A major goal of evolutionary developmental biology (evo-devo) is to understand how multicellular body plans of increasing complexity have evolved, and how the corresponding developmental programs are genetically encoded. It has been repeatedly argued that key to the evolution of increased body plan complexity is the modularity of the underlying developmental gene regulatory networks (GRNs). This modularity is considered essential for network robustness and evolvability. In our opinion, these ideas, appealing as they may sound, have not been sufficiently tested. Here we use computer simulations to study the evolution of GRNs' underlying body plan patterning. We select for body plan segmentation and differentiation, as these are considered to be major innovations in metazoan evolution. To allow modular networks to evolve, we independently select for segmentation and differentiation. We study both the occurrence and relation of robustness, evolvability and modularity of evolved networks. Interestingly, we observed two distinct evolutionary strategies to evolve a segmented, differentiated body plan. In the first strategy, first segments and then differentiation domains evolve (SF strategy). In the second scenario segments and domains evolve simultaneously (SS strategy). We demonstrate that under indirect selection for robustness the SF strategy becomes dominant. In addition, as a byproduct of this larger robustness, the SF strategy is also more evolvable. Finally, using a combined functional and architectural approach, we determine network modularity. We find that while SS networks generate segments and domains in an integrated manner, SF networks use largely independent modules to produce segments and domains. Surprisingly, we find that widely used, purely architectural methods for determining network modularity completely fail to establish this higher modularity of SF networks. Finally, we observe that, as a free side effect of evolving segmentation and differentiation in combination, we obtained in-silico developmental mechanisms resembling mechanisms used in vertebrate development.
An important question in evolutionary developmental biology is how the complex organisms we see around us have evolved, and how this complexity is encoded in their DNA. An often heard statement is that the gene regulatory networks underlying developmental processes are modular; that is, different functions are carried out by largely independent network parts. It is argued that this network modularity allows both for robust functioning and evolutionary tinkering, and that selection thus produces modular networks. Here we use a simulation model for the evolution of animal body plan patterning to investigate these ideas. To allow for the evolution of modular networks we independently select for both body plan segmentation and differentiation. We find two distinct evolutionary trajectories, one in which segments evolve before domains, and one in which segments and domains evolve simultaneously. In addition, the two evolved network types also differ in terms of developmental dynamics. We show that indirect selection for robustness favors the segments first type networks. Furthermore, as a free side effect, these more robust networks are also more evolvable. Finally, we take into account both functional and architectural aspects to determine the modularity of the network types. We show that segments simultaneous networks generate segments and domains in a integrated manner, whereas segments first networks use largely independent modules to generate segments and domains. Finally, although mimicking natural developmental mechanisms was not part of our model design, the segments first developmental mechanisms resembles vertebrate axial patterning mechanisms. This resemblance arises for free, simply from considering segmentation and differentiation in combination.
The term “micro-evo-devo” refers to the combined study of the genetic and developmental bases of natural variation in populations and the evolutionary forces that have shaped this variation. It thus represents a synthesis of the fields of evolutionary developmental biology and population genetics. As has been pointed out by several others, this synthesis can provide insights into the evolution of organismal form and function that have not been possible within these individual disciplines separately. Despite a number of important successes in micro-evo-devo, however, it appears that evo devo and population genetics remain largely separate spheres of research, limiting their ability to address evolutionary questions. This also risks pushing contemporary evo devo to the fringes of evolutionary biology because it does not describe the causative molecular changes underlying evolution or the evolutionary forces involved. Here we reemphasize the theoretical and practical importance of micro-evo-devo as a strategy for understanding phenotypic evolution, review the key recent insights that it has provided, and present a perspective on both the potential and the remaining challenges of this exciting interdisciplinary field.
Robustness seems to be the opposite of evolvability. If phenotypes are robust against mutation, we might expect that a population will have difficulty adapting to an environmental change, as several studies have suggested1–4. However, other studies contend that robust organisms are more adaptable5–8. A quantitative understanding of the relationship between robustness and evolvability will help resolve these conflicting reports and will clarify outstanding problems in molecular and experimental evolution, evo-devo, and protein engineering. Using a general population-genetic model, we demonstrate here that mutational robustness can either impede or facilitate adaptation, depending on the population size, the mutation rate, and the structure of the fitness landscape. In particular, neutral diversity in a robust population can accelerate adaptation provided the number of phenotypes accessible to an individual by mutation is smaller than the total number of phenotypes in the fitness landscape. These results provide a quantitative resolution to a significant ambiguity in evolutionary theory.
The authors present the development and validation of the EvoDevoCI, a concept inventory for evolutionary developmental biology. This CI measures student understanding of six core evolutionary developmental biology (evo-devo) concepts using four scenarios and 11 multiple-choice items, all inspired by authentic scientific examples. Distracters were designed to represent the common conceptual difficulties students have with each evo-devo concept.
