Identification and functional characterization of two rare LDLR stop gain variants (p.C231* and p.R744*) in Saudi familial hypercholesterolemia patients.

BACKGROUND: Familial hypercholesterolemia (FH) is a globally underdiagnosed inherited metabolic disorder. Owing to limited published data from Arab world, this study was conducted with the aim of identifying the genetic and molecular basis of FH in highly consanguineous Saudi population. METHODS: We performed clinical screening, biochemical profiling, whole exome sequencing and variant segregation analysis of two Saudi FH families. Additionally, 500 normolipic individuals were screened to ensure the absence of FH variant in general Saudi population. Functional characterization of FH variants on secondary structure characteristics of RNA and protein molecules was performed using different bioinformatics modelling approaches. RESULTS: WES analysis identified two independent rare LDLR gene stop gain variants (p.C231* and p.R744*) consistent to the clinical presentation of FH patients from two different families. RNAfold analysis has shown that both variants were predicted to disturb the free energy dynamics of LDLR mRNA molecule and destabilize its folding pattern and function. PSIPRED based structural modelling analysis has suggested that both variants bring drastic changes disturbing the secondary structural elements of LDLR molecule. The p.C231* and p.R744* variants are responsible for partial or no protein product, thus they are class 1 variants causing loss of function (LoF) LDLR variants. CONCLUSIONS: This study highlights the effectiveness of the WES, sanger sequencing, and computational analysis in expanding FH variant spectrum in culturally distinct populations like Saudi Arabia. Genetic testing of FH patients is very essential in better clinical diagnosis, screening, treatment, and management and prevention of cardiovascular disease burden in the society.

Integrative weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer.

Chronic obstructive pulmonary disease (COPD) is a multifactorial progressive airflow obstruction in the lungs, accounting for high morbidity and mortality across the world. This study aims to identify potential COPD blood-based biomarkers by analyzing the dysregulated gene expression patterns in blood and lung tissues with the help of robust computational approaches. The microarray gene expression datasets from blood (136 COPD and 6 controls) and lung tissues (16 COPD and 19 controls) were analyzed to detect shared differentially expressed genes (DEGs). Then these DEGs were used to construct COPD protein network-clusters and functionally enrich them against gene ontology annotation terms. The hub genes in the COPD network clusters were then queried in GWAS catalog and in several cancer expression databases to explore their pathogenic roles in lung cancers. The comparison of blood and lung tissue datasets revealed 63 shared DEGs. Of these DEGs, 12 COPD hub gene-network clusters (SREK1, TMEM67, IRAK2, MECOM, ASB4, C1QTNF2, CDC42BPA, DPF3, DET1, CCDC74B, KHK, and DDX3Y) connected to dysregulations of protein degradation, inflammatory cytokine production, airway remodeling, and immune cell activity were prioritized with the help of protein interactome and functional enrichment analysis. Interestingly, IRAK2 and MECOM hub genes from these COPD network clusters are known for their involvement in different pulmonary diseases. Additional COPD hub genes like SREK1, TMEM67, CDC42BPA, DPF3, and ASB4 were identified as prognostic markers in lung cancer, which is reported in 1% of COPD patients. This study identified 12 gene network- clusters as potential blood based genetic biomarkers for COPD diagnosis and prognosis.

Integrative global co-expression analysis identifies key microRNA-target gene networks as key blood biomarkers for obesity.

BACKGROUND: Obesity is associated with the quantitative changes in miRNAs and their target genes. However, the molecular basis of their dysregulation and expression status correlations is incompletely understood. Therefore, this study aims to examine the shared differentially expressed miRNAs and their target genes between blood and adipose tissues of obese individuals to identify potential blood-based biomarkers. METHODS: In this study, 3 gene expression datasets (two mRNA and one miRNA), generated from blood and adipose tissues of 68 obese and 39 lean individuals, were analyzed by a series of robust computational concepts, like protein interactome mapping, functional enrichment of biological pathways and construction of miRNA-mRNA and transcription factor gene networks. RESULTS: The comparison of blood versus tissue datasets has revealed the shared differential expression of 210 genes (59.5% upregulated) involved in lipid metabolism and inflammatory reactions. The blood miRNA (GSE25470) analysis has identified 79 differentially expressed miRNAs (71% downregulated). The miRNA-target gene scan identified regulation of 30 shared genes by 22miRNAs. The gene network analysis has identified the inverse expression correlation between 8 target genes (TP53, DYSF, GAB2, GFRA2, NACC2, FAM53C, JNK and GAB2) and 3 key miRNAs (hsa-mir-940, hsa-mir-765, hsa-mir-612), which are further regulated by 24 key transcription factors. CONCLUSIONS: This study identifies potential obesity related blood biomarkers from large-scale gene expression data by computational miRNA-target gene interactome and transcription factor network construction methods.

Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes.

Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1, and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.

Saudi Familial Hypercholesterolemia Patients With Rare LDLR Stop Gain Variant Showed Variable Clinical Phenotype and Resistance to Multiple Drug Regimen.

Familial hypercholesterolemia (FH), a well-known lipid disease caused by inherited genetic defects in cholesterol uptake and metabolism is underdiagnosed in many countries including Saudi Arabia. The present study aims to identify the molecular basis of severe clinical manifestations of FH patients from unrelated Saudi consanguineous families. Two Saudi families with multiple FH patients fulfilling the combined FH diagnostic criteria of Simon Broome Register, and the Dutch Lipid Clinic Network (DLCN) were recruited. LipidSeq, a targeted resequencing panel for monogenic dyslipidemias, was used to identify causative pathogenic mutation in these two families and in 92 unrelated FH cases. Twelve FH patients from two unrelated families were sharing a very rare, pathogenic and founder LDLR stop gain mutation i.e., c.2027delG (p.Gly676Alafs*33) in both the homozygous or heterozygous states, but not in unrelated patients. Based on the variant zygosity, a marked phenotypic heterogeneity in terms of LDL-C levels, clinical presentations and resistance to anti-lipid treatment regimen (ACE inhibitors, ß-blockers, ezetimibe, statins) of the FH patients was observed. This loss-of-function mutation is predicted to alter the free energy dynamics of the transcribed RNA, leading to its instability. Protein structural mapping has predicted that this non-sense mutation eliminates key functional domains in LDLR, which are essential for the receptor recycling and LDL particle binding. In conclusion, by combining genetics and structural bioinformatics approaches, this study identified and characterized a very rare FH causative LDLR pathogenic variant determining both clinical presentation and resistance to anti-lipid drug treatment.

Multilevel systems biology analysis of lung transcriptomics data identifies key miRNAs and potential miRNA target genes for SARS-CoV-2 infection.

BACKGROUND: The spread of a novel severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) has affected both the public health and the global economy. The current study was aimed at analysing the genetic sequence of this highly contagious corona virus from an evolutionary perspective, comparing the genetic variation features of different geographic strains, and identifying the key miRNAs as well as their gene targets from the transcriptome data of infected lung tissues. METHODS: A multilevel robust computational analysis was undertaken for viral genetic sequence alignment, phylogram construction, genome-wide transcriptome data interpretation of virus-infected lung tissues, miRNA mapping, and functional biology networking. RESULTS: Our findings show both genetic similarities as well as notable differences in the S protein length among SARS-CoV-1, SARS-CoV-2 and MERS viruses. All SARS-CoV-2 strains showed a high genetic similarity with the parent Wuhan strain, but Saudi Arabian, South African, USA, Russia and New Zealand strains carry 3 additional genetic variations like P333L (RNA -dependant RNA polymerase), D614G (spike), and P4715L (ORF1ab). The infected lung tissues demonstrated the upregulation of 282 (56.51%) antiviral defensive response pathway genes and downregulation of 217 (43.48%) genes involved in autophagy and lung repair pathways. By miRNA mapping, 4 key miRNAs (hsa-miR-342-5p, hsa-miR-432-5p, hsa-miR-98-5p and hsa-miR-17-5p), targeting multiple host genes (MYC, IL6, ICAM1 and VEGFA) as well as SARS-CoV2 gene (ORF1ab) were identified. CONCLUSION: Systems biology methods offer a new perspective in understanding the molecular basis for the faster spread of SARS-CoV-2 infection. The antiviral miRNAs identified in this study may aid in the ongoing search for novel personalized therapeutic avenues for COVID patients.

Myocardial infarction biomarker discovery with integrated gene expression, pathways and biological networks analysis.

