Distribution of Genetic Marker Concentrations for Fecal Indicator Bacteria in Sewage and Animal Feces

Abstract

Very little is known about the density and distribution of fecal indicator bacteria (FIB) genetic markers measured by quantitative real-time PCR (qPCR) in fecal pollution sources. Before qPCR-based FIB technologies can be applied to waste management and public health risk applications, it is vital to characterize the concentrations of these genetic markers in pollution sources (i.e., untreated wastewater and animal feces). We report the distribution of rRNA genetic markers for several general FIB groups, including Clostridium spp., Escherichia coli, enterococci, and Bacteroidales, as determined by qPCR on reference collections consisting of 54 primary influent sewage samples collected from treatment facilities across the United States and fecal samples representing 20 different animal species. Based on raw sewage sample collection data, individual FIB genetic markers exhibited a remarkable similarity in concentration estimates from locations across the United States ranging from Hawaii to Florida. However, there was no significant correlation between genetic markers for most FIB combinations (P > 0.05). In addition, large differences (up to 5 log₁₀ copies) in the abundance of FIB genetic markers were observed between animal species, emphasizing the importance of indicator microorganism selection and animal source contribution for future FIB applications.

INTRODUCTION

The use of fecal indicator bacteria (FIB) methods for applications such as quantitative microbial risk assessment (29, 38, 39), estimation of microbial fecal loads from different pollution sources (43, 44), and evaluation of waste management practices in agricultural settings may (14, 26) require the estimation of the concentration of an indicator organism from each contributing animal source. The majority of data available on FIB distributions rely on the detection of viable cells directly from samples of interest by cultivation on selective media. However, quantitative real-time PCR (qPCR) technologies are becoming more popular due to decreased sample processing times (10) and smaller amounts of variability compared to some cultivation methods (32). As a result, numerous qPCR methods have been developed to measure a variety of FIB microorganisms (5, 7, 16, 19, 25, 28, 35). As researchers explore the use of qPCR methods for FIB measurements, there is a growing body of evidence suggesting that the enumeration of indicator microorganisms by cultivation methods is not always equivalent to enumeration with qPCR technologies (1, 12, 18, 24, 29).

There are several possible explanations for this lack of agreement. First, cultivation methods measure viable cells, whereas qPCR methods measure nucleic acids isolated from viable cells, viable but not culturable cells, dead cells, or even free DNA released from cells after lysis (dependent on extraction protocol). Second, Enterococcus and Escherichia coli nucleic acids measured by qPCR have been shown to decay at different rates compared to viable cell counts determined by cultivation in a number of environmental conditions (9, 41). Third, the specificity of selective culture and qPCR methods may differ. The realization that there can be a disconnect between culture and qPCR FIB enumeration has led some researchers to focus on characterizing the distribution of targeted genetic markers in fecal pollution sources (16, 19, 25, 30, 36, 42). The most thorough research report to date characterized enterococci, E. coli, and Bacteroidales genetic marker concentrations in 12 untreated sewage samples and fecal samples from four different animal sources, including cow, horse, dog, and Canada goose, collected from the southern California region (36). Based on this geographically localized data set, the authors reported substantial differences in the distribution of these FIB groups in the different animal sources. As the application of qPCR methods becomes more common, there is clearly a need to generate FIB genetic marker distribution data from sewage and fecal samples representative of a broader geographic range and number of animal species. In turn, this information can be used to confirm previously reported trends and identify new patterns between FIB genetic marker concentrations and different animal sources.

The goal of this study was to characterize the concentration of four FIB genetic markers measured by qPCR in sewage and fecal matter from a large array of pollution sources and geographic locations. We report the estimated concentration of genetic targets from Enterococcus (20, 35), E. coli (4), Clostridium spp. (28), and Bacteroidales (7) FIB qPCR assays in 54 primary influent sewage samples collected from facilities across the United States and fecal samples representing 20 different animal species. Based on raw sewage sample collection data, individual FIB genetic markers exhibited a remarkable similarity in concentration estimates from locations across the United States, ranging from Hawaii to Florida. In addition, large differences in the abundance of FIB genetic markers are observed between animal species, emphasizing the importance of indicator microorganism selection and animal source contribution for FIB applications.

