Neutralism and selectionism: a network-based reconciliation

Evolution of gene regulatory networks by means of selection and random genetic drift

The evolution of a population by means of genetic drift and natural selection operating on a gene regulatory network (GRN) of an individual has not been scrutinized in depth. Thus, the relative importance of various evolutionary forces and processes on shaping genetic variability in GRNs is understudied. In this study, we implemented a simulation framework, called EvoNET, that simulates forward-in-time the evolution of GRNs in a population. The fitness effect of mutations is not constant, rather fitness of each individual is evaluated on the phenotypic level, by measuring its distance from an optimal phenotype. Each individual goes through a maturation period, where its GRN may reach an equilibrium, thus deciding its phenotype. Afterwards, individuals compete to produce the next generation. We examine properties of the GRN evolution, such as robustness against the deleterious effect of mutations and the role of genetic drift. We are able to confirm previous hypotheses regarding the effect of mutations and we provide new insights on the interplay between random genetic drift and natural selection.

Robust genetic codes enhance protein evolvability

The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability-the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether such robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively parallel sequence-to-function assays to construct and analyze 6 empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. However, the standard genetic code is rarely exceptional in this regard, because many alternative codes render smoother landscapes than the standard code. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we show that such alternative codes radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering nonstandard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the bio-containment of synthetic organisms, respectively.

The simplicity of protein sequence-function relationships

How complicated is the genetic architecture of proteins - the set of causal effects by which sequence determines function? High-order epistatic interactions among residues are thought to be pervasive, making a protein's function difficult to predict or understand from its sequence. Most studies, however, used methods that overestimate epistasis, because they analyze genetic architecture relative to a designated reference sequence - causing measurement noise and small local idiosyncrasies to propagate into pervasive high-order interactions - or have not effectively accounted for global nonlinearity in the sequence-function relationship. Here we present a new reference-free method that jointly estimates global nonlinearity and specific epistatic interactions across a protein's entire genotype-phenotype map. This method yields a maximally efficient explanation of a protein's genetic architecture and is more robust than existing methods to measurement noise, partial sampling, and model misspecification. We reanalyze 20 combinatorial mutagenesis experiments from a diverse set of proteins and find that additive and pairwise effects, along with a simple nonlinearity to account for limited dynamic range, explain a median of 96% of total variance in measured phenotypes (and >92% in every case). Only a tiny fraction of genotypes are strongly affected by third- or higher-order epistasis. Genetic architecture is also sparse: the number of terms required to explain the vast majority of variance is smaller than the number of genotypes by many orders of magnitude. The sequence-function relationship in most proteins is therefore far simpler than previously thought, opening the way for new and tractable approaches to characterize it.

Moderating the neutralist-selectionist debate: exactly which propositions are we debating, and which arguments are valid?

Half a century after its foundation, the neutral theory of molecular evolution continues to attract controversy. The debate has been hampered by the coexistence of different interpretations of the core proposition of the neutral theory, the 'neutral mutation-random drift' hypothesis. In this review, we trace the origins of these ambiguities and suggest potential solutions. We highlight the difference between the original, the revised and the nearly neutral hypothesis, and re-emphasise that none of them equates to the null hypothesis of strict neutrality. We distinguish the neutral hypothesis of protein evolution, the main focus of the ongoing debate, from the neutral hypotheses of genomic and functional DNA evolution, which for many species are generally accepted. We advocate a further distinction between a narrow and an extended neutral hypothesis (of which the latter posits that random non-conservative amino acid substitutions can cause non-ecological phenotypic divergence), and we discuss the implications for evolutionary biology beyond the domain of molecular evolution. We furthermore point out that the debate has widened from its initial focus on point mutations, and also concerns the fitness effects of large-scale mutations, which can alter the dosage of genes and regulatory sequences. We evaluate the validity of neutralist and selectionist arguments and find that the tested predictions, apart from being sensitive to violation of underlying assumptions, are often derived from the null hypothesis of strict neutrality, or equally consistent with the opposing selectionist hypothesis, except when assuming molecular panselectionism. Our review aims to facilitate a constructive neutralist-selectionist debate, and thereby to contribute to answering a key question of evolutionary biology: what proportions of amino acid and nucleotide substitutions and polymorphisms are adaptive?

Open-endedness in synthetic biology: A route to continual innovation for biological design

Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.

Gene expression bias between the subgenomes of allopolyploid hybrids is an emergent property of the kinetics of expression

Hybridization coupled to polyploidy, or allopolyploidy, has dramatically shaped the evolution of flowering plants, teleost fishes, and other lineages. Studies of recently formed allopolyploid plants have shown that the two subgenomes that merged to form that new allopolyploid do not generally express their genes equally. Instead, one of the two subgenomes expresses its paralogs more highly on average. Meanwhile, older allopolyploidy events tend to show biases in duplicate losses, with one of the two subgenomes retaining more genes than the other. Since reduced expression is a pathway to duplicate loss, understanding the origins of expression biases may help explain the origins of biased losses. Because we expect gene expression levels to experience stabilizing selection, our conceptual frameworks for how allopolyploid organisms form tend to assume that the new allopolyploid will show balanced expression between its subgenomes. It is then necessary to invoke phenomena such as differences in the suppression of repetitive elements to explain the observed expression imbalances. Here we show that, even for phenotypically identical diploid progenitors, the inherent kinetics of gene expression give rise to biases between the expression levels of the progenitor genes in the hybrid. Some of these biases are expected to be gene-specific and not give rise to global differences in progenitor gene expression. However, particularly in the case of allopolyploids formed from progenitors with different genome sizes, global expression biases favoring one subgenome are expected immediately on formation. Hence, expression biases are arguably the expectation upon allopolyploid formation rather than a phenomenon needing explanation. In the future, a deeper understanding of the kinetics of allopolyploidy may allow us to better understand both biases in duplicate losses and hybrid vigor.

Robustness and complexity

When a system robustly corrects component-level errors, the direct pressure on component performance declines. Components become less reliable, maintain more genetic variability, or drift neutrally, creating new forms of complexity. Examples include the hourglass pattern of biological development and the hourglass architecture for robustly complex systems in engineering.

The evolutionary consequences of epigenesis and neutral change: A conceptual approach at the organismal level

Living beings are autopoietic systems with highly context-dependent structural dynamics and interactions, that determine whether a disturbance in the genotype or environment will lead or not to phenotypic change. The concept of epigenesis entails how a change in the phenotype may not correspond to a change in the structure of an earlier developmental stage, including the genome. Disturbances of embryonic structure may fail to change the phenotype, as in regulated development, or when different genotypes are associated to a single phenotype. Likewise, the same genotype or early embryonic structure may develop different phenotypes, as in phenotypic plasticity. Disturbances that fail to trigger phenotypic change are considered neutral, but even so, they can alter unexpressed developmental potential. Here, we present conceptual diagrams of the "epigenic field": similar to Waddington's epigenetic landscapes, but including the ontogenic niche (organism/environment interactional dynamics during ontogeny) as a factor in defining epigenic fields, rather than just selecting among possible pathways. Our diagrams illustrate transgenerational changes of genotype, ontogenic niche, and their correspondence (or lack thereof) with changes of phenotype. Epigenic fields provide a simple way to understand developmental constraints on evolution, for instance: how constraints evolve as a result of developmental system drift; how neutral changes can be involved in genetic assimilation and de-assimilation; and how constraints can evolve as a result of neutral changes in the ontogenic niche (not only the genotype). We argue that evolutionary thinking can benefit from a framework for evolution with conceptual foundations at the organismal level.

