Inferring the molecular and phenotypic impact of amino acid variants with MutPred2

Neurodevelopmental disorders and cancer networks share pathways, but differ in mechanisms, signaling strength, and outcome

Epidemiological studies suggest that individuals with neurodevelopmental disorders (NDDs) are more prone to develop certain types of cancer. Notably, however, the case statistics can be impacted by late discovery of cancer in individuals afflicted with NDDs, such as intellectual disorders, autism, and schizophrenia, which may bias the numbers. As to NDD-associated mutations, in most cases, they are germline while cancer mutations are sporadic, emerging during life. However, somatic mosaicism can spur NDDs, and cancer-related mutations can be germline. NDDs and cancer share proteins, pathways, and mutations. Here we ask (i) exactly which features they share, and (ii) how, despite their commonalities, they differ in clinical outcomes. To tackle these questions, we employed a statistical framework followed by network analysis. Our thorough exploration of the mutations, reconstructed disease-specific networks, pathways, and transcriptome levels and profiles of autism spectrum disorder (ASD) and cancers, point to signaling strength as the key factor: strong signaling promotes cell proliferation in cancer, and weaker (moderate) signaling impacts differentiation in ASD. Thus, we suggest that signaling strength, not activating mutations, can decide clinical outcome.

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.

Novel Mutations in the Non-Structure Protein 2 of SARS-CoV-2

Introduction

Mutation in the genome of SARS-CoV-2 may play a role in immune evasion, pathogenicity, and speed of its transmission. Our investigation aimed to evaluate the mutations that exist in the NSP2.

Materials and Method

RNA was extracted from nasopharyngeal swabs from 100 COVID-19 patients. RT-PCR was performed on all samples using NSP2-specific primers. Following gel electrophoresis, the bands were cut, purified, and sequenced using the Sanger method. After sequencing, 90 sequences could be used for further analysis. Bioinformatics analysis was conducted to investigate the effect of mutations on protein structure, stability, prediction of homology models, and phylogeny tree.

Results

The patients' mean age was 51.08. The results revealed that 8 of the 17 NSP2 mutations (R207C, T224I, G262V, T265I, K337D, N348S, G392D, and I431M) were missense. One deletion was also found in NSP2. Among NSP2 missense mutations studied, K337D and G392D increased structural stability while the others decreased it. The homology-designed models demonstrated that the homologies were comparable to the sequences of the Wuhan-HU-1 virus.

Conclusion

Our study suggested that the mutations K337D and G392D modulate the stability of NSP2, and tracking viral evolution should be implemented and vaccine development updated.

CUX1-related neurodevelopmental disorder: deep insights into phenotype-genotype spectrum and underlying pathology

Heterozygous, pathogenic CUX1 variants are associated with global developmental delay or intellectual disability. This study delineates the clinical presentation in an extended cohort and investigates the molecular mechanism underlying the disorder in a Cux1+/- mouse model. Through international collaboration, we assembled the phenotypic and molecular information for 34 individuals (23 unpublished individuals). We analyze brain CUX1 expression and susceptibility to epilepsy in Cux1+/- mice. We describe 34 individuals, from which 30 were unrelated, with 26 different null and four missense variants. The leading symptoms were mild to moderate delayed speech and motor development and borderline to moderate intellectual disability. Additional symptoms were muscular hypotonia, seizures, joint laxity, and abnormalities of the forehead. In Cux1+/- mice, we found delayed growth, histologically normal brains, and increased susceptibility to seizures. In Cux1+/- brains, the expression of Cux1 transcripts was half of WT animals. Expression of CUX1 proteins was reduced, although in early postnatal animals significantly more than in adults. In summary, disease-causing CUX1 variants result in a non-syndromic phenotype of developmental delay and intellectual disability. In some individuals, this phenotype ameliorates with age, resulting in a clinical catch-up and normal IQ in adulthood. The post-transcriptional balance of CUX1 expression in the heterozygous brain at late developmental stages appears important for this favorable clinical course.

Transcript-Level In Silico Analysis of Alzheimer's Disease-Related Gene Biomarkers and Their Evaluation with Bioactive Flavonoids to Explore Therapeutic Interactions

Alzheimer's disease (AD) is a progressive brain disorder that can significantly affect the quality of life. We used a variety of in silico tools to investigate the transcript-level mutational impact of exonic missense rare variations (single nucleotide polymorphisms, SNPs) on protein function and to identify potential druggable protein cavities that correspond to potential therapeutic targets for the management of AD. According to the NIA-AA (National Institute on Aging-Alzheimer's Association) framework, we selected three AD biomarker genes (APP, NEFL, and MAPT). We systematically screened transcript-level exonic rare SNPs from these genes with a minor allele frequency of 1% in 1KGD (1000 Genomes Project Database) and gnomAD (Genome Aggregation Database). With downstream functional effect predictions, a single variation (rs182024939: K > N) of the MAPT gene with nine transcript SNPs was identified as the most pathogenic variation from the large dataset of mutations. The machine learning consensus classifier predictor categorized these transcript-level SNPs as the most deleterious variations, resulting in a large decrease in protein structural stability (ΔΔG kcal/mol). The bioactive flavonoid library was screened for drug-likeness and toxicity risk. Virtual screening of eligible flavonoids was performed using the MAPT protein. Identification of druggable protein-binding cavities showed VAL305, GLU655, and LYS657 as consensus-interacting residues present in the MAPT-docked top-ranked flavonoid compounds. The MM/PB(GB)SA analysis indicated hesperetin (-5.64 kcal/mol), eriodictyol (-5.63 kcal/mol), and sakuranetin (-5.60 kcal/mol) as the best docked flavonoids with the near-native binding pose. The findings of this study provide important insights into the potential of hesperetin as a promising flavonoid that can be utilized for further rational drug design and lead optimization to open new gateways in the field of AD therapeutics.

Toward a high-resolution mechanism of intrinsically disordered protein self-assembly

Membraneless organelles formed via the self-assembly of intrinsically disordered proteins (IDPs) play a crucial role in regulating various physiological functions. Elucidating the mechanisms behind IDP self-assembly is of great interest not only from a biological perspective but also for understanding how amino acid mutations in IDPs contribute to the development of neurodegenerative diseases and other disorders. Currently, two proposed mechanisms explain IDP self-assembly: (1) the sticker-and-spacer framework, which considers amino acid residues as beads to simulate the intermolecular interactions, and (2) the cross-β hypothesis, which focuses on the β-sheet interactions between the molecular surfaces constructed by multiple residues. This review explores the advancement of new models that provide higher resolution insights into the IDP self-assembly mechanism based on new findings obtained from structural studies of IDPs.

Most monogenic disorders are caused by mutations altering protein folding free energy

Revealing the molecular effect that pathogenic missense mutations cause on the corresponding protein is crucial for developing therapeutic solutions. This is especially important for monogenic diseases since, for most of them, there is no treatment available, while typically, the treatment should be provided in the early development stages. This requires fast, targeted drug development at a low cost. Here, we report a database of monogenic disorders (MOGEDO), which includes 768 proteins, the corresponding 2559 pathogenic and 1763 benign mutations, along with the functional classification of the corresponding proteins. Using the database and various computational tools that predict folding free energy change (ΔΔG), we demonstrate that, on average, 70% of pathogenic cases result in decreased protein stability. Such a large fraction indicates that one should aim at in-silico screening for small molecules stabilizing the structure of the mutant protein. We emphasize that knowledge of ΔΔG is essential because one wants to develop stabilizers that compensate for ΔΔG but not to make protein over-stable since over-stable protein may be dysfunctional. We demonstrate that using ΔΔG and predicted solvent exposure of the mutation site; one can develop a predictive method that distinguishes pathogenic from benign mutation with a success rate even better than some of the leading pathogenicity predictors. Furthermore, hydrophobic-hydrophobic mutations have stronger correlations between folding free energy change and pathogenicity compared with others. Also, mutations involving Cys, Gly, Arg, Trp and Tyr amino acids being replaced by any other amino acid are more likely to be pathogenic. To facilitate further detection of pathogenic mutations, the wild type of amino acids in the 768 proteins mentioned above was mutated to other 19 residues (14,847,817 mutations), and the ΔΔG was calculated with SAAFEC-SEQ, and 5,506,051 mutations were predicted to be pathogenic.

