Partitioning transcript variation in Drosophila: abundance, isoforms, and alleles

What the X has to do with it: differences in regulatory variability between the sexes in Drosophila simulans

The mechanistic basis of regulatory variation and the prevailing evolutionary forces shaping that variation are known to differ between sexes and between chromosomes. Regulatory variation of gene expression can be due to functional changes within a gene itself (cis) or in other genes elsewhere in the genome (trans). The evolutionary properties of cis mutations are expected to differ from mutations affecting gene expression in trans. We analyze allele-specific expression across a set of X substitution lines in intact adult Drosophila simulans to evaluate whether regulatory variation differs for cis and trans, for males and females, and for X-linked and autosomal genes. Regulatory variation is common (56% of genes), and patterns of variation within D. simulans are consistent with previous observations in Drosophila that there is more cis than trans variation within species (47% vs. 25%, respectively). The relationship between sex-bias and sex-limited variation is remarkably consistent across sexes. However, there are differences between cis and trans effects: cis variants show evidence of purifying selection in the sex toward which expression is biased, while trans variants do not. For female-biased genes, the X is depleted for trans variation in a manner consistent with a female-dominated selection regime on the X. Surprisingly, there is no evidence for depletion of trans variation for male-biased genes on X. This is evidence for regulatory feminization of the X, trans-acting factors controlling male-biased genes are more likely to be found on the autosomes than those controlling female-biased genes.

A flexible Bayesian method for detecting allelic imbalance in RNA-seq data

Background

One method of identifying cis regulatory differences is to analyze allele-specific expression (ASE) and identify cases of allelic imbalance (AI). RNA-seq is the most common way to measure ASE and a binomial test is often applied to determine statistical significance of AI. This implicitly assumes that there is no bias in estimation of AI. However, bias has been found to result from multiple factors including: genome ambiguity, reference quality, the mapping algorithm, and biases in the sequencing process. Two alternative approaches have been developed to handle bias: adjusting for bias using a statistical model and filtering regions of the genome suspected of harboring bias. Existing statistical models which account for bias rely on information from DNA controls, which can be cost prohibitive for large intraspecific studies. In contrast, data filtering is inexpensive and straightforward, but necessarily involves sacrificing a portion of the data.

Results

Here we propose a flexible Bayesian model for analysis of AI, which accounts for bias and can be implemented without DNA controls. In lieu of DNA controls, this Poisson-Gamma (PG) model uses an estimate of bias from simulations. The proposed model always has a lower type I error rate compared to the binomial test. Consistent with prior studies, bias dramatically affects the type I error rate. All of the tested models are sensitive to misspecification of bias. The closer the estimate of bias is to the true underlying bias, the lower the type I error rate. Correct estimates of bias result in a level alpha test.

Conclusions

To improve the assessment of AI, some forms of systematic error (e.g., map bias) can be identified using simulation. The resulting estimates of bias can be used to correct for bias in the PG model, without data filtering. Other sources of bias (e.g., unidentified variant calls) can be easily captured by DNA controls, but are missed by common filtering approaches. Consequently, as variant identification improves, the need for DNA controls will be reduced. Filtering does not significantly improve performance and is not recommended, as information is sacrificed without a measurable gain. The PG model developed here performs well when bias is known, or slightly misspecified. The model is flexible and can accommodate differences in experimental design and bias estimation.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-920) contains supplementary material, which is available to authorized users.

Pleiotropic mutations are subject to strong stabilizing selection

The assumption that pleiotropic mutations are more deleterious than mutations with more restricted phenotypic effects is an important premise in models of evolution. However, empirical evidence supporting this assumption is limited. Here, we estimated the strength of stabilizing selection on mutations affecting gene expression in male Drosophila serrata. We estimated the mutational variance (VM) and the standing genetic variance (VG) from two well-matched panels of inbred lines: a panel of mutation accumulation (MA) lines derived from a single inbred ancestral line and a panel of inbred lines derived from an outbred population. For 855 gene-expression traits, we estimated the strength of stabilizing selection as s = VM/VG. Selection was observed to be relatively strong, with 17% of traits having s > 0.02, a magnitude typically associated with life-history traits. Randomly assigning expression traits to five-trait sets, we used factor analytic mixed modeling in the MA data set to identify covarying traits that shared pleiotropic mutations. By assigning traits to the same trait sets in the outbred line data set, we then estimated s for the combination of traits affected by pleiotropic mutation. For these pleiotropic combinations, the median s was three times greater than s acting on the individual component traits, and 46% of the pleiotropic trait combinations had s > 0.02. Although our analytical approach was biased toward detecting mutations with relatively large effects, likely overestimating the average strength of selection, our results provide widespread support for the prediction that stronger selection can act against mutations with pleiotropic effects.