Exploring the Protein Sequence Space with Global Generative Models

Recent advancements in specialized large-scale architectures for training images and language have profoundly impacted the field of computer vision and natural language processing (NLP). Language models, such as the recent ChatGPT and GPT-4, have demonstrated exceptional capabilities in processing, translating, and generating human language. These breakthroughs have also been reflected in protein research, leading to the rapid development of numerous new methods in a short time, with unprecedented performance. Several of these models have been developed with the goal of generating sequences in novel regions of the protein space. In this work, we provide an overview of the use of protein generative models, reviewing (1) language models for the design of novel artificial proteins, (2) works that use non-transformer architectures, and (3) applications in directed evolution approaches.

Purging and accumulation of genetic load in conservation

Our ability to assess the threat posed by the genetic load to small and declining populations has been greatly improved by advances in genome sequencing and computational approaches. Yet, considerable confusion remains around the definitions of the genetic load and its dynamics, and how they impact individual fitness and population viability. We illustrate how both selective purging and drift affect the distribution of deleterious mutations during population size decline and recovery. We show how this impacts the composition of the genetic load, and how this affects the extinction risk and recovery potential of populations. We propose a framework to examine load dynamics and advocate for the introduction of load estimates in the management of endangered populations.

Environment-dependent epistasis increases phenotypic diversity in gene regulatory networks

Mutations to gene regulatory networks can be maladaptive or a source of evolutionary novelty. Epistasis confounds our understanding of how mutations affect the expression patterns of gene regulatory networks, a challenge exacerbated by the dependence of epistasis on the environment. We used the toolkit of synthetic biology to systematically assay the effects of pairwise and triplet combinations of mutant genotypes on the expression pattern of a gene regulatory network expressed in Escherichia coli that interprets an inducer gradient across a spatial domain. We uncovered a preponderance of epistasis that can switch in magnitude and sign across the inducer gradient to produce a greater diversity of expression pattern phenotypes than would be possible in the absence of such environment-dependent epistasis. We discuss our findings in the context of the evolution of hybrid incompatibilities and evolutionary novelties.

Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments

Novel and improved biocatalysts are increasingly sourced from libraries via experimental screening. The success of such campaigns is crucially dependent on the number of candidates tested. Water-in-oil emulsion droplets can replace the classical test tube, to provide in vitro compartments as an alternative screening format, containing genotype and phenotype and enabling a readout of function. The scale-down to micrometer droplet diameters and picoliter volumes brings about a >107-fold volume reduction compared to 96-well-plate screening. Droplets made in automated microfluidic devices can be integrated into modular workflows to set up multistep screening protocols involving various detection modes to sort >107 variants a day with kHz frequencies. The repertoire of assays available for droplet screening covers all seven enzyme commission (EC) number classes, setting the stage for widespread use of droplet microfluidics in everyday biochemical experiments. We review the practicalities of adapting droplet screening for enzyme discovery and for detailed kinetic characterization. These new ways of working will not just accelerate discovery experiments currently limited by screening capacity but profoundly change the paradigms we can probe. By interfacing the results of ultrahigh-throughput droplet screening with next-generation sequencing and deep learning, strategies for directed evolution can be implemented, examined, and evaluated.

Mitochondrial genomes of soft scales (Hemiptera: Coccidae): features, structures and significance

BACKGROUND

Soft scales (Hemiptera: Coccidae), including important agricultural and forestry pests, are difficult to identify directly by morphological characters. Mitochondrial genomes (mitogenomes) have been widely used in species identification and phylogenetic research. However, only three complete mitogenomes, and very few mitochondrial genes of scale insects (Hemiptera: Coccoidea) can be searched in GenBank. Mitogenome comparisons between scale insects or between scale insects and other hemipteran species have not yet been reported.

RESULTS

In this study, detailed annotation of three new mitogenomes and comparative analysis of scale insects were completed, as well as comparative analysis of the gene composition, gene arrangement, codon usage and evolutionary forces between scale insects and 488 other hemipteran species for the first time. We found that high A + T content, gene rearrangement and truncated tRNAs are common phenomena in soft scales. The average A + T content and codon usage bias of scale insects are higher and stronger than those of other hemipteran insects, respectively. The atp8 gene of Hemiptera and nine other protein-coding genes of scale insects are under positive selection with higher evolutionary rates.

CONCLUSIONS

The study revealed the particularity of the scale insect mitogenomes, which will provide a good reference for future research on insect phylogenetic relationships, insect pest control, biogeography and identification.

Molecular Evolution of the Bactericidal/Permeability-Increasing Protein (BPIFA1) Regulating the Innate Immune Responses in Mammals

Bactericidal/permeability-increasing protein, a primary factor of the innate immune system of mammals, participates in natural immune protection against invading bacteria. BPIFA1 actively contributes to host defense via multiple mechanisms, such as antibacterial, surfactant, airway surface liquid control, and immunomodulatory activities. However, the evolutionary history and selection forces on the BPIFA1 gene in mammals during adaptive evolution are poorly understood. This study examined the BPIFA1 gene of humans compared with that of other mammalian species to estimate the selective pressure derived by adaptive evolution. To assess whether or not positive selection occurred, we employed several different possibility tests (M1 vs. M2 and M7 vs. M8). The proportions of positively selected sites were significant, with a likelihood log value of 93.63 for the BPIFA1 protein. The Selecton server was used on the same dataset to reconfirm positive selection for specific sites by employing the Mechanistic-Empirical Combination model, thus providing additional evidence supporting the findings of positive selection. There was convincing evidence for positive selection signals in the BPIFA1 genes of mammalian species, which was more significant for selection signs and creating signals. We performed probability tests comparing various models based on dN/dS ratios to recognize specific codons under positive selection pressure. We identified positively selected sites in the LBP-BPI domain of BPIFA1 proteins in the mammalian genome, including a lipid-binding domain with a very high degree of selectivity for DPPC. BPIFA1 activates the upper airway's innate immune system in response to numerous genetic signals in the mammalian genome. These findings highlight evolutionary advancements in immunoregulatory effects that play a significant role in the antibacterial and antiviral defenses of mammalian species.