In-silico prediction of TGF-β1 non-synonymous variants and their impact on binding affinity to Fresolimumab

TGF-β1 is a potent immunoregulatory cytokine that plays diverse roles in development, bone healing, fibrosis, and cancer. However, characterizing TGF-β1 gene variants is challenging because the structural and functional consequences of these variants are still undetermined. In this study, we aimed to perform an in-silico analysis of TGF-β1 non-synonymous variants and their pathogenic effects on the TGF-β1 protein. A total of 10,252 TGF-β1 SNPs were collected from the NCBI dbSNP database and in-silico tools (SIFT, PROVEAN, Mutation Taster, ClinVar, PolyPhen-2, CScape, MutPred, and ConSurf) were used. The in-silico predicted potential variants were further investigated for their binding to the TGF-β1 targeting drug "Fresolimumab". Molecular docking was performed using HADDOCK and confirmed by PRODIGY and PDBsum. The in-silico analysis predicted four potential TGF-β1 nsSNPs: E47G in the LAP domain of the propeptide and I22T, L28F, and E35D in the mature TGF-β1 peptide. HADDOCK and molecular dynamics simulations revealed that the I22T and E35D variants have higher binding affinity for Fresolimumab as compared to the wild type and L28F variants. Molecular dynamics simulations (100 ns) and principal component analysis showed that TGF-β1 variants influenced the protein structure and caused variations in the internal dynamics of protein complexes with the antibody. Among them, the E35D variant significantly destabilized the TGF-β1 protein structure, resulting in rearrangement in the binding site and affecting the interactions with the Fresolimumab. This study identified four variants that can affect the TGF-β1 protein structure and result in functional consequences such as impaired response to Fresolimumab.Communicated by Ramaswamy H. Sarma.

Identification of Pathogenic Missense Mutations in the CHRNA5 Gene: A Computational Approach

Aim The CHRNA5/A3/B4 gene locus is closely related to nicotine dependence and other smoking-related disorders. Coupling genetic and clinical studies of nicotine dependence and smoking behaviors may open new avenues for medication development. The aim of this study is to investigate the functional missense mutations in the CHRNA5 gene. Methodology The Ensembl database was used to gather data on missense mutations of the human CHRNA5 gene. Computational tools viz. SIFT (Sorting Intolerant From Tolerant), PolyPhen (Polymorphism Phenotyping), PROVEAN (Protein Variation Effect Analyzer), I-Mutant, and MutPred were used to uncover the pathogenic mutations in the gene under investigation.  Results Among 161 missense variants reported inthe CHRNA5 gene, 94 variants were found to be highly pathogenic. Moreover, 20 were pathogenic and 4 were not pathogenic. Conclusion The computational analysis disclosed harmful mutations in the CHRNA5 gene which could be potentially associated with smoking-related traits.

Identification of High-Risk Single Nucleotide Polymorphisms in the Human CYB5R3 Gene Responsible for Recessive Congenital Methemoglobinemia: A Computational Approach

Introduction

NADH-cytochrome b5 reductase deficiency due to pathogenic variants in the CYB5R3 gene causes recessive congenital methemoglobinemia (RCM) type I or type II. In type I, cyanosis from birth is the only major symptom, and the enzyme deficiency is restricted only to erythrocytes. Whereas in type II, cyanosis is associated with severe neurological manifestations, and the enzyme deficiency is generalized to all tissues.

Methods

In this study, several computational methods (SIFT, Polyphen-2, PROVEAN, Mutation Assessor, Panther, Phd-SNP, SNPs&GO, SNAP2, Align, GVGD, MutPred2, I-Mutant 2.0, MUpro, Duet, ConSurf and Netsurf-2.0 tools) were used to find the most deleterious nsSNPs in the CYB5R3 gene. Furthermore, structural analysis by Swiss-PDB viewer, protein-ligand docking using FTSite, and protein-protein interaction using STRING were carried out to evaluate the impact of these nsSNPs on the protein structure and function.

Results

Our in silico analysis suggested that out of 339 nsSNPs of the CYB5R3 gene, 17 (L47H, L47P, R61P, L73R G76D, G76C, P96H, G104C, S128P, G144D, P145S, L149P, Y151H, M177T, I178T, I216N, and G251V), are the most deleterious. Among them, two (P96H and S128P) were reported to be associated with the severe form RCM type II, six are related to RCM type I (G104C, G144D, P145S, L149P, M177T, and I178T), and the remaining nine high-risk nsSNPs have not yet been reported in RCM patients.

Discussion

This study highlighted the potential pathogenic nsSNPs of the CYB5R3 gene. To comprehend how these most harmful nsSNPs contribute to disease, it is crucial to experimentally validate their functional effects.

Uncovering the impact of SARS-CoV2 spike protein variants on human receptors: A molecular dynamics docking and simulation approach

BACKGROUND

The SARS-CoV-2 pandemic, caused by the novel coronavirus, has posed a significant global health threat since its emergence in late 2019. The World Health Organization declared the outbreak a pandemic on March 11, 2020, due to its rapid global spread and impact on public health. New variants have raised concerns about their potential impact on the transmission of the virus and the effectiveness of current diagnostic tools, treatments, and vaccines. This study aims to investigate the effect of new variants in Pakistani virus strains on human receptors, specifically ACE2 and NRP1. In-silico analysis provides a powerful tool to analyze the potential impact of new variants on protein structure, function, and interactions.

OBJECTIVES

The SARS-CoV-2 virus is evolving quickly. After being exposed in Wuhan, SARS-CoV-2 underwent numerous mutations, leading to several variants' emergence. These variants stabilize the interaction of spike protein with human receptors ACE2 and NRP1. The study aims to check the molecular effect of these variants on human receptors using the in-silico approach.

MATERIAL AND METHODS

We use in-silico mutational tools to analyze new variants in SARS-CoV-2 and to check the molecular interaction of spike protein with human receptors (ACE2 and NRP1). Genomic sequences of 41 SARS-CoV-2 strains were sequenced using Ion Torrent (NGS) and submitted to the GISAID database. Spike protein of SARS-CoV-2 sequence trimmed and translated into a protein sequence using ExPasy. We used multiple sequence alignments to check for variants in the spike protein of strains. We utilized mutation tools such as Mupro, SIFT, SNAP2, and Mutpred2.3D structures of SARS-CoV-2 spike proteins (wild and mutated) to analyze further the mutations, ACE2 and NRP1 modelled by the ITASSER protein modelling server. Interactions of spike proteins (wild and mutant) analyzed by MD Docking, Simulation, and MMGBSA RESULTS: Variants I210T, V213G, S371F, S373P, T478K, F486V, Y505H, and D796Y were identified in SARS-CoV-2 Pakistani strains' spike protein. Variant Y505H were found to affect protein function. MD Docking, MMGBSA and MD simulation revealed that these variants increased spike protein's binding affinity with human receptors (ACE2 and NRP1). MD simulation revealed that mutated spike protein stabilized earlier than wild when interacting with ACE2 after 40 ns and interaction with NRP1 stabilized after 30 ns for mutated spike protein compared to wild.

CONCLUSION

These variants in Pakistani strains of SARS-CoV-2 are increasing the stability of spike protein with human receptors. These findings provide insight into how the SARS-CoV-2 virus evolves and adapts to human hosts. This information may help develop strategies to control the virus's spread and develop effective treatments and vaccines in the future.

CRISPR-dependent Base Editing Screens Identify Separation of Function Mutants of RADX with Altered RAD51 Regulatory Activity

RAD51 forms nucleoprotein filaments to promote homologous recombination, replication fork reversal, and fork protection. Numerous factors regulate the stability of these filaments and improper regulation leads to genomic instability and ultimately disease including cancer. RADX is a single stranded DNA binding protein that modulates RAD51 filament stability. Here, we utilize a CRISPR-dependent base editing screen to tile mutations across RADX to delineate motifs required for RADX function. We identified separation of function mutants of RADX that bind DNA and RAD51 but have a reduced ability to stimulate its ATP hydrolysis activity. Cells expressing these RADX mutants accumulate RAD51 on chromatin, exhibit replication defects, have reduced growth, accumulate DNA damage, and are hypersensitive to DNA damage and replication stress. These results indicate that RADX must promote RAD51 ATP turnover to regulate RAD51 and genome stability during DNA replication.

