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bioRxiv2019

Extrinsic noise and heavy-tailed laws in gene expression

Noise in gene expression is one of the hallmarks of life at the molecular scale. Here we derive analytical solutions to a set of models describing the molecular mechanisms underlying transcription of DNA into RNA. Our Ansatz allows us to incorporate the effects of extrinsic noise - encompassing factors external to the transcription of the individual gene - and discuss the ramifications for heterogeneity in gene product abundance that has been widely observed in single cell data. Crucially, we are able to show that heavy-tailed distributions of RNA copy numbers cannot result from the intrinsic stochasticity in gene expression alone, but must instead reflect extrinsic sources of variability.

systems biology↗

bioRxiv2021

Structural variants are a major source of gene expression differences in humans and often affect multiple nearby genes

Structural variants (SVs) are an important source of human genome diversity but their functional effects are not well understood. We mapped 61,668 SVs in 613 individuals with deep genome sequencing data from the GTEx project and measured their effects on gene expression. We estimate that common SVs are causal at 2.66% of eQTLs, which is a 10.5-fold enrichment relative to their abundance in the genome and consistent with prior work using smaller sample sizes. Duplications and deletions were the most impactful variant types, whereas the contribution of mobile element insertions was surprisingly small (0.12% of eQTLs, 1.9-fold enriched). Multi-tissue analysis of expression effects revealed that gene-altering SVs show significantly more constitutive effects than other variant types, with 62.09% of coding SV-eQTLs active in all tissues with known eQTL activity compared to 23.08% of coding SNV- and indel-eQTLs, whereas noncoding SVs, SNVs and indels show broadly similar patterns. We also identified 539 rare SVs associated with nearby gene expression outliers. Of these, 62.34% are noncoding SVs that show strong effects on gene expression yet modest enrichment at known regulatory elements, demonstrating that rare noncoding SVs are a major source of gene expression differences but remain difficult to predict from current annotations. Remarkably, both common and rare noncoding SVs often show strong regional effects on the expression of multiple genes: SV-eQTLs affect an average of 1.82 nearby genes compared to 1.09 genes affected by SNV- and indel-eQTLs, and 21.34% of rare expression-altering SVs show strong effects on 2-9 different genes. We also observe significant effects on gene expression extending 1 Mb from the SV. This provides a mechanism by which individual noncoding SVs may have strong and/or pleiotropic effects on phenotypic variation and disease.

genomics↗

bioRxiv2020

Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells

The discovery that somatic cells can be reprogrammed to induced pluripotent stem cells (iPSCs) - cells that can be differentiated into any cell type of the three germ layers - has provided a foundation for in vitro human disease modelling1,2, drug development1,2, and population genetics studies3,4. In the majority of instances, the expression levels of genes, plays a critical role in contributing to disease risk, or the ability to identify therapeutic targets. However, while the effect of the genetic background of cell lines has been shown to strongly influence gene expression, the effect has not been evaluated at the level of individual cells. Differences in the effect of genetic variation on the gene expression of different cell-types, would provide significant resolution for in vitro research using preprogramed cells. By bringing together single cell RNA sequencing15-21 and population genetics, we now have a framework in which to evaluate the cell-types specific effects of genetic variation on gene expression. Here, we performed single cell RNA-sequencing on 64,018 fibroblasts from 79 donors and we mapped expression quantitative trait loci (eQTL) at the level of individual cell types. We demonstrate that the large majority of eQTL detected in fibroblasts are specific to an individual sub-type of cells. To address if the allelic effects on gene expression are dynamic across cell reprogramming, we generated scRNA-seq data in 19,967 iPSCs from 31 reprogramed donor lines. We again identify highly cell type specific eQTL in iPSCs, and show that that the eQTL in fibroblasts are almost entirely disappear during reprogramming. This work provides an atlas of how genetic variation influences gene expression across cell subtypes, and provided evidence for patterns of genetic architecture that lead to cell-types specific eQTL effects.

genomics↗

bioRxiv2024

Multiplexed ultrasound imaging of gene expression

Acoustic reporter genes (ARGs) have enabled the imaging of gene expression with ultrasound, which provides high-resolution access to deep, optically opaque living tissues. However, unlike their fluorescent counterparts, ARGs have so far been limited to a single "color," preventing multiplexed imaging of cellular states or populations. Here, we use rational protein design and directed evolution to develop two novel ARGs that can be distinguished from each other based on their acoustic pressure response profiles, enabling "two-color" ultrasound imaging of gene expression. We demonstrate the utility of multiplexed ARGs for delineating bacterial cell species and cell states in vitro, and then apply them towards imaging distinct subpopulations of probiotics in the mouse gastrointestinal tract and in tumor-colonizing bacterial agents in vivo. Just as the first wavelength-shifted derivatives of fluorescent proteins opened a vivid world for optical microscopy, our next-generation acoustic proteins set the stage for a richer symphony of ultrasound signals from living subjects.

