Showing papers on "Evolvability published in 2016"

Genetic drift, selection and the evolution of the mutation rate

Abstract: Mutation is the source of genetic diversity on which natural selection acts, therefore understanding the rates of mutations is crucial for understanding evolutionary trajectories. In this Opinion article, the authors discuss how emerging experimental mutation-rate data from genome-wide sequencing studies, combined with population-genetic theory, can provide unifying explanations for the diversity in mutation rates between species and across genomic locations.

How Can Evolution Learn

Abstract: The theory of evolution links random variation and selection to incremental adaptation. In a different intellectual domain, learning theory links incremental adaptation (e.g., from positive and/or negative reinforcement) to intelligent behaviour. Specifically, learning theory explains how incremental adaptation can acquire knowledge from past experience and use it to direct future behaviours toward favourable outcomes. Until recently such cognitive learning seemed irrelevant to the ‘uninformed’ process of evolution. In our opinion, however, new results formally linking evolutionary processes to the principles of learning might provide solutions to several evolutionary puzzles – the evolution of evolvability, the evolution of ecological organisation, and evolutionary transitions in individuality. If so, the ability for evolution to learn might explain how it produces such apparently intelligent designs.

Adaptive Protein Evolution in Animals and the Effective Population Size Hypothesis

Abstract: The rate at which genomes adapt to environmental changes and the prevalence of adaptive processes in molecular evolution are two controversial issues in current evolutionary genetics. Previous attempts to quantify the genome-wide rate of adaptation through amino-acid substitution have revealed a surprising diversity of patterns, with some species (e.g. Drosophila) experiencing a very high adaptive rate, while other (e.g. humans) are dominated by nearly-neutral processes. It has been suggested that this discrepancy reflects between-species differences in effective population size. Published studies, however, were mainly focused on model organisms, and relied on disparate data sets and methodologies, so that an overview of the prevalence of adaptive protein evolution in nature is currently lacking. Here we extend existing estimators of the amino-acid adaptive rate by explicitly modelling the effect of favourable mutations on non-synonymous polymorphism patterns, and we apply these methods to a newly-built, homogeneous data set of 44 non-model animal species pairs. Data analysis uncovers a major contribution of adaptive evolution to the amino-acid substitution process across all major metazoan phyla-with the notable exception of humans and primates. The proportion of adaptive amino-acid substitution is found to be positively correlated to species effective population size. This relationship, however, appears to be primarily driven by a decreased rate of nearly-neutral amino-acid substitution because of more efficient purifying selection in large populations. Our results reveal that adaptive processes dominate the evolution of proteins in most animal species, but do not corroborate the hypothesis that adaptive substitutions accumulate at a faster rate in large populations. Implications regarding the factors influencing the rate of adaptive evolution and positive selection detection in humans vs. other organisms are discussed.

The Evolutionary Origins of Hierarchy

Abstract: Hierarchical organization—the recursive composition of sub-modules—is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force–the cost of connections–promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics.

Horizontal gene transfer: essentiality and evolvability in prokaryotes, and roles in evolutionary transitions.

Abstract: The wide spread of gene exchange and loss in the prokaryotic world has prompted the concept of ‘lateral genomics’ to the point of an outright denial of the relevance of phylogenetic trees for evolution. However, the pronounced coherence congruence of the topologies of numerous gene trees, particularly those for (nearly) universal genes, translates into the notion of a statistical tree of life (STOL), which reflects a central trend of vertical evolution. The STOL can be employed as a framework for reconstruction of the evolutionary processes in the prokaryotic world. Quantitatively, however, horizontal gene transfer (HGT) dominates microbial evolution, with the rate of gene gain and loss being comparable to the rate of point mutations and much greater than the duplication rate. Theoretical models of evolution suggest that HGT is essential for the survival of microbial populations that otherwise deteriorate due to the Muller’s ratchet effect. Apparently, at least some bacteria and archaea evolved dedicated vehicles for gene transfer that evolved from selfish elements such as plasmids and viruses. Recent phylogenomic analyses suggest that episodes of massive HGT were pivotal for the emergence of major groups of organisms such as multiple archaeal phyla as well as eukaryotes. Similar analyses appear to indicate that, in addition to donating hundreds of genes to the emerging eukaryotic lineage, mitochondrial endosymbiosis severely curtailed HGT. These results shed new light on the routes of evolutionary transitions, but caution is due given the inherent uncertainty of deep phylogenies.

