Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel

Unravelling the Genetic Landscape of Hemiplegic Migraine: Exploring Innovative Strategies and Emerging Approaches

Migraine is a severe, debilitating neurovascular disorder. Hemiplegic migraine (HM) is a rare and debilitating neurological condition with a strong genetic basis. Sequencing technologies have improved the diagnosis and our understanding of the molecular pathophysiology of HM. Linkage analysis and sequencing studies in HM families have identified pathogenic variants in ion channels and related genes, including CACNA1A, ATP1A2, and SCN1A, that cause HM. However, approximately 75% of HM patients are negative for these mutations, indicating there are other genes involved in disease causation. In this review, we explored our current understanding of the genetics of HM. The evidence presented herein summarises the current knowledge of the genetics of HM, which can be expanded further to explain the remaining heritability of this debilitating condition. Innovative bioinformatics and computational strategies to cover the entire genetic spectrum of HM are also discussed in this review.

FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework

Nonsynonymous single-nucleotide polymorphisms (nsSNPs), implicated in over 6000 diseases, necessitate accurate prediction for expedited drug discovery and improved disease diagnosis. In this study, we propose FCMSTrans, a novel nsSNP predictor that innovatively combines the transformer framework and multiscale modules for comprehensive feature extraction. The distinctive attribute of FCMSTrans resides in a deep feature combination strategy. This strategy amalgamates evolutionary-scale modeling (ESM) and ProtTrans (PT) features, providing an understanding of protein biochemical properties, and position-specific scoring matrix, secondary structure, predicted relative solvent accessibility, and predicted disorder (PSPP) features, which are derived from four protein sequences and structure-oriented characteristics. This feature combination offers a comprehensive view of the molecular dynamics involving nsSNPs. Our model employs the transformer's self-attention mechanisms across multiple layers, extracting higher-level and abstract representations. Simultaneously, varied-level features are captured by multiscale convolutions, enriching feature abstraction at multiple echelons. Our comparative analyses with existing methodologies highlight significant improvements made possible by the integrated feature fusion approach adopted in FCMSTrans. This is further substantiated by performance assessments based on diverse data sets, such as PredictSNP, MMP, and PMD, with areas under the curve (AUCs) of 0.869, 0.819, and 0.693, respectively. Furthermore, FCMSTrans shows robustness and superiority by outperforming the current best predictor, PROVEAN, in a blind test conducted on a third-party data set, achieving an impressive AUC score of 0.7838. The Python code of FCMSTrans is available at https://github.com/gc212/FCMSTrans for academic usage.

Nonsynonymous Mutations in Intellectual Disability and Autism Spectrum Disorder Gene PTCHD1 Disrupt N-Glycosylation and Reduce Protein Stability

PTCHD1 has been implicated in Autism Spectrum Disorders (ASDs) and/or intellectual disability, where copy-number-variant losses or loss-of-function coding mutations segregate with disease in an X-linked recessive fashion. Missense variants of PTCHD1 have also been reported in patients. However, the significance of these mutations remains undetermined since the activities, subcellular localization, and regulation of the PTCHD1 protein are currently unknown. This paucity of data concerning PTCHD1 prevents the effective evaluation of sequence variants identified during diagnostic screening. Here, we characterize PTCHD1 protein binding partners, extending previously reported interactions with postsynaptic scaffolding protein, SAP102. Six rare missense variants of PTCHD1 were also identified from patients with neurodevelopmental disorders. After modelling these variants on a hypothetical three-dimensional structure of PTCHD1, based on the solved structure of NPC1, PTCHD1 variants harboring these mutations were assessed for protein stability, post-translational processing, and protein trafficking. We show here that the wild-type PTCHD1 post-translational modification includes complex N-glycosylation and that specific mutant proteins disrupt normal N-link glycosylation processing. However, regardless of their processing, these mutants still localized to PSD95-containing dendritic processes and remained competent for complexing SAP102.

Computational analysis and molecular dynamics simulation of high-risk single nucleotide polymorphisms of the ADAM10 gene

Polymorphisms of the disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) are linked to pathophysiological changes in lung inflammation, cancer, Alzheimer's disease (AD), encephalopathy, liver fibrosis, and cardiovascular diseases. In this study, we predicted the pathogenicity of ADAM10 non-synonymous single nucleotide polymorphisms (nsSNPs) in a wide array of mutation analyzing bioinformatics tools. We retrieved 423 nsSNPs from dbSNP-NCBI for the analysis, and 13 were predicted deleterious by each of the ten tools: SIFT, PROVEAN, CONDEL, PANTHER-PSEP, SNAP2, SuSPect, PolyPhen-2, Meta-SNP, Mutation Assessor and Predict-SNP. Further assessment of amino acid sequences, homology models, conservation profiles, and inter-atomic interactions identified C222G, G361E and C639Y as the most pathogenic mutations. We validated this prediction through structural stability analysis using DUET, I-Mutant Suite, SNPeffect and Dynamut. Molecular dynamics simulations and principal component analysis also indicated considerable instability of the C222G, G361E and C639Y variants. Therefore, these ADAM10 nsSNPs could be candidates for diagnostic genetic screening and therapeutic molecular targeting.Communicated by Ramaswamy H. Sarma.

Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set

Gain-of-function (GOF) variants give rise to increased/novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. Experimental approaches for identifying GOF and LOF are generally slow and costly, whilst available computational methods have not been optimized to discriminate between GOF and LOF variants. We have developed LoGoFunc, a machine learning method for predicting pathogenic GOF, pathogenic LOF, and neutral genetic variants, trained on a broad range of gene-, protein-, and variant-level features describing diverse biological characteristics. LoGoFunc outperforms other tools trained solely to predict pathogenicity for identifying pathogenic GOF and LOF variants and is available at https://itanlab.shinyapps.io/goflof/ .

MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction

Understanding the pathogenicity of missense mutation (MM) is essential for shed light on genetic diseases, gene functions, and individual variations. In this study, we propose a novel computational approach, called MMPatho, for enhancing missense mutation pathogenic prediction. First, we established a large-scale nonredundant MM benchmark data set based on the entire Ensembl database, complemented by a focused blind test set specifically for pathogenic GOF/LOF MM. Based on this data set, for each mutation, we utilized Ensembl VEP v104 and dbNSFP v4.1a to extract variant-level, amino acid-level, individuals' outputs, and genome-level features. Additionally, protein sequences were generated using ENSP identifiers with the Ensembl API, and then encoded. The mutant sites' ESM-1b and ProtTrans-T5 embeddings were subsequently extracted. Then, our model group (MMPatho) was developed by leveraging upon these efforts, which comprised ConsMM and EvoIndMM. To be specific, ConsMM employs individuals' outputs and XGBoost with SHAP explanation analysis, while EvoIndMM investigates the potential enhancement of predictive capability by incorporating evolutionary information from ESM-1b and ProtT5-XL-U50, large protein language embeddings. Through rigorous comparative experiments, both ConsMM and EvoIndMM were capable of achieving remarkable AUROC (0.9836 and 0.9854) and AUPR (0.9852 and 0.9902) values on the blind test set devoid of overlapping variations and proteins from the training data, thus highlighting the superiority of our computational approach in the prediction of MM pathogenicity. Our Web server, available at http://csbio.njust.edu.cn/bioinf/mmpatho/, allows researchers to predict the pathogenicity (alongside the reliability index score) of MMs using the ConsMM and EvoIndMM models and provides extensive annotations for user input. Additionally, the newly constructed benchmark data set and blind test set can be accessed via the data page of our web server.

