Comprehensive Identification and Alternative Splicing of Microexons in Drosophila

Regulation of micro- and small-exon retention and other splicing processes by GRP20 for flower development

Pre-mRNA splicing is crucial for gene expression and depends on the spliceosome and splicing factors. Plant exons have an average size of ~180 nucleotides and typically contain motifs for interactions with spliceosome and splicing factors. Micro exons (<51 nucleotides) are found widely in eukaryotes and in genes for plant development and environmental responses. However, little is known about transcript-specific regulation of splicing in plants and about the regulators for micro exon splicing. Here we report that glycine-rich protein 20 (GRP20) is an RNA-binding protein and required for splicing of ~2,100 genes including those functioning in flower development and/or environmental responses. Specifically, GRP20 is required for micro-exon retention in transcripts of floral homeotic genes; these micro exons are conserved across angiosperms. GRP20 is also important for small-exon (51-100 nucleotides) splicing. In addition, GRP20 is required for flower development. Furthermore, GRP20 binds to poly-purine motifs in micro and small exons and a spliceosome component; both RNA binding and spliceosome interaction are important for flower development and micro-exon retention. Our results provide new insights into the mechanisms of micro-exon retention in flower development.

To RNA-binding and beyond: Emerging facets of the role of Rbfox proteins in development and disease

The RNA-binding Fox-1 homologue (Rbfox) proteins represent an ancient family of splicing factors, conserved through evolution. All members share an RNA recognition motif (RRM), and a particular affinity for the GCAUG signature in target RNA molecules. The role of Rbfox, as a splice factor, deciding the tissue-specific inclusion/exclusion of an exon, depending on its binding position on the flanking introns, is well known. Rbfox often acts in concert with other splicing factors, and forms splicing regulatory networks. Apart from this canonical role, recent studies show that Rbfox can also function as a transcription co-factor, and affects mRNA stability and translation. The repertoire of Rbfox targets is vast, including genes involved in the development of tissue lineages, such as neurogenesis, myogenesis, and erythropoeiesis, and molecular processes, including cytoskeletal dynamics, and calcium handling. A second layer of complexity is added by the fact that Rbfox expression itself is regulated by multiple mechanisms, and, in vertebrates, exhibits tissue-specific expression. The optimum dosage of Rbfox is critical, and its misexpression is etiological to various disease conditions. In this review, we discuss the contextual roles played by Rbfox as a tissue-specific regulator for the expression of many important genes with diverse functions, through the lens of the emerging data which highlights its involvement in many human diseases. Furthermore, we explore the mechanistic details provided by studies in model organisms, with emphasis on the work with Drosophila. This article is categorized under: RNA Processing > Splicing Mechanisms RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Turnover and Surveillance > Regulation of RNA Stability RNA Processing > Splicing Regulation/Alternative Splicing.

Dietary Stimuli, Intestinal Bacteria and Peptide Hormones Regulate Female Drosophila Defecation Rate

Peptide hormones control Drosophila gut motility, but the intestinal stimuli and the gene networks coordinating this trait remain poorly defined. Here, we customized an assay to quantify female Drosophila defecation rate as a proxy of intestinal motility. We found that bacterial infection with the human opportunistic bacterial pathogen Pseudomonas aeruginosa (strain PA14) increases defecation rate in wild-type female flies, and we identified specific bacteria of the fly microbiota able to increase defecation rate. In contrast, dietary stress, imposed by either water-only feeding or high ethanol consumption, decreased defecation rate and the expression of enteroendocrine-produced hormones in the fly midgut, such as Diuretic hormone 31 (Dh31). The decrease in defecation due to dietary stress was proportional to the impact of each stressor on fly survival. Furthermore, we exploited the Drosophila Genetic Reference Panel wild type strain collection and identified strains displaying high and low defecation rates. We calculated the narrow-sense heritability of defecation rate to be 91%, indicating that the genetic variance observed using our assay is mostly additive and polygenic in nature. Accordingly, we performed a genome-wide association (GWA) analysis revealing 17 candidate genes linked to defecation rate. Downregulation of four of them (Pmp70, CG11307, meso18E and mub) in either the midgut enteroendocrine cells or in neurons reduced defecation rate and altered the midgut expression of Dh31, that in turn regulates defecation rate via signaling to the visceral muscle. Hence, microbial and dietary stimuli, and Dh31-controlling genes, regulate defecation rate involving signaling within and among neuronal, enteroendocrine, and visceral muscle cells.

CStone: A de novo transcriptome assembler for short-read data that identifies non-chimeric contigs based on underlying graph structure

With the exponential growth of sequence information stored over the last decade, including that of de novo assembled contigs from RNA-Seq experiments, quantification of chimeric sequences has become essential when assembling read data. In transcriptomics, de novo assembled chimeras can closely resemble underlying transcripts, but patterns such as those seen between co-evolving sites, or mapped read counts, become obscured. We have created a de Bruijn based de novo assembler for RNA-Seq data that utilizes a classification system to describe the complexity of underlying graphs from which contigs are created. Each contig is labelled with one of three levels, indicating whether or not ambiguous paths exist. A by-product of this is information on the range of complexity of the underlying gene families present. As a demonstration of CStones ability to assemble high-quality contigs, and to label them in this manner, both simulated and real data were used. For simulated data, ten million read pairs were generated from cDNA libraries representing four species, Drosophila melanogaster, Panthera pardus, Rattus norvegicus and Serinus canaria. These were assembled using CStone, Trinity and rnaSPAdes; the latter two being high-quality, well established, de novo assembers. For real data, two RNA-Seq datasets, each consisting of ≈30 million read pairs, representing two adult D. melanogaster whole-body samples were used. The contigs that CStone produced were comparable in quality to those of Trinity and rnaSPAdes in terms of length, sequence identity of aligned regions and the range of cDNA transcripts represented, whilst providing additional information on chimerism. Here we describe the details of CStones assembly and classification process, and propose that similar classification systems can be incorporated into other de novo assembly tools. Within a related side study, we explore the effects that chimera’s within reference sets have on the identification of differentially expression genes. CStone is available at: https://sourceforge.net/projects/cstone/.

Within transcriptome reference sets, non-chimeric sequences are representations of transcribed genes, while artificially generated chimeric ones are mosaics of two or more pieces of DNA incorrectly pieced together. One area where such sets are utilized is in the quantification of gene expression patterns; where RNA-Seq reads are mapped to the sequences within, and subsequent count values reflect expression levels. Artificial chimeras can have a negative impact on count values by erroneously increasing variation in relation to the reads being mapped. Reference sets can be created from de novo assembled contigs, but chimeras can be introduced during the assembly process via the required traversal of graphs, representing gene families, constructed from the RNA-Seq data. Graph complexity determines how likely chimeras will arise. We have created CStone, a de novo assembler that utilizes a classification system to describe such complexity. Contigs created by CStone are labelled in a manner that indicates whether or not they are non-chimeric. This encourages contig dependent results to be presented with increased objectivity by maintaining the context of ambiguity associated with the assembly process. CStone has been tested extensively. Additionally, we have quantified the relationship between chimeras within reference sets and the identification of differentially expressed genes.