You are here

Article Text

Article menu
Basic and translational research
Extended report
Next-generation sequencing and its initial applications for molecular diagnosis of systemic auto-inflammatory diseases
  1. Marta Rusmini1,
  2. Silvia Federici2,
  3. Francesco Caroli1,
  4. Alice Grossi1,
  5. Maurizia Baldi3,
  6. Laura Obici4,
  7. Antonella Insalaco5,
  8. Alberto Tommasini6,
  9. Roberta Caorsi2,
  10. Eleonora Gallo7,
  11. Alma Nunzia Olivieri8,
  12. AngeloValerio Marzano9,
  13. Domenico Coviello3,
  14. Roberto Ravazzolo1,10,
  15. Alberto Martini2,10,
  16. Marco Gattorno2,
  17. Isabella Ceccherini1
  1. 1UOC Medical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
  2. 2UOC Pediatric Rheumatology, Lab Immunology Rheumatic Diseases, IRCCS Istituto Giannina Gaslini, Genoa, Italy
  3. 3Laboratory of Human Genetics, Ospedali Galliera, Genoa, Italy
  4. 4Amyloidosis Research and Treatment Centre, Biotechnology Research Laboratories, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
  5. 5Division of Rheumatology, Department of Pediatric Medicine, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
  6. 6Department of Pediatrics, Institute for Maternal and Child Health, IRCCS Burlo Garofolo, Trieste, Italy
  7. 7Dipartimento di Salute Pubblica e Pediatria, Universita’ di Torino, Torino, Italy
  8. 8Dipartimento della Donna, del Bambino e di Chirurgia Generale e Specialistica, Seconda Universita’ degli Studi di Napoli, Napoli, Italia
  9. 9UO Dermatologia, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico—Dipartimento di Fisiopatologia Medico-Chirurgica e dei Trapianti, Università degli Studi di Milano, Milan, Italy
  10. 10Dipartimento Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno-Infantili (DiNOGMI), Università degli Studi di Genova, Genova, Italy
  1. Correspondence to Dr Isabella Ceccherini, UOC Genetica Medica, Istituto Giannina Gaslini, Genova 16148, Italy; isa.c{at}unige.it

Abstract

Objectives Systemic auto-inflammatory disorders (SAIDs) are a heterogeneous group of monogenic diseases sharing a primary dysfunction of the innate immune system. More than 50% of patients with SAID does not show any mutation at gene(s) tested because of lack of precise clinical classification criteria and/or incomplete gene screening. To improve the molecular diagnosis and genotype interpretation of SAIDs, we undertook the development of a next-generation sequencing (NGS)-based protocol designed to simultaneous screening of 10 genes.

Methods Fifty patients with SAID, already genotyped for the respective causative gene(s), were massively sequenced for the coding portions of MEFV, MVK, TNFRSF1A, NLRP3, NLRP12, NOD2, PSTPIP1, IL1RN, LPIN2 and PSMB8. Three different bioinformatic pipelines (Ion Reporter, CLC Bio Genomics Workbench, GATK-based in-house workflow) were compared.

Results Once resulting variants were compared with the expected mutation list, no workflow turned out to be able to detect all the 79 variants known in the 50 DNAs. Additional variants were also detected, validated by Sanger sequencing and compared to assess true and false positive detection rates of the three workflows. Finally, the overall clinical picture of 34 patients was re-evaluated in the light of the new mutations found.

Conclusions The present gene panel has resulted suitable for molecular diagnosis of SAIDs. Moreover, genotype–phenotype correlation has confirmed that the interpretation of NGS data in patients with an undefined inflammatory phenotype is remarkably difficult, thus supporting the need of evidence-based and validated clinical criteria to be used concurrently with the genetic analysis for the final diagnosis and classification of patients with SAIDs.