Functional Characterization of a Novel SLC4A4 Variant and Uniparental Isodisomy in Proximal Renal Tubular Acidosis Patient

SLC4A4 variants are the etiologies of inherited proximal renal tubular acidosis (pRTA), which results in metabolic acidosis, hypokalemia, glaucoma, band keratopathy, and cataract. This study aims to characterize SLC4A4 variant and uniparental isodisomy of chromosome 4 in a patient, and analyse the functional characterization of SLC4A4 variants. This study analyzed renal tubular acidosis disease genes by whole exome sequencing (WES). H3M2 algorithm was used to analyze the run of homozygosity region in chromosomal regions in trio-WES data. The pathogenicity analysis of variants was performed using bioinformatics tools. Additionally, protein stability was analyzed by cycloheximide chase assay. Whole-cell patch clamping was used to examine the electrophysiological properties of NBCe1-A. A novel homozygous SLC4A4 variant was identified in the patient: a missense variant c.496C > T, p. Arg166Trp (NM_003759.4). But the father was heterozygous variant carrier, and the mother did not detect the variant. The H3M2 and UPDio algorithm revealed paternal uniparental isodisomy on chromosome 4 in the patient. SIFT, Poly Phen-2, FATHMM and Mutant Taster showed that the variant might be pathogenic. The tertiary structure analysis showed that the variant could cause structural damage to NBCe1 protein. Foldx results showed that the protein stability of the variant was slightly reduced. Cycloheximide chase assay demonstrated that the variant affects protein stability. The result of electrophysiological studies showed that the variant altered Na+/HCO3- cotransport activity of protein. In conclusion, the study is the first to report a pRTA patient with Arg166Trp variant with UPiD (4) pat and analyze the function of Arg166Trp variant.

Computational study of the potential impact of AURKC missense SNPs on AURKC-INCENP interaction and their correlation to macrozoospermia

Aurora Kinase C (AURKC) is considered an important element in Chromosome Passenger Complex (CPC), its interaction with Inner Centromere Protein (INCENP) plays a critical role in the establishment and the recruitment of a stable CPC during spermatogenesis. Genetic variations of AURKC gene are susceptible to impact AURKC-INCENP interaction, which may affect CPC stability and predispose male subjects to macrozoospermia. In this study, we systematically applied computational approaches using different bioinformatic tools to predict the effect of missense SNPs reported on AURKC gene, we selected the deleterious ones and we introduced their corresponding amino acid substitutions on AURKC protein structure. Then we did a protein-protein docking between AURKC variants and INCENP followed by a structural assessment of each resulting complex using PRODIGY server, Yassara view, Ligplot + and we choose the complexes of the most impactful variants for molecular dynamics (MD) simulation study. Seventeen missense SNPs of AURKC were identified as deleterious between all reported ones. All of them were located on relatively conserved positions on AURKC protein according to Consurf server. Only the four missense SNPs; E91K, D166V, D221Y and G235V were ranked as the most impactful ones and were chosen for MD simulation. D221Y and G235V were responsible for the most remarkable changes on AURKC-INCENP structural stability, therefore, they were selected as the most deleterious ones. Experimental studies are recommended to test the actual effect of these two variants and their actual impact on the morphology of sperm cells.Communicated by Ramaswamy H. Sarma.

In silico analysis of missense SNPs in TNFR1a and their possible therapeutic or pathogenic role in immune diseases

Tumor necrosis factor alpha (TNFa) is an inflammatory cytokine that is involved in the pathogenesis of various inflammatory disorders including rheumatoid arthritis. TNF-alpha receptor I (TNFR1a) is one of the receptors TNFa binds with for its activation. Any variation in this receptor might affect the role of TNFa in successive events. Amino acid residue substitutions might happen in TNFR1a through non-synonymous single nucleotide polymorphisms (nsSNPs) which may alter the functioning of TNFa, hence, identifying any such substitutions is of paramount significance. In this study, six nsSNPs at five different evolutionary conserved regions are predicted to be detrimental to the structure and/or function of TNFR1a by using numerous computational tools. Their 3D models are also proposed in this study. Besides, they were found to reduce the stability and affect the molecular mechanisms of this protein. Two contrasting possibilities might happen because of these substitutions. One, they might reduce the production of TNFa which is overexpressed in inflammatory diseases, hence can play therapeutic role in such diseases. Second, they might possibly hinder the apoptosis to occur which can effectuate the uncontrolled division of cells, hence can be pathogenic in diseases like cancer. Further investigations on these nsSNPs using animal models and at cellular level will open doors to understand the underlying mechanisms behind various diseases.

Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies

PURPOSE OF REVIEW

Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH.

RECENT FINDINGS

In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

Comprehensive analysis of mitochondrial DNA variants, mitochondrial DNA copy number and oxidative damage in psoriatic arthritis

Growing evidence suggests that abnormalities in mitochondrial DNA (mtDNA) are involved in the pathogenesis of various inflammatory and immuno-mediated diseases. The present study analysed the entire mitochondrial genome by next-generation sequencing (NGS) in 23 patients with psoriatic arthritis (PsA) and 20 healthy controls to identify PsA-related variants. Changes in mtDNA copy number (mtDNAcn) were also evaluated by quantitative polymerase chain reaction (qPCR) and mtDNA oxidative damage was measured using an 8-hydroxy-2'-deoxyguanosine assay. NGS analysis revealed a total of 435 variants including 187 in patients with PsA only and 122 in controls only. Additionally, 126 common variants were found, of which 2 variants differed significantly in their frequencies among patients and controls (P<0.05), and may be associated with susceptibility to PsA. A total of 33 missense variants in mtDNA-encoded genes for complexes I, III, IV and V were identified only in patients with PsA. Of them, 25 variants were predicted to be deleterious by affecting the functions and structures of encoded proteins, and 13 variants were predicted to affect protein's stability. mtDNAcn analysis revealed decreased mtDNA content in patients with PsA compared with controls (P=0.0001) but the decrease in mtDNAcn was not correlated with patients' age or inflammatory biomarkers (P>0.05). Moreover, a higher level of oxidative damage was observed in patients with PsA compared with controls (P=0.03). The results of the present comprehensive analysis of mtDNA in PsA revealed that certain mtDNA variants may be implicated in the predisposition/pathogenesis of PsA, highlighting the importance of NGS in the identification of mtDNA variants in PsA. The current results also demonstrated that decreased mtDNAcn in PsA may be a consequence of increased oxidative stress. These data provide valuable insights into the contribution of mtDNA defects to the pathogenesis of PsA. Additional studies in larger cohorts are needed to elucidate the role of mtDNA defects in PsA.

Genetic signature detected in T cell receptors from patients with severe COVID-19

Characterization of host genetic factors contributing to COVID-19 severity promises advances on drug discovery to fight the disease. Most genetic analyses to date have identified genome-wide significant associations involving loss-of-function variants for immune response pathways. Despite accumulating evidence supporting a role for T cells in COVID-19 severity, no definitive genetic markers have been found to support an involvement of T cell responses. We analyzed 205 whole exomes from both a well-characterized cohort of hospitalized severe COVID-19 patients and controls. Significantly enriched high impact alleles were found for 25 variants within the T cell receptor beta (TRB) locus on chromosome 7. Although most of these alleles were found in heterozygosis, at least three or more in TRBV6-5, TRBV7-3, TRBV7-6, TRBV7-7, and TRBV10-1 suggested a possible TRB loss of function via compound heterozygosis. This loss-of-function in TRB genes supports suboptimal or dysfunctional T cell responses as a major contributor to severe COVID-19 pathogenesis.

