Selection to minimise noise in living systems and its implications for the evolution of gene expression.
Ontology highlight
ABSTRACT:
SUBMITTER: Lehner B
PROVIDER: S-EPMC2290932 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:It is well known that the expression noise is lessened by natural selection for genes that are important for cell growth or are sensitive to dosage. In theory, expression noise can also be elevated by natural selection when noisy gene expression is advantageous. Here we analyze yeast genome-wide gene expression noise data and show that plasma-membrane transporters show significantly elevated expression noise after controlling all confounding factors. We propose a model that explains why and under what conditions elevated expression noise may be beneficial and subject to positive selection. Our model predicts and the simulation confirms that, under certain conditions, expression noise also increases the evolvability of gene expression by promoting the fixation of favorable expression level-altering mutations. Indeed, yeast genes with higher noise show greater between-strain and between-species divergences in expression, even when all confounding factors are excluded. Together, our theoretical model and empirical results suggest that, for yeast genes such as plasma-membrane transporters, elevated expression noise is advantageous, is subject to positive selection, and is a facilitator of adaptive gene expression evolution.
| S-EPMC2736655 | biostudies-literature
Project description:Phenotype of biological systems needs to be robust against mutation in order to sustain themselves between generations. On the other hand, phenotype of an individual also needs to be robust against fluctuations of both internal and external origins that are encountered during growth and development. Is there a relationship between these two types of robustness, one during a single generation and the other during evolution? Could stochasticity in gene expression have any relevance to the evolution of these types of robustness? Robustness can be defined by the sharpness of the distribution of phenotype; the variance of phenotype distribution due to genetic variation gives a measure of 'genetic robustness', while that of isogenic individuals gives a measure of 'developmental robustness'. Through simulations of a simple stochastic gene expression network that undergoes mutation and selection, we show that in order for the network to acquire both types of robustness, the phenotypic variance induced by mutations must be smaller than that observed in an isogenic population. As the latter originates from noise in gene expression, this signifies that the genetic robustness evolves only when the noise strength in gene expression is larger than some threshold. In such a case, the two variances decrease throughout the evolutionary time course, indicating increase in robustness. The results reveal how noise that cells encounter during growth and development shapes networks' robustness to stochasticity in gene expression, which in turn shapes networks' robustness to mutation. The necessary condition for evolution of robustness, as well as the relationship between genetic and developmental robustness, is derived quantitatively through the variance of phenotypic fluctuations, which are directly measurable experimentally.
| S-EPMC1855988 | biostudies-literature
Project description:Biochemical reactions within individual cells result from the interactions of molecules, typically in small numbers. Consequently, the inherent stochasticity of binding and diffusion processes generates noise along the cascade that leads to the synthesis of a protein from its encoding gene. As a result, isogenic cell populations display phenotypic variability even in homogeneous environments. The extent and consequences of this stochastic gene expression have only recently been assessed on a genome-wide scale, owing, in particular, to the advent of single-cell transcriptomics. However, the evolutionary forces shaping this stochasticity have yet to be unraveled. Here, we take advantage of two recently published data sets for the single-cell transcriptome of the domestic mouse Mus musculus to characterize the effect of natural selection on gene-specific transcriptional stochasticity. We show that noise levels in the mRNA distributions (also known as transcriptional noise) significantly correlate with three-dimensional nuclear domain organization, evolutionary constraints on the encoded protein, and gene age. However, the position of the encoded protein in a biological pathway is the main factor that explains observed levels of transcriptional noise, in agreement with models of noise propagation within gene networks. Because transcriptional noise is under widespread selection, we argue that it constitutes an important component of the phenotype and that variance of expression is a potential target of adaptation. Stochastic gene expression should therefore be considered together with the mean expression level in functional and evolutionary studies of gene expression.
| S-EPMC5753856 | biostudies-literature
Project description:Changes in gene expression can affect phenotypes and therefore both its level and stochastic variability are frequently under selection. It has recently been proposed that epistatic interactions influence gene expression evolution: gene pairs where simultaneous knockout is more deleterious than expected should evolve reduced expression noise to avoid concurrent low expression of both proteins. In apparent support, yeast genes with many epistatic partners have low expression variation both among isogenic individuals and between species. However, the specific predictions and basic assumptions of this verbal model remain untested. Using bioinformatics analysis, we first demonstrate that the model's predictions are unsupported by available large-scale data. Based on quantitative biochemical modeling, we then show that epistasis between expression reductions (epigenetic epistasis) is not expected to aggravate the fitness cost of stochastic expression, which is in sharp contrast to the verbal argument. This nonintuitive result can be readily explained by the typical diminishing return of fitness on gene activity and by the fact that expression noise not only decreases but also increases the abundance of proteins. Overall, we conclude that stochastic variation in epistatic partners is unlikely to drive noise minimization or constrain gene expression divergence on a genomic scale.
| S-EPMC6544514 | biostudies-literature
Project description: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.
