Identification and in silico Characterization of Deleterious Single Nucleotide Variations in Human ZP2 Gene

ZP2, an important component of the zona matrix, surrounds mammalian oocytes and facilitates fertilization. Recently, some studies have documented the association of mutations in genes encoding the zona matrix with the infertile status of human females. Single nucleotide polymorphisms are the most common type of genetic variations observed in a population and as per the dbSNP database, around 5,152 SNPs are reported to exist in the human ZP2 (hZP2) gene. Although a wide range of computational tools are publicly available, yet no computational studies have been done to date to identify and analyze structural and functional effects of deleterious SNPs on hZP2. In this study, we conducted a comprehensive in silico analysis of all the SNPs found in hZP2. Six different computational tools including SIFT and PolyPhen-2 predicted 18 common nsSNPs as deleterious of which 12 were predicted to most likely affect the structure/functional properties. These were either present in the N-term region crucial for sperm-zona interaction or in the zona domain. 31 additional SNPs in both coding and non-coding regions were also identified. Interestingly, some of these SNPs have been found to be present in infertile females in some recent studies.

Evaluation of in silico tools for variant classification in clinically actionable NSCLC variants.

e13545 Background: Genetic variants beyond FDA-approved drug targets are often identified in NSCLC patients. To address this challenge, in silico variant classification tools are available to determine whether specific variants contribute to disease pathogenicity or remain benign. Although the performance of in silico tools has been analyzed in previous studies, it has not been analyzed for actionable targets of FDA-approved therapies for NSCLC. The aim of this study is to compare the performance of commonly used in silico tools in classifying the pathogenicity of actionable variants in NSCLC. Methods: We evaluated the performance of several in silico tools: PolyPhen-2, Align-GVGD, and MutationTaster2. A curated set of targetable NSCLC missense variants (n = 179) was used. The dataset consisted of variants in the BRAF, EGFR, ERBB2, KRAS, MET, ALK, and ROS1 genes based on their indications as molecular targets in the NCCN Guidelines for NSCLC. Pathogenic variants (n = 80) were curated based on available literature and annotations according to the NCCN Guidelines, OncoKB, My Cancer Genome, and AACR Project GENIE. Benign variants (n = 99) were curated from the dbSNP database with the inclusion criteria of a benign or likely benign ClinVar assertion. The overall accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) of each in silico tool were determined. The performance of each in silico tool in predicting pathogenicity for subsets of sensitizing (n = 18) and resistant (n = 57) variants was also evaluated. Results: PolyPhen-2 HumVar demonstrated the highest overall accuracy (0.80), specificity (0.69), and MCC (0.63) of the in silico tools analyzed. PolyPhen-2 HumDiv (0.75) and MutationTaster2 (0.69) had similar overall accuracies while Align-GVGD (0.50) had the lowest overall accuracy. Align-GVGD also had the lowest MCC (0.08), with the other in silico tools ranging from 0.50 to 0.63. All the in silico tools achieved high sensitivities, with MutationTaster2 performing the highest (1.00) and Align-GVGD performing the lowest (0.86). The specificities were remarkably low (0.20-0.69) for all the in silico tools, with the lowest specificity demonstrated by Align-GVGD. The overall accuracies when classifying the subsets of sensitizing and resistant variants were generally high, ranging from 0.84 to 1.00. Conclusions: These results suggest that the performance of the evaluated in silico tools to predict the pathogenicity of clinically actionable NSCLC missense variants is not fully reliable. The tools analyzed in this study could be acceptable to rule out pathogenicity in variants given their higher sensitivities, but are limited when it comes to identifying pathogenicity in variants due to low specificities.