  • Fever Syndromes
  • Inflammation
  • Gene Polymorphism

https://doi.org/10.1136/annrheumdis-2015-207701

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Introduction

    Systemic auto-inflammatory diseases (SAIDs) are a group of disorders characterised by inflammation that occurs in the absence of pathogenic autoantibodies, autoreactive T lymphocytes or other infective causes.1 ,2 The inflammatory attacks are predominantly mediated by cells of the innate immune system, neutrophils and monocytes, and by molecules, such as interleukin (IL)-1β, IL-6 and tumour necrosis factor (TNF)-α.3 ,4

    Depending on gene defect(s) and diagnosis, patients affected with SAIDs exhibit continuous or recurrent acute phase response.3 SAID conditions have a heterogeneous genetics characterised by monogenic, and at a lesser extent, multifactorial inheritance, with causative genes involved in the innate branch of the immune response.5 Manifestations usually start in early infancy or neonatal period,6 ,7 but rare adult onset has also been described.8 ,9 SAID clinical picture is extremely wide, ranging from recurrent and self-limiting fever episodes associated with arthralgia and myalgia, rash, chest and abdominal pain (periodic fevers) to chronic and persistent inflammatory disease course with skin rash, arthritis and possible severe neurological inflammation (cryopyrinopathies). Some diseases are characterised by more localised inflammatory manifestations at skin, joint and bones (Pyogenic Arthritis, Pyoderma gangrenosum and Acne (PAPA) and Blau's syndromes). Finally, variable combinations of symptoms in patients with uncharacterised systemic inflammatory diseases may make the diagnosis very difficult.10 ,11

    A great improvement in SAID diagnosis has been achieved since 1997, when the first SAID gene was identified.12 ,13 To date, there are 25 known SAID genes reported in the Infevers database (http://fmf.igh.cnrs.fr/ISSAID/infevers), responsible for many more SAID conditions, due to the wide phenotypic heterogeneity typical of these disorders. Molecular genetics has greatly contributed to correct diagnosis, especially in atypical presentations.14 On the other hand, mutational screening may not be exhaustive if focused on a minority of known genes and/or mutational hotspots.6 Indeed, it has been reported that at least 50% of patients with SAID do show a negative genetics.3 Reasons for such a low mutation detection rate include polygenic complex phenotypes, such as systemic onset juvenile idiopathic arthritis and likely also PFAPA syndrome,15 ,16 lack of precise clinical classification criteria and missing mutations because of partial gene screening, namely the screening of either a limited number of candidate genes or a subset of coding portions. As a further complication, a number of polymorphisms have frequently been detected in many SAID genes and investigated to verify their possible association with specific phenotypes.17–20Moreover, even though SAIDs are characterised by early onset, adult patients carrying low penetrant mutations have also been described for cryoyrin-associated periodic syndrome (CAPS), tumor necrosis factor (TNF) receptor-associated periodic syndrome (TRAPS) and familial Mediterranean fever (FMF),21–24 thus indicating genetic and clinical complexity. Finally, somatic mosaicism has been demonstrated, by means of very deep sequencing, in patients presenting with typical CAPS and Blau syndromes, but negative for germline mutations.25 ,26

    The very fast development of new technologies for massive sequencing has made it possible to focus on many different genes simultaneously, a comprehensive approach that effectively fulfils the diagnosis of complex and/or heterogeneous diseases.27–30

    In an attempt to improve the molecular diagnosis and genotype interpretation of patients affected with SAIDs, here we report the development of a next-generation sequencing (NGS)-based protocol designed to simultaneously screen 10 genes. Fifty DNA samples, from patients with SAID already genotyped for the respective causative gene(s), were sequenced and three different bioinformatic pipelines compared. Sensitivity and specificity of data analysis were assessed, thus prompting both the application of the protocol to routine diagnosis and the study of the correlation between genotypes and the vast range of SAID phenotypes.

    Materials and methods

    DNA samples

    Forty-eight genomic DNA samples from patients with SAID and two asymptomatic transmitting parents had already been found to carry at least one mutation in one of the known causative genes previously tested through Sanger sequencing. These mutation-positive patients were taken into consideration for developing an NGS-based diagnostic protocol.

    Genes panel design, libraries preparation and sequencing

    A panel of gene amplicons specific for SAIDs was designed through the Ion AmpliSeq designer software (https://www.ampliseq.com/browse.action), including 10 genes selected from those reported on the Infevers database (http://fmf.igh.cnrs.fr/ISSAID/infevers/), most of which have been known for a long time to be involved in the pathogenesis of SAIDs (see online supplementary table S1). Coding regions, plus 20 bp flanking each exon, were included in the design, which ended up with 21.8 kb of target DNA subdivided into 191 amplicons (2 primer pools) covering 116 exons. Libraries preparation and successive sequencing were carried out according to Life Technologies’ protocols. Further details are reported in the online supplementary text.

    Bioinformatic analysis

    FastQ data, generated by Ion PGM semiconductor, were analysed by three different workflows: Ion Reporter 4.0, CLC Bio Genomics Workbench 6.5 and GATK-based in-house pipeline, as outlined in the online supplementary figure S1, and reported in detail in the online supplementary text.

