Deciphering the splicing code

Functional analysis of the GPAT4 gene mutation predicted to affect splicing

The GPAT4 gene is considered as a potential functional candidate for single nucleotide polymorphism (SNP) studies in dairy cattle breeding due to its association with dairy performance in cattle by encoding an enzyme responsible for the presence of diacylglycerols and triacylglycerols in milk. Using the example of the GPAT4 gene, we applied the minigene splicing assay to analyze the functional consequences of its variant that was predicted to affect normal splicing. The results of functional analysis revealed the sequence variations (rs442541537), transfection experiments in a wild type and mutant cell line model system demonstrated that the investigated mutation in the second intron of the GPAT4 gene was responsible for the presence of a second exon in mature messenger RNA (mRNA). The cases of its absence in the spliced mature mRNA transcript resulted in a truncated dysfunctional protein due to the appearance of a stop codon. Thus, the discovered SNP led to alternative splicing in pre-mRNA by the 'cassette exon' ('exon skipping') mechanism. The studied mutation can potentially be a molecular genetic marker for alternative splicing for the GPAT4 gene and, therefore contributes to economic benefits in cattle breeding programs.

From computational models of the splicing code to regulatory mechanisms and therapeutic implications

Since the discovery of RNA splicing and its role in gene expression, researchers have sought a set of rules, an algorithm or a computational model that could predict the splice isoforms, and their frequencies, produced from any transcribed gene in a specific cellular context. Over the past 30 years, these models have evolved from simple position weight matrices to deep-learning models capable of integrating sequence data across vast genomic distances. Most recently, new model architectures are moving the field closer to context-specific alternative splicing predictions, and advances in sequencing technologies are expanding the type of data that can be used to inform and interpret such models. Together, these developments are driving improved understanding of splicing regulatory mechanisms and emerging applications of the splicing code to the rational design of RNA- and splicing-based therapeutics.

Dynamic interactions drive early spliceosome assembly

Splicing is a critical processing step during pre-mRNA maturation in eukaryotes. The correct selection of splice sites during the early steps of spliceosome assembly is highly important and crucial for the regulation of alternative splicing. Splice site recognition and alternative splicing depend on cis-regulatory sequence elements in the RNA and trans-acting splicing factors that recognize these elements and crosstalk with the canonical splicing machinery. Structural mechanisms involving early spliceosome complexes are governed by dynamic RNA structures, protein-RNA interactions and conformational flexibility of multidomain RNA binding proteins. Here, we highlight structural studies and integrative structural biology approaches, which provide complementary information from cryo-EM, NMR, small angle scattering, and X-ray crystallography to elucidate mechanisms in the regulation of early spliceosome assembly and quality control, highlighting the role of conformational dynamics.

Cell-type-specific splicing of transcription regulators and Ptbp1 by Rbfox1/2/3 in the developing neocortex

How master splicing regulators crosstalk with each other and to what extent transcription regulators are differentially spliced remain unclear in the developing brain. Here, cell-type-specific RNA-Seq of the developing neocortex uncover that transcription regulators are enriched for differential splicing, altering protein isoforms or inducing nonsense-mediated mRNA decay. Transient expression of Rbfox proteins in radial glia progenitors induces neuronal splicing events preferentially in transcription regulators such as Meis2 and Tead1. Surprisingly, Rbfox proteins promote the inclusion of a mammal-specific alternative exon and a previously undescribed poison exon in Ptbp1. Simultaneous ablation of Rbfox1/2/3 in the neocortex downregulates neuronal isoforms and disrupts radial neuronal migration. Furthermore, the progenitor isoform of Meis2 promotes Tgfb3 transcription, while the Meis2 neuron isoform promotes neuronal differentiation. These observations indicate that transcription regulators are differentially spliced between cell types in the developing neocortex.

Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning

BACKGROUND

Alternative splicing is a pivotal mechanism of post-transcriptional modification that contributes to the transcriptome plasticity and proteome diversity in metazoan cells. Although many splicing regulations around the exon/intron regions are known, the relationship between promoter-bound transcription factors and the downstream alternative splicing largely remains unexplored.

RESULTS

In this study, we present computational approaches to unravel the regulatory relationship between promoter-bound transcription factor binding sites (TFBSs) and the splicing patterns. We curated a fine dataset that includes DNase I hypersensitive site sequencing and transcriptomes across fifteen human tissues from ENCODE. Specifically, we proposed different representations of TF binding context and splicing patterns to examine the associations between the promoter and downstream splicing events. While machine learning models demonstrated potential in predicting splicing patterns based on TFBS occupancies, the limitations in the generalization of predicting the splicing forms of singleton genes across diverse tissues was observed with carefully examination using different cross-validation methods. We further investigated the association between alterations in individual TFBS at promoters and shifts in exon splicing efficiency. Our results demonstrate that the convolutional neural network (CNN) models, trained on TF binding changes in the promoters, can predict the changes in splicing patterns. Furthermore, a systemic in silico substitutions analysis on the CNN models highlighted several potential splicing regulators. Notably, using empirical validation using K562 CTCFL shRNA knock-down data, we showed the significant role of CTCFL in splicing regulation.

CONCLUSION

In conclusion, our finding highlights the potential role of promoter-bound TFBSs in influencing the regulation of downstream splicing patterns and provides insights for discovering alternative splicing regulations.

A Deep Dive into Statistical Modeling of RNA Splicing QTLs Reveals New Variants that Explain Neurodegenerative Disease

Genome-wide association studies (GWAS) have identified thousands of putative disease causing variants with unknown regulatory effects. Efforts to connect these variants with splicing quantitative trait loci (sQTLs) have provided functional insights, yet sQTLs reported by existing methods cannot explain many GWAS signals. We show current sQTL modeling approaches can be improved by considering alternative splicing representation, model calibration, and covariate integration. We then introduce MAJIQTL, a new pipeline for sQTL discovery. MAJIQTL includes two new statistical methods: a weighted multiple testing approach for sGene discovery and a model for sQTL effect size inference to improve variant prioritization. By applying MAJIQTL to GTEx, we find significantly more sGenes harboring sQTLs with functional significance. Notably, our analysis implicates the novel variant rs582283 in Alzheimer's disease. Using antisense oligonucleotides, we validate this variant's effect by blocking the implicated YBX3 binding site, leading to exon skipping in the gene MS4A3.

Large-scale evaluation of the ability of RNA-binding proteins to activate exon inclusion

RNA-binding proteins (RBPs) modulate alternative splicing outcomes to determine isoform expression and cellular survival. To identify RBPs that directly drive alternative exon inclusion, we developed tethered function luciferase-based splicing reporters that provide rapid, scalable and robust readouts of exon inclusion changes and used these to evaluate 718 human RBPs. We performed enhanced cross-linking immunoprecipitation, RNA sequencing and affinity purification-mass spectrometry to investigate a subset of candidates with no prior association with splicing. Integrative analysis of these assays indicates surprising roles for TRNAU1AP, SCAF8 and RTCA in the modulation of hundreds of endogenous splicing events. We also leveraged our tethering assays and top candidates to identify potent and compact exon inclusion activation domains for splicing modulation applications. Using these identified domains, we engineered programmable fusion proteins that outperform current artificial splicing factors at manipulating inclusion of reporter and endogenous exons. This tethering approach characterizes the ability of RBPs to induce exon inclusion and yields new molecular parts for programmable splicing control.

