Effects of intronic mutations in the LDLR gene on pre-mRNA splicing: Comparison of wet-lab and bioinformatics analyses

• Published in: Molecular genetics and metabolism Vol. 96; no. 4; pp. 245 - 252
• Main Authors: Holla, Øystein L., Nakken, Sigve, Mattingsdal, Morten, Ranheim, Trine, Berge, Knut Erik, Defesche, Joep C., Leren, Trond P.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2009
• Subjects: Base Sequence; Bioinformatics; Blotting, Northern; Cell Line, Transformed; Computational Biology; Computer applications; Computer programs; DNA Mutational Analysis; Epstein-Barr virus; Exons; Familial hypercholesterolemia; Flow cytometry; Gene Expression Regulation; Herpesvirus 4, Human; Humans; Hypercholesterolemia; Intron; Introns; Introns - genetics; Laboratories; LDL receptor; LDLR gene; Lipoprotein (low density) receptors; Lymphocytes; Lymphocytes - metabolism; Lymphocytes - virology; Molecular Sequence Data; mRNA; Mutant Proteins - genetics; Mutant Proteins - metabolism; Mutation; Mutation - genetics; Polymerase chain reaction; Receptors, LDL - genetics; Reverse Transcriptase Polymerase Chain Reaction; Reverse transcription; RNA Precursors - genetics; RNA Splice Sites - genetics; RNA Splicing - genetics; RNA, Messenger - genetics; RNA, Messenger - metabolism; software; Splicing; Intron; Splicing; mRNA; Familial hypercholesterolemia; Mutation; Bioinformatics; LDL receptor
• DOI: 10.1016/j.ymgme.2008.12.014
• ISSN: 1096-7192; 1096-7206

Screening for mutations in the low density lipoprotein receptor (LDLR) gene has identified more than 1000 mutations as the cause of familial hypercholesterolemia (FH). In addition, numerous intronic mutations with uncertain effects on pre-mRNA splicing have also been identified. In this study, we have selected 18 intronic mutations in the LDLR gene for comprehensive studies of their effects on pre-mRNA splicing. Epstein-Barr virus (EBV) transformed lymphocytes from subjects heterozygous for these mutations were established and mRNA was studied by Northern blot analyses and reverse transcription polymerase chain reactions. Furthermore, functional studies of the LDLRs were performed by flow cytometry. The results of the wet-lab analyses were compared to the predictions obtained from bioinformatics analyses using the programs MaxEntScan, NetGene2 and NNSplice 0.9, which are commonly used software packages for prediction of abnormal splice sites. Thirteen of the 18 intronic mutations were found to affect pre-mRNA splicing in a biologically relevant way as determined by wet-lab analyses. Skipping of one or two exons was observed for eight of the mutations, intron inclusion was observed for four of the mutations and activation of a cryptic splice site was observed for two of the mutations. Transcripts from eight of the mutant alleles were subjected to degradation. The computational analyses of the normal and mutant splice sites, predicted abnormal splicing with a sensitivity of 100% and a specificity of 60%. Thus, bioinformatics analyses are valuable tools as a first screening of the effects of intronic mutations in the LDLR gene on pre-mRNA splicing.