The complete local genotype–phenotype landscape for the alternative splicing of a human exon

Abstract: The properties of genotype-phenotype landscapes are crucial for understanding evolution but are not characterized for most traits. Here, we present a >95% complete local landscape for a defined molecular function-the alternative splicing of a human exon (FAS/CD95 exon 6, involved in the control of apoptosis). The landscape provides important mechanistic insights, revealing that regulatory information is dispersed throughout nearly every nucleotide in an exon, that the exon is more robust to the effects of mutations than its immediate neighbours in genotype space, and that high mutation sensitivity (evolvability) will drive the rapid divergence of alternative splicing between species unless it is constrained by selection. Moreover, the extensive epistasis in the landscape predicts that exonic regulatory sequences may diverge between species even when exon inclusion levels are functionally important and conserved by selection.

Global Rebalancing of Cellular Resources by Pleiotropic Point Mutations Illustrates a Multi-scale Mechanism of Adaptive Evolution.

Abstract: Pleiotropic regulatory mutations affect diverse cellular processes, posing a challenge to our understanding of genotype-phenotype relationships across multiple biological scales. Adaptive laboratory evolution (ALE) allows for such mutations to be found and characterized in the context of clear selection pressures. Here, several ALE-selected single-mutation variants in RNA polymerase (RNAP) of Escherichia coli are detailed using an integrated multi-scale experimental and computational approach. While these mutations increase cellular growth rates in steady environments, they reduce tolerance to stress and environmental fluctuations. We detail structural changes in the RNAP that rewire the transcriptional machinery to rebalance proteome and energy allocation toward growth and away from several hedging and stress functions. We find that while these mutations occur in diverse locations in the RNAP, they share a common adaptive mechanism. In turn, these findings highlight the resource allocation trade-offs organisms face and suggest how the structure of the regulatory network enhances evolvability.

Gap gene regulatory dynamics evolve along a genotype network

Abstract: Developmental gene networks implement the dynamic regulatory mechanisms that pattern and shape the organism. Over evolutionary time, the wiring of these networks changes, yet the patterning outcome is often preserved, a phenomenon known as "system drift." System drift is illustrated by the gap gene network-involved in segmental patterning-in dipteran insects. In the classic model organism Drosophila melanogaster and the nonmodel scuttle fly Megaselia abdita, early activation and placement of gap gene expression domains show significant quantitative differences, yet the final patterning output of the system is essentially identical in both species. In this detailed modeling analysis of system drift, we use gene circuits which are fit to quantitative gap gene expression data in M. abdita and compare them with an equivalent set of models from D. melanogaster. The results of this comparative analysis show precisely how compensatory regulatory mechanisms achieve equivalent final patterns in both species. We discuss the larger implications of the work in terms of "genotype networks" and the ways in which the structure of regulatory networks can influence patterns of evolutionary change (evolvability).

Phenotypic plasticity as an adaptation to a functional trade-off

Abstract: We report the evolution of a phenotypically plastic behavior that circumvents the hardwired trade-off that exists when resources are partitioned between growth and motility in Escherichia coli. We propagated cultures in a cyclical environment, alternating between growth up to carrying capacity and selection for chemotaxis. Initial adaptations boosted overall swimming speed at the expense of growth. The effect of the trade-off was subsequently eased through a change in behavior; while individual cells reduced motility during exponential growth, the faction of the population that was motile increased as the carrying capacity was approached. This plastic behavior was produced by a single amino acid replacement in FliA, a regulatory protein central to the chemotaxis network. Our results illustrate how phenotypic plasticity potentiates evolvability by opening up new regions of the adaptive landscape.