| S-EPMC3080991 | biostudies-literature
Project description:Feature selection in high dimensional gene expression datasets not only reduces the dimension of the data, but also the execution time and computational cost of the underlying classifier. The current study introduces a novel feature selection method called weighted signal to noise ratio (WSNR) by exploiting the weights of features based on support vectors and signal to noise ratio, with an objective to identify the most informative genes in high dimensional classification problems. The combination of two state-of-the-art procedures enables the extration of the most informative genes. The corresponding weights of these procedures are then multiplied and arranged in decreasing order. Larger weight of a feature indicates its discriminatory power in classifying the tissue samples to their true classes. The current method is validated on eight gene expression datasets. Moreover, results of the proposed method (WSNR) are also compared with four well known feature selection methods. We found that the (WSNR) outperform the other competing methods on 6 out of 8 datasets. Box-plots and Bar-plots of the results of the proposed method and all the other methods are also constructed. The proposed method is further assessed on simulated data. Simulation analysis reveal that (WSNR) outperforms all the other methods included in the study.
| S-EPMC10128961 | biostudies-literature
Project description:Intra-population heterogeneity is commonly observed in natural and cellular populations and has profound influence on their evolution. For example, intra-tumor heterogeneity is prevalent in most tumor types and can result in the failure of tumor therapy. However, the evolutionary process of heterogeneity remains poorly characterized at both genotypic and phenotypic level. Here we study the evolution of intra-population heterogeneities of gene regulatory networks (GRN), in particular mutational robustness and gene expression noise as contributors to the development of heterogeneities. By in silico simulations, it was found that the impact of these factors on GRN can, under certain conditions, promote phenotypic heterogeneity. We also studied the effect of population bottlenecks on the evolution of GRN. When the GRN population passes through such bottlenecks, neither mutational robustness nor population fitness was observed to be substantially altered. Interestingly, however, we did detect a significant increase in the number of potential "generator" genes which can substantially induce population fitness, when stimulated by mutational hits.
| S-EPMC4277480 | biostudies-literature
Project description:The profound and pervasive differences in gene expression observed between males and females, and the unique evolutionary properties of these genes in many species, have led to the widespread assumption that they are the product of sexual selection and sexual conflict. However, we still lack a clear understanding of the connection between sexual selection and transcriptional dimorphism, often termed sex-biased gene expression. Moreover, the relative contribution of sexual selection vs. drift in shaping broad patterns of expression, divergence, and polymorphism remains unknown. To assess the role of sexual selection in shaping these patterns, we assembled transcriptomes from an avian clade representing the full range of sexual dimorphism and sexual selection. We use these species to test the links between sexual selection and sex-biased gene expression evolution in a comparative framework. Through ancestral reconstruction of sex bias, we demonstrate a rapid turnover of sex bias across this clade driven by sexual selection and show it to be primarily the result of expression changes in males. We use phylogenetically controlled comparative methods to demonstrate that phenotypic measures of sexual selection predict the proportion of male-biased but not female-biased gene expression. Although male-biased genes show elevated rates of coding sequence evolution, consistent with previous reports in a range of taxa, there is no association between sexual selection and rates of coding sequence evolution, suggesting that expression changes may be more important than coding sequence in sexual selection. Taken together, our results highlight the power of sexual selection to act on gene expression differences and shape genome evolution.
| S-EPMC4394296 | biostudies-literature
Project description:The elevated rate of evolution for genes on sex chromosomes compared with autosomes (Fast-X or Fast-Z evolution) can result either from positive selection in the heterogametic sex or from nonadaptive consequences of reduced relative effective population size. Recent work in birds suggests that Fast-Z of coding sequence is primarily due to relaxed purifying selection resulting from reduced relative effective population size. However, gene sequence and gene expression are often subject to distinct evolutionary pressures; therefore, we tested for Fast-Z in gene expression using next-generation RNA-sequencing data from multiple avian species. Similar to studies of Fast-Z in coding sequence, we recover clear signatures of Fast-Z in gene expression; however, in contrast to coding sequence, our data indicate that Fast-Z in expression is due to positive selection acting primarily in females. In the soma, where gene expression is highly correlated between the sexes, we detected Fast-Z in both sexes, although at a higher rate in females, suggesting that many positively selected expression changes in females are also expressed in males. In the gonad, where intersexual correlations in expression are much lower, we detected Fast-Z for female gene expression, but crucially, not males. This suggests that a large amount of expression variation is sex-specific in its effects within the gonad. Taken together, our results indicate that Fast-Z evolution of gene expression is the product of positive selection acting on recessive beneficial alleles in the heterogametic sex. More broadly, our analysis suggests that the adaptive potential of Z chromosome gene expression may be much greater than that of gene sequence, results which have important implications for the role of sex chromosomes in speciation and sexual selection.
| S-EPMC4576705 | biostudies-other
Project description:Human communication systems evolve culturally, but the evolutionary mechanisms that drive this evolution are not well understood. Against a baseline that communication variants spread in a population following neutral evolutionary dynamics (also known as drift models), we tested the role of two cultural selection models: coordination- and content-biased. We constructed a parametrized mixed probabilistic model of the spread of communicative variants in four 8-person laboratory micro-societies engaged in a simple communication game. We found that selectionist models, working in combination, explain the majority of the empirical data. The best-fitting parameter setting includes an egocentric bias and a content bias, suggesting that participants retained their own previously used communicative variants unless they encountered a superior (content-biased) variant, in which case it was adopted. This novel pattern of results suggests that (i) a theory of the cultural evolution of human communication systems must integrate selectionist models and (ii) human communication systems are functionally adaptive complex systems.
| S-EPMC4083785 | biostudies-literature