Myocardial infarction (MI) is the most prevalent coronary heart disease caused by the complex molecular interactions between multiple genes and environment. Here, we aim to identify potential biomarkers for the disease development and for prognosis of MI. We have used gene expression dataset (GSE66360) generated from 51 healthy controls and 49 patients experiencing acute MI and analyzed the differentially expressed genes (DEGs), protein-protein interactions (PPI), gene network-clusters to annotate the candidate pathways relevant to MI pathogenesis. Bioinformatic analysis revealed 810 DEGs. Their functional annotations have captured several MI targeting biological processes and pathways like immune response, inflammation and platelets degranulation. PPI network identify seventeen hub and bottleneck genes, whose involvement in MI was further confirmed by DisGeNET database. OpenTarget Platform reveal unique bottleneck genes as potential target for MI. Our findings identify several potential biomarkers associated with early stage MI providing a new insight into molecular mechanism underlying the disease.

Molecular insights into the coding region mutations of low-density lipoprotein receptor adaptor protein 1 (LDLRAP1) linked to familial hypercholesterolemia.

BACKGROUND: Familial hypercholesterolemia (FH) is a lipid disorder caused by pathogenic mutations in LDLRAP1 gene. The present study has aimed to deepen our understanding about the pathogenicity predictions of FH causative genetic mutations, as well as their relationship to phenotype changes in LDLRAP1 protein, by utilizing multidirectional computational analysis. METHODS: FH linked LDLRAP1 mutations were mined from databases, and the prediction ability of several pathogenicity classifiers against these clinical variants, was assessed through different statistical measures. Furthermore, these mutations were 3D modelled in protein structures to assess their impact on protein phenotype changes. RESULTS: Our findings suggest that Polyphen-2, when compared with SIFT, M-CAP and CADD tools, can make better pathogenicity predictions for FH causative LDLRAP1 mutations. Through, 3D simulation and superimposition analysis of LDLRAP1 protein structures, it was found that missense mutations do not create any gross changes in the protein structure, although they could induce subtle structural changes at the level of amino acid residues. Near native molecular dynamic analysis revealed that missense mutations could induce variable degrees of fluctuation differences guiding the protein flexibility. Stability analysis showed that most missense mutations shifts the free energy equilibrium and hence they destabilize the protein. Molecular docking analysis demonstrates the molecular shifts in hydrogen and ionic bonds and Van der waals bonding properties, which further cause differences in the binding energy of LDLR-LDLRAP1 proteins. CONCLUSIONS: The diverse computational approaches used in the present study may provide a new dimension for exploring the structure-function relationship of the novel and deleterious LDLRAP1 mutations linked to FH.

Unraveling the role of salt-sensitivity genes in obesity with integrated network biology and co-expression analysis.

Obesity is a multifactorial disease caused by complex interactions between genes and dietary factors. Salt-rich diet is related to the development and progression of several chronic diseases including obesity. However, the molecular basis of how salt sensitivity genes (SSG) contribute to adiposity in obesity patients remains unexplored. In this study, we used the microarray expression data of visceral adipose tissue samples and constructed a complex protein-interaction network of salt sensitivity genes and their co-expressed genes to trace the molecular pathways connected to obesity. The Salt Sensitivity Protein Interaction Network (SSPIN) of 2691 differentially expressed genes and their 15474 interactions has shown that adipose tissues are enriched with the expression of 23 SSGs, 16 hubs and 84 bottlenecks (p = 2.52 x 10-16) involved in diverse molecular pathways connected to adiposity. Fifteen of these 23 SSGs along with 8 other SSGs showed a co-expression with enriched obesity-related genes (r ≥ 0.8). These SSGs and their co-expression partners are involved in diverse metabolic pathways including adipogenesis, adipocytokine signaling pathway, renin-angiotensin system, etc. This study concludes that SSGs could act as molecular signatures for tracing the basis of adipogenesis among obese patients. Integrated network centered methods may accelerate the identification of new molecular targets from the complex obesity genomics data.

Whole exome sequencing identifies rare biallelic ALMS1 missense and stop gain mutations in familial Alström syndrome patients.

