Detecting signatures of selection on gene expression

Transcriptomic signatures of atheroresistance in the human atrium and ventricle highlight potential candidates for targeted atherosclerosis therapeutics

Atherosclerosis risk is not uniform throughout the cardiovascular system. This study therefore aimed to compare the transcriptomes of atheroresistant human atrium and ventricle with atheroprone coronary arteries to identify transcriptomic signatures of atheroresistance and potential targets for atherosclerosis therapeutics. Using publicly available gene read counts, differentially expressed genes between the atrium, ventricle, and coronary artery were identified for each contrast and validated against the Swiss Institute of Bioinformatics' Bgee database. Over-representation analysis and active-subnetwork-oriented enrichment assessment then identified enriched terms, which were grouped into endothelial dysfunction-related processes. Potential biological significance was further explored with pathway analysis. Among 21474 features, 12656 differentially expressed genes were identified across the three contrasts and associated with 1215 enriched terms. There were 315 down-regulated and 133 up-regulated genes associated with endothelial dysfunction-related processes across the contrasts, including immune modulators, cell adhesion molecules, and lipid metabolism- and coagulation-related molecules. Differentially expressed genes were associated with six down-regulated Kyoto Encyclopedia of Genes and Genomes pathways, related to immune cell and associated endothelium functions. Review of regulated genes associated with endothelial dysfunction-related processes and included in these pathways, indicate immune cell-associated B cell scaffold protein with ankyrin repeats 1, as well as arterial endothelial cell-associated vascular cell adhesion molecule 1 and cadherin 5, as potential atherosclerosis targets.

Natural Variation in Sexual Traits and Gene Expression between Selfing and Outcrossing Arabidopsis lyrata Suggests Sexual Selection at Work

Flowering plants show significant diversity in sexual strategies, profoundly impacting the evolution of sexual traits and associated genes. Sexual selection is one of the primary evolutionary forces driving sexual trait variation, particularly evident during pollen-pistil interactions, where pollen grains compete for fertilization and females select mating partners. Multiple mating may intensify competition among pollen donors for siring, while in contrast, self-fertilization reduces sire-sire competition, relaxing the sexual selection pressure. Traits involved in male-male competition and female choice are well described, and molecular mechanisms underlying pollen development and pollen-pistil interactions have been extensively studied in the model species Arabidopsis thaliana. However, whether these molecular mechanisms are involved in sexual selection in nature remains unclear. To address this gap, we measured intrinsic pollen performance and its interaction with female choice and investigated the associated gene expression patterns in a selfing and an outcrossing population of Arabidopsis lyrata. We found that pollen germination and pollen tube growth were significantly higher in outcrossers than selfers, and this difference was accompanied by changes in the expression of genes involved in vesicle transport and cytoskeleton. Outcrosser mother plants showed a negative impact on pollen tube growth compared to selfer mother plants, together with a difference of expression for genes involved in auxin and stress response, suggesting a potential mechanism for female choice through molecular cross talk at the post-pollination stage. Our study provides insight into the impact of sexual selection on the evolution of sexual gene expression in plants.

Unveiling cell-type-specific mode of evolution in comparative single-cell expression data

While methodology for determining the mode of evolution in coding sequences has been well established, evaluation of adaptation events in emerging types of phenotype data needs further development. Here we propose an analysis framework (expression variance decomposition, EVaDe) for comparative single-cell expression data based on phenotypic evolution theory. After decomposing the gene expression variance into separate components, we use two strategies to identify genes exhibiting large between-taxon expression divergence and small within-cell-type expression noise in certain cell types, attributing this pattern to putative adaptive evolution. In a dataset of primate prefrontal cortex, we find that such human-specific key genes enrich with neurodevelopment-related functions, while most other genes exhibit neutral evolution patterns. Specific neuron types are found to harbor more of these key genes than other cell types, thus likely to have experienced more extensive adaptation. Reassuringly, at molecular sequence level, the key genes are significantly associated with the rapidly evolving conserved non-coding elements. An additional case analysis comparing the naked mole-rat (NMR) with the mouse suggests that innate-immunity-related genes and cell types have undergone putative expression adaptation in NMR. Overall, the EVaDe framework may effectively probe adaptive evolution mode in single-cell expression data.

Constraints on the optimization of gene product diversity

RNA and proteins can have diverse isoforms due to post-transcriptional and post-translational modifications. A fundamental question is whether these isoforms are mostly beneficial or the result of noisy molecular processes. To assess the plausibility of these explanations, we developed mathematical models depicting different regulatory architectures and investigated isoform evolution under multiple population genetic regimes. We found that factors beyond selection, such as effective population size and the number of cis-acting loci, significantly influence evolutionary outcomes. We found that sub-optimal phenotypes are more likely to evolve when populations are small and/or when the number of cis-loci is large. We also discovered that opposing selection on cis- and trans-acting loci can constrain adaptation, leading to a non-monotonic relationship between effective population size and optimization. More generally, our models provide a quantitative framework for developing statistical tests to analyze empirical data; as a demonstration of this, we analyzed A-to-I RNA editing levels in coleoids and found these to be largely consistent with non-adaptive explanations.

Macroevolutionary divergence of gene expression driven by selection on protein abundance

The regulation of messenger RNA (mRNA) and protein abundances is well-studied, but less is known about the evolutionary processes shaping their relationship. To address this, we derived a new phylogenetic model and applied it to multispecies mammalian data. Our analyses reveal (i) strong stabilizing selection on protein abundances over macroevolutionary time, (ii) mutations affecting mRNA abundances minimally impact protein abundances, (iii) mRNA abundances evolve under selection to align with protein abundances, and (iv) mRNA abundances adapt faster than protein abundances owing to greater mutational opportunity. These conclusions are supported by comparisons of model parameters with independent functional genomic data. By decomposing mutational and selective influences on mRNA-protein dynamics, our approach provides a framework for discovering the evolutionary rules that drive divergence in gene expression.