    Coverage analysis

    Coverage assessment was carried out for every run by the Ion Coverage Analysis plug-in v4.0-r77897 and amplimers at <10× were discarded as unsuitable for further analysis.

    Validation

    Additional missense and frameshift variants were all validated by Sanger sequencing. PCR products were purified by ExoSAP-IT (GE Healthcare) and directly sequenced using Big Dye V.1.1 and a ABI3130 automated sequencer (Applied Biosystems, Foster City, California, USA). Validated variants were studied by SIFT (http://sift.jcvi.org) and PolyPhen (http://genetics.bwh.harvard.edu/pph2/) software in order to predict their possible functional impact.

    Statistical tests

    Two-tailed Fisher's test was performed to compare variant allele frequencies assessed in the present study with those reported in the 1000 Genomes Project website (http://www.1000genomes.org/). p Values corrected for multiple testing are reported and considered significant when <0.05.

    Results

    SAID gene panel

    An NGS-based gene panel screening was developed in order to identify causative mutations in SAIDs. To this end, 10 genes were selected, whose known mutations represent approximately 75% of all the entries reported in the Infevers database (http://fmf.igh.cnrs.fr/ISSAID/infevers/). Overall, the panel includes the MEFV, MVK, TNFRSF1A, NLRP3, NLRP12, NOD2, PSTPIP1, IL1RN, LPIN2 and PSMB8 genes, known to be associated with five SAID spectra, as reported in the online supplementary table S1. More than 99% of the input gene regions could be included in the experimental design. Missed regions are reported in online supplementary table S2.

    Samples and PGM runs

    Fifty DNA samples, from subjects previously genotyped by Sanger sequencing and carrying at least one coding mutation in one of the 10 SAID genes under analysis, were selected and processed through the new screening protocol. The whole target was captured by means of 191 multiplex amplification reactions subdivided into two PCR pools including 95 and 96 amplicons, respectively. Not all the amplicons could be amplified with the same efficiency. Indeed, a mean coverage of 336× was assessed for the whole target of 191 amplimers in the 50 DNA samples, with 98.5% of amplicons at >10× and 96.06% at >30×. The mean coverage of each amplimer for all the 50 samples is reported in online supplementary figure S2. In figure 1, the 191 amplimers are ordered from the least (left) to the most (right) represented, with those at <30× framed by a red box. In order to find the minimum coverage able to detect true positive variants, amplimers at <10× were assessed and found to be only three, when estimated as mean among the 50 samples. However, there are amplimers showing such a little coverage in a significant proportion of DNA samples, as reported in online supplementary table S3 listing amplimers with a coverage of ≤10× in >50% and 20% of samples. Amplimers presenting with a high rate of low coverage, thus identified, are checked after every run and eventually screened by Sanger sequencing.

    Figure 1
    Figure 1

    Coverage analysis of all the amplimer reads obtained from all 50 samples. The 191 amplimers used for the enrichment of our gene target are ordered according to the mean coverage, from the least to the most represented in the reads pool, as calculated among the 50 samples tested. Each amplimer is, therefore, represented by its mean coverage (dots) and the SD (bar) resulting from all the runs. Amplimers with a mean coverage <30× are framed in the thick black box on the left.

    Expected mutations

    Due to sample ascertainment, at least one mutation in one of the 10 genes under study was expected to be found per patient. Therefore, analyses performed by means of the three bioinformatic tools aimed at maximising detection of the expected variants, and implying that, once these variants were correctly called, we would detect also unknown variants of genes not previously analysed in each patient. Resulting variants were compared with the expected mutation list. Surprisingly, no workflow was able to detect all the 79 variants known in the 50 DNA samples (table 1). The CLC Genomic Workbench missed 11 variants, 10 of which are the same missense substitution p.V377I of the MVK gene, while one is the p.M694I mutation of the MEFV gene. The p.V377I change affects a codon lying in the amplimer named MV10 that is known to be covered <10× in >20% of samples (see online supplementary table S3). This can also explain the eight p.V377I variants missed by the Ion Reporter workflow. The GATK-based pipeline has returned the most reliable results, missing only two variants: p.F433L and p.E277D in the NLRP3 and PSTPIP1 genes, respectively. In an attempt to force the calling of these two mutations, we changed the default setting of the ‘maximum deletion fraction’, which normally avoids calls for variants rounded by >5% of reads with deletions. Unfortunately, besides detecting both mutations, with the new settings the analysis suffered from drawbacks and three false variants (p.Q345K and p.A946T in the NOD2 gene and p.R760G in the LPIN2 gene) were called in addition, as confirmed by Sanger sequencing.