Benefit of transferred mutations is better predicted by the fitness of recipients than by their ecological or genetic relatedness

Abstract: The effect of a mutation depends on its interaction with the genetic background in which it is assessed. Studies in experimental systems have demonstrated that such interactions are common among beneficial mutations and often follow a pattern consistent with declining evolvability of more fit genotypes. However, these studies generally examine the consequences of interactions between a small number of focal mutations. It is not clear, therefore, that findings can be extrapolated to natural populations, where new mutations may be transferred between genetically divergent backgrounds. We build on work that examined interactions between four beneficial mutations selected in a laboratory-evolved population of Escherichia coli to test how they interact with the genomes of diverse natural isolates of the same species. We find that the fitness effect of transferred mutations depends weakly on the genetic and ecological similarity of recipient strains relative to the donor strain in which the mutations were selected. By contrast, mutation effects were strongly inversely correlated to the initial fitness of the recipient strain. That is, there was a pattern of diminishing returns whereby fit strains benefited proportionally less from an added mutation. Our results strengthen the view that the fitness of a strain can be a major determinant of its ability to adapt. They also support a role for barriers of transmission, rather than differential selection of transferred DNA, as an explanation of observed phylogenetically determined patterns of restricted recombination among E. coli strains.

Just how Lamarckian is CRISPR-Cas immunity: the continuum of evolvability mechanisms

Abstract: The CRISPR-Cas system of prokaryotic adaptive immunity displays features of a mechanism for directional, Lamarckian evolution. Indeed, this system modifies a specific locus in a bacterial or archaeal genome by inserting a piece of foreign DNA into a CRISPR array which results in acquired, heritable resistance to the cognate selfish element. A key element of the Lamarckian scheme is the specificity and directionality of the mutational process whereby an environmental cue causes only mutations that provide specific adaptations to the original challenge. In the case of adaptive immunity, the specificity of mutations is equivalent to self-nonself discrimination. Recent studies on the CRISPR mechanism have shown that the levels of discrimination can substantially differ such that in some CRISPR-Cas variants incorporation of DNA is random whereas discrimination occurs by selection of cells that carry cognate inserts. In other systems, a higher level of specificity appears to be achieved via specialized mechanisms. These findings emphasize the continuity between random and directed mutations and the critical importance of evolved mechanisms that govern the mutational process. Reviewers: This article has been reviewed by Yitzhak Pilpel, Martijn Huynen, and Bojan Zagrovic.

Strong Selection Significantly Increases Epistatic Interactions in the Long-Term Evolution of a Protein.

Abstract: Epistatic interactions between residues determine a protein’s adaptability and shape its evolutionary trajectory. When a protein experiences a changed environment, it is under strong selection to find a peak in the new fitness landscape. It has been shown that strong selection increases epistatic interactions as well as the ruggedness of the fitness landscape, but little is known about how the epistatic interactions change under selection in the long-term evolution of a protein. Here we analyze the evolution of epistasis in the protease of the human immunodeficiency virus type 1 (HIV-1) using protease sequences collected for almost a decade from both treated and untreated patients, to understand how epistasis changes and how those changes impact the long-term evolvability of a protein. We use an information-theoretic proxy for epistasis that quantifies the co-variation between sites, and show that positive information is a necessary (but not sufficient) condition that detects epistasis in most cases. We analyze the “fossils” of the evolutionary trajectories of the protein contained in the sequence data, and show that epistasis continues to enrich under strong selection, but not for proteins whose environment is unchanged. The increase in epistasis compensates for the information loss due to sequence variability brought about by treatment, and facilitates adaptation in the increasingly rugged fitness landscape of treatment. While epistasis is thought to enhance evolvability via valley-crossing early-on in adaptation, it can hinder adaptation later when the landscape has turned rugged. However, we find no evidence that the HIV-1 protease has reached its potential for evolution after 9 years of adapting to a drug environment that itself is constantly changing. We suggest that the mechanism of encoding new information into pairwise interactions is central to protein evolution not just in HIV-1 protease, but for any protein adapting to a changing environment.

Heritability and evolvability of fitness and nonfitness traits: Lessons from livestock

Abstract: Data from natural populations have suggested a disconnection between trait heritability (variance standardized additive genetic variance, VA ) and evolvability (mean standardized VA ) and emphasized the importance of environmental variation as a determinant of trait heritability but not evolvability. However, these inferences are based on heterogeneous and often small datasets across species from different environments. We surveyed the relationship between evolvability and heritability in >100 traits in farmed cattle, taking advantage of large sample sizes and consistent genetic approaches. Heritability and evolvability estimates were positively correlated (r = 0.37/0.54 on untransformed/log scales) reflecting a substantial impact of VA on both measures. Furthermore, heritabilities and residual variances were uncorrelated. The differences between this and previously described patterns may reflect lower environmental variation experienced in farmed systems, but also low and heterogeneous quality of data from natural populations. Similar to studies on wild populations, heritabilities for life-history and behavioral traits were lower than for other traits. Traits having extremely low heritabilities and evolvabilities (17% of the studied traits) were almost exclusively life-history or behavioral traits, suggesting that evolutionary constraints stemming from lack of genetic variability are likely to be most common for classical "fitness" (cf. life-history) rather than for "nonfitness" (cf. morphological) traits.

