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Modeling the rate at which adaptive phenotypes appear in a population is a key to predicting evolutionary processes. Given random mutations, should this rate be modeled by a simple Poisson process, or is a more complex dynamics needed? Here we use analytic calculations and simulations of evolving populations on explicit genotype–phenotype maps to show that the introduction of novel phenotypes can be "bursty" or overdispersed. In other words, a novel phenotype either appears multiple times in quick succession or not at all for many generations. These bursts are fundamentally caused by statistical fluctuations and other structure in the map from genotypes to phenotypes. Their strength depends on population parameters, being highest for "monomorphic" populations with low mutation rates. They can also be enhanced by additional inhomogeneities in the mapping from genotypes to phenotypes. We mainly investigate the effect of bursts using the well-studied genotype–phenotype map for RNA secondary structure, but find similar behavior in a lattice protein model and in Richard Dawkins's biomorphs model of morphological development. Bursts can profoundly affect adaptive dynamics. Most notably, they imply that fitness differences play a smaller role in determining which phenotype fixes than would be the case for a Poisson process without bursts. [ABSTRACT FROM AUTHOR]

Epistasis is often portrayed as unimportant in evolution. While random patterns of epistasis may have limited effects on the response to selection, systematic directional epistasis can have substantial effects on evolutionary dynamics. Directional epistasis occurs when allele substitutions that change a trait also modify the effects of allele substitution at other loci in a systematic direction. In this case, trait evolution may induce correlated changes in allelic effects and additive genetic variance (evolvability) that modify further evolution. Although theory thus suggests a potentially important role for directional epistasis in evolution, we still lack empirical evidence about its prevalence and magnitude. Using a new framework to estimate systematic patterns of epistasis from line-crosses experiments, we quantify its effects on 197 size-related traits from diverging natural populations in 24 animal and 17 plant species. We show that directional epistasis is common and tends to become stronger with increasing morphological divergence. In animals, most traits displayed negative directionality toward larger size, suggesting that epistatic constraints reducing evolvability toward larger size. Dominance was also common but did not systematically alter the effects of epistasis. [ABSTRACT FROM AUTHOR]

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

While the role of selection in divergence along the speciation continuum is theoretically well understood, defining specific signatures of selection in the genomic landscape of divergence is empirically challenging. Modelling approaches can provide insight into the potential role of selection on the emergence of a heterogenous genomic landscape of divergence. Here, we extend and apply an individual‐based approach that simulates the phenotypic and genotypic distributions of two populations under a variety of selection regimes, genotype–phenotype maps, modes of migration, and genotype‐environment interactions. We show that genomic islands of high differentiation and genomic valleys of similarity may respectively form under divergent and parallel selection between populations. For both types of between‐population selection, negative and positive frequency‐dependent selection within populations generated genomic islands of higher magnitude and genomic valleys of similarity, respectively. Divergence rates decreased under strong dominance with divergent selection, as well as in models including genotype‐environment interactions under parallel selection. For both divergent and parallel selection models, divergence rate was higher under an intermittent migration regime between populations, in contrast to a constant level of migration across generations, despite an equal number of total migrants. We highlight that interpreting a particular evolutionary history from an observed genomic pattern must be done cautiously, as similar patterns may be obtained from different combinations of evolutionary processes. Modelling approaches such as ours provide an opportunity to narrow the potential routes that generate the genomic patterns of specific evolutionary histories. [ABSTRACT FROM AUTHOR]

A novel budding yeast species was isolated from a soil sample collected in the United States of America. Phylogenetic analyses of multiple loci and phylogenomic analyses conclusively placed the species within the genus Pichia. Strain yHMH446 falls within a clade that includes Pichia norvegensis, Pichia pseudocactophila, Candida inconspicua, and Pichia cactophila. Whole genome sequence data were analyzed for the presence of genes known to be important for carbon and nitrogen metabolism, and the phenotypic data from the novel species were compared to all Pichia species with publicly available genomes. Across the genus, including the novel species candidate, we found that the inability to use many carbon and nitrogen sources correlated with the absence of metabolic genes. Based on these results, Pichia galeolata sp. nov. is proposed to accommodate yHMH446T (=NRRL Y‐64187 = CBS 16864). This study shows how integrated taxogenomic analysis can add mechanistic insight to species descriptions. Take‐away: The novel species Pichia galeolata is discovered and formally described.The genome sequence of P. galeolata is reported.Taxogenomic analysis shows how genome content correlates with traditional physiological assays. [ABSTRACT FROM AUTHOR]

