Climate change has caused drastic declines in corals. As sessile organisms, corals acclimate to environmental shifts through genome-wide changes in gene expression, epigenetic modifications, and alterations in microbiome composition. However, alternative splicing (AS), a conserved mechanism of stress response in many organisms, has been under-explored in corals. Using short-term acute thermal stress assays, we investigated patterns of AS in the scleractinian coral Acropora cervicornis during response to low (33°C), medium (35°C), and high (37°C) heat stress and subsequent overnight recovery. Our findings demonstrate reproducible dynamic shifts in AS of at least 40 percent of all genes during response to heat treatment and the recovery phase. The relative proportion of AS increased in response to heat stress and was primarily dominated by intron retention in specific classes of transcripts, including those related to splicing regulation itself. While AS returned to baseline levels post-exposure to low heat, AS persisted even after reprieve from higher levels of heat stress, which was associated with irreversible loss of photosynthetic efficiency of the symbiont. Our findings demonstrate that, although animals, corals are more plant-like in their likely usage of AS for regulating thermal stress response and recovery.

Tuberous sclerosis complex (TSC) is characterized by abnormalities in cell proliferation and migration, leading to the development of hamartomas, benign tumors, or malignant cancers, affecting both the skin and brain, as well as potentially impacting the heart, kidneys, lungs, and eyes, with varying patterns of involvement over a lifetime. It is primarily caused by mutations in the TSC1 and TSC2 genes. Aberrant splicing is a crucial factor in hereditary diseases. Alternative splicing is a key mechanism for expanding the diversity of the human proteome. Mutations disrupting canonical splice sites or splicing regulatory elements impede the utilization of splice sites, leading to exon skipping and intron retention. We comprehensively analyzed missense and nonsense mutations of TSC1 and TSC2 genes using bioinformatics tools and identified 10 candidate mutations affecting pre-mRNA splicing through minigene analysis. Mutations in TSC genes can lead to partial or complete exon skipping and/or intron retention through complex mechanisms. This study emphasizes the importance of evaluating their roles in the splicing of suspected pathogenic variants in TSC.

Aberrant mRNA splicing is a well-established pathogenic mechanism for human disease, but its real impact is hardly predictable and underestimated. Splicing can be therefore modulated for therapeutic purposes, and splicing-switching molecules are in clinics for some diseases. Here, conscious that over 10% of all pathogenic mutations occurs at 5'ss, we aimed at characterizing and rescuing nine 5'ss mutations in three models of defective F8 exons whose skipping would lead to factor VIII (FVIII) deficiency (Hemophilia A), the most frequent coagulation factor disorder.

HEK293T cells were transfected with F8 minigene variants, alone or with engineered U1 small nuclear RNAs (U1snRNAs), and splicing patterns analysed via RT-PCR.

All 5'ss mutations induced exon skipping, and the proportion of correct transcripts, not predictable by computational analysis, was consistent with residual FVIII levels in patients. For each exon we identified a unique engineered U1snRNAs, either compensatory or Exon Specific (ExSpeU1), able to rescue all mutations. Overall, ExSpeU1s were more effective than compensatory U1snRNAs, particularly in the defective exons 6 and 22.

Data highlight the importance of splicing assays to elucidate genotype-phenotype relationships and proved the correction efficacy of ExSpeU1s for each targeted defective F8 exon, thus expanding their translational potential for HA.

Variants which disrupt splicing are a frequent cause of rare disease that have been under-ascertained clinically. Accurate and efficient methods to predict a variant's impact on splicing are needed to interpret the growing number of variants of unknown significance (VUS) identified by exome and genome sequencing. Here, we present the results of the CAGI6 Splicing VUS challenge, which invited predictions of the splicing impact of 56 variants ascertained clinically and functionally validated to determine splicing impact. The performance of 12 prediction methods, along with SpliceAI and CADD, was compared on the 56 functionally validated variants. The maximum accuracy achieved was 82% from two different approaches, one weighting SpliceAI scores by minor allele frequency, and one applying the recently published Splicing Prediction Pipeline (SPiP). SPiP performed optimally in terms of sensitivity, while an ensemble method combining multiple prediction tools and information from databases exceeded all others for specificity. Several challenge methods equalled or exceeded the performance of SpliceAI, with ultimate choice of prediction method likely to depend on experimental or clinical aims. One quarter of the variants were incorrectly predicted by at least 50% of the methods, highlighting the need for further improvements to splicing prediction methods for successful clinical application.

Aberrant alternative splicing, prevalent in cancer, impacts various cancer hallmarks involving proliferation, angiogenesis, and invasion. Splicing disruption often results from somatic point mutations rewiring functional pathways to support cancer cell survival. We introduce iSoMAs (iSoform expression and somatic Mutation Association), an efficient computational pipeline leveraging principal component analysis technique, to explore how somatic mutations influence transcriptome-wide gene expression at the isoform level. Applying iSoMAs to 33 cancer types comprising 9,738 tumor samples in The Cancer Genome Atlas, we identified 908 somatically mutated genes significantly associated with altered isoform expression across three or more cancer types. Mutations linked to differential isoform expression occurred through both cis- and trans-acting mechanisms, involving well-known oncogenes/suppressor genes, RNA binding protein and splicing factor genes. With wet-lab experiments, we verified direct association between TP53 mutations and differential isoform expression in cell cycle genes. Additional iSoMAs genes have been validated in the literature with independent cohorts and/or methods. Despite the complexity of cancer, iSoMAs attains computational efficiency via dimension reduction strategy and reveals critical associations between regulatory factors and transcriptional landscapes.

