Energy costs constrain the evolution of gene expression

Multisubstrate specificity shaped the complex evolution of the aminotransferase family across the tree of life

Aminotransferases (ATs) are an ancient enzyme family that play central roles in core nitrogen metabolism, essential to all organisms. However, many of the AT enzyme functions remain poorly defined, limiting our fundamental understanding of the nitrogen metabolic networks that exist in different organisms. Here, we traced the deep evolutionary history of the AT family by analyzing AT enzymes from 90 species spanning the tree of life (ToL). We found that each organism has maintained a relatively small and constant number of ATs. Mapping the distribution of ATs across the ToL uncovered that many essential AT reactions are carried out by taxon-specific AT enzymes due to wide-spread nonorthologous gene displacements. This complex evolutionary history explains the difficulty of homology-based AT functional prediction. Biochemical characterization of diverse aromatic ATs further revealed their broad substrate specificity, unlike other core metabolic enzymes that evolved to catalyze specific reactions today. Interestingly, however, we found that these AT enzymes that diverged over billion years share common signatures of multisubstrate specificity by employing different nonconserved active site residues. These findings illustrate that AT family enzymes had leveraged their inherent substrate promiscuity to maintain a small yet distinct set of multifunctional AT enzymes in different taxa. This evolutionary history of versatile ATs likely contributed to the establishment of robust and diverse nitrogen metabolic networks that exist throughout the ToL. The study provides a critical foundation to systematically determine diverse AT functions and underlying nitrogen metabolic networks across the ToL.

Models for the retention of duplicate genes and their biological underpinnings

Gene content in genomes changes through several different processes, with gene duplication being an important contributor to such changes. Gene duplication occurs over a range of scales from individual genes to whole genomes, and the dynamics of this process can be context dependent. Still, there are rules by which genes are retained or lost from genomes after duplication, and probabilistic modeling has enabled characterization of these rules, including their context-dependence. Here, we describe the biology and corresponding mathematical models that are used to understand duplicate gene retention and its contribution to the set of biochemical functions encoded in a genome.

Dominant constraints on the evolution of rhythmic gene expression

Although the individual transcriptional regulators of the core circadian clock are distinct among different organisms, the autoregulatory feedback loops they form are conserved. This unified design principle explains how daily physiological activities oscillate across species. However, it is unknown whether analogous design principles govern the gene expression output of circadian clocks. In this study, we performed a comparative analysis of rhythmic gene expression in eight diverse species and identified four common distribution patterns of cycling gene expression across these species. We hypothesized that the maintenance of reduced energetic costs constrains the evolution of rhythmic gene expression. Our large-scale computational simulations support this hypothesis by showing that selection against high-energy expenditure completely regenerates all cycling gene patterns. Moreover, we find that the peaks of rhythmic expression have been subjected to this type of selective pressure. The results suggest that selective pressure from circadian regulation efficiently removes unnecessary gene products from the transcriptome, thereby significantly impacting its evolutionary path.

Copy number variation alters local and global mutational tolerance

Copy number variants (CNVs), duplications and deletions of genomic sequences, contribute to evolutionary adaptation but can also confer deleterious effects and cause disease. Whereas the effects of amplifying individual genes or whole chromosomes (i.e., aneuploidy) have been studied extensively, much less is known about the genetic and functional effects of CNVs of differing sizes and structures. Here, we investigated Saccharomyces cerevisiae (yeast) strains that acquired adaptive CNVs of variable structures and copy numbers following experimental evolution in glutamine-limited chemostats. Although beneficial in the selective environment, CNVs result in decreased fitness compared with the euploid ancestor in rich media. We used transposon mutagenesis to investigate mutational tolerance and genome-wide genetic interactions in CNV strains. We find that CNVs increase mutational target size, confer increased mutational tolerance in amplified essential genes, and result in novel genetic interactions with unlinked genes. We validated a novel genetic interaction between different CNVs and BMH1 that was common to multiple strains. We also analyzed global gene expression and found that transcriptional dosage compensation does not affect most genes amplified by CNVs, although gene-specific transcriptional dosage compensation does occur for ∼12% of amplified genes. Furthermore, we find that CNV strains do not show previously described transcriptional signatures of aneuploidy. Our study reveals the extent to which local and global mutational tolerance is modified by CNVs with implications for genome evolution and CNV-associated diseases, such as cancer.

Engineering resource allocation in artificially minimized cells: Is genome reduction the best strategy?

The elimination of the expression of cellular functions that are not needed in a certain well-defined artificial environment, such as those used in industrial production facilities, has been the goal of many cellular minimization projects. The generation of a minimal cell with reduced burden and less host-function interactions has been pursued as a tool to improve microbial production strains. In this work, we analysed two cellular complexity reduction strategies: genome and proteome reduction. With the aid of an absolute proteomics data set and a genome-scale model of metabolism and protein expression (ME-model), we quantitatively assessed the difference of reducing genome to the correspondence of reducing proteome. We compare the approaches in terms of energy consumption, defined in ATP equivalents. We aim to show what is the best strategy for improving resource allocation in minimized cells. Our results show that genome reduction by length is not proportional to reducing resource use. When we normalize calculated energy savings, we show that strains with the larger calculated proteome reduction show the largest resource use reduction. Furthermore, we propose that reducing highly expressed proteins should be the target as the translation of a gene uses most of the energy. The strategies proposed here should guide cell design when the aim of a project is to reduce the maximum amount or cellular resources.

