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ACS ES T Water
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ACS Es&t Water
2690-0637
American Chemical Society

10.1021/acsestwater.3c00169
Article
High-Throughput Microfluidic Quantitative PCR Platform for the Simultaneous Quantification of Pathogens, Fecal Indicator Bacteria, and Microbial Source Tracking Markers
Hill Elizabeth R. †
Chun Chan Lan †‡§
https://orcid.org/0000-0003-2991-7325
Hamilton Kerry ∥⊥
https://orcid.org/0000-0003-3600-9165
Ishii Satoshi *†#∇
† Water Resource Science Graduate Program, University of Minnesota, 173 McNeal Hall, 1985 Buford Avenue, St. Paul, Minnesota 55108, United States
‡ Natural Resources Research Institute, University of Minnesota, 5013 Miller Trunk Highway, Duluth, Minnesota 55811, United States
§ Department of Civil Engineering, University of Minnesota, 221 Swenson Civil Engineering, 1405 University Drive, Duluth, Minnesota 55812, United States
∥ School of Sustainable Engineering and the Built Environment, Arizona State University, 660 S. College Avenue, Tempe, Arizona 85281, United States
⊥ Biodesign Center for Environmental Health Engineering, Arizona State University, 727 E. Tyler Street, Tempe, Arizona 85281, United States
# BioTechnology Institute, University of Minnesota, 140 Gortner Laboratory, 1479 Gortner Avenue, St. Paul, Minnesota 55108, United States
∇ Department of Soil, Water, and Climate, University of Minnesota, 439 Borlaug Hall, 1991 Upper Buford Circle, St. Paul, Minnesota 55108, United States
* Email: ishi0040@umn.edu.
24 07 2023
11 08 2023
24 07 2024
3 8 26472658
04 04 2023
05 07 2023
03 07 2023
© 2023 The Authors. Published by American Chemical Society
2023
The Authors
https://creativecommons.org/licenses/by-nc-nd/4.0/ Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Contamination of water with bacterial, viral, and protozoan pathogens can cause human diseases. Both humans and nonhumans can release these pathogens through their feces. To identify the sources of fecal contamination in the water environment, microbial source tracking (MST) approaches have been developed; however, the relationship between MST markers and pathogens is still not well understood most likely due to the lack of comprehensive datasets of pathogens and MST marker concentrations. In this study, we developed a novel microfluidic quantitative PCR (MFQPCR) platform for the simultaneous quantification of 37 previously validated MST markers, two fecal indicator bacteria (FIB), 22 bacterial, 11 viral, and five protozoan pathogens, and three internal amplification/process controls in many samples. The MFQPCR chip was applied to analyze pathogen removal rates during the wastewater treatment processes. In addition, multiple host-specific MST markers, FIB, and pathogens were successfully quantified in human and avian-impacted surface waters. While the genes for pathogens were relatively infrequently detected, positive correlations were observed between some potential pathogens such as Clostridium perfringens and Mycobacterium spp., and human MST markers. The MFQPCR chips developed in this study, therefore, can provide useful information to monitor and improve water quality.

A high-throughput tool was developed to quantify various pathogens and microbial source tracking markers to monitor and improve water quality.

water quality
fecal pollution
microbial source tracking
pathogens
quantitative PCR
Division of Chemical, Bioengineering, Environmental, and Transport Systems 10.13039/100000146 CBET 1916025 University of Minnesota 10.13039/100007249 NA document-id-old-9ew3c00169
document-id-new-14ew3c00169
ccc-price
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pmcIntroduction

Waters contaminated with bacterial, viral, and protozoan pathogens pose an increased risk of infections.1 Currently, fecal indicator bacteria (FIB) such as E. coli and Enterococci are commonly used to assess water quality.2,3 While FIB tests are convenient and relatively inexpensive, the test results do not necessarily indicate the recent occurrence of fecal contamination because some FIB can survive for extended time periods and even grow in natural environments.4−6 Poor correlations were also reported between the levels of FIB and the occurrence of human pathogens,7−12 and as such, are also ineffective predictors of human health outcomes.3,13 In addition, because FIB are largely ubiquitous in the digestive tracts of most warm-blooded animals, the levels of FIB alone cannot be used to elucidate sources of fecal pollution, making the regulation and remediation of pathogen-impaired waters a challenge.

Microbial source tracking (MST) is an approach to identify host-specific fecal microorganisms and pinpoint major sources of fecal contamination (and thus pathogens) in the environment.1 Early applications of MST methods were primarily employed to discriminate between human fecal sources and other animal fecal sources, as human sewage was expected to attribute greater risks to the environment.1 It is now understood that nonhuman fecal sources may contribute specific pathogens to the environment as well. For example, excrement from pigs may contain infectious Hepatitis E virus.14 Wild and domestic avian species are known to excrete Campylobacter spp.15,16 Ruminants (i.e., cattle, sheep) are also known to be significant reservoirs of Shiga-toxin producing E. coli.17,18 MST methods have expanded significantly in recent years in attempts to attribute pathogen loading to the correct fecal host. This methodology is being applied to new developments in the Total Maximum Daily Load.19 To improve water quality and public health, it is important to analyze the co-occurrences of pathogens and MST markers. However, the relationship between various MST markers and pathogens is still not well understood,12,20,21 most likely because of the lack of comprehensive datasets of pathogens and MST marker concentrations.

