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Host factors influencing viral persistence.

A R Thomsen1, A Nansen, S O Andreasen

  • 1Institute of Medical Microbiology & Immunology, The Panum Institute, University of Copenhagen, Denmark. a.r.thomsen@immi.ku.dk

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|February 24, 2001
PubMed
Summary

Antiviral immunity depends on host genetics and virus strain. Impaired immune surveillance, particularly CD8+ T-cells and interferon-gamma, can lead to viral resurgence or fatal wasting syndrome.

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Area of Science:

  • Immunobiology
  • Virology
  • Infectious Disease

Background:

  • Characterizing antiviral immune responses is crucial for understanding host-pathogen dynamics.
  • Lymphocytic choriomeningitis virus (LCMV) serves as a model for studying non-cytocidal viral infections.
  • Host immunocompetence significantly influences viral infection outcomes.

Purpose of the Study:

  • To investigate the role of specific immune components in controlling LCMV infection.
  • To determine how virus strain and host genetic background affect infection outcomes.
  • To explore the interplay between viral replication, immune surveillance, and disease pathogenesis.

Main Methods:

  • Utilized gene knockout mouse models to assess the function of CD4+ cells, B cells, CD40 ligand, and interferon-gamma.

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  • Employed two LCMV strains with distinct replication and spread capacities.
  • Integrated mathematical modeling with in vivo experimental data.

    • Main Results:

    • CD4+ cells are not critical for initial viral control, but their absence leads to viral resurgence.
    • Impaired long-term CD8+ T-cell surveillance, B cells, or CD40 ligand function also results in virus reappearance.
    • Interferon-gamma is central; its deficiency can cause fatal wasting syndrome or chronic infection, depending on viral replication rates.
    • Viral resurgence time correlates inversely with viral replication rate.

    Conclusions:

    • Antiviral immune responses are complex and strain/host-dependent.
    • Mathematical modeling can predict critical host and virus parameters in immunobiology.
    • Combining quantitative in vivo analysis with mathematical modeling offers a promising approach to understanding immune responses.