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Epstein-Barr virus: a paradigm for persistent infection – for real and in virtual reality
Review
Epstein-Barr virus: a paradigm for persistent infection – for real and in virtual reality David A. Thorley-Lawson1, Karen A. Duca2 and Michael Shapiro1 1 2
Department of Pathology, Tufts University School of Medicine, Boston, Massachusetts 02111, USA Department of Biochemistry and Biotechnology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
The really interesting thing about herpesviruses is that they can establish lifelong persistant infections in immunocompetent hosts. At first glance, they would seem to have very different ways of doing this. Here we will use as a model our current understanding of how the human herpesvirus Epstein-Barr virus establishes and maintains such an infection. We apply information from a wide range of sources including laboratory experimentation, clinical observation, animal models and a new computer simulation. We propose that the detailed mechanisms for establishing infection are dependent on the virus and tissues involved, but the strategy is the same – to persist in a long-lived cell type where the virus is invisible to the immune system and nonpathogenic. Introduction Herpesviruses are fascinating because they establish lifelong persistent infection in normal healthy hosts [1]. Human herpesviruses are also of practical importance because they are associated with severe, sometimes lifethreatening, diseases that are particularly problematic in the immunocompromised. All herpesviruses are believed to have two phases to their life cycle that together describe persistent infection; these are latency and reactivation/ replication [1]. Together they constitute the mechanism by which these viruses establish and maintain lifetime infections and spread to new hosts. In the most simplistic interpretation, latency refers to an infected state where the cell(s) contain an intact viral genome(s) but are not producing infectious virus and reactivation/replication involves the production of infectious virus. However, as we have begun to learn more about the details of herpesvirus biology, it has become apparent that these terms mean different things to different people with different viruses in different settings. What is the difference between persistence and latency? How do we distinguish different states of latency from each other? How do we distinguish latency in a cell type where the virus persists from one where it does not? How do we distinguish latency from abortive replication or replication that just has not finished yet? Several of these questions arise because of the limitations on what we can measure. For example, any cell that lacks an infectious virion could be perceived as latent. However, this might include latent states that may be irrelevant to persistence Corresponding author: Thorley-Lawson, D.A. ([email protected]).
and cells initiating viral replication that have not yet produced intact virions or are abortive and never produce virus. Similarly, cells that fail to produce infectious virus in culture might be abortively infected or we may just not know the correct signal to reactivate the virus. One of the best understood of the human herpesviruses is Epstein-Barr virus (EBV) [2–4], for which there is an established biological model of infection and persistence (reviewed in [5–7]). In this article, we will discuss a general model of herpesvirus persistence based on EBV. Biological models and experimental limitations The proposed generic model for herpesvirus infection is summarized in Figure 1. The central notion is that a ‘true’ form of latency exists where no viral proteins are expressed and the viral genomes are episomal (i.e. circular not linear). This is where the virus persists for the lifetime of the host and is clearly distinguished from other types of infected cells that harbor viral genomes but do not contain infectious virus. To arrive at this destination, the virus infects a progenitor that may go through several different states before arriving at the condition of true latency. These states are referred to as ’transitional latency’. A consequence of this distinction is that pathogenesis may be associated with the transitional states, because viral proteins are expressed that could impact the target cell and the organism. However, at the site of true latency, there are no viral proteins expressed and so the virus is neither pathogenic to the host nor visible to the immune system. For all intents and purposes, the cell carrying the truly latent virus is normal, as far as the host is concerned, until viral replication is activated. Viral replication is divided into several stages [8]. The first is initiation in response to a signal. Because the truly latent cell is essentially normal from the point of view of the host, this signal by definition must be one that is part of the normal biology of the cell. This in turn implies that the viral gene or genes that initiate the replicative cycle must be responsive to transcription factors that are transiently expressed during some part of the normal biology of the infected cell. The next stage involves organizing the viral gene products needed to replicate the DNA leading to production of the proteins necessary to package the DNA in intact infectious virions. Note that only at this last stage can infectious virus be recovered from the cells. This virus infects a secondary tissue that provides
1471-4906/$ – see front matter ß 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.it.2008.01.006 Available online 6 March 2008
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Figure 1. The generic biological model for herpesviruses. Free virus infects the primary target tissue and passes although a variable number of latency states (transient latency) until it reaches the site of true latency where viral protein expression ceases. The virus is reactivated on receiving an appropriate external signal (red lightning bolt) ultimately leading to viral DNA replication, the production of infectious virus and cell death. This virus infects a secondary tissue where it replicates aggressively, leading to a rapid transient amplification of infectious virus. This virus is then shed for tranmission to new hosts. The death of cells entering viral replication requires that there be a mechanism of either self-renewal and/or reinfection (dashed arrows) to sustain stable levels of persistence in the truly latent cells. For Epstein-Barr virus (EBV), these states are well defined with the growth program in naı¨ve B cells and the default program in germinal center (GC) cells being transient latency states. True latency is in peripheral memory B cells. Reactivation of the virus is caused by signals that drive terminal differentiation. The virus first expresses immediate early transcription factors (IE) that turn on early antigens (EA, primarily components of the DNA replication complex) followed by DNA replication, packaging of the virus (late antigens), lysis of the cells and release of infectious virus for infection and amplification in epithelial cells. At the bottom of the figure are shown the equivalent stages for EBV from the biological and computer models.
a favorable environment for replication but not latency. The classical example of this is replication of herpes simplex virus (HSV) in the skin after reactivation from latently infected ganglia [8]. By definition, this form of replication is not a prerequisite for persistence only for spread. Since replication is occurring in a healthy host, successful production of virus has to be transient, rapid and relatively rare to maximize the opportunities for spread to new hosts before the immune system shuts replication down. Not surprisingly, herpesviruses have developed strategies to evade the immune response to maximize the chances of successfully producing infectious virus (reviewed in [9]). Because viral replication is assumed to result in the death of the cell, there must exist some mechanism for replacement of truly latent cells; otherwise, there would be a steady decay in the level of persistent infection through life. This could occur through new infection or occasional division of the truly latent cell (dashed lines in Figure 1), in which case the transient latency states become irrelevant once the virus has reached the site of true latency. Although this issue is not settled, it is also not trivial. If reinfection is needed for stable persistence, it provides a window of attack against persistent virus with antivirals and/or vaccine strategies to which a self-renewing system would be invulnerable. EBV as an unreliable paradigm of herpesvirus biology: a brief history EBV does not have a stellar record as a model for herpesvirus studies. It was discovered in Burkitt’s lymphoma and 196
subsequently found in several other lymphomas and carcinomas and has the capacity to transform B lymphocytes in culture [3,4]. Together this provided a compelling argument for EBV as the first human tumor virus and led to an outburst of interest in the role of herpesviruses in human cancer. Unfortunately, this was an ill-fated field, and no further candidates emerged. It took AIDS to reveal the second example, Kaposi’s sarcoma herpes virus (KSHV) [10]. Similarly, EBV has been misleading to thinking about herpesvirus latency. When EBV transforms B cells, it establishes a latent infection associated with the expression of nine latent proteins [2,4]. EBV is also latent in the tumors within which it is found, and different tumors express different subsets of the nine latent proteins [3,4]. This created the notion that herpesviruses express latent proteins at the site of latent persistence. Indeed, one of the goals of herpesvirus research was to define latent sites of persistence through the detection of latent genes often defined as those whose expression is independent of viral replication. Only recently have we come to realize that the expression of latent proteins by EBV is a mechanism to get to the site of persistence not the mechanism of persistence itself. From this discussion, it is apparent that the expression of such genes identifies ‘transient latent states’ not the site of ‘true latency’. Ironically, it seems that HSV is the paradigm for herpesvirus persistence after all and at the site of true latent persistence all viral protein expression ceases.