The nature and extent of mutational pleiotropy in gene expression of male Drosophila serrata

The nature and extent of mutational pleiotropy remain largely unknown, despite the central role that pleiotropy plays in many areas of biology, including human disease, agricultural production, and evolution. Here, we investigate the variation in 11,604 gene expression traits among 41 mutation accumulation (MA) lines of Drosophila serrata. We first confirmed that these expression phenotypes were heritable, detecting genetic variation in 96% of them in an outbred, natural population of D. serrata. Among the MA lines, 3385 (29%) of expression traits were variable, with a mean mutational heritability of 0.0005. In most traits, variation was generated by mutations of relatively small phenotypic effect; putative mutations with effects of greater than one phenotypic standard deviation were observed for only 8% of traits. With most (71%) traits unaffected by any mutation, our data provide no support for universal pleiotropy. We further characterized mutational pleiotropy in the 3385 variable traits, using sets of 5, randomly assigned, traits. Covariance among traits chosen at random with respect to their biological function is expected only if pleiotropy is extensive. Taking an analytical approach in which the variance unique to each trait in the random 5-trait sets was partitioned from variance shared among traits, we detected significant (at 5% false discovery rate) mutational covariance in 21% of sets. This frequency of statistically supported covariance implied that at least some mutations must pleiotropically affect a substantial number of traits (>70; 0.6% of all measured traits).

Transcriptomic analysis of sexual differentiation in somatic tissues of Drosophila melanogaster: successes and caveats

The advent of high-throughput technologies to analyze RNA expression levels and transcript structure has brought renewed attention to the age-old question of what differentiates males from females. In Drosophila, the characterized somatic sex determination cascade includes proteins implicated in the regulation of pre-mRNA splicing as well as at least 2 transcription factors at its base. Both DNA microarrays and RNA-Seq have been applied in a number of studies to determine the identities, expression levels and structure of transcripts expressed differentially in the 2 sexes, with remarkably divergent results in the number, structure and identity of affected transcripts. We briefly summarize these reports and discuss the reasons for the apparent discrepancies based upon the different conditions used for sample preparation and data analysis.

New levels of transcriptome complexity at upper thermal limits in wild Drosophila revealed by exon expression analysis

While the cellular heat-shock response has been a paradigm for studying the impact of thermal stress on RNA metabolism and gene expression, the genome-wide response to thermal stress and its connection to physiological stress resistance remain largely unexplored. Here, we address this issue using an array-based exon expression analysis to interrogate the transcriptome in recently established Drosophila melanogaster stocks during severe thermal stress and recovery. We first demonstrated the efficacy of exon-level analyses to reveal a level of thermally induced transcriptome complexity extending well beyond gene-level analyses. Next, we showed that the upper range of both the cellular and physiological thermal stress response profoundly affected message expression and processing in D. melanogaster, limiting expression to a small subset of transcripts, many that share features of known rapidly responding stress genes. As predicted from cellular heat-shock research, constitutive splicing was blocked in a set of novel genes; we did not detect changes to alternative splicing during heat stress, but rather induction of intronless isoforms of known heat-responsive genes. We observed transcriptome plasticity in the form of differential isoform expression during recovery from heat shock, mediated by multiple mechanisms including alternative transcription and alternative splicing. This affected genes involved in DNA regulation, immune response, and thermotolerance. These patterns highlight the complex nature of innate transcriptome responses under stress and potential for adaptive shifts through plasticity and evolved genetic responses at different hierarchical levels.

Research progress in allele-specific expression and its regulatory mechanisms

Although the majority of genes are expressed equally from both alleles, some genes are differentially expressed. Organisms possess characteristics to preferentially express a particular allele under regulatory factors, which is termed allele-specific expression (ASE). It is one of the important genetic factors that lead to phenotypic variation and can be used to identify the variance of gene regulation factors. ASE indicates mechanisms such as DNA methylation, histone modifications, and non-coding RNAs function. Here, we review a broad survey of progress in ASE studies, and what this simple yet very effective approach can offer in functional genomics, and possible implications toward our better understanding of the underlying mechanisms of complex traits.