Experimental and In Silico Analysis of TEM β-Lactamase Adaptive Evolution

Multiple mutations often have non-additive (epistatic) phenotypic effects. Epistasis is of fundamental biological relevance but is not well understood mechanistically. Adaptive evolution, i.e., the evolution of new biochemical activities, is rich in epistatic interactions. To better understand the principles underlying epistasis during genetic adaptation, we studied the evolution of TEM-1 β-lactamase variants exhibiting cefotaxime resistance. We report the collection of a library of 487 observed evolutionary trajectories for TEM-1 and determine the epistasis status based on cefotaxime resistance phenotype for 206 combinations of 2-3 TEM-1 mutations involving 17 positions under adaptive selective pressure. Gain-of-function (GOF) mutations are gatekeepers for adaptation. To see if GOF phenotypes can be inferred based solely on sequence data, we calculated the enrichment of GOF mutations in the different categories of epistatic pairs. Our results suggest that this is possible because GOF mutations are particularly enriched in sign and reciprocal sign epistasis, which leave a major imprint on the sequence space accessible to evolution. We also used FoldX to explore the relationship between thermodynamic stability and epistasis. We found that mutations in observed evolutionary trajectories tend to destabilize the folded structure of the protein, albeit their cumulative effects are consistently below the protein's free energy of folding. The destabilizing effect is stronger for epistatic pairs, suggesting that modest or local alterations in folding stability can modulate catalysis. Finally, we report a significant relationship between epistasis and the degree to which two protein positions are structurally and dynamically coupled, even in the absence of ligand.

Early effects of gene duplication on the robustness and phenotypic variability of gene regulatory networks

Background

Research on gene duplication is abundant and comes from a wide range of approaches, from high-throughput analyses and experimental evolution to bioinformatics and theoretical models. Notwithstanding, a consensus is still lacking regarding evolutionary mechanisms involved in evolution through gene duplication as well as the conditions that affect them. We argue that a better understanding of evolution through gene duplication requires considering explicitly that genes do not act in isolation. It demands studying how the perturbation that gene duplication implies percolates through the web of gene interactions. Due to evolution’s contingent nature, the paths that lead to the final fate of duplicates must depend strongly on the early stages of gene duplication, before gene copies have accumulated distinctive changes.

Methods

Here we use a widely-known model of gene regulatory networks to study how gene duplication affects network behavior in early stages. Such networks comprise sets of genes that cross-regulate. They organize gene activity creating the gene expression patterns that give cells their phenotypic properties. We focus on how duplication affects two evolutionarily relevant properties of gene regulatory networks: mitigation of the effect of new mutations and access to new phenotypic variants through mutation.

Results

Among other observations, we find that those networks that are better at maintaining the original phenotype after duplication are usually also better at buffering the effect of single interaction mutations and that duplication tends to enhance further this ability. Moreover, the effect of mutations after duplication depends on both the kind of mutation and genes involved in it. We also found that those phenotypes that had easier access through mutation before duplication had higher chances of remaining accessible through new mutations after duplication.

Conclusion

Our results support that gene duplication often mitigates the impact of new mutations and that this effect is not merely due to changes in the number of genes. The work that we put forward helps to identify conditions under which gene duplication may enhance evolvability and robustness to mutations.

Adaptive evolvability through direct selection instead of indirect, second-order selection

Can evolvability itself be the product of adaptive evolution? To answer this question is challenging, because any DNA mutation that alters only evolvability is subject to indirect, "second order" selection on the future effects of this mutation. Such indirect selection is weaker than "first-order" selection on mutations that alter fitness, in the sense that it can operate only under restrictive conditions. Here I discuss a route to adaptive evolvability that overcomes this challenge. Specifically, a recent evolution experiment showed that some mutations can enhance both fitness and evolvability through a combination of direct and indirect selection. Unrelated evidence from gene duplication and the evolution of gene regulation suggests that mutations with such dual effects may not be rare. Through such mutations, evolvability may increase at least in part because it provides an adaptive advantage. These observations suggest a research program on the adaptive evolution of evolvability, which aims to identify such mutations and to disentangle their direct fitness effects from their indirect effects on evolvability. If evolvability is itself adaptive, Darwinian evolution may have created more than life's diversity. It may also have helped create the very conditions that made the success of Darwinian evolution possible.

Selection in a growing colony biases results of mutation accumulation experiments

Mutations provide the raw material for natural selection to act. Therefore, understanding the variety and relative frequency of different type of mutations is critical to understanding the nature of genetic diversity in a population. Mutation accumulation (MA) experiments have been used in this context to estimate parameters defining mutation rates, distribution of fitness effects (DFE), and spectrum of mutations. MA experiments can be performed with different effective population sizes. In MA experiments with bacteria, a single founder is grown to a size of a colony (~ 108). It is assumed that natural selection plays a minimal role in dictating the dynamics of colony growth. In this work, we simulate colony growth via a mathematical model, and use our model to mimic an MA experiment. We demonstrate that selection ensures that, in an MA experiment, fraction of all mutations that are beneficial is over-represented by a factor of almost two, and that the distribution of fitness effects of beneficial and deleterious mutations are inaccurately captured in an MA experiment. Given this, the estimate of mutation rates from MA experiments is non-trivial. We then perform an MA experiment with 160 lines of E. coli, and show that due to the effect of selection in a growing colony, the size and sector of a colony from which the experiment is propagated impacts the results. Overall, we demonstrate that the results of MA experiments need to be revisited taking into account the action of selection in a growing colony.

A Family of Fitness Landscapes Modeled through Gene Regulatory Networks

Fitness landscapes are a powerful metaphor for understanding the evolution of biological systems. These landscapes describe how genotypes are connected to each other through mutation and related through fitness. Empirical studies of fitness landscapes have increasingly revealed conserved topographical features across diverse taxa, e.g., the accessibility of genotypes and "ruggedness". As a result, theoretical studies are needed to investigate how evolution proceeds on fitness landscapes with such conserved features. Here, we develop and study a model of evolution on fitness landscapes using the lens of Gene Regulatory Networks (GRNs), where the regulatory products are computed from multiple genes and collectively treated as phenotypes. With the assumption that regulation is a binary process, we prove the existence of empirically observed, topographical features such as accessibility and connectivity. We further show that these results hold across arbitrary fitness functions and that a trade-off between accessibility and ruggedness need not exist. Then, using graph theory and a coarse-graining approach, we deduce a mesoscopic structure underlying GRN fitness landscapes where the information necessary to predict a population's evolutionary trajectory is retained with minimal complexity. Using this coarse-graining, we develop a bottom-up algorithm to construct such mesoscopic backbones, which does not require computing the genotype network and is therefore far more efficient than brute-force approaches. Altogether, this work provides mathematical results of high-dimensional fitness landscapes and a path toward connecting theory to empirical studies.