Identification of candidate genes for developmental colour agnosia in a single unique family

Colour agnosia is a disorder that impairs colour knowledge (naming, recognition) despite intact colour perception. Previously, we have identified the first and only-known family with hereditary developmental colour agnosia. The aim of the current study was to explore genomic regions and candidate genes that potentially cause this trait in this family. For three family members with developmental colour agnosia and three unaffected family members CGH-array analysis and exome sequencing was performed, and linkage analysis was carried out using DominantMapper, resulting in the identification of 19 cosegregating chromosomal regions. Whole exome sequencing resulted in 11 rare coding variants present in all affected family members with developmental colour agnosia and absent in unaffected members. These variants affected genes that have been implicated in neural processes and functions (CACNA2D4, DDX25, GRINA, MYO15A) or that have an indirect link to brain function, development or disease (MAML2, STAU1, TMED3, RABEPK), and a remaining group lacking brain expression or involved in non-neural traits (DEPDC7, OR1J1, OR8D4). Although this is an explorative study, the small set of candidate genes that could serve as a starting point for unravelling mechanisms of higher level cognitive functions and cortical specialization, and disorders therein such as developmental colour agnosia.

Identification of compound heterozygous variants in MSH4 as a novel genetic cause of diminished ovarian reserve

BACKGROUND

Diminished ovarian reserve (DOR) is a common cause of female infertility, with genetic factors being a significant contributor. However, due to high genetic heterogeneity, the etiology of DOR in many cases remains unknown. In this study, we analyzed the phenotype of a young woman with primary infertility and performed molecular genetic analysis to identify the genetic cause of her condition, thus providing important insights for genetic counseling and reproductive guidance.

METHODS

We collected the patient's basic information, clinical data, as well as diagnostic and therapeutic history and performed whole-exome sequencing on her peripheral blood. Candidate pathogenic variants were validated by Sanger sequencing in family members, and the pathogenicity of variants was analyzed using ACMG guidelines. We used bioinformatics tools to predict variant effects on splicing and protein function, and performed in vitro experiments including minigene assay and expression analysis to evaluate their functional effects on HEK293T.

RESULTS

We identified biallelic MSH4 variants, c.2374 A > G (p.Thr792Ala) and c.2222_2225delAAGA (p.Lys741Argfs*2) in the DOR patient. According to ACMG guidelines, the former was classified as likely pathogenic, while the latter was classified as pathogenic. The patient presented with poor oocyte quantity and quality, resulting in unsuccessful in vitro fertilization cycles. Bioinformatics and in vitro functional analysis showed that the c.2374 A > G variant altered the local conformation of the MutS_V domain without decreasing MSH4 protein expression, while the c.2222_2225delAAGA variant led to a reduction in MSH4 protein expression without impacting splicing.

CONCLUSIONS

In this study, we present evidence of biallelic variants in MSH4 as a potential cause of DOR. Our findings indicate a correlation between MSH4 variants and reduced oocyte quality, as well as abnormal morphology of the first polar body, thereby expanding the phenotypic spectrum associated with MSH4 variants. Furthermore, Our study emphasizes the importance of utilizing whole-exome sequencing and functional analysis in diagnosing genetic causes, as well as providing effective genetic counseling and reproductive guidance for DOR patients.

eyeVarP: A computational framework for the identification of pathogenic variants specific to eye disease

PURPOSE

Disease-specific pathogenic variant prediction tools that differentiate pathogenic variants from benign have been improved through disease specificity recently. However, they have not been evaluated on disease-specific pathogenic variants compared with other diseases, which would help to prioritize disease-specific variants from several genes or novel genes. Thus, we hypothesize that features of pathogenic variants alone would provide a better model.

METHODS

We developed an eye disease-specific variant prioritization tool (eyeVarP), which applied the random forest algorithm to the data set of pathogenic variants of eye diseases and other diseases. We also developed the VarP tool and generalized pipeline to filter missense and insertion-deletion variants and predict their pathogenicity from exome or genome sequencing data, thus we provide a complete computational procedure.

RESULTS

eyeVarP outperformed pan disease-specific tools in identifying eye disease-specific pathogenic variants under the top 10. VarP outperformed 12 pathogenicity prediction tools with an accuracy of 95% in correctly identifying the pathogenicity of missense and insertion-deletion variants. The complete pipeline would help to develop disease-specific tools for other genetic disorders.

CONCLUSION

eyeVarP performs better in identifying eye disease-specific pathogenic variants using pathogenic variant features and gene features. Implementing such complete computational procedure would significantly improve the clinical variant interpretation for specific diseases.

High-throughput deep learning variant effect prediction with Sequence UNET

Understanding coding mutations is important for many applications in biology and medicine but the vast mutation space makes comprehensive experimental characterisation impossible. Current predictors are often computationally intensive and difficult to scale, including recent deep learning models. We introduce Sequence UNET, a highly scalable deep learning architecture that classifies and predicts variant frequency from sequence alone using multi-scale representations from a fully convolutional compression/expansion architecture. It achieves comparable pathogenicity prediction to recent methods. We demonstrate scalability by analysing 8.3B variants in 904,134 proteins detected through large-scale proteomics. Sequence UNET runs on modest hardware with a simple Python package.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

S. B, Chatterjee M, Prabhash K, Sreekanthreddy P, Rishi KD, Goswami HM, Veldore VH. An absolute approach to using whole exome DNA and RNA workflow for cancer biomarker testing

Introduction

The concept of personalized medicine in cancer has emerged rapidly with the advancement of genome sequencing and the identification of clinically relevant variants that contribute to disease prognosis and facilitates targeted therapy options. In this study, we propose to validate a whole exome-based tumor molecular profiling for DNA and RNA from formalin-fixed paraffin-embedded (FFPE) tumor tissue.

Methods

The study included 166 patients across 17 different cancer types. The scope of this study includes the identification of single-nucleotide variants (SNVs), insertions/deletions (INDELS), copy number alterations (CNAs), gene fusions, tumor mutational burden (TMB), and microsatellite instability (MSI). The assay yielded a mean read depth of 200×, with &gt;80% of on-target reads and a mean uniformity of &gt;90%. Clinical maturation of whole exome sequencing (WES) (DNA and RNA)- based assay was achieved by analytical and clinical validations for all the types of genomic alterations in multiple cancers. We here demonstrate a limit of detection (LOD) of 5% for SNVs and 10% for INDELS with 97.5% specificity, 100% sensitivity, and 100% reproducibility.

Results

The results were &gt;98% concordant with other orthogonal techniques and appeared to be more robust and comprehensive in detecting all the clinically relevant alterations. Our study demonstrates the clinical utility of the exome-based approach of comprehensive genomic profiling (CGP) for cancer patients at diagnosis and disease progression.

