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

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

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

Discovery of eQTL Alleles Associated with Autism Spectrum Disorder: A Case-Control Study

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by challenges in social communication as well as repetitive or restrictive behaviors. Many genetic associations with ASD have been identified, but most associations occur in a fraction of the ASD population. Here, we searched for eQTL-associated DNA variants with significantly different allele distributions between ASD-affected and control. Thirty significant DNA variants associated with 174 tissue-specific eQTLs from ASD individuals in the SPARK project were identified. Several significant variants fell within brain-specific regulatory regions or had been associated with a significant change in gene expression in the brain. These eQTLs are a new class of biomarkers that could control the myriad of brain and non-brain phenotypic traits seen in ASD-affected individuals.

[TCOF1 Gene variation in Treacher Collins syndrome and evaluation of speech rehabilitation after bone bridge surgery]

Objective:By analyzing the clinical phenotypic characteristics and gene sequences of two patients with Treacher Collins syndrome（TCS）, the biological causes of the disease were determined. Then discuss the therapeutic effect of hearing intervention after bone bridge implantation. Methods:All clinical data of the two family members were collected, and the patients signed the informed consent. The peripheral blood of the proband and family members was extracted, DNA was extracted for whole exome sequencing, and Sanger sequencing was performed on the family members for the mutation site.TCOF1genetic mutations analysis was performed on the paitents. Then, the hearing threshold and speech recognition rate of family 2 proband were evaluated and compared under the sound field between bare ear and wearing bone bridge. Results:In the two pedigrees, the probands of both families presented with auricle deformity, zygomatic and mandibular hypoplasia, micrognathia, hypotropia of the eye fissure, and hypoplasia of the medial eyelashes. The proband of Family 1 also presents with specific features including right-sided narrow anterior nasal aperture and dental hypoplasia, which were consistent with the clinical diagnosis of Treacher Collins syndrome. Genetic testing was conducted on both families, and two heterozygous mutations were identified in the TCOF1 gene: c. 1350_1351dupGG（p. A451Gfs*43） and c. 4362_4366del（p. K1457Efs*12）, resulting in frameshift mutations in the amino acid sequence. Sanger sequencing validation of the TCOF1 gene in the parents of the proband in Family 1 did not detect any mutations. Proband 1 TCOF1 c. 1350_1351dupGG heterozygous variants have not been reported previously. The postoperative monosyllabic speech recognition rate of family 2 proband was 76%, the Categories of Auditory Performance（CAP） score was 6, and the Speech Intelligibility Rating（SIR） score was 4. Assessment using the Meaningful Auditory Integration Scale（MAIS） showed notable improvement in the patient's auditory perception, comprehension, and usage of hearing aids. Evaluation using the Glasgow Children's Benefit Inventory and quality of life assessment revealed significant improvements in the child's self care abilities, daily living and learning, social interactions, and psychological well being, as perceived by the parents. Conclusion:This study has elucidated the biological cause of Treacher Collins syndrome, enriched the spectrum of TCOF1 gene mutations in the Chinese population, and demonstrated that bone bridge implantation can improve the auditory and speech recognition rates in TCS patients.

High-Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels

Due to the large chemical space, the design of functional and responsive soft materials poses many challenges but also offers a wide range of opportunities in terms of the scope of possible properties. Herein, an experimental workflow for miniaturized combinatorial high-throughput screening of functional hydrogel libraries is reported. The data created from the analysis of the photodegradation process of more than 900 different types of hydrogel pads are used to train a machine learning model for automated decision making. Through iterative model optimization based on Bayesian optimization, a substantial improvement in response properties is achieved and thus expanded the scope of material properties obtainable within the chemical space of hydrogels in the study. It is therefore demonstrated that the potential of combining miniaturized high-throughput experiments with smart optimization algorithms for cost and time efficient optimization of materials properties.

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

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

Novel combinations of variations in KCNQ1 were associated with patients with long QT syndrome or Jervell and Lange-Nielsen syndrome

OBJECTIVES

Long QT syndrome (LQTS) is one of the primary causes of sudden cardiac death (SCD) in youth. Studies have identified mutations in ion channel genes as key players in the pathogenesis of LQTS. However, the specific etiology in individual families remains unknown.

METHODS

Three unrelated Chinese pedigrees diagnosed with LQTS or Jervell and Lange-Nielsen syndrome (JLNS) were recruited clinically. Whole exome sequencing (WES) was performed and further validated by multiplex ligation-dependent probe amplification (MLPA) and Sanger sequencing.

RESULTS

All of the probands in our study experienced syncope episodes and featured typically prolonged QTc-intervals. Two probands also presented with congenital hearing loss and iron-deficiency anemia and thus were diagnosed with JLNS. A total of five different variants in KCNQ1, encoding a subunit of the voltage-gated potassium channel, were identified in 3 probands. The heterozygous variants, KCNQ1 c.749T > C was responsible for LQTS in Case 1, transmitting in an autosomal dominant pattern. Two patterns of compound heterozygous variants were responsible for JLNS, including a large deletion causing loss of the exon 16 and missense variant c.1663 C > T in Case 2, and splicing variant c.605-2 A > G and frame-shift variant c.1265del in Case 3. To our knowledge, the compound heterozygous mutations containing a large deletion and missense variant were first reported in patients with JLNS.

CONCLUSION

Our study expanded the LQTS genetic spectrum, thus favoring disease screening and diagnosis, personalized treatment, and genetic consultation.