Genetic regulation of nascent RNA maturation revealed by direct RNA nanopore sequencing

Quantitative trait loci analyses have revealed an important role for genetic variants in regulating alternative splicing (AS) and alternative cleavage and polyadenylation (APA) in humans. Yet, these studies are generally performed with mature mRNA, so they report on the outcome rather than the processes of RNA maturation and thus may overlook how variants directly modulate pre-mRNA processing. The order in which the many introns of a human gene are removed can substantially influence AS, while nascent RNA polyadenylation can affect RNA stability and decay. However, how splicing order and poly(A) tail length are regulated by genetic variation has never been explored. Here, we used direct RNA nanopore sequencing to investigate allele-specific pre-mRNA maturation in 12 human lymphoblastoid cell lines. We found frequent splicing order differences between alleles and uncovered significant single nucleotide polymorphism (SNP)-splicing order associations in 17 genes. This included SNPs located in or near splice sites as well as more distal intronic and exonic SNPs. Moreover, several genes showed allele-specific poly(A) tail lengths, many of which also had a skewed allelic abundance ratio. HLA class I transcripts, which encode proteins that play an essential role in antigen presentation, showed the most allele-specific splicing orders, which frequently co-occurred with allele-specific AS, APA or poly(A) tail length differences. Together, our results expose new layers of genetic regulation of pre-mRNA maturation and highlight the power of long-read RNA sequencing for allele-specific analyses.

Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes, and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods on various prediction tasks, including evolutionary sequence divergence on lineage-specific splicing and splicing-altering mutations in human populations and neurodevelopmental disorders, and demonstrated that DeltaSplice outperformed consistently. DeltaSplice predicted ∼15% of splicing quantitative trait loci (sQTLs) in the human brain as causal splicing-altering variants. It also predicted splicing-altering de novo mutations outside the splice sites in a subset of patients affected by autism and other neurodevelopmental disorders (NDDs), including 19 genes with recurrent splicing-altering mutations. Integration of splicing-altering mutations with other types of de novo mutation burdens allowed the prediction of eight novel NDD-risk genes. Our work expanded the capacity of in silico splicing models with potential applications in genetic diagnosis and the development of splicing-based precision medicine.

Splice-switching antisense oligonucleotides for pediatric neurological disorders

Pediatric neurological disorders are frequently devastating and present unmet needs for effective medicine. The successful treatment of spinal muscular atrophy with splice-switching antisense oligonucleotides (SSO) indicates a feasible path to targeting neurological disorders by redirecting pre-mRNA splicing. One direct outcome is the development of SSOs to treat haploinsufficient disorders by targeting naturally occurring non-productive splice isoforms. The development of personalized SSO treatment further inspired the therapeutic exploration of rare diseases. This review will discuss the recent advances that utilize SSOs to treat pediatric neurological disorders.

Accurate isoform quantification by joint short- and long-read RNA-sequencing

Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing RNA sequencing methods have significant limitations: short-read (SR) sequencing provides high depth but struggles with isoform deconvolution, whereas long-read (LR) sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. By applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of transcript abundances, we show that untranslated regions (UTRs) are major determinants of isoform proportion and exon usage; this effect is mediated through isoform-specific sequence features embedded in UTRs, which likely interact with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQT's potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.

Interferon-Regulated Expression of Cellular Splicing Factors Modulates Multiple Levels of HIV-1 Gene Expression and Replication

Type I interferons (IFN-Is) are pivotal in innate immunity against human immunodeficiency virus I (HIV-1) by eliciting the expression of IFN-stimulated genes (ISGs), which encompass potent host restriction factors. While ISGs restrict the viral replication within the host cell by targeting various stages of the viral life cycle, the lesser-known IFN-repressed genes (IRepGs), including RNA-binding proteins (RBPs), affect the viral replication by altering the expression of the host dependency factors that are essential for efficient HIV-1 gene expression. Both the host restriction and dependency factors determine the viral replication efficiency; however, the understanding of the IRepGs implicated in HIV-1 infection remains greatly limited at present. This review provides a comprehensive overview of the current understanding regarding the impact of the RNA-binding protein families, specifically the two families of splicing-associated proteins SRSF and hnRNP, on HIV-1 gene expression and viral replication. Since the recent findings show specifically that SRSF1 and hnRNP A0 are regulated by IFN-I in various cell lines and primary cells, including intestinal lamina propria mononuclear cells (LPMCs) and peripheral blood mononuclear cells (PBMCs), we particularly discuss their role in the context of the innate immunity affecting HIV-1 replication.

Concordant Gene Expression and Alternative Splicing Regulation under Abiotic Stresses in Arabidopsis

The current investigation endeavors to identify differentially expressed alternatively spliced (DAS) genes that exhibit concordant expression with splicing factors (SFs) under diverse multifactorial abiotic stress combinations in Arabidopsis seedlings. SFs serve as the post-transcriptional mechanism governing the spatiotemporal dynamics of gene expression. The different stresses encompass variations in salt concentration, heat, intensive light, and their combinations. Clusters demonstrating consistent expression profiles were surveyed to pinpoint DAS/SF gene pairs exhibiting concordant expression. Through rigorous selection criteria, which incorporate alignment with documented gene functionalities and expression patterns observed in this study, four members of the serine/arginine-rich (SR) gene family were delineated as SFs concordantly expressed with six DAS genes. These regulated SF genes encompass cactin, SR1-like, SR30, and SC35-like. The identified concordantly expressed DAS genes encode diverse proteins such as the 26.5 kDa heat shock protein, chaperone protein DnaJ, potassium channel GORK, calcium-binding EF hand family protein, DEAD-box RNA helicase, and 1-aminocyclopropane-1-carboxylate synthase 6. Among the concordantly expressed DAS/SF gene pairs, SR30/DEAD-box RNA helicase, and SC35-like/1-aminocyclopropane-1-carboxylate synthase 6 emerge as promising candidates, necessitating further examinations to ascertain whether these SFs orchestrate splicing of the respective DAS genes. This study contributes to a deeper comprehension of the varied responses of the splicing machinery to abiotic stresses. Leveraging these DAS/SF associations shows promise for elucidating avenues for augmenting breeding programs aimed at fortifying cultivated plants against heat and intensive light stresses.