Functional Classification and Characterization of the Fungal Glycoside Hydrolase 28 Protein Family

Pectin is a major constituent of the plant cell wall, comprising compounds with important industrial applications such as homogalacturonan, rhamnogalacturonan and xylogalacturonan. A large array of enzymes is involved in the degradation of this amorphous substrate. The Glycoside Hydrolase 28 (GH28) family includes polygalacturonases (PG), rhamnogalacturonases (RG) and xylogalacturonases (XG) that share a structure of three to four pleated β-sheets that form a rod with the catalytic site amidst a long, narrow groove. Although these enzymes have been studied for many years, there has been no systematic analysis. We have collected a comprehensive set of GH28 encoding sequences to study their evolution in fungi, directed at obtaining a functional classification, as well as at the identification of substrate specificity as functional constraint. Computational tools such as Alphafold, Consurf and MEME were used to identify the subfamilies’ characteristics. A hierarchic classification defines the major classes of endoPG, endoRG and endoXG as well as three exoPG classes. Ascomycete endoPGs are further classified in two subclasses whereas we identify four exoRG subclasses. Diversification towards exomode is explained by loops that appear inserted in a number of turns. Substrate-driven diversification can be identified by various specificity determining positions that appear to surround the binding groove.

Toward a theory of evolution as multilevel learning

We apply the theory of learning to physically renormalizable systems in an attempt to outline a theory of biological evolution, including the origin of life, as multilevel learning. We formulate seven fundamental principles of evolution that appear to be necessary and sufficient to render a universe observable and show that they entail the major features of biological evolution, including replication and natural selection. It is shown that these cornerstone phenomena of biology emerge from the fundamental features of learning dynamics such as the existence of a loss function, which is minimized during learning. We then sketch the theory of evolution using the mathematical framework of neural networks, which provides for detailed analysis of evolutionary phenomena. To demonstrate the potential of the proposed theoretical framework, we derive a generalized version of the Central Dogma of molecular biology by analyzing the flow of information during learning (back propagation) and predicting (forward propagation) the environment by evolving organisms. The more complex evolutionary phenomena, such as major transitions in evolution (in particular, the origin of life), have to be analyzed in the thermodynamic limit, which is described in detail in the paper by Vanchurin et al. [V. Vanchurin, Y. I. Wolf, E. V. Koonin, M. I. Katsnelson, Proc. Natl. Acad. Sci. U.S.A. 119, 10.1073/pnas.2120042119 (2022)].

Toward a theory of evolution as multilevel learning

We apply the theory of learning to physically renormalizable systems in an attempt to outline a theory of biological evolution, including the origin of life, as multilevel learning. We formulate seven fundamental principles of evolution that appear to be necessary and sufficient to render a universe observable and show that they entail the major features of biological evolution, including replication and natural selection. It is shown that these cornerstone phenomena of biology emerge from the fundamental features of learning dynamics such as the existence of a loss function, which is minimized during learning. We then sketch the theory of evolution using the mathematical framework of neural networks, which provides for detailed analysis of evolutionary phenomena. To demonstrate the potential of the proposed theoretical framework, we derive a generalized version of the Central Dogma of molecular biology by analyzing the flow of information during learning (back propagation) and predicting (forward propagation) the environment by evolving organisms. The more complex evolutionary phenomena, such as major transitions in evolution (in particular, the origin of life), have to be analyzed in the thermodynamic limit, which is described in detail in the paper by Vanchurin et al. [V. Vanchurin, Y. I. Wolf, E. V. Koonin, M. I. Katsnelson, Proc. Natl. Acad. Sci. U.S.A. 119, 10.1073/pnas.2120042119 (2022)].

Dynamic RNA fitness landscapes of a group I ribozyme during changes to the experimental environment

Fitness landscapes of protein and RNA molecules can be studied experimentally using high-throughput techniques to measure the functional effects of numerous combinations of mutations. The rugged topography of these molecular fitness landscapes is important for understanding and predicting natural and experimental evolution. Mutational effects are also dependent upon environmental conditions, but the effects of environmental changes on fitness landscapes remains poorly understood. Here, we investigate the changes to the fitness landscape of a catalytic RNA molecule while changing a single environmental variable that is critical for RNA structure and function. Using high-throughput sequencing of in vitro selections, we mapped a fitness landscape of the Azoarcus group I ribozyme under eight different concentrations of magnesium ions (1–48 mM MgCl₂). The data revealed the magnesium dependence of 16,384 mutational neighbors, and from this, we investigated the magnesium induced changes to the topography of the fitness landscape. The results showed that increasing magnesium concentration improved the relative fitness of sequences at higher mutational distances while also reducing the ruggedness of the mutational trajectories on the landscape. As a result, as magnesium concentration was increased, simulated populations evolved toward higher fitness faster. Curve-fitting of the magnesium dependence of individual ribozymes demonstrated that deep sequencing of in vitro reactions can be used to evaluate the structural stability of thousands of sequences in parallel. Overall, the results highlight how environmental changes that stabilize structures can also alter the ruggedness of fitness landscapes and alter evolutionary processes.

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.

Redundancy-selection trade-off in phenotype-structured populations

Realistic fitness landscapes generally display a redundancy-fitness trade-off: highly fit trait configurations are inevitably rare, while less fit trait configurations are expected to be more redundant. The resulting sub-optimal patterns in the fitness distribution are typically described by means of effective formulations, where redundancy provided by the presence of neutral contributions is modelled implicitly, e.g. with a bias of the mutation process. However, the extent to which effective formulations are compatible with explicitly redundant landscapes is yet to be understood, as well as the consequences of a potential miss-match. Here we investigate the effects of such trade-off on the evolution of phenotype-structured populations, characterised by continuous quantitative traits. We consider a typical replication-mutation dynamics, and we model redundancy by means of two dimensional landscapes displaying both selective and neutral traits. We show that asymmetries of the landscapes will generate neutral contributions to the marginalised fitness-level description, that cannot be described by effective formulations, nor disentangled by the full trait distribution. Rather, they appear as effective sources, whose magnitude depends on the geometry of the landscape. Our results highlight new important aspects on the nature of sub-optimality. We discuss practical implications for rapidly mutant populations such as pathogens and cancer cells, where the qualitative knowledge of their trait and fitness distributions can drive disease management and intervention policies.

Gene duplication and subsequent diversification strongly affect phenotypic evolvability and robustness

We study the effects of non-determinism and gene duplication on the structure of genotype-phenotype (GP) maps by introducing a non-deterministic version of the Polyomino self-assembly model. This model has previously been used in a variety of contexts to model the assembly and evolution of protein quaternary structure. Firstly, we show the limit of the current deterministic paradigm which leads to built-in anti-correlation between evolvability and robustness at the genotypic level. We develop a set of metrics to measure structural properties of GP maps in a non-deterministic setting and use them to evaluate the effects of gene duplication and subsequent diversification. Our generalized versions of evolvability and robustness exhibit positive correlation for a subset of genotypes. This positive correlation is only possible because non-deterministic phenotypes can contribute to both robustness and evolvability. Secondly, we show that duplication increases robustness and reduces evolvability initially, but that the subsequent diversification that duplication enables has a stronger, inverse effect, greatly increasing evolvability and reducing robustness relative to their original values.

Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs

Tumour cell heterogeneity is a major barrier for efficient design of targeted anti-cancer therapies. A diverse distribution of phenotypically distinct tumour-cell subpopulations prior to drug treatment predisposes to non-uniform responses, leading to the elimination of sensitive cancer cells whilst leaving resistant subpopulations unharmed. Few strategies have been proposed for quantifying the variability associated to individual cancer-cell heterogeneity and minimizing its undesirable impact on clinical outcomes. Here, we report a computational approach that allows the rational design of combinatorial therapies involving epigenetic drugs against chromatin modifiers. We have formulated a stochastic model of a bivalent transcription factor that allows us to characterise three different qualitative behaviours, namely: bistable, high- and low-gene expression. Comparison between analytical results and experimental data determined that the so-called bistable and high-gene expression behaviours can be identified with undifferentiated and differentiated cell types, respectively. Since undifferentiated cells with an aberrant self-renewing potential might exhibit a cancer/metastasis-initiating phenotype, we analysed the efficiency of combining epigenetic drugs against the background of heterogeneity within the bistable sub-ensemble. Whereas single-targeted approaches mostly failed to circumvent the therapeutic problems represented by tumour heterogeneity, combinatorial strategies fared much better. Specifically, the more successful combinations were predicted to involve modulators of the histone H3K4 and H3K27 demethylases KDM5 and KDM6A/UTX. Those strategies involving the H3K4 and H3K27 methyltransferases MLL2 and EZH2, however, were predicted to be less effective. Our theoretical framework provides a coherent basis for the development of an in silico platform capable of identifying the epigenetic drugs combinations best-suited to therapeutically manage non-uniform responses of heterogenous cancer cell populations.

Neutral quasispecies evolution and the maximal entropy random walk

Even if they have no impact on phenotype, neutral mutations are not equivalent in the eyes of evolution: A robust neutral variant-one which remains functional after further mutations-is more likely to spread in a large, diverse population than a fragile one. Quasispecies theory shows that the equilibrium frequency of a genotype is proportional to its eigenvector centrality in the neutral network. This paper explores the link between the selection for mutational robustness and the navigability of neutral networks. I show that sequences of neutral mutations follow a "maximal entropy random walk," a canonical Markov chain on graphs with nonlocal, nondiffusive dynamics. I revisit M. Smith's word-game model of evolution in this light, finding that the likelihood of certain sequences of substitutions can decrease with the population size. These counterintuitive results underscore the fertility of the interface between evolutionary dynamics, information theory, and physics.

Ab Initio Construction and Evolutionary Analysis of Protein-Coding Gene Families with Partially Homologous Relationships: Closely Related Drosophila Genomes as a Case Study

How have genes evolved within a well-known genome phylogeny? Many protein-coding genes should have evolved as a whole at the gene level, and some should have evolved partly through fragments at the subgene level. To comprehensively explore such complex homologous relationships and better understand gene family evolution, here, with de novo-identified modules, the subgene units which could consecutively cover proteins within a set of closely related species, we applied a new phylogeny-based approach that considers evolutionary models with partial homology to classify all protein-coding genes in nine Drosophila genomes. Compared with two other popular methods for gene family construction, our approach improved practical gene family classifications with a more reasonable view of homology and provided a much more complete landscape of gene family evolution at the gene and subgene levels. In the case study, we found that most expanded gene families might have evolved mainly through module rearrangements rather than gene duplications and mainly generated single-module genes through partial gene duplication, suggesting that there might be pervasive subgene rearrangement in the evolution of protein-coding gene families. The use of a phylogeny-based approach with partial homology to classify and analyze protein-coding gene families may provide us with a more comprehensive landscape depicting how genes evolve within a well-known genome phylogeny.

Symmetry-related residues as promising hotspots for the evolution of de novo oligomeric enzymes

Directed evolution has provided us with great opportunities and prospects in the synthesis of tailor-made proteins. It, however, often requires at least mid to high throughput screening, necessitating more effective strategies for laboratory evolution. We herein demonstrate that protein symmetry can be a versatile criterion for searching for promising hotspots for the directed evolution of de novo oligomeric enzymes. The randomization of symmetry-related residues located at the rotational axes of artificial metallo-β-lactamase yields drastic effects on catalytic activities, whereas that of non-symmetry-related, yet, proximal residues to the active site results in negligible perturbations. Structural and biochemical analysis of the positive hits indicates that seemingly trivial mutations at symmetry-related spots yield significant alterations in overall structures, metal-coordination geometry, and chemical environments of active sites. Our work implicates that numerous artificially designed and natural oligomeric proteins might have evolutionary advantages of propagating beneficial mutations using their global symmetry.

Symmetry-related residues located at the rotational axes can be promising hotspots for the evolution of de novo oligomeric enzymes even though they are distantly located from the active site pocket.

Evolutionary stalling and a limit on the power of natural selection to improve a cellular module

Cells consist of molecular modules which perform vital biological functions. Cellular modules are key units of adaptive evolution because organismal fitness depends on their performance. Theory shows that in rapidly evolving populations, such as those of many microbes, adaptation is driven primarily by common beneficial mutations with large effects, while other mutations behave as if they are effectively neutral. As a consequence, if a module can be improved only by rare and/or weak beneficial mutations, its adaptive evolution would stall. However, such evolutionary stalling has not been empirically demonstrated, and it is unclear to what extent stalling may limit the power of natural selection to improve modules. Here we empirically characterize how natural selection improves the translation machinery (TM), an essential cellular module. We experimentally evolved populations of Escherichia coli with genetically perturbed TMs for 1,000 generations. Populations with severe TM defects initially adapted via mutations in the TM, but TM adaptation stalled within about 300 generations. We estimate that the genetic load in our populations incurred by residual TM defects ranges from 0.5 to 19%. Finally, we found evidence that both epistasis and the depletion of the pool of beneficial mutations contributed to evolutionary stalling. Our results suggest that cellular modules may not be fully optimized by natural selection despite the availability of adaptive mutations.

Adaptation of turnip mosaic potyvirus to a specific niche reduces its genetic and environmental robustness

Robustness is the preservation of the phenotype in the face of genetic and environmental perturbations. It has been argued that robustness must be an essential fitness component of RNA viruses owed to their small and compacted genomes, high mutation rates and living in ever-changing environmental conditions. Given that genetic robustness might hamper possible beneficial mutations, it has been suggested that genetic robustness can only evolve as a side-effect of the evolution of robustness mechanisms specific to cope with environmental perturbations, a theory known as plastogenetic congruence. However, empirical evidences from different viral systems are contradictory. To test how adaptation to a particular environment affects both environmental and genetic robustness, we have used two strains of turnip mosaic potyvirus (TuMV) that differ in their degree of adaptation to Arabidopsis thaliana at a permissive temperature. We show that the highly adapted strain is strongly sensitive to the effect of random mutations and to changes in temperature conditions. In contrast, the non-adapted strain shows more robustness against both the accumulation of random mutations and drastic changes in temperature conditions. Together, these results are consistent with the predictions of the plastogenetic congruence theory, suggesting that genetic and environmental robustnesses may be two sides of the same coin for TuMV.