In-silico predictions of deleterious SNPs in Human ephrin type-A receptor 3 (EPHA3) gene

Ephrin type-A receptor 3 (EPHA3) is a receptor tyrosine kinase encoded by the EPHA3 gene, involves in many biological functions, such as cell migration, adhesion, etc. Overexpression or inactivation of EPHA3 receptors can be complicit in various oncogenic events. Therefore, SNP analysis is required to screen out the deleterious SNPs, such as non-synonymous SNPs (nsSNPs), and in-silico analysis is an effective way to do this screening process. Using eight bioinformatics tools, 81 nsSNPs were found as potential harmful nsSNPs by in-silico analysis among 631 nsSNPs that were retrieved from the dbSNP database. 77 out of 81 substitutions were found as highly conserved by ConSurf analysis. G628E, I652M nsSNPs were found to be present in the ATP-binding site. N751D was found in one of the catalytic residues and P824S in the αF-αG loop in the substrate-binding region. Y596H, Y779C & D598G nsSNPs were found to be associated with activation and regulatory mechanism. R66S, R160H, R104Q nsSNPs were present in the EPH-ligand binding domain that has a higher probability of interfering with ligand-receptor interactions. 21 SNPs associated with the alteration of miRNA target site at 3’ UTR region, which may be responsible for affecting post-transcriptional regulation of EPHA3 gene. These findings will provide necessary data to explore various diseases or cancers pertinent to the EPHA3 receptor protein, will provide remedial biomarkers, may help in molecular diagnosis, and help to design target-specific therapeutic agents.

Structure analysis of deleterious nsSNPs in human PALB2 protein for functional inference

Partner and Localizer of BRCA2 or PALB2 is a typical tumor suppressor protein, that responds to DNA double stranded breaks through homologous recombination repair. Heterozygous mutations in PALB2 are known to contribute to the susceptibility of breast and ovarian cancer. However, there is no comprehensive study characterizing the structural and functional impacts of SNPs located in the PALB2 gene.Therefore, it is of interest to document a comprehensive analysis of coding and non-coding SNPs located at the PALB2 loci using in silico tools. The data for 1455 non-synonymous SNPs (nsSNPs) located in the PALB2 loci were retrieved from the dbSNP database. Comprehensive characterization of the SNPs using a combination of in silico tools such as SIFT, PROVEAN, PolyPhen, PANTHER, PhDSNP, Pmut, MutPred 2.0 and SNAP-2, identified 28 functionally important SNPs. Among these, 16 nsSNPs were further selected for structural analysis using conservation profile and protein stability. The most deleterious nsSNPs were documented within the WD40 domain of PALB2. A general outline of the structural consequences of each variant was developed using the HOPE project data. These 16 mutant structures were further modelled using SWISS Model and three most damaging mutant models (rs78179744, rs180177123 and rs45525135) were identified. The non-coding SNPs in the 3’ UTR region of the PALB2 gene were analyzed for altered miRNA target sites. The comprehensive characterization of the coding and non-coding SNPs in the PALB2 locus has provided a list of damaging SNPs with potential disease association. Further validation through genetic association study will reveal their clinical significance.

Validation of Susceptibility Loci for Vitiligo Identified by GWAS in the Chinese Han Population

Forty-nine susceptible loci have been reported to be significantly associated with vitiligo by genome-wide association studies (GWASs) in European-derived whites. To date, some of these reported susceptibility loci have not yet been validated in the Chinese Han population. The purpose of this study was to examine whether the 16 reported susceptible loci in European-derived whites were associated with vitiligo in the Chinese Han population. Imputation was performed using our previous GWAS dataset by IMPUTE v2.2.2. The 16 imputed top single-nucleotide polymorphisms (SNPs) with suggestive signals, together with the reported SNPs, were genotyped in a total of 2581 patients and 2579 controls by the Sequenom MassARRAY system. PLINK 2.0 software was used to perform association analysis. The dbSNP database, HaploReg, and eQTL data were adopted to annotate the biological function of the SNPs. Finally, four SNPs from three loci were significantly associated with vitiligo, including rs3747517 (P = 1.29 × 10–3, OR = 0.87) in 2q24.2, rs4807000 (P = 7.78 × 10–24, OR = 0.66) and rs6510827 (P = 3.65 × 10–5, OR = 1.19) in 19p13.3, and rs4822024 (P = 6.37 × 10–10, OR = 0.67) in 22q13.2. According to the dbSNP database, rs3747517 is a missense variant of IFIH1, rs4807000 and rs6510827 are located in TICAM1, and rs4822024 is located 6 kb upstream of TEF. Further bioinformatics analysis by HaploReg and eQTL found that rs4807000, rs6510827, and rs4822024 are involved in regulating gene expression. Our study revealed the strong association of 2q24.2 (rs3747517), 19p13.3 (rs4807000, rs6510827), and 22q13.2 (rs4822024) with the risk of vitiligo in the Chinese Han population, which implicates common factors for vitiligo across different ethnicities, and helps expand the understanding of the genetic basis of this disease.