Sensitivity of transcriptomics: Different samples and methodology alter conclusions in Gulf pipefish (Syngnathus scovelli)

Transcriptome analysis has become a central tool in evolutionary and functional genomics. However, variation among biological samples and analysis techniques can greatly influence results, potentially compromising insights into the phenomenon under study. Here, we evaluate differences in the brain transcriptome between female and male Gulf pipefish (Syngnathus scovelli). We perform comparisons between results from entire pipelines for brain transcriptome assembly, quantification, and analysis. We also offer a unique biological comparison between two sampling instances (Redfish Bay: n = 15, Port Lavaca: n = 7). Our results demonstrate crucial shortcomings with current experimental approaches. We found high variation within our results that was driven by both technical differences between pipelines and biological differences between pipefish samples. In our analysis of highly expressed genes, we found that the choice of methods influenced the degree of contamination or noise included in the identified genes. Notably, genes identified within the same pipeline were more similar than any other comparison. Our differential expression analysis revealed that both methodology and sampling location influenced the quantity and consistency of statistically significant transcripts. In the context of these results, we offer modifications to current practices that may increase the robustness of transcriptome-based conclusions. In particular, the use of a reference-guided assembly and an increase in sample sizes are likely to improve resistance to noise or error.

Archaic introgression and the distribution of shared variation under stabilizing selection

Many phenotypic traits are under stabilizing selection, which maintains a population's mean phenotypic value near some optimum. The dynamics of traits and trait architectures under stabilizing selection have been extensively studied for single populations at steady state. However, natural populations are seldom at steady state and are often structured in some way. Admixture and introgression events may be common, including over human evolutionary history. Because stabilizing selection results in selection against the minor allele at a trait-affecting locus, alleles from the minor parental ancestry will be selected against after admixture. We show that the site-frequency spectrum can be used to model the genetic architecture of such traits, allowing for the study of trait architecture dynamics in complex multi-population settings. We use a simple deterministic two-locus model to predict the reduction of introgressed ancestry around trait-contributing loci. From this and individual-based simulations, we show that introgressed-ancestry is depleted around such loci. When introgression between two diverged populations occurs in both directions, as has been inferred between humans and Neanderthals, the locations of such regions with depleted introgressed ancestry will tend to be shared across populations. We argue that stabilizing selection for shared phenotypic optima may explain recent observations in which regions of depleted human-introgressed ancestry in the Neanderthal genome overlap with Neanderthal-ancestry deserts in humans.

Cross-species imputation and comparison of single-cell transcriptomic profiles

Cross-species comparison and prediction of gene expression profiles are important to understand regulatory changes during evolution and to transfer knowledge learned from model organisms to humans. Single-cell RNA-seq (scRNA-seq) profiles enable us to capture gene expression profiles with respect to variations among individual cells; however, cross-species comparison of scRNA-seq profiles is challenging because of data sparsity, batch effects, and the lack of one-to-one cell matching across species. Moreover, single-cell profiles are challenging to obtain in certain biological contexts, limiting the scope of hypothesis generation. Here we developed Icebear, a neural network framework that decomposes single-cell measurements into factors representing cell identity, species, and batch factors. Icebear enables accurate prediction of single-cell gene expression profiles across species, thereby providing high-resolution cell type and disease profiles in under-characterized contexts. Icebear also facilitates direct cross-species comparison of single-cell expression profiles for conserved genes that are located on the X chromosome in eutherian mammals but on autosomes in chicken. This comparison, for the first time, revealed evolutionary and diverse adaptations of X-chromosome upregulation in mammals.

Network properties constrain natural selection on gene expression in Caenorhabditis elegans

Gene regulatory networks (GRNs) integrate genetic and environmental signals to coordinate complex phenotypes and evolve through a balance of selection and drift. Using publicly available datasets from Caenorhabditis elegans, we investigated the extent of natural selection on transcript abundance by linking population-scale variation in gene expression to fecundity, a key fitness component. While the expression of most genes covaried only weakly with fitness, which is typical for polygenic traits, we identified seven transcripts under significant directional selection. These included nhr-114 and feh-1, implicating variation in nutrient-sensing and metabolic pathways as impacting fitness. Stronger directional selection on tissue-specific and older genes highlighted the germline and nervous system as focal points of adaptive change. Network position further constrained selection on gene expression; high-connectivity genes faced stronger stabilizing and directional selection, highlighting GRN architecture as a key factor in microevolutionary dynamics. The activity of transcription factors such as zip-3, which regulates mitochondrial stress responses, emerged as targets of selection, revealing potential links between energy homeostasis and fitness. Our findings demonstrate how GRNs mediate the interplay between selection and drift, shaping microevolutionary trajectories of gene expression and phenotypic diversity.

Predicting Fitness-Related Traits Using Gene Expression and Machine Learning

Evolution by natural selection occurs at its most basic through the change in frequencies of alleles; connecting those genomic targets to phenotypic selection is an important goal for evolutionary biology in the genomics era. The relative abundance of gene products expressed in a tissue can be considered a phenotype intermediate to the genes and genomic regulatory elements themselves and more traditionally measured macroscopic phenotypic traits such as flowering time, size, or growth. The high dimensionality, low sample size nature of transcriptomic sequence data is a double-edged sword, however, as it provides abundant information but makes traditional statistics difficult. Machine learning (ML) has many features which handle high-dimensional data well and is thus useful in genetic sequence applications. Here, we examined the association of fitness components with gene expression data in Ipomoea hederacea (Ivyleaf morning glory) grown under field conditions. We combine the results of two different ML approaches and find evidence that expression of photosynthesis-related genes is likely under selection. We also find that genes related to stress and light responses were overall important in predicting fitness. With this study, we demonstrate the utility of ML models for smaller samples and their potential application for understanding natural selection.

