Pathogenicity Prediction Research Articles

Characterization of 13 Novel Genetic Variants in Genes Associated with Epilepsy: Implications for Targeted Therapeutic Strategies.

Childhood epilepsies are caused by heterogeneous underlying disorders where approximately 40% of the origins of epilepsy can be attributed to genetic factors. The application of next-generation sequencing (NGS)has revolutionized molecular diagnostics and has enabled the identification of disease-causing genes and variants in childhood epilepsies.The objective of this study was to use NGS to identify variants in patients with childhood epilepsy, to expand the variant spectrum and discover potential therapeutic targets. In our study, 55 children with epilepsy of unknown etiology were analyzed bycombining clinical-exome and whole-exome sequencing. Novel variants were characterized using various in silico algorithms for pathogenicity and structure prediction. The molecular genetic cause of epilepsy was identified in 28 patients and the overall diagnostic success rate was 50.9%. We identified variants in 22 different genes associated with epilepsy that correlate well with the described phenotype. SCN1A gene variants were found in five unrelated patients, while ALDH7A1 and KCNQ2 gene variants were found twice. In the other 19 genes, variants were found only in a single patient. This includes genes such as ASH1L, CSNK2B, RHOBTB2, and SLC13A5, which have only recently been associated with epilepsy. Almost half of diagnosed patients (46.4%) carried novel variants. Interestingly, we identified variants in ALDH7A1, KCNQ2, PNPO, SCN1A, and SCN2A resulting ingene-directed therapy decisions for 11 children from our study, including four children who all carried novel SCN1A genetic variants. Described novel variants will contribute to a better understanding of the European genetic landscape, while insights into the genotype-phenotype correlation will contribute to a better understanding of childhood epilepsies worldwide. Given the expansion of molecular-based approaches, each newly identified genetic variant could become a potential therapeutic target.

INDELpred: Improving the prediction and interpretation of indel pathogenicity within the clinical genome

Small insertions and deletions (InDels) are critical yet challenging genetic variations with significant clinical implications. However, the identification of pathogenic InDels from neutral variants in clinical contexts remains an understudied problem. Here, we developed INDELpred, a machine learning-based predictive model for discerning pathogenic from benign InDels. INDELpred was established based on key features, including allele frequency, InDel length, function-based features, and gene-based features. A set of comprehensive evaluation analyses demonstrated that INDELpred exhibited superior performance over competing methods in terms of computational efficiency and prediction accuracy. Importantly, INDELpred highlighted the crucial role of function-based features in identifying pathogenic InDels, with a clear interpretability of the features in understanding the disease-causing variants. We envisage INDELpred as a desirable tool for the detection of pathogenic InDels within large-scale genomic datasets, and thereby enhancing the precision of genetic diagnoses in clinical settings.

PHACTboost: A Phylogeny-Aware Pathogenicity Predictor for Missense Mutations via Boosting.

Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. By learning from data, PHACTboost outperforms PHACT. Furthermore, the results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, metapredictors, and deep learning-based approaches as well as more recent tools such as AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 215 million amino acid alterations over 20,191 proteins. PHACTboost is available at https://github.com/CompGenomeLab/PHACTboost. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.

Exploring Pathogen Presence Prediction in Pastured Poultry Farms through Transformer-Based Models and Attention Mechanism Explainability.

In this study, we explore how transformer models, which are known for their attention mechanisms, can improve pathogen prediction in pastured poultry farming. By combining farm management practices with microbiome data, our model outperforms traditional prediction methods in terms of the F1 score-an evaluation metric for model performance-thus fulfilling an essential need in predictive microbiology. Additionally, the emphasis is on making our model's predictions explainable. We introduce a novel approach for identifying feature importance using the model's attention matrix and the PageRank algorithm, offering insights that enhance our comprehension of established techniques such as DeepLIFT. Our results showcase the efficacy of transformer models in pathogen prediction for food safety and mark a noteworthy contribution to the progress of explainable AI within the biomedical sciences. This study sheds light on the impact of effective farm management practices and highlights the importance of technological advancements in ensuring food safety.

