cycling and fluorescence detection were carried out in the iQ5 Real-Time PCR detection system (BioRad, Hercules, CA). The reactions were performed in a total volume of 25 μL containing 1× PCR buffer (50 mM KCl, 20 mM Tris−HCl, pH 8.4), 5 mM MgCl2, 800 μM deoxynucleotide triphosphates (dNTPs), 800 nM primers, 240 nM of the Mnif probe, 2.3 U Taq DNA polymerase, and 3 μL of the M. smithii quantification standard or DNA extract. Standards were diluted in nuclease-free water and stored in single use aliquots at −80 °C. A five-point 10-fold serial dilution of the M. smithii genomic DNA (10 to 100 000 fg) was run in triplicate with each set of reactions to generate the standard curve, and the lower limit of detection was set using the lowest quantity of genomic DNA detected for the standard curve. All samples were run in triplicate with the standards acting as positive controls and no-template negative controls. The cycling conditions were an initial denaturation for 10 min at 95 °C and 50 cycles of denaturation for 10 s at 95 °C and annealing/extension for 30 s at 57 °C. Real-time fluorescence measurements were collected by the iQ5 instrument beginning after the first 3 cycles to prevent any residual bubbles from causing any background fluorescence signal. Background well factors were collected from the experimental plate, and the fluorescent thresholds were set automatically by the iQ5 software in the PCR baseline subtracted curve fit analysis mode. The cycle thresholds (CT) at which the sample fluorescence exceeds background fluorescence were recorded for the extracted samples and quantitative standards. The numbers of M. smithii nif H gene targets are interpolated from the standard curve generated from the quantification standards in relation to their CT. M. smithii nif H Conventional PCR. A conventional PCR method for detecting the M. smithii nif H gene was first developed by Ufnar et al.12 PCR reactions were carried out in either 10 or 20 μL volumes containing 1× PCR buffer, 0.1% BSA, 200 μM dNTPs, 0.5 U Taq polymerase, 0.5 μM each primer, and 1 μL of DNA template. Cycling conditions for the reaction consisted of initial denaturation for 2 min at 92 °C and 30 cycles of denaturation for 1 min at 92 °C, annealing for 30 s at 55 °C, and elongation for 1 min at 72 °C. A final elongation was performed for 6 min at 72 °C. PCR products were separated on a 1% agarose gel stained with ethidium bromide and viewed under UV light. Enterococcus esp. The Enterococcus esp gene has been proposed as a marker of human pollution in environmental waters.25 PCR was performed as described by Whitman et al.24 and using E. faecium C68 as a positive control. Reactions were performed in 50 μL volumes containing 1× PCR buffer, 1.5 mM MgCl2, 200 μM dNTPs, 0.3 μM each primer, 2.5 U AmpliTaq DNA polymerase, and 5 μL of DNA template. The
P(M +|H ) × P(H ) P(M |H ) × P(H ) + P(M +|H c) × P(H c) r × P(H ) = r × P(H ) + (1 − s) × P(H c) (1)
P(H |M +) =
+
where H represents the event that human fecal contamination is present, Hc represents the absence of human fecal contamination, and M+ represents the event that the sample tests positive for the marker. Similarly, using Bayes’ Theorem, the probability that human fecal contamination is present given that two markers are detected in a sample can be computed from P(H |M1+ ∩ M 2+) =
r1 × r2 × P(H ) r1 × r2 × P(H ) + (1 − s1) × (1 − s2) × P(H c) (2)
where ri represents the sensitivity of marker i and si represents the specificity of marker i. 13623
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Table 1. PCR Results from the M. smithii nif H, Bacteroides HF183, and Enterococcus esp Gene Markers against a Panel of Human and Nonhuman Fecal Sources Collected in Indianaa source
M. smithii nif H qPCR
Bacteroides HF183 PCR
Enterococcus espfmb PCR
human
pit toilet septic truck sewage influent
47 (7/15) 45 (9/20) 100 (24/24)
47 (7/15) 5 (1/20) 100 (24/24)
0 (0/15) 30 (6/20) 100 (24/24)
individual animals
cow bird cat dog goose gull mouse raccoon chicken goat sheep deer chipmunk rabbit
46 (6/13) 0 (0/5) 0 (0/5) 0 (0/5) 0 (0/10) 0 (0/5) 0 (0/5) 0 (0/5) 0 (0/5) 100 (2/2) 50 (1/2) 0 (0/13) 0 (0/1) 0 (0/1)
0 0 0 0 0 0 0 0 0 0 0 0 0 0
(0/13) (0/5) (0/5) (0/5) (0/10) (0/5) (0/5) (0/5) (0/5) (0/2) (0/2) (0/13) (0/1) (0/1)
N/A 0 (0/4) N/A 35 (9/26) N/A 10 (2/20) N/A N/A N/A N/A N/A 0 (0/1) 0 (0/1) 0 (0/1)
composite animals
bird cat dog goose gull mouse raccoon chicken horse squirrel turkey rooster deer
0 (0/8) 0 (0/7) 0 (0/10) 0 (0/3) 0 (0/4) 0 (0/3) 0 (0/3) 0 (0/1) 0 (0/1) 0 (0/1) 0 (0/1) 0 (0/1) N/A
0 (0/8) 0 (0/7) 0 (0/10) 0 (0/3) 0 (0/4) 0 (0/3) 0 (0/3) 0 (0/1) 0 (0/1) 100 (1/1) 0 (0/1) 0 (0/1) N/A
0 (0/5) 0 (0/7) 60 (6/10) 0 (0/2) 25 (1/4) 0 (0/3) 0 (0/3) N/A N/A N/A N/A N/A 0 (0/1)
