Dynamic effects of antibody-dependent enhancement on the fitness of viruses

Abstract

Antibody-dependent enhancement (ADE), a phenomenon in which viral replication is increased rather than decreased by immune sera, has been observed in vitro for a large number of viruses of public health importance, including flaviviruses, coronaviruses, and retroviruses. The most striking in vivo example of ADE in humans is dengue hemorrhagic fever, a disease in which ADE is thought to increase the severity of clinical manifestations of dengue virus infection by increasing virus replication. We examine the epidemiological impact of ADE on the prevalence and persistence of viral serotypes. Using a dynamical system model of n cocirculating dengue serotypes, we find that ADE may provide a competitive advantage to those serotypes that undergo enhancement compared with those that do not, and that this advantage increases with increasing numbers of cocirculating serotypes. Paradoxically, there are limits to the selective advantage provided by increasing levels of ADE, because greater levels of enhancement induce large amplitude oscillations in incidence of all dengue virus infections, threatening the persistence of both the enhanced and nonenhanced serotypes. Although the models presented here are specifically designed for dengue, our results are applicable to any epidemiological system in which partial immunity increases pathogen replication rates. Our results suggest that enhancement is most advantageous in settings where multiple serotypes circulate and where a large host population is available to support pathogen persistence during the deep troughs of ADE-induced large amplitude oscillations of virus replication.dengue ͉ adaptive ͉ trade-off

Instabilities in multiserotype disease models with antibody-dependent enhancement

“…However, there is not much advantage in generating a large number of serotypes if the goal is to lower the ADE factor necessary to induce oscillatory dynamics. Also, there is a balance between the number of co-circulating serotypes and the magnitude of ADE factors that do not induce oscillations that may threaten the persistence of each serotype (as discussed in Cummings et al, 2005).…”

“…Synchronization can occur between two, three, or all four serotypes. As discussed in Cummings et al (2005), synchronization can act in a boom-bust fashion, driving the numbers of infected individuals to greater extremes. If the percentage of infected individuals is sufficiently low, it could represent the fadeout of one or more serotypes.…”

“…Similarly, we can follow approaches used in previous dengue models without ADE, as in Vargas (2000, 2003). Here, we examine a dynamical system with a general number of cocirculating serotypes and an ADE factor that modifies the transmissibility of secondary infections, providing derivations of and extending previous work in Cummings et al (2005) and Schwartz et al (2005). As we begin to understand the complexity of the dengue model, we gain a better perspective in formulating optimal vaccination strategies.…”

“…Understanding the dynamics of a mathematical model of a multiserotype disease with ADE could help in this effort. Foundational work on ADE factors can be found in Ferguson et al (1999a,b), Cummings et al (2005), and Schwartz et al (2005). Similarly, we can follow approaches used in previous dengue models without ADE, as in Vargas (2000, 2003).…”

Models of dengue virus infection

“…Therefore, it is unclear whether these phenomena actually occur in nature. These reservations also apply to the study of the effects of antibody-dependent enhancement of dengue virus infection of monocytes in the host (demonstrated only in vitro), especially when estimating its long-range effects on virus epidemiology and population dynamics [47]. Other computer models of dengue virus infection have included clinical applications, such as distinguishing dengue from other fevers, based on logistic regression analysis of clinical and laboratory features [48], and measuring pathophysiological parameters of severe dengue (DHF/DSS) and risk in DF patients, using neural network programs to predict outcome, and to determine patient management or treatment [49].…”