Stochastic modeling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones

Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present a formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein-Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Sublines previously observed to be resistant to anti-CTLA4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression. A record of this paper's transparent peer review process is included in the supplemental information.

Fixation of Expression Divergences by Natural Selection in Arabidopsis Coding Genes

Functional divergences of coding genes can be caused by divergences in their coding sequences and expression. However, whether and how expression divergences and coding sequence divergences coevolve is not clear. Gene expression divergences in differentiated cells and tissues recapitulate developmental models within a species, while gene expression divergences between analogous cells and tissues resemble traditional phylogenies in different species, suggesting that gene expression divergences are molecular traits that can be used for evolutionary studies. Using transcriptomes and evolutionary proxies to study gene expression divergences among differentiated cells and tissues in Arabidopsis, expression divergences of coding genes are shown to be strongly anti-correlated with phylostrata (gene ages), indicators of selective constraint Ka/Ks (nonsynonymous replacement rate/synonymous substitution rate) and indicators of positive selection (frequency of loci with Ka/Ks > 1), but only weakly or not correlated with indicators of neutral selection (Ks). Our results thus suggest that expression divergences largely coevolve with coding sequence divergences, suggesting that expression divergences of coding genes are selectively fixed by natural selection but not neutral selection, which provides a molecular framework for trait diversification, functional adaptation and speciation. Our findings therefore support that positive selection rather than negative or neutral selection is a major driver for the origin and evolution of Arabidopsis genes, supporting the Darwinian theory at molecular levels.

Selfing Shapes Fixation of a Mutant Allele Under Flux Equilibrium

Sexual reproduction with alternative generations in a life cycle is an important feature in eukaryotic evolution. Partial selfing can regulate the efficacy of purging deleterious alleles in the gametophyte phase and the masking effect in heterozygotes in the sporophyte phase. Here, we develop a new theory to analyze how selfing shapes fixation of a mutant allele that is expressed in the gametophyte or the sporophyte phase only or in two phases. In an infinitely large population, we analyze a critical selfing rate beyond which the mutant allele tends to be fixed under equilibrium between irreversible mutation and selection effects. The critical selfing rate varies with genes expressed in alternative phases. In a finite population with partial self-fertilization, we apply Wright's method to calculate the fixation probability of the mutant allele under flux equilibrium among irreversible mutation, selection, and drift effects and compare it with the fixation probability derived from diffusion model under equilibrium between selection and drift effects. Selfing facilitates fixation of the deleterious allele expressed in the gametophyte phase only but impedes fixation of the deleterious allele expressed in the sporophyte phase only. Selfing facilitates or impedes fixation of the deleterious allele expressed in two phases, depending upon how phase variation in selection occurs in a life cycle. The overall results help to understand the adaptive strategy that sexual reproductive plant species evolve through the joint effects of partial selfing and alternative generations in a life cycle.

Detecting diversifying selection for a trait from within and between-species genotypes and phenotypes

To quantify selection acting on a trait, methods have been developed using either within or between-species variation. However, methods using within-species variation do not integrate the changes at the macro-evolutionary scale. Conversely, current methods using between-species variation usually discard within-species variation, thus not accounting for processes at the micro-evolutionary scale. The main goal of this study is to define a neutrality index for a quantitative trait, by combining within- and between-species variation. This neutrality index integrates nucleotide polymorphism and divergence for normalizing trait variation. As such, it does not require estimation of population size nor of time of speciation for normalization. Our index can be used to seek deviation from the null model of neutral evolution, and test for diversifying selection. Applied to brain mass and body mass at the mammalian scale, we show that brain mass is under diversifying selection. Finally, we show that our test is not sensitive to the assumption that population sizes, mutation rates and generation time are constant across the phylogeny, and automatically adjust for it.

Phenotype prediction in plants is improved by integrating large-scale transcriptomic datasets

Research on the dynamic expression of genes in plants is important for understanding different biological processes. We used the large amounts of transcriptomic data from various plant sample sources that are publicly available to investigate whether the expression levels of a subset of highly variable genes (HVGs) can be used to accurately identify the phenotypes of plants. Using maize (Zea mays L.) as an example, we built machine learning (ML) models to predict phenotypes using a gene expression dataset of 21 612 bulk RNA sequencing samples. We showed that the ML models achieved excellent prediction accuracy using only the HVGs to identify different phenotypes, including tissue types, developmental stages, cultivars and stress conditions. By ML models, several important functional genes were found to be associated with different phenotypes. We performed a similar analysis in rice (Orzya sativa L.) and found that the ML models could be generalized across species. However, the models trained from maize did not perform well in rice, probably because of the expression divergence of the conserved HVGs between the two species. Overall, our results provide an ML framework for phenotype prediction using gene expression profiles, which may contribute to precision management of crops in agricultural practices.

Stem cell transcriptional profiles from mouse subspecies reveal cis-regulatory evolution at translation genes

A key goal of evolutionary genomics is to harness molecular data to draw inferences about selective forces that have acted on genomes. The field progresses in large part through the development of advanced molecular-evolution analysis methods. Here we explored the intersection between classical sequence-based tests for selection and an empirical expression-based approach, using stem cells from Mus musculus subspecies as a model. Using a test of directional, cis-regulatory evolution across genes in pathways, we discovered a unique program of induction of translation genes in stem cells of the Southeast Asian mouse M. m. castaneus relative to its sister taxa. We then mined population-genomic sequences to pursue underlying regulatory mechanisms for this expression divergence, finding robust evidence for alleles unique to M. m. castaneus at the upstream regions of the translation genes. We interpret our data under a model of changes in lineage-specific pressures across Mus musculus in stem cells with high translational capacity. Our findings underscore the rigor of integrating expression and sequence-based methods to generate hypotheses about evolutionary events from long ago.

