Combining Land Use Information and Small Stream Sampling with PCR-Based Methods for Better Characterization of Diffuse Sources of Human Fecal Pollution

h Over 1,400 water samples were collected biweekly over 6 years from an intermittent stream protected and unprotected from pasturing cattle. The samples were monitored for host-specific Bacteroidales markers, Cryptosporidium species/genotypes, viruses and coliphages associated with humans or animals, and bacterial zoonotic pathogens. Ruminant Bacteroidales markers did not increase within the restricted cattle access reach of the stream, whereas the ruminant Bacteroidales marker increased significantly in the unrestricted cattle access reach. Human Bacteroidales markers significantly increased downstream of homes where septic issues were documented. Wildlife Bacteroidales markers were detected downstream of the cattle exclusion practice where stream and riparian habitat was protected, but detections decreased after the unrestricted pasture, where the stream and riparian zone was unprotected from livestock. Detection of a large number of human viruses was shown to increase downstream of homes, and similar trends were observed for the human Bacteroidales marker. There was considerable interplay among biomarkers with stream flow, season, and the cattle exclusion practices. There were no to very weak associations with Bacteroidales markers and bacterial, viral, and parasitic pathogens. Overall, discrete sample-by-sample coherence among the different microbial source tracking markers that expressed a similar microbial source was minimal, but spatial trends were physically meaningful in terms of land use (e.g., beneficial management practice) effects on sources of fecal pollution. Microbial source tracking (MST) can help reveal the sources of fecal contamination in water resources (1-5). There are a suite of MST tools that have been used in this capacity (6); some of these include the antimicrobial resistance patterns of target bacteria (7, 8), bacterial markers (9), such as Bacteroidales markers (2), host specificity associated with parasitic organisms like Cryptosporidium (10, 11), mitochondrial DNA methods (12, 13), and virus host specificity (14-16). There is potential for these tools to help identify how different land uses can impact the sources of fecal pollution in open-surface-water systems like agricultural watersheds, systems typically prone to variable inputs of human, livestock, and wildlife feces. While these tools have potential utility for such assessments, they are different in their limits of detection, source discrimination power, spatial and temporal stability, persistence in different environmental matrices, and what they express biophysically in water samples; and as such, results from one method may not necessarily be coherent with those of another method (5,17,18). Under some circumstances, however, coherency, or a lack of it, can reveal what biomarkers are appropriate for the use at hand, and MST coherence with the occurrence of other fecal indicator organisms and pathogens can be viewed as an important factor for helping to identify mitigation measures that will reduce public health risks (19)...

Section: Comparison Of Ruminant Bacteroidales Marker Occurrence the supporting

confidence: 85%

Coherence among Different Microbial Source Tracking Markers in a Small Agricultural Stream with or without Livestock Exclusion Practices

Wilkes

Brassard

Edge

et al. 2013

“…If amplification interference was present, confirmation of inhibition was achieved by comparison to a competition threshold. The competition threshold is the C q value where the upper bound of the IAC ROQ intersects the respective multiplexed master calibration curve (31). Thus, evidence of inhibition occurred when the IAC C q for a given test sample failed the interference criterion and the respective HF183 FAM test sample C q did not exceed the competition threshold.…”

mentioning

confidence: 99%

Improved HF183 Quantitative Real-Time PCR Assay for Characterization of Human Fecal Pollution in Ambient Surface Water Samples