sample type
a Results for each gene marker shown as percent positive detection (positive PCR results/total number of samples tested). bData from Whitman et al.24
and 45% of samples collected from pit toilet and septic trucks in Indiana, respectively, as well as 78% of individual human feces sampled in Mississippi. The conventional PCR method for M. smithii nif H was only tested on the Mississippi DNA extract panel (Table 2) because there was not enough genomic DNA from the Indiana panel, and this gene was detected in 93% of the sewage samples and 29% of the individual fecal samples. While the conventional PCR method did not detect the marker in any of the nonhuman sources, the qPCR method did display cross reactivity, notably in fecal samples from cow, goats, sheep, deer, and pigs from both Mississippi and Indiana. This marker was also detected in 48% of the pig samples from the Mississippi panel, which were not part of the host sources for the Indiana panel. Fecal waste tested from birds, horses, and household pets were all negative (Table 2). Quantitative results for the nif H qPCR marker varied depending on the host source as well as among individuals from the same source (data not shown). As a general trend, sheep and goat fecal samples were found to have higher levels of the nif H marker (at levels comparable to humans) than pigs or cows. The nif H qPCR marker was detected at the lowest levels in deer samples. The Bacteroides HF183 gene marker was tested on the Indiana DNA extract panel (Table 1). It was detected in 100% of the sewage samples, 47% of pit toilet samples, and 5% of
When one marker is detected and the second is not, then the posterior probability of human fecal contamination is given by P(H |M1+ ∩ M 2−) =
r1 × (1 − r2) × P(H ) r1 × (1 − r2) × P(H ) + (1 − s1) × s2 × P(H c) (3)
Mi−
where represents the event that marker i is absent. Bayes’ Theorem was used to derive similar equations for the posterior probability that human fecal contamination is present given various other combinations of results for the genetic markers tested in this research (for example, two markers positive and the third marker negative, and so on).
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RESULTS The panel of DNA extracts, representing 485 individual samples from 20 different host fecal sources, was tested using PCR methods proposed for microbial source tracking.12,24,30 The M. smithii nif H qPCR method was tested on the complete panel of DNA extracts from both Mississippi and Indiana (Tables 1 and 2). This method detected the marker in 100% of the sewage samples (both influent and effluent) from both Mississippi and Indiana. The marker was also detected in 47% 13624
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marker was detected in 100% of sewage samples and 30% of septic truck samples. It was not detected in any of the pit toilet samples. The marker was also detected in both individual and composite samples from dogs and gulls and was absent in all other animal samples. A subset of the positive samples was confirmed by sequencing as described in Byappanahalli et al.31 The overall specificity of the M. smithii qPCR marker was 0.93 and 0.58 for the Indiana and Mississippi panels, respectively (Tables 3 and 4). In comparison, the M. smithii conventional PCR method specificity was 1.0 for the Mississippi panel; however, the sensitivity of the conventional PCR method was 0.46, while the sensitivity of the qPCR method was 0.68 and 0.82 for the Indiana and Mississippi panels. The Enterococcus esp marker specificity and sensitivity were 0.80 and 0.51 for the Indiana panel (Table 3). The Bacteroides HF183 marker specificity and sensitivity were 0.99 and 0.54, respectively for the Indiana panel (Table 3). In order to explore the potential usefulness of the results presented here for analysis of fecal source contributions to a given water body, we employed Bayes’ Theorem to estimate the posterior probability that a fecal organism isolated from a water sample originates from a human source, given various combinations of the presence or absence of the markers. Figure 1 shows the results for differing prior probabilities that contamination originates from human sources. The prior probability is defined in the typical manner employed in Bayesian statistics (see, for example, DeGroot and Schervish32): it is the decision-maker’s advance prediction (before collecting microbial source-tracking data) of the likelihood that the water body is impacted by a human source. Such prior probabilities might be based on characteristics of the watershed, for example, or on previous test results using traditional fecal indicator
