The effect of host heterogeneity and parasite intragenomic interactions on parasite population structure

Understanding the processes that shape the genetic structure of parasite populations and the functional consequences of different parasite genotypes is critical for our ability to predict how an infection can spread through a host population and for the design of effective vaccines to combat infection and disease. Here, we examine how the genetic structure of parasite populations responds to host genetic heterogeneity. We consider the well-characterized molecular specificity of major histocompatibility complex binding of antigenic peptides to derive deterministic and stochastic models. We use these models to ask, firstly, what conditions favour the evolution of generalist parasite genotypes versus specialist parasite genotypes? Secondly, can parasite genotypes coexist in a population? We find that intragenomic interactions between parasite loci encoding antigenic peptides are pivotal in determining the outcome of evolution. Where parasite loci interact synergistically (i.e. the recognition of additional antigenic peptides has a disproportionately large effect on parasite fitness), generalist parasite genotypes are favoured. Where parasite loci act multiplicatively (have independent effects on fitness) or antagonistically (have diminishing effects on parasite fitness), specialist parasite genotypes are favoured. A key finding is that polymorphism is not stable and that, with respect to functionally important antigenic peptides, parasite populations are dominated by a single genotype

Divergent selection on locally adapted major histocompatibility complex immune genes experimentally proven in the field

Although crucial for the understanding of adaptive evolution, genetically resolved examples of local adaptation are rare. To maximize survival and reproduction in their local environment, hosts should resist their local parasites and pathogens. The major histocompatibility complex (MHC) with its key function in parasite resistance represents an ideal candidate to investigate parasite-mediated local adaptation. Using replicated field mesocosms, stocked with second-generation lab-bred three-spined stickleback hybrids of a lake and a river population, we show local adaptation of MHC genotypes to population-specific parasites, independently of the genetic background. Increased allele divergence of lake MHC genotypes allows lake fish to fight the broad range of lake parasites, whereas more specific river genotypes confer selective advantages against the less diverse river parasites. Hybrids with local MHC genotype gained more body weight and thus higher fitness than those with foreign MHC in either habitat, suggesting the evolutionary significance of locally adapted MHC genotypes

Robust Inference of Trees

This paper is concerned with the reliable inference of optimal tree-approximations to the dependency structure of an unknown distribution generating data. The traditional approach to the problem measures the dependency strength between random variables by the index called mutual information. In this paper reliability is achieved by Walley's imprecise Dirichlet model, which generalizes Bayesian learning with Dirichlet priors. Adopting the imprecise Dirichlet model results in posterior interval expectation for mutual information, and in a set of plausible trees consistent with the data. Reliable inference about the actual tree is achieved by focusing on the substructure common to all the plausible trees. We develop an exact algorithm that infers the substructure in time O(m^4), m being the number of random variables. The new algorithm is applied to a set of data sampled from a known distribution. The method is shown to reliably infer edges of the actual tree even when the data are very scarce, unlike the traditional approach. Finally, we provide lower and upper credibility limits for mutual information under the imprecise Dirichlet model. These enable the previous developments to be extended to a full inferential method for trees.Comment: 26 pages, 7 figure

Three-spined stickleback armour predicted by body size, minimum winter temperature and pH

Similar phenotypes evolve under equivalent environmental conditions through parallel evolution. Because they have repeatedly invaded and adapted to new freshwater environments, the three-spined stickleback (Gasterosteus aculeatus) offers a powerful system for understanding the agents of selection in nature that drive parallel evolution. Here we examine the ecological and environmental variables responsible for morphological variation in three-spined stickleback populations across its European range. We collected fish from 85 populations, encompassing much of the European latitudinal range of the species and including lowland rivers and lakes, coastal lagoons, and moorland ponds. We measured biotic and environmental variables at all sites along with morphological traits for 2,358 individuals. Using an information theory approach, we identified body size, minimum average winter temperature and pH as primary predictors of stickleback armour evolution, challenging current hypotheses for stickleback morphological diversification and demonstrating the fundamental role played by body size and scaling in mediating responses to selection. Stickleback lateral plate phenotype represents a potentially powerful tool for monitoring change in climate variables across the northern temperate region

Species-level selection reduces selfishness through competitive exclusion.

Adaptation does not necessarily lead to traits which are optimal for the population. This is because selection is often the strongest at the individual or gene level. The evolution of selfishness can lead to a 'tragedy of the commons', where traits such as aggression or social cheating reduce population size and may lead to extinction. This suggests that species-level selection will result whenever species differ in the incentive to be selfish. We explore this idea in a simple model that combines individual-level selection with ecology in two interacting species. Our model is not influenced by kin or trait-group selection. We find that individual selection in combination with competitive exclusion greatly increases the likelihood that selfish species go extinct. A simple example of this would be a vertebrate species that invests heavily into squabbles over breeding sites, which is then excluded by a species that invests more into direct reproduction. A multispecies simulation shows that these extinctions result in communities containing species that are much less selfish. Our results suggest that species-level selection and community dynamics play an important role in regulating the intensity of conflicts in natural populations

Mathematical Manipulative Models: In Defense of Beanbag Biology

Mathematical manipulative models have had a long history of influence in biological research and in secondary school education, but they are frequently neglected in undergraduate biology education. By linking mathematical manipulative models in a four-step process-1) use of physical manipulatives, 2) interactive exploration of computer simulations, 3) derivation of mathematical relationships from core principles, and 4) analysis of real data sets-we demonstrate a process that we have shared in biological faculty development workshops led by staff from the BioQUEST Curriculum Consortium over the past 24 yr. We built this approach based upon a broad survey of literature in mathematical educational research that has convincingly demonstrated the utility of multiple models that involve physical, kinesthetic learning to actual data and interactive simulations. Two projects that use this approach are introduced: The Biological Excel Simulations and Tools in Exploratory, Experiential Mathematics (ESTEEM) Project (http://bioquest.org/esteem) and Numerical Undergraduate Mathematical Biology Education (NUMB3R5 COUNT; http://bioquest.org/numberscount). Examples here emphasize genetics, ecology, population biology, photosynthesis, cancer, and epidemiology. Mathematical manipulative models help learners break through prior fears to develop an appreciation for how mathematical reasoning informs problem solving, inference, and precise communication in biology and enhance the diversity of quantitative biology education

Origin of Life

The evolution of life has been a big enigma despite rapid advancements in the fields of biochemistry, astrobiology, and astrophysics in recent years. The answer to this puzzle has been as mind-boggling as the riddle relating to evolution of Universe itself. Despite the fact that panspermia has gained considerable support as a viable explanation for origin of life on the Earth and elsewhere in the Universe, the issue remains far from a tangible solution. This paper examines the various prevailing hypotheses regarding origin of life like abiogenesis, RNA World, Iron-sulphur World, and panspermia; and concludes that delivery of life-bearing organic molecules by the comets in the early epoch of the Earth alone possibly was not responsible for kick-starting the process of evolution of life on our planet.Comment: 32 pages, 8 figures,invited review article, minor additio