High-throughput quantitative PCR is a powerful method to quantify multiple genes of interest for many samples.22 When microfluidic technology is used to dispense and mix reagents and samples for high-throughput qPCR, it is called microfluidic qPCR (MFQPCR). We previously applied the MFQPCR technology (Fluidigm BioMark) to simultaneously quantify various pathogens and FIB in multiple samples;23−25 however, these MFQPCR chips did not include assays for MST markers. Recently, Shahraki et al.26 reported an MFQPCR OpenArray chip to simultaneously quantify bacterial pathogens, FIB, and MST markers. However, their chip includes a limited number of assays (15 bacterial pathogens, two FIB, and seven MST markers), and therefore, may not provide comprehensive data enough to analyze correlations between pathogen and MST marker concentrations.

We hypothesized that it is possible to simultaneously quantify dozens of pathogens, FIB, and MST markers on an MFQPCR platform. The high-throughput quantitative data obtained by the MFQPCR can be used for various applications, such as to analyze pathogen removal rates, assess human health risks, identify potential sources of fecal contamination, and examine the co-occurrences of pathogens and MST markers in the same water samples. In MFQPCR, identical detection chemistry (e.g., TaqMan or SYBR green) and PCR conditions (e.g., annealing temperature) should be used.23 In addition, because the volume of the reaction chambers is small (<10 nL), a preamplification reaction called specific target amplification (STA) is often necessary to increase the amount of template DNA for MFQPCR.23 Therefore, assays to include in the MFQPCR platform should be carefully selected and evaluated.

Consequently, the objectives of this study were to (i) develop MFQPCR chips to simultaneously quantify pathogens (bacteria, viruses, and protozoa), FIB, and MST markers, (ii) apply the chips to quantify target organisms in wastewater, surface water, and fecal samples, and (iii) analyze correlations between pathogen and MST marker concentrations.

Experimental/Methods

Quantitative PCR Assays

A total of 80 TaqMan probe assays were selected from previous studies to be used in this research (Tables S1–S3), including those for two FIB, 22 bacterial pathogens, 11 viral pathogens, five protozoan pathogens, 37 MST markers for various host species (general, human, dog, cow, pig, ruminant, avian, poultry, gull, goose, deer, beaver, muskrat), and three internal amplification/process controls. These assays were selected based on the following criteria: they (1) use TaqMan probe chemistry, (2) have primer annealing temperatures of around 60 °C, and (3) previously demonstrated high sensitivity and specificity to the target organism. Pathogens selected for this chip were frequently reported as the etiological agents of waterborne disease outbreaks1,12 and included pathogenic Escherichia coli, Shigella spp., Salmonella spp., Campylobacter spp., Salmonella spp., Cryptosporidium spp., Giardia spp., noroviruses, and adenoviruses (see Tables S1–S3 for the full list). These assays were divided into two groups, DNA targets (bacteria, eukaryotes, and DNA viruses) and RNA targets (RNA viruses) and were run on separate MFQPCR chips.

All primers were synthesized and purified through a cartridge by Eurofins Genomics. All TaqMan probes were synthesized by Integrated DNA Technologies with 6-carboxyfluorescein (6-FAM) at their 5′ ends regardless of the fluorophore used in the original literature. When probes in the original literature were labeled with minor glove binder (MGB) at their 3′ ends, they were synthesized with MGB and a nonfluorescent quencher at their 3′ ends. When probes in the original literature were not labeled with MGB, they were synthesized with an Iowa Black fluorescent quencher and an internal ZEN quencher that was inserted between the 9th and 10th bases from their 5′ ends. The addition of an internal ZEN quencher was previously shown useful to reduce the background fluorescent signals in TaqMan qPCR assays with non-MGB probes.25

The gBlock DNA fragments containing target gene sequences were synthesized by Integrated DNA Technologies and used to generate the standard curves for qPCR (Table S4). The gBlocks DNA solutions (109 copies/μL) for all 80 assays were pooled together and serially diluted to make 2 × 106–2 × 100 copies/μL of the standard DNA solutions.

Surface Water Samples

Surface water samples were collected from two areas of southwestern Lake Superior (46°43′N, 92°00′N) near the Duluth-Superior Harbor: Rice Point public water access and the Western Lake Superior Sanitary District (WLSSD) wastewater outfall point, six times between June and October 2020 (Figure S1). Rice Point (RP) is adjacent (∼0.4 km distance) to the Interstate Island State Wildlife Management Area, where thousands of common terns and ring-billed gulls nest each year. The Duluth Wastewater Outfall (DWWO) is located approximately 1.1 km from Interstate Island. These two sites were chosen to show the applicability of the MFQPCR approach to MST. The main sources of fecal contamination at RP and DWWO were previously identified as waterfowl and wastewater, respectively.27,28

To collect a sample at RP, autoclave-sterilized L/S 18 precision pump tubing (Cole-Parmer) was weighted with an autoclave-sterilized hose barb and lowered into the water at the end of the dock (ca. 30 cm below the water surface). With a battery-powered peristaltic pump, approximately 40 L of water was pumped through a dead-end hollow-fiber REXEED 25S ultrafiltration membrane (Asahi Kasei) as described previously.29 After filtration, the ultrafiltration membrane was capped and transported on ice back to the laboratory, where the filter was backflushed within 48 h (see the Supporting Information for the detailed procedure). To collect samples from the DWWO, a canoe was launched from the RP dock and paddled to the outfall location. The water sample (10 L) was collected 30 cm below the surface of the water as described above.