Stabilizing selection and adaptation shape cis and trans gene expression variation in C. elegans

An outstanding question in the evolution of gene expression is the relative influence of neutral processes versus natural selection, including adaptive change driven by directional selection as well as stabilizing selection, which may include compensatory dynamics. These forces shape patterns of gene expression variation within and between species, including the regulatory mechanisms governing expression in cis and trans. In this study, we interrogate intraspecific gene expression variation among seven wild C. elegans strains, with varying degrees of genomic divergence from the reference strain N2, leveraging this system's unique advantages to comprehensively evaluate gene expression evolution. By capturing allele-specific and between-strain changes in expression, we characterize the regulatory architecture and inheritance mode of gene expression variation within C. elegans and assess their relationship to nucleotide diversity, genome evolutionary history, gene essentiality, and other biological factors. We conclude that stabilizing selection is a dominant influence in maintaining expression phenotypes within the species, and the discovery that genes with higher overall expression tend to exhibit fewer expression differences supports this conclusion, as do widespread instances of cis differences compensated in trans. Moreover, analyses of human expression data replicate our finding that higher expression genes have less variable expression. We also observe evidence for directional selection driving expression divergence, and that expression divergence accelerates with increasing genomic divergence. To provide community access to the data from this first analysis of allele-specific expression in C. elegans, we introduce an interactive web application, where users can submit gene-specific queries to view expression, regulatory pattern, inheritance mode, and other information: https://wildworm.biosci.gatech.edu/ase/.

Gene Expression Divergence in Eugenia uniflora Highlights Adaptation across Contrasting Atlantic Forest Ecosystems

Understanding the evolution and the effect of plasticity in plant responses to environmental changes is crucial to combat global climate change. It is particularly interesting in species that survive in distinct environments, such as Eugenia uniflora, which thrives in contrasting ecosystems within the Atlantic Forest (AF). In this study, we combined transcriptome analyses of plants growing in nature (Restinga and Riparian Forest) with greenhouse experiments to unveil the DEGs within and among adaptively divergent populations of E. uniflora. We compared global gene expression among plants from two distinct ecological niches. We found many differentially expressed genes between the two populations in natural and greenhouse-cultivated environments. The changes in how genes are expressed may be related to the species' ability to adapt to specific environmental conditions. The main difference in gene expression was observed when plants from Restinga were compared with their offspring cultivated in greenhouses, suggesting that there are distinct selection pressures underlying the local environmental and ecological factors of each Restinga and Riparian Forest ecosystem. Many of these genes engage in the stress response, such as water and nutrient transport, temperature, light intensity, and gene regulation. The stress-responsive genes we found are potential genes for selection in these populations. These findings revealed the adaptive potential of E. uniflora and contributed to our understanding of the role of gene expression reprogramming in plant evolution and niche adaptation.

Host and venom evolution in parasitoid wasps: does independently adapting to the same host shape the evolution of the venom gland transcriptome?

BACKGROUND

Venoms have repeatedly evolved over 100 occasions throughout the animal tree of life, making them excellent systems for exploring convergent evolutionary novelty. Growing evidence supports that venom evolution is predominantly driven by prey or host-related selection pressures, and the expression patterns of venom glands reflect adaptive evolution. However, it remains elusive whether the evolution of expression patterns in venom glands is likewise a convergent evolution driven by their prey/host species.

RESULTS

We utilized parasitoid wasps that had independently adapted to Drosophila hosts as models to investigate the convergent evolution of venom gland transcriptomes in 19 hymenopteran species spanning ~ 200 million years of evolution. Comparative transcriptome analysis reveals that the global expression patterns among the venom glands of Drosophila parasitoid wasps do not achieve higher similarity compared to non-Drosophila parasitoid wasps. Further evolutionary analyses of expression patterns at the single gene, orthogroup, and Gene Ontology (GO) term levels indicate that some orthogroups/GO terms show correlation with the Drosophila parasitoid wasps. However, these groups rarely include genes highly expressed in venom glands or putative venom genes in the Drosophila parasitoid wasps.

CONCLUSIONS

Our study suggests that convergent evolution may not play a predominant force shaping gene expression levels in the venom gland of the Drosophila parasitoid wasps, offering novel insights into the co-evolution between venom and prey/host.

Cross-species imputation and comparison of single-cell transcriptomic profiles

Cross-species comparison and prediction of gene expression profiles are important to understand regulatory changes during evolution and to transfer knowledge learned from model organisms to humans. Single-cell RNA-seq (scRNA-seq) profiles enable us to capture gene expression profiles with respect to variations among individual cells; however, cross-species comparison of scRNA-seq profiles is challenging because of data sparsity, batch effects, and the lack of one-to-one cell matching across species. Moreover, single-cell profiles are challenging to obtain in certain biological contexts, limiting the scope of hypothesis generation. Here we developed Icebear, a neural network framework that decomposes single-cell measurements into factors representing cell identity, species, and batch factors. Icebear enables accurate prediction of single-cell gene expression profiles across species, thereby providing high-resolution cell type and disease profiles in under-characterized contexts. Icebear also facilitates direct cross-species comparison of single-cell expression profiles for conserved genes that are located on the X chromosome in eutherian mammals but on autosomes in chicken. This comparison, for the first time, revealed evolutionary and diverse adaptations of X-chromosome upregulation in mammals.

Genetic and selective constraints on the optimization of gene product diversity

RNA and protein expressed from the same gene can have diverse isoforms due to various post-transcriptional and post-translational modifications. For the vast majority of alternative isoforms, It is unknown whether they are adaptive or simply biological noise. As we cannot experimentally probe the function of each isoform, we can ask whether the distribution of isoforms across genes and across species is consistent with expectations from different evolutionary processes. However, there is currently no theoretical framework that can generate such predictions. To address this, we developed a mathematical model where isoform abundances are determined collectively by cis-acting loci, trans-acting factors, gene expression levels, and isoform decay rates to predict isoform abundance distributions across species and genes in the face of mutation, genetic drift, and selection. We found that factors beyond selection, such as effective population size and the number of cis-acting loci, significantly influence evolutionary outcomes. Notably, suboptimal phenotypes are more likely to evolve when the population is small and/or when the number of cis-loci is large. We also explored scenarios where modification processes have both beneficial and detrimental effects, revealing a non-monotonic relationship between effective population size and optimization, demonstrating how opposing selection pressures on cis- and trans-acting loci can constrain the optimization of gene product diversity. As a demonstration of the power of our theory, we compared the expected distribution of A-to-I RNA editing levels in coleoids and found this to be largely consistent with non-adaptive explanations.