Discussion

The assay provides a consolidated picture of tumor heterogeneity and prognostic and predictive biomarkers, thus helping in precision oncology practice. The primary intended use of WES (DNA+RNA) assay would be for patients with rare cancers as well as for patients with unknown primary tumors, and this category constitutes nearly 20–30% of all cancers. The WES approach may also help us understand the clonal evolution during disease progression to precisely plan the treatment in advanced stage disease.

Gene mutations in comorbidity of epilepsy and arrhythmia

Epilepsy is one of the most common neurological disorders, and sudden unexpected death in epilepsy (SUDEP) is the most severe outcome of refractory epilepsy. Arrhythmia is one of the heterogeneous factors in the pathophysiological mechanism of SUDEP with a high incidence in patients with refractory epilepsy, increasing the risk of premature death. The gene co-expressed in the brain and heart is supposed to be the genetic basis between epilepsy and arrhythmia, among which the gene encoding ion channel contributes to the prevalence of "cardiocerebral channelopathy" theory. Nevertheless, this theory could only explain the molecular mechanism of comorbid arrhythmia in part of patients with epilepsy (PWE). Therefore, we summarized the mutant genes that can induce comorbidity of epilepsy and arrhythmia and the possible corresponding treatments. These variants involved the genes encoding sodium, potassium, calcium and HCN channels, as well as some non-ion channel coding genes such as CHD4, PKP2, FHF1, GNB5, and mitochondrial genes. The relationship between genotype and clinical phenotype was not simple linear. Indeed, genes co-expressed in the brain and heart could independently induce epilepsy and/or arrhythmia. Mutant genes in brain could affect cardiac rhythm through central or peripheral regulation, while in the heart it could also affect cerebral electrical activity by changing the hemodynamics or internal environment. Analysis of mutations in comorbidity of epilepsy and arrhythmia could refine and expand the theory of "cardiocerebral channelopathy" and provide new insights for risk stratification of premature death and corresponding precision therapy in PWE.

The in-silico evaluation of important GLUT9 residue for uric acid transport based on renal hypouricemia type 2

Uric acid is the end product of purine metabolism. Uric acid transporters in the renal proximal tubule plays a key role in uric acid transport. Functional abnormalities in these transporters could lead to high or low levels of uric acid in the blood plasma, known as hyperuricemia and hypouricemia, respectively. GLUT9 has been reported as a key transporter for uric acid reuptake in renal proximal tubule. GLUT9 mutation is known as causal gene for renal hypouricemia due to defective uric acid uptake, with more severe cases resulting in urolithiasis and exercise induced acute kidney injury (EIAKI). However, the effect of mutation is not fully investigated and hard to predict the change of binding affinity. We comprehensively described the effect of GLUT9 mutation for uric acid transport using molecular dynamics and investigated the specific site for uric acid binding differences. R171C and R380W showed the significant disruption of the structure not affecting transport dynamics whereas L75R, G216R, N333S, and P412R showed the reduced affinity of the extracellular vestibular area towards urate. Interestingly, T125 M showed a significant increase in intracellular binding energy, associated with distorted geometries. We can use this classification to consider the effect mutations by comparing the transport profiles of mutants against those of chemical candidates for transport and providing new perspectives to urate lowering drug discovery using GLUT9.

Structural Consequences of IRS-2 nsSNPs and Implication for Insulin Receptor Substrate-2 Protein Stability

Single-Nucleotide Polymorphisms (SNPs) are common genetic variations implicated in human diseases. The non-synonymous SNPs (nsSNPs) affect the proteins' structures and their molecular interactions with other interacting proteins during the accomplishment of biochemical processes. This ultimately causes proteins functional perturbation and disease phenotypes. The Insulin receptor substrate-2 (IRS-2) protein promotes glucose absorption and participates in the biological regulation of glucose metabolism and energy production. Several IRS-2 SNPs are reported in association with type 2 diabetes and obesity in human populations. However, there are no comprehensive reports about the protein structural consequences of these nsSNPs. Keeping in view the pathophysiological consequences of the IRS-2 nsSNPs, we designed the current study to understand their possible structural impact on coding protein. The prioritized list of the deleterious IRS-2 nsSNPs was acquired from multiple bioinformatics resources, including VEP (SIFT, PolyPhen, and Condel), PROVEAN, SNPs&GO, PMut, and SNAP2. The protein structure stability assessment of these nsSNPs was performed by MuPro and I-Mutant-3.0 servers via structural modeling approaches. The atomic-level structural and molecular dynamics (MD) impact of these nsSNPs were examined using GROMACS 2019.2 software package. The analyses initially predicted 8 high-risk nsSNPs located in the highly conserved regions of IRS-2. The MD simulation analysis eventually prioritized the N232Y, R218C, and R104H nsSNPs that predicted to significantly compromise the structure stability and may affect the biological function of IRS-2. These nsSNPs are predicted as high-risk candidates for diabetes and obesity. The validation of protein structural impact of these shortlisted nsSNPs may provide biochemical insight into the IRS-2-mediated type-2 diabetes.

High-throughput sequencing analysis of nuclear-encoded mitochondrial genes reveals a genetic signature of human longevity

Mitochondrial dysfunction is a well-known contributor to aging and age-related diseases. The precise mechanisms through which mitochondria impact human lifespan, however, remain unclear. We hypothesize that humans with exceptional longevity harbor rare variants in nuclear-encoded mitochondrial genes (mitonuclear genes) that confer resistance against age-related mitochondrial dysfunction. Here we report an integrated functional genomics study to identify rare functional variants in ~ 660 mitonuclear candidate genes discovered by target capture sequencing analysis of 496 centenarians and 572 controls of Ashkenazi Jewish descent. We identify and prioritize longevity-associated variants, genes, and mitochondrial pathways that are enriched with rare variants. We provide functional gene variants such as those in MTOR (Y2396Lfs*29), CPS1 (T1406N), and MFN2 (G548*) as well as LRPPRC (S1378G) that is predicted to affect mitochondrial translation. Taken together, our results suggest a functional role for specific mitonuclear genes and pathways in human longevity.

Heritable defects in telomere and mitotic function selectively predispose to sarcomas

Cancer genetics has to date focused on epithelial malignancies, identifying multiple histotype-specific pathways underlying cancer susceptibility. Sarcomas are rare malignancies predominantly derived from embryonic mesoderm. To identify pathways specific to mesenchymal cancers, we performed whole-genome germline sequencing on 1644 sporadic cases and 3205 matched healthy elderly controls. Using an extreme phenotype design, a combined rare-variant burden and ontologic analysis identified two sarcoma-specific pathways involved in mitotic and telomere functions. Variants in centrosome genes are linked to malignant peripheral nerve sheath and gastrointestinal stromal tumors, whereas heritable defects in the shelterin complex link susceptibility to sarcoma, melanoma, and thyroid cancers. These studies indicate a specific role for heritable defects in mitotic and telomere biology in risk of sarcomas.

Targeted deep sequencing analyses of long QT syndrome in a Japanese population

Long QT syndrome (LQTS) is one of the most common inherited arrhythmias and multiple genes have been reported as causative. Presently, genetic diagnosis for LQTS patients is becoming widespread and contributing to implementation of therapies. However, causative genetic mutations cannot be detected in about 20% of patients. To elucidate additional genetic mutations in LQTS, we performed deep-sequencing of previously reported 15 causative and 85 candidate genes for this disorder in 556 Japanese LQTS patients. We performed in-silico filtering of the sequencing data and found 48 novel variants in 33 genes of 53 cases. These variants were predicted to be damaging to coding proteins or to alter the binding affinity of several transcription factors. Notably, we found that most of the LQTS-related variants in the RYR2 gene were in the large cytoplasmic domain of the N-terminus side. They might be useful for screening of LQTS patients who had no known genetic factors. In addition, when the mechanisms of these variants in the development of LQTS are revealed, it will be useful for early diagnosis, risk stratification, and selection of treatment.