Review The germinal center model of EBV infection? The biological model of EBV persistence (Figure 2, left) has been described elsewhere [5–7] and will not be detailed here except in as far as the discussion relates to the ideas in Figure 1. The underlying principle of the model is that EBV uses normal B-cell biology to establish infection, persist and replicate. EBV is spread by saliva contact [11]. It enters the epithelium of Waldeyer’s ring (the adenoids and tonsils) where it infects normal, resting, naı¨ve B cells. Here it expresses the first of its transient latency states (the ‘growth program’ in Figure 1 [2]), where all of the latent genes are expressed and which drives the activation and proliferation of the B cell [12,13]. These cells are postulated to enter a follicle and undergo a germinal center reaction involving a switch to a second state of transient latency (the ‘default program’ in Figure 1). Here only three latent proteins are expressed, of which LMP1 (latent membrane protein 1) and LMP2 are crucial because they have the potential to provide the signals necessary to drive the differentiation of the latently infected B cell into the memory compartment [14–18]. Once in the memory cell, viral protein expression is shut down [19], so this is the site of true latency. Furthermore, by every criterion measured thus far, these latently infected memory cells are indistinguishable from normal memory B cells. The biggest challenge to this concept may come from herpesvirus-encoded
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microRNAs [20,21], which could profoundly influence our understanding of how much the otherwise dormant viral genome regulates the host cell [22]. Reactivation of EBV from latently infected memory cells is thought to occur in response to the normal physiologic signals that drive the cell to become a plasma cell [23–26]. Our most recent measurements suggest that healthy carriers of the virus continually shed large amounts of virus over time (V. Hadinoto and D.A.T.-L. unpublished). Indeed, so much virus is produced that it cannot be accounted for by replication in plasma cells alone, suggesting amplification at a secondary site. There is now increasing evidence that this site is the epithelium surrounding Waldeyer’s ring (secondary target in Figure 1) [27–30]. This is not a latent infection but a rapid and efficient lytic infection that releases a stream of virus into the saliva for infectious spread to new hosts. Computer modeling of EBV infection EBV is the first herpesvirus to be successfully modeled with a computer simulation [31,32]. Despite rapidly growing literature in host–pathogen modeling, little has been done previously with herpes viruses. Ordinary differential equations (ODEs) have been used to study T-cell senescence in the context of EBV [33]; however, they did not analyze the dynamics of B-cell infection nor follow-up on the study. Our attempts to modify their equations to model
Figure 2. A diagrammatic representation of the computer simulation of Epstein-Barr virus (EBV) persistence and the biological model on which it is based. In the biological model, EBV from saliva latently infects naı¨ve B cells in Waldeyer’s ring (the tonsils and adenoids) and uses its growth transcription program to activate the cell to become a proliferating blast so that it can then pass through a germinal center, where it receives survival and progression signals from its default transcription program. These are ’transitional latency’ states in Figure 1. Ultimately, the cell leaves the germinal center as a resting memory B cell, where no viral proteins are expressed: the site of ’true latency’. Here the latently infected cells are maintained like normal B cells through homeostasis mechanisms. The latently infected memory cells return to the Waldeyer’s ring and occasionally receive differentiation signals that cause the virus to reactivate, replicate and be shed. This virus infects and replicates in epithelial cells (secondary target tissue in Figure 1) before being shed to infect new B cells or hosts. This one-way circuit of infection is controlled by the immune system, which targets latently infected blasts and lytically infected plasma cells with ctyotoxic T cells (CTLs) and free virus with neutralizing antibodies. The computer model is based on the biological model with the simplification that all latently infected B cells are represented by a single agent (termed BLat). These cells are not subject to CTL predation while in the peripheral circulation. This is to model the lack of immune surveillance of latently infected memory B cells in real infection. The consequence is that, in the simulation, the BLat are subject to immunosurveillance when they return to the Waldeyer’s ring, a situation we believe is not true for real latently infected memory B cells. This is an important limitation of the simulation that is discussed further in the main text.
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Review infection have, to date, been unsuccessful (K.A.D., unpublished). We have used an agent-based approach (Box 1) to investigate EBV infection. In a pilot study, we used an existing platform (C-ImmSim), consisting of a small, simple two-dimensional virtual grid, with some success [34]. Concurrently, we developed a more detailed model (PathSim; Pathogen Simulation; Figure 3a–d; Box 1) based on the biological model described above (Figure 2, left). Despite shortcomings (discussed below and detailed in [31,32]), the simulation does demonstrate the characteristic features of an EBV infection with an acute phase, occurring around 40–50 days after virtual infection [35], that resolves into a long-term, low-level persistent phase (Figure 3e). It was also able to make predictions about previously unknown aspects of viral dynamics that were confirmed experimentally [31]. Just like animal models, the computer model makes certain assumptions to generalize about the biology (Figure 2, right), and these may become problematic. For example, we confounded all the transitional latency states into the single entity ‘Blat’ (Figure 1). This is computationally equivalent to the empirical assumption that any cell lacking infectious virus is ’latently infected’. In hindsight, this was an important oversimplification because it does not allow us to dissect the relative roles of latently infected B cell blasts, that are proliferating and under immunosurveillance by cytotoxic T cells (CTLs) versus resting memory cells that are not under immunosurveillance. One of the most difficult issues in modeling biological systems is determining what features to include. Moreover, each feature requires parameters (e.g. B cells have volumes, lifespans, rates at which they perform functions) for which values are not always available (Box 2). The more Box 1. Rationale for choosing agent-based simulation and a virtual world Computer modeling is beginning to be applied to complex biological systems. Traditionally, biologists have been skeptical of this approach. This is because there is an inherent language and conceptual gulf between biologists and mathematicians in the way they describe and think about systems and because biologists feel that such models are intrinsically ’not real’. This reaction is particularly acute for models that involve equations, such as the ordinary differential equations (ODEs) that have been used extensively to model HIV, for example (reviewed in [47–49]). ODEs describe biological systems based on mass action laws. The inherent smoothness of the output from these equations and the assumptions that the system is homogeneous and well mixed (like a chemical reaction in a beaker) are intuitively unsatisfying for biological systems that are not evenly dispersed and where local events can have global impacts. To address these concerns a simulation was designed that consists of a virtual grid that represents Waldeyer’s ring and the peripheral circulation (Figure 3) and on which various agents (B cells, T cells, and EB virions) can move and interact. The simulation can be run from the command line (i.e. like regular software) or by a user-friendly visual interface on a standard computer monitor (i.e. in a format that is more familiar – that of a computer game; Figure 3d). This allows the simulation to be launched and output to be accessed and analyzed in a visual way that is simple and easily comprehensible to the nonspecialist. This simulation may be used as a way of experimenting with hypotheses about the biological system and examining their implications.
