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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2010-10-01
|
| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20967291/?tool=EBI |
| _version_ | 1818573607743258624 |
|---|---|
| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-15T00:13:36Z |
| format | Article |
| id | doaj.art-b98c5fdefa094046ba58519912ec889d |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-15T00:13:36Z |
| publishDate | 2010-10-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-b98c5fdefa094046ba58519912ec889d2022-12-21T22:42:30ZengPublic Library of Science (PLoS)PLoS ONE1932-62032010-10-01510e1313910.1371/journal.pone.0013139Inference of surface membrane factors of HIV-1 infection through functional interaction networks.Samira JaegerGokhan ErtaylanDavid van DijkUlf LeserPeter Sloot<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.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20967291/?tool=EBI |
| spellingShingle | Samira Jaeger Gokhan Ertaylan David van Dijk Ulf Leser Peter Sloot Inference of surface membrane factors of HIV-1 infection through functional interaction networks. PLoS ONE |
| title | 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_short | 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 |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20967291/?tool=EBI |
| work_keys_str_mv | AT samirajaeger inferenceofsurfacemembranefactorsofhiv1infectionthroughfunctionalinteractionnetworks AT gokhanertaylan inferenceofsurfacemembranefactorsofhiv1infectionthroughfunctionalinteractionnetworks AT davidvandijk inferenceofsurfacemembranefactorsofhiv1infectionthroughfunctionalinteractionnetworks AT ulfleser inferenceofsurfacemembranefactorsofhiv1infectionthroughfunctionalinteractionnetworks AT petersloot inferenceofsurfacemembranefactorsofhiv1infectionthroughfunctionalinteractionnetworks |