Literature DB >> 26231650

Toolbox Approaches Using Molecular Markers and 16S rRNA Gene Amplicon Data Sets for Identification of Fecal Pollution in Surface Water.

W Ahmed1, C Staley2, M J Sadowsky2, P Gyawali3, J P S Sidhu3, A Palmer4, D J Beale4, S Toze3.   

Abstract

In this study, host-associated molecular markers and bacterial 16S rRNA gene community analysis using high-throughput sequencing were used to identify the sources of fecal pollution in environmental waters in Brisbane, Australia. A total of 92 fecal and composite wastewater samples were collected from different host groups (cat, cattle, dog, horse, human, and kangaroo), and 18 water samples were collected from six sites (BR1 to BR6) along the Brisbane River in Queensland, Australia. Bacterial communities in the fecal, wastewater, and river water samples were sequenced. Water samples were also tested for the presence of bird-associated (GFD), cattle-associated (CowM3), horse-associated, and human-associated (HF183) molecular markers, to provide multiple lines of evidence regarding the possible presence of fecal pollution associated with specific hosts. Among the 18 water samples tested, 83%, 33%, 17%, and 17% were real-time PCR positive for the GFD, HF183, CowM3, and horse markers, respectively. Among the potential sources of fecal pollution in water samples from the river, DNA sequencing tended to show relatively small contributions from wastewater treatment plants (up to 13% of sequence reads). Contributions from other animal sources were rarely detected and were very small (<3% of sequence reads). Source contributions determined via sequence analysis versus detection of molecular markers showed variable agreement. A lack of relationships among fecal indicator bacteria, host-associated molecular markers, and 16S rRNA gene community analysis data was also observed. Nonetheless, we show that bacterial community and host-associated molecular marker analyses can be combined to identify potential sources of fecal pollution in an urban river. This study is a proof of concept, and based on the results, we recommend using bacterial community analysis (where possible) along with PCR detection or quantification of host-associated molecular markers to provide information on the sources of fecal pollution in waterways.
Copyright © 2015, American Society for Microbiology. All Rights Reserved.

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Year:  2015        PMID: 26231650      PMCID: PMC4579440          DOI: 10.1128/AEM.02032-15

Source DB:  PubMed          Journal:  Appl Environ Microbiol        ISSN: 0099-2240            Impact factor:   4.792


  64 in total

1.  Evaluation of the nifH gene marker of Methanobrevibacter smithii for the detection of sewage pollution in environmental waters in Southeast Queensland, Australia.

Authors:  W Ahmed; J P S Sidhu; S Toze
Journal:  Environ Sci Technol       Date:  2011-12-12       Impact factor: 9.028

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

Authors:  Valerie J Harwood; Bruce Wiggins; Charles Hagedorn; R D Ellender; Jan Gooch; James Kern; Mansour Samadpour; Annie C H Chapman; Brian J Robinson; Brian C Thompson
Journal:  J Water Health       Date:  2003-12       Impact factor: 1.744

3.  Human and bovine adenoviruses for the detection of source-specific fecal pollution in coastal waters in Australia.

Authors:  W Ahmed; A Goonetilleke; T Gardner
Journal:  Water Res       Date:  2010-09       Impact factor: 11.236

4.  Comparison of Enterococcus measurements in freshwater at two recreational beaches by quantitative polymerase chain reaction and membrane filter culture analysis.

Authors:  Richard A Haugland; Shawn C Siefring; Larry J Wymer; Kristen P Brenner; Alfred P Dufour
Journal:  Water Res       Date:  2004-12-24       Impact factor: 11.236

Review 5.  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

6.  Prevalence of human pathogens and indicators in stormwater runoff in Brisbane, Australia.

Authors:  J P S Sidhu; L Hodgers; W Ahmed; M N Chong; S Toze
Journal:  Water Res       Date:  2012-03-16       Impact factor: 11.236

7.  Bioaugmentation as a tool to protect the structure and function of an activated-sludge microbial community against a 3-chloroaniline shock load.

Authors:  Nico Boon; Eva M Top; Willy Verstraete; Steven D Siciliano
Journal:  Appl Environ Microbiol       Date:  2003-03       Impact factor: 4.792

8.  Massive parallel 16S rRNA gene pyrosequencing reveals highly diverse fecal bacterial and fungal communities in healthy dogs and cats.

