Nonlinear fitness consequences of variation in expression level of a eukaryotic gene

The potential of inversions to accumulate balanced sexual antagonism is supported by simulations and Drosophila experiments

Chromosomal inversion polymorphisms can be common, but the causes of their persistence are often unclear. We propose a model for the maintenance of inversion polymorphism, which requires that some variants contribute antagonistically to two phenotypes, one of which has negative frequency-dependent fitness. These conditions yield a form of frequency-dependent disruptive selection, favoring two predominant haplotypes segregating alleles that favor opposing antagonistic phenotypes. An inversion associated with one haplotype can reduce the fitness load incurred by generating recombinant offspring, reinforcing its linkage to the haplotype and enabling both haplotypes to accumulate more antagonistic variants than expected otherwise. We develop and apply a forward simulator to examine these dynamics under a tradeoff between survival and male display. These simulations indeed generate inversion-associated haplotypes with opposing sex-specific fitness effects. Antagonism strengthens with time, and can ultimately yield karyotypes at surprisingly predictable frequencies, with striking genotype frequency differences between sexes and between developmental stages. To test whether this model may contribute to well-studied yet enigmatic inversion polymorphisms in Drosophila melanogaster, we track inversion frequencies in laboratory crosses to test whether they influence male reproductive success or survival. We find that two of the four tested inversions show significant evidence for the tradeoff examined, with In(3 R)K favoring survival and In(3 L)Ok favoring male reproduction. In line with the apparent sex-specific fitness effects implied for both of those inversions, In(3 L)Ok was also found to be less costly to the viability and/or longevity of males than females, whereas In(3 R)K was more beneficial to female survival. Based on this work, we expect that balancing selection on antagonistically pleiotropic traits may provide a significant and underappreciated contribution to the maintenance of natural inversion polymorphism.

Inversions Can Accumulate Balanced Sexual Antagonism: Evidence from Simulations and Drosophila Experiments

Chromosomal inversion polymorphisms can be common, but the causes of their persistence are often unclear. We propose a model for the maintenance of inversion polymorphism, which requires that some variants contribute antagonistically to two phenotypes, one of which has negative frequency-dependent fitness. These conditions yield a form of frequency-dependent disruptive selection, favoring two predominant haplotypes segregating alleles that favor opposing antagonistic phenotypes. An inversion associated with one haplotype can reduce the fitness load incurred by generating recombinant offspring, reinforcing its linkage to the haplotype and enabling both haplotypes to accumulate more antagonistic variants than expected otherwise. We develop and apply a forward simulator to examine these dynamics under a tradeoff between survival and male display. These simulations indeed generate inversion-associated haplotypes with opposing sex-specific fitness effects. Antagonism strengthens with time, and can ultimately yield karyotypes at surprisingly predictable frequencies, with striking genotype frequency differences between sexes and between developmental stages. To test whether this model may contribute to well-studied yet enigmatic inversion polymorphisms in Drosophila melanogaster, we track inversion frequencies in laboratory crosses to test whether they influence male reproductive success or survival. We find that two of the four tested inversions show significant evidence for the tradeoff examined, with ln(3R)K favoring survival and ln(3L)Ok favoring male reproduction. In line with the apparent sex-specific fitness effects implied for both of those inversions, ln(3L)Ok was also found to be less costly to the viability and/or longevity of males than females, whereas ln(3R)K was more beneficial to female survival. Based on this work, we expect that balancing selection on antagonistically pleiotropic traits may provide a significant and underappreciated contribution to the maintenance of natural inversion polymorphism.

Plasticity and environment-specific relationships between gene expression and fitness in Saccharomyces cerevisiae

The environment influences how an organism's genotype determines its phenotype and how this phenotype affects its fitness. Here, to better understand this dual role of environment in the production and selection of phenotypic variation, we determined genotype-phenotype-fitness relationships for mutant strains of Saccharomyces cerevisiae in four environments. Specifically, we measured how promoter mutations of the metabolic gene TDH3 modified expression level and affected growth for four different carbon sources. In each environment, we observed a clear relationship between TDH3 expression level and fitness, but this relationship differed among environments. Mutations with similar effects on expression in different environments often had different effects on fitness and vice versa. Such environment-specific relationships between phenotype and fitness can shape the evolution of phenotypic plasticity. We also found that mutations disrupting binding sites for transcription factors had more variable effects on expression among environments than those disrupting the TATA box, which is part of the core promoter. However, mutations with the most environmentally variable effects on fitness were located in the TATA box, because of both the lack of plasticity of TATA box mutations and environment-specific fitness functions. This observation suggests that mutations affecting different molecular mechanisms contribute unequally to regulatory sequence evolution in changing environments.