Splicing Analysis of MYO5B Noncanonical Variants in Patients with Low Gamma-Glutamyltransferase Cholestasis

Biallelic MYO5B variants have been associated with familial intrahepatic cholestasis (FIC) with low serum gamma-glutamyltransferase (GGT). Intronic or synonymous variants outside of canonical splice sites (hereinafter referred to as noncanonical variants) with uncertain significance were identified in MYO5B posing a challenge in clinical interpretation. This study is aimed at assessing the effects of these variants on premessenger RNA (pre-mRNA) splicing to improve recognition of pathogenic spliceogenic variants in MYO5B and better characterize the MYO5B genetic variation spectrum. Disease-associated MYO5B noncanonical variants were collected from the literature or newly identified low GGT cholestasis patients. In silico splicing predictions were performed to prioritize potential pathogenic variants. Minigene splicing assays were performed to determine their splicing patterns, with confirmation by blood RNA analysis in one case. Eleven (five novel) noncanonical variants with uncertain significance were identified. Minigene splicing assays revealed that three variants (c.2090+3A>T, c.2414+5G>T, and c.613-11G>A) caused complete aberrations, five variants (c.2349A>G/p.(=), c.4221G>A/p.(=), c.1322+5G>A, c.1669-35A>C, and c.3045+3A>T) caused predominant aberrations, and three variants (c.4852+11A>G, c.455+8T>C, and c.2415-6C>G) had no effect on pre-mRNA splicing. Patient-derived RNA analysis showed consistent results. Based on our results, eight variants were reclassified as likely pathogenic and three as likely benign. Combining the clinical features and the above analysis, the diagnosis of MYO5B-associated FIC could be made in three new patients. In conclusion, we characterized the splicing patterns of MYO5B noncanonical variants and suggest that RNA analysis should be routinely included in clinical diagnostics to provide essential evidence for the interpretation of variants.

Clinical Characterization of Alagille Syndrome in Patients with Cholestatic Liver Disease

Alagille syndrome (ALGS) is a multisystem condition characterized by cholestasis and bile duct paucity on liver biopsy and variable involvement of the heart, skeleton, eyes, kidneys, and face and caused by pathogenic variants in the JAG1 or NOTCH2 gene. The variable expressivity of the clinical phenotype and the lack of genotype-phenotype correlations lead to significant diagnostic difficulties. Here we present an analysis of 18 patients with cholestasis who were diagnosed with ALGS. We used an NGS panel targeting coding exons of 52 genes, including the JAG1 and NOTCH2 genes. Sanger sequencing was used to verify the mutation in the affected individuals and family members. The specific facial phenotype was seen in 16/18 (88.9%). Heart defects were seen in 8/18 (44.4%) patients (pulmonary stenosis in 7/8). Butterfly vertebrae were seen in 5/14 (35.7%) patients. Renal involvement was detected in 2/18 (11.1%) cases-one patient had renal cysts, and one had obstructive hydronephrosis. An ophthalmology examination was performed on 12 children, and only one had posterior embryotoxon (8.3%). A percutaneous liver biopsy was performed in nine cases. Bile duct paucity was detected in six/nine cases (66.7%). Two patients required liver transplantation because of cirrhosis. We identified nine novel variants in the JAG1 gene-eight frameshift variants (c.1619_1622dupGCTA (p.Tyr541X), c.1160delG (p.Gly387fs), c.964dupT (p.C322fs), c.120delG (p.L40fs), c.1984dupG (p.Ala662Glyfs), c.3168_3169delAG (p.R1056Sfs*51), c.2688delG (p.896CysfsTer49), c.164dupG (p.Cys55fs)) and one missense variant, c.2806T > G (p.Cys936Gly). None of the patients presented with NOTCH2 variants. In accordance with the classical criteria, only six patients could meet the diagnostic criteria in our cohort without genetic analysis. Genetic testing is important in the diagnosis of ALGS and can help differentiate it from other types of cholestasis.

Intronic position +9 and -9 are potentially splicing sites boundary from intronic variants analysis of whole exome sequencing data

Whole exome sequencing (WES) can also detect some intronic variants, which may affect splicing and gene expression, but how to use these intronic variants, and the characteristics about them has not been reported. This study aims to reveal the characteristics of intronic variant in WES data, to further improve the clinical diagnostic value of WES. A total of 269 WES data was analyzed, 688,778 raw variants were called, among these 367,469 intronic variants were in intronic regions flanking exons which was upstream/downstream region of the exon (default is 200 bps). Contrary to expectation, the number of intronic variants with quality control (QC) passed was the lowest at the +2 and -2 positions but not at the +1 and -1 positions. The plausible explanation was that the former had the worst effect on trans-splicing, whereas the latter did not completely abolish splicing. And surprisingly, the number of intronic variants that passed QC was the highest at the +9 and -9 positions, indicating a potential splicing site boundary. The proportion of variants which could not pass QC filtering (false variants) in the intronic regions flanking exons generally accord with "S"-shaped curve. At +5 and -5 positions, the number of variants predicted damaging by software was most. This was also the position at which many pathogenic variants had been reported in recent years. Our study revealed the characteristics of intronic variant in WES data for the first time, we found the +9 and -9 positions might be a potentially splicing sites boundary and +5 and -5 positions were potentially important sites affecting splicing or gene expression, the +2 and -2 positions seem more important splicing site than +1 and -1 positions, and we found variants in intronic regions flanking exons over ± 50 bps may be unreliable. This result can help researchers find more useful variants and demonstrate that WES data is valuable for intronic variants analysis.

Molecular Diagnosis and Identification of Novel Pathogenic Variants in a Large Cohort of Italian Patients Affected by Polycystic Kidney Diseases

Polycystic Kidney Diseases (PKDs) consist of a genetically and phenotypically heterogeneous group of inherited disorders characterized by numerous renal cysts. PKDs include autosomal dominant ADPKD, autosomal recessive ARPKD and atypical forms. Here, we analyzed 255 Italian patients using an NGS panel of 63 genes, plus Sanger sequencing of exon 1 of the PKD1 gene and MPLA (PKD1, PKD2 and PKHD1) analysis. Overall, 167 patients bore pathogenic/likely pathogenic variants in dominant genes, and 5 patients in recessive genes. Four patients were carriers of one pathogenic/likely pathogenic recessive variant. A total of 24 patients had a VUS variant in dominant genes, 8 patients in recessive genes and 15 patients were carriers of one VUS variant in recessive genes. Finally, in 32 patients we could not reveal any variant. Regarding the global diagnostic status, 69% of total patients bore pathogenic/likely pathogenic variants, 18.4% VUS variants and in 12.6% of patients we could not find any. PKD1 and PKD2 resulted to be the most mutated genes; additional genes were UMOD and GANAB. Among recessive genes, PKHD1 was the most mutated gene. An analysis of eGFR values showed that patients with truncating variants had a more severe phenotype. In conclusion, our study confirmed the high degree of genetic complexity at the basis of PKDs and highlighted the crucial role of molecular characterization in patients with suspicious clinical diagnosis. An accurate and early molecular diagnosis is essential to adopt the appropriate therapeutic protocol and represents a predictive factor for family members.