Highly recurrent somatic mutations in the gene encoding the core splicing factor SF3B1 are drivers of multiple cancer types. SF3B1 is a scaffold protein that orchestrates multivalent protein-protein interactions within the spliceosome that are essential for recognizing the branchsite (BS) and selecting the 3' splice site during the earliest stage of pre-mRNA splicing. In this review, we first describe the molecular mechanism by which multiple oncogenic SF3B1 mutations disrupt splicing. This involves perturbation of an early spliceosomal trimeric protein complex necessary for accurate BS recognition in a subset of introns, which leads to activation of upstream branchpoints and selection of cryptic 3' splice sites. We next discuss how specific transcripts affected by aberrant splicing in SF3B1-mutant cells contribute to the initiation and progression of cancer. Finally, we highlight the prognostic value and disease phenotypes of different cancer-associated SF3B1 mutations, which is critical for developing new targeted therapeutics against SF3B1-mutant cancers still lacking in the clinic.

Splicing is the molecular mechanism to produce mature messenger RNA (mRNA) before its translation into protein. It is estimated that 50% of disease-causing mutations disrupt splicing, mostly of them affecting canonical positions. However, variants occurring in coding regions or deep-intronic variants can also affect splicing. In these cases, interpretation of the results may be challenging and molecular validation is required.

The study includes 23 patients with splicing variants out of a cohort of 187 patients diagnosed with inborn errors of immunity (IEI). Clinical features and immunophenotypes are shown. Reverse transcription-polymerase chain reaction (RT-PCR) is the molecular assay employed for pathogenicity validation.

We detected 23 patients of 20 pedigrees with splicing variants in IEI genes, which constitutes the 12.3% of our cohort. In total, 21 splicing variants were analyzed, 10 of which had previously been reported in the literature and 11 novel ones. Among the 23 patients, 16 showed variants at canonical splice sites. Molecular validation was required only in the cases of genes of uncertain significance (GUS), high homology pseudogenes or incompatible clinical phenotype. Seven patients showed variants outside canonical positions. All of them needed molecular validation, with the exception of two patients, whose variants had previously been well characterized in the medical literature.

This study shows the proportion of splicing variants in a cohort of IEI patients, providing their clinical phenotypic characteristics and the methodology used to validate the splicing defects. Based on the results, an algorithm is proposed to clarify when a splicing variant should be validated by complementary methodology and when, by contrast, it can be directly considered disease causing.

Primary ciliary dyskinesia (PCD) is a rare autosomal recessive disorder characterized by dysfunction of motile cilia. While approximately 50 genes have been identified, around 25% of PCD patients remain genetically unexplained; elucidating the pathogenicity of specific variants remains a challenge.

Whole exome sequencing (WES) and Sanger sequencing were conducted to identify potential pathogenic variants of PCD. Minigene assays were performed to evaluate the pathogenicity of variants. Transmission electron microscopy (TEM) and high-speed video analysis (HSVA) were conducted to analyze the function of cilia in respiratory epithelial cells.

We identified two variants of DNAAF3: c.557G>A, p.G186E in exon 5, and c.1364G>A, p.G455D at the terminal nucleotide of exon 10 in a 16-year-old male patient. Through a minigene assay, we demonstrated that the c.1364G>A variant led to a four-nucleotide skipping. The cilia in epithelial ciliary cells of the proband were almost immotile. The absence of outer dynein arms and inner dynein arms was also observed.

Our study identified two compound heterozygous variants of DNAAF3, a pathogenic gene for PCD, and proved that a novel missense variant c.1364G>A affects splicing. Our findings not only expanded the spectrum of mutations in the DNAAF3 gene but also highlighted the importance of investigating variants of uncertain significance (VUS) for comprehensive genetic diagnoses.

Mutations that impact splicing play a significant role in disease etiology but are not fully understood. To characterize the impact of exonic variants on splicing in 71 clinically-actionable disease genes in asymptomatic people, we analyzed 32,112 exonic mutations from ClinVar and Geisinger MyCode using a minigene reporter assay. We identify 1,733 splice-disrupting mutations, of which the most extreme 1-2% of variants are likely to be deleterious. We report that these variants are not distributed evenly across exons but are mostly concentrated in the ∼8% of exons that are most susceptible to splicing mutations (i.e. hotspot exons). We demonstrate that splicing defects in these exons can be reverted by ASOs targeting the splice sites of either their upstream or downstream flanking exons. This finding supports the feasibility of developing single therapeutic ASOs that could revert all splice-altering variants localized to a particular exon.