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complex and ‘realistic’ the model, the larger the number of parameter values needed. Of necessity, a simulation is a simplification and feature sets and parameters by their very nature can never exactly map ‘true biology’. One value of simulation is in identifying the important components and events behind the dynamics under study, compared with those that make minor or no contributions. This is a nontrivial issue because, among life scientists, one great but underappreciated value of simulation is its ability to give insights into what we actually do and do not know and how well we understand what we know (Box 2). Put simply, if we build a computer simulation based on a biological model and it gives nonbiological output, something is wrong/missing with our biological model. If it produces credible output, we may have captured, at least crudely, essential features of the biology. We will briefly discuss several examples to illuminate this point.
Box 2. The art and science of parameter choice and simulation design For engineers, an established body of knowledge allows routine, accurate simulation of many (although certainly not all) processes. Physical processes are often well understood and governed by equations whose parameter values are known physical constants. Currently, this is not the case with biological simulation. We don’t know all the structures/components that are crucial, and many of the values of biological functions are also unknown and may even be unmeasurable. This reflects the limitations of our biological understanding, because the simulation is directly informed by what we know about the biology. Indeed, the case could be made that this is the state of the art right now – developing methods to figure out which parameters are important, how to get at their value and how to interpret their biological significance. Currently, many parameters have to be estimated or guessed based on available information or simply subjective opinion (for a complete discussion of the parameters used in PathSim, see [32]). These parameters then have to be ‘fitted’ to produce credible output and can be thought of as ‘simulation parameters’ rather than ‘biological parameters’. There are several reasons why a parameter value may need to be ‘fitted’: The actual value is unknown. The simulation must be scaled down because of computational constraints. The virtual process is a simplification that does not map directly to biology. In the event that it works, there are three possible interpretations. The parameter: is correct is wrong but has little impact on the simulation is wrong but its effects are counterbalanced by errors in another parameter(s). It is important to emphasize that distinguishing these possibilities is not a trivial exercise but is a central issue in simulating biological systems given the current level of sophistication. Despite these constraints, biological simulations are already yielding useful information. We can use them: to give perspectives on the biological system that allows us to ask new questions and derive new insights. to question what we really know about a biological system. to investigate the consequences of limitations in our understanding of the biology and/or the simulation. This may provide important insights (see main body of the text) into what is and what is not crucial in the biological model/system. to produce values for parameters we do not have information for and ask what meaning they have biologically. to study the sensitivity of the model system to varying parameters in a way that cannot be performed experimentally.
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Figure 3. The structure and typical output of PathSim, the agent-based model of Epstein-Barr virus (EBV) infection. The simulation is constructed as a virtual grid representing Waldeyer’s ring and the peripheral circulation. Agents (lymphocytes and EB virions) move and interact on this grid according to a defined rule set (e.g. when a virion meets a B cell, it infects it with some probability; when a T cell meets an infected cell, it becomes an activated cytotoxic T cell. (a) The basic ’hex’ unit that makes up the grid. The white lines indicate the grid on which the agents move and the red boxes the nodes where the agents interact. (b) A cross-section of part of a real tonsil that includes a single germinal center/follicle and the proximal tissue. The mapping from the tissue to the ’hex’ unit is indicated by the labels and arrows. Virus enters from the surface. Lymphocytes enter through high endothelial venules (HEVs) and leave through the draining, efferent, lymphatics. HEV and lymphatics are not easily resolved in the tissue section but are indicated for the ’hex’ unit. (c) Multiple hex units are combined to form a single tonsil. (d) Complete Waldeyer’s ring as it appears on the computer screen. The four tonsils and adenoids are shown in red, the draining lymphatics in white and the peripheral circulation in red and blue. The figure also shows the control panel (resembling a typical VCR-like controller) for running the simulation at the bottom and typical output graphs in various styles. (e) Simulation output, using the default parameter set (for a complete list and discussion, see [32]), of the total number of latently infected virtual B cells (Blats) over time in a series of superimposed simulation runs (colored lines, each line corresponds to a different run) versus the number of actual latently infected memory B cells in the peripheral blood of 15 acutely infected individuals over time (B latent, colored circles). Note the overall congruity of the simulated and clinical kinetics. For a discussion of the disparities and their significance, see text. (f) Persistence is highly sensitive to the rate of Vir (virtual virus) reactivation. The simulation was run as described in (e) except the percentage of latently infected virtual B cells (BLat) that initiate virus replication on return to the Waldeyer’s ring was varied as shown. Note that as the percent that initiate reactivation increases, the infection rapidly overwhelms the virtual host.