HPV and RNA Binding Proteins: What We Know and What Remains to Be Discovered

Papillomavirus gene regulation is largely post-transcriptional due to overlapping open reading frames and the use of alternative polyadenylation and alternative splicing to produce the full suite of viral mRNAs. These processes are controlled by a wide range of cellular RNA binding proteins (RPBs), including constitutive splicing factors and cleavage and polyadenylation machinery, but also factors that regulate these processes, for example, SR and hnRNP proteins. Like cellular RNAs, papillomavirus RNAs have been shown to bind many such proteins. The life cycle of papillomaviruses is intimately linked to differentiation of the epithelial tissues the virus infects. For example, viral late mRNAs and proteins are expressed only in the most differentiated epithelial layers to avoid recognition by the host immune response. Papillomavirus genome replication is linked to the DNA damage response and viral chromatin conformation, processes which also link to RNA processing. Challenges with respect to elucidating how RBPs regulate the viral life cycle include consideration of the orchestrated spatial aspect of viral gene expression in an infected epithelium and the epigenetic nature of the viral episomal genome. This review discusses RBPs that control viral gene expression, and how the connectivity of various nuclear processes might contribute to viral mRNA production.

An interpretable model of pre-mRNA splicing for animal and plant genes

Pre-mRNA splicing is a fundamental step in gene expression, conserved across eukaryotes, in which the spliceosome recognizes motifs at the 3' and 5' splice sites (SSs), excises introns, and ligates exons. SS recognition and pairing is often influenced by protein splicing factors (SFs) that bind to splicing regulatory elements (SREs). Here, we describe SMsplice, a fully interpretable model of pre-mRNA splicing that combines models of core SS motifs, SREs, and exonic and intronic length preferences. We learn models that predict SS locations with 83 to 86% accuracy in fish, insects, and plants and about 70% in mammals. Learned SRE motifs include both known SF binding motifs and unfamiliar motifs, and both motif classes are supported by genetic analyses. Our comparisons across species highlight similarities between non-mammals, increased reliance on intronic SREs in plant splicing, and a greater reliance on SREs in mammalian splicing.

CRISPR-dCas13d-based deep screening of proximal and distal splicing-regulatory elements

Pre-mRNA splicing, a key process in gene expression, can be therapeutically modulated using various drug modalities, including antisense oligonucleotides (ASOs). However, determining promising targets is hampered by the challenge of systematically mapping splicing-regulatory elements (SREs) in their native sequence context. Here, we use the catalytically inactive CRISPR-RfxCas13d RNA-targeting system (dCas13d/gRNA) as a programmable platform to bind SREs and modulate splicing by competing against endogenous splicing factors. SpliceRUSH, a high-throughput screening method, was developed to map SREs in any gene of interest using a lentivirus gRNA library that tiles the genetic region, including distal intronic sequences. When applied to SMN2, a therapeutic target for spinal muscular atrophy, SpliceRUSH robustly identifies not only known SREs but also a previously unknown distal intronic SRE, which can be targeted to alter exon 7 splicing using either dCas13d/gRNA or ASOs. This technology enables a deeper understanding of splicing regulation with applications for RNA-based drug discovery.

Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods on various prediction tasks, including evolutionary sequence divergence on lineage-specific splicing and splicing-altering mutations in human populations and neurodevelopmental disorders, and demonstrated that DeltaSplice outperformed consistently. DeltaSplice predicted ~15% of splicing quantitative trait loci (sQTLs) in the human brain as causal splicing-altering variants. It also predicted splicing-altering de novo mutations outside the splice sites in a subset of patients affected by autism and other neurodevelopmental disorders, including 19 genes with recurrent splicing-altering mutations. Among the new candidate disease risk genes, MFN1 is involved in mitochondria fusion, which is frequently disrupted in autism patients. Our work expanded the capacity of in silico splicing models with potential applications in genetic diagnosis and the development of splicing-based precision medicine.

CIRI-Deep Enables Single-Cell and Spatial Transcriptomic Analysis of Circular RNAs with Deep Learning

Circular RNAs (circRNAs) are a crucial yet relatively unexplored class of transcripts known for their tissue- and cell-type-specific expression patterns. Despite the advances in single-cell and spatial transcriptomics, these technologies face difficulties in effectively profiling circRNAs due to inherent limitations in circRNA sequencing efficiency. To address this gap, a deep learning model, CIRI-deep, is presented for comprehensive prediction of circRNA regulation on diverse types of RNA-seq data. CIRI-deep is trained on an extensive dataset of 25 million high-confidence circRNA regulation events and achieved high performances on both test and leave-out data, ensuring its accuracy in inferring differential events from RNA-seq data. It is demonstrated that CIRI-deep and its adapted version enable various circRNA analyses, including cluster- or region-specific circRNA detection, BSJ ratio map visualization, and trans and cis feature importance evaluation. Collectively, CIRI-deep's adaptability extends to all major types of RNA-seq datasets including single-cell and spatial transcriptomic data, which will undoubtedly broaden the horizons of circRNA research.

EF1α-associated protein complexes affect dendritic spine plasticity by regulating microglial phagocytosis in Fmr1 knock-out mice

Fragile X syndrome (FXS) is the most common inherited cause of intellectual disability. There is no specific treatment for FXS due to the lack of therapeutic targets. We report here that Elongation Factor 1α (EF1α) forms a complex with two other proteins: Tripartite motif-containing protein 3 (TRIM3) and Murine double minute (Mdm2). Both EF1α-Mdm2 and EF1α-TRIM3 protein complexes are increased in the brain of Fmr1 knockout mice as a result of FMRP deficiency, which releases the normal translational suppression of EF1α mRNA and increases EF1α protein levels. Increased EF1α-Mdm2 complex decreases PSD-95 ubiquitination (Ub-PSD-95) and Ub-PSD-95-C1q interaction. The elevated level of TRIM3-EF1α complex is associated with decreased TRIM3-Complement Component 3 (C3) complex that inhibits the activation of C3. Both protein complexes thereby contribute to a reduction in microglia-mediated phagocytosis and dendritic spine pruning. Finally, we created a peptide that disrupts both protein complexes and restores dendritic spine plasticity and behavioural deficits in Fmr1 knockout mice. The EF1α-Mdm2 and EF1α-TRIM3 complexes could thus be new therapeutic targets for FXS.

Splicing Factor PQBP1 Curtails BAX Expression to Promote Ovarian Cancer Progression

Splicing factor polyglutamine binding protein-1 (PQBP1) is abundantly expressed in the central nervous system during development, and mutations in the gene cause intellectual disability. However, the roles of PQBP1 in cancer progression remain largely unknown. Here, it is shown that PQBP1 overexpression promotes tumor progression and indicates worse prognosis in ovarian cancer. Integrative analysis of spyCLIP-seq and RNA-seq data reveals that PQBP1 preferentially binds to exon regions and modulates exon skipping. Mechanistically, it is shown that PQBP1 regulates the splicing of genes related to the apoptotic signaling pathway, including BAX. PQBP1 promotes BAX exon 2 skipping to generate a truncated isoform that undergoes degradation by nonsense-mediated mRNA decay, thus making cancer cells resistant to apoptosis. In contrast, PQBP1 depletion or splice-switching antisense oligonucleotides promote exon 2 inclusion and thus increase BAX expression, leading to inhibition of tumor growth. Together, the results demonstrate an oncogenic role of PQBP1 in ovarian cancer and suggest that targeting the aberrant splicing mediated by PQBP1 has therapeutic potential in cancer treatment.

