Open access peer-reviewed chapter

New Molecular Diagnosis Approaches — From the Identification of Mutations to their Characterization

By Anne Bergougnoux, Magali Taulan-Cadars, Mireille Claustres and Caroline Raynal

Submitted: November 4th 2014Reviewed: April 22nd 2015Published: August 24th 2015

DOI: 10.5772/60679

Downloaded: 1794


Molecular diagnosis of cystic fibrosis is based on the detection of mutation in the CFTR gene, identified in 1989. During the past 20 years, thanks to evolutions of diagnostic techniques, our knowledge of mutation spectrum and pathophysiological mechanisms involved in the disease has significantly improved. Sanger sequencing and quantitative methods greatly contributed to the identification of the 2,000 sequence variations reported worldwide in CFTR. We are now entering the new technological age with the generalisation of Next Generation Sequencing (NGS) technologies in diagnostics laboratories. These high throughput approaches allow scanning for the entire CFTR locus, including deep intronic regions, and in parallel other candidate genes that possibly influence the clinical evolution of patients. However, this powerful technology poses new challenge in test interpretation. In this chapter, we review the current and new technologies used in molecular diagnostics of cystic fibrosis, particularly NGS approaches. We also present current and new bioinformatics tools available for the interpretation of variants and in vitro/ex vivo and in vivo techniques that can be used to improve the characterization of the functional impact of CFTR variations.


  • Next generation sequencing
  • CFTR
  • sequence variations
  • interpretation
  • functional characterization

1. Introduction

The autosomal recessive mode of inheritance of Cystic Fibrosis (CF) was suggested by Andersen and Hodges in 1945 when they described this disease as a pathological entity.

Since the discovery of the CFTRgene (and the predicted protein cystic fibrosis transmembrane conductance regulator) in 1989 [1], close to 2, 000 variations have been identified on this locus. CFTRgene studies represent one of the most frequent genetic analyses routinely performed worldwide, either to confirm the clinical diagnosis of CF or CFTR-related disorders (CFTR-RDs), or to offer carrier testing, prenatal or pre-implantation genetic diagnosis.

The most common severe mutation, p.Phe508del, is found in approximately 70% of CF alleles of European descent (therefore it is present in 49% of homozygous and 42% of compound heterozygous CF patients), with significant variations depending on ethnicities [2]. There is a clear decreasing northwest to southwest gradient in p.Phe508del frequency across Europe. Only four other mutations represent more than 1% of CF cases: p.Gly542*, p.Gly551Asp, p.Asn1303Lys, and p.Trp1282*. All other mutations are rare and many are private, only detected within a single family.

Figure 1.

Two models for the classification of CFTR variants: (1) Functional: five classes of defective CFTR protein [3,4,5]; and (2) Clinical: four classes based on phenotypic expression in patients [6].

Therefore, a good knowledge of CFTR diseases and their molecular pathology is required when choosing tools and strategy and when interpreting results. Organization of regional or national networks of specialist clinical and molecular genetic laboratories is needed. Two models for the classification of sequence alterations have been proposed (Figure 1): (1) depending on their molecular impact on the CFTR chloride channel, which requires in vitro/in vivofunctional assays, now less used due to the difficulty in accurately classifying the new reported variants [3, 4] or (2) by their clinical consequences on patients based on detailed and prospective clinical evaluation of patients associated with molecular findings [5].

However, even if the most severe mutations were studied in vitro, mild mutations or variants of unknown (clinical) significance (VUCs or VUS) were barely analysed. Thus, only one third of CFTRvariations are functionally classified. Finally, in CF, as in other genetic diseases, new molecular diagnosis techniques such as Next Generation Sequencing (NGS) allow the analysis of the whole CFTRlocus (including deep intronic regions) but increase the number of VUCS reported on patients.

Therefore, geneticists and clinicians have to combine CFTR epidemiological databases, functional in vitro/in vivoanalysis and exhaustive clinical data to perform pertinent genotype/phenotype correlations in patients in order to provide appropriate genetic counselling to families.


2. Technical aspects of molecular diagnosis

The CFTRgenetic testing strategy depends on the clinical and familial context and is classically performed step by step, as recommended [7]. As shown in Figure 2, tests are carried out in various situations, including confirmation of a clinical diagnosis, prenatal diagnosis (familial context or foetal suspicion of CF) and carrier screening.

A wide range of techniques is still used to identify CFTRgene sequence variations (presented in Table 1) and there is no gold standard or preferred method for routine testing. However, laboratories should be aware of the limitations of their chosen method (e.g. some mutations are not identified). Moreover, assay performance should always be verified before diagnostic use, even though commercial kits are CE-marked in vitrodiagnostic devices (IVDD). In the perspective of accreditation (expected in France in 2020 for hospital diagnostic laboratories), all methods used in molecular biology should be validated before diagnostic use, notably by comparison with a reference method (Sanger sequencing for Single Nucleotide Variations and quantitative PCR for Copy Number Variations detection). This technical validation should be undertaken for NGS in the step 1 and 2 diagnosis.

Undetected CFTRmutations may lie within the introns or regulatory regions, which are not routinely explored but that will be soon analysed by NGS. Comparison between NGS and reference methods for the identification of CFTRvariants on these unexplored regions will be a challenge.

Figure 2.

CF molecular diagnosis: a step-by-step strategy (modified from [7]).NBS: newborn screening;SNV: Single Nucleotide Variation;CNV: Copy Number Variation;MLPA: Multiplex Ligation-dependent Probe Amplification.

2.1. Current techniques to detect CFTRvariants

2.1.1. Screening techniques (Step 1)

These approaches consist of genotyping a panel of frequent CF mutations using commercial kits (Table 1) that classically cover more than 80% of CF known mutations in European populations. Additional search for mutations specific to certain regions or ethnicities (frequency higher than 1% of CF alleles in the targeted population) completes the analyses.

Data on disease and carrier frequencies or mutation frequencies in various populations are available in the WHO report [8] and should be accurately known and used by laboratories.

For many patients carrying CF-causing mutations included in commercial panels, CFTRmolecular analysis generally stops at this step. There is no need for additional studies, except the confirmation of mutations by a second method, as recommended by international guidelines for genetics diagnosis.

2.1.2. Scanning techniques (Step 2)

The high heterogeneity of CFTRmutations in CF and CFTR-RD populations makes the complete molecular screening of the 27 exons and parts of the regulatory regions (5’UTR, 3’UTR and partial intronic regions) essential.

Therefore, the analysis of the CFTRlocus can be performed as follows:

  • To detect SNV: scanning the 27 exons of the CFTRgene, intronic boundaries and four intronic regions with reported deep intronic mutations [9-12] and a part of the promoter. As an example, one can use Sanger sequencing with Single Condition Amplification Primers (SCAP) technique [13].

