Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.

A large number of highly pathogenic bacteria utilize secretion systems to translocate effector proteins into host cells. Using these effectors, the bacteria subvert host cell processes during infection. Legionella pneumophila translocates effectors via the Icm/Dot type-IV secretion system and to dat...

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Autores principales:	David Burstein, Tal Zusman, Elena Degtyar, Ram Viner, Gil Segal, Tal Pupko
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Immunologic diseases. Allergy RC581-607 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/1725796ca8db49a49120915e6d246dd4
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spelling	oai:doaj.org-article:1725796ca8db49a49120915e6d246dd42021-11-25T05:47:47ZGenome-scale identification of Legionella pneumophila effectors using a machine learning approach.1553-73661553-737410.1371/journal.ppat.1000508https://doaj.org/article/1725796ca8db49a49120915e6d246dd42009-07-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19593377/?tool=EBIhttps://doaj.org/toc/1553-7366https://doaj.org/toc/1553-7374A large number of highly pathogenic bacteria utilize secretion systems to translocate effector proteins into host cells. Using these effectors, the bacteria subvert host cell processes during infection. Legionella pneumophila translocates effectors via the Icm/Dot type-IV secretion system and to date, approximately 100 effectors have been identified by various experimental and computational techniques. Effector identification is a critical first step towards the understanding of the pathogenesis system in L. pneumophila as well as in other bacterial pathogens. Here, we formulate the task of effector identification as a classification problem: each L. pneumophila open reading frame (ORF) was classified as either effector or not. We computationally defined a set of features that best distinguish effectors from non-effectors. These features cover a wide range of characteristics including taxonomical dispersion, regulatory data, genomic organization, similarity to eukaryotic proteomes and more. Machine learning algorithms utilizing these features were then applied to classify all the ORFs within the L. pneumophila genome. Using this approach we were able to predict and experimentally validate 40 new effectors, reaching a success rate of above 90%. Increasing the number of validated effectors to around 140, we were able to gain novel insights into their characteristics. Effectors were found to have low G+C content, supporting the hypothesis that a large number of effectors originate via horizontal gene transfer, probably from their protozoan host. In addition, effectors were found to cluster in specific genomic regions. Finally, we were able to provide a novel description of the C-terminal translocation signal required for effector translocation by the Icm/Dot secretion system. To conclude, we have discovered 40 novel L. pneumophila effectors, predicted over a hundred additional highly probable effectors, and shown the applicability of machine learning algorithms for the identification and characterization of bacterial pathogenesis determinants.David BursteinTal ZusmanElena DegtyarRam VinerGil SegalTal PupkoPublic Library of Science (PLoS)articleImmunologic diseases. AllergyRC581-607Biology (General)QH301-705.5ENPLoS Pathogens, Vol 5, Iss 7, p e1000508 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Immunologic diseases. Allergy RC581-607 Biology (General) QH301-705.5
spellingShingle	Immunologic diseases. Allergy RC581-607 Biology (General) QH301-705.5 David Burstein Tal Zusman Elena Degtyar Ram Viner Gil Segal Tal Pupko Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.
description	A large number of highly pathogenic bacteria utilize secretion systems to translocate effector proteins into host cells. Using these effectors, the bacteria subvert host cell processes during infection. Legionella pneumophila translocates effectors via the Icm/Dot type-IV secretion system and to date, approximately 100 effectors have been identified by various experimental and computational techniques. Effector identification is a critical first step towards the understanding of the pathogenesis system in L. pneumophila as well as in other bacterial pathogens. Here, we formulate the task of effector identification as a classification problem: each L. pneumophila open reading frame (ORF) was classified as either effector or not. We computationally defined a set of features that best distinguish effectors from non-effectors. These features cover a wide range of characteristics including taxonomical dispersion, regulatory data, genomic organization, similarity to eukaryotic proteomes and more. Machine learning algorithms utilizing these features were then applied to classify all the ORFs within the L. pneumophila genome. Using this approach we were able to predict and experimentally validate 40 new effectors, reaching a success rate of above 90%. Increasing the number of validated effectors to around 140, we were able to gain novel insights into their characteristics. Effectors were found to have low G+C content, supporting the hypothesis that a large number of effectors originate via horizontal gene transfer, probably from their protozoan host. In addition, effectors were found to cluster in specific genomic regions. Finally, we were able to provide a novel description of the C-terminal translocation signal required for effector translocation by the Icm/Dot secretion system. To conclude, we have discovered 40 novel L. pneumophila effectors, predicted over a hundred additional highly probable effectors, and shown the applicability of machine learning algorithms for the identification and characterization of bacterial pathogenesis determinants.
format	article
author	David Burstein Tal Zusman Elena Degtyar Ram Viner Gil Segal Tal Pupko
author_facet	David Burstein Tal Zusman Elena Degtyar Ram Viner Gil Segal Tal Pupko
author_sort	David Burstein
title	Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.
title_short	Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.
title_full	Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.
title_fullStr	Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.
title_full_unstemmed	Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.
title_sort	genome-scale identification of legionella pneumophila effectors using a machine learning approach.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/1725796ca8db49a49120915e6d246dd4
work_keys_str_mv	AT davidburstein genomescaleidentificationoflegionellapneumophilaeffectorsusingamachinelearningapproach AT talzusman genomescaleidentificationoflegionellapneumophilaeffectorsusingamachinelearningapproach AT elenadegtyar genomescaleidentificationoflegionellapneumophilaeffectorsusingamachinelearningapproach AT ramviner genomescaleidentificationoflegionellapneumophilaeffectorsusingamachinelearningapproach AT gilsegal genomescaleidentificationoflegionellapneumophilaeffectorsusingamachinelearningapproach AT talpupko genomescaleidentificationoflegionellapneumophilaeffectorsusingamachinelearningapproach
_version_	1718414445095419904

Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.

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