Self-supervised learning on millions of primary RNA sequences from 72 vertebrates improves sequence-based RNA splicing prediction

Language models pretrained by self-supervised learning (SSL) have been widely utilized to study protein sequences, while few models were developed for genomic sequences and were limited to single species. Due to the lack of genomes from different species, these models cannot effectively leverage evolutionary information. In this study, we have developed SpliceBERT, a language model pretrained on primary ribonucleic acids (RNA) sequences from 72 vertebrates by masked language modeling, and applied it to sequence-based modeling of RNA splicing. Pretraining SpliceBERT on diverse species enables effective identification of evolutionarily conserved elements. Meanwhile, the learned hidden states and attention weights can characterize the biological properties of splice sites. As a result, SpliceBERT was shown effective on several downstream tasks: zero-shot prediction of variant effects on splicing, prediction of branchpoints in humans, and cross-species prediction of splice sites. Our study highlighted the importance of pretraining genomic language models on a diverse range of species and suggested that SSL is a promising approach to enhance our understanding of the regulatory logic underlying genomic sequences.

ASAPA: a bioinformatic pipeline based on Iso-Seq that identifies the links among alternative splicing, alternative transcription initiation and alternative polyadenylation

BACKGROUND

Although the events associated with alternative splicing (AS), alternative polyadenylation (APA) and alternative transcription initiation (ATI) can be identified by many approaches based on isoform sequencing (Iso-Seq), these analyses are generally independent of each other and the links between these events are still rarely mentioned. However, an interdependency analysis can be achieved because the transcriptional start site, splice sites and polyA site could be simultaneously included in a long, full-length read from Iso-Seq.

RESULTS

We create ASAPA pipeline that enables streamlined analysis for a robust detection of potential links among AS, ATI and APA using Iso-Seq data. We tested this pipeline using Arabidopsis data and found some interesting results: some adjacent introns tend to be simultaneously spliced or retained; coupling between AS and ATI or APA is limited to the initial or terminal intron; and ATI and APA are potentially linked in some special cases.

CONCLUSION

Our pipeline enables streamlined analysis for a robust detection of potential links among AS, ATI and APA using Iso-Seq data, which is conducive to a better understanding of transcription landscape generation.

Detecting and understanding meaningful cancerous mutations based on computational models of mRNA splicing

Cancer research has long relied on non-silent mutations. Yet, it has become overwhelmingly clear that silent mutations can affect gene expression and cancer cell fitness. One fundamental mechanism that apparently silent mutations can severely disrupt is alternative splicing. Here we introduce Oncosplice, a tool that scores mutations based on models of proteomes generated using aberrant splicing predictions. Oncosplice leverages a highly accurate neural network that predicts splice sites within arbitrary mRNA sequences, a greedy transcript constructor that considers alternate arrangements of splicing blueprints, and an algorithm that grades the functional divergence between proteins based on evolutionary conservation. By applying this tool to 12M somatic mutations we identify 8K deleterious variants that are significantly depleted within the healthy population; we demonstrate the tool's ability to identify clinically validated pathogenic variants with a positive predictive value of 94%; we show strong enrichment of predicted deleterious mutations across pan-cancer drivers. We also achieve improved patient survival estimation using a proposed set of novel cancer-involved genes. Ultimately, this pipeline enables accelerated insight-gathering of sequence-specific consequences for a class of understudied mutations and provides an efficient way of filtering through massive variant datasets - functionalities with immediate experimental and clinical applications.

Improved modeling of RNA-binding protein motifs in an interpretable neural model of RNA splicing

Sequence-specific RNA-binding proteins (RBPs) play central roles in splicing decisions. Here, we describe a modular splicing architecture that leverages in vitro-derived RNA affinity models for 79 human RBPs and the annotated human genome to produce improved models of RBP binding and activity. Binding and activity are modeled by separate Motif and Aggregator components that can be mixed and matched, enforcing sparsity to improve interpretability. Training a new Adjusted Motif (AM) architecture on the splicing task not only yields better splicing predictions but also improves prediction of RBP-binding sites in vivo and of splicing activity, assessed using independent data.

An intronic RNA element modulates Factor VIII exon-16 splicing

Pathogenic variants in the human Factor VIII (F8) gene cause Hemophilia A (HA). Here, we investigated the impact of 97 HA-causing single-nucleotide variants on the splicing of 11 exons from F8. For the majority of F8 exons, splicing was insensitive to the presence of HA-causing variants. However, splicing of several exons, including exon-16, was impacted by variants predicted to alter exonic splicing regulatory sequences. Using exon-16 as a model, we investigated the structure-function relationship of HA-causing variants on splicing. Intriguingly, RNA chemical probing analyses revealed a three-way junction structure at the 3'-end of intron-15 (TWJ-3-15) capable of sequestering the polypyrimidine tract. We discovered antisense oligonucleotides (ASOs) targeting TWJ-3-15 partially rescue splicing-deficient exon-16 variants by increasing accessibility of the polypyrimidine tract. The apical stem loop region of TWJ-3-15 also contains two hnRNPA1-dependent intronic splicing silencers (ISSs). ASOs blocking these ISSs also partially rescued splicing. When used in combination, ASOs targeting both the ISSs and the region sequestering the polypyrimidine tract, fully rescue pre-mRNA splicing of multiple HA-linked variants of exon-16. Together, our data reveal a putative RNA structure that sensitizes F8 exon-16 to aberrant splicing.

SplicingLore: a web resource for studying the regulation of cassette exons by human splicing factors

One challenge faced by scientists from the alternative RNA splicing field is to decode the cooperative or antagonistic effects of splicing factors (SFs) to understand and eventually predict splicing outcomes on a genome-wide scale. In this manuscript, we introduce SplicingLore, an open-access database and web resource that help to fill this gap in a straightforward manner. The database contains a collection of RNA-sequencing-derived lists of alternative exons regulated by a total of 75 different SFs. All datasets were processed in a standardized manner, ensuring valid comparisons and correlation analyses. The user can easily retrieve a factor-specific set of differentially included exons from the database or provide a list of exons and search which SF(s) control(s) their inclusion. Our simple workflow is fast and easy to run, and it ensures a reliable calculation of correlation scores between the tested datasets. As a proof of concept, we predicted and experimentally validated a novel functional cooperation between the RNA helicases DDX17 and DDX5 and the heterogeneous nuclear ribonucleoprotein C (HNRNPC) protein. SplicingLore is available at https://splicinglore.ens-lyon.fr/. Database URL:  https://splicinglore.ens-lyon.fr/.

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

BACKGROUND

Variants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes.