MATERIALS AND METHODS

Sewage and fecal collections.

Fecal samples from individual animals and primary effluent sewage from wastewater treatment plants (WWTP) were collected for analysis as previously described (34). Animal fecal samples represent 20 distinct animal species, including Homo sapiens (human, n = 16), Anser sp. (Canada goose; n = 12), Felis catus (cat, n = 10), Gallus gallus (chicken, n = 10), Odocoileus virginianus (white-tail deer, n = 5), Odocoileus hemionus (mule deer, n = 5), Cervus elaphus (elk, n = 5), Canis familiaris (dog, n = 10), Anas sp. (duck, n = 12), Capra aegagrus (goat, n = 7), Pelecanus sp. (pelican, n = 5), Sus scrofa (pig, n = 10), Laridae (gull, n = 12), Ovis aries, (sheep, n = 10), Procyon loter (raccoon, n = 2), Meleagris sp. (turkey, n = 7), Bos taurus (cow, n = 18), Delphinidae (dolphin, n = 3), Zalophus californianus (sea lion, n = 5), and Phocidae (elephant seal, n = 5). All wildlife samples were collected from feral animals. Each sample was collected from a different individual. A single grab sewage sample was collected at each geographical location (n = 54) across the United States over a 6-month period of time (Fig. 1). The sewage treatment facilities represented human population sizes ranging from 3,000 to 1.2 × 10⁶ individuals based on population-served statistics and generated between 0.25 and 200 million gallons per day of raw sewage.

Fig 1.

Geographic information systems map of the United States indicating the location of each wastewater treatment facility. The color of each circle denotes the population served by the facility, and the diameter of each circle indicates the average daily inflow of sewage in million gallons per day.

DNA extraction of fecal and untreated wastewater samples.

Fecal DNA extractions were performed with the FastDNA kit for soils (Q-Biogene, Carlsbad, CA) as previously described (31). For untreated wastewater samples, 25 ml of each sample was filtered through a 0.2-μm-pore-size Supor-200 filter (GE HealthCare, North America), and each filter was placed in a sterile 1.5-ml microtube and stored at −80°C until time of analysis. DNA from sewage filters was extracted using the DNA-EZ kit (GeneRite, New Brunswick, NJ) according to the manufacturer's instructions. DNA extraction yields were determined with a NanoDrop ND-1000 UV spectrophotometer (NanoDrop Technologies, Wilmington, DE). Individual fecal DNA extracts were then combined into composites for each animal source to estimate the concentration of FIB genetic markers. Composites ranged from two (raccoon) to 16 (human) separate samples where an identical mass of DNA from each extract was combined to allow for equivalent representation of each individual. Composite extracts were used to reduce cost and allow for the testing of a larger number of animal sources. In addition, fecal pollution sources affecting a body of water will typically consist of more than one individual. The same mass of total DNA was added to each qPCR mixture to (i) standardize the test concentration of each DNA extract across samples, (ii) avoid the need to measure and correct for DNA extraction efficiencies between samples, (iii) eliminate error introduced by variability in sample consistencies (solid, liquid, or something in between), and (iv) eliminate the potential for differences in DNA concentration between reaction mixtures that could affect amplification chemistry.

Genomic DNA standards.

