In Silico Analysis of Coding/Noncoding SNPs of Human RETN Gene and Characterization of Their Impact on Resistin Stability and Structure

Metabolic Blood Hormones in Young People With Electrocardiographic Signs of Ischemic Myocardial Changes

Aim    To study changes in blood concentrations of metabolic hormones and adipocytokines in people aged 25-44 years with electrocardiographic (ECG) signs of ischemic changes in the myocardium.Material and methods This study was a part of a cross-sectional survey of a random sample of Novosibirsk population aged 25-44 years. The study included 1363 people divided into two groups: group 1, subjects with ECG signs of ischemic changes in the myocardium and group 2, subjects without ECG changes. Blood serum concentrations of adipocytokines and metabolic hormones were measured by multiplex assay on a Luminex MAGPIX flow-through fluorometer.Results    The group with ECG signs of myocardial ischemia had higher blood concentrations of adiponectin, resistin, glucagon, and interleukin 6 (IL-6) than in the comparison group. A multivariate logistic regression analysis showed that the glucagon concentration was associated with the presence of ECG signs of myocardial ischemia (OR, 1.019; CI, 1.018-1.034; p=0.017).Conclusion    In young people aged 25-44 years, higher blood concentrations of glucagon are associated with the presence of ECG signs of myocardial ischemia.

In silico prediction of deleterious non-synonymous SNPs in STAT3

Background

STAT3, a pleiotropic transcription factor, plays a critical role in the pathogenesis of autoimmunity, cancer, and many aspects of the immune system, as well as having a link with inflammatory bowel disease. Changes caused by non-synonymous single nucleotide polymorphisms (nsSNPs) have the potential to damage the protein's structure and function.

Objective

We identified disease susceptible single nucleotide polymorphisms (SNPs) in STAT3 and predicted structural changes associated with mutants that disrupt normal protein-protein interactions using different computational algorithms.

Methods

Several in silico tools, such as SIFT, PolyPhen v2, PROVEAN, PhD-SNP, and SNPs&GO, were used to determine nsSNPs of the STAT3. Further, the potentially deleterious SNPs were evaluated using I-Mutant, ConSurf, and other computational tools like DynaMut for structural prediction.

Result

417 nsSNPs of STAT3 were identified, 6 of which are considered deleterious by in silico SNP prediction algorithms. Amino acid changes in V507F, R335W, E415K, K591M, F561Y, and Q32K were identified as the most deleterious nsSNPs based on the conservation profile, structural conformation, relative solvent accessibility, secondary structure prediction, and protein-protein interaction tools.

Conclusion

The in silico prediction analysis could be beneficial as a diagnostic tool for both genetic counseling and mutation confirmation. The 6 deleterious nsSNPs of STAT3 may serve as potential targets for different proteomic studies, large population-based studies, diagnoses, and therapeutic interventions.

Functional characterization of human genomic variation linked to polygenic diseases

The burden of human disease lies predominantly in polygenic diseases. Since the early 2000s, genome-wide association studies (GWAS) have identified genetic variants and loci associated with complex traits. These have ranged from variants in coding sequences to mutations in regulatory regions, such as promoters and enhancers, as well as mutations affecting mediators of mRNA stability and other downstream regulators, such as 5' and 3'-untranslated regions (UTRs), long noncoding RNA (lncRNA), and miRNA. Recent research advances in genetics have utilized a combination of computational techniques, high-throughput in vitro and in vivo screening modalities, and precise genome editing to impute the function of diverse classes of genetic variants identified through GWAS. In this review, we highlight the vastness of genomic variants associated with polygenic disease risk and address recent advances in how genetic tools can be used to functionally characterize them.

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.

Functionally Significant Variants in Genes Associated with Abdominal Obesity: A Review

The high prevalence of obesity and of its associated diseases is a major problem worldwide. Genetic predisposition and the influence of environmental factors contribute to the development of obesity. Changes in the structure and functional activity of genes encoding adipocytokines are involved in the predisposition to weight gain and obesity. In this review, variants in genes associated with adipocyte function are examined, as are variants in genes associated with metabolic aberrations and the accompanying disorders in visceral obesity.

A computational in silico approach to predict high-risk coding and non-coding SNPs of human PLCG1 gene

PLCG1 gene is responsible for many T-cell lymphoma subtypes, including peripheral T-cell lymphoma (PTCL), angioimmunoblastic T-cell lymphoma (AITL), cutaneous T-cell lymphoma (CTCL), adult T-cell leukemia/lymphoma along with other diseases. Missense mutations of this gene have already been found in patients of CTCL and AITL. The non-synonymous single nucleotide polymorphisms (nsSNPs) can alter the protein structure as well as its functions. In this study, probable deleterious and disease-related nsSNPs in PLCG1 were identified using SIFT, PROVEAN, PolyPhen-2, PhD-SNP, Pmut, and SNPS&GO tools. Further, their effect on protein stability was checked along with conservation and solvent accessibility analysis by I-mutant 2.0, MUpro, Consurf, and Netsurf 2.0 server. Some SNPs were finalized for structural analysis with PyMol and BIOVIA discovery studio visualizer. Out of the 16 nsSNPs which were found to be deleterious, ten nsSNPs had an effect on protein stability, and six mutations (L411P, R355C, G493D, R1158H, A401V and L455F) were predicted to be highly conserved. Among the six highly conserved mutations, four nsSNPs (R355C, A401V, L411P and L455F) were part of the catalytic domain. L411P, L455F and G493D made significant structural change in the protein structure. Two mutations-Y210C and R1158H had post-translational modification. In the 5' and 3' untranslated region, three SNPs, rs139043247, rs543804707, and rs62621919 showed possible miRNA target sites and DNA binding sites. This in silico analysis has provided a structured dataset of PLCG1 gene for further in vivo researches. With the limitation of computational study, it can still prove to be an asset for the identification and treatment of multiple diseases associated with the target gene.

Comprehensive in-silico analysis of damage associated SNPs in hOCT1 affecting Imatinib response in chronic myeloid leukemia

Non-synonymous single nucleotide polymorphisms (nsSNPs) in hOCT1 (encoded by SLC22A1 gene) are expected to affect Imatinib uptake in chronic myeloid leukemia (CML). In this study, sequence homology-based genetic analysis of a set of 270 coding SNPs identified 18 nsSNPs to be putatively damaging/deleterious using eight different algorithms. Subsequently, based on conservation of amino acid residues, stability analysis, posttranscriptional modifications, and solvent accessibility analysis, the possible structural-functional relationship was established for high-confidence nsSNPs. Furthermore, based on the modeling results, some dissimilarities of mutant type amino acids from wild-type amino acids such as size, charge, interaction and hydrophobicity were revealed. Three highly deleterious mutations consisting of P283L, G401S and R402G in SLC22A1 gene that may alter the protein structure, function and stability were identified. These results provide a filtered data to explore the effect of uncharacterized nsSNP and find their association with Imatinib resistance in CML.