Alström syndrome (AS, OMIM ID 203800) is a rare childhood multiorgan disorder, which is widely studied in non-Arab ethnic patients. The clinical and molecular basis of AS and the mode of disease inheritance in consanguineous Arab populations is not well investigated. Therefore, to identify the molecular basis of AS in familial forms, the present study performed whole exome sequencing of 5 AS patients belonging to 2 different Bedouin families from Saudi Arabia. The present study identified the AS causative rare biallelic mutations in ALMS gene:T376S in exon 5 and S909* in exon 8 for family A and an R2721* in exon 10 (R2721*) for family B. ALMS1 targeted genetic sequencing of healthy population controls and family members has confirmed its extremely rare frequency and autosomal recessive mode of inheritance. The truncating mutations S909* and R2721* could cause the loss of CC domains and ALMS motif on C-terminal end of the protein and creates unstable protein, which eventually undergoes intracellular degradation. The premature protein truncating mutations described in our study may eventually provide further insight into the functional domains of the ALMS1 protein and contribute to the understanding of the phenotypic spectrum of AS. Whole exome sequencing based molecular diagnosis is expected to rule out ambiguity surrounding clinical diagnosis of suspected AS cases.

Exome sequencing and metabolomic analysis of a chronic kidney disease and hearing loss patient family revealed RMND1 mutation induced sphingolipid metabolism defects.

Mitochondrial disorders (MIDs) shows overlapping clinical presentations owing to the genetic and metabolic defects of mitochondria. However, specific relationship between inherited mutations in nuclear encoded mitochondrial proteins and their functional impacts in terms of metabolic defects in patients is not yet well explored. Therefore, using high throughput whole exome sequencing (WES), we screened a chronic kidney disease (CKD) and sensorineural hearing loss (SNHL) patient, and her family members to ascertain the mode of inheritance of the mutation, and healthy population controls to establish its rare frequency. The impact of mutation on biophysical characteristics of the protein was further studied by mapping it in 3D structure. Furthermore, LC-MS tandem mass spectrophotometry based untargeted metabolomic profiling was done to study the fluctuations in plasma metabolites relevant to disease causative mutations and kidney damage. We identified a very rare homozygous c.631G > A (p.Val211Met) pathogenic mutation in RMND1 gene in the proband, which is inherited in an autosomal recessive fashion. This gene is involved in the mitochondrial translational pathways and contribute in mitochondrial energy metabolism. The p.Val211Met mutation is found to disturb the structural orientation (RMSD is -2.95 Å) and stability (ΔΔG is -0.552 Kcal/mol) of the RMND1 protein. Plasma metabolomics analysis revealed the aberrant accumulation of metabolites connected to lipid and amino acid metabolism pathways. Of these metabolites, pathway networking has discovered ceramide, a metabolite of sphingolipids, which plays a role in different signaling cascades including mitochondrial membrane biosynthesis, is highly elevated in this patient. This study suggests that genetic defects in RMND1 gene alters the mitochondrial energy metabolism leading to the accumulation of ceramide, and subsequently promote dysregulated apoptosis and tissue necrosis in kidneys.

Molecular modelling and dynamics of CA2 missense mutations causative to carbonic anhydrase 2 deficiency syndrome.

Carbonic anhydrase 2 (CA2) enzyme deficiency caused by CA2 gene mutations is an inherited disorder characterized by symptoms like osteopetrosis, renal tubular acidosis, and cerebral calcification. This study has collected the CA2 deficiency causal missense mutations and assessed their pathogenicity using diverse computational programs. The 3D protein models for all missense mutations were built, and analyzed for structural divergence, protein stability, and molecular dynamics properties. We found M-CAP as the most sensitive prediction method to measure the deleterious potential of CA2 missense mutations. Free energy dynamics of tertiary structure models of CA2 mutants with DUET, mCSM, and SDM based consensus methods predicted only 50% of the variants as destabilizing. Superimposition of native and mutant CA2 models revealed the minor structural fluctuations at the amino acid residue level but not at the whole protein structure level. Near native molecular dynamic simulation analysis indicated that CA2 causative missense variants result in residue level fluctuation pattern in the protein structure. This study expands the understanding of genotype-protein phenotype correlations underlying CA2 variant pathogenicity and presents a potential avenue for modifying the CA2 deficiency by targeting biophysical structural features of CA2 protein. Communicated by Ramaswamy H. Sarma.

The genetic association study of TP53 polymorphisms in Saudi obese patients.