The peripheral memory B-cell compartment In the biological model, access to the peripheral memory pool is essential for EBV persistence. We found this to hold true in the simulation but now we can ask the following: how robust is this dependence? We found that if we denied EBV access to this pool, we were unable to obtain an infection process that even remotely resembled a real persistent EBV infection no matter how much we changed other parameters. This is an example of how we can
examine sensitivity to a parameter in a way not possible with real patients. It also shows how, lacking a crucial component of the biology, it is not necessarily possible to force a simulation to ‘look’ biological by arbitrary fitting of parameters. Reactivation rate Latently infected B cells returning to Waldeyer’s ring reactivate to produce infectious virus. In the simulation, 199
Review virtual infection was extremely sensitive to this rate (Figure 3f). If it was increased slightly, the host was overwhelmed by acute infection. If it was reduced slightly, the virus was cleared. Strikingly, the rate that allowed persistence was the same as we had previously measured experimentally in Waldeyer’s ring [24]. Such quantitative verification adds credibility to the simulation but also provides an example of how simulations can stimulate biological thinking. When we measured the rate of reactivation, we had no idea what it meant: we simply had a value. We can now speculate that host and virus must have evolved to produce a reactivation rate that was high enough to maximize the chances for establishing persistence and spread to other hosts without putting the host at risk. This in turn implies a biological mechanism precisely regulating this rate that is yet to be identified. T-cell memory The simulation was imprecise at the quantitative level, because the infection resolved too slowly and levels of persistent infection were too high and demonstrated marked oscillations that are not seen in normal persistence. All three discrepancies may be caused by the lack of T-cell memory and T-cell proliferation in our simulation. These two attributes would allow a more sustained T-cell response that would produce a more rapid decline of infected cells, lower levels of sustained persistence and tend to flatten out oscillatory behavior, thus making the simulation more quantitatively accurate. This is an example of how thinking about the limitations of the simulation can lead to a testable prediction. Sensitivity to antivirals Antiviral drugs that target EBV DNA replication are ineffective in ameliorating acute infection [36], clearing persistent infection [37] and preventing EBV-associated lymphoma in the immunosuppressed [38]. This has led investigators to conclude that viral replication and infection may not be important in these processes. With the simulation, we could ask how effective such a drug would need to be to impact the process of infection, assuming our biological model is correct [32]. The results suggested that the drug may need to be up to 95% effective at blocking viral replication – well beyond the efficacy of available drugs. Thus, the question remains open as to whether viral replication is crucial in these processes and if more efficient drugs, or a vaccine that induced strong neutralizing antibodies, might be efficacious. This is an example of how the simulation can be used to provide insights for interpretation of experimental results and drug design. PathSim and the murine gammaherpesvirus (MHV68) MHV68 is a potentially useful animal model for EBV [39– 43]. It also persists in memory B cells [44] but lacks the EBV latency states, including the oncogenic latent genes. Therefore, it must access the memory compartment through a different mechanism. However, one of the simplifications of PathSim is that we have generalized the process by which the virus proceeds from free virion to the site of persistence in such a way that it may be applicable to both EBV and MHV68. Thus, the overall dynamics of 200
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infection might be similar. As a first test of this concept, we analyzed the effect of varying input virus, at the time of infection, on the levels of infected B cells at persistence. Just as with MHV68 [45], we found the surprising result that varying input virus had no impact on the level of persistent infection. This suggests that MHV68 may be useful for examining quantitative aspects of EBV infection dynamics. Conclusions We sought to develop a virtual representation of Waldeyer’s ring and the peripheral circulation that was reasonably realistic with the intent of using EBV to validate its accuracy and potentially applying it to other pathogens. As yet we have not investigated the contrary question: how much of this complexity is actually needed to accurately model EBV infection? Lastly, our results raise the question of whether animal models that to date have provided little insight into EBV (MHV68 and the rhesus models [46]) are of more or less value and relevance than a computer model that already has yielded new insights? The unraveling of EBV persistence and disease will require the combination of clinical and experimental studies with animal and computer models. There is now a substantial body of evidence supporting the germinal center (GC) model of EBV persistence. This model provides a possible paradigm for how herpesviruses establish and maintain a persistent infection. The core of this paradigm is that these viruses persist in an otherwise healthy cell where all viral protein expression is expunged – the truly latent cell. Application of this biological model to a computer model has allowed the development of the first successful simulation of a herpesvirus infection. Ultimately, the hope is that simulations will allow the identification of crucial ’switch points’ in the process of infection where therapeutic intervention could alter the outcome from death to persistence or even clearance. Our preliminary studies suggest that biological simulations may already have this potential. Acknowledgements The authors thank Herbert ‘Skip’ Virgin for the stimulating conversations that spawned this review article. D.T.-L. is supported by Public Health Research Grants CA65883, AI18757, and AI062989.
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8 Roizman, B. and Knipe, D.M. (2001) Herpes simplex viruses and their replication. In Virology (Vol.2) (Knipe, D.M. and Howley, P.M., eds) , pp. 2399–2459, Lippincott Williams and Wilkins 9 Vossen, M.T. et al. (2002) Viral immune evasion: a masterpiece of evolution. Immunogenetics 54, 527–542 10 Chang, Y. and Moore, P.S. (1996) Kaposi’s Sarcoma (KS)-associated herpesvirus and its role in KS. Infect. Agents Dis. 5, 215–222 11 Hoagland, R.J. (1955) The transmission of infectious mononucleosis. Am. J. Med. Sci. 229, 262–272 12 Babcock, G.J. et al. (2000) The expression pattern of Epstein-Barr virus latent genes in vivo is dependent upon the differentiation stage of the infected B cell. Immunity 13, 497–506 13 Joseph, A.M. et al. (2000) EBV persistence involves strict selection of latently infected B cells. J. Immunol. 165, 2975–2981 14 Caldwell, R.G. et al. (1998) Epstein-Barr virus LMP2A drives B cell development and survival in the absence of normal B cell receptor signals. Immunity 9, 405–411 15 Casola, S. et al. (2004) B cell receptor signal strength determines B cell fate. Nat. Immunol. 5, 317–327 16 Gires, O. et al. (1997) Latent membrane protein 1 of Epstein-Barr virus mimics a constitutively active receptor molecule. EMBO J. 16, 6131– 6140 17 He, B. et al. (2003) EBV-encoded latent membrane protein 1 cooperates with BAFF/BLyS and APRIL to induce T cell-independent Ig heavy chain class switching. J. Immunol. 