Much of the theory of host-pathogen interactions is aimed at understanding the circumstances that lead to cycles in pathogen incidence or host density. This theory usually assumes that host and pathogen have strong effects on each other, which should in principle lead to strong natural selection on both host and pathogen. Natural selection on host resistance and pathogen virulence may therefore play a role in host-pathogen cycles, but this possibility is almost never allowed for in standard theory. This conceptual issue is of great applied importance in the case of out breaking forest insects, such as the gypsy moth that the Pi's propose to study. Like many forest insects, the gypsy moth undergoes huge swings in density, and the economic damage caused by peak populations is reduced by epizootics (= epidemics in animal species) of fatal baculoviruses. Because insect larvae are not very mobile, and because the biology of baculovirus transmission is relatively simple, it is possible to accurately quantify baculovirus transmission using small-scale experiments in the field. The PI and colleagues therefore propose to use the gypsy moth-baculovirus interaction as a model experimental system to ask, does the evolution of host resistance or pathogen virulence play a significant role in insect outbreaks? To answer this question, they will first carry out small scale experiments that disentangle components of host and pathogen fitness. These experiments will focus on estimating the parameters of a range of mathematical models, each making different assumptions about the effects of natural selection on resistance and virulence. Based on these parameter values, the models will be used to generate predictions, and the model predictions will be tested through comparison to data on outbreaks in nature. If models that include host or pathogen evolution do a better job of explaining data on outbreaks, the Pi's will conclude that host and pathogen evolution play a role in insect outbreaks in nature.

Public Health Relevance

Mathematical models are widely used to predict the circumstances under which hosts and pathogens will evolve in response to each other, a phenomenon known as coevolution. Existing coevolutioriary models, however, have not been tested with data. In the proposed research, the scientists will use insect viruses as a model system for testing mathematical models of coevolution.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BDA-A (80))
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Eckstrand, Irene A
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University of Chicago
Schools of Medicine
United States
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Kennedy, David A; Dukic, Vanja; Dwyer, Greg (2014) Pathogen growth in insect hosts: inferring the importance of different mechanisms using stochastic models and response-time data. Am Nat 184:407-23
Elderd, Bret D; Rehill, Brian J; Haynes, Kyle J et al. (2013) Induced plant defenses, host-pathogen interactions, and forest insect outbreaks. Proc Natl Acad Sci U S A 110:14978-83
Elderd, Bret D; Dwyer, Greg; Dukic, Vanja (2013) Population-level differences in disease transmission: a Bayesian analysis of multiple smallpox epidemics. Epidemics 5:146-56