Evolvability Search: Directly Selecting for Evolvability in order to Study and Produce It

Abstract: One hallmark of natural organisms is their significant evolvability, i.e.,their increased potential for further evolution. However, reproducing such evolvability in artificial evolution remains a challenge, which both reduces the performance of evolutionary algorithms and inhibits the study of evolvable digital phenotypes. Although some types of selection in evolutionary computation indirectly encourage evolvability, one unexplored possibility is to directly select for evolvability. To do so, we estimate an individual's future potential for diversity by calculating the behavioral diversity of its immediate offspring, and select organisms with increased offspring variation. While the technique is computationally expensive, we hypothesized that direct selection would better encourage evolvability than indirect methods. Experiments in two evolutionary robotics domains confirm this hypothesis: in both domains, such Evolvability Search produces solutions with higher evolvability than those produced with Novelty Search or traditional objective-based search algorithms. Further experiments demonstrate that the higher evolvability produced by Evolvability Search in a training environment also generalizes, producing higher evolvability in a new test environment without further selection. Overall, Evolvability Search enables generating evolvability more easily and directly, facilitating its study and understanding, and may inspire future practical algorithms that increase evolvability without significant computational overhead.

Regulatory mechanisms link phenotypic plasticity to evolvability

Abstract: Organisms have a remarkable capacity to respond to environmental change. They can either respond directly, by means of phenotypic plasticity, or they can slowly adapt through evolution. Yet, how phenotypic plasticity links to evolutionary adaptability is largely unknown. Current studies of plasticity tend to adopt a phenomenological reaction norm (RN) approach, which neglects the mechanisms underlying plasticity. Focusing on a concrete question – the optimal timing of bacterial sporulation – we here also consider a mechanistic approach, the evolution of a gene regulatory network (GRN) underlying plasticity. Using individual-based simulations, we compare the RN and GRN approach and find a number of striking differences. Most importantly, the GRN model results in a much higher diversity of responsive strategies than the RN model. We show that each of the evolved strategies is pre-adapted to a unique set of unseen environmental conditions. The regulatory mechanisms that control plasticity therefore critically link phenotypic plasticity to the adaptive potential of biological populations.

Hierarchy and extremes in selections from pools of randomized proteins

Abstract: Variation and selection are the core principles of Darwinian evolution, but quantitatively relating the diversity of a population to its capacity to respond to selection is challenging. Here, we examine this problem at a molecular level in the context of populations of partially randomized proteins selected for binding to well-defined targets. We built several minimal protein libraries, screened them in vitro by phage display, and analyzed their response to selection by high-throughput sequencing. A statistical analysis of the results reveals two main findings. First, libraries with the same sequence diversity but built around different “frameworks” typically have vastly different responses; second, the distribution of responses of the best binders in a library follows a simple scaling law. We show how an elementary probabilistic model based on extreme value theory rationalizes the latter finding. Our results have implications for designing synthetic protein libraries, estimating the density of functional biomolecules in sequence space, characterizing diversity in natural populations, and experimentally investigating evolvability (i.e., the potential for future evolution).

Nonlinear Dynamics in Gene Regulation Promote Robustness and Evolvability of Gene Expression Levels.

