RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease

EIciRNAs in focus: current understanding and future perspectives

Circular RNAs (circRNAs) are a unique class of covalently closed single-stranded RNA molecules that play diverse roles in normal physiology and pathology. Among the major types of circRNA, exon-intron circRNA (EIciRNA) distinguishes itself by its sequence composition and nuclear localization. Recent RNA-seq technologies and computational methods have facilitated the detection and characterization of EIciRNAs, with features like circRNA intron retention (CIR) and tissue-specificity being characterized. EIciRNAs have been identified to exert their functions via mechanisms such as regulating gene transcription, and the physiological relevance of EIciRNAs has been reported. Within this review, we present a summary of the current understanding of EIciRNAs, delving into their identification and molecular functions. Additionally, we emphasize factors regulating EIciRNA biogenesis and the physiological roles of EIciRNAs based on recent research. We also discuss the future challenges in EIciRNA exploration, underscoring the potential for novel functions and functional mechanisms of EIciRNAs for further investigation.

Homozygous familial hypercholesterolemia evaluation and survival single center study in Saudi Arabia: The HESSA registry

BACKGROUND AND AIMS BACKGROUND

Homozygous Familial Hypercholesterolemia (HoFH) is a rare, life-threatening genetic disorder causing extremely high low density lipoprotein cholesterol (LDL-C) levels, leading to early cardiovascular disease (CVD) and premature death. In Saudi Arabia, where consanguinity is common, HoFH prevalence is higher with unique genetic pathogenic familial hypercholesterolemia (FH) causing variants and treatment challenges. This study aims to analyze the clinical, genetic, treatment, and cardiovascular outcomes data of Saudi pediatric and adult HoFH patients treated at King Faisal Specialist Hospital & Research Centre (KFSHRC) over 23 years.

METHODS

A retrospective review of all patients (LDL-C >8 mmol/L) at KFSHRC (2000-2023) using European Atherosclerosis Society 2023 criteria to confirm HoFH. Data from those confirmed included demographics, lipid profiles, pathogenic FH-causing variants, treatments, mortality, and cardiovascular outcomes.

RESULTS

Among 514 severe hypercholesterolemia cases, 127 had HoFH. Diagnosis occurred at an average age of 14.3 ± 9.7 years. The mortality was 16 %, and 12 % were lost to follow-up. Cardiovascular interventions were performed in 31 % (coronary interventions in 28 % and aortic valve replacement in 17 %). The most common pathogenic FH-causing variants (57 %) was the founder null mutation c.2027del p.(Gly676Alafs∗33). Statins and ezetimibe were the primary treatments (73 %), but many required LDL-apheresis (36 %) or liver transplantation (LTx) (21 %). The peri-operative mortality for LTx was 7 %, but there was no long-term mortality on average follow-up of 6.2 ± 3.6 years, with only one patient requiring percutaneous coronary intervention. Adults were more likely to receive statins/ezetimibe (94 %/91 % vs. 50 %/53 % in pediatrics, p < 0.01) and LDL-apheresis (64 % vs. 8 %, p < 0.001), while liver transplantation was more common in children (38 % vs. 7 %, p < 0.001).

CONCLUSIONS

This study highlights the burden of null LDL-R pathogenic FH-causing variants and the frequent need for invasive treatments in Saudi HoFH patients. Liver transplantation is a viable option with low peri-operative mortality and favorable long-term disease-free survival. Early diagnosis, regional genetic screening, and access to advanced therapies are essential in achieving better outcomes.

Reclassification of candidate splicing variants refines clinically conflicting interpretations in SLC26A4-associated hearing loss

PURPOSE

Variants in the human SLC26A4 gene are a major cause of hereditary hearing loss. Many splice site variants have been identified, but their pathogenicity is not well understood.

METHODS

In accordance with the guidelines from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology, we analysed the spectrum of SLC26A4 gene variants. We performed in silico analysis and in vitro splicing assays to evaluate novel or known variants of uncertain significance that may contribute to aberrant alternative splicing.

RESULTS

In a cohort of 178 patients carrying SLC26A4 variants, selected from 202 hearing loss patients with or without inner ear malformations who underwent SLC26A4 gene testing, we identified a total of 50 variants. Among these, 10 intronic variants potentially affecting splicing collectively accounted for 54.8% of the total allele frequency of all identified variant types and were prioritised for messenger RNA (mRNA) splicing analysis. Further investigation demonstrated that four variants led to distinct types of aberrant splicing outcomes. Overall, the clinical significance of seven splice site variants was reclassified, representing at least 4.34% (14/323) of the variants within our cohort.

CONCLUSION

By using the standard classification of SLC26A4 variants, our results were able to interpret novel or uncertain SLC26A4 gene variants in a pathogenic or benign variant direction. This approach facilitates more refined genetic counselling for patients carrying SLC26A4 gene variants.

A series of reviews in familial cancer: genetic cancer risk in context variants of uncertain significance in MMR genes: which procedures should be followed?

Interpreting variants of uncertain significance (VUS) in mismatch repair (MMR) genes remains a major challenge in managing Lynch syndrome and other hereditary cancer syndromes. This review outlines recommended VUS classification procedures, encompassing foundational and specialized methodologies tailored for MMR genes by expert organizations, including InSiGHT and ClinGen's Hereditary Colorectal Cancer/Polyposis Variant Curation Expert Panel (VCEP). Key approaches include: (1) functional data, encompassing direct assays measuring MMR proficiency such as in vitro MMR assays, deep mutational scanning, and MMR cell-based assays, as well as techniques like methylation-tolerant assays, proteomic-based approaches, and RNA sequencing, all of which provide critical functional evidence supporting variant pathogenicity; (2) computational data/tools, including in silico meta-predictors and models, which contribute to robust VUS classification when integrated with experimental evidence; and (3) enhanced variant detection to identify the actual causal variant through whole-genome sequencing and long-read sequencing to detect pathogenic variants missed by traditional methods. These strategies improve diagnostic precision, support clinical decision-making for Lynch syndrome, and establish a flexible framework that can be applied to other OMIM-listed genes.

Defects in mRNA splicing and implications for infertility: a comprehensive review and in silico analysis

BACKGROUND

mRNA splicing is a fundamental process in the reproductive system, playing a pivotal role in reproductive development and endocrine function, and ensuring the proper execution of meiosis, mitosis, and gamete function. Trans-acting factors and cis-acting elements are key players in mRNA splicing whose dysfunction can potentially lead to male and female infertility. Although hundreds of trans-acting factors have been implicated in mRNA splicing, the mechanisms by which these factors influence reproductive processes are fully understood for only a subset. Furthermore, the clinical impact of variations in cis-acting elements on human infertility has not been comprehensively characterized, leading to probable omissions of pathogenic variants in standard genetic analyses.

OBJECTIVE AND RATIONALE

This review aimed to summarize our current understanding of the factors involved in mRNA splicing regulation and their association with infertility disorders. We introduced methods for prioritizing and functionally validating splicing variants associated with human infertility. Additionally, we explored corresponding abnormal splicing therapies that could potentially provide insight into treating human infertility.