DNA standards for all qPCR assays were prepared from bacterial cultures as previously described (4, 13, 28). Cells were lysed in a bead mill for 60 s at maximum speed, and the debris was removed by centrifugation. Approximately 400 μl of supernatant containing genomic DNA was transferred to a sterile 1.7-ml low-retention microcentrifuge tube and incubated for 1 h at 37°C with 16.6 ng/μl RNase A (catalog number D-5006; Sigma-Aldrich, St. Louis, MO). RNase-treated genomic DNA extracts were then purified with a commercially available silica column adsorption kit according to the vendor's directions (DNA-EZ; GeneRite, Inc., North Brunswick, NJ). Genomic DNA concentrations were determined with a NanoDrop ND-1000 UV spectrophotometer (NanoDrop Technologies, Wilmington, DE). rRNA gene copy number concentrations were determined from reported estimates based on genome size and rRNA gene copy numbers per genome (17). Dilutions were then prepared containing 10 to 4 × 10⁴ target copy sequences per 5 μl. Genomic DNA standards were stored at 4°C during the course of this study.

qPCR amplification.

The four real-time PCR (qPCR) assays used in this study were Entero1, uidA, Cperf, and GenBac3 (4, 7, 20, 28, 35), for enterococci, E. coli, Clostridium spp., and Bacteroidales, respectively. The primer, TaqMan probe, and presumptive target organism(s) for each qPCR assay are shown in Table 1. Real-time PCR assays were conducted using a 7900 HT fast real-time sequence detector (Life Technologies, United States). Simplex reaction mixtures contained 1× TaqMan universal master mix, 0.2 mg/ml bovine serum albumin (Sigma-Aldrich, St. Louis, MO), 1 μM each primer, 80 nM FAM-labeled TaqMan probe (Life Technologies), and fecal DNA extracts containing 1 ng total DNA or 10 to 4 × 10⁴ target gene copies (genomic DNA standards) in a total reaction mixture volume of 25 μl. Multiplex reaction mixtures were prepared in the same manner except that 80 nM TET-labeled UC1P1 TaqMan probe (Table 1) and 25 copies of an internal amplification control (IAC) template were added to each reaction mixture. All reactions were performed in duplicate. The thermal cycling conditions were 2 min at 95°C, followed by 40 cycles of 5 s at 95°C and 30 s at 60°C. Data were initially analyzed with Sequence Detector software (version 2.3.2; Life Technologies) at a threshold determination of 0.03. Quantification cycle (C_q) values were exported to Microsoft Excel in preparation for further statistical analysis.

Table 1.

Real-time PCR primers and probes

Assay	Reported targeta	Locusb	Primer and probe sequences (5′ to 3′)	Reference(s)
Entero1	Enterococcus spp.	23S rRNA	ECST748F: AGAAATTCCAAACGAACTTG	20, 35
			ENC854R: CAGTGCTCTACCTCCATCATT
			GPL813TQ: (6-FAM)TGGTTCTCTCCGAAATAGCTTTAGGGCTA(TAMRA)
uidA	E. coli	uidA	F: CAACGAACTGAACTGGCAGA	4
			R: CATTACGCTGCGATGGAT
			Probe: (6-FAM)CCCGCCGGGAATGGTGATTAC(TAMRA)
GenBac3	Bacteroidales spp.	16S rRNA	GenBactF3: GGGGTTCTGAGAGGAAGGT	7
			GenBactR4: CCGTCATCCTTCACGCTACT
			GenBactP2: (6-FAM)CAATATTCCTCACTGCTGCCTCCCGTA(TAMRA)
Cperf	Clostridium spp.	16S rRNA	F: CATGCAAGTCGAGCGAKG	28
			R: TATGCGGTATTAATCTYCCTTT
			Probe: (6-FAM)CCCACGTGTTACTCACCCGTCCG(TAMRA)
Multiplex		IAC	UC1P1: (TET)CCTGCCGTCTCGTGCTCCTCA(TAMRA)	13, 37

^aIntended fecal microorganism source target.

^bAssay's gene target.

Screening for amplification interference.