Obesity is a multifactorial metabolic disorder characterized by low grade chronic inflammation. Rare and novel mutations in genes which are vital in several key pathways have been reported to alter the energy expenditure which regulates body weight. The TP53 or p53 gene plays a prominent role in regulating various metabolic activities such as glycolysis, lipolysis, and glycogen synthesis. Recent genome-wide association studies reported that tumor suppressor gene p53 variants play a critical role in the predisposition of type 2 diabetes and obesity. Till date, no reports are available from the Arabian population; hence the present study was intended to assess the association between p53 variants with risk of obesity development in the Saudi population. We have selected three p53 polymorphisms, rs1642785 (C > G), and rs9894946 (A > G), and rs1042522 (Pro72Arg; C > G) and assessed their association with obesity risk in the Saudi population. Phenotypic and biochemical parameters were also evaluated to check their association with p53 genotypes and obesity. Genotyping was carried out on 136 obese and 122 normal samples. We observed that there is significantly increased prevalence p52 Pro72Arg (rs1042522) polymorphism in obese persons when compared to controls at GG genotype in overall comparison (OR: 2.169, 95% CI: 1.086-4.334, p = 0.02716). Male obese subjects showed three-fold higher risk at GG genotype (OR: 3.275, 95% CI: 1.230-8.716, p = 0.01560) and two-fold risk at G allele (OR: 1.827, 95% CI: 1.128-2.958, p = 0.01388) of p53 variant Pro72Arg respectively. This variant has also shown significant influence on cholesterol, LDL level, and random insulin levels in obese subjects (p ≤ 0.05). In conclusion, p53 Pro72Arg variant is highly prevalent among obese individuals and may act as a genetic modifier for obesity development among Saudis.

Identification of key regulatory genes connected to NF-κB family of proteins in visceral adipose tissues using gene expression and weighted protein interaction network.

Obesity is connected to the activation of chronic inflammatory pathways in both adipocytes and macrophages located in adipose tissues. The nuclear factor (NF)-κB is a central molecule involved in inflammatory pathways linked to the pathology of different complex metabolic disorders. Investigating the gene expression data in the adipose tissue would potentially unravel disease relevant gene interactions. The present study is aimed at creating a signature molecular network and at prioritizing the potential biomarkers interacting with NF-κB family of proteins in obesity using system biology approaches. The dataset GSE88837 associated with obesity was downloaded from Gene Expression Omnibus (GEO) database. Statistical analysis represented the differential expression of a total of 2650 genes in adipose tissues (p = <0.05). Using concepts like correlation, semantic similarity, and theoretical graph parameters we narrowed down genes to a network of 23 genes strongly connected with NF-κB family with higher significance. Functional enrichment analysis revealed 21 of 23 target genes of NF-κB were found to have a critical role in the pathophysiology of obesity. Interestingly, GEM and PPP1R13L were predicted as novel genes which may act as potential target or biomarkers of obesity as they occur with other 21 target genes with known obesity relationship. Our study concludes that NF-κB and prioritized target genes regulate the inflammation in adipose tissues through several molecular signaling pathways like NF-κB, PI3K-Akt, glucocorticoid receptor regulatory network, angiogenesis and cytokine pathways. This integrated system biology approaches can be applied for elucidating functional protein interaction networks of NF-κB protein family in different complex diseases. Our integrative and network-based approach for finding therapeutic targets in genomic data could accelerate the identification of novel drug targets for obesity.

Protein phenotype diagnosis of autosomal dominant calmodulin mutations causing irregular heart rhythms.

The life-threatening group of irregular cardiac rhythmic disorders also known as Cardiac Arrhythmias (CA) are caused by mutations in highly conserved Calmodulin (CALM/CaM) genes. Herein, we present a multidimensional approach to diagnose changes in phenotypic, stability, and Ca2+ ion binding properties of CA-causing mutations. Mutation pathogenicity was determined by diverse computational machine learning approaches. We further modeled the mutations in 3D protein structure and analyzed residue level phenotype plasticity. We have also examined the influence of torsion angles, number of H-bonds, and free energy dynamics on the stability, near-native simulation dynamic potential of residue fluctuations in protein structures, Ca2+ ion binding potentials, of CaM mutants. Our study recomends to use M-CAP method for measuring the pathogenicity of CA causing CaM variants. Interestingly, most CA-causing variants we analyzed, exists in either third (V/H-96, S/I-98, V-103) or fourth (G/V-130, V/E/H-132, H-134, P-136, G-141, and L-142) EF-hands located in carboxyl domains of the CaM molecule. We observed that the minor structural fluctuations caused by these variants are likely tolerable owing to the highly flexible nature of calmodulin's globular domains. However, our molecular docking results supports that these variants disturb the affinity of CaM toward Ca2+ ions and corroborate previous findings from functional studies. Taken together, these computational findings can explain the molecular reasons for subtle changes in structure, flexibility, and stability aspects of mutant CaM molecule. Our comprehensive molecular scanning approach demonstrates the utility of computational methods in quick preliminary screening of CA- CaM mutations before undertaking time consuming and complicated functional laboratory assays.