At both sites, water pH, conductivity, and temperature were measured by using the YSI Professional Plus Multiparameter Instrument. Turbidity was measured by the Hach 2100P ISO portable turbidimeter. Upon returning to the laboratory (<12 h), total coliform and E. coli concentrations were measured using the IDEXX Colilert test kits.

Domestic Wastewater Samples

From November 2021 to May 2022, domestic wastewater influent and effluent samples were received from 11 wastewater treatment facilities throughout the United States. These facilities were geographically diverse, used various treatment strategies, and treated varied volumes of daily inflow. The locations and specific characteristics of participating facilities will remain anonymous, and samples are herein labeled as Facility “A”–“K.” Each facility collected a 10 L grab sample of the finished effluent and a 1 L grab sample of the raw influent. These samples were placed in a cooler on ice and shipped overnight to the laboratory at the University of Minnesota, St. Paul, MN, where they were processed within 48 h of collection (see the Supporting Information). One effluent sample was unusable for filtration due to substantial leaking during shipment. To capture biomass from the effluent samples received, 10 L of effluent was pumped through a dead-end hollow-fiber REXEED 25S ultrafiltration membrane as described above. For influent samples, the filtration step was omitted. Approximately 700 mL of well-mixed influent was poured into a 1 L glass bottle and received the post-backflushing treatment (see the Supporting Information).

Fecal Sample Collection

Fresh gull and goose fecal samples were collected using sterilized plastic scoops from beaches in Duluth, MN, in August 2021. Samples were immediately placed into 15 mL tubes, kept on ice, and transported back to the laboratory. They were stored at −80 °C until DNA extraction. A total of ten gull and nine goose fecal samples were collected.

Propidium Monoazide (PMA) Treatment

To differentiate between viable and non-viable cells and viruses in qPCR, half of the aliquots described in the Supporting Information (i.e., the second set of triplicates) received a PMAxx treatment. Aliquots of the biomass samples (230 μL) were mixed with 5.75 μL of 2 mM PMAxx (Biotium; final PMAxx conc. of 50 μM) and incubated at room temperature in a dark box for 10 min, then exposed to light for 20 min in the PMA-Lite LED Photolysis Device (Biotium). After treatment, these samples were stored at −80 °C until DNA/RNA extractions.

DNA and RNA Extractions

DNA and RNA were co-extracted from the triplicate biomass samples (PMA-treated and non-treated) originating from surface water, wastewater, and blanks, by using the AllPrep PowerViral DNA/RNA kit (Qiagen). To assess RNA and DNA recovery efficiencies, known amounts of murine norovirus S7-PP3 strain (5 × 106 PFU, kindly provided by Masaaki Kitajima at Hokkaido University and Yukinobu Tohya at Nihon University)30 and Pseudogulbenkiania sp. NH8B-2D9 strain (3.9–4.2 × 106 cells)24 were spiked to the pellets before DNA/RNA extractions as process controls. The amount of these process controls in the resultant DNA/RNA solutions was later quantified by MFQPCR (see below). Prior to bead-beating, 100 μL of phenol–chloroform:isoamyl alcohol (25:24:1, pH 6.6) and 6 μL of β-mercaptoethanol were added to extraction tubes. Following this step, extraction was performed following the manufacturer-recommended protocol. Resultant DNA/RNA solutions (100 μL) were stored at −80 °C.

To synthesize cDNA from viral RNA, reverse transcription (RT) was performed by using the PrimeScript RT Kit (Takara Bio) according to the manufacturer’s protocol. The RT reaction (10 μL) was done with 2.5 μM oligo dT primer, 20 μM random 6mers, and 2 μL of 10-fold diluted RNA samples. The RT product was stored at −20 °C.

DNA was also extracted from gull and goose fecal samples by using the QIAamp PowerFecal Pro DNA Kit (Qiagen) according to the manufacturer’s protocol. Resultant DNA solutions (100 μL) were stored at −80 °C.

Conventional qPCR

Conventional qPCR was used to quantify select MST markers. The reaction mixture (10 μL) contained 1× SsoAdvanced Universal Probe Supermix (BioRad), 0.4 μM each of forward and reverse primers, 0.2 μM probe, and 1 μL DNA solution. Standard DNA (2 × 106–2 × 100 copies/μL) and no template control (NTC) were included in each run. The qPCR was done using StepOnePlus Real-Time PCR System (Applied Biosystems) under the following thermal conditions: initial denaturation at 98 °C for 3 min, 40 cycles of 98 °C for 10 s, and 60 °C for 30 s. ROX was used as a passive reference dye.