Pangenomes are becoming a powerful framework to perform many bioinformatics analyses taking into account the genetic variability of a population, thus reducing the bias introduced by a single reference genome. With the wider diffusion of pangenomes, integrating genetic variability with transcriptome diversity is becoming a natural extension that demands specific methods for its exploration. In this work, we extend the notion of spliced pangenomes to that of annotated spliced pangenomes; this allows us to introduce a formal definition of Alternative Splicing (AS) events on a graph structure. To investigate the usage of graph pangenomes for the quantification of AS events across conditions, we developed pantas, the first pangenomic method for the detection and differential analysis of AS events from short RNA-Seq reads. A comparison with state-of-the-art linear reference-based approaches proves that pantas achieves competitive accuracy, making spliced pangenomes effective for conducting AS events quantification and opening future directions for the analysis of population-based transcriptomes.

Congenital heart disease (CHD) is the most common birth defect and heart valve defects are the most common cardiac defect, accounting for over 25% of all congenital heart diseases. To date, more than 400 genes have been linked to CHD, the genetic analysis of CHD cases is crucial for both clinical management and etiological determination. Patients with autosomal-recessive variants of PLD1 are predisposed to Cardiac Valvular Dysplasia-1 (CVDP1), which predominantly affects the right-sided heart valves, including the pulmonic, tricuspid, and mitral valves.

Databases were utilized to predict the impact of the c.1062-59A>G variant on splicing. Whole-exome sequencing (WES), reverse transcription polymerase chain reaction (RT-PCR), Sanger sequencing, and TA clone sequencing were conducted on both the parents and the fetus.

A compound heterozygous variation in the PLD1(NM_002662.5):c.1937G>C (p. G646A) from the father and PLD1(NM_002662.5):c.1062-59A>G from the mother, was identified and confirmed in the fetus. The c.1937G>C (p. G646A) and the c.1062-59A>G variants were all classified as variant of uncertain significance (VUS) per ACMG guidelines. RT-PCR and TA clone sequencing revealed a 76-bp intronic insertion and exon 11 skipping in the proband and her mother's transcripts, causing a frameshift and premature stop codon in PLD1. Consequently, after being informed about the risks of their variant of unknown significance (VUS), the couple chose pre-implantation genetic testing for monogenic disorders (PGT-M) and had a healthy child.

Our study identified novel variants to expand the mutation spectrum of CHD and provided reliable evidence for the recurrent risk and reproductive care options.

Chimeric RNAs, which can arise from gene recombination at the DNA level or non-canonical splicing events at the RNA level, have been identified as important roles in human tumors. Dysregulated gene expression caused by somatic mutations and altered splicing patterns of oncogenes or tumor suppressor genes can contribute to the development of tumors. Therefore, investigating the formation mechanism of chimeric RNAs via somatic mutations is critical for understanding tumor pathogenesis. This project is the first to propose studying the association between somatic single nucleotide variants and chimeric RNAs, identifying around 2900 somatic SNVs affecting chimeric RNAs in pan-cancer level. The somatic SNVs on chimeric RNAs were commonly observed in various types of tumor tissues, providing a valuable resource for future study. Additionally, these SNVs show distinct tumor specificity, and those with high frequency had a significant impact on the survival time of patients with tumors. Further research revealed that somatic SNVs associated with chimeric RNA (chiR-SNVs) were typically found within 10 nt of the junction site of chimeric RNAs and had a particularly significant effect on chimeric RNAs from different chromosomes. The enrichment analysis revealed that chiR-SNVs were significantly overrepresented in oncogenes and genes related to RNA binding proteins involved in RNA splicing, which could imply that chiR-SNVs may disrupt the process of RNA splicing and induce the occurrence of chimeric RNAs. This study sheds light on the potential molecular interaction mechanism between somatic SNVs and chimeric RNAs, which opens up a new avenue for researching disease pathway and tumorigenesis development.

We report the clinical presentation and genetic screening of a 31-year-old man with dilatation of the aortic root and ascending aorta and a positive family history for aortic dissection and sudden death. A novel heterozygous variant in a splice acceptor site (c.1600-1G>T) of TGFβR2 gene was identified by using a targeted multi-gene panel analysis. Bioinformatics tools predicted that the c.1600-1G>T variant is pathogenic by altering acceptor splice site at - 1 position affecting pre-mRNA splicing. These data confirm that the diverging splicing in the TGF-β pathway genes may be an important process in aneurismal disease and emphasize the utility of genetic sequencing in the identification of high-risk patients for a more patient's management able to improve outcomes and minimize costs for the care of patients with heritable thoracic aortic aneurysm and dissection.

Two well-known facets in protein synthesis in eukaryotic cells are transcription of DNA to pre-RNA in the nucleus and the translation of messenger-RNA (mRNA) to proteins in the cytoplasm. A critical intermediate step is the removal of segments (introns) containing ∼97% of the nucleic-acid sites in pre-RNA and sequential alignment of the retained segments (exons) to form mRNA through a process referred to as splicing. Alternative forms of splicing enrich the proteome while abnormal splicing can enhance the likelihood of a cell developing cancer or other diseases. Mechanisms for splicing and origins of splicing errors are only partially deciphered. Our goal is to determine if rules on splicing can be inferred from data analytics on nucleic-acid sequences. Toward that end, we represent a nucleic-acid site as a point in a plane defined in terms of the anterior and posterior sub-sequences of the site. The "point-set" representation expands analytical approaches, including the use of statistical tools, to characterize genome sequences. It is found that point-sets for exons and introns are visually different, and that the differences can be quantified using a family of generalized moments. We design a machine-learning algorithm that can recognize individual exons or introns with 91% accuracy. Point-set distributions and generalized moments are found to differ between organisms.