The American Association for the Advancement of Science 2011 report Vision and Change in Undergraduate Biology Education encourages the teaching of developmental biology as an important part of teaching evolution. Recently, however, we found that biology majors often lack the developmental knowledge needed to understand evolutionary developmental biology, or “evo-devo.” To assist in efforts to improve evo-devo instruction among undergraduate biology majors, we designed a concept inventory (CI) for evolutionary developmental biology, the EvoDevoCI. The CI measures student understanding of six core evo-devo concepts using four scenarios and 11 multiple-choice items, all inspired by authentic scientific examples. Distracters were designed to represent the common conceptual difficulties students have with each evo-devo concept. The tool was validated by experts and administered at four institutions to 1191 students during preliminary (n = 652) and final (n = 539) field trials. We used student responses to evaluate the readability, difficulty, discriminability, validity, and reliability of the EvoDevoCI, which included items ranging in difficulty from 0.22–0.55 and in discriminability from 0.19–0.38. Such measures suggest the EvoDevoCI is an effective tool for assessing student understanding of evo-devo concepts and the prevalence of associated common conceptual difficulties among both novice and advanced undergraduate biology majors.
In this study we used surveys of evo-devo experts to identify the core concepts of evo-devo and outline an underlying conceptual framework. We also use interviews and surveys of conceptual difficulties with these concepts.
To examine how well biology majors have achieved the necessary foundation in evolution, numerous studies have examined how students learn natural selection. However, no studies to date have examined how students learn developmental aspects of evolution (evo-devo). Although evo-devo plays an increasing role in undergraduate biology curricula, we find that instruction often addresses development cursorily, with most of the treatment embedded within instruction on evolution. Based on results of surveys and interviews with students, we suggest that teaching core concepts (CCs) within a framework that integrates supporting concepts (SCs) from both evolutionary and developmental biology can improve evo-devo instruction. We articulate CCs, SCs, and foundational concepts (FCs) that provide an integrative framework to help students master evo-devo concepts and to help educators address specific conceptual difficulties their students have with evo-devo. We then identify the difficulties that undergraduates have with these concepts. Most of these difficulties are of two types: those that are ubiquitous among students in all areas of biology and those that stem from an inadequate understanding of FCs from developmental, cell, and molecular biology.
Robustness, maintaining a constant phenotype despite perturbations, is a fundamental property of biological systems that is incorporated at various levels of biological complexity. Although robustness has been frequently observed in nature, its evolutionary origin remains unknown. Current hypotheses suggest that robustness originated as a direct consequence of natural selection, as an intrinsic property of adaptations, or as a congruent correlate of environment robustness. To elucidate the evolutionary origins of robustness, a convenient computational package is strongly needed.
In this study, we developed the open-source integrated system EvoRSR (Evolution of RNA Structural Robustness) to explore the evolution of robustness based on biologically important landscapes induced by RNA folding. EvoRSR is object-oriented, modular, and freely available at under the GNU/GPL license. We present an overview of EvoRSR package and illustrate its features with the miRNA gene cel-mir-357.
EvoRSR is a novel and flexible package for exploring the evolution of robustness. Accordingly, EvoRSR can be used for future studies to investigate the evolution and origin of robustness and to address other common questions about robustness. While the current EvoRSR environment is a versatile analysis framework, future versions can include features to enhance evolutionary studies of robustness.
A central question in brain evolution is how species-typical behaviors, and the neural function-structure mappings supporting them, can be acquired and inherited. Advocates of brain modularity, in its different incarnations across scientific subfields, argue that natural selection must target domain-dedicated, separately modifiable neural subsystems, resulting in genetically-specified functional modules. In such modular systems, specification of neuron number and functional connectivity are necessarily linked. Mounting evidence, however, from allometric, developmental, comparative, systems-physiological, neuroimaging and neurological studies suggests that brain elements are used and reused in multiple functional systems. This variable allocation can be seen in short-term neuromodulation, in neuroplasticity over the lifespan and in response to damage. We argue that the same processes are evident in brain evolution. Natural selection must preserve behavioral functions that may co-locate in variable amounts with other functions. In genetics, the uses and problems of pleiotropy, the re-use of genes in multiple networks have been much discussed, but this issue has been sidestepped in neural systems by the invocation of modules. Here we highlight the interaction between evolutionary and developmental mechanisms to produce distributed and overlapping functional architectures in the brain. These adaptive mechanisms must be robust to perturbations that might disrupt critical information processing and action selection, but must also recognize useful new sources of information arising from internal genetic or environmental variability, when those appear. These contrasting properties of “robustness” and “evolvability” have been discussed for the basic organization of body plan and fundamental cell physiology. Here we extend them to the evolution and development, “evo-devo,” of brain structure.
cortex; modularity; evo-devo; visual system; neural re-use
Regulatory and developmental systems produce phenotypes that are robust to environmental and genetic variation. A gene product that normally contributes to this robustness is termed a phenotypic capacitor. When a phenotypic capacitor fails, for example when challenged by a harsh environment or mutation, the system becomes less robust and thus produces greater phenotypic variation. A functional phenotypic capacitor provides a mechanism by which hidden polymorphism can accumulate, whereas its failure provides a mechanism by which evolutionary change might be promoted. The primary example to date of a phenotypic capacitor is Hsp90, a molecular chaperone that targets a large set of signal transduction proteins. In both Drosophila and Arabidopsis, compromised Hsp90 function results in pleiotropic phenotypic effects dependent on the underlying genotype. For some traits, Hsp90 also appears to buffer stochastic variation, yet the relationship between environmental and genetic buffering remains an important unresolved question. We previously used simulations of knockout mutations in transcriptional networks to predict that many gene products would act as phenotypic capacitors. To test this prediction, we use high-throughput morphological phenotyping of individual yeast cells from single-gene deletion strains to identify gene products that buffer environmental variation in Saccharomyces cerevisiae. We find more than 300 gene products that, when absent, increase morphological variation. Overrepresented among these capacitors are gene products that control chromosome organization and DNA integrity, RNA elongation, protein modification, cell cycle, and response to stimuli such as stress. Capacitors have a high number of synthetic-lethal interactions but knockouts of these genes do not tend to cause severe decreases in growth rate. Each capacitor can be classified based on whether or not it is encoded by a gene with a paralog in the genome. Capacitors with a duplicate are highly connected in the protein–protein interaction network and show considerable divergence in expression from their paralogs. In contrast, capacitors encoded by singleton genes are part of highly interconnected protein clusters whose other members also tend to affect phenotypic variability or fitness. These results suggest that buffering and release of variation is a widespread phenomenon that is caused by incomplete functional redundancy at multiple levels in the genetic architecture.
