In silico analysis of deleterious single nucleotide polymorphisms in human BUB1 mitotic checkpoint serine/threonine kinase B gene

Molecular Dynamics Simulation of Kir6.2 Variants Reveals Potential Association with Diabetes Mellitus

Diabetes mellitus (DM) represents a problem for the healthcare system worldwide. DM has very serious complications such as blindness, kidney failure, and cardiovascular disease. In addition to the very bad socioeconomic impacts, it influences patients and their families and communities. The global costs of DM and its complications are huge and expected to rise by the year 2030. DM is caused by genetic and environmental risk factors. Genetic testing will aid in early diagnosis and identification of susceptible individuals or populations using ATP-sensitive potassium (KATP) channels present in different tissues such as the pancreas, myocardium, myocytes, and nervous tissues. The channels respond to different concentrations of blood sugar, stimulation by hormones, or ischemic conditions. In pancreatic cells, they regulate the secretion of insulin and glucagon. Mutations in the KCNJ11 gene that encodes the Kir6.2 protein (a major constituent of KATP channels) were reported to be associated with Type 2 DM, neonatal diabetes mellitus (NDM), and maturity-onset diabetes of the young (MODY). Kir6.2 harbors binding sites for ATP and phosphatidylinositol 4,5-diphosphate (PIP2). The ATP inhibits the KATP channel, while the (PIP2) activates it. A Kir6.2 mutation at tyrosine330 (Y330) was demonstrated to reduce ATP inhibition and predisposes to NDM. In this study, we examined the effect of mutations on the Kir6.2 structure using bioinformatics tools and molecular dynamic simulations (SIFT, PolyPhen, SNAP2, PANTHER, PhD&SNP, SNP&Go, I-Mutant, MuPro, MutPred, ConSurf, HOPE, and GROMACS). Our results indicated that M199R, R201H, R206H, and Y330H mutations influence Kir6.2 structure and function and therefore may cause DM. We conclude that MD simulations are useful techniques to predict the effects of mutations on protein structure. In addition, the M199R, R201H, R206H, and Y330H variant in the Kir6.2 protein may be associated with DM. These results require further verification in protein-protein interactions, Kir6.2 function, and case-control studies.

Bioinformatics screening of biomarkers related to liver cancer

Background

Liver cancer is a common malignant tumor in China, with high mortality. Its occurrence and development were thoroughly studied by high-throughput expression microarray, which produced abundant data on gene expression, mRNA quantification and the clinical data of liver cancer. However, the hub genes, which can be served as biomarkers for diagnosis and treatment of early liver cancer, are not well screened.

Results

Here we present a new method for getting 6 key genes, aiming to diagnose and treat the early liver cancer. We firstly analyzed the different expression microarrays based on TCGA database, and a total of 1564 differentially expressed genes were obtained, of which 1400 were up-regulated and 164 were down-regulated. Furthermore, these differentially expressed genes were studied by using GO and KEGG enrichment analysis, a PPI network was constructed based on the STRING database, and 15 hub genes were obtained. Finally, 15 hub genes were verified by applying the survival analysis method on Oncomine database, and 6 key genes were ultimately identified, including PLK1, CDC20, CCNB2, BUB1, MAD2L1 and CCNA2. The robustness analysis of four independent data sets verifies the accuracy of the key gene’s classification of the data set.

Conclusions

Although there are complicated differences between cancer and normal cells in gene functions, cancer cells could be differentiated in case that a group of special genes expresses abnormally. Here we presented a new method to identify the 6 key genes for diagnosis and treatment of early liver cancer, and these key genes can help us understand the pathogenesis of liver cancer more deeply.

Identification of a novel HEXB Mutation in an Iranian Family with suspected patient to GM2-gangliosidoses

Sandhoff disease is one of the GM2-gangliosidoses which is caused by a mutation in the HEXB preventing the breakdown of GM2-ganglioside. We report a novel HEXB variant in a family with a history of a dead girl with Sandhoff disease which was not found in controls.

Predicting the most deleterious missense nsSNPs of the protein isoforms of the human HLA-G gene and in silico evaluation of their structural and functional consequences

BACKGROUND

The Human Leukocyte Antigen G (HLA-G) protein is an immune tolerogenic molecule with 7 isoforms. The change of expression level and some polymorphisms of the HLA-G gene are involved in various pathologies. Therefore, this study aimed to predict the most deleterious missense non-synonymous single nucleotide polymorphisms (nsSNPs) in HLA-G isoforms via in silico analyses and to examine structural and functional effects of the predicted nsSNPs on HLA-G isoforms.

RESULTS

Out of 301 reported SNPs in dbSNP, 35 missense SNPs in isoform 1, 35 missense SNPs in isoform 5, 8 missense SNPs in all membrane-bound HLA-G isoforms and 8 missense SNPs in all soluble HLA-G isoforms were predicted as deleterious by all eight servers (SIFT, PROVEAN, PolyPhen-2, I-Mutant 3.0, SNPs&GO, PhD-SNP, SNAP2, and MUpro). The Structural and functional effects of the predicted nsSNPs on HLA-G isoforms were determined by MutPred2 and HOPE servers, respectively. Consurf analyses showed that the majority of the predicted nsSNPs occur in conserved sites. I-TASSER and Chimera were used for modeling of the predicted nsSNPs. rs182801644 and rs771111444 were related to creating functional patterns in 5'UTR. 5 SNPs in 3'UTR of the HLA-G gene were predicted to affect the miRNA target sites. Kaplan-Meier analysis showed the HLA-G deregulation can serve as a prognostic marker for some cancers.