Investigation of a Large Kindred Reveals Cardiac Calsequestrin (CASQ2) as a Cause of Brugada Syndrome.

Brugada syndrome (BrS) is an inherited primary channelopathy syndrome associated with the risk of ventricular fibrillation (VF) and sudden cardiac death in a structurally normal heart. The aim of this study was to clinically and genetically evaluate a large family with severe autosomal dominant Brugada syndrome. Clinical and genetic studies were performed. Genetic analysis was conducted with NGS technologies (WES) using the Illumina instrument. According to the standard procedure, variants found by WES were confirmed in all available families by Sanger sequencing. The effect of the variants was studied by using in silico prediction of pathogenicity. The proband was a 52-year-old man who was admitted to the emergency department for syncope at rest. WES of the index case identified a heterozygous VUS CASQ2, c.532T>C, p.(Tyr178His). We studied the segregation of the variation in all pedigree members. All the patients were heterozygous for the variation CASQ2 p.(Tyr178His), whereas the remaining healthy individuals in the family were homozygous for the normal allele. Structural analysis of CASQ2 p.(Tyr178His) was performed and revealed an important effect of the missense variation on monomer stability. The CASQ2 Tyr180 residue is located inside the sarcoplasmic reticulum (SR) junctional face membrane interaction domain and is predicted to disrupt filamentation. Our data suggest that the p.Tyr178His substitution is associated with BrS in the family investigated, affecting the stability of the protein, disrupting filamentation at the interdimer interface, and affecting the subsequent formation of tetramers and polymers that contain calcium-binding sites.

Rare pathogenic structural variants show potential to enhance prostate cancer germline testing for African men.

Prostate cancer (PCa) is highly heritable, with men of African ancestry at greatest risk and associated lethality. Lack of representation in genomic data means germline testing guidelines exclude for African men. Established that structural variations (SVs) are major contributors to human disease and prostate tumourigenesis, their role is under-appreciated in familial and therapeutic testing. Utilising a clinico-methodologically matched African (n = 113) versus European (n = 57) deep-sequenced PCa resource, we interrogated 42,966 high-quality germline SVs using a best-fit pathogenicity prediction workflow. We identified 15 potentially pathogenic SVs representing 12.4% African and 7.0% European patients, of which 72% and 86% met germline testing standard-of-care recommendations, respectively. Notable African-specific loss-of-function gene candidates include DNA damage repair MLH1 and BARD1 and tumour suppressors FOXP1, WASF1 and RB1. Representing only a fraction of the vast African diaspora, this study raises considerations with respect to the contribution of kilo-to-mega-base rare variants to PCa pathogenicity and African associated disparity.

Multimodal framework to resolve variants of uncertain significance in TSC2.

Efforts to resolve the functional impact of variants of uncertain significance (VUS) have lagged behind the identification of new VUS; as such, there is a critical need for scalable VUS resolution technologies. Computational variant effect predictors (VEPs), once trained, can predict pathogenicity for all missense variants in a gene, set of genes, or the exome. Existing tools have employed information on known pathogenic and benign variants throughout the genome to predict pathogenicity of VUS. We hypothesize that taking a gene-specific approach will improve pathogenicity prediction over globally-trained VEPs. We tested this hypothesis using the gene TSC2, whose loss of function results in tuberous sclerosis, a multisystem mTORopathy affecting about 1 in 6,000 individuals born in the United States. TSC2 has been identified as a high-priority target for VUS resolution, with (1) well-characterized molecular and patient phenotypes associated with loss-of-function variants, and (2) more than 2,700 VUS already documented in ClinVar. We developed Tuberous sclerosis classifier to Resolve variants of Uncertain Significance in T SC2 (TRUST), a machine learning model to predict pathogenicity of TSC2 missense VUS. To test whether these predictions are accurate, we further introduce curated loci prime editing (cliPE) as an accessible strategy for performing scalable multiplexed assays of variant effect (MAVEs). Using cliPE, we tested the effects of more than 200 TSC2 variants, including 106 VUS. It is highly likely this functional data alone would be sufficient to reclassify 92 VUS with most being reclassified as likely benign. We found that TRUST's classifications were correlated with the functional data, providing additional validation for the in silico predictions. We provide our pathogenicity predictions and MAVE data to aid with VUS resolution. In the near future, we plan to host these data on a public website and deposit into relevant databases such as MAVEdb as a community resource. Ultimately, this study provides a framework to complete variant effect maps of TSC1 and TSC2 and adapt this approach to other mTORopathy genes.