Most species maintain abundant genetic variation and experience a wide range of environmental conditions, yet phenotypic differences between individuals are usually small. This phenomenon, known as phenotypic robustness, presents an apparent contradiction: if biological systems are so resistant to variation, how do they diverge and adapt through evolutionary time? Here, we address this question by investigating the molecular mechanisms that underlie phenotypic robustness and how these mechanisms can be broken to produce phenotypic heterogeneity. We identify genes that contribute to phenotypic robustness in yeast by analyzing the variance of morphological phenotypes in a comprehensive collection of single-gene knockout strains. We find that ∼5% of yeast genes break phenotypic robustness when knocked out. The products of these genes tend to be involved in critical cellular processes, including maintaining DNA stability, processing RNA, modifying proteins, and responding to stressful environments. These genes tend to interact genetically with a large number of other genes, and their products tend to interact physically with a large number of other gene products. Our results suggest that loss of phenotypic robustness might be a common phenomenon during evolution that occurs when cellular networks are disrupted.
A genome-wide screen inSaccharomyces cerevisiae identifies over 300 gene products that buffer environmental variation--dubbed phenotypic capacitors--and function as hubs in protein-protein and synthetic-lethal interaction networks.
The rich phenotypic diversity that characterizes the vertebrate skeleton results from evolutionary changes in regulation of genes that drive development. Although relatively little is known about the genes that underlie the skeletal variation among fish species, significant knowledge of genetics and development is available for zebrafish. Because developmental processes are highly conserved, this knowledge can be leveraged for understanding the evolution of skeletal diversity. We developed the Phenoscape Knowledgebase (KB; http://kb.phenoscape.org) to yield testable hypotheses of candidate genes involved in skeletal evolution. We developed a community anatomy ontology for fishes and ontology-based methods to represent complex free-text character descriptions of species in a computable format. With these tools, we populated the KB with comparative morphological data from the literature on over 2,500 teleost fishes (mainly Ostariophysi) resulting in over 500,000 taxon phenotype annotations. The KB integrates these data with similarly structured phenotype data from zebrafish genes (http://zfin.org). Using ontology-based reasoning, candidate genes can be inferred for the phenotypes that vary across taxa, thereby uniting genetic and phenotypic data to formulate evo-devo hypotheses. The morphological data in the KB can be browsed, sorted, and aggregated in ways that provide unprecedented possibilities for data mining and discovery.
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.
An individual-oriented evolutionary model is used to shed light on this matter. Each individual has a genome from which its gene regulatory network is derived. Mutations, such as gene duplications and deletions, alter the genome, while the resulting network determines the gene expression pattern and hence fitness. With this protocol we let a population of individuals evolve under Darwinian selection in an environment that changes through time.
Our work demonstrates that long-term evolution of complex gene regulatory networks in a changing environment can lead to a striking increase in the efficiency of generating beneficial mutations. We show that the population evolves towards genotype-phenotype mappings that allow for an orchestrated network-wide change in the gene expression pattern, requiring only a few specific gene indels. The genes involved are hubs of the networks, or directly influencing the hubs. Moreover, throughout the evolutionary trajectory the networks maintain their mutational robustness. In other words, evolution in an alternating environment leads to a network that is sensitive to a small class of beneficial mutations, while the majority of mutations remain neutral: an example of evolution of evolvability.
A cell receives signals both from its internal and external environment and responds by changing the expression of genes. In this manner the cell adjusts to heat, osmotic pressures and other circumstances during its lifetime. Over long timescales, the network of interacting genes and its regulatory actions also undergo evolutionary adaptation. Yet how do such networks evolve and become adapted?
In this paper we describe the study of a simple model of gene regulatory networks, focusing solely on evolutionary adaptation. We let a population of individuals evolve, while the external environment changes through time. To ensure evolution is the only source of adaptation, we do not provide the individuals with a sensor to the environment. We show that the interplay between the long-term process of evolution and short-term gene regulation dynamics leads to a striking increase in the efficiency of creating well-adapted offspring. Beneficial mutations become more frequent, nevertheless robustness to the majority of mutations is maintained. Thus we demonstrate a clear example of the evolution of evolvability.