MFQPCR

Prior to MFQPCR, specific target amplification (STA) was done to increase the number of target molecules in the samples and standards. The STA reaction mixture (8 μL) contained 2× Prelude PreAmp master mix (Takarabio), 0.2 μM each primer, 1 μL of the gBlock solution (2 × 104 copies/μL) for HF183-IAC assay, and 2 μL of the 10-fold diluted DNA samples or undiluted cDNA samples. The HF183-IAC gBlock solution was added as an internal amplification control.31 Standard DNA (2 × 106–2 × 100 copies/μL) and NTC were included in each run. The STA reaction was done using a Veriti 96-Well Thermal Cycler (Applied Biosystems) with the following protocol: 95 °C for 10 min, followed by 14 cycles with 95 °C for 15 s and 60 °C for 4 min. Upon completion, the STA product was diluted 5-folds by mixing with 32 μL of TE buffer (10 mM Tris, 0.1 mM EDTA [pH 8.0]) and stored at −20 °C.

To confirm the success of target amplification and assess any substantial amplification bias in the STA reaction, conventional qPCR was performed with pre- and post-STA standards as described above. STA reactions were deemed successful if the Ct values for the 50-fold diluted post-STA standards were smaller by approximately 4–8 than the Ct values for pre-STA standards.

MFQPCR was done using the BioMark HD Real-Time PCR system (Fluidigm) with the 96.96 Dynamic Array IFC or 192.24 Dynamic Array IFC for DNA or RNA targets, respectively. Aliquots (5 μL) of 10× assay mix (1× Assay Loading Reagent [Fluidigm], 2 μM each primer, 1 μM probe) and the sample mix (1× SsoAdvanced Universal Probe Supermix [BioRad], 1× Loading Reagent [Fluidigm], and 2.25 μL of the 5-fold diluted STA product) were loaded into the 96.96 or 192.24 chip and mixed using an IFC controller MX or RX (Fluidigm) for the 96.96 chip or the 192.24 chip, respectively, according to the manufacturer’s instructions. The final primer and probe concentrations after mixing were 200 and 100 nM, respectively. The STA-amplified standards (2 × 106–2 × 100 copies/μL) and NTC were included in each run. To fill the 96.96 and 192.24 chips, some assays were run in duplicates. The qPCR was done under the following conditions: thermal mixing at 70 °C for 40 min and 60 °C for 30 s (only for the 96.96 chip), 98 °C for 3 min, followed by 40 cycles of 98 °C for 10 s and 60 °C for 30 s. ROX was used as a passive reference dye.

Data Analysis

The MFQPCR results were analyzed using the Fluidigm Real-Time PCR Analysis software version 4.8.1. Threshold fluorescence intensity was manually set for each assay based on the logarithmic view of the amplification curve to obtain quantification cycle (Cq) values. Data were then exported to Microsoft Excel, where qPCR standard curves were generated based on the Cq value of the serially diluted standard DNA. Amplification efficiency (E) was calculated based on the slope of the standard curves with the following equation: E = −1 + 10(1/slope). For environmental samples, the quantity of a target gene was calculated from the Cq value by using the standard curve.

Samples were considered quantifiable if the measured quantity was greater than the observable quantity in the lowest concentration standard (i.e., limit of quantification; LOQ) and the background signals in the NTC (if any). Samples that exhibited amplification but did not meet these criteria were considered Detected but Not Quantifiable (DNQ). Samples were considered Non-Detects (ND) for an assay if no amplification was observed within 40 cycles. Duplicate quantities (i.e., technical replicates) were averaged to obtain one value per sample.

We ran MFQPCR with three biological replicates (i.e., triplicate DNA samples per water sample). To obtain a single quantity per water sample, the observed quantities were averaged across triplicate DNA samples. If two out of three replicates were quantifiable, the sample was considered quantifiable, and the mean gene quantity and standard error (SE) were calculated. If two out of three replicates were DNQ or if only one out of three replicates was quantifiable and another replicate was DNQ, the sample was considered DNQ. If samples did not meet these criteria, they were designated as ND.

The gene copy number per liter of water was calculated using the gene quantity value (copies/μL DNA), the mass of biomass pellet used for DNA/RNA extraction, the concentrated pellet mass, and the filtration volume for the water sample. For wastewater influent samples that omitted the filtration step, the filtration volume was substituted with the volume used for subsequent flocculation steps. Log reduction of microbes during wastewater treatment processes was calculated as follows: log reduction value (LRV) = log10 (influent gene concentration/effluent gene concentration).

Kendall’s rank correlations were analyzed between gene concentrations and water quality parameters by using corrplot package in R.32 The correlation tests were performed with Bonferroni-adjusted p-values by using the psych package.33 Only assays that were detected ≥50% of the samples were used for the correlation analysis. In addition, a log gene quantity was randomly imputed for each of the DNQ and ND samples by using the NADA package34 and the Monte Carlo simulation with a sample size of 1 × 106. To estimate the values for DNQ samples, the mean and standard deviation were estimated for each assay by using the cenfit function and the assay-specific limit of quantification (Table S5). Similarly, the values for ND samples were randomly imputed using the mean and standard deviation estimated based on the limit of detection for each assay, which is the log gene quantity at Cq = 40 calculated using each assay’s standard curve.

Results and Discussion

MFQPCR Chip Design

High-throughput qPCR, including MFQPCR, has been shown useful to quantify genes of interest in environmental samples. We previously reported MFQPCR chips to simultaneously quantify various bacterial23,24 and viral pathogens;25 however, MST markers were not included on these chips. In addition, the TaqMan probes reported in Ishii et al.23 are no longer available; therefore, we needed to re-establish a new chip format. We updated our MFQPCR chip format by adding MST markers (37 assays), increasing the number of pathogens targeted (22, 11, and 5 assays for bacterial, viral, and protozoan pathogens, respectively) and adding internal amplification and process controls (three assays) as well as FIB (two assays). Because all assays need to be amplified under the same conditions on the MFQPCR platform, we used previously validated TaqMan qPCR assays that demonstrated high analytical sensitivity and specificity to the target organism and have primer annealing temperatures of around 60 °C.

