Dataset Information

Microbial Source Tracking in Adjacent Karst Springs.

ABSTRACT: Modern man-made environments, including urban, agricultural, and industrial environments, have complex ecological interactions among themselves and with the natural surroundings. Microbial source tracking (MST) offers advanced tools to resolve the host source of fecal contamination beyond indicator monitoring. This study was intended to assess karst spring susceptibilities to different fecal sources using MST quantitative PCR (qPCR) assays targeting human, bovine, and swine markers. It involved a dual-time monitoring frame: (i) monthly throughout the calendar year and (ii) daily during a rainfall event. Data integration was taken from both monthly and daily MST profile monitoring and improved identification of spring susceptibility to host fecal contamination; three springs located in close geographic proximity revealed different MST profiles. The Giach spring showed moderate fluctuations of MST marker quantities amid wet and dry samplings, while the Zuf spring had the highest rise of the GenBac3 marker during the wet event, which was mirrored in other markers as well. The revelation of human fecal contamination during the dry season not connected to incidents of raining leachates suggests a continuous and direct exposure to septic systems. Pigpens were identified in the watersheds of Zuf, Shefa, and Giach springs and on the border of the Gaaton spring watershed. Their impact was correlated with partial detection of the Pig-2-Bac marker in Gaaton spring, which was lower than detection levels in all three of the other springs. Ruminant and swine markers were detected intermittently, and their contamination potential during the wet samplings was exposed. These results emphasized the importance of sampling design to utilize the MST approach to delineate subtleties of fecal contamination in the environment.

SUBMITTER: Ohad S

PROVIDER: S-EPMC4495197 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Microbial Source Tracking in Adjacent Karst Springs.

Ohad Shoshanit S Vaizel-Ohayon Dalit D Rom Meir M Guttman Joseph J Berger Diego D Kravitz Valeria V Pilo Shlomo S Huberman Zohar Z Kashi Yechezkel Y Rorman Efrat E

Applied and environmental microbiology 20150522 15

Modern man-made environments, including urban, agricultural, and industrial environments, have complex ecological interactions among themselves and with the natural surroundings. Microbial source tracking (MST) offers advanced tools to resolve the host source of fecal contamination beyond indicator monitoring. This study was intended to assess karst spring susceptibilities to different fecal sources using MST quantitative PCR (qPCR) assays targeting human, bovine, and swine markers. It involved ...[more]

PMID: 26002893

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Project description:Several microbes and chemicals have been considered as potential tracers to identify fecal sources in the environment. However, to date, no one approach has been shown to accurately identify the origins of fecal pollution in aquatic environments. In this multilaboratory study, different microbial and chemical indicators were analyzed in order to distinguish human fecal sources from nonhuman fecal sources using wastewaters and slurries from diverse geographical areas within Europe. Twenty-six parameters, which were later combined to form derived variables for statistical analyses, were obtained by performing methods that were achievable in all the participant laboratories: enumeration of fecal coliform bacteria, enterococci, clostridia, somatic coliphages, F-specific RNA phages, bacteriophages infecting Bacteroides fragilis RYC2056 and Bacteroides thetaiotaomicron GA17, and total and sorbitol-fermenting bifidobacteria; genotyping of F-specific RNA phages; biochemical phenotyping of fecal coliform bacteria and enterococci using miniaturized tests; specific detection of Bifidobacterium adolescentis and Bifidobacterium dentium; and measurement of four fecal sterols. A number of potentially useful source indicators were detected (bacteriophages infecting B. thetaiotaomicron, certain genotypes of F-specific bacteriophages, sorbitol-fermenting bifidobacteria, 24-ethylcoprostanol, and epycoprostanol), although no one source identifier alone provided 100% correct classification of the fecal source. Subsequently, 38 variables (both single and derived) were defined from the measured microbial and chemical parameters in order to find the best subset of variables to develop predictive models using the lowest possible number of measured parameters. To this end, several statistical or machine learning methods were evaluated and provided two successful predictive models based on just two variables, giving 100% correct classification: the ratio of the densities of somatic coliphages and phages infecting Bacteroides thetaiotaomicron to the density of somatic coliphages and the ratio of the densities of fecal coliform bacteria and phages infecting Bacteroides thetaiotaomicron to the density of fecal coliform bacteria. Other models with high rates of correct classification were developed, but in these cases, higher numbers of variables were required.

Dataset Information

Microbial Source Tracking in Adjacent Karst Springs.

Publications

Microbial Source Tracking in Adjacent Karst Springs.

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