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

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

Impact of Genome Reduction in Microsporidia

Microsporidia represent an evolutionary outlier in the tree of life and occupy the extreme edge of the eukaryotic domain with some of their biological features. Many of these unicellular fungi-like organisms have reduced their genomic content to potentially the lowest limit. With some of the most compacted eukaryotic genomes, microsporidia are excellent model organisms to study reductive evolution and its functional consequences. While the growing number of sequenced microsporidian genomes have elucidated genome composition and organization, a recent increase in complementary post-genomic studies has started to shed light on the impacts of genome reduction in these unique pathogens. This chapter will discuss the biological framework enabling genome minimization and will use one of the most ancient and essential macromolecular complexes, the ribosome, to illustrate the effects of extreme genome reduction on a structural, molecular, and cellular level. We outline how reductive evolution in microsporidia has shaped DNA organization, the composition and function of the ribosome, and the complexity of the ribosome biogenesis process. Studying compacted mechanisms, processes, or macromolecular machines in microsporidia illuminates their unique lifestyle and provides valuable insights for comparative eukaryotic structural biology.

Natural variation in the consequences of gene overexpression and its implications for evolutionary trajectories

Copy number variation through gene or chromosome amplification provides a route for rapid phenotypic variation and supports the long-term evolution of gene functions. Although the evolutionary importance of copy-number variation is known, little is understood about how genetic background influences its tolerance. Here, we measured fitness costs of over 4000 overexpressed genes in 15 Saccharomyces cerevisiae strains representing different lineages, to explore natural variation in tolerating gene overexpression (OE). Strain-specific effects dominated the fitness costs of gene OE. We report global differences in the consequences of gene OE, independent of the amplified gene, as well as gene-specific effects that were dependent on the genetic background. Natural variation in the response to gene OE could be explained by several models, including strain-specific physiological differences, resource limitations, and regulatory sensitivities. This work provides new insight on how genetic background influences tolerance to gene amplification and the evolutionary trajectories accessible to different backgrounds.

Transcription factors and chaperone proteins play a role in launching a faster response to heat stress and aggregation

Proteins, under conditions of cellular stress, typically tend to unfold and form lethal aggregates leading to neurological diseases like Parkinson's and Alzheimer's. A clear understanding of the conditions that favor dis-aggregation and restore the cell to its healthy state after they have been stressed is therefore important in dealing with these diseases. The heat shock response (HSR) mechanism is a signaling network that deals with these undue protein aggregates and aids in the maintenance of homeostasis within a cell. This framework, on its own, is a mathematically well studied mechanism. However, not much is known about how the various intermediate mis-folded protein states of the aggregation process interact with some of the key components of the HSR pathway such as the Heat Shock Protein (HSP), the Heat Shock Transcription Factor (HSF) and the HSP-HSF complex. In this article, using kinetic parameters from the literature, we propose and analyze two mathematical models for HSR that also include explicit reactions for the formation of protein aggregates. Deterministic analysis and stochastic simulations of these models show that the folded proteins and the misfolded aggregates exhibit bistability in a certain region of the parameter space. Further, the models also highlight the role of HSF and the HSF-HSP complex in reducing the time lag of response to stress and in re-folding all the mis-folded proteins back to their native state. These models, therefore, call attention to the significance of studying related pathways such as the HSR and the protein aggregation and re-folding process in conjunction with each other.

A Candida parapsilosis Overexpression Collection Reveals Genes Required for Pathogenesis

Relative to the vast data regarding the virulence mechanisms of Candida albicans, there is limited knowledge on the emerging opportunistic human pathogen Candida parapsilosis. The aim of this study was to generate and characterize an overexpression mutant collection to identify and explore virulence factors in C. parapsilosis. With the obtained mutants, we investigated stress tolerance, morphology switch, biofilm formation, phagocytosis, and in vivo virulence in Galleria mellonella larvae and mouse models. In order to evaluate the results, we compared the data from the C. parapsilosis overexpression collection analysis to the results derived from previous deletion mutant library characterizations. Of the 37 overexpression C. parapsilosis mutants, we identified eight with altered phenotypes compared to the controls. This work is the first report to identify CPAR2_107240, CPAR2_108840, CPAR2_302400, CPAR2_406400, and CPAR2_602820 as contributors to C. parapsilosis virulence by regulating functions associated with host-pathogen interactions and biofilm formation. Our findings also confirmed the role of CPAR2_109520, CPAR2_200040, and CPAR2_500180 in pathogenesis. This study was the first attempt to use an overexpression strategy to systematically assess gene function in C. parapsilosis, and our results demonstrate that this approach is effective for such investigations.

