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In silico identification of bacteriocin gene clusters in the gastrointestinal tract, based on the Human Microbiome Project’s reference genome database

BACKGROUND: The human gut microbiota comprises approximately 100 trillion microbial cells which significantly impact many aspects of human physiology - including metabolism, nutrient absorption and immune function. Disturbances in this population have been implicated in many conditions and diseases,...

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Autores principales: Walsh, Calum J., Guinane, Caitriona M., Hill, Colin, Ross, R. Paul, O’Toole, Paul W., Cotter, Paul D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4573289/
https://www.ncbi.nlm.nih.gov/pubmed/26377179
http://dx.doi.org/10.1186/s12866-015-0515-4
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author Walsh, Calum J.
Guinane, Caitriona M.
Hill, Colin
Ross, R. Paul
O’Toole, Paul W.
Cotter, Paul D.
author_facet Walsh, Calum J.
Guinane, Caitriona M.
Hill, Colin
Ross, R. Paul
O’Toole, Paul W.
Cotter, Paul D.
author_sort Walsh, Calum J.
collection PubMed
description BACKGROUND: The human gut microbiota comprises approximately 100 trillion microbial cells which significantly impact many aspects of human physiology - including metabolism, nutrient absorption and immune function. Disturbances in this population have been implicated in many conditions and diseases, including obesity, type-2 diabetes and inflammatory bowel disease. This suggests that targeted manipulation or shaping of the gut microbiota, by bacteriocins and other antimicrobials, has potential as a therapeutic tool for the prevention or treatment of these conditions. With this in mind, several studies have used traditional culture-dependent approaches to successfully identify bacteriocin-producers from the mammalian gut. In silico-based approaches to identify novel gene clusters are now also being utilised to take advantage of the vast amount of data currently being generated by next generation sequencing technologies. In this study, we employed an in silico screening approach to mine potential bacteriocin clusters in genome-sequenced isolates from the gastrointestinal tract (GIT). More specifically, the bacteriocin genome-mining tool BAGEL3 was used to identify potential bacteriocin producers in the genomes of the GIT subset of the Human Microbiome Project’s reference genome database. Each of the identified gene clusters were manually annotated and potential bacteriocin-associated genes were evaluated. RESULTS: We identified 74 clusters of note from 59 unique members of the Firmicutes, Bacteroidetes, Actinobacteria, Fusobacteria and Synergistetes. The most commonly identified class of bacteriocin was the >10 kDa class, formerly known as bacteriolysins, followed by lantibiotics and sactipeptides. CONCLUSIONS: Multiple bacteriocin gene clusters were identified in a dataset representative of the human gut microbiota. Interestingly, many of these were associated with species and genera which are not typically associated with bacteriocin production. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12866-015-0515-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-45732892015-09-18 In silico identification of bacteriocin gene clusters in the gastrointestinal tract, based on the Human Microbiome Project’s reference genome database Walsh, Calum J. Guinane, Caitriona M. Hill, Colin Ross, R. Paul O’Toole, Paul W. Cotter, Paul D. BMC Microbiol Research Article BACKGROUND: The human gut microbiota comprises approximately 100 trillion microbial cells which significantly impact many aspects of human physiology - including metabolism, nutrient absorption and immune function. Disturbances in this population have been implicated in many conditions and diseases, including obesity, type-2 diabetes and inflammatory bowel disease. This suggests that targeted manipulation or shaping of the gut microbiota, by bacteriocins and other antimicrobials, has potential as a therapeutic tool for the prevention or treatment of these conditions. With this in mind, several studies have used traditional culture-dependent approaches to successfully identify bacteriocin-producers from the mammalian gut. In silico-based approaches to identify novel gene clusters are now also being utilised to take advantage of the vast amount of data currently being generated by next generation sequencing technologies. In this study, we employed an in silico screening approach to mine potential bacteriocin clusters in genome-sequenced isolates from the gastrointestinal tract (GIT). More specifically, the bacteriocin genome-mining tool BAGEL3 was used to identify potential bacteriocin producers in the genomes of the GIT subset of the Human Microbiome Project’s reference genome database. Each of the identified gene clusters were manually annotated and potential bacteriocin-associated genes were evaluated. RESULTS: We identified 74 clusters of note from 59 unique members of the Firmicutes, Bacteroidetes, Actinobacteria, Fusobacteria and Synergistetes. The most commonly identified class of bacteriocin was the >10 kDa class, formerly known as bacteriolysins, followed by lantibiotics and sactipeptides. CONCLUSIONS: Multiple bacteriocin gene clusters were identified in a dataset representative of the human gut microbiota. Interestingly, many of these were associated with species and genera which are not typically associated with bacteriocin production. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12866-015-0515-4) contains supplementary material, which is available to authorized users. BioMed Central 2015-09-16 /pmc/articles/PMC4573289/ /pubmed/26377179 http://dx.doi.org/10.1186/s12866-015-0515-4 Text en © Walsh et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Walsh, Calum J.
Guinane, Caitriona M.
Hill, Colin
Ross, R. Paul
O’Toole, Paul W.
Cotter, Paul D.
In silico identification of bacteriocin gene clusters in the gastrointestinal tract, based on the Human Microbiome Project’s reference genome database
title In silico identification of bacteriocin gene clusters in the gastrointestinal tract, based on the Human Microbiome Project’s reference genome database
title_full In silico identification of bacteriocin gene clusters in the gastrointestinal tract, based on the Human Microbiome Project’s reference genome database
title_fullStr In silico identification of bacteriocin gene clusters in the gastrointestinal tract, based on the Human Microbiome Project’s reference genome database
title_full_unstemmed In silico identification of bacteriocin gene clusters in the gastrointestinal tract, based on the Human Microbiome Project’s reference genome database
title_short In silico identification of bacteriocin gene clusters in the gastrointestinal tract, based on the Human Microbiome Project’s reference genome database
title_sort in silico identification of bacteriocin gene clusters in the gastrointestinal tract, based on the human microbiome project’s reference genome database
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4573289/
https://www.ncbi.nlm.nih.gov/pubmed/26377179
http://dx.doi.org/10.1186/s12866-015-0515-4
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