Phenotypic plasticity can facilitate adaptive evolution in gene regulatory circuits

Body plan evolvability: The role of variability in gene regulatory networks

Recent computational modeling of early fruit fly (Drosophila) development has characterized the degree to which gene regulation networks can be robust to natural variability. In the first few hours of development, broad spatial gradients of maternally derived transcription factors activate embryonic gap genes. These gap patterns determine the subsequent segmented insect body plan through pair-rule gene expression. Gap genes are expressed with greater spatial precision than the maternal patterns. Computational modeling of the gap-gap regulatory interactions provides a mechanistic understanding for this robustness to maternal variability in wild-type (WT) patterning. A long-standing question in evolutionary biology has been how a system which is robust, such as the developmental program creating any particular species' body plan, is also evolvable, i.e. how can a system evolve or speciate, if the WT form is strongly buffered and protected? In the present work, we use the WT model to explore the breakdown of such Waddington-type 'canalization'. What levels of variability will push the system out of the WT form; are there particular pathways in the gene regulatory mechanism which are more susceptible to losing the WT form; and when robustness is lost, what types of forms are most likely to occur (i.e. what forms lie near the WT)? Manipulating maternal effects in several different pathways, we find a common gap 'peak-to-step' pattern transition in the loss of WT. We discuss these results in terms of the evolvability of insect segmentation, and in terms of experimental perturbations and mutations which could test the model predictions. We conclude by discussing the prospects for using continuum models of pattern dynamics to investigate a wider range of evo-devo problems.

Utilizing developmental dynamics for evolutionary prediction and control

Understanding, predicting, and controlling the phenotypic consequences of genetic and environmental change is essential to many areas of fundamental and applied biology. In evolutionary biology, the generative process of development is a major source of organismal evolvability that constrains or facilitates adaptive change by shaping the distribution of phenotypic variation that selection can act upon. While the complex interactions between genetic and environmental factors during development may appear to make it impossible to infer the consequences of perturbations, the persistent observation that many perturbations result in similar phenotypes indicates that there is a logic to what variation is generated. Here, we show that a general representation of development as a dynamical system can reveal this logic. We build a framework that allows predicting the phenotypic effects of perturbations, and conditions for when the effects of perturbations of different origins are concordant. We find that this concordance is explained by two generic features of development, namely the dynamical dependence of the phenotype on itself and the fact that all perturbations must affect the developmental process to have an effect on the phenotype. We apply our theoretical framework to classical models of development and show that it can be used to predict the evolutionary response to selection using information of plasticity and to accelerate evolution in a desired direction. The framework we introduce provides a way to quantitatively interchange perturbations, opening an avenue of perturbation design to control the generation of variation.

Plasticity-led and mutation-led evolutions are different modes of the same developmental gene regulatory network

The standard theory of evolution proposes that mutations cause heritable variations, which are naturally selected, leading to evolution. However, this mutation-led evolution (MLE) is being questioned by an alternative theory called plasticity-led evolution (PLE). PLE suggests that an environmental change induces adaptive phenotypes, which are later genetically accommodated. According to PLE, developmental systems should be able to respond to environmental changes adaptively. However, developmental systems are known to be robust against environmental and mutational perturbations. Thus, we expect a transition from a robust state to a plastic one. To test this hypothesis, we constructed a gene regulatory network (GRN) model that integrates developmental processes, hierarchical regulation, and environmental cues. We then simulated its evolution over different magnitudes of environmental changes. Our findings indicate that this GRN model exhibits PLE under large environmental changes and MLE under small environmental changes. Furthermore, we observed that the GRN model is susceptible to environmental or genetic fluctuations under large environmental changes but is robust under small environmental changes. This indicates a breakdown of robustness due to large environmental changes. Before the breakdown of robustness, the distribution of phenotypes is biased and aligned to the environmental changes, which would facilitate rapid adaptation should a large environmental change occur. These observations suggest that the evolutionary transition from mutation-led to plasticity-led evolution is due to a developmental transition from robust to susceptible regimes over increasing magnitudes of environmental change. Thus, the GRN model can reconcile these conflicting theories of evolution.

The Boltzmann distributions of molecular structures predict likely changes through random mutations

New folded molecular structures can only evolve after arising through mutations. This aspect is modeled using genotype-phenotype maps, which connect sequence changes through mutations to changes in molecular structures. Previous work has shown that the likelihood of appearing through mutations can differ by orders of magnitude from structure to structure and that this can affect the outcomes of evolutionary processes. Thus, we focus on the phenotypic mutation probabilities φqp, i.e., the likelihood that a random mutation changes structure p into structure q. For both RNA secondary structures and the HP protein model, we show that a simple biophysical principle can explain and predict how this likelihood depends on the new structure q: φqp is high if sequences that fold into p as the minimum-free-energy structure are likely to have q as an alternative structure with high Boltzmann frequency. This generalizes the existing concept of plastogenetic congruence from individual sequences to the entire neutral spaces of structures. Our result helps us understand why some structural changes are more likely than others, may be useful for estimating these likelihoods via sampling and makes a connection to alternative structures with high Boltzmann frequency, which could be relevant in evolutionary processes.