One of the surprising insights gained from research in evolutionary developmental biology (evo-devo) is that increasing diversity in body plans and morphology in organisms across animal phyla are not reflected in similarly dramatic changes at the level of gene composition of their genomes. For instance, simplicity at the tissue level of organization often contrasts with a high degree of genetic complexity. Also intriguing is the observation that the coding regions of several genes of invertebrates show high sequence similarity to those in humans. This lack of change (conservation) indicates that evolutionary novelties may arise more frequently through combinatorial processes, such as changes in gene regulation and the recruitment of novel genes into existing regulatory gene networks (co-option), and less often through adaptive evolutionary processes in the coding portions of a gene. As a consequence, it is of great interest to examine whether the widespread conservation of the genetic machinery implies the same developmental function in a last common ancestor, or whether homologous genes acquired new developmental roles in structures of independent phylogenetic origin. To distinguish between these two possibilities one must refer to current concepts of phylogeny reconstruction and carefully investigate homology relationships. Particularly problematic in terms of homology decisions is the use of gene expression patterns of a given structure. In the future, research on more organisms other than the typical model systems will be required since these can provide insights that are not easily obtained from comparisons among only a few distantly related model species.
Developmental biology, as all experimental science, is empowered by technological advances. The availability of genetic tools in some species - designated as model organisms - has driven their use as major platforms for understanding development, physiology and behavior. Extending these tools to a wider range of species determines whether (and how) we can experimentally approach developmental diversity and evolution. During the last two decades, comparative developmental biology (evo-devo) was marked by the introduction of gene knockdown and deep sequencing technologies that are applicable to a wide range of species. These approaches allowed us to test the developmental role of specific genes in diverse species, to study biological processes that are not accessible in established models and, in some cases, to conduct genome-wide screens that overcome the limitations of the candidate gene approach. The recent discovery of CRISPR/Cas as a means of precise alterations into the genome promises to revolutionize developmental genetics. In this review we describe the development of gene editing tools, from zinc-finger nucleases to TALENs and CRISPR, and examine their application in gene targeting, their limitations and the opportunities they present for evo-devo. We outline their use in gene knock-out and knock-in approaches, and in manipulating gene functions by directing molecular effectors to specific sites in the genome. The ease-of-use and efficiency of CRISPR in diverse species provide an opportunity to close the technology gap that exists between established model organisms and emerging genetically-tractable species.
Comparative developmental biology; Model organisms; Gene targeting; Homologous recombination; Gene-editing nucleases; CRISPR
MicroRNAs (miRNAs) control many important aspects of plant development, suggesting these molecules may also have played key roles in the evolution of developmental processes in plants. However, evolutionary-developmental (evo-devo) studies of miRNAs have been held back by technical difficulties in gene identification. To help solve this problem, we have developed a two-step procedure for the efficient identification of miRNA genes in any plant species. As a test case, we have studied the evolution of the MIR164 family in the angiosperms. We have identified novel MIR164 genes in three species occupying key phylogenetic positions and used these, together with published sequence data, to partially reconstruct the evolution of the MIR164 family since the last common ancestor of the extant flowering plants. We use our evolutionary reconstruction to discuss potential roles for MIR164 genes in the evolution of leaf shape and carpel closure in the angiosperms. The techniques we describe may be applied to any miRNA family and should thus enable plant evo-devo to begin to investigate the contributions miRNAs have made to the evolution of plant development.
microRNA; miR164; angiosperm; flower; carpel; leaf dissection
Understanding the role of gene duplications in establishing vertebrate innovations is one of the main challenges of Evo-Devo (evolution of development) studies. Data on evolutionary changes in gene expression (i.e., evolution of transcription factor-cis-regulatory elements relationships) tell only part of the story; protein function, best studied by biochemical and functional assays, can also change. In this study, we have investigated how gene duplication has affected both the expression and the ligand-binding specificity of retinoic acid receptors (RARs), which play a major role in chordate embryonic development. Mammals have three paralogous RAR genes—RARα, β, and γ—which resulted from genome duplications at the origin of vertebrates. By using pharmacological ligands selective for specific paralogues, we have studied the ligand-binding capacities of RARs from diverse chordates species. We have found that RARβ-like binding selectivity is a synapomorphy of all chordate RARs, including a reconstructed synthetic RAR representing the receptor present in the ancestor of chordates. Moreover, comparison of expression patterns of the cephalochordate amphioxus and the vertebrates suggests that, of all the RARs, RARβ expression has remained most similar to that of the ancestral RAR. On the basis of these results together, we suggest that while RARβ kept the ancestral RAR role, RARα and RARγ diverged both in ligand-binding capacity and in expression patterns. We thus suggest that neofunctionalization occurred at both the expression and the functional levels to shape RAR roles during development in vertebrates.
In eukaryotic organisms, each gene is a stretch of DNA composed of control regions that bind transcription factors and coding regions that transcribe the mRNA that is later translated into proteins. At the molecular level, changes in control regions can affect the time and place at which a protein is synthesized, whereas changes in the coding region can alter the protein's function. Retinoic acid receptors (RARs) are chordate-specific transcription factors which, upon binding the natural morphogen retinoic acid, bind to and activate transcription from target genes. Here, the authors show how the ligand specificity of RARs has changed during vertebrate evolution in parallel with changes in expression. Through functional characterization of the RARs from several vertebrates, the chordate amphioxus, and the reconstructed ancestral RAR sequence, the authors show that of the three vertebrate RARs, RARβ has retained the ancestral characteristics in terms of both function and expression, while RARα and γ have evolved by acquiring new functions, both new binding specificity and new expression patterns. Thus both types of evolution have been important in the diversification of vertebrate RARs.
