BaRTv2: A highly resolved barley reference transcriptome for accurate transcript-specific RNA-seq quantification

Accurate characterization of splice junctions as well as transcription start and end sites in reference transcriptomes allows precise quantification of transcripts from RNA-seq data and enable detailed investigations of transcriptional and post-transcriptional regulation. Using novel computational methods and a combination of PacBio Iso-seq and Illumina short read sequences from 20 diverse tissues and conditions, we generated a comprehensive and highly resolved barley reference transcript dataset (RTD) from the European 2-row spring barley cultivar Barke (BaRTv2.18). Stringent and thorough filtering was carried out to maintain the quality and accuracy of the splice junctions and transcript start and end sites. BaRTv2.18 shows increased transcript diversity and completeness compared to an earlier version, BaRTv1.0. The accuracy of transcript level quantification, splice junctions and transcript start and end sites has been validated extensively using parallel technologies and analysis, including high resolution RT PCR and 5 prime RACE. BaRTv2.18 contains 39,434 genes and 148,260 transcripts, representing the most comprehensive and resolved reference transcriptome in barley to date. It provides an important and high-quality resource for advanced transcriptomic analyses, including both transcriptional and post-transcriptional regulation, with exceptional resolution and precision.

Sex-specific transcript diversity is regulated by a maternal pioneer factor in early Drosophila embryos

Maternally deposited RNAs and proteins play a crucial role in initiating zygotic transcription during early embryonic development. However, the mechanisms by which maternal factors regulate zygotic transcript diversity early in development remain poorly understood. Furthermore, how early in development sex-specific transcript diversity occurs is not known genome-wide in any organism. Here, we determine that sex-specific transcript diversity occurs much earlier in development than previously thought in Drosophila, concurrent with Zygotic genome activation (ZGA). We use genetic, biochemical, and genomic approaches to demonstrate that the essential maternally-deposited pioneer factor CLAMP (Chromatin linked adapter for MSL proteins) is a key regulator of sex-specific transcript diversity in the early embryo via the following mechanisms: 1) In both sexes, CLAMP directly binds to the gene bodies of female and male sex-specifically spliced genes. 2) In females, CLAMP modulates chromatin accessibility of an alternatively-spliced exon within Sex-lethal, the master regulator of sex determination, to promote protein production. 3) In males, CLAMP regulates Maleless (MLE) distribution, a spliceosome component to prevent aberrant sex-specific splicing. Thus, we demonstrate for the first time how a maternal factor regulates early zygotic transcriptome diversity sex-specifically. We also developed a new tool to measure how splicing changes over time called time2splice.

Full-length transcript sequencing of human and mouse identifies widespread isoform diversity and alternative splicing in the cerebral cortex

Alternative splicing is a post-transcriptional regulatory mechanism producing multiple distinct mRNA molecules from a single pre-mRNA. Alternative splicing has a prominent role in the central nervous system, impacting neurodevelopment and various neuronal functions as well as being increasingly implicated in brain disorders including autism, schizophrenia and Alzheimer’s disease. Standard short-read RNA-Seq approaches only sequence fragments of the mRNA molecule, making it difficult to accurately characterize the true nature of RNA isoform diversity. In this study, we used long-read isoform sequencing (Iso-Seq) to generate full-length cDNA sequences and map transcript diversity in the human and mouse cerebral cortex. We identify widespread RNA isoform diversity amongst expressed genes in the cortex, including many novel transcripts not present in existing genome annotations. Alternative splicing events were found to make a major contribution to RNA isoform diversity in the cortex, with intron retention being a relatively common event associated with nonsense-mediated decay and reduced transcript expression. Of note, we found evidence for transcription from novel (unannotated genes) and fusion events between neighbouring genes. Although global patterns of RNA isoform diversity were found to be generally similar between human and mouse cortex, we identified some notable exceptions. We also identified striking developmental changes in transcript diversity, with differential transcript usage between human adult and fetal cerebral cortex. Finally, we found evidence for extensive isoform diversity in genes associated with autism, schizophrenia and Alzheimer’s disease. Our data confirm the importance of alternative splicing in the cerebral cortex, dramatically increasing transcriptional diversity and representing an important mechanism underpinning gene regulation in the brain. We provide this transcript level data as a resource to the scientific community.

RNA editing in mesothelioma: a look forward

RNA editing is a post-transcriptional process increasing transcript diversity, thereby regulating different biological processes. We recently observed that mutations resulting from RNA editing due to hydrolytic deamination of adenosine increase during the development of mesothelioma, a rare cancer linked to chronic exposure to asbestos. This review gathers information from the published literature and public data mining to explore several aspects of RNA editing and their possible implications for cancer growth and therapy. We address possible links between RNA editing and particular types of mesothelioma genetic and epigenetic alterations and discuss the relevance of an edited substrate in the context of current chemotherapy or immunotherapy.