Plasticity-led evolution as an intrinsic property of developmental gene regulatory networks

The modern evolutionary synthesis seemingly fails to explain how a population can survive a large environmental change: the pre-existence of heritable variants adapted to the novel environment is too opportunistic, whereas the search for new adaptive mutations after the environmental change is so slow that the population may go extinct. Plasticity-led evolution, the initial environmental induction of a novel adaptive phenotype followed by genetic accommodation, has been proposed to solve this problem. However, the mechanism enabling plasticity-led evolution remains unclear. Here, we present computational models that exhibit behaviors compatible with plasticity-led evolution by extending the Wagner model of gene regulatory networks. The models show adaptive plastic response and the uncovering of cryptic mutations under large environmental changes, followed by genetic accommodation. Moreover, these behaviors are consistently observed over distinct novel environments. We further show that environmental cues, developmental processes, and hierarchical regulation cooperatively amplify the above behaviors and accelerate evolution. These observations suggest plasticity-led evolution is a universal property of complex developmental systems independent of particular mutations.

The non-deterministic genotype-phenotype map of RNA secondary structure

Selection and variation are both key aspects in the evolutionary process. Previous research on the mapping between molecular sequence (genotype) and molecular fold (phenotype) has shown the presence of several structural properties in different biological contexts, implying that these might be universal in evolutionary spaces. The deterministic genotype-phenotype (GP) map that links short RNA sequences to minimum free energy secondary structures has been studied extensively because of its computational tractability and biologically realistic nature. However, this mapping ignores the phenotypic plasticity of RNA. We define a GP map that incorporates non-deterministic (ND) phenotypes, and take RNA as a case study; we use the Boltzmann probability distribution of folded structures and examine the structural properties of ND GP maps for RNA sequences of length 12 and coarse-grained RNA structures of length 30 (RNAshapes30). A framework is presented to study robustness, evolvability and neutral spaces in the ND map. This framework is validated by demonstrating close correspondence between the ND quantities and sample averages of their deterministic counterparts. When using the ND framework we observe the same structural properties as in the deterministic GP map, such as bias, negative correlation between genotypic robustness and evolvability, and positive correlation between phenotypic robustness and evolvability.

A computational investigation of cis-gene regulation in evolution

In biological processes involving gene networks, genes regulate other genes that determine the phenotypic traits. Gene regulation plays an important role in evolutionary dynamics. In a genetic algorithm, a trans-gene regulatory mechanism was shown to speed up adaptation and evolution. Here, we examine the effect of cis-gene regulation on an adaptive system. The model is haploid. A chromosome is partitioned into regulatory loci and structural loci. The regulatory genes regulate the expression and functioning of structural genes via the cis-elements in a probabilistic manner. In the simulation, the change in the allele frequency, the mean population fitness and the efficiency of phenotypic selection are monitored. Cis-gene regulation increases adaption and accelerates the evolutionary process in comparison with the case involving absence of gene regulation. Some special features of the simulation results are as follows. A low ratio of regulatory loci and structural loci gives higher adaptation for fixed total number of loci. Plasticity is advantageous beyond a threshold value. Adaptation is better for large number of total loci when the ratio of regulatory loci to structural loci is one. However, it reaches a saturation beyond which the increase in the total loci is not advantageous. Efficiency of the phenotypic selection is higher for larger value of the initial plasticity.

Computational modelling of plasticity-led evolution

Plasticity-led evolution is a form of evolution where a change in the environment induces novel traits via phenotypic plasticity, after which the novel traits are genetically accommodated over generations under the novel environment. This mode of evolution is expected to resolve the problem of gradualism (i.e., evolution by the slow accumulation of mutations that induce phenotypic variation) implied by the Modern Evolutionary Synthesis, in the face of a large environmental change. While experimental works are essential for validating that plasticity-led evolution indeed happened, we need computational models to gain insight into its underlying mechanisms and make qualitative predictions. Such computational models should include the developmental process and gene-environment interactions in addition to genetics and natural selection. We point out that gene regulatory network models can incorporate all the above notions. In this review, we highlight results from computational modelling of gene regulatory networks that consolidate the criteria of plasticity-led evolution. Since gene regulatory networks are mathematically equivalent to artificial recurrent neural networks, we also discuss their analogies and discrepancies, which may help further understand the mechanisms underlying plasticity-led evolution.

The phenotypic plasticity of an evolving digital organism

Climate change will fundamentally reshape life on Earth in the coming decades. Therefore, understanding the extent to which species will cope with rising temperatures is of paramount importance. Phenotypic plasticity is the ability of an organism to change the morphological and functional traits encoded by its genome in response to the environment. I show here that plasticity pervades not only natural but also artificial systems that mimic the developmental process of biological organisms, such as self-replicating and evolving computer programs-digital organisms. Specifically, the environment can modify the sequence of instructions executed from a digital organism's genome (i.e. its transcriptome), which results in changes in its phenotype (i.e. the ability of the digital organism to perform Boolean logic operations). This genetic-based pathway for plasticity comes at a fitness cost to an organism's viability and generation time: the longer the transcriptome (higher fitness cost), the more chances for the environment to modify the genetic execution flow control, and the higher the likelihood for the genome to encode novel phenotypes. By studying to what extent a digital organism's phenotype is influenced by both its genome and the environment, I make a parallelism between natural and artificial evolving systems on how natural selection might slide trait regulation anywhere along a continuum from total environmental control to total genomic control, which harbours lessons not only for designing evolvable artificial systems, but also for synthetic biology.