Differences in structure and hibernation mechanism highlight diversification of the microsporidian ribosome

Assembling and powering ribosomes are energy-intensive processes requiring fine-tuned cellular control mechanisms. In organisms operating under strict nutrient limitations, such as pathogenic microsporidia, conservation of energy via ribosomal hibernation and recycling is critical. The mechanisms by which hibernation is achieved in microsporidia, however, remain poorly understood. Here, we present the cryo–electron microscopy structure of the ribosome from Paranosema locustae spores, bound by the conserved eukaryotic hibernation and recycling factor Lso2. The microsporidian Lso2 homolog adopts a V-shaped conformation to bridge the mRNA decoding site and the large subunit tRNA binding sites, providing a reversible ribosome inactivation mechanism. Although microsporidian ribosomes are highly compacted, the P. locustae ribosome retains several rRNA segments absent in other microsporidia, and represents an intermediate state of rRNA reduction. In one case, the near complete reduction of an expansion segment has resulted in a single bound nucleotide, which may act as an architectural co-factor to stabilize a protein–protein interface. The presented structure highlights the reductive evolution in these emerging pathogens and sheds light on a conserved mechanism for eukaryotic ribosome hibernation.

Tiny pathogenic eukaryotes called microsporidia have evolved highly compact ribosomes, smaller than bacterial ribosomes, and employ diverse hibernation factors to facilitate ribosome inactivation and recovery from the latent spore stage, including the conserved eukaryotic hibernation and recycling factor Lso2.

High Transcriptional Error Rates Vary as a Function of Gene Expression Level

Errors in gene transcription can be costly, and organisms have evolved to prevent their occurrence or mitigate their costs. The simplest interpretation of the drift barrier hypothesis suggests that species with larger population sizes would have lower transcriptional error rates. However, Escherichia coli seems to have a higher transcriptional error rate than species with lower effective population sizes, for example Saccharomyces cerevisiae. This could be explained if selection in E. coli were strong enough to maintain adaptations that mitigate the consequences of transcriptional errors through robustness, on a gene by gene basis, obviating the need for low transcriptional error rates and associated costs of global proofreading. Here, we note that if selection is powerful enough to evolve local robustness, selection should also be powerful enough to locally reduce error rates. We therefore predict that transcriptional error rates will be lower in highly abundant proteins on which selection is strongest. However, we only expect this result when error rates are high enough to significantly impact fitness. As expected, we find such a relationship between expression and transcriptional error rate for non-C→U errors in E. coli (especially G→A), but not in S. cerevisiae. We do not find this pattern for C→U changes in E. coli, presumably because most deamination events occurred during sample preparation, but do for C→U changes in S. cerevisiae, supporting the interpretation that C→U error rates estimated with an improved protocol, and which occur at rates comparable with E. coli non-C→U errors, are biological.

Molecular adaptation of molluscan biomineralisation to high-CO2 oceans - The known and the unknown

High-CO₂ induced ocean acidification (OA) reduces the calcium carbonate (CaCO₃) saturation level (Ω) and the pH of oceans. Consequently, OA is causing a serious threat to several ecologically and economically important biomineralising molluscs. Biomineralisation is a highly controlled biochemical process by which molluscs deposit their calcareous structures. In this process, shell matrix proteins aid the nucleation, growth and assemblage of the CaCO₃ crystals in the shell. These molluscan shell proteins (MSPs) are, ultimately, responsible for determination of the diverse shell microstructures and mechanical strength. Recent studies have attempted to integrate gene and protein expression data of MSPs with shell structure and mechanical properties. These advances made in understanding the molecular mechanism of biomineralisation suggest that molluscs either succumb or adapt to OA stress. In this review, we discuss the fate of biomineralisation process in future high-CO₂ oceans and its ultimate impact on the mineralised shell's structure and mechanical properties from the perspectives of limited substrate availability theory, proton flux limitation model and the omega myth theory. Furthermore, studying the interplay of energy availability and differential gene expression is an essential first step towards understanding adaptation of molluscan biomineralisation to OA, because if there is a need to change gene expression under stressors, any living system would require more energy than usual. To conclude, we have listed, four important future research directions for molecular adaptation of molluscan biomineralisation in high-CO₂ oceans: 1) Including an energy budgeting factor while understanding differential gene expression of MSPs and ion transporters under OA. 2) Unraveling the genetic or epigenetic changes related to biomineralisation under stressors to help solving a bigger picture about future evolution of molluscs, and 3) Understanding Post Translational Modifications of MSPs with and without stressors. 4) Understanding carbon uptake mechanisms across taxa with and without OA to clarify the OA theories on Ω.

Adaptive colour change and background choice behaviour in peppered moth caterpillars is mediated by extraocular photoreception

Light sensing by tissues distinct from the eye occurs in diverse animal groups, enabling circadian control and phototactic behaviour. Extraocular photoreceptors may also facilitate rapid colour change in cephalopods and lizards, but little is known about the sensory system that mediates slow colour change in arthropods. We previously reported that slow colour change in twig-mimicking caterpillars of the peppered moth (Biston betularia) is a response to achromatic and chromatic visual cues. Here we show that the perception of these cues, and the resulting phenotypic responses, does not require ocular vision. Caterpillars with completely obscured ocelli remained capable of enhancing their crypsis by changing colour and choosing to rest on colour-matching twigs. A suite of visual genes, expressed across the larval integument, likely plays a key role in the mechanism. To our knowledge, this is the first evidence that extraocular colour sensing can mediate pigment-based colour change and behaviour in an arthropod.