Computational Protein Phenotype Characterization of IL10RA Mutations Causative to Early Onset Inflammatory Bowel Disease (IBD).

The deleterious amino acid substitution mutations in IL-10 receptor alpha gene are most frequently reported in several autoimmune diseases including early onset-inflammatory bowel disease (IBD). Despite the important role of IL-10 RA in maintaining immune homeostasis, the specific structural and functional implications of these mutations on protein phenotype, stability, ligand binding and post translational characteristics is not well explored. Therefore, this study performed the multidimensional computational analysis of IL10RA missense variations causative to pediatric or early onset inflammatory bowel disease (<5 years of age). Our computational algorithmic screening identified the deleterious nature of p. W45G, p. Y57C, p. W69G, p.T84I, p.Y91C, p.R101W, p.R117C, and p.R117H, IBD causative IL10-RA mutations. The sensitivity and specificity analysis of different computational methods showed that CADD outperform SIFT, PolyPhen 2.0, FATHMM, LRT, MetaLR, MetaSVM, PROVEAN and Condel in predicting the pathogenicity of IL10RA mutations. Our three-dimensional protein modeling assays showed that the point mutations cause major drifts in the structural plasticity of IL10 RA molecule and negatively influence its stability. Findings from molecular docking analysis have shown that these point mutations decrease the binding affinity of IL10RA toward IL10 and may likely to disturb the IL10 signaling pathway. This study provides an easy frame work for phenotypic characterization of mutant IL10RA molecule in terms of structure, flexibility and stability aspects. Our approach may also add a new dimension to conventional functional biology assays in quickly studying IL10 RA mutations and also for designing and developing inhibitors for mutant IL10RA molecule.

In Silico Approach to Investigate the Structural and Functional Attributes of Familial Hypercholesterolemia Variants Reported in the Saudi Population.

Familial hypercholesterolemia (FH) is a metabolic disorder that leads primarily to premature cardiovascular diseases, the main cause of mortality in Saudi Arabia (SA). FH is underreported and underdiagnosed in SA with statistical evidence of high expected prevalence in such a consanguineous community. Lacking knowledge of which and how these alterations are actually impacting lipid metabolism is one of the main reasons why FH is insufficiently diagnosed in the region. The aim of this study was to develop a fast prediction approach using an integrated bioinformatics method for future screening of the potential causative variants from national registries. A total of 21 variants were detected with majority rate in LDLR (81%). Variants were classified based on the type of mutation. Missense variants resulting in amino acid changes, c.1429G>A (p.D477N), c.1474G>A (p.D492N), c.1731G>T (p.W577C), and c.1783C>T (p.R595W) in LDLR gene, in addition to c.9835A>G (p.S3279G) in APOB, were shown to be deleterious by concordant analysis. Furthermore, functional interaction deformities showed a significant loss and gain of energies in the mutated proteins. These findings will help in distinguishing the most harmful mutations needed to be screened for clinically diagnosed FH patients in SA. Such computational research is necessary to avoid time consumption and the usage of expensive biological experiments. This can be a fast track to facilitate the future filtering and screening of causative mutations from national registries.

Computational Analysis of Breast Cancer GWAS Loci Identifies the Putative Deleterious Effect of STXBP4 and ZNF404 Gene Variants.

