Date Published: October 17, 2013
Publisher: Public Library of Science
Author(s): Jared B. Hawkins, Edgar Delgado-Eckert, David A. Thorley-Lawson, Michael Shapiro, Rustom Antia.
Previous analysis of Epstein-Barr virus (EBV) persistent infection has involved biological and immunological studies to identify and quantify infected cell populations and the immune response to them. This led to a biological model whereby EBV infects and activates naive B-cells, which then transit through the germinal center to become resting memory B-cells where the virus resides quiescently. Occasionally the virus reactivates from these memory cells to produce infectious virions. Some of this virus infects new naive B-cells, completing a cycle of infection. What has been lacking is an understanding of the dynamic interactions between these components and how their regulation by the immune response produces the observed pattern of viral persistence. We have recently provided a mathematical analysis of a pathogen which, like EBV, has a cycle of infected stages. In this paper we have developed biologically credible values for all of the parameters governing this model and show that with these values, it successfully recapitulates persistent EBV infection with remarkable accuracy. This includes correctly predicting the observed patterns of cytotoxic T-cell regulation (which and by how much each infected population is regulated by the immune response) and the size of the infected germinal center and memory populations. Furthermore, we find that viral quiescence in the memory compartment dictates the pattern of regulation but is not required for persistence; it is the cycle of infection that explains persistence and provides the stability that allows EBV to persist at extremely low levels. This shifts the focus away from a single infected stage, the memory B-cell, to the whole cycle of infection. We conclude that the mathematical description of the biological model of EBV persistence provides a sound basis for quantitative analysis of viral persistence and provides testable predictions about the nature of EBV-associated diseases and how to curb or prevent them.
Epstein-Barr virus (EBV) is a herpesvirus that benignly infects more than 95% of the world’s adult human population , but is occasionally associated with certain tumors including 3 forms of lymphoma . One prominent feature of EBV is that it persists as a lifelong low-level infection in the memory B-cells of healthy carriers , . Our laboratory has measured the level of infection in the peripheral blood memory B-cells of healthy carriers over the course of decades (,  and unpublished observations) and shown that it remains stable. If there is a real decline (or expansion), it is happening too slowly to detect. Persistent infection is also associated with an active humoral and cellular immune response by the host that is also stable over time , . We see this stability as a balance between infection and the immune response which returns to equilibrium when perturbed. Two biological models have been proposed to account for this persistence: the germinal center (GC) model ,  and the direct infection model , .
The studies presented here suggest a shift in our understanding of EBV persistence. The mechanism of EBV infection is well understood to involve a cycle of infected stages, but until now it was believed that EBV persists solely because it resides in resting memory B-cells that cannot be recognized by the immune response. Previously, we were able to describe this cycle of infection in terms of a set of differential equations (the cyclic pathogen model or CPM) and show that the solution of these equations at steady state produced one and only one solution that was stable and biologically possible . Put simply, the CPM shows how the rates governing such processes as proliferation, death and differentiation of infected B-cells,, amplification of the virus, and proliferation, loss and killing efficiency of the immune response collectively determine a stable set point for the coexistence of the host and the pathogen. In doing so, it gives us the key to understanding the sizes of infected populations and which fall under CTL regulation. We proposed that the stable set point described by the CPM represented persistent infection. In this paper we have now validated this assertion.