  • To detect CNV (large CFTRrearrangements): scanning of exon by multiplex fluorescent quantitative PCR (Multiplex Ligation-dependent Probe Amplification or MLPA) or array-CGH [14].

These robust methods allow the detection of more than 97% of CFTRmutations involved in CF.

2.1.3. Advantages and limits

Classical screening or scanning methods used to detect mutations (in step 1 and 2) have high specificity and sensitivity (Table 1). Older techniques such as RFLP (restriction fragment length polymorphism), DGGE (denaturing gradient gel electrophoresis) or DHPLC (denaturing high performance liquid chromatography) were widely used in the past 20 years but were difficult to set up and showed inconstant performance. They have been progressively replaced by easy-to-use commercial kits, more appropriate for larger sample series. These kits allow PCR multiplexing and thus enable the detection of the most common CF mutations in one reaction. Sanger sequencing and quantitative PCR are the most effective to identify unknown mutations. However, all these classical methods are labour-intensive and time-consuming.

Method nameMutations detectedAdvantagesLimits
Screening (step 1)
Reverse dot blot hybridizationUp to 20 mutations per multiplexAppropriate for large series
High specificity
Cost effective
Partial hybridization if SNP on probe
ARMS (amplification refractory mutation system)Up to 50 mutationsPrimer Design difficult
Need of high agarose gel quality
OLA (Oligonucleotide ligation assay)32 mutations (Abbott kit)Partial hybridization if SNP on probe
NGS139 to 166 CFTR variantsNeed of sufficient coverage
In development (Illumina)
Scanning (Step 2)
HRM (High Resolution Melting curve analysis)Heterozygous variantsAppropriate for large series (e.g. epidemiological studies)Exon by exon
Need of positive cases
Not adapted for homozygous variants
Sanger SequencingAll sequenced basesClose to 100% sensitivityExpensive method
Can not detect CNV
Quantitative fluorescent multiplex PCRDeletions, duplications, insertionsSimple and rapidFalse positive if SNP on probe
Performance dependent on DNA quality
NGS (used for step 3)All sequenced basesAppropriate for large series
Cost effective
Need of sufficient coverage
In development (Illumina)

Table 1.

Overview, advantages and limits of screening and scanning techniques used in molecular genetics diagnosis (adapted from [7])

2.2. Next Generation Sequencing (NGS)

2.2.1. Principle of NGS technologies

The development of Next Generation Sequencing (NGS) or Massively Parallel Sequencing (MPS) technologies with an immense capacity (up to 1 Terabase (Tb) of data per run with the HiSeq2500 system, Illumina) is a major technical progress in the field of human genetics. Since 2004, three principal NGS platforms have been commercially available, including (i) 454 GS FLX & GS junior from Roche (ii) Genome Analyzer, HiSeq & MiSeq from Illumina and (iii) SOLiD & Ion Torrent PGM from Life Technologies-Applied Biosystems (AB) [15]. These technologies differ in terms of sample library preparation workflow, enabling sequencing on any of the current NGS sequencers (e.g. Illumina, Life technologies, Roche). Since late 2004, three principal NGS firms developed sequencers commercially available (listed in Table 2).

454 pyrosequencing is a sequencing-by-synthesis method that measures the release of inorganic pyrophosphate upon incorporation of nucleotides, by converting it into luciferase chemiluminescent signals using a series of enzymatic reactions. Ion Torrent semiconductor technology is also based on a sequencing-by-synthesis approach but measures pH changes (instead of light) induced by the release of hydrogen ions as nucleotides are incorporated. SOLiD technology is a ligation-based sequencing system. DNA ligase is used to identify the nucleotide present at a given position in a DNA sequence; each base is read twice, which increases accuracy, even for homopolymeric regions. Base detection uses a mixture of labelled oligonucleotides, which queries the input strand with ligase. In Illumina system, clonal amplification is performed using a process termed ‘bridge amplification’ followed by two basic steps, initial priming and extending of the single-stranded, single-molecule template and bridge amplification of the immobilized template with immediately adjacent primers to form clusters. For sequencing, only dye-labelled terminators are added; then the sequence at that position is determined for all clusters; next, the dye is cleaved and another round of dye-labelled terminators is added.

PlatformGAII, HiSeq & MiSeqIon TorrentSOLiDGS FLX & GS Junior
MethodologySequencing by synthesis (Reversible termination)Sequencing by synthesisSequencing by LigationSequencing by synthesis (Pyrosequencing)
LoadingAdaptors on template DNA bind high density primers across surface of slideAdaptors on template DNA bind primers on beads, one molecule per beadAdaptors on template DNA bind primers on beads, one molecule per beadAdaptors on template DNA bind primers on beads, one molecule per bead
Clonal amplificationBridge PCR: Surface array on flow cellEmulsion PCR: clusters on beadsEmulsion PCR: clusters on beadsEmulsion PCR: clusters on beads
ParallelisationRandom array on flow cellBeads loaded on a chipBeads bonded to high density glass slideBeads loaded onto high density plate
DetectionFluorescenceH+ ions (sensitive pH meter)FluorescenceLight (luciferase)

Table 2.

Overview of the main NGS technologies Target enrichment

Targeted re-sequencing isolates genomic regions of interest in a sample library, allowing to focus efficiently and cost-effectively on a small subset of the genome, such as an exome, a particular chromosome, a set of genes or a region of interest such as a whole gene. Two main strategies can be envisioned: capture [16, 17] or amplification relevant genomic DNA [18-20] as shown in Table 3.

StrategyCapture/hybridisation-based methodAmplicon-based method
MethodsHaloPlexSureSelect (Agilent) /SeqCap (NimbleGen)Nextera (Illumina)Microdoplet (RainDance Technologies)Multiplex PCR (AmpliSeq, Life Technology & TrueSeq, Illumina)Long-Range PCR Qiagen & "home" design
Main stepsRestriction enzyme digestionSonication/ nebulisationTagmentation with transposonSonicationPCR amplificationPCR amplification
Probe hybridisationPCR amplification + probe hybridisationPCR amplification + probe hybridisationSonication/ nebulisation
Ligation of sequencing adaptors

Table 3.

Main strategies proposed for target enrichment Advantages and limits

The main advantages of the technology are related to its capability to process a large number of samples in parallel. NGS technologies are time saving and lower the costs per patient, of step 1 and particularly step 2 molecular analyses. But, they also have significant limitations such as high error rates, enrichment of rare variants and large proportion of missing values, as well as the fact that most current analytical methods are designed for population-based association studies. With second generation sequencing, it is necessary to clonally amplify the isolated targets in order to generate sufficient signal for detection during the sequencing run generating clusters of many thousands of identical DNA targets. In addition, each NGS platform generates different read lengths that range from short (e.g. 35 bases) to long reads (over 500 bases). For a number of applications, including targeted re-sequencing, ChIP-Seq and RNA-Seq, short reads are highly informative and adequate. Conversely, longer reads are more suitable for de novogenome assembly, mapping of high homology regions (related gene family and pseudogenes) and sequencing of repetitive DNA regions, such as introns. This is an important consideration since short read length can make accurate assembly and alignment computationally challenging.

