ScienceDirect® Home Skip Main Navigation Links
You have guest access to ScienceDirect. Find out more.
 
Home
Browse
My Settings
Alerts
Help
 Quick Search
 Search tips (Opens new window)
    Clear all fields    
advertisementadvertisement
Journal of Theoretical Biology
Volume 234, Issue 3, 7 June 2005, Pages 439-454
 
Font Size: Decrease Font Size  Increase Font Size
 Abstract - selected
Article
Purchase PDF (554 K)

 
 
 
Related Articles in ScienceDirect
View More Related Articles
 
View Record in Scopus
 
doi:10.1016/j.jtbi.2004.12.007    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2005 Elsevier Ltd All rights reserved.

Mathematical analysis demonstrates that interferons-β and -γ interact in a multiplicative manner to disrupt herpes simplex virus replication

William P. Halforda, Corresponding Author Contact Information, E-mail The Corresponding Author, Keith J. Halfordb and Amy T. Piercea

aDepartment of Microbiology and Immunology, Tulane University Health Sciences Center, New Orleans, LA 70112, USA bDepartment of Water Resources, United States Geological Survey, Carson City, NV 89706, USA

Received 19 August 2004; 
revised 10 November 2004; 
accepted 6 December 2004. 
Communicated by Robert Root-Bernstein. 
Available online 25 January 2005.

Purchase the full-text article



References and further reading may be available for this article. To view references and further reading you must purchase this article.

Abstract

Several studies suggest that the innate interferons (IFNs), IFN-α and IFN-β, can act in concert with IFN-γ to synergistically inhibit the replication of cytomegalovirus and herpes simplex virus type 1 (HSV-1). The significance of this observation is not yet agreed upon in large part because the nature and magnitude of the interaction between IFN-α/β and IFN-γ is not well defined. In the current study, we resolve this issue by demonstrating three points. First, the hyperbolic tangent function, tanh (x), can be used to describe the individual effects of IFN-β or IFN-γ on HSV-1 replication over a 320,000-fold range of IFN concentration. Second, pharmacological methods prove that IFN-β and IFN-γ interact in a greater-than-additive manner to inhibit HSV-1 replication. Finally, the potency with which combinations of IFN-β and IFN-γ inhibit HSV-1 replication is accurately predicted by multiplying the individual inhibitory effects of each cytokine. Thus, IFN-β and IFN-γ interact in a multiplicative manner. We infer that a primary antiviral function of IFN-γ lies in its capacity to multiply the potency with which IFN-α/β restricts HSV-1 replication in vivo. This hypothesis has important ramifications for understanding how T lymphocyte-secreted cytokines such as IFN-γ can force herpesviruses into a latent state without destroying the neurons or leukocytes that continue to harbor these viral infections for the lifetime of the host.

Keywords: Interferon; Synergy; Multiplicative interaction; Herpesvirus; Latent infection

Article Outline

1. Introduction
2. Materials and methods
2.1. Cells, viruses, and interferons
2.2. Dose–response analysis of the combined effects of interferons on HSV-1 replication
2.2.1. Additive composite curve analysis
2.2.2. Three-dimensional response surface analysis
2.3. Dotblot analysis of viral DNA yields in HSV-1 infected Vero cells
2.4. Quantitative and statistical analysis of dose–response data
3. Results
3.1. Viral DNA yields provide a precise and accurate measure of HSV-1 replication
3.2. Human IFN-β inhibits HSV-1 replication in a dose-dependent manner
3.3. Derivation of an equation that describes inhibition of HSV-1 by human IFN-β
3.4. Derivation of an equation that describes inhibition of HSV-1 by human IFN-γ
3.5. Combined effects of IFN-β and IFN-γ on HSV-1: additive composite curve analysis
3.6. Combined effects of IFN-β and IFN-γ on HSV-1 replication: response surface analysis
4. Discussion
4.1. Individual effects of IFN-β or IFN-γ
4.2. Combined effects of IFN-β and IFN-γ
4.2.1. Derivation of a general V(β+γ) equation
4.2.2. Dose-additive model of the interaction between IFN-β and IFN-γ
4.2.3. Multiplicative model of the interaction between IFN-β and IFN-γ
4.2.4. Fitting the multiplicative model to the response surface data
4.2.5. Predicted versus observed interaction of IFN-β and IFN-γ
4.3. Application of these methods to other systems
4.3.1. General
4.3.2. Pharmacological
4.4. Implications of a multiplicative interaction between IFN-β and IFN-γ
4.5. Physical basis of the multiplicative interaction between IFN-β and IFN-γ?
Acknowledgements
References