Pharmacogenetics of CYP2A6, CYP2B6, and UGT2B7 in the Context of HIV Treatments in African Populations

OBJECTIVES

This study focuses on identifying variations in selected CYP genes related to treatment responses in patients with HIV in African populations by investigating variant characteristics and effects in African cohorts.

DESIGN

Cytochrome P450 (CYP) 2A6, 2B6, and Uridine 5'-diphospho-glucuronosyltransferase (UGT) 2B7 allele frequencies were studied using public-domain datasets obtained from the 1000 Genomes Phase 3 project, the African Genome Variation Project (AGVP), and the South African Human Genome Programme (SAHGP).

METHODS

Variant annotations were performed using self-identified ethnicities to conduct allele frequency analysis in a population-stratification-sensitive manner. The NCBI DB-SNP database was used to identify documented variants and standard frequencies, and the E! Ensembl Variant Effect Predictor tool was used to perform the prediction of possible deleterious variants.

RESULTS

A total of 4468 variants were identified across 3676 individuals following pre-filtering. Seventy-one variants were identified at an allelic frequency (1% or more in at least one population), which were predicted to be linked to existing disease associations and, in some cases, linked to drug metabolisms. This list was further studied to identify 23 alleles with disease considerations found at significantly different frequencies in one or more populations.

CONCLUSIONS

This study describes allele frequencies observed in African populations at significantly different frequencies relative to at least one other reference population and identifies a subset of variants of clinical interest. Despite the inclusion of mixed sequence coverage datasets, the variants identified pose notable avenues for future inquiries. A subset of variants of clinical interest with statistically significant inter-population frequency differences was identified for further inspection, which provides evidence of an African population-specific variant frequency profile. This study highlights the need for additional research and African genetics data given the presence of this unique frequency profile to better facilitate the genetic pre-screening of patients as a standard of practice in HIV care, particularly on the African continent where HIV is highly prevalent.

Structural and Functional Impact of Damaging Nonsynonymous Single Nucleotide Polymorphisms (nsSNPs) on Human VPS35 Protein Using Computational Approaches

Parkinson's disease is the second most common progressive neurodegenerative movement disorder. Mutations in retromer complex subunit and VPS35 represent the second most common cause of late-onset familial Parkinson's disease. The mutation in VPS35 can disrupt the normal protein functions resulting in Parkinson's disease. The aim of this study was the identification of deleterious missense Single Nucleotide Polymorphisms (nsSNPs) and their structural and functional impact on the VPS35 protein. In this study, several insilico tools were used to identify deleterious and disease-associated nsSNPs. 3D structure of VPS35 protein was constructed through MODELLER 9.2, normalized using FOLDX, and evaluated through RAMPAGE and ERRAT whereas, FOLDX was used for mutagenesis. 25 ligands were obtained from literature and docked using PyRx 0.8 software. Based on the binding affinity, five ligands i.e., PG4, MSE, GOL, EDO, and CAF were further analyzed. Molecular Dynamic simulation analysis was performed using GROMACS 5.1.4, where temperature, pressure, density, RMSD, RMSF, Rg, and SASA graphs were analyzed. The results showed that the mutations Y67H, R524W, and D620N had a structural and functional impact on the VPS35 protein. The current findings will help in appropriate drug design against the disease caused by these mutations in a large population using in-vitro study.

Epigenetic regulator genes direct lineage switching in MLL/AF4 leukemia

The fusion gene MLL/AF4 defines a high-risk subtype of pro-B acute lymphoblastic leukemia. Relapse can be associated with a lineage switch from acute lymphoblastic to acute myeloid leukemia, resulting in poor clinical outcomes caused by resistance to chemotherapies and immunotherapies. In this study, the myeloid relapses shared oncogene fusion breakpoints with their matched lymphoid presentations and originated from various differentiation stages from immature progenitors through to committed B-cell precursors. Lineage switching is linked to substantial changes in chromatin accessibility and rewiring of transcriptional programs, including alternative splicing. These findings indicate that the execution and maintenance of lymphoid lineage differentiation is impaired. The relapsed myeloid phenotype is recurrently associated with the altered expression, splicing, or mutation of chromatin modifiers, including CHD4 coding for the ATPase/helicase of the nucleosome remodelling and deacetylation complex. Perturbation of CHD4 alone or in combination with other mutated epigenetic modifiers induces myeloid gene expression in MLL/AF4+ cell models, indicating that lineage switching in MLL/AF4 leukemia is driven and maintained by disrupted epigenetic regulation.

A Novel Homozygous Founder Variant of RTN4IP1 in Two Consanguineous Saudi Families

The genetic architecture of mitochondrial disease continues to expand and currently exceeds more than 350 disease-causing genes. Bi-allelic variants in RTN4IP1, also known as Optic Atrophy-10 (OPA10), lead to early-onset recessive optic neuropathy, atrophy, and encephalopathy in the afflicted patients. The gene is known to encode a mitochondrial ubiquinol oxidoreductase that interacts with reticulon 4 and is thought to be a mitochondrial antioxidant NADPH oxidoreductase. Here, we describe two unrelated consanguineous families from the northern region of Saudi Arabia harboring a missense variant (RTN4IP1:NM_032730.5; c.475G Collapse

Key Words

• RTN4IP1
• age of variant
• encephalopathy
• founder variant
• in silico pathogenicity prediction
• missense
• optic atrophy
• structural modeling

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MESH Headings

• Antioxidants
• Brain Diseases
• Carrier Proteins/genetics
• Humans
• Mitochondrial Proteins/genetics
• Mutation/genetics
• NADP/genetics
• Optic Atrophy/genetics
• Oxidoreductases/genetics
• Saudi Arabia

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Grants

• MR/W019027/1 Medical Research Council
• MR/S005021/1 Medical Research Council
• Department of Health
• 203105/Z/16/Z Wellcome Trust
• G0800674 Medical Research Council

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Affiliation(s)

Mazhor Aldosary Translational Genomics Department, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
Maysoon Alsagob Translational Genomics Department, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia Center of Excellence for Biomedicine, Joint Centers for Excellence Program, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
Hanan AlQudairy Translational Genomics Department, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
Ana C. González-Álvarez Bioscience Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia Computational Biology Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
Stefan T. Arold Bioscience Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia Computational Biology Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia Centre de Biologie Structurale (CBS), INSERM, CNRS, Université de Montpellier, F-34090 Montpellier, France
Mohammad Anas Dababo Department of Pathology and Laboratory Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
Omar A. Alharbi Radiology Department, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
Rawan Almass Department of Medical Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
AlBandary AlBakheet Translational Genomics Department, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
Dalia AlSarar Translational Genomics Department, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
Alya Qari Department of Medical Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
Mysoon M. Al-Ansari Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia Department of Molecular Oncology, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
Monika Oláhová Welcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
Saif A. Al-Shahrani Department of Medical Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
Moeenaldeen AlSayed Department of Medical Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia College of Medicine, Alfaisal University, P.O. Box 50927, Riyadh 11533, Saudi Arabia
Dilek Colak Department of Molecular Oncology, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
Robert W. Taylor Welcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK NHS Highly Specialized Service for Rare Mitochondrial Disorders, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE1 4LP, UK
Mohammed AlOwain Department of Medical Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia
Namik Kaya Translational Genomics Department, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), P.O. Box 3354, Riyadh 11211, Saudi Arabia

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Computational approaches for predicting variant impact: An overview from resources, principles to applications

One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.