IAC templates were used to evaluate the suitability of DNA extracts for qPCR amplification. The amplification interference criterion for each multiplex assay was based on repeated experiments measuring the mean C_q of a 25-copy IAC spike in buffer only. Evidence of amplification interference was defined as any observed IAC C_q value measured in a sample DNA extract that was greater than the respective laboratory control mean C_q plus 1.5 for each assay (32). Amplification interference was further classified as either inhibition or competition based on observed competition thresholds calculated separately for each multiplex assay. Competition thresholds were defined as the intersection C_q of a multiplex calibration curve and the IAC range of quantification (ROQ) upper bound (Fig. 2). An IAC ROQ is the range of calibration curve standard concentrations where the IAC C_q does not significantly change based on analysis of variance (ANOVA). Any DNA extracts exhibiting amplification interference where the respective sample DNA extract C_q is greater than the competition threshold were designated inhibition. Any sample DNA extract C_q values lower than the competition threshold were influenced by competition between the IAC and the sample DNA target.

Fig 2.

Determination of internal amplification control (IAC) range of quantification (ROQ) and competition threshold for the GenBac3 multiplex qPCR assay. The solid diagonal line indicates the fitted multiplex calibration curve. The mean IAC cycle threshold (C_q) values were plotted for each standard concentration based on a 25-copy IAC spike and are connected with a blue dotted line. Error bars indicate standard deviations. The IAC ROQ (top dashed horizontal line) delineates the range of concentrations where no significant change in IAC C_q measurements was observed, based on ANOVA. The competition threshold (bottom red, dashed horizontal line) shows where the multiplex calibration curve and IAC ROQ intersect.

Monitoring for extraneous DNA.

To monitor for potential sources of extraneous DNA during laboratory analyses, a minimum of three no-template amplifications with purified water substituted for template DNA were performed for each 96-well qPCR experiment.

Calculations and statistics.

Master calibration curves, unknown DNA concentration estimates, and credible intervals were determined using a Bayesian Markov chain Monte Carlo (MCMC) approach as previously described (37). MCMC calculations were performed using the publicly available software WinBUGS, version 1.4.1 (21). ANOVA tests were performed using SAS (Cary, NC) with the procedures PROC MIXED and PROC GLM. Amplification efficiencies (E) were based on the following equation: E = 10^(−1/slope) − 1. To determine the lower limit of quantification (LLOQ), the mean C_q value was calculated from running the 10-copy genomic standard a minimum of 15 times for each assay and application (simplex and multiplex). Potential relationships between FIB genetic marker concentrations and sewage reference samples were evaluated by testing the null hypothesis that the Pearson correlation coefficient (r) is zero.

RESULTS

Calibration curves and performance metrics.

Master calibration curve equations and associated performance metrics are reported for simplex and multiplex applications in Table 2 and 3, respectively. All qPCR assay applications indicated R² values greater than 0.965 and E values greater than 86%, and the range of quantification (ROQ) values spanned 10 to 4 × 10⁴ copies of target DNA (entire ranged tested in study). LLOQ C_q thresholds ranged from 33.96 to 37.69 (Tables 2 and 3).

Table 2.

Simplex qPCR calibration curves and performance metrics

Assay	Equation	Ea	R2	LLOQb
Cperf	Y = 38.8 − 3.55X	0.95	0.978	35.36
uidA	Y = 41.4 − 3.69X	0.86	0.992	37.69
Entero1	Y = 37.5 − 3.15X	1.08	0.965	33.99
GenBac3	Y = 37.7 − 3.44X	0.95	0.993	34.17

^aAmplification efficiency [E = 10^(−1/slope) − 1].

^bLower limit of quantification (mean C_q of 10-copy-standard data).

Table 3.

Multiplex qPCR calibration curves, performance metrics, and interference parameters

Assay	Equation	Ea	R2	LLOQb	IAC ROQc	Interference Cq	Competition Cq
Cperf	Y = 38.3 − 3.44X	0.95	0.978	35.36	1 to 2	36.9	31.4
uidA	Y = 39.8 − 3.30X	1.01	0.965	36.38	1	36.5	32.47
Entero1	Y = 37.7 − 3.34X	0.99	0.975	34.09	1 to 2	37.7	31.0
GenBac3	Y = 37.5 − 3.42X	0.96	0.995	33.96	1 to 3.6	35.5	25.19

^aAmplification efficiency [E = 10^(−1/slope) − 1].