Integrating Networks, Phylogenomics, and Population Genomics for the Study of Polyploidy

Duplication events are regarded as sources of evolutionary novelty, but our understanding of general trends for the long-term trajectory of additional genomic material is still lacking. Organisms with a history of whole genome duplication (WGD) offer a unique opportunity to study potential trends in the context of gene retention and/or loss, gene and network dosage, and changes in gene expression. In this review, we discuss the prevalence of polyploidy across the tree of life, followed by an overview of studies investigating genome evolution and gene expression. We then provide an overview of methods in network biology, phylogenomics, and population genomics that are critical for advancing our understanding of evolution post-WGD, highlighting the need for models that can accommodate polyploids. Finally, we close with a brief note on the importance of random processes in the evolution of polyploids with respect to neutral versus selective forces, ancestral polymorphisms, and the formation of autopolyploids versus allopolyploids.

Dosage-sensitive genes in evolution and disease

For a subset of genes in our genome a change in gene dosage, by duplication or deletion, causes a phenotypic effect. These dosage-sensitive genes may confer an advantage upon copy number change, but more typically they are associated with disease, including heart disease, cancers and neuropsychiatric disorders. This gene copy number sensitivity creates characteristic evolutionary constraints that can serve as a diagnostic to identify dosage-sensitive genes. Though the link between copy number change and disease is well-established, the mechanism of pathogenicity is usually opaque. We propose that gene expression level may provide a common basis for the pathogenic effects of many copy number variants.

A Theoretical Lower Bound for Selection on the Expression Levels of Proteins

We use simple models of the costs and benefits of microbial gene expression to show that changing a protein's expression away from its optimum by 2-fold should reduce fitness by at least [Formula: see text], where P is the fraction the cell's protein that the gene accounts for. As microbial genes are usually expressed at above 5 parts per million, and effective population sizes are likely to be above 10(6), this implies that 2-fold changes to gene expression levels are under strong selection, as [Formula: see text], where Ne is the effective population size and s is the selection coefficient. Thus, most gene duplications should be selected against. On the other hand, we predict that for most genes, small changes in the expression will be effectively neutral.

Cycling Transcriptional Networks Optimize Energy Utilization on a Genome Scale

Genes expressing circadian RNA rhythms are enriched for metabolic pathways, but the adaptive significance of cyclic gene expression remains unclear. We estimated the genome-wide synthetic and degradative cost of transcription and translation in three organisms and found that the cost of cycling genes is strikingly higher compared to non-cycling genes. Cycling genes are expressed at high levels and constitute the most costly proteins to synthesize in the genome. We demonstrate that metabolic cycling is accelerated in yeast grown under higher nutrient flux and the number of cycling genes increases ∼40%, which are achieved by increasing the amplitude and not the mean level of gene expression. These results suggest that rhythmic gene expression optimizes the metabolic cost of global gene expression and that highly expressed genes have been selected to be downregulated in a cyclic manner for energy conservation.

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.

The evolution and adaptive potential of transcriptional variation in sticklebacks--signatures of selection and widespread heritability

Evidence implicating differential gene expression as a significant driver of evolutionary novelty continues to accumulate, but our understanding of the underlying sources of variation in expression, both environmental and genetic, is wanting. Heritability in particular may be underestimated when inferred from genetic mapping studies, the predominant "genetical genomics" approach to the study of expression variation. Such uncertainty represents a fundamental limitation to testing for adaptive evolution at the transcriptomic level. By studying the inheritance of expression levels in 10,495 genes (10,527 splice variants) in a threespine stickleback pedigree consisting of 563 individuals, half of which were subjected to a thermal treatment, we show that 74-98% of transcripts exhibit significant additive genetic variance. Dominance variance is also prevalent (41-99% of transcripts), and genetic sources of variation seem to play a more significant role in expression variance in the liver than a key environmental variable, temperature. Among-population comparisons suggest that the majority of differential expression in the liver is likely due to neutral divergence; however, we also show that signatures of directional selection may be more prevalent than those of stabilizing selection. This predominantly aligns with the neutral model of evolution for gene expression but also suggests that natural selection may still act on transcriptional variation in the wild. As genetic variation both within- and among-populations ultimately defines adaptive potential, these results indicate that broad adaptive potential may be found within the transcriptome.

Analog synthetic biology

We analyse the pros and cons of analog versus digital computation in living cells. Our analysis is based on fundamental laws of noise in gene and protein expression, which set limits on the energy, time, space, molecular count and part-count resources needed to compute at a given level of precision. We conclude that analog computation is significantly more efficient in its use of resources than deterministic digital computation even at relatively high levels of precision in the cell. Based on this analysis, we conclude that synthetic biology must use analog, collective analog, probabilistic and hybrid analog-digital computational approaches; otherwise, even relatively simple synthetic computations in cells such as addition will exceed energy and molecular-count budgets. We present schematics for efficiently representing analog DNA-protein computation in cells. Analog electronic flow in subthreshold transistors and analog molecular flux in chemical reactions obey Boltzmann exponential laws of thermodynamics and are described by astoundingly similar logarithmic electrochemical potentials. Therefore, cytomorphic circuits can help to map circuit designs between electronic and biochemical domains. We review recent work that uses positive-feedback linearization circuits to architect wide-dynamic-range logarithmic analog computation in Escherichia coli using three transcription factors, nearly two orders of magnitude more efficient in parts than prior digital implementations.