bioengineering↗

bioRxiv2019

Optimal bang--bang feedback for bursty gene expression

Stochasticity in gene expression poses a critical challenge to the precise control of cellular function. In this paper we examine how precisely can a stochastically expressed protein attain a given target expression level. We consider a protein which is produced in bursts and which is able to control its expression via a negative feedback loop; we specifically focus on feedback of a bang-bang type which turns off the production of the protein whenever its concentration exceeds a given threshold. Using a piecewise deterministic mathematical formalism, we derive explicit expressions for the probabilistic distribution of the protein concentration, and for the mean square deviation from the target level. Employing a combination of analytic and numerical optimization, we identify the optimal value of the bang-bang threshold, in terms of minimising the deviation, and examine the dependence of the optimal value on the target level and the sub-threshold burst frequency. The systematic analysis allows us to formulate a number of quantitative and qualitative conclusions about the controllability of burst like gene expression. Finally, we outline directions for future research into the topic.

systems biology↗

bioRxiv2014

The Genetic Architecture of Gene Expression Levels in Wild Baboons

Gene expression variation is well documented in human populations and its genetic architecture has been extensively explored. However, we still know little about the genetic architecture of gene expression variation in other species, particularly our closest living relatives, the nonhuman primates. To address this gap, we performed an RNA sequencing (RNA-seq)-based study of 63 wild baboons, members of the intensively studied Amboseli baboon population in Kenya. Our study design allowed us to measure gene expression levels and identify genetic variants using the same data set, enabling us to perform complementary mapping of putative cis-acting expression quantitative trait loci (eQTL) and measurements of allele-specific expression (ASE) levels. We discovered substantial evidence for genetic effects on gene expression levels in this population. Surprisingly, we found more power to detect individual eQTL in the baboons relative to a HapMap human data set of comparable size, probably as a result of greater genetic variation, enrichment of SNPs with high minor allele frequencies, and longer-range linkage disequilibrium in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes. Interestingly, genes with eQTL significantly overlapped between the baboon and human data sets, suggesting that some genes may tolerate more genetic perturbation than others, and that this property may be conserved across species. Finally, we used a Bayesian sparse linear mixed model to partition genetic, demographic, and early environmental contributions to variation in gene expression levels. We found a strong genetic contribution to gene expression levels for almost all genes, while individual demographic and environmental effects tended to be more modest. Together, our results establish the feasibility of eQTL mapping using RNA-seq data alone, and act as an important first step towards understanding the genetic architecture of gene expression variation in nonhuman primates.

Genomics↗

bioRxiv2024

Endogenous Retrovirus Loci and Induced Changes in Gene Expression in Japanese Indigenous Chickens

When retroviruses infect germ cells and are transmitted to offspring, they become endogenous retroviruses (ERVs), whose insertions influence the expression of nearby genes. This study aimed to identify the genomic loci of ERVs in commercial broiler (Ross308), Tosa-Jidori, and Yakido chickens as well as to elucidate their impact on neighboring gene expression. Whole-genome data were obtained using next-generation sequencing, and candidate ERV loci were identified using the RetroSeq software. The Integrative Genomics Viewer tool was used to confirm target site duplications (TSDs) as evidence of ERV insertions. All reads within 200 bp of these TSDs were extracted to create contigs, confirming the presence of ERV sequences in the contigs using BLASTN. Gene expression levels were estimated by focusing on genes located near the 172 identified ERV loci. Among these, 119 loci were detected in broiler chickens, 80 in Tosa-Jidori chickens, and 86 in Yakido chickens, with 28 loci shared among them. Moreover, of these 172 loci, 75 were located within or near genes. Significant differences in gene expression were observed for N-acetylated alpha-linked acidic dipeptidase 2, glypican 6, and phospholipid scramblase family member 5 depending on the presence of ERV insertions. These results suggest that ERV insertions may influence the expression of certain genes, providing insights into the genetic diversity and evolutionary background of commercial and indigenous chickens. Understanding the effects of ERV insertions on gene expression can inform future genetic research and poultry breeding programs aimed at improving health and productivity.