Analytical Sensitivity of MFQPCR Assays

Out of the 80 assays included in the MFQPCR platform, 76 assays showed consistent amplification over multiple chip runs. Four assays (BacCow-UCD, Gull2Taqman, HNoV-GII, LA35) showed an absence of or inconsistent standard DNA amplification, and therefore, were removed from the downstream analyses. For the 76 assays, r2 values of the standard curves were >0.97 and the LOQ ranged from 2 to 20 copies/μL for most assays (Table S2). The LOQ of the assay used to detect human adenovirus (ADV) was high because of the background signals in NTC.

The high analytical sensitivity on the MFQPCR platform was made possible by the STA reaction. The STA reaction is a multiplex PCR with all primers used for MFQPCR and with a small number of PCR cycles (10–14).23 Theoretically, at 100% amplification efficiency, a 14-cycle STA reaction should increase template DNA yield by 214 times. The STA reaction is necessary to provide enough amount of template DNA to the MFQPCR reaction chambers (i.e., 6.7 nL) and allow for the detection of low-copy-number genes.23 The impact of STA was examined by conventional qPCR with six randomly selected assays (H8, Gull-4, Entero1, EV, HNoV-GI, Ehf) (Figure 1). These assays demonstrated amplification efficiencies ranging from 78–104% and the LOQ as low as 2–20 gene copies/μL. These values were similar between pre-STA and post-STA standards. The Cq values for post-STA standards were smaller by four to eight than those for pre-STA standards. This means the preamplification of samples increased the target DNA molecules by approximately 800–25,000 (210–214) times, considering the 1/50 dilution of the STA product prior to qPCR (cf. 24 × 50 = 800 ≈ 210). Similar to this study, STA reaction was previously used to increase template DNA molecules without major amplification biases.23−25,35,36 Although we did not examine the effects of STA for all assays, based on the previous literature,37 it is unlikely that our STA conditions caused significant biases in the amount of target DNA molecules.

Figure 1 PCR standard curves for six, randomly selected assays for STA method validation. Standard curves were created by linear regression analysis of the reaction Cq value versus the known concentration of standard DNA (log copies/μL). The two standard curves are shown per assay: one with pre-STA (○) and another with post-STA standards (●). Regression equations for the standard curve and r2 values are shown.

Specificity of the MFQPCR Assays

The high specificity of the MST assays used in this study was previously examined using conventional qPCR. This study confirmed their specificity, especially of human MST markers, on the MFQPCR platform. Human MST markers were detected in >81% of the influent wastewater samples but not from goose and gull fecal samples (Figure 2). Among the human MST markers, crAssphage markers (CPQ_056 and CPQ_064 assays) were detected most frequently (100%) in the influent wastewater samples. While PMMoV has been reported to widely and abundantly occur in wastewater samples,38 we did not detect PMMoV in some of the wastewater influent samples. Because PMMoV was previously detected by MFQPCR,39 no detection of PMMoV in some of our samples is probably related to the samples themselves, not to the use of MFQPCR.

Figure 2 Proportion of quantifiable samples per sample and assay type. Samples were considered quantifiable if the quantity detected was greater than the observable quantity in the lowest concentration standard (i.e., limit of quantification; LOQ). Legend: INF, wastewater influent; EFF: wastewater effluent; DWWO, Duluth wastewater outfall; RP, Rice Point; GOOSE, goose fecal samples; GULL; gull fecal samples.

Some nonhuman markers, especially those for dogs and cows, were also detected in wastewater samples by MFQPCR. Detection of dog and cow markers in wastewater samples was confirmed by conventional qPCR in this study (see below) and was also reported by previous studies.40−42 Although some dog MST markers (e.g., BacCan-UCD) were reported to cross-react with human feces,41 it is also possible that wastewater actually contained both human and dog secreta.40 Similarly, some cow MST markers are also known to cross-react with human wastewater.20 Because most MST markers cross-react with non-target host DNA to a limited extent, it is important to analyze multiple assays for the same MST host (which was done on our MFQPCR chip format) to increase the accuracy of the source identification.20,43

The MST markers for avian (Av4143 assay) and goose (CGOF1-ND2) were detected only in the avian (goose/gull) and goose feces, respectively, while gull markers (Gull-4 and LeeSeaGull assays) were detected in goose and gull feces. Their frequency of detection in the target organisms was relatively low (10–56%). Other goose markers (CGOF1-Bac1 and CGOF1-Bac2 assays) were not detected in the goose and gull feces collected in this study. In addition, the detection frequency of general Bacteroides MST markers (AllBac and BacGeneral assays) was also low (<22%) in these samples. The infrequent and low levels of these MST markers in the fecal samples may be related to microbial decay. An effort was made to select the most fresh possible fecal samples; however, some samples may have been sitting on the beach for a long period and exposed to sunlight and other environmental stresses, which could impact the analysis of the MST marker presence for goose and gull samples.44