Overexpression of human SAMD9 inhibits protein translation and alters MYC signaling resulting in cell cycle arrest

Inherited bone marrow failure syndromes often result from pathogenic mutations in genes that are important for ribosome function, namely, Diamond-Blackfan anemia, Shwachman-Diamond syndrome, and dyskeratosis congenita. Germline mutations in SAMD9 are a frequent genetic lesion resulting in an inherited bone marrow failure syndrome with monosomy 7; some patients have severe multisystem syndromes that include myelodysplasia. The association of germline SAMD9 mutations and bone marrow failure is clear; however, to date, there is no reliable method to predict whether a novel SAMD9 mutation is pathogenic unless it is accompanied by an obvious family history and/or clinical syndrome. The difficulty with pathogenicity prediction is, in part, due to the incomplete understanding of the biological functions of SAMD9. We used a SAMD9-targeted, inducible CRISPRa system and RNA sequencing to better understand the global transcriptional changes that result from transcriptional manipulation of SAMD9. Supporting recent discoveries that SAMD9 acts as a ACNase specific for phenylalanine tRNA (tRNA-Phe), we confirmed with crosslinking and solid-phase purification that SAMD9 is an RNA binding protein and analyzed how overexpression of tRNA-Phe may reverse transcriptomic changes caused by SAMD9 activation. Our data show that overexpression of SAMD9 from the endogenous locus results in decreased cell proliferation, cell cycle progression, and global protein translation. When SAMD9 contains a gain-of-function mutation (p.E1136Q), these functional phenotypes are exacerbated but only partially rescued with tRNA-Phe overexpression, suggesting additional molecular actions of SAMD9. Additionally, we demonstrate that gene expression pathways important for ribosome biogenesis and MYC signaling are the most significantly impacted by SAMD9 overexpression.

Are Next-Generation Pathogenicity Predictors Applicable to Cancer?

Next-generation pathogenicity predictors are designed to identify pathogenic mutations in genetic disorders but are increasingly used to detect driver mutations in cancer. Despite this, their suitability for cancer is not fully established. Here we have assessed the effectiveness of next-generation pathogenicity predictors when applied to cancer by using a comprehensive experimental benchmark of cancer driver and neutral mutations. Our findings indicate that state-of-the-art methods AlphaMissense and VARITY demonstrate commendable performance despite generally underperforming compared to cancer-specific methods. This is notable considering that these methods do not explicitly incorporate cancer-related data in their training and have made concerted efforts to prevent data leakage from the human-curated training and test sets. Nevertheless, it should be mentioned that a significant limitation of using pathogenicity predictors for cancer arises from their inability to detect cancer potential driver mutations specific for a particular cancer type.

Comparing Occurrence of Bovine Respiratory Pathogens Detected by High-Throughput Real-Time PCR in Nasal Swabs and Non-Endoscopic Bronchoalveolar Lavage Samples from Dairy and Veal Calves.