Table 2. Frequency of M. smithii nif H Detection by PCR and qPCR Methods against a Panel of Human and Nonhuman Fecal Sources Collected in Mississippia M. smithii nif H qPCR
M. smithii nif H PCRb
individuals sewage influent treated sewage
78 (38/49) 100 (12/12)
29 (20/70) 93 (25/27)
100 (1/1)
ND
individual animals
cow horse goat sheep deer rat pig chicken
41 (18/44) 0 (0/40) 91 (21/23) 100 (23/23) 38 (9/24) 0 (0/20) 48 (12/25) 0 (0/24)
0 (0/46) 0 (0/23) 0 (0/2) 0 (0/2) 0 (0/20) ND 0 (0/24) 0 (0/23)
composite animals
cow lagoon pig lagoon
89 (8/9) 91 (10/11)
0 (0/2) ND
sample type human
source
a
Results for each gene marker shown as percent positive detection (positive PCR results/total number of samples tested). bData from Ufnar et al.12
septic truck samples. The only nonhuman source that the Bacteroides HF183 marker was detected in from this study was a squirrel composite sample. Not enough genetic material was available to test this marker against the samples collected in Mississippi. The Enterococcus esp gene frequency was similarly tested on a portion of the samples in the Indiana panel (Table 1). The
Table 3. Determination of the Specificity (s) and Sensitivity (r) of the M. smithii nif H, Bacteroides HF183, and Enterococcus esp Gene Markers against a Panel of Human and Nonhuman Fecal Sources Collected in Indiana M. smithii nif H source human nonhuman pit toilet septic truck sewage influent cow bird cat dog goose gull mouse raccoon chicken goat sheep deer chipmunk rabbit horse squirrel turkey rooster a
s (%)
r (%)
n
67.8
59 120 15 20 24 13 13 12 15 13 9 8 8 6 2 2 13 1 1 1 1 1 1
92.5 46.7 45 100 53.9 100 100 100 100 100 100 100 100 0 50 100 100 100 100 100 100 100
Enterococcus espfma
Bacteroides HF183 s (%)
r (%)
n
54.2
59 120 15 20 24 13 13 12 15 13 9 8 8 6 2 2 13 1 1 1 1 1 1
99.2 46.7 5 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 0 100 100
s (%)
r (%)
n
50.9
59 88 15 20 24 0 9 7 36 2 24 3 3 0 0 0 2 1 1 0 0 0 0
79.6 0 30 100 N/A 100 100 58.3 100 87.5 100 100 N/A N/A N/A 100 100 100 N/A N/A N/A N/A
Data from Whitman et al.24 13625
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with low prior probabilities that human fecal contamination is present, the presence of this marker results in a very high posterior probability that the contamination is, in fact, of human origin. Near certainty (probability close to 100%) that a sample is impacted by contamination of human origin can be obtained if both the M. smithii nif H and Bacteroides HF183 markers are present, even in cases where the prior probability of human fecal contamination is very low. Both of these markers are more powerful in confirming human fecal contamination than is the Enterococcus esp marker.
Table 4. Determination of the Specificity (s) and Sensitivity (r) of the M. smithii nif H Gene Markers against a Panel of Human and Nonhuman Fecal Sources Collected in Mississippi M. smithii nif H qPCR source human nonhuman
a
s (%)
r (%)
n
82.3
62 243
58.4
individual human sewage influent treated sewage
77.5
cow horse goat sheep deer rat pig chicken cow lagoon pig lagoon
59.1 100 8.7 0 62.5 100 52 100 11.1 9.1
49 100 100
M. smithii nif H PCRa s (%)
n
46.4
97 142
100 28.6
12 1 44 40 23 23 24 20 25 24 9 11
r (%)
100 100 100 100 100 N/A 100 100 100 N/A
70 92.6 N/A
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27 0
DISCUSSION This study serves to establish the probability that detecting a particular MST marker or combination of markers is accurate in identifying contaminant sources in impacted waters, although the approach still needs to best tested for environmental samples. The current results show that Bacteroides HF183 is the most effective single marker for determining fecal inputs from human sources. Greater than 98% certainty that a sample is impacted by fecal contamination of human origin can be obtained if a combination of markers, HF183 and M. smithii nif H, is present, even in cases where the prior probability of human fecal contamination is as low as 8.5%. Using one of the largest panels of reference samples available, results of this study show high but not absolute sensitivity of the markers for their intended targets. Both HF183 and M. smithii nif H markers were consistently detected in humansource fecal samples, indicating their widespread occurrence in these sources. Exceptions were noted in the ability to consistently detect these markers from pit toilets. The toilets were in use at the time of sampling, so it is not clear whether the bacteria or target DNA were highly degraded in negative samples or if individuals contributing wastes to the toilets did not actually harbor the bacterial targets. Among the animal samples tested, M. smithii was detected in ruminants (cows, goats, and sheep) using the qPCR assay and HF183 was detected in a composite scat sample from squirrels.