Abstract: Cellular phenotypes underpinned by regulatory networks need to respond to evolutionary pressures to allow adaptation, but at the same time be robust to perturbations. This creates a conflict in which mutations affecting regulatory networks must both generate variance but also be tolerated at the phenotype level. Here, we perform mathematical analyses and simulations of regulatory networks to better understand the potential trade-off between robustness and evolvability. Examining the phenotypic effects of mutations, we find an inverse correlation between robustness and evolvability that breaks only with nonlinearity in the network dynamics, through the creation of regions presenting sudden changes in phenotype with small changes in genotype. For genotypes embedding low levels of nonlinearity, robustness and evolvability correlate negatively and almost perfectly. By contrast, genotypes embedding nonlinear dynamics allow expression levels to be robust to small perturbations, while generating high diversity (evolvability) under larger perturbations. Thus, nonlinearity breaks the robustness-evolvability trade-off in gene expression levels by allowing disparate responses to different mutations. Using analytical derivations of robustness and system sensitivity, we show that these findings extend to a large class of gene regulatory network architectures and also hold for experimentally observed parameter regimes. Further, the effect of nonlinearity on the robustness-evolvability trade-off is ensured as long as key parameters of the system display specific relations irrespective of their absolute values. We find that within this parameter regime genotypes display low and noisy expression levels. Examining the phenotypic effects of mutations, we find an inverse correlation between robustness and evolvability that breaks only with nonlinearity in the network dynamics. Our results provide a possible solution to the robustness-evolvability trade-off, suggest an explanation for the ubiquity of nonlinear dynamics in gene expression networks, and generate useful guidelines for the design of synthetic gene circuits.

Functional Trade-Offs in Promiscuous Enzymes Cannot Be Explained by Intrinsic Mutational Robustness of the Native Activity.

Abstract: The extent to which an emerging new function trades off with the original function is a key characteristic of the dynamics of enzyme evolution. Various cases of laboratory evolution have unveiled a characteristic trend; a large increase in a new, promiscuous activity is often accompanied by only a mild reduction of the native, original activity. A model that associates weak trade-offs with “evolvability” was put forward, which proposed that enzymes possess mutational robustness in the native activity and plasticity in promiscuous activities. This would enable the acquisition of a new function without compromising the original one, reducing the benefit of early gene duplication and therefore the selection pressure thereon. Yet, to date, no experimental study has examined this hypothesis directly. Here, we investigate the causes of weak trade-offs by systematically characterizing adaptive mutations that occurred in two cases of evolutionary transitions in enzyme function: (1) from phosphotriesterase to arylesterase, and (2) from atrazine chlorohydrolase to melamine deaminase. Mutational analyses in various genetic backgrounds revealed that, in contrast to the prevailing model, the native activity is less robust to mutations than the promiscuous activity. For example, in phosphotriesterase, the deleterious effect of individual mutations on the native phosphotriesterase activity is much larger than their positive effect on the promiscuous arylesterase activity. Our observations suggest a revision of the established model: weak trade-offs are not caused by an intrinsic robustness of the native activity and plasticity of the promiscuous activity. We propose that upon strong adaptive pressure for the new activity without selection against the original one, selected mutations will lead to the largest possible increases in the new function, but whether and to what extent they decrease the old function is irrelevant, creating a bias towards initially weak trade-offs and the emergence of generalist enzymes.

Adaptation to Parasites and Costs of Parasite Resistance in Mutator and Nonmutator Bacteria

Abstract: Parasitism creates selection for resistance mechanisms in host populations and is hypothesized to promote increased host evolvability. However, the influence of these traits on host evolution when parasites are no longer present is unclear. We used experimental evolution and whole-genome sequencing of Escherichia coli to determine the effects of past and present exposure to parasitic viruses (phages) on the spread of mutator alleles, resistance, and bacterial competitive fitness. We found that mutator alleles spread rapidly during adaptation to any of four different phage species, and this pattern was even more pronounced with multiple phages present simultaneously. However, hypermutability did not detectably accelerate adaptation in the absence of phages and recovery of fitness costs associated with resistance. Several lineages evolved phage resistance through elevated mucoidy, and during subsequent evolution in phage-free conditions they rapidly reverted to nonmucoid, phage-susceptible phenotypes. Genome sequencing revealed that this phenotypic reversion was achieved by additional genetic changes rather than by genotypic reversion of the initial resistance mutations. Insertion sequence (IS) elements played a key role in both the acquisition of resistance and adaptation in the absence of parasites; unlike single nucleotide polymorphisms, IS insertions were not more frequent in mutator lineages. Our results provide a genetic explanation for rapid reversion of mucoidy, a phenotype observed in other bacterial species including human pathogens. Moreover, this demonstrates that the types of genetic change underlying adaptation to fitness costs, and consequently the impact of evolvability mechanisms such as increased point-mutation rates, depend critically on the mechanism of resistance.

