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Comparison of in silico prediction and experimental assessment of ABCB4 variants identified in patients with biliary diseases.

Authors :
Khabou B
Durand-Schneider AM
Delaunay JL
Aït-Slimane T
Barbu V
Fakhfakh F
Housset C
Maurice M
Source :
The international journal of biochemistry & cell biology [Int J Biochem Cell Biol] 2017 Aug; Vol. 89, pp. 101-109. Date of Electronic Publication: 2017 Jun 03.
Publication Year :
2017

Abstract

Genetic variations of the phosphatidylcholine transporter, ABCB4 cause several biliary diseases. The large number of reported variations makes it difficult to foresee a comprehensive study of each variation. To appreciate the reliability of in silico prediction programs, 1) we confronted them with the assessment in cell models of two ABCB4 variations (E528D and P1161S) identified in patients with low phospholipid-associated cholelithiasis (LPAC); 2) we extended the confrontation to 19 variations that we had previously characterized in cellulo. Four programs (Provean, Polyphen-2, PhD-SNP and MutPred) were used to predict the degree of pathogenicity. The E528D and P1161S variants were studied in transfected HEK293 and HepG2 cells by immunofluorescence, immunoblotting and measurement of phosphatidylcholine secretion. All prediction tools qualified the P1161S variation as deleterious, but provided conflicting results for E528D. In cell models, both mutants were expressed and localized as the wild type but their activity was significantly reduced, by 48% (P1161S) and 33% (E528D). These functional defects best correlated with MutPred predictions. MutPred program also proved the most accurate to predict the pathogenicity of the 19 ABCB4 variants that we previously characterized in cell models, and the most sensitive to predict the pathogenicity of 65 additional mutations of the Human Gene Mutation Database. These results confirm the pathogenicity of E528D and P1161S variations and suggest that even a moderate decrease (by less than 50%) of phosphatidylcholine secretion can cause LPAC syndrome. They highlight the reliability of in silico prediction tools, most notably MutPred, as a first approach to predict the pathogenicity of ABCB4 variants.<br /> (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Details

Language :
English
ISSN :
1878-5875
Volume :
89
Database :
MEDLINE
Journal :
The international journal of biochemistry & cell biology
Publication Type :
Academic Journal
Accession number :
28587926
Full Text :
https://doi.org/10.1016/j.biocel.2017.05.028