Comprehensive double-mutant analysis of the Bacillus subtilis envelope using double-CRISPRi

Understanding bacterial gene function remains a major biological challenge. Double-mutant genetic interaction (GI) analysis addresses this challenge by uncovering the functional partners of targeted genes, allowing us to associate genes of unknown function with novel pathways and unravel connections between well-studied pathways, but is difficult to implement at the genome-scale. Here, we develop and use double-CRISPRi to systematically quantify genetic interactions at scale in the Bacillus subtilis envelope, including essential genes. We discover > 1000 known and novel genetic interactions. Our analysis pipeline and experimental follow-ups reveal the distinct roles of paralogous genes such as the mreB and mbl actin homologs, and identify new genes involved in the well-studied process of cell division. Overall, our study provides valuable insights into gene function and demonstrates the utility of double-CRISPRi for high-throughput dissection of bacterial gene networks, providing a blueprint for future studies in diverse bacterial species.

Plasticity and environment-specific relationships between gene expression and fitness in Saccharomyces cerevisiae

Phenotypic evolution is shaped by interactions between organisms and their environments. The environment influences how an organism's genotype determines its phenotype and how this phenotype affects its fitness. To better understand this dual role of the environment in the production and selection of phenotypic variation, we empirically determined and compared the genotype-phenotype-fitness relationship for mutant strains of the budding yeast Saccharomyces cerevisiae in four environments. Specifically, we measured how mutations in the promoter of the metabolic gene TDH3 modified its expression level and affected its growth on media with four different carbon sources. In each environment, we observed a clear relationship between TDH3 expression level and fitness, but this relationship differed among environments. Genetic variants with similar effects on TDH3 expression in different environments often had different effects on fitness and vice versa. Such environment-specific relationships between phenotype and fitness can shape the evolution of phenotypic plasticity. The set of mutants we examined also allowed us to compare the effects of mutations disrupting binding sites for key transcriptional regulators and the TATA box, which is part of the core promoter sequence. Mutations disrupting the binding sites for the transcription factors had more variable effects on expression among environments than mutations disrupting the TATA box, yet mutations with the most environmentally variable effects on fitness were located in the TATA box. This observation suggests that mutations affecting different molecular mechanisms are likely to contribute unequally to regulatory sequence evolution in changing environments.

Mechanisms of regulatory evolution in yeast

Studies of regulatory variation in yeast - at the level of new mutations, polymorphisms within a species, and divergence between species - have provided great insight into the molecular and evolutionary processes responsible for the evolution of gene expression in eukaryotes. The increasing ease with which yeast genomes can be manipulated and expression quantified in a high-throughput manner has recently accelerated mechanistic studies of cis- and trans-regulatory variation at multiple evolutionary timescales. These studies have, for example, identified differences in the properties of cis- and trans-acting mutations that affect their evolutionary fate, experimentally characterized the molecular mechanisms through which cis- and trans-regulatory variants act, and illustrated how regulatory networks can diverge between species with or without changes in gene expression.

On the incongruence of genotype-phenotype and fitness landscapes

The mapping from genotype to phenotype to fitness typically involves multiple nonlinearities that can transform the effects of mutations. For example, mutations may contribute additively to a phenotype, but their effects on fitness may combine non-additively because selection favors a low or intermediate value of that phenotype. This can cause incongruence between the topographical properties of a fitness landscape and its underlying genotype-phenotype landscape. Yet, genotype-phenotype landscapes are often used as a proxy for fitness landscapes to study the dynamics and predictability of evolution. Here, we use theoretical models and empirical data on transcription factor-DNA interactions to systematically study the incongruence of genotype-phenotype and fitness landscapes when selection favors a low or intermediate phenotypic value. Using the theoretical models, we prove a number of fundamental results. For example, selection for low or intermediate phenotypic values does not change simple sign epistasis into reciprocal sign epistasis, implying that genotype-phenotype landscapes with only simple sign epistasis motifs will always give rise to single-peaked fitness landscapes under such selection. More broadly, we show that such selection tends to create fitness landscapes that are more rugged than the underlying genotype-phenotype landscape, but this increased ruggedness typically does not frustrate adaptive evolution because the local adaptive peaks in the fitness landscape tend to be nearly as tall as the global peak. Many of these results carry forward to the empirical genotype-phenotype landscapes, which may help to explain why low- and intermediate-affinity transcription factor-DNA interactions are so prevalent in eukaryotic gene regulation.