Genotyping, in silico screening and molecular dynamics simulation of SNPs of MGMT and ERCC1 gene in lung cancer patients treated with platinum-based doublet chemotherapy

Lung cancer, the leading cause of death worldwide, arises from an intricate combination of genetic and environmental factors. Genetic variations can influence the chemotherapeutic response of lung cancer patients in DNA repair genes. This study examines the response to platinum-based drugs among lung cancer patients of North Indian descent who possess genetic variations in the MGMT and ERCC1 genes. P CR-RFLP method was used for genotypic analysis. MedCalc statistical software was used to calculate odds ratios and Median Survival Time (MST). GROMACS software was used to perform Molecular dynamic simulation. ADCC Patients revealed a significant association with MGMT in the heterozygous genotype (HR= 1.56, p=0.02) and also with ERCC1 in both mutant and combined variants (HR= 1.25, p=0.01; HR=0.78, p=0.03). SQCC subjects harbouring ERCC1 polymorphism also reported a 2-fold increase in hazard ratio and a corresponding decrease in survival time for heterozygous and combined variants (HR= 2.55, p=0.02; HR 2.33, p=0.01, respectively). MD simulation results demonstrate a lower RMSD, stable radius of gyration, and lower RMSF, indicating the mutated MGMT protein is more stable than the wild. Further, the docking score for DNA-Wild and DNA-L84F mutants are -201.6 and -131.8, respectively. MD Simulation of the complexes further validated the results. Our study concludes that MGMT and ERCC1 polymorphisms are associated with decreased overall survival. Further, computational analysis of MGMT (rs12917) polymorphism revealed that mutated MGMT cannot bind properly to the DNA and hence cannot properly repair DNA, resulting in lower overall survival.Communicated by Ramaswamy H. Sarma.

Novel Genetic and Phenotypic Expansion in GOSR2-Related Progressive Myoclonus Epilepsy

Biallelic variants in the Golgi SNAP receptor complex member 2 gene (GOSR2) have been reported in progressive myoclonus epilepsy with neurodegeneration. Typical clinical features include ataxia and areflexia during early childhood, followed by seizures, scoliosis, dysarthria, and myoclonus. Here, we report two novel patients from unrelated families with a GOSR2-related disorder and novel genetic and clinical findings. The first patient, a male compound heterozygous for the GOSR2 splice site variant c.336+1G>A and the novel c.364G>A,p.Glu122Lys missense variant showed global developmental delay and seizures at the age of 2 years, followed by myoclonus at the age of 8 years with partial response to clonazepam. The second patient, a female homozygous for the GOSR2 founder variant p.Gly144Trp, showed only mild fine motor developmental delay and generalized tonic-clonic seizures triggered by infections during adolescence, with seizure remission on levetiracetam. The associated movement disorder progressed atypically slowly during adolescence compared to its usual speed, from initial intention tremor and myoclonus to ataxia, hyporeflexia, dysmetria, and dystonia. These findings expand the genotype-phenotype spectrum of GOSR2-related disorders and suggest that GOSR2 should be included in the consideration of monogenetic causes of dystonia, global developmental delay, and seizures.

PreHom-PCLM: protein remote homology detection by combing motifs and protein cubic language model

Protein remote homology detection is essential for structure prediction, function prediction, disease mechanism understanding, etc. The remote homology relationship depends on multiple protein properties, such as structural information and local sequence patterns. Previous studies have shown the challenges for predicting remote homology relationship by protein features at sequence level (e.g. position-specific score matrix). Protein motifs have been used in structure and function analysis due to their unique sequence patterns and implied structural information. Therefore, designing a usable architecture to fuse multiple protein properties based on motifs is urgently needed to improve protein remote homology detection performance. To make full use of the characteristics of motifs, we employed the language model called the protein cubic language model (PCLM). It combines multiple properties by constructing a motif-based neural network. Based on the PCLM, we proposed a predictor called PreHom-PCLM by extracting and fusing multiple motif features for protein remote homology detection. PreHom-PCLM outperforms the other state-of-the-art methods on the test set and independent test set. Experimental results further prove the effectiveness of multiple features fused by PreHom-PCLM for remote homology detection. Furthermore, the protein features derived from the PreHom-PCLM show strong discriminative power for proteins from different structural classes in the high-dimensional space. Availability and Implementation: http://bliulab.net/PreHom-PCLM.

Allele mining of eukaryotic translation initiation factor genes in Prunus for the identification of new sources of resistance to sharka

Members of the eukaryotic translation initiation complex are co-opted in viral infection, leading to susceptibility in many crop species, including stone fruit trees (Prunus spp.). Therefore, modification of one of those eukaryotic translation initiation factors or changes in their gene expression may result in resistance. We searched the crop and wild Prunus germplasm from the Armeniaca and Amygdalus taxonomic sections for allelic variants in the eIF4E and eIFiso4E genes, to identify alleles potentially linked to resistance to Plum pox virus (PPV). Over one thousand stone fruit accessions (1397) were screened for variation in eIF4E and eIFiso4E transcript sequences which are in single copy within the diploid Prunus genome. We identified new alleles for both genes differing from haplotypes associated with PPV susceptible accessions. Overall, analyses showed that eIFiso4E is genetically more constrained since it displayed less polymorphism than eIF4E. We also demonstrated more variations at both loci in the related wild species than in crop species. As the eIFiso4E translation initiation factor was identified as indispensable for PPV infection, a selection of ten different eIFiso4E haplotypes along 13 accessions were tested by infection with PPV and eight of them displayed a range of reduced susceptibility to resistance, indicating new potential sources of resistance to sharka.

In Silico Evaluation of the Potential Association of the Pathogenic Mutations of Alpha Synuclein Protein with Induction of Synucleinopathies

Alpha synuclein (α-Syn) is a neuronal protein encoded by the SNCA gene and is involved in the development of Parkinson's disease (PD). The objective of this study was to examine in silico the functional implications of non-synonymous single nucleotide polymorphisms (nsSNPs) in the SNCA gene. We used a range of computational algorithms such as sequence conservation, structural analysis, physicochemical properties, and machine learning. The sequence of the SNCA gene was analyzed, resulting in the mapping of 42,272 SNPs that are classified into different functional categories. A total of 177 nsSNPs were identified within the coding region; there were 20 variants that may influence the α-Syn protein structure and function. This identification was made by employing different analytical tools including SIFT, PolyPhen2, Mut-pred, SNAP2, PANTHER, PhD-SNP, SNP&Go, MUpro, Cosurf, I-Mut, and HOPE. Three mutations, V82A, K80E, and E46K, were selected for further examinations due to their spatial positioning within the α-Syn as determined by PyMol. Results indicated that these mutations may affect the stability and function of α-Syn. Then, a molecular dynamics simulation was conducted for the SNCA wildtype and the four mutant variants (p.A18G, p.V82A, p.K80E, and p.E46K). The simulation examined temperature, pressure, density, root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), and radius of gyration (Rg). The data indicate that the mutations p.V82A, p.K80E, and p.E46K reduce the stability and functionality of α-Syn. These findings highlight the importance of understanding the impact of nsSNPs on α-syn structure and function. Our results required verifications in further protein functional and case-control studies. After being verified these findings can be used in genetic testing for the early diagnosis of PD, the evaluation of the risk factors, and therapeutic approaches.

Novel genetic variant associated with globoid cell leukodystrophy in a family of mixed breed dogs

BACKGROUND

Globoid cell leukodystrophy (GCL) is a fatal autosomal recessive disease caused by variants in the galactosylceramidase (GALC) gene. Two dog breed-specific variants are reported.

OBJECTIVES

Characterize the putatively causative GALC variant for GCL in a family of dogs and determine population allele frequency.

ANIMALS

Four related mixed-breed puppies with signs of neurologic disease were evaluated. Subsequently, 33 related dogs were tested for genetic markers for parentage and the identified GALC variant. Additional GALC genotyping was performed on 278 banked samples from various breeds.