Green

Haugland

Varma

et al. 2014

Self Cite

246

215

h Quantitative real-time PCR (qPCR) assays that target the human-associated HF183 bacterial cluster within members of the genus Bacteroides are among the most widely used methods for the characterization of human fecal pollution in ambient surface waters. In this study, we show that a current TaqMan HF183 qPCR assay (HF183/BFDrev) routinely forms nonspecific amplification products and introduce a modified TaqMan assay (HF183/BacR287) that alleviates this problem. The performance of each qPCR assay was compared in head-to-head experiments investigating limits of detection, analytical precision, predicted hybridization to 16S rRNA gene sequences from a reference database, and relative marker concentrations in fecal and sewage samples. The performance of the modified HF183/BacR287 assay is equal to or improves upon that of the original HF183/BFDrev assay. In addition, a qPCR chemistry designed to combat amplification inhibition and a multiplexed internal amplification control are included. In light of the expanding use of PCR-based methods that rely on the detection of extremely low concentrations of DNA template, such as qPCR and digital PCR, the new TaqMan HF183/BacR287 assay should provide more accurate estimations of human-derived fecal contaminants in ambient surface waters. N umerous host-associated indicators are used to identify human fecal pollution in ambient surface waters, with many relying on molecular methods such as quantitative real-time PCR (qPCR). The most widely used methods target the HF183 16S rRNA gene cluster of members of the genus Bacteroides initially identified in human fecal samples collected from the United States Pacific Northwest (1) and targeted by PCR to distinguish humanassociated contamination (2). Over the past decade, researchers have developed (2-9) and implemented (10-15) many PCR-based methods targeting the HF183 cluster worldwide.In most HF183 PCR applications, the forward primer targets the Bacteroides HF183 cluster, reported to include Bacteroides dorei (3). The reverse primer typically hybridizes to a wider diversity of Bacteroidales so as not to further restrict the range of targeted bacteria. Researchers have employed this strategy for endpoint PCR applications (2), as well as a variety of quantitative real-time PCR chemistries, such as the SYBR green (8, 16) and TaqMan (4, 5, 9, 17) chemistries.In recent performance evaluation studies, PCR-based methods targeting the HF183 cluster, in particular, the TaqMan HF183/ BFDrev assay (3), consistently outperformed other tested approaches in terms of specificity and sensitivity (18-21). TaqMan chemistry utilizes an internal oligonucleotide probe, greatly reducing the chance of false-positive results due to the accumulation of unintended amplification by-products (e.g., primer dimerization [PD] molecules). The TaqMan HF183/BFDrev qPCR assay was recently tested in a five-laboratory repeatability study and shown to be highly reproducible across laboratories when key components of the protocol were standardized (22). However, the ...

“…As a result, a number of fecal source identification technologies have been developed (2)(3)(4)(5)(6)(7)(8)(9). These methods have been employed to address challenges such as the identification of septic pollution (10)(11)(12), the evaluation of agricultural waste management practices (13)(14)(15), the assessment of combined sewer overflow water quality impact (16,17), and the estimation of recreational water public health risk (18). This growing interest in fecal source identification technologies signals the need to transform these experimental tools into mainstream water quality management protocols.…”

mentioning

confidence: 99%

Data Acceptance Criteria for Standardized Human-Associated Fecal Source Identification Quantitative Real-Time PCR Methods

Shanks

Kelty

Oshiro

et al. 2016

Self Cite

There is growing interest in the application of human-associated fecal source identification quantitative real-time PCR (qPCR) technologies for water quality management. The transition from a research tool to a standardized protocol requires a high degree of confidence in data quality across laboratories. Data quality is typically determined through a series of specifications that ensure good experimental practice and the absence of bias in the results due to DNA isolation and amplification interferences. However, there is currently a lack of consensus on how best to evaluate and interpret human fecal source identification qPCR experiments. This is, in part, due to the lack of standardized protocols and information on interlaboratory variability under conditions for data acceptance. The aim of this study is to provide users and reviewers with a complete series of conditions for data acceptance derived from a multiple laboratory data set using standardized procedures. To establish these benchmarks, data from HF183/BacR287 and HumM2 human-associated qPCR methods were generated across 14 laboratories. Each laboratory followed a standardized protocol utilizing the same lot of reference DNA materials, DNA isolation kits, amplification reagents, and test samples to generate comparable data. After removal of outliers, a nested analysis of variance (ANOVA) was used to establish proficiency metrics that include lab-to-lab, replicate testing within a lab, and random error for amplification inhibition and sample processing controls. Other data acceptance measurements included extraneous DNA contamination assessments (notemplate and extraction blank controls) and calibration model performance (correlation coefficient, amplification efficiency, and lower limit of quantification). To demonstrate the implementation of the proposed standardized protocols and data acceptance criteria, comparable data from two additional laboratories were reviewed. The data acceptance criteria proposed in this study should help scientists, managers, reviewers, and the public evaluate the technical quality of future findings against an established benchmark. Fecal pollution remains a significant challenge for ambient water quality managers worldwide. In the United States alone, it is estimated that 39.2% of all rivers, lakes, and streams are unsafe for recreational use, with fecal pathogens as the number one cause of impairment (1). General fecal indicator bacteria (FIB), such as Escherichia coli and enterococci, shed by nearly all warm-blooded animals, are routinely used to assess water quality. However, general FIB cannot discriminate between different animal groups, making it challenging to pinpoint the origin of fecal pollution. Researchers and managers alike recognize the advantages of animal source information to help solve long-standing ambient water quality problems. As a result, a number of fecal source identification technologies have been developed (2-9). These methods have been employed to address challenges such as the identification ...