SEARCH METHODS

Systematic literature searches of human and model organisms were performed in the PubMed database between May 1977 and July 2024. To identify mRNA splicing-related genes and pathogenic variants in infertility, the search terms 'splice', 'splicing', 'variant', and 'mutation' were combined with azoospermia, oligozoospermia, asthenozoospermia, multiple morphological abnormalities of the sperm flagella, acephalic spermatozoa, disorders of sex development, early embryonic arrest, reproductive endocrine disorders, oocyte maturation arrest, premature ovarian failure, primary ovarian insufficiency, zona pellucida, fertilization defects, infertile, fertile, infertility, fertility, reproduction, and reproductive.

OUTCOMES

Our search identified 5014 publications, of which 291 were included in the final analysis. This review provided a comprehensive overview of the biological mechanisms of mRNA splicing, with a focus on the roles of trans-acting factors and cis-acting elements. We highlighted the disruption of 52 trans-acting proteins involved in spliceosome assembly and catalytic activity and recognized splicing regulatory regions and epigenetic regulation associated with infertility. The 73 functionally validated splicing variants in the cis-acting elements of 54 genes have been reported in 20 types of human infertility; 27 of them were located outside the canonical splice sites and potentially overlooked in standard genetic analysis due to likely benign or of uncertain significance. The in silico prediction of splicing can prioritize potential splicing abnormalities that may be true pathogenic mechanisms. We also summarize the methods for prioritizing splicing variants and strategies for functional validation and review splicing therapy approaches for other diseases, providing a reference for abnormal reproduction treatment.

WIDER IMPLICATIONS

Our comprehensive review of trans-acting factors and cis-acting elements in mRNA splicing will further promote a more thorough understanding of reproductive regulatory processes, leading to improved pathogenic variant identification and potential treatments for human infertility.

REGISTRATION NUMBER

N/A.

Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study

Introduction

The fields of allergy and immunology are increasingly recognizing the transformative potential of artificial intelligence (AI). Its adoption is reshaping research directions, clinical practices, and healthcare systems. However, a systematic overview identifying current statuses, emerging trends, and future research hotspots is lacking.

Methods

This study applied bibliometric analysis methods to systematically evaluate the global research landscape of AI applications in allergy and immunology. Data from 3,883 articles published by 21,552 authors across 1,247 journals were collected and analyzed to identify leading contributors, prevalent research themes, and collaboration patterns.

Results

Analysis revealed that the USA and China are currently leading in research output and scientific impact in this domain. AI methodologies, especially machine learning (ML) and deep learning (DL), are predominantly applied in drug discovery and development, disease classification and prediction, immune response modeling, clinical decision support, diagnostics, healthcare system digitalization, and medical education. Emerging trends indicate significant movement toward personalized medical systems integration.

Discussion

The findings demonstrate the dynamic evolution of AI in allergy and immunology, highlighting the broadening scope from basic diagnostics to comprehensive personalized healthcare systems. Despite advancements, critical challenges persist, including technological limitations, ethical concerns, and regulatory frameworks that could potentially hinder further implementation and integration.

Conclusion

AI holds considerable promise for advancing allergy and immunology globally by enhancing healthcare precision, efficiency, and accessibility. Addressing existing technological, ethical, and regulatory challenges will be crucial to fully realizing its potential, ultimately improving global health outcomes and patient well-being.

Alternative splicing analysis in a Spanish ASD (Autism Spectrum Disorders) cohort: in silico prediction and characterization

Autism Spectrum Disorders (ASD) are complex and genetically heterogeneous neurodevelopmental conditions. Although alternative splicing (AS) has emerged as a potential contributor to ASD pathogenesis, its role in large-scale genomic studies has remained relatively unexplored. In this comprehensive study, we utilized computational tools to identify, predict, and validate splicing variants within a Spanish ASD cohort (360 trios), shedding light on their potential contributions to the disorder. We utilized SpliceAI, a newly developed machine-learning tool, to identify high-confidence splicing variants in the Spanish ASD cohort and applied a stringent threshold (Δ ≥ 0.8) to ensure robust confidence in the predictions. The in silico validation was then conducted using SpliceVault, which provided compelling evidence of the predicted splicing effects, using 335,663 reference RNA-sequencing (RNA-seq) datasets from GTEx v8 and the sequence read archive (SRA). Furthermore, ABSplice was employed for additional orthogonal in silico confirmation and to elucidate the tissue-specific impacts of the splicing variants. Notably, our analysis suggested the contribution of splicing variants within CACNA1I, CBLB, CLTB, DLGAP1, DVL3, KIAA0513, OFD1, PKD1, SLC13A3, and SCN2A. Complementary datasets, including more than 42,000 ASD cases, were employed for gene validation and gene ontology (GO) analysis. These analyses revealed potential tissue-specific effects of the splicing variants, particularly in adipose tissue, testis, and the brain. These findings suggest the involvement of these tissues in ASD etiology, which opens up new avenues for further functional testing. Enrichments in molecular functions and biological processes imply the presence of separate pathways and mechanisms involved in the progression of the disorder, thereby distinguishing splicing genes from other ASD-related genes. Notably, splicing genes appear to be predominantly associated with synaptic organization and transmission, in contrast to non-splicing genes (i.e., genes harboring de novo and inherited coding variants not predicted to alter splicing), which have been mainly implicated in chromatin remodeling processes. In conclusion, this study advances our comprehension of the role of AS in ASD and calls for further investigations, including in vitro validation and integration with multi-omics data, to elucidate the functional roles of the highlighted genes and the intricate interplay of the splicing process with other regulatory mechanisms and tissues in ASD.

OpenSpliceAI: An efficient, modular implementation of SpliceAI enabling easy retraining on non-human species

The SpliceAI deep learning system is currently one of the most accurate methods for identifying splicing signals directly from DNA sequences. However, its utility is limited by its reliance on older software frameworks and human-centric training data. Here we introduce OpenSpliceAI, a trainable, open-source version of SpliceAI implemented in PyTorch to address these challenges. OpenSpliceAI supports both training from scratch and transfer learning, enabling seamless re-training on species-specific datasets and mitigating human-centric biases. Our experiments show that it achieves faster processing speeds and lower memory usage than the original SpliceAI code, allowing large-scale analyses of extensive genomic regions on a single GPU. Additionally, OpenSpliceAI's flexible architecture makes for easier integration with established machine learning ecosystems, simplifying the development of custom splicing models for different species and applications. We demonstrate that OpenSpliceAI's output is highly concordant with SpliceAI. In silico mutagenesis (ISM) analyses confirm that both models rely on similar sequence features, and calibration experiments demonstrate similar score probability estimates.

Functional analysis of a novel FBN1 deep intronic variant causing Marfan syndrome in a Chinese patient

Marfan syndrome (MFS MIM#154700), due to pathogenic variants in the FBN1 gene, is an autosomal dominant connective tissue disorder, typically involving the skeletal, cardiovascular and ocular systems. Currently, over 3000 MFS patients were reported, and approximately 1800 pathogenic variants in FBN1 were identified. However, the molecular diagnosis still remains challenging for 8%-10% of patients with clinical features suggestive of MFS. In this study, we reported a 2-month-old Chinese female patient whose clinical features were compatible with the MFS. Whole-exome sequencing (WES) identified a novel de novo deep intronic variant, c.4943-8_4943-7insTATGTGATATTCAT TCAC in intron 40 of FBN1 that was predicted to affect the RNA splicing. Minigene analysis showed that this variant causes skipping of exon 41, leading to the deletion of 41 amino acids (c.4943_5065del, p.Val1649_Asp1689del). It confirmed the pathogenic nature of the variant and established the genotype-phenotype relationship. Our study expands the mutation spectrum of FBN1 and emphasizes the importance of deep intronic variant interpretation and the need for additional functional studies to verify the pathogenicity of these variants.