Internal Amplification and Blank Controls

The internal amplification control (HF183-IAC assays) was detected in all samples at similar Cq values (13.05 ± 0.33 SD). The lack of false negatives and the consistent quantification of the IAC in all samples suggest that the PCR inhibition by potential DNA impurities was negligible. In addition, blank filters did not show quantifiable detection of genes, suggesting that minimal background contamination occurred during sample processing. DNA recovery of the process control strains (Pseudogulbenkiania sp. NH8B-2D9 and murine noroviruses) were 82 ± 46 and 88 ± 59%, respectively, which are similar to the previously reported values.24,29,30

Quantification of Pathogens, FIB, and MST Markers in Wastewater and Surface Water Samples

Various pathogens, FIB, and MST markers were quantitatively detected by MFQPCR in wastewater and surface water samples (Figure 2). Fecal indicators Enterococcus spp. (Entero1 assay) and E. coli (uidA assay) as well as general Bacteroides markers (All Bac and BacGen assays) were frequently (50–100%) detected, while most bacterial, viral, and protozoan pathogens were detected relatively infrequently (<30%) across all samples similar to previous studies.20,45 Exceptions to this include Clostridium perfringens (CPerf16S assay) which were detected in 67–100% of wastewater and surface water samples and Mycobacterium spp. (atpE assay), which were detected in 80–100% of wastewater and surface water samples. Additionally, Legionella spp. (ssrA assay) was detected in 83–100% of RP and DWWO samples. It is important to note, however, that these bacteria include both pathogenic and nonpathogenic strains. Because the three assays mentioned above do not target virulence factor genes, the quantities of Legionella spp., Mycobacterium spp., and Clostridium perfringens measured by these assays do not necessarily indicate the level of human pathogens. Multiple species of Legionella, Mycobacterium, and Clostridium are known to be naturally occurring in aquatic environments.46−49 Assays that target virulence factor genes (e.g., cpe assay for Clostridium perfringens enterotoxin) or more pathogen-related subgroups (e.g., mip assay for L. pneumophila, wzm assay for L. pneumophila serogroup 1, and MAC ITS assay for Mycobacterium avium complex) could provide more pathogen-related information. The infrequent detection of these assays in this study suggested low pathogenic potential.

Comparison with Conventional qPCR

To verify the quantitative results of MFQPCR, conventional qPCR was performed with wastewater samples (n = 20; without STA) for nine assays that were relatively frequently detected (Entero1, uidA, All Bac, CPQ_056, CPQ_064, BacCan-UCD, BacBov1, CPerf16S, and atpE assays). Strong positive correlations near the y = x line were observed between gene concentrations (log copies/μL) measured by MFQPCR and those by conventional qPCR (Figure 3). This indicates that the quantitative results obtained by MFQPCR were comparable to those by conventional qPCR similar to other previous MFQPCR studies.9,50

Figure 3 Correlations between gene concentrations in wastewater samples (n = 20) measured by MFQPCR and those by conventional qPCR. Pearson’s correlation coefficient is shown for each assay. Blue dotted lines indicate y = x.

We also compared the cost and time required for MFQPCR and qPCR. The cost for consumables to run STA and MFQPCR was estimated as $1981 per chip (one 96.96 chip to run 88 samples +7 standards +1 NTC; $22.52/sample for up to 96 assays) (Table S6). We included 68 assays shown in Tables S1 and S2 in one 96.96 chip; therefore, the cost per sample per assay was $0.33, It takes about 2.5 h to run STA (0.5 h for reagent preparation and 2 h for reaction) and 3.5 h for MFQPCR (0.5 h for chip conditioning, 1.5 h for reagent loading, and 1.5 h for reaction). The conventional qPCR was estimated to cost $86.44 per plate (88 samples +7 standards +1 NTC; $0.98/sample for one assay) (Table S7). It takes about 1.5 h to run qPCR (0.5 h for reagent preparation and 1 h for reaction). Therefore, if we need to run multiple assays, which is often the case for MST studies, the cost and time for MFQPCR can become competitive with conventional qPCR.

Log Reduction of Microbes during Wastewater Treatment Processes

Quantitative information obtained by MFQPCR can be used to evaluate the log reduction of microbes during wastewater treatment processes. The concentrations of various bacteria and crAssphage in wastewater decreased, on average, by about 1–2 logs after treatment, although the LRV varied by assays (Table 1; p < 0.001 by ANOVA). The LRV for PMMoV was negative for both PMA-treated and nontreated samples (−0.53 and −0.72, respectively), indicating that their concentration was greater in effluent than in influent. Similar to this study, greater removal of crAssphage than PMMoV was previously reported,51 although the concentrations of both viruses decreased (3.3 ± 1.0 and 2.0 ± 0.4 for crAssphage and PMMoV, respectively). The different LRV between Tandukar et al.51 and this study may be related to different treatment systems and/or water temperatures.52 It is also possible that different water sample processing between WWTP effluent and influent samples could have influenced the recovery of viral particle results in this study. While ultrafiltration was used to concentrate microbial biomass for the effluent samples, ultrafiltration was not used for the influent samples. This is because the influent samples had smaller volumes and higher suspended solids than the effluent samples. As a result, recovery of PMMoV might be higher for the influent samples than the effluent samples.