The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.

High-throughput omics technologies have generated a wealth of large protein, gene, and transcript datasets that have exacerbated the need for new methods to analyse and compare big datasets. Rank-rank hypergeometric overlap is an important threshold-free method to combine and visualize two ranked lists of P-values or fold-changes, usually from differential gene expression analyses. Here, we introduce a new rank-rank hypergeometric overlap-based method aimed at gene level and alternative splicing analyses at transcript or exon level, hitherto unreachable as transcript numbers are an order of magnitude larger than gene numbers. We tested the tool on synthetic and real datasets at gene and transcript levels to detect correlation and anticorrelation patterns and found it to be fast and accurate, even on very large datasets thanks to an evolutionary algorithm-based minimal P-value search. The tool comes with a ready-to-use permutation scheme allowing the computation of adjusted P-values at low time cost. The package compatibility mode is a drop-in replacement to previous packages. RedRibbon holds the promise to accurately extricate detailed information from large comparative analyses.

Although gene splicing occurs throughout the body, the phenotype of spliceosomal defects is largely limited to specific tissues. Cerebro-costo-mandibular syndrome (CCMS) is one such spliceosomal disease, which presents as congenital skeletal dysmorphism and is caused by mutations of SNRPB gene encoding Small Nuclear Ribonucleoprotein Polypeptides B/B' (SmB/B'). This study employed in vitro cell cultures to monitor osteo- and chondro-differentiation and examined the role of SmB/B' in the differentiation process. We found that low levels of SmB/B' by knockdown or mutations of SNRPB led to suppressed osteodifferentiation in Saos-2 osteoprogenitor-like cells, which was accompanied by affected splicing of Dlx5. On the other hand, low SmB/B' led to promoted chondrogenesis in HEPM mesenchymal stem cells. Consistent with other reports, osteogenesis was promoted by the Wnt/β-catenin pathway activator and suppressed by Wnt and BMP blockers, whereas chondrogenesis was promoted by Wnt inhibitors. Suppressed osteogenic markers by SNRPB knockdown were partly rescued by Wnt/β-catenin pathway activation. Reporter analysis revealed that suppression of SNRPB results in attenuated Wnt pathway and/or enhanced BMP pathway activities. SNRPB knockdown altered splicing of TCF7L2 which impacts Wnt/β-catenin pathway activities. This work helps unravel the mechanism underlying CCMS whereby reduced expression of spliceosomal proteins causes skeletal phenotypes.

Genomic variants outside of the canonical splicing site (±2) may generate abnormal mRNA splicing, which are defined as non-canonical splicing variants (NCSVs). However, the clinical interpretation of NCSVs in neurodevelopmental disorders (NDDs) is largely unknown.

We investigated the contribution of NCSVs to NDDs from 345,787 de novo variants (DNVs) in 47,574 patients with NDDs. We performed functional enrichment and protein-protein interaction analysis to assess the association between genes carrying prioritised NCSVs and NDDs. Minigene was used to validate the impact of NCSVs on mRNA splicing.

We observed significantly more NCSVs (p = 0.02, odds ratio [OR] = 2.05) among patients with NDD than in controls. Both canonical splicing variants (CSVs) and NCSVs contributed to an equal proportion of patients with NDD (0.76% vs. 0.82%). The candidate genes carrying NCSVs were associated with glutamatergic synapse and chromatin remodelling. Minigene successfully validated 59 of 79 (74.68%) NCSVs that led to abnormal splicing in 40 candidate genes, and 9 of the genes (ARID1B, KAT6B, TCF4, SMARCA2, SHANK3, PDHA1, WDR45, SCN2A, SYNGAP1) harboured recurrent NCSVs with the same variant present in more than two unrelated patients with NDD. Moreover, 36 of 59 (61.02%) NCSVs are novel clinically relevant variants, including 34 unreported and 2 clinically conflicting interpretations or of uncertain significance NCSVs in the ClinVar database.

This study highlights the common pathology and clinical importance of NCSVs in unsolved patients with NDD.

The present study was funded by grants from the National Natural Science Foundation of China, China Postdoctoral Science Foundation, the Hunan Youth Science and Technology Innovation Talent Project, the Provincial Natural Science Foundation of Hunan, The Scientific Research Program of FuRong laboratory, and the Natural Science Project of the University of Anhui Province.

