skip to main content

DOE PAGESDOE PAGES

Title: Inferring HIV Escape Rates from Multi-Locus Genotype Data

Cytotoxic T-lymphocytes (CTLs) recognize viral protein fragments displayed by major histocompatibility complex molecules on the surface of virally infected cells and generate an anti-viral response that can kill the infected cells. Virus variants whose protein fragments are not efficiently presented on infected cells or whose fragments are presented but not recognized by CTLs therefore have a competitive advantage and spread rapidly through the population. We present a method that allows a more robust estimation of these escape rates from serially sampled sequence data. The proposed method accounts for competition between multiple escapes by explicitly modeling the accumulation of escape mutations and the stochastic effects of rare multiple mutants. Applying our method to serially sampled HIV sequence data, we estimate rates of HIV escape that are substantially larger than those previously reported. The method can be extended to complex escapes that require compensatory mutations. We expect our method to be applicable in other contexts such as cancer evolution where time series data is also available.
Authors:
 [1] ;  [2] ;  [1]
  1. Max Planck Inst. for Developmental Biology, Tubingen (Germany)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-13-24089
Journal ID: ISSN 1664-3224
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Frontiers in Immunology
Additional Journal Information:
Journal Volume: 4; Journal ID: ISSN 1664-3224
Publisher:
Frontiers Research Foundation
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
National Institutes of Health (NIH); USDOE
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; Biological Science
OSTI Identifier:
1396110

Kessinger, Taylor A., Perelson, Alan S., and Neher, Richard A.. Inferring HIV Escape Rates from Multi-Locus Genotype Data. United States: N. p., Web. doi:10.3389/fimmu.2013.00252.
Kessinger, Taylor A., Perelson, Alan S., & Neher, Richard A.. Inferring HIV Escape Rates from Multi-Locus Genotype Data. United States. doi:10.3389/fimmu.2013.00252.
Kessinger, Taylor A., Perelson, Alan S., and Neher, Richard A.. 2013. "Inferring HIV Escape Rates from Multi-Locus Genotype Data". United States. doi:10.3389/fimmu.2013.00252. https://www.osti.gov/servlets/purl/1396110.
@article{osti_1396110,
title = {Inferring HIV Escape Rates from Multi-Locus Genotype Data},
author = {Kessinger, Taylor A. and Perelson, Alan S. and Neher, Richard A.},
abstractNote = {Cytotoxic T-lymphocytes (CTLs) recognize viral protein fragments displayed by major histocompatibility complex molecules on the surface of virally infected cells and generate an anti-viral response that can kill the infected cells. Virus variants whose protein fragments are not efficiently presented on infected cells or whose fragments are presented but not recognized by CTLs therefore have a competitive advantage and spread rapidly through the population. We present a method that allows a more robust estimation of these escape rates from serially sampled sequence data. The proposed method accounts for competition between multiple escapes by explicitly modeling the accumulation of escape mutations and the stochastic effects of rare multiple mutants. Applying our method to serially sampled HIV sequence data, we estimate rates of HIV escape that are substantially larger than those previously reported. The method can be extended to complex escapes that require compensatory mutations. We expect our method to be applicable in other contexts such as cancer evolution where time series data is also available.},
doi = {10.3389/fimmu.2013.00252},
journal = {Frontiers in Immunology},
number = ,
volume = 4,
place = {United States},
year = {2013},
month = {9}
}