171, 5215–5224 18 Panagopoulos, D. et al. (2004) Comparative analysis of signal transduction by CD40 and the Epstein-Barr virus oncoprotein LMP1 in vivo. J. Virol. 78, 13253–13261 19 Hochberg, D. et al. (2004) Demonstration of the Burkitt’s lymphoma Epstein-Barr virus phenotype in dividing latently infected memory cells in vivo. Proc. Natl. Acad. Sci. U. S. A. 101, 239–244 20 Cui, C. et al. (2006) Prediction and identification of herpes simplex virus 1-encoded microRNAs. J. Virol. 80, 5499–5508 21 Pfeffer, S. et al. (2005) Identification of microRNAs of the herpesvirus family. Nat. Methods 2, 269–276 22 Gupta, A. et al. (2006) Anti-apoptotic function of a microRNA encoded by the HSV-1 latency-associated transcript. Nature 442, 82–85 23 Anagnostopoulos, I. et al. (1995) Morphology, immunophenotype, and distribution of latently and/or productively Epstein-Barr virus-infected cells in acute infectious mononucleosis: implications for the interindividual infection route of Epstein-Barr virus. Blood 85, 744– 750 24 Laichalk, L.L. and Thorley-Lawson, D.A. (2005) Terminal differentiation into plasma cells initiates the replicative cycle of Epstein-Barr virus in vivo. J. Virol. 79, 1296–1307 25 Niedobitek, G. et al. (1997) Epstein-Barr virus (EBV) infection in infectious mononucleosis: virus latency, replication and phenotype of EBV-infected cells. J. Pathol. 182, 151–159 26 Laichalk, L.L. et al. (2002) The dispersal of mucosal memory B cells: evidence from persistent EBV infection. Immunity 16, 745–754 27 Borza, C.M. and Hutt-Fletcher, L.M. (2002) Alternate replication in B cells and epithelial cells switches tropism of Epstein-Barr virus. Nat. Med. 8, 594–599
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28 Hudnall, S.D. et al. (2005) Distribution and phenotype of Epstein-Barr virus-infected cells in human pharyngeal tonsils. Mod. Pathol. 18, 519– 527 29 Pegtel, D.M. et al. (2004) Epstein-Barr virus infection in ex vivo tonsil epithelial cell cultures of asymptomatic carriers. J. Virol. 78, 12613– 12624 30 Walling, D.M. et al. (2001) Persistent productive Epstein-Barr virus replication in normal epithelial cells in vivo. J. Infect. Dis. 184, 1499– 1507 31 Duca, K.A. et al. (2007) A virtual look at Epstein-Barr virus infection: biological interpretations. PLoS Pathog 3, 1388–1400 32 Shapiro, M. et al. A virtual look at Epstein-Barr Virus infection: simulation mechanism. J. Theor. Biol. (in press) 33 Davenport, M.P. et al. (2002) Clonal selection, clonal senescence, and clonal succession: The evolution of the T cell response to infection with a persistent virus. J. Immunol. 168, 3309–3317 34 Castiglione, F. et al. (2007) Simulating Epstein-Barr virus infection with C-ImmSim. Bioinformatics 23, 1371–1377 35 Hoagland, R. (1964) The incubation period of infectious mononucleosis. Am. J. Public Health Nations Health 54, 1699–1704 36 Torre, D. and Tambini, R. (1999) Acyclovir for treatment of infectious mononucleosis: a meta-analysis. Scand. J. Infect. Dis. 31, 543–547 37 Yao, Q.Y. et al. (1989) Epstein-Barr virus-infected B cells persist in the circulation of acyclovir-treated virus carriers. Int. J. Cancer 43, 67–71 38 Mentzer, S.J. et al. (2001) Epstein–Barr virus post-transplant lymphoproliferative disease and virus-specific therapy: pharmacological re-activation of viral target genes with arginine butyrate. Transpl. Infect. Dis. 3, 177–185 39 Doherty, P.C. et al. (2001) Dissecting the host response to a gammaherpesvirus. Philos. Trans. R. Soc. Lond. B Biol. Sci. 356, 581–593 40 Flano, E. et al. (2002) A mouse model for infectious mononucleosis. Immunol. Res. 25, 201–217 41 Stevenson, P.G. and Efstathiou, S. (2005) Immune mechanisms in murine gammaherpesvirus-68 infection. Viral Immunol. 18, 445–456 42 Virgin, H.W. (2005) Immune regulation of viral infection and vice versa. Immunol. Res. 32, 293–315 43 Speck, S.H. and Virgin, H.W. (1999) Host and viral genetics of chronic infection: a mouse model of gamma-herpesvirus pathogenesis. Curr. Opin. Microbiol. 2, 403–409 44 Willer, D.O. and Speck, S.H. (2003) Long-term latent murine Gammaherpesvirus 68 infection is preferentially found within the surface immunoglobulin D-negative subset of splenic B cells in vivo. J. Virol. 77, 8310–8321 45 Tibbetts, S.A. et al. (2003) Establishment and maintenance of gammaherpesvirus latency are independent of infective dose and route of infection. J. Virol. 77, 7696–7701 46 Wang, F. (2001) A new animal model for Epstein-Barr virus pathogenesis. Curr. Topics Microbiol. Immunol. 258, 201–219 47 Nowak, M.A. and May, R.M. (2000) Virus Dynamics: Mathematical Principles of Immunology and Virology, Oxford University Press 48 Perelson, A.S. (2002) Modelling viral and immune system dynamics. Nat. Rev. Immunol. 2, 28–36 49 Wodarz, D. (2007) Killer Cell Dynamics: Mathematical and Computational Approaches to Immunology, Springer
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Epstein-Barr virus: a paradigm for persistent infection – for real and in virtual reality David A. Thorley-Lawson1, Karen A. Duca2 and Michael Shapiro1 1 2
Department of Pathology, Tufts University School of Medicine, Boston, Massachusetts 02111, USA Department of Biochemistry and Biotechnology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
The really interesting thing about herpesviruses is that they can establish lifelong persistant infections in immunocompetent hosts. At first glance, they would seem to have very different ways of doing this. Here we will use as a model our current understanding of how the human herpesvirus Epstein-Barr virus establishes and maintains such an infection. We apply information from a wide range of sources including laboratory experimentation, clinical observation, animal models and a new computer simulation. We propose that the detailed mechanisms for establishing infection are dependent on the virus and tissues involved, but the strategy is the same – to persist in a long-lived cell type where the virus is invisible to the immune system and nonpathogenic. Introduction Herpesviruses are fascinating because they establish lifelong persistent infection in normal healthy hosts [1]. Human herpesviruses are also of practical importance because they are associated with severe, sometimes lifethreatening, diseases that are particularly problematic in the immunocompromised. All herpesviruses are believed to have two phases to their life cycle that together describe persistent infection; these are latency and reactivation/ replication [1]. Together they constitute the mechanism by which these viruses establish and maintain lifetime infections and spread to new hosts. In the most simplistic interpretation, latency refers to an infected state where the cell(s) contain an intact viral genome(s) but are not producing infectious virus and reactivation/replication involves the production of infectious virus. However, as we have begun to learn more about the details of herpesvirus biology, it has become apparent that these terms mean different things to different people with different viruses in different settings. What is the difference between persistence and latency? How do we distinguish different states of latency from each other? How do we distinguish latency in a cell type where the virus persists from one where it does not? How do we distinguish latency from abortive replication or replication that just has not finished yet? Several of these questions arise because of the limitations on what we can measure. For example, any cell that lacks an infectious virion could be perceived as latent. However, this might include latent states that may be irrelevant to persistence Corresponding author: Thorley-Lawson, D.A. ([email protected]).