CONCLUSIONS

The implementation of in silico SNP prioritization methods provides a great framework for the recognition of functional SNPs. The results obtained from the current study would be called laboratory investigations.

Computational Analysis of High-Risk SNPs in Human DBY Gene Responsible for Male Infertility: A Functional and Structural Impact

BACKGROUND

DEAD-box helicase 3, Y-linked (DBY) is a candidate gene of the AZF region which is involved in spermatogenesis process. Mutations in the DBY gene may disrupt the spermatogenesis and lead to infertility in men. Identification of functionally neutral mutation from the disease-causing mutation is the biggest challenge in human genetic variation analysis. Owing to the importance of DBY in male infertility, functional analysis was carried out to reveal the association between genetic mutation and phenotypic variation through various in silico approaches.

METHODS

The present study analyzed the functional consequences of the nsSNPs in human DBY gene using SIFT, PolyPhen 2, PROVEAN, SNAP2, PMut, nsSNPAnalyzer, PhD-SNP and SNPs&GO along with stability analysis through I-Mutant2.0, MuPro and iPTREE-STAB. The conservational analysis of amino acid residues, biophysical properties and conserved domains of the DBY protein was analyzed using various computational tools. The 3D structure of the protein was generated using SPARKS-X and validated using RAMPAGE.

RESULTS

Out of 1130 SNPs reported in dbSNP, only one nsSNP (G300D) was found to have a functional effect on stability as well as the function of the DBY protein. The results showed the presence of G300 in the putative structure of DBY domain.

CONCLUSION

To the best of our knowledge, this is the first study to detect pathologically significant nsSNPs (G300D) through a computational approach in the DBY which can be useful for development in potent drug discovery studies.

Phenotype prediction for mucopolysaccharidosis type I by in silico analysis

BACKGROUND

Mucopolysaccharidosis type I (MPS I) is an autosomal recessive disease due to deficiency of α-L-iduronidase (IDUA), a lysosomal enzyme that degrades glycosaminoglycans (GAG) heparan and dermatan sulfate. To achieve optimal clinical outcomes, early and proper treatment is essential, which requires early diagnosis and phenotype severity prediction.

RESULTS

To establish a genotype/phenotype correlation of MPS I disease, a combination of bioinformatics tools including SIFT, PolyPhen, I-Mutant, PROVEAN, PANTHER, SNPs&GO and PHD-SNP are utilized. Through analyzing single nucleotide polymorphisms (SNPs) by these in silico approaches, 28 out of 285 missense SNPs were predicted to be damaging. By integrating outcomes from these in silico approaches, a prediction algorithm (sensitivity 94%, specificity 80%) was thereby developed. Three dimensional structural analysis of 5 candidate SNPs (P533R, P496R, L346R, D349G, T374P) were performed by SWISS PDB viewer, which revealed specific structural changes responsible for the functional impacts of these SNPs. Additionally, SNPs in the untranslated region were analyzed by UTRscan and PolymiRTS. Moreover, by investigating known pathogenic mutations and relevant patient phenotypes in previous publications, phenotype severity (severe, intermediate or mild) of each mutation was deduced.

CONCLUSIONS

Collectively, these results identified potential candidate SNPs with functional significance for studying MPS I disease. This study also demonstrates the effectiveness, reliability and simplicity of these in silico approaches in addressing complexity of underlying genetic basis of MPS I disease. Further, a step-by-step guideline for phenotype prediction of MPS I disease is established, which can be broadly applied in other lysosomal diseases or genetic disorders.

Computational Analysis of High Risk Missense Variant in Human UTY Gene: A Candidate Gene of AZFa Sub-region

BACKGROUND

The human Ubiquitously transcribed tetratricopeptide repeat gene, Y-linked (UTY) gene encodes histone demethylase involved in protein-protein interactions. UTY protein evidence at protein level predicted intracellular and secreted protein. UTY is also involved in spermatogenesis process.

METHODS

The high-risk non-synonymous single nucleotide polymorphism in the coding region of the UTY gene was screened by SNP database and identified missense variants were subjected to computational analysis to understand the effect on protein function, stability and structure by SIFT, PolyPhen 2, PANTHER, PROVEAN, I-Mutant 2, iPTREE-STAB, ConSurf, ModPred, SPARKS-X, QMEAN, PROCHECK, project HOPE and STRING.

RESULTS

A total of 151 nsSNPs variants were retrieved in UTY gene out of which one missense variant (E18D) was predicted to be damaging or deleterious using SIFT, PolyPhen 2, PANTHER and PROVEAN. Additionally, E18D variant showed less stability, high conservation and having role in post translation modification using i-Mutant 2 and iPTREE-STAB, ConSurf and ModPred, respectively. The predicted 3D model of UTY using SPARKS-X with z-score of 15.16 was generated and validated via QMEAN (Z-score of 0.472) and PROCHECK which plots Ramachandran plot (85.3% residues in most favored regions, 12.3% in additionally allowed regions, 2.0% in generously allowed regions and 4.0% were in disallowed regions) and it indicates a good quality model. STRING showed that UTY interacts with ten different proteins.

CONCLUSION

This study revealed that SNP data available on database was deduced to find out the most damaging nsSNPs i.e. rs3212293 (E18D). Therefore, it provides useful information about functional SNPs for future prospects concerning infertility in men.