Comprehensive Computational Analysis Revealed Seven Novel Mutations in Human Insulin gene (original) (raw)
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Diabetes, 1991
We used the recently described technique of singlestranded conformation-polymorphism (SSCP) analysis to examine the insulin-receptor locus. First, the ability of the method to detect known mutations and polymorphisms in the insulin-receptor coding sequence was assessed. Regions of the insulinreceptor sequence containing 16 different nucleotide changes, 9 in patient genomic DNA and 7 as cloned cDNA in plasmids, were analyzed. All 9 patient genomic DNA mutants and 5 of 7 plasmid mutants exhibited variant SSCP patterns. To investigate the potential of the technique for screening many patients, the 5 exons that encode the tyrosine kinase domain of the insulin receptor were examined in 30 unrelated white subjects with non-insulin-dependent diabetes mellitus (NIDDM). Exons 17-21 were amplified from genomic DNA with polymerase chain reaction and subjected to SSCP analysis. Exons 19, 20, and 21 revealed no bands of aberrant migration, suggesting a high degree of conservation of these sequences. One diabetic subject had an SSCP variant in exon 18. Direct sequencing of this subject's genomic DNA revealed a heterozygous missense mutation (Lys 1068 -> Glu 1068 ). Five different SSCP patterns were detected in exon 17. Based on direct sequencing, these patterns were explained by combinations of three different nucleotide substitutions, two of which were common silent polymorphisms. One subject had a heterozygous missense mutation Val 985 -• Met 985 . Allele-specific oligonucleotide hybridization confirmed the presence of these mutations in the appropriate diabetic subjects and also detected the Val 985 mutation in heterozygous form in 1 of 13 nondiabetic white subjects. SSCP analysis is a sensitive rapid method for screening for mutations in the insulin-receptor gene. Using SSCP,
Diabetes mellitus seems to be a complex metabolic disorder due to its association with several complications like cardiovascular, ocular, neurologic, skeletal, hepatic and renal abnormalities etc. Current estimations by WHO suggest that most of the low and middle-income countries of the world are worst affected by this disorder with a prediction that the prevalence may double between 2020 and 2030. Thus both doctors and scientists across the globe are involved in research to disclose the complex genetics of this disorder associated with several environmental and demographic factors. In the last 10 years, several predictions have been made in the lane of omics approaches and computational biology which makes the process quite generous. In the current work, we present a computational analysis of potential candidate genes for diabetes mellitus and their differential expressions in targeted human tissue systems. About 220 reported genes for diabetes mellitus were selected for the study ...
Journal of biomolecular structure & dynamics, 2016
Several naturally occuring mutations in the human insulin gene are associated with diabetes mellitus. The three known mutant molecules, Wakayama, Los Angeles and Chicago were evaluated using molecular docking and molecular dynamics(MD) to analyse mechanisms of deprived binding affinity for insulin receptor (IR). Insulin Wakayama, is a variant in which valine at position A3 is substituted by leucine, while in insulin Los Angeles and Chicago, phenylalanine at position B24 and B25 are replaced by serine and leucine respectively. These mutations show radical changes in binding affinity for insulin receptor. The ZDOCK server was used for molecular docking while AMBER 14 was used for the molecular dynamics study. The published crystal structure of insulin receptor bound to natural insulin was also used for molecular dynamics. The binding interactions and molecular dynamics trajectories clearly explained the critical factors for deprived binding to the insulin receptor. The surface area ar...
2016
Genomic insulin is located on the short arm of chromosome 11 in human genome. It is a well studied polypeptide hormone, consists of 110 amino acids which start with signaling peptide of 1-24 amino acids, B-chain of 25-54 amino acids, C-peptide of 55-89 amino acids and end with A-chain of 90-110 amino acids. Insulin, produced by the beta cell of the pancreas in response to glucose stimuli, binds to its receptor rapidly due to receptor autophosphorylation and primordially regulates nutritional metabolic pathways. In this study we have depicted and explored evolutionary conservation rate, insight into structure and phylogenetic connection of insulin molecule among eight mammalian species; Homo sapiens (Human), Bos taurus (Cattle), Cavia porcellus (Guinea pig), Canis lupus familiaris (Dog), Gorilla gorilla (Western gorilla), Ovis aries (Sheep), Pan troglodytes (Chimpanzee), Pongo pygmaeus (Orangutan) using Computational Biology. The analysis of physico-chemical characteristics, secondar...