and cells initiating viral replication that have not yet produced intact virions or are abortive and never produce virus. Similarly, cells that fail to produce infectious virus in culture might be abortively infected or we may just not know the correct signal to reactivate the virus. One of the best understood of the human herpesviruses is Epstein-Barr virus (EBV) [2–4], for which there is an established biological model of infection and persistence (reviewed in [5–7]). In this article, we will discuss a general model of herpesvirus persistence based on EBV. Biological models and experimental limitations The proposed generic model for herpesvirus infection is summarized in Figure 1. The central notion is that a ‘true’ form of latency exists where no viral proteins are expressed and the viral genomes are episomal (i.e. circular not linear). This is where the virus persists for the lifetime of the host and is clearly distinguished from other types of infected cells that harbor viral genomes but do not contain infectious virus. To arrive at this destination, the virus infects a progenitor that may go through several different states before arriving at the condition of true latency. These states are referred to as ’transitional latency’. A consequence of this distinction is that pathogenesis may be associated with the transitional states, because viral proteins are expressed that could impact the target cell and the organism. However, at the site of true latency, there are no viral proteins expressed and so the virus is neither pathogenic to the host nor visible to the immune system. For all intents and purposes, the cell carrying the truly latent virus is normal, as far as the host is concerned, until viral replication is activated. Viral replication is divided into several stages [8]. The first is initiation in response to a signal. Because the truly latent cell is essentially normal from the point of view of the host, this signal by definition must be one that is part of the normal biology of the cell. This in turn implies that the viral gene or genes that initiate the replicative cycle must be responsive to transcription factors that are transiently expressed during some part of the normal biology of the infected cell. The next stage involves organizing the viral gene products needed to replicate the DNA leading to production of the proteins necessary to package the DNA in intact infectious virions. Note that only at this last stage can infectious virus be recovered from the cells. This virus infects a secondary tissue that provides
1471-4906/$ – see front matter ß 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.it.2008.01.006 Available online 6 March 2008
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Figure 1. The generic biological model for herpesviruses. Free virus infects the primary target tissue and passes although a variable number of latency states (transient latency) until it reaches the site of true latency where viral protein expression ceases. The virus is reactivated on receiving an appropriate external signal (red lightning bolt) ultimately leading to viral DNA replication, the production of infectious virus and cell death. This virus infects a secondary tissue where it replicates aggressively, leading to a rapid transient amplification of infectious virus. This virus is then shed for tranmission to new hosts. The death of cells entering viral replication requires that there be a mechanism of either self-renewal and/or reinfection (dashed arrows) to sustain stable levels of persistence in the truly latent cells. For Epstein-Barr virus (EBV), these states are well defined with the growth program in naı¨ve B cells and the default program in germinal center (GC) cells being transient latency states. True latency is in peripheral memory B cells. Reactivation of the virus is caused by signals that drive terminal differentiation. The virus first expresses immediate early transcription factors (IE) that turn on early antigens (EA, primarily components of the DNA replication complex) followed by DNA replication, packaging of the virus (late antigens), lysis of the cells and release of infectious virus for infection and amplification in epithelial cells. At the bottom of the figure are shown the equivalent stages for EBV from the biological and computer models.
a favorable environment for replication but not latency. The classical example of this is replication of herpes simplex virus (HSV) in the skin after reactivation from latently infected ganglia [8]. By definition, this form of replication is not a prerequisite for persistence only for spread. Since replication is occurring in a healthy host, successful production of virus has to be transient, rapid and relatively rare to maximize the opportunities for spread to new hosts before the immune system shuts replication down. Not surprisingly, herpesviruses have developed strategies to evade the immune response to maximize the chances of successfully producing infectious virus (reviewed in [9]). Because viral replication is assumed to result in the death of the cell, there must exist some mechanism for replacement of truly latent cells; otherwise, there would be a steady decay in the level of persistent infection through life. This could occur through new infection or occasional division of the truly latent cell (dashed lines in Figure 1), in which case the transient latency states become irrelevant once the virus has reached the site of true latency. Although this issue is not settled, it is also not trivial. If reinfection is needed for stable persistence, it provides a window of attack against persistent virus with antivirals and/or vaccine strategies to which a self-renewing system would be invulnerable. EBV as an unreliable paradigm of herpesvirus biology: a brief history EBV does not have a stellar record as a model for herpesvirus studies. It was discovered in Burkitt’s lymphoma and 196
subsequently found in several other lymphomas and carcinomas and has the capacity to transform B lymphocytes in culture [3,4]. Together this provided a compelling argument for EBV as the first human tumor virus and led to an outburst of interest in the role of herpesviruses in human cancer. Unfortunately, this was an ill-fated field, and no further candidates emerged. It took AIDS to reveal the second example, Kaposi’s sarcoma herpes virus (KSHV) [10]. Similarly, EBV has been misleading to thinking about herpesvirus latency. When EBV transforms B cells, it establishes a latent infection associated with the expression of nine latent proteins [2,4]. EBV is also latent in the tumors within which it is found, and different tumors express different subsets of the nine latent proteins [3,4]. This created the notion that herpesviruses express latent proteins at the site of latent persistence. Indeed, one of the goals of herpesvirus research was to define latent sites of persistence through the detection of latent genes often defined as those whose expression is independent of viral replication. Only recently have we come to realize that the expression of latent proteins by EBV is a mechanism to get to the site of persistence not the mechanism of persistence itself. From this discussion, it is apparent that the expression of such genes identifies ‘transient latent states’ not the site of ‘true latency’. Ironically, it seems that HSV is the paradigm for herpesvirus persistence after all and at the site of true latent persistence all viral protein expression ceases.
Review The germinal center model of EBV infection? The biological model of EBV persistence (Figure 2, left) has been described elsewhere [5–7] and will not be detailed here except in as far as the discussion relates to the ideas in Figure 1. The underlying principle of the model is that EBV uses normal B-cell biology to establish infection, persist and replicate. EBV is spread by saliva contact [11]. It enters the epithelium of Waldeyer’s ring (the adenoids and tonsils) where it infects normal, resting, naı¨ve B cells. Here it expresses the first of its transient latency states (the ‘growth program’ in Figure 1 [2]), where all of the latent genes are expressed and which drives the activation and proliferation of the B cell [12,13]. These cells are postulated to enter a follicle and undergo a germinal center reaction involving a switch to a second state of transient latency (the ‘default program’ in Figure 1). Here only three latent proteins are expressed, of which LMP1 (latent membrane protein 1) and LMP2 are crucial because they have the potential to provide the signals necessary to drive the differentiation of the latently infected B cell into the memory compartment [14–18]. Once in the memory cell, viral protein expression is shut down [19], so this is the site of true latency. Furthermore, by every criterion measured thus far, these latently infected memory cells are indistinguishable from normal memory B cells. The biggest challenge to this concept may come from herpesvirus-encoded
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microRNAs [20,21], which could profoundly influence our understanding of how much the otherwise dormant viral genome regulates the host cell [22]. Reactivation of EBV from latently infected memory cells is thought to occur in response to the normal physiologic signals that drive the cell to become a plasma cell [23–26]. Our most recent measurements suggest that healthy carriers of the virus continually shed large amounts of virus over time (V. Hadinoto and D.A.T.-L. unpublished). Indeed, so much virus is produced that it cannot be accounted for by replication in plasma cells alone, suggesting amplification at a secondary site. There is now increasing evidence that this site is the epithelium surrounding Waldeyer’s ring (secondary target in Figure 1) [27–30]. This is not a latent infection but a rapid and efficient lytic infection that releases a stream of virus into the saliva for infectious spread to new hosts. Computer modeling of EBV infection EBV is the first herpesvirus to be successfully modeled with a computer simulation [31,32]. Despite rapidly growing literature in host–pathogen modeling, little has been done previously with herpes viruses. Ordinary differential equations (ODEs) have been used to study T-cell senescence in the context of EBV [33]; however, they did not analyze the dynamics of B-cell infection nor follow-up on the study. Our attempts to modify their equations to model
Figure 2. A diagrammatic representation of the computer simulation of Epstein-Barr virus (EBV) persistence and the biological model on which it is based. In the biological model, EBV from saliva latently infects naı¨ve B cells in Waldeyer’s ring (the tonsils and adenoids) and uses its growth transcription program to activate the cell to become a proliferating blast so that it can then pass through a germinal center, where it receives survival and progression signals from its default transcription program. These are ’transitional latency’ states in Figure 1. Ultimately, the cell leaves the germinal center as a resting memory B cell, where no viral proteins are expressed: the site of ’true latency’. Here the latently infected cells are maintained like normal B cells through homeostasis mechanisms. The latently infected memory cells return to the Waldeyer’s ring and occasionally receive differentiation signals that cause the virus to reactivate, replicate and be shed. This virus infects and replicates in epithelial cells (secondary target tissue in Figure 1) before being shed to infect new B cells or hosts. This one-way circuit of infection is controlled by the immune system, which targets latently infected blasts and lytically infected plasma cells with ctyotoxic T cells (CTLs) and free virus with neutralizing antibodies. The computer model is based on the biological model with the simplification that all latently infected B cells are represented by a single agent (termed BLat). These cells are not subject to CTL predation while in the peripheral circulation. This is to model the lack of immune surveillance of latently infected memory B cells in real infection. The consequence is that, in the simulation, the BLat are subject to immunosurveillance when they return to the Waldeyer’s ring, a situation we believe is not true for real latently infected memory B cells. This is an important limitation of the simulation that is discussed further in the main text.
