Bayesian Inference of Allele-Specific Gene Expression Indicates Abundant Cis-Regulatory Variation in Natural Flycatcher Populations

Evolution and genetics of accessory gland transcriptome divergence between Drosophila melanogaster and D. simulans

Studies of allele-specific expression in interspecific hybrids have provided important insights into gene-regulatory divergence and hybrid incompatibilities. Many such investigations in Drosophila have used transcriptome data from complex mixtures of many tissues or from gonads, however, regulatory divergence may vary widely among species, sexes, and tissues. Thus, we lack sufficiently broad sampling to be confident about the general biological principles of regulatory divergence. Here, we seek to fill some of these gaps in the literature by characterizing regulatory evolution and hybrid misexpression in a somatic male sex organ, the accessory gland, in F1 hybrids between Drosophila melanogaster and D. simulans. The accessory gland produces seminal fluid proteins, which play an important role in male and female fertility and may be subject to adaptive divergence due to male-male or male-female interactions. We find that trans differences are relatively more abundant than cis, in contrast to most of the interspecific hybrid literature, though large effect-size trans differences are rare. Seminal fluid protein genes have significantly elevated levels of expression divergence and tend to be regulated through both cis and trans divergence. We find limited misexpression (over- or underexpression relative to both parents) in this organ compared to most other Drosophila studies. As in previous studies, male-biased genes are overrepresented among misexpressed genes and are much more likely to be underexpressed. ATAC-Seq data show that chromatin accessibility is correlated with expression differences among species and hybrid allele-specific expression. This work identifies unique regulatory evolution and hybrid misexpression properties of the accessory gland and suggests the importance of tissue-specific allele-specific expression studies.

Gene age shapes the transcriptional landscape of sexual morphogenesis in mushroom forming fungi (Agaricomycetes)

Multicellularity has been one of the most important innovations in the history of life. The role of gene regulatory changes in driving transitions to multicellularity is being increasingly recognized; however, factors influencing gene expression patterns are poorly known in many clades. Here, we compared the developmental transcriptomes of complex multicellular fruiting bodies of eight Agaricomycetes and Cryptococcus neoformans, a closely related human pathogen with a simple morphology. In-depth analysis in Pleurotus ostreatus revealed that allele-specific expression, natural antisense transcripts, and developmental gene expression, but not RNA editing or a ‘developmental hourglass,’ act in concert to shape its transcriptome during fruiting body development. We found that transcriptional patterns of genes strongly depend on their evolutionary ages. Young genes showed more developmental and allele-specific expression variation, possibly because of weaker evolutionary constraint, suggestive of nonadaptive expression variance in fruiting bodies. These results prompted us to define a set of conserved genes specifically regulated only during complex morphogenesis by excluding young genes and accounting for deeply conserved ones shared with species showing simple sexual development. Analysis of the resulting gene set revealed evolutionary and functional associations with complex multicellularity, which allowed us to speculate they are involved in complex multicellular morphogenesis of mushroom fruiting bodies.

System drift and speciation

Even if a species' phenotype does not change over evolutionary time, the underlying mechanism may change, as distinct molecular pathways can realize identical phenotypes. Here we use linear system theory to explore the consequences of this idea, describing how a gene network underlying a conserved phenotype evolves, as the genetic drift of small changes to these molecular pathways causes a population to explore the set of mechanisms with identical phenotypes. To do this, we model an organism's internal state as a linear system of differential equations for which the environment provides input and the phenotype is the output, in which context there exists an exact characterization of the set of all mechanisms that give the same input-output relationship. This characterization implies that selectively neutral directions in genotype space should be common and that the evolutionary exploration of these distinct but equivalent mechanisms can lead to the reproductive incompatibility of independently evolving populations. This evolutionary exploration, or system drift, is expected to proceed at a rate proportional to the amount of intrapopulation genetic variation divided by the effective population size ( Ne$N_e$ ). At biologically reasonable parameter values this could lead to substantial interpopulation incompatibility, and thus speciation, on a time scale of Ne$N_e$ generations. This model also naturally predicts Haldane's rule, thus providing a concrete explanation of why heterogametic hybrids tend to be disrupted more often than homogametes during the early stages of speciation.

Power calculator for detecting allelic imbalance using hierarchical Bayesian model

OBJECTIVE

Allelic imbalance (AI) is the differential expression of the two alleles in a diploid. AI can vary between tissues, treatments, and environments. Methods for testing AI exist, but methods are needed to estimate type I error and power for detecting AI and difference of AI between conditions. As the costs of the technology plummet, what is more important: reads or replicates?

RESULTS

We find that a minimum of 2400, 480, and 240 allele specific reads divided equally among 12, 5, and 3 replicates is needed to detect a 10, 20, and 30%, respectively, deviation from allelic balance in a condition with power > 80%. A minimum of 960 and 240 allele specific reads divided equally among 8 replicates is needed to detect a 20 or 30% difference in AI between conditions with comparable power. Higher numbers of replicates increase power more than adding coverage without affecting type I error. We provide a Python package that enables simulation of AI scenarios and enables individuals to estimate type I error and power in detecting AI and differences in AI between conditions.