^bLower limit of quantification (mean C_q of 10-copy-standard data).

^cInternal amplification control range of quantification.

Quantification of FIB genes in untreated sewage.

FIB genetic markers were detected in all 54 sewage samples at a test concentration of 1 ng total DNA for each FIB qPCR assay. However, a few sewage samples contained genetic marker concentrations below the LLOQ for the Entero1 (n = 2) and uidA (n = 1) assays. The mean log₁₀ and standard deviation values for each genetic marker across the entire sewage collection were 2.19 ± 0.51 (Entero1), 2.26 ± 0.21 (uidA), 2.86 ± 0.53 (Cperf), and 4.47 ± 0.35 (GenBac3). A Pearson correlation test indicated a significant positive correlation between the results for Cperf and GenBac3 (r = 0.36; P = 0.0074) and uidA and GenBac3 (r = 0.67; P < 0.0001). All other assay combinations resulted in no significant correlations (P > 0.05).

Quantification of FIB genes in fecal samples.

The mean log₁₀ concentration of FIB genetic markers per nanogram of total DNA was determined in 20 different animal species (Fig. 3). The GenBac3 genetic marker was the only indicator detected in all animal sources, in concentrations ranging from below the LLOQ (pelican and gull) to 5.01 ± 0.028 log₁₀ copies per nanogram of total DNA (human). Detectable levels of the Cperf genetic marker were present in all but two animal sources (whitetail deer and Canada goose), with the highest concentration observed in raccoon (4.42 ± 0.054). Entero1 genetic markers were absent in two animal sources (Canada goose and pelican), with the highest concentrations in sea lion (3.52 ± 0.081). E. coli measured with the uidA qPCR assay was undetectable in five samples, including four ruminants (dairy cattle, elk, goat, and whitetail deer) and pelican. The highest concentration for uidA genetic markers was observed in raccoon (4.34 ± 0.029).

Fig 3.

Plots depict mean log₁₀ copies per nanogram of total DNA ± standard deviation for each FIB qPCR genetic marker, including Cperf (A), uidA (B), Entero1 (C), and GenBac3 (D) assays, tested on 21 animal sources. Horizontal dashed line represents respective lower limit of quantification (LLOQ estimated from mean C_q of respective 10-copy standard data). Black dots indicate log₁₀ copies per nanogram of total DNA. Green dots denote a positive detection (detectable but below LLOQ). Red dots represent no detection result.

Amplification interference and other quality controls.

Each DNA extract was screened for amplification interference using a multiplex approach where 25-copy spikes of IAC target copies were measured simultaneously with FIB genetic markers. To identify amplification interference, an interference C_q was calculated for each multiplex assay (Table 3). No fecal DNA extracts exhibited amplification interference based on Entero1 IAC multiplex reactions. For sewage DNA samples, each extract was evaluated with all four FIB qPCR multiplex assays. The frequency of amplification interference between assays ranged from 0% (Entero1 and uidA) to 41.7% (GenBac3) based on a total of 108 individual measurements (two per sewage sample). Amplification interference can arise from two possible scenarios, inhibition from substances that coextract during DNA purification or competition between the IAC and the respective FIB DNA target. A competition threshold was calculated to delineate between these two possibilities. Competition thresholds ranged from 25.19 to 32.47 C_q (Table 3) and indicate that all instances of amplification interference, regardless of the FIB assay, were due to competition and not inhibition. The results of 116 no-template amplifications with purified water substituted for sample DNA indicated the absence of extraneous DNA molecules in 99.1% (1 false positive with the uidA assay) of experiments.

DISCUSSION

Trends in FIB distribution in raw sewage.