Comment: “Evaluation of the Association of Omentin 1 rs2274907 A>T and rs2274908 G>A Gene Polymorphisms with Coronary Artery Disease in Indian Population: A Case Control Study”

The study by Jha et al. (2019) demonstrated an association of the single nucleotide polymorphism (SNP) rs2274907 A>T with coronary artery disease (CAD) in 100 CAD patients and 100 matched healthy controls from a South Indian population. There are serious concerns with regard to the interpretations of the study findings. The genotypes of the SNP are not in Hardy–Weinberg equilibrium (HWE) in both cases (p < 0.0001) and controls (p = 0.006), which is indicative of a technical error due to a problematic genotyping method. In addition, the genotype and allele frequencies reported in the study do not match with the frequencies listed in the SNP database for Asian Indians. While the study by Jha et al. reported ”T” allele as the minor allele, the dbSNP database reported ”A” as the minor allele. In summary, it can be concluded that the data presented in the study suffer from genotyping as well as data interpretation error and, hence, the findings should be considered by the reader with caution.

In silico analysis of SNPs in human phosphofructokinase, Muscle (PFKM) gene: An apparent therapeutic target of aerobic glycolysis and cancer

ABSTRACTPhosphofructokinase, muscle (PFKM), a key glycolytic regulatory enzyme is a potential target for cancer therapeutic studies accredited to the employed inefficient phenomenon known as Warburg effect. PFKM is encoded by PFKM gene located at chromosome 12q13.11. Single nucleotide polymorphisms (SNPs) are known to profoundly affect gene expression and protein function. Therefore, the first attempt was made to computationally identify putative functional PFKM variants. These SNPs were further explored to find their probable association with different cancer types. A total of 9694 SNPs were retrieved from dbSNP database. Of which, only 85 validated SNPs with ≥10% minor allele frequency (MAF) were subjected to analysis by softwares including Ensembl Genome browser, FuncPred (SNPinfo), regulomeDB (v 2.0), SIFT and PolyPhen-2. The relative analysis of output obtained classified the selected-SNPs into 11 highly prioritized (HP), 20 moderately prioritized and 54 not/poorly prioritized SNPs. The 11 HP-SNPs were found to have the highest likelihood of being functionally important, evidenced by previous association of rs2269935, rs11168417, rs11609399 and rs2228500 HP-SNPs with cachexia, lung and breast cancer. The study warrants further experiments to confirm the predictive role of prioritized SNPs in cancer etiology and also provides directions to fellow researchers.

