Selection Mosaics or environmental interactions

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

Vale, P., & Little, T. (2009). Measuring parasite fitness under genetic and thermal variation Heredity DOI: 10.1038/hdy.2009.54

Universal understanding of host-parasite adaptation

We recently read a theory paper by Gandon and Day (2009). In this paper they describe a valuable method for dissecting how interactions between a host and parasite alter mean fitness. Their method uses an understanding built from Fisher's fundamental theorem. They partition changes in mean fitness based on three different factors: natural selection, environmental change, and mutation. We know that the rate of adaptation is going to result from the amount of genetic variance in the focal organism (Fisher's theorem), but what about the impact of an interacting species that evolves as well (i.e. a coevolving parasite? Here is the real beauty of their analysis because the coevolving species becomes the environment. By separating the changes in a population mean fitness into changes driven by different forces, the authors provide not only a mathematically useful model, but also a useful intuition for understanding how hosts and parasites coevolve.

There are several ways that theoreticians often describe a host-parasite interaction (e.g. gene-for-gene, matching alleles) and these describe natural systems to some degree of accuracy. The authors use their method to analyze some recent empirical evidence (Buckling and Rainey 2002; Decaestecker et al 2007). They use the time series data on the interaction to test hypotheses of the nature of the interaction. These empirical studies compare the fitness of parasites against hosts from the past that they have coevolved with and those from the future (hosts that evolve later in the study). By making these comparisons, they have the ability to hold other factors constant (the genetic variance of the parasite population) and vary the environment (the hosts). Their model makes different predictions that should be evident from empirical evidence about how parasite mean fitness should change when the environment is varied.

The authors very elegant proposed method of looking at changes over time works well for systems where archives of past populations are possible as in experimental evolution systems (Buckling and Rainey 2002) or clever natural systems (Decaestecker et al 2007), but what about the rest of us? Addressed in at the very end, but only briefly, is a comparison of spatial patterns of coevolution when temporal data is missing. I think this issue of substituting space for time is potentially very powerful, but also somewhat more complicated. Temporal samples of a coevolutionary system could be predicted to have a certain amount of autocorrelation, but does this hold for spatially distributed systems. It certainly would nice to assume that there is a relationship between distance and time and this will of course depend on gene flow. How would selection mosaics (Gomulkiewicz et al 2007; Thompson 1999, 2005) impact this potential relationship? I look forward to future research as it provides some answers.

References

Buckling, A., and P. B. Rainey. 2002. Antagonistic coevolution between a bacterium and a bacteriophage. P Roy Soc Lond B Bio 269:931-936.

Decaestecker, E., S. Gaba, J. A. M. Raeymaekers, R. Stoks, L. Van Kerckhoven, D. Ebert, and L. De Meester. 2007. Host-parasite 'Red Queen' dynamics archived in pond sediment. Nature 450:870-873.

Gandon, S., and T. Day. 2009. Evolutionary epidemiology and the dynamics of adaptation. Evolution 63:826-838.

Gomulkiewicz, R., D. M. Drown, M. F. Dybdahl, W. Godsoe, S. L. Nuismer, K. M. Pepin, B. J. Ridenhour, C. I. Smith, and J. B. Yoder. 2007. Dos and don'ts of testing the geographic mosaic theory of coevolution. Heredity 98:249-258.

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.

Paper read

Gandon, S., & Day, T. (2009). EVOLUTIONARY EPIDEMIOLOGY AND THE DYNAMICS OF ADAPTATION Evolution, 63 (4), 826-838 DOI: 10.1111/j.1558-5646.2009.00609.x