Cytotoxic T-Lymphocyte Escape Mutations Identified by HLA Association Favor Those Which Escape and Revert Rapidly

Author:

Fryer Helen R.12,Frater John13,Duda Anna3,Palmer Duncan4,Phillips Rodney E.13,McLean Angela R.12

Affiliation:

1. The Institute for Emerging Infections, The Oxford Martin School, University of Oxford, Old Indian Institute, Oxford, United Kingdom

2. Department of Zoology, University of Oxford, Oxford, United Kingdom

3. The Peter Medawar Building for Pathogen Research, Nuffield Department of Clinical Medicine, Oxford University, Oxford, United Kingdom

4. Department of Statistics, University of Oxford, Oxford, United Kingdom

Abstract

ABSTRACT Identifying human immunodeficiency virus (HIV) immune escape mutations has implications for understanding the impact of host immunity on pathogen evolution and guiding the choice of vaccine antigens. One means of identifying cytotoxic-T-lymphocyte (CTL) escape mutations is to search for statistical associations between mutations and host human leukocyte antigen (HLA) class I alleles at the population level. The impact of evolutionary rates on the strength of such associations is not well defined. Here, we address this topic using a mathematical model of within-host evolution and between-host transmission of CTL escape mutants that predicts the prevalence of escape mutants at the population level. We ask how the rates at which an escape mutation emerges in a host who bears the restricting HLA and reverts when transmitted to a host who does not bear the HLA affect the strength of an association. We consider the impact of these factors when using a standard statistical method to test for an association and when using an adaptation of that method that corrects for phylogenetic relationships. We show that with both methods, the average sample size required to identify an escape mutation is smaller if the mutation escapes and reverts quickly. Thus, escape mutations identified as HLA associated systematically favor those that escape and revert rapidly. We also present expressions that can be used to infer escape and reversion rates from cross-sectional escape prevalence data.

Publisher

American Society for Microbiology

Subject

Virology,Insect Science,Immunology,Microbiology

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