Gene expression analysis of HIV-1 linked p24-specific CD4+ T-cell responses for identifying genetic markers

The Human Immunodeficiency Virus (HIV) presents a complex knot for scientists to unravel. After initial contact and attachment to a cell of the immune system (e.g. lymphocytes, monocytes), there is a cascade of intracellular events. The endproduct of these events is the production of massive numbers of new viral particles, death of the infected cells, and ultimate devastation of the immune system. HIV is an epidemic and a crisis in many continents. Since there are many variations of the virus and differences in people's genetic make-up, rapid diagnosis and monitoring of tailored treatments are essential for future medicine. To combat this problem, microarray technology can perform a single scan on thousands of genes. However, without a proper research design and data mining techniques, the results from such a technology can be very skewed. Thus, using a normalized, clean dataset (time-series) from the CD4+ T-cell line CEM-CCRF, we designed and implemented hierarchical clustering and pattern-based clustering algorithms to identify specific cellular genes influenced by the HIV-1 viral infection. This research can contribute to the HIV Pharmacogenomics field by confirming HIV genetic markers, which would lead to rapid diagnosis and customized treatments.