Detection of aberrantly spliced genes is an important step in RNA-seq-based rare-disease diagnostics. We recently developed FRASER, a denoising autoencoder-based method that outperformed alternative methods of detecting aberrant splicing. However, because FRASER's three splice metrics are partially redundant and tend to be sensitive to sequencing depth, we introduce here a more robust intron-excision metric, the intron Jaccard index, that combines the alternative donor, alternative acceptor, and intron-retention signal into a single value. Moreover, we optimized model parameters and filter cutoffs by using candidate rare-splice-disrupting variants as independent evidence. On 16,213 GTEx samples, our improved algorithm, FRASER 2.0, called typically 10 times fewer splicing outliers while increasing the proportion of candidate rare-splice-disrupting variants by 10-fold and substantially decreasing the effect of sequencing depth on the number of reported outliers. To lower the multiple-testing correction burden, we introduce an option to select the genes to be tested for each sample instead of a transcriptome-wide approach. This option can be particularly useful when prior information, such as candidate variants or genes, is available. Application on 303 rare-disease samples confirmed the relative reduction in the number of outlier calls for a slight loss of sensitivity; FRASER 2.0 recovered 22 out of 26 previously identified pathogenic splicing cases with default cutoffs and 24 when multiple-testing correction was limited to OMIM genes containing rare variants. Altogether, these methodological improvements contribute to more effective RNA-seq-based rare diagnostics by drastically reducing the amount of splicing outlier calls per sample at minimal loss of sensitivity.

Pure hereditary spastic paraplegia (SPG) type 4 (SPG4) is caused by mutations of SPAST gene. This study aimed to analyze SPAST variants in SPG4 patients to highlight the occurrence of splicing mutations and combine functional studies to assess the relevance of these variants in the molecular mechanisms of the disease.

We performed an NGS panel in 105 patients, in silico analysis for splicing mutations, and in vitro minigene assay.

The NGS panel was applied to screen 105 patients carrying a clinical phenotype corresponding to upper motor neuron syndrome (UMNS), selectively affecting motor control of lower limbs. Pathogenic mutations in SPAST were identified in 12 patients (11.42%), 5 missense, 3 frameshift, and 4 splicing variants. Then, we focused on the patients carrying splicing variants using a combined approach of in silico and in vitro analysis through minigene assay and RNA, if available. For two splicing variants (i.e., c.1245+1G>A and c.1414-2A>T), functional assays confirm the types of molecular alterations suggested by the in silico analysis (loss of exon 9 and exon 12). In contrast, the splicing variant c.1005-1delG differed from what was predicted (skipping exon 7), and the functional study indicates the loss of frame and formation of a premature stop codon. The present study evidenced the high splice variants in SPG4 patients and indicated the relevance of functional assays added to in silico analysis to decipher the pathogenic mechanism.

High-throughput sequencing has identified vast numbers of variants in genetic disorders. However, the significance of variants at the exon-intron junction remains controversial. Even though most cases of Mowat-Wilson syndrome (MOWS) are caused by heterozygous loss-of-function variants in ZEB2, the pathogenicity of variants at exon-intron junction is often indeterminable. We identified four intronic variants in 5/173 patients with clinical suspicion for MOWS, and evaluated their pathogenicity by in vitro analyses. The minigene analysis showed that c.73+2T>G caused most of the transcripts skipping exon 2, while c.916+6T>G led to partial skipping of exon 7. No splicing abnormalities were detected in both c.917-21T>C and c.3067+6A>T. The minigene analysis reproduced the splicing observed in the blood cells of the patient with c.73+2T>G. The degree of the exon skipping was concordant with the severity of MOWS; while the patient with c.73+2T>G was typical MOWS, the patient with c.916+6T>G showed milder phenotype which has been seldom reported. Our results demonstrate that mRNA splicing assays using the minigenes are valuable for determining the clinical significance of intronic variants in patients with not only MOWS but also other genetic diseases with splicing aberrations and may explain atypical or milder cases, such as the current patient.

Autoimmune polyendocrine syndrome type 1 (APS-1) is caused by mutations in the autoimmune regulator (AIRE) gene. Most patients present with severe chronic mucocutaneous candidiasis and organ-specific autoimmunity from early childhood, but the clinical picture is highly variable. AIRE is crucial for negative selection of T cells, and scrutiny of different patient mutations has previously highlighted many of its molecular mechanisms. In patients with a milder adult-onset phenotype sharing a mutation in the canonical donor splice site of intron 7 (c.879+1G>A), both the predicted altered splicing pattern with loss of exon 7 (AireEx7-/-) and normal full-length AIRE mRNA were found, indicating leaky rather than abolished mRNA splicing. Analysis of a corresponding mouse model demonstrated that the AireEx7-/- mutant had dramatically impaired transcriptional capacity of tissue-specific antigens in medullary thymic epithelial cells but still retained some ability to induce gene expression compared with the complete loss-of-function AireC313X-/- mutant. Our data illustrate an association between AIRE activity and the severity of autoimmune disease, with implications for more common autoimmune diseases associated with AIRE variants, such as primary adrenal insufficiency, pernicious anemia, type 1 diabetes, and rheumatoid arthritis.

Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy. The development and optimisation of tools and methods for analysing the vast quantity of genomic data is a rapidly evolving area of research. Deep learning (DL) is a subset of machine learning (ML) that brings opportunity for novel investigative strategies that can be harnessed to gain new insights into the genomic risk of people with epilepsy. DL is being harnessed to address limitations in accuracy of long-read sequencing technologies, which improve on short-read methods. Tools that predict the functional consequence of genetic variation can represent breaking ground in addressing critical knowledge gaps, while methods that integrate independent but complimentary data enhance the predictive power of genetic data. We provide an overview of these DL tools and discuss how they may be applied to the analysis of genetic data for epilepsy research.