For some clinical applications, as NGS produces massive amounts of data, their analysis and interpretation are time-consuming, not trivial and a real challenge even if specific portions of a genome is analysed. Bioinformatics software to analyse NGS data

Then, generated data are analysed by bioinformatics tools. Quality criteria such as confidence or coverage are guarantees for optimal sensitivity and specificity of a sequencing run. Bioinformatics analyses are frequently performed by using software developed by sequencers’ companies. The three steps include (1) base calling and quality score computation, (2) assembly and alignment and (3) variant calling and annotation. Laboratories developed their own ‘in-house’ pipeline mainly to apply filters allowing an easy focus on causing-mutations. Others use free web resources to realign files including BWA ( or to perform a new variant calling such as Samtools and GATK [21-23]. Others serve as Viewer such as Integrative Genomics Viewer ( Galaxy website is an integrative platform offering the possibility to view and process files generated by NGS. Many databases, useful for variant annotation and sometimes inserted in others programs or pipelines, are publicly available including the 1000 Genomes Project [24] and the dbSNP database [25] (further described in part 3.1.1).

Many tracks are currently studied (i.e. improvement of bioinformatics tools, comparison of NGS approaches between CF laboratories, development of databases including newly detected NGS variants, publication of guidelines and definition of a diagnosis report model) to make CF molecular diagnosis by NGS suitable for different clinical and familial cases.

2.2.2. Indications and choice of CFTRanalysed regions

In the case of Mendelian diseases caused by mutations in a single gene, like CFTRin Cystic Fibrosis, NGS sequencing of entire genome or exome is still useless and expensive. Collaborations between companies specialized in molecular diagnosis and academic laboratories recently led to the establishment of new molecular diagnosis tools based on NGS. Combination of (i) the enrichment of regions of interest by hybrid capture, circularization or Polymerase Chain Reaction (PCR) and (ii) high throughput sequencing now allows time-efficient and economical way to perform analyses. Use of NGS technology as a first intention is currently set up in laboratories, which will soon question the CF diagnosis strategy as a tree. As shown in Figure 2, in our actual CF molecular diagnosis strategy, NGS technology can be used instead of ‘classical’ techniques for the detection of a panel of common mutations (step 1) or to analyse the ‘CFTRexome’ (step 2). Technical manipulations are similar for both, but filters can be used to focus on regions that contain mutations. Multiplicom® and Illumina® propose a locus specific design to library preparation for molecular analysis of the ‘CFTRexome’. CE-marked kits for in vitrodiagnostic (CE-IVD) and efficient bioinformatics tools are commercialized. The latter could be performed by in-house pipelines or subcontract on commercial firms (Sophia Genetics®). SNV and indels are correctly detected (high specificity and sensitivity) and CNV detection is now available for some amplicon-based methods.

Furthermore, in patients with definite CF clinical diagnosis (positive sweat test, CF clinical features) and who carry no or only one CF mutation, step 3 analysis could be ultimately proposed, in combination with the analysis of potential modifiers genes (see next section)

In literature, three studies reported NGS CFTRsequencing (Table 4) on CF patients, CF carriers or controls samples [18, 26, 27]. Abou Tayoun and colleagues [26] first proposed a proof-of-concept for a ‘CFTRexome’ analysis by NGS on 79 samples. Target enrichment was performed by PCR amplification (AmpliSeq Panel, Life technologies) and Sequencing on Ion Torrent Platform (PGM®). Their sequencing offered minimal coverage of 100X (depending on the Ion 314 or 318 chip used). Two others studies realized sequencing of the whole CFTRlocus (close to 250 kb) including deep intronic CFTRregions (Figure 2, Step 3). Trujillano et al. [27] reported the CFTRre-sequencing by hybridization capture on a custom NimbleGen SeqCap EZ Choise array using HiSeq2000 (Illumina®) in a set of 92 samples. They highlighted the precise characterization of breakpoints of seven genomic rearrangements in CFTR.

We proposed a complete CFTRgene sequencing of DNA samples from patients with a confirmed CF clinical diagnosis but with an incomplete genotype [18]. Although large unexplored intronic regions might contain few mutations (about 1%–3% of CF mutations), we identified a new pathogenic mutation, which creates a pseudo-exon (Table 4). Moreover, we compared hybridization capture and Long-Range PCR to target enrichment and used a small-scale NGS platform for sequencing (GS Junior Sequencer, 454 Life Sciences®). Some promising variants were then confirmed as deleterious by in vitro/ex vivofunctional assays. However, for most detected intronic variants, classification will be a long and difficult way. This approach is currently under development for CF diagnosis.