This study aimed to enhance our understanding of the agreement between two sampling methods for the detection of bovine respiratory disease (BRD) pathogens in calves using high-throughput real-time qPCR (ht-RT-qPCR). In total, 233 paired nasal swab (NS) and non-endoscopic bronchoalveolar lavage (nBAL) samples were collected from 152 calves from 12 Danish cattle herds. In 202 of the observations, the calves were examined using a standardized clinical protocol. Samples were tested for three viruses (bovine respiratory syncytial virus, bovine corona virus, and influenza D virus) and six bacteria (Histophilus somni, Mannheimia haemolytica, Mycoplasma bovis, Mycoplasma species, Pasteurella multocida, and Truepurella pyogenes). The results showed age-related differences in disease and pathogen occurrence, with the highest detection rates in calves aged 35 days or older. Poor to moderate agreement was found between the NS and nBAL results. The presence of Mannheimia haemolytica in both NS and nBAL in younger calves and in nBAL in older calves was associated with clinical BRD. There was a potential link between BRD and influenza D virus in older calves, although it was only found in one herd in a small sample size. Overall, NS was a relatively poor predictor of pathogens in the lower respiratory tract. The present study confirms the complexity of pathogen detection in BRD, with marked influences of age and the sampling method on pathogen detection and disease associations.

Clinical utility of AlphaMissense in predicting pathogenicity of DNA damage repair genes in cancer.

10581 Background: Deficiencies in DNA damage repair (DDR) impact cancer predisposition and various treatment responses.Functional implications of missense variants in DDR genes remain elusive.AlphaMissense, a new AI-based method, accurately predicts the pathogenicity of missense variants, but its clinical utility requires validation. We evaluated the accuracy of AlphaMissense predictions by analyzing known mutational signatures, genomic characteristics, and therapeutic vulnerabilities of DDR-deficient tumors. Methods: We analyzed data from TCGA (n=8,178) and MSK-IMPACT targeted panel sequencing (n=56,695) to evaluate the performance of AlphaMissense in predicting clinically actionable DDR genes, including core homologous recombination (HR) genes ( BRCA1/2, PALB2, RAD51C), POLE, and ATM. Missense mutations were classified as known pathogenic by OncoKB or previous literature, newly pathogenic identified with AlphaMisense, or benign. Mutational signatures were computed using SigMA algorithm, classifying each signature as positive at >20%. The irradiated tumor control was evaluated in patients with ATM missense mutations who received radiotherapy. Results: Among 6,743 unique DDR missense mutations from MSK-IMPACT, 372 (3.5%) were known pathogenic, and 1,182 (17.5%) were new pathogenic mutations. In 205 tumors with bi-allelic core-HR missense mutations in breast, ovarian, pancreatic, and prostate cancers, 20 (9.8%) had new pathogenic mutations. Tumors with new pathogenic mutations more frequently exhibited HR-deficient signatures (65%) than wild-type (31.8%, p < .001), similar to tumors with known pathogenic mutations (62.8%, p = 1.0). In TCGA, HR-deficient signatures tended to be more frequent in tumors with new pathogenic missense mutations than in wild-type tumors (41.7% vs. 21.0%, p = .16). Of 1,010 tumors with POLE missense mutations from MSK-IMPACT, 300 (30%) had new pathogenic mutations. The presence of POLE signatures in tumors with new pathogenic mutations (11.9%) was not significantly different from those with benign mutations (4.3%, p = .19) and less frequent than in tumors with known pathogenic mutations (91.0%, p < .001). In MSK-IMPACT, tumors with new pathogenic ATM missense mutations were less likely to have TP53 mutations than benign missense mutations (32.7% vs. 56.6%, p < .001), consistent with bona-fide pathogenicity. Further, patients with new pathogenic ATM missense mutations showed increased radiosensitivity, characteristic of ATM deficiency, and improved irradiated tumor control compared to those with benign missense mutations (Hazard ratio 0.58, 95% Cl 0.35-0.95, p = .03). Conclusions: AlphaMissense can identify previously unknown pathogenic DDR missense mutations, but its accuracy is gene-dependent. AlphaMissense pathogenicity predictions for DDR genes can utilized to help classify mutations, but may not be sufficient on their own.