Fetal alcohol spectrum disorders (FASD) are leading causes of neurodevelopmental disability. The mechanisms by which alcohol (EtOH) disrupts fetal brain development are incompletely understood, as are the genetic factors that modify individual vulnerability. Because the phenotype abnormalities of FASD are so varied and widespread, we investigated whether fetal exposure to EtOH disrupts ribosome biogenesis and the processing of pre-ribosomal RNAs and ribosome assembly, by determining the effect of exposure to EtOH on the developmental expression of 18S rRNA and its cleaved forms, members of a novel class of short non-coding RNAs (srRNAs). In vitro neuronal cultures and fetal brains (11-22 weeks) were collected according to an IRB-approved protocol. Twenty EtOH-exposed brains from the first and second trimester were compared with ten unexposed controls matched for gestational age and fetal gender. Twenty fetal-brain-derived exosomes (FB-Es) were isolated from matching maternal blood. RNA was isolated using Qiagen RNA isolation kits. Fetal brain srRNA expression was quantified by ddPCR. srRNAs were expressed in the human brain and FB-Es during fetal development. EtOH exposure slightly decreased srRNA expression (1.1-fold; p = 0.03). Addition of srRNAs to in vitro neuronal cultures inhibited EtOH-induced caspase-3 activation (1.6-fold, p = 0.002) and increased cell survival (4.7%, p = 0.034). The addition of exogenous srRNAs reversed the EtOH-mediated downregulation of srRNAs (2-fold, p = 0.002). EtOH exposure suppressed expression of srRNAs in the developing brain, increased activity of caspase-3, and inhibited neuronal survival. Exogenous srRNAs reversed this effect, possibly by stabilizing endogenous srRNAs, or by increasing the association of cellular proteins with srRNAs, modifying gene transcription. Finally, the reduction in 18S rRNA levels correlated closely with the reduction in fetal eye diameter, an anatomical hallmark of FASD. The findings suggest a potential mechanism for EtOH-mediated neurotoxicity via alterations in 18S rRNA processing and the use of FB-Es for early diagnosis of FASD. Ribosome biogenesis may be a novel target to ameliorate FASD in utero or after birth. These findings are consistent with observations that gene-environment interactions contribute to FASD vulnerability.

Variants in GCK, HNF1A, and HNF4A genes are the three main causes of monogenic diabetes. Determining the molecular etiology is essential for patients with monogenic diabetes to benefit from the most appropriate treatment. The increasing number of variants of unknown significance (VUS) is a major issue in genetic diagnosis, and assessing the impact of variants on RNA splicing is challenging, particularly for genes expressed in tissues not easily accessible as in monogenic diabetes. The in vitro functional splicing assay based on a minigene construct is an appropriate approach. Here, we performed in silico analysis using SpliceAI and SPiP and prioritized 36 spliceogenic variants in GCK, HNF1A, and HNF4A. Predictions were secondarily compared with Pangolin and AbSplice-DNA bioinformatics tools which include tissue-specific annotations. We assessed the effect of selected variants on RNA splicing using minigene assays. These assays validated splicing defects for 33 out of 36 spliceogenic variants consisting of exon skipping (15%), exonic deletions (18%), intronic retentions (24%), and complex splicing patterns (42%). This provided additional evidence to reclassify 23 out of 31 (74%) VUS including missense, synonymous, and intronic noncanonical splice site variants as likely pathogenic variants. Comparison of in silico analysis with minigene results showed the robustness of bioinformatics tools to prioritize spliceogenic variants, but revealed inconsistencies in the location of cryptic splice sites underlying the importance of confirming predicted splicing alterations with functional splicing assays. Our study underlines the feasibility and the benefits of implementing minigene-splicing assays in the genetic testing of monogenic diabetes after a prior in-depth in silico analysis.

Autosomal dominant polycystic kidney disease (ADPKD) is a common monogenic multisystem disease caused primarily by mutations in the PKD1 gene or PKD2 gene. There is increasing evidence that some of these variants, which are described as missense, synonymous or nonsense mutations in the literature or databases, may be deleterious by affecting the pre-mRNA splicing process.

This study aimed to determine the effect of these PKD1 and PKD2 variants on exon splicing combined with predictive bioinformatics tools and minigene assay. As a result, among the 19 candidate single nucleotide alterations, 11 variants distributed in PKD1 (c.7866C > A, c.7960A > G, c.7979A > T, c.7987C > T, c.11248C > G, c.11251C > T, c.11257C > G, c.11257C > T, c.11346C > T, and c.11393C > G) and PKD2 (c.1480G > T) were identified to result in exon skipping.

We confirmed that 11 variants in the gene of PKD1 and PKD2 affect normal splicing by interfering the recognition of classical splicing sites or by disrupting exon splicing enhancers and generating exon splicing silencers. This is the most comprehensive study to date on pre-mRNA splicing of exonic variants in ADPKD-associated disease-causing genes in consideration of the increasing number of identified variants in PKD1 and PKD2 gene in recent years. These results emphasize the significance of assessing the effect of exon single nucleotide variants in ADPKD at the mRNA level.

