IMMUNODYNAMICS OF HIV-1 IN GENETICALLY DIVERSE COHORTS

Abstract

Under the influence of host immune pressures, human immunodeficiency virus (HIV) rapidly accumulates and selects mutations that confer survival advantage to the virus. The human leukocyte antigen (HLA) represents one of the major host selection pressures that drive the antigenic evolution of HIV. During the course of infection, the interplay of host and virus factors determines the eventual outcome of the disease as well as the repertoire of predominant viral mutants in a given host milieu. In global perspective, this cross-talk between the host and the virus is observed as population-specific amplification of particular HIV subtypes, recombinant forms, or mutation variants. This study focuses on the HIV immunogenic protein Gag to analyze; a) the association of host immunity and viral genetic variability with disease progression, b) HIV subtype A divergence and epitope evolution at global as well as at population level, and c) co-occurring epitope mutations in HIV Gag, using a new in-house bioinformatics tool. Methodology: In this study, a total of 1893 subtype A sequences, from mid-1980s to late 2000s, representing 19 different countries, were included for global analysis. For cohort study, 15 Afghan, 50 Kenyan and 74 Pakistani HIV positive samples were collected. Using a variety of bioinformatics software, the sequences from HIV-1 Gag region p24 and p2p7p1p6 were analyzed for mutations affecting genetic divergence and epitope evolution (predominantly V303 to T303 mutation in the Pakistani and Kenyan cohorts, respectively). Subsequently, the population-specific Gag mutations V303 and T303 were focused for in vitro analysis. Proteasomal assays followed by Mass Spectrometry were performed to evaluate the significance of V303 and T303 mutation in epitope processing. The HIV divergence was analyzed using phylogenetic networks and Bayesian Skyline plot, whereas, the genomic variability of Gag was measured in terms of GA substitutions and Shannon entropy. Finally, a new Bioinformatics software, I-CAN (Identification of Co-occurring Amino acids and Nucleotides), was developed and used to analyze Gag epitope mutations co-occurring with the mutation V303T in Pakistani and Kenyan sequences. Results: In the Kenyan cohort, a linear trend between HIV genomic variability, and high viral load and low CD4 count was observed. Furthermore, certain Gag mutations unique to either Pakistani or Kenyan cohort were observed to affect the epitope processing in a population-specific manner. As a consequence of these mutations, epitope pattern in the two cohorts was uniquely altered. In the global analysis, it was observed that the HIV subtype A diverged around mid-90s from Kenya, exhibiting an upward trend in genomic variability that peaked in the last 5 years (2005-2010) of the analysis. A similar trend was also observed in Gag epitopes, where point mutations gave rise to novel Gag epitopes, evolving especially in the years 2005-2010. Finally, the results from the tool I-CAN, indicated a strong association in certain Gag co- occurring epitope mutations and the patient‟s HLA types. Conclusion: A pattern of population-specific Gag epitopes was observed that appeared to be evolving under selection pressures from the host HLA. Further investigation of these mutations will enhance the understanding of the evolution of HIV under host/population-specific selection pressures. This information may be helpful in designing vaccine and treatment strategies against HIV.