Novel gene-specific Bayesian Gaussian mixture model to predict the missense variants pathogenicity of Sanfilippo syndrome.

MPS III is an autosomal recessive lysosomal storage disease caused mainly by missense variants in theNAGLU, GNS, HGSNAT, and SGSH genes. Thepathogenicity interpretation of missense variants is still challenging. We aimed to develop unsupervised clustering-based pathogenicity predictor scores using extracted features from eight in silico predictors to predict the impact of novel missense variants of Sanfilippo syndrome. The model was trained on a dataset consisting of 415 uncertain significant (VUS) missense NAGLU variants. Performance The SanfilippoPred tool was evaluated by validation and test datasets consisting of 197-labelled NAGLU missense variants, and its performance was compared versus individual pathogenicity predictors using receiver operating characteristic (ROC) analysis. Moreover, we tested theSanfilippoPred tool using extra-labelled 427 missense variants to assess its specificity and sensitivity threshold. Application of the trained machine learning (ML) model on the test dataset of labelled NAGLU missense variants showed that SanfilippoPred has an accuracy of 0.93 (0.86-0.97 at CI 95%), sensitivity of 0.93, and specificity of 0.92. The comparative performance of theSanfilippoPred showed better performance (AUC = 0.908) than the individual predictors SIFT (AUC = 0.756), Polyphen-2 (AUC = 0.788), CADD (AUC = 0.568), REVEL (AUC = 0.548), MetaLR (AUC = 0.751), and AlphMissense (AUC = 0.885). Using high-confidence labelled NAGLU variants, showed that SanfilippoPred has an85.7% sensitivity threshold. The poor correlation between the Sanfilippo syndrome phenotype and genotype represents a demand for a new tool to classify its missense variants. This study provides asignificant tool forpreventing themisinterpretation of missense variants of the Sanfilippo syndrome-relevant genes. Finally, it seems that ML-based pathogenicity predictors and Sanfilippo syndrome-specific prediction tools could be feasible andefficient pathogenicity predictors in the future.

Pathogenicity Prediction of Gene Fusion in Structural Variations: A Knowledge Graph-Infused Explainable Artificial Intelligence (XAI) Framework.

When analyzing cancer sample genomes in clinical practice, many structural variants (SVs), other than single nucleotide variants (SNVs), have been identified. To identify driver variants, the leading candidates must be narrowed down. When fusion genes are involved, selection is particularly difficult, and highly accurate predictions from AI is important. Furthermore, we also wanted to determine how the prediction can make more reliable diagnoses. Here, we developed an explainable AI (XAI) suitable for SVs with gene fusions, based on the XAI technology we previously developed for the prediction of SNV pathogenicity. To cope with gene fusion variants, we added new data to the previous knowledge graph for SVs and we improved the algorithm. Its prediction accuracy was as high as that of existing tools. Moreover, our XAI could explain the reasons for these predictions. We used some variant examples to demonstrate that the reasons are plausible in terms of pathogenic basic mechanisms. These results can be seen as a hopeful step toward the future of genomic medicine, where efficient and correct decisions can be made with the support of AI.

Detection of a novel pathogenic variant in KCNH2 associated with long QT syndrome 2 using whole exome sequencing