RESULTS

We benchmark eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compare experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms' concordance with MPSA measurements, and with each other, is lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieve the best overall performance at distinguishing disruptive and neutral variants, and controlling for overall call rate genome-wide, SpliceAI and Pangolin have superior sensitivity. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues.

CONCLUSION

SpliceAI and Pangolin show the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.

Decoding the role of aberrant RNA alternative splicing in hepatocellular carcinoma: a comprehensive review

During eukaryotic gene expression, alternative splicing of messenger RNA precursors is critical in increasing protein diversity and regulatory complexity. Multiple transcript isoforms could be produced by alternative splicing from a single gene; they could eventually be translated into protein isoforms with deleted, added, or altered domains or produce transcripts containing premature termination codons that could be targeted by nonsense-mediated mRNA decay. Alternative splicing can generate proteins with similar, different, or even opposite functions. Increasingly strong evidence indicates that abnormal RNA splicing is a prevalent and crucial occurrence in cellular differentiation, tissue advancement, and the development and progression of cancer. Aberrant alternative splicing could affect cancer cell activities such as growth, apoptosis, invasiveness, drug resistance, angiogenesis, and metabolism. This systematic review provides a comprehensive overview of the impact of abnormal RNA alternative splicing on the development and progression of hepatocellular carcinoma.

The human genome contains over a million autonomous exons

Mammalian mRNA and lncRNA exons are often small compared to introns. The exon definition model predicts that exons splice autonomously, dependent on proximal exon sequence features, explaining their delineation within large introns. This model has not been examined on a genome-wide scale, however, leaving open the question of how often mRNA and lncRNA exons are autonomous. It is also unknown how frequently such exons can arise by chance. Here, we directly assayed large fragments (500-1000 bp) of the human genome by exon trapping, which detects exons spliced into a heterologous transgene, here designed with a large intron context. We define the trapped exons as "autonomous." We obtained ∼1.25 million trapped exons, including most known mRNA and well-annotated lncRNA internal exons, demonstrating that human exons are predominantly autonomous. mRNA exons are trapped with the highest efficiency. Nearly a million of the trapped exons are unannotated, most located in intergenic regions and antisense to mRNA, with depletion from the forward strand of introns. These exons are not conserved, suggesting they are nonfunctional and arose from random mutations. They are nonetheless highly enriched with known splicing promoting sequence features that delineate known exons. Novel autonomous exons are more numerous than annotated lncRNA exons, and computational models also indicate they will occur with similar frequency in any randomly generated sequence. These results show that most human coding exons splice autonomously, and provide an explanation for the existence of many unconserved lncRNAs, as well as a new annotation and inclusion levels of spliceable loci in the human genome.

Specific recognition and sensitive quantification of mRNA splice variants via one-pot ligation-dependent loop-mediated isothermal amplification

Specific recognition and sensitive quantification of mRNA alternative splice variants have been a necessity for exploring the regulatory mechanism of RNA splicing and revealing the association between pre-mRNA splicing and transcriptome function, as well as disease diagnosis. However, their wide abundance range and high sequence homology pose enormous challenges for high sensitivity and selectivity quantification of splice variants. Herein, taking advantage of the excellent specificity of ligation and the powerful nucleic acid replication feature of loop-mediated isothermal amplification (LAMP), we developed a one-pot method (termed one-pot ligation-LAMP) for specific recognition and sensitive quantification of mRNA splicing variants based on two splicing junction-specific stem-loop DNA probe ligation and the subsequently initiating LAMP. The one-pot ligation-LAMP can specifically detect as low as 100 aM mRNA splice variants without any nonspecific signals and quantify them with a wide dynamics range spanning at least six orders of magnitude. We have demonstrated that the one-pot ligation-LAMP is a versatile and practical strategy for accurately quantifying different splicing variants in complex biological samples with high sensitivity all in one tube within 90 min, thereby providing an attractive tool for mRNA splice variant-related studies.

Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models

The data explosion driven by advancements in genomic research, such as high-throughput sequencing techniques, is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in various fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning, since we expect a superhuman intelligence that explores beyond our knowledge to interpret the genome from deep learning. A powerful deep learning model should rely on the insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with proper deep learning-based architecture, and we remark on practical considerations of developing deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research and point out current challenges and potential research directions for future genomics applications. We believe the collaborative use of ever-growing diverse data and the fast iteration of deep learning models will continue to contribute to the future of genomics.

Cell-type specific regulator RBPMS switches alternative splicing via higher-order oligomerization and heterotypic interactions with other splicing regulators

Alternative pre-mRNA splicing decisions are regulated by RNA binding proteins (RBPs) that can activate or repress regulated splice sites. Repressive RBPs typically harness multivalent interactions to bind stably to target RNAs. Multivalency can be achieved by homomeric oligomerization and heteromeric interactions with other RBPs, often mediated by intrinsically disordered regions (IDRs), and by possessing multiple RNA binding domains. Cell-specific splicing decisions often involve the action of widely expressed RBPs, which are able to bind multivalently around target exons, but without effect in the absence of a cell-specific regulator. To address how cell-specific regulators can collaborate with constitutive RBPs in alternative splicing regulation, we used the smooth-muscle specific regulator RBPMS. Recombinant RBPMS is sufficient to confer smooth muscle cell specific alternative splicing of Tpm1 exon 3 in cell-free assays by preventing assembly of ATP-dependent splicing complexes. This activity depends upon a C-terminal IDR that facilitates dynamic higher-order self-assembly, cooperative binding to multivalent RNA and interactions with widely expressed splicing co-regulators, including MBNL1 and RBFOX2, allowing cooperative assembly of stable cell-specific regulatory complexes.

USP39 promotes hepatocellular carcinogenesis through regulating alternative splicing in cooperation with SRSF6/HNRNPC

Abnormal alternative splicing (AS) caused by alterations in spliceosomal factors is implicated in cancers. Standard models posit that splice site selection is mainly determined by early spliceosomal U1 and U2 snRNPs. Whether and how other mid/late-acting spliceosome components such as USP39 modulate tumorigenic splice site choice remains largely elusive. We observed that hepatocyte-specific overexpression of USP39 promoted hepatocarcinogenesis and potently regulated splice site selection in transgenic mice. In human liver cancer cells, USP39 promoted tumor proliferation in a spliceosome-dependent manner. USP39 depletion deregulated hundreds of AS events, including the oncogenic splice-switching of KANK2. Mechanistically, we developed a novel RBP-motif enrichment analysis and found that USP39 modulated exon inclusion/exclusion by interacting with SRSF6/HNRNPC in both humans and mice. Our data represented a paradigm for the control of splice site selection by mid/late-acting spliceosome proteins and their interacting RBPs. USP39 and possibly other mid/late-acting spliceosome proteins may represent potential prognostic biomarkers and targets for cancer therapy.