High-order interactions distort the functional landscape of microbial consortia

Understanding the link between community composition and function is a major challenge in microbial population biology, with implications for the management of natural microbiomes and the design of synthetic consortia. Specifically, it is poorly understood whether community functions can be quantitatively predicted from traits of species in monoculture. Inspired by the study of complex genetic interactions, we have examined how the amylolytic rate of combinatorial assemblages of six starch-degrading soil bacteria depend on the separate functional contributions from each species and their interactions. Filtering our results through the theory of biochemical kinetics, we show that this simple function is additive in the absence of interactions among community members. For about half of the combinatorially assembled consortia, the amylolytic function is dominated by pairwise and higher-order interactions. For the other half, the function is additive despite the presence of strong competitive interactions. We explain the mechanistic basis of these findings and propose a quantitative framework that allows us to separate the effect of behavioral and population dynamics interactions. Our results suggest that the functional robustness of a consortium to pairwise and higher-order interactions critically affects our ability to predict and bottom-up engineer ecosystem function in complex communities.

High-order interactions distort the functional landscape of microbial consortia

Understanding the link between community composition and function is a major challenge in microbial population biology, with implications for the management of natural microbiomes and the design of synthetic consortia. Specifically, it is poorly understood whether community functions can be quantitatively predicted from traits of species in monoculture. Inspired by the study of complex genetic interactions, we have examined how the amylolytic rate of combinatorial assemblages of six starch-degrading soil bacteria depend on the separate functional contributions from each species and their interactions. Filtering our results through the theory of biochemical kinetics, we show that this simple function is additive in the absence of interactions among community members. For about half of the combinatorially assembled consortia, the amylolytic function is dominated by pairwise and higher-order interactions. For the other half, the function is additive despite the presence of strong competitive interactions. We explain the mechanistic basis of these findings and propose a quantitative framework that allows us to separate the effect of behavioral and population dynamics interactions. Our results suggest that the functional robustness of a consortium to pairwise and higher-order interactions critically affects our ability to predict and bottom-up engineer ecosystem function in complex communities.

Inactivation of a Mismatch-Repair System Diversifies Genotypic Landscape of Escherichia coli During Adaptive Laboratory Evolution

Adaptive laboratory evolution (ALE) is used to find causal mutations that underlie improved strain performance under the applied selection pressure. ALE studies have revealed that mutator populations tend to outcompete their non-mutator counterparts following the evolutionary trajectory. Among them, mutS-inactivated mutator cells, characterize d by a dysfunctional methyl-mismatch repair system, are frequently found in ALE experiments. Here, we examined mutS inactivation as an approach to facilitate ALE of Escherichia coli. The wild-type E. coli MG1655 and mutS knock-out derivative (ΔmutS) were evolved in parallel for 800 generations on lactate or glycerol minimal media in a serial-transfer experiment. Whole-genome re-sequencing of each lineage at 100-generation intervals revealed that (1) mutations emerge rapidly in the ΔmutS compared to in the wild-type strain; (2) mutations were more than fourfold higher in the ΔmutS strain at the end-point populations compared to the wild-type strain; and (3) a significant number of random mutations accumulated in the ΔmutS strains. We then measured the fitness of the end-point populations on an array of non-adaptive carbon sources. Interestingly, collateral fitness increases on non-adaptive carbon sources were more pronounced in the ΔmutS strains than the parental strain. Fitness measurement of single mutants revealed that the collateral fitness increase seen in the mutator lineages can be attributed to a pool of random mutations. Together, this study demonstrates that short-term mutator ALE extensively expands possible genotype space, resulting in versatile bacteria with elevated fitness levels across various carbon sources.

Variation across Species and Levels: Implications for Model Species Research

The selection of model species tends to involve two typically unstated assumptions, namely: (1) that the similarity between species decreases steadily with phylogenetic distance, and (2) that similarities are greater at lower levels of biological organization. The first assumption holds on average, but species similarities tend to decrease with the square root of divergence time, rather than linearly, and lineages with short generation times (which includes most model species) tend to diverge faster than average, making the decrease in similarity non-monotonic. The second assumption is more difficult to test. Comparative molecular research has traditionally emphasized species similarities over differences, whereas comparative research at higher levels of organization frequently highlights the species differences. However, advances in comparative genomics have brought to light a great variety of species differences, not just in gene regulation but also in protein coding genes. Particularly relevant are cases in which homologous high-level characters are based on non-homologous genes. This phenomenon of non-orthologous gene displacement, or "deep non-homology," indicates that species differences at the molecular level can be surprisingly large. Given these observations, it is not surprising that some findings obtained in model species do not generalize across species as well as researchers had hoped, even if the research is molecular.

Genotype network intersections promote evolutionary innovation

Evolutionary innovations are qualitatively novel traits that emerge through evolution and increase biodiversity. The genetic mechanisms of innovation remain poorly understood. A systems view of innovation requires the analysis of genotype networks—the vast networks of genetic variants that produce the same phenotype. Innovations can occur at the intersection of two different genotype networks. However, the experimental characterization of genotype networks has been hindered by the vast number of genetic variants that need to be functionally analyzed. Here, we use high-throughput sequencing to study the fitness landscape at the intersection of the genotype networks of two catalytic RNA molecules (ribozymes). We determined the ability of numerous neighboring RNA sequences to catalyze two different chemical reactions, and we use these data as a proxy for a genotype to fitness map where two functions come in close proximity. We find extensive functional overlap, and numerous genotypes can catalyze both functions. We demonstrate through evolutionary simulations that these numerous points of intersection facilitate the discovery of a new function. However, the rate of adaptation of the new function depends upon the local ruggedness around the starting location in the genotype network. As a consequence, one direction of adaptation is more rapid than the other. We find that periods of neutral evolution increase rates of adaptation to the new function by allowing populations to spread out in their genotype network. Our study reveals the properties of a fitness landscape where genotype networks intersect and the consequences for evolutionary innovations. Our results suggest that historic innovations in natural systems may have been facilitated by overlapping genotype networks.

The determination of the empirical fitness landscape at the genotypic intersection between two different catalytic RNA (ribozyme) functions reveals details about how novel traits can emerge through evolutionary innovation.