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.

The evolution of genetic architectures underlying quantitative traits

In the classic view introduced by R. A. Fisher, a quantitative trait is encoded by many loci with small, additive effects. Recent advances in quantitative trait loci mapping have begun to elucidate the genetic architectures underlying vast numbers of phenotypes across diverse taxa, producing observations that sometimes contrast with Fisher's blueprint. Despite these considerable empirical efforts to map the genetic determinants of traits, it remains poorly understood how the genetic architecture of a trait should evolve, or how it depends on the selection pressures on the trait. Here, we develop a simple, population-genetic model for the evolution of genetic architectures. Our model predicts that traits under moderate selection should be encoded by many loci with highly variable effects, whereas traits under either weak or strong selection should be encoded by relatively few loci. We compare these theoretical predictions with qualitative trends in the genetics of human traits, and with systematic data on the genetics of gene expression levels in yeast. Our analysis provides an evolutionary explanation for broad empirical patterns in the genetic basis for traits, and it introduces a single framework that unifies the diversity of observed genetic architectures, ranging from Mendelian to Fisherian.

Inducing extra copies of the Hsp70 gene in Drosophila melanogaster increases energetic demand

Background

Mutations that increase gene expression are predicted to increase energy allocation to transcription, translation and protein function. Despite an appreciation that energetic tradeoffs may constrain adaptation, the energetic costs of increased gene expression are challenging to quantify and thus easily ignored when modeling the evolution of gene expression, particularly for multicellular organisms. Here we use the well-characterized, inducible heat-shock response to test whether expressing additional copies of the Hsp70 gene increases energetic demand in Drosophila melanogaster.

Results

We measured metabolic rates of larvae with different copy numbers of the Hsp70 gene to quantify energy expenditure before, during, and after exposure to 36°C, a temperature known to induce robust expression of Hsp70. We observed a rise in metabolic rate within the first 30 minutes of 36°C exposure above and beyond the increase in routine metabolic rate at 36°C. The magnitude of this increase in metabolic rate was positively correlated with Hsp70 gene copy number and reflected an increase as great as 35% of the 22°C metabolic rate. Gene copy number also affected Hsp70 mRNA levels as early as 15 minutes after larvae were placed at 36°C, demonstrating that gene copy number affects transcript abundance on the same timescale as the metabolic effects that we observed. Inducing Hsp70 also had lasting physiological costs, as larvae had significantly depressed metabolic rate when returned to 22°C after induction.

Conclusions

Our results demonstrate both immediate and persistent energetic consequences of gene copy number in a multicellular organism. We discuss these consequences in the context of existing literature on the pleiotropic effects of variation in Hsp70 copy number, and argue that the increased energetic demand of expressing extra copies of Hsp70 may contribute to known tradeoffs in physiological performance of extra-copy larvae. Physiological costs of mutations that greatly increase gene expression, such as these, may constrain their utility for adaptive evolution.

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

Levels of gene expression show considerable variation in eukaryotes, but no fine-scale maps have been made of the fitness consequences of such variation in controlled genetic backgrounds and environments. To address this, we assayed fitness at many levels of up- and down-regulated expression of a single essential gene, LCB2, involved in sphingolipid synthesis in budding yeast Saccharomyces cerevisiae. Reduced LCB2 expression rapidly decreases cellular fitness, yet increased expression has little effect. The wild-type expression level is therefore perched on the edge of a nonlinear fitness cliff. LCB2 is upregulated when cells are exposed to osmotic stress; consistent with this, the entire fitness curve is shifted upward to higher expression under osmotic stress, illustrating the selective force behind gene regulation. Expression levels of LCB2 are lower in wild yeast strains than in the experimental lab strain, suggesting that higher levels in the lab strain may be idiosyncratic. Reports indicate that the effect sizes of alleles contributing to variation in complex phenotypes differ among environments and genetic backgrounds; our results suggest that such differences may be explained as simple shifts in the position of nonlinear fitness curves.

Network context and selection in the evolution to enzyme specificity

Enzymes are thought to have evolved highly specific catalytic activities from promiscuous ancestral proteins. By analyzing a genome-scale model of Escherichia coli metabolism, we found that 37% of its enzymes act on a variety of substrates and catalyze 65% of the known metabolic reactions. However, it is not apparent why these generalist enzymes remain. Here, we show that there are marked differences between generalist enzymes and specialist enzymes, known to catalyze a single chemical reaction on one particular substrate in vivo. Specialist enzymes (i) are frequently essential, (ii) maintain higher metabolic flux, and (iii) require more regulation of enzyme activity to control metabolic flux in dynamic environments than do generalist enzymes. Furthermore, these properties are conserved in Archaea and Eukarya. Thus, the metabolic network context and environmental conditions influence enzyme evolution toward high specificity.

Expressed sequence tags from Atta laevigata and identification of candidate genes for the control of pest leaf-cutting ants

Background

Leafcutters are the highest evolved within Neotropical ants in the tribe Attini and model systems for studying caste formation, labor division and symbiosis with microorganisms. Some species of leafcutters are agricultural pests controlled by chemicals which affect other animals and accumulate in the environment. Aiming to provide genetic basis for the study of leafcutters and for the development of more specific and environmentally friendly methods for the control of pest leafcutters, we generated expressed sequence tag data from Atta laevigata, one of the pest ants with broad geographic distribution in South America.

