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|Title:||Prediction of deleterious non-synonymous single-nucleotide polymorphisms of human uridine diphosphate glucuronosyltransferase genes||Authors:||Di, Y.M.
|Issue Date:||Sep-2009||Citation:||Di, Y.M., Chan, E., Wei, M.Q., Liu, J.-P., Zhou, S.-F. (2009-09). Prediction of deleterious non-synonymous single-nucleotide polymorphisms of human uridine diphosphate glucuronosyltransferase genes. AAPS Journal 11 (3) : 469-480. ScholarBank@NUS Repository. https://doi.org/10.1208/s12248-009-9126-z||Abstract:||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. © 2009 American Association of Pharmaceutical Scientists.||Source Title:||AAPS Journal||URI:||http://scholarbank.nus.edu.sg/handle/10635/106241||ISSN:||15507416||DOI:||10.1208/s12248-009-9126-z|
|Appears in Collections:||Staff Publications|
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