Trio-based whole exome sequencing in patients with suspected sporadic inborn errors of immunity: A retrospective cohort study

  1. Anne Hebert
  2. Annet Simons
  3. Janneke HM Schuurs-Hoeijmakers
  4. Hans JPM Koenen
  5. Evelien Zonneveld-Huijssoon
  6. Stefanie SV Henriet
  7. Ellen JH Schatorjé
  8. Esther PAH Hoppenreijs
  9. Erika KSM Leenders
  10. Etienne JM Janssen
  11. Gijs WE Santen
  12. Sonja A de Munnik
  13. Simon V van Reijmersdal
  14. Esther van Rijssen
  15. Simone Kersten
  16. Mihai G Netea
  17. Ruben L Smeets
  18. Frank L van de Veerdonk
  19. Alexander Hoischen  Is a corresponding author
  20. Caspar I van der Made
  1. Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Netherlands
  2. Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, Netherlands
  3. Department of Genetics, University of Groningen, University Medical Center Groningen, Netherlands
  4. Department of Pediatric Infectious Diseases and Immunology, Amalia Children’s Hospital, Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Netherlands
  5. Department of Pediatric Rheumatology and Immunology, Amalia Children’s Hospital, Radboud University Medical Center, Netherlands
  6. Department of Clinical Genetics, Maastricht University Medical Center+, Netherlands
  7. Center for Human and Clinical Genetics, Leiden University Medical Center, Netherlands
  8. Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Netherlands
  9. Department for Immunology and Metabolism, Life and Medical Sciences Institute (LIMES), University of Bonn, Germany
  10. Department of Laboratory Medicine, Laboratory for Diagnostics, Radboud University Medical Center, Netherlands

Abstract

Background:

De novo variants (DNVs) are currently not routinely evaluated as part of diagnostic whole exome sequencing (WES) analysis in patients with suspected inborn errors of immunity (IEI).

Methods:

This study explored the potential added value of systematic assessment of DNVs in a retrospective cohort of 123 patients with a suspected sporadic IEI that underwent patient-parent trio-based WES.

Results:

A (likely) molecular diagnosis for (part) of the immunological phenotype was achieved in 12 patients with the diagnostic in silico IEI WES gene panel. Systematic evaluation of rare, non-synonymous DNVs in coding or splice site regions led to the identification of 14 candidate DNVs in genes with an annotated immune function. DNVs were found in IEI genes (NLRP3 and RELA) and in potentially novel candidate genes, including PSMB10, DDX1, KMT2C, and FBXW11. The FBXW11 canonical splice site DNV was shown to lead to defective RNA splicing, increased NF-κB p65 signalling, and elevated IL-1β production in primary immune cells extracted from the patient with autoinflammatory disease.

Conclusions:

Our findings in this retrospective cohort study advocate the implementation of trio-based sequencing in routine diagnostics of patients with sporadic IEI. Furthermore, we provide functional evidence supporting a causal role for FBXW11 loss-of-function mutations in autoinflammatory disease.

Funding:

This research was supported by grants from the European Union, ZonMW and the Radboud Institute for Molecular Life Sciences.

Editor's evaluation

This is an important paper that reports on the diagnostic utility of TRIO-based whole-exome sequencing (WES) for patients with suspected monogenic inborn errors of immunity, which is supported by solid data. This manuscript will be of particular interest to medical geneticists, immunologists, and physicians working with patients with primary immunodeficiencies.

https://doi.org/10.7554/eLife.78469.sa0

Introduction

Although we inherit the vast majority of genomic variants from our parents, a small fraction of variants arises de novo during parental gametogenesis or after zygosis (Acuna-Hidalgo et al., 2016). The biological rate at which these variants develop in humans translates to an average of 50-100 de novo single nucleotide variants (SNVs) per genome per generation, only one or two of which affect coding regions (Acuna-Hidalgo et al., 2016; Lynch, 2010). De novo variants (DNVs) are often very rare or unique (absent from population databases) and have a higher a priori chance to be pathogenic than inherited variants (Meyts et al., 2016; Veltman and Brunner, 2012). In contrast to inherited variants, DNVs emerge between two generations and are subjected to minimal evolutionary selection pressure that would normally purify damaging mutations (Acuna-Hidalgo et al., 2016). DNVs affecting nucleotides or genes that have been targeted by strong purifying selection can therefore be highly damaging to their respective non-redundant biological functions, as has for example been shown for genes involved in innate immunity, an ancient host defence mechanism that developed under constant environmental selection pressure by microorganisms (Veltman and Brunner, 2012; Quintana-Murci and Clark, 2013).