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

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

Discovering cryptic splice mutations in cancers via a deep neural network framework

Somatic mutations can disrupt splicing regulatory elements and have dramatic effects on cancer genes, yet the functional consequences of mutations located in extended splice regions is difficult to predict. Here, we use a deep neural network (SpliceAI) to characterize the landscape of splice-altering mutations in cancer. In our in-house series of 401 liver cancers, SpliceAI uncovers 1244 cryptic splice mutations, located outside essential splice sites, that validate at a high rate (66%) in matched RNA-seq data. We then extend the analysis to a large pan-cancer cohort of 17 714 tumors, revealing >100 000 cryptic splice mutations. Taking into account these mutations increases the power of driver gene discovery, revealing 126 new candidate driver genes. It also reveals new driver mutations in known cancer genes, doubling the frequency of splice alterations in tumor suppressor genes. Mutational signature analysis suggests mutational processes that could give rise preferentially to splice mutations in each cancer type, with an enrichment of signatures related to clock-like processes and DNA repair deficiency. Altogether, this work sheds light on the causes and impact of cryptic splice mutations in cancer, and highlights the power of deep learning approaches to better annotate the functional consequences of mutations in oncology.

Whole-exome sequencing applications in prenatal diagnosis of fetal bowel dilatation

This study introduced whole-exome sequencing (WES) in prenatal diagnosis of fetal bowel dilatation to improve the detection outcome when karyotype analysis and copy number variation sequencing (CNV-seq) were uninformative in detecting pathogenic variants. The work reviewed 28 cases diagnosed with fetal bowel dilatation and analyzed the results of karyotype analysis, CNV-seq, and WES. Among the 28 cases, the detection rate in cases with low risk of aneuploidy was 11.54% (3/26), which is lower than 100% (2/2) in cases with high risk of aneuploidy. Ten low-risk aneuploidy cases with isolated fetal bowel dilatation had normal genetic testing results, while the remaining 16 cases with other ultrasound abnormalities were detected for genetic variants at a rate of 18.75% (3/16). The detection rate of gene variation was 3.85% (1/26) by CNV-seq and 7.69% (2/26) by WES. This study suggested that WES could reveal more genetic risk in prenatal diagnosis of fetal bowel dilatation and has value in prenatal diagnosis to reduce birth defects.

Introme accurately predicts the impact of coding and noncoding variants on gene splicing, with clinical applications

Predicting the impact of coding and noncoding variants on splicing is challenging, particularly in non-canonical splice sites, leading to missed diagnoses in patients. Existing splice prediction tools are complementary but knowing which to use for each splicing context remains difficult. Here, we describe Introme, which uses machine learning to integrate predictions from several splice detection tools, additional splicing rules, and gene architecture features to comprehensively evaluate the likelihood of a variant impacting splicing. Through extensive benchmarking across 21,000 splice-altering variants, Introme outperformed all tools (auPRC: 0.98) for the detection of clinically significant splice variants. Introme is available at https://github.com/CCICB/introme .

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

Background

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

Results

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

Conclusion

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

Aberrant splicing prediction across human tissues

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

XAI-enabled neural network analysis of metabolite spatial distributions

We used deep neural networks to process the mass spectrometry imaging (MSI) data of mouse muscle (young vs aged) and human cancer (tumor vs normal adjacent) tissues, with the aim of using explainable artificial intelligence (XAI) methods to rapidly identify biomarkers that can distinguish different classes of tissues, from several thousands of metabolite features. We also modified classic neural network architectures to construct a deep convolutional neural network that is more suitable for processing high-dimensional MSI data directly, instead of using dimension reduction techniques, and compared it to seven other machine learning analysis methods' performance in classification accuracy. After ascertaining the superiority of Channel-ResNet10, we used a novel channel selection-based XAI method to identify the key metabolite features that were responsible for its learning accuracy. These key metabolite biomarkers were then processed using MetaboAnalyst for pathway enrichment mapping. We found that Channel-ResNet10 was superior to seven other machine learning methods for MSI analysis, reaching > 98% accuracy in muscle aging and colorectal cancer datasets. We also used a novel channel selection-based XAI method to find that in young and aged muscle tissues, the differentially distributed metabolite biomarkers were especially enriched in the propanoate metabolism pathway, suggesting it as a novel target pathway for anti-aging therapy.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

The non-coding genome in Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders (NDDs) characterized by difficulties in social interaction and communication, repetitive behavior, and restricted interests. While ASD have been proven to have a strong genetic component, current research largely focuses on coding regions of the genome. However, non-coding DNA, which makes up for ∼99% of the human genome, has recently been recognized as an important contributor to the high heritability of ASD, and novel sequencing technologies have been a milestone in opening up new directions for the study of the gene regulatory networks embedded within the non-coding regions. Here, we summarize current progress on the contribution of non-coding alterations to the pathogenesis of ASD and provide an overview of existing methods allowing for the study of their functional relevance, discussing potential ways of unraveling ASD's "missing heritability".

Improved detection of aberrant splicing using the Intron Jaccard Index

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.

Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank

Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions that contribute to the COVID-19 related mortality risk using the UK Biobank data (28,097 affected cases and 1656 deaths). We refer to such groups of variants as super variants. We identify 15 super variants with various levels of significance as susceptibility loci for COVID-19 mortality. Specifically, we identify a super variant (odds ratio [OR] = 1.594, p = 5.47 × 10-9 ) on Chromosome 7 that consists of the minor allele of rs76398985, rs6943608, rs2052130, 7:150989011_CT_C, rs118033050, and rs12540488. We also discover a super variant (OR = 1.353, p = 2.87 × 10-8 ) on Chromosome 5 that contains rs12517344, rs72733036, rs190052994, rs34723029, rs72734818, 5:9305797_GTA_G, and rs180899355.

Regulation of pre-mRNA splicing: roles in physiology and disease, and therapeutic prospects

The removal of introns from mRNA precursors and its regulation by alternative splicing are key for eukaryotic gene expression and cellular function, as evidenced by the numerous pathologies induced or modified by splicing alterations. Major recent advances have been made in understanding the structures and functions of the splicing machinery, in the description and classification of physiological and pathological isoforms and in the development of the first therapies for genetic diseases based on modulation of splicing. Here, we review this progress and discuss important remaining challenges, including predicting splice sites from genomic sequences, understanding the variety of molecular mechanisms and logic of splicing regulation, and harnessing this knowledge for probing gene function and disease aetiology and for the design of novel therapeutic approaches.

