Hammerschmidt and colleagues (2009) recently published an empirical investigation of optimal host switching. Parasites that must infect multiple hosts to complete their life cycle face a complex set of challenges. One of these is determining the timing of the switch. The authors of this paper look at the trade-off involved in staying in an intermediate host so as to become larger and more fecund in the next host and the increased chance of mortality in the current host. The authors conduct two different experiments with a tapeworm parasite, Schistocephalus solidus. In one experiment they examined the behavior of the first intermediate host, cyclopoid copepods (Macrocyclops albidus). In the second experiment they directly measured differences in fecundity among different host switch timing between the first and second intermediate hosts (in this case the three-spine stickleback, Gasterosteus aculeatus). The authors also build an optimality model and use the data from these experiments as well as some previously published data to confirm that the switch from the first to second host occurs at an optimal time for parasite fecundity.
What was most novel about this paper to me was the modification of the host behavior that had the effect of reducing parasite transmission, at least in the short run. Since the parasite was transmitted trophically, the next host eats the previous host, predation enhancement or avoidance directly influences the rate of transmission. The authors found some evidence of predation enhancement after the optimal switch time, but the stronger evidence was at least a shift in behavior of the current host. Before the parasite is mature in the first intermediate host, or before the optimal switching time to the second intermediate host, there was a reduction in movement which translates into predator avoidance behavior. Manipulating the host so as to allow the parasite a longer time to grow is a very clever strategy. In hosts that have a high potential mortality, this strategy may be found among a diversity of trophically transmitted parasites.
Reference
Hammerschmidt, K., K. Koch, M. Milinski, J. C. Chubb, and G. A. Parker. 2009. When to go: Optimization of host switching in parasites with complex life cycles. Evolution 63:1976-1986.
Vale and Little (2009) published recent work on parasite infection variation across a temperature gradient. Specific parasite infections are often the result of genetic interactions of both the host and parasite, sometimes referred to as genotype by genotype interactions (GxG). The authors of this paper used an ideal interaction between Daphnia magna and a bacterial parasite, Pasteuria ramose. The experiment was such that they could test multiple levels on interactions. They isolated multiple host clonal lines (n = 4) as well as parasite lines (n = 4) and compared infection rates as well as parasite growth rates across three different temperatures. The paper details the experiment very well, so I'll spare details here, but a good model for future studies.
The authors found significant GxG interactions for most of the traits measured in the infection process, including both early (probability of infection) and later (parasite growth rate). However differences in genotype by environment (GxE) interactions showed up for different places in the infection timeline. The probability of infection showed a host genotype by temperature interaction, but this was a weak affect and the authors make the important point that the relative rank order wasn't changed. The reason this is key is that it is often emphasized that GxE interactions are a mechanism of the maintenance of different genotypes. If each genotype has high fitness in only some environments, and the environment varies, then there can be some period of time where polymorphism is maintained. In terms of interactions of the parasite genotype and the environment, there were initially some interactions with transmission potential and growth rate, however rank differences were again absent. The paper makes one further step and examines the combined transmission potential (spore production and infectivity). This isn't quite a measure of R0 because of complications with the effect of dose on infection rate and the interaction between parasite genotype and temperature disappears.
The study failed to find evidence of a GxGxE interaction, but the authors were correct to point out, that this is only the case for the environmental variable measured (temperature). Given that temperature is an important component of the environment for this interaction, I was surprised by this result. Perhaps, it would have been different if the difference were not just in constant temperature, but in some sort of variable environment. In the very last paragraph, Vale and Little (2009) emphasize that the lack of GxGxE interactions mean that the specificity of the interactions are robust to environmental noise. However, it is just such noise that others have proposed as important in maintaining variation. These interactions are the selection mosaics in the Geographic Mosaic Theory of Coevolution (Thompson 1999, 2005).
References
Thompson, J. N. 1999. Specific hypotheses on the geographic mosaic of coevolution. American Naturalist 153:S1-S14.
Thompson, J. N. 2005.
The Geographic Mosaic of Coevolution. University of Chicago Press, Chicago.
Vale, P. F., and T. J. Little. 2009. Measuring parasite fitness under genetic and thermal variation. Heredity online early.
Paper read
This week we continued along our current path of pathogen models and looked at a recent paper (Sorrell et al 2009) investigating covert infections, a common and unexplained phenomenon of some pathogens exhibiting long periods of infection where they are silent (or covert in the language of the paper). During this silent/covert stage, the infections are mostly avirulent and non-infectious. These authors extend a previous SI type model that incorporated a covert state (Boots et al 2003) to understand what forces select for this kind of pathogen.
Extending a previous SI model (Boots et al 2003), the authors build a two strain model that includes susceptible hosts and multiple classes of infected hosts. With two strains, there are two broad types of infected hosts. Each of these is split again. The hosts can carry a covert infection or an overt infection. Covert infections are allowed to become overt but not the other way around. There are multiple trade-offs built into this model. A covert infection does not cause an increased host death rate (avirulent), but it does impose a cost to host fecundity where as an overt infection is virulent but does not decrease fecundity. In addition, covert infections are only transmitted vertically (from parent to offspring), while on the other hand overt infections are only transmitted horizontally (among individuals within the population).
Without additional forces, they find no selection for covert infections. However, given the abundance of such pathogens in nature, there must be some forces that are generating the proper conditions. The paper explores three different mechanisms that may be involved in selection for covert infections. The first examines the effect of superinfection (multiple pathogen strains in the same host). They conclude that selection will favor covert infections that are protective, that is they prevent superinfection. The other two mechanisms consider nonequilibrium host dynamics, temporal variation in host density and transmission. When variation is high and the potential to be lost from the population because of a lack of hosts or a lack of transmission events, then covert infections which again are vertically transmitted become more likely.
A question that was brought up during our discussion was: are these results different from a horizontal vertical transmission trade-off? When transmission opportunities are likely (high populations), then horizontally transmitting virulent pathogens are favored. In situations when there are fewer opportunities (e.g. during host population declines), then a pathogen that retains some vertical transmission and will be favored. Favoring a more covert pathogen is really just selecting for these two fixed trade-offs. I think what this paper contributes thought is a more thorough mechanistic explanation for how this trade-off works. They provide many biological examples of pathogens with complex covert behavior and this study certainly provides evidence of how they may have arisen.
This paper was quite interesting to me in that it was the first adaptive dynamics analysis that I've really understood. The authors walk through their methods and explain how to read the pairwise invisibility plots (PIPs) and provide some helpful but uncomplicated simulations too. Recently Dercole and Rinaldi (2008) published an introduction to this modeling/analysis technique that I'm looking forward to reading in the near future.
References
Boots, M., J. Greenman, D. Ross, R. Norman, R. Hails, and S. Sait. 2003. The population dynamical implications of covert infections in host-microparasite interactions. Journal of Animal Ecology 72:1064-1072.
Dercole, F., and S. Rinaldi. 2008. Analysis of Evolutionary Processes: The Adaptive Dynamics Approach and its Applications. Princeton University Press, Princeton.
Sorrell, I., A. White, A. B. Pedersen, R. S. Hails, and M. Boots. 2009. The evolution of covert, silent infection as a parasite strategy. Proceedings of the Royal Society B: Biological Sciences: online early.
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