    View this table:
    Table 1

    List of expected mutations, namely known to be present in the 50 selected samples, analysed

    Additional mutations

    The targeted re-sequencing of many genes not previously analysed in the patients has also allowed us to detect a number of additional variants. These new missense and indel variants, either common or unique, were validated by Sanger sequencing in order to compare the true and false positive detection rates of the three workflows. Interestingly, only 1 out of 50 DNA samples showed no additional rare or common variant, while the remaining 49 individuals turned out to carry more variants in the gene set under analysis.

    A total of 204 variants were detected, only 151 of which were called by all the pipelines (table 2). CLC-Bio Workbench and Ion Reporter analysis returned 151 and 153 variants, respectively. Sanger sequencing could not confirm 2 CLC and 3 Ion Reporter calls. GATK showed the largest amount of false positive calls, with 54 out of 204 variants that could not be confirmed by Sanger sequencing. Therefore, false positive variants were detected by CLC, Ion Reporter and GATK at a rate of 1.32%, 1.96% and 26.47%, respectively.

    View this table:
    Table 2

    List of additional mutations, namely previously unknown to be present in the 50 selected samples, analysed

    Allelic frequencies

    To test the hypothesis that some of the variants are either causative or predisposing to SAID, allele frequencies were compared with data available from the 1000 Genomes Project (http://www.1000genomes.org/), both all the samples (All) and European samples only (EU), as reported in online supplementary table S4, along with prediction data about possible variant effects, obtained using SIFT (http://sift.jcvi.org/) and PolyPhen (http://genetics.bwh.harvard.edu/pph2/) software. Only two single-nucleotide polymorphisms, MEFV p.V726A and LPIN2 p.P626S, resulted in a statistically significant over-representation of the variant alleles in SAID samples compared with 1000 Genome data (All and EU). The MEFV variant resulted also deleterious and possibly damaging, supporting its possible pathogenic effect. On the other hand, the LPIN2 variant was recognised as tolerated and benign, thus suggesting it is a neutral polymorphism. Finally, two NOD2 variants, p.P268S and p.V955I already known as genetic variations of uncertain significance, turned out with allele frequencies higher in SAID than controls (All but not EU).

    Impact of different variants on the clinical phenotype

    The availability of complete genotyping data at 10 loci has prompted us to re-evaluate the overall clinical picture of the patients also in the light of the new mutations found. Complete clinical information at the last follow-up was available for 34 out of 50 individuals (48 patients and 2 transmitting asymptomatic parents). This has allowed us to draw a genotype–phenotype correlation, as displayed in table 3, whose details about groups and single patients are reported in the online supplementary information file.

    View this table:
    Table 3

    classification of 34 out of 50 patients tested using the present systemic auto-inflammatory disorder (SAID) gene panel

    Seven patients with definitive diagnosis of SAID (two CAPS, three mevalonate kinase deficiency (MKD) and two FMF) did not display any additional possibly causative variant/mutation, besides those already known, and were included in group 1. Consistently, these patients showed a clinical phenotype typical of the specific disease. Conversely, 16 patients with a definitive diagnosis of SAID (four CAPS, five MKD, two TRAPS, three FMF, one PAPA and one FCAS2) were carriers of one or more additional possible effective variants in at least another gene included in the NGS panel (group 2, table 3). Notably, in the majority of these patients, the additional variants seemed not to modify the clinical picture that was therefore consistent with the principal diagnosis. The third group includes patients without a definitive diagnosis of SAID, who show no clear causative mutation, but rather have variants that might have had a role in their disease-like phenotypes. Also in this case, additional mutations did not influence significantly the clinical picture (group 3, table 3). Finally, four patients with undefined clinical diagnosis and non-confirmatory genetic test belong to group 4. Further clinical details about these patients can be found in table 3 and the online supplementary text.

    Discussion

    SAIDs are a heterogeneous group of both monogenic and, at a lesser extent, multifactorial diseases caused by primary dysfunctions of the innate immune system.5 Previous diagnosis by Sanger sequencing, performed by us on a restricted number of genes, namely NLRP3, MVK and TNFRSF1A, and/or gene portions, has resulted time consuming and expensive, failing to detect mutations in around 86% of >2000 patients recruited among several Italian Pediatric Rheumatologic Units (unpublished data).