Analysis of BRCA1 and BRCA2 alternative splicing in predisposition to ovarian cancer

BACKGROUND

The mRNA splicing is regulated on multiple levels, resulting in the proper distribution of genes' transcripts in each cell and maintaining cell homeostasis. At the same time, the expression of alternative transcripts can change in response to underlying genetic variants, often missed during routine diagnostics.

AIM

The main aim of this study was to define the frequency of aberrant splicing in BRCA1 and BRCA2 genes in blood RNA extracted from ovarian cancer patients who were previously found negative for the presence of pathogenic alterations in the 25 most commonly analysed ovarian cancer genes, including BRCA1 and BRCA2.

MATERIAL AND METHODS

Frequency and spectrum of splicing alterations in BRCA1 and BRCA2 genes were analysed in blood RNA from 101 ovarian cancer patients and healthy controls (80 healthy women) using PCR followed by gel electrophoresis and Sanger sequencing. The expression of splicing events was examined using RT-qPCR.

RESULTS

We did not identify any novel, potentially pathogenic splicing alterations. Nevertheless, we detected six naturally occurring transcripts, named BRCA1ΔE9-10, BRCA1ΔE11, BRCA1ΔE11q, and BRCA2ΔE3, BRCA2ΔE12 and BRCA2ΔE17-18 of which three (BRCA1ΔE11q, BRCA1ΔE11 and BRCA2ΔE3) were significantly higher expressed in the ovarian cancer cohort than in healthy controls (p ≤ 0.0001).

CONCLUSIONS

This observation indicates that the upregulation of selected naturally occurring transcripts can be stimulated by non-genetic mechanisms and be a potential systemic response to disease progression and/or treatment. However, this hypothesis requires further examination.

Splicing accuracy varies across human introns, tissues and age

Alternative splicing impacts most multi-exonic human genes. Inaccuracies during this process may have an important role in ageing and disease. Here, we investigated mis-splicing using RNA-sequencing data from ~14K control samples and 42 human body sites, focusing on split reads partially mapping to known transcripts in annotation. We show that mis-splicing occurs at different rates across introns and tissues and that these splicing inaccuracies are primarily affected by the abundance of core components of the spliceosome assembly and its regulators. Using publicly available data on short-hairpin RNA-knockdowns of numerous spliceosomal components and related regulators, we found support for the importance of RNA-binding proteins in mis-splicing. We also demonstrated that age is positively correlated with mis-splicing, and it affects genes implicated in neurodegenerative diseases. This in-depth characterisation of mis-splicing can have important implications for our understanding of the role of splicing inaccuracies in human disease and the interpretation of long-read RNA-sequencing data.

Unsupervised Domain Adaptation with Differentially Private Gradient Projection

Domain adaptation is a viable solution for deep learning with small data. However, domain adaptation models trained on data with sensitive information may be a violation of personal privacy. In this article, we proposed a solution for unsupervised domain adaptation, called DP-CUDA, which is based on differentially private gradient projection and contradistinguisher. Compared with the traditional domain adaptation process, DP-CUDA involves searching for domain-invariant features between the source domain and target domain first and then transferring knowledge. Specifically, the model is trained in the source domain by supervised learning from labeled data. During the training of the target model, feature learning is used to solve the classification task in an end-to-end manner using unlabeled data directly, and the differentially private noise is injected into the gradient. We conducted extensive experiments on a variety of benchmark datasets, including MNIST, USPS, SVHN, VisDA-2017, Office-31, and Amazon Review, to demonstrate our proposed method’s utility and privacy-preserving properties.

PredDSMC: A predictor for driver synonymous mutations in human cancers

Introduction: Driver mutations play a critical role in the occurrence and development of human cancers. Most studies have focused on missense mutations that function as drivers in cancer. However, accumulating experimental evidence indicates that synonymous mutations can also act as driver mutations.

Methods: Here, we proposed a computational method called PredDSMC to accurately predict driver synonymous mutations in human cancers. We first systematically explored four categories of multimodal features, including sequence features, splicing features, conservation scores, and functional scores. Further feature selection was carried out to remove redundant features and improve the model performance. Finally, we utilized the random forest classifier to build PredDSMC.

Results: The results of two independent test sets indicated that PredDSMC outperformed the state-of-the-art methods in differentiating driver synonymous mutations from passenger mutations.

Discussion: In conclusion, we expect that PredDSMC, as a driver synonymous mutation prediction method, will be a valuable method for gaining a deeper understanding of synonymous mutations in human cancers.

Deep Learning-Based Modeling of Drug–Target Interaction Prediction Incorporating Binding Site Information of Proteins

Chemogenomics, also known as proteochemometrics, covers various computational methods for predicting interactions between related drugs and targets on large-scale data. Chemogenomics is used in the early stages of drug discovery to predict the off-target effects of proteins against therapeutic candidates. This study aims to predict unknown ligand–target interactions using one-dimensional SMILES as inputs for ligands and binding site residues for proteins in a computationally efficient manner. We first formulate a Deep learning CNN model using one-dimensional SMILES for drugs and motif-rich binding pocket subsequences of proteins as inputs. We evaluate and compare the proposed deep learning model trained on expert-based features against shallow feature-based machine learning methods. The proposed method achieved better or similar performance on the MSE and AUPR metrics than the shallow methods. Additionally, We show that our deep learning model, DeepPS is computationally more efficient than the deep learning model trained on full-length raw sequences of proteins. We conclude that a beneficial research approach would be to integrate structural information of proteins for modeling drug-target interaction prediction of large datasets for more interpretability, high throughput, and broad applicability.

Graphical abstract

Evidence for the role of transcription factors in the co-transcriptional regulation of intron retention

BACKGROUND

Alternative splicing is a widespread regulatory phenomenon that enables a single gene to produce multiple transcripts. Among the different types of alternative splicing, intron retention is one of the least explored despite its high prevalence in both plants and animals. The recent discovery that the majority of splicing is co-transcriptional has led to the finding that chromatin state affects alternative splicing. Therefore, it is plausible that transcription factors can regulate splicing outcomes.

