SCS Colloquium: Research Overview - Protein interactions to Co-receptor Switch

In this colloquium talk I will give a brief overview of the research I have been conducting over the years in University of Amsterdam. 

First, I will start with  protein interactions between HIV and human proteins and the underlying HIV-1 human protein interaction network. I will explain the analysis of this network for network centrality, connectivity as well as overrepresented significant network motifs. I will present our results on the network analysis indicating that infection with HIV results in a reprioritization of cellular processes reflected by an increase in the relative importance of the transcriptional machinery and proteasome formation. We argue that during the evolution of HIV some interaction patterns were favorable for the virus and thus are conserved. This resulted in a system where virus proteins interact with central host proteins for direct control, and with proteasomal proteins for indirect control over the cellular processes.

Later, I will introduce and validate a domain independent algorithm for discovering potential interaction partners based on similarity and graph theory. We have applied this algorithm for predicting “potential missing links in our protein interaction network” based on their functional similarities with the proteins readily available. This resulted in identification of 21 SMPs potentially permit, mediate or enhance HIV infection in different cell/tissue types in HIV-infected individuals. Among those 21 SMPs, ten were involved in a cascade of events in HIV infection from serving as co-receptors for cell entry (CCR1 and CCBP2), mediating transinfection (DARC), activating immune cells (CD97) to inducing viral production from latently infected cells (CSF3R, TNFRSF3 and CD2). We also presented eleven original predictions that are potential HIV interacting factors.

Finally I will focus on two of the co-receptors of HIV-1 (CCR5 and CXCR4)  and describe an evolutionary model of HIV-1 co-receptor tropism. This computational model with  a clear temporal scale has been implemented for  studying the longitudinal dynamics of HIV tropism and the co-receptor switch. This talk will be finalized with conclusions and future work.