    Clinical misdiagnosis, mutations in untested gene regions, genetic heterogeneity and/or a complex mode of inheritance are all possible explanations. To improve molecular diagnosis, and to draw genotype–phenotype correlations, we sought to approach the mutation search in patients with SAID by using an NGS procedure. To this end, we launched a pilot project consisting in the mutational screening of 10 genes, through amplification capture of their coding portions and deep sequencing by an NGS platform, in 50 already diagnosed patients with SAID. The genes selected for the analysis were highly representative of the known SAID genes carrying, at the time of the experimental design, >75% of all the mutation entries reported in the Infevers database (http://fmf.igh.cnrs.fr/ISSAID/infevers/). Patients were known to carry mutations at one or more of these genes, and altogether guaranteed a heterogeneous set of genotypes. Results of our study have laid bare a number of not only technical but also genetic and clinical aspects that deserve consideration.

    As expected, the gene input did not coincide with the amplification design obtained from the AmpliSeq online tool, and 6 regions of 5 genes, for a total of 58 coding basepairs, did not result to be part of the initial target capture. Further sequences were missed as, despite the very high mean coverage of the whole target among the 50 samples (336×), some amplicons did not achieve a reliable coverage. Overall, around 1.6 kb of coding target sequences (7.3% of total target) were not included because of either lack of original design or poor amplification rate. Once precisely mapped, these regions need to be tested by standard Sanger sequencing approach in order to implement the analysis results with variants possibly located within these gene portions, refractory with the present capture tool to the NGS assay. Indeed, this is a matter we need to be aware of as we have experienced, during the validation of our NGS procedure, one frequent MVK mutation (p.V377I) missed several times from two out of three bioinformatic pipelines because of low coverage of the corresponding amplicon (in each case <10×).

    Variant calling also represents a tricky step, which we undertook by comparing three different pipelines: (i) Ion Reporter, (ii) GATK-based and (iii) CLC-Bio software. Mutations previously detected by standard analysis in the 50 subjects (48 patients with SAID +2 parents), hence referred to as ‘expected’ mutations, were correctly called, except a few cases, due to either low coverage or setting conditions. These confounding circumstances need to be kept in mind to minimise the false negative rate. On the other hand, due to our experimental design, many additional variants were called, yet unknown in the corresponding patients as they had not been analysed for all the 10 genes before. To estimate the rate of false positives among these latter variants, 204 of them were validated through standard Sanger analysis. A quite high number of variants called by the GATK-based workflow could not be confirmed while the specificity of the two other pipelines turned out to be much higher (≥98% vs 74%). Altogether, we can conclude that the Ion Reporter 4.0 has returned the most significant results, which are highly reliable provided a careful consideration of the gene portions presenting with low coverage. Finally, the minimum coverage needed for a reliable diagnostic test has already been recommended to be 30×; 31 however, according to our results, which missed variants in amplicons at ≤10×, we are tempted to suggest a coverage of ≥15× to call germline variants.

    Besides mere technical evaluations, we also assessed the potential impact of the additional variants on patients’ phenotypes by (i) comparing allele frequencies in our set with those deduced by the 1000 Genomes Project and (ii) drawing a genotype–phenotype correlation.

    A few common variants have turned out to be possibly associated with SAID, an observation that may account for the wide phenotypic variability of these disorders, and deserving further investigations. Interestingly, some genes, such as IL1RN and PSMB8, did not show many variants, while others, that is, NOD2, LPIN2 and NLRP12, presented with a high frequency of mutations, some of which might be responsible for mild or atypical symptoms.

