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  4. Effects of thymic selection of the T cell repertoire on HLA-class I associated control of HIV infection

Effects of thymic selection of the T cell repertoire on HLA-class I associated control of HIV infection

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Author(s)
Kosmrlj, Andrej
•
Read, Elizabeth L.
•
Qi, Ying
•
Allen, Todd M.
•
Altfeld, Marcus
•
Deeks, Steven G.
•
Pereyra, Florencia
•
Carrington, Mary
•
Walker, Bruce D.
•
Chakraborty, Arup K
Date Issued
May 2010
Journal
Nature
Publisher
Nature Publishing Group
Citation
Košmrlj, Andrej et al. “Effects of Thymic Selection of the T-cell Repertoire on HLA Class I-associated Control of HIV Infection.” Nature 465.7296 (2010): 350–354. CrossRef. Web.
Version
Author's final manuscript
Abstract
Without therapy, most people infected with human immunodeficiency virus (HIV) ultimately progress to AIDS. Rare individuals (‘elite controllers’) maintain very low levels of HIV RNA without therapy, thereby making disease progression and transmission unlikely. Certain HLA class I alleles are markedly enriched in elite controllers, with the highest association observed for HLA-B57 (ref. 1). Because HLA molecules present viral peptides that activate CD8+ T cells, an immune-mediated mechanism is probably responsible for superior control of HIV. Here we describe how the peptide-binding characteristics of HLA-B57 molecules affect thymic development such that, compared to other HLA-restricted T cells, a larger fraction of the naive repertoire of B57-restricted clones recognizes a viral epitope, and these T cells are more cross-reactive to mutants of targeted epitopes. Our calculations predict that such a T-cell repertoire imposes strong immune pressure on immunodominant HIV epitopes and emergent mutants, thereby promoting efficient control of the virus. Supporting these predictions, in a large cohort of HLA-typed individuals, our experiments show that the relative ability of HLA-B alleles to control HIV correlates with their peptide-binding characteristics that affect thymic development. Our results provide a conceptual framework that unifies diverse empirical observations, and have implications for vaccination strategies.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Physics
Ragon Institute of MGH, MIT and Harvard
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
http://creativecommons.org/licenses/by-nc-sa/3.0/
Persistent DSpace Link
http://hdl.handle.net/1721.1/77959
DOI of Published Version
https://doi.org/10.1038/nature08997
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