RESULTS

We provide evidence for the hypothesis that transcription factors are involved in the regulation of intron retention by studying regions of open chromatin in retained and excised introns. Using deep learning models designed to distinguish between regions of open chromatin in retained introns and non-retained introns, we identified motifs enriched in IR events with significant hits to known human transcription factors. Our model predicts that the majority of transcription factors that affect intron retention come from the zinc finger family. We demonstrate the validity of these predictions using ChIP-seq data for multiple zinc finger transcription factors and find strong over-representation for their peaks in intron retention events.

CONCLUSIONS

This work opens up opportunities for further studies that elucidate the mechanisms by which transcription factors affect intron retention and other forms of splicing.

AVAILABILITY

Source code available at https://github.com/fahadahaf/chromir.

Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer

Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools ( www.regtools.org ), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. We apply RegTools to over 9000 tumor samples with both tumor DNA and RNA sequence data. RegTools discovers 235,778 events where a splice-associated variant significantly increases the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotate them with the Variant Effect Predictor, SpliceAI, and Genotype-Tissue Expression junction counts and compare our results to other tools that integrate genomic and transcriptomic data. While many events are corroborated by the aforementioned tools, the flexibility of RegTools also allows us to identify splice-associated variants in known cancer drivers, such as TP53, CDKN2A, and B2M, and other genes.

An Integrated Pipeline for Trio-Rapid Genome Sequencing in Critically Ill Infants

Trio-rapid genome sequencing (trio-rGS) can assist the genetic diagnosis of critically ill infants given its ability to detect a broad range of pathogenic variants, as well as microbes, simultaneously with high efficiency. To achieve more comprehensive clinical diagnoses, it is essential to propose a recommended protocol in clinical practice. Here, we introduced an integrated pipeline to detect germline variants and microorganisms simultaneously from trio-RGS in critically ill infants, which provides step-by-step criteria for the semi-automatic processing procedures. With this pipeline in clinical application, only 1 ml of peripheral blood is needed for clinicians to provide both genetic and infectious causal information to a patient. The establishment and clinical practice of the method is of great significance for further mining of high-throughput sequencing data and for assisting clinicians in promoting diagnosis efficiency and accuracy. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Experimental pipeline for rapid whole-genome sequencing for the simultaneous detection of germline variants and microorganisms Basic Protocol 2: Computational pipeline for rapid whole-genome sequencing for the simultaneous detection of germline variants and microorganisms.

Genetic Variants of HOTAIR Associated with Colorectal Cancer: A Case-Control Study in the Saudi Population

Genetic polymorphism in long noncoding RNA (lncRNA) HOTAIR is linked with the risk and susceptibility of various cancers in humans. The mechanism involved in the development of CRC is not fully understood but single nucleotide polymorphisms (SNPs) can be used to predict its risk and prognosis. In the present case-control study, we investigated the relationship between HOTAIR (rs12826786, rs920778, and rs1899663) polymorphisms and CRC risk in the Saudi population by genotyping using a TaqMan genotyping assay in 144 CRC cases and 144 age- and sex-matched controls. We found a significant (p < 0.05) association between SNP rs920778 G > A and CRC risk, and a protective role of SNPs rs12826786 (C > T) and rs1899663 (C > A) was noticed. The homozygous mutant "AA" genotype at rs920778 (G > A) showed a significant correlation with the female sex and colon tumor site. The homozygous TT in SNP rs12816786 (C > T) showed a significant protective association in the male and homozygous AA of SNP rs1899663 (C > A) with colon tumor site. These results indicate that HOTAIR can be a powerful biomarker for predicting the risk of colorectal cancer in the Saudi population. The association between HOTAIR gene polymorphisms and the risk of CRC in the Saudi population was reported for the first time here.

Integrative multi-ancestry genetic analysis of gene regulation in coronary arteries prioritizes disease risk loci

Genome-wide association studies (GWAS) have identified hundreds of genetic risk loci for coronary artery disease (CAD). However, non-European populations are underrepresented in GWAS and the causal gene-regulatory mechanisms of these risk loci during atherosclerosis remain unclear. We incorporated local ancestry and haplotype information to identify quantitative trait loci (QTL) for gene expression and splicing in coronary arteries obtained from 138 ancestrally diverse Americans. Of 2,132 eQTL-associated genes (eGenes), 47% were previously unreported in coronary arteries and 19% exhibited cell-type-specific expression. Colocalization analysis with GWAS identified subgroups of eGenes unique to CAD and blood pressure. Fine-mapping highlighted additional eGenes of interest, including TBX20 and IL5 . Splicing (s)QTLs for 1,690 genes were also identified, among which TOR1AIP1 and ULK3 sQTLs demonstrated the importance of evaluating splicing events to accurately identify disease-relevant gene expression. Our work provides the first human coronary artery eQTL resource from a patient sample and exemplifies the necessity of diverse study populations and multi-omic approaches to characterize gene regulation in critical disease processes.

Study Design Overview

STR-based feature extraction and selection for genetic feature discovery in neurological disease genes

Gene expression, often determined by single nucleotide polymorphisms, short repeated sequences known as short tandem repeats (STRs), structural variants, and environmental factors, provides means for an organism to produce gene products necessary to live. Variation in expression levels, sometimes known as enrichment patterns, has been associated with disease progression. Thus, the STR enrichment patterns have recently gained interest as potential genetic markers for disease progression. However, to the best of our knowledge, we are unaware of any study that evaluates and explores STRs, particularly trinucleotide sequences, as machine learning features for classifying neurological disease genes for the purpose of discovering genetic features. Thus, in this paper, we proposed a new metric and a novel feature extraction and selection algorithm based on statistically significant STR-based features and their respective enrichment patterns to create a statistically significant feature set. The proposed new metric has shown that the neurological disease family genes have a non-random AA, AT, TA, TG, and TT enrichment pattern. This is an important result, as it supports prior research that has established that certain trinucleotides, such as AAT, ATA, ATT, TAT, and TTA, are favored during protein misfolding. In contrast, trinucleotides, such as TAA, TAG, and TGA, are favored during premature termination codon mutations as they are stop codons. This suggests that the metric has the potential to identify patterns that may be genetic features in a sample of neurological genes. Moreover, the practical performance and high prediction results of the statistically significant STR-based feature set indicate that variations in STR enrichment patterns can distinguish neurological disease genes. In conclusion, the proposed approach may have the potential to discover differential genetic features for other diseases.

Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review

This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.

Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data

BACKGROUND

It remains an important challenge to predict the functional consequences or clinical impacts of genetic variants in human diseases, such as cancer. An increasing number of genetic variants in cancer have been discovered and documented in public databases such as COSMIC, but the vast majority of them have no functional or clinical annotations. Some databases, such as CiVIC are available with manual annotation of functional mutations, but the size of the database is small due to the use of human annotation. Since the unlabeled data (millions of variants) typically outnumber labeled data (thousands of variants), computational tools that take advantage of unlabeled data may improve prediction accuracy.

RESULT

To leverage unlabeled data to predict functional importance of genetic variants, we introduced a method using semi-supervised generative adversarial networks (SGAN), incorporating features from both labeled and unlabeled data. Our SGAN model incorporated features from clinical guidelines and predictive scores from other computational tools. We also performed comparative analysis to study factors that influence prediction accuracy, such as using different algorithms, types of features, and training sample size, to provide more insights into variant prioritization. We found that SGAN can achieve competitive performances with small labeled training samples by incorporating unlabeled samples, which is a unique advantage compared to traditional machine learning methods. We also found that manually curated samples can achieve a more stable predictive performance than publicly available datasets.

CONCLUSIONS

By incorporating much larger samples of unlabeled data, the SGAN method can improve the ability to detect novel oncogenic variants, compared to other machine-learning algorithms that use only labeled datasets. SGAN can be potentially used to predict the pathogenicity of more complex variants such as structural variants or non-coding variants, with the availability of more training samples and informative features.

Hereditary Diffuse Gastric Cancer

Hereditary diffuse gastric cancer (HDGC) is a dominantly inherited cancer syndrome characterized by a high incidence of diffuse gastric cancer (DGC) and lobular breast cancer (LBC). HDGC is caused by germline mutations in 2 genes involved in the epithelial adherens junction complex, CDH1 and CTNNA1. We discuss the genetics of HDGC and the variability of its clinical phenotype, in particular the variable penetrance of advanced DGC and LBC, both within and between families. We review the pathology of the disease, the mechanism of tumor initiation, and its natural history. Finally, we describe current best practice for the clinical management of HDGC, including emerging genetic testing criteria for the identification of new families, methods for endoscopic surveillance, the complications associated with prophylactic surgery, postoperative quality of life, and the emerging field of HDGC chemoprevention.

Obtaining genetics insights from deep learning via explainable artificial intelligence

Artificial intelligence (AI) models based on deep learning now represent the state of the art for making functional predictions in genomics research. However, the underlying basis on which predictive models make such predictions is often unknown. For genomics researchers, this missing explanatory information would frequently be of greater value than the predictions themselves, as it can enable new insights into genetic processes. We review progress in the emerging area of explainable AI (xAI), a field with the potential to empower life science researchers to gain mechanistic insights into complex deep learning models. We discuss and categorize approaches for model interpretation, including an intuitive understanding of how each approach works and their underlying assumptions and limitations in the context of typical high-throughput biological datasets.

High Bone Mass Disorders: New Insights From Connecting the Clinic and the Bench

Monogenic high bone mass (HBM) disorders are characterized by an increased amount of bone in general, or at specific sites in the skeleton. Here, we describe 59 HBM disorders with 50 known disease-causing genes from the literature, and we provide an overview of the signaling pathways and mechanisms involved in the pathogenesis of these disorders. Based on this, we classify the known HBM genes into HBM (sub)groups according to uniform Gene Ontology (GO) terminology. This classification system may aid in hypothesis generation, for both wet lab experimental design and clinical genetic screening strategies. We discuss how functional genomics can shape discovery of novel HBM genes and/or mechanisms in the future, through implementation of omics assessments in existing and future model systems. Finally, we address strategies to improve gene identification in unsolved HBM cases and highlight the importance for cross-laboratory collaborations encompassing multidisciplinary efforts to transfer knowledge generated at the bench to the clinic. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Clinical manifestations of 17 Chinese children with hereditary spherocytosis caused by novel mutations of the ANK1 gene and phenotypic analysis

Background: Hereditary spherocytosis (HS) is an autosomal dominant (AD) and autosomal recessive (AR) disorder that is mostly caused by mutations of the erythrocyte membrane-related gene ANK1. Methods: Clinical and genetic testing data of 17 HS children with ANK1 gene mutations were retrospectively collected. Clinical manifestations and phenotypic analysis of HS were summarized based on our experience and literature review. Results: A total of 17 mutations of the ANK1 gene were identified from 17 probands (12 sporadic cases and five familial cases), including 15 novel mutations and two previously reported ones. Among the 15 novel variants of ANK1, there were four non-sense mutations, four frameshift mutations, three splicing mutations, three missense mutations and one in-frame deletion of three amino acids. In the present study, HS patients with mutations in membrane binding domains had significantly lower hemoglobin (Hb) levels and higher total bilirubin (T-Bil) levels than those with mutations in regulatory domains. After reviewing and analyzing all available published reports of Chinese HS patients carrying ANK1 mutations in PubMed and Chinese journals, there were no significant differences in Hb, Ret and T-Bil between different mutation types or mutation regions. Conclusion: Mutations of the ANK1 can be inherited or de novo. Clinical manifestations of HS in children caused by ANK1 mutations are similar to those of other types of hemolytic anemia. Our report expands the mutation spectrum of HS, thus providing references for clinical management and genetic counseling of HS.

FRMD7 Gene Alterations in a Pakistani Family Associated with Congenital Idiopathic Nystagmus

Congenital idiopathic nystagmus (CIN) is an oculomotor disorder characterized by repetitive and rapid involuntary movement of the eye that usually develops in the first six months after birth. Unlike other forms of nystagmus, CIN is widely associated with mutations in the FRMD7 gene. This study involves the molecular genetic analysis of a consanguineous Pakistani family with individuals suffering from CIN to undermine any potential pathogenic mutations. Blood samples were taken from affected and normal individuals of the family. Genomic DNA was extracted using an in-organic method. Whole Exome Sequencing (WES) and analysis were performed to find any mutations in the causative gene. To validate the existence and co-segregation of the FRMD7 gene variant found using WES, sanger sequencing was also carried out using primers that targeted all of the FRMD7 coding exons. Additionally, the pathogenicity of the identified variant was assessed using different bioinformatic tools. The WES results identified a novel nonsense mutation in the FRMD7 (c.443T>A; p. Leu148 *) gene in affected individuals from the Pakistani family, with CIN resulting in a premature termination codon, further resulting in the formation of a destabilized protein structure that was incomplete. Co-segregation analysis revealed that affected males are hemizygous for the mutated allele c.443T>A; p. Leu148 * and the affected mother is heterozygous. Overall, such molecular genetic studies expand our current knowledge of the mutations associated with the FRMD7 gene in Pakistani families with CIN and significantly enhance our understanding of the molecular mechanisms involved in genetic disorders.