Correlation among multiple phenotypes across related individuals may reflect some pattern of shared genetic architecture: individual genetic loci affect multiple phenotypes (an effect known as pleiotropy), creating observable relationships between phenotypes. A natural hypothesis is that pleiotropic effects reflect a relatively small set of common "core" cellular processes: each genetic locus affects one or a few core processes, and these core processes in turn determine the observed phenotypes. Here, we propose a method to infer such structure in genotype-phenotype data. Our approach, sparse structure discovery (SSD) is based on a penalized matrix decomposition designed to identify latent structure that is low-dimensional (many fewer core processes than phenotypes and genetic loci), locus-sparse (each locus affects few core processes), and/or phenotype-sparse (each phenotype is influenced by few core processes). Our use of sparsity as a guide in the matrix decomposition is motivated by the results of a novel empirical test indicating evidence of sparse structure in several recent genotype-phenotype datasets. First, we use synthetic data to show that our SSD approach can accurately recover core processes if each genetic locus affects few core processes or if each phenotype is affected by few core processes. Next, we apply the method to three datasets spanning adaptive mutations in yeast, genotoxin robustness assay in human cell lines, and genetic loci identified from a yeast cross, and evaluate the biological plausibility of the core process identified. More generally, we propose sparsity as a guiding prior for resolving latent structure in empirical genotype-phenotype maps. [ABSTRACT FROM AUTHOR]

Accurate phenotype prediction based on genetic information has numerous societal applications, such as crop design or cellular factories. Epistasis, when biological components interact, complicates modelling phenotypes from genotypes. Here we show an approach to mitigate this complication for polarity establishment in budding yeast, where mechanistic information is abundant. We coarse-grain molecular interactions into a so-called mesotype, which we combine with gene expression noise into a physical cell cycle model. First, we show with computer simulations that the mesotype allows validation of the most current biochemical polarity models by quantitatively matching doubling times. Second, the mesotype elucidates epistasis emergence as exemplified by evaluating the predicted mutational effect of key polarity protein Bem1p when combined with known interactors or under different growth conditions. This example also illustrates how unlikely evolutionary trajectories can become more accessible. The tractability of our biophysically justifiable approach inspires a road-map towards bottom-up modelling complementary to statistical inferences. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'. [ABSTRACT FROM AUTHOR]

Viral populations are large and highly heterogeneous. Despite the evolutionary relevance of such heterogeneity, statistical approaches to quantifying the extent to which viruses maintain a high genotypic and/or phenotypic diversity have been rarely pursued. Here, we address this issue by analyzing a nucleotide-to-protein sequence map through deep sequencing of populations of the Qβ phage adapted to high temperatures. Tens of thousands of different sequences corresponding to two fragments of the gene coding for the viral replicase were recovered. A diversity analysis of two independent populations consistently revealed that about 40% of the mutations identified caused changes in protein amino acids, leading to an almost complete exploration of the protein neighborhood of (non-silent) mutants at a distance of one. The functional form of the empirical distribution of phenotype abundance agreed with analytical calculations that assumed random mutations in the nucleotide sequence. Our results concur with the idea that viral populations maintain a high diversity as an efficient adaptive mechanism and support the hypothesis of universality for a lognormal distribution of phenotype abundances in biologically meaningful genotype–phenotype maps, highlighting the relevance of entropic effects in molecular evolution.

