Evolution: the evolvability enigma

A report that a switch of a yeast protein to a 'prion' state triggers diverse phenotypic changes has prompted re-examination of the processes of evolution. To what extent should processes of gene expression and control be interpreted in terms of their capacity to allow future evolution as well as present adaptation?     

Pleiotropic mutation, modularity and evolvability

The relationship between pleiotropy and the rate of evolution of a phenotypic character (evolvability) in a population is explored using computer simulations. I present results that suggest the rate of evolution of a phenotypic character may not decline when that character is pleiotropically associated to an increasing number of other characters, provided that the characters are under pure directional selection such that they are far from their optima relative to the average magnitude of a mutation. These conditions may be relevant during adaptive radiations. Adding pleiotropic associations to a set of characters in which one is under directional selection and the other is under stabilizing selection increases the rate of adaptation of the character under directional selection provided that the new characters that come to be pleiotropically associated are under directional selection. Thus, increasing the number of pleiotropic associations under these conditions increases the rate of adaptation of a character.     

Evolution of evolvability in gene regulatory networks

Gene regulatory networks are perhaps the most important organizational level in the cell where signals from the cell state and the outside environment are integrated in terms of activation and inhibition of genes. For the last decade, the study of such networks has been fueled by large-scale experiments and renewed attention from the theoretical field. Different models have been proposed to, for instance, investigate expression dynamics, explain the network topology we observe in bacteria and yeast, and for the analysis of evolvability and robustness of such networks. Yet how these gene regulatory networks evolve and become evolvable remains an open question.     

Enhancing evolvability with mutation buffering mediated through multiple weak interactions

The evolutionary adaptability of a system is dependent on three organizational properties, self-organizing dynamics that are hierarchically organized, component redundancy, and multiple weak interactions [Towards high evolvability dynamics, in: G. van de Vijver, S. Salthe, M. Delpos (Eds.), Evolutionary Systems, Kluwer Academic Publishers, Dordrecht, 1998, pp. 147-169]. This study reports on the use of the dual dynamics network model as an aid in understanding the role multiple weak interactions play in enhancing evolutionary adaptability. Dual dynamics networks are self-organizing systems that consist of simple components that change local state due to the coupled influences from connected components exerting strong discrete decision-making influences and from groups of components exerting multiple weak influences [J. Theor. Biol. 193 (1998) 287]. The dual dynamics model has been enhanced to support investigations of properties relevant to a system's capacity for evolvability, such as structure-function relationships, neutrality, adaptive tolerance, and evolutionary search performance.Three network types are investigated, each utilizing a different method of coupling strong and weak influences. The results demonstrate that the manner of coupling multiple weak interactions into the systems dynamics significantly affects the structure-function maps and the consequent evolvability characteristics. Specifically it is found that a form of coupling, denoted as linear modulation, enhances evolutionary adaptability. Linear modulation coupling requires that the weak interactions be integrated with strong interactions in a manner that implies a linear ordered relation between the possible state values of the components of the systems. When coupling functions that do not imply such an ordering of local state values are used, evolutionary adaptability is decreased.     

Evolution of adaptive phenotypic variation patterns by direct selection for evolvability

A basic assumption of the Darwinian theory of evolution is that heritable variation arises randomly. In this context, randomness means that mutations arise irrespective of the current adaptive needs imposed by the environment. It is broadly accepted, however, that phenotypic variation is not uniformly distributed among phenotypic traits, some traits tend to covary, while others vary independently, and again others barely vary at all. Furthermore, it is well established that patterns of trait variation differ among species. Specifically, traits that serve different functions tend to be less correlated, as for instance forelimbs and hind limbs in bats and humans, compared with the limbs of quadrupedal mammals. Recently, a novel class of genetic elements has been identified in mouse gene-mapping studies that modify correlations among quantitative traits. These loci are called relationship loci, or relationship Quantitative Trait Loci (rQTL), and affect trait correlations by changing the expression of the existing genetic variation through gene interaction. Here, we present a population genetic model of how natural selection acts on rQTL. Contrary to the usual neo-Darwinian theory, in this model, new heritable phenotypic variation is produced along the selected dimension in response to directional selection. The results predict that selection on rQTL leads to higher correlations among traits that are simultaneously under directional selection. On the other hand, traits that are not simultaneously under directional selection are predicted to evolve lower correlations. These results and the previously demonstrated existence of rQTL variation, show a mechanism by which natural selection can directly enhance the evolvability of complex organisms along lines of adaptive change.     

Natural selection fails to optimize mutation rates for long-term adaptation on rugged fitness landscapes

The rate of mutation is central to evolution. Mutations are required for adaptation, yet most mutations with phenotypic effects are deleterious. As a consequence, the mutation rate that maximizes adaptation will be some intermediate value. Here, we used digital organisms to investigate the ability of natural selection to adjust and optimize mutation rates. We assessed the optimal mutation rate by empirically determining what mutation rate produced the highest rate of adaptation. Then, we allowed mutation rates to evolve, and we evaluated the proximity to the optimum. Although we chose conditions favorable for mutation rate optimization, the evolved rates were invariably far below the optimum across a wide range of experimental parameter settings. We hypothesized that the reason that mutation rates evolved to be suboptimal was the ruggedness of fitness landscapes. To test this hypothesis, we created a simplified landscape without any fitness valleys and found that, in such conditions, populations evolved near-optimal mutation rates. In contrast, when fitness valleys were added to this simple landscape, the ability of evolving populations to find the optimal mutation rate was lost. We conclude that rugged fitness landscapes can prevent the evolution of mutation rates that are optimal for long-term adaptation. This finding has important implications for applied evolutionary research in both biological and computational realms.     