Extensive transgressive gene expression in testis but not ovary in the homoploid hybrid Italian sparrow

Hybridization can result in novel allelic combinations which can impact the hybrid phenotype through changes in gene expression. While misexpression in F₁ hybrids is well documented, how gene expression evolves in stabilized hybrid taxa remains an open question. As gene expression evolves in a stabilizing manner, break‐up of co‐evolved cis‐ and trans‐regulatory elements could lead to transgressive patterns of gene expression in hybrids. Here, we address to what extent gonad gene expression has evolved in an established and stable homoploid hybrid, the Italian sparrow (Passer italiae). Through comparison of gene expression in gonads from individuals of the two parental species (i.e., house and Spanish sparrow) to that of Italian sparrows, we find evidence for strongly transgressive expression in male Italian sparrows—2530 genes (22% of testis genes tested for inheritance) exhibit expression patterns outside the range of both parent species. In contrast, Italian sparrow ovary expression was similar to that of one of the parent species, the house sparrow (Passer domesticus). Moreover, the Italian sparrow testis transcriptome is 26 times as diverged from those of the parent species as the parental transcriptomes are from each other, despite being genetically intermediate. This highlights the potential for regulation of gene expression to produce novel variation following hybridization. Genes involved in mitochondrial respiratory chain complexes and protein synthesis are enriched in the subset that is over‐dominantly expressed in Italian sparrow testis, suggesting that selection on key functions has moulded the hybrid Italian sparrow transcriptome.

The evolution, evolvability and engineering of gene regulatory DNA

Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal phenotype and fitness^1-3. Constructing complete fitness landscapes, in which DNA sequences are mapped to fitness, is a long-standing goal in biology, but has remained elusive because it is challenging to generalize reliably to vast sequence spaces^4-6. Here we build sequence-to-expression models that capture fitness landscapes and use them to decipher principles of regulatory evolution. Using millions of randomly sampled promoter DNA sequences and their measured expression levels in the yeast Saccharomyces cerevisiae, we learn deep neural network models that generalize with excellent prediction performance, and enable sequence design for expression engineering. Using our models, we study expression divergence under genetic drift and strong-selection weak-mutation regimes to find that regulatory evolution is rapid and subject to diminishing returns epistasis; that conflicting expression objectives in different environments constrain expression adaptation; and that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for using such models to detect signatures of selection on expression from natural variation in regulatory sequences and use it to discover an instance of convergent regulatory evolution. We assess mutational robustness, finding that regulatory mutation effect sizes follow a power law, characterize regulatory evolvability, visualize promoter fitness landscapes, discover evolvability archetypes and illustrate the mutational robustness of natural regulatory sequence populations. Our work provides a general framework for designing regulatory sequences and addressing fundamental questions in regulatory evolution.

Molecular and evolutionary processes generating variation in gene expression

Heritable variation in gene expression is common within and between species. This variation arises from mutations that alter the form or function of molecular gene regulatory networks that are then filtered by natural selection. High-throughput methods for introducing mutations and characterizing their cis- and trans-regulatory effects on gene expression (particularly, transcription) are revealing how different molecular mechanisms generate regulatory variation, and studies comparing these mutational effects with variation seen in the wild are teasing apart the role of neutral and non-neutral evolutionary processes. This integration of molecular and evolutionary biology allows us to understand how the variation in gene expression we see today came to be and to predict how it is most likely to evolve in the future.

Mismatch-CRISPRi Reveals the Co-varying Expression-Fitness Relationships of Essential Genes in Escherichia coli and Bacillus subtilis

Essential genes are the hubs of cellular networks, but lack of high-throughput methods for titrating gene expression has limited our understanding of the fitness landscapes against which their expression levels are optimized. We developed a modified CRISPRi system leveraging the predictable reduction in efficacy of imperfectly matched sgRNAs to generate defined levels of CRISPRi activity and demonstrated its broad applicability. Using libraries of mismatched sgRNAs predicted to span the full range of knockdown levels, we characterized the expression-fitness relationships of most essential genes in Escherichia coli and Bacillus subtilis. We find that these relationships vary widely from linear to bimodal but are similar within pathways. Notably, despite ∼2 billion years of evolutionary separation between E. coli and B. subtilis, most essential homologs have similar expression-fitness relationships with rare but informative differences. Thus, the expression levels of essential genes may reflect homeostatic or evolutionary constraints shared between the two organisms.

Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs

A lack of tools to precisely control gene expression has limited our ability to evaluate relationships between expression levels and phenotypes. Here, we describe an approach to titrate expression of human genes using CRISPR interference and series of single-guide RNAs (sgRNAs) with systematically modulated activities. We used large-scale measurements across multiple cell models to characterize activities of sgRNAs containing mismatches to their target sites and derived rules governing mismatched sgRNA activity using deep learning. These rules enabled us to synthesize a compact sgRNA library to titrate expression of ~2,400 genes essential for robust cell growth and to construct an in silico sgRNA library spanning the human genome. Staging cells along a continuum of gene expression levels combined with single-cell RNA-seq readout revealed sharp transitions in cellular behaviors at gene-specific expression thresholds. Our work provides a general tool to control gene expression, with applications ranging from tuning biochemical pathways to identifying suppressors for diseases of dysregulated gene expression.

Promoter Activity Buffering Reduces the Fitness Cost of Misregulation

Organisms regulate gene expression through changes in the activity of transcription factors (TFs). In yeast, the response of genes to changes in TF activity is generally assumed to be encoded in the promoter. To directly test this assumption, we chose 42 genes and, for each, replaced the promoter with a synthetic inducible promoter and measured how protein expression changes as a function of TF activity. Most genes exhibited gene-specific TF dose-response curves not due to differences in mRNA stability, translation, or protein stability. Instead, most genes have an intrinsic ability to buffer the effects of promoter activity. This can be encoded in the open reading frame and the 3' end of genes and can be implemented by both autoregulatory feedback and by titration of limiting trans regulators. We show experimentally and computationally that, when misexpression of a gene is deleterious, this buffering insulates cells from fitness defects due to misregulation.

DNA variants affecting the expression of numerous genes in trans have diverse mechanisms of action and evolutionary histories

DNA variants that alter gene expression contribute to variation in many phenotypic traits. In particular, trans-acting variants, which are often located on different chromosomes from the genes they affect, are an important source of heritable gene expression variation. However, our knowledge about the identity and mechanism of causal trans-acting variants remains limited. Here, we developed a fine-mapping strategy called CRISPR-Swap and dissected three expression quantitative trait locus (eQTL) hotspots known to alter the expression of numerous genes in trans in the yeast Saccharomyces cerevisiae. Causal variants were identified by engineering recombinant alleles and quantifying the effects of these alleles on the expression of a green fluorescent protein-tagged gene affected by the given locus in trans. We validated the effect of each variant on the expression of multiple genes by RNA-sequencing. The three variants differed in their molecular mechanism, the type of genes they reside in, and their distribution in natural populations. While a missense leucine-to-serine variant at position 63 in the transcription factor Oaf1 (L63S) was almost exclusively present in the reference laboratory strain, the two other variants were frequent among S. cerevisiae isolates. A causal missense variant in the glucose receptor Rgt2 (V539I) occurred at a poorly conserved amino acid residue and its effect was strongly dependent on the concentration of glucose in the culture medium. A noncoding variant in the conserved fatty acid regulated (FAR) element of the OLE1 promoter influenced the expression of the fatty acid desaturase Ole1 in cis and, by modulating the level of this essential enzyme, other genes in trans. The OAF1 and OLE1 variants showed a non-additive genetic interaction, and affected cellular lipid metabolism. These results demonstrate that the molecular basis of trans-regulatory variation is diverse, highlighting the challenges in predicting which natural genetic variants affect gene expression.

Differences in the DNA sequence of individual genomes contribute to differences in many traits, such as appearance, physiology, and the risk for common diseases. An important group of these DNA variants influences how individual genes across the genome are turned on or off. In this paper, we describe a strategy for identifying such “trans-acting” variants in different strains of baker’s yeast. We used this strategy to reveal three single DNA base changes that each influences the expression of dozens of genes. These three DNA variants were very different from each other. Two of them changed the protein sequence, one in a transcription factor and the other in a sugar sensor. The third changed the expression of an enzyme, a change that in turn caused other genes to alter their expression. One variant existed in only a few yeast isolates, while the other two existed in many isolates collected from around the world. This diversity of DNA variants that influence the expression of many other genes illustrates how difficult it is to predict which DNA variants in an individual’s genome will have effects on the organism.