Results

The analysis of the expressed sequence tags allowed us to characterize 2,006 unique sequences in Atta laevigata. Sixteen of these genes had a high number of transcripts and are likely positively selected for high level of gene expression, being responsible for three basic biological functions: energy conservation through redox reactions in mitochondria; cytoskeleton and muscle structuring; regulation of gene expression and metabolism. Based on leafcutters lifestyle and reports of genes involved in key processes of other social insects, we identified 146 sequences potential targets for controlling pest leafcutters. The targets are responsible for antixenobiosis, development and longevity, immunity, resistance to pathogens, pheromone function, cell signaling, behavior, polysaccharide metabolism and arginine kynase activity.

Conclusion

The generation and analysis of expressed sequence tags from Atta laevigata have provided important genetic basis for future studies on the biology of leaf-cutting ants and may contribute to the development of a more specific and environmentally friendly method for the control of agricultural pest leafcutters.

Impact of gene expression noise on organismal fitness and the efficacy of natural selection

Gene expression noise is a universal phenomenon across all life forms. Although beneficial under certain circumstances, expression noise is generally thought to be deleterious. However, neither the magnitude of the deleterious effect nor the primary mechanism of this effect is known. Here, we model the impact of expression noise on the fitness of unicellular organisms by considering the influence of suboptimal expressions of enzymes on the rate of biomass production and the energetic cost associated with imprecise amounts of protein synthesis. Our theoretical modeling and empirical analysis of yeast data show four findings. (i) Expression noise reduces the mean fitness of a cell by at least 25%, and this reduction cannot be substantially alleviated by gene overexpression. (ii) Higher sensitivity of fitness to the expression fluctuations of essential genes than nonessential genes creates stronger selection against noise in essential genes, resulting in a decrease in their noise. (iii) Reduction of expression noise by genome doubling offers a substantial fitness advantage to diploids over haploids, even in the absence of sex. (iv) Expression noise generates fitness variation among isogenic cells, which lowers the efficacy of natural selection similar to the effect of population shrinkage. Thus, expression noise renders organisms both less adapted and less adaptable. Because expression noise is only one of many manifestations of the stochasticity in cellular molecular processes, our results suggest a much more fundamental role of molecular stochasticity in evolution than is currently appreciated.

Evolution of asymmetric damage segregation : a modelling approach

Mother cell-specific ageing is a well-known phenomenon in budding yeast Saccharomyces cerevisiae. Asymmetric segregation of damage and its accumulation in the mother cell has been proposed as one important mechanism. There are, however, unicellular organisms such as the fission yeast Schizosaccharomyces pombe, which replicates with almost no asymmetry of segregation of damage and the pathogenic yeast Candida albicans, which falls around the middle of the segregation spectrum far from both complete symmetry and complete asymmetry. The ultimate evolutionary cause that determines the way damage segregates in a given organism is not known. Here we develop a mathematical model to examine the selective forces that drive the evolution of asymmetry and discover the conditions in which symmetry is the optimal strategy. Three main processes are included in the model: protein synthesis (growth), protein damage, and degradation of damage. We consider, for the first time, the costs to the cell that might accompany the evolution of asymmetry and incorporate them into the model along with known trade-offs between reproductive and maintenance investments and their energy requirements. The model provides insight into the relationship between ecology and cellular trade-off physiology in the context of unicellular ageing, and applications of the model may extend to multicellular organisms.

Positive selection at high temperature reduces gene transcription in the bacteriophage ϕX174

Background

Gene regulation plays a central role in the adaptation of organisms to their environments. There are many molecular components to gene regulation, and it is often difficult to determine both the genetic basis of adaptation and the evolutionary forces that influence regulation. In multiple evolution experiments with the bacteriophage ϕX174, adaptive substitutions in cis-acting regulatory sequences sweep through the phage population as the result of strong positive selection at high temperatures that are non-permissive for laboratory-adapted phage. For one cis-regulatory region, we investigate the individual effects of four adaptive substitutions on transcript levels and fitness for phage growing on three hosts at two temperatures.

Results

The effect of the four individual substitutions on transcript levels is to down-regulate gene expression, regardless of temperature or host. To ascertain the conditions under which these substitutions are adaptive, fitness was measured by a variety of methods for several bacterial hosts growing at two temperatures, the control temperature of 37°C and the selective temperature of 42°C. Time to lysis and doublings per hour indicate that the four substitutions individually improve fitness over the ancestral strain at high temperature independent of the bacterial host in which the fitness was measured. Competition assays between the ancestral strain and either of two mutant strains indicate that both mutants out-compete the ancestor at high temperature, but the relative frequencies of each phage remain the same at the control temperature.

Conclusions

Our results strongly suggest that gene transcription plays an important role in influencing fitness in the bacteriophage ϕX174, and different point mutations in a single cis-regulatory region provided the genetic basis for this role in adaptation to high temperature. We speculate that the adaptive nature of these substitutions is due to the physiology of the host at high temperature or the need to maintain particular ratios of phage proteins during capsid assembly. Our investigation of regulatory evolution contributes to interpreting genome-level assessments of regulatory variation, as well as to understanding the molecular basis of adaptation.