Functional Diversification after Gene Duplication: Paralog Specific Regions of Structural Disorder and Phosphorylation in p53, p63, and p73.

Abstract: Conformational and functional flexibility promote protein evolvability. High evolvability allows related proteins to functionally diverge and perhaps to neostructuralize. p53 is a multifunctional protein frequently referred to as the Guardian of the Genome–a hub for e.g. incoming and outgoing signals in apoptosis and DNA repair. p53 has been found to be structurally disordered, an extreme form of conformational flexibility. Here, p53, and its paralogs p63 and p73, were studied for further insights into the evolutionary dynamics of structural disorder, secondary structure, and phosphorylation. This study is focused on the post gene duplication phase for the p53 family in vertebrates, but also visits the origin of the protein family and the early domain loss and gain events. Functional divergence, measured by rapid evolutionary dynamics of protein domains, structural properties, and phosphorylation propensity, is inferred across vertebrate p53 proteins, in p63 and p73 from fish, and between the three paralogs. In particular, structurally disordered regions are redistributed among paralogs, but within clades redistribution of structural disorder also appears to be an ongoing process. Despite its deemed importance as the Guardian of the Genome, p53 is indeed a protein with high evolvability as seen not only in rearranged structural disorder, but also in fluctuating domain sequence signatures among lineages.

Efficient escape from local optima in a highly rugged fitness landscape by evolving RNA virus populations.

Abstract: Predicting viral evolution has proven to be a particularly difficult task, mainly owing to our incomplete knowledge of some of the fundamental principles that drive it. Recently, valuable information has been provided about mutation and recombination rates, the role of genetic drift and the distribution of mutational, epistatic and pleiotropic fitness effects. However, information about the topography of virus' adaptive landscapes is still scarce, and to our knowledge no data has been reported so far on how its ruggedness may condition virus' evolvability. Here, we show that populations of an RNA virus move efficiently on a rugged landscape and scape from the basin of attraction of a local optimum. We have evolved a set of Tobacco etch virus genotypes located at increasing distances from a local adaptive optimum in a highly rugged fitness landscape, and we observed that few evolved lineages remained trapped in the local optimum, while many others explored distant regions of the landscape. Most of the diversification in fitness among the evolved lineages was explained by adaptation, while historical contingency and chance events contribution was less important. Our results demonstrate that the ruggedness of adaptive landscapes is not an impediment for RNA viruses to efficiently explore remote parts of it.

Rapid changes in genetic architecture of behavioural syndromes following colonization of a novel environment.

Abstract: Behavioural syndromes, that is correlated behaviours, may be a result from adaptive correlational selection, but in a new environmental setting, the trait correlation might act as an evolutionary constraint. However, knowledge about the quantitative genetic basis of behavioural syndromes, and the stability and evolvability of genetic correlations under different ecological conditions, is limited. We investigated the quantitative genetic basis of correlated behaviours in the freshwater isopod Asellus aquaticus. In some Swedish lakes, A. aquaticus has recently colonized a novel habitat and diverged into two ecotypes, presumably due to habitat-specific selection from predation. Using a common garden approach and animal model analyses, we estimated quantitative genetic parameters for behavioural traits and compared the genetic architecture between the ecotypes. We report that the genetic covariance structure of the behavioural traits has been altered in the novel ecotype, demonstrating divergence in behavioural correlations. Thus, our study confirms that genetic correlations behind behaviours can change rapidly in response to novel selective environments.