On the effects of the modularity of gene regulatory networks on phenotypic variability and its association with robustness

Biological adaptations depend on natural selection sorting out those individuals that exhibit characters fit to their environment. Selection, in turn, depends on the phenotypic variation present in a population. Thus, evolutionary outcomes depend, to a certain extent, on the kind of variation that organisms can produce through random genetic perturbation, that is, their phenotypic variability. Moreover, the properties of developmental mechanisms that produce the organisms affect their phenotypic variability. Two of these properties are modularity and robustness. Modularity is the degree to which interactions occur mostly within groups of the system's elements and scarcely between elements in different groups. Robustness is the propensity of a system to endure perturbations while preserving its phenotype. In this paper, we used a model of gene regulatory networks (GRNs) to study the relationship between modularity and robustness in developmental processes and how modularity affects the variation that random genetic mutations produce in the expression patterns of GRNs. Our results show that modularity and robustness are correlated in multifunctional GRNs and that selection for one of these properties affects the other as well. We contend that these observations may help to understand why modularity and robustness are widespread in biological systems. Additionally, we found that modular networks tend to produce new expression patterns with subtle changes localized in the expression of a few groups of genes. This effect in the phenotypic variability of modular GRNs may bear important consequences for adaptive evolution: it may help to adjust the expression of one group of genes at a time, with few alterations on other previously evolved expression patterns.

Using phenotypic plasticity to understand the structure and evolution of the genotype-phenotype map

Deciphering the genotype-phenotype map necessitates relating variation at the genetic level to variation at the phenotypic level. This endeavour is inherently limited by the availability of standing genetic variation, the rate of spontaneous mutation to novo genetic variants, and possible biases associated with induced mutagenesis. An interesting alternative is to instead rely on the environment as a source of variation. Many phenotypic traits change plastically in response to the environment, and these changes are generally underlain by changes in gene expression. Relating gene expression plasticity to the phenotypic plasticity of more integrated organismal traits thus provides useful information about which genes influence the development and expression of which traits, even in the absence of genetic variation. We here appraise the prospects and limits of such an environment-for-gene substitution for investigating the genotype-phenotype map. We review models of gene regulatory networks, and discuss the different ways in which they can incorporate the environment to mechanistically model phenotypic plasticity and its evolution. We suggest that substantial progress can be made in deciphering this genotype-environment-phenotype map, by connecting theory on gene regulatory network to empirical patterns of gene co-expression, and by more explicitly relating gene expression to the expression and development of phenotypes, both theoretically and empirically.

Recombination facilitates genetic assimilation of new traits in gene regulatory networks

A new phenotypic variant may appear first in organisms through plasticity, that is, as a response to an environmental signal or other nongenetic perturbation. If such trait is beneficial, selection may increase the frequency of alleles that enable and facilitate its development. Thus, genes may take control of such traits, decreasing dependence on nongenetic disturbances, in a process called genetic assimilation. Despite an increasing amount of empirical studies supporting genetic assimilation, its significance is still controversial. Whether genetic assimilation is widespread depends, to a great extent, on how easily mutation and recombination reduce the trait's dependence on nongenetic perturbations. Previous research suggests that this is the case for mutations. Here we use simulations of gene regulatory network dynamics to address this issue with respect to recombination. We find that recombinant offspring of parents that produce a new phenotype through plasticity are more likely to produce the same phenotype without requiring any perturbation. They are also prone to preserve the ability to produce that phenotype after genetic and nongenetic perturbations. Our work also suggests that ancestral plasticity can play an important role for setting the course that evolution takes. In sum, our results indicate that the manner in which phenotypic variation maps unto genetic variation facilitates evolution through genetic assimilation in gene regulatory networks. Thus, we contend that the importance of this evolutionary mechanism should not be easily neglected.

Emerging Role of Chimeric RNAs in Cell Plasticity and Adaptive Evolution of Cancer Cells

Gene fusions can give rise to somatic alterations in cancers. Fusion genes have the potential to create chimeric RNAs, which can generate the phenotypic diversity of cancer cells, and could be associated with novel molecular functions related to cancer cell survival and proliferation. The expression of chimeric RNAs in cancer cells might impact diverse cancer-related functions, including loss of apoptosis and cancer cell plasticity, and promote oncogenesis. Due to their recurrence in cancers and functional association with oncogenic processes, chimeric RNAs are considered biomarkers for cancer diagnosis. Several recent studies demonstrated that chimeric RNAs could lead to the generation of new functionality for the resistance of cancer cells against drug therapy. Therefore, targeting chimeric RNAs in drug resistance cancer could be useful for developing precision medicine. So, understanding the functional impact of chimeric RNAs in cancer cells from an evolutionary perspective will be helpful to elucidate cancer evolution, which could provide a new insight to design more effective therapies for cancer patients in a personalized manner.

From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.

Resurrecting the metabolome: Rapid evolution magnifies the metabolomic plasticity to predation in a natural Daphnia population

Populations rely on already present plastic responses (ancestral plasticity) and evolution (including both evolution of mean trait values, constitutive evolution, and evolution of plasticity) to adapt to novel environmental conditions. Because of the lack of evidence from natural populations, controversy remains regarding the interplay between ancestral plasticity and rapid evolution in driving responses to new stressors. We addressed this topic at the level of the metabolome utilizing a resurrected natural population of the water flea Daphnia magna that underwent a human-caused increase followed by a reduction in predation pressure within ~16 years. Predation risk induced plastic changes in the metabolome which were mainly related to shifts in amino acid and sugar metabolism, suggesting predation risk affected protein and sugar utilization to increase energy supply. Both the constitutive and plastic components of the metabolic profiles showed rapid, probably adaptive evolution whereby ancestral plasticity and evolution contributed nearly equally to the total changes of the metabolomes. The subpopulation that experienced the strongest fish predation pressure and showed the strongest phenotypic response, also showed the strongest metabolomic response to fish kairomones, both in terms of the number of responsive metabolites and in the amplitude of the multivariate metabolomic reaction norm. More importantly, the metabolites with higher ancestral plasticity showed stronger evolution of plasticity when predation pressure increased, while this pattern reversed when predation pressure relaxed. Our results therefore highlight that the evolution in response to a novel pressure in a natural population magnified the metabolomic plasticity to this stressor.