research article, 2020
Background: The Peroxisome proliferator-activated receptor gamma gene (PPARG), encodes a member of the peroxisome-activated receptor subfamily of nuclear receptors. PPARs form heterodimers with retinoid X receptors (RXRs) which regulate transcription of various genes. Three subtypes of PPARs are known: PPAR-alpha, PPAR-delta and PPAR-gamma. The protein encoded by this gene is PPAR-gamma which is a regulator of adipocyte differentiation. PPARG-gamma has been implicated in the pathology of numerous diseases including obesity, diabetes, atherosclerosis and cancer. Aim: This study aimed to perform insilico analysis to predict the effects that can be imposed by SNPs reported in PPARG gene. Methodology: This gene was investigated in NCBI database (http://www.ncbi.nlm.nih.gov/) during the year 2016 and the SNPs in coding region (exonal SNPs) that are non-synonymous (ns SNPs) were analyzed by computational softwares. SIFT, Polyphen, I-Mutant and PHD-SNP softwares). SIFT was used to filter the deleterious SNPs, Polyphen was used to determine the degree of pathogenicity, I-Mutant was used to determine the effect of mutation on protein stability while PHD-SNP software was used to investigate the effect of mutation on protein function. Furthermore, Structural and functional analysis of ns SNPs was also studied using Project HOPE software and modeling was conducted by Chimera. Results: A total of 34,035 SNPs from NCBI, were found, 21,235 of them were found in Homo sapiens, 134 in coding non synonymous (missense) and 89 were synonymous. Only SNPs present in coding regions were selected for analysis. Out of 12 deleterious SNPs sorted by SIFT, 10 were predicted by Polyphen to be probably damaging with PISC score = 1 and only two were benign. All these 10 double positive SNPs were disease related as predicted by PHD-SNPs and revealed decreased stability indicated by I-Mutant. Conclusion: Based on the findings of this study, it can be concluded that the deleterious ns SNPs (rs72551364 and rs121909244SNPs) of PPARG are important candidates for the cause of different types of human diseases including diabetes mellitus.
Type-2 Diabetes Mellitus and Glucagon-Like Peptide-1 Receptor toward Predicting Possible Association
Computational Molecular Bioscience
Aim: This study aimed to investigate the effect of non-synonymous SNPs (nsSNPs) of the Glucagon-like peptide-1 Receptor (GLP-1R) gene in protein function and structure using different computational software. Introduction: The GLP1R gene provides the necessary instruction for the synthesis of the insulin hormones which is needed for glucose catabolism. Polymorphisms in this gene are associated with diabetes. The protein is an important drug target for the treatment of type-2 diabetes and stroke. Material and Methods: Different nsSNPs and protein-related sequences were obtained from NCBI and ExPASY database. Gene associations and interactions were predicted using GeneMANIA software. Deleterious and damaging effects of nsSNPs were analyzed using SIFT, Provean, and Polyphen-2. The association of the nsSNPs with the disease was predicted using SNPs & GO software. Protein stability was investigated using I-Mutant and MUpro software. The structural and functional impact of point mutations was predicted using Project Hope software. Project Hope analyzes the mutations according to their size, charge, hydrophobicity, and conservancy. Results: The GLP1R gene was found to have an association with 20 other different genes. Among the most important ones is the GCG (glucagon) gene which is also a trans membrane protein. Overall 7229 variants were seen, and the missense variants or nsSNPs (146) were selected for further analysis. The total number of nsSNPs obtained in this study was 146. After being subjected to SIFT software (27 Deleterious and 119 Tolerated) were predicted. Analysis with Provean showed that (20 deleterious and 7 neutral). Analysis using Polyphen-2 revealed 17 probably damaging, 2 possibly damaging and 1 benign nsSNPs. Using two additional software SNPs & GO and PHD-SNPs showed that 14 and 17 nsSNPs had a
Insulin gene structure and function: a review of studies using recombinant DNA methodology
Diabetes Care, 1984
This review focuses on recent advances in molecular biology as they pertain to the insulin gene and diabetes mellitus. The structure of the human insulin gene is examined, and factors related to its normal functioning in the beta cells of the pancreas are explored. DNA polymorphisms near the insulin locus and their relationship with certain types of diabetes are considered, as are recently characterized human insulin gene mutations. Events in animal models for diabetes that reflect altered insulin gene expression are discussed and the potential application of gene therapy in human diabetes is examined. Recombinant DNA methodology holds great promise as a tool for providing better understanding of the causes of diabetes and potential curative treatment, DIABETES CARE 1984; 7:386-94.
Bioinformatics Analysis of Genes Associted with Type 2 Diabetes Mellitus
International Journal of Research -GRANTHAALAYAH
Type 2 Diabetes mellitus is a multi-factorial disease caused due to gene defect as well as environmental factor. GWAS have played a primary role in demonstrating that genetic variation in a number of loci, SNPs, affects the risk of T2DM. there are our objective is to find out Disease pathway map by taking all genes of T2DM which are 35 in numbers and but in all there are 10 mostly involve in T2Dm from all over world population and it is find out by GWAS method then after we analyzed the KEGG pathway by analyzing T2DM pathway, Insulin signaling pathway, and WNT signalling pathway to find out common protein then after by bioinformatics analysis combined and expend these pathways toward common protein for understanding the Diseases mechanism. We do Protein-protein interaction and find out their complete target hub protein and target prediction for network hub. so for all these analysis I collect the total genes involve in T2DM and take those gene which are common for all popula...
Mathematical Analysis of Diabetes Related Proteins Having High Sequence Complexity
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06), 2006
Background: The objective of this study is to examine the relationship between the protein variates and to infer the variation across the 3 species namely man (Homo sapiens), house mouse (Mus Musculus) and Norway rat (Rattus Norvegicus). Results For this purpose, a dataset of size 639 proteins has been taken representing 213 type 2 diabetes related proteins each belonging to Man, Mouse and Rat. Principal Component Analysis technique is used to reduce the dimensionality of the variables. The results show that the protein variates variation in man differs from those of two species. Conclusion Principal Component analysis of type 2 diabetes related genes showed that those of house mouse and Norway rat were closer to each other than that of human being.