genomics↗

bioRxiv2026

Genomic signatures of selection are enriched in differentially expressed genes in sticklebacks adapting to contrasting environments

Whole genome scans have identified numerous adaptive alleles in many species; however, linking these alleles to specific phenotypes remains a major challenge. A promising alternative to direct genotype-phenotype mapping, particularly given the complexities introduced by epistasis, pleiotropy, and environmental variability, is to assess whether differentially expressed genes are enriched in regions of genetic divergence between populations adapted to contrasting environments. Here, we study gene expression patterns in threespine stickleback populations adapting to contrasting environments (marine vs freshwater) and investigate how signatures of selection interact with patterns of gene expression. We performed transcriptomic experiments of the brain and gill tissues of wild-caught sticklebacks sampled from one marine and two freshwater environments using TagSeq. We found that differentially expressed genes in the freshwater environments are enriched for single nucleotide polymorphisms (SNPs) previously identified to be involved in rapid adaptation and FST outliers. A majority of these SNPs were located in cis-regulatory regions with predicted low to moderate effects on protein function and structure, although we found a high-impact SNP in the gene col8a1b. Genes such as pvalb4 and acsl4a, involved in calcium regulation in the gill and fatty acid metabolism in the brain, respectively, were enriched with SNPs showing signatures of selection. By linking signatures of selection to tissue-specific gene expression patterns, our study bridges the gap between genomic divergence and the molecular mechanisms underlying physiological adaptation to new environments. Significance StatementUnderstanding how genetic variation translates into adaptive traits remains a central challenge in evolutionary biology. While whole-genome scans routinely identify candidate adaptive alleles, connecting these variants to functional phenotypes is complicated by epistasis, pleiotropy, and environmental effects. Here, we integrate signatures of selection with tissue-specific gene expression in threespine stickleback adapting to contrasting environments (marine and freshwater). We demonstrate that differentially expressed genes in freshwater populations are enriched for previously identified adaptive SNPs and FST outliers, many of which are located in cis-regulatory regions with predicted low to moderate functional effects. Notably, we identify a high-impact variant leading to a premature stop codon in col8a1b and highlight genes such as pvalb4 and acsl4a that link selection to key physiological processes, including ion regulation in gills and fatty acid metabolism in the brain. By connecting genomic divergence to regulatory and tissue-specific expression changes, this work provides a mechanistic framework for understanding how natural selection shapes complex physiological adaptation.

evolutionary biology↗

bioRxiv2023

When is gene expression noise advantageous?

The variability of gene expression levels, also known as gene expression noise, is an evolvable trait subject to selection. While gene expression noise is detrimental in constant environments where the expression level is under stabilizing selection, it may be beneficial in changing environments when the phenotype is far from the optimum. However, expression noise propagates along the gene network, making the evolution of connected genes interdependent. Here, we explore how their position in the gene network constrains the evolution of genes under selection using an in silico evolution experiment. We simulate the evolution of populations of model gene regulatory networks under directional and fluctuating selection on the gene expression level while allowing the basal expression level and expression noise level to mutate. We find that expression noise is only transiently favoured under directional selection, but high levels of noise can be maintained in a fluctuating selection regime. Furthermore, target genes, regulated by other genes, were more likely to increase their gene-specific expression noise than regulator genes. These findings suggest that both the mean and variance of gene expression levels respond to selection due to changing environments - and do so in a network-dependent manner. They further point at gene expression noise as a putative mechanism for populations to escape extinction when facing environmental changes.

evolutionary biology↗

bioRxiv2016

Survey of the Heritability and Sparsity of Gene Expression Traits Across Human Tissues

