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.

Computational Analysis of Deleterious nsSNPs in INS Gene Associated with Permanent Neonatal Diabetes Mellitus.

Insulin gene mutations affect the structure of insulin and are considered a leading cause of neonatal diabetes and permanent neonatal diabetes mellitus PNDM. These mutations can affect the production and secretion of insulin, resulting in inadequate insulin levels and subsequent hyperglycemia. Early discovery or prediction of PNDM can aid in better management and treatment. The current study identified potential deleterious non-synonymous single nucleotide polymorphisms nsSNPs in the INS gene. The analysis of the nsSNPs in the INS gene was conducted using bioinformatics tools by implementing computational algorithms including SIFT, PolyPhen2, SNAP2, SNPs & GO, PhD-SNP, MutPred2, I-Mutant, MuPro, and HOPE tools to investigate the prediction of the potential association between nsSNPs in the INS gene and PNDM. Three mutations, C96Y, P52R, and C96R, were shown to potentially reduce the stability and function of the INS protein. These mutants were subjected to MDSs for structural analysis. Results suggested that these three potential pathogenic mutations may affect the stability and functionality of the insulin protein encoded by the INS gene. Therefore, these changes may influence the development of PNDM. Further researches are required to fully understand the various effects of mutations in the INS gene on insulin synthesis and function. These data can aid in genetic testing for PNDM to evaluate its risk and create treatment and prevention strategies in personalized medicine.

Novel Therapeutic Agents for Management of Diabetes Mellitus: A Hope for Drug Designing against Diabetes Mellitus.

Diabetes mellitus (DM) is characterized by an absolute decline in insulin secretion and peripheral resistance and is the most prevalent metabolic and endocrine disorder. However, the pathogenesis of DM also includes adipocyte insulin resistance, increased glucagon secretion, increased renal glomerular glucose absorption, and neurotransmitter dysfunction. Although there is a wide spectrum of therapeutics available for glycemic control, owing to the identification of various pathogenic determinants of DM, management of DM remains challenging and complex. Current therapeutic interventions against DM focus mostly on glycemic control without considering the other pathological determinants that eventually lead to treatment failure and the progression of DM. Furthermore, long-term use of these conventionally available anti-diabetic drugs leads to various side effects, henceforth development of novel drugs against DM remains an unending search strategy for researchers. Various studies conducted in various parts of the world have proposed that these novel therapeutic interventions target multiple and alternate pathogenic hotspots involved in DM. The current review article discusses novel therapeutic options that hold particular promise to support their safety and discuss the side effects resulting from their use so that these novel candidate drugs can be effectively fabricated into potential drugs for the treatment of DM.

In Silico Evaluation of the Potential Association of the Pathogenic Mutations of Alpha Synuclein Protein with Induction of Synucleinopathies.

Alpha synuclein (α-Syn) is a neuronal protein encoded by the SNCA gene and is involved in the development of Parkinson's disease (PD). The objective of this study was to examine in silico the functional implications of non-synonymous single nucleotide polymorphisms (nsSNPs) in the SNCA gene. We used a range of computational algorithms such as sequence conservation, structural analysis, physicochemical properties, and machine learning. The sequence of the SNCA gene was analyzed, resulting in the mapping of 42,272 SNPs that are classified into different functional categories. A total of 177 nsSNPs were identified within the coding region; there were 20 variants that may influence the α-Syn protein structure and function. This identification was made by employing different analytical tools including SIFT, PolyPhen2, Mut-pred, SNAP2, PANTHER, PhD-SNP, SNP&Go, MUpro, Cosurf, I-Mut, and HOPE. Three mutations, V82A, K80E, and E46K, were selected for further examinations due to their spatial positioning within the α-Syn as determined by PyMol. Results indicated that these mutations may affect the stability and function of α-Syn. Then, a molecular dynamics simulation was conducted for the SNCA wildtype and the four mutant variants (p.A18G, p.V82A, p.K80E, and p.E46K). The simulation examined temperature, pressure, density, root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), and radius of gyration (Rg). The data indicate that the mutations p.V82A, p.K80E, and p.E46K reduce the stability and functionality of α-Syn. These findings highlight the importance of understanding the impact of nsSNPs on α-syn structure and function. Our results required verifications in further protein functional and case-control studies. After being verified these findings can be used in genetic testing for the early diagnosis of PD, the evaluation of the risk factors, and therapeutic approaches.

Clinical Characterization and Outcomes of Patients with Hypercreatinemia Affected by COVID-19.

The present study evaluated the clinical presentation and outcome of COVID-19 patients with underlying hypercreatinemia at the time of hospitalization. A retrospective observational study was conducted from the 23rd of March 2020 to the 15th of April 2021 in 1668 patients confirmed positive for COVID-19 in the Chest Disease Hospital in Srinagar, India. The results of the present study revealed that out of 1668 patients, 339 with hypercreatinemia had significantly higher rates of admission to the intensive care unit (ICU), severe manifestations of the disease, need for mechanical ventilation, and all-cause mortality. Multivariable analysis revealed that age, elevated creatinine concentrations, IL-1, D-Dimer, and Hs-Crp were independent risk factors for in-hospital mortality. After adjusted analysis, the association of creatinine levels remained strongly predictive of all-cause, in-hospital mortality (HR-5.34; CI-4.89-8.17; p ≤ 0.001). The amelioration of kidney function may be an effective method for achieving creatinemic targets and, henceforth, might be beneficial for improving outcomes in patients with COVID-19.