BackgroundLong QT syndrome (LQTS) is a cardiac channelopathy characterized by impaired myocardial repolarization that predisposes to life-threatening arrhythmias. This study aimed to elucidate the genetic basis of LQTS in an affected Iranian family using whole exome sequencing (WES).MethodsA 37-year-old woman with a personal and family history of sudden cardiac arrest and LQTS was referred for genetic study after losing her teenage daughter due to sudden cardiac death (SCD). WES was performed and variants were filtered and prioritized based on quality, allele frequency, pathogenicity predictions, and conservation scores. Sanger sequencing confirmed segregation in the family.ResultsWES identified a novel heterozygous frameshift variant (NM_000238.4:c.3257_3258insG; pGly1087Trpfs*32) in the KCNH2 encoding the α-subunit of the rapid delayed rectifier potassium channel responsible for cardiac repolarization. This variant, predicted to cause a truncated protein, is located in the C-terminal region of the channel and was classified as likely pathogenic based on ACMG guidelines. The variant was absent in population databases and unaffected family members.ConclusionThis study reports a novel KCNH2 frameshift variant in an Iranian family with LQTS, expanding the spectrum of disease-causing variants in this gene. Our findings highlight the importance of the C-terminal region in KCNH2 for proper channel function and the utility of WES in identifying rare variants in genetically heterogeneous disorders like LQTS. Functional characterization of this variant is warranted to fully elucidate its pathogenic mechanisms and inform personalized management strategies.

Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes.

Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.

Regional-specific calibration enables application of computational evidence for clinical classification of 5′ cis-regulatory variants in Mendelian disease

To date, clinical genetic testing for Mendelian disease variants has focused heavily on exonic coding and intronic gene regions. This multi-step study was undertaken to provide an evidence base for selecting and applying computational approaches for use in clinical classification of 5' cis-regulatory region variants. Curated datasets of clinically reported disease-causing 5' cis-regulatory region variants and variants from matched genomic regions in population controls were used to calibrate six bioinformatic tools as predictors of variant pathogenicity. Likelihood ratio estimates were aligned to code weights following ClinGen recommendations for application of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) classification scheme. Considering code assignment across all reference dataset variants, performance was best for CADD (81.2%) and REMM (81.5%). Optimized thresholds provided moderate evidence toward pathogenicity (CADD, REMM) and moderate (CADD) or supporting (REMM) evidence against pathogenicity. Both sensitivity and specificity of prediction were improved when further categorizing variants based on location in an EPDnew-defined promoter region. Combining predictions (CADD, REMM, and location in a promoter region) increased specificity at the expense of sensitivity. Importantly, the optimal CADD thresholds for assigning ACMG/AMP codes PP3 (≥10) and BP4 (≤8) were vastly different from recommendations for protein-coding variants (PP3≥25.3; BP4≤22.7); CADD <22.7 would incorrectly assign BP4 for >90% of reported disease-causing cis-regulatory region variants. Our results demonstrate the need to consider a tiered approach and tailored score thresholds to optimize bioinformatic impact prediction for clinical classification of 5' cis-regulatory region variants.

Identification of a novel de novo mutation in SOX4 for syndromic tooth agenesis.

Coffin-Siris Syndrome (CSS) is a congenital disorder characterized by delayed growth, dysmorphic facial features, hypoplastic nails and phalanges of the fifth digit, and dental abnormalities. Tooth agenesis has been reported in CSS patients, but the mechanisms regulating this syndromic tooth agenesis remain largely unknown. This study aims to identify the pathogenic mutation of CSS presenting tooth genesis and explore potential regulatory mechanisms. We utilized whole-exome sequencing to identify variants in a CSS patient, followed by Sanger validation. In silico analysis including conservation analysis, pathogenicity predictions, and 3D structural assessments were carried out. Additionally, single-cell RNA sequencing and fluorescence in situ hybridization (FISH) were applied to explore the spatio-temporal expression of Sox4 expression during murine tooth development. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to examine the functional role of SOX4. A novel de novo SOX4 missense mutation (c.1255C > G, p.Leu419Val) was identified in a Chinese CSS patient exhibiting tooth agenesis. Single-cell RNA sequencing and FISH further verified high expression of Sox4 during murine tooth development, and WGCNA confirmed its central role in tooth development pathways. Enriched functions included cell-substrate junctions, focal adhesion, and RNA splicing. Our findings link a novel SOX4 mutation to syndromic tooth agenesis in CSS. This is the first report of SOX4 missense mutation causing syndromic tooth agenesis. This study not only enhances our understanding of the pathogenic mutation for syndromic tooth agenesis but also provides genetic diagnosis and potential therapeutic insights for syndromic tooth agenesis.