Comparative studies of developmental processes are one of the main approaches to evolutionary developmental biology (evo-devo). Over recent years, there has been a shift of focus from the comparative study of particular regulatory genes to the level of whole gene networks. Reverse-engineering methods can be used to computationally reconstitute and analyze the function and dynamics of such networks. These methods require quantitative spatio-temporal expression data for model fitting. Obtaining such data in non-model organisms remains a major technical challenge, impeding the wider application of data-driven mathematical modeling to evo-devo.
We have raised antibodies against four segmentation gene products in the moth midge Clogmia albipunctata, a non-drosophilid dipteran species. We have used these antibodies to create a quantitative atlas of protein expression patterns for the gap gene hunchback (hb), and the pair-rule gene even-skipped (eve). Our data reveal differences in the dynamics of Hb boundary positioning and Eve stripe formation between C. albipunctata and Drosophila melanogaster. Despite these differences, the overall relative spatial arrangement of Hb and Eve domains is remarkably conserved between these two distantly related dipteran species.
We provide a proof of principle that it is possible to acquire quantitative gene expression data at high accuracy and spatio-temporal resolution in non-model organisms. Our quantitative data extend earlier qualitative studies of segmentation gene expression in C. albipunctata, and provide a starting point for comparative reverse-engineering studies of the evolutionary and developmental dynamics of the segmentation gene system.
Clogmia albipunctata; Non-drosophilid diptera; Non-model organism; Pattern formation; Comparative network analysis; Segmentation gene network; Hunchback; Even-skipped; Image bioinformatics; Quantitative expression data
The topology of cellular circuits (the who-interacts-with-whom) is key to understand their robustness to both mutations and noise. The reason is that many biochemical parameters driving circuit behavior vary extensively and are thus not fine-tuned. Existing work in this area asks to what extent the function of any one given circuit is robust. But is high robustness truly remarkable, or would it be expected for many circuits of similar topology? And how can high robustness come about through gradual Darwinian evolution that changes circuit topology gradually, one interaction at a time? We here ask these questions for a model of transcriptional regulation networks, in which we explore millions of different network topologies. Robustness to mutations and noise are correlated in these networks. They show a skewed distribution, with a very small number of networks being vastly more robust than the rest. All networks that attain a given gene expression state can be organized into a graph whose nodes are networks that differ in their topology. Remarkably, this graph is connected and can be easily traversed by gradual changes of network topologies. Thus, robustness is an evolvable property. This connectedness and evolvability of robust networks may be a general organizational principle of biological networks. In addition, it exists also for RNA and protein structures, and may thus be a general organizational principle of all biological systems.
Living things are astonishingly complex, yet unlike houses of cards they are also highly robust. That is, they have persisted for billions of years, despite being exposed to an endless stream of environmental stressors and random mutations. Is this robustness an evolvable property? Do different biological systems vary in their robustness? Has natural selection shaped this robustness? These questions are very difficult to answer experimentally for most systems, be they proteins or large gene networks. Here we address these questions with a model of the transcription regulation networks that regulate both cellular functions and embryonic development in many organisms. We examine millions of such networks that differ in the topology or architecture of their regulatory interactions, that is, in the “who interacts with whom” of a network. We find that radically different network architectures can show the same gene expression pattern. The networks' robustness to both mutations and gene expression noise shows a broad distribution: some network architectures are highly robust, whereas others are quite fragile. Importantly, the entire space of network architectures can be traversed through small changes of individual regulatory interactions, without changing a network's gene expression pattern. This means that high robustness in gene expression can evolve through gradual and neutral evolution in the space of network architectures. Our results show that the robustness of transcriptional regulation networks is an evolvable trait that natural selection can change like any other trait.
Developmental constraints have been postulated to limit the space of feasible phenotypes and thus shape animal evolution. These constraints have been suggested to be the strongest during either early or mid-embryogenesis, which corresponds to the early conservation model or the hourglass model, respectively. Conflicting results have been reported, but in recent studies of animal transcriptomes the hourglass model has been favored. Studies usually report descriptive statistics calculated for all genes over all developmental time points. This introduces dependencies between the sets of compared genes and may lead to biased results. Here we overcome this problem using an alternative modular analysis. We used the Iterative Signature Algorithm to identify distinct modules of genes co-expressed specifically in consecutive stages of zebrafish development. We then performed a detailed comparison of several gene properties between modules, allowing for a less biased and more powerful analysis. Notably, our analysis corroborated the hourglass pattern at the regulatory level, with sequences of regulatory regions being most conserved for genes expressed in mid-development but not at the level of gene sequence, age, or expression, in contrast to some previous studies. The early conservation model was supported with gene duplication and birth that were the most rare for genes expressed in early development. Finally, for all gene properties, we observed the least conservation for genes expressed in late development or adult, consistent with both models. Overall, with the modular approach, we showed that different levels of molecular evolution follow different patterns of developmental constraints. Thus both models are valid, but with respect to different genomic features.