Understanding the genetic architecture of gene expression traits is key to elucidating the underlying mechanisms of complex traits. Here, for the first time, we perform a systematic survey of the heritability and the distribution of effect sizes across all representative tissues in the human body. We find that local h2 can be relatively well characterized with 59% of expressed genes showing significant h2 (FDR < 0.1) in the DGN whole blood cohort. However, current sample sizes (n [≤] 922) do not allow us to compute distal h2. Bayesian Sparse Linear Mixed Model (BSLMM) analysis provides strong evidence that the genetic contribution to local expression traits is dominated by a handful of genetic variants rather than by the collective contribution of a large number of variants each of modest size. In other words, the local architecture of gene expression traits is sparse rather than polygenic across all 40 tissues (from DGN and GTEx) examined. This result is confirmed by the sparsity of optimal performing gene expression predictors via elastic net modeling. To further explore the tissue context specificity, we decompose the expression traits into cross-tissue and tissue-specific components using a novel Orthogonal Tissue Decomposition (OTD) approach. Through a series of simulations we show that the cross-tissue and tissue-specific components are identifiable via OTD. Heritability and sparsity estimates of these derived expression phenotypes show similar characteristics to the original traits. Consistent properties relative to prior GTEx multi-tissue analysis results suggest that these traits reflect the expected biology. Finally, we apply this knowledge to develop prediction models of gene expression traits for all tissues. The prediction models, heritability, and prediction performance R2 for original and decomposed expression phenotypes are made publicly available (https://github.com/hakyimlab/PrediXcan).\n\nAuthor SummaryGene regulation is known to contribute to the underlying mechanisms of complex traits. The GTEx project has generated RNA-Seq data on hundreds of individuals across more than 40 tissues providing a comprehensive atlas of gene expression traits. Here, we systematically examined the local versus distant heritability as well as the sparsity versus polygenicity of protein coding gene expression traits in tissues across the entire human body. To determine tissue context specificity, we decomposed the expression levels into cross-tissue and tissue-specific components. Regardless of tissue type, we found that local heritability, but not distal heritability, can be well characterized with current sample sizes. We found that the distribution of effect sizes is more consistent with a sparse local architecture in all tissues. We also show that the cross-tissue and tissue-specific expression phenotypes constructed with our orthogonal tissue decomposition model recapitulate complex Bayesian multi-tissue analysis results. This knowledge was applied to develop prediction models of gene expression traits for all tissues, which we make publicly available.

Genomics↗

bioRxiv2020

Transcriptome analysis reveals the effects of transgenic expression of the Gal4 protein on normal gene expression in silkworm tissues

The Gal4/upstream activating sequence(UAS) system, a well-known genetic tool, has been widely used to analyze gene function in many organisms, including the silkworm (Bombyx mori), a model lepidopteran insect. Several studies have suggested that Gal4 protein activation in tissues can negatively affect transgenic individuals; however, whether and to what extent the Gal4 protein affects normal endogenous gene expression have rarely been studied. Here, we analyzed the transcriptomes of transgenic silkworms expressing the Gal4 protein at high levels in both the wing disc (WD) and epidermis (EP) and investigated gene expression changes in both tissues. Overall, 24,593 genes were identified in the WD and EP libraries, and 2,025 and 2,488 were identified as significant differentially expressed genes(DEGs) in the WD and EP between the transgenic and control groups, respectively. These DEGs were further annotated by gene function classification and pathway assessment using public databases. In addition, 506 DEGs were shared (common) between both tissues. Of these, 97 genes were commonly upregulated, and 234 were commonly downregulated; many of them were annotated to be involved in metabolic processes such as "fat digestion and absorption", "glycine, serine and threonine metabolism" and "glutathione metabolism" and in signal transduction pathways such as the "Rap1 signaling pathway", "MAPK signaling pathway" and "Hippo signaling pathway". Overall, this work enhances understanding of the effects of transgenic Gal4 protein expression on normal gene expression in silkworm tissues and suggests that researchers should pay attention to unexpected effects when using the Gal4/UAS system to study gene function.

genomics↗

bioRxiv2017

Rapid and scalable preparation of bacterial lysates for cell-free gene expression

Cell-free gene expression systems are emerging as an important platform for a diverse range of synthetic biology and biotechnology applications, including production of robust field-ready biosensors. Here, we combine programmed cellular autolysis with a freeze-thaw or freeze-dry cycle to create a practical, reproducible, and a labor- and cost-effective approach for rapid production of bacterial lysates for cell-free gene expression. Using this method, ro-bust and highly active bacterial cell lysates can be produced without specialized equipment at a wide range of scales, making cell-free gene expression easily and broadly accessible. More-over, live autolysis strain can be freeze-dried directly and subsequently lysed upon rehydration to produce active lysate. We demonstrate the utility of autolysates for synthetic biology by reg-ulating protein production and degradation, implementing quorum sensing, and showing quan-titative protection of linear DNA templates by GamS protein. To allow versatile and sensitive {beta}-galactosidase (LacZ) based readout we produce autolysates with no detectable background LacZ activity and use them to produce sensitive mercury(II) biosensors with LacZ-mediated colorimetric and fluorescent outputs. The autolysis approach can facilitate wider adoption of cell-free technology for cell-free gene expression as well as other synthetic biology and biotechnology applications, such as metabolic engineering, natural product biosynthesis, or proteomics.

