Bioinformatic Analysis of HIV-1 Entry and Pathogenesis
Benjamas Aiamkitsumrit, Will Dampier, Gregory Antell, Nina Rivera, Julio Martin-Garcia, Vanessa Pirrone, Michael R. Nonnemacher and Brian Wigdahl
Affiliation: Department of Microbiology and Immunology, Drexel University College of Medicine, 245 N. 15th Street, Philadelphia, PA 19102.
Keywords: CCR5, coreceptor, CXCR4, HIV, mutual information, PSSM.
The evolution of human immunodeficiency virus type 1 (HIV-1) with respect to co-receptor utilization has
been shown to be relevant to HIV-1 pathogenesis and disease. The CCR5-utilizing (R5) virus has been shown to be
important in the very early stages of transmission and highly prevalent during asymptomatic infection and chronic disease.
In addition, the R5 virus has been proposed to be involved in neuroinvasion and central nervous system (CNS) disease. In
contrast, the CXCR4-utilizing (X4) virus is more prevalent during the course of disease progression and concurrent with
the loss of CD4+ T cells. The dual-tropic virus is able to utilize both co-receptors (CXCR4 and CCR5) and has been
thought to represent an intermediate transitional virus that possesses properties of both X4 and R5 viruses that can be
encountered at many stages of disease. The use of computational tools and bioinformatic approaches in the prediction of
HIV-1 co-receptor usage has been growing in importance with respect to understanding HIV-1 pathogenesis and disease,
developing diagnostic tools, and improving the efficacy of therapeutic strategies focused on blocking viral entry. Current
strategies have enhanced the sensitivity, specificity, and reproducibility relative to the prediction of co-receptor use;
however, these technologies need to be improved with respect to their efficient and accurate use across the HIV-1
subtypes. The most effective approach may center on the combined use of different algorithms involving sequences within
and outside of the env-V3 loop. This review focuses on the HIV-1 entry process and on co-receptor utilization, including
bioinformatic tools utilized in the prediction of co-receptor usage. It also provides novel preliminary analyses for enabling
identification of linkages between amino acids in V3 with other components of the HIV-1 genome and demonstrates that
these linkages are different between X4 and R5 viruses.
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