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

• Main Authors: Sloot, Peter M. A., Jaeger, Samira., Ertaylan, Gokhan., van Dijk, David., Leser, Ulf.
• Other Authors: School of Computer Engineering
• Format: Article
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences
• Online Access: https://hdl.handle.net/10356/84471; http://hdl.handle.net/10220/9872
• Institution: Nanyang Technological University

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. Methodology/Principal Findings 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. Conclusions 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.