Evolutionary Biosemiotics and Multilevel Construction Networks

Abstract: In contrast to the traditional relational semiotics, biosemiotics decisively deviates towards dynamical aspects of signs at the evolutionary and developmental time scales. The analysis of sign dynamics requires constructivism (in a broad sense) to explain how new components such as subagents, sensors, effectors, and interpretation networks are produced by developing and evolving organisms. Semiotic networks that include signs, tools, and subagents are multilevel, and this feature supports the plasticity, robustness, and evolvability of organisms. The origin of life is described here as the emergence of simple self-constructing semiotic networks that progressively increased the diversity of their components and relations. Primitive organisms have no capacity to classify and track objects; thus, we need to admit the existence of proto-signs that directly regulate activities of agents without being associated with objects. However, object recognition and handling became possible in eukaryotic species with the development of extensive rewritable epigenetic memory as well as sensorial and effector capacities. Semiotic networks are based on sequential and recursive construction, where each step produces components (i.e., agents, scaffolds, signs, and resources) that are needed for the following steps of construction. Construction is not limited to repair and reproduction of what already exists or is unambiguously encoded, it also includes production of new components and behaviors via learning and evolution. A special case is the emergence of new levels of organization known as metasystem transition. Multilevel semiotic networks reshape the phenotype of organisms by combining a mosaic of features developed via learning and evolution of cooperating and/or conflicting subagents.

Resolving the paradox of evolvability with learning theory: How evolution learns to improve evolvability on rugged fitness landscapes

Abstract: It has been hypothesized that one of the main reasons evolution has been able to produce such impressive adaptations is because it has improved its own ability to evolve -- "the evolution of evolvability". Rupert Riedl, for example, an early pioneer of evolutionary developmental biology, suggested that the evolution of complex adaptations is facilitated by a developmental organization that is itself shaped by past selection to facilitate evolutionary innovation. However, selection for characteristics that enable future innovation seems paradoxical: natural selection cannot favor structures for benefits they have not yet produced, and favoring characteristics for benefits that have already been produced does not constitute future innovation. Here we resolve this paradox by exploiting a formal equivalence between the evolution of evolvability and learning systems. We use the conditions that enable simple learning systems to generalize, i.e., to use past experience to improve performance on previously unseen, future test cases, to demonstrate conditions where natural selection can systematically favor developmental organizations that benefit future evolvability. Using numerical simulations of evolution on highly epistatic fitness landscapes, we illustrate how the structure of evolved gene regulation networks can result in increased evolvability capable of avoiding local fitness peaks and discovering higher fitness phenotypes. Our findings support Riedl's intuition: Developmental organizations that "mimic" the organization of constraints on phenotypes can be favored by short-term selection and also facilitate future innovation. Importantly, the conditions that enable the evolution of such surprising evolvability follow from the same non-mysterious conditions that permit generalization in learning systems.

Parasites Sustain and Enhance RNA-Like Replicators through Spatial Self-Organisation.

Abstract: In a prebiotic RNA world, parasitic behaviour may be favoured because template dependent replication happens in trans, thus being altruistic. Spatially extended systems are known to reduce harmful effects of parasites. Here we present a spatial system to show that evolution of replication is (indirectly) enhanced by strong parasites, and we characterise the phase transition that leads to this mode of evolution. Building on the insights of this analysis, we identify two scenarios, namely periodic disruptions and longer replication time-span, in which speciation occurs and an evolved parasite-like lineage enables the evolutionary increase of replication rates in replicators. Finally, we show that parasites co-evolving with replicators are selected to become weaker, i.e. worse templates for replication when the duration of replication is increased. We conclude that parasites may not be considered a problem for evolution in a prebiotic system, but a degree of freedom that can be exploited by evolution to enhance the evolvability of replicators, by means of emergent levels of selection.

Additive genetic variance in polyandry enables its evolution, but polyandry is unlikely to evolve through sexy or good sperm processes.

Abstract: Polyandry is widespread despite its costs. The sexually selected sperm hypotheses ('sexy' and 'good' sperm) posit that sperm competition plays a role in the evolution of polyandry. Two poorly studied assumptions of these hypotheses are the presence of additive genetic variance in polyandry and sperm competitiveness. Using a quantitative genetic breeding design in a natural population of Drosophila melanogaster, we first established the potential for polyandry to respond to selection. We then investigated whether polyandry can evolve through sexually selected sperm processes. We measured lifetime polyandry and offensive sperm competitiveness (P2 ) while controlling for sampling variance due to male × male × female interactions. We also measured additive genetic variance in egg-to-adult viability and controlled for its effect on P2 estimates. Female lifetime polyandry showed significant and substantial additive genetic variance and evolvability. In contrast, we found little genetic variance or evolvability in P2 or egg-to-adult viability. Additive genetic variance in polyandry highlights its potential to respond to selection. However, the low levels of genetic variance in sperm competitiveness suggest that the evolution of polyandry may not be driven by sexy sperm or good sperm processes.