Stem cell transcriptional profiles from mouse subspecies reveal cis -regulatory evolution at translation genes

A key goal of evolutionary genomics is to harness molecular data to draw inferences about selective forces that have acted on genomes. The field progresses in large part through the development of advanced molecular-evolution analysis methods. Here we explored the intersection between classical sequence-based tests for selection and an empirical expression-based approach, using stem cells from Mus musculus subspecies as a model. Using a test of directional, cis -regulatory evolution across genes in pathways, we discovered a unique program of induction of translation genes in stem cells of the Southeast Asian mouse M. m. castaneus relative to its sister taxa. We then mined population-genomic sequences to pursue underlying regulatory mechanisms for this expression divergence, finding robust evidence for alleles unique to M. m. castaneus at the upstream regions of the translation genes. We interpret our data under a model of changes in lineage-specific pressures across Mus musculus in stem cells with high translational capacity. Our findings underscore the rigor of integrating expression and sequence-based methods to generate hypotheses about evolutionary events from long ago.

Stochastic modelling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones

Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present the first formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein-Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Interestingly, sublines previously observed to be resistant to anti-CTLA-4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression.

DNA-based molecular classifiers for the profiling of gene expression signatures

Although gene expression signatures offer tremendous potential in diseases diagnostic and prognostic, but massive gene expression signatures caused challenges for experimental detection and computational analysis in clinical setting. Here, we introduce a universal DNA-based molecular classifier for profiling gene expression signatures and generating immediate diagnostic outcomes. The molecular classifier begins with feature transformation, a modular and programmable strategy was used to capture relative relationships of low-concentration RNAs and convert them to general coding inputs. Then, competitive inhibition of the DNA catalytic reaction enables strict weight assignment for different inputs according to their importance, followed by summation, annihilation and reporting to accurately implement the mathematical model of the classifier. We validated the entire workflow by utilizing miRNA expression levels for the diagnosis of hepatocellular carcinoma (HCC) in clinical samples with an accuracy 85.7%. The results demonstrate the molecular classifier provides a universal solution to explore the correlation between gene expression patterns and disease diagnostics, monitoring, and prognosis, and supports personalized healthcare in primary care.

Sex Differences in Cochlear Transcriptomes in Horseshoe Bats

Sexual dimorphism of calls is common in animals, whereas studies on the molecular basis underlying this phenotypic variation are still scarce. In this study, we used comparative transcriptomics of cochlea to investigate the sex-related difference in gene expression and alternative splicing in four Rhinolophus taxa. Based on 31 cochlear transcriptomes, we performed differential gene expression (DGE) and alternative splicing (AS) analyses between the sexes in each taxon. Consistent with the degree of difference in the echolocation pulse frequency between the sexes across the four taxa, we identified the largest number of differentially expressed genes (DEGs) and alternatively spliced genes (ASGs) in R. sinicus. However, we also detected multiple DEGs and ASGs in taxa without sexual differences in echolocation pulse frequency, suggesting that these genes might be related to other parameters of echolocation pulse rather than the frequency component. Some DEGs and ASGs are related to hearing loss or deafness genes in human or mice and they can be considered to be candidates associated with the sexual differences of echolocation pulse in bats. We also detected more than the expected overlap of DEGs and ASGs in two taxa. Overall, our current study supports the important roles of both DGE and AS in generating or maintaining sexual differences in animals.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these two fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we derive a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., Genome-Wide Association Studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur using analytical theory and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate this by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study of this, we re-examine an analysis testing for co-evolution of expression levels between genes across a fungal phylogeny, and show that including covariance matrix eigenvectors as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

Deciphering the origin of developmental stability: The role of intracellular expression variability in evolutionary conservation

Progress in evolutionary developmental biology (evo-devo) has deepened our understanding of how intrinsic properties of embryogenesis, along with natural selection and population genetics, shape phenotypic diversity. A focal point of recent empirical and theoretical research is the idea that highly developmentally stable phenotypes are more conserved in evolution. Previously, we demonstrated that in Japanese medaka (Oryzias latipes), embryonic stages and genes with high stability, estimated through whole-embryo RNA-seq, are highly conserved in subsequent generations. However, the precise origin of the stability of gene expression levels evaluated at the whole-embryo level remained unclear. Such stability could be attributed to two distinct sources: stable intracellular expression levels or spatially stable expression patterns. Here we demonstrate that stability observed in whole-embryo RNA-seq can be attributed to stability at the cellular level (low variability in gene expression at the cellular levels). We quantified the intercellular variations in expression levels and spatial gene expression patterns for seven key genes involved in patterning dorsoventral and rostrocaudal regions during early development in medaka. We evaluated intracellular variability by counting transcripts and found its significant correlation with variation observed in whole-embryo RNA-seq data. Conversely, variation in spatial gene expression patterns, assessed through intraindividual left-right asymmetry, showed no correlation. Given the previously reported correlation between stability and conservation of expression levels throughout embryogenesis, our findings suggest a potential general trend: the stability or instability of developmental systems-and the consequent evolutionary diversity-may be primarily anchored in intrinsic fundamental elements such as the variability of intracellular states.