Regulation of Flowering Time and Other Developmental Plasticities by 3' Splicing Factor-Mediated Alternative Splicing in Arabidopsis thaliana

Plants, as sessile organisms, show a high degree of plasticity in their growth and development and have various strategies to cope with these alterations under continuously changing environments and unfavorable stress conditions. In particular, the floral transition from the vegetative and reproductive phases in the shoot apical meristem (SAM) is one of the most important developmental changes in plants. In addition, meristem regions, such as the SAM and root apical meristem (RAM), which continually generate new lateral organs throughout the plant life cycle, are important sites for developmental plasticity. Recent findings have shown that the prevailing type of alternative splicing (AS) in plants is intron retention (IR) unlike in animals; thus, AS is an important regulatory mechanism conferring plasticity for plant growth and development under various environmental conditions. Although eukaryotes exhibit some similarities in the composition and dynamics of their splicing machinery, plants have differences in the 3' splicing characteristics governing AS. Here, we summarize recent findings on the roles of 3' splicing factors and their interacting partners in regulating the flowering time and other developmental plasticities in Arabidopsis thaliana.

Altered splicing factor and alternative splicing events in a mouse model of diet- and polychlorinated biphenyl-induced liver disease

Non-alcoholic fatty liver disease (NAFLD) is associated with human environmental exposure to polychlorinated biphenyls (PCBs). Alternative splicing (AS) is dysregulated in steatotic liver disease and is regulated by splicing factors (SFs) and N-6 methyladenosine (m6A) modification. Here integrated analysis of hepatic mRNA-sequencing data was used to identify differentially expressed SFs and differential AS events (ASEs) in the livers of high fat diet-fed C57BL/6 J male mice exposed to Aroclor1260, PCB126, Aroclor1260 + PCB126, or vehicle control. Aroclor1260 + PCB126 co-exposure altered 100 SFs and replicate multivariate analysis of transcript splicing (rMATS) identified 449 ASEs in 366 genes associated with NAFLD pathways. These ASEs were similar to those resulting from experimental perturbations in m6A writers, readers, and erasers. These results demonstrate specific hepatic SF and AS regulatory mechanisms are disrupted by HFD and PCB exposures, contributing to the expression of altered isoforms that may play a role in NAFLD progression to NASH.

MkcDBGAS: a reference-free approach to identify comprehensive alternative splicing events in a transcriptome

Alternative splicing (AS) is an essential post-transcriptional mechanism that regulates many biological processes. However, identifying comprehensive types of AS events without guidance from a reference genome is still a challenge. Here, we proposed a novel method, MkcDBGAS, to identify all seven types of AS events using transcriptome alone, without a reference genome. MkcDBGAS, modeled by full-length transcripts of human and Arabidopsis thaliana, consists of three modules. In the first module, MkcDBGAS, for the first time, uses a colored de Bruijn graph with dynamic- and mixed- kmers to identify bubbles generated by AS with precision higher than 98.17% and detect AS types overlooked by other tools. In the second module, to further classify types of AS, MkcDBGAS added the motifs of exons to construct the feature matrix followed by the XGBoost-based classifier with the accuracy of classification greater than 93.40%, which outperformed other widely used machine learning models and the state-of-the-art methods. Highly scalable, MkcDBGAS performed well when applied to Iso-Seq data of Amborella and transcriptome of mouse. In the third module, MkcDBGAS provides the analysis of differential splicing across multiple biological conditions when RNA-sequencing data is available. MkcDBGAS is the first accurate and scalable method for detecting all seven types of AS events using the transcriptome alone, which will greatly empower the studies of AS in a wider field.

Long-read sequencing reveals the landscape of aberrant alternative splicing and novel therapeutic target in colorectal cancer

BACKGROUND

Alternative splicing complexity plays a vital role in carcinogenesis and cancer progression. Improved understanding of novel splicing events and the underlying regulatory mechanisms may contribute new insights into developing new therapeutic strategies for colorectal cancer (CRC).

METHODS

Here, we combined long-read sequencing technology with short-read RNA-seq methods to investigate the transcriptome complexity in CRC. By using experiment assays, we explored the function of newly identified splicing isoform TIMP1 Δ4-5. Moreover, a CRISPR/dCasRx-based strategy to induce the TIMP1 exon 4-5 exclusion was introduced to inhibit neoplasm growth.

RESULTS

A total of 90,703 transcripts were identified, of which > 62% were novel compared with current transcriptome annotations. These novel transcripts were more likely to be sample specific, expressed at relatively lower levels with more exons, and oncogenes displayed a characteristic to generate more transcripts in CRC. Clinical outcome data analysis showed that 1472 differentially expressed alternative splicing events (DEAS) were tightly associated with CRC patients' prognosis, and many novel isoforms were likely to be important determinants for patient survival. Among these, newly identified splicing isoform TIMP1 Δ4-5 was significantly downregulated in CRC. Further in vitro and in vivo assays demonstrated that ectopic expression of TIMP1 Δ4-5 significantly suppresses tumor cell growth and metastasis. Serine/arginine-rich splicing factor 1 (SRSF1) acts as a onco-splicing regulator through sustaining the inclusion of TIMP1 exon 4-5. Furthermore, CRISPR/dCasRx-based strategies designed to induce TIMP1 exon 4-5 exclusion have the potential to restrain the CRC growth.

CONCLUSIONS

This data provides a rich resource for deeper studies of gastrointestinal malignancies. Newly identified splicing isoform TIMP1 Δ4-5 plays an important role in mediating CRC progression and may be a potential therapy target in CRC.

Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis

Genomic variants affecting pre-messenger RNA splicing and its regulation are known to underlie many rare genetic diseases. However, common workflows for genetic diagnosis and clinical variant interpretation frequently overlook splice-altering variants. To better serve patient populations and advance biomedical knowledge, it has become increasingly important to develop and refine approaches for detecting and interpreting pathogenic splicing variants. In this review, we will summarize a few recent developments and challenges in using RNA sequencing technologies for rare disease investigation. Moreover, we will discuss how recent computational splicing prediction tools have emerged as complementary approaches for revealing disease-causing variants underlying splicing defects. We speculate that continuous improvements to sequencing technologies and predictive modeling will not only expand our understanding of splicing regulation but also bring us closer to filling the diagnostic gap for rare disease patients.

Transposons contribute to the functional diversification of the head, gut, and ovary transcriptomes across Drosophila natural strains

Transcriptomes are dynamic, with cells, tissues, and body parts expressing particular sets of transcripts. Transposable elements (TEs) are a known source of transcriptome diversity; however, studies often focus on a particular type of chimeric transcript, analyze single body parts or cell types, or are based on incomplete TE annotations from a single reference genome. In this work, we have implemented a method based on de novo transcriptome assembly that minimizes the potential sources of errors while identifying a comprehensive set of gene-TE chimeras. We applied this method to the head, gut, and ovary dissected from five Drosophila melanogaster natural strains, with individual reference genomes available. We found that ∼19% of body part-specific transcripts are gene-TE chimeras. Overall, chimeric transcripts contribute a mean of 43% to the total gene expression, and they provide protein domains for DNA binding, catalytic activity, and DNA polymerase activity. Our comprehensive data set is a rich resource for follow-up analysis. Moreover, because TEs are present in virtually all species sequenced to date, their role in spatially restricted transcript expression is likely not exclusive to the species analyzed in this work.

