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Home/Equine Research Bank/PLoS Comput Biol/Modeling within-host dynamics of influenza virus infection i...
PLoS computational biology2012; 8(6); e1002588; doi: 10.1371/journal.pcbi.1002588

Modeling within-host dynamics of influenza virus infection including immune responses.

Abstract: Influenza virus infection remains a public health problem worldwide. The mechanisms underlying viral control during an uncomplicated influenza virus infection are not fully understood. Here, we developed a mathematical model including both innate and adaptive immune responses to study the within-host dynamics of equine influenza virus infection in horses. By comparing modeling predictions with both interferon and viral kinetic data, we examined the relative roles of target cell availability, and innate and adaptive immune responses in controlling the virus. Our results show that the rapid and substantial viral decline (about 2 to 4 logs within 1 day) after the peak can be explained by the killing of infected cells mediated by interferon activated cells, such as natural killer cells, during the innate immune response. After the viral load declines to a lower level, the loss of interferon-induced antiviral effect and an increased availability of target cells due to loss of the antiviral state can explain the observed short phase of viral plateau in which the viral level remains unchanged or even experiences a minor second peak in some animals. An adaptive immune response is needed in our model to explain the eventual viral clearance. This study provides a quantitative understanding of the biological factors that can explain the viral and interferon kinetics during a typical influenza virus infection.
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Publication Date: 2012-06-28 PubMed ID: 22761567PubMed Central: PMC3386161DOI: 10.1371/journal.pcbi.1002588Google Scholar: Lookup
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  • Extramural
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  • Non-U.S. Gov't
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  • U.S. Gov't
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research article is about the development and application of a mathematical model to better understand the dynamics of an influenza virus infection within a host, with a focus on both innate and adaptive immune responses.

Development of the Mathematical Model

  • The researchers created a mathematical model to study how equine influenza virus infection works within horses, with a particular focus on both innate and adaptive immune responses.
  • This model is designed to help gain a deeper understanding of the mechanisms underlying how a host controls an uncomplicated influenza virus infection.

Utilization of the Mathematical Model

  • The model was compared with data from both interferon and viral kinetics to examine how several factors, including target cell availability and both kinds of immune responses, play a role in the control of the virus.
  • The results of the model show that the significant reduction of viral load observed (2 to 4 logs within a day) after its peak could be explained by infected cells being killed by interferon-activated cells, such as natural killer cells.
  • This phase corresponds to the role of the innate immune response, eradicating the infected cells quickly and effectively.

Understanding Different Phases of Infection

  • Once the viral load declines to a lower level, the post peak phase of infection is characterized by a loss of the antiviral effect induced by interferon and an increased availability of target cells resulting from the loss of the antiviral state.
  • This explains the brief phase in which viral level plateaus, remaining unchanged or even experiencing a secondary minor peak in some cases.

The Role of Adaptive Immune Response

  • The model further indicates that for eventual viral clearance to happen, an adaptive immune response is necessary.
  • This suggests that the body’s ability to create specific antibodies to the virus strain is crucial to completely get rid of the virus.

Conclusion

  • In conclusion, this study provides quantifiable insight into the complex interactions between the influenza virus, the host, and the host’s immune response.
  • It aids in the understanding of why and how certain phases of the disease progress as they do and reveals the very different roles of innate and adaptive immune responses play in combating a common influenza virus infection.

Cite This Article

APA
Pawelek KA, Huynh GT, Quinlivan M, Cullinane A, Rong L, Perelson AS. (2012). Modeling within-host dynamics of influenza virus infection including immune responses. PLoS Comput Biol, 8(6), e1002588. https://doi.org/10.1371/journal.pcbi.1002588

Publication

PLoS computational biology
ISSN: 1553-7358
NlmUniqueID: 101238922
Country: United States
Language: English
Volume: 8
Issue: 6
Pages: e1002588
PII: e1002588

Researcher Affiliations

Pawelek, Kasia A
  • Department of Mathematics and Statistics, Oakland University, Rochester, Michigan, United States of America.
Huynh, Giao T
    Quinlivan, Michelle
      Cullinane, Ann
        Rong, Libin
          Perelson, Alan S

            MeSH Terms

            • Adaptive Immunity
            • Animals
            • Computational Biology
            • Computer Simulation
            • Horse Diseases / immunology
            • Horse Diseases / virology
            • Horses
            • Host-Pathogen Interactions / immunology
            • Humans
            • Immunity, Innate
            • Influenza A Virus, H3N8 Subtype / immunology
            • Influenza A Virus, H3N8 Subtype / pathogenicity
            • Influenza, Human / immunology
            • Influenza, Human / virology
            • Models, Immunological
            • Orthomyxoviridae Infections / immunology
            • Orthomyxoviridae Infections / veterinary
            • Orthomyxoviridae Infections / virology
            • Time Factors
            • Viral Load / immunology

            Grant Funding

            • R01 OD011095 / NIH HHS
            • P30 EB011339 / NIBIB NIH HHS
            • 8R01-OD011095-21 / NIH HHS
            • P30-EB011339 / NIBIB NIH HHS
            • HHSN272201000055C / NIAID NIH HHS

            Conflict of Interest Statement

            The authors have declared that no competing interests exist.

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