Sex linkage, sex-specific selection, and the role of recombination in the evolution of sexually dimorphic gene expression

Sex-biased genes--genes that are differentially expressed within males and females--are nonrandomly distributed across animal genomes, with sex chromosomes and autosomes often carrying markedly different concentrations of male- and female-biased genes. These linkage patterns are often gene- and lineage-dependent, differing between functional genetic categories and between species. Although sex-specific selection is often hypothesized to shape the evolution of sex-linked and autosomal gene content, population genetics theory has yet to account for many of the gene- and lineage-specific idiosyncrasies emerging from the empirical literature. With the goal of improving the connection between evolutionary theory and a rapidly growing body of genome-wide empirical studies, we extend previous population genetics theory of sex-specific selection by developing and analyzing a biologically informed model that incorporates sex linkage, pleiotropy, recombination, and epistasis, factors that are likely to vary between genes and between species. Our results demonstrate that sex-specific selection and sex-specific recombination rates can generate, and are compatible with, the gene- and species-specific linkage patterns reported in the genomics literature. The theory suggests that sexual selection may strongly influence the architectures of animal genomes, as well as the chromosomal distribution of fixed substitutions underlying sexually dimorphic traits.

A novel approach for determining environment-specific protein costs: the case of Arabidopsis thaliana

Motivation: Comprehensive understanding of cellular processes requires development of approaches which consider the energetic balances in the cell. The existing approaches that address this problem are based on defining energy-equivalent costs which do not include the effects of a changing environment. By incorporating these effects, one could provide a framework for integrating ‘omics’ data from various levels of the system in order to provide interpretations with respect to the energy state and to elicit conclusions about putative global energy-related response mechanisms in the cell.

Results: Here we define a cost measure for amino acid synthesis based on flux balance analysis of a genome-scale metabolic network, and develop methods for its integration with proteomics and metabolomics data. This is a first measure which accounts for the effect of different environmental conditions. We applied this approach to a genome-scale network of Arabidopsis thaliana and calculated the costs for all amino acids and proteins present in the network under light and dark conditions. Integration of function and process ontology terms in the analysis of protein abundances and their costs indicates that, during the night, the cell favors cheaper proteins compared with the light environment. However, this does not imply that there is squandering of resources during the day. The results from the association analysis between the costs, levels and well-defined expenses of amino acid synthesis, indicate that our approach not only captures the adjustment made at the switch of conditions, but also could explain the anticipation of resource usage via a global energy-related regulatory mechanism of amino acid and protein synthesis.

Contact:nikoloski@mpimp-golm.mpg.de

Supplementary information:Supplementary data are available at Bioinformatics online.

Economical evolution: microbes reduce the synthetic cost of extracellular proteins

Protein evolution is not simply a race toward improved function. Because organisms compete for limited resources, fitness is also affected by the relative economy of an organism’s proteome. Indeed, many abundant proteins contain relatively high percentages of amino acids that are metabolically less taxing for the cell to make, thus reducing cellular cost. However, not all abundant proteins are economical, and many economical proteins are not particularly abundant. Here we examined protein composition and found that the relative synthetic cost of amino acids constrains the composition of microbial extracellular proteins. In Escherichia coli, extracellular proteins contain, on average, fewer energetically expensive amino acids independent of their abundance, length, function, or structure. Economic pressures have strategically shaped the amino acid composition of multicomponent surface appendages, such as flagella, curli, and type I pili, and extracellular enzymes, including type III effector proteins and secreted serine proteases. Furthermore, in silico analysis of Pseudomonas syringae, Mycobacterium tuberculosis, Saccharomyces cerevisiae, and over 25 other microbes spanning a wide range of GC content revealed a broad bias toward more economical amino acids in extracellular proteins. The synthesis of any protein, especially those rich in expensive aromatic amino acids, represents a significant investment. Because extracellular proteins are lost to the environment and not recycled like other cellular proteins, they present a greater burden on the cell, as their amino acids cannot be reutilized during translation. We hypothesize that evolution has optimized extracellular proteins to reduce their synthetic burden on the cell.

Microbes secrete proteins to perform essential interactions with their environment, such as motility, pathogenesis, biofilm formation, and resource acquisition. However, because microbes generally lack protein import systems, secretion is often a one-way street. Consequently, secreted proteins are less likely to be recycled by the cell due to environmental loss. We demonstrate that evolution has in turn selected these extracellular proteins for increased economy at the level of their amino acid composition. Compared to their cellular counterparts, extracellular proteins have fewer synthetically expensive amino acids and more inexpensive amino acids. The resulting bias lessens the loss of cellular resources due to secretion. Furthermore, this economical bias was observed regardless of the abundance, length, structure, or function of extracellular proteins. Thus, it appears that economy may address the compositional bias seen in many extracellular proteins and deliver further insight into the forces driving their evolution.