During development, vertebrate embryos pass through a “phylotypic” stage, during which their morphology is most similar between different species. This gave rise to the hourglass model, which predicts the highest developmental constraints during mid-embryogenesis. In the last decade, a large effort has been made to uncover the relation between developmental constraints and the evolution of genome. Several studies reported gene characteristics that change according to the hourglass model, e.g. sequence conservation, age, or expression. Here, we first show that some of the previous conclusions do not hold out under detailed analysis of the data. Then, we discuss the disadvantages of the standard evo-devo approach, i.e. comparing descriptive statistics of all genes across development. Results of such analysis are biased by genes expressed constantly during development (housekeeping genes). To overcome this limitation, we use a modularization approach, which reduces the complexity of the data and assures independency between the sets of genes which are compared. We identified distinct sets of genes (modules) with time-specific expression in zebrafish development and analyzed their conservation of sequence, gene expression, and regulatory elements, as well as their age and orthology relationships. Interestingly, we found different patterns of developmental constraints for different gene properties. Only conserved regulatory regions follow an hourglass pattern.
Even though pigmentation traits have had substantial impacts on the field of animal evolutionary developmental biology, they have played only relatively minor roles in plant evo-devo. This is surprising given the often direct connection between flower color and fitness variation mediated through the effects of pollinators. At the same time, ecological and evolutionary genetic studies have utilized the molecular resources available for the anthocyanin pathway to generate several examples of the molecular basis of putatively adaptive transitions in flower color. Despite this opportunity to synthesize experimental approaches in ecology, evolution, and developmental biology, the investigation of many fundamental questions in evo-devo using this powerful model is only at its earliest stages. For example, a long-standing question is whether predictable genetic changes accompany the repeated evolution of a trait. Due to the conserved nature of the biochemical and regulatory control of anthocyanin biosynthesis, it has become possible to determine whether, and under what circumstances, different types of mutations responsible for flower color variation are preferentially targeted by natural selection. In addition, because plants use anthocyanin and related compounds in vegetative tissue for other important physiological functions, the identification of naturally occurring transitions from unpigmented to pigmented flowers provides the opportunity to examine the mechanisms by which regulatory networks are co-opted into new developmental domains. Here, we review what is known about the ecological and molecular basis of anthocyanic flower color transitions in natural systems, focusing on the evolutionary and developmental features involved. In doing so, we provide suggestions for future work on this trait and suggest that there is still much to be learned from the evolutionary development of flower color transitions in nature.
anthocyanin; flower color; R2R3-MYB; predictability; co-option; pleiotropy
Many genetic networks are astonishingly robust to quantitative variation,
allowing these networks to continue functioning in the face of mutation and
environmental perturbation. However, the evolution of such robustness remains
poorly understood for real genetic networks. Here we explore whether and how
ploidy and recombination affect the evolution of robustness in a detailed
computational model of the segment polarity network. We introduce a novel
computational method that predicts the quantitative values of biochemical
parameters from bit sequences representing genotype, allowing our model to
bridge genotype to phenotype. Using this, we simulate 2,000 generations of
evolution in a population of individuals under stabilizing and truncation
selection, selecting for individuals that could sharpen the initial pattern of
engrailed and wingless expression. Robustness was measured by simulating a
mutation in the network and measuring the effect on the engrailed and wingless
patterns; higher robustness corresponded to insensitivity of this pattern to
perturbation. We compared robustness in diploid and haploid populations, with
either asexual or sexual reproduction. In all cases, robustness increased, and
the greatest increase was in diploid sexual populations; diploidy and sex
synergized to evolve greater robustness than either acting alone. Diploidy
conferred increased robustness by allowing most deleterious mutations to be
rescued by a working allele. Sex (recombination) conferred a robustness
advantage through “survival of the compatible”: those
alleles that can work with a wide variety of genetically diverse partners
persist, and this selects for robust alleles.
Most so-called “higher organisms” are diploid (have two
copies of each gene) and reproduce sexually. Diploidy may be advantageous if one
functional copy can mask the effects of a mutation in the other copy; however,
it is a liability if most mutations are dominant. Sex can increase genetic
diversity and the rate of evolution by creating new combinations of alleles that
might function better together but can also disrupt working combinations. Given
these trade-offs, why are sex and diploidy so common, and why do they occur so
often together? We hypothesize that sex and diploidy allow gene networks to
evolve to function more robustly in the face of genetic and environmental
variation. This robustness would be advantageous because organisms are exposed
to constantly changing environments and all genes undergo mutation. To test this
hypothesis, we simulated evolution in a model of the segment polarity network, a
well-studied group of genes essential for proper development in many organisms.
We compared the robustness of haploid and diploid populations that reproduced
either sexually or asexually. Sexually reproducing diploid populations evolved
the greatest robustness, suggesting an explanation for the selective advantage
of diploid sexual reproduction.
The origin of novel traits and their subsequent diversification represent central themes in evo-devo and evolutionary ecology. Here we explore the genetic and genomic basis of a class of traits that is both novel and highly diverse, in a group of organisms that is ecologically complex and experimentally tractable: horned beetles.