Species concepts have been defined through a number of lenses, but are almost entirely empirical in nature. Fundamentally linked to various existing species concepts, an interpretation of genomic data through a species classification filter based upon a theoretical genotype–phenotype map with a monophyly requirement is discussed. [ABSTRACT FROM AUTHOR]

Natural populations experience continuous and often transient changes of environmental conditions. These in turn may result in fluctuating selection pressures leading to variable demographic and evolutionary population responses. Rapid adaptation as short‐term response to a sudden environmental change has in several cases been attributed to polygenic traits, but the underlying genomic dynamics and architecture are poorly understood. In this study, we took advantage of a natural experiment in an insect population of the non‐biting midge Chironomus riparius by monitoring genome‐wide allele frequencies before and after a cold snap event. Whole genome pooled sequencing of time series samples revealed 10 selected haplotypes carrying ancient polymorphisms, partially with signatures of balancing selection. By constantly cold exposing genetically variable individuals in the laboratory, we could demonstrate with whole genome resequencing (i) that among the survivors, the same alleles rose in frequency as in the wild, and (ii) that the identified variants additively predicted fitness (survival time) of its bearers. Finally, by simultaneously sequencing the genome and the transcriptome of cold exposed individuals we could tentatively link some of the selected SNPs to the cis‐ and trans‐regulation of genes and pathways known to be involved in cold response of insects, such as cytochrome P450 and fatty acid metabolism. Altogether, our results shed light on the strength and speed of selection in natural populations and the genomic architecture of its underlying polygenic trait. Population genomic time series data thus appear as promising tool for measuring the selective tracking of fluctuating selection in natural populations. [ABSTRACT FROM AUTHOR]

Viral populations are large and highly heterogeneous. Despite the evolutionary relevance of such heterogeneity, statistical approaches to quantifying the extent to which viruses maintain a high genotypic and/or phenotypic diversity have been rarely pursued. Here, we address this issue by analyzing a nucleotide-to-protein sequence map through deep sequencing of populations of the Q β phage adapted to high temperatures. Tens of thousands of different sequences corresponding to two fragments of the gene coding for the viral replicase were recovered. A diversity analysis of two independent populations consistently revealed that about 40 % of the mutations identified caused changes in protein amino acids, leading to an almost complete exploration of the protein neighborhood of (non-silent) mutants at a distance of one. The functional form of the empirical distribution of phenotype abundance agreed with analytical calculations that assumed random mutations in the nucleotide sequence. Our results concur with the idea that viral populations maintain a high diversity as an efficient adaptive mechanism and support the hypothesis of universality for a lognormal distribution of phenotype abundances in biologically meaningful genotype–phenotype maps, highlighting the relevance of entropic effects in molecular evolution. [ABSTRACT FROM AUTHOR]

Evolutionary biology is paying increasing attention to the mechanisms that enable phenotypic plasticity, evolvability, and extra‐genetic inheritance. Yet, there is a concern that these phenomena remain insufficiently integrated within evolutionary theory. Understanding their evolutionary implications would require focusing on phenotypes and their variation, but this does not always fit well with the prevalent genetic representation of evolution that screens off developmental mechanisms. Here, we instead use development as a starting point, and represent it in a way that allows genetic, environmental and epigenetic sources of phenotypic variation to be independent. We show why this representation helps to understand the evolutionary consequences of both genetic and non‐genetic phenotype determinants, and discuss how this approach can instigate future areas of empirical and theoretical research. [ABSTRACT FROM AUTHOR]

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation., Competing Interests: KS, SA, DN, AS, GK, KG No competing interests declared, (© 2024, Schmidlin, Apodaca et al.)

The relationship between the evolutionary dynamics observed in contemporary populations (microevolution) and evolution on timescales of millions of years (macroevolution) has been a topic of considerable debate. Historically, this debate centers on inconsistencies between microevolutionary processes and macroevolutionary patterns. Here, we characterize a striking exception: emerging evidence indicates that standing variation in contemporary populations and macroevolutionary rates of phenotypic divergence are often positively correlated. This apparent consistency between micro- and macroevolution is paradoxical because it contradicts our previous understanding of phenotypic evolution and is so far unexplained. Here, we explore the prospects for bridging evolutionary timescales through an examination of this "paradox of predictability." We begin by explaining why the divergence-variance correlation is a paradox, followed by data analysis to show that the correlation is a general phenomenon across a broad range of temporal scales, from a few generations to tens of millions of years. Then we review complementary approaches from quantitative-genetics, comparative morphology, evo-devo, and paleontology to argue that they can help to address the paradox from the shared vantage point of recent work on evolvability. In conclusion, we recommend a methodological orientation that combines different kinds of short-term and long-term data using multiple analytical frameworks in an interdisciplinary research program. Such a program will increase our general understanding about how evolution works within and across timescales., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Evolutionary Biology.)