Signaling pathways have an inherent need for noise to acquire information

BACKGROUND

All living systems acquire information about their environment. At the cellular level, they do so through signaling pathways. Such pathways rely on reversible binding interactions between molecules that detect and transmit the presence of an extracellular cue or signal to the cell's interior. These interactions are inherently stochastic and thus noisy. On the one hand, noise can cause a signaling pathway to produce the same response for different stimuli, which reduces the amount of information a pathway acquires. On the other hand, in processes such as stochastic resonance, noise can improve the detection of weak stimuli and thus the acquisition of information. It is not clear whether the kinetic parameters that determine a pathway's operation cause noise to reduce or increase the acquisition of information.

RESULTS

We analyze how the kinetic properties of the reversible binding interactions used by signaling pathways affect the relationship between noise, the response to a signal, and information acquisition. Our results show that, under a wide range of biologically sensible parameter values, a noisy dynamic of reversible binding interactions is necessary to produce distinct responses to different stimuli. As a consequence, noise is indispensable for the acquisition of information in signaling pathways.

CONCLUSIONS

Our observations go beyond previous work by showing that noise plays a positive role in signaling pathways, demonstrating that noise is essential when such pathways acquire information.

Phenotypic noise and the cost of complexity

Experimental studies demonstrate the existence of phenotypic diversity despite constant genotype and environment. Theoretical models based on a single phenotypic character predict that during an adaptation event, phenotypic noise should be positively selected far from the fitness optimum because it increases the fitness of the genotype, and then be selected against when the population reaches the optimum. It is suggested that because of this fitness gain, phenotypic noise should promote adaptive evolution. However, it is unclear how the selective advantage of phenotypic noise is linked to the rate of evolution, and whether any advantage would hold for more realistic, multidimensional phenotypes. Indeed, complex organisms suffer a cost of complexity, where beneficial mutations become rarer as the number of phenotypic characters increases. Using a quantitative genetics approach, we first show that for a one-dimensional phenotype, phenotypic noise promotes adaptive evolution on plateaus of positive fitness, independently from the direct selective advantage on fitness. Second, we show that for multidimensional phenotypes, phenotypic noise evolves to a low-dimensional configuration, with elevated noise in the direction of the fitness optimum. Such a dimensionality reduction of the phenotypic noise promotes adaptive evolution and numerical simulations show that it reduces the cost of complexity.

Seasonal Variation in Genome-Wide DNA Methylation Patterns and the Onset of Seasonal Timing of Reproduction in Great Tits

In seasonal environments, timing of reproduction is a trait with important fitness consequences, but we know little about the molecular mechanisms that underlie the variation in this trait. Recently, several studies put forward DNA methylation as a mechanism regulating seasonal timing of reproduction in both plants and animals. To understand the involvement of DNA methylation in seasonal timing of reproduction, it is necessary to examine within-individual temporal changes in DNA methylation, but such studies are very rare. Here, we use a temporal sampling approach to examine changes in DNA methylation throughout the breeding season in female great tits (Parus major) that were artificially selected for early timing of breeding. These females were housed in climate-controlled aviaries and subjected to two contrasting temperature treatments. Reduced representation bisulfite sequencing on red blood cell derived DNA showed genome-wide temporal changes in more than 40,000 out of the 522,643 CpG sites examined. Although most of these changes were relatively small (mean within-individual change of 6%), the sites that showed a temporal and treatment-specific response in DNA methylation are candidate sites of interest for future studies trying to understand the link between DNA methylation patterns and timing of reproduction.

Developmental Bias and Evolution: A Regulatory Network Perspective

Phenotypic variation is generated by the processes of development, with some variants arising more readily than others-a phenomenon known as "developmental bias." Developmental bias and natural selection have often been portrayed as alternative explanations, but this is a false dichotomy: developmental bias can evolve through natural selection, and bias and selection jointly influence phenotypic evolution. Here, we briefly review the evidence for developmental bias and illustrate how it is studied empirically. We describe recent theory on regulatory networks that explains why the influence of genetic and environmental perturbation on phenotypes is typically not uniform, and may even be biased toward adaptive phenotypic variation. We show how bias produced by developmental processes constitutes an evolving property able to impose direction on adaptive evolution and influence patterns of taxonomic and phenotypic diversity. Taking these considerations together, we argue that it is not sufficient to accommodate developmental bias into evolutionary theory merely as a constraint on evolutionary adaptation. The influence of natural selection in shaping developmental bias, and conversely, the influence of developmental bias in shaping subsequent opportunities for adaptation, requires mechanistic models of development to be expanded and incorporated into evolutionary theory. A regulatory network perspective on phenotypic evolution thus helps to integrate the generation of phenotypic variation with natural selection, leaving evolutionary biology better placed to explain how organisms adapt and diversify.