METHODS

The 4 affected puppies had neurological exams and necropsies. DNA was isolated from blood samples. Variants in GALC were identified via Sanger sequencing. Parentage testing was performed using short tandem repeat markers. Prevalence of the GALC variant of interest was investigated in other breeds.

RESULTS

GCL was confirmed histopathologically. A novel missense variant in GALC (NC_006590.4:g.58893972G>A) was homozygous in all affected animals (n = 4). A recessive mode of inheritance was confirmed by parentage testing as was variant linkage with the phenotype (LOD = 3.36). Among the related dogs (n = 33), 3 dogs were homozygous and 7 heterozygous. The variant allele was not detected in screening 278 dogs from 5 breeds. The novel variant is either unique to this family or has an extremely low allele frequency in the general population.

CONCLUSIONS AND CLINICAL IMPORTANCE

A novel GALC variant was identified that likely explains GCL in this cohort. The identification of multiple causal variants for GCL in dogs is consistent with findings in humans.

Dysfunction of ubiquitin protein ligase MYCBP2 leads to cell resilience in human breast cancers

Breast cancer is the most common type of cancer among women worldwide, and it is estimated that 294 000 new diagnoses and 37 000 deaths will occur each year in the United States alone by 2030. Large-scale genomic studies have identified a number of genetic loci with alterations in breast cancer. However, identification of the genes that are critical for tumorgenicity still remains a challenge. Here, we perform a comprehensive functional multi-omics analysis of somatic mutations in breast cancer and identify previously unknown key regulators of breast cancer tumorgenicity. We identify dysregulation of MYCBP2, an E3 ubiquitin ligase and an upstream regulator of mTOR signaling, is accompanied with decreased disease-free survival. We validate MYCBP2 as a key target through depletion siRNA using in vitro apoptosis assays in MCF10A, MCF7 and T47D cells. We demonstrate that MYCBP2 loss is associated with resistance to apoptosis from cisplatin-induced DNA damage and cell cycle changes, and that CHEK1 inhibition can modulate MYCBP2 activity and caspase cleavage. Furthermore, we show that MYCBP2 knockdown is associated with transcriptomic responses in TSC2 and in apoptosis genes and interleukins. Therefore, we show that MYCBP2 is an important genetic target that represents a key node regulating multiple molecular pathways in breast cancer corresponding with apparent drug resistance in our study.

Comprehensive analysis predicting effects of deleterious SNPs of human progesterone receptor gene on its structure and functions: a computational approach

Progesterone receptor plays a crucial role in the development of the mammary gland and breast cancer. Single nucleotide polymorphisms (SNPs) within its gene, PGR, are associated with the risk of miscarriages and preterm birth as well as many cancers across different populations. The main aim of this work is to investigate the most deleterious SNPs in the PGR gene to identify potential biomarkers for various disease susceptibility and treatments. Both sequence and structure-based computational approaches were adopted and in total 11 nsSNPs have been filtered out of 674 nsSNPs along with seven non-coding SNPs. R740Q, I744T and D746E belonged to a mutation cluster. R740Q, D746E along with S865L altered H-bond interactions within the receptor. The same mutations have been found to be associated with several cancers including uterine and breast cancer among others. It is, therefore, possible that the high-risk SNPs associated with cancers may exert their effect by causing changes in the protein structure, particularly in its bonding patterns, and thus affecting its function. In addition, seven non-coding SNPs that were located in the UTR region created a new miRNA site while three SNPs disrupted a conserved miRNA site. These high-risk SNPs can play an instrumental role in generating a dataset of the PGR gene's SNPs. Thus, the present study may pave the way to design and develop novel therapeutics for overcoming the challenges associated with certain cancers and pregnancy that result from a change in the protein structure and function due to the SNP mutations in the PGR gene.Communicated by Ramaswamy H. Sarma.

A Homozygous MAN2B1 Missense Mutation in a Doberman Pinscher Dog with Neurodegeneration, Cytoplasmic Vacuoles, Autofluorescent Storage Granules, and an α-Mannosidase Deficiency

A 7-month-old Doberman Pinscher dog presented with progressive neurological signs and brain atrophy suggestive of a hereditary neurodegenerative disorder. The dog was euthanized due to the progression of disease signs. Microscopic examination of tissues collected at the time of euthanasia revealed massive accumulations of vacuolar inclusions in cells throughout the central nervous system, suggestive of a lysosomal storage disorder. A whole genome sequence generated with DNA from the affected dog contained a likely causal, homozygous missense variant in MAN2B1 that predicted an Asp104Gly amino acid substitution that was unique among whole genome sequences from over 4000 dogs. A lack of detectable α-mannosidase enzyme activity confirmed a diagnosis of a-mannosidosis. In addition to the vacuolar inclusions characteristic of α-mannosidosis, the dog exhibited accumulations of autofluorescent intracellular inclusions in some of the same tissues. The autofluorescence was similar to that which occurs in a group of lysosomal storage disorders called neuronal ceroid lipofuscinoses (NCLs). As in many of the NCLs, some of the storage bodies immunostained strongly for mitochondrial ATP synthase subunit c protein. This protein is not a substrate for α-mannosidase, so its accumulation and the development of storage body autofluorescence were likely due to a generalized impairment of lysosomal function secondary to the accumulation of α-mannosidase substrates. Thus, it appears that storage body autofluorescence and subunit c accumulation are not unique to the NCLs. Consistent with generalized lysosomal impairment, the affected dog exhibited accumulations of intracellular inclusions with varied and complex ultrastructural features characteristic of autophagolysosomes. Impaired autophagic flux may be a general feature of this class of disorders that contributes to disease pathology and could be a target for therapeutic intervention. In addition to storage body accumulation, glial activation indicative of neuroinflammation was observed in the brain and spinal cord of the proband.

Evolutionary history of MEK1 illuminates the nature of deleterious mutations

Mutations in signal transduction pathways lead to various diseases including cancers. MEK1 kinase, encoded by the human MAP2K1 gene, is one of the central components of the MAPK pathway and more than a hundred somatic mutations in the MAP2K1 gene were identified in various tumors. Germline mutations deregulating MEK1 also lead to congenital abnormalities, such as the cardiofaciocutaneous syndrome and arteriovenous malformation. Evaluating variants associated with a disease is a challenge, and computational genomic approaches aid in this process. Establishing evolutionary history of a gene improves computational prediction of disease-causing mutations; however, the evolutionary history of MEK1 is not well understood. Here, by revealing a precise evolutionary history of MEK1, we construct a well-defined dataset of MEK1 metazoan orthologs, which provides sufficient depth to distinguish between conserved and variable amino acid positions. We matched known and predicted disease-causing and benign mutations to evolutionary changes observed in corresponding amino acid positions and found that all known and many suspected disease-causing mutations are evolutionarily intolerable. We selected several variants that cannot be unambiguously assessed by automated prediction tools but that are confidently identified as "damaging" by our approach, for experimental validation in Drosophila. In all cases, evolutionary intolerant variants caused increased mortality and severe defects in fruit fly embryos confirming their damaging nature. We anticipate that our analysis will serve as a blueprint to help evaluate known and novel missense variants in MEK1 and that our approach will contribute to improving automated tools for disease-associated variant interpretation.

APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants

Mitochondrial dysfunction has pleiotropic effects and is frequently caused by mitochondrial DNA mutations. However, factors such as significant variability in clinical manifestations make interpreting the pathogenicity of variants in the mitochondrial genome challenging. Here, we present APOGEE 2, a mitochondrially-centered ensemble method designed to improve the accuracy of pathogenicity predictions for interpreting missense mitochondrial variants. Built on the joint consensus recommendations by the American College of Medical Genetics and Genomics/Association for Molecular Pathology, APOGEE 2 features an improved machine learning method and a curated training set for enhanced performance metrics. It offers region-wise assessments of genome fragility and mechanistic analyses of specific amino acids that cause perceptible long-range effects on protein structure. With clinical and research use in mind, APOGEE 2 scores and pathogenicity probabilities are precompiled and available in MitImpact. APOGEE 2's ability to address challenges in interpreting mitochondrial missense variants makes it an essential tool in the field of mitochondrial genetics.