46 23 2 2 20 0 24 23 2 0
Data from Ufnar et al.42
bacteria. Where no prior knowledge is available, the prior probability can be assumed to be 50%. That is, the decisionmaker or analyst has no information about whether contamination is more likely than not to be present. The results in Figure 1 assume the sensitivity and specificity of the markers are as observed in the data set from Indiana because the sensitivity and specificity data needed for similar computations using the Mississippi data were not available. These data show that the Bacteroides HF183 marker is the most effective marker for determining whether a sample may have been impacted by a human source of fecal contamination: even
Figure 1. Predicted probability that a sample may be impacted by fecal contamination of human origin, given various combinations of the presence or absence of the genetic markers tested in this study and different prior assumptions about the likelihood of human contamination. 13626
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linkages between discharges, nonpoint source pollution, and potential ecological impacts such as receiving water quality.
These results are consistent with other reports in the literature that show exceptions to the host-specificity of source tracking markers. For example, cross-reactivity has been reported between the HF183 marker and samples from chickens, dogs, and birds.9,33,34 Ahmed et al.21 reported 81% sensitivity of the nif H marker, with cross-reactivity noted for one bird (n = 30) and six composite pig (n = 20) samples. Such nonspecific reactions in domesticated animals (chickens, dogs, pigs) may emphasize the need for additional confirmatory tests, especially in watersheds impacted by intense animal husbandry operations, such as dairy, poultry, and swine farms. In contrast to the cross-reactivity noted for the nif H qPCR assay, the conventional PCR assay tested against the Mississippi panel showed 100% specificity. That is, this marker was not detected in any of the animal fecal samples tested. Discordant results between the PCR and qPCR method may be due to greater sensitivity of the qPCR assay. The qPCR method has been shown to be more sensitive with probe-based detection (limit of detection 10 fg of genomic DNA) compared to the conventional PCR method with gel-based detection (1 pg of genomic DNA).12,13 We recognize that our study did not fully address potential geographic or temporal variability for the tested markers. Geographic and temporal variation was determined to be an issue with library-based source-tracking methods, where characteristics of organisms were compared to databases to determine their sources.35−38 However, it is not clear the extent to which this will be an issue for library-independent source tracking markers, such as the ones tested in this study. A collaborative study among scientists in the European Union reported consistently high specificity of the HF183 marker among all countries that participated, although regional variations in specificity of the ruminant marker CF128 were noted.34 The panel of challenge samples used in this study consisted of feces and wastewaters obtained from two geographically different locations in the United States: Indiana and Mississippi. Although the sensitivity and specificity of the human markers differed slightly between the Indiana and Mississippi panels (Tables 3 and 4), the results were generally consistent with each other and with similar validation studies conducted in Australia9,21,39 and California.40 Other locations may differ in host distributions of the tested markers, and validation of MST markers for a particular geographic area should be conducted prior to using the markers in field studies. Application of probabilistic analysis to MST data, as demonstrated in this study, could offer a powerful tool to translate results from field studies into information needed for decision-making. This approach could also help with selection of the most appropriate tools available in the MST “toolbox”.27,41 Identifying waters with the highest probability of human-source contamination could help prioritize those most likely to pose a threat to human health.20 Identifying particular animal sources of contamination could help identify effective and practical strategies for mitigating impacted waters. This approach could also allow managers and regulators to link contaminant sources with impacts. Apportioning impacts is important to water quality managers, utilities, and others who are tasked with bringing water bodies into compliance with standards, but who lack appropriate means to assess the impacts of improved treatment or reduced discharges.11 The move toward tools that identify the probability of water contamination from specific sources could help confirm
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AUTHOR INFORMATION
Corresponding Author
*Phone: 919-966-7553; fax: 919-966-7911; e-mail: Jill.
[email protected]. Notes
This publication does not constitute an endorsement of any commercial product or intend to be an opinion beyond scientific or other results obtained by the National Oceanic and Atmospheric Administration (NOAA). No reference shall be made to NOAA, or this publication furnished by NOAA, to any advertising or sales promotion which would indicate or imply that NOAA recommends or endorses any proprietary product mentioned herein, or which has as its purpose an interest to cause the advertised product to be used or purchased because of this publication. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors would like to thank Dawn Shively for help with sample collection and analyses. This work was funded in part by the U.S. Geological Survey Ocean Research Priorities Plan. This article is contribution 1802 of the U.S. Geological Survey Great Lakes Science Center.
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REFERENCES
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