The genome-wide association studies (GWAS) have enabled us in identifying different breast cancer (BC) susceptibility loci. However, majority of these are non-coding variants with no annotated biological function. We investigated such 78 noncoding genome wide associated SNPs of BC and further expanded the list to 2,162 variants with strong linkage-disequilibrium (LD, r2 ≥0.8). Using multiple publically available algorithms such as CADD, GWAVA, and FATHAMM, we classified all these variants into deleterious, damaging, or benign categories. Out of total 2,241 variants, 23 (1.02%) variants were extreme deleterious (rank 1), 70 (3.12%) variants were deleterious (rank 2), and 1,937 (86.43%) variants were benign (rank 3). The results show 14% of lead or associated variants are under strong negative selection (GERP++ RS ≥2), and ∼22% are under balancing selection (Tajima's D score >2) in CEU population of 1KGP-the regions being positively selected (GERP++ RS <0) in mammalian evolution. The expression quantitative trait loci of highest deleteriously ranked genes were tested on relevant adipose and breast tissues, the results of which were extended for protein expression on breast tissues. From the concordance analysis of ranking system of GWAVA, CADD, and FATHMM, eQTL and protein expression, we identified the deleterious SNPs localized in STXBP4 and ZNF404 genes which might play a role in BC development by dysregulating its gene expression. This simple approach will be easier to implement and to prioritize large scale GWAS data for variety of diseases and link to the potentially unrecognized functional roles of genes. J. Cell. Biochem. 118: 4296-4307, 2017. © 2017 Wiley Periodicals, Inc.

Down regulated expression of Claudin-1 and Claudin-5 and up regulation of ß-catenin: association with human glioma progression.

Glioblastoma multiforme is the most common form of intracranial malignancy in humans, and is characterized by aggressive tumor growth, tissue invasion and neurodegenerative properties. The present study investigated the expression status of tight junction associated Claudin 1 (CLDN1), Claudin 5 (CLDN5) and Adheren junction associated ß-catenin genes in the light of their critical role in the progression of both low- and high-grade human gliomas. Using quantitative PCR and Western blot methods the mRNA and protein status of CLDN1, CLDN5 and ß-catenin genes were studied in a total of 25 human gliomas of World Health Organization (WHO) grades I-IV, non-cancerous control brain tissues and their corresponding model cell lines (C6, U373, U118, T98 and U87MG). Quantitative analysis of the transcript and protein expression data showed that CLDN1 and CLDN5 were significantly down regulated (p=<0.001) in tumors of all four grades and model cell lines. This decrease in expression pattern was in accordance with the increasing grade of the tumor. A 4-fold stronger reduction of CLDN1 when compared to CLDN5 was evident in high-grade tumors. Interestingly, ß-catenin was up regulated in all tumor types we studied (p=<0.005). Our findings, suggest that down regulated CLDN1 and CLDN5 genes have potential relevance in relation to the progression of glioblastoma multiforme. Hence, their therapeutic targeting may provide both insight and leads to control the cellular proliferation and subsequent invasiveness among affected individuals.

Structural and functional characterization of pathogenic non- synonymous genetic mutations of human insulin-degrading enzyme by in silico methods.

Insulin-degrading enzyme (IDE) is a key protease involved in degrading insulin and amyloid peptides in human body. Several non-synonymous genetic mutations of IDE gene have been recently associated with susceptibility to both diabetes and Alzheimer's diseases. However, the consequence of these mutations on the structure of IDE protein and its substrate binding characteristics is not well elucidated. The computational investigation of genetic mutation consequences on structural level of protein is recently found to be an effective alternate to traditional in vivo and in vitro approaches. Hence, by using a combination of empirical rule and support vector machine based in silico algorithms, this study was able to identify that the pathogenic nonsynonymous genetic mutations corresponding to p.I54F, p.P122T, p.T533R, p.P581A and p.Y609A have more potential role in structural and functional deviations of IDE activity. Moreover, molecular modeling and secondary structure analysis have also confirmed their impact on the stability and secondary properties of IDE protein. The molecular docking analysis of IDE with combinational substrates has revealed that peptide inhibitors compared to small non-peptide inhibitor molecules possess good inhibitory activity towards mutant IDE. This finding may pave a way to design novel potential small peptide inhibitors for mutant IDE. Additionally by un-translated region (UTR) scanning analysis, two regulatory pathogenic genetic mutations i.e., rs5786997 (3' UTR) and rs4646954 (5' UTR), which can influence the translation pattern of IDE gene through sequence alteration of upstream-Open Reading Frame and Internal Ribosome Entry Site elements were identified. Our findings are expected to help in narrowing down the number of IDE genetic variants to be screened for disease association studies and also to select better competitive inhibitors for IDE related diseases.