Abou Tayoun et al., 2013 [25]Trujillano et al., 2014 [26]Bonini et al., 2015 [17]
Samples57 subjects already genotyped – 22 cell lines: 24 CF, 46 CF carriers, 9 controls92 subjects already genotyped: 45 CF, 27 CF carriers, 20 CFTR-RD18 CF subjects in whom only one mutation had identified
Study objectifProof of concept of CFTR assay by NGSResequencing mutations
Validation technique
Identification of the second mutation
Comparison of two target enrichment approaches
Sequencing protocol
Regions analyzedAll CFTRexons and 20bp of all intron/splice sites (10 343bp)All CFTRlocus (208kb181kb after masking repetitive DNA elements)All CFTRlocus (250kb)
2.1million probes (60-90pb)36 overlapping fragments (7.3-14.5kb)
Target enrichmentCustom AmpliSeq panel (Life technologies)Hybrid capture: Custom NimbleGen SeqCap EZHybrid capture: Custom NimbleGen SeqCap EZLong-Range -PCR (LR-PCR) enrichment
Initial DNA quantity per sample20ng500ng750ng
Library preparation2 separate primer pools (2x36 amplicons), end-repair, ligation to adaptators, quantification of Library pooled bar-coded libraries
Clonal PCR
Sonication (Covaris
End-repair, A-tailing, ligation to index adaptators (TrueSeq protocol
CR), PCR amplification, quantification of Library (bioanalyzer), in-solution hybridization
Nebulization; End-repair, ligation to adaptators, quantification of Library, Clonal emulsion PCRNebulization; LR-PCREnd-repair, phosphorylation, ligation to MID adaptators, quantification of Library,
Clonal emulsion PCR
Sample Multiplexing and cost5 to 35 samples8 to 24 samplesNo multiplexing4 samples
NGS technology200bp single reads
Ion 314TM (10Mb) or Ion 318 (1Gb)
Ion torrent Personal Genome Machine (PGM)
2x100bp paired-end reads
HiSeq 2000 instrument (Illumina)
Single or Paired-end reads, up to 450 bp
GS Junior Sequencer (454 Life Sciences)
MappingHuman genomeHuman genome (GATK pipeline and picard-tools)Human Genome (454 Roche GS Junior data analysis pipeline)Chromosome 7
(454 Roche GS Junior data analysis pipeline)
Minimal coverageMean: 231X
99,7% covered by minimum 5 reads
CFTR Sequencing statistics
Specificity97% (22 on 23 mutations)100%ND
Sensitivity100% (23 on 23 mutations)100% (122 on 122 variants)ND
Reproducibility100% (56 on 56 variants in 3 independent runs)100% (8 on 8 variants)ND
Bioinformatics tools
CNVDepth-Of-Coverage tool from the Genome Analysis ToolKit (GATK)
--> Log2 ratios < -0.7: deletion
Pindel; Conifer; PeSV-Fisher
5 large deletions, 1 duplication and 1 genomic rearrangement
SNV7 variants per sample on average115 SNV + 28 InDels on average per sample197 variants on average per sample (118 in introns)
Variant identification and callingNot notifiedVariant prediction tools: GATK Unified Genotyper, samtools mpileup, SHOREReference Mapper
SeqNext (JSI medical systems)
Variant filterMin coverage: 20 reads
Threshold: 25% reads
Both strands
GATK Variant filtration (MQ<30.0; QUAL<25.0; QD<4.0; DP<5; DP<2000; GQ<15)
GATK Combine Variant
Threshold 30% of reads
Heterozygous: Read support: 30-65%
Homozygous >70%
DatabasesVariant frequency > 1%
Exome Variant Server
1000 Genomes project
Exome Variant Server
1000 Genomes project
In silicoanalysisSIFT
Mutation Taster
SIFT, PolyPhen2, PhyloP, Mutation Taster, phastCons
UCSC Genome Browser
Annovar, HSF, MaxEnt, NNSplice, SIFT, Polyphen2, PhyloP, UCSC Genome Browser, Ensembl, MutationTaster
Diagnostic rateND98.91%88.9% (16 / 18)
Particular casesFalse positive c.2052del (2184delA)
Sanger sequencing to determine the c.1210-34TG(11-13)T(5-9) haplotype
In-house script to determine the c.1210-34TG(11-13)T(5-9) haplotypeFalse positive c.2052del (2184delA)
Sanger sequencing to determine the c.1210-34TG(11-13);-12T(5-9) haplotype
AdvantagesRobust, specific, limited number of VUCsBreakpoints of large genomic rearrangement accurately determinedLR-PCR more adapted for complete gene resequencing
New intronic mutations identified
LimitsSINE/LINE repeats (amplification and mapping)
Homopolymer stretches (Sequencing)
GC rich genomic segments (target enrichment)
Errors in index tags (sequencing)
SINE/LINE repeats (amplification and mapping)
Homopolymer stretches (Sequencing)
Uncaptured region in intron 3

Table 4.

Comparison of three NGS strategies for CFTRsequencing

2.3. Other loci

With the perspectives of high throughput molecular diagnosis in genetics laboratories, CF, CFTR-RDs and CF-like diseases could be simultaneously explored in patients using NGS. Various gene panels could be investigated according to patients’ phenotypes. Therefore, NGS approaches could contribute to (i) identify and confirm the implication of modifiers genes and (ii) improve molecular diagnosis of atypical Cystic Fibrosis, CFTR-RDs or CF-like diseases. Several sequence changes located in the so-called ‘modifier genes’ have been associated with progression of lung disease in CF patients. A decrease of pulmonary function measured by FEV1 (the forced expired volume after 1 s of blowing out) was associated with SNVs in EDNRA, ACER, IFRD1, IL8, MUC5ACand TGF-β1genes. Haplotype 8.1 and variants in MBL2 gene were related to Pseudomonas aeruginosacolonization [28, 29]. SNVs in SNAP23, PPP2R4, PPP2R1A and KRT19were recently associated with a decrease of lung function. Interactions between CFTR and these altered proteins may modify CFTR trafficking and membrane stability and therefore modify phenotype of CF patients [30]. Moreover, changes in other modifier genes were suspected to have an effect on intestinal obstruction (DCTN4, ADIPOR2and MSRAgenes), CF-associated diabetes (TCF7L2gene) or liver disease (SERPIN1A1) [31].

CFTR-Related disorders comprise congenital bilateral absence of vas deferens (CBAVD), pancreatitis, diffuse bronchiectasis and nasal polyposis. Classically, two CFTRmutations (a severe and a mild mutation or two mild mutations) are found in well-characterized CBAVD patients [32]. In addition, Sharma et al. reported 2 SNVs on TGF-β1and ENDRAgenes associated with urogenital anomalies [33]. Chronic pancreatitis is caused by CFTRvariations in some cases, and mutations in CTRC, PRSS1, PRSS2or SPINK1are also involved and must be analysed.

The implication of CFTRin diffuse bronchiectasis or nasal polyposis is more controversial [34]; variants in other genes were previously reported as possibly causative. However, the identification of mutations in other genes with sufficient significance remains difficult and needs large patient cohorts. In fact, in airway tract diseases, the influence of environment (pollutants, drugs/therapy and way of life) complicated the achievement of unbiased studies. Nevertheless, the hypothesis of oligogenism is supported by a study that reported mutations in ENaC channel genes (SCNN1A, SCNN1B, SCNN1G) or SERPIN1A1in CF-like patients (borderline sweat-test and suggestive CF clinical features without two CFTRmutations) [35].

Finally, a pleiotropic effect of SLC26A9on meconium ileus, pancreatic damage and lung disease has been identified [36], as well as SLC9A3for meconium ileus and lung disease and SLC6A14for meconium ileus and both lung disease and age at first P. aeruginosainfection [37]. Thus, the existence of pleiotropic effect of modifier genes on CF evolution may encourage the development of new therapeutic targets with multi-organ benefits.

3. Functional characterization of CFTRsequence variations

To provide appropriate diagnosis and prognosis to CF patients and also appropriate genetic counselling to families, the impact of variants identified by the techniques detailed above has to be functionally characterized.