Gene expression and alternative splicing contribute to adaptive divergence of ecotypes

Regulation of gene expression is a critical link between genotype and phenotype explaining substantial heritable variation within species. However, we are only beginning to understand the ways that specific gene regulatory mechanisms contribute to adaptive divergence of populations. In plants, the post-transcriptional regulatory mechanism of alternative splicing (AS) plays an important role in both development and abiotic stress response, making it a compelling potential target of natural selection. AS allows organisms to generate multiple different transcripts/proteins from a single gene and thus may provide a source of evolutionary novelty. Here, we examine whether variation in alternative splicing and gene expression levels might contribute to adaptation and incipient speciation of dune-adapted prairie sunflowers in Great Sand Dunes National Park, Colorado, USA. We conducted a common garden experiment to assess transcriptomic variation among ecotypes and analyzed differential expression, differential splicing, and gene coexpression. We show that individual genes are strongly differentiated for both transcript level and alternative isoform proportions, even when grown in a common environment, and that gene coexpression networks are disrupted between ecotypes. Furthermore, we examined how genome-wide patterns of sequence divergence correspond to divergence in transcript levels and isoform proportions and find evidence for both cis and trans-regulation. Together, our results emphasize that alternative splicing has been an underappreciated mechanism providing source material for natural selection at short evolutionary time scales.

Hybridization and gene expression: Beyond differentially expressed genes

Gene expression has a key role in reproductive isolation, and studies of hybrid gene expression have identified mechanisms causing hybrid sterility. Here, we review the evidence for altered gene expression following hybridization and outline the mechanisms shown to contribute to altered gene expression in hybrids. Transgressive gene expression, transcending that of both parental species, is pervasive in early generation sterile hybrids, but also frequently observed in viable, fertile hybrids. We highlight studies showing that hybridization can result in transgressive gene expression, also in established hybrid lineages or species. Such extreme patterns of gene expression in stabilized hybrid taxa suggest that altered hybrid gene expression may result in hybridization-derived evolutionary novelty. We also conclude that while patterns of misexpression in hybrids are well documented, the understanding of the mechanisms causing misexpression is lagging. We argue that jointly assessing differences in cell composition and cell-specific changes in gene expression in hybrids, in addition to assessing changes in chromatin and methylation, will significantly advance our understanding of the basis of altered gene expression. Moreover, uncovering to what extent evolution of gene expression results in altered expression for individual genes, or entire networks of genes, will advance our understanding of how selection moulds gene expression. Finally, we argue that jointly studying the dual roles of altered hybrid gene expression, serving both as a mechanism for reproductive isolation and as a substrate for hybrid ecological adaptation, will lead to significant advances in our understanding of the evolution of gene expression.

Evolution of chemosensory tissues and cells across ecologically diverse Drosophilids

Chemosensory tissues exhibit significant between-species variability, yet the evolution of gene expression and cell types underlying this diversity remain poorly understood. To address these questions, we conducted transcriptomic analyses of five chemosensory tissues from six Drosophila species and integrated the findings with single-cell datasets. While stabilizing selection predominantly shapes chemosensory transcriptomes, thousands of genes in each tissue have evolved expression differences. Genes that have changed expression in one tissue have often changed in multiple other tissues but at different past epochs and are more likely to be cell type-specific than unchanged genes. Notably, chemosensory-related genes have undergone widespread expression changes, with numerous species-specific gains/losses including novel chemoreceptors expression patterns. Sex differences are also pervasive, including a D. melanogaster-specific excess of male-biased expression in sensory and muscle cells in its forelegs. Together, our analyses provide new insights for understanding evolutionary changes in chemosensory tissues at both global and individual gene levels.

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred models for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best-fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.

Evolutionary Analyses of Gene Expression Divergence in Panicum hallii: Exploring Constitutive and Plastic Responses Using Reciprocal Transplants

The evolution of gene expression is thought to be an important mechanism of local adaptation and ecological speciation. Gene expression divergence occurs through the evolution of cis- polymorphisms and through more widespread effects driven by trans-regulatory factors. Here, we explore expression and sequence divergence in a large sample of Panicum hallii accessions encompassing the species range using a reciprocal transplantation experiment. We observed widespread genotype and transplant site drivers of expression divergence, with a limited number of genes exhibiting genotype-by-site interactions. We used a modified FST-QST outlier approach (QPC analysis) to detect local adaptation. We identified 514 genes with constitutive expression divergence above and beyond the levels expected under neutral processes. However, no plastic expression responses met our multiple testing correction as QPC outliers. Constitutive QPC outlier genes were involved in a number of developmental processes and responses to abiotic environments. Leveraging earlier expression quantitative trait loci results, we found a strong enrichment of expression divergence, including for QPC outliers, in genes previously identified with cis and cis-environment interactions but found no patterns related to trans-factors. Population genetic analyses detected elevated sequence divergence of promoters and coding sequence of constitutive expression outliers but little evidence for positive selection on these proteins. Our results are consistent with a hypothesis of cis-regulatory divergence as a primary driver of expression divergence in P. hallii.

Phylogenetic modeling of enhancer shifts in African mole-rats reveals regulatory changes associated with tissue-specific traits

Changes in gene regulation are thought to underlie most phenotypic differences between species. For subterranean rodents such as the naked mole-rat, proposed phenotypic adaptations include hypoxia tolerance, metabolic changes, and cancer resistance. However, it is largely unknown what regulatory changes may associate with these phenotypic traits, and whether these are unique to the naked mole-rat, the mole-rat clade, or are also present in other mammals. Here, we investigate regulatory evolution in the heart and liver from two African mole-rat species and two rodent outgroups using genome-wide epigenomic profiling. First, we adapted and applied a phylogenetic modeling approach to quantitatively compare epigenomic signals at orthologous regulatory elements and identified thousands of promoter and enhancer regions with differential epigenomic activity in mole-rats. These elements associate with known mole-rat adaptations in metabolic and functional pathways and suggest candidate genetic loci that may underlie mole-rat innovations. Second, we evaluated ancestral and species-specific regulatory changes in the study phylogeny and report several candidate pathways experiencing stepwise remodeling during the evolution of mole-rats, such as the insulin and hypoxia response pathways. Third, we report nonorthologous regulatory elements overlap with lineage-specific repetitive elements and appear to modify metabolic pathways by rewiring of HNF4 and RAR/RXR transcription factor binding sites in mole-rats. These comparative analyses reveal how mole-rat regulatory evolution informs previously reported phenotypic adaptations. Moreover, the phylogenetic modeling framework we propose here improves upon the state of the art by addressing known limitations of inter-species comparisons of epigenomic profiles and has broad implications in the field of comparative functional genomics.