A multiscale model of epigenetic heterogeneity-driven cell fate decision-making

The inherent capacity of somatic cells to switch their phenotypic status in response to damage stimuli in vivo might have a pivotal role in ageing and cancer. However, how the entry-exit mechanisms of phenotype reprogramming are established remains poorly understood. In an attempt to elucidate such mechanisms, we herein introduce a stochastic model of combined epigenetic regulation (ER)-gene regulatory network (GRN) to study the plastic phenotypic behaviours driven by ER heterogeneity. To deal with such complex system, we additionally formulate a multiscale asymptotic method for stochastic model reduction, from which we derive an efficient hybrid simulation scheme. Our analysis of the coupled system reveals a regime of tristability in which pluripotent stem-like and differentiated steady-states coexist with a third indecisive state, with ER driving transitions between these states. Crucially, ER heterogeneity of differentiation genes is for the most part responsible for conferring abnormal robustness to pluripotent stem-like states. We formulate epigenetic heterogeneity-based strategies capable of unlocking and facilitating the transit from differentiation-refractory (stem-like) to differentiation-primed epistates. The application of the hybrid numerical method validates the likelihood of such switching involving solely kinetic changes in epigenetic factors. Our results suggest that epigenetic heterogeneity regulates the mechanisms and kinetics of phenotypic robustness of cell fate reprogramming. The occurrence of tunable switches capable of modifying the nature of cell fate reprogramming might pave the way for new therapeutic strategies to regulate reparative reprogramming in ageing and cancer.

Certain modifications of the structure and functioning of the protein/DNA complex called chromatin can allow adult, fully differentiated, cells to adopt a stem cell-like pluripotent state in a purely epigenetic manner, not involving changes in the underlying DNA sequence. Such reprogramming-like phenomena may constitute an innate reparative route through which human tissues respond to injury and could also serve as a novel regenerative strategy in human pathological situations in which tissue or organ repair is impaired. However, it should be noted that in vivo reprogramming would be capable of maintaining tissue homeostasis provided the acquisition of pluripotency features is strictly transient and accompanied by an accurate replenishment of the specific cell types being lost. Crucially, an excessive reprogramming in the absence of controlled re-differentiation would impair the repair or the replacement of damaged cells, thereby promoting pathological alterations of cell fate. A mechanistic understanding of how the degree of chromatin plasticity dictates the reparative versus pathological behaviour of in vivo reprogramming to rejuvenate aged tissues while preventing tumorigenesis is urgently needed, including especially the intrinsic epigenetic heterogeneity of the tissue resident cells being reprogrammed. We here introduce a novel method that mathematically captures how epigenetic heterogeneity is actually the driving force that governs the routes and kinetics to entry into and exit from a pathological stem-like state. Moreover, our approach computationally validates the likelihood of unlocking chronic, unrestrained plastic states and drive their differentiation down the correct path by solely manipulating the intensity and direction of few epigenetic control switches. Our approach could inspire new therapeutic approaches based on in vivo cell reprogramming for efficient tissue regeneration and rejuvenation and cancer treatment.

Experimental Evolution of Innovation and Novelty

How does novelty, a new, genetically based function, evolve? A compelling answer has been elusive because there are few model systems where both the genetic mechanisms generating novel functions and the ecological conditions that govern their origin and spread can be studied in detail. This review article considers what we have learned about the evolution of novelty from microbial selection experiments. This work reveals that the genetic routes to novelty can be more highly variable than standard models have led us to believe and underscores the importance of considering both genetics and ecology in this process.

Comparative Genomics Between Saccharomyces kudriavzevii and S. cerevisiae Applied to Identify Mechanisms Involved in Adaptation

Yeasts belonging to the Saccharomyces genus play an important role in human-driven fermentations. The species S. cerevisiae has been widely studied because it is the dominant yeast in most fermentations and it has been widely used as a model eukaryotic organism. Recently, other species of the Saccharomyces genus are gaining interest to solve the new challenges that the fermentation industry are facing. One of these species is S. kudriavzevii, which exhibits interesting physiological properties compared to S. cerevisiae, such as a better adaptation to grow at low temperatures, a higher glycerol synthesis and lower ethanol production. The aim of this study is to understand the molecular basis behind these phenotypic differences of biotechnological interest by using a species-based comparative genomics approach. In this work, we sequenced, assembled and annotated two new genomes of S. kudriavzevii. We used a combination of different statistical methods to identify functional divergence, signatures of positive selection and acceleration of substitution rates at specific amino acid sites of proteins in S. kudriavzevii when compared to S. cerevisiae, and vice versa. We provide a list of candidate genes in which positive selection could be acting during the evolution of both S. cerevisiae and S. kudriavzevii clades. Some of them could be related to certain important differences in metabolism previously reported by other authors such us DAL3 and ARO4, involved in nitrogen assimilation and amino acid biosynthesis. In addition, three of those genes (FBA1, ZIP1, and RQC2) showed accelerated evolutionary rates in Sk branch. Finally, genes of the riboflavin biosynthesis were also among those genes with a significant higher rate of nucleotide substitution and those proteins have amino acid positions contributing to functional divergence.

Cryptic genetic variation shapes the adaptive evolutionary potential of enzymes

Genetic variation among orthologous proteins can cause cryptic phenotypic properties that only manifest in changing environments. Such variation may impact the evolvability of proteins, but the underlying molecular basis remains unclear. Here, we performed comparative directed evolution of four orthologous metallo-β-lactamases toward a new function and found that different starting genotypes evolved to distinct evolutionary outcomes. Despite a low initial fitness, one ortholog reached a significantly higher fitness plateau than its counterparts, via increasing catalytic activity. By contrast, the ortholog with the highest initial activity evolved to a less-optimal and phenotypically distinct outcome through changes in expression, oligomerization and activity. We show how cryptic molecular properties and conformational variation of active site residues in the initial genotypes cause epistasis, that could lead to distinct evolutionary outcomes. Our work highlights the importance of understanding the molecular details that connect genetic variation to protein function to improve the prediction of protein evolution.

Different mitogenomic codon usage patterns between damselflies and dragonflies and nine complete mitogenomes for odonates

Damselflies and dragonflies, of the order Odonata, have distinct body plans and predatory abilities. Knowledge of their various evolutionary histories will allow for an understanding of the genetic and phenotypic evolution of insects. Mitogenomes are suitable materials to elucidate this, but the mitogenome of only a few odonates have been annotated. Herein, we report the complete mitogenome of nine odonates, including seven dragonflies and two damselflies, and a comprehensive analysis of the codon usage in 31 Odonata mitogenomes with the aim to estimate their evolutionary characteristics. Overall, a weak codon bias exists among odonate mitogenomes, although this favours AT-ending codons. Damselflies have a weaker codon usage bias than dragonflies, and 37 codons have significantly different usages. Both directional mutation and purifying selection shape damselfly and dragonfly mitogenomes. Although inevitable, directional mutation bias plays a minor role, whereas purifying selection pressure is the dominant evolutionary force. A higher selection pressure is observed in dragonflies than in damselflies, but it mainly acts on codon usage patterns rather than amino acid translation. Our findings suggest that dragonflies might have more efficient mitochondrial gene expression levels than damselflies, producing more proteins that support their locomotion and predatory abilities.