At odds with a traditional view of molecular evolution that seeks a descent-with-modification relationship between functional sequences, new functions can emerge de novo with relative ease. At early times of molecular evolution, random polymers could have sufficed for the appearance of incipient chemical activity, while the cellular environment harbours a myriad of proto-functional molecules. The emergence of function is facilitated by several mechanisms intrinsic to molecular organization, such as redundant mapping of sequences into structures, phenotypic plasticity, modularity or cooperative associations between genomic sequences. It is the availability of niches in the molecular ecology that filters new potentially functional proposals. New phenotypes and subsequent levels of molecular complexity could be attained through combinatorial explorations of currently available molecular variants. Natural selection does the rest. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'. [ABSTRACT FROM AUTHOR]

Determining how adaptive possibilities do or do not become evolutionary realities is central to understanding the tempo and mode of evolutionary change. Some of the simplest evolutionary landscapes arise from underdominance at a single locus where the fitness valley consists of only one less-fit genotype. Despite their potential for rapid evolutionary change, few such examples have been investigated. We capitalized on an experimental system in which a significant evolutionary shift, the transition from uni-to-multicellularity, was observed in asexual diploid populations of Saccharomyces cerevisiae experimentally selected for increased settling rates. The multicellular phenotype results from recessive single-locus mutations that undergo loss-of-heterozygosity (LOH) events. By reconstructing the necessary heterozygous intermediate steps, we found that the evolution of multicellularity involves a decrease in size during the first steps. Heterozygous genotypes are 20% smaller in size than genotypes with functional alleles. Nevertheless, populations of heterozygotes give rise to multicellular genotypes more readily than unicellular genotypes with two functional alleles, by rapid LOH events. LOH drives adaptation that may enable rapid evolution in diploid yeast. Together these results show discordance between the phenotypic and genotypic multicellular transition. The evolutionary path to multicellularity, and the adaptive benefits of increased size, requires initial size reductions. [ABSTRACT FROM AUTHOR]

'Evolvability' – the ability to undergo adaptive evolution – is a key concept for understanding and predicting the response of biological systems to environmental change. Evolvability has various facets and is applied in many ways, easily leading to misunderstandings among researchers. To clarify matters, we first categorize the mechanisms and organismal features underlying evolvability into determinants providing variation, determinants shaping the effect of variation on fitness, and determinants shaping the selection process. Second, we stress the importance of timescale when studying evolvability. Third, we distinguish between evolvability determinants with a broad and a narrow scope. Finally, we highlight two contrasting perspectives on evolvability: general evolvability and specific evolvability. We hope that this framework facilitates communication and guides future research. Evolvability, the capability to undergo adaptive evolution, is determined by a staggering diversity of mechanisms and organismal features. When discussing evolvability, it is useful to distinguish three categories of determinants: those providing variation, those shaping the effect of variation on fitness, and those shaping the selection process. Some determinants of evolvability have a broad scope in that they affect adaptive evolution across many different environments; others have a narrower scope in that they impact evolvability only with respect to particular challenges. Being explicit about the scope of evolvability determinants would largely facilitate communication across disciplines. On different timescales, the comparison of organisms regarding their evolvability and the comparison of mechanisms regarding their effects on evolvability can lead to very different conclusions. [ABSTRACT FROM AUTHOR]

Phenotypic (co)variation is a prerequisite for evolutionary change, and understanding how (co)variation evolves is of crucial importance to the biological sciences. Theoretical models predict that under directional selection, phenotypic (co)variation should evolve in step with the underlying adaptive landscape, increasing the degree of correlation among co-selected traits as well as the amount of genetic variance in the direction of selection. Whether either of these outcomes occurs in natural populations is an open question and thus an important gap in evolutionary theory. Here, we documented changes in the phenotypic (co)variation structure in two separate natural populations in each of two chipmunk species (Tamias alpinus and T. speciosus) undergoing directional selection. In populations where selection was strongest (those of T. alpinus), we observed changes, at least for one population, in phenotypic (co)variation that matched theoretical expectations, namely an increase of both phenotypic integration and (co)variance in the direction of selection and a re-alignment of the major axis of variation with the selection gradient.

