Exonic Splicing Mutations Are More Prevalent than Currently Estimated and Can Be Predicted by Using In Silico Tools

The identification of a causal mutation is essential for molecular diagnosis and clinical management of many genetic disorders. However, even if next-generation exome sequencing has greatly improved the detection of nucleotide changes, the biological interpretation of most exonic variants remains challenging. Moreover, particular attention is typically given to protein-coding changes often neglecting the potential impact of exonic variants on RNA splicing. Here, we used the exon 10 of MLH1, a gene implicated in hereditary cancer, as a model system to assess the prevalence of RNA splicing mutations among all single-nucleotide variants identified in a given exon. We performed comprehensive minigene assays and analyzed patient’s RNA when available. Our study revealed a staggering number of splicing mutations in MLH1 exon 10 (77% of the 22 analyzed variants), including mutations directly affecting splice sites and, particularly, mutations altering potential splicing regulatory elements (ESRs). We then used this thoroughly characterized dataset, together with experimental data derived from previous studies on BRCA1, BRCA2, CFTR and NF1, to evaluate the predictive power of 3 in silico approaches recently described as promising tools for pinpointing ESR-mutations. Our results indicate that ΔtESRseq and ΔHZ_EI-based approaches not only discriminate which variants affect splicing, but also predict the direction and severity of the induced splicing defects. In contrast, the ΔΨ-based approach did not show a compelling predictive power. Our data indicates that exonic splicing mutations are more prevalent than currently appreciated and that they can now be predicted by using bioinformatics methods. These findings have implications for all genetically-caused diseases.     

Global control of aberrant splice-site activation by auxiliary splicing sequences: evidence for a gradient in exon and intron definition

Auxiliary splicing signals play a major role in the regulation of constitutive and alternative pre-mRNA splicing, but their relative importance in selection of mutation-induced cryptic or de novo splice sites is poorly understood. Here, we show that exonic sequences between authentic and aberrant splice sites that were activated by splice-site mutations in human disease genes have lower frequencies of splicing enhancers and higher frequencies of splicing silencers than average exons. Conversely, sequences between authentic and intronic aberrant splice sites have more enhancers and less silencers than average introns. Exons that were skipped as a result of splice-site mutations were smaller, had lower SF2/ASF motif scores, a decreased availability of decoy splice sites and a higher density of silencers than exons in which splice-site mutation activated cryptic splice sites. These four variables were the strongest predictors of the two aberrant splicing events in a logistic regression model. Elimination or weakening of predicted silencers in two reporters consistently promoted use of intron-proximal splice sites if these elements were maintained at their original positions, with their modular combinations producing expected modification of splicing. Together, these results show the existence of a gradient in exon and intron definition at the level of pre-mRNA splicing and provide a basis for the development of computational tools that predict aberrant splicing outcomes.     

In vitro and in silico analysis reveals an efficient algorithm to predict the splicing consequences of mutations at the 5' splice sites

We have found that two previously reported exonic mutations in the PINK1 and PARK7 genes affect pre-mRNA splicing. To develop an algorithm to predict underestimated splicing consequences of exonic mutations at the 5′ splice site, we constructed and analyzed 31 minigenes carrying exonic splicing mutations and their derivatives. We also examined 189 249 U2-dependent 5′ splice sites of the entire human genome and found that a new variable, the SD-Score, which represents a common logarithm of the frequency of a specific 5′ splice site, efficiently predicts the splicing consequences of these minigenes. We also employed the information contents (R_i) to improve the prediction accuracy. We validated our algorithm by analyzing 32 additional minigenes as well as 179 previously reported splicing mutations. The SD-Score algorithm predicted aberrant splicings in 198 of 204 sites (sensitivity = 97.1%) and normal splicings in 36 of 38 sites (specificity = 94.7%). Simulation of all possible exonic mutations at positions −3, −2 and −1 of the 189 249 sites predicts that 37.8, 88.8 and 96.8% of these mutations would affect pre-mRNA splicing, respectively. We propose that the SD-Score algorithm is a practical tool to predict splicing consequences of mutations affecting the 5′ splice site.     

DNA methylation profiling to assess pathogenicity of BRCA1 unclassified variants in breast cancer

Germline pathogenic mutations in BRCA1 increase risk of developing breast cancer. Screening for mutations in BRCA1 frequently identifies sequence variants of unknown pathogenicity and recent work has aimed to develop methods for determining pathogenicity. We previously observed that tumor DNA methylation can differentiate BRCA1-mutated from BRCA1-wild type tumors. We hypothesized that we could predict pathogenicity of variants based on DNA methylation profiles of tumors that had arisen in carriers of unclassified variants. We selected 150 FFPE breast tumor DNA samples [47 BRCA1 pathogenic mutation carriers, 65 BRCAx (BRCA1-wild type), 38 BRCA1 test variants] and analyzed a subset (n=54) using the Illumina 450K methylation platform, using the remaining samples for bisulphite pyrosequencing validation. Three validated markers (BACH2, C8orf31, and LOC654342) were combined with sequence bioinformatics in a model to predict pathogenicity of 27 variants (independent test set).  Predictions were compared with standard multifactorial likelihood analysis. Prediction was consistent for c.5194-12G>A (IVS 19-12 G>A) (P>0.99); 13 variants were considered not pathogenic or likely not pathogenic using both approaches. We conclude that tumor DNA methylation data alone has potential to be used in prediction of BRCA1 variant pathogenicity but is not independent of estrogen receptor status and grade, which are used in current multifactorial models to predict pathogenicity.     

A method of predicting changes in human gene splicing induced by genetic variants in context of cis-acting elements

Background  

Polymorphic variants and mutations disrupting canonical splicing isoforms are among the leading causes of human hereditary disorders. While there is a substantial evidence of aberrant splicing causing Mendelian diseases, the implication of such events in multi-genic disorders is yet to be well understood. We have developed a new tool (SpliceScan II) for predicting the effects of genetic variants on splicing and cis-regulatory elements. The novel Bayesian non-canonical 5'GC splice site (SS) sensor used in our tool allows inference on non-canonical exons.     

Detection of exon skipping events in BRCA1 RNA using MLPA kit P002

A rapid and easy method to screen for aberrant cDNA would be a very useful diagnostic tool in genetics since a fraction of the DNA variants found affect RNA splicing. The currently used RT-PCR methods require new primer combinations to study each variant that might affect splicing. Since MLPA is routinely used to detect large genomic deletions and successfully detected exon skipping events in Duchenne muscular dystrophy in cDNA, we performed a pilot study to evaluate its value for BRCA1 cDNA. The effect of puromycin, DNase I and two different DNA cleaning protocols were tested in the RNA analysis of lymphocyte cultures. We used two samples from unrelated families with two different BRCA1 exon deletion events, two healthy unrelated controls and six samples from hereditary breast/ovarian cancer syndrome (HBOC) patients without BRCA1/2 mutations. Using RNA treated with DNase I and cleaned in a column system from puromycin-treated fractions, we were able to identify the two BRCA1 deletions. Additional HBOC patients did not show additional splice events. However, we were not able to get reproducible results. Therefore, the cDNA-MLPA technique using kit BRCA1 P002 is in our hands currently not reliable enough for routine RNA analysis and needs further optimization.