Importance of genomic surveillance of SARS-CoV-2 in cats during reverse zoonosis events: potential viral evolution may occur

The apparition of new variants of severe acute respiratory syndrome coronavirus 2 and lineages is constantly happening because of the high viral mutation rate. Since numerous reverse zoonosis events have been reported so far, genomic surveillance should be conducted in susceptible species to evaluate potential adaptations that may trigger the apparition of new variants. Here, we evaluate the evolution of the infection in a cat naturally infected in parallel with its owner, performing a comparative phylogenetic analysis. Sequencing analysis showed that both were infected with the Omicron BA.5/BF.1 lineage and revealed the presence of nucleotide substitution in the viral genome recovered from the cat with respect to the viral genome from the human sample. This nucleotide substitution (C11897A) produced the amino acid change Orf1a: Q3878K. Therefore, genomic surveillance in the case of reverse zoonosis events is still necessary in order to control possible adaptations of the virus to other susceptible species. IMPORTANCE Genomic surveillance of pets for severe acute respiratory syndrome coronavirus 2 is important to monitor the emergence of new variants of the virus associated with these animals. Pets can serve as a potential reservoir for the virus, and their close contact with humans increases the risk of transmission. By conducting genomic surveillance in pets, it is possible to detect and track new variants early on, allowing for more effective control measures to be put in place. This can help prevent the spread of these variants to human populations and potentially mitigate the impact of the pandemic. Furthermore, it may also provide insight into the evolution and spread of the virus within the animal population.

Computational Identification of Most Deleterious Missense Mutations in Human PD-1 Gene

Traditional cancer treatment approaches are often hindered by the presence of toxic side effects and the high rate of relapse observed in treated organs. In contrast, novel immunotherapeutic strategies targeting immune checkpoint inhibitors, particularly PD-1, have demonstrated promising results with minimal adverse effects. However, the emergence of immunotherapeutic-resistant tumors, predominantly caused by intrinsic mutations, poses a significant obstacle to successful treatment outcomes. Consequently, the primary objective of this study was to screen for the most detrimental missense mutations in the PD-1 gene associated with immunotherapeutic resistance. To achieve this aim, a comprehensive screening process utilizing 20 web servers, incorporating both sequence- and structure-based methodologies, was undertaken. Through meticulous analysis and mutual disease association sorting, four specific missense mutations were successfully identified. These mutations, namely, R38C, D61V, R94C, and D117V, emerged as the leading contributors to genetic cancer progression and immunotherapeutic resistance against PD-1 blockers. The findings presented in this study are supported by multiple lines of evidence. A thorough examination of protein topology, structural alignment, docking interactions with PD-L1, and protein flexibility collectively confirmed the pathogenic nature of these sorted mutations. By considering these various aspects, we have gained a comprehensive understanding of the underlying mechanisms driving immunotherapeutic resistance. In conclusion, the comprehensive screening process undertaken in this study has successfully identified R38C, D61V, R94C, and D117V as the primary mutations contributing to genetic cancer progression and immunotherapeutic resistance against PD-1 blockers. The integration of protein topology analysis, structural alignment, docking studies with PD-L1, and assessment of protein flexibility have collectively provided robust evidence to support the pathogenic significance of these mutations.

Structure-Informed Protein Language Models are Robust Predictors for Variant Effects

Predicting protein variant effects through machine learning is often challenged by the scarcity of experimentally measured effect labels. Recently, protein language models (pLMs) emerge as zero-shot predictors without the need of effect labels, by modeling the evolutionary distribution of functional protein sequences. However, biological contexts important to variant effects are implicitly modeled and effectively marginalized. By assessing the sequence awareness and the structure awareness of pLMs, we find that their improvements often correlate with better variant effect prediction but their tradeoff can present a barrier as observed in over-finetuning to specific family sequences. We introduce a framework of structure-informed pLMs (SI-pLMs) to inject protein structural contexts purposely and controllably, by extending masked sequence denoising in conventional pLMs to cross-modality denoising. Our SI-pLMs are applicable to revising any sequence-only pLMs through model architecture and training objectives. They do not require structure data as model inputs for variant effect prediction and only use structures as context provider and model regularizer during training. Numerical results over deep mutagenesis scanning benchmarks show that our SI-pLMs, despite relatively compact sizes, are robustly top performers against competing methods including other pLMs, regardless of the target protein family's evolutionary information content or the tendency to overfitting / over-finetuning. Learned distributions in structural contexts could enhance sequence distributions in predicting variant effects. Ablation studies reveal major contributing factors and analyses of sequence embeddings provide further insights. The data and scripts are available at https://github.com/Stephen2526/Structure-informed_PLM.git.

CAGI6 ID-Challenge: Assessment of phenotype and variant predictions in 415 children with Neurodevelopmental Disorders (NDDs)

In the context of the Critical Assessment of the Genome Interpretation, 6th edition (CAGI6), the Genetics of Neurodevelopmental Disorders Lab in Padua proposed a new ID-challenge to give the opportunity of developing computational methods for predicting patient's phenotype and the causal variants. Eight research teams and 30 models had access to the phenotype details and real genetic data, based on the sequences of 74 genes (VCF format) in 415 pediatric patients affected by Neurodevelopmental Disorders (NDDs). NDDs are clinically and genetically heterogeneous conditions, with onset in infant age. In this study we evaluate the ability and accuracy of computational methods to predict comorbid phenotypes based on clinical features described in each patient and causal variants. Finally, we asked to develop a method to find new possible genetic causes for patients without a genetic diagnosis. As already done for the CAGI5, seven clinical features (ID, ASD, ataxia, epilepsy, microcephaly, macrocephaly, hypotonia), and variants (causative, putative pathogenic and contributing factors) were provided. Considering the overall clinical manifestation of our cohort, we give out the variant data and phenotypic traits of the 150 patients from CAGI5 ID-Challenge as training and validation for the prediction methods development.

Prediction of pathogenic single amino acid substitutions using molecular fragment descriptors

MOTIVATION

Next Generation Sequencing technologies make it possible to detect rare genetic variants in individual patients. Currently, more than a dozen software and web services have been created to predict the pathogenicity of variants related with changing of amino acid residues. Despite considerable efforts in this area, at the moment there is no ideal method to classify pathogenic and harmless variants, and the assessment of the pathogenicity is often contradictory. In this article, we propose to use peptides structural formulas of proteins as an amino acid residues substitutions description, rather than a single-letter code. This allowed us to investigate the effectiveness of chemoinformatics approach to assess the pathogenicity of variants associated with amino acid substitutions.

RESULTS

The structure-activity relationships analysis relying on protein-specific data and atom centric substructural multilevel neighborhoods of atoms (MNA) descriptors of molecular fragments appeared to be suitable for predicting the pathogenic effect of single amino acid variants. MNA-based Naïve Bayes classifier algorithm, ClinVar and humsavar data were used for the creation of structure-activity relationships models for 10 proteins. The performance of the models was compared with 11 different predicting tools: 8 individual (SIFT 4G, Polyphen2 HDIV, MutationAssessor, PROVEAN, FATHMM, MVP, LIST-S2, MutPred) and 3 consensus (M-CAP, MetaSVM, MetaLR). The accuracy of MNA-based method varies for the proteins (AUC: 0.631-0.993; MCC: 0.191-0.891). It was similar for both the results of comparisons with the other individual predictors and third-party protein-specific predictors. For several proteins (BRCA1, BRCA2, COL1A2, and RYR1), the performance of the MNA-based method was outstanding, capable of capturing the pathogenic effect of structural changes in amino acid substitutions.

AVAILABILITY AND IMPLEMENTATION

The datasets are available as supplemental data at Bioinformatics online. A python script to convert amino acid and nucleotide sequences from single-letter codes to SD files is available at https://github.com/SmirnygaTotoshka/SequenceToSDF. The authors provide trial licenses for MultiPASS software to interested readers upon request.