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Review infection have, to date, been unsuccessful (K.A.D., unpublished). We have used an agent-based approach (Box 1) to investigate EBV infection. In a pilot study, we used an existing platform (C-ImmSim), consisting of a small, simple two-dimensional virtual grid, with some success [34]. Concurrently, we developed a more detailed model (PathSim; Pathogen Simulation; Figure 3a–d; Box 1) based on the biological model described above (Figure 2, left). Despite shortcomings (discussed below and detailed in [31,32]), the simulation does demonstrate the characteristic features of an EBV infection with an acute phase, occurring around 40–50 days after virtual infection [35], that resolves into a long-term, low-level persistent phase (Figure 3e). It was also able to make predictions about previously unknown aspects of viral dynamics that were confirmed experimentally [31]. Just like animal models, the computer model makes certain assumptions to generalize about the biology (Figure 2, right), and these may become problematic. For example, we confounded all the transitional latency states into the single entity ‘Blat’ (Figure 1). This is computationally equivalent to the empirical assumption that any cell lacking infectious virus is ’latently infected’. In hindsight, this was an important oversimplification because it does not allow us to dissect the relative roles of latently infected B cell blasts, that are proliferating and under immunosurveillance by cytotoxic T cells (CTLs) versus resting memory cells that are not under immunosurveillance. One of the most difficult issues in modeling biological systems is determining what features to include. Moreover, each feature requires parameters (e.g. B cells have volumes, lifespans, rates at which they perform functions) for which values are not always available (Box 2). The more Box 1. Rationale for choosing agent-based simulation and a virtual world Computer modeling is beginning to be applied to complex biological systems. Traditionally, biologists have been skeptical of this approach. This is because there is an inherent language and conceptual gulf between biologists and mathematicians in the way they describe and think about systems and because biologists feel that such models are intrinsically ’not real’. This reaction is particularly acute for models that involve equations, such as the ordinary differential equations (ODEs) that have been used extensively to model HIV, for example (reviewed in [47–49]). ODEs describe biological systems based on mass action laws. The inherent smoothness of the output from these equations and the assumptions that the system is homogeneous and well mixed (like a chemical reaction in a beaker) are intuitively unsatisfying for biological systems that are not evenly dispersed and where local events can have global impacts. To address these concerns a simulation was designed that consists of a virtual grid that represents Waldeyer’s ring and the peripheral circulation (Figure 3) and on which various agents (B cells, T cells, and EB virions) can move and interact. The simulation can be run from the command line (i.e. like regular software) or by a user-friendly visual interface on a standard computer monitor (i.e. in a format that is more familiar – that of a computer game; Figure 3d). This allows the simulation to be launched and output to be accessed and analyzed in a visual way that is simple and easily comprehensible to the nonspecialist. This simulation may be used as a way of experimenting with hypotheses about the biological system and examining their implications.
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complex and ‘realistic’ the model, the larger the number of parameter values needed. Of necessity, a simulation is a simplification and feature sets and parameters by their very nature can never exactly map ‘true biology’. One value of simulation is in identifying the important components and events behind the dynamics under study, compared with those that make minor or no contributions. This is a nontrivial issue because, among life scientists, one great but underappreciated value of simulation is its ability to give insights into what we actually do and do not know and how well we understand what we know (Box 2). Put simply, if we build a computer simulation based on a biological model and it gives nonbiological output, something is wrong/missing with our biological model. If it produces credible output, we may have captured, at least crudely, essential features of the biology. We will briefly discuss several examples to illuminate this point.
Box 2. The art and science of parameter choice and simulation design For engineers, an established body of knowledge allows routine, accurate simulation of many (although certainly not all) processes. Physical processes are often well understood and governed by equations whose parameter values are known physical constants. Currently, this is not the case with biological simulation. We don’t know all the structures/components that are crucial, and many of the values of biological functions are also unknown and may even be unmeasurable. This reflects the limitations of our biological understanding, because the simulation is directly informed by what we know about the biology. Indeed, the case could be made that this is the state of the art right now – developing methods to figure out which parameters are important, how to get at their value and how to interpret their biological significance. Currently, many parameters have to be estimated or guessed based on available information or simply subjective opinion (for a complete discussion of the parameters used in PathSim, see [32]). These parameters then have to be ‘fitted’ to produce credible output and can be thought of as ‘simulation parameters’ rather than ‘biological parameters’. There are several reasons why a parameter value may need to be ‘fitted’: The actual value is unknown. The simulation must be scaled down because of computational constraints. The virtual process is a simplification that does not map directly to biology. In the event that it works, there are three possible interpretations. The parameter: is correct is wrong but has little impact on the simulation is wrong but its effects are counterbalanced by errors in another parameter(s). It is important to emphasize that distinguishing these possibilities is not a trivial exercise but is a central issue in simulating biological systems given the current level of sophistication. Despite these constraints, biological simulations are already yielding useful information. We can use them: to give perspectives on the biological system that allows us to ask new questions and derive new insights. to question what we really know about a biological system. to investigate the consequences of limitations in our understanding of the biology and/or the simulation. This may provide important insights (see main body of the text) into what is and what is not crucial in the biological model/system. to produce values for parameters we do not have information for and ask what meaning they have biologically. to study the sensitivity of the model system to varying parameters in a way that cannot be performed experimentally.