Empirical mean-noise fitness landscapes reveal the fitness impact of gene expression noise

The effects of cell-to-cell variation (noise) in gene expression have proven difficult to quantify because of the mechanistic coupling of noise to mean expression. To independently quantify the effects of changes in mean expression and noise we determine the fitness landscapes in mean-noise expression space for 33 genes in yeast. For most genes, short-lived (noise) deviations away from the expression optimum are nearly as detrimental as sustained (mean) deviations. Fitness landscapes can be classified by a combination of each gene’s sensitivity to protein shortage or surplus. We use this classification to explore evolutionary scenarios for gene expression and find that certain landscape topologies can break the mechanistic coupling of mean and noise, thus promoting independent optimization of both properties. These results demonstrate that noise is detrimental for many genes and reveal non-trivial consequences of mean-noise-fitness topologies for the evolution of gene expression systems.

Quantifying the effects of noise in gene expression is difficult since noise and mean expression are coupled. Here the authors determine fitness landscapes in mean-noise expression space to uncouple these two parameters and show that changes in noise and mean expression are similarly detrimental to fitness.

Dominance reversals and the maintenance of genetic variation for fitness

Antagonistic selection between different fitness components (e.g., survival versus fertility) or different types of individuals in a population (e.g., females versus males) can potentially maintain genetic diversity and thereby account for the high levels of fitness variation observed in natural populations. However, the degree to which antagonistic selection can maintain genetic variation critically depends on the dominance relations between antagonistically selected alleles in diploid individuals. Conditions for stable polymorphism of antagonistically selected alleles are narrow, particularly when selection is weak, unless the alleles exhibit "dominance reversals"-in which each allele is partially or completely dominant in selective contexts in which it is favored and recessive in contexts in which it is harmful. Although theory predicts that dominance reversals should emerge under biologically plausible conditions, evidence for dominance reversals is sparse. In this primer, we review theoretical arguments and data supporting a role for dominance reversals in the maintenance of genetic variation. We then highlight an illuminating new study by Grieshop and Arnqvist, which reports a genome-wide signal of dominance reversals between male and female fitness in seed beetles.

Heterosis Is a Systemic Property Emerging From Non-linear Genotype-Phenotype Relationships: Evidence From in Vitro Genetics and Computer Simulations

Heterosis, the superiority of hybrids over their parents for quantitative traits, represents a crucial issue in plant and animal breeding as well as evolutionary biology. Heterosis has given rise to countless genetic, genomic and molecular studies, but has rarely been investigated from the point of view of systems biology. We hypothesized that heterosis is an emergent property of living systems resulting from frequent concave relationships between genotypic variables and phenotypes, or between different phenotypic levels. We chose the enzyme-flux relationship as a model of the concave genotype-phenotype (GP) relationship, and showed that heterosis can be easily created in the laboratory. First, we reconstituted in vitro the upper part of glycolysis. We simulated genetic variability of enzyme activity by varying enzyme concentrations in test tubes. Mixing the content of "parental" tubes resulted in "hybrids," whose fluxes were compared to the parental fluxes. Frequent heterotic fluxes were observed, under conditions that were determined analytically and confirmed by computer simulation. Second, to test this model in a more realistic situation, we modeled the glycolysis/fermentation network in yeast by considering one input flux, glucose, and two output fluxes, glycerol and acetaldehyde. We simulated genetic variability by randomly drawing parental enzyme concentrations under various conditions, and computed the parental and hybrid fluxes using a system of differential equations. Again we found that a majority of hybrids exhibited positive heterosis for metabolic fluxes. Cases of negative heterosis were due to local convexity between certain enzyme concentrations and fluxes. In both approaches, heterosis was maximized when the parents were phenotypically close and when the distributions of parental enzyme concentrations were contrasted and constrained. These conclusions are not restricted to metabolic systems: they only depend on the concavity of the GP relationship, which is commonly observed at various levels of the phenotypic hierarchy, and could account for the pervasiveness of heterosis.

Fitness Effects of Cis-Regulatory Variants in the Saccharomyces cerevisiae TDH3 Promoter

Variation in gene expression is widespread within and between species, but fitness consequences of this variation are generally unknown. Here, we use mutations in the Saccharomyces cerevisiae TDH3 promoter to assess how changes in TDH3 expression affect cell growth. From these data, we predict the fitness consequences of de novo mutations and natural polymorphisms in the TDH3 promoter. Nearly all mutations and polymorphisms in the TDH3 promoter were found to have no significant effect on fitness in the environment assayed, suggesting that the wild-type allele of this promoter is robust to the effects of most new cis-regulatory mutations.