On the networked architecture of genotype spaces and its critical effects on molecular evolution

Evolutionary dynamics is often viewed as a subtle process of change accumulation that causes a divergence among organisms and their genomes. However, this interpretation is an inheritance of a gradualistic view that has been challenged at the macroevolutionary, ecological and molecular level. Actually, when the complex architecture of genotype spaces is taken into account, the evolutionary dynamics of molecular populations becomes intrinsically non-uniform, sharing deep qualitative and quantitative similarities with slowly driven physical systems: nonlinear responses analogous to critical transitions, sudden state changes or hysteresis, among others. Furthermore, the phenotypic plasticity inherent to genotypes transforms classical fitness landscapes into multiscapes where adaptation in response to an environmental change may be very fast. The quantitative nature of adaptive molecular processes is deeply dependent on a network-of-networks multilayered structure of the map from genotype to function that we begin to unveil.

Molecular investigation of genetic assimilation during the rapid adaptive radiations of East African cichlid fishes

Adaptive radiations are characterized by adaptive diversification intertwined with rapid speciation within a lineage resulting in many ecologically specialized, phenotypically diverse species. It has been proposed that adaptive radiations can originate from ancestral lineages with pronounced phenotypic plasticity in adaptive traits, facilitating ecologically driven phenotypic diversification that is ultimately fixed through genetic assimilation of gene regulatory regions. This study aimed to investigate how phenotypic plasticity is reflected in gene expression patterns in the trophic apparatus of several lineages of East African cichlid fishes, and whether the observed patterns support genetic assimilation. This investigation used a split brood experimental design to compare adaptive plasticity in species from within and outside of adaptive radiations. The plastic response was induced in the crushing pharyngeal jaws through feeding individuals either a hard or soft diet. We find that nonradiating, basal lineages show higher levels of adaptive morphological plasticity than the derived, radiated lineages, suggesting that these differences have become partially genetically fixed during the formation of the adaptive radiations. Two candidate genes that may have undergone genetic assimilation, gif and alas1, were identified, in addition to alterations in the wiring of LPJ patterning networks. Taken together, our results suggest that genetic assimilation may have dampened the inducibility of plasticity related genes during the adaptive radiations of East African cichlids, flattening the reaction norms and canalizing their feeding phenotypes, driving adaptation to progressively more narrow ecological niches.

Robustness versus evolvability analysis for regulatory feed-forward loops

From the definition, it appears that phenotypic robustness and evolvability of an organism are inversely related to each other. However, a number of studies exploring this question have found conflicting evidences in this regard. This question motivated the current work where we explore the relationship between robustness and evolvability. As a model system, we pick the Feed Forward Loops (FFLs), and develop a framework to characterize their performance in terms of their ability to resist changes to steady state expression (robustness), and their ability to evolve towards novel phenotypes (evolvability). We demonstrate that robustness and evolvability are positively correlated in some FFL topologies. We compare this against other small regulatory topologies, and show that the same trend does not hold among them. We postulate that the ability to positively link robustness and evolvability could be an additional reason for over-representation of FFLs in living organisms, as compared to other regulatory topologies.

Modulation of yellow expression contributes to thermal plasticity of female abdominal pigmentation in Drosophila melanogaster

Phenotypic plasticity describes the ability of a given genotype to produce distinct phenotypes in different environments. We use the temperature sensitivity of abdominal pigmentation in Drosophila melanogaster females as a model to analyse the effect of the environment on development. We reported previously that thermal plasticity of abdominal pigmentation in females involves the pigmentation gene tan (t). However, the expression of the pigmentation gene yellow (y) was also modulated by temperature in the abdominal epidermis of pharate females. We investigate here the contribution of y to female abdominal pigmentation plasticity. First, we show that y is required for the production of black Dopamine-melanin. Then, using in situ hybridization, we show that the expression of y is strongly modulated by temperature in the abdominal epidermis of pharate females but not in bristles. Interestingly, these two expression patterns are known to be controlled by distinct enhancers. However, the activity of the y-wing-body epidermal enhancer only partially mediates the effect of temperature suggesting that additional regulatory sequences are involved. In addition, we show that y and t co-expression is needed to induce strong black pigmentation indicating that y contributes to female abdominal pigmentation plasticity.

Why and how genetic canalization evolves in gene regulatory networks

Background

Genetic canalization reflects the capacity of an organism’s phenotype to remain unchanged in spite of mutations. As selection on genetic canalization is weak and indirect, whether or not genetic canalization can reasonably evolve in complex genetic architectures is still an open question. In this paper, we use a quantitative model of gene regulatory network to describe the conditions in which substantial canalization is expected to emerge in a stable environment.

Results

Through an individual-based simulation framework, we confirmed that most parameters associated with the network topology (complexity and size of the network) have less influence than mutational parameters (rate and size of mutations) on the evolution of genetic canalization. We also established that selecting for extreme phenotypic optima (nil or full gene expression) leads to much higher canalization levels than selecting for intermediate expression levels. Overall, constrained networks evolve less canalization than networks in which some genes could evolve freely (i.e. without direct stabilizing selection pressure on gene expression).

Conclusions

Taken together, these results lead us to propose a two-fold mechanism involved in the evolution of genetic canalization in gene regulatory networks: the shrinkage of mutational target (useless genes are virtually removed from the network) and redundancy in gene regulation (so that some regulatory factors can be lost without affecting gene expression).