Deep screening of proximal and distal splicing-regulatory elements in a native sequence context

Pre-mRNA splicing, a key process in gene expression, can be therapeutically modulated using various drug modalities, including antisense oligonucleotides (ASOs). However, determining promising targets is impeded by the challenge of systematically mapping splicing-regulatory elements (SREs) in their native sequence context. Here, we use the catalytically dead CRISPR-RfxCas13d RNA-targeting system (dCas13d/gRNA) as a programmable platform to bind SREs and modulate splicing by competing against endogenous splicing factors. SpliceRUSH, a high-throughput screening method, was developed to map SREs in any gene of interest using a lentivirus gRNA library that tiles the genetic region, including distal intronic sequences. When applied to SMN2, a therapeutic target for spinal muscular atrophy, SpliceRUSH robustly identified not only known SREs, but also a novel distal intronic splicing enhancer, which can be targeted to alter exon 7 splicing using either dCas13d/gRNA or ASOs. This technology enables a deeper understanding of splicing regulation with applications for RNA-based drug discovery.

Pre-mRNA splicing order is predetermined and maintains splicing fidelity across multi-intronic transcripts

Combinatorially, intron excision within a given nascent transcript could proceed down any of thousands of paths, each of which would expose different dynamic landscapes of cis-elements and contribute to alternative splicing. In this study, we found that post-transcriptional multi-intron splicing order in human cells is largely predetermined, with most genes spliced in one or a few predominant orders. Strikingly, these orders were conserved across cell types and stages of motor neuron differentiation. Introns flanking alternatively spliced exons were frequently excised last, after their neighboring introns. Perturbations to the spliceosomal U2 snRNA altered the preferred splicing order of many genes, and these alterations were associated with the retention of other introns in the same transcript. In one gene, early removal of specific introns was sufficient to induce delayed excision of three proximal introns, and this delay was caused by two distinct cis-regulatory mechanisms. Together, our results demonstrate that multi-intron splicing order in human cells is predetermined, is influenced by a component of the spliceosome and ensures splicing fidelity across long pre-mRNAs.

Tnpo3 controls splicing of the pre-mRNA encoding the canonical TCR α chain of iNKT cells

Unconventional T cells, such as innate natural killer T cells (iNKT) cells, are an important part of vertebrate immune defences. iNKT recognise glycolipids through a T cell receptor (TCR) that is composed of a semi-invariant TCR α chain, paired with a restricted set of TCR β chains. Here, we show that splicing of the cognate Trav11-Traj18-Trac pre-mRNA encoding the characteristic Vα14Jα18 variable region of this semi-invariant TCR depends on the presence of Tnpo3. The Tnpo3 gene encodes a nuclear transporter of the β-karyopherin family whose cargo includes various splice regulators. The block of iNKT cell development in the absence of Tnpo3 can be overcome by transgenic provision of a rearranged Trav11-Traj18-Trac cDNA, indicating that Tnpo3 deficiency does not interfere with the development of iNKT cells per se. Our study thus identifies a role for Tnpo3 in regulating the splicing of the pre-mRNA encoding the cognate TCRα chain of iNKT cells.

PIE-seq: identifying RNA-binding protein targets by dual RNA-deaminase editing and sequencing

RNA-binding proteins (RBPs) are essential for gene regulation, but it remains a challenge to identify their RNA targets across cell types. Here we present PIE-Seq to investigate Protein-RNA Interaction with dual-deaminase Editing and Sequencing by conjugating C-to-U and A-to-I base editors to RBPs. We benchmark PIE-Seq and demonstrate its sensitivity in single cells, its application in the developing brain, and its scalability with 25 human RBPs. Bulk PIE-Seq identifies canonical binding features for RBPs such as PUM2 and NOVA1, and nominates additional target genes for most tested RBPs such as SRSF1 and TDP-43/TARDBP. Homologous RBPs frequently edit similar sequences and gene sets in PIE-Seq while different RBP families show distinct targets. Single-cell PIE-PUM2 uncovers comparable targets to bulk samples and applying PIE-PUM2 to the developing mouse neocortex identifies neural-progenitor- and neuron-specific target genes such as App. In summary, PIE-Seq provides an orthogonal approach and resource to uncover RBP targets in mice and human cells.

Identification of Significant RNA-Binding Proteins in the Process of CD44 Splicing Using the Boosted Beta Regression Algorithm

The expression of RNA-binding proteins and their interaction with the spliced pre-mRNA are the key factors in determining the final isoform profile. Transmembrane protein CD44 is involved in differentiation, invasion, motility, growth and survival of tumor cells, and is also a commonly accepted marker of cancer stem cells and epithelial-mesenchymal transition. However, the functions of the isoforms of this protein differ significantly. In this paper, we developed a method based on the boosted beta regression algorithm for identification of the significant RNA-binding proteins in the splicing process by modeling the isoform ratio. The application of this method to the analysis of CD44 splicing in colorectal cancer cells revealed 20 significant RNA-binding proteins. Many of them were previously shown as EMT regulators, but for the first time presented as potential CD44 splicing factors.

Aberrant splicing prediction across human tissues

Aberrant splicing is a major cause of genetic disorders but its direct detection in transcriptomes is limited to clinically accessible tissues such as skin or body fluids. While DNA-based machine learning models can prioritize rare variants for affecting splicing, their performance in predicting tissue-specific aberrant splicing remains unassessed. Here we generated an aberrant splicing benchmark dataset, spanning over 8.8 million rare variants in 49 human tissues from the Genotype-Tissue Expression (GTEx) dataset. At 20% recall, state-of-the-art DNA-based models achieve maximum 12% precision. By mapping and quantifying tissue-specific splice site usage transcriptome-wide and modeling isoform competition, we increased precision by threefold at the same recall. Integrating RNA-sequencing data of clinically accessible tissues into our model, AbSplice, brought precision to 60%. These results, replicated in two independent cohorts, substantially contribute to noncoding loss-of-function variant identification and to genetic diagnostics design and analytics.

