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The joint distribution of selection coefficients and mutation rates is a key determinant of the genetic architecture of molecular adaptation. Three different distributions are of immediate interest: (1) thenominaldistribution of possible changes, prior to mutation or selection, (2) thede novodistribution of realized mutations, and (3) thefixeddistribution of selectively established mutations. Here, we formally characterize the relationships between these joint distributions under the strong selection, weak mutation (SSWM) regime. Thede novodistribution is enriched relative to the nominal distribution for the highest rate mutations, and the fixed distribution is further enriched for the most highly beneficial mutations. Whereas mutation rates and selection coefficients are often assumed to be uncorrelated, we show that even with no correlation in the nominal distribution, the resultingde novoand fixed distributions can have correlations with any combination of signs. Nonetheless, we suggest that natural systems with a finite number of beneficial mutations will frequently have the kind of nominal distribution that induces negative correlations in the fixed distribution. We apply our mathematical framework, along with population simulations, to explore joint distributions of selection coefficients and mutation rates from deep mutational scanning and cancer informatics. Finally, we consider the evolutionary implications of these joint distributions together with two additional joint distributions relevant to parallelism and the rate of adaptation.

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., Science Advances, 9 (21), ISSN:2375-2548

Predicting evolutionary outcomes is an important research goal in a diversity of contexts. The focus of evolutionary forecasting is usually on adaptive processes, and efforts to improve prediction typically focus on selection. However, adaptive processes often rely on new mutations, which can be strongly influenced by predictable biases in mutation. Here, we provide an overview of existing theory and evidence for such mutation-biased adaptation and consider the implications of these results for the problem of prediction, in regard to topics such as the evolution of infectious diseases, resistance to biochemical agents, as well as cancer and other kinds of somatic evolution. We argue that empirical knowledge of mutational biases is likely to improve in the near future, and that this knowledge is readily applicable to the challenges of short-term prediction. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'., Philosophical Transactions of the Royal Society B: Biological Sciences, 378 (1877), ISSN:0962-8436, ISSN:1471-2970, ISSN:0080-4622

Evolutionary adaptation often occurs by the fixation of beneficial mutations. This mode of adaptation can be characterized quantitatively by a spectrum of adaptive substitutions, i.e., a distribution for types of changes fixed in adaptation. Recent work establishes that the changes involved in adaptation reflect common types of mutations, raising the question of how strongly the mutation spectrum shapes the spectrum of adaptive substitutions. We address this question with a codon-based model for the spectrum of adaptive amino acid substitutions, applied to three large datasets covering thousands of amino acid changes identified in natural and experimental adaptation in Saccharomyces cerevisiae, Escherichia coli, and Mycobacterium tuberculosis Using species-specific mutation spectra based on prior knowledge, we find that the mutation spectrum has a proportional influence on the spectrum of adaptive substitutions in all three species. Indeed, we find that by inferring the mutation rates that best explain the spectrum of adaptive substitutions, we can accurately recover the species-specific mutation spectra. However, we also find that the predictive power of the model differs substantially between the three species. To better understand these differences, we use population simulations to explore the factors that influence how closely the spectrum of adaptive substitutions mirrors the mutation spectrum. The results show that the influence of the mutation spectrum decreases with increasing mutational supply ([Formula: see text]) and that predictive power is strongly affected by the number and diversity of beneficial mutations., Proceedings of the National Academy of Sciences of the United States of America, 119 (7), ISSN:0027-8424, ISSN:1091-6490

Non-heritable somatic mutations are typically associated with deleterious effects such as in cancer and senescence, so their role in adaptive evolution has received little attention. However, most somatic mutations are harmless and some even confer a fitness advantage to the organism carrying them. We hypothesized that heritable, germline genotypes that are likely to express an advantageous phenotype via non-heritable somatic mutation will have a selective advantage over other germline genotypes, and this advantage will channel evolving populations toward more fit germline genotypes, thus promoting adaptation. We tested this hypothesis by simulating evolving populations of developing organisms with an impermeable germline-soma separation navigating a minimal fitness landscape. The simulations revealed the conditions under which non-heritable somatic mutations promote adaptation. Specifically, this can occur when the somatic mutation supply is high, when only very few cells with the advantageous somatic mutation are required to increase organismal fitness, and when the somatic mutation also confers a selective advantage to cells with that mutation. We therefore provide proof-of-principle that non-heritable somatic mutations can promote adaptive evolution via a process we call somatic genotypic exploration. We discuss the biological plausibility of this phenomenon, as well as its evolutionary implications.SignificanceThe immensity of non-heritable genetic diversity arising in the soma has been largely disregarded in evolutionary theory as a source of adaptation. Here, we introduce a model in which non-heritable somatic mutations arising during development confer an organismal fitness advantage. Analysis of this model shows how such mutations channel evolving populations toward adaptive germline genotypes. This is most likely to occur when somatic mutations confer a fitness benefit at both the cellular and organismal levels, evoking a synergistic form of multi-level selection that contrasts with the antagonistic forms typically associated with somatic mutations in cancer and senescence. As such, our study invites a new view of somatic genetic diversity in evolutionary theory as a potential source of adaptation.

