Literature DB >> 27598696

Microbial source tracking in impaired watersheds using PhyloChip and machine-learning classification.

Eric A Dubinsky1, Steven R Butkus2, Gary L Andersen3.   

Abstract

Sources of fecal indicator bacteria are difficult to identify in watersheds that are impacted by a variety of non-point sources. We developed a molecular source tracking test using the PhyloChip microarray that detects and distinguishes fecal bacteria from humans, birds, ruminants, horses, pigs and dogs with a single test. The multiplexed assay targets 9001 different 25-mer fragments of 16S rRNA genes that are common to the bacterial community of each source type. Both random forests and SourceTracker were tested as discrimination tools, with SourceTracker classification producing superior specificity and sensitivity for all source types. Validation with 12 different mammalian sources in mixtures found 100% correct identification of the dominant source and 84-100% specificity. The test was applied to identify sources of fecal indicator bacteria in the Russian River watershed in California. We found widespread contamination by human sources during the wet season proximal to settlements with antiquated septic infrastructure and during the dry season at beaches during intense recreational activity. The test was more sensitive than common fecal indicator tests that failed to identify potential risks at these sites. Conversely, upstream beaches and numerous creeks with less reliance on onsite wastewater treatment contained no fecal signal from humans or other animals; however these waters did contain high counts of fecal indicator bacteria after rain. Microbial community analysis revealed that increased E. coli and enterococci at these locations did not co-occur with common fecal bacteria, but rather co-varied with copiotrophic bacteria that are common in freshwaters with high nutrient and carbon loading, suggesting runoff likely promoted the growth of environmental strains of E. coli and enterococci. These results indicate that machine-learning classification of PhyloChip microarray data can outperform conventional single marker tests that are used to assess health risks, and is an effective tool for distinguishing numerous fecal and environmental sources of pathogen indicators.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Fecal indicator bacteria; Machine learning; Microbial community analysis; Microbial source tracking; Pathogen TMDL; PhyloChip microarray

Mesh:

Substances:

Year:  2016        PMID: 27598696     DOI: 10.1016/j.watres.2016.08.035

Source DB:  PubMed          Journal:  Water Res        ISSN: 0043-1354            Impact factor:   11.236


  8 in total

1.  Elucidating Waterborne Pathogen Presence and Aiding Source Apportionment in an Impaired Stream.

Authors:  Jennifer Weidhaas; Angela Anderson; Rubayat Jamal
Journal:  Appl Environ Microbiol       Date:  2018-03-01       Impact factor: 4.792

Review 2.  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

3.  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

4.  Tracking Major Sources of Water Contamination Using Machine Learning.

Authors:  Jianyong Wu; Conghe Song; Eric A Dubinsky; Jill R Stewart
Journal:  Front Microbiol       Date:  2021-01-20       Impact factor: 5.640

Review 5.  Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges.

Authors:  James M W R McElhinney; Mary Krystelle Catacutan; Aurelie Mawart; Ayesha Hasan; Jorge Dias
Journal:  Front Microbiol       Date:  2022-04-25       Impact factor: 6.064

6.  Bacterial community structure transformed after thermophilically composting human waste in Haiti.

Authors:  Yvette M Piceno; Gabrielle Pecora-Black; Sasha Kramer; Monika Roy; Francine C Reid; Eric A Dubinsky; Gary L Andersen
Journal:  PLoS One       Date:  2017-06-01       Impact factor: 3.240

7.  Transcriptome Changes of Escherichia coli, Enterococcus faecalis, and Escherichia coli O157:H7 Laboratory Strains in Response to Photo-Degraded DOM.

Authors:  Adelumola Oladeinde; Erin Lipp; Chia-Ying Chen; Richard Muirhead; Travis Glenn; Kimberly Cook; Marirosa Molina
Journal:  Front Microbiol       Date:  2018-05-08       Impact factor: 5.640

8.  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

  8 in total

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