Choosing Variant Interpretation Tools for Clinical Applications: Context Matters

Pathogenicity predictors are computational tools that classify genetic variants as benign or pathogenic; this is currently a major challenge in genomic medicine. With more than fifty such predictors available, selecting the most suitable tool for clinical applications like genetic screening, molecular diagnostics, and companion diagnostics has become increasingly challenging. To address this issue, we have developed a cost-based framework that naturally considers the various components of the problem. This framework encodes clinical scenarios using a minimal set of parameters and treats pathogenicity predictors as rejection classifiers, a common practice in clinical applications where low-confidence predictions are routinely rejected. We illustrate our approach in four examples where we compare different numbers of pathogenicity predictors for missense variants. Our results show that no single predictor is optimal for all clinical scenarios and that considering rejection yields a different perspective on classifiers.

Identification and characterization of putative biomarkers and therapeutic axis in Glioblastoma multiforme microenvironment

Non-cellular secretory components, including chemokines, cytokines, and growth factors in the tumor microenvironment, are often dysregulated, impacting tumorigenesis in Glioblastoma multiforme (GBM) microenvironment, where the prognostic significance of the current treatment remains unsatisfactory. Recent studies have demonstrated the potential of post-translational modifications (PTM) and their respective enzymes, such as acetylation and ubiquitination in GBM etiology through modulating signaling events. However, the relationship between non-cellular secretory components and post-translational modifications will create a research void in GBM therapeutics. Therefore, we aim to bridge the gap between non-cellular secretory components and PTM modifications through machine learning and computational biology approaches. Herein, we highlighted the importance of BMP1, CTSB, LOX, LOXL1, PLOD1, MMP9, SERPINE1, and SERPING1 in GBM etiology. Further, we demonstrated the positive relationship between the E2 conjugating enzymes (Ube2E1, Ube2H, Ube2J2, Ube2C, Ube2J2, and Ube2S), E3 ligases (VHL and GNB2L1) and substrate (HIF1A). Additionally, we reported the novel HAT1-induced acetylation sites of Ube2S (K211) and Ube2H (K8, K52). Structural and functional characterization of Ube2S (8) and Ube2H (1) have identified their association with protein kinases. Lastly, our results found a putative therapeutic axis HAT1-Ube2S(K211)-GNB2L1-HIF1A and potential predictive biomarkers (CTSB, HAT1, Ube2H, VHL, and GNB2L1) that play a critical role in GBM pathogenesis.

Structure-based network analysis predicts mutations associated with inherited retinal disease

With continued advances in gene sequencing technologies comes the need to develop better tools to understand which mutations cause disease. Here we validate structure-based network analysis (SBNA)1,2 in well-studied human proteins and report results of using SBNA to identify critical amino acids that may cause retinal disease if subject to missense mutation. We computed SBNA scores for genes with high-quality structural data, starting with validating the method using 4 well-studied human disease-associated proteins. We then analyzed 47 inherited retinal disease (IRD) genes. We compared SBNA scores to phenotype data from the ClinVar database and found a significant difference between benign and pathogenic mutations with respect to network score. Finally, we applied this approach to 65 patients at Massachusetts Eye and Ear (MEE) who were diagnosed with IRD but for whom no genetic cause was found. Multivariable logistic regression models built using SBNA scores for IRD-associated genes successfully predicted pathogenicity of novel mutations, allowing us to identify likely causative disease variants in 37 patients with IRD from our clinic. In conclusion, SBNA can be meaningfully applied to human proteins and may help predict mutations causative of IRD.

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.

Sequence Analysis of Hot Spot Regions of Spike and RNA-dependent-RNA polymerase (RdRp) Genes of SARS-CoV-2 in Kerman, Iran

Background

Mutations in the SARS-CoV-2 genome might influence pathogenicity, transmission rate, and evasion of the host immune system. Therefore, the purpose of the present study was to investigate the genetic alteration as well as assess their effects on the receptor binding domain (RBD) of the spike and the putative RNA binding site of the RdRp genes of SARS-CoV-2 using bioinformatics tools.

Materials and Method

In this cross-sectional study, 45 confirmed COVID-19 patients using qRT-PCR were included and divided into mild, severe, and critical groups based on the severity of the disease. RNA was extracted from nasopharyngeal swab samples using a commercial kit. RT-PCR was performed to amplify the target sequences of the spike and RdRp genes and sequence them by the Sanger method. Clustal OMEGA, MEGA 11 software, I-mutant tools, SWISS-MODEL, and HDOCK web servers were used for bioinformatics analyses.

Results

The mean age of the patients was 50.68±2.73. The results showed that four of six mutations (L452R, T478K, N501Y, and D614G) in RBD and three of eight in the putative RNA binding site (P314L, E1084D, V1883T) were missense. In the putative RNA binding site, another deletion was discovered. Among missense mutations, N501Y and V1883T were responsible for increasing structural stability, while others were responsible for decreasing it. The various homology models designed showed that these homologies were like the Wuhan model. The molecular docking analysis revealed that the T478K mutation in RBD had the highest binding affinity. In addition, 35 RBD samples (89.7%) and 33 putative RNA binding site samples (84.6%) were similar to the Delta variant.

Conclusion

Our results indicated that double mutations (T478K and N501Y) in the S protein might increase the binding affinity of SARS-CoV-2 to human ACE2 compared to the wild-type (WT) strain. Moreover, variations in the spike and RdRp genes might influence the stability of encoded proteins.

Deciphering the enigma of RNA editing in the ATP1_alpha subunit of ATP synthase in Triticum aestivum

There is evidence that RNA editing is related to plant cellular stress as well as electron transport organelles, such as mitochondria. The mitochondrial atp1 gene encodes the alpha-subunit of Atp synthase. Control as well as two periods of drought stress treatments were analyzed in the cDNAs generated from the mitochondrial atp1 gene of two cultivars of Triticum aestivum [Giza 168 (G168) and Gemmiza 10 (GM10)]. Following RNA-seq data assembly, atp1 cDNAs from the control (acc. no. OQ129415), 2-hour (acc. no. OQ129416), and 12-hour (acc. no. OQ129417) time points of the T. aestivum cultivar G168 were obtained. Control (acc. no. OQ129419), 2-hour (acc. no. OQ129420), and 12-hour (acc. no. OQ129421) samples all included reconstructed atp1 transcripts from Gemmiza 10. Atp1 transcripts were assembled using the wheat atp1 gene (acc. no. NC_036024). RNA-seq raw data was utilized to identify 11 RNA editing sites in atp1 in the tolerant cultivar Giza168 and 6 in the sensitive cultivar Gemmiza10. The significant difference in RNA editing observed between control and drought stress conditions in sites led to synonymous amino acids. This led to no change in tertiary structure between tolerant and sensitive cultivars. But the change was focused between produced protein and its correspondence sequence on DNA.

Non-synonymous SNPs variants of PRKCG and its association with oncogenes predispose to hepatocellular carcinoma

BACKGROUND

PRKCG encodes PKC γ, which is categorized under the classical protein kinase C family. No studies have specifically established the relationship between PRKCG nsSNPs with structural and functional variations in PKC γ in the context of hepatocellular carcinoma (HCC). The present study aims to uncover this link through in-silico and experimental studies.

METHODS

The 3D structure of PKC γ was predicted. Molecular Dynamic (MD) Simulations were run and estimates were made for interactions, stability, conservation and post-translational alterations between wild and mutant structures. The association of PRKCG levels with HCC survival rate was determined. Genotyping analyses were conducted to investigate the deleterious PRKCG nsSNP association with HCC. mRNA expression of PKC γ, HIF-1 alpha, AKT, SOCS3 and VEGF in the blood of controls and HCC patients was analyzed and a genetic cascade was constructed depicting these interactions.

RESULTS

The expression level of studied oncogenes was compared to tumour suppressor genes. Through Alphafold, the 3D structure of PKC γ was explored. Fifteen SNPs were narrowed down for in-silico analyses that were identified in exons 5, 10 and 18 and the regulatory and kinase domain of PKC γ. Root mean square deviation and fluctuation along with the radius of gyration unveiled potential changes between the wild and mutated variant structures. Mutant genotype AA (homozygous) corresponding to nsSNP, rs386134171 had more frequency in patients with OR (2.446), RR (1.564) and P-values (< 0.0029) that highlights its significant association with HCC compared to controls in which the wild genotype GG was found more prevalent.

CONCLUSION

nsSNP rs386134171 can be a genetic marker for HCC diagnosis and therapeutic studies. This study has laid down a road map for future studies to be conducted on HCC.

