IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1002307.html
   My bibliography  Save this article

Mathematical Model of Viral Kinetics In Vitro Estimates the Number of E2-CD81 Complexes Necessary for Hepatitis C Virus Entry

Author

Listed:
  • Pranesh Padmanabhan
  • Narendra M Dixit

Abstract

Interaction between the hepatitis C virus (HCV) envelope protein E2 and the host receptor CD81 is essential for HCV entry into target cells. The number of E2-CD81 complexes necessary for HCV entry has remained difficult to estimate experimentally. Using the recently developed cell culture systems that allow persistent HCV infection in vitro, the dependence of HCV entry and kinetics on CD81 expression has been measured. We reasoned that analysis of the latter experiments using a mathematical model of viral kinetics may yield estimates of the number of E2-CD81 complexes necessary for HCV entry. Here, we constructed a mathematical model of HCV viral kinetics in vitro, in which we accounted explicitly for the dependence of HCV entry on CD81 expression. Model predictions of viral kinetics are in quantitative agreement with experimental observations. Specifically, our model predicts triphasic viral kinetics in vitro, where the first phase is characterized by cell proliferation, the second by the infection of susceptible cells and the third by the growth of cells refractory to infection. By fitting model predictions to the above data, we were able to estimate the threshold number of E2-CD81 complexes necessary for HCV entry into human hepatoma-derived cells. We found that depending on the E2-CD81 binding affinity, between 1 and 13 E2-CD81 complexes are necessary for HCV entry. With this estimate, our model captured data from independent experiments that employed different HCV clones and cells with distinct CD81 expression levels, indicating that the estimate is robust. Our study thus quantifies the molecular requirements of HCV entry and suggests guidelines for intervention strategies that target the E2-CD81 interaction. Further, our model presents a framework for quantitative analyses of cell culture studies now extensively employed to investigate HCV infection. Author Summary: The interaction between the hepatitis C virus (HCV) envelope protein E2 and the host cell surface receptor CD81 is critical for HCV entry into hepatocytes and presents a promising drug and vaccine target. Yet, the number of E2-CD81 complexes that must be formed between a virus and a target cell to enable viral entry remains unknown. Direct observation of the E2-CD81 complexes preceding viral entry has not been possible. We constructed a mathematical model of HCV viral kinetics in vitro and using it to analyze data from recent cell culture studies obtained estimates of the threshold number of E2-CD81 complexes necessary for HCV entry. We found that depending on the E2-CD81 binding affinity, between 1 and 13 complexes are necessary for HCV entry into human hepatoma-derived cells. Our study thus presents new, quantitative insights into the molecular requirements of HCV entry, which may serve as a guideline for intervention strategies targeting the E2-CD81 interaction. Further, our study shows that HCV viral kinetics in vitro can be described using a mathematical model, thus facilitating quantitative analyses of the wealth of data now emanating from cell culture studies of HCV infection.

Suggested Citation

  • Pranesh Padmanabhan & Narendra M Dixit, 2011. "Mathematical Model of Viral Kinetics In Vitro Estimates the Number of E2-CD81 Complexes Necessary for Hepatitis C Virus Entry," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-11, December.
  • Handle: RePEc:plo:pcbi00:1002307
    DOI: 10.1371/journal.pcbi.1002307
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002307
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002307&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1002307?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Marcus Dorner & Joshua A. Horwitz & Justin B. Robbins & Walter T. Barry & Qian Feng & Kathy Mu & Christopher T. Jones & John W. Schoggins & Maria Teresa Catanese & Dennis R. Burton & Mansun Law & Char, 2011. "A genetically humanized mouse model for hepatitis C virus infection," Nature, Nature, vol. 474(7350), pages 208-211, June.
    2. Matthew J. Evans & Thomas von Hahn & Donna M. Tscherne & Andrew J. Syder & Maryline Panis & Benno Wölk & Theodora Hatziioannou & Jane A. McKeating & Paul D. Bieniasz & Charles M. Rice, 2007. "Claudin-1 is a hepatitis C virus co-receptor required for a late step in entry," Nature, Nature, vol. 446(7137), pages 801-805, April.
    3. Narendra M. Dixit & Jennifer E. Layden-Almer & Thomas J. Layden & Alan S. Perelson, 2004. "Modelling how ribavirin improves interferon response rates in hepatitis C virus infection," Nature, Nature, vol. 432(7019), pages 922-924, December.
    4. Alexander Ploss & Matthew J. Evans & Valeriya A. Gaysinskaya & Maryline Panis & Hana You & Ype P. de Jong & Charles M. Rice, 2009. "Human occludin is a hepatitis C virus entry factor required for infection of mouse cells," Nature, Nature, vol. 457(7231), pages 882-886, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mphatso Kalemera & Dilyana Mincheva & Joe Grove & Christopher J R Illingworth, 2019. "Building a mechanistic mathematical model of hepatitis C virus entry," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-26, March.
    2. Pranesh Padmanabhan & Narendra M Dixit, 2012. "Viral Kinetics Suggests a Reconciliation of the Disparate Observations of the Modulation of Claudin-1 Expression on Cells Exposed to Hepatitis C Virus," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pranesh Padmanabhan & Narendra M Dixit, 2012. "Viral Kinetics Suggests a Reconciliation of the Disparate Observations of the Modulation of Claudin-1 Expression on Cells Exposed to Hepatitis C Virus," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.
    2. Mphatso Kalemera & Dilyana Mincheva & Joe Grove & Christopher J R Illingworth, 2019. "Building a mechanistic mathematical model of hepatitis C virus entry," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-26, March.
    3. Ashish Kumar & Tiana C. Rohe & Elizabeth J. Elrod & Abdul G. Khan & Altaira D. Dearborn & Ryan Kissinger & Arash Grakoui & Joseph Marcotrigiano, 2023. "Regions of hepatitis C virus E2 required for membrane association," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    4. Libin Rong & Jeremie Guedj & Harel Dahari & Daniel J Coffield Jr & Micha Levi & Patrick Smith & Alan S Perelson, 2013. "Analysis of Hepatitis C Virus Decline during Treatment with the Protease Inhibitor Danoprevir Using a Multiscale Model," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-12, March.
    5. Pan, Sonjoy & Chakrabarty, Siddhartha P., 2022. "Analysis of a reaction–diffusion HCV model with general cell-to-cell incidence function incorporating B cell activation and cure rate," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 431-450.
    6. Santosh Kumar Sharma & Amar Nath Chatterjee & Bashir Ahmad, 2023. "Effect of Antiviral Therapy for HCV Treatment in the Presence of Hepatocyte Growth Factor," Mathematics, MDPI, vol. 11(3), pages 1-20, February.
    7. Tao Lu & Yangxin Huang & Min Wang & Feng Qian, 2014. "A refined parameter estimating approach for HIV dynamic model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1645-1657, August.
    8. Luo, Yantao & Zhang, Long & Zheng, Tingting & Teng, Zhidong, 2019. "Analysis of a diffusive virus infection model with humoral immunity, cell-to-cell transmission and nonlinear incidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    9. Sonjoy Pan & Siddhartha P. Chakrabarty, 2020. "Hopf Bifurcation and Stability Switches Induced by Humoral Immune Delay in Hepatitis C," Indian Journal of Pure and Applied Mathematics, Springer, vol. 51(4), pages 1673-1695, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1002307. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.