Cone Rod Homeobox (CRX): literature review and new insights

The development of the neural retina requires a complex, spatiotemporally regulated network of gene expression. Here we review the role of the cone rod homeobox (CRX) transcription factor in specification and differentiation of retinal photoreceptors and its function in inherited retinal diseases such as cone-rod dystrophy (CoRD), dominant retinitis pigmentosa (RP), and Leber's congenital amaurosis (LCA). We delineate the findings of animal models and, more recently, human retinal organoids in elucidating molecular mechanisms of CRX activity and the pathogenesis of inherited photoreceptor degenerations. Lastly, we discuss implications of these findings in the development of therapies for inherited retinal diseases.

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.

Evaluating reproductive carrier screening using biotinidase deficiency as a model: Variants identified, variant rates, and management

PURPOSE

To review biotinidase gene (BTD) variants identified in a large, diverse, reproductive carrier screening (RCS) cohort and outline management of heterozygotes with pathogenic or likely pathogenic (P/LP) variants.

METHODS

This retrospective observational study included samples tested from January 2020 to September 2022 in a 274-gene panel. The study involved females aged 18 to 55 years. Screening was performed using next-generation sequencing covering exons and 10 base-pair flanking introns. The heterozygote frequency was calculated for P/LP variants for the entire population and individual racial/ethnic groups.

RESULTS

Of the 91,637 women tested, 5625 (6.1%) had a P/LP variant in BTD. NM_000060.4:c.1330G>C p.(Asp444His) (referred to as D444H or D424H) alone, or in combination with another variant, accounted for 5193 (92.3%) of the positive tests. P/LP heterozygote rates differed between racial and ethnic groups. We ascertained 7 novel P/LP variants not previously recorded in databases.

CONCLUSION

The BTD P/LP variants identified through RCS were substantially compatible with those found through positive newborn screening. Therefore, RCS provides a potential for earlier diagnosis. We observed significant differences in P/LP heterozygote rates for biotinidase deficiency among different racial and ethnic groups. Most reported variants can be interpreted without requiring determination of serum biotinidase activity.

MAJIQ-CLIN: A novel tool for the identification of Mendelian disease-causing variants from RNA-Seq data

The current diagnostic rate for patients with suspected Mendelian genetic disorders is only 25 to 58%, even though whole exome sequencing (WES) is part of the standard of care. One reason for the low diagnostic rate is that traditional WES analysis methods struggle to detect RNA splicing aberrations. It is estimated that 15-50% of human pathogenic variants alter splicing, with numerous splice-altering variants being causal for known Mendelian disorders. Developing reliable diagnostic tools to detect, quantify, prioritize, and visualize RNA splicing aberrations from patient RNA sequencing is therefore crucial. We present MAJIQ-CLIN, a method to address this need to augment clinical diagnostic using RNA-Seq and compare it to existing tools. We include the first systematic evaluation of the accuracy of such tools using synthetic data across several aberration types and transcript inclusion levels; we also evaluate accuracy on several datasets of biologically validated solved test cases. We show that MAJIQ-CLIN compares favorably to existing tools in both accuracy and efficiency, then use MAJIQ-CLIN to investigate several unsolved patient cases from the Undiagnosed Diseases Network.

Molecular assessment of splicing variants in a cohort of patients with inborn errors of immunity: methodological approach and interpretation remarks

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Splicing accuracy varies across human introns, tissues, age and disease

Alternative splicing impacts most multi-exonic human genes. Inaccuracies during this process may have an important role in ageing and disease. Here, we investigate splicing accuracy using RNA-sequencing data from >14k control samples and 40 human body sites, focusing on split reads partially mapping to known transcripts in annotation. We show that splicing inaccuracies occur at different rates across introns and tissues and are affected by the abundance of core components of the spliceosome assembly and its regulators. We find that age is positively correlated with a global decline in splicing fidelity, mostly affecting genes implicated in neurodegenerative diseases. We find support for the latter by observing a genome-wide increase in splicing inaccuracies in samples affected with Alzheimer's disease as compared to neurologically normal individuals. In this work, we provide an in-depth characterisation of splicing accuracy, with implications for our understanding of the role of inaccuracies in ageing and neurodegenerative disorders.

Generative modeling for RNA splicing predictions and design

Alternative splicing (AS) of pre-mRNA plays a crucial role in tissue-specific gene regulation, with disease implications due to splicing defects. Predicting and manipulating AS can therefore uncover new regulatory mechanisms and aid in therapeutics design. We introduce TrASPr+BOS, a generative AI model with Bayesian Optimization for predicting and designing RNA for tissue-specific splicing outcomes. TrASPr is a multi-transformer model that can handle different types of AS events and generalize to unseen cellular conditions. It then serves as an oracle, generating labeled data to train a Bayesian Optimization for Splicing (BOS) algorithm to design RNA for condition-specific splicing outcomes. We show TrASPr+BOS outperforms existing methods, enhancing tissue-specific AUPRC by up to 2.4 fold and capturing tissue-specific regulatory elements. We validate hundreds of predicted novel tissue-specific splicing variations and confirm new regulatory elements using dCas13. We envision TrASPr+BOS as a light yet accurate method researchers can probe or adopt for specific tasks.

Pre-T cell receptor-α immunodeficiency detected exclusively using whole genome sequencing

Maturation of αβ lineage T cells in the thymus relies on the formation and cell surface expression of a pre-T cell receptor (TCR) complex, composed of TCRβ chain and pre-TCRα (pTCRα) chain heterodimers, giving rise to a diverse T cell repertoire. Genetic aberrations in key molecules involved in T cell development lead to profound T cell immunodeficiency. Definitive genetic diagnosis guides treatment choices and counseling. In this study, we describe the role of whole genome sequencing (WGS) in providing a definitive diagnosis for a child with T cell deficiency, where targeted panel sequencing of SCID genes and whole exome sequencing had failed. A novel homozygous 8kb deletion in PTCRA, encoding pTCRα, was identified. To date, use of WGS remains restricted and for many geographical regions, is clinically unavailable.

A family with normal sperm motility carrying a sY86 deletion in AZFa region and partial deletion in AZFc region

Introduction

Usually, patients with sY84 or sY86 deficiency present with azoospermia, but recent studies have shown that some males with partial AZFa deletions, including sY84 or sY86, exhibit normal fertility. Here, we reported a rare case of AZF deletion in a family, where both father and son exhibited a deletion at the sY86 site in the AZFa region and a partial deletion in the AZFc region.

Methods and Results

Detection was performed using classical multiplex polymerase chain reaction and the "Male AZF Full-region Detection" Panel, revealing specific deletions in AZFa: Yq11.21 (14,607,372-14,637,973), 30.6 kb; AZFc: Yq11.223-11.23 (25,848,831-27,120,665), 1.3 M for the father; and Yq11.223-11.23 (25,505,378-27,120,665), 1.6 M for the son. Notably, although the son's sperm motility parameters showed no significant abnormalities, there was a history of failed pregnancies for twice, with sperm exhibiting a high rate of head defect.