Some variants such as frequent mutations (found in more than 1% of CF mutated alleles), nonsense or frameshift mutations are readily classified as pathogenic mutations. However, the frequent identification of rare sequence alterations of unknown pathogenicity (VUCS, VUS) substantially complicates test interpretation. Moreover, their number will increase with the diffusion of NGS technologies. To facilitate classification of these variants, CF laboratories have to combine several tools like central mutation databases or CFTRlocus specific databases, in silicoprediction tools and ex vivo/in vivofunctional analyses [38, 39].

3.1. Epidemiological data and locus specific databases dedicated to CFTR

3.1.1. Core or central mutation databases

Their goal is to collect all sequence variations detected in all genes and to describe each mutation briefly. These databases are used to assess the frequency of a variation (minor allele frequency (MAF) lower or higher than 1%) in the general population (i.e. unaffected individuals). Since a MAF higher than 1% reflects a low probability for the variant to be pathogenic, such data may be highly informative for the interpretation of variants. The National Center for Biotechnology Information (NCBI) short genetic variations database dbSNP

dbSNP [25] is the most comprehensive directory of single nucleotide variations. It catalogues short variations in nucleotide sequences from a wide range of organisms. Genetic Variations may be common, thus representing true polymorphisms, or they may be rare. Some of these rare human entries have additional information associated with them, including disease associations, genotype information and allele origin, as some variations are somatic rather than germ-line events. Genotypes and allele frequencies information for various populations from different studies, including data form the HapMap project, are also available. Databases specifically collecting data from NGS projects

1000Genomes [24] aimed to find most genetic variants that have frequencies of at least 1% in samples from five populations: East Asian, South Asian, African, European and American ancestries. As in dbSNP, genotypes and allele frequencies information are available for a large number of variants [40].

Exome Variant Server [41] is a database that collects data of the NHLBI GO Exome Sequencing Project (ESP). This project aimed to discover novel genes and mechanisms contributing to various disorders by sequencing the protein coding regions of the human genome (i.e. exome) using NGS technology. As the CFTRgene is widely studied, this tool would not be of added value compared to dbSNP and 1000 Genomes.

3.1.2. Locus Specific Databases (LSDBs) dedicated to CFTR

LSDBs are now recognized as the best mode of collecting and curating lists of mutations related to human genetic diseases [42]. They compile in a single bioinformatics tool disease-causing and non-disease-causing sequence variations identified by genetics laboratories in families with a history of a given Mendelian disease. The most sophisticated ones integrate clinical and biological data, information on the geographic and/or ethnic origin, frequency of variations in the general population, mutation hot spots and all useful information for diagnosis, prognosis and the evaluation of genotype/phenotype relationships [43].

Here we choose to detail three LSDB dedicated to CFTRthat provide complementary information for the interpretation and the characterization of variants identified in diagnostics practice. Cystic fibrosis mutation database

The Cystic Fibrosis Mutation Database (CFMDB) [44] also called ‘CFTR1’ is an open access database dedicated to the collection of sequence variations in the CFTRgene for the international CF genetics research community. It was initiated by the Cystic Fibrosis Genetic Analysis Consortium (CFGAC) in 1989 and is maintained by the Cystic Fibrosis Centre at the Hospital for Sick Children in Toronto. CFMDB allows the direct submission of new variants by laboratories, by filling out an on- line standardized form with the possibility to detail phenotypic data, genotype (i.e. other variants identified in patient) or epidemiological data. The key point of this database is to collect the largest number of CFTRsequence variations identified in patients, relatives and partners. On the other side, because the submission procedure applies only to the initial report of each variant, CFMDB does not provide frequency data, available with the two other databases described below. Finally, contributors do not always follow HGVS recommendations and a same variant can be reported by several laboratories under different names, possibly leading to misinterpretation or misreporting in diagnosis reports. Clinical and functional translation of CFTR database CFTR2

CFTR2 [45] is a website designed to provide information about specific CF mutations to patients, researchers and the general public. For each mutation included in the database, it provides information about whether a given mutation causes cystic fibrosis when combined with another CF-causing mutation and clinical and biological information (sweat chloride, lung function, pancreatic status and pseudomonas infection rates) in patients carrying the mutation. A specific section for health practitioners and scientists provides more in-depth and research-related information.

The goal of the CFTR2 project is to categorize all mutations seen in CF patients as disease-causing (always resulting in CF when combined with another CF-causing mutation), neutral or mutation of varying clinical consequences (CF and CFTR-RD). Mutations that have not been fully analysed are considered of unknown clinical significance.

The major advantages of CFTR2 are (i) the collection of detailed clinical characteristics on large cohorts of individuals [46] that provide useful information related to a given genotype, and (ii) results of functional testing that are key arguments for their final interpretation [47].

However, this database only collects clinical and genetic data of CF patients (from national registers) that can lead to a bias of phenotypic spectrum assessment of several mutations considered as CF-causing mutations while they were also reported in CFTR-RD patients in transof other CF-causing mutations. The French molecular database CFTR-France

CFTR-France [48] has been developed since 2012 with the aim to collect, store and process any category of variants identified in the CFTRgene, thanks to the collaboration of nine French laboratories with high expertise in the molecular analyses of this gene. Its specificity is to compile and annotate any category of variations (disease-causing, non-disease-causing and variants of unknown clinical significance) that have been identified by collaborators in patients affected with CF or CFTR-RD, in foetuses with abnormal ultrasonography (e.g. echogenic bowel), newborns with pending or inconclusive diagnosis and asymptomatic individuals carrying at least one sequence variation on each CFTR gene (i.e. carrying two variations in trans). The database includes the main clinical data of these individuals, genetic information from familial segregation studies and various variant annotations (frequency in patients and controls populations, sequence homology, predicted or experimentally assessed functional impact, etc.), allowing the analysis of genotype/phenotype relationships.

Thus, CFTR-France, by collecting all phenotypes, reflects the phenotypic spectrum of a large number of mutations. It also reports mutations in complex alleles with association frequencies (related to all individuals recorded in the database), and gives the up-to-date HGVS nomenclature of mutations.

Data collected in CFTR-France are provided by level 2 (specialised) and reference laboratories, so that patients analysed only by level 1 laboratories (searching for the most common mutations) are not included in the database.

Note: Access to CFTR-France is currently restricted to collaborators. A public access program is in progress for the medical and scientific community and for patients and families.

3.2. In silicoprediction analyses

3.2.1. Variants located in exons and exon-intron boundaries Prediction tools for the assessment of the impact on protein

Prediction methods of amino acid substitutions use protein sequence, structure and/or annotation. Disease-causing mutations that affect protein function tend to occur at evolutionarily conserved sites and/or at key positions in protein structure. Multiple sequence alignment of orthologous sequences reveal what positions have been conserved through evolution, and these positions are supposed to be important for protein function. Annotation can enhance prediction for variants located in structurally and functionally important domains, but this information is often sparse.