Authors:  Stefanie Handl; Scot E Dowd; Jose F Garcia-Mazcorro; Jörg M Steiner; Jan S Suchodolski
Journal:  FEMS Microbiol Ecol       Date:  2011-02-14       Impact factor: 4.194

9.  Validity of the indicator organism paradigm for pathogen reduction in reclaimed water and public health protection.

Authors:  Valerie J Harwood; Audrey D Levine; Troy M Scott; Vasanta Chivukula; Jerzy Lukasik; Samuel R Farrah; Joan B Rose
Journal:  Appl Environ Microbiol       Date:  2005-06       Impact factor: 4.792

10.  Ironing out the wrinkles in the rare biosphere through improved OTU clustering.

Authors:  Susan M Huse; David Mark Welch; Hilary G Morrison; Mitchell L Sogin
Journal:  Environ Microbiol       Date:  2010-03-11       Impact factor: 5.491

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  15 in total

1.  Quantifying the Relative Contributions of Environmental Sources to the Microbial Community in an Urban Stream under Dry and Wet Weather Conditions.

Authors:  Darshan Baral; Allison Speicher; Bruce Dvorak; David Admiraal; Xu Li
Journal:  Appl Environ Microbiol       Date:  2018-07-17       Impact factor: 4.792

2.  Regional Similarities and Consistent Patterns of Local Variation in Beach Sand Bacterial Communities throughout the Northern Hemisphere.

Authors:  Christopher Staley; Michael J Sadowsky
Journal:  Appl Environ Microbiol       Date:  2016-04-18       Impact factor: 4.792

3.  Evidence of Avian and Possum Fecal Contamination in Rainwater Tanks as Determined by Microbial Source Tracking Approaches.

Authors:  W Ahmed; K A Hamilton; P Gyawali; S Toze; C N Haas
Journal:  Appl Environ Microbiol       Date:  2016-06-30       Impact factor: 4.792

4.  Complete Microbiota Engraftment Is Not Essential for Recovery from Recurrent Clostridium difficile Infection following Fecal Microbiota Transplantation.

Authors:  Christopher Staley; Colleen R Kelly; Lawrence J Brandt; Alexander Khoruts; Michael J Sadowsky
Journal:  MBio       Date:  2016-12-20       Impact factor: 7.867

5.  Regional Assessment of Human Fecal Contamination in Southern California Coastal Drainages.

Authors:  Yiping Cao; Meredith R Raith; Paul D Smith; John F Griffith; Stephen B Weisberg; Alexander Schriewer; Andrew Sheldon; Chris Crompton; Geremew G Amenu; Jason Gregory; Joe Guzman; Kelly D Goodwin; Laila Othman; Mayela Manasjan; Samuel Choi; Shana Rapoport; Syreeta Steele; Tommy Nguyen; Xueyuan Yu
Journal:  Int J Environ Res Public Health       Date:  2017-08-04       Impact factor: 3.390

6.  A Community Multi-Omics Approach towards the Assessment of Surface Water Quality in an Urban River System.

Authors:  David J Beale; Avinash V Karpe; Warish Ahmed; Stephen Cook; Paul D Morrison; Christopher Staley; Michael J Sadowsky; Enzo A Palombo
Journal:  Int J Environ Res Public Health       Date:  2017-03-14       Impact factor: 3.390

7.  Tracking antibiotic resistance gene pollution from different sources using machine-learning classification.

Authors:  Li-Guan Li; Xiaole Yin; Tong Zhang
Journal:  Microbiome       Date:  2018-05-24       Impact factor: 14.650

8.  The Use of Ribosomal RNA as a Microbial Source Tracking Target Highlights the Assay Host-Specificity Requirement in Water Quality Assessments.

Authors:  Annastiina Rytkönen; Ananda Tiwari; Anna-Maria Hokajärvi; Sari Uusheimo; Asko Vepsäläinen; Tiina Tulonen; Tarja Pitkänen
Journal:  Front Microbiol       Date:  2021-06-02       Impact factor: 5.640

9.  Fecal source identification using random forest.

Authors:  Adélaïde Roguet; A Murat Eren; Ryan J Newton; Sandra L McLellan
Journal:  Microbiome       Date:  2018-10-18       Impact factor: 14.650

10.  Accounting for Bacterial Overlap Between Raw Water Communities and Contaminating Sources Improves the Accuracy of Signature-Based Microbial Source Tracking.

Authors:  Moa Hägglund; Stina Bäckman; Anna Macellaro; Petter Lindgren; Emmy Borgmästars; Karin Jacobsson; Rikard Dryselius; Per Stenberg; Andreas Sjödin; Mats Forsman; Jon Ahlinder
Journal:  Front Microbiol       Date:  2018-10-02       Impact factor: 5.640

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