New views on the selection acting on genetic polymorphism in central metabolic genes

Studies of the polymorphism of central metabolic genes as a source of fitness variation in natural populations date back to the discovery of allozymes in the 1960s. The unique features of these genes and their enzymes and our knowledge base greatly facilitates the systems-level study of this group. The expectation that pathway flux control is central to understanding the molecular evolution of genes is discussed, as well as studies that attempt to place gene-specific molecular evolution and polymorphism into a context of pathway and network architecture. There is an increasingly complex picture of the metabolic genes assuming additional roles beyond their textbook anabolic and catabolic reactions. In particular, this review emphasizes the potential role of these genes as part of the energy-sensing machinery. It is underscored that the concentrations of key cellular metabolites are the reflections of cellular energy status and nutritional input. These metabolites are the top-down signaling messengers that set signaling through signaling pathways that are involved in energy economy. I propose that the polymorphisms in central metabolic genes shift metabolite concentrations and in that fashion act as genetic modifiers of the energy-state coupling to the transcriptional networks that affect physiological trade-offs with significant fitness consequences.

Massively Parallel Interrogation of the Effects of Gene Expression Levels on Fitness

Data of gene expression levels across individuals, cell types, and disease states is expanding, yet our understanding of how expression levels impact phenotype is limited. Here, we present a massively parallel system for assaying the effect of gene expression levels on fitness in Saccharomyces cerevisiae by systematically altering the expression level of ∼100 genes at ∼100 distinct levels spanning a 500-fold range at high resolution. We show that the relationship between expression levels and growth is gene and environment specific and provides information on the function, stoichiometry, and interactions of genes. Wild-type expression levels in some conditions are not optimal for growth, and genes whose fitness is greatly affected by small changes in expression level tend to exhibit lower cell-to-cell variability in expression. Our study addresses a fundamental gap in understanding the functional significance of gene expression regulation and offers a framework for evaluating the phenotypic effects of expression variation.

Developing maps of fitness consequences for plant genomes

Predicting the fitness consequences of mutations, and their concomitant impacts on molecular and cellular function as well as organismal phenotypes, is an important challenge in biology that has new relevance in an era when genomic data is readily available. The ability to construct genomewide maps of fitness consequences in plant genomes is a recent development that has profound implications for our ability to predict the fitness effects of mutations and discover functional elements. Here we highlight approaches to building fitness consequence maps to infer regions under selection. We emphasize computational methods applied primarily to the study of human disease that translate physical maps of within-species genome variation into maps of fitness effects of individual natural mutations. Maps of fitness consequences in plants, combined with traditional genetic approaches, could accelerate discovery of functional elements such as regulatory sequences in non-coding DNA and genetic polymorphisms associated with key traits, including agronomically-important traits such as yield and environmental stress responses.

Contrasting Frequencies and Effects of cis- and trans-Regulatory Mutations Affecting Gene Expression

Heritable differences in gene expression are caused by mutations in DNA sequences encoding cis-regulatory elements and trans-regulatory factors. These two classes of regulatory change differ in their relative contributions to expression differences in natural populations because of the combined effects of mutation and natural selection. Here, we investigate how new mutations create the regulatory variation upon which natural selection acts by quantifying the frequencies and effects of hundreds of new cis- and trans-acting mutations altering activity of the TDH3 promoter in the yeast Saccharomyces cerevisiae in the absence of natural selection. We find that cis-regulatory mutations have larger effects on expression than trans-regulatory mutations and that while trans-regulatory mutations are more common overall, cis- and trans-regulatory changes in expression are equally abundant when only the largest changes in expression are considered. In addition, we find that cis-regulatory mutations are skewed toward decreased expression while trans-regulatory mutations are skewed toward increased expression. We also measure the effects of cis- and trans-regulatory mutations on the variability in gene expression among genetically identical cells, a property of gene expression known as expression noise, finding that trans-regulatory mutations are much more likely to decrease expression noise than cis-regulatory mutations. Because new mutations are the raw material upon which natural selection acts, these differences in the frequencies and effects of cis- and trans-regulatory mutations should be considered in models of regulatory evolution.

Divergent MLS1 Promoters Lie on a Fitness Plateau for Gene Expression

Qualitative patterns of gene activation and repression are often conserved despite an abundance of quantitative variation in expression levels within and between species. A major challenge to interpreting patterns of expression divergence is knowing which changes in gene expression affect fitness. To characterize the fitness effects of gene expression divergence, we placed orthologous promoters from eight yeast species upstream of malate synthase (MLS1) in Saccharomyces cerevisiae. As expected, we found these promoters varied in their expression level under activated and repressed conditions as well as in their dynamic response following loss of glucose repression. Despite these differences, only a single promoter driving near basal levels of expression caused a detectable loss of fitness. We conclude that the MLS1 promoter lies on a fitness plateau whereby even large changes in gene expression can be tolerated without a substantial loss of fitness.