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well-described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred model for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.

On the Decoupling of Evolutionary Changes in mRNA and Protein Levels

Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here, we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level; this observation held true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and the translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic data.

Impact of the acquired subgenome on the transcriptional landscape in Brettanomyces bruxellensis allopolyploids

Gene expression variation can provide an overview of the changes in regulatory networks that underlie phenotypic diversity. Certain evolutionary trajectories such as polyploidization events can have an impact on the transcriptional landscape. Interestingly, the evolution of the yeast species Brettanomyces bruxellensis has been punctuated by diverse allopolyploidization events leading to the coexistence of a primary diploid genome associated with various haploid acquired genomes. To assess the impact of these events on gene expression, we generated and compared the transcriptomes of a set of 87 B. bruxellensis isolates, selected as being representative of the genomic diversity of this species. Our analysis revealed that acquired subgenomes strongly impact the transcriptional patterns and allow discrimination of allopolyploid populations. In addition, clear transcriptional signatures related to specific populations have been revealed. The transcriptional variations observed are related to some specific biological processes such as transmembrane transport and amino acids metabolism. Moreover, we also found that the acquired subgenome causes the overexpression of some genes involved in the production of flavor-impacting secondary metabolites, especially in isolates of the beer population.

The roles of sexual selection and sexual conflict in shaping patterns of genome and transcriptome variation

Sexual dimorphism is one of the most prevalent, and often the most extreme, examples of phenotypic variation within species, and arises primarily from genomic variation that is shared between females and males. Many sexual dimorphisms arise through sex differences in gene expression, and sex-biased expression is one way that a single, shared genome can generate multiple, distinct phenotypes. Although many sexual dimorphisms are expected to result from sexual selection, and many studies have invoked the possible role of sexual selection to explain sex-specific traits, the role of sexual selection in the evolution of sexually dimorphic gene expression remains difficult to differentiate from other forms of sex-specific selection. In this Review, we propose a holistic framework for the study of sex-specific selection and transcriptome evolution. We advocate for a comparative approach, across tissues, developmental stages and species, which incorporates an understanding of the molecular mechanisms, including genomic variation and structure, governing gene expression. Such an approach is expected to yield substantial insights into the evolution of genetic variation and have important applications in a variety of fields, including ecology, evolution and behaviour.

Sex-biased gene expression at single-cell resolution: cause and consequence of sexual dimorphism

Gene expression differences between males and females are thought to be key for the evolution of sexual dimorphism, and sex-biased genes are often used to study the molecular footprint of sex-specific selection. However, gene expression is often measured from complex aggregations of diverse cell types, making it difficult to distinguish between sex differences in expression that are due to regulatory rewiring within similar cell types and those that are simply a consequence of developmental differences in cell-type abundance. To determine the role of regulatory versus developmental differences underlying sex-biased gene expression, we use single-cell transcriptomic data from multiple somatic and reproductive tissues of male and female guppies, a species that exhibits extensive phenotypic sexual dimorphism. Our analysis of gene expression at single-cell resolution demonstrates that nonisometric scaling between the cell populations within each tissue and heterogeneity in cell-type abundance between the sexes can influence inferred patterns of sex-biased gene expression by increasing both the false-positive and false-negative rates. Moreover, we show that, at the bulk level, the subset of sex-biased genes that are the product of sex differences in cell-type abundance can significantly confound patterns of coding-sequence evolution. Taken together, our results offer a unique insight into the effects of allometry and cellular heterogeneity on perceived patterns of sex-biased gene expression and highlight the power of single-cell RNA-sequencing in distinguishing between sex-biased genes that are the result of regulatory change and those that stem from sex differences in cell-type abundance, and hence are a consequence rather than a cause of sexual dimorphism.

A gene-level test for directional selection on gene expression

Most variants identified in human genome-wide association studies and scans for selection are noncoding. Interpretation of their effects and the way in which they contribute to phenotypic variation and adaptation in human populations is therefore limited by our understanding of gene regulation and the difficulty of confidently linking noncoding variants to genes. To overcome this, we developed a gene-wise test for population-specific selection based on combinations of regulatory variants. Specifically, we use the QX statistic to test for polygenic selection on cis-regulatory variants based on whether the variance across populations in the predicted expression of a particular gene is higher than expected under neutrality. We then applied this approach to human data, testing for selection on 17,388 protein-coding genes in 26 populations from the Thousand Genomes Project. We identified 45 genes with significant evidence (FDR<0.1) for selection, including FADS1, KHK, SULT1A2, ITGAM, and several genes in the HLA region. We further confirm that these signals correspond to plausible population-level differences in predicted expression. While the small number of significant genes (0.2%) is consistent with most cis-regulatory variation evolving under genetic drift or stabilizing selection, it remains possible that there are effects not captured in this study. Our gene-level QX score is independent of standard genomic tests for selection, and may therefore be useful in combination with traditional selection scans to specifically identify selection on regulatory variation. Overall, our results demonstrate the utility of combining population-level genomic data with functional data to understand the evolution of gene expression.