    Nevertheless, their contribution to the clinics is still doubtful, representing the most challenging issue for the actual use of NGS panels in the daily clinical practice in patients with suspected SAID. Indeed, our findings are going to have an impact on diagnosis assessment and interactions between geneticists and clinicians to interpret genotypes and report combinations of new and/or known NGS variants. The contribution of multiple genes to SAIDs has already been reported, representing not only a fortuitous coexistence of highly prevalent mutations in a single individual but digenic inheritance could be proven in some cases too.32 In the present study, we had the possibility to analyse for the first time the possible impact of additional variants in other genes in the clinical phenotype of 34 patients that we could carefully and systematically review in the light of the novel NGS findings. Our initial experience shows that, with a few exceptions, the presence of additional possibly pathogenic variants in genes included in the present small gene panel and not related to the original diagnosed disorder apparently did not affect the clinical phenotype (group 2), even in patients with a non-confirmatory genetic test (group 3). Conversely, the interpretation of the actual NGS data in patients with an undefined inflammatory phenotype (group 4) is remarkably difficult. Though variant combinations may explain the presence of a complex and ambiguous phenotype, like in patient FP1418, in other cases the presence of several mutations could lead to overestimate the NGS findings and to misdiagnosis. A combination of NGS and clinical information may provide diagnosis for some patients, thus supporting the need of evidence-based and validated clinical criteria as crucial tools to be used concurrently with the genetic analysis for the final diagnosis and classification of patients with SAIDs.33 The unanswered question on the possible role of multi-gene analysis with NGS in the characterisation of undiagnosed patients with an undefined phenotype will be analysed in a larger number of patients through a larger panel of genes.