A synthetic growth switch based on controlled expression of RNA polymerase

The ability to control growth is essential for fundamental studies of bacterial physiology and biotechnological applications. We have engineered an Escherichia coli strain in which the transcription of a key component of the gene expression machinery, RNA polymerase, is under the control of an inducible promoter. By changing the inducer concentration in the medium, we can adjust the RNA polymerase concentration and thereby switch bacterial growth between zero and the maximal growth rate supported by the medium. We show that our synthetic growth switch functions in a medium-independent and reversible way, and we provide evidence that the switching phenotype arises from the ultrasensitive response of the growth rate to the concentration of RNA polymerase. We present an application of the growth switch in which both the wild-type E. coli strain and our modified strain are endowed with the capacity to produce glycerol when growing on glucose. Cells in which growth has been switched off continue to be metabolically active and harness the energy gain to produce glycerol at a twofold higher yield than in cells with natural control of RNA polymerase expression. Remarkably, without any further optimization, the improved yield is close to the theoretical maximum computed from a flux balance model of E. coli metabolism. The proposed synthetic growth switch is a promising tool for gaining a better understanding of bacterial physiology and for applications in synthetic biology and biotechnology.

Population perspectives on functional genomic variation in yeast

Advances in high-throughput sequencing have facilitated large-scale surveys of genomic variation in the budding yeast,Saccharomyces cerevisiae These surveys have revealed extensive sequence variation between yeast strains. However, much less is known about how such variation influences the amount and nature of variation for functional genomic traits within and between yeast lineages. We review population-level studies of functional genomic variation, with a particular focus on how population functional genomic approaches can provide insights into both genome function and the evolutionary process. Although variation in functional genomics phenotypes is pervasive, our understanding of the consequences of this variation, either in physiological or evolutionary terms, is still rudimentary and thus motivates increased attention to appropriate null models. To date, much of the focus of population functional genomic studies has been on gene expression variation, but other functional genomic data types are just as likely to reveal important insights at the population level, suggesting a pressing need for more studies that go beyond transcription. Finally, we discuss how a population functional genomic perspective can be a powerful approach for developing a mechanistic understanding of the processes that link genomic variation to organismal phenotypes through gene networks.

Gene Expression Evolves under a House-of-Cards Model of Stabilizing Selection

Divergence in gene regulation is hypothesized to underlie much of phenotypic evolution, but the role of natural selection in shaping the molecular phenotype of gene expression continues to be debated. To resolve the mode of gene expression, evolution requires accessible theoretical predictions for the effect of selection over long timescales. Evolutionary quantitative genetic models of phenotypic evolution can provide such predictions, yet those predictions depend on the underlying hypotheses about the distributions of mutational and selective effects that are notoriously difficult to disentangle. Here, we draw on diverse genomic data sets including expression profiles of natural genetic variation and mutation accumulation lines, empirical estimates of genomic mutation rates, and inferences of genetic architecture to differentiate contrasting hypotheses for the roles of stabilizing selection and mutation in shaping natural expression variation. Our analysis suggests that gene expression evolves in a domain of phenotype space well fit by the House-of-Cards (HC) model. Although the strength of selection inferred is sensitive to the number of loci controlling gene expression, the model is not. The consistency of these results across evolutionary time from budding yeast through fruit fly implies that this model is general and that mutational effects on gene expression are relatively large. Empirical estimates of the genetic architecture of gene expression traits imply that selection provides modest constraints on gene expression levels for most genes, but that the potential for regulatory evolution is high. Our prediction using data from laboratory environments should encourage the collection of additional data sets allowing for more nuanced parameterizations of HC models for gene expression.