Evolutionary changes in mutation rates and spectra and their influence on the adaptation of pathogens

The evolutionary tuning of mutational processes may play a key role in prokaryotic evolution, particularly among pathogens. In this paper we review the evidence that genetic systems controlling the rate and spectrum of heritable mutations have evolved to optimize levels of adaptive variation and rates of genetic change.     

Cold adaptation of enzyme reaction rates

A major issue for organisms living at extreme temperatures is to preserve both stability and activity of their enzymes. Cold-adapted enzymes generally have a reduced thermal stability, to counteract freezing, and show a lower enthalpy and a more negative entropy of activation compared to mesophilic and thermophilic homologues. Such a balance of thermodynamic activation parameters can make the reaction rate decrease more linearly, rather than exponentially, as the temperature is lowered, but the structural basis for rate optimization toward low working temperatures remains unclear. In order to computationally address this problem, it is clear that reaction simulations rather than standard molecular dynamics calculations are needed. We have thus carried out extensive computer simulations of the keto-enol(ate) isomerization steps in differently adapted citrate synthases to explore the structure-function relationships behind catalytic rate adaptation to different temperatures. The calculations reproduce the absolute rates of the psychrophilic and mesophilic enzymes at 300 K, as well as the lower enthalpy and more negative entropy of activation of the cold-adapted enzyme, where the latter simulation result is obtained from high-precision Arrhenius plots. The overall catalytic effect originates from electrostatic stabilization of the transition state and enolate and the reduction of reorganization free energy. The simulations, however, show psychrophilic, mesophilic, and hyperthermophilic citrate synthases to have increasingly stronger electrostatic stabilization of the transition state, while the energetic penalty in terms of internal protein interactions follows the reverse order with the cold-adapted enzyme having the most favorable energy term. The lower activation enthalpy and more negative activation entropy observed for cold-adapted enzymes are found to be associated with a decreased protein stiffness. The origin of this effect is, however, not localized to the active site but to other regions of the protein structure.     

Environmental tuning of mutation rates

Through their life cycles, bacteria experience many different environments in which the relationship between available energy resources and the frequency and the nature of various stresses is highly variable. In order to survive in such changeable environments, bacteria must balance the need for nutritional competence with stress resistance. In Escherichia coli natural populations, this is most frequently achieved by changing the regulation of the RpoS sigma factor-dependent general stress response. One important secondary consequence of altered regulation of the RpoS regulon is the modification of mutation rates. For example, under nutrient limitation during stationary phase, the high intracellular concentration of RpoS diminishes nutritional competence, increases stress resistance, and, by downregulating the mismatch repair system and upregulating [corrected] the expression of the dinB gene (coding for PolIV translesion synthesis polymerase) increases mutation rates. The reduction of the intracellular concentration of RpoS has exactly opposite effects on nutritional competence, stress resistance and mutation rates. Therefore, the natural selection that favours variants having the highest fitness under different environmental conditions results in high variability of stress-associated mutation rates in those variants.     

Evolution of cardiovascular adaptation to gravity

The present paper considers the evolutionary responses of vertebrates to gravitational stressors acting on the cardiovascular system. Inasmuch as the range of published studies addressing gravity's effects on cardiovascular function is limited to relatively few taxa, the perspective developed here is based largely on studies of snakes and a few mammals. A brief review and synthesis of the available data suggest several generalizations concerning the evolutionary responses of form and function to gravity's stress on blood circulation.     

Evolution of stepping-stone dispersal rates

We present a general model of the evolution of dispersal in a population with any distribution of dispersal distance. We use this model to analyse evolutionarily stable (ES) dispersal rates for the classical island model of dispersal and for three different stepping-stone models. Using general techniques to compute relatedness coefficients in the different dispersal models which we consider, we find that the distribution of dispersal distance may affect the ES dispersal rate when the cost of dispersal is low. In this case the ES dispersal rate increases with the number of demes that can be reached by one dispersal event. However, for increasing cost the ES dispersal rate converges to a value independent of the distribution of dispersal distance. These results are in contrast to previous analyses of similar models. The effects of the size (number of demes) and shape (ratio between the width and the length) of the population on the evolution of dispersal are also studied. We find that larger and more elongated populations lead generally to higher ES dispersal rates. However, both of these effects can only be observed for extreme parameter values (i.e. for very small and very elongated populations). The direct fitness method and the analytical techniques used here to compute relatedness coefficients provide an efficient way to analyse ES strategies in subdivided populations.     

Likelihood-based estimation of microsatellite mutation rates

Microsatellites are widely used in genetic analyses, many of which require reliable estimates of microsatellite mutation rates, yet the factors determining mutation rates are uncertain. The most straightforward and conclusive method by which to study mutation is direct observation of allele transmissions in parent-child pairs, and studies of this type suggest a positive, possibly exponential, relationship between mutation rate and allele size, together with a bias toward length increase. Except for microsatellites on the Y chromosome, however, previous analyses have not made full use of available data and may have introduced bias: mutations have been identified only where child genotypes could not be generated by transmission from parents' genotypes, so that the probability that a mutation is detected depends on the distribution of allele lengths and varies with allele length. We introduce a likelihood-based approach that has two key advantages over existing methods. First, we can make formal comparisons between competing models of microsatellite evolution; second, we obtain asymptotically unbiased and efficient parameter estimates. Application to data composed of 118,866 parent-offspring transmissions of AC microsatellites supports the hypothesis that mutation rate increases exponentially with microsatellite length, with a suggestion that contractions become more likely than expansions as length increases. This would lead to a stationary distribution for allele length maintained by mutational balance. There is no evidence that contractions and expansions differ in their step size distributions.