Carbon source utilization profiles as a method to identify sources of faecal pollution in water

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 ...

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confidence: 99%

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

Shanks

Kelty

Oshiro

et al. 2016

“…A group of methods termed "library-based" methods rely on a common approach: indicator bacteria from various fecal sources are isolated and phylotyped by a genotypic (5,10,27) or phenotypic (18,20,36,40) technique. The database, or library, of phylotype patterns from known sources then forms the basis for classifying bacteria isolated from water into a source category (39).…”

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confidence: 99%

Persistence and Differential Survival of Fecal Indicator Bacteria in Subtropical Waters and Sediments

Anderson

Whitlock

Harwood

2005

469

346

Fecal coliforms and enterococci are indicator organisms used worldwide to monitor water quality. These bacteria are used in microbial source tracking (MST) studies, which attempt to assess the contribution of various host species to fecal pollution in water. Ideally, all strains of a given indicator organism (IO) would experience equal persistence (maintenance of culturable populations) in water; however, some strains may have comparatively extended persistence outside the host, while others may persist very poorly in environmental waters. Assessment of the relative contribution of host species to fecal pollution would be confounded by differential persistence of strains. Here, freshwater and saltwater mesocosms, including sediments, were inoculated with dog feces, sewage, or contaminated soil and were incubated under conditions that included natural stressors such as microbial predators, radiation, and temperature fluctuations. Persistence of IOs was measured by decay rates (change in culturable counts over time). Decay rates were influenced by IO, inoculum, water type, sediment versus water column location, and Escherichia coli strain. Fecal coliform decay rates were significantly lower than those of enterococci in freshwater but were not significantly different in saltwater. IO persistence according to mesocosm treatment followed the trend: contaminated soil > wastewater > dog feces. E. coli ribotyping demonstrated that certain strains were more persistent than others in freshwater mesocosms, and the distribution of ribotypes sampled from mesocosm waters was dissimilar from the distribution in fecal material. These results have implications for the accuracy of MST methods, modeling of microbial populations in water, and efficacy of regulatory standards for protection of water quality.

“…These methods, which require the generation of a "library" of patterns from bacteria from known sources, include genotypic methods such as ribotyping (6,8,16,24), pulsed-field gel electrophoresis (22,28,29,31), amplified fragment length polymorphism (13,31), 16S rRNA sequencing (13), and PCR-based methods, such as randomly amplified polymorphic DNA (RAPD) (31) and rep-PCR (7). Phenotypic library-based methods have also been developed, such as those that measure differences in carbonsource utilization patterns (15) and resistance to antibiotics. Differences in antibiotic resistance among bacteria from various potential fecal sources have been measured by the multiple antibiotic resistance method, which uses single concentrations of each drug (13,19,23), and by antibiotic resistance analysis (ARA), which uses multiple concentrations of each antibiotic (5,12,14,17,20,(33)(34)(35).…”

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confidence: 99%

Use of Antibiotic Resistance Analysis for Representativeness Testing of Multiwatershed Libraries

Wiggins

Cash

Creamer

et al. 2003

104

The use of antibiotic resistance analysis (ARA) for microbial source tracking requires the generation of a library of isolates collected from known sources in the watershed. The size and composition of the library are critical in determining if it represents the diversity of patterns found in the watershed. This study was performed to determine the size that an ARA library needs to be to be representative of the watersheds for which it will be used and to determine if libraries from different watersheds can be merged to create multiwatershed libraries. Fecal samples from known human, domesticated, and wild animal sources were collected from six Virginia watersheds. From these samples, enterococci were isolated and tested by ARA. Based on cross-validation discriminant analysis, only the largest of the libraries (2,931 isolates) were found to be able to classify nonlibrary isolates as well as library isolates (i.e., were representative). Small libraries tended to have higher average rates of correct classification, but were much less able to correctly classify nonlibrary isolates. A merged multiwatershed library (6,587 isolates) was created and was found to be large enough to be representative of the isolates from the contributing watersheds. When isolates that were collected from the contributing watersheds approximately 1 year later were analyzed with the multiwatershed library, they were classified as well as the isolates in the library, suggesting that the resistance patterns are temporally stable for at least 1 year. The ability to obtain a representative, temporally stable library demonstrates that ARA can be used to identify sources of fecal pollution in natural waters.