The Landscape of Aberrant Alternative Splicing Events in Steatotic Liver Graft Post Transplantation via Transcriptome-Wide Analysis

The application of steatotic liver graft has been increased significantly due to the severe donor shortage and prevalence of non-alcoholic fatty liver disease. However, steatotic donor livers are vulnerable to acute phase inflammatory injury, which may result in cancer recurrence. Alternative splicing events (ASEs) are critical for diverse transcriptional variants in hepatocellular carcinoma (HCC). Here, we aimed to depict the landscape of ASEs, as well as to identify the differential ASEs in steatotic liver graft and their association with tumor recurrence after transplantation. The overall portrait of intragraft transcripts and ASEs were elucidated through RNA sequencing with the liver graft biopsies from patients and rat transplant models. Various differential ASEs were identified in steatotic liver grafts. CYP2E1, ADH1A, CYP2C8, ADH1C, and HGD, as corresponding genes to the common pathways involved differential ASEs in human and rats, were significantly associated with HCC patients' survival. The differential ASEs related RNA-binding proteins (RBPs) were enriched in metabolic pathways. The altered immune cell distribution, particularly macrophages and neutrophils, were perturbated by differential ASEs. The cancer hallmarks were enriched in steatotic liver grafts and closely associated with differential ASEs. Our work identified the differential ASE network with metabolic RBPs, immune cell distribution, and cancer hallmarks in steatotic liver grafts. We verified the link between steatotic liver graft injury and tumor recurrence at post-transcriptional level, offered new evidence to explore metabolism and immune responses, and provided the potential prognostic and therapeutic markers for tumor recurrence.

mRNA isoform balance in neuronal development and disease

Balanced mRNA isoform diversity and abundance are spatially and temporally regulated throughout cellular differentiation. The proportion of expressed isoforms contributes to cell type specification and determines key properties of the differentiated cells. Neurons are unique cell types with intricate developmental programs, characteristic cellular morphologies, and electrophysiological potential. Neuron-specific gene expression programs establish these distinctive cellular characteristics and drive diversity among neuronal subtypes. Genes with neuron-specific alternative processing are enriched in key neuronal functions, including synaptic proteins, adhesion molecules, and scaffold proteins. Despite the similarity of neuronal gene expression programs, each neuronal subclass can be distinguished by unique alternative mRNA processing events. Alternative processing of developmentally important transcripts alters coding and regulatory information, including interaction domains, transcript stability, subcellular localization, and targeting by RNA binding proteins. Fine-tuning of mRNA processing is essential for neuronal activity and maintenance. Thus, the focus of neuronal RNA biology research is to dissect the transcriptomic mechanisms that underlie neuronal homeostasis, and consequently, predispose neuronal subtypes to disease. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA in Disease and Development > RNA in Development.

Transcriptomic Complexity of Culm Growth and Development in Different Types of Moso Bamboo

Moso bamboo is capable of both sexual and asexual reproduction during natural growth, resulting in four distinct types of culms: the bamboo shoot-culm, the seedling stem, the leptomorph rhizome, and a long-ignored culm-the outward-rhizome. Sometimes, when the outward rhizomes break through the soil, they continue to grow longitudinally and develop into a new individual. However, the roles of alternative transcription start sites (aTSS) or termination sites (aTTS) as well as alternative splicing (AS) have not been comprehensively studied for their development. To re-annotate the moso bamboo genome and identify genome-wide aTSS, aTTS, and AS in growing culms, we utilized single-molecule long-read sequencing technology. In total, 169,433 non-redundant isoforms and 14,840 new gene loci were identified. Among 1311 lncRNAs, most of which showed a positive correlation with their target mRNAs, one-third of these IncRNAs were preferentially expressed in winter bamboo shoots. In addition, the predominant AS type observed in moso bamboo was intron retention, while aTSS and aTTS events occurred more frequently than AS. Notably, most genes with AS events were also accompanied by aTSS and aTTS events. Outward rhizome growth in moso bamboo was associated with a significant increase in intron retention, possibly due to changes in the growth environment. As different types of moso bamboo culms grow and develop, a significant number of isoforms undergo changes in their conserved domains due to the regulation of aTSS, aTTS, and AS. As a result, these isoforms may play different roles than their original functions. These isoforms then performed different functions from their original roles, contributing to the transcriptomic complexity of moso bamboo. Overall, this study provided a comprehensive overview of the transcriptomic changes underlying different types of moso bamboo culm growth and development.

Splicing complexity as a pivotal feature of alternative exons in mammalian species

BACKGROUND

As a significant process of post-transcriptional gene expression regulation in eukaryotic cells, alternative splicing (AS) of exons greatly contributes to the complexity of the transcriptome and indirectly enriches the protein repertoires. A large number of studies have focused on the splicing inclusion of alternative exons and have revealed the roles of AS in organ development and maturation. Notably, AS takes place through a change in the relative abundance of the transcript isoforms produced by a single gene, meaning that exons can have complex splicing patterns. However, the commonly used percent spliced-in (Ψ) values only define the usage rate of exons, but lose information about the complexity of exons' linkage pattern. To date, the extent and functional consequence of splicing complexity of alternative exons in development and evolution is poorly understood.

RESULTS

By comparing splicing complexity of exons in six tissues (brain, cerebellum, heart, liver, kidney, and testis) from six mammalian species (human, chimpanzee, gorilla, macaque, mouse, opossum) and an outgroup species (chicken), we revealed that exons with high splicing complexity are prevalent in mammals and are closely related to features of genes. Using traditional machine learning and deep learning methods, we found that the splicing complexity of exons can be moderately predicted with features derived from exons, among which length of flanking exons and splicing strength of downstream/upstream splice sites are top predictors. Comparative analysis among human, chimpanzee, gorilla, macaque, and mouse revealed that, alternative exons tend to evolve to an increased level of splicing complexity and higher tissue specificity in splicing complexity. During organ development, not only developmentally regulated exons, but also 10-15% of non-developmentally regulated exons show dynamic splicing complexity.

CONCLUSIONS

Our analysis revealed that splicing complexity is an important metric to characterize the splicing dynamics of alternative exons during the development and evolution of mammals.

Rescue of blood coagulation Factor VIII exon-16 mis-splicing by antisense oligonucleotides

The human Factor VIII ( F8 ) protein is essential for the blood coagulation cascade and specific F8 mutations cause the rare bleeding disorder Hemophilia A (HA). Here, we investigated the impact of HA-causing single-nucleotide mutations on F8 pre-mRNA splicing. We found that 14/97 (∼14.4%) coding sequence mutations tested in our study induced exon skipping. Splicing patterns of 4/11 (∼36.4%) F8 exons tested were especially sensitive to the presence of common disease-causing mutations. RNA-chemical probing analyses revealed a three-way junction structure at the 3' end of intron 15 (TWJ-3-15). TWJ-3-15 sequesters the polypyrimidine tract, a key determinant of 3' splice site strength. Using exon-16 of the F8 gene as a model, we designed specific antisense oligonucleotides (ASOs) that target TWJ-3-15 and identified three that promote the splicing of F8 exon-16. Interaction of TWJ-3-15 with ASOs increases accessibility of the polypyrimidine tract and inhibits the binding of hnRNPA1-dependent splicing silencing factors. Moreover, ASOs targeting TWJ-3-15 rescue diverse splicing-sensitive HA-causing mutations, most of which are distal to the 3' splice site being impacted. The TWJ-3-15 structure and its effect on mRNA splicing provide a model for HA etiology in patients harboring specific F8 mutations and provide a framework for precision RNA-based HA therapies.