Mutualisms such as those between flowering plants and their pollinators are common in nature. Yet understanding their persistence in the face of cheaters and identifying the mechanisms behind their stunning diversity provide formidable challenges for evolutionary biologists. To shed light onto these questions, we introduce an individual-based model of two coevolving species in which individuals of one species use a Boolean circuit to discriminate between cooperators and cheaters in the other species. This conveys the idea that interactions are often mediated by complex biological processes rather than the matching of a single trait, as often assumed in models of coevolution. Our results show that cheating promotes diversification and complex discrimination mechanisms at the cost of a higher risk for mutualism to collapse. This result is mediated by an inverse relationship between mutational robustness and organismal complexity. [ABSTRACT FROM AUTHOR]

The G matrix is a statistical summary of the genetic basis of a set of traits and a central pillar of quantitative genetics. A persistent controversy is whether G changes slowly or quickly over time. The evolution of G is important because it affects the ability to predict, or reconstruct, evolution by selection. Empirical studies have found mixed results on how fast G evolves. Theoretical work has largely been developed under the assumption that the relationship between genetic variation and phenotypic variation—the genotype-phenotype map (GPM)—is linear. Under this assumption, G is expected to remain constant over long periods of time. However, according to developmental biology, the GPM is typically complex and nonlinear. Here, we use a GPM model based on the development of a multicellular organ to study how G evolves. We find that G can change relatively fast and in qualitative different ways, which we describe in detail. Changes can be particularly large when the population crosses between regions of the GPM that have different properties. This can result in the additive genetic variance in the direction of selection fluctuating over time and even increasing despite the eroding effect of selection. [ABSTRACT FROM AUTHOR]

Understanding the molecular consequences of mutations in proteins is essential to map genotypes to phenotypes and interpret the increasing wealth of genomic data. While mutations are known to disrupt protein structure and function, their potential to create new structures and localization phenotypes has not yet been mapped to a sequence space. To map this relationship, we employed two homo-oligomeric protein complexes in which the internal symmetry exacerbates the impact of mutations. We mutagenized three surface residues of each complex and monitored the mutations' effect on localization and assembly phenotypes in yeast cells. While surface mutations are classically viewed as benign, our analysis of several hundred mutants revealed they often trigger three main phenotypes in these proteins: nuclear localization, the formation of puncta, and fibers. Strikingly, more than 50% of random mutants induced one of these phenotypes in both complexes. Analyzing the mutant's sequences showed that surface stickiness and net charge are two key physicochemical properties associated with these changes. In one complex, more than 60% of mutants self-assembled into fibers. Such a high frequency is explained by negative design: charged residues shield the complex from self-interacting with copies of itself, and the sole removal of the charges induces its supramolecular self-assembly. A subsequent analysis of several other complexes targeted with alanine mutations suggested that such negative design is common. These results highlight that minimal perturbations in protein surfaces' physicochemical properties can frequently drive assembly and localization changes in a cellular context. [ABSTRACT FROM AUTHOR]

Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ70 binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10–20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ70-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought.

Complexity in biology is often described using a multi-map hierarchical architecture, where the genotype, representing the encoded information, is mapped to the functional level, known as the phenotype, which is then connected to a latent phenotype we refer to as fitness. This underlying architecture governs the processes driving evolution. Furthermore, natural selection, along with other neutral forces, can, in turn, modify these maps. At each level, variation is observed. Here, I propose the need to establish principles that can aid in understanding the transformation of variation within this multi-map architecture. Specifically, I will introduce three, related to the presence of modulators, constraints, and the modular channeling of variation. By comprehending these design principles in various biological systems, we can gain better insights into the mechanisms underlying these maps and how they ultimately contribute to evolutionary dynamics., (Creative Commons Attribution license.)

