Marín-Martín Francisco R, Soler-Rivas Cristina, Martín-Hernández Roberto, Rodriguez-Casado Arantxa
Department of Production and Characterization of New Foods, Institute of Food Science Research (CIAL), UAM-CSIC, Campus de Cantoblanco, 28049 Madrid, Spain.
IMDEA Food Institute, Campus de Cantoblanco, 28049 Madrid, Spain.
Cholesterol. 2014;2014:639751. doi: 10.1155/2014/639751. Epub 2014 Aug 19.
Disease phenotypes and defects in function can be traced to nonsynonymous single nucleotide polymorphisms (nsSNPs), which are important indicators of action sites and effective potential therapeutic approaches. Identification of deleterious nsSNPs is crucial to characterize the genetic basis of diseases, assess individual susceptibility to disease, determinate molecular and therapeutic targets, and predict clinical phenotypes. In this study using PolyPhen2 and MutPred in silico algorithms, we analyzed the genetic variations that can alter the expression and function of the ABCA1 gene that causes the allelic disorders familial hypoalphalipoproteinemia and Tangier disease. Predictions were validated with published results from in vitro, in vivo, and human studies. Out of a total of 233 nsSNPs, 80 (34.33%) were found deleterious by both methods. Among these 80 deleterious nsSNPs found, 29 (12.44%) rare variants resulted highly deleterious with a probability >0.8. We have observed that mostly variants with verified functional effect in experimental studies are correctly predicted as damage variants by MutPred and PolyPhen2 tools. Still, the controversial results of experimental approaches correspond to nsSNPs predicted as neutral by both methods, or contradictory predictions are obtained for them. A total of seventeen nsSNPs were predicted as deleterious by PolyPhen2, which resulted neutral by MutPred. Otherwise, forty two nsSNPs were predicted as deleterious by MutPred, which resulted neutral by PolyPhen2.
疾病表型和功能缺陷可追溯到非同义单核苷酸多态性（nsSNPs），它们是作用位点和有效潜在治疗方法的重要指标。鉴定有害的nsSNPs对于表征疾病的遗传基础、评估个体对疾病的易感性、确定分子和治疗靶点以及预测临床表型至关重要。在本研究中，我们使用PolyPhen2和MutPred这两种计算机算法，分析了可能改变ABCA1基因表达和功能的基因变异，该基因会导致等位基因疾病家族性低α脂蛋白血症和Tangier病。我们用已发表的体外、体内和人体研究结果对预测进行了验证。在总共233个nsSNPs中，两种方法均发现80个（34.33%）是有害的。在这80个被发现的有害nsSNPs中，29个（12.44%）罕见变异的有害概率>0.8，结果显示为高度有害。我们观察到，在实验研究中具有已证实功能效应的大多数变异，被MutPred和PolyPhen2工具正确预测为有害变异。然而，实验方法的有争议结果对应于两种方法均预测为中性的nsSNPs，或者对它们获得了相互矛盾的预测。共有17个nsSNPs被PolyPhen2预测为有害，但被MutPred预测为中性。否则，有42个nsSNPs被MutPred预测为有害，但被PolyPhen2预测为中性。