Single-Cell Transcriptomics Reveal a Correlation between Genome Architecture and Gene Family Evolution in Ciliates

ABSTRACT Ciliates, a eukaryotic clade that is over 1 billion years old, are defined by division of genome function between transcriptionally inactive germline micronuclei and functional somatic macronuclei. To date, most analyses of gene family evolution have been limited to cultivable model lineages (e.g., Tetrahymena, Paramecium, Oxytricha, and Stylonychia). Here, we focus on the uncultivable Karyorelictea and its understudied sister class Heterotrichea, which represent two extremes in genome architecture. Somatic macronuclei within the Karyorelictea are described as nearly diploid, while the Heterotrichea have hyperpolyploid somatic genomes. Previous analyses indicate that genome architecture impacts ciliate gene family evolution as the most diverse and largest gene families are found in lineages with extensively processed somatic genomes (i.e., possessing thousands of gene-sized chromosomes). To further assess ciliate gene family evolution, we analyzed 43 single-cell transcriptomes from 33 ciliate species representing 10 classes. Focusing on conserved eukaryotic genes, we use estimates of transcript diversity as a proxy for the number of paralogs in gene families among four focal clades: Karyorelictea, Heterotrichea, extensive fragmenters (with gene-size somatic chromosomes), and non-extensive fragmenters (with more traditional somatic chromosomes), the latter two within the subphylum Intramacronucleata. Our results show that (i) the Karyorelictea have the lowest average transcript diversity, while Heterotrichea are highest among the four groups; (ii) proteins in Karyorelictea are under the highest functional constraints, and the patterns of selection in ciliates may reflect genome architecture; and (iii) stop codon reassignments vary among members of the Heterotrichea and Spirotrichea but are conserved in other classes. IMPORTANCE To further our understanding of genome evolution in eukaryotes, we assess the relationship between patterns of molecular evolution within gene families and variable genome structures found among ciliates. We combine single-cell transcriptomics with bioinformatic tools, focusing on understudied and uncultivable lineages selected from across the ciliate tree of life. Our analyses show that genome architecture correlates with patterns of protein evolution as lineages with more canonical somatic genomes, such as the class Karyorelictea, have more conserved patterns of molecular evolution compared to other classes. This study showcases the power of single-cell transcriptomics for investigating genome architecture and evolution in uncultivable microbial lineages and provides transcriptomic resources for further research on genome evolution.

Venom Diversity and Evolution in the Most Divergent Cone Snail Genus Profundiconus

Profundiconus is the most divergent cone snail genus and its unique phylogenetic position, sister to the rest of the family Conidae, makes it a key taxon for examining venom evolution and diversity. Venom gland and foot transcriptomes of Profundiconus cf. vaubani and Profundiconus neocaledonicus were de novo assembled, annotated, and analyzed for differential expression. One hundred and thirty-seven venom components were identified from P. cf. vaubani and 82 from P. neocaledonicus, with only four shared by both species. The majority of the transcript diversity was composed of putative peptides, including conotoxins, profunditoxins, turripeptides, insulin, and prohormone-4. However, there were also a significant percentage of other putative venom components such as chymotrypsin and L-rhamnose-binding lectin. The large majority of conotoxins appeared to be from new gene superfamilies, three of which are highly different from previously reported venom peptide toxins. Their low conotoxin diversity and the type of insulin found suggested that these species, for which no ecological information are available, have a worm or molluscan diet associated with a narrow dietary breadth. Our results indicate that Profundiconus venom is highly distinct from that of other cone snails, and therefore important for examining venom evolution in the Conidae family.

Visualization and analysis of RNA-Seq assembly graphs

RNA-Seq is a powerful transcriptome profiling technology enabling transcript discovery and quantification. Whilst most commonly used for gene-level quantification, the data can be used for the analysis of transcript isoforms. However, when the underlying transcript assemblies are complex, current visualization approaches can be limiting, with splicing events a challenge to interpret. Here, we report on the development of a graph-based visualization method as a complementary approach to understanding transcript diversity from short-read RNA-Seq data. Following the mapping of reads to a reference genome, a read-to-read comparison is performed on all reads mapping to a given gene, producing a weighted similarity matrix between reads. This is used to produce an RNA assembly graph, where nodes represent reads and edges similarity scores between them. The resulting graphs are visualized in 3D space to better appreciate their sometimes large and complex topology, with other information being overlaid on to nodes, e.g. transcript models. Here we demonstrate the utility of this approach, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity.

A NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data

Single-cell RNA sequencing has enabled researchers to quantify the transcriptomes of individual cells, infer cell types, and investigate differential expression among cell types, which will lead to a better understanding of the regulatory mechanisms of cell states. Transcript diversity caused by phenomena such as aberrant splicing events have been revealed, and differential expression of previously unannotated transcripts might be overlooked by annotation-based analyses.Accordingly, we have developed an approach to discover overlooked differentially expressed (DE) gene regions that complements annotation-based methods. We applied our algorithm to two datasets and discovered several intriguing DE transcripts, including a transcript related to the modulation of neural stem/progenitor cell differentiation.