Clinical significance of genetic variation in hypertrophic cardiomyopathy: comparison of computational tools to prioritize missense variants

Hypertrophic cardiomyopathy (HCM) is a common heart disease associated with sudden cardiac death. Early diagnosis is critical to identify patients who may benefit from implantable cardioverter defibrillator therapy. Although genetic testing is an integral part of the clinical evaluation and management of patients with HCM and their families, in many cases the genetic analysis fails to identify a disease-causing mutation. This is in part due to difficulties in classifying newly detected rare genetic variants as well as variants-of-unknown-significance (VUS). Multiple computational algorithms have been developed to predict the potential pathogenicity of genetic variants, but their relative performance in HCM has not been comprehensively assessed. Here, we compared the performance of 39 currently available prediction tools in distinguishing between high-confidence HCM-causing missense variants and benign variants, and we developed an easy-to-use-tool to perform variant prediction benchmarks based on annotated VCF files (VETA). Our results show that tool performance increases after HCM-specific calibration of thresholds. After excluding potential biases due to circularity type I issues, we identified ClinPred, MISTIC, FATHMM, MPC and MetaLR as the five best performer tools in discriminating HCM-associated variants. We propose combining these tools in order to prioritize unknown HCM missense variants that should be closely followed-up in the clinic.

Screening of Pathogenic Missense Single Nucleotide Variants From LHPP Gene Associated With the Hepatocellular Carcinoma: An In silico Approach

LHPP gene encodes a phospholysine phosphohistidine inorganic pyrophosphate phosphatase, which functions as a tumor-suppressor protein. The tumor suppression by this protein has been confirmed in various cancers, including hepatocellular carcinoma (HCC). LHPP downregulation promotes cell growth and proliferation by modulating the PI3K/AKT signaling pathway. This study identifies potentially deleterious missense single nucleotide variants (SNVs) associated with the LHPP gene using multiple computational tools based on different algorithms. A total of 4 destabilizing mutants are identified as L22P, I212T, G227R, and G236R, from the conserved region of the phosphatase. The 3-dimensional (3D) modeling and structural comparison of variants with the native protein reveals significant structural and conformational variations after mutations, suggesting disruption in the function of phospholysine phosphohistidine inorganic pyrophosphate phosphatase. The identified mutations might, therefore, participate in the cause of HCC.

Dynamic insights into the effects of nonsynonymous polymorphisms (nsSNPs) on loss of TREM2 function

Single nucleotide variations in Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) are associated with many neurodegenerative diseases, including Nasu-Hakola disease (NHD), frontotemporal dementia (FTD), and late-onset Alzheimer's disease because they disrupt ligand binding to the extracellular domain of TREM2. However, the effects of nonsynonymous single nucleotide polymorphisms (nsSNPs) in TREM2 on disease progression remain unknown. In this study, we identified several high-risk nsSNPs in the TREM2 gene using various deleterious SNP predicting algorithms and analyzed their destabilizing effects on the ligand recognizing region of the TREM2 immunoglobulin (Ig) domain by molecular dynamics (MD) simulation. Cumulative prediction by all tools employed suggested the three most deleterious nsSNPs involved in loss of TREM2 function are rs549402254 (W50S), rs749358844 (R52C), and rs1409131974 (D104G). MD simulation showed that these three variants cause substantial structural alterations and conformational remodeling of the apical loops of the TREM2 Ig domain, which is responsible for ligand recognition. Detailed analysis revealed that these variants substantially increased distances between apical loops and induced conformation remodeling by changing inter-loop nonbonded contacts. Moreover, all nsSNPs changed the electrostatic potentials near the putative ligand-interacting region (PLIR), which suggested they might reduce specificity or loss of binding affinity for TREM2 ligands. Overall, this study identifies three potential high-risk nsSNPs in the TREM2 gene. We propose further studies on the molecular mechanisms responsible for loss of TREM2 function and the associations between TREM2 nsSNPs and neurodegenerative diseases.

Interpreting protein variant effects with computational predictors and deep mutational scanning

Computational predictors of genetic variant effect have advanced rapidly in recent years. These programs provide clinical and research laboratories with a rapid and scalable method to assess the likely impacts of novel variants. However, it can be difficult to know to what extent we can trust their results. To benchmark their performance, predictors are often tested against large datasets of known pathogenic and benign variants. These benchmarking data may overlap with the data used to train some supervised predictors, which leads to data re-use or circularity, resulting in inflated performance estimates for those predictors. Furthermore, new predictors are usually found by their authors to be superior to all previous predictors, which suggests some degree of computational bias in their benchmarking. Large-scale functional assays known as deep mutational scans provide one possible solution to this problem, providing independent datasets of variant effect measurements. In this Review, we discuss some of the key advances in predictor methodology, current benchmarking strategies and how data derived from deep mutational scans can be used to overcome the issue of data circularity. We also discuss the ability of such functional assays to directly predict clinical impacts of mutations and how this might affect the future need for variant effect predictors.

Impact of natural selection on global patterns of genetic variation and association with clinical phenotypes at genes involved in SARS-CoV-2 infection

Human genomic diversity has been shaped by both ancient and ongoing challenges from viruses. The current coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a devastating impact on population health. However, genetic diversity and evolutionary forces impacting host genes related to SARS-CoV-2 infection are not well understood. We investigated global patterns of genetic variation and signatures of natural selection at host genes relevant to SARS-CoV-2 infection (angiotensin converting enzyme 2 [ACE2], transmembrane protease serine 2 [TMPRSS2], dipeptidyl peptidase 4 [DPP4], and lymphocyte antigen 6 complex locus E [LY6E]). We analyzed data from 2,012 ethnically diverse Africans and 15,977 individuals of European and African ancestry with electronic health records and integrated with global data from the 1000 Genomes Project. At ACE2, we identified 41 nonsynonymous variants that were rare in most populations, several of which impact protein function. However, three nonsynonymous variants (rs138390800, rs147311723, and rs145437639) were common among central African hunter-gatherers from Cameroon (minor allele frequency 0.083 to 0.164) and are on haplotypes that exhibit signatures of positive selection. We identify signatures of selection impacting variation at regulatory regions influencing ACE2 expression in multiple African populations. At TMPRSS2, we identified 13 amino acid changes that are adaptive and specific to the human lineage compared with the chimpanzee genome. Genetic variants that are targets of natural selection are associated with clinical phenotypes common in patients with COVID-19. Our study provides insights into global variation at host genes related to SARS-CoV-2 infection, which have been shaped by natural selection in some populations, possibly due to prior viral infections.

Innovative in Silico Approaches for Characterization of Genes and Proteins

Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine.

