A logical model of HIV-1 interactions with the T-cell activation signalling pathway

Oyeyemi, O. J., Davies, O., Robertson, D. L. and Schwartz, J.-M. (2015) A logical model of HIV-1 interactions with the T-cell activation signalling pathway. Bioinformatics, 31(7), pp. 1075-1083. (doi: 10.1093/bioinformatics/btu787) (PMID:25431332)

Abstract

Motivation: Human immunodeficiency virus type 1 (HIV-1) hijacks host cellular processes to replicate within its host. Through interactions with host proteins, it perturbs and interrupts signaling pathways that alter key cellular functions. Although networks of viral–host interactions have been relatively well characterized, the dynamics of the perturbation process is poorly understood. Dynamic models of infection have the potential to provide insights into the HIV-1 host interaction. Results: We employed a logical signal flow network to model the dynamic interactions between HIV-1 proteins and key human signal transduction pathways necessary for activation of CD4+ T lymphocytes. We integrated viral–host interaction and host signal transduction data into a dynamic logical model comprised of 137 nodes (16 HIV-1 and 121 human proteins) and 336 interactions collected from the HIV-1 Human Interaction Database. The model reproduced expected patterns of T-cell activation, co-stimulation and co-inhibition. After simulations, we identified 26 host cell factors, including MAPK1&3, Ikkb-Ikky-Ikka and PKA, which contribute to the net activation or inhibition of viral proteins. Through in silico knockouts, the model identified a further nine host cell factors, including members of the PI3K signalling pathway that are essential to viral replication. Simulation results intersected with the findings of three siRNA gene knockout studies and identified potential drug targets. Our results demonstrate how viral infection causes the cell to lose control of its signalling system. Logical Boolean modelling therefore provides a useful approach for analysing the dynamics of host–viral interactions with potential applications for drug discovery.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Robertson, Professor David
• Authors: Oyeyemi, O. J., Davies, O., Robertson, D. L., and Schwartz, J.-M.
• College/School: College of Medical Veterinary and Life Sciences > School of Infection & Immunity; College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Virus Research
• Journal Name: Bioinformatics
• Publisher: Oxford University Press
• ISSN: 1367-4803
• ISSN (Online): 1367-4811
• Published Online: 26 November 2014