Exploring the impact of CYP11A1's missense SNPs on the interaction between CYP11A1 and cholesterol: A comprehensive structural analysis and MD simulation study.

The process of steroidogenesis plays a vital role in human physiology as it governs the biosynthesis of mineralocorticoids, glucocorticoids, and androgens. These three classes of steroid hormones are primarily produced in the adrenal and gonadal glands through steroidogenesis pathways. Initiated by the side chain cleavage of cholesterol (CLR), this process leads to the conversion of cholesterol into pregnenolone and isocaproic aldehyde. The enzyme CYP11A1, encoded by the CYP11A1 gene, plays a key role in catalyzing the side chain cleavage of CLR. Several single nucleotide polymorphisms (SNPs) have been identified in the CYP11A1 gene, which may predispose carriers to disorders associated with abnormal steroidogenesis. Specifically, missense SNPs in the CYP11A1 gene have the potential to negatively impact the interaction between CYP11A1 and CLR, thus affecting the overall metabolome of steroid hormones. In this computational study, we focused on a specific set of missense SNPs reported in the CYP11A1 gene, aiming to identify variants that directly impact the interaction between CYP11A1 and CLR. The three-dimensional structure of the CYP11A1-CLR complex was obtained from the RCSB Protein Data Bank, while missense SNPs in the CYP11A1 gene were retrieved from Ensembl. To predict the most deleterious variants, we utilized the ConSurf server, SIFT, and PolyPhen. Furthermore, we assessed the impact of induced amino acid (AA) substitutions on the CYP11A1-CLR interaction using the PRODIGY server, PyMol, and Ligplot programs. Additionally, molecular dynamics (MD) simulations were conducted to analyze the effects of deleterious variants on the structural dynamics of the CYP11A1-CLR complex. Among the 8096 retrieved variants, we identified ten missense SNPs (E91K, W147G, R151W, R151Q, S391C, V392M, Q395K, Q416E, R460W, and R460Q) as deleterious for the interaction between CYP11A1 and CLR. MD simulations of the CYP11A1-CLR complexes carrying these deleterious AA substitutions revealed that Q416E, W147G, R460Q, and R460W had the most pronounced impacts on the structural dynamics of the complex. Consequently, these missense SNPs were considered the most deleterious ones. Further functional tests are recommended to assess the impact of these four missense SNPs on the enzymatic activity of CYP11A1. Moreover, Genome-Wide Association Studies (GWAS) should be conducted to determine the significance of their association with abnormal steroidogenesis diseases in various patient groups.

Dominant ACO2 mutations are a frequent cause of isolated optic atrophy.

Biallelic mutations in ACO2, encoding the mitochondrial aconitase 2, have been identified in individuals with neurodegenerative syndromes, including infantile cerebellar retinal degeneration and recessive optic neuropathies (locus OPA9). By screening European cohorts of individuals with genetically unsolved inherited optic neuropathies, we identified 61 cases harbouring variants in ACO2, among whom 50 carried dominant mutations, emphasizing for the first time the important contribution of ACO2 monoallelic pathogenic variants to dominant optic atrophy. Analysis of the ophthalmological and clinical data revealed that recessive cases are affected more severely than dominant cases, while not significantly earlier. In addition, 27% of the recessive cases and 11% of the dominant cases manifested with extraocular features in addition to optic atrophy. In silico analyses of ACO2 variants predicted their deleterious impacts on ACO2 biophysical properties. Skin derived fibroblasts from patients harbouring dominant and recessive ACO2 mutations revealed a reduction of ACO2 abundance and enzymatic activity, and the impairment of the mitochondrial respiration using citrate and pyruvate as substrates, while the addition of other Krebs cycle intermediates restored a normal respiration, suggesting a possible short-cut adaptation of the tricarboxylic citric acid cycle. Analysis of the mitochondrial genome abundance disclosed a significant reduction of the mitochondrial DNA amount in all ACO2 fibroblasts. Overall, our data position ACO2 as the third most frequently mutated gene in autosomal inherited optic neuropathies, after OPA1 and WFS1, and emphasize the crucial involvement of the first steps of the Krebs cycle in the maintenance and survival of retinal ganglion cells.

Computational Analysis of nsSNPs of ADA Gene in Severe Combined Immunodeficiency Using Molecular Modeling and Dynamics Simulation.

Severe combined immunodeficiency (SCID) is the most severe form of primary immunodeficiency (PID), characterized by fatal opportunistic infections. The ADA gene encodes adenosine deaminase, an enzyme that catalyzes the irreversible deamination of adenosine and deoxyadenosine in the catabolic pathway of purine. Mutations of the ADA gene have been identified in patients with severe combined immunodeficiency. In this study, we performed a bioinformatics analysis of the human ADA gene to identify potentially harmful nonsynonymous SNPs and their effect on protein structure and stability. Using eleven prediction tools, we identified 15 nsSNPs (H15D, H15P, H17Q, H17Y, D19N, T26I, G140E, C153F, A183D, G216R, H258Y, C262Y, S291L, S291W, and K34OE) as harmful. The results of ConSurf's analysis revealed that all these nsSNPs are localised in the highly conserved positions and affect the structure of the native proteins. In addition, our computational analysis showed that the H15D, G140E, G216R, and S291L mutations identified as being associated with severe combined immunodeficiency affect protein structure. Similarly, the results of the analyses of Rmsd, Rmsf, and Rg showed that all these factors influence protein stability, flexibility, and compaction with different levels of impact. This study is the first comprehensive computational analysis of nsSNPs of the ADA gene. However, functional analyses are needed to elucidate the biological mechanisms of these polymorphisms in severe combined immunodeficiency.

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

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.