Prediction of Deleterious Non-synonymous Single-Nucleotide Polymorphisms of Human Uridine Diphosphate Glucuronosyltransferase Genes
Prediction of Deleterious Non-synonymous Single-Nucleotide Polymorphisms of Human Uridine Diphosphate Glucuronosyltransferase Genes
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DOI：
10.1208/s12248-009-9126-z
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发表时间：
2009-09-01
期刊：
影响因子：
4.5
通讯作者：
Zhou, Shu-Feng
中科院分区：
文献类型：
--
作者：
Di, Yuan Ming;Chan, Eli;Zhou, Shu-Feng
UDP 葡萄糖醛酸基转移酶 (UGT) 是一类重要的 II 期酶，参与多种外源物质（包括治疗药物和内源性化合物（例如胆红素））的代谢和解毒。迄今为止，已鉴定出21个人类UGT基因，其中大多数含有单核苷酸多态性（SNP）。人类 UGT 基因的非同义 SNP (nsSNP) 可能导致酶活性缺失或降低，并且 UGT 多态性已被发现与药物清除和/或药物反应改变、高胆红素血症、吉尔伯特综合征和克里格勒-纳贾综合征密切相关。然而，由于其数量巨大，不可能使用实验室方法研究所有已识别的 nsSNP 对人类的功能影响。我们研究了基于已知 nsSNP 的生物信息学方法预测表型的潜力。我们从人类 UGT 基因中鉴定出总共 248 个 nsSNP。不耐受与耐受排序 (SIFT) 和多态性表型分析 (PolyPhen) 这两种算法工具用于预测这些 nsSNP 对蛋白质功能的影响。 SIFT 将 35.5% 的 UGT nsSNP 分类为“有害”； PolyPhen 则将 46.0% 的 UGT nsSNP 鉴定为“潜在破坏性”和“破坏性”。两种算法的结果高度相关。在 UGT 中 63 个具有功能特征的 nsSNP 中，24 个显示酶表达/活性改变，45 个与疾病易感性相关。 SIFT 和 Polyphen 的预测正确率分别为 57.1% 和 66.7%。这些发现证明了生物信息学技术预测基因型-表型关系的潜在用途，这可能构成未来功能研究的基础。
UDP glucuronosyltransferases (UGTs) are an important class of Phase II enzymes involved in the metabolism and detoxification of numerous xenobiotics including therapeutic drugs and endogenous compounds (e.g. bilirubin). To date, there are 21 human UGT genes identified, and most of them contain single-nucleotide polymorphisms (SNPs). Non-synonymous SNPs (nsSNPs) of the human UGT genes may cause absent or reduced enzyme activity and polymorphisms of UGT have been found to be closely related to altered drug clearance and/or drug response, hyperbilirubinemia, Gilbert's syndrome, and Crigler-Najjar syndrome. However, it is unlikely to study the functional impact of all identified nsSNPs in humans using laboratory approach due to its giant number. We have investigated the potential for bioinformatics approach for the prediction of phenotype based on known nsSNPs. We have identified a total of 248 nsSNPs from human UGT genes. The two algorithms tools, sorting intolerant from tolerant (SIFT) and polymorphism phenotyping (PolyPhen), were used to predict the impact of these nsSNPs on protein function. SIFT classified 35.5% of the UGT nsSNPs as "deleterious"; while PolyPhen identified 46.0% of the UGT nsSNPs as "potentially damaging" and "damaging". The results from the two algorithms were highly associated. Among 63 functionally characterized nsSNPs in the UGTs, 24 showed altered enzyme expression/activities and 45 were associated with disease susceptibility. SIFT and Polyphen had a correct prediction rate of 57.1% and 66.7%, respectively. These findings demonstrate the potential use of bioinformatics techniques to predict genotype-phenotype relationships which may constitute the basis for future functional studies.