Table 1 Summary of Log Removal Value (LRV) for FIB, MST Marker, and Pathogen Genes in Wastewater Samplesa

target organism	assay	PMA-treated samples	nontreated samples	
n	mean	SD	min.	max.	mean	SD	min.	max.	
FIB	Enterococci spp.	Entero1	7	2.68	1.87	–0.48	5.44	3.14	1.63	0.36	4.81	
Escherichia coli	uidA	4	2.12	1.86	0.45	3.85	2.34	1.03	1.34	3.78	
MST marker	human	all Bac	8	2.69	1.50	0.68	5.01	2.45	2.17	–1.54	6.24	
BacGeneral	8	2.71	1.54	0.62	5.17	2.49	2.23	–1.62	4.78	
HF183	0	 	 	 	 	1.28	 	1.28	1.28	
gyrB	0	 	 	 	 	1.17	0.02	1.16	1.18	
HumM2	4	1.70	1.76	–0.04	3.57	2.23	0.80	1.39	2.98	
H8	3	1.31	1.91	–0.72	3.06	1.63	1.57	0.10	3.88	
H12	1	3.09	 	3.09	3.09	1.97	0.90	1.18	2.96	
Mnif	0	 	 	 	 	1.41	 	1.41	1.41	
CPQ_056	7	2.65	1.33	0.71	4.23	2.12	1.53	–0.21	3.82	
CPQ_064	7	2.65	1.34	0.69	4.31	2.17	1.56	–0.23	3.97	
human-ND5	1	1.85	 	1.85	1.85	1.57	0.53	1.20	2.18	
PMMoV	5	–0.53	1.03	–0.53	–1.11	–0.72	1.19	–1.97	3.23	
dog	BacCan-UCD	1	0.86	 	0.86	0.86	2.32	0.96	1.57	3.41	
DogBact	1	2.93	 	2.93	2.93	1.47	0.46	1.15	1.80	
cow	BacBov1	1	0.87	 	0.87	0.87	1.80	0.54	1.42	2.18	
Cow-ND5	0	 	 	 	 	1.10	0.03	1.08	1.12	
Muskrat	MuBa01	0	 	 	 	 	1.34	 	1.34	1.34	
pathogens	Clostridium perfringens	CPerf16S	5	1.68	0.76	0.43	2.38	1.62	0.92	0.08	2.48	
Mycobacterium spp.	atpE	8	1.07	0.91	–0.37	2.03	1.01	1.01	–0.95	2.14	
M. avium complex	MAC ITS	1	1.02	 	1.02	1.02	 	 	 	 	
Acanthamoeba spp.	Acant	0	 	 	 	 	0.38	 	0.38	0.38	
a The LRV was calculated for each facility that had the quantifiable values in both influent and effluent samples. The number of facilities used for the LRV calculation (n), and the mean, standard deviation (SD), minimum, and maximum LRV values are shown for each assay.

PMA-MFQPCR

Pathogen concentrations measured by MFQPCR can be also useful for quantitative microbial risk assessment (QMRA). One of the criticisms associated with the qPCR-based QMRA is the overestimation of risk by detecting signals from dead cells and viral particles.9 To overcome this issue, we used PMA treatment before qPCR. Gene concentrations were lower in PMA-treated samples than those in nontreated samples across all assays (p < 0.05 by ANOVA) (Figure 4), suggesting that PMA treatment successfully reduced the signals from dead or damaged cells/particles. It is important to note, however, that the effectiveness of PMA treatment can vary by target organisms53 and be influenced by various factors such as the concentration of suspended solids54 and organic matter.55 Therefore, the results of PMA-qPCR should be carefully interpreted.

Figure 4 Heatmap of log gene copies per liter of wastewater influent and effluent from Facilities A–K. Gene quantification was done using MFQPCR with and without PMA treatment.

Identification of Fecal Contamination Sources

The MFQPCR chips developed in this study were also applied to identify potential sources of fecal contamination at two locations, Rice Point (RP) and Duluth wastewater outfall (DWWO). While both human and gull MST markers were detected from the two sites, the human MST markers (CPQ_056, CPQ_064, and Human-ND5 assays) were more abundant in the DWWO samples than in the RP samples (Figure 5), suggesting that the DWWO samples were more impacted by human sources than the RP samples. In contrast, the concentrations of gull markers (LeeSeaGull and Gull-4 assays) were higher in the RP samples than in the DWWO sample. This was likely due to RP’s proximity to the gull population on Interstate Island. Similar results were obtained by previous MST studies done at or near these sites.27,28 More accurate source apportionment may be possible by increasing the number of samples and by using statistical modeling (e.g., decision trees).56

Figure 5 Concentrations of viable FIB, MST markers, and pathogens in surface water samples. Biomass from the water samples was treated with PMA to make the DNA from dead cells unavailable for PCR. The results of PMA-untreated samples are shown in Figure S2. Legend: RP, Rice Point; DWWO, Duluth wastewater outfall.

Correlations between Pathogen, FIB, and MST Marker Concentrations

Correlation analysis was done with the PMA-treated surface water samples. Positive correlations (tau > 0.4) were seen between most of the human MST markers (Figure 6), some of which were significantly correlated (Table S8). A positive correlation (tau = 0.48) was also seen between gull markers (Gull_4 vs LeeSeaGull), although this was not statistically significant.