Discussion

Given the complexities of the reproductive phenotype following AZF region deletions, additional extended genetic testing is necessary when partial deletions in the AZF region are detected, thus providing more accurate predictions of the spermatogenesis in patient. This study provides valuable insights and guidance for clinical decision-making and the implementation of assisted reproductive technologies in such cases.

Profiling of pathogenic variants in Japanese patients with sarcoglycanopathy

BACKGROUND

Sarcoglycanopathies (SGPs) are limb-girdle muscular dystrophies (LGMDs) that can be classified into four types, LGMDR3, LGMDR4, LGMDR5, and LGMDR6, caused by mutations in the genes, SGCA, SGCB, SGCG, and SGCD, respectively. SGPs are relatively rare in Japan. This study aims to profile the genetic variants that cause SGPs in Japanese patients.

METHODS

Clinical course and pathological findings were retrospectively reviewed in Japanese patients with SGP. Genetic analyses were performed using a combination of targeted resequencing with a hereditary muscle disease panel, whole genome sequencing, multiplex ligation-dependent probe amplification, and long-read sequencing. The structures of transcripts with aberrant splicing were also determined by RT-PCR, RNA-seq, and in silico prediction.

RESULTS

We identified biallelic variants in SGC genes in 53 families, including three families with LGMDR6, which had not been identified in Japan so far. SGCA was the most common causative gene, accounting for 56% of cases, followed by SGCG, SGCB, and SGCD, at 17%, 21%, and 6%, respectively. Missense variants in SGCA were very frequent at 78.3%, while they were relatively rare in SGCB, SGCG, and SGCD at 11.1%, 18.2%, and 16.6%, respectively. We also analyzed the haplotypes of alleles carrying three variants found in multiple cases: c.229C > T in SGCA, c.325C > T in SGCB, and exon 6 deletion in SGCG; two distinct haplotypes were found for c.229C > T in SGCA, while each of the latter two variants was on single haplotypes.

CONCLUSIONS

We present genetic profiles of Japanese patients with SGPs. Haplotype analysis indicated common ancestors of frequent variants. Our findings will support genetic diagnosis and gene therapy.

Orthrus: Towards Evolutionary and Functional RNA Foundation Models

In the face of rapidly accumulating genomic data, our ability to accurately predict key mature RNA properties that underlie transcript function and regulation remains limited. Pre-trained genomic foundation models offer an avenue to adapt learned RNA representations to biological prediction tasks. However, existing genomic foundation models are trained using strategies borrowed from textual or visual domains that do not leverage biological domain knowledge. Here, we introduce Orthrus, a Mamba-based mature RNA foundation model pre-trained using a novel self-supervised contrastive learning objective with biological augmentations. Orthrus is trained by maximizing embedding similarity between curated pairs of RNA transcripts, where pairs are formed from splice isoforms of 10 model organisms and transcripts from orthologous genes in 400+ mammalian species from the Zoonomia Project. This training objective results in a latent representation that clusters RNA sequences with functional and evolutionary similarities. We find that the generalized mature RNA isoform representations learned by Orthrus significantly outperform existing genomic foundation models on five mRNA property prediction tasks, and requires only a fraction of fine-tuning data to do so. Finally, we show that Orthrus is capable of capturing divergent biological function of individual transcript isoforms.

Artificial intelligence and deep learning algorithms for epigenetic sequence analysis: A review for epigeneticists and AI experts

Epigenetics encompasses mechanisms that can alter the expression of genes without changing the underlying genetic sequence. The epigenetic regulation of gene expression is initiated and sustained by several mechanisms such as DNA methylation, histone modifications, chromatin conformation, and non-coding RNA. The changes in gene regulation and expression can manifest in the form of various diseases and disorders such as cancer and congenital deformities. Over the last few decades, high-throughput experimental approaches have been used to identify and understand epigenetic changes, but these laboratory experimental approaches and biochemical processes are time-consuming and expensive. To overcome these challenges, machine learning and artificial intelligence (AI) approaches have been extensively used for mapping epigenetic modifications to their phenotypic manifestations. In this paper we provide a narrative review of published research on AI models trained on epigenomic data to address a variety of problems such as prediction of disease markers, gene expression, enhancer-promoter interaction, and chromatin states. The purpose of this review is twofold as it is addressed to both AI experts and epigeneticists. For AI researchers, we provided a taxonomy of epigenetics research problems that can benefit from an AI-based approach. For epigeneticists, given each of the above problems we provide a list of candidate AI solutions in the literature. We have also identified several gaps in the literature, research challenges, and recommendations to address these challenges.

Canada's contributions to RNA research: past, present, and future perspectives

The field of RNA research has provided profound insights into the basic mechanisms modulating the function and adaption of biological systems. RNA has also been at the center stage in the development of transformative biotechnological and medical applications, perhaps most notably was the advent of mRNA vaccines that were critical in helping humanity through the Covid-19 pandemic. Unbeknownst to many, Canada boasts a diverse community of RNA scientists, spanning multiple disciplines and locations, whose cutting-edge research has established a rich track record of contributions across various aspects of RNA science over many decades. Through this position paper, we seek to highlight key contributions made by Canadian investigators to the RNA field, via both thematic and historical viewpoints. We also discuss initiatives underway to organize and enhance the impact of the Canadian RNA research community, particularly focusing on the creation of the not-for-profit organization RNA Canada ARN. Considering the strategic importance of RNA research in biology and medicine, and its considerable potential to help address major challenges facing humanity, sustained support of this sector will be critical to help Canadian scientists play key roles in the ongoing RNA revolution and the many benefits this could bring about to Canada.

Studying protein-protein interactions: Latest and most popular approaches

PPIs, or protein-protein interactions, are essential for many biological processes. According to the findings, abnormal PPIs have been linked to several diseases, such as cancer and infectious and neurological disorders. Consequently, focusing on PPIs is a path toward disease treatment and a crucial tool for producing novel medications. Many methods exist to investigate PPIs, including low- and high-throughput studies. Since many PPIs have been discovered using in vitro and in vivo experimental approaches, the use of computational methods to predict PPIs has grown due to the expanding scale of PPI data and the intrinsic complexity of interacting mechanisms. Recognizing PPI networks offers a systematic means of predicting protein functions, and pathways that are included. These investigations can help uncover the underlying molecular mechanisms of complex phenotypes and clarify the biological processes related to health and diseases. Therefore, our goal in this study is to provide an overview of the latest and most popular approaches for investigating PPIs. We also overview some important clinical approaches based on the PPIs and how these interactions can be targeted.

