Inference of surface membrane factors of HIV-1 infection through functional interaction networks.

<h4>Background</h4>HIV infection affects the populations of T helper cells, dendritic cells and macrophages. Moreover, it has a serious impact on the central nervous system. It is yet not clear whether this list is complete and why specifically those cell types are affected. To address t...

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Autores principales:	Samira Jaeger, Gokhan Ertaylan, David van Dijk, Ulf Leser, Peter Sloot
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b98c5fdefa094046ba58519912ec889d
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spelling	oai:doaj.org-article:b98c5fdefa094046ba58519912ec889d2021-11-18T07:03:25ZInference of surface membrane factors of HIV-1 infection through functional interaction networks.1932-620310.1371/journal.pone.0013139https://doaj.org/article/b98c5fdefa094046ba58519912ec889d2010-10-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20967291/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>HIV infection affects the populations of T helper cells, dendritic cells and macrophages. Moreover, it has a serious impact on the central nervous system. It is yet not clear whether this list is complete and why specifically those cell types are affected. To address this question, we have developed a method to identify cellular surface proteins that permit, mediate or enhance HIV infection in different cell/tissue types in HIV-infected individuals. Receptors associated with HIV infection share common functions and domains and are involved in similar cellular processes. These properties are exploited by bioinformatics techniques to predict novel cell surface proteins that potentially interact with HIV.<h4>Methodology/principal findings</h4>We compiled a set of surface membrane proteins (SMP) that are known to interact with HIV. This set is extended by proteins that have direct interaction and share functional similarity. This resulted in a comprehensive network around the initial SMP set. Using network centrality analysis we predict novel surface membrane factors from the annotated network. We identify 21 surface membrane factors, among which three have confirmed functions in HIV infection, seven have been identified by at least two other studies, and eleven are novel predictions and thus excellent targets for experimental investigation.<h4>Conclusions</h4>Determining to what extent HIV can interact with human SMPs is an important step towards understanding patient specific disease progression. Using various bioinformatics techniques, we generate a set of surface membrane factors that constitutes a well-founded starting point for experimental testing of cell/tissue susceptibility of different HIV strains as well as for cohort studies evaluating patient specific disease progression.Samira JaegerGokhan ErtaylanDavid van DijkUlf LeserPeter SlootPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 10, p e13139 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Samira Jaeger Gokhan Ertaylan David van Dijk Ulf Leser Peter Sloot Inference of surface membrane factors of HIV-1 infection through functional interaction networks.
description	<h4>Background</h4>HIV infection affects the populations of T helper cells, dendritic cells and macrophages. Moreover, it has a serious impact on the central nervous system. It is yet not clear whether this list is complete and why specifically those cell types are affected. To address this question, we have developed a method to identify cellular surface proteins that permit, mediate or enhance HIV infection in different cell/tissue types in HIV-infected individuals. Receptors associated with HIV infection share common functions and domains and are involved in similar cellular processes. These properties are exploited by bioinformatics techniques to predict novel cell surface proteins that potentially interact with HIV.<h4>Methodology/principal findings</h4>We compiled a set of surface membrane proteins (SMP) that are known to interact with HIV. This set is extended by proteins that have direct interaction and share functional similarity. This resulted in a comprehensive network around the initial SMP set. Using network centrality analysis we predict novel surface membrane factors from the annotated network. We identify 21 surface membrane factors, among which three have confirmed functions in HIV infection, seven have been identified by at least two other studies, and eleven are novel predictions and thus excellent targets for experimental investigation.<h4>Conclusions</h4>Determining to what extent HIV can interact with human SMPs is an important step towards understanding patient specific disease progression. Using various bioinformatics techniques, we generate a set of surface membrane factors that constitutes a well-founded starting point for experimental testing of cell/tissue susceptibility of different HIV strains as well as for cohort studies evaluating patient specific disease progression.
format	article
author	Samira Jaeger Gokhan Ertaylan David van Dijk Ulf Leser Peter Sloot
author_facet	Samira Jaeger Gokhan Ertaylan David van Dijk Ulf Leser Peter Sloot
author_sort	Samira Jaeger
title	Inference of surface membrane factors of HIV-1 infection through functional interaction networks.
title_short	Inference of surface membrane factors of HIV-1 infection through functional interaction networks.
title_full	Inference of surface membrane factors of HIV-1 infection through functional interaction networks.
title_fullStr	Inference of surface membrane factors of HIV-1 infection through functional interaction networks.
title_full_unstemmed	Inference of surface membrane factors of HIV-1 infection through functional interaction networks.
title_sort	inference of surface membrane factors of hiv-1 infection through functional interaction networks.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/b98c5fdefa094046ba58519912ec889d
work_keys_str_mv	AT samirajaeger inferenceofsurfacemembranefactorsofhiv1infectionthroughfunctionalinteractionnetworks AT gokhanertaylan inferenceofsurfacemembranefactorsofhiv1infectionthroughfunctionalinteractionnetworks AT davidvandijk inferenceofsurfacemembranefactorsofhiv1infectionthroughfunctionalinteractionnetworks AT ulfleser inferenceofsurfacemembranefactorsofhiv1infectionthroughfunctionalinteractionnetworks AT petersloot inferenceofsurfacemembranefactorsofhiv1infectionthroughfunctionalinteractionnetworks
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Inference of surface membrane factors of HIV-1 infection through functional interaction networks.

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