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into 6 classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

• Understanding of how genotypes map onto molecular phenotypes and organismal functions is partial. • The networked organization of genotype-phenotype (GP) maps conditions evolution and adaptation. • Several topological properties of genotype spaces are universal. • The evolution of GP maps (not on GP maps) drives the emergence of phenotypes at the species level. • Technical advances permit the exhaustive empirical characterization of small GP maps. Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics—cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis. [ABSTRACT FROM AUTHOR]

Quantitative analysis of gene expression domains and investigation of relationships between gene expression and developmental and phenotypic outcomes are central to advancing our understanding of the genotype-phenotype map. Gene expression domains typically have smooth but irregular shapes lacking homologous landmarks, making it difficult to analyze shape variation with the tools of landmark-based geometric morphometrics. In addition, 3D image acquisition and processing introduce many artifacts that further exacerbate the problem. To overcome these difficulties, this paper presents a method that combines optical projection tomography scanning, a shape regularization technique and a landmark-free approach to quantify variation in the morphology of Sonic hedgehog expression domains in the frontonasal ectodermal zone (FEZ) of avians and investigate relationships with embryonic craniofacial shape. The model reveals axes in FEZ and embryonic-head morphospaces along which variation exhibits a sharp linear relationship at high statistical significance. The technique should be applicable to analyses of other 3D biological structures that can be modeled as smooth surfaces and have ill-defined shape.

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.

The ever-growing genome-wide association studies (GWAS) have revealed widespread pleiotropy. To exploit this, various methods that jointly consider associations of a genetic variant with multiple traits have been developed. Most efforts have been made concerning improving GWAS discovery power. However, how to replicate these discovered pleiotropic loci has yet to be discussed thoroughly. Unlike a single-trait scenario, multi-trait replication is not trivial considering the underlying genotype-multi-phenotype map of the associations. Here, we evaluate four methods for replicating multi-trait associations, corresponding to four levels of replication strength. Weak replication cannot justify pleiotropic genetic effects, whereas strong replication using our developed correlation methods can inform consistent pleiotropic genetic effects across the discovery and replication samples. We provide a protocol for replicating multi-trait genetic associations in practice. The described methods are implemented in the free and open-source R package MultiABEL.

The fitness impact of loss-of-function mutations is generally assumed to reflect the loss of specific molecular functions associated with the perturbed gene. Here, we propose that rewiring of the transcriptome upon deleterious gene inactivation is frequently nonspecific and mimics stereotypic responses to external environmental change. Consequently, transcriptional response to gene deletion could be suboptimal and incur an extra fitness cost. Analysis of the transcriptomes of ∼1,500 single-gene deletion Saccharomyces cerevisiae strains supported this scenario. First, most transcriptomic changes are not specific to the deleted gene but are rather triggered by perturbations in functionally diverse genes. Second, gene deletions that alter the expression of dosage-sensitive genes are especially harmful. Third, by elevating the expression level of downregulated genes, we could experimentally mitigate the fitness defect of gene deletions. Our work shows that rewiring of genomic expression upon gene inactivation shapes the harmful effects of mutations. [ABSTRACT FROM AUTHOR]

The ever-growing genome-wide association studies (GWAS) have revealed widespread pleiotropy. To exploit this, various methods that jointly consider associations of a genetic variant with multiple traits have been developed. Most efforts have been made concerning improving GWAS discovery power. However, how to replicate these discovered pleiotropic loci has yet to be discussed thoroughly. Unlike a single-trait scenario, multi-trait replication is not trivial considering the underlying genotype-multi-phenotype map of the associations. Here, we evaluate four methods for replicating multi-trait associations, corresponding to four levels of replication strength. Weak replication cannot justify pleiotropic genetic effects, whereas strong replication using our developed correlation methods can inform consistent pleiotropic genetic effects across the discovery and replication samples. We provide a protocol for replicating multi-trait genetic associations in practice. The described methods are implemented in the free and open-source R package MultiABEL. [ABSTRACT FROM AUTHOR]