Critical Analysis of Cytoplasmic Progression of Inflammatory Signaling Suggests Potential Pharmacologic Targets for Wound Healing and Fibrotic Disorders

Successful skin wound healing is dependent on an interplay between epidermal keratinocytes and dermal fibroblasts as they react to local extracellular factors (DAMPs, PAMPs, cytokines, etc.) surveyed from that environment by numerous membrane receptors (e.g., TLRs, cytokine receptors, etc.). In turn, those receptors are the start of a cytoplasmic signaling pathway where balance is key to effective healing and, as needed, cell and matrix regeneration. When directed through NF-κB, these signaling routes lead to transient responses to the benefit of initiating immune cell recruitment, cell replication, local chemokine and cytokine production, and matrix protein synthesis. The converse can also occur, where ongoing canonical NF-κB activation leads to chronic, hyper-responsive states. Here, we assess three key players, TAK1, TNFAIP3, and TNIP1, in cytoplasmic regulation of NF-κB activation, which, because of their distinctive and yet inter-related functions, either promote or limit that activation. Their balanced function is integral to successful wound healing, given their significant control over the expression of inflammation-, fibrosis-, and matrix remodeling-associated genes. Intriguingly, these three proteins have also been emphasized in dysregulated NF-κB signaling central to systemic sclerosis (SSc). Notably, diffuse SSc shares some tissue features similar to an excessive inflammatory/fibrotic wound response without eventual resolution. Taking a cue from certain instances of aberrant wound healing and SSc having some shared aspects, e.g., chronic inflammation and fibrosis, this review looks for the first time, to our knowledge, at what those pathologies might have in common regarding the cytoplasmic progression of NF-κB-mediated signaling. Additionally, while TAK1, TNFAIP3, and TNIP1 are often investigated and reported on individually, we propose them here as three proteins whose consequences of function are very highly interconnected at the signaling focus of NF-κB. We thus highlight the emerging promise for the eventual clinical benefit derived from an improved understanding of these integral signal progression modulators. Depending on the protein, its indirect or direct pharmacological regulation has been reported. Current findings support further intensive studies of these points in NF-κB regulation both for their basic function in healthy cells as well as with the goal of targeting them for translational benefit in multiple cutaneous wound healing situations, whether stemming from acute injury or a dysregulated inflammatory/fibrotic response.

A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity

Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene therapy. Despite numerous high-throughput methods facilitating genome-wide enhancer identification, deciphering the sequence determinants of their activity remains challenging. Here, we present the DREAM (DNA cis-Regulatory Elements with controllable Activity design platforM) framework, a novel deep learning-based approach for synthetic enhancer design. Proficient in uncovering subtle and intricate patterns within extensive enhancer screening data, DREAM achieves cutting-edge sequence-based enhancer activity prediction and highlights critical sequence features implicating strong enhancer activity. Leveraging DREAM, we have engineered enhancers that surpass the potency of the strongest enhancer within the Drosophila genome by approximately 3.6-fold. Remarkably, these synthetic enhancers exhibited conserved functionality across species that have diverged more than billion years, indicating that DREAM was able to learn highly conserved enhancer regulatory grammar. Additionally, we designed silencers and cell line-specific enhancers using DREAM, demonstrating its versatility. Overall, our study not only introduces an interpretable approach for enhancer design but also lays out a general framework applicable to the design of other types of cis-regulatory elements.

Case report: A case of holocarboxylase synthetase deficiency with respiratory tract as the initial symptom

Introduction

Holocarboxylase synthetase deficiency (HLCSD) is a rare autosomal recessive genetic disorder caused by mutations in the holocarboxylase synthetase (HLCS) gene, which affects multiple systems. Common clinical manifestations include metabolic acidosis, rash, feeding difficulties, and growth retardation, with predominant involvement of the nervous system, skin, and hair. However, respiratory symptoms as the initial manifestation are relatively rare.

Case Presentation

We report the case of a 1 year and 4-month-old Chinese male patient who presented with a 2-day history of cough, followed by half a day of wheezing and shortness of breath. Despite supportive treatment with antibiotics upon admission, the infant continued to experience rapid and deep breathing accompanied by groaning, and obvious wheezing. Blood gas analysis revealed metabolic acidosis that was difficult to correct. Blood tandem mass spectrometry showed elevations in C50H, C3, C4OH, and urine organic acid analysis revealed elevations in lactate, 3-hydroxybutyric acid, 3-hydroxyisovaleric acid, acetoacetic acid, 3-methylcrotonylglycine, and methylcitric acid. Genetic testing revealed two variants in the HLCS gene in the infant: NM_001352514: exon6: c.1088T>A: p.V363D variant and exon11: c.2434C>T: p.R812* heterozygous variant, resulting in HLCSD. Ultimately, the diagnosis of HLCSD was established, and oral biotin treatment achieved good clinical efficacy.

Conclusion

This article summarizes the clinical data of a case of HLCSD in an infant, primarily presenting with respiratory symptoms. It provides a comprehensive summary of the etiology, diagnosis, and treatment, offering insights for the diagnosis of rare diseases by clinical physicians.

DMD mutations in pediatric patients with phenotypes of Duchenne/Becker muscular dystrophy

Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) are common X-inherited neuromuscular diseases. The genetic diagnosis has been used as the diagnostic choice for DMD/BMD. The study subjects consisted of 37 patients from Southwest China. Peripheral blood was collected for the extraction of genomic DNA. DMD mutation was sequenced using the next-generation sequencing approach. The detected mutation was validated using the multiplex ligation-dependent probe amplification or Sanger sequencing methods. Variation annotation and pathogenicity prediction were performed using the online databases. Pathogenic mutations were identified 3 splicing site, 7 single nucleotide, 1 indel, 23 deletion, and 3 duplication mutations. Novel DMD variants were discovered, including two novel splicing variations (c.1890 + 1G>T; c.1923 + 1G>A), one missense mutation (c.1946G>T), one nonsense mutation (c.7441G>T), one indel mutation (INDEL EX20), and one duplication mutation (DUP EX75-78). The current study provides mutation information of DMD for the genetic diagnosis of DMD/BMD.

Profiling genetically driven alternative splicing across the Indonesian archipelago

One of the regulatory mechanisms influencing the functional capacity of genes is alternative splicing (AS). Previous studies exploring the splicing landscape of human tissues have shown that AS has contributed to human biology, especially in disease progression and the immune response. Nonetheless, this phenomenon remains poorly characterized across human populations, and it is unclear how genetic and environmental variation contribute to AS. Here, we examine a set of 115 Indonesian samples from three traditional island populations spanning the genetic ancestry cline that characterizes Island Southeast Asia. We conduct a global AS analysis between islands to ascertain the degree of functionally significant AS events and their consequences. Using an event-based statistical model, we detected over 1,500 significant differential AS events across all comparisons. Additionally, we identify over 6,000 genetic variants associated with changes in splicing (splicing quantitative trait loci [sQTLs]), some of which are driven by Papuan-like genetic ancestry, and only show partial overlap with other publicly available sQTL datasets derived from other populations. Computational predictions of RNA binding activity reveal that a fraction of these sQTLs directly modulate the binding propensity of proteins involved in the splicing regulation of immune genes. Overall, these results contribute toward elucidating the role of genetic variation in shaping gene regulation in one of the most diverse regions in the world.

Compare three deep learning-based artificial intelligence models for classification of calcified lumbar disc herniation: a multicenter diagnostic study

Objective

To develop and validate an artificial intelligence diagnostic model for identifying calcified lumbar disc herniation based on lateral lumbar magnetic resonance imaging(MRI).

