{"id":322994,"date":"2016-11-16T12:47:42","date_gmt":"2016-11-16T20:47:42","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&p=322994"},"modified":"2022-04-06T16:22:27","modified_gmt":"2022-04-06T23:22:27","slug":"correlates-protective-cellular-immunity-revealed-analysis-population-level-immune-escape-pathways-hiv-1","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/correlates-protective-cellular-immunity-revealed-analysis-population-level-immune-escape-pathways-hiv-1\/","title":{"rendered":"Correlates of Protective Cellular Immunity Revealed by Analysis of Population-Level Immune Escape Pathways in HIV-1"},"content":{"rendered":"

A full proteome look at HLA-mediated escape<\/h1>\n

Citation and access<\/h2>\n

Correlates of protective cellular immunity revealed by analysis of population-level immune escape pathways in HIV-1 (opens in new tab)<\/span><\/a><\/p>\n

Jonathan M. Carlson*, Chanson J. Brumme*, Eric Martin*, Jennifer Listgarten, Mark A. Brockman, Anh Q. Le, Celia Chui, Laura A. Cotton, David J.H.F. Knapp, Sharon A. Riddler, Richard Haubrich, George Nelson, Nico Pfeifer, Charles E. DeZiel, David Heckerman, Richard Apps, Mary Carrington, Simon Mallal, P. Richard Harrigan, Mina John, Zabrina L. Brumme, and the International HIV Adaptation Collaborative<\/p>\n

Journal of Virology<\/span> (opens in new tab)<\/span><\/a>, 86<\/b>(24):13187-13201, December 2012.<\/p>\n

Cover image (opens in new tab)<\/span><\/a><\/p>\n

Hla Associations (xlsx) (opens in new tab)<\/span><\/a><\/p><\/blockquote>\n

Abstract<\/h2>\n

HLA class I-associated polymorphisms identified at the population level mark viral sites under immune pressure by individual HLA alleles. As such, analysis of their distribution, frequency, location, statistical strength, sequence conservation, and other properties offers a unique perspective from which to identify correlates of protective cellular immunity. We analyzed HLA-associated HIV-1 subtype B polymorphisms in 1,888 treatment-na\u00efve, chronically infected individuals using phylogenetically informed methods and identified characteristics of HLA-associated immune pressures that differentiate protective and nonprotective alleles. Over 2,100 HLA-associated HIV-1 polymorphisms were identified, approximately one-third of which occurred inside or within 3 residues of an optimally defined cytotoxic T-lymphocyte (CTL) epitope. Differential CTL escape patterns between closely related HLA alleles were common and increased with greater evolutionary distance between allele group members. Among 9-mer epitopes, mutations at HLA-specific anchor residues represented the most frequently detected escape type: these occurred nearly 2-fold more frequently than expected by chance and were computationally predicted to reduce peptide-HLA binding nearly 10-fold on average. Characteristics associated with protective HLA alleles (defined using hazard ratios for progression to AIDS from natural history cohorts) included the potential to mount broad immune selection pressures across all HIV-1 proteins except Nef, the tendency to drive multisite and\/or anchor residue escape mutations within known CTL epitopes, and the ability to strongly select mutations in conserved regions within HIV’s structural and functional proteins. Thus, the factors defining protective cellular immune responses may be more complex than simply targeting conserved viral regions. The results provide new information to guide vaccine design and immunogenicity studies.<\/p>\n

Overview<\/h2>\n

\"\"As part of the IHAC collaboration, we published (opens in new tab)<\/span><\/a> the largest HIV escape study ever done today. We studied the full HIV proteomes from 1,888 chronically HIV clade B-infected individuals who had never been given drugs to identify HLA escape mutations. These data (opens in new tab)<\/span><\/a> will be a major resource for the community moving forward as they update the current standard (opens in new tab)<\/span><\/a> we had published by adding more individuals, covering the full proteome, and developing new methods.<\/p>\n

But we also show some important new insights into HIV escape. For example, we now know that escape frequently happens at anchor residues (20% of the time; see Fig at right) and that a hallmark of protective HLA alleles is the ability to drive escape across the proteome, especially at anchors. What’s special about anchors? Primarily, once HIV mutates there, it is unlikely that the TcR will be able to adapt. Why would good HLAs drive escape at anchors? Presumably it’s a marker of a very broad TcR response, such that anchors are the only viable escape option. Also, if you can only escape at one or two sites, instead of 9 or 10, odds are higher a mutation will result in a fitness cost.<\/p>\n

Interestingly, we were able to build more on the differential escape paper by showing that a majority (62%) of escape associations are HLA subtype (4-digit) specific, while an additional 31% are type (2-digit) specific. Only 7% are shared across supertypes. This is important because we tend to group alleles to simplify things. Notably, we found a fairly strong correlation (Spearman rho=0.6) between the extent of genetic divergence of HLA exons 2 and 3 (which largely define peptide binding) and the extent of differential escape.<\/p>\n

Amusingly enough, a sytlized version of a supplemental figure ended up on the cover (opens in new tab)<\/span><\/a> of the January 2013 issue.<\/p>\n

Resources<\/h2>\n

The supplement (opens in new tab)<\/span><\/a> contains some key references for HLA-escape lists. The HLA escape lists (opens in new tab)<\/span><\/a> are probably the most useful. Note that we computed escapes at 3 different HLA resolutions: 4-digit, 2-digit and supertype. Each list includes more specific associations as well. So, for example, the supertype lists includes 2- and 4- digit escapes. To do this, we ran the model including as independent variables all supertype, 2- and 4-digit alleles (that passed our pre-processing filters), then used forward selection to identify the most significant associations. In the paper, we validate the forward selection proceedure as typically pulling out differenceds that are statistically significant. Which list you choose in use-dependent. We find the 4-digit lists the easiest to work with in most cases, but if I want to, for example, look at all HLA-linked mutations happening over time, or ask how adapted a sequence is to an HLA-repertoire, I’ll use the supertype list. Otherwise, the covariation associations are here (opens in new tab)<\/span><\/a> (one per escape list) and, for ease of use, we mapped all escapes to optimally defined epitopes here (opens in new tab)<\/span><\/a>. If you just want to lookup escapes for an epitope, this will be the easiest resource. Finally, visualizations of the escape maps are in graphical form are here (opens in new tab)<\/span><\/a>.<\/p>\n

Principal collaborators<\/h2>\n

Simon Fraser University (opens in new tab)<\/span><\/a><\/b><\/p>\n