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Figure 3. The structure and typical output of PathSim, the agent-based model of Epstein-Barr virus (EBV) infection. The simulation is constructed as a virtual grid representing Waldeyer’s ring and the peripheral circulation. Agents (lymphocytes and EB virions) move and interact on this grid according to a defined rule set (e.g. when a virion meets a B cell, it infects it with some probability; when a T cell meets an infected cell, it becomes an activated cytotoxic T cell. (a) The basic ’hex’ unit that makes up the grid. The white lines indicate the grid on which the agents move and the red boxes the nodes where the agents interact. (b) A cross-section of part of a real tonsil that includes a single germinal center/follicle and the proximal tissue. The mapping from the tissue to the ’hex’ unit is indicated by the labels and arrows. Virus enters from the surface. Lymphocytes enter through high endothelial venules (HEVs) and leave through the draining, efferent, lymphatics. HEV and lymphatics are not easily resolved in the tissue section but are indicated for the ’hex’ unit. (c) Multiple hex units are combined to form a single tonsil. (d) Complete Waldeyer’s ring as it appears on the computer screen. The four tonsils and adenoids are shown in red, the draining lymphatics in white and the peripheral circulation in red and blue. The figure also shows the control panel (resembling a typical VCR-like controller) for running the simulation at the bottom and typical output graphs in various styles. (e) Simulation output, using the default parameter set (for a complete list and discussion, see [32]), of the total number of latently infected virtual B cells (Blats) over time in a series of superimposed simulation runs (colored lines, each line corresponds to a different run) versus the number of actual latently infected memory B cells in the peripheral blood of 15 acutely infected individuals over time (B latent, colored circles). Note the overall congruity of the simulated and clinical kinetics. For a discussion of the disparities and their significance, see text. (f) Persistence is highly sensitive to the rate of Vir (virtual virus) reactivation. The simulation was run as described in (e) except the percentage of latently infected virtual B cells (BLat) that initiate virus replication on return to the Waldeyer’s ring was varied as shown. Note that as the percent that initiate reactivation increases, the infection rapidly overwhelms the virtual host.
The peripheral memory B-cell compartment In the biological model, access to the peripheral memory pool is essential for EBV persistence. We found this to hold true in the simulation but now we can ask the following: how robust is this dependence? We found that if we denied EBV access to this pool, we were unable to obtain an infection process that even remotely resembled a real persistent EBV infection no matter how much we changed other parameters. This is an example of how we can
examine sensitivity to a parameter in a way not possible with real patients. It also shows how, lacking a crucial component of the biology, it is not necessarily possible to force a simulation to ‘look’ biological by arbitrary fitting of parameters. Reactivation rate Latently infected B cells returning to Waldeyer’s ring reactivate to produce infectious virus. In the simulation, 199
Review virtual infection was extremely sensitive to this rate (Figure 3f). If it was increased slightly, the host was overwhelmed by acute infection. If it was reduced slightly, the virus was cleared. Strikingly, the rate that allowed persistence was the same as we had previously measured experimentally in Waldeyer’s ring [24]. Such quantitative verification adds credibility to the simulation but also provides an example of how simulations can stimulate biological thinking. When we measured the rate of reactivation, we had no idea what it meant: we simply had a value. We can now speculate that host and virus must have evolved to produce a reactivation rate that was high enough to maximize the chances for establishing persistence and spread to other hosts without putting the host at risk. This in turn implies a biological mechanism precisely regulating this rate that is yet to be identified. T-cell memory The simulation was imprecise at the quantitative level, because the infection resolved too slowly and levels of persistent infection were too high and demonstrated marked oscillations that are not seen in normal persistence. All three discrepancies may be caused by the lack of T-cell memory and T-cell proliferation in our simulation. These two attributes would allow a more sustained T-cell response that would produce a more rapid decline of infected cells, lower levels of sustained persistence and tend to flatten out oscillatory behavior, thus making the simulation more quantitatively accurate. This is an example of how thinking about the limitations of the simulation can lead to a testable prediction. Sensitivity to antivirals Antiviral drugs that target EBV DNA replication are ineffective in ameliorating acute infection [36], clearing persistent infection [37] and preventing EBV-associated lymphoma in the immunosuppressed [38]. This has led investigators to conclude that viral replication and infection may not be important in these processes. With the simulation, we could ask how effective such a drug would need to be to impact the process of infection, assuming our biological model is correct [32]. The results suggested that the drug may need to be up to 95% effective at blocking viral replication – well beyond the efficacy of available drugs. Thus, the question remains open as to whether viral replication is crucial in these processes and if more efficient drugs, or a vaccine that induced strong neutralizing antibodies, might be efficacious. This is an example of how the simulation can be used to provide insights for interpretation of experimental results and drug design. PathSim and the murine gammaherpesvirus (MHV68) MHV68 is a potentially useful animal model for EBV [39– 43]. It also persists in memory B cells [44] but lacks the EBV latency states, including the oncogenic latent genes. Therefore, it must access the memory compartment through a different mechanism. However, one of the simplifications of PathSim is that we have generalized the process by which the virus proceeds from free virion to the site of persistence in such a way that it may be applicable to both EBV and MHV68. Thus, the overall dynamics of 200
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infection might be similar. As a first test of this concept, we analyzed the effect of varying input virus, at the time of infection, on the levels of infected B cells at persistence. Just as with MHV68 [45], we found the surprising result that varying input virus had no impact on the level of persistent infection. This suggests that MHV68 may be useful for examining quantitative aspects of EBV infection dynamics. Conclusions We sought to develop a virtual representation of Waldeyer’s ring and the peripheral circulation that was reasonably realistic with the intent of using EBV to validate its accuracy and potentially applying it to other pathogens. As yet we have not investigated the contrary question: how much of this complexity is actually needed to accurately model EBV infection? Lastly, our results raise the question of whether animal models that to date have provided little insight into EBV (MHV68 and the rhesus models [46]) are of more or less value and relevance than a computer model that already has yielded new insights? The unraveling of EBV persistence and disease will require the combination of clinical and experimental studies with animal and computer models. There is now a substantial body of evidence supporting the germinal center (GC) model of EBV persistence. This model provides a possible paradigm for how herpesviruses establish and maintain a persistent infection. The core of this paradigm is that these viruses persist in an otherwise healthy cell where all viral protein expression is expunged – the truly latent cell. Application of this biological model to a computer model has allowed the development of the first successful simulation of a herpesvirus infection. Ultimately, the hope is that simulations will allow the identification of crucial ’switch points’ in the process of infection where therapeutic intervention could alter the outcome from death to persistence or even clearance. Our preliminary studies suggest that biological simulations may already have this potential. Acknowledgements The authors thank Herbert ‘Skip’ Virgin for the stimulating conversations that spawned this review article. D.T.-L. is supported by Public Health Research Grants CA65883, AI18757, and AI062989.
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