The issue of the efficiency of prediction tools in assessing possible pathogenicity of missense variants in the CFTRgene is of major interest, since they constitute the vast majority of VUS identified in patients. Diagnostics laboratories frequently use those tools and particularly in problematic situations. Unfortunately their performance has not been clearly established and results (i.e. score of pathogenicity) may be discordant for a given variant.

Predictions of the impact of non-synonymous substitutions in CFTR are mainly based on multiple sequence alignment of orthologous sequences. Indeed, even if a partial 3D model of the CFTR protein has been established [49, 50], prediction tools do not take into account these elements in the final ‘score of pathogenicity’. It is classically recommended to use several prediction tools to obtain concordant predictions that could be considered for variant interpretation.

Table 5 summarizes bioinformatics programs classically used by diagnostics laboratories [51-53] and the new software SuSPect [54].

Method and Web siteAlgorithmOutput
PolyPhen 2 [54]Sequence conservation, structure to model position of amino acid substitution, and SWISS-PROT annotationScore ranges from 0 to a positive number, where 0 is neutral, and a high positive number is damaging
SIFT [55]Sequence homology:
scores are calculated using position-specific scoring matrices with Dirichlet priors
Score ranges from 0 to 1, where 0 is damaging and 1 is neutral
Align GVGD [56]Multiple sequence alignments to characterise the biochemical properties (composition, polarity and volume) of the observed amino acids at each positionThe prediction classes form a spectrum (C0, C15, C25, C35, C45, C55, C65) with C65 most likely to interfere with function and C0 least likely.
SuSPect [57]Sequence-, structure*- and systems biology-based features to predict the phenotypic effects of missense mutations.
Algorithm trained using VariBench (contains information for experimentally verified effects)
*Structure-based comparison with other ABC transporters
Table of scores from 0-100, colour-coded according to predicted deleteriousness (blue=neutral, red=disease-causing).
A score of 50 is recommended as a cut-off between neutral and disease-causing variants
Alamut© [58]
Commercial software (Interactive Biosoftware)
Databases: RefSeq, dbSNP, Uniprot, InterPro, UCSC Genome Browser Database, PubMed, Ensembl.
Automated access to on-line prediction tools: PolyPhen2, SIFT, Align GVGD
Access to splicing prediction tools (detailed in Section III.2.1.2.)
“Alamut Batch”
High-throughput annotation software for NGS analysis
Summarized report of the results of different prediction tools, according to their output format
Designed for intensive variant analysis workflows

Table 5.

Bioinformatics tools for the prediction of amino acid changes: websites, characteristics and output format [55-59]

A recent work has emphasised the importance of sequence alignments on the performance of prediction tools [60]. The authors constructed custom multiple sequence alignments called phenotype-optimized sequence ensembles (POSEs) that was tested on a training set of CFTRmutations.

A previous work already suggested that providing SIFT or PolyPhen-2 with custom alignments increased their performance relative to the default alignments employed by the algorithms [61]. This could explain that, if Alamut© is a highly interesting tool with its ease of use, some predictions obtained by using each tool separately with a custom algorithm could differ from Alamut© results (obtained with default alignments). Prediction tools for the assessment of the impact on pre-mRNA splicing

Splicing mechanisms comprise exon recognition within large pre-mRNA molecules and the precise removal of flanking introns. Three elements constitute the core splicing signals: the intronic branch point, the acceptor site (or 3’splice site), including an inconstant upstream polypyrimidine tract (PPT), and the donor site (or 5’ splice site). These core human splice site motifs contain only a part of the information that defines exons, whereas the rest corresponds to less conserved splicing regulatory elements. The latter are located within the exon or flanking introns, promoting or inhibiting exon recognition through exonic/intronic splicing enhancers (ESE or ISE) or silencers (ESS or ISS), respectively (Figure 3).

Figure 3.

A schematic of key splicing motifs and regulatory elements. Adapted by Le Guédard-Méreuze S. from Wang and Burge, 2008 [62,63].

Many bioinformatics tools have been developed to predict which splicing modification is the most probable for a given sequence variation — exon skipping, cryptic splice sites activation, use of de novosplice sites — or if the variant may be considered as neutral regarding its impact on splicing. Most algorithms were developed based on biostatistical and experimental analyses of information contained in the genomic sequence. They provide a score depending on the strength of the considered splice site. Indeed, the strength of splicing motifs is a key parameter to predict the impact of a sequence variation. Performance of these tools has been widely studied by comparing the results of predictions with experimental assays for various genes including CFTR[39, 63-66]. In 2012, Houdayer and collaborators performed a large-scale study of VUCS in BRCA genes in order to assess the performance of six prediction tools [67]. This work provided guidelines for the proper use of these tools and for the interpretation of prediction results.

Table 6 summarizes principle and main characteristics of the most ‘popular’ bioinformatics programs and ASSEDA, a recently developed program [68-73].

Method and Web siteAlgorithmOutput
MaxEntScan [72]Based on a Maximum Entropy Model (MEM):
Modelling the sequences of short sequence motifs such as those involved in RNA splicing which simultaneously accounts for non-adjacent as well as adjacent dependencies between positions.
Human Splicing Finder (HSF) [73]
Associates weight matrix model (WMM) and
Maximum Entropy Model (MEM)
Consensus splice sites
HSF (WMM): Quantification of the relative likelihood of candidate splice site sequence to show coincidence with consensus sequenceNumerical Score
MaxEnt (MEM): Statistical approach representing the least biased approximation for the distribution of sequence motifs, from available data (real and decoy splice sites). Incorporates local adjacent and non-adjacent position dependenciesNumerical Score
cis-regulatory elements
Predicted PESE AND PESS Octamers from Zhang & Chasin
*See Prediction tools for cis-regulatory elements
See Prediction tools for cis-regulatory elements
NNSplice [74]Machine learning approach that recognizes sequence patterns once it is trained with sets of DNA sequences encompassing authentic and decoy splice sites.
Based on a hidden Markov Model (HMM): Incorporates local adjacent position dependencies
Score between 0 and 1
Automated Splice Site and Exon Definition Analysis (ASSEDA) [75]
Restricted access: registration required
Analyses splicing mutations according to changes in total exon information: information content of a spliced exon from the cumulative contributions of sequences recognized by the spliceosomal machinery and the distribution distances separating binding sites within the same exon.∆Ri,totalvalues (differences in probability of relative inclusion or exclusion of the wild type and mutated exon in mature mRNA)
Prediction tools for cis-regulatory elements
ESEfinder [76]Prediction of SR protein specific putative ESE, based on an in vitro SELEX approach dependent on addition of individual SR proteinsESE motif score
(Relative enhancer and silencer classification by unanimous enrichment)
Statistical approach based upon different distribution of hexamers in exons and introns with different properties (e.g. weak and strong splice sites)Z-score
(Highlights extremely over- or under- represented hexamers)

Table 6.