Figure 6 Correlation plot showing the associations between MST markers, FIB, pathogen concentrations, and water quality parameters. This plot was generated using a matrix of Kendall’s Tau correlation coefficients. Statistical significance values of these correlations are shown in Table S8.

Concentrations for FIB (uidA and Entero1 assays) and general Bacteroides (All Bac and BacGeneral assays) were positively correlated with many of the human MST markers, suggesting that the main source of fecal contamination was humans in the DWWO and RP samples. Similar results from multiple markers can increase the confidence of the source identification as suggested previously.43 Genes targeting Clostridium perfringens (CPerf16S assay) and Mycobacterium spp. (atpE assay) were also positively correlated with human MST markers, suggesting that these potential human pathogens likely originated from humans in these water samples. In contrast, a positive correlation was seen between Legionella spp. ssrA and a cow MST marker BacBov1 (tau = 0.64, p = 0.026), suggesting that Legionella spp. may be originated from cows. This is also supported by the isolation of Legionella spp. from cattle.57,58 However, since only one out of six cow MST markers showed this relationship, care should be taken when interpreting the result. For example, we cannot exclude the possibility of the BacBov1 assay detecting signals from other hosts (e.g., humans) similar to other cow MST markers.20 More samples need to be analyzed to clarify the relationship between Legionella and cow MST markers.

Correlations were also seen between human MST markers and water quality parameters (e.g., water temperature, turbidity, and conductivity). This suggests that multivariate regression or similar modeling approach can be used to predict the occurrence of pathogens in a given water sample based on the concentrations of MST markers, FIB, and/or additional physicochemical parameters.59 Such a modeling approach could expand the toolkit available for water quality monitoring and QMRA, and therefore, should be tested in the near future.

Conclusions

The MFQPCR chips developed in this study can provide quantitative information on various pathogens, FIB, and MST markers in environmental water samples. Our MFQPCR chips can provide much more comprehensive data than a previous system26 because they can quantify (i) both bacterial, viral, and protozoan pathogens, (ii) larger number of MST assays, (iii) internal amplification and process controls, and (iv) with more samples per run. Multiple assays included for the same pathogen or host can increase the accuracy of the pathogen or source identification. The MFQPCR results are useful to analyze pathogen removal rates, assess human health risks, identify potential sources of fecal contamination, and examine correlations between pathogen and MST marker concentrations.

While not all genes listed in our MFQPCR chips are always needed for a specific MST project, testing various MST and pathogen markers in one chip run could allow us to detect unexpected pathogen and fecal contamination sources. In most MST studies, qPCR markers are selected based on the land use of the study area (e.g., the presence of pastureland, wastewater treatment plants, etc.). However, this approach may miss some of the important contributors to fecal contamination. Based on our recent MST study done with the MFQPCR approach, we identified beavers as one of the important fecal contributors in an urban watershed (unpublished data), which was unexpected. The presence of beaver dams in some of the tributaries was later confirmed.

Comprehensive data on various pathogens, FIB, and MST markers obtained by MFQPCR could also provide an opportunity to develop predictive models to estimate the presence of pathogens in a given water sample based on the concentrations of MST markers, FIB, and/or additional physicochemical parameters. This remains as a future task.59

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsestwater.3c00169.Detailed methods for water sample processing; locations of Rice Point boat launch and Duluth Wastewater Outfall in Duluth, MN; concentrations of FIB, MST markers, and pathogens in surface water samples; primer and probe sequences used in this study to quantify microbial source tracking markers, fecal indicator bacteria, pathogenic bacteria, protozoans, DNA and RNA viruses, and internal amplification controls; gBlock sequences used as DNA standards; amplification efficiency (E), limit of quantification (LOQ), and r2 value of the MFQPCR assays; running cost for qPCR and MFQPCR; and statistical significance (p value) of Kendall’s rank correlations between gene concentrations and water quality parameters (PDF)

Supplementary Material

ew3c00169_si_001.pdf

Author Contributions

CRediT: Elizabeth R Hill conceptualization (supporting), formal analysis (lead), investigation (lead), methodology (lead), visualization (lead), writing-original draft (lead); Chanlan Chun resources (supporting), supervision (equal), writing-review & editing (supporting); Kerry A. Hamilton conceptualization (supporting), funding acquisition (equal), supervision (supporting), writing-review & editing (supporting); Satoshi Ishii conceptualization (lead), formal analysis (supporting), funding acquisition (equal), methodology (supporting), project administration (lead), resources (lead), supervision (equal), visualization (supporting), writing-review & editing (lead).

The authors declare no competing financial interest.

Notes

The original version of this manuscript has been deposited to bioRxiv. https://www.biorxiv.org/content/10.1101/2023.02.25.529995v1.

Acknowledgments

We thank John Griffith and Josh Steele (SCCWRP) and John Meschke (University of Washington) for assistance in connecting with local WWTP facilities. We also thank those participating anonymous wastewater treatment facilities for providing water samples to this research, and Tamara Walsky, Caitlin Graeber, Maxwell Brubaker, Collin Krochalk, and Braeden Cox for their work in sample collection and processing. This work was supported by the National Science Foundation award CBET 1916025 and the MnDRIVE Initiative of the University of Minnesota. E.H. was supported by the Moos Graduate Research Fellowship in Aquatic Biology.
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