Molecular and evolutionary processes generating variation in gene expression

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

Genome-wide analysis of spatiotemporal allele-specific expression in F1 hybrids of meat- and egg-type chickens

In diploid organisms, each gene locus is composed of two parental alleles, which would interact with each other for determining the phenotypic variation. Better understanding of the allele-specific expression (ASE) in farm animals is much important to explore the genetic basis underlying economically important traits, which have been poorly understood yet. In this study, genome-wide analysis was applied to explore the spatiotemporal pattern of ASE in the F1 hybrids of chicken. First, meat- and egg-type chickens were selected for producing a full-sib F1 hybrid population (n = 57). Then, genome resequencing of two parents and 38 offspring were performed and liver and breast muscle samples (n = 38) were subjected to strand-specific RNA sequencing (ssRNA-seq) for ASE detection at 1, 28, and 56 days of age, respectively. The results accurately identified a total of 465 informative genes that could be distinguished with respect to their parental origins. There were 0.4% - 4.1% of informative genes showing ASE, and 57 of them were found across different tissues and time points. Besides, most ASE genes in chickens were tissue-specific, and no matter what the time-point pattern of one ASE gene, the same parental allele of this gene almost showed consistently higher or lower expression across all time points in the same type tissue. In conclusion, this study indicated that most of ASE genes were tissue-specific and time-dependent.

Gene regulatory divergence between locally adapted ecotypes in their native habitats

Local adaptation is a key driver of ecological specialization and the formation of new species. Despite its importance, the evolution of gene regulatory divergence among locally adapted populations is poorly understood, especially how that divergence manifests in nature. Here, we evaluate gene expression divergence and allele-specific gene expression responses for locally adapted coastal perennial and inland annual accessions of the yellow monkeyflower, Mimulus guttatus, in a field reciprocal transplant experiment. Overall, 6765 (73%) of surveyed genes were differentially expressed between coastal and inland habitats, while 7213 (77%) were differentially expressed between the coastal perennial and inland annual accessions. Cis-regulatory variation was pervasive, affecting 79% (5532) of differentially expressed genes. We detected trans effects for 52% (3611) of differentially expressed genes. Expression plasticity of alleles across habitats (G × E interactions) appears to be relatively common (affecting 18% of transcripts) and could minimize fitness trade-offs at loci that contribute to local adaptation. We also found evidence that at least one chromosomal inversion may act as supergene by holding together haplotypes of differentially expressed genes, but this pattern depends on habitat context. Our results highlight multiple key patterns regarding the relationship between gene expression and the evolution of locally adapted populations.

The Evolution of Gene Expression in cis and trans

There is abundant variation in gene expression between individuals, populations, and species. The evolution of gene regulation and expression within and between species is thought to frequently contribute to adaptation. Yet considerable evidence suggests that the primary evolutionary force acting on variation in gene expression is stabilizing selection. We review here the results of recent studies characterizing the evolution of gene expression occurring in cis (via linked polymorphisms) or in trans (through diffusible products of other genes) and their contribution to adaptation and response to the environment. We review the evidence for buffering of variation in gene expression at the level of both transcription and translation, and the possible mechanisms for this buffering. Lastly, we summarize unresolved questions about the evolution of gene regulation.

RPASE: Individual-based allele-specific expression detection without prior knowledge of haplotype phase

Variation in gene expression is believed to make a significant contribution to phenotypic diversity and divergence. The analysis of allele-specific expression (ASE) can reveal important insights into gene expression regulation. We developed a novel method called RPASE (Read-backed Phasing-based ASE detection) to test for genes that show ASE. With mapped RNA-seq data from a single individual and a list of SNPs from the same individual as the only input, RPASE is capable of aggregating information across multiple dependent SNPs and producing individual-based gene-level tests for ASE. RPASE performs well in simulations and comparisons. We applied RPASE to multiple bird species and found a potentially rich landscape of ASE.

Direct Testing for Allele-Specific Expression Differences Between Conditions

Allelic imbalance (AI) indicates the presence of functional variation in cis regulatory regions. Detecting cis regulatory differences using AI is widespread, yet there is no formal statistical methodology that tests whether AI differs between conditions. Here, we present a novel model and formally test differences in AI across conditions using Bayesian credible intervals. The approach tests AI by environment (G×E) interactions, and can be used to test AI between environments, genotypes, sex, and any other condition. We incorporate bias into the modeling process. Bias is allowed to vary between conditions, making the formulation of the model general. As gene expression affects power for detection of AI, and, as expression may vary between conditions, the model explicitly takes coverage into account. The proposed model has low type I and II error under several scenarios, and is robust to large differences in coverage between conditions. We reanalyze RNA-seq data from a Drosophila melanogaster population panel, with F1 genotypes, to compare levels of AI between mated and virgin female flies, and we show that AI × genotype interactions can also be tested. To demonstrate the use of the model to test genetic differences and interactions, a formal test between two F1s was performed, showing the expected 20% difference in AI. The proposed model allows a formal test of G×E and G×G, and reaffirms a previous finding that cis regulation is robust between environments.