A common TMPRSS2 variant has a protective effect against severe COVID-19

BACKGROUND

The human protein transmembrane protease serine type 2 (TMPRSS2) plays a key role in SARS-CoV-2 infection, as it is required to activate the virus' spike protein, facilitating entry into target cells. We hypothesized that naturally-occurring TMPRSS2 human genetic variants affecting the structure and function of the TMPRSS2 protein may modulate the severity of SARS-CoV-2 infection.

METHODS

We focused on the only common TMPRSS2 non-synonymous variant predicted to be damaging (rs12329760 C>T, p.V160M), which has a minor allele frequency ranging from 0.14 in Ashkenazi Jewish to 0.38 in East Asians. We analysed the association between the rs12329760 and COVID-19 severity in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units recruited as part of the GenOMICC (Genetics Of Mortality In Critical Care) study. Logistic regression analyses were adjusted for sex, age and deprivation index. For in vitro studies, HEK293 cells were co-transfected with ACE2 and either TMPRSS2 wild type or mutant (TMPRSS2V160M). A SARS-CoV-2 pseudovirus entry assay was used to investigate the ability of TMPRSS2V160M to promote viral entry.

RESULTS

We show that the T allele of rs12329760 is associated with a reduced likelihood of developing severe COVID-19 (OR 0.87, 95%CI:0.79-0.97, p = 0.01). This association was stronger in homozygous individuals when compared to the general population (OR 0.65, 95%CI:0.50-0.84, p = 1.3 × 10-3). We demonstrate in vitro that this variant, which causes the amino acid substitution valine to methionine, affects the catalytic activity of TMPRSS2 and is less able to support SARS-CoV-2 spike-mediated entry into cells.

CONCLUSION

TMPRSS2 rs12329760 is a common variant associated with a significantly decreased risk of severe COVID-19. Further studies are needed to assess the expression of TMPRSS2 across different age groups. Moreover, our results identify TMPRSS2 as a promising drug target, with a potential role for camostat mesilate, a drug approved for the treatment of chronic pancreatitis and postoperative reflux esophagitis, in the treatment of COVID-19. Clinical trials are needed to confirm this.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

The Genetic and Molecular Analyses of RAD51C and RAD51D Identifies Rare Variants Implicated in Hereditary Ovarian Cancer from a Genetically Unique Population

To identify candidate variants in RAD51C and RAD51D ovarian cancer (OC) predisposing genes by investigating French Canadians (FC) exhibiting unique genetic architecture. Candidates were identified by whole exome sequencing analysis of 17 OC families and 53 early-onset OC cases. Carrier frequencies were determined by the genetic analysis of 100 OC or HBOC families, 438 sporadic OC cases and 1025 controls. Variants of unknown function were assayed for their biological impact and/or cellular sensitivity to olaparib. RAD51C c.414G>C;p.Leu138Phe and c.705G>T;p.Lys235Asn and RAD51D c.137C>G;p.Ser46Cys, c.620C>T;p.Ser207Leu and c.694C>T;p.Arg232Ter were identified in 17.6% of families and 11.3% of early-onset cases. The highest carrier frequency was observed in OC families (1/44, 2.3%) and sporadic cases (15/438, 3.4%) harbouring RAD51D c.620C>T versus controls (1/1025, 0.1%). Carriers of c.620C>T (n = 7), c.705G>T (n = 2) and c.137C>G (n = 1) were identified in another 538 FC OC cases. RAD51C c.705G>T affected splicing by skipping exon four, while RAD51D p.Ser46Cys affected protein stability and conferred olaparib sensitivity. Genetic and functional assays implicate RAD51C c.705G>T and RAD51D c.137C>G as likely pathogenic variants in OC. The high carrier frequency of RAD51D c.620C>T in FC OC cases validates previous findings. Our findings further support the role of RAD51C and RAD51D in hereditary OC.

Emerging of composition variations of SARS-CoV-2 spike protein and human ACE2 contribute to the level of infection: in silico approaches

SARS-CoV-2 is causative of pandemic COVID-19. There is a sequence similarity between SARS-CoV-2 and SARS-CoV; however, SARS-CoV-2 RBDs (receptor-binding domain) binds 20-fold strongly with human angiotensin-converting enzyme 2 (hACE2) than SARS-CoV. The study aims to investigate protein-protein interactions (PPI) of hACE2 with SARS-CoV-2 RBD between wild and variants to detect the most influential interaction. Variants of hACE2 were retrieved from NCBI and subjected to determine the most pathogenic nsSNPs. Probability of PPIs determines the binding affinity of hACE2 genetic variants with RBD was investigated. Composition variations at the hACE2 and RBD were processed for PatchDock and refined by FireDock for the PPIs. Twelve nsSNPs were identified as the top pathogenic from SNPs (n = 7489) in hACE2 using eight bioinformatics tools. Eight RBD variants were complexed with 12 nSNPS of hACE2, and the global energy scores (Kcal/mol) were calculated and classified as very weak (-3.93 to -18.43), weak (-18.42 to -32.94), moderate (-32.94 to -47.44), strong (-47.44 to -61.95) and very strong (-61.95 to -76.46) zones. Seven composition variants in the very strong zone [G726R-G476S; R768W-V367F; Y252N-V483A; Y252N-V367F; G726R-V367F; N720D-V367F and N720D-F486L], and three in very weak [P263S-S383C; RBD-H378R; G726R-A348T] are significantly (p < 0.00001) varied for global energy score. Zonation of the five zones was established based on the scores to differentiate the effect of hACE2 and RBD variants on the binding affinity. Moreover, our findings support that the combination of hACE2 and RBD is key players for the risk of infection that should be done by further laboratory studies.Communicated by Ramaswamy H. Sarma.

Machine learning methods for prediction of cancer driver genes: a survey paper

Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.

Structural Consequence of Non-Synonymous Single-Nucleotide Variants in the N-Terminal Domain of LIS1

Disruptive neuronal migration during early brain development causes severe brain malformation. Characterized by mislocalization of cortical neurons, this condition is a result of the loss of function of migration regulating genes. One known neuronal migration disorder is lissencephaly (LIS), which is caused by deletions or mutations of the LIS1 (PAFAH1B1) gene that has been implicated in regulating the microtubule motor protein cytoplasmic dynein. Although this class of diseases has recently received considerable attention, the roles of non-synonymous polymorphisms (nsSNPs) in LIS1 on lissencephaly progression remain elusive. Therefore, the present study employed combined bioinformatics and molecular modeling approach to identify potential damaging nsSNPs in the LIS1 gene and provide atomic insight into their roles in LIS1 loss of function. Using this approach, we identified three high-risk nsSNPs, including rs121434486 (F31S), rs587784254 (W55R), and rs757993270 (W55L) in the LIS1 gene, which are located on the N-terminal domain of LIS1. Molecular dynamics simulation highlighted that all variants decreased helical conformation, increased the intermonomeric distance, and thus disrupted intermonomeric contacts in the LIS1 dimer. Furthermore, the presence of variants also caused a loss of positive electrostatic potential and reduced dimer binding potential. Since self-dimerization is an essential aspect of LIS1 to recruit interacting partners, thus these variants are associated with the loss of LIS1 functions. As a corollary, these findings may further provide critical insights on the roles of LIS1 variants in brain malformation.