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-016-0801-2) contains supplementary material, which is available to authorized users.

Phenotypic Plasticity through Transcriptional Regulation of the Evolutionary Hotspot Gene tan in Drosophila melanogaster

Phenotypic plasticity is the ability of a given genotype to produce different phenotypes in response to distinct environmental conditions. Phenotypic plasticity can be adaptive. Furthermore, it is thought to facilitate evolution. Although phenotypic plasticity is a widespread phenomenon, its molecular mechanisms are only beginning to be unravelled. Environmental conditions can affect gene expression through modification of chromatin structure, mainly via histone modifications, nucleosome remodelling or DNA methylation, suggesting that phenotypic plasticity might partly be due to chromatin plasticity. As a model of phenotypic plasticity, we study abdominal pigmentation of Drosophila melanogaster females, which is temperature sensitive. Abdominal pigmentation is indeed darker in females grown at 18°C than at 29°C. This phenomenon is thought to be adaptive as the dark pigmentation produced at lower temperature increases body temperature. We show here that temperature modulates the expression of tan (t), a pigmentation gene involved in melanin production. t is expressed 7 times more at 18°C than at 29°C in female abdominal epidermis. Genetic experiments show that modulation of t expression by temperature is essential for female abdominal pigmentation plasticity. Temperature modulates the activity of an enhancer of t without modifying compaction of its chromatin or level of the active histone mark H3K27ac. By contrast, the active mark H3K4me3 on the t promoter is strongly modulated by temperature. The H3K4 methyl-transferase involved in this process is likely Trithorax, as we show that it regulates t expression and the H3K4me3 level on the t promoter and also participates in female pigmentation and its plasticity. Interestingly, t was previously shown to be involved in inter-individual variation of female abdominal pigmentation in Drosophila melanogaster, and in abdominal pigmentation divergence between Drosophila species. Sensitivity of t expression to environmental conditions might therefore give more substrate for selection, explaining why this gene has frequently been involved in evolution of pigmentation.

Environmental conditions can strongly modulate the phenotype produced by a particular genotype. This process, called phenotypic plasticity, has major implications in medicine and agricultural sciences, and is thought to facilitate evolution. Phenotypic plasticity is observed in many animals and plants but its mechanisms are only partially understood. As a model of phenotypic plasticity, we study the effect of temperature on female abdominal pigmentation in the fruit fly Drosophila melanogaster. Here we show that temperature affects female abdominal pigmentation by modulating the expression of tan (t), a gene involved in melanin production, in female abdominal epidermis. This effect is mediated at least partly by a particular regulatory sequence of t, the t_MSE enhancer. However we detected no modulation of chromatin structure of t_MSE by temperature. By contrast, the level of the active chromatin mark H3K4me3 on the t promoter is strongly increased at lower temperature. We show that the H3K4 methyl-transferase Trithorax is involved in female abdominal pigmentation and its plasticity and regulates t expression and H3K4me3 level on the t promoter. Several studies have linked t to pigmentation evolution within and between Drosophila species. Our results suggest that sensitivity of t expression to temperature might facilitate its role in pigmentation evolution.

Evolutionary Developmental Biology (Evo-Devo) Research in Latin America

Famous for its blind cavefish and Darwin's finches, Latin America is home to some of the richest biodiversity hotspots of our planet. The Latin American fauna and flora inspired and captivated naturalists from the nineteenth and twentieth centuries, including such notable pioneers such as Fritz Müller, Florentino Ameghino, and Léon Croizat who made a significant contribution to the study of embryology and evolutionary thinking. But, what are the historical and present contributions of the Latin American scientific community to Evo-Devo? Here, we provide the first comprehensive overview of the Evo-Devo laboratories based in Latin America and describe current lines of research based on endemic species, focusing on body plans and patterning, systematics, physiology, computational modeling approaches, ecology, and domestication. Literature searches reveal that Evo-Devo in Latin America is still in its early days; while showing encouraging indicators of productivity, it has not stabilized yet, because it relies on few and sparsely distributed laboratories. Coping with the rapid changes in national scientific policies and contributing to solve social and health issues specific to each region are among the main challenges faced by Latin American researchers. The 2015 inaugural meeting of the Pan-American Society for Evolutionary Developmental Biology played a pivotal role in bringing together Latin American researchers eager to initiate and consolidate regional and worldwide collaborative networks. Such networks will undoubtedly advance research on the extremely high genetic and phenotypic biodiversity of Latin America, bound to be an almost infinite source of amazement and fascinating findings for the Evo-Devo community.

Root Architecture Diversity and Meristem Dynamics in Different Populations of Arabidopsis thaliana

Arabidopsis thaliana has been an excellent model system for molecular genetic approaches to development and physiology. More recently, the potential of studying various accessions collected from diverse habitats has been started to exploit. Col-0 has been the best-studied accession but we now know that several traits show significant divergences among them. In this work, we focused in the root that has become a key system for development. We studied root architecture and growth dynamics of 12 Arabidopsis accessions. Our data reveal a wide variability in root architecture and root length among accessions. We also found variability in the root apical meristem (RAM), explained mainly by cell size at the RAM transition domain and possibly by peculiar forms of organization at the stem cell niche in some accessions. Contrary to Col-0 reports, in some accessions the RAM size not always explains the variations in the root length; indicating that elongated cell size could be more relevant in the determination of root length than the RAM size itself. This study contributes to investigations dealing with understanding the molecular and cellular basis of phenotypic variation, the role of plasticity on adaptation, and the developmental mechanisms that may restrict phenotypic variation in response to contrasting environmental conditions.