On the evolution of natural product biosynthesis

Natural products are the raw material for drug discovery programmes. Bioactive natural products are used extensively in medicine and agriculture and have found utility as antibiotics, immunosuppressives, anti-cancer drugs and anthelminthics. Remarkably, the natural role and what mechanisms drive evolution of these molecules is relatively poorly understood. The exponential increase in genome and chemical data in recent years, coupled with technical advances in bioinformatics and genetics have enabled progress to be made in understanding the evolution of biosynthetic gene clusters and the products of their enzymatic machinery. Here we discuss the diversity of natural products, incorporating the mechanisms that govern evolution of metabolic pathways and how this can be applied to biosynthetic gene clusters. We build on the nomenclature of natural products in terms of primary, integrated, secondary and specialised metabolism and place this within an ecology-evolutionary-developmental biology framework. This eco-evo-devo framework we believe will help to clarify the nature and use of the term specialised metabolites in the future.

Recent progress in understanding the genomic architecture of sexual conflict

Genomic conflict between the sexes over shared traits is widely assumed to be resolved through the evolution of sex-biased expression and the subsequent emergence of sexually dimorphic phenotypes. However, while there is support for a broad relationship between genome-wide patterns of expression level and sexual conflict, recent studies suggest that sex differences in the nature and strength of interactions between loci are instead key to conflict resolution. Furthermore, the advent of new technologies for measuring and perturbing expression means we now have much more power to detect genomic signatures of sexual conflict. Here, we review our current understanding of the genomic architecture of sexual conflict in the light of these new studies and highlight the potential for novel approaches to address outstanding knowledge gaps.

CAGEE: Computational Analysis of Gene Expression Evolution

Despite the increasing abundance of whole transcriptome data, few methods are available to analyze global gene expression across phylogenies. Here, we present a new software package (Computational Analysis of Gene Expression Evolution [CAGEE]) for inferring patterns of increases and decreases in gene expression across a phylogenetic tree, as well as the rate at which these changes occur. In contrast to previous methods that treat each gene independently, CAGEE can calculate genome-wide rates of gene expression, along with ancestral states for each gene. The statistical approach developed here makes it possible to infer lineage-specific shifts in rates of evolution across the genome, in addition to possible differences in rates among multiple tissues sampled from the same species. We demonstrate the accuracy and robustness of our method on simulated data and apply it to a data set of ovule gene expression collected from multiple self-compatible and self-incompatible species in the genus Solanum to test hypotheses about the evolutionary forces acting during mating system shifts. These comparisons allow us to highlight the power of CAGEE, demonstrating its utility for use in any empirical system and for the analysis of most morphological traits. Our software is available at https://github.com/hahnlab/CAGEE/.

Social insect transcriptomics and the molecular basis of caste diversity

Studies of gene expression provide fundamentally important information on the molecular mechanisms underlying variation in phenotype. Recent technological advances have allowed for the robust study of gene expression through analysis of whole transcriptomes. Here, we review current advances in social insect transcriptomics and discuss their implications in understanding phenotypic diversity. Recent transcriptomic studies provide detailed inventories of the genes involved in producing distinct phenotypes in social species. These investigations have identified key genes and networks involved in producing distinct social insect castes. Nevertheless, questions concerning the evolution of gene expression patterns remain. We suggest a path forward for studying gene expression in future studies of biological systems.

Gene expression differentials driven by mass rearing and artificial selection in black soldier fly colonies

The micro-evolutionary forces that shape genetic diversity during domestication have been assessed in many plant and animal systems. However, the impact of these processes on gene expression, and consequent functional adaptation to artificial environments, remains under-investigated. In this study, whole-transcriptome dynamics associated with the early stages of domestication of the black soldier fly (BSF), Hermetia illucens, were assessed. Differential gene expression (DGE) was evaluated in relation to (i) generational time within the cultured environment (F2 vs. F3), and (ii) two selection strategies [no artificial selective pressure (NS); and selection for greater larval mass (SEL)]. RNA-seq was conducted on 5th instar BSF larvae (n = 36), representing equal proportions of the NS (F2 = 9; F3 = 9) and SEL (F2 = 9; F3 = 9) groups. A multidimensional scaling plot revealed greater gene expression variability within the NS and F2 subgroups, while the SEL group clustered separately with lower levels of variation. Comparisons between generations revealed 898 differentially expressed genes (DEGs; FDR-corrected p < 0.05), while between selection strategies, 213 DEGs were observed (FDR-corrected p < 0.05). Enrichment analyses revealed that metabolic, developmental, and defence response processes were over-expressed in the comparison between F2 and F3 larvae, while metabolic processes were the main differentiating factor between NS and SEL lines. This illustrates the functional adaptations that occur in BSF colonies across generations due to mass rearing; as well as highlighting genic dynamics associated with artificial selection for production traits that might inform future selective breeding strategies.

Gene expression phylogenies and ancestral transcriptome reconstruction resolves major transitions in the origins of pregnancy

Structural and physiological changes in the female reproductive system underlie the origins of pregnancy in multiple vertebrate lineages. In mammals, the glandular portion of the lower reproductive tract has transformed into a structure specialized for supporting fetal development. These specializations range from relatively simple maternal nutrient provisioning in egg-laying monotremes to an elaborate suite of traits that support intimate maternal-fetal interactions in Eutherians. Among these traits are the maternal decidua and fetal component of the placenta, but there is considerable uncertainty about how these structures evolved. Previously, we showed that changes in uterine gene expression contributes to several evolutionary innovations during the origins of pregnancy (Mika et al., 2021b). Here, we reconstruct the evolution of entire transcriptomes ('ancestral transcriptome reconstruction') and show that maternal gene expression profiles are correlated with degree of placental invasion. These results indicate that an epitheliochorial-like placenta evolved early in the mammalian stem-lineage and that the ancestor of Eutherians had a hemochorial placenta, and suggest maternal control of placental invasiveness. These data resolve major transitions in the evolution of pregnancy and indicate that ancestral transcriptome reconstruction can be used to study the function of ancestral cell, tissue, and organ systems.