Literature DB >> 15382721

Phenotypic library-based microbial source tracking methods: efficacy in the California collaborative study.

Valerie J Harwood1, Bruce Wiggins, Charles Hagedorn, R D Ellender, Jan Gooch, James Kern, Mansour Samadpour, Annie C H Chapman, Brian J Robinson, Brian C Thompson.   

Abstract

As part of a larger microbial source tracking (MST) study, several laboratories used library-based, phenotypic subtyping techniques to analyse fecal samples from known sources (human, sewage, cattle, dogs and gulls) and blinded water samples that were contaminated with the fecal sources. The methods used included antibiotic resistance analysis (ARA) of fecal streptococci, enterococci, fecal coliforms and E. coli; multiple antibiotic resistance (MAR) and Kirby-Bauer antibiotic susceptibility testing of E. coli; and carbon source utilization for fecal streptococci and E. coli. Libraries comprising phenotypic patterns of indicator bacteria isolated from known fecal sources were used to predict the sources of isolates from water samples that had been seeded with fecal material from the same sources as those used to create the libraries. The accuracy of fecal source identification in the water samples was assessed both with and without a cut-off termed the minimum detectable percentage (MDP). The libraries (approximately 300 isolates) were not large enough to avoid the artefact of source-independent grouping, but some important conclusions could still be drawn. Use of a MDP decreased the percentage of false-positive source identifications, and had little effect on the high percentage of true-positives in the most accurate libraries. In general, the methods were more prone to false-positive than to false-negative errors. The most accurate method, with a true-positive rate of 100% and a false-positive rate of 39% when analysed with a MDP, was ARA of fecal streptococci. The internal accuracy of the libraries did not correlate with the accuracy of source prediction in water samples, showing that one should not rely solely on parameters such as the average rate of correct classification of a library to indicate its predictive capabilities.

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Year:  2003        PMID: 15382721

Source DB:  PubMed          Journal:  J Water Health        ISSN: 1477-8920            Impact factor:   1.744


  16 in total

1.  Development of Bacteroides 16S rRNA gene TaqMan-based real-time PCR assays for estimation of total, human, and bovine fecal pollution in water.

Authors:  Alice Layton; Larry McKay; Dan Williams; Victoria Garrett; Randall Gentry; Gary Sayler
Journal:  Appl Environ Microbiol       Date:  2006-06       Impact factor: 4.792

Review 2.  Performance, design, and analysis in microbial source tracking studies.

Authors:  Donald M Stoeckel; Valerie J Harwood
Journal:  Appl Environ Microbiol       Date:  2007-02-16       Impact factor: 4.792

3.  Evaluation of two library-independent microbial source tracking methods to identify sources of fecal contamination in French estuaries.

Authors:  Michèle Gourmelon; Marie Paule Caprais; Raphaël Ségura; Cécile Le Mennec; Solen Lozach; Jean Yves Piriou; Alain Rincé
Journal:  Appl Environ Microbiol       Date:  2007-06-08       Impact factor: 4.792

4.  Fecal source tracking by antibiotic resistance analysis on a watershed exhibiting low resistance.

Authors:  Yolanda Olivas; Barton R Faulkner
Journal:  Environ Monit Assess       Date:  2007-06-12       Impact factor: 2.513

5.  Antibiotic resistance in Escherichia coli isolates from roof-harvested rainwater tanks and urban pigeon faeces as the likely source of contamination.

Authors:  Lizyben Chidamba; Lise Korsten
Journal:  Environ Monit Assess       Date:  2015-06-05       Impact factor: 2.513

6.  Identifying host sources of fecal pollution: diversity of Escherichia coli in confined dairy and swine production systems.

Authors:  Zexun Lu; David Lapen; Andrew Scott; Angela Dang; Edward Topp
Journal:  Appl Environ Microbiol       Date:  2005-10       Impact factor: 4.792

Review 7.  Microbial source tracking using metagenomics and other new technologies.

Authors:  Shahbaz Raza; Jungman Kim; Michael J Sadowsky; Tatsuya Unno
Journal:  J Microbiol       Date:  2021-02-10       Impact factor: 3.422

8.  A comparison of BOX-PCR and pulsed-field gel electrophoresis to determine genetic relatedness of enterococci from different environments.

Authors:  Charlene R Jackson; Vesna Furtula; Erin G Farrell; John B Barrett; Lari M Hiott; Patricia Chambers
Journal:  Microb Ecol       Date:  2012-03-02       Impact factor: 4.552

9.  Ultrafiltration and Microarray for Detection of Microbial Source Tracking Marker and Pathogen Genes in Riverine and Marine Systems.

Authors:  Xiang Li; Valerie J Harwood; Bina Nayak; Jennifer L Weidhaas
Journal:  Appl Environ Microbiol       Date:  2016-01-04       Impact factor: 4.792

10.  Tracking host sources of Cryptosporidium spp. in raw water for improved health risk assessment.

Authors:  Norma J Ruecker; Shannon L Braithwaite; Edward Topp; Thomas Edge; David R Lapen; Graham Wilkes; Will Robertson; Diane Medeiros; Christoph W Sensen; Norman F Neumann
Journal:  Appl Environ Microbiol       Date:  2007-05-04       Impact factor: 4.792

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