synthetic biology↗

bioRxiv2022

Hypergraph factorisation for multi-tissue gene expression imputation

AO_SCPLOWBSTRACTC_SCPLOWIntegrating gene expression across scales and tissues is crucial for understanding the biological mechanisms that drive disease and characterise homeostasis. However, traditional multi-tissue integration methods cannot handle uncollected tissues or rely on genotype information, which is subject to privacy concerns and often unavailable. To address these challenges, we present HYFA (Hypergraph Factorisation), a novel method for joint imputation of multi-tissue and cell-type gene expression. HYFA imputes tissue-specific gene expression via a specialised graph neural network operating on a hypergraph of individuals, metagenes, and tissues. HYFA is genotype- agnostic, supports a variable number of collected tissues per individual, and imposes strong inductive biases to leverage the shared regulatory architecture of tissues. In performance comparison on data from the Genotype Tissue Expression project, HYFA achieves superior performance over existing transcriptome imputation methods, especially when multiple reference tissues are available. Through transfer learning on a paired single-nucleus RNA-seq (snRNA-seq) dataset, we further show that HYFA can accurately resolve cell-type signatures from bulk gene expression, highlighting the methods ability to leverage gene expression programs underlying cell-type identity, even in tissues that were never observed in the training set. Using Gene Set Enrichment Analysis, we find that the metagenes learned by HYFA capture information about known biological pathways. Notably, the HYFA-imputed dataset can be used to identify regulatory genetic variations (eQTLs), with substantial gains over the original incomplete dataset. Our framework can accelerate effective and scalable integration of tissue and cell-type gene expression biorepositories.

bioinformatics↗

bioRxiv2020

DELocal: Chromosomal neighbourhoods having genes of diverse functions allow improved identification of differentially expressed genes

Although most genes share their chromosomal neighbourhood with other genes, distribution of genes has not been explored in the context of individual organ development; the common focus of developmental biology studies. Because developmental processes are often associated with initially subtle changes in gene expression, here we explored whether neighbouring genes are informative in the identification of differentially expressed genes. First, we quantified the chromosomal neighbourhood patterns of genes having related functional roles in the mammalian genome. Although the majority of protein coding genes have at least five neighbours within 1 Mb window around each gene, very few of these neighbours regulate development of the same organ. Analyses of transcriptomes of developing mouse molar teeth revealed that whereas expression of genes regulating tooth development changes, their neighbouring genes show no marked changes, irrespective of their level of expression. Finally, we test whether inclusion of gene neighbourhood in the analyses of differential expression could provide additional benefits. For the analyses, we developed an algorithm, called DELocal that identifies differentially expressed genes by comparing their expression changes to changes in adjacent genes in their chromosomal regions. Our results show that DELocal removes detection bias towards large changes in expression, thereby allowing identification of even subtle changes in development. Future studies, including the detection of differential expression, may benefit from, and further characterize the significance of gene-gene neighbour relationships. Author SummaryDevelopment of organs is typically associated with small and hard to detect changes in gene expression. Here we examined how often genes regulating specific organs are neighbours to each other in the genome, and whether this gene neighbourhood helps in the detection of changes in gene expression. We found that genes regulating individual organ development are very rarely close to each other in the mouse and human genomes. We built an algorithm, called DELocal, to detect changes in gene expression that incorporates information about neighbouring genes. Using transcriptomes of developing mouse molar teeth containing gene expression profiles of thousands of genes, we show how genes regulating tooth development are ranked high by DELocal even if their expression level changes are subtle. We propose that developmental biology studies can benefit from gene neighbourhood analyses in the detection of differential expression and identification of organ specific genes.

bioinformatics↗

bioRxiv2020

Local adaptation contributes to gene expression divergence in maize

Gene expression links genotypes to phenotypes, so identifying genes whose expression is shaped by selection will be important for understanding the traits and processes underlying local adaptation. However, detecting local adaptation for gene expression will require distinguishing between divergence due to selection and divergence due to genetic drift. Here, we adapt a QST -FST framework to detect local adaptation for transcriptome-wide gene expression levels in a population of diverse maize genotypes. We compare the number and types of selected genes across a wide range of maize populations and tissues, as well as selection on cold-response genes, drought-response genes, and coexpression clusters. We identify a number of genes whose expression levels are consistent with local adaptation and show that genes involved in stress-response show enrichment for selection. Due to its history of intense selective breeding and domestication, maize evolution has long been of interest to researchers, and our study provides insight into the genes and processes important for in local adaptation of maize.