In-silico assessment of high-risk non-synonymous SNPs in ADAMTS3 gene associated with Hennekam syndrome and their impact on protein stability and function

Hennekam Lymphangiectasia-Lymphedema Syndrome 3 (HKLLS3) is a rare genetical disorder caused by mutations in a few genes including ADAMTS3. It is characterized by lymphatic dysplasia, intestinal lymphangiectasia, severe lymphedema and distinctive facial appearance. Up till now, no extensive studies have been conducted to elucidate the mechanism of the disease caused by various mutations. As a preliminary investigation of HKLLS3, we sorted out the most deleterious nonsynonymous single nucleotide polymorphisms (nsSNPs) that might affect the structure and function of ADAMTS3 protein by using a variety of in silico tools. A total of 919 nsSNPs in the ADAMTS3 gene were identified. 50 nsSNPs were predicted to be deleterious by multiple computational tools. 5 nsSNPs (G298R, C567Y, A370T, C567R and G374S) were found to be the most dangerous and can be associated with the disease as predicted by different bioinformatics tools. Modelling of the protein shows it can be divided into segments 1, 2 and 3, which are connected by short loops. Segment 3 mainly consists of loops without substantial secondary structures. With prediction tools and molecular dynamics simulation, some SNPs were found to significantly destabilize the protein structure and disrupt the secondary structures, especially in segment 2. The deleterious effects of mutations in segment 1 are possibly not from destabilization but from other factors such as the change in phosphorylation as suggested by post-translational modification (PTM) studies. This is the first-ever study of ADAMTS3 gene polymorphism, and the predicted nsSNPs in ADAMST3, some of which have not been reported yet in patients, will serve for diagnostic purposes and further therapeutic implications in Hennekam syndrome, contributing to better diagnosis and treatment.

In silico identification and characterization of the SNPs in the human ASTL gene and their probable role in female infertility

Ovastacin (ASTL), a zinc metalloprotease, is released from a fertilized egg during exocytosis of cortical granules which occurs minutes after the sperm and egg fuse. ASTL cleaves ZP2, one of the four primary glycoproteins of human zona pellucida, and this cleavage prevents polyspermy, causes zona pellucida hardening, and also protects the pre-implantation embryo. Any perturbation in the activity of ASTL can thus disturb this process and may lead to infertility without changing the gross morphology of the oocyte. A small amount of ASTL is also released by unfertilized oocytes but its catalytic activity is absent as it is bound by its inhibitor, Fetuin-B (FETUB). Pre-mature release of ASTL when FETUB is absent also causes infertility. To identify and understand the structural and functional effects of deleterious SNPs of ASTL on its interaction with ZP2 and FETUB and hence on fertility, a total of 4,748 SNPs from the dbSNP database were evaluated using a variety of in silico tools. All of the 40 shortlisted nsSNPs were present in the catalytic domain of the protein. Comparison of the wild type with mutants using MutPred2 suggests an alteration in the catalytic activity/zinc binding site in many SNPs. Docking studies show the involvement of hydrophobic interactions and H bonding between ASTL and ZP2 and also between ASTL and FETUB. Four positions in ASTL involved in the hydrophobic interactions (P¹⁰⁵ and D²⁰⁰ between ASTL and ZP2; D¹⁹⁸ and L²⁷⁸ between ASTL and FETUB) and 5 in H bonding (E⁷⁵ and R¹⁵⁹ between ASTL and ZP2; and K⁹³, R^159, and C²⁸¹ between ASTL and FETUB) have SNP's associated with them validating their importance. Interestingly, a cluster of multiple SNPs was found in the motif ¹⁹⁸DRD²⁰⁰, which is also a well-conserved region among several species. Statistical Coupling Analysis (SCA) suggested that the deleterious SNPs were present in the functionally important amino acid positions of ASTL and are evolutionarily coupled. Thus, these results attempt to identify the regions in ASTL, mutations in which can affect its binding with ZP2 or FETUB and cause female infertility.

SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation

Our incomplete knowledge of the human transcriptome impairs the detection of disease-causing variants, in particular if they affect transcripts only expressed under certain conditions. These transcripts are often lacking from reference transcript sets, such as Ensembl/GENCODE and RefSeq, and could be relevant for establishing genetic diagnoses. We present SUsPECT (Solving Unsolved Patient Exomes/gEnomes using Custom Transcriptomes), a pipeline based on the Ensembl Variant Effect Predictor (VEP) to predict variant impact on custom transcript sets, such as those generated by long-read RNA-sequencing, for downstream prioritization. Our pipeline predicts the functional consequence and likely deleteriousness scores for missense variants in the context of novel open reading frames predicted from any transcriptome. We demonstrate the utility of SUsPECT by uncovering potential mutational mechanisms of pathogenic variants in ClinVar that are not predicted to be pathogenic using the reference transcript annotation. In further support of SUsPECT's utility, we identified an enrichment of immune-related variants predicted to have a more severe molecular consequence when annotating with a newly generated transcriptome from stimulated immune cells instead of the reference transcriptome. Our pipeline outputs crucial information for further prioritization of potentially disease-causing variants for any disease and will become increasingly useful as more long-read RNA sequencing datasets become available.

A heterozygous missense variant in DLX3 leads to uterine leiomyomas and pregnancy losses in a consanguineous Iranian family

Uterine leiomyomas (ULs) are benign solid tumors arising from the uterine myometrium. They are the most common pelvic tumors among females of reproductive age. Despite the universal prevalence of ULs and its huge impact on women's lives, the exact etiology and pathophysiologic mechanisms have not been fully understood. Numerous studies indicate that genetic factors play a crucial role in ULs development. This study aims to identify the probable genetic causes of ULs in a consanguineous Iranian family. Whole-exome sequencing (WES) on five family members with ULs revealed a likely pathogenic missense variant encoding for Y88C in the transactivation (TA) domain of DLX3 gene (c.263A > G; p.Y88C). Sanger sequencing of a total of 9 affected and non-affected family members indicated a segregation with disease with autosomal dominant inheritance. Moreover, targeted Sanger sequencing on 32 additional non-related patients with ULs showed none was heterozygous for this variant. MutPred2 predicted the pathogenicity of candidate variant by both phosphorylation and sulfation loss as actionable hypotheses. Project HOPE revealed that the identified variant residue is smaller and more hydrophobic comparing to the wild-type residue. I-TASSER and UCSF Chimera were also used for modeling and visualizing the predicted variant, respectively. This WES analysis is the first to report a variant in DLX3 variation associated with ULs pathogenicity in Iranian population highlighting the effectiveness of WES as a strong diagnostic method. However, further functional studies on this variant are needed to confirm the potential pathogenicity of this mutation.

Lipid Peroxidation Products Influence Calpain-1 Functionality In Vitro by Covalent Binding

The objective of the current study was to evaluate the effects of lipid peroxidation products, malondialdehyde (MDA), hexenal, and 4-hydroxynonenal (HNE), on calpain-1 function, and liquid chromatography and tandem mass spectrometry (LC-MS/MS) identification of adducts on calpain-1. Calpain-1 activity slightly increased after incubation with 100 μM MDA but not with 500 and 1000 μM MDA. However, calpain-1 activity was lowered by hexenal and HNE at 100, 500, and 1000 μM. No difference in calpain-1 autolysis was observed between the control and 1000 μM MDA. However, 1000 μM hexenal and HNE treatments slowed the calpain-1 autolysis. Adducts of MDA were detected on glutamine, arginine, lysine, histidine, and asparagine residues via Schiff base formation, while HNE adducts were detected on histidine, lysine, glutamine, and asparagine residues via Michael addition. These results are the first to demonstrate that lipid peroxidation products can impact calpain-1 activity in a concentration-dependent manner and may impact the development of meat tenderness postmortem.

Resources and tools for rare disease variant interpretation

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

Protein structure-based evaluation of missense variants: Resources, challenges and future directions

We provide an overview of the methods that can be used for protein structure-based evaluation of missense variants. The algorithms can be broadly divided into those that calculate the difference in free energy (ΔΔG) between the wild type and variant structures and those that use structural features to predict the damaging effect of a variant without providing a ΔΔG. A wide range of machine learning approaches have been employed to develop those algorithms. We also discuss challenges and opportunities for variant interpretation in view of the recent breakthrough in three-dimensional structural modelling using deep learning.