Long QT syndrome type 2 (LQT2) is a genetic disorder caused by mutations in the KCNH2 gene, also known as the human ether-a-go-go-related gene (hERG). Over 30% of hERG mutations result in a premature termination codon (PTC) that triggers a process called nonsense-mediated mRNA decay (NMD), where the mRNA transcript is degraded. NMD is a quality control mechanism that removes faulty mRNA to prevent the translation of truncated proteins. Recent advances in antisense oligonucleotide (ASO) technology in the field of cystic fibrosis (CF) have yielded significant progress, including the ASO-mediated comprehensive characterization of key NMD factors and exon-skipping therapy. These advances have contributed to our understanding of the role of PTC-containing mutations in disease phenotypes and have also led to the development of potentially useful therapeutic strategies. Historically, studies of CF have provided valuable insights for the research on LQT2, particularly concerning increasing the expression of hERG. In this article, we outline the current state of knowledge regarding ASO, NMD, and hERG and discuss the introduction of ASO technology in the CF to elucidate the pathogenic mechanisms through targeting NMD. We also discuss the potential clinical therapeutic benefits and limitations of ASO for the management of LQT2. By drawing on lessons learned from CF research, we explore the potential translational values of these advances into LQT2 studies.

Breast cancer is a frequent disease for which the discovery of markers that enable early detection or prognostic assessment remains challenging. Circular RNAs (circRNAs) are single-stranded structures in closed loops that are produced by backsplicing. CircRNA and messenger RNA (mRNA) are generated co-transcriptionally, and backsplicing and linear splicing compete against each other. As mRNAs are key players in tumorigenesis, we hypothesize that a disruption of the balance between circRNAs and mRNAs could promote breast cancer. Hence, we developed an assay for a simultaneous study of circRNAs and mRNAs, which we have called splice and expression analyses by exon ligation and high-throughput sequencing (SEALigHTS). Following SEALigHTS validation for BRCA1 and BRCA2, our hypothesis was tested using an independent research set of 95 pairs from tumor and adjacent normal breast tissues. In this research set, ratios of BRCA1 and BRCA2 circRNAs/mRNAs were significantly lower in the tumor breast tissue compared to normal tissue (p = 1.6 × 10-9 and p = 4.4 × 10-5 for BRCA1 and BRCA2, respectively). Overall, we developed an innovative method to study linear splicing and backsplicing, described the repertoire of BRCA1 and BRCA2 circRNAs, including 15 novel ones, and showed for the first time that a disequilibrium between BRCA1 and BRCA2 circRNAs and mRNAs plays a role in breast cancer.

Detection of aberrantly spliced genes is an important step in RNA-seq-based rare disease diagnostics. We recently developed FRASER, a denoising autoencoder-based method for aberrant splicing detection that outperformed alternative approaches. However, as FRASER's three splice metrics are partially redundant and tend to be sensitive to sequencing depth, we introduce here a more robust intron excision metric, the Intron Jaccard Index, that combines alternative donor, alternative acceptor, and intron retention signal into a single value. Moreover, we optimized model parameters and filter cutoffs using candidate rare splice-disrupting variants as independent evidence. On 16,213 GTEx samples, our improved algorithm called typically 10 times fewer splicing outliers while increasing the proportion of candidate rare splice-disrupting variants by 10 fold and substantially decreasing the effect of sequencing depth on the number of reported outliers. Application on 303 rare disease samples confirmed the reduction fold-change of the number of outlier calls for a slight loss of sensitivity (only 2 out of 22 previously identified pathogenic splicing cases not recovered). Altogether, these methodological improvements contribute to more effective RNA-seq-based rare diagnostics by a drastic reduction of the amount of splicing outlier calls per sample at minimal loss of sensitivity.

Cancer is among the leading causes of mortality worldwide. While considerable attention has been given to genetic and epigenetic sources of cancer-specific cellular activities, the role of alternative mRNA splicing has only recently received attention as a major contributor to cancer initiation and progression. The distribution of alternate mRNA splicing variants in cancer cells is different from their non-cancer counterparts, and cancer cells are more sensitive than non-cancer cells to drugs that target components of the splicing regulatory network. While many of the alternatively spliced mRNAs in cancer cells may represent "noise" from splicing dysregulation, certain recurring splicing variants have been shown to contribute to tumor progression. Some pathogenic splicing disruption events result from mutations in cis-acting splicing regulatory sequences in disease-associated genes, while others may result from shifts in balance among naturally occurring alternate splicing variants among mRNAs that participate in cell cycle progression and the regulation of apoptosis. This review provides examples of cancer-related alternate splicing events resulting from each step of mRNA processing and the promising therapies that may be used to address them.

SpliceAI is an open-source deep learning splicing prediction algorithm that has demonstrated in the past few years its high ability to predict splicing defects caused by DNA variations. However, its outputs present several drawbacks: (1) although the numerical values are very convenient for batch filtering, their precise interpretation can be difficult, (2) the outputs are delta scores which can sometimes mask a severe consequence, and (3) complex delins are most often not handled. We present here SpliceAI-visual, a free online tool based on the SpliceAI algorithm, and show how it complements the traditional SpliceAI analysis. First, SpliceAI-visual manipulates raw scores and not delta scores, as the latter can be misleading in certain circumstances. Second, the outcome of SpliceAI-visual is user-friendly thanks to the graphical presentation. Third, SpliceAI-visual is currently one of the only SpliceAI-derived implementations able to annotate complex variants (e.g., complex delins). We report here the benefits of using SpliceAI-visual and demonstrate its relevance in the assessment/modulation of the PVS1 classification criteria. We also show how SpliceAI-visual can elucidate several complex splicing defects taken from the literature but also from unpublished cases. SpliceAI-visual is available as a Google Colab notebook and has also been fully integrated in a free online variant interpretation tool, MobiDetails ( https://mobidetails.iurc.montp.inserm.fr/MD ).