A recent theoretical, deterministic model of the effects of phenotypic robustness on adaptive evolutionary dynamics showed that a certain level of phenotypic robustness (critical robustness) is a required condition for adaptation to occur and to be maintained during evolution in most real organismal systems. We built an individual-based heuristic model to verify the soundness of these theoretical results through computer simulation, testing expectations under a range of scenarios for the relevant parameters of the evolutionary dynamics. These include the mutation probability, the presence of stochastic effects, the introduction of environmental influences and the possibility for some features of the population (like selection coefficients and phenotypic robustness) to change themselves during adaptation. Overall, we found a good match between observed and expected results, even for evolutionary parameter values that violate some of the assumptions of the deterministic model, and that robustness can itself evolve. However, from more than one simulation it appears that very high robustness values, higher than the critical value, can limit or slow-down adaptation. This possible trade-off was not predicted by the deterministic model. [ABSTRACT FROM AUTHOR]

In many animal species, traits associated with male fitness evolve rapidly. Intersexual conflict and male-male competition have been suggested to drive this rapid evolution. These fast evolutionary dynamics result in elevated rates of amino acid replacement and modification of gene expression attributes. Gene acquisition is another mechanism that might contribute to fitness differences among males. However, empirical evidence of fitness effects associated with newly evolved genes is scarce. The Sdic multigene family originated within the last 5.4 myr in the lineage that leads to D. melanogaster and encodes a sperm dynein intermediate chain presumably involved in sperm motility. The silencing of the Sdic multigene family, followed by the screening of relevant phenotypes, supports the role of the Sdic multigene family in sperm competition. The case of the Sdic multigene family illustrates the flexibility of genetic networks in incorporating lineage-specific gene novelties that can trigger an evolutionary arms race between males.

Living systems evolve one mutation at a time, but a single mutation can alter the effect of subsequent mutations. The underlying mechanistic determinants of such epistasis are unclear. Here, we demonstrate that the physical dynamics of a biological system can generically constrain epistasis. We analyze models and experimental data on proteins and regulatory networks. In each, we find that if the long-time physical dynamics is dominated by a slow, collective mode, then the dimensionality of mutational effects is reduced. Consequently, epistatic coefficients for different combinations of mutations are no longer independent, even if individually strong. Such epistasis can be summarized as resulting from a global nonlinearity applied to an underlying linear trait, that is, as global epistasis. This constraint, in turn, reduces the ruggedness of the sequence-to-function map. By providing a generic mechanistic origin for experimentally observed global epistasis, our work suggests that slow collective physical modes can make biological systems evolvable. [ABSTRACT FROM AUTHOR]

Genotype–phenotype mapping plays an essential role in the design of an evolutionary algorithm. Variation occurs at the genotypic level but fitness is evaluated at the phenotypic level, therefore, this mapping determines if and how variations are effectively translated into quality improvements. In evolutionary algorithms, this mapping has often been observed as highly redundant, i.e., multiple genotypes can map to the same phenotype, as well as heterogeneous, i.e., some phenotypes are represented by a large number of genotypes while some phenotypes only have few. We numerically study the redundant genotype–phenotype mapping of a simple Boolean linear genetic programming system and quantify the mutational connections among phenotypes using tools of complex network analysis. The analysis yields several interesting statistics of the phenotype network. We show the evidence and provide explanations for the observation that some phenotypes are much more difficult to find as the target of a search than others. Our study provides a quantitative analysis framework to better understand the genotype–phenotype map, and the results may be utilized to inspire algorithm design that allows the search of a difficult target to be more effective. [ABSTRACT FROM AUTHOR]

A fundamental aim of post‐genomic 21st century biology is to understand the genotype–phenotype map (GPM) or how specific genetic variation relates to specific phenotypic variation. Quantitative genetics approximates such maps using linear models, and has developed methods to predict the response to selection in a population. The other major field of research concerned with the GPM, developmental evolutionary biology, or evo‐devo, has found the GPM to be highly nonlinear and complex. Here, we quantify how the predictions of quantitative genetics are affected by a complex, nonlinear map based on the development of a multicellular organ. We compared the predicted change in mean phenotype for a single generation using the multivariate breeder's equation, with the change observed from the model of development. We found that there are frequent disagreements between predicted and observed responses to selection due to the nonlinear nature of the genotype–phenotype map. Our results are a step toward integrating the fields studying the GPM. [ABSTRACT FROM AUTHOR]