Unique cost dynamics elucidate the role of frameshifting errors in promoting translational robustness

There is now considerable evidence supporting the view that codon usage is frequently under selection for translational accuracy. There are, however, multiple forms of inaccuracy (missense, premature termination, and frameshifting errors) and pinpointing a particular error process behind apparently adaptive mRNA anatomy is rarely straightforward. Understanding differences in the fitness costs associated with different types of translational error can help us devise critical tests that can implicate one error process to the exclusion of others. To this end, we present a model that captures distinct features of frameshifting cost and apply this to 641 prokaryotic genomes. We demonstrate that, although it is commonly assumed that the ribosome encounters an off-frame stop codon soon after the frameshift and costs of mis-elongation are therefore limited, genomes with high GC content typically incur much larger per-error costs. We go on to derive the prediction, unique to frameshifting errors, that differences in translational robustness between the 5' and 3' ends of genes should be less pronounced in genomes with higher GC content. This prediction we show to be correct. Surprisingly, this does not mean that GC-rich organisms necessarily carry a greater fitness burden as a consequence of accidental frameshifting. Indeed, increased per-error costs are often more than counterbalanced by lower predicted error rates owing to more diverse anticodon repertoires in GC-rich genomes. We therefore propose that selection on tRNA repertoires may operate to reduce frameshifting errors.

The evolutionary origins of gene regulation

One way that organisms cope with constantly changing physical and biological conditions is by regulating the expression of genes and thereby altering protein production. Clearly, altering the protein production to match the environmental demands can be adaptive, but there may be evolutionary barriers to the transition from constitutive expression to regulated expression. In particular, down-regulating a gene when it is not needed means that there will necessarily be a delay in protein production when the protein is up-regulated in the future. We develop a model of simple gene regulation in response to randomly changing environmental conditions. We calculate the long-term behavior of gene expression and determine the fitness consequences of changes in the gene regulation. We then embed this model into a population genetic framework in order to determine the conditions that allow populations to evolve environment-specific transcription rates. The population genetic model follows the evolutionary transition from constitutive expression to regulated expression. There are three distinct possible evolutionary outcomes. The gene may be stuck in the always on position, the gene may first evolve to an intermediate constitutive expression level and then evolve regulation, or regulation can evolve directly from the ancestral state in a smooth fashion. Regulation is most likely to evolve when the costs of mis-expression are low and the transcript decay rate is high. This suggests that genes that have less severe reductions in fitness when mis-expressed are more likely to initially evolve regulation.

Metabolic modeling and analysis of the metabolic switch in Streptomyces coelicolor

BACKGROUND

The transition from exponential to stationary phase in Streptomyces coelicolor is accompanied by a major metabolic switch and results in a strong activation of secondary metabolism. Here we have explored the underlying reorganization of the metabolome by combining computational predictions based on constraint-based modeling and detailed transcriptomics time course observations.

RESULTS

We reconstructed the stoichiometric matrix of S. coelicolor, including the major antibiotic biosynthesis pathways, and performed flux balance analysis to predict flux changes that occur when the cell switches from biomass to antibiotic production. We defined the model input based on observed fermenter culture data and used a dynamically varying objective function to represent the metabolic switch. The predicted fluxes of many genes show highly significant correlation to the time series of the corresponding gene expression data. Individual mispredictions identify novel links between antibiotic production and primary metabolism.

CONCLUSION

Our results show the usefulness of constraint-based modeling for providing a detailed interpretation of time course gene expression data.

Gene expression dynamics in randomly varying environments

A simple model of gene regulation in response to stochastically changing environmental conditions is developed and analyzed. The model consists of a differential equation driven by a continuous time 2-state Markov process. The density function of the resulting process converges to a beta distribution. We show that the moments converge to their stationary values exponentially in time. Simulations of a two-stage process where protein production depends on mRNA concentrations are also presented demonstrating that protein concentration tracks the environment whenever the rate of protein turnover is larger than the rate of environmental change. Single-celled organisms are therefore expected to have relatively high mRNA and protein turnover rates for genes that respond to environmental fluctuations.

Evolutionary dynamics of redundant regulatory control

Many complex regulatory processes concern tracking a constant or variable set point. Examples include temperature homeostasis, rhythmic oscillation, and the concentration of key metabolites and enzymes. Control over homeostatic or tracking phenotypes often depends on multiple, overlapping regulatory systems. In this paper, I develop a theory for the evolutionary dynamics of redundant regulatory control architecture. Prior theories analyzed the evolution of redundant control architectures by the balance between improved performance for additional redundant control weighed against the decay by germline mutation that arises in characters with overlapping function. By contrast, I argue that germline mutation is likely to be a very weak balancing force in evolutionary dynamics. Instead, I analyze the evolutionary dynamics of redundant control by a balance between the benefits of reduced tracking error and the costs of building and running the multiple control systems. In one particular mathematical model that highlights key features of evolutionary dynamics, additional redundant control reduces tracking error multiplicatively but contributes to costs additively. In that model, the performance landscape has multiple peaks of the same height, one peak for each level of redundancy and the associated optimal investment per control structure. The multipeak landscape imposes evolutionary stasis, in which control systems resist invasion by increased or decreased levels of redundancy. However, fluctuating environments likely favor a rise in redundancy over time. With greater redundancy, investment per individual control structure declines, causing a decay in the performance of each individual dimension of control. I conclude that the costs of control structures may influence regulatory architecture.