Even for essential splice-site variants that are almost guaranteed to alter mRNA splicing, no current method can reliably predict whether exon-skipping, cryptic activation or multiple events will result, greatly complicating clinical interpretation of pathogenicity. Strikingly, ranking the four most common unannotated splicing events across 335,663 reference RNA-sequencing (RNA-seq) samples (300K-RNA Top-4) predicts the nature of variant-associated mis-splicing with 92% sensitivity. The 300K-RNA Top-4 events correctly identify 96% of exon-skipping events and 86% of cryptic splice sites for 140 clinical cases subject to RNA testing, showing higher sensitivity and positive predictive value than SpliceAI. Notably, RNA re-analyses showed we had missed 300K-RNA Top-4 events for several clinical cases tested before the development of this empirical predictive method. Simply, mis-splicing events that happen around a splice site in RNA-seq data are those most likely to be activated by a splice-site variant. The SpliceVault web portal allows users easy access to 300K-RNA for informed splice-site variant interpretation and classification.

RNA is a major player in cellular function, and consequently can drive a number of disease pathologies. Over the past several years, small molecule-RNA targeting (smRNA targeting) has developed into a promising drug discovery approach. Numerous techniques, tools, and assays have been developed to support this field, and significant investments have been made by pharmaceutical and biotechnology companies. To date, the focus has been on identifying disease validated primary targets for smRNA drug development, yet RNA as a secondary (off) target for all small molecule drug programs largely has been unexplored. In this perspective, we discuss structure, target, and mechanism-driven safety aspects of smRNAs and highlight how these parameters can be evaluated in drug discovery programs to produce potentially safer drugs.

Modeling splicing is essential for tackling the challenge of variant interpretation as each nucleotide variation can be pathogenic by affecting pre-mRNA splicing via disruption/creation of splicing motifs such as 5'/3' splice sites, branch sites, or splicing regulatory elements. Unfortunately, most in silico tools focus on a specific type of splicing motif, which is why we developed the Splicing Prediction Pipeline (SPiP) to perform, in one single bioinformatic analysis based on a machine learning approach, a comprehensive assessment of the variant effect on different splicing motifs. We gathered a curated set of 4616 variants scattered all along the sequence of 227 genes, with their corresponding splicing studies. The Bayesian analysis provided us with the number of control variants, that is, variants without impact on splicing, to mimic the deluge of variants from high-throughput sequencing data. Results show that SPiP can deal with the diversity of splicing alterations, with 83.13% sensitivity and 99% specificity to detect spliceogenic variants. Overall performance as measured by area under the receiving operator curve was 0.986, better than SpliceAI and SQUIRLS (0.965 and 0.766) for the same data set. SPiP lends itself to a unique suite for comprehensive prediction of spliceogenicity in the genomic medicine era. SPiP is available at: https://sourceforge.net/projects/splicing-prediction-pipeline/.

The molecular mechanisms underlying heat stress tolerance in animals to high temperatures remain unclear. This study identified the differentially expressed mRNA isoforms which narrowed down the most reliable DEG markers and molecular pathways that underlie the mechanisms of thermoregulation. This experiment was performed on Sprague Dawley rats housed at 22 °C (control group; CT), and three acute heat-stressed groups housed at 42 °C for 30 min (H30), 60 min (H60), and 120 min (H120). Earlier, we demonstrated that acute heat stress increased the rectal temperature of rats, caused abnormal changes in the blood biochemical parameters, as well as induced dramatic changes in the expression levels of genes through epigenetics and post-transcriptional regulation. Transcriptomic analysis using RNA-Sequencing (RNA-Seq) data obtained previously from blood (CT and H120), liver (CT, H30, H60, and H120), and adrenal glands (CT, H30, H60, and H120) was performed. The differentially expressed mRNA isoforms (DEIs) were identified and annotated by the CLC Genomics Workbench. Biological process and metabolic pathway analyses were performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. A total of 225, 5764, and 4988 DEIs in the blood, liver, and adrenal glands were observed. Furthermore, the number of novel differentially expressed transcript lengths with annotated genes and novel differentially expressed transcript with non-annotated genes were 136 and 8 in blood, 3549 and 120 in the liver, as well as 3078 and 220 in adrenal glands, respectively. About 35 genes were involved in the heat stress response, out of which, Dnaja1, LOC680121, Chordc1, AABR07011951.1, Hsp90aa1, Hspa1b, Cdkn1a, Hmox1, Bag3, and Dnaja4 were commonly identified in the liver and adrenal glands, suggesting that these genes may regulate heat stress response through interactions between the liver and adrenal glands. In conclusion, this study would enhance our understanding of the complex underlying mechanisms of acute heat stress, and the identified mRNA isoforms and genes can be used as potential candidates for thermotolerance selection in mammals.