Cryptic alleles make a bridge for adaptation Protein function is generally constrained by selective parameters that can inhibit evolutionary potential. It has thus been difficult to determine how novelties arise. Zheng et al. allowed bacterial populations to accumulate mutations and then used directed evolution to evolve green fluorescent protein function from a gene that expressed yellow fluorescent protein (see the Perspective by Lee and Marx). Protein alternatives could evolve in cases where cryptic alleles—selectively neutral or mildly deleterious genetic variants with no apparent phenotypic differences—were present in the population. Thus, cryptic alleles provide an evolutionary bridge between diversity and selection and facilitate the generation of novel adaptations. Science , this issue p. 347 ; see also p. 318

The relationship between genotype and phenotype is central to our understanding of development, evolution, and disease. This relationship is known as the genotype- phenotype map. Gene regulatory circuits occupy a central position in this map, because they control when, where, and to what extent genes are expressed, and thus drive fundamental physiological, developmental, and behavioral processes in living organisms as different as bacteria and humans. Mutations that affect these gene expression patterns are often implicated in disease, so it is important that gene regulatory circuits are robust to mutation. Such mutations can also bring forth beneficial phenotypic variation that embodies or leads to evolutionary adaptations or innovations. Here we review recent theoretical and experimental work that sheds light on the robustness and evolvability of gene regulatory circuits.

The relationship between genotype and phenotype is central to our understanding of development, evolution, and disease. This relationship is known as the genotype-phenotype map. Gene regulatory circuits occupy a central position in this map, because they control when, where, and to what extent genes are expressed, and thus drive fundamental physiological, developmental, and behavioral processes in living organisms as different as bacteria and humans. Mutations that affect these gene expression patterns are often implicated in disease, so it is important that gene regulatory circuits are robust to mutation. Such mutations can also bring forth beneficial phenotypic variation that embodies or leads to evolutionary adaptations or innovations. Here, we review recent theoretical and experimental work that sheds light on the robustness and evolvability of gene regulatory circuits.

Regulatory networks control the spatiotemporal gene expression patterns that give rise to and define the individual cell types of multicellular organisms. In eumetazoa, distal regulatory elements called enhancers play a key role in determining the structure of such networks, particularly the wiring diagram of “who regulates whom.” Mutations that affect enhancer activity can therefore rewire regulatory networks, potentially causing adaptive changes in gene expression. Here, we use whole-tissue and single-cell transcriptomic and chromatin accessibility data from mouse to show that enhancers play an additional role in the evolution of regulatory networks: They facilitate network growth by creating transcriptionally active regions of open chromatin that are conducive to de novo gene evolution. Specifically, our comparative transcriptomic analysis with three other mammalian species shows that young, mouse-specific intergenic open reading frames are preferentially located near enhancers, whereas older open reading frames are not. Mouse-specific intergenic open reading frames that are proximal to enhancers are more highly and stably transcribed than those that are not proximal to enhancers or promoters, and they are transcribed in a limited diversity of cellular contexts. Furthermore, we report several instances of mouse-specific intergenic open reading frames proximal to promoters showing evidence of being repurposed enhancers. We also show that open reading frames gradually acquire interactions with enhancers over macroevolutionary timescales, helping integrate genes—those that have arisen de novo or by other means—into existing regulatory networks. Taken together, our results highlight a dual role of enhancers in expanding and rewiring gene regulatory networks.

Mutation is a biased stochastic process, with some types of mutations occurring more frequently than others. Previous work has used synthetic genotype-phenotype landscapes to study how such mutation bias affects adaptive evolution. Here, we consider 746 empirical genotype-phenotype landscapes, each of which describes the binding affinity of target DNA sequences to a transcription factor, to study the influence of mutation bias on adaptive evolution of increased binding affinity. By using empirical genotype-phenotype landscapes, we need to make only few assumptions about landscape topography and about the DNA sequences that each landscape contains. The latter is particularly important because the set of sequences that a landscape contains determines the types of mutations that can occur along a mutational path to an adaptive peak. That is, landscapes can exhibit a composition bias—a statistical enrichment of a particular type of mutation relative to a null expectation, throughout an entire landscape or along particular mutational paths—that is independent of any bias in the mutation process. Our results reveal the way in which composition bias interacts with biases in the mutation process under different population genetic conditions, and how such interaction impacts fundamental properties of adaptive evolution, such as its predictability, as well as the evolution of genetic diversity and mutational robustness., PLoS Computational Biology, 16 (9), ISSN:1553-734X, ISSN:1553-7358

Understanding the conservation and evolution of protein complexes is of critical value to decode their function in physiological and pathological processes. One prominent proposal posits gene duplication as a potential mechanism for protein complex evolution. In this study we take advantage of large-scale proteome expression datasets to systematically investigate the role of paralogues, and specifically self-interacting paralogues, in shaping the evolutionary trajectories of protein complexes. First, we show that protein co-expression derived from quantitative proteomic matrices is a good indicator for complex membership and is conserved across species. Second, we suggest that paralogues are commonly strongly co-expressed and that for the subset of paralogues that show diverging co-expression patterns, the divergent co-expression patterns reflect both sequence and functional divergence. Finally, on this basis, we show that homomeric paralogues known to be part of protein complexes display a unique co-expression pattern distribution, with a subset of them being highly diverging. These findings support the idea that homomeric paralogues can avoid cross-interference by diversifying their expression patterns, and corroborates the role of this mechanism as a force shaping protein complex evolution and specialization.

