Microexons: novel regulators of the transcriptome

Unveiling a Microexon Switch: Novel Regulation of the Activities of Sugar Assimilation and Plant-Cell-Wall-Degrading Xylanases and Cellulases by Xlr2 in Trichoderma virens

Functional microexons have not previously been described in filamentous fungi. Here, we describe a novel mechanism of transcriptional regulation in Trichoderma requiring the inclusion of a microexon from the Xlr2 gene. In low-glucose environments, a long mRNA including the microexon encodes a protein with a GAL4-like DNA-binding domain (Xlr2-α), whereas in high-glucose environments, a short mRNA that is produced encodes a protein lacking this DNA-binding domain (Xlr2-β). Interestingly, the protein isoforms differ in their impact on cellulase and xylanase activity. Deleting the Xlr2 gene reduced both xylanase and cellulase activity and growth on different carbon sources, such as carboxymethylcellulose, xylan, glucose, and arabinose. The overexpression of either Xlr2-α or Xlr2-β in T. virens showed that the short isoform (Xlr2-β) caused higher xylanase activity than the wild types or the long isoform (Xlr2-α). Conversely, cellulase activity did not increase when overexpressing Xlr2-β but was increased with the overexpression of Xlr2-α. This is the first report of a novel transcriptional regulation mechanism of plant-cell-wall-degrading enzyme activity in T. virens. This involves the differential expression of a microexon from a gene encoding a transcriptional regulator.

Identification and Quantification of Microexons Using Bulk and Single-Cell RNA-Seq Data

The analysis of RNA-seq has greatly improved the characterization and understanding of the transcriptome. In particular, RNA-seq experiments have extended catalogs of alternative splicing events. However, the analysis of RNAs-seq data for detection and quantification of microexons, extremely short exons of length up to 30 nt, require specialized computational workflows. Here, we describe MicroExonator, a reproducible computational workflow for microexon splicing analysis using bulk or single-cell RNA-seq data.

Prediction of functional microexons by transfer learning

Background

Microexons are a particular kind of exon of less than 30 nucleotides in length. More than 60% of annotated human microexons were found to have high levels of sequence conservation, suggesting their potential functions. There is thus a need to develop a method for predicting functional microexons.

Results

Given the lack of a publicly available functional label for microexons, we employed a transfer learning skill called Transfer Component Analysis (TCA) to transfer the knowledge obtained from feature mapping for the prediction of functional microexons. To provide reference knowledge, microindels were chosen because of their similarities to microexons. Then, Support Vector Machine (SVM) was used to train a classification model in the newly built feature space for the functional microindels. With the trained model, functional microexons were predicted. We also built a tool based on this model to predict other functional microexons. We then used this tool to predict a total of 19 functional microexons reported in the literature. This approach successfully predicted 16 out of 19 samples, giving accuracy greater than 80%.

Conclusions

In this study, we proposed a method for predicting functional microexons and applied it, with the predictive results being largely consistent with records in the literature.

Nanopore sequencing of brain-derived full-length circRNAs reveals circRNA-specific exon usage, intron retention and microexons

Circular RNA (circRNA) is a class of covalently joined non-coding RNAs with functional roles in a wide variety of cellular processes. Their composition shows extensive overlap with exons found in linear mRNAs making it difficult to delineate their composition using short-read RNA sequencing, particularly for long and multi-exonic circRNAs. Here, we use long-read nanopore sequencing of nicked circRNAs (circNick-LRS) and characterize a total of 18,266 and 39,623 circRNAs in human and mouse brain, respectively. We further develop an approach for targeted long-read sequencing of a panel of circRNAs (circPanel-LRS), eliminating the need for prior circRNA enrichment and find >30 circRNA isoforms on average per targeted locus. Our data show that circRNAs exhibit a large number of splicing events such as novel exons, intron retention and microexons that preferentially occur in circRNAs. We propose that altered exon usage in circRNAs may reflect resistance to nonsense-mediated decay in the absence of translation.

Alternative microexon splicing by RBFOX2 and PTBP1 is associated with metastasis in colorectal cancer

The splicing of microexons (very small exons) is frequently dysregulated in the brain of individuals with autism spectrum disorder. However, little is known of the patterns, regulatory mechanisms and roles of microexon splicing in cancer. We here examined the transcriptome-wide profile of microexon splicing in matched colorectal cancer (CRC) and normal tissue specimens. Out of 1492 microexons comprising 3 to 15 nucleotides, 21 (1%) manifested differential splicing between CRC and normal tissue. The 21 genes harboring the differentially spliced microexons were enriched in gene ontology terms related to cell adhesion and migration. RNA interference-mediated knockdown experiments identified two splicing factors, RBFOX2 and PTBP1, as regulators of microexon splicing in CRC cells. RBFOX2 and PTBP1 were found to directly bind to microexon-containing pre-mRNAs and to control their splicing in such cells. Differential microexon splicing was shown to be due, at least in part, to altered expression of RBFOX2 and PTBP1 in CRC tissue compared to matched normal tissue. Finally, we found that changes in the pattern of microexon splicing were associated with CRC metastasis. Our data thus suggest that altered expression of RBFOX2 and PTBP1 might influence CRC metastasis through the regulation of microexon splicing.

FINDER: an automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences

BACKGROUND

Gene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative.

RESULTS

We present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species.

CONCLUSIONS

FINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision-ideal for bench researchers with limited experience in handling computational tools.