Methods

During the period from January 2019 to March 2024, patients meeting the inclusion criteria were collected. All patients had undergone both lumbar spine MRI and computed tomography(CT) examinations, with regions of interest (ROI) clearly marked on the lumbar sagittal MRI images. The participants were then divided into separate sets for training, testing, and external validation. Ultimately, we developed a deep learning model using the ResNet-34 algorithm model and evaluated its diagnostic efficacy.

Results

A total of 1,224 eligible patients were included in this study, consisting of 610 males and 614 females, with an average age of 53.34 ± 10.61 years. Notably, the test datasets displayed an impressive classification accuracy rate of 91.67%, whereas the external validation datasets achieved a classification accuracy rate of 88.76%. Among the test datasets, the ResNet34 model outperformed other models, yielding the highest area under the curve (AUC) of 0.96 (95% CI: 0.93, 0.99). Additionally, the ResNet34 model also exhibited superior performance in the external validation datasets, exhibiting an AUC of 0.88 (95% CI: 0.80, 0.93).

Conclusion

In this study, we established a deep learning model with excellent performance in identifying calcified intervertebral discs, thereby offering a valuable and efficient diagnostic tool for clinical surgeons.

Integrated Biochemical and Computational Methods for Deciphering RNA-Processing Codes

RNA processing involves steps such as capping, splicing, polyadenylation, modification, and nuclear export. These steps are essential for transforming genetic information in DNA into proteins and contribute to RNA diversity and complexity. Many biochemical methods have been developed to profile and quantify RNAs, as well as to identify the interactions between RNAs and RNA-binding proteins (RBPs), especially when coupled with high-throughput sequencing technologies. With the rapid accumulation of diverse data, it is crucial to develop computational methods to convert the big data into biological knowledge. In particular, machine learning and deep learning models are commonly utilized to learn the rules or codes governing the transformation from DNA sequences to intriguing RNAs based on manually designed or automatically extracted features. When precise enough, the RNA codes can be incredibly useful for predicting RNA products, decoding the molecular mechanisms, forecasting the impact of disease variants on RNA processing events, and identifying driver mutations. In this review, we systematically summarize the biochemical and computational methods for deciphering five important RNA codes related to alternative splicing, alternative polyadenylation, RNA localization, RNA modifications, and RBP binding. For each code, we review the main types of experimental methods used to generate training data, as well as the key features, strategic model structures, and advantages of representative tools. We also discuss the challenges encountered in developing predictive models using large language models and extensive domain knowledge. Additionally, we highlight useful resources and propose ways to improve computational tools for studying RNA codes.

Genome Sequencing Identifies 13 Novel Candidate Risk Genes for Autism Spectrum Disorder in a Qatari Cohort

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social communication, restricted interests, and repetitive behaviors. Despite considerable research efforts, the genetic complexity of ASD remains poorly understood, complicating diagnosis and treatment, especially in the Arab population, with its genetic diversity linked to migration, tribal structures, and high consanguinity. To address the scarcity of ASD genetic data in the Middle East, we conducted genome sequencing (GS) on 50 ASD subjects and their unaffected parents. Our analysis revealed 37 single-nucleotide variants from 36 candidate genes and over 200 CGG repeats in the FMR1 gene in one subject. The identified variants were classified as uncertain, likely pathogenic, or pathogenic based on in-silico algorithms and ACMG criteria. Notably, 52% of the identified variants were homozygous, indicating a recessive genetic architecture to ASD in this population. This finding underscores the significant impact of high consanguinity within the Qatari population, which could be utilized in genetic counseling/screening program in Qatar. We also discovered single nucleotide variants in 13 novel genes not previously associated with ASD: ARSF, BAHD1, CHST7, CUL2, FRMPD3, KCNC4, LFNG, RGS4, RNF133, SCRN2, SLC12A8, USP24, and ZNF746. Our investigation categorized the candidate genes into seven groups, highlighting their roles in cognitive development, including the ubiquitin pathway, transcription factors, solute carriers, kinases, glutamate receptors, chromatin remodelers, and ion channels.

Analysis of Bone Phenotype Differences in MEN1-Related and Sporadic Primary Hyperparathyroidism Using 3D-DXA

Background: The rarity and variability of MEN1-related primary hyperparathyroidism (mPHPT) has led to contradictory data regarding the bone phenotype in this patient population. Methods: A single-center retrospective study was conducted among young age- and sex-matched patients with mPHPT and sporadic hyperparathyroidism (sPHPT). The main parameters of calcium-phosphorus metabolism, bone remodeling markers, and bone mineral density (BMD) measurements were obtained during the active phase of hyperparathyroidism before parathyroidectomy (PTE) and 1 year after. Trabecular Bone Score (TBS) and 3D-DXA analysis of the proximal femur were used to evaluate the differences in bone architecture disruption between groups. Results: Patients with mPHPT had significant lower preoperative BMD compared to sPHPT at lumbar spine-LS (p = 0.002); femur neck-FN (p = 0.001); and total hip-TH (p = 0.002). 3D-DXA analysis showed the prevalence of cortical rather than trabecular bone damage in mPHPT compared to sPHPT: cortical thickness (p < 0.001); cortical surface BMD (p = 0.001); cortical volumetric BMD (p = 0.007); and trabecular volumetric BMD (p = 0.029). One year after, PTE DXA and 3D-DXA parameters were similar between groups, while 3D-visualisation showed more extensive regeneration in cortical sBMD and cortical thickness in mPHPT. Conclusions: mPHPT is associated with lower preoperative BMD values with predominant architecture disruption in the cortical bone. The absence of differences in DXA and 3D-DXA parameters 1 year after PTE between mPHPT/sPHPT combined with significantly lower BMD in mPHPT at the initial stage may indicate faster bone recovery after surgery in mPHPT than in sPHPT.

SpliceTransformer predicts tissue-specific splicing linked to human diseases

We present SpliceTransformer (SpTransformer), a deep-learning framework that predicts tissue-specific RNA splicing alterations linked to human diseases based on genomic sequence. SpTransformer outperforms all previous methods on splicing prediction. Application to approximately 1.3 million genetic variants in the ClinVar database reveals that splicing alterations account for 60% of intronic and synonymous pathogenic mutations, and occur at different frequencies across tissue types. Importantly, tissue-specific splicing alterations match their clinical manifestations independent of gene expression variation. We validate the enrichment in three brain disease datasets involving over 164,000 individuals. Additionally, we identify single nucleotide variations that cause brain-specific splicing alterations, and find disease-associated genes harboring these single nucleotide variations with distinct expression patterns involved in diverse biological processes. Finally, SpTransformer analysis of whole exon sequencing data from blood samples of patients with diabetic nephropathy predicts kidney-specific RNA splicing alterations with 83% accuracy, demonstrating the potential to infer disease-causing tissue-specific splicing events. SpTransformer provides a powerful tool to guide biological and clinical interpretations of human diseases.