Ultra-Rare Variants Identify Biological Pathways and Candidate Genes in the Pathobiology of Non-Syndromic Cleft Palate Only

In recent decades, many efforts have been made to elucidate the genetic causes of non-syndromic cleft palate (nsCPO), a complex congenital disease caused by the interaction of several genetic and environmental factors. Since genome-wide association studies have evidenced a minor contribution of common polymorphisms in nsCPO inheritance, we used whole exome sequencing data to explore the role of ultra-rare variants in this study. In a cohort of 35 nsCPO cases and 38 controls, we performed a gene set enrichment analysis (GSEA) and a hypergeometric test for assessing significant overlap between genes implicated in nsCPO pathobiology and genes enriched in ultra-rare variants in our cohort. GSEA highlighted an enrichment of ultra-rare variants in genes principally belonging to cytoskeletal protein binding pathway (Probability Density Function corrected p-value = 1.57 × 10^-4); protein-containing complex binding pathway (p-value = 1.06 × 10^-2); cell adhesion molecule binding pathway (p-value = 1.24 × 10^-2); ECM-receptor interaction pathway (p-value = 1.69 × 10^-2); and in the Integrin signaling pathway (p-value = 1.28 × 10^-2). Two genes implicated in nsCPO pathobiology, namely COL2A1 and GLI3, ranked among the genes (n = 34) with nominal enrichment in the ultra-rare variant collapsing analysis (Fisher's exact test p-value < 0.05). These genes were also part of an independent list of genes highly relevant to nsCPO biology (n = 25). Significant overlap between the two sets of genes (hypergeometric test p-value = 5.86 × 10^-3) indicated that enriched genes are likely to be implicated in physiological palate development and/or the pathological processes of oral clefting. In conclusion, ultra-rare variants collectively impinge on biological pathways crucial to nsCPO pathobiology and point to candidate genes that may contribute to the individual risk of disease. Sequencing can be an effective approach to identify candidate genes and pathways for nsCPO.

Applications of deep learning in understanding gene regulation

Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area.

Position-dependent effects of RNA-binding proteins in the context of co-transcriptional splicing

Alternative splicing is an important step in eukaryotic mRNA pre-processing which increases the complexity of gene expression programs, but is frequently altered in disease. Previous work on the regulation of alternative splicing has demonstrated that splicing is controlled by RNA-binding proteins (RBPs) and by epigenetic DNA/histone modifications which affect splicing by changing the speed of polymerase-mediated pre-mRNA transcription. The interplay of these different layers of splicing regulation is poorly understood. In this paper, we derived mathematical models describing how splicing decisions in a three-exon gene are made by combinatorial spliceosome binding to splice sites during ongoing transcription. We additionally take into account the effect of a regulatory RBP and find that the RBP binding position within the sequence is a key determinant of how RNA polymerase velocity affects splicing. Based on these results, we explain paradoxical observations in the experimental literature and further derive rules explaining why the same RBP can act as inhibitor or activator of cassette exon inclusion depending on its binding position. Finally, we derive a stochastic description of co-transcriptional splicing regulation at the single-cell level and show that splicing outcomes show little noise and follow a binomial distribution despite complex regulation by a multitude of factors. Taken together, our simulations demonstrate the robustness of splicing outcomes and reveal that quantitative insights into kinetic competition of co-transcriptional events are required to fully understand this important mechanism of gene expression diversity.

Effect of SMAD4 gene polymorphism on breast cancer risk in Bangladeshi women

Background

Breast cancer, one of the most prevalent cancer types among women worldwide as well as in Bangladesh, is the leading cause of cancer death in women throughout the globe. The risk of breast cancer development was found to be associated with genetic polymorphism according to several studies. As a convenient prognostic marker, a biomarker helps to identify disease progression, can lead to an effective therapeutic strategy, development of prognostic marker is very important for any cancer to initiate treatment strategy early to increase the possibility of the success rate of the treatment along with reduction of the treatment cost. This study aims to establish the correlation between polymorphism of SMAD4 rs10502913 and risk of breast cancer development in Bangladeshi women. This study was conducted on 70 breast cancer patients and 60 healthy volunteers through blood sample collection followed by DNA separation between the intervals of August 2019–October 2019. The collected DNA sample was arranged for the RFLP analysis of a PCR amplified fragments followed by gel electrophoresis. The obtained data was analyzed by structured multinomial logistic regression model.

Results

Obtained different fragment size after gel electrophoresis indicated different genotypes in this experiment. Our findings demonstrated that mutant homozygous A/A genotype, plays a significant role in breast cancer development among Bangladeshi women (P = 0.006, OR = 4.9626, 95% CI = 1.9980–12.3261) compared to the reference homozygous G/G genotype. Moreover, heterozygous G/A genotype was also found to be significantly associated with the risk of breast cancer development (P = 0.0252, OR = 2.6574, CI = 1.1295–6.2525). Considering the A/A genotype and G/A genotype combined, it also indicates a strong association of breast cancer development in Bangladeshi women (P = 0.008, OR = 3.5630, CI = 1.6907–7.5068).

Conclusion

Our study indicated a novel association between SMAD4 (rs10502913) polymorphism and increased risk of breast cancer development in Bangladeshi women.

The landscape of expression and alternative splicing variation across human traits

Understanding the consequences of individual transcriptome variation is fundamental to deciphering human biology and disease. We implement a statistical framework to quantify the contributions of 21 individual traits as drivers of gene expression and alternative splicing variation across 46 human tissues and 781 individuals from the Genotype-Tissue Expression project. We demonstrate that ancestry, sex, age, and BMI make additive and tissue-specific contributions to expression variability, whereas interactions are rare. Variation in splicing is dominated by ancestry and is under genetic control in most tissues, with ribosomal proteins showing a strong enrichment of tissue-shared splicing events. Our analyses reveal a systemic contribution of types 1 and 2 diabetes to tissue transcriptome variation with the strongest signal in the nerve, where histopathology image analysis identifies novel genes related to diabetic neuropathy. Our multi-tissue and multi-trait approach provides an extensive characterization of the main drivers of human transcriptome variation in health and disease.