evolutionary biology↗

bioRxiv2017

Modularity Facilitates Flexible Tuning Of Plastic And Evolutionary Gene Expression Responses During Early Divergence

Gene expression changes have been recognized as important drivers of adaptation to changing environmental conditions. Little is known about the relative roles of plastic and evolutionary responses in complex gene expression networks during the early stages of divergence. Large gene expression data sets coupled with in silico methods for identifying co-expressed modules now enable systems genetics approaches also in non-model species for better understanding of gene expression responses during early divergence. Here, we combined gene co-expression analyses with population genetics to separate plastic and population (evolutionary) effects in expression networks using small salmonid populations as a model system. We show that plastic and population effects were highly variable among the six identified modules and that the plastic effects explained larger proportion of the total eigengene expression than population effects. A more detailed analysis of the population effects using a QST - FST comparison across 16622 annotated transcripts revealed that gene expression followed neutral expectations within modules and at the global level. Furthermore, two modules showed enrichment for genes coding for early developmental traits that have been previously identified as important phenotypic traits in thermal responses in the same model system indicating that co-expression analysis can capture expression patterns underlying ecologically important traits. We suggest that module-specific responses may facilitate the flexible tuning of expression levels to local thermal conditions. Overall, our study indicates that plasticity and neutral evolution are the main drivers of gene expression variance in the early stages of thermal adaptation in this system.

evolutionary biology↗

bioRxiv2017

Genetics of trans-regulatory variation in gene expression

Heritable variation in gene expression provides a critical bridge between differences in genome sequence and the biology of many traits, including common human diseases. However, the sources of most regulatory genetic variation remain unknown. Here, we used transcriptome profiling in 1,012 yeast segregants to map the genetic basis of variation in gene expression with high statistical power. We identified expression quantitative trait loci (eQTL) that together account for over 70% of the total genetic contribution to variation in mRNA levels, allowing us to examine the sources of regulatory variation comprehensively. We found that variation in the expression of a typical gene has a complex genetic architecture involving multiple eQTL. We also detected hundreds of eQTL pairs with significant non-additive interactions in an unbiased genome-wide scan. Although most genes were influenced by a local eQTL located close to the gene, most expression variation arose from distant, trans-acting eQTL located far from their target genes. Nearly all distant eQTL clustered at 102 \"hotspot\" locations, some of which influenced the expression of thousands of genes. Hotspot regions were enriched for transcription factor genes and altered expression of their target genes though both direct and indirect mechanisms. Many local eQTL had no detectable effects on the expression of other genes in trans. These results reveal the complexity of genetic influences on transcriptome variation in unprecedented depth and detail.

genetics↗

bioRxiv2024

Inter-individual gene expression variability implies stable regulation of brain-biased genes across organs

Phenotypic variation among individuals provides the raw material for evolution, and gene expression is a key mediator between genetic and phenotypic variation. As for phenotypes, the range of gene expression is limited and biased by evolutionary and developmental constraints. Observed expression variability due to biomolecular stochasticity and cell-to-cell heterogeneity has been well-studied in isogenic populations of unicellular organisms. However, for multicellular organisms with a diversity of cells and tissues sharing the same genetic background, the interplay between expression variability, gene and organ function, and gene regulation remains an open question. Here, we use highly multiplexed 3-end bulk RNA sequencing to generate transcriptome profiles spanning at least nine organs in outbred individuals of three ray-finned fish species: zebrafish, Northern pike, and spotted gar. Per organ, we quantify individual-to-individual gene expression variability independent of mean expression level. Lowly variable genes are enriched in cellular housekeeping functions whereas highly variable genes are enriched in stimulus-response functions. Furthermore, highly variable genes evolve under weaker purifying selection at the protein-coding sequence, indicating that intra-species expression variability predicts inter-species protein sequence divergence. Genes that are broadly expressed across organs are both highly expressed and lowly variable, whereas organ-biased genes are typically highly variable within their top organ. Among organ-biased genes, patterns of expression variance are dependent on the top organ. Specifically, brain- and gonads-biased genes have lowly variable expression across different organs, suggesting stabilizing selection. These patterns suggest that gene regulatory mechanisms evolve under organ-specific selective pressures.

evolutionary biology↗