Δ10(E)-Sphingolipid Desaturase Involved in Fusaruside Mycosynthesis and Stress Adaptation in Fusarium graminearum

Sphingolipids are biologically important and structurally distinct cell membrane components. Fusaruside (1) is a 10,11-unsaturated immunosuppressive fungal sphingolipid with medical potentials for treating liver injury and colitis, but its poor natural abundance bottlenecks its druggability. Here, fusaruside is clarified biosynthetically, and its efficacy-related 10,11-double bond can be generated under the regioselective catalysis of an unprecedented Δ10(E)-sphingolipid desaturase (Δ10(E)-SD). Δ10(E)-SD shares 17.7% amino acid sequence similarity with a C9-unmethylated Δ10-sphingolipid desaturase derived from a marine diatom, and 55.7% with Δ8(E)-SD from Fusarium graminearum. Heterologous expression of Δ10(E)-SD in Pichia pastoris has been established to facilitate a reliable generation of 1 through the Δ10(E)-SD catalyzed desaturation of cerebroside B (2), an abundant fungal sphingolipid. Site directed mutageneses show that the conserved histidines of Δ10(E)-SD are essential for the 10,11-desaturation catalysis, which is also preconditioned by the C9-methylation of the substrate. Moreover, Δ10(E)-SD confers improved survival and faster growth to fungal strains at low temperature and high salinity, in parallel with to higher contents of 1 in the mycelia. Collectively, the investigation describes a new Δ10(E)-sphingolipid desaturase with its heterologous expression fundamentalizing a biotechnological supply of 1, and eases the follow-up clarification of the immunosuppression and stress-tolerance mechanism.

How fast-growing bacteria robustly tune their ribosome concentration to approximate growth-rate maximization

Maximization of growth rate is an important fitness strategy for bacteria. Bacteria can achieve this by expressing proteins at optimal concentrations, such that resources are not wasted. This is exemplified for Escherichia coli by the increase of its ribosomal protein-fraction with growth rate, which precisely matches the increased protein synthesis demand. These findings and others have led to the hypothesis that E. coli aims to maximize its growth rate in environments that support growth. However, what kind of regulatory strategy is required for a robust, optimal adjustment of the ribosome concentration to the prevailing condition is still an open question. In the present study, we analyze the ppGpp-controlled mechanism of ribosome expression used by E. coli and show that this mechanism maintains the ribosomes saturated with its substrates. In this manner, overexpression of the highly abundant ribosomal proteins is prevented, and limited resources can be redirected to the synthesis of other growth-promoting enzymes. It turns out that the kinetic conditions for robust, optimal protein-partitioning, which are required for growth rate maximization across conditions, can be achieved with basic biochemical interactions. We show that inactive ribosomes are the most suitable ‘signal’ for tracking the intracellular nutritional state and for adjusting gene expression accordingly, as small deviations from optimal ribosome concentration cause a huge fractional change in ribosome inactivity. We expect to find this control logic implemented across fast-growing microbial species because growth rate maximization is a common selective pressure, ribosomes are typically highly abundant and thus costly, and the required control can be implemented by a small, simple network.

Cytoplasmic 5'-3' exonuclease Xrn1p is also a genome-wide transcription factor in yeast

The 5′ to 3′ exoribonuclease Xrn1 is a large protein involved in cytoplasmatic mRNA degradation as a critical component of the major decaysome. Its deletion in the yeast Saccharomyces cerevisiae is not lethal, but it has multiple physiological effects. In a previous study, our group showed that deletion of all tested components of the yeast major decaysome, including XRN1, results in a decrease in the synthetic rate and an increase in half-life of most mRNAs in a compensatory manner. Furthermore, the same study showed that the all tested decaysome components are also nuclear proteins that bind to the 5′ region of a number of genes. In the present work, we show that disruption of Xrn1 activity preferentially affects both the synthesis and decay of a distinct subpopulation of mRNAs. The most affected mRNAs are the transcripts of the highly transcribed genes, mainly those encoding ribosome biogenesis and translation factors. Previously, we proposed that synthegradases play a key role in regulating both mRNA synthesis and degradation. Evidently, Xrn1 functions as a synthegradase, whose selectivity might help coordinating the expression of the protein synthetic machinery. We propose to name the most affected genes “Xrn1 synthegradon.”

Integrating phenotypic plasticity within an Ecological Genomics framework: recent insights from the genomics, evolution, ecology, and fitness of plasticity

E.B. Ford's 1964 book Ecological Genetics was a call for biologists to engage in multidisciplinary work in order to elucidate the link between genotype, phenotype, and fitness for ecologically relevant traits. In this review, we argue that the integration of an ecological genomics framework in studies of phenotypic plasticity is a promising approach to elucidate the causal links between genes and the environment, particularly during colonization of novel environments, environmental change, and speciation. This review highlights some of the questions and hypotheses generated from a mechanistic, evolutionary, and ecological perspective, in order to direct the continued and future use of genomic tools in the study of phenotypic plasticity.