The concentrations of four FIB genetic markers were estimated for a series of untreated sewage samples collected from 54 different geographic locations across the United States. Bacteroidales genetic markers were always the most abundant, affirming previous observations (36) that this microorganism group is a very sensitive indicator of fecal pollution. Genetic markers from Clostridium spp. were the next most abundant, followed by E. coli and enterococci. Although there was a difference of up to 1.6 log₁₀ mean copies per nanogram of total DNA between FIB genetic markers, the respective standard deviations were ≤0.53. The relatively low level of variability compared to the mean concentration estimates, especially for the uidA genetic marker (standard deviation, 0.21), suggests that any of these FIB qPCR assays can be remarkably predictable across the geographic range tested in this study. In contrast, little evidence of significant correlations in FIB genetic marker concentrations between the assays was observed among sewage samples. It remains unclear whether the lack of correlation between most FIB genetic marker pairings is due to geographic differences or other factors, such as amplification efficiency (E). For instance, there is a >|0.1| difference in E between Entero1 and the other three FIB qPCR assays (Table 3). Regardless of the source(s) of variability, the uncertain relationship between qPCR estimates of these different FIB groups may prove challenging for applications that attempt to combine multiple indicator data sets.

Trends in animal fecal sources.

Fecal samples from 20 different animal species allowed the documentation of several novel and previously reported trends between FIB genetic markers and animal sources. For example, experiments indicate that FIB genetic marker concentrations are high for California sea lions, with the mean log₁₀ and standard deviation ranging from 3.52 ± 0.08 (Entero1) to 4.53 ± 0.03 (GenBac3). The United States National Marine Fisheries Service estimates that California sea lion populations exceed 334,000 individuals, based on statewide aerial surveys and a 5% population increase per year (23). Large California sea lion populations combined with the shedding of high concentrations of FIB represent a potential confounder for water quality testing when these animals are present. Another interesting trend emerges with ruminant animal sources, where enterococci are undetectable or below the LLOQ for all species except mule deer (2.91 ± 0.08), suggesting that enterococci may not be the most representative indicator of waters impacted by animals with this physiology (Fig. 3). E. coli may also be a poor choice, because uidA genetic markers are undetectable in more than half of the ruminant sources and never exceed 1.64 ± 0.02 log₁₀ copies per nanogram of total DNA (Fig. 3).

A closer examination of FIB genetic marker concentrations in bird samples provides some additional host-associated trends. Six different bird sources were examined in this study, including poultry (turkey and chicken), waterfowl (duck and Canada goose), and shorebirds (pelicans and gull). FIB genetic marker concentrations ranged from no detection up to 4.29 ± 0.03 log₁₀ copies per nanogram of total DNA, suggesting high variability in contributions by different bird species to FIB loading (Fig. 3). Large differences in enterococcus concentrations between different bird species have also been observed using the standardized membrane filtration cultivation method (3). The high concentrations of Entero1 and uidA genetic markers in poultry, especially chicken (mean log₁₀ ≥ 2.92 ± 0.08), are of particular interest because these fecal sources have been shown to harbor an array of human pathogens (2, 11, 22). It is also worth noting that Cperf genetic markers were not detected or were below LLOQ for all bird sources except chicken, hinting that the qPCR measurement of Clostridium spp. may not be useful in bird-impacted waters. Our results also support previous findings, including the observation that Bacteroidales genetic markers occur at a lower abundance than enterococcus genetic markers in gulls (15). However, our data contradict previous reports that E. coli genetic marker concentrations are higher than those of Bacteroidales in gulls (15) and that enterococcus genetic marker concentrations are lower in gulls than in pelicans (44).

