This work was supported by grant no
This work was supported by grant no. antigenic drift being Rabbit polyclonal to ACTG a side effect of these binding avidity changes. Here, we present a mathematical formulation of this Methotrexate (Abitrexate) new antigenic drift model and use it to show how rates of antigenic drift depend on epidemiological parameters. We further use the model to evaluate how two different vaccination strategies can impact antigenic drift rates and ultimately disease incidence levels. Finally, we discuss the assumptions present in the model formulation, predictions of the model, and future work that needs to be done to determine the consistency of this hypothesis with known patterns of influenza’s genetic and antigenic evolution.  have recently questioned this model of antigenic drift, noting that viral escape from polyclonal antibodies by this mechanism would be exceptionally difficult. This is because escape mutants, having all the necessary epitope changes to allow for polyclonal immune escape, are extremely unlikely to arise within single hosts given current mutation rate estimates. In place of this model, they suggest that the evolutionary dynamics of influenza’s HA are predominantly driven by cellular receptor binding avidity changes Methotrexate (Abitrexate) and that antigenic drift is a side effect of these mutational changes. Evolution can act on receptor binding avidity because this phenotype affects the rate at which viruses enter host cells, and thereby their ability to escape neutralization by circulating polyclonal antibodies. The authors support this new model of antigenic drift with passage experiments in mice: when passaged through immune mice, influenza A strains accumulate HA mutations that increase receptor binding avidity, with a subset of these mutations located in previously identified HA epitopes; when passaged through na?ve mice, influenza A strains instead accumulate HA mutations that decrease receptor binding avidity, Methotrexate (Abitrexate) with a subset of these mutations again lying in known HA epitopes. Being appreciably different from the current model of antigenic drift, this new model may change our understanding of influenza’s ecological and evolutionary dynamics. It may also affect the design of control strategies that aim to reduce disease incidence. Although some of the dynamical consequences of this new antigenic drift model could presumably be intuited, others may be more difficult to predict. This is because the model, as verbally described, has nonlinear feedbacks between viral changes in receptor binding avidity, rates of antigenic drift and host immunity at the population level. Furthermore, it would be difficult for the verbal model to lead to quantitative predictions; this is particularly limiting when it comes to choosing between alternative disease-control strategies. Here, we therefore develop a mathematical model for the receptor binding avidity hypothesis outlined by Hensley and co-authors, with the assumption that selection acts solely on cellular receptor binding avidity. Through numerical simulation of the model, we show how epidemiological parameters, such as contact rates and host lifespans, affect receptor binding avidity levels and rates of antigenic drift. Finally, we use the model to quantitatively explore the consequences of alternative vaccination strategies on the rates of antigenic drift and ultimately on rates of disease incidence. 2.?A mathematical formulation for the new model of antigenic drift We formulate Hensley and co-authors new model of antigenic drift mathematically by specifying an SIRS model, with hosts classified into Methotrexate (Abitrexate) discrete classes of susceptible hosts ( 1, 2.1 where is the birth/death rate, is the transmission rate, is the recovery rate, is the rate of waning immunity, is the total population size and because we use as a proxy for immune status: individuals with a higher number of previous infections are assumed to have higher levels of circulating antibodies with which to counter a challenging infection, and thereby lower susceptibility to infection. A schematic of this model is shown in the electronic supplementary material, figure.