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CYP2R1和CYP27A1基因:一种通过计算机模拟方法来识别有害突变、对结构的影响及其在疾病状态下的差异表达

CYP2R1 and CYP27A1 genes: An in silico approach to identify the deleterious mutations, impact on structure and their differential expression in disease conditions.

作者信息

Sunkar Swetha, Neeharika Desam

机构信息

Center for Molecular Data science and Systems Biology, Department of Bioinformatics, Sathyabama Institute of Science and Technology, Chennai 119, India.

Center for Molecular Data science and Systems Biology, Department of Bioinformatics, Sathyabama Institute of Science and Technology, Chennai 119, India.

出版信息

Genomics. 2020 Sep;112(5):3677-3686. doi: 10.1016/j.ygeno.2020.04.017. Epub 2020 Apr 25.

Abstract

Mutations in CYP2R1 and CYP27A1 involved in the conversion of Cholecalciferol into Calcidiol were associated with the impaired 25-hydroxylase activity therefore affecting the Vitamin D metabolism. Hence, this study attempted to understand the influence of genetic variations at the sequence and structural level via computational approach. The non-synonymous mutations retrieved from dbSNP database were assessed for their pathogenicity, stability as well as conservancy using various computational tools. The above analysis predicted 11/260 and 35/489 non-synonymous mutations to be deleterious in CYP2R1 and CYP27A1 genes respectively. Native and mutant forms of the corresponding proteins were modeled. Further, interacting native and mutant proteins with cholecalciferol showed difference in hydrogen bonds, hydrophobic bonds and their binding affinities suggesting the possible influence of these mutations in their function. Also, expression of these genes in various disease conditions was investigated using GEO datasets which predicted that there is a differential expression in cancer and arthritis.

摘要

参与胆钙化醇转化为骨化二醇的CYP2R1和CYP27A1突变与25-羟化酶活性受损相关,因此影响维生素D代谢。因此,本研究试图通过计算方法了解序列和结构水平上基因变异的影响。使用各种计算工具评估从dbSNP数据库检索到的非同义突变的致病性、稳定性和保守性。上述分析预测,分别有11/260和35/489个非同义突变在CYP2R1和CYP27A1基因中有害。对相应蛋白质的天然形式和突变形式进行建模。此外,与胆钙化醇相互作用的天然和突变蛋白质在氢键、疏水键及其结合亲和力方面存在差异,表明这些突变可能影响其功能。此外,使用GEO数据集研究了这些基因在各种疾病条件下的表达,预测在癌症和关节炎中存在差异表达。

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