Paulo R. A. Campos

Can intra-Y gene conversion oppose the degeneration of the human Y chromosome? A simulation study

The human Y is a genetically degenerate chromosome, which has lost about 97% of the genes originally present. Most of the remaining human Y genes are in large duplicated segments (ampliconic regions) undergoing intense Y–Y gene conversion. It has been suggested that Y–Y gene conversion may help these genes getting rid of deleterious mutations that would inactivate them otherwise. Here, we tested this idea by simulating the evolution of degenerating Y chromosomes with or without gene conversion using the most up-to-date population genetics parameters for humans. We followed the fate of a variant with Y–Y gene conversion in a population of Y chromosomes where Y–Y gene conversion is originally absent. We found that this variant gets fixed more frequently than the neutral expectation, which supports the idea that gene conversion is beneficial for a degenerating Y chromosome. Interestingly, a very high rate of gene conversion is needed for an effect of gene conversion to be observed. This suggests that high levels of Y-Y gene conversion observed in humans may have been selected to oppose the Y degeneration. We also studied with a similar approach the evolution of ampliconic regions on the Y chromosomes and found that the fixation of many copies at once is unlikely, which suggest these regions probably evolved gradually unless selection for increased dosage favored large-scale duplication events. Exploring the parameter space showed that Y–Y gene conversion may be beneficial in most mammalian species, which is consistent with recent data in chimpanzees and mice.

Sex and Deleterious Mutations

The evolutionary advantage of sexual reproduction has been considered as one of the most pressing questions in evolutionary biology. While a pluralistic view of the evolution of sex and recombination has been suggested by some, here we take a simpler view and try to quantify the conditions under which sex can evolve given a set of minimal assumptions. Since real populations are finite and also subject to recurrent deleterious mutations, this minimal model should apply generally to all populations. We show that the maximum advantage of recombination occurs for an intermediate value of the deleterious effect of mutations. Furthermore we show that the conditions under which the biggest advantage of sex is achieved are those that produce the fastest fitness decline in the corresponding asexual population and are therefore the conditions for which Mullers ratchet has the strongest effect. We also show that the selective advantage of a modifier of the recombination rate depends on its strength. The quantification of the range of selective effects that favors recombination then leads us to suggest that, if in stressful environments the effect of deleterious mutations is enhanced, a connection between sex and stress could be expected, as it is found in several species.

Adaptive evolution in a spatially structured asexual population.

We study the process of adaptation in a spatially structured asexual haploid population. The model assumes a local competition for replication, where each organism interacts only with its nearest neighbors. We observe that the substitution rate of beneficial mutations is smaller for a spatially structured population than that seen for populations without structure. The difference between structured and unstructured populations increases as the adaptive mutation rate increases. Furthermore, the substitution rate decreases as the number of neighbors for local competition is reduced. We have also studied the impact of structure on the distribution of adaptive mutations that fix during adaptation.

The effect of spatial structure on adaptation in Escherichia coli

Populations of organisms are generally organized in a given spatial structure. However, the vast majority of population genetic studies are based on populations in which every individual competes globally. Here we use experimental evolution in Escherichia coli to directly test a recently made prediction that spatial structure slows down adaptation and that this cost increases with the mutation rate. This was studied by comparing populations of different mutation rates adapting to a liquid (unstructured) medium with populations that evolved in a Petri dish on solid (structured) medium. We find that mutators adapt faster to both environments and that adaptation is slower if there is spatial structure. We observed no significant difference in the cost of structure between mutator and wild-type populations, which suggests that clonal interference is intense in both genetic backgrounds.

THE ADAPTATION RATE OF ASEXUALS: DELETERIOUS MUTATIONS, CLONAL INTERFERENCE AND POPULATION BOTTLENECKS

The rate at which a population adapts to its environment is a cornerstone of evolutionary theory, and recent experimental advances in microbial populations have renewed interest in predicting and testing this rate. Efforts to understand the adaptation rate theoretically are complicated by high mutation rates, to both beneficial and deleterious mutations, and by the fact that beneficial mutations compete with each other in asexual populations (clonal interference). Testable predictions must also include the effects of population bottlenecks, repeated reductions in population size imposed by the experimental protocol. In this contribution, we integrate previous work that addresses each of these issues, developing an overall prediction for the adaptation rate that includes: beneficial mutations with probabilistically distributed effects, deleterious mutations of arbitrary effect, population bottlenecks, and clonal interference.