We developed two high quality, normalized cDNA libraries for larval and pupal Onthophagus taurus and sequenced 3,488 ESTs that assembled into 451 contigs and 2,330 singletons. We present the annotation and a comparative analysis of the conservation of the sequences. Microarrays developed from the combined libraries were then used to contrast the transcriptome of developing primordia of head horns, prothoracic horns, and legs. Our experiments identify a first comprehensive list of candidate genes for the evolution and diversification of beetle horns. We find that developing horns and legs show many similarities as well as important differences in their transcription profiles, suggesting that the origin of horns was mediated partly, but not entirely, by the recruitment of genes involved in the formation of more traditional appendages such as legs. Furthermore, we find that horns developing from the head and prothorax differ in their transcription profiles to a degree that suggests that head and prothoracic horns are not serial homologs, but instead may have evolved independently from each other.
We have laid the foundation for a systematic analysis of the genetic basis of horned beetle development and diversification with the potential to contribute significantly to several major frontiers in evolutionary developmental biology.
The evolutionary potential of a gene is constrained not only by the amino acid sequence of its product, but by its DNA sequence as well. The topology of the genetic code is such that half of the amino acids exhibit synonymous codons that can reach different subsets of amino acids from each other through single mutation. Thus, synonymous DNA sequences should access different regions of the protein sequence space through a limited number of mutations, and this may deeply influence the evolution of natural proteins. Here, we demonstrate that this feature can be of value for manipulating protein evolvability. We designed an algorithm that, starting from an input gene, constructs a synonymous sequence that systematically includes the codons with the most different evolutionary perspectives; i.e., codons that maximize accessibility to amino acids previously unreachable from the template by point mutation. A synonymous version of a bacterial antibiotic resistance gene was computed and synthesized. When concurrently submitted to identical directed evolution protocols, both the wild type and the recoded sequence led to the isolation of specific, advantageous phenotypic variants. Simulations based on a mutation isolated only from the synthetic gene libraries were conducted to assess the impact of sub-functional selective constraints, such as codon usage, on natural adaptation. Our data demonstrate that rational design of synonymous synthetic genes stands as an affordable improvement to any directed evolution protocol. We show that using two synonymous DNA sequences improves the overall yield of the procedure by increasing the diversity of mutants generated. These results provide conclusive evidence that synonymous coding sequences do experience different areas of the corresponding protein adaptive landscape, and that a sequence's codon usage effectively constrains the evolution of the encoded protein.
Evolutionary processes largely rely on the production of diversity. Genetic robustness, by allowing the accumulation of neutral diversity within a population, has been associated with increase in evolutionary potential (evolvability). In this work, we propose to use a well-known source of robustness, the redundancy of the genetic code, to alter the evolvability of any protein. The topology of the code allows synonymous codons to sample different mutational neighborhoods. Using this property, we developed an algorithm to design synonymous sequences with maximally divergent evolutionary potentials relative to the input sequences. At the population level, each of these sequences expands the scope of the evolutionary landscape that can be explored by the encoded protein, and ultimately increase the odds of uncovering adaptive mutants. We applied this principle to evolve new antibiotic resistance phenotype variants. Fundamentally, our results provide an example of how neutral diversity may favor evolvability. Moreover, in light of the rapid development in nucleic acid synthesis, the use of rationally designed synonymous genes offers a profitable enhancement to any directed evolution procedure.
A commonly held view in evolutionary biology is that speciation (the emergence of genetically distinct and reproductively incompatible subpopulations) is driven by external environmental constraints, such as localized barriers to dispersal or habitat-based variation in selection pressures. We have developed a spatially explicit model of a biological population to study the emergence of spatial and temporal patterns of genetic diversity in the absence of predetermined subpopulation boundaries. We propose a 2-D cellular automata model showing that an initially homogeneous population might spontaneously subdivide into reproductively incompatible species through sheer isolation-by-distance when the viability of offspring decreases as the genomes of parental gametes become increasingly different. This simple implementation of the Dobzhansky-Muller model provides the basis for assessing the process and completion of speciation, which is deemed to occur when there is complete postzygotic isolation between two subpopulations. The model shows an inherent tendency toward spatial self-organization, as has been the case with other spatially explicit models of evolution. A well-mixed version of the model exhibits a relatively stable and unimodal distribution of genetic differences as has been shown with previous models. A much more interesting pattern of temporal waves, however, emerges when the dispersal of individuals is limited to short distances. Each wave represents a subset of comparisons between members of emergent subpopulations diverging from one another, and a subset of these divergences proceeds to the point of speciation. The long-term persistence of diverging subpopulations is the essence of speciation in biological populations, so the rhythmic diversity waves that we have observed suggest an inherent disposition for a population experiencing isolation-by-distance to generate new species.
A commonly held view in evolutionary biology is that new species form in response to environmental factors, such as habitat differences or barriers to individual movements that sever a population. We have developed a computer model, called EvoSpace, that illustrates how new species can emerge when a species range becomes very large compared with the dispersal distances of its individuals. This situation has been called isolation-by-distance because remote parts of the range can take different evolutionary paths even though there is no particular place where we would expect different populations to separate. When the extent of genetic difference between individuals is coupled with decreasing offspring viability (e.g., resulting from developmental problems), EvoSpace predicts that sharp spatial boundaries can emerge in arbitrary locations, separating subpopulations that occasionally persist long enough to become reproductively incompatible species. The model shows an inherent tendency toward spatial self-organization, in contrast with the traditional view of environmentally forced origins of new species. We think that isolation-by-distance is a common aspect of the evolutionary process and that spatial self-organization of gene pools may often facilitate the evolution of new species.
