In silico explanation for the causalities of deleterious RNF213 SNPs in Moyamoya disease and insulin resistance. (June 2021)

• Record Type: Journal Article
• Authors: Sarkar, Priyanka; Thirumurugan, Kavitha
• Is Part Of: Computational biology and chemistry. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: Cerebrovascular disease moyamoya -- RNF213 -- SNP analysis -- Obesity -- Ligand binding analysis -- Gene expression omnibus-microarray data -- Post-translational modification -- Insulin resistance; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2021.107488
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: In silico analysis of the basic characteristics of RNF213 as a protein. A detailed analysis of clinically reported and predicted SNPs of RNF213. Analysis of the effect of deleterious SNPs on the stability of RNF213 protein. R4810K might increase iron binding by RNF213 causing the pathological phenotype of MMD. RNF213 might be playing a major role in insulin resistance, obesity and cancer. Abstract: Moyamoya disease (MMD), a cerebrovascular disorder caused by the RNF213 gene, is a cerebrovascular, neurological disorder leading to ischemic strokes. Our previous work suggested that RNF213 might be involved in the pro-inflammatory TNFα-mediated insulin-resistance pathway in adipocytes. Insulin resistance can lead to cerebrovascular diseases and ischemic strokes. Though p. R4810 K has been reported as the founder mutation for Asian population with this disease, there are several mutations continuously reported in clinical diagnosis. We are interested to know whether these mutations can modulate insulin resistance. Also, we are intended to understand the causalities of RNF213 and its associated mutations in MMD. For this, we have adopted a computational approach to characterize RNF213 and its naturally occurring SNPs. Clinically reported SNPs and the predicted SNPs were analyzed for their pathogenicity and effect on the biological function of the protein. To increase accuracy, this was performed through three different analysis software (PROVEAN, SIFT, and SNAP2). The mutations that were found to be deleterious in all the three platforms were further analyzed for their effect on the thermal stability of the protein through I-mutant and iStable. It was found that R4810 K and other mutations decreased the thermodynamic stability of the protein. Loss of function of RNF213 was suggested in some reports. Contrary to this, some studies reported a gain of function state due to the R4810K mutation. To understand this we have measured the ligand-binding ability of this mutated protein through COFACTOR and COACH. An increase in ligand binding is always related to the functional stability of a protein. We have observed that the R4810K mutation might increase the iron-binding efficiency of the amino acid residues. This increase in binding was further validated by analyzing the binding efficiencies by docking. Since RNF213 was previously reported as a target for Protein Tyrosine Phosphatase 1B (PTP1B), we have also analyzed whether PTP1B-binding positions are susceptible to mutations. We have re-analyzed our earlier report on the differential expression pattern of RNF213 in cancer and obese samples. We have provided a detailed analysis of the most deleterious SNPs related to RNF213. Also, we provide a prediction for the loss of function and gain of function attributes of RNF213 and its predicted causalities in MMD and insulin resistance.