Regulatory mechanisms link phenotypic plasticity to evolvability

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.

Relative stability of network states in Boolean network models of gene regulation in development

Progress in cell type reprogramming has revived the interest in Waddington's concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington's landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols.

Can biological complexity be reverse engineered?

Concerns with the use of engineering approaches in biology have recently been raised. I examine two related challenges to biological research that I call the synchronic and diachronic underdetermination problem. The former refers to challenges associated with the inference of design principles underlying system capacities when the synchronic relations between lower-level processes and higher-level systems capacities are degenerate (many-to-many). The diachronic underdetermination problem regards the problem of reverse engineering a system where the non-linear relations between system capacities and lower-level mechanisms are changing over time. Braun and Marom argue that recent insights to biological complexity leave the aim of reverse engineering hopeless - in principle as well as in practice. While I support their call for systemic approaches to capture the dynamic nature of living systems, I take issue with the conflation of reverse engineering with naïve reductionism. I clarify how the notion of design principles can be more broadly conceived and argue that reverse engineering is compatible with a dynamic view of organisms. It may even help to facilitate an integrated account that bridges the gap between mechanistic and systems approaches.

Robustness to noise in gene expression evolves despite epistatic constraints in a model of gene networks

Stochastic noise in gene expression causes variation in the development of phenotypes, making such noise a potential target of stabilizing selection. Here, we develop a new simulation model of gene networks to study the adaptive landscape underlying the evolution of robustness to noise. We find that epistatic interactions between the determinants of the expression of a gene and its downstream effect impose significant constraints on evolution, but these interactions do allow the gradual evolution of increased robustness. Despite strong sign epistasis, adaptation rarely proceeds via deleterious intermediate steps, but instead occurs primarily through small beneficial mutations. A simple mathematical model captures the relevant features of the single-gene fitness landscape and explains counterintuitive patterns, such as a correlation between the mean and standard deviation of phenotypes. In more complex networks, mutations in regulatory regions provide evolutionary pathways to increased robustness. These results chart the constraints and possibilities of adaptation to reduce expression noise and demonstrate the potential of a novel modeling framework for gene networks.

Epigenetic resolution of the 'curse of complexity' in adaptive evolution of complex traits

The age of most genes exceeds the longevity of their genomic and physiological associations by many orders of magnitude. Such transient contexts modulate the expression of ancient genes to produce currently appropriate and often highly distinct developmental and functional outcomes. The efficacy of such adaptive modulation is diminished by the high dimensionality of complex organisms and associated vast areas of neutrality in their genotypic and developmental networks (and, thus, weak natural selection). Here I explore whether epigenetic effects facilitate adaptive modulation of complex phenotypes by effectively reducing the dimensionality of their deterministic networks and thus delineating their developmental and evolutionary trajectories even under weak selection. Epigenetic effects that link unconnected or widely dispersed elements of genotype space in ecologically relevant time could account for the rapid appearance of functionally integrated adaptive modifications. On an organismal time scale, conceptually similar processes occur during recurrent epigenetic reprogramming of somatic stem cells to produce, recurrently and reversibly, a bewildering array of differentiated and persistent cell lineages, all sharing identical genomic sequences despite strongly distinct phenotypes. I discuss whether close dependency of onset, scope and duration of epigenetic effects on cellular and genomic context in stem cells could provide insights into contingent modulation of conserved genomic material on a much longer evolutionary time scale. I review potential empirical examples of epigenetic bridges that reduce phenotype dimensionality and accomplish rapid adaptive modulation in the evolution of novelties, expression of behavioural types, and stress-induced ossification schedules.

Bioattractors: dynamical systems theory and the evolution of regulatory processes

In this paper, we illustrate how dynamical systems theory can provide a unifying conceptual framework for evolution of biological regulatory systems. Our argument is that the genotype-phenotype map can be characterized by the phase portrait of the underlying regulatory process. The features of this portrait--such as attractors with associated basins and their bifurcations--define the regulatory and evolutionary potential of a system. We show how the geometric analysis of phase space connects Waddington's epigenetic landscape to recent computational approaches for the study of robustness and evolvability in network evolution. We discuss how the geometry of phase space determines the probability of possible phenotypic transitions. Finally, we demonstrate how the active, self-organizing role of the environment in phenotypic evolution can be understood in terms of dynamical systems concepts. This approach yields mechanistic explanations that go beyond insights based on the simulation of evolving regulatory networks alone. Its predictions can now be tested by studying specific, experimentally tractable regulatory systems using the tools of modern systems biology. A systematic exploration of such systems will enable us to understand better the nature and origin of the phenotypic variability, which provides the substrate for evolution by natural selection.

Modeling the evolution of complex genetic systems: the gene network family tree

In 1994 and 1996, Andreas Wagner introduced a novel model in two papers addressing the evolution of genetic regulatory networks. This work, and a suite of papers that followed using similar models, helped integrate network thinking into biology and motivate research focused on the evolution of genetic networks. The Wagner network has its mathematical roots in the Ising model, a statistical physics model describing the activity of atoms on a lattice, and in neural networks. These models have given rise to two branches of applications, one in physics and biology and one in artificial intelligence and machine learning. Here, we review development along these branches, outline similarities and differences between biological models of genetic regulatory circuits and neural circuits models used in machine learning, and identify ways in which these models can provide novel insights into biological systems.