Pathogen genetic variation in small-world host contact structures

We introduce a model for assessing the levels and patterns of genetic diversity in pathogen populations, whose epidemiology follows a susceptible–infected–susceptible model. We assume a population which is structured into many small subpopulations (hosts) that exchange migrants (transmission) between their neighbours. We consider that the hosts are connected according to a small-world network topology, and in this way our model interpolates between two classical population genetics models: the stepping-stone and the island model. We have observed that the level of diversity has a maximum at intermediate values of the basic reproductive number R0. This result is independent of the topology considered, but depends on the relation between parasite load and the rate at which the immune system clears the pathogen. We show that, for a given R0 of the pathogen, as the host contact structure changes, by increasing the rewiring probability p, the level of pathogen diversity decreases. Its level is higher in regular lattices and smaller in random graphs. The latter topology presents a similar diversity level to the island model (a fully connected network), but also presents a clear pattern of isolation by distance, which is observed in some pathogen populations.

Bounded fitness landscapes and the evolution of the linguistic diversity

We have recently introduced a simple spatial computer simulation model to study the evolution of the linguistic diversity. The model considers processes of selective geographic colonization, linguistic anomalous diffusion and mutation. In the approach, we ascribe to each language a fitness function which depends on the number of people that speak that language. Here, we extend the aforementioned model to examine the role of saturation of the fitness on the language dynamics. We found that the dependence of the linguistic diversity on the area after colonization displays a power law regime with a nontrivial exponent in very good agreement with the measured exponent associated with the actual distribution of languages on the Earth.

Excitable scale free networks

Abstract.When a simple excitable system is continuously stimulated by a Poissonian external source, the response function (mean activity versus stimulus rate) generally shows a linear saturating shape. This is experimentally verified in some classes of sensory neurons, which accordingly present a small dynamic range (defined as the interval of stimulus intensity which can be appropriately coded by the mean activity of the excitable element), usually about one or two decades only. The brain, on the other hand, can handle a significantly broader range of stimulus intensity, and a collective phenomenon involving the interaction among excitable neurons has been suggested to account for the enhancement of the dynamic range. Since the role of the pattern of such interactions is still unclear, here we investigate the performance of a scale-free (SF) network topology in this dynamic range problem. Specifically, we study the transfer function of disordered SF networks of excitable Greenberg-Hastings cellular automata. We observe that the dynamic range is maximum when the coupling among the elements is critical, corroborating a general reasoning recently proposed. Although the maximum dynamic range yielded by general SF networks is slightly worse than that of random networks, for special SF networks which lack loops the enhancement of the dynamic range can be dramatic, reaching nearly five decades. In order to understand the role of loops on the transfer function we propose a simple model in which the density of loops in the network can be gradually increased, and show that this is accompanied by a gradual decrease of dynamic range.

Genetic Diversity in the SIR Model of Pathogen Evolution

We introduce a model for assessing the levels and patterns of genetic diversity in pathogen populations, whose epidemiology follows a susceptible-infected-recovered model (SIR). We model the population of pathogens as a metapopulation composed of subpopulations (infected hosts), where pathogens replicate and mutate. Hosts transmit pathogens to uninfected hosts. We show that the level of pathogen variation is well predicted by analytical expressions, such that pathogen neutral molecular variation is bounded by the level of infection and increases with the duration of infection. We then introduce selection in the model and study the invasion probability of a new pathogenic strain whose fitness (R 0(1+s)) is higher than the fitness of the resident strain (R 0). We show that this invasion probability is given by the relative increment in R 0 of the new pathogen (s). By analyzing the patterns of genetic diversity in this framework, we identify the molecular signatures during the replacement and compare these with those observed in sequences of influenza A.

Patterns of genetic variation in populations of infectious agents

BackgroundThe analysis of genetic variation in populations of infectious agents may help us understand their epidemiology and evolution. Here we study a model for assessing the levels and patterns of genetic diversity in populations of infectious agents. The population is structured into many small subpopulations, which correspond to their hosts, that are connected according to a specific type of contact network. We considered different types of networks, including fully connected networks and scale free networks, which have been considered as a model that captures some properties of real contact networks. Infectious agents transmit between hosts, through migration, where they grow and mutate until elimination by the host immune system.ResultsWe show how our model is closely related to the classical SIS model in epidemiology and find that: depending on the relation between the rate at which infectious agents are eliminated by the immune system and the within host effective population size, genetic diversity increases with R0 or peaks at intermediate R0 levels; patterns of genetic diversity in this model are in general similar to those expected under the standard neutral model, but in a scale free network and for low values of R0 a distortion in the neutral mutation frequency spectrum can be observed; highly connected hosts (hubs in the network) show patterns of diversity different from poorly connected individuals, namely higher levels of genetic variation, lower levels of genetic differentiation and larger values of Tajimas D.ConclusionWe have found that levels of genetic variability in the population of infectious agents can be predicted by simple analytical approximations, and exhibit two distinct scenarios which are met according to the relation between the rate of drift and the rate at which infectious agents are eliminated. In one scenario the diversity is an increasing function of the level of transmission and in a second scenario it is peaked around intermediate levels of transmission. This is independent of the type of host contact structure. Furthermore for low values of R0, very heterogeneous host contact structures lead to lower levels of diversity.