Understanding genetic variation in the CFTR gene by next-generation sequencing

Despite extensive genetic screening, 1-5% of cystic fibrosis (CF) patients still lack a definite molecular diagnosis. The advent of next-generation sequencing (NGS), combined with advancements in target enrichment and in multiplexing techniques, are making increasingly affordable a sequencing-based approach to the identification of genetic variants in extended genomic regions. In this frame, we chose to analyze 24 selected CF patients, by full resequencing of the CFTR gene. We used the Nimblegen SeqCap EZ Library (Roche) for DNA capture and an HiSeq 2000 platform (Illumina) for the resequencing. Multiplexing 6 samples in the capture phase and 12 in the sequencing step, we obtained a mean depth >1,000X, with a 98% coverage of the target region. Data were analyzed using an in-house developed pipeline, which allowed the identification of all variants already detected by routine CFTR screening (17 pathogenic mutations). In addition, we identified 3 variants that are already reported in the literature to be clinically associated with CF but were not identified by Sanger sequencing and a total of 9 novel deep-intronic variants. Newly identified mutations, bioinformatically predicted to potentially interfere with normal splicing, are currently being functionally characterized by in-vivo and in-vitro analyses. The first approach, when possible, will be performed by RT-PCR assays on RNA extracted from patients' nasal epithelial cells. The different CFTR isoforms will be quantitatively evaluated by semi-quantitative real-time RT-PCR or competitive RT-PCR assays. As for the in-vitro approach, each relevant genomic region containing a putative mutation-activated exon has already been cloned into a mammalian expression vector. The wild-type and mutant transcripts will be produced in HeLa cells by transient transfections and aberrant splicing events analyzed both qualitatively and quantitatively by digital RT-PCR. A detailed knowledge on CFTR splicing defects will improve the genetic diagnosis and pave the way for future RNA-based correction strategies.