Therefore, DNVs are important candidates to pursue as a cause for disease, particularly in rare, sporadic phenotypes (Lynch, 2010; Veltman and Brunner, 2012; Vissers et al., 2010). The presence of such candidate DNVs can be assessed by trio-based sequencing, in which the patient is sequenced together with the (healthy) parents (Acuna-Hidalgo et al., 2016). Most experience with the systematic diagnostic assessment of DNVs has been gained in the field of developmental disorders, in which DNVs have been shown to constitute up to 50% of disease-causing mutations (Vissers et al., 2010; Martin et al., 2018; Kaplanis et al., 2020). However, the contribution of DNVs in the pathogenesis of other disorders such as inborn errors of immunity (IEI) is less clear.

DNVs as the underlying cause in IEI patients have been widely reported in literature, but most of these mutations were determined to be de novo through subsequent segregation analysis and not by trio-based sequencing (Stray-Pedersen et al., 2017; Arts et al., 2019; Rudilla et al., 2019; Bradshaw et al., 2018; Liu et al., 2011). IEI can present at different stages of life with a variable phenotype ranging from recurrent, life-threatening infections to immune dysregulation and cancer (Arts et al., 2019; Bousfiha et al., 2020). Particularly in IEI patients with early-onset and severe complex phenotypes, there is an increased chance for an underlying causative DNV (Veltman and Brunner, 2012; Vorsteveld et al., 2021). Moreover, DNVs that arise post-zygotically or somatically are recognized as an important underlying cause for IEI patients with autoinflammatory disease (Labrousse et al., 2018; de Inocencio et al., 2015; Mensa-Vilaro et al., 2016; Kawasaki et al., 2017; Holzelova et al., 2004; Aluri et al., 2021; Beck et al., 2020; van der Made et al., 2022; Zhou et al., 2012). The potentially added value of systematic DNV assessment in IEI patients is supported by the findings of an international cohort study, which reported a diagnosis in 44% of cases after patient-parent trio sequencing, compared to 36% by single whole exome sequencing (WES) (Stray-Pedersen et al., 2017). However, trio-based sequencing has not yet been implemented as part of the routine diagnostic procedure of IEI patients.

The current study has aimed to explore the potential added value of systematic assessment of DNVs in a retrospective cohort of 123 patients with a suspected, sporadic IEI that underwent trio-based WES.

Materials and methods

Patients and samples

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We retrospectively screened patient-parent trios that were submitted to Genome Diagnostics at the Department of Human Genetics in the Radboud University Medical Center (RUMC) between May 2013 and November 2021. Patient-parent trios were selected for systematic DNV analysis when fulfilling the following inclusion criteria: (1) the patient’s phenotype was sporadic, (2) the clinical description was suspect for an inborn error of immunity (IEI), and (3) the in silico IEI whole exome sequencing (WES) panel was requested and analysed. The in silico IEI gene panel of the RUMC is periodically updated after literature review and currently encompasses 456 genes (version DG3.1.0 Radboudumc, 2021). During the study period, the in silico IEI WES panel was analysed in 146 patient-parent trios, of which 123 trios met the inclusion criteria for our retrospective cohort study (Figure 1).

Figure 1 with 1 supplement see all
Schematic overview of patient inclusion, de novo variant filtering strategy and variant evaluation.

Of the 146 eligible patient-parent trios, 123 trios met the inclusion criteria for this IEI cohort study. Whole exome sequencing data from these patient-parent trios was filtered to retain rare, non-synonymous candidate de novo variants in coding regions. Subsequently, variants were systematically evaluated at variant and gene level for their potential involvement in the patient’s immunological phenotype. Abbreviations: IEI = inborn errors of immunity; dbSNP = Single Nucleotide Polymorphism Database; ExAC = Exome Aggregation Consortium; GnomAD = Genome Aggregation Database; AF = allele frequency; GoNL = Genome of the Netherlands.

Figure 1—source code 1

R script for de novo variant filtering.

https://cdn.elifesciences.org/articles/78469/elife-78469-fig1-code1-v2.zip
Figure 1—source data 1

List of 123 patient-parent trios with patient characteristics and whole exome sequencing performance statistics.

https://cdn.elifesciences.org/articles/78469/elife-78469-fig1-data1-v2.docx
Figure 1—source data 2

List of all candidate rare, coding de novo variants found in the cohort of IEI patients.

https://cdn.elifesciences.org/articles/78469/elife-78469-fig1-data2-v2.docx
Figure 1—source data 3

De novo variant rate and distribution of de novo variant types across our IEI cohort in comparison to a reference cohort from Kaplanis et al., 2020.

The total amount of candidate de novo variants was retrieved from our cohort, consisting of 123 individuals. All identified de novo variants with at least 20% variation reads of the Kaplanis et al., 2020. cohort were obtained. The amount of predicted loss-of-function, synonymous and non-synonymous de novo single nucleotide variants or small insertion-deletions were extracted from both cohorts. The total amount of all, predicted loss-of-function, synonymous and non-synonymous de novo variants were divided by the respective cohort size to obtain the average number of respective variants per individual for each cohort.

https://cdn.elifesciences.org/articles/78469/elife-78469-fig1-data3-v2.docx

As described previously (Arts et al., 2019), patients and their parents provided written informed consent for in silico IEI WES gene panel analysis with or without exome-wide variant analysis that is in line with the diagnostic clinical question, as approved by the Medical Ethics Review Committee Arnhem-Nijmegen (2011/188 and 2020–7142). This research is in compliance with the principles of the Declaration of Helsinki (World Medical, 2013).