3D genome organization around nuclear speckles drives mRNA splicing efficiency

The nucleus is highly organized such that factors involved in transcription and processing of distinct classes of RNA are organized within specific nuclear bodies. One such nuclear body is the nuclear speckle, which is defined by high concentrations of protein and non-coding RNA regulators of pre-mRNA splicing. What functional role, if any, speckles might play in the process of mRNA splicing remains unknown. Here we show that genes localized near nuclear speckles display higher spliceosome concentrations, increased spliceosome binding to their pre-mRNAs, and higher co-transcriptional splicing levels relative to genes that are located farther from nuclear speckles. We show that directed recruitment of a pre-mRNA to nuclear speckles is sufficient to drive increased mRNA splicing levels. Finally, we show that gene organization around nuclear speckles is highly dynamic with differential localization between cell types corresponding to differences in Pol II occupancy. Together, our results integrate the longstanding observations of nuclear speckles with the biochemistry of mRNA splicing and demonstrate a critical role for dynamic 3D spatial organization of genomic DNA in driving spliceosome concentrations and controlling the efficiency of mRNA splicing.

Downregulation of RBM17 enhances cisplatin sensitivity and inhibits cell invasion in human hypopharyngeal cancer cells

Most of advanced hypopharyngeal squamous cell carcinoma (HSCC) are resistant to chemotherapy, and there is still lack of effective treatment for HSCC now. The present study aimed to investigate whether downregulation of RNA-binding motif protein 17 (RBM17) could enhance cisplatin sensitivity and inhibit cell invasion in HSCC and the underlying mechanism. We observed that RBM17 was upregulated in tumor tissues and associated with poor progression. Treatment of FaDu cells with cisplatin increased RBM17 expression in mRNA levels. Downregulation of RBM17 enhanced cisplatin-mediated inhibition of FaDu cells. In addition, downregulation of RBM17 effectively suppressed tumor cell migration and invasion through the reversion of epithelial-mesenchymal transition. Moreover, downregulation of RBM17 could significantly slow tumor growth in FaDu xenograft tumor model. Liquid chromatography-mass spectrometry/mass spectrometry detection and independent PRM analysis showed that 21 differentially expressed proteins were associated with the downregulation of RBM17. Taken together, our study implied that downregulation of RBM17 could serve as a novel approach to enhance cisplatin sensitivity in HSCC.

frDSM: An Ensemble Predictor With Effective Feature Representation for Deleterious Synonymous Mutation in Human Genome

With the discovery of causality between synonymous mutations and diseases, it has become increasingly important to identify deleterious synonymous mutations for better understanding of their functional mechanisms. Although several machine learning methods have been proposed to solve the task, an effective feature representation method that can make use of the inner difference and relevance between deleterious and benign synonymous mutations is still challenging considering the vast number of synonymous mutations in human genome. In this work, we developed a robust and accurate predictor called frDSM for deleterious synonymous mutation prediction using logistic regression. More specifically, we introduced an effective feature representation learning method which exploits multiple feature descriptors from different perspectives including functional scores obtained from previously computational methods, evolutionary conservation, splicing and sequence feature descriptors, and these features descriptors were input into the 76 XGBoost classifiers to obtain the predictive probabilities values. These probabilities were concatenated to generate the 76-dimension new feature vector, and feature selection method was used to remove redundant and irrelevant features. Experimental results show that frDSM enables robust and accurate prediction than the competing prediction methods with 31 optimal features, which demonstrated the effectiveness of the feature representation learning method. frDSM is freely available at http://frdsm.xialab.info.

Roles of minor spliceosome in intron recognition and the convergence with the better understood major spliceosome

Catalyzed by spliceosomes in the nucleus, RNA splicing removes intronic sequences from precursor RNAs in eukaryotes to generate mature RNA, which also significantly increases proteome complexity and fine-tunes gene expression. Most metazoans have two coexisting spliceosomes; the major spliceosome, which removes >99.5% of introns, and the minor spliceosome, which removes far fewer introns (only 770 at present have been predicted in the human genome). Both spliceosomes are large and dynamic machineries, each consisting of five small nuclear RNAs (snRNAs) and more than 100 proteins. However, the dynamic assembly, catalysis, and protein composition of the minor spliceosome are still poorly understood. With different splicing signals, minor introns are rare and usually distributed alone and flanked by major introns in genes, raising questions of how they are recognized by the minor spliceosome and how their processing deals with the splicing of neighboring major introns. Due to large numbers of introns and close similarities between the two machinery, cooperative, and competitive recognition by the two spliceosomes has been investigated. Functionally, many minor-intron-containing genes are evolutionarily conserved and essential. Mutations in the minor spliceosome exhibit a variety of developmental defects in plants and animals and are linked to numerous human diseases. Here, we review recent progress in the understanding of minor splicing, compare currently known components of the two spliceosomes, survey minor introns in a wide range of organisms, discuss cooperation and competition of the two spliceosomes in splicing of minor-intron-containing genes, and contributions of minor splicing mutations in development and diseases. This article is categorized under: RNA Processing > Processing of Small RNAs RNA Processing > Splicing Mechanisms RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry.

Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy

Over the last decade, next-generation sequencing (NGS) methods have become increasingly used in various areas of human genomics. In routine clinical care, their use is already implemented in oncology to profile the mutational landscape of a tumor, as well as in rare disease diagnostics. However, its utilization in pharmacogenomics is largely lacking behind. Recent population-scale genome data has revealed that human pharmacogenes carry a plethora of rare genetic variations that are not interrogated by conventional array-based profiling methods and it is estimated that these variants could explain around 30% of the genetically encoded functional pharmacogenetic variability.To interpret the impact of such variants on drug response a multitude of computational tools have been developed, but, while there have been major advancements, it remains to be shown whether their accuracy is sufficient to improve personalized pharmacogenetic recommendations in robust trials. In addition, conventional short-read sequencing methods face difficulties in the interrogation of complex pharmacogenes and high NGS test costs require stringent evaluations of cost-effectiveness to decide about reimbursement by national healthcare programs. Here, we illustrate current challenges and discuss future directions toward the clinical implementation of NGS to inform genotype-guided decision-making.