Widespread Impact of HLA Restriction on Immune Control and Escape Pathways of HIV-1

Abstract

jThe promiscuous presentation of epitopes by similar HLA class I alleles holds promise for a universal T-cell-based HIV-1 vaccine. However, in some instances, cytotoxic T lymphocytes (CTL) restricted by HLA alleles with similar or identical binding motifs are known to target epitopes at different frequencies, with different functional avidities and with different apparent clinical outcomes. Such differences may be illuminated by the association of similar HLA alleles with distinctive escape pathways. Using a novel computational method featuring phylogenetically corrected odds ratios, we systematically analyzed differential patterns of immune escape across all optimally defined epitopes in Gag, Pol, and Nef in 2,126 HIV-1 clade C-infected adults. Overall, we identified 301 polymorphisms in 90 epitopes associated with HLA alleles belonging to shared supertypes. We detected differential escape in 37 of 38 epitopes restricted by more than one allele, which included 278 instances of differential escape at the polymorphism level. The majority (66 to 97%) of these resulted from the selection of unique HLA-specific polymorphisms rather than differential epitope targeting rates, as confirmed by gamma interferon (IFN-␥) enzyme-linked immunosorbent spot assay (ELISPOT) data. Discordant associations between HLA alleles and viral load were frequently observed between allele pairs that selected for differential escape. Furthermore, the total number of associated polymorphisms strongly correlated with average viral load. These studies confirm that differential escape is a widespread phenomenon and may be the norm when two alleles present the same epitope. Given the clinical correlates of immune escape, such heterogeneity suggests that certain epitopes will lead to discordant outcomes if applied universally in a vaccine.

Host-Specific Adaptation of HIV-1 Subtype B in the Japanese Population

“…HLA-APs can be identified in large cross-sectional linked data sets of host (HLA) and HIV genotypes using statistical-association approaches that identify viral polymorphisms significantly over-or underrepresented in individuals harboring a specific HLA class I allele (1,2,4,(16)(17)(18)21). HLA-APs that are overrepresented in individuals harboring the relevant HLA are commonly referred to as "adapted" forms, while those underrepresented in individuals harboring the relevant HLA are referred to as "nonadapted" forms (2,18).…”

“…As such, "nonadapted" and "adapted" forms can be conceptualized to represent the "immunologically susceptible" and "escape mutant" forms, respectively, for the specific HLA allele in question at that HIV codon position. Statistical association approaches for the identification of HLA-APs also correct for the confounding influences of viral phylogeny, HIV codon covariation, and linkage disequilibrium (LD) between HLA class I alleles (2,16,17,21).…”

“…Associations between HLA class I alleles and HIV-1 amino acid polymorphisms in the Japanese and IHAC data sets were identified using a published phylogenetically corrected logistical-regression model that corrects for HLA LD, HIV phylogeny, and HIV codon covariation as potential confounders (17,20). Briefly, maximum-likelihood phylogenetic trees were constructed using Gag, Pol, and Nef sequences (one tree per gene), and a model of conditional adaptation was inferred for each observed amino acid at each codon.…”

“…First, the identification of HLA-APs must be undertaken at the HLA subtype level. This is because the majority (Ͼ60%) of HLA-associated polymorphisms are best defined at the subtype level (16), even for closely related HLA subtype members that present the same or similar peptide epitopes (16,17,18,19). Comparative studies undertaken at allele level (two-digit) resolution cannot disentangle whether population-specific HLA-APs are attributable to differential HLA subtype distributions between cohorts or whether they are "true" cases where the same HLA subtype drives different escape pathways across populations.…”

“…Second, the identification of population-specific escape pathways driven by the same HLA allele requires a method to do so. Here, we adapt phylogenetically corrected statistical methods originally developed to assess differential escape among related HLA subtypes (17) and apply them to investigate differential escape across host populations.…”