Landscape of TP53 Alterations in Chronic Lymphocytic Leukemia via Data Mining Mutation Databases

Locus-specific databases are invaluable tools for both basic and clinical research. The extensive information they contain is gathered from the literature and manually curated by experts. Cancer genome sequencing projects generate an immense amount of data, which are stored directly in large repositories (cancer genome databases). The presence of a TP53 defect (17p deletion and/or TP53 mutations) is an independent prognostic factor in chronic lymphocytic leukemia (CLL) and TP53 status analysis has been adopted in routine clinical practice. For that reason, TP53 mutation databases have become essential for the validation of the plethora of TP53 variants detected in tumor samples. TP53 profiles in CLL are characterized by a great number of subclonal TP53 mutations with low variant allelic frequencies and the presence of multiple minor subclones harboring different TP53 mutations. In this review, we describe the various characteristics of the multiple levels of heterogeneity of TP53 variants in CLL through the analysis of TP53 mutation databases and the utility of their diagnosis in the clinic.

Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity

We used a machine learning approach to analyze the within-gene distribution of missense variants observed in hereditary conditions and cancer. When applied to 840 genes from the ClinVar database, this approach detected a significant non-random distribution of pathogenic and benign variants in 387 (46%) and 172 (20%) genes, respectively, revealing that variant clustering is widespread across the human exome. This clustering likely occurs as a consequence of mechanisms shaping pathogenicity at the protein level, as illustrated by the overlap of some clusters with known functional domains. We then took advantage of these findings to develop a pathogenicity predictor, MutScore, that integrates qualitative features of DNA substitutions with the new additional information derived from this positional clustering. Using a random forest approach, MutScore was able to identify pathogenic missense mutations with very high accuracy, outperforming existing predictive tools, especially for variants associated with autosomal-dominant disease and cancer. Thus, the within-gene clustering of pathogenic and benign DNA changes is an important and previously underappreciated feature of the human exome, which can be harnessed to improve the prediction of pathogenicity and disambiguation of DNA variants of uncertain significance.

Comparative Panel Sequencing of DNA Variants in cf-, ev- and tumorDNA for Pancreatic Ductal Adenocarcinoma Patients

Simple Summary

Pancreatic ductal adenocarcinoma (PDAC) has still a dismal prognosis. To improve treatment, personalized medicine uses next-generation DNA sequencing to monitor disease and guide treatment decisions. Tumor samples for sequencing are usually obtained by invasive fine-needle biopsy. Recently, the focus has been increasingly shifting to blood-based liquid biopsies, including circulating free (cf)DNA or DNA isolated from extracellular vesicles (evDNA). To evaluate the detection performance of DNA alterations, we directly compared tumor-, cf- and evDNA from patients with advanced PDAC upon panel sequencing. Copy number variations (CNVs), single nucleotide variants (SNVs) and insertions and deletions (indels) were compared for their concordance with tumorDNA. Compared to cfDNA, evDNA contained significantly larger DNA fragments, which improved the concordance of SNVs and indels with tumorDNA. In line with previous observations, CNV detection was mostly uninformative for cf- and evDNA. However, the combination of both liquid biopsy analytes was clearly superior for SNV detection, pointing to potentially improved actionable variant prediction.

Abstract

Pancreatic ductal adenocarcinomas (PDACs) are tumors with poor prognosis and limited treatment options. Personalized medicine aims at characterizing actionable DNA variants by next-generation sequencing, thereby improving treatment strategies and outcomes. Fine-needle tumor biopsies are currently the gold standard to acquire samples for DNA profiling. However, liquid biopsies have considerable advantages as they are minimally invasive and frequently obtainable and thus may help to monitor tumor evolution over time. However, which liquid analyte works best for this purpose is currently unclear. Our study aims to directly compare tumor-, circulating free (cf-) and extracellular vesicle-derived (ev)DNA by panel sequencing of matching patient material. We evaluated copy number variations (CNVs), single nucleotide variants (SNVs) and insertions and deletions (indels). Our data show that evDNA contains significantly larger DNA fragments up to 5.5 kb, in line with previous observations. Stringent bioinformatic processing revealed a significant advantage of evDNA with respect to cfDNA concerning detection performance for SNVs and a numerical increase for indels. A combination of ev- and cfDNA was clearly superior for SNV detection, as compared to either single analyte, thus potentially improving actionable variant prediction upon further optimization. Finally, calling of CNVs from liquid biopsies still remained challenging and uninformative.

Association of recessive c.430G>A (p.(Gly144Arg)) thyroid peroxidase variant with primary congenital hypothyroidism in cats

BACKGROUND

Primary congenital hypothyroidism (CH) is a rare endocrine disorder in cats with a largely unknown genetic cause.

OBJECTIVES

Describe the clinical presentation of CH in 11 affected cats and identify the causal genetic variant.

ANIMALS

Eleven CH-cats from 10 unrelated families, 11 CH-free family members, 21 unrelated CH-free cats, and 155 unrelated nondiagnosed cats from different breeds.

METHODS

Case control study of CH-cats and their siblings (2019-2021). Diagnosis was based on low to low-normal serum thyroxine (T4) concentrations, high thyroid-stimulating hormone (TSH) concentrations and clinical signs compatible with CH. We identified the causal variant using Sanger sequencing, genotyping via PCR-RFLP and variant interpretation using ACMG/AMP guidelines.

RESULTS

All CH-cats (5 weeks-8 years) had disproportionate dwarfism. A goiter was not palpable in all. Thyroid scintigraphy with radiopertechnetate showed abnormally high uptake by thyroid glands, whereas scintigraphy with radioiodine showed abnormally low uptake, compatible with a defect in iodine organification by thyroid peroxidase (TPO). All cases were homozygous for TPO variant XM_006930524.4:c.430G>A(p.(Gly144Arg)), while none of the CH-free cats were. All sampled parents were heterozygous for this recessive variant. This variant was found in 15 cat breeds with an estimated allele frequency of 9%.

CONCLUSIONS AND CLINICAL IMPORTANCE

Disproportionate dwarfism, abnormally high TSH and abnormally low to low-normal T4 concentrations are diagnostic for CH in cats. All cases had dyshormonogenesis demonstrated by thyroid scintigraphy. This novel TPO missense variant (not described in humans) causes CH in cats and awareness of it can assist in diagnosis and breeding.

Evaluation of in silico predictors on short nucleotide variants in HBA1, HBA2, and HBB associated with haemoglobinopathies

Haemoglobinopathies are the commonest monogenic diseases worldwide and are caused by variants in the globin gene clusters. With over 2400 variants detected to date, their interpretation using the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) guidelines is challenging and computational evidence can provide valuable input about their functional annotation. While many in silico predictors have already been developed, their performance varies for different genes and diseases. In this study, we evaluate 31 in silico predictors using a dataset of 1627 variants in HBA1, HBA2, and HBB. By varying the decision threshold for each tool, we analyse their performance (a) as binary classifiers of pathogenicity and (b) by using different non-overlapping pathogenic and benign thresholds for their optimal use in the ACMG/AMP framework. Our results show that CADD, Eigen-PC, and REVEL are the overall top performers, with the former reaching moderate strength level for pathogenic prediction. Eigen-PC and REVEL achieve the highest accuracies for missense variants, while CADD is also a reliable predictor of non-missense variants. Moreover, SpliceAI is the top performing splicing predictor, reaching strong level of evidence, while GERP++ and phyloP are the most accurate conservation tools. This study provides evidence about the optimal use of computational tools in globin gene clusters under the ACMG/AMP framework.