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In Silico Prediction of Human Pathogenicity in the γ-Proteobacteria

BACKGROUND: Although the majority of bacteria are innocuous or even beneficial for their host, others are highly infectious pathogens that can cause widespread and deadly diseases. When investigating the relationships between bacteria and other living organisms, it is therefore essential to be able...

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Autores principales: Andreatta, Massimo, Nielsen, Morten, Møller Aarestrup, Frank, Lund, Ole
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2965111/
https://www.ncbi.nlm.nih.gov/pubmed/21048922
http://dx.doi.org/10.1371/journal.pone.0013680
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author Andreatta, Massimo
Nielsen, Morten
Møller Aarestrup, Frank
Lund, Ole
author_facet Andreatta, Massimo
Nielsen, Morten
Møller Aarestrup, Frank
Lund, Ole
author_sort Andreatta, Massimo
collection PubMed
description BACKGROUND: Although the majority of bacteria are innocuous or even beneficial for their host, others are highly infectious pathogens that can cause widespread and deadly diseases. When investigating the relationships between bacteria and other living organisms, it is therefore essential to be able to separate pathogenic organisms from non-pathogenic ones. Using traditional experimental methods for this purpose can be very costly and time-consuming, and also uncertain since animal models are not always good predictors for pathogenicity in humans. Bioinformatics-based methods are therefore strongly needed to mine the fast growing number of genome sequences and assess in a rapid and reliable way the pathogenicity of novel bacteria. METHODOLOGY/PRINCIPAL FINDINGS: We describe a new in silico method for the prediction of bacterial pathogenicity, based on the identification in microbial genomes of features that appear to correlate with virulence. The method does not rely on identifying genes known to be involved in pathogenicity (for instance virulence factors), but rather it inherently builds families of proteins that, irrespective of their function, are consistently present in only one of the two kinds of organisms, pathogens or non-pathogens. Whether a new bacterium carries proteins contained in these families determines its prediction as pathogenic or non-pathogenic. The application of the method on a set of known genomes correctly classified the virulence potential of 86% of the organisms tested. An additional validation on an independent test-set assigned correctly 22 out of 24 bacteria. CONCLUSIONS: The proposed approach was demonstrated to go beyond the species bias imposed by evolutionary relatedness, and performs better than predictors based solely on taxonomy or sequence similarity. A set of protein families that differentiate pathogenic and non-pathogenic strains were identified, including families of yet uncharacterized proteins that are suggested to be involved in bacterial pathogenicity.
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spelling pubmed-29651112010-11-03 In Silico Prediction of Human Pathogenicity in the γ-Proteobacteria Andreatta, Massimo Nielsen, Morten Møller Aarestrup, Frank Lund, Ole PLoS One Research Article BACKGROUND: Although the majority of bacteria are innocuous or even beneficial for their host, others are highly infectious pathogens that can cause widespread and deadly diseases. When investigating the relationships between bacteria and other living organisms, it is therefore essential to be able to separate pathogenic organisms from non-pathogenic ones. Using traditional experimental methods for this purpose can be very costly and time-consuming, and also uncertain since animal models are not always good predictors for pathogenicity in humans. Bioinformatics-based methods are therefore strongly needed to mine the fast growing number of genome sequences and assess in a rapid and reliable way the pathogenicity of novel bacteria. METHODOLOGY/PRINCIPAL FINDINGS: We describe a new in silico method for the prediction of bacterial pathogenicity, based on the identification in microbial genomes of features that appear to correlate with virulence. The method does not rely on identifying genes known to be involved in pathogenicity (for instance virulence factors), but rather it inherently builds families of proteins that, irrespective of their function, are consistently present in only one of the two kinds of organisms, pathogens or non-pathogens. Whether a new bacterium carries proteins contained in these families determines its prediction as pathogenic or non-pathogenic. The application of the method on a set of known genomes correctly classified the virulence potential of 86% of the organisms tested. An additional validation on an independent test-set assigned correctly 22 out of 24 bacteria. CONCLUSIONS: The proposed approach was demonstrated to go beyond the species bias imposed by evolutionary relatedness, and performs better than predictors based solely on taxonomy or sequence similarity. A set of protein families that differentiate pathogenic and non-pathogenic strains were identified, including families of yet uncharacterized proteins that are suggested to be involved in bacterial pathogenicity. Public Library of Science 2010-10-27 /pmc/articles/PMC2965111/ /pubmed/21048922 http://dx.doi.org/10.1371/journal.pone.0013680 Text en Andreatta et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Andreatta, Massimo
Nielsen, Morten
Møller Aarestrup, Frank
Lund, Ole
In Silico Prediction of Human Pathogenicity in the γ-Proteobacteria
title In Silico Prediction of Human Pathogenicity in the γ-Proteobacteria
title_full In Silico Prediction of Human Pathogenicity in the γ-Proteobacteria
title_fullStr In Silico Prediction of Human Pathogenicity in the γ-Proteobacteria
title_full_unstemmed In Silico Prediction of Human Pathogenicity in the γ-Proteobacteria
title_short In Silico Prediction of Human Pathogenicity in the γ-Proteobacteria
title_sort in silico prediction of human pathogenicity in the γ-proteobacteria
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2965111/
https://www.ncbi.nlm.nih.gov/pubmed/21048922
http://dx.doi.org/10.1371/journal.pone.0013680
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