Main characteristics of several Splicing prediction tools [74-79]

It is important to note that consequences on splicing of exonic synonymous and non-synonymous CFTRvariants must be assessed, as suggested by recent of experimental studies [39, 80].

3.2.2. Deep intronic variants

The examples of insertion of intronic sequences called pseudo-exons (or cryptic exons) in mature transcripts of various genes are becoming ever more numerous and their role in human diseases has been largely demonstrated. We saw in section 2.2 that NGS strategies currently allow scanning of CFTRdeep intronic regions [18], resulting in a growing number of new identified deep intronic variants.

Bioinformatics tools described above, which assess the impact of variants on splicing, can also be used to evaluate deep intronic mutations. We tested these algorithms on mutations identified in CF patients after NGS sequencing of the entire CFTRlocus and they showed satisfactory results [18]. Indeed, prediction tools allowed the selection of possible disease-causing mutations (i.e. predicted impact on splicing by inclusion of pseudo-exons) and predictions were confirmed by in vitrofunctional studies using minigene constructs (see section and by direct analysis of aberrant transcripts from nasal epithelial cells of patients (see

3.3. In vitro/ex vivofunctional analyses

3.3.1. Cell lines transfection experiments

The type of cells used for transfection depends on the tissue that is studied and the clinical context. Pulmonary (BEAS-2B, A549, Calu-3) or intestinal/colic (CACO-2, T84) immortalized cells (by SV40 or carcinoma) contain an appropriate concentration of transcriptional and splicing factors for CFTR protein synthesis. Cells stably transfected with mutated CFTRcan also be used (CFBe41o-, CFPAC-1). Stable expression is usually obtained by lentivirus transduction and transient transfection by chemical agent (Polyfect, interferin). In this case, the endogenous CF molecular and cellular context (inflammation) should also be considered. Splicing assessment

Minigenes are autonomic cyclic entity containing promoter and exons and are produced by clonal amplification in bacteria [81, 82]. They contain a genomic segment from the gene of interest (here CFTR) that includes exon and flanking intronic regions (length can range from ten to thousands of nucleotides, an average of 300 bp) or only intronic regions in the case of evaluation of potential creation of a pseudo-exon. To determine whether a mutation is responsible for altered splicing, minigenes can also include cis-regulatory elements if affected (ESE, ESS, ISE or/and ISS) [83]. These regions of interest are framed by two invariable exons, which are part of the system. Every assay of transfection in cell lines compares the wild-type and mutated (through directed mutagenesis) constructs [84]. All CFTRexons are needed to produce a mature and functional protein. Thus, a modification of transcript in the in vitrosystem suggests that the assessed CFTRchange has a deleterious effect on exon splicing. An ever-increasing number of mini-gene studies have been performed to assess the pathogenicity of CFTRvariants [39, 60, 80, 84]. This strategy, despite its limitations, is of high interest in the overall strategy for the characterization of rare sequence variations. Expression vectors for the quantification of mRNA, protein or CFTR-specific chloride conductance

Full-length CFTRcDNA is classically inserted in expression vector system (e.g. pcDNA3 or p-Tracer) upon a promoter that may be drug-activated (G418, tetracycline or doxycycline-activation). To assess point variants or small indels, directed mutagenesis is carried out (usually QuikChange Mutagenesis kits®, Agilent Technologies). To assess molecular consequences of large rearrangements concerning one or more exons, a truncated CFTRcDNA can be inserted in the expression vector [85-87]. Transient or stable transfection can be performed in eukaryotic cells (describe in III.3.1. section). 3-HA tag (in the fourth loop of CFTR) can be introduced to easily visualize protein expression. Then, measurement of mRNA expression and evaluation of function and localization of the CFTR protein can be performed for each alternative transcript construct, compared to wild-type.

Automated real-time RT-PCR allows the relative straightforward quantification of mRNA transcripts with specific primers and appropriate reference genes for normalization. mRNA level informs about future protein quality and quantity.

Protein assessment consists in the implementation of complementary experiments for protein quantification, evaluation of its maturation or its cellular localization. Main techniques are detailed below. The effect of variants on CFTR expression and maturation is assessed based on the detection of immature (core-glycosylated, B band, ~150-kD) and/or mature (additional glycosylation in the Golgi, C band ~170-190 kDa) CFTR forms by immunoblotting. Long-term pulse-chase experiments can provide additional information on the lifetime of CFTR on cellular compartments [88]. Immunocytochemical assays (Immunofluorescence (IF) based) can highlight the cellular localization of the CFTR protein. However, most difficulties noted in IF experiments relate to non-specific antibody staining and the effect of sample processing on characteristics of cell development. Moreover, confusion between cell surface (where CFTR is active) and subsurface (where it would not) may occur. Therefore, more sensitive and specific antibodies as well as co-localization assays with other cell surface markers (such as β-tubulin or WGA) are needed. Finally, this remains a qualitative or semi-quantitative method.

CFTR function and activity, i.e. CFTR-specific chloride conductance, can be determined by patch-clamp electrophysiology, halide selective electrode technique, radioisotope efflux assays and by fluorescence-based halide efflux measurement. The use of a CFTR-activating appropriate drug (such as forskolin, isobutylmethylxanthine (IBMX), isoproterenol, terbutaline, genistein, adenosine, etc.) or ATP followed by specific CFTR inhibitor CFTRinh-172 permits CFTR-dependant or independent chloride transport, respectively. To date, the easiest approach developed consists in Iodide efflux based on fluorescence measurement. YFP fluorescence is dependent on YFP expression levels and iodide concentration. Compared with conventional plate-bound CFTR functional assays, the flow cytometric approach can be used to study CFTR function in cell suspension. It may be further adjusted to study CFTR function in heterologous cell populations using cell surface markers and selection of cells that display high CFTR function. Technical limitations include the need to perform this assay in specialized centres (using expensive imaging equipment).

All these methods offer the possibility to evaluate the functional consequences of molecular abnormalities on CFTR and finally improve the classification of variants.