Recent advances in high-throughput technologies are bringing the study of empirical genotype-phenotype (GP) maps to the fore. Here, we use data from protein-binding microarrays to study an empirical GP map of transcription factor (TF) -binding preferences. In this map, each genotype is a DNA sequence. The phenotype of this DNA sequence is its ability to bind one or more TFs. We study this GP map using genotype networks, in which nodes represent genotypes with the same phenotype, and edges connect nodes if their genotypes differ by a single small mutation. We describe the structure and arrangement of genotype networks within the space of all possible binding sites for 525 TFs from three eukaryotic species encompassing three kingdoms of life (animal, plant, and fungi). We thus provide a high-resolution depiction of the architecture of an empirical GP map. Among a number of findings, we show that these genotype networks are "small-world" and assortative, and that they ubiquitously overlap and interface with one another. We also use polymorphism data from Arabidopsis thaliana to show how genotype network structure influences the evolution of TF-binding sites in vivo. We discuss our findings in the context of regulatory evolution.

Regulatory networks control the spatiotemporal gene expression patterns that give rise to and define the individual cell types of multicellular organisms. In eumetazoa, distal regulatory elements called enhancers play a key role in determining the structure of such networks, particularly the wiring diagram of “who regulates whom.” Mutations that affect enhancer activity can therefore rewire regulatory networks, potentially causing changes in gene expression that are adaptive. Here, we use whole-tissue and single-cell transcriptomic and chromatin accessibility data from mouse to show that enhancers play an additional role in the evolution of regulatory networks: They facilitate network growth by creating transcriptionally active regions of open chromatin that are conducive to de novo gene evolution. Specifically, our comparative transcriptomic analysis with three other mammalian species shows that young, mouse-specific intergenic open reading frames are preferentially located near enhancers, whereas older open reading frames are not. Mouse-specific intergenic open reading frames that are proximal to enhancers are more highly and stably transcribed than those that are not proximal to enhancers or promoters, and they are transcribed in a limited diversity of cellular contexts. Furthermore, we report several instances of mouse-specific intergenic open reading frames that are proximal to promoters that show evidence of being repurposed enhancers. We also show that open reading frames gradually acquire specific interactions with enhancers over macro-evolutionary timescales, helping integrate new genes into existing regulatory networks. Taken together, our results highlight a dual role of enhancers in expanding and rewiring gene regulatory networks.

Circuit motifs are small directed subgraphs that appear in real-world networks significantly more often than in randomized networks. In the Boolean model of gene circuits, most motifs are realized by multiple circuit genotypes. Each of a motif’s constituent circuit genotypes may have one or more functions, which are embodied in the expression patterns the circuit forms in response to specific initial conditions. Recent enumeration of a space of nearly 17 million three-gene circuit genotypes revealed that all circuit motifs have more than one function, with the number of functions per motif ranging from 12 to nearly 30,000. This indicates that some motifs are more functionally versatile than others. However, the individual circuit genotypes that constitute each motif are less robust to mutation if they have many functions, hinting that functionally versatile motifs may be less robust to mutation than motifs with few functions. Here, I explore the relationship between versatility and robustness in circuit motifs, demonstrating that functionally versatile motifs are robust to mutation despite the inherent tradeoff between versatility and robustness at the level of an individual circuit genotype.

The adaptive landscape is an iconic metaphor that pervades evolutionary biology. It was mostly applied in theoretical models until recent years, when empirical data began to allow partial landscape reconstructions. Here, we exhaustively analyse 1,137 complete landscapes from 129 eukaryotic species, each describing the binding affinity of a transcription factor to all possible short DNA sequences. We find that the navigability of these landscapes through single mutations is intermediate to that of additive and shuffled null models, suggesting that binding affinity-and thereby gene expression-is readily fine-tuned via mutations in transcription factor binding sites. The landscapes have few peaks that vary in their accessibility and in the number of sequences they contain. Binding sites in the mouse genome are enriched in sequences found in the peaks of especially navigable landscapes and the genetic diversity of binding sites in yeast increases with the number of sequences in a peak. Our findings suggest that landscape navigability may have contributed to the enormous success of transcriptional regulation as a source of evolutionary adaptations and innovations.

On the one hand, organisms are to some extent robust to DNA mutations – their phenotypes do not change in response to such mutations. On the other hand, DNA mutations are essential for life’s ability to bring forth novel and beneficial phenotypes, that is, for its evolvability. We here discuss that robustness can facilitate evolvability in proteins, RNA, and regulatory circuits even though it causes the preservation of existing phenotypes.