An ABCC9 Missense Variant Is Associated with Sudden Cardiac Death and Dilated Cardiomyopathy in Juvenile Dogs

Sudden cardiac death in the young (SCDY) is a devastating event that often has an underlying genetic basis. Manchester Terrier dogs offer a naturally occurring model of SCDY, with sudden death of puppies as the manifestation of an inherited dilated cardiomyopathy (DCM). We performed a genome-wide association study for SCDY/DCM in Manchester Terrier dogs and identified a susceptibility locus harboring the cardiac ATP-sensitive potassium channel gene ABCC9. Sanger sequencing revealed an ABCC9 p.R1186Q variant present in a homozygous state in all SCDY/DCM-affected dogs (n = 26). None of the controls genotyped (n = 398) were homozygous for the variant, but 69 were heterozygous carriers, consistent with autosomal recessive inheritance with complete penetrance (p = 4 × 10^-42 for the association of homozygosity for ABCC9 p.R1186Q with SCDY/DCM). This variant exists at low frequency in human populations (rs776973456) with clinical significance previously deemed uncertain. The results of this study further the evidence that ABCC9 is a susceptibility gene for SCDY/DCM and highlight the potential application of dog models to predict the clinical significance of human variants.

SOAT1 missense variant in two cats with sebaceous gland dysplasia

Spontaneously arisen hereditary diseases in domestic animals provide an excellent opportunity to study the physiological functions of the altered genes. We investigated two 4-month-old sibling domestic short haired kittens with dry dark debris around the eyes, nose, and ears, dark crusting on the legs and a thin poor hair coat. Skin biopsies revealed abnormal sebaceous gland morphology with lack of normal sebocyte arrangement and differentiation. Hair follicles had a distorted silhouette, interpreted as a change secondary to the observed sebaceous gland dysplasia. Whole genome sequencing on both affected kittens and 65 genetically diverse feline genomes was performed. Filtering for variants that were present in both kittens but absent from the control genomes revealed a homozygous missense variant in SOAT1, encoding sterol O-acyltransferase 1. The protein is localized in the endoplasmic reticulum and catalyzes the formation of cholesteryl esters, an essential component of sebum and meibum. The identified SOAT1:c.1531G > A variant is predicted to change a highly conserved glycine residue within the last transmembrane domain of SOAT1, p.Gly511Arg. In mice, variants in Soat1 or complete knockout of the gene lead to the "hair interior defect" (hid) or abnormal Meibomian glands, respectively. SOAT1:c.1531G > A represents a plausible candidate variant for the observed sebaceous gland dysplasia in both kittens of this study. The variant was not present in 10 additional cats with a similar clinical and histopathological phenotype suggesting genetic heterogeneity. SOAT1 variants should be considered as potential cause in hereditary sebaceous gland dysplasias of humans and domestic animals.

Structural and Pathogenic Impacts of ABCA4 Variants in Retinal Degenerations-An In-Silico Study

The retina-specific ATP-binding cassette transporter protein ABCA4 is responsible for properly continuing the visual cycle by removing toxic retinoid byproducts of phototransduction. Functional impairment caused by ABCA4 sequence variations is the leading cause of autosomal recessive inherited retinal disorders, including Stargardt disease, retinitis pigmentosa, and cone-rod dystrophy. To date, more than 3000 ABCA4 genetic variants have been identified, approximately 40 percent of which have not been able to be classified for pathogenicity assessments. This study examined 30 missense ABCA4 variants using AlphaFold2 protein modeling and computational structure analysis for pathogenicity prediction. All variants classified as pathogenic (n = 10) were found to have deleterious structural consequences. Eight of the ten benign variants were structurally neutral, while the remaining two resulted in mild structural changes. This study's results provided multiple lines of computational pathogenicity evidence for eight ABCA4 variants of uncertain clinical significance. Overall, in silico analyses of ABCA4 can provide a valuable tool for understanding the molecular mechanisms of retinal degeneration and their pathogenic impact.

Using In Silico Bioinformatics Algorithms for the Accurate Prediction of the Impact of Spike Protein Mutations on the Pathogenicity, Stability, and Functionality of the SARS-CoV-2 Virus and Analysis of Potential Therapeutic Targets

Coronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We have used bioinformatics to investigate seventeen mutations in the spike protein of SARS-CoV-2, as this mediates infection of human cells and is the target of most vaccine strategies and antibody-based therapies. Two mutations, H146Y and S221W, were identified as being most pathogenic. Mutations at positions D614G, A829T, and P1263L might also have deleterious effects on protein function. We hypothesized that candidate small molecules may be repurposed to combat viral infection. We investigated changes in binding energies of the ligands and the mutant proteins by assessing molecular docking. For an understanding of cellular function and organization, protein-protein interactions are also critical. Protein-protein docking for naïve and mutated structures of SARS-CoV-2 S protein was evaluated for their binding energy with the angiotensin-converting enzyme 2 (ACE2). These interactions might limit the binding of the SARS-CoV-2 spike protein to the ACE2 receptor or may have a deleterious effect on protein function that may limit infection. These results may have important implications for the transmission of SARS-CoV-2, its pathogenesis, and the potential for drug repurposing and immune therapies.

In silico exploration and molecular dynamics of deleterious SNPs on the human TERF1 protein triggering male infertility

By limiting chromosome erosion and end-to-end fusions, telomere integrity is critical for chromosome stability and cell survival. During mitotic cycles or due to environmental stresses, telomeres become progressively shorter and dysfunctional, thus triggering cellular senescence, genomic instability and cell death. To avoid such consequences, the telomerase action, as well as the Shelterin and CST complexes, assure the telomere's protection. Telomeric repeat binding factor 1 (TERF1), which is one of the primary components of the Shelterin complex, binds directly to the telomere and controls its length and function by regulating the telomerase activity. Several reports about TERF1 gene variations have been associated with different diseases, and some of them have linked these variations to male infertility. Hence, this paper can be advantageous to investigate the association between the missense variants of the TERF1 gene and the susceptibility to male infertility. The stepwise prediction of SNPs pathogenicity followed in this study was based on stability and conservation analysis, post-translational modification, secondary structure, functional interaction prediction, binding energy evaluation and finally molecular dynamic simulation. Prediction matching among the tools revealed that out of 18 SNPs, only four (rs1486407144, rs1259659354, rs1257022048 and rs1320180267) were predicted as the most damaging and highly deleterious SNPs affecting the TERF1 protein and its molecular dynamics when interacting with the TERB1 protein by influencing the function, structural stability, flexibility and compaction of the overall complex. Interestingly, these polymorphisms should be considered during genetic screening so they can be used effectively as genetic biomarkers for male infertility diagnosis.Communicated by Ramaswamy H. Sarma.

Heterogeneity in Lowe Syndrome: Mutations Affecting the Phosphatase Domain of OCRL1 Differ in Impact on Enzymatic Activity and Severity of Cellular Phenotypes

Lowe Syndrome (LS) is a condition due to mutations in the OCRL1 gene, characterized by congenital cataracts, intellectual disability, and kidney malfunction. Unfortunately, patients succumb to renal failure after adolescence. This study is centered in investigating the biochemical and phenotypic impact of patient's OCRL1 variants (OCRL1VAR). Specifically, we tested the hypothesis that some OCRL1VAR are stabilized in a non-functional conformation by focusing on missense mutations affecting the phosphatase domain, but not changing residues involved in binding/catalysis. The pathogenic and conformational characteristics of the selected variants were evaluated in silico and our results revealed some OCRL1VAR to be benign, while others are pathogenic. Then we proceeded to monitor the enzymatic activity and function in kidney cells of the different OCRL1VAR. Based on their enzymatic activity and presence/absence of phenotypes, the variants segregated into two categories that also correlated with the severity of the condition they induce. Overall, these two groups mapped to opposite sides of the phosphatase domain. In summary, our findings highlight that not every mutation affecting the catalytic domain impairs OCRL1's enzymatic activity. Importantly, data support the inactive-conformation hypothesis. Finally, our results contribute to establishing the molecular and structural basis for the observed heterogeneity in severity/symptomatology displayed by patients.