Each of 3,323 metabolic innovations in the evolution of E. coli arose through the horizontal transfer of a single DNA segment

Even closely related prokaryotes often show an astounding diversity in their ability to grow in different nutritional environments. It has been hypothesized that complex metabolic adaptations-those requiring the independent acquisition of multiple new genes-can evolve via selectively neutral intermediates. However, it is unclear whether this neutral exploration of phenotype space occurs in nature, or what fraction of metabolic adaptations is indeed complex. Here, we reconstruct metabolic models for the ancestors of a phylogeny of 53 Escherichia coli strains, linking genotypes to phenotypes on a genome-wide, macroevolutionary scale. Based on the ancestral and extant metabolic models, we identify 3,323 phenotypic innovations in the history of the E. coli clade that arose through changes in accessory genome content. Of these innovations, 1,998 allow growth in previously inaccessible environments, while 1,325 increase biomass yield. Strikingly, every observed innovation arose through the horizontal acquisition of a single DNA segment less than 30 kb long. Although we found no evidence for the contribution of selectively neutral processes, 10.6% of metabolic innovations were facilitated by horizontal gene transfers on earlier phylogenetic branches, consistent with a stepwise adaptation to successive environments. Ninety-eight percent of metabolic phenotypes accessible to the combined E. coli pangenome can be bestowed on any individual strain by transferring a single DNA segment from one of the extant strains. These results demonstrate an amazing ability of the E. coli lineage to adapt to novel environments through single horizontal gene transfers (followed by regulatory adaptations), an ability likely mirrored in other clades of generalist bacteria.

Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates

Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. Here, we combine genome-scale metabolic modeling with population genetics models to simulate the evolution of enzyme turnover numbers (k_cats) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved k_cats. Diminishing returns epistasis prevents enzymes from developing higher k_cats in all reactions and keeps the organism far from the potential fitness optimum. Multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape allows k_cat evolution to be convergent. Predicted k_cat parameters show a significant correlation with experimental data, validating our modeling approach. Our analysis reveals how evolutionary forces shape modern k_cats and the whole of metabolism.

Ontogenetic allometry conservatism across five teleost orders

Geometric morphometrics were used to analyse ontogenetic trajectories in representatives of the Characiformes, Cichliformes, Cyprinodontiformes, Siluriformes, and Tetraodontiformes. It was not possible to differentiate any allometric growth patterns across groups, indicating that a phylogenetically conserved developmental pattern is widespread throughout Teleostei.

RNA-mediated gene regulation is less evolvable than transcriptional regulation

Cells regulate the activity of genes in a variety of ways. For example, they regulate transcription through DNA binding proteins called transcription factors, and they regulate mRNA stability and processing through RNA binding proteins. Based on current knowledge, transcriptional regulation is more widespread and is involved in many more evolutionary adaptations than posttranscriptional regulation. The reason could be that transcriptional regulation is studied more intensely. We suggest instead that transcriptional regulation harbors an intrinsic evolutionary advantage: when mutations change transcriptional regulation, they are more likely to bring forth novel patterns of such regulation. That is, transcriptional regulation is more evolvable. Our analysis suggests a reason why a specific kind of gene regulation is especially abundant in the living world.

Much of gene regulation is carried out by proteins that bind DNA or RNA molecules at specific sequences. One class of such proteins is transcription factors, which bind short DNA sequences to regulate transcription. Another class is RNA binding proteins, which bind short RNA sequences to regulate RNA maturation, transport, and stability. Here, we study the robustness and evolvability of these regulatory mechanisms. To this end, we use experimental binding data from 172 human and fruit fly transcription factors and RNA binding proteins as well as human polymorphism data to study the evolution of binding sites in vivo. We find little difference between the robustness of regulatory protein–RNA interactions and transcription factor–DNA interactions to DNA mutations. In contrast, we find that RNA-mediated regulation is less evolvable than transcriptional regulation, because mutations are less likely to create interactions of an RNA molecule with a new RNA binding protein than they are to create interactions of a gene regulatory region with a new transcription factor. Our observations are consistent with the high level of conservation observed for interactions between RNA binding proteins and their target molecules as well as the evolutionary plasticity of regulatory regions bound by transcription factors. They may help explain why transcriptional regulation is implicated in many more evolutionary adaptations and innovations than RNA-mediated gene regulation.

Embryonic lethality is not sufficient to explain hourglass-like conservation of vertebrate embryos

Background

Understanding the general trends in developmental changes during animal evolution, which are often associated with morphological diversification, has long been a central issue in evolutionary developmental biology. Recent comparative transcriptomic studies revealed that gene expression profiles of mid-embryonic period tend to be more evolutionarily conserved than those in earlier or later periods. While the hourglass-like divergence of developmental processes has been demonstrated in a variety of animal groups such as vertebrates, arthropods, and nematodes, the exact mechanism leading to this mid-embryonic conservation remains to be clarified. One possibility is that the mid-embryonic period (pharyngula period in vertebrates) is highly prone to embryonic lethality, and the resulting negative selections lead to evolutionary conservation of this phase. Here, we tested this “mid-embryonic lethality hypothesis” by measuring the rate of lethal phenotypes of three different species of vertebrate embryos subjected to two kinds of perturbations: transient perturbations and genetic mutations.

Results

By subjecting zebrafish (Danio rerio), African clawed frog (Xenopus laevis), and chicken (Gallus gallus) embryos to transient perturbations, namely heat shock and inhibitor treatments during three developmental periods [early (represented by blastula and gastrula), pharyngula, and late], we found that the early stages showed the highest rate of lethal phenotypes in all three species. This result was corroborated by perturbation with genetic mutations. By tracking the survival rate of wild-type embryos and embryos with genetic mutations induced by UV irradiation in zebrafish and African clawed frogs, we found that the highest decrease in survival rate was at the early stages particularly around gastrulation in both these species.

Conclusion

In opposition to the “mid-embryonic lethality hypothesis,” our results consistently showed that the stage with the highest lethality was not around the conserved pharyngula period, but rather around the early period in all the vertebrate species tested. These results suggest that negative selection by embryonic lethality could not explain hourglass-like conservation of animal embryos. This highlights the potential contribution of alternative mechanisms such as the diversifying effect of positive selections against earlier and later stages, and developmental constraints which lead to conservation of mid-embryonic stages.

Electronic supplementary material

The online version of this article (10.1186/s13227-018-0095-0) contains supplementary material, which is available to authorized users.

Analysis of the codon usage pattern in Middle East Respiratory Syndrome Coronavirus

Middle East Respiratory Syndrome Coronavirus (MERS-CoV), which first broken out in Jeddah in 2012, causes a severe acute respiratory illness with a high mortality rate. To better understand the molecular characteristics of isolated MERS-CoV genomes, we first analysed the codon usage pattern of the zoonotic MERS-CoV strains comprehensively to gain an insight into the mechanism of cross-species transmission. We found that MERS human/camel isolates showed a low codon usage bias. Both mutation and nature selection pressure have contributed to this low codon usage bias, with the former being the main determining factor. We also observed that gene function, evolution time and the different host species of the virus all contributed to the bias of MERS-CoV, to some extent. Additionally, the codon usage pattern of MERS-CoV isolates is different from other related Nidovirales viruses isolated from bats and hedgehogs. In the future, more epidemiological surveys are required to examine the factors that resulted in the emergence and outbreak of this virus.