    References

    1. ↵
      1. Doria A,
      2. Dayer J-M,
      3. Punzi L
      . Autoinflammatory diseases: how to put the fire inside the body out? Autoimmun Rev 2012;12:1–4. doi:10.1016/j.autrev.2012.07.013
      OpenUrlCrossRefPubMed
    2. ↵
      1. Kastner DL,
      2. Aksentijevich I,
      3. Goldbach-Mansky R
      . Autoinflammatory disease reloaded: a clinical perspective. Cell 2010;140:784–90. doi:10.1016/j.cell.2010.03.002
      OpenUrlCrossRefPubMedWeb of Science
    3. ↵
      1. Russo RA,
      2. Brogan PA
      . Monogenic autoinflammatory diseases. Rheumatol Oxf Engl 2014;53:1927–39. http://dx.doi.org/10.1093/rheumatology/keu170
      OpenUrlAbstract/FREE Full Text
    4. ↵
      1. Zen M,
      2. Gatto M,
      3. Domeneghetti M, et al
      . Clinical guidelines and definitions of autoinflammatory diseases: contrasts and comparisons with autoimmunity-a comprehensive review. Clin Rev Allergy Immunol 2013;45:227–35. doi:10.1007/s12016-013-8355-1
      OpenUrlCrossRefPubMed
    5. ↵
      1. Masters SL,
      2. Simon A,
      3. Aksentijevich I, et al
      . Horror autoinflammaticus: the molecular pathophysiology of autoinflammatory disease (*). Annu Rev Immunol 2009;27:621–68. doi:10.1146/annurev.immunol.25.022106.141627
      OpenUrlCrossRefPubMedWeb of Science
    6. ↵
      1. Touitou I
      . New genetic interpretation of old diseases. Autoimmun Rev 2012;12:5–9. doi:10.1016/j.autrev.2012.07.014
      OpenUrlCrossRefPubMed
    7. ↵
      1. Punzi L,
      2. Scanu A,
      3. Ramonda R, et al
      . Gout as autoinflammatory disease: new mechanisms for more appropriated treatment targets. Autoimmun Rev 2012;12:66–71. doi:10.1016/j.autrev.2012.07.024
      OpenUrlCrossRefPubMed
    8. ↵
      1. Cantarini L,
      2. Lucherini OM,
      3. Iacoponi F, et al
      . Development and preliminary validation of a diagnostic score for identifying patients affected with adult-onset autoinflammatory disorders. Int J Immunopathol Pharmacol 2010;23:1133–41.
      OpenUrlAbstract/FREE Full Text
    9. ↵
      1. Sayarlioglu M,
      2. Cefle A,
      3. Inanc M, et al
      . Characteristics of patients with adult-onset familial Mediterranean fever in Turkey: analysis of 401 cases. Int J Clin Pract 2005;59:202–5. doi:10.1111/j.1742-1241.2004.00294.x
      OpenUrlCrossRefPubMedWeb of Science
    10. ↵
      1. Yalçinkaya F,
      2. Ozen S,
      3. Ozçakar ZB, et al
      . A new set of criteria for the diagnosis of familial Mediterranean fever in childhood. Rheumatol Oxf Engl 2009;48:395–8. doi:10.1093/rheumatology/ken509
      OpenUrlAbstract/FREE Full Text
    11. ↵
      1. Kondi A,
      2. Hentgen V,
      3. Piram M, et al
      . Validation of the new paediatric criteria for the diagnosis of familial Mediterranean fever: data from a mixed population of 100 children from the French reference centre for auto-inflammatory disorders. Rheumatol Oxf Engl 2010;49:2200–3. doi:10.1093/rheumatology/keq252
      OpenUrlAbstract/FREE Full Text
    12. ↵
      The International FMF Consortium. Ancient missense mutations in a new member of the RoRet gene family are likely to cause familial Mediterranean fever. Cell 1997;90:797–807. doi:10.1016/S0092-8674(00)80539-5
      OpenUrlCrossRefPubMedWeb of Science
    13. ↵
      French FMF Consortium. A candidate gene for familial Mediterranean fever. Nat Genet 1997;17:25–31. doi:10.1038/ng0997-25
      OpenUrlCrossRefPubMedWeb of Science
    14. ↵
      1. Marcuzzi A,
      2. Piscianz E,
      3. Kleiner G, et al
      . Clinical genetic testing of periodic fever syndromes. BioMed Res Int 2013;2013:501305. doi:10.1155/2013/501305
      OpenUrlPubMed
    15. ↵
      1. Gattorno M,
      2. Caorsi R,
      3. Meini A, et al
      . Differentiating PFAPA syndrome from monogenic periodic fevers. Pediatrics 2009;124:e721–8. doi:10.1542/peds.2009-0088
      OpenUrlAbstract/FREE Full Text
    16. ↵
      1. Federici L,
      2. Rittore-Domingo C,
      3. Koné-Paut I, et al
      . A decision tree for genetic diagnosis of hereditary periodic fever in unselected patients. Ann Rheum Dis 2006;65:1427–32. doi:10.1136/ard.2006.054304
      OpenUrlAbstract/FREE Full Text
    17. ↵
      1. Ogura Y,
      2. Bonen DK,
      3. Inohara N, et al
      . A frameshift mutation in NOD2 associated with susceptibility to Crohn's disease. Nature 2001;411:603–6. doi:10.1038/35079114
      OpenUrlCrossRefPubMedWeb of Science
    18. ↵
      1. Gazouli M,
      2. Mantzaris G,
      3. Kotsinas A, et al
      . Association between polymorphisms in the Toll-like receptor 4, CD14, and CARD15/NOD2 and inflammatory bowel disease in the Greek population. World J Gastroenterol WJG 2005;11:681–5. doi:10.3748/wjg.v11.i5.681
      OpenUrlCrossRefPubMed
    19. ↵
      1. Schoultz I,
      2. Verma D,
      3. Halfvarsson J, et al
      . Combined polymorphisms in genes encoding the inflammasome components NALP3 and CARD8 confer susceptibility to Crohn's disease in Swedish men. Am J Gastroenterol 2009;104:1180–8. doi:10.1038/ajg.2009.29
      OpenUrlCrossRefPubMed
    20. ↵
      1. Ozen S,
      2. Bilginer Y
      . A clinical guide to autoinflammatory diseases: familial Mediterranean fever and next-of-kin. Nat Rev Rheumatol 2014;10:135–47. doi:10.1038/nrrheum.2013.174