Functional role of UNC13D in immune diseases and its therapeutic applications

UNC13 family (also known as Munc13) proteins are evolutionarily conserved proteins involved in the rapid and regulated secretion of vesicles, including synaptic vesicles and cytotoxic granules. Fast and regulated secretion at the neuronal and immunological synapses requires multiple steps, from the biogenesis of vesicles to membrane fusion, and a complex array of proteins for each step. Defects at these steps can lead to various genetic disorders. Recent studies have shown multiple roles of UNC13D in the secretion of cytotoxic granules by immune cells. Here, the molecular structure and detailed roles of UNC13D in the biogenesis, tethering, and priming of cytotoxic vesicles and in endoplasmic reticulum are summarized. Moreover, its association with immune diseases, including familial hemophagocytic lymphohistiocytosis type 3, macrophage activation syndrome, juvenile idiopathic arthritis, and autoimmune lymphoproliferative syndrome, is reviewed. Finally, the therapeutic application of CRISPR/Cas9-based gene therapy for genetic diseases is introduced.

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.

Congenital anterior segment ocular disorders: Genotype-phenotype correlations and emerging novel mechanisms

Development of the anterior segment of the eye requires reciprocal sequential interactions between the arising tissues, facilitated by numerous genetic factors. Disruption of any of these processes results in congenital anomalies in the affected tissue(s) leading to anterior segment disorders (ASD) including aniridia, Axenfeld-Rieger anomaly, congenital corneal opacities (Peters anomaly, cornea plana, congenital primary aphakia), and primary congenital glaucoma. Current understanding of the genetic factors involved in ASD remains incomplete, with approximately 50% overall receiving a genetic diagnosis. While some genes are strongly associated with a specific clinical diagnosis, the majority of known factors are linked with highly variable phenotypic presentations, with pathogenic variants in FOXC1, CYP1B1, and PITX2 associated with the broadest spectrum of ASD conditions. This review discusses typical clinical presentations including associated systemic features of various forms of ASD; the latest functional data and genotype-phenotype correlations related to 25 ASD factors including newly identified genes; promising novel candidates; and current and emerging treatments for these complex conditions. Recent developments of interest in the genetics of ASD include identification of phenotypic expansions for several factors, discovery of multiple modes of inheritance for some genes, and novel mechanisms including a growing number of non-coding variants and alleles affecting specific domains/residues and requiring further studies.

The recommendation of re-classification of variants of uncertain significance (VUS) in adult genetic disorders patients

Since variants of uncertain significance (VUS) reported in genetic testing cannot be acted upon clinically, this classification may delay or prohibit precise diagnosis and genetic counseling in adult genetic disorders patients. Large-scale analyses about qualitatively distinct lines of evidence used for VUS can make them re-classification more accurately. We analyzed 458 Chinese adult patients WES data, within 15 pathogenic evidence PS1, PS2, PM1, PM6 and PP4 were not used for VUS pathogenic classification, meanwhile the PP3, BP4, PP2 were used much more frequently. The PM2_Supporting was used most widely for all reported variants. There were also 31 null variants (nonsense, frameshift, canonical ±1 or 2 splice sites) which were probably the disease-causing variants of the patients were classified as VUS. By analyzed the evidence used for all VUS we recommend that appropriate genetic counseling, reliable releasing of in-house data, allele frequency comparison between case and control, expanded verification in patient family, co-segregation analysis and functional assays were urgent need to gather more evidence to reclassify VUS. We also found adult patients with nervous system disease were reported the most phenotype-associated VUS and the lower the phenotypic specificity, the more reported VUS. This result emphasized the importance of pretest genetic counseling which would make less reporting of VUS. Our result revealed the characteristics of the pathogenic classification evidence used for VUS in adult genetic disorders patients for the first time, recommend a rules-based process to evaluate the pathogenicity of VUS which could provide a strong basis for accurately evaluating the pathogenicity and clinical grade information of VUS. Meanwhile, we further expanded the genetic spectrum and improve the diagnostic rate of adult genetic disorders.

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors

BACKGROUND

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

RESULTS

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

CONCLUSIONS

VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning

We are witnessing a steep increase in model development initiatives in genomics that employ high-end machine learning methodologies. Of particular interest are models that predict certain genomic characteristics based solely on DNA sequence. These models, however, treat the DNA as a mere collection of four, A, T, G and C, letters, dismissing the past advancements in science that can enable the use of more intricate information from nucleic acid sequences. Here, we provide a comprehensive database of quantum mechanical (QM) and geometric features for all the permutations of 7-meric DNA in their representative B, A and Z conformations. The database is generated by employing the applicable high-cost and time-consuming QM methodologies. This can thus make it seamless to associate a wealth of novel molecular features to any DNA sequence, by scanning it with a matching k-meric window and pulling the pre-computed values from our database for further use in modelling. We demonstrate the usefulness of our deposited features through their exclusive use in developing a model for A->C mutation rates.

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.

Clinical, pathological and genetic characteristics of 17 unrelated children with Alagille Syndrome

BACKGROUND

Alagille syndrome (ALGS) is a multisystem genetic disorder frequently characterized by hepatic manifestations. This study analyzed the clinical, pathological, and molecular genetic features of ALGS to improve the efficiency of clinical diagnosis.

METHODS

We retrospectively analyzed the clinical manifestations, pathological examination findings, and genetic testing results of 17 children diagnosed with ALGS based on the revised criteria and hospitalized at our center from January 2012 to January 2022.

RESULTS

The clinical manifestations are as follows: Cholestasis (16/17, 94%), characteristic facies (15/17, 88%), heart disease (12/16, 75%), butterfly vertebrae (12/17, 71%) and posterior embryotoxon (7/12, 58%). Among the 15 patients who underwent liver pathology examination, 13 (87%) were found to have varying degrees of bile duct paucity. Genetic testing was performed on 15 children, and pathogenic variants of the jagged canonical Notch ligand 1 (JAG1) gene were identified in 13 individuals, including 4 novel variants. No pathogenic variant in the notch homolog 2 (NOTCH2) gene were identified, and 2 children exhibited none of the aforementioned gene pathogenic variants. The median follow-up duration was 7 years. Of the remaining 15 patients (excluding 2 lost to follow-up), 11 remained stable, 4 deteriorated, and no patient died during the follow-up period.

CONCLUSIONS

Among children diagnosed with ALGS, cholestasis stands as the most common feature. To minimize the risk of misdiagnosis, genetic testing should be performed on children exhibiting cholestasis, followed by the application of the revised diagnostic criteria for ALGS. While pharmacological therapy has shown effectiveness for ALGS patients, liver transplantation may be considered in instances of severe pruritus.

7p22.3 microdeletion: a case study of a patient with congenital heart defect, neurodevelopmental delay and epilepsy

BACKGROUND

Chromosome 7 has regions enriched with low copy repeats (LCRs), which increase the likelihood of chromosomal microdeletion disorders. Documented microdeletion disorders on chromosome 7 include both well-known Williams syndrome and more rare cases. It is noteworthy that most cases of various microdeletions are characterized by phenotypic signs of neuropsychological developmental disorders, which, however, have a different genetic origin. The localization of the microdeletions, the genes included in the region, as well as the structural features of the sequences of these genes have a cumulative influence on the phenotypic characteristics of the individuals for each specific case and the severity of the manifestations of disorders. The consideration of these features and their detailed analysis is important for a correct and comprehensive assessment of the disease.

RESULTS

The article describes a clinical case of 7p22.3 microdeletion in a patient with congenital heart defect and neurological abnormalities - epilepsy, combined with moderate mental and motor developmental delay.

CONCLUSIONS

Through detailed genetic analyses, we are improving the clinical description of the rare 7p22.3 microdeletion and thus creating a basis for future genetic counseling and research into targeted therapies.

