Epithelial sodium channel (ENaC) family: Phylogeny, structure-function, tissue distribution, and associated inherited diseases.

Candidate novel long noncoding RNAs, MicroRNAs and putative drugs for Parkinson's disease using a robust and efficient genome-wide association study.

Mendelian diseases have shown to be an and efficient model for connecting genotypes to phenotypes and for elucidating the function of genes. Whole-exome sequencing (WES) accelerated the study of rare Mendelian diseases in families, allowing for directly pinpointing rare causal mutations in genic regions without the need for linkage analysis. However, the low diagnostic rates of 20-30% reported for multiple WES disease studies point to the need for improved variant pathogenicity classification and causal variant prioritization methods. Here, we present the exome Disease Variant Analysis (eDiVA; http://ediva.crg.eu), an automated computational framework for identification of causal genetic variants (coding/splicing single-nucleotide variants and small insertions and deletions) for rare diseases using WES of families or parent-child trios. eDiVA combines next-generation sequencing data analysis, comprehensive functional annotation, and causal variant prioritization optimized for familial genetic disease studies. eDiVA features a machine learning-based variant pathogenicity predictor combining various genomic and evolutionary signatures. Clinical information, such as disease phenotype or mode of inheritance, is incorporated to improve the precision of the prioritization algorithm. Benchmarking against state-of-the-art competitors demonstrates that eDiVA consistently performed as a good or better than existing approach in terms of detection rate and precision. Moreover, we applied eDiVA to several familial disease cases to demonstrate its clinical applicability.

/pdf/ediva-classification-and-prioritization-of-pathogenic-18jfvas9d1.pdf

eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics.

Cystic fibrosis is the most common autosomal recessive genetic disease in Caucasians and has been extensively studied for many decades. The cystic fibrosis transmembrane conductance regulator gene was identified in 1989. It encodes a complex protein which has numerous cellular functions. Our understanding of cystic fibrosis pathophysiology and genetics is constantly expanding and being refined, leading to improved management of the disease and increased life expectancy in affected individuals.

Recent advances in understanding and managing cystic fibrosis transmembrane conductance regulator dysfunction

Despite extensive screening, 1-5% of cystic fibrosis (CF) patients lack a definite molecular diagnosis. Next-generation sequencing (NGS) is making affordable genetic testing based on the identification of variants in extended genomic regions. In this frame, we analyzed 23 CF patients and one carrier by whole-gene CFTR resequencing: 4 were previously characterized and served as controls; 17 were cases lacking a complete diagnosis after a full conventional CFTR screening; 3 were consecutive subjects referring to our centers, not previously submitted to any screening. We also included in the custom NGS design the coding portions of the SCNN1A, SCNN1B and SCNN1G genes, encoding the subunits of the sodium channel ENaC, which were found to be mutated in CF-like patients. Besides 2 novel SCNN1B missense mutations, we identified 22 previously-known CFTR mutations, including 2 large deletions (whose breakpoints were precisely mapped), and novel deep-intronic variants, whose role on splicing was excluded by ex-vivo analyses. Finally, for 2 patients, compound heterozygotes for a CFTR mutation and the intron-9c.1210-34TG[11-12]T5 allele-known to be associated with decreased CFTR mRNA levels-the molecular diagnosis was implemented by measuring the residual level of wild-type transcript by digital reverse transcription polymerase chain reaction performed on RNA extracted from nasal brushing.

/pdf/whole-gene-cftr-sequencing-combined-with-digital-rt-pcr-30tc4ucm9n.pdf

Whole-gene CFTR sequencing combined with digital RT-PCR improves genetic diagnosis of cystic fibrosis.

The term cystic fibrosis (CF)-like disease is used to describe patients with a borderline sweat test and suggestive CF clinical features but without two CFTR(cystic fibrosis transmembrane conductance regulator) mutations. We have performed the extensive molecular analysis of four candidate genes (SCNN1A, SCNN1B, SCNN1G and SERPINA1) in a cohort of 10 uncharacterized patients with CF and CF-like disease. We have used whole-exome sequencing to characterize mutations in the CFTR gene and these four candidate genes. CFTR molecular analysis allowed a complete characterization of three of four CF patients. Candidate variants in SCNN1A, SCNN1B, SCNN1G and SERPINA1 in six patients with CF-like phenotypes were confirmed by Sanger sequencing and were further supported by in silico predictive analysis, pedigree studies, sweat test in other family members, and analysis in CF patients and healthy subjects. Our results suggest that CF-like disease probably results from complex genotypes in several genes in an oligogenic form, with rare variants interacting with environmental factors.

Extensive sequence analysis of CFTR, SCNN1A, SCNN1B, SCNN1G and SERPINA1 suggests an oligogenic basis for cystic fibrosis-like phenotypes.

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Extensive sequence analysis of CFTR, SCNN1A, SCNN1B, SCNN1G and SERPINA1 suggests an oligogenic basis for cystic fibrosis-like phenotypes.