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

Abstract

Resistin (RETN) is a gene coding for proinflammatory adipokine called resistin secreted by macrophages in humans. Single nucleotide polymorphisms (SNPs) in RETN are linked to obesity and insulin resistance in various populations. Using dbSNP, 78 nonsynonymous SNPs (nsSNPs) were retrieved and tested on a PredictSNP 1.0 megaserver. Among these, 15 nsSNPs were predicted as highly deleterious and thus subjected to further analyses, such as conservation, posttranscriptional modifications, and stability. The 3D structure of human resistin was generated by homology modeling using Swiss model. Root-mean-square deviation (RMSD), hydrogen bonds (h-bonds), and interactions were estimated. Furthermore, UTRscan served to identify UTR functional SNPs. Among the 15 most deleterious nsSNPs, 13 were predicted to be highly conserved including variants in posttranslational modification sites. Stability analysis predicted 9 nsSNPs (I32S, C51Y, G58E, G58R, C78S, G79C, W98C, C103G, and C104Y) which can decrease protein stability with at least three out of the four algorithms used in this study. These nsSNPs were chosen for structural analysis. Both variants C51Y and C104Y showed the highest RMS deviations (1.137 Å and 1.308 Å, respectively) which were confirmed by the important decrease in total h-bonds. The analysis of hydrophobic and hydrophilic interactions showed important differences between the native protein and the 9 mutants, particularly I32S, G79C, and C104Y. Six SNPs in the 3′UTR (rs920569876, rs74176247, rs1447199134, rs943234785, rs76346269, and rs78048640) were predicted to be implicated in polyadenylation signal. This study revealed 9 highly deleterious SNPs located in the human RETN gene coding region and 6 SNPs within the 3′UTR that may alter the protein structure. Interestingly, these SNPs are worth to be analyzed in functional studies to further elucidate their effect on metabolic phenotype occurrence.

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

“…Therefore, the primary purpose of this study was to find possible coding and non-coding SNPs, which can affect the protein function by utilizing various computational approach and bioinformatics tools. These tools find out the possible conserved residues, mutations with the chance of most functionality, possible altered molecular mechanism, structural change in the protein, decreasing protein stability, post-translational modifications (PTM), and other predictable changes to recognize the most significant SNPs [17,18]. Now-a-days such computational research has become popular to find pathogenicity of genes, such as CSN3, RETN, FOXC2, CHK2 and so on [17][18][19][20].…”

“…These tools find out the possible conserved residues, mutations with the chance of most functionality, possible altered molecular mechanism, structural change in the protein, decreasing protein stability, post-translational modifications (PTM), and other predictable changes to recognize the most significant SNPs [17,18]. Now-a-days such computational research has become popular to find pathogenicity of genes, such as CSN3, RETN, FOXC2, CHK2 and so on [17][18][19][20]. Through our study, it may be possible to identify and predict new SNPs that can be associated with possible diseases.…”

In Silico Analysis of Effect of Non-synonymous SNPs associated with Permanent Neonatal Diabetes Mellitus on Stability and Structure of Human Insulin

“…Using UCSF Chimera, each mutant model was superimposed on the native structure in turn to determine the root mean square deviation (RMSD) values, which measures the quality of the structural alignment. Higher the difference, greater the differences in the two structures and correspondingly higher probability of activity alteration (Elkhattabi et al, 2019). The total H-bonds were also compared between the structures, a difference indicating changes in folding and stability.…”

“…Consequently, it is reasonable to assume that non-synonymous SNPs are likely to alter the structure of the native protein. Several studies have focused on determining the impact of deleterious SNPs on the structure of native proteins (Elkhattabi et al, 2019, Younus et al, 2018. Usually SNPs that arise in highly conserved positions can significantly affect the functional and structural characteristics of a protein, which makes these SNPs strong candidates for being associated with diseases (Younus et al, 2018).…”

“…Usually SNPs that arise in highly conserved positions can significantly affect the functional and structural characteristics of a protein, which makes these SNPs strong candidates for being associated with diseases (Younus et al, 2018). Elkhattabi et al (2019) studied the effect of SNPs on the structure of human resistin; out of 78 SNPs studied, 15 were predicted to be deleterious. 9 of these were found to exist in highly conserved regions, presenting significant impact on the structure.…”