The evolution of heterogeneities altered by mutational robustness, gene expression noise and bottlenecks in gene regulatory networks

Intra-population heterogeneity is commonly observed in natural and cellular populations and has profound influence on their evolution. For example, intra-tumor heterogeneity is prevalent in most tumor types and can result in the failure of tumor therapy. However, the evolutionary process of heterogeneity remains poorly characterized at both genotypic and phenotypic level. Here we study the evolution of intra-population heterogeneities of gene regulatory networks (GRN), in particular mutational robustness and gene expression noise as contributors to the development of heterogeneities. By in silico simulations, it was found that the impact of these factors on GRN can, under certain conditions, promote phenotypic heterogeneity. We also studied the effect of population bottlenecks on the evolution of GRN. When the GRN population passes through such bottlenecks, neither mutational robustness nor population fitness was observed to be substantially altered. Interestingly, however, we did detect a significant increase in the number of potential “generator” genes which can substantially induce population fitness, when stimulated by mutational hits.

Reproduction cost reduces demographic stochasticity and enhances inter-individual compatibility

A population׳s survival depends on its ability to adapt to constraints impinging upon it. As such, adaptation is at the heart of an increasing number of theoretical models. In this paper, we propose a bottom-up evolutionary model to explore the relationship between individual evolutionary dynamics and population-level survival. To do so, we extend a well-established model of gene network evolution by introducing a cost for reproduction. As a result population sizes fluctuate and populations can even go extinct. We find that if a population survives a small and critical number of generations, it will reach a quasi-stationary state which ensures long-term survival. In a constant environment, individual adaptation occurs in response to changes in a populations genetic composition. We show that genetic compatibility increases over generations as a by-product of selection for robustness, thus preventing extinction. We also demonstrate that the number of reproductive opportunities per individual, initial population size, and mutation rates all influence population survival. Finally, mixing different populations reveals that individual properties of gene networks co-evolve with the genetic composition of the population in order to maximize an individuals reproductive success.

Mutational robustness accelerates the origin of novel RNA phenotypes through phenotypic plasticity

Novel phenotypes can originate either through mutations in existing genotypes or through phenotypic plasticity, the ability of one genotype to form multiple phenotypes. From molecules to organisms, plasticity is a ubiquitous feature of life, and a potential source of exaptations, adaptive traits that originated for nonadaptive reasons. Another ubiquitous feature is robustness to mutations, although it is unknown whether such robustness helps or hinders the origin of new phenotypes through plasticity. RNA is ideal to address this question, because it shows extensive plasticity in its secondary structure phenotypes, a consequence of their continual folding and unfolding, and these phenotypes have important biological functions. Moreover, RNA is to some extent robust to mutations. This robustness structures RNA genotype space into myriad connected networks of genotypes with the same phenotype, and it influences the dynamics of evolving populations on a genotype network. In this study I show that both effects help accelerate the exploration of novel phenotypes through plasticity. My observations are based on many RNA molecules sampled at random from RNA sequence space, and on 30 biological RNA molecules. They are thus not only a generic feature of RNA sequence space but are relevant for the molecular evolution of biological RNA.

VCF2Networks: applying genotype networks to single-nucleotide variants data

SUMMARY

A wealth of large-scale genome sequencing projects opens the doors to new approaches to study the relationship between genotype and phenotype. One such opportunity is the possibility to apply genotype networks analysis to population genetics data. Genotype networks are a representation of the set of genotypes associated with a single phenotype, and they allow one to estimate properties such as the robustness of the phenotype to mutations, and the ability of its associated genotypes to evolve new adaptations. So far, though, genotype networks analysis has rarely been applied to population genetics data. To help fill this gap, here we present VCF2Networks, a tool to determine and study genotype network structure from single-nucleotide variant data.

AVAILABILITY AND IMPLEMENTATION

VCF2Networks is available at https://bitbucket.org/dalloliogm/vcf2networks.

CONTACT

giovanni.dallolio@kcl.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mutation rates and evolution of multiple coding in RNA-based protocells

RNA has a myriad of biological roles in contemporary life. We use the RNA paradigm for genotype-phenotype mappings to study the evolution of multiple coding in dependence to mutation rates. We study three different one-to-many genotype-phenotype mappings which have the potential to encode the information for multiple functions on a single sequence. These three different maps are (i) cofolding, where two sequences can bind and “cofold,” (ii) suboptimal folding, where the alternative foldings within a certain range of the native state of sequences are considered, and (iii) adapter-based folding, in which protocells can evolve adapter-mediated alternative foldings. We study how protocells with a set of sequences can code for a set of predefined functional structures, while avoiding all other structures, which are considered to be misfoldings. Note that such misfolded structures are far more prevalent than functional ones. Our results highlight the flexibility of the RNA sequence to secondary structure mapping and the power of evolution to shape the genotype-phenotype mapping. We show that high fitness can be achieved even at high mutation rates. Mutation rates affect genome size, but differently depending on which folding method is used. We observe that cofolding limits the possibility to avoid misfolded structures and that adapters are always beneficial for fitness, but even more beneficial at low mutation rates. In all cases, the evolution procedure selects for molecules that can form additional structures. Our results indicate that inherent properties of RNA molecules and their interactions allow the evolution of complexity even at high mutation rates.