3.3.2. Ex vivoCF patients’ cells assays Analyses on primary airway cells and tissues

Characterization of CFTRmolecular defect can partially be performed directly after biopsies of tissues that show sufficient CFTR expression. Nasal or lung airway epitheliums are optimal. These tissues are accessible by minimally invasive interventions and display an endogenous expression of CFTR transcripts and protein. Moreover, nasal and bronchial epitheliums show the same cellular composition (ciliated, goblet, columnar and immune cells) although ratios differ slightly [89]. Since quantification and detection of aberrant splicing and quantification or localization of proteins are possible in human tissues, information that they bring is crucial to assess the effect of variants and to propose a functional classification. However, this approach has its limitations and requires other functional assays to perform large-scale genotype–phenotype correlation studies. In addition, especially for nasal tissue, the low quantity of cells collected (out of 500.000 cells per brushing) only allows ‘one-shot’ tests and hinders mechanistic assays. Moreover, highly variable CFTR expression in heterogeneous cell types, in healthy individuals and in p.Phe508del homozygous patients has been described, varying from 0 to 100% [90]. Other genetic (see below) or environmental parameters could also modify CFTR expression levels. Ex vivoculture of primary airway cells

Culture of primary cells from CF patients can be performed with brushed nasal or bronchial cells after biopsies. Wild-type endogenous CFTR protein is expressed at the apical membrane of polarized cells. Therefore, in vitromonolayer culture seems no longer adapted. Obtaining polarized cells is promoted by air–liquid interface culture (ALI), proposed since the 2000s, by an ex vivosystem of collagen-coated porous membrane on which cells are platted after a phase of monolayer amplification or directly after nasal brushing. Basal adherent cells differentiate in all airways epithelial cell types, which organize into a pseudo-stratified epithelium [91].

This model offers the opportunity to perform functional assays described above to determine CFTR dysfunction. Molecular defect induced by a specific mutation can be qualitatively determined if the cell donor is homozygous for this mutation.

Technical limitations such as bacterial or fungi contaminations or absence of adherence complicate culture of cells obtained from CF patients. Moreover, further studies are needed to determine if extrapolation is possible between observations in primary cells directly after brushing and after several weeks in culture media, particularly for quantitative level assessment. Intestinal current measurement (ICM)

ICM was developed as a research tool to assess CFTR function in the 1990s and has been used as a diagnostic test since the early 2000s [92, 93]. At least four superficial rectal biopsies per patient, obtained by suction, are needed and mounted on adequate tissue sliders. ICM consists in blocking epithelial sodium channels by amiloride and stimulates cAMP-mediated CFTR-depending chloride transport in a chloride-free solution with forskolin or IBMX. Cholinergic chloride transport and histaminic reaction were also evaluated and the sum of the response ∆Iscforskolin/IBMX+carbachol+histamine appears to be the best parameter to evaluate CFTR function by ICM, but reference values and ranges have not been established. This combination of ionic responses discriminates patients with CF from healthy subjects but not CF patients with pancreatic insufficiency (PI) or sufficiency (PS). ICM can detect a loss of CFTR function above 80%, therefore CF carriers and CFTR-RD patients may not be identified by this method. Moreover, mild mutations could result in a false-negative ICM.

ICM is not altered by secondary damage on tissue and thus better reveals the primary CFTR dysfunction (compared with nasal potential difference, see below). A new functional CFTR assay using primary CF intestinal organoïds derived from patients and cultured in vitromay offer new tools to screen for therapy [94].

Finally, setup and maintenance of dedicated equipment by experienced and trained staff limits its use.

3.3.3. In vivobiomarkers assays of CFTR function

Sweat chloride Test (ST) and Nasal potential difference (NPD) measurement are used as diagnostic tests for CF. In atypical clinical context these in vivotests can give additional arguments to further explore the CFTRlocus (cf. Figure 2 that describes the molecular diagnostics step-by-step strategy). Furthermore, they provide complementary information for the interpretation of CFTRvariations.

ST higher than 60 mmol/L is the ‘gold standard’ and discriminates between healthy and typical CF. Sweat electrolytes are higher in the most severely affected and are lower in those with mild mutation, who have partial rescue of channel function [95-97]. There are two major advantages for the use of sweat test in the evaluation of the CFTRmutation severity: (i) stability of the measure throughout life and (ii) non-invasive way of measurement which maintains skin integrity. Indeed, sweat electrolytes levels reflect the primary defect on the CFTR protein and do not highlight secondary consequences of its absence or dysfunction on affected organs. Furthermore, a new means for assessing the secretory function of CFTR has been recently developed based on β-adrenergic-mediated sweating. This method provides a unique evaluation of the purely secretory function of CFTR in vivo. It has been shown to be more sensitive in individuals carrying mutations that commonly exhibit normal or borderline sweat chloride rates, such as c.3718-2477C>T (legacy name: 3849+10kbC>T) [98].

The transepithelial NPD measurement estimates the net ion conductance across the nasal airway epithelium and indirectly measure CFTR activity. Sodium conductance is determined after amiloride perfusion (Na+). NPD parameters discriminate CF and CFTR-RD phenotypes and a correlation between NPD and disease severity has been shown [99], although there is an overlap in subjects with mild phenotype [97]. However, other sources of phenotypic variability (modifiers genes or environment) and technical limitations (i.e. maintenance of dedicated equipment and trained personnel) contribute to the inconsistencies of NPD across genotype-phenotype correlation studies. CFTR expression and NPD response can also be modified if nasal epithelium is affected by rhinosinusitis, polyposis or exposure to smoking [100]. However, despite the difficulty of setting up this technique, NPD assessment could be used as a complementary step to support CFTR dysfunction in inconclusive clinical cases and then to confirm the deleterious effect of CFTRvariants identified in these patients.

4. Conclusion

New issues are emerging from the use of NGS technology in CF molecular diagnosis. On one hand, NGS approaches offer new possibilities by multiplexing samples and provide a wider coverage of the CFTRlocus including deep intronic regions. NGS assay design can also include additional modifiers genes [31]. On the other hand, molecular diagnoses in emergency contexts challenge the possibility of sample multiplexing, and the increased number of VUCS will require complex functional analyses. However, as tools described above are constantly improving, the knowledge about CFTRvariations is rapidly expanding, allowing geneticists and clinicians to provide patients with high quality information and adequate genetic counselling. Finally, the functional characterization of CFTR variations will provide rationale for a personalised medicine strategy driven by patients’ genotype in the very near future.


We acknowledge all members of the technical team for CF molecular diagnosis: Jean-Pierre Altieri, Fanny Verneau and Lise Larrieu. We also thank the coordination team of the CFTR-France genetic database: Corinne Bareil and Corinne Thèze and all the collaborating members of this project. We are also grateful to the French Network of laboratories specialized in molecular diagnosis of CF (GenMucoFrance). Finally, we thank the French Association against CF (Vaincre la Mucoviscidose) for its constant support.

© 2015 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Anne Bergougnoux, Magali Taulan-Cadars, Mireille Claustres and Caroline Raynal (August 24th 2015). New Molecular Diagnosis Approaches — From the Identification of Mutations to their Characterization, Cystic Fibrosis in the Light of New Research, Dennis Wat, IntechOpen, DOI: 10.5772/60679. Available from:

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