Splicing-specific transcriptome-wide association uncovers genetic mechanisms for schizophrenia

Recent studies have highlighted the essential role of RNA splicing, a key mechanism of alternative RNA processing, in establishing connections between genetic variations and disease. Genetic loci influencing RNA splicing variations show considerable influence on complex traits, possibly surpassing those affecting total gene expression. Dysregulated RNA splicing has emerged as a major potential contributor to neurological and psychiatric disorders, likely due to the exceptionally high prevalence of alternatively spliced genes in the human brain. Nevertheless, establishing direct associations between genetically altered splicing and complex traits has remained an enduring challenge. We introduce Spliced-Transcriptome-Wide Associations (SpliTWAS) to integrate alternative splicing information with genome-wide association studies to pinpoint genes linked to traits through exon splicing events. We applied SpliTWAS to two schizophrenia (SCZ) RNA-sequencing datasets, BrainGVEX and CommonMind, revealing 137 and 88 trait-associated exons (in 84 and 67 genes), respectively. Enriched biological functions in the associated gene sets converged on neuronal function and development, immune cell activation, and cellular transport, which are highly relevant to SCZ. SpliTWAS variants impacted RNA-binding protein binding sites, revealing potential disruption of RNA-protein interactions affecting splicing. We extended the probabilistic fine-mapping method FOCUS to the exon level, identifying 36 genes and 48 exons as putatively causal for SCZ. We highlight VPS45 and APOPT1, where splicing of specific exons was associated with disease risk, eluding detection by conventional gene expression analysis. Collectively, this study supports the substantial role of alternative splicing in shaping the genetic basis of SCZ, providing a valuable approach for future investigations in this area.

Neurodevelopmental disorder in a patient with HMBS and SCN3A variants-A possibly blended phenotype further delineating autosomal recessive HMBS related disease

Monoallelic pathogenic HMBS variants are a well-established cause of acute intermittent porphyria (AIP), whereas biallelic pathogenic variants may cause HMBS-related leukoencephalopathy which remains a poorly characterized disorder. We describe an 8-year-old girl with hypotonia, hearing impairment, horizontal nystagmus, bilateral strabismus, impaired visual acuity, and optic nerve atrophy. She had no epilepsy but sleep electroencephalogram showed paroxysmal changes in the right hemisphere with secondary generalizations. Brain magnetic resonance imaging was unremarkable apart from a few small white matter hyperintensities. Exome sequencing (ES) initially prioritized a SCN3A c.3822G>A de novo variant whose sole causative role was eventually questioned as not fully compatible with symptoms. ES reanalysis revealed a homozygous c.674G>A HMBS variant. In the monoallelic form this variant is a known cause of AIP, whereas in trans with another HMBS pathogenic variant it was associated with the HMBS-related leukoencephalopathy in four individuals. Despite lack of signs/symptoms of porphyria, literature analysis suggested that HMBS c.674G>A likely contributed to the disease either as the sole cause or together with SCN3A c.3822G>A as a part of blended phenotype. Our report adds to the relatively small number of described cases of HMBS-related leukoencephalopathy and emphasizes that autosomal recessive form of HMBS disease can be present in the absence of porphyria symptoms.

Integrative genomics identifies SHPRH as a tumor suppressor gene in lung adenocarcinoma that regulates DNA damage response

BACKGROUND

Identification of driver mutations and development of targeted therapies has considerably improved outcomes for lung cancer patients. However, significant limitations remain with the lack of identified drivers in a large subset of patients. Here, we aimed to assess the genomic landscape of lung adenocarcinomas (LUADs) from individuals without a history of tobacco use to reveal new genetic drivers of lung cancer.

METHODS

Integrative genomic analyses combining whole-exome sequencing, copy number, and mutational information for 83 LUAD tumors was performed and validated using external datasets to identify genetic variants with a predicted functional consequence and assess association with clinical outcomes. LUAD cell lines with alteration of identified candidates were used to functionally characterize tumor suppressive potential using a conditional expression system both in vitro and in vivo.

RESULTS

We identified 21 genes with evidence of positive selection, including 12 novel candidates that have yet to be characterized in LUAD. In particular, SNF2 Histone Linker PHD RING Helicase (SHPRH) was identified due to its frequency of biallelic disruption and location within the familial susceptibility locus on chromosome arm 6q. We found that low SHPRH mRNA expression is associated with poor survival outcomes in LUAD patients. Furthermore, we showed that re-expression of SHPRH in LUAD cell lines with inactivating alterations for SHPRH reduces their in vitro colony formation and tumor burden in vivo. Finally, we explored the biological pathways associated SHPRH inactivation and found an association with the tolerance of LUAD cells to DNA damage.

CONCLUSIONS

These data suggest that SHPRH is a tumor suppressor gene in LUAD, whereby its expression is associated with more favorable patient outcomes, reduced tumor and mutational burden, and may serve as a predictor of response to DNA damage. Thus, further exploration into the role of SHPRH in LUAD development may make it a valuable biomarker for predicting LUAD risk and prognosis.

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.

A comprehensive review of deep learning-based variant calling methods

Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspection or predefined rules, which can be time-consuming and prone to errors. Consequently, deep learning-based approaches for variation detection have gained attention due to their ability to automatically learn genomic features that distinguish between variants. In our review, we discuss the recent advancements in deep learning-based algorithms for detecting small variations and structural variations in genomic data, as well as their advantages and limitations.

Spectrum of pathogenic variants and high prevalence of pathogenic BBS7 variants in Russian patients with Bardet-Biedl syndrome

Introduction

Bardet-Biedl syndrome is a rare condition characterized by obesity, retinitis pigmentosa, polydactyly, development delay, and structural kidney anomalies. This syndrome has an autosomal recessive type of inheritance. For the first time, molecular genetic testing has been provided for a large cohort of Russian patients with Bardet-Biedl syndrome.

Materials and methods

Genetic testing was provided to 61 unrelated patients using an MPS panel that includes coding regions and intronic areas of all genes (n = 21) currently associated with Bardet-Biedl syndrome.

Results

The diagnosis was confirmed for 41% of the patients (n = 25). Disease-causing variants were observed in BBS1, BBS4, BBS7, TTC8, BBS9, BBS10, BBS12, and MKKS genes. In most cases, pathogenic and likely pathogenic variants were localized in BBS1, BBS10, and BBS7 genes; recurrent variants were also observed in these genes.

Discussion

The frequency of pathogenic and likely pathogenic variants in the BBS1 and BBS10 genes among Russian patients matches the research data in other countries. The frequency of pathogenic variants in the BBS7 gene is about 1.5%-2% of patients with Bardet-Biedl syndrome, while in the cohort of Russian patients, the fraction is 24%. In addition, the recurrent pathogenic variant c.1967_1968delinsC was detected in the BBS7 gene. The higher frequency of this variant in the Russian population, as well as the lack of association of this pathogenic variant with Bardet-Biedl syndrome in other populations, suggests that the variant c.1967_1968delinsC in the BBS7 gene is major and has a founder effect in the Russian population. Results provided in this article show the significant role of pathogenic variants in the BBS7 gene for patients with Bardet-Biedl syndrome in the Russian population.

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.