Within-host stochastic emergence dynamics of immune-escape mutants (bibtex)
by Hartfield, Matthew; Alizon, Samuel
Abstract:
Predicting the emergence of new pathogenic strains is a key goal of evolutionary epidemiology. However, the majority of existing studies have focussed on emergence at the population level, and not within a host. In particular, the coexistence of pre-existing and mutated strains triggers a heightened immune response due to the larger total pathogen population; this feedback can smother mutated strains before they reach an ample size and establish. Here, we extend previous work for measuring emergence probabilities in non-equilibrium populations, to within-host models of acute infections. We create a mathematical model to investigate the emergence probability of a fitter strain if it mutates from a self-limiting strain that is guaranteed to go extinct in the long-term. We show that ongoing immune cell proliferation during the initial stages of infection causes a drastic reduction in the probability of emergence of mutated strains; we further outline how this effect can be accurately measured. Further analysis of the model shows that, in the short-term, mutant strains that enlarge their replication rate due to evolving an increased growth rate are more favoured than strains that suffer a lower immune-mediated death rate ('immune tolerance'), as the latter does not completely evade ongoing immune proliferation due to inter-parasitic competition. We end by discussing the model in relation to within-host evolution of human pathogens (including HIV, hepatitis C virus, and cancer), and how ongoing immune growth can affect their evolutionary dynamics.
Reference:
Hartfield M. & Alizon S. (2015) Within-host stochastic emergence dynamics of immune-escape mutants. PLoS Comput Biol. 11(3): e1004149.
Bibtex Entry:
@article{HartfieldAlizon2015,
	Abstract = {Predicting the emergence of new pathogenic strains is a key goal of evolutionary epidemiology. However, the majority of existing studies have focussed on emergence at the population level, and not within a host. In particular, the coexistence of pre-existing and mutated strains triggers a heightened immune response due to the larger total pathogen population; this feedback can smother mutated strains before they reach an ample size and establish. Here, we extend previous work for measuring emergence probabilities in non-equilibrium populations, to within-host models of acute infections. We create a mathematical model to investigate the emergence probability of a fitter strain if it mutates from a self-limiting strain that is guaranteed to go extinct in the long-term. We show that ongoing immune cell proliferation during the initial stages of infection causes a drastic reduction in the probability of emergence of mutated strains; we further outline how this effect can be accurately measured. Further analysis of the model shows that, in the short-term, mutant strains that enlarge their replication rate due to evolving an increased growth rate are more favoured than strains that suffer a lower immune-mediated death rate ('immune tolerance'), as the latter does not completely evade ongoing immune proliferation due to inter-parasitic competition. We end by discussing the model in relation to within-host evolution of human pathogens (including HIV, hepatitis C virus, and cancer), and how ongoing immune growth can affect their evolutionary dynamics.},
	Author = {Hartfield, Matthew and Alizon, Samuel},
	Date-Added = {2015-04-08 14:32:48 +0000},
	Date-Modified = {2015-04-08 14:35:18 +0000},
	Doi = {10.1371/journal.pcbi.1004149},
  Url = {http://www.ploscompbiol.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pcbi.1004149&representation=PDF},
  Bdsk-Url-1 = {http://dx.doi.org/10.1371/journal.pcbi.1004149},
	Journal = {PLoS Comput Biol},
	Journal-Full = {PLoS computational biology},
	Keywords = {emergence, within-host, stochasticity, model, evolution, escape},
	Number = {3},
	Pages = {e1004149},
	Pmc = {PMC4365036},
	Pmid = {25785434},
	Pst = {epublish},
	Title = {Within-host stochastic emergence dynamics of immune-escape mutants},
	Volume = {11},
	Year = {2015},
	}
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