Identification of Novel Key Biomarkers in Simpson-Golabi-Behmel Syndrome (SGBS)

The Simpson-Golabi-Behmel Syndrome (SGBS) or overgrowth Syndrome is an uncommon genetic X-linked disorder highlighted by macrosomia, renal defects, cardiac weaknesses and skeletal abnormalities. The purpose of the work was to classify the functional nsSNPs of GPC3 to serve as genetic biomarkers for overgrowth syndrome. The raw data of GPC3 gene were retrieved from dbSNP database and used to examine the most damaging effect using eight functional analysis tools, while we used I-mutant and MUPro to examine the effect of SNPs on GPC3 protein structure; The 3D structure of GPC3 protein is not found in the PDB, so RaptorX was used to create a 3D structural prototype to visualize the amino acids alterations by UCSF Chimera; For biophysical validation we used project HOPE; Lastly we run conservational analysis by BioEdit and Consurf web server respectively. Our results revealed three novel missense mutations (rs1460413167, rs1295603457 and rs757475450) that are that are more likely to be responsible for disturbance in the function and structure of GPC3. This work provides new insight into the molecular basis of overgrowth Syndrome by evidence from bioinformatics analysis. Three novel missense mutations (rs757475450, rs1295603457 and rs1460413167) are more likely to be responsible for disturbance in the function and structure of GPC3; therefore, they may be assisting as genetic biomarkers for overgrowth syndrome. As well as these SNPs can be used for the larger population-based studies of overgrowth syndrome.

In Silico Genetics Revealing Novel Mutations inCEBPAGene Associated with Acute Myeloid Leukemia

BackgroundMyelodysplastic syndrome/Acute myeloid leukemia (MDS/AML) is a highly heterogeneous malignant disease; affects children and adults of all ages. AML is one of the main causes of death in children with cancer. However, It is the most common acute leukemia in adults, with a frequency of over 20 000 cases per year in the United States of America alone.MethodsThe SNPs were retrieved from the dbSNP database. this SNPs were submitted into various functional analysis tools that done by SIFT, PolyPhen-2, PROVEAN, SNAP2, SNPs&GO, PhD-SNP and PANTHER, while structural analysis were done by I-mutant3 and MUPro. The most damaging SNPs were selected for further analysis by Mutation3D, Project hope, ConSurf and BioEdit softwares.ResultsA total of five novel nsSNPs out of 248 missense mutations were predicted to be responsible for the structural and functional variations of CEBPA protein.ConclusionIn this study the impact of functional SNPs in the CEBPA gene was investigated through different computational methods, which determined that (R339W, R288P, N292S N292T and D63N) are novel SNPs have a potential functional effect and can thus be used as diagnostic markers and may facilitate in genetic studies with a special consideration of the large heterogeneity of AML among the different populations.

In Silico Analysis of Non Synonymous Single Nucleotide Polymorphisms (nsSNPs) of SMPX Gene in Hearing Impairment

ABSTRACTBackgroundMutations in SMPX gene can disrupt the normal activity of the SMPX protein which is involved in hearing process.ObjectiveIn this study, deleterious non-synonymous single nucleotide polymorphisms were isolated from the neutral variants by using several bioinformatics tools.MethodFirstly, dbSNP database hosted by NCBI was used to retrieve the SNPs of SMPX gene, secondly, SIFT was used primarily to screen the damaging SNPs. Further, for validation PROVEAN, PredictSNP and PolyPhen 2 were used. I-Mutant 3 was utilized to analyze the protein stability change and MutPred predicted the molecular mechanism of protein stability change. Finally evolutionary conservation was done to study their conservancy by using ConSurf server.ResultsA total of 26 missense (0.6517%) and 3 nonsense variants (0.075%) were retrieved and among them 4 mutations were found deleterious by all the tools of this experiment and are also highly conserved according to ConSurf server. rs772775896, rs759552778, rs200892029 and rs1016314772 are the reference IDs of deleterious mutations where the substitutions are S71L, N19D, A29T and K54N. Loss of Ubiquitination, loss of methylation, loss of glycosylation, and loss of MoRF binding motifs are the root causes of protein stability change.ConclusionThis is the first study regarding nsSNPs of SMPX gene where the most damaging SNPs were screened that are associated with the SMPX gene and can be used for further research to study their effect on protein structure and function, their dynamic behavior and how they actually affect protein’s flexibility.