A unified analysis of evolutionary and population constraint in protein domains highlights structural features and pathogenic sites

Protein evolution is constrained by structure and function, creating patterns in residue conservation that are routinely exploited to predict structure and other features. Similar constraints should affect variation across individuals, but it is only with the growth of human population sequencing that this has been tested at scale. Now, human population constraint has established applications in pathogenicity prediction, but it has not yet been explored for structural inference. Here, we map 2.4 million population variants to 5885 protein families and quantify residue-level constraint with a new Missense Enrichment Score (MES). Analysis of 61,214 structures from the PDB spanning 3661 families shows that missense depleted sites are enriched in buried residues or those involved in small-molecule or protein binding. MES is complementary to evolutionary conservation and a combined analysis allows a new classification of residues according to a conservation plane. This approach finds functional residues that are evolutionarily diverse, which can be related to specificity, as well as family-wide conserved sites that are critical for folding or function. We also find a possible contrast between lethal and non-lethal pathogenic sites, and a surprising clinical variant hot spot at a subset of missense enriched positions.

Recessive GNE Mutations in Korean Nonaka Distal Myopathy Patients with or without Peripheral Neuropathy.

Autosomal recessive Nonaka distal myopathy is a rare autosomal recessive genetic disease characterized by progressive degeneration of the distal muscles, causing muscle weakness and decreased grip strength. It is primarily associated with mutations in the GNE gene, which encodes a key enzyme of sialic acid biosynthesis (UDP-N-acetylglucosamine 2-epimerase/N-acetylmannosamine kinase). This study was performed to find GNE mutations in six independent distal myopathy patients with or without peripheral neuropathy using whole-exome sequencing (WES). In silico pathogenic prediction and simulation of 3D structural changes were performed for the mutant GNE proteins. As a result, we identified five pathogenic or likely pathogenic missense variants: c.86T>C (p.Met29Thr), c.527A>T (p.Asp176Val), c.782T>C (p.Met261Thr), c.1714G>C (p.Val572Leu), and c.1771G>A (p.Ala591Thr). Five affected individuals showed compound heterozygous mutations, while only one patient revealed a homozygous mutation. Two patients revealed unreported combinations of combined heterozygous mutations. We observed some specific clinical features, such as complex phenotypes of distal myopathy with distal hereditary peripheral neuropathy, an earlier onset of weakness in legs than that of hands, and clinical heterogeneity between two patients with the same set of compound heterozygous mutations. Our findings on these genetic causes expand the clinical spectrum associated with the GNE mutations and can help prepare therapeutic strategies.

Enhancing missense variant pathogenicity prediction with protein language models using VariPred

Computational approaches for predicting the pathogenicity of genetic variants have advanced in recent years. These methods enable researchers to determine the possible clinical impact of rare and novel variants. Historically these prediction methods used hand-crafted features based on structural, evolutionary, or physiochemical properties of the variant. In this study we propose a novel framework that leverages the power of pre-trained protein language models to predict variant pathogenicity. We show that our approach VariPred (Variant impact Predictor) outperforms current state-of-the-art methods by using an end-to-end model that only requires the protein sequence as input. Using one of the best-performing protein language models (ESM-1b), we establish a robust classifier that requires no calculation of structural features or multiple sequence alignments. We compare the performance of VariPred with other representative models including 3Cnet, Polyphen-2, REVEL, MetaLR, FATHMM and ESM variant. VariPred performs as well as, or in most cases better than these other predictors using six variant impact prediction benchmarks despite requiring only sequence data and no pre-processing of the data.