      OpenUrlPubMed
    21. ↵
      1. Vitale A,
      2. Lucherini OM,
      3. Galeazzi M, et al
      . Long-term clinical course of patients carrying the Q703K mutation in the NLRP3 gene: a case series. Clin Exp Rheumatol 2012;30:943–6.
      OpenUrlPubMed
    22. ↵
      1. Ravet N,
      2. Rouaghe S,
      3. Dodé C, et al
      . Clinical significance of P46L and R92Q substitutions in the tumour necrosis factor superfamily 1A gene. Ann Rheum Dis 2006;65:1158–62. doi:10.1136/ard.2005.048611
      OpenUrlAbstract/FREE Full Text
    23. ↵
      1. Cantarini L,
      2. Lucherini OM,
      3. Brucato A, et al
      . Clues to detect tumor necrosis factor receptor-associated periodic syndrome (TRAPS) among patients with idiopathic recurrent acute pericarditis: results of a multicentre study. Clin Res Cardiol Off J Ger Card Soc 2012;101:525–31. doi:10.1007/s00392-012-0422-8
      OpenUrlCrossRef
    24. ↵
      1. Tunca M,
      2. Akar S,
      3. Onen F, et al
      . Familial Mediterranean fever (FMF) in Turkey: results of a nationwide multicenter study. Medicine (Baltimore) 2005;84:1–11. doi:10.1097/01.md.0000152370.84628.0c
      OpenUrlCrossRefPubMedWeb of Science
    25. ↵
      1. Nakagawa K,
      2. Gonzalez-Roca E,
      3. Souto A, et al
      . Somatic NLRP3 mosaicism in Muckle-Wells syndrome. A genetic mechanism shared by different phenotypes of cryopyrin-associated periodic syndromes. Ann Rheum Dis 2015;74:603–10. doi:10.1136/annrheumdis-2013-204361
      OpenUrlAbstract/FREE Full Text
    26. ↵
      1. de Inocencio J,
      2. Mensa-Vilaro A,
      3. Tejada-Palacios P, et al
      . Somatic NOD2 mosaicism in Blau syndrome. J Allergy Clin Immunol 2015;136:484–7.e2. doi:10.1016/j.jaci.2014.12.1941
      OpenUrlCrossRef
    27. ↵
      1. Walsh T,
      2. Lee MK,
      3. Casadei S, et al
      . Detection of inherited mutations for breast and ovarian cancer using genomic capture and massively parallel sequencing. Proc Natl Acad Sci USA 2010;107:12629–33. doi:10.1073/pnas.1007983107
      OpenUrlAbstract/FREE Full Text
    28. ↵
      1. Tung N,
      2. Battelli C,
      3. Allen B, et al
      . Frequency of mutations in individuals with breast cancer referred for BRCA1 and BRCA2 testing using next-generation sequencing with a 25-gene panel. Cancer 2015;121:25–33. doi:10.1002/cncr.29010
      OpenUrlCrossRefPubMed
    29. ↵
      1. Onoufriadis A,
      2. Shoemark A,
      3. Schmidts M, et al
      . Targeted NGS gene panel identifies mutations in RSPH1 causing primary ciliary dyskinesia and a common mechanism for ciliary central pair agenesis due to radial spoke defects. Hum Mol Genet 2014;23:3362–74. doi:10.1093/hmg/ddu046
      OpenUrlAbstract/FREE Full Text
    30. ↵
      1. Kammermeier J,
      2. Drury S,
      3. James CT, et al
      . Targeted gene panel sequencing in children with very early onset inflammatory bowel disease-evaluation and prospective analysis. J Med Genet 2014;51:748–55. doi:10.1136/jmedgenet-2014-102624
      OpenUrlAbstract/FREE Full Text
    31. ↵
      1. Tarabeux J,
      2. Zeitouni B,
      3. Moncoutier V, et al
      . Streamlined ion torrent PGM-based diagnostics: BRCA1 and BRCA2 genes as a model. Eur J Hum Genet EJHG 2014;22:535–41. doi:10.1038/ejhg.2013.181
      OpenUrlCrossRefPubMed
    32. ↵
      1. Touitou I,
      2. Perez C,
      3. Dumont B, et al
      . Refractory autoinflammatory syndrome associated with digenic transmission of low-penetrance tumour necrosis factor receptor-associated periodic syndrome and cryopyrin associated periodic syndrome mutations. Ann Rheum Dis 2006;65:1530–1. doi:10.1136/ard.2006.054312
      OpenUrlFREE Full Text
    33. ↵
      1. Federici S,
      2. Sormani MP,
      3. Ozen S, et al
      . Evidence-based provisional clinical classification criteria for autoinflammatory periodic fevers. Ann Rheum Dis 2015;74: 799–805. doi:10.1136/annrheumdis-2014-206580
      OpenUrlAbstract/FREE Full Text
    View Abstract

    Supplementary materials

    • Supplementary Data

      This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

    Footnotes

    • Handling editor Tore K Kvien

    • Contributors MG and IC contributed equally. MR, MG and IC designed the research. FC, AG and MB performed the experiments (sequencing and validation). MR performed the bioinformatic analysis. MR and IC analysed genetic results. LO, AI, AT, RC, EG, ANO, AVM, SF and MG provided and discussed the clinical pictures of patients re-evaluated here. SF and MG integrated clinical data with patients’ genotypes. DC, RR, AM, MR, SF, MG and IC discussed the results. MR, SF, MG and IC wrote the manuscript. All the authors edited the manuscript.

    • Funding This work has been performed with the contribution of the Italian Ministry of Health (“Cinque per mille” and Ricerca Corrente to the Gaslini Institute).

    • Competing interests None declared.

    • Ethics approval Gaslini Institute Ethical Committee approved this study.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data sharing statement All sequencing data reported in the article will be made available in their raw, original format upon request.