Disagreements in FIB concentration trends between the current study and previous research could arise for several reasons. First, the fecal sample collections originate from different animal populations in different geographic locations. Factors such as animal diet have been correlated with dramatic shifts in the shedding of host-associated genetic markers in cattle subjected to different feeding regimes (33). It is possible that regional differences in food sources could influence the shedding of FIB as well. Another factor to consider is the method of measurement. As discussed earlier, there are many reasons to expect differences between cultivation and molecular measurements of FIB. An often overlooked detail that most likely influences the measurement of FIB concentrations from fecal samples is the unit of measure. A number of strategies have been used to standardize FIB measurements of fecal samples, including fecal wet mass (3, 25, 36), fecal dry mass (44), and estimated copies of a Bacteroidales genetic marker (16). Standardization to a wet or dry fecal mass is the most common tactic; however, moisture content can vary from one animal source to another, as well as fluctuate based on the disease state, freshness of the sample, or storage conditions. Dry mass measurements can also fluctuate based on animal diet, age, and disease state. For example, a cow feeding predominately on grass will have a larger proportion of dry mass from undigested cellulose than a cow fed high percentages of processed grain (data not shown). An alternative to standardizing to a mass of feces (wet or dry) is the use of the same mass of total DNA in each qPCR amplification. Standardizing the total DNA per amplification not only helps prevent qPCR inhibition, eliminate the effect of variability from different extraction efficiencies between samples, and help ensure similar qPCR chemistry conditions from one reaction mixture to another but also provides a convenient approach to avoid confounding factors associated with wet and dry fecal mass determinations.

Implications for future FIB qPCR applications.

Genetic marker concentration estimates also reinforce several themes relevant to the application of qPCR technologies. Based on raw sewage sample collection data, individual FIB genetic markers exhibit a high degree of similarity in concentration estimates across the United States. This geographic predictability suggests that any of these FIB qPCR methods may be suitable for application in the United States in locations where sewage is a contributing source of fecal pollution, especially if sewage is the dominant source.

Numerous epidemiology studies conducted worldwide report associations between sewage-impacted recreational water and adverse health outcomes (27, 40, 45). However, the relative human health risks from other animal sources are not as well understood. There is a growing body of evidence suggesting that some animal sources, such as birds, pose a smaller health risk than sewage (6, 8, 38) but that other animal sources may have an equivalent health risk (38). Therefore, if a water body of interest is impacted from multiple nonhuman animal sources, it becomes more challenging to predict associated health risk based on the interpretation of FIB qPCR results. Observations from this study further confound risk predictions in this scenario because different animal sources shed different concentrations of the same FIB genetic marker. For instance, estimates from animal sources in this study indicate that there can be more than 5 log₁₀ copies per nanogram of total DNA difference between sources for a given FIB genetic marker (Fig. 3). This enormous discrepancy between fecal sources strongly suggests that a FIB concentration can exceed estimated safety levels (for enterococcus and E. coli groups) at different rates depending on the mixture and proportion of contributing sources.

Other applications attempt to pair FIB and host-associated qPCR genetic marker concentrations to estimate the relative proportions of different animal sources to the total fecal load in a polluted water sample (36, 42). Thus, the observed variability in FIB genetic marker concentrations between animal sources that can confound health risk predictions could be an advantage for source allocation methods. The results from this study support the concept that Bacteroidales may be the FIB group of choice for these applications, based on the broad range of genetic marker concentrations across animal sources (Fig. 3). However, it is important to note that the potential does exist to attain different relative-proportion predictions using different FIB genetic markers. For example, the uidA genetic marker was not detected in dairy cattle, but estimated concentrations were as high as 3.38 ± 0.03 log₁₀ copies per nanogram of total DNA with the GenBac3 assay. Large differences in genetic marker concentrations between different FIB from the same animal source suggest that FIB selection will influence the interpretation of FIB source allocations and, in extreme cases, may lead to the identification of different dominant sources of fecal pollution in the same water sample.

The use of qPCR to quantify FIB genetic markers in environmental samples impacted by fecal pollution offers several benefits; however, it is becoming more evident that there are still gaps in the knowledge required to apply these methods, especially when multiple nonhuman animal sources could be present. This study provides novel information regarding the distribution of FIB genetic markers in primary effluent sewage collected from sites across a large geographic area. In addition, we identify trends in FIB genetic marker shedding across a wide range of animal species. However, more extensive studies are needed to establish the robustness of these trends. Future research should continue to expand the number of reference samples over a larger geographic range and characterize any correlations between FIB group and pathogen shedding, as well as determine the influence of animal dietary practices and physiological development on FIB genetic marker concentrations.