A Quantitative Quasispecies Theory-Based Model of Virus Escape Mutation Under Immune Selection

Abstract

Viral infections involve a complex interplay of the immune response and escape mutation of the virus quasispecies inside a single host. Although fundamental aspects of such a balance of mutation and selection pressure have been established by the quasispecies theory decades ago, its implications have largely remained qualitative. Here, we present a quantitative approach to model the virus evolution under cytotoxic T-lymphocyte immune response. The virus quasispecies dynamics are explicitly represented by mutations in the combined sequence space of a set of epitopes within the viral genome. We stochastically simulated the growth of a viral population originating from a single wild-type founder virus and its recognition and clearance by the immune response, as well as the expansion of its genetic diversity. Applied to the immune escape of a simian immunodeficiency virus epitope, model predictions were quantitatively comparable to the experimental data. Within the model parameter space, we found two qualitatively different regimes of infectious disease pathogenesis, each representing alternative fates of the immune response: It can clear the infection in finite time or eventually be overwhelmed by viral growth and escape mutation. The latter regime exhibits the characteristic disease progression pattern of human immunodeficiency virus, while the former is bounded by maximum mutation rates that can be suppressed by the immune response. Our results demonstrate that, by explicitly representing epitope mutations and thus providing a genotype phenotype map, the quasispecies theory can form the basis of a detailed sequence-specific model of real-world viral pathogens evolving under immune selection.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2012
Accession Number
ADA570913

Entities

People

  • Hyung-june Woo
  • Jaques Reifman

Organizations

  • United States Army Medical Research and Development Command

Tags

DTIC Thesaurus Topics

  • Amino Acids
  • Application Software
  • Cells
  • Disease Attributes
  • Genetic Code
  • Genetic Variation
  • Genetics
  • Immune System
  • Infectious Diseases
  • Lymphocytes
  • Mathematical Models
  • Mutations
  • Rna Viruses
  • T Lymphocytes
  • Viral Load
  • Virus Diseases
  • Viruses

Fields of Study

  • Biology
  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Immunology
  • Molecular Genetics

Technology Areas

  • Biotechnology
  • Biotechnology - Cancer Biotech
  • Space