For the systematic DNV analysis in this study, WES data of all subjects was pseudonymised. This entailed the at random enciphering of patient DNA numbers to ascending numbers by a Genome Diagnostics member. In addition, clinical descriptions were condensed and classified according to the International Union of Immunological Societies (IUIS) classification (Bousfiha et al., 2020). Some of the included trios were part of previous publications: one was published as a clinical case report by D’hauw et al., 19 were part of the IEI cohort of Arts et al., and one was part of a study by Konrad et al. (Figure 1—source data 1; Arts et al., 2019; Konrad et al., 2019; D’hauw et al., 2008).

Diagnostic whole exome sequencing

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WES was performed as described earlier with minor modifications (Lelieveld et al., 2016). In brief, genomic DNA samples isolated from whole blood were processed at the Beijing Genomics Institute (BGI) Europe (BGI Europe, Copenhagen, Denmark) or the in-house sequencing facility. All samples were enriched for exonic DNA using Agilent (Agilent Technologies, Santa Clara, CA, United States) or Twist (Twist Bioscience, San Francisco, CA, United States) exome kits. DNA samples at BGI were sequenced on Illumina HiSeq4000 (Illumina Sequencing, San Diego, CA, United States) or DNBseq (MGI Tech, Shenzhen, China). In-house DNA samples were sequenced on Illumina NovaSeq6000 (Illumina Sequencing). Sequencing was performed with 2x100 base pair (DNBseq) or 2x150 base pair (HiSeq4000 and NovaSeq6000) paired-end sequencing reads. The average median sequence coverage was 124x with an average of 96% target coverage greater than 20x (Figure 1—source data 1).

Downstream processing was performed by an automated data analysis pipeline, including mapping of sequencing reads to the GRCh37/hg19 reference genome with the Burrows-Wheeler Aligner algorithm and Genome Analysis Toolkit variant calling and additional custom-made annotation (Li and Durbin, 2010; McKenna et al., 2010). The DeNovoCheck tool is part of the custom-made annotation and was used to align variants called in each member of the patient-parent trios, providing an indication whether variants were inherited or de novo. DNVs were filtered out if the variation reads in either parent exceeded 2% (Lelieveld et al., 2016; de Ligt et al., 2012). Subsequently, all single nucleotide variants (SNVs) or small insertion-deletions (indels) were annotated by a custom, in-house annotation pipeline. Copy number variants (CNVs) were assessed by the copy number inference from exome reads (CoNIFER) method, as of 2018 (Krumm et al., 2012).

Subsequently, variants in genes included in the in silico IEI panel were filtered to retain both inherited and de novo coding, non-synonymous variants with population frequencies below 1% in our in-house database or population databases (GnomAD and dbSNP) (Karczewski et al., 2020; Sherry et al., 1999). Variant prioritisation was performed by clinical laboratory geneticists of the Department of Human Genetics at the RUMC. SNVs, small indels or CNVs that were considered to be (partially) related to the phenotype were classified (five-tier classification) and reported according to guidelines of the Association for Clinical Genetic Science and the American College of Medical Genetics and Genomics (ACMG; Richards et al., 2015; Wallis et al., 2013). Variants that were denoted or classified as carriership of a variant in a known recessive disease gene, known risk factors or variants of uncertain significance or (likely) pathogenic variants in disease genes other than those associated with IEI and candidate variants in genes without any disease association, were additionally reported and are listed in Table 1—source data 1.

De novo variant analysis

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In this study, a research-based re-analysis was performed on 123 patient-parent trio WES datasets to assess the presence of candidate DNVs. For this, a standardised variant filtering strategy was scripted using R Studio version 3.6.2 (Figure 1—source code 1). Variants were filtered to retain rare (≤0.1% allele frequency in our in-house database and the population databases from Exome Aggregation Consortium (ExAC), Genome Aggregation Database (GnomAD) genomes and dbSNP as well as ≤0.5% in the Genome of the Netherlands (GoNL) database), coding, non-synonymous, possible DNVs, as annotated by the DeNovoCheck tool (Figure 1; Lelieveld et al., 2016; de Ligt et al., 2012; Karczewski et al., 2020; Sherry et al., 1999; Lek et al., 2016; Boomsma et al., 2014). Variants with ≤10 variation reads, ≤20% variant allele fraction or low coverage DNVs (de Ligt et al., 2012) were excluded. Moreover, synonymous SNVs and small indels were removed from the analysis. DNVs excluded by this filtering