In a now classic paper published in 1991, Alberch introduced the concept of genotype–phenotype (G→P) mapping to provide a framework for a more sophisticated discussion of the integration between genetics and developmental biology that was then available. The advent of evo-devo first and of the genomic era later would seem to have superseded talk of transitions in phenotypic space and the like, central to Alberch's approach. On the contrary, this paper shows that recent empirical and theoretical advances have only sharpened the need for a different conceptual treatment of how phenotypes are produced. Old-fashioned metaphors like genetic blueprint and genetic programme are not only woefully inadequate but positively misleading about the nature of G→P, and are being replaced by an algorithmic approach emerging from the study of a variety of actual G→P maps. These include RNA folding, protein function and the study of evolvable software. Some generalities are emerging from these disparate fields of analysis, and I suggest that the concept of ‘developmental encoding’ (as opposed to the classical one of genetic encoding) provides a promising computational–theoretical underpinning to coherently integrate ideas on evolvability, modularity and robustness and foster a fruitful framing of the G→P mapping problem.
modularity; developmental encoding; evolvability; genotype–phenotype mapping; robustness
Crustaceans of the genus Daphnia are one of the oldest model organisms in ecotoxicology, ecology and evolutionary biology. The publication of the Daphnia pulex genome has facilitated the development of genetic tools to answer long-standing questions in these research fields (Science 331: 555-561, 2011). A particular focus is laid on understanding the genetic basis of the striking ability of daphnids to change their phenotype in response to environmental stressors. Furthermore, Daphnia have recently been developed into crustacean model organisms for EvoDevo research, contributing to the ongoing attempt to resolve arthropod phylogeny. These problems require the comparative analyses of gene expression and functional data, which in turn require a standardized developmental staging system for Daphnia.
Here we provide a detailed staging system of the embryonic development of Daphnia magna based on morphological landmarks. The staging system does not rely on developmental hours and is therefore suitable for functional and ecological experiments, which often cause developmental delays in affected embryos and thus shifts in time reference points. We provide a detailed description of each stage and include schematic drawings of all stages showing relevant morphological landmarks in order to facilitate the application of this staging scheme.
We present here a staging system for Daphnia magna, which is based on morphological landmarks. The staging system can be adopted for other daphnids with minor variations since the sequence of development is highly conserved during early stages and only minor heterochronic shifts occur in late embryonic stages.
Daphnia magna; Embryonic development; Staging system; Morphogenesis; Cladocerans; Water flea
Discovering links between the genotype of an organism and its metabolite levels can increase our understanding of metabolism, its controls, and the indirect effects of metabolism on other quantitative traits. Recent technological advances in both DNA sequencing and metabolite profiling allow the use of broad-spectrum, untargeted metabolite profiling to generate phenotypic data for genome-wide association studies that investigate quantitative genetic control of metabolism within species. We conducted a genome-wide association study of natural variation in plant metabolism using the results of untargeted metabolite analyses performed on a collection of wild Arabidopsis thaliana accessions. Testing 327 metabolites against >200,000 single nucleotide polymorphisms identified numerous genotype–metabolite associations distributed non-randomly within the genome. These clusters of genotype–metabolite associations (hotspots) included regions of the A. thaliana genome previously identified as subject to recent strong positive selection (selective sweeps) and regions showing trans-linkage to these putative sweeps, suggesting that these selective forces have impacted genome-wide control of A. thaliana metabolism. Comparing the metabolic variation detected within this collection of wild accessions to a laboratory-derived population of recombinant inbred lines (derived from two of the accessions used in this study) showed that the higher level of genetic variation present within the wild accessions did not correspond to higher variance in metabolic phenotypes, suggesting that evolutionary constraints limit metabolic variation. While a major goal of genome-wide association studies is to develop catalogues of intraspecific variation, the results of multiple independent experiments performed for this study showed that the genotype–metabolite associations identified are sensitive to environmental fluctuations. Thus, studies of intraspecific variation conducted via genome-wide association will require analyses of genotype by environment interaction. Interestingly, the network structure of metabolite linkages was also sensitive to environmental differences, suggesting that key aspects of network architecture are malleable.
Understanding how genetic variation can control phenotypic variation is a fundamental goal of modern biology. We combined genome-wide association mapping with metabolomics in the plant Arabidopsis thaliana to explore how species-wide genetic variation controls metabolism. We identified numerous naturally-variable genes that may influence plant metabolism, often clustering in “hotspots.” These hotspots were proximal to selective sweeps, regions of the genome showing decreased diversity possibly from a strong selective advantage of specific variants within the region. This suggests that metabolism may be connected to the selective advantage. Interestingly, metabolite variation in wild Arabidopsis is highly constrained despite the significant genetic variation, thus providing the plant un-sampled metabolic space if the environment shifts. The observed structuring of genetic and metabolic variation suggests individual convergence upon similar phenotypes via different genotypes, possibly intra-specific parallel evolution. This phenotypic convergence couples with a pattern of genotype—phenotype association consistent with metabolite variation largely controlled by numerous small effect genetic variants. This supports the supposition that large magnitude variation is likely unstable in a complex and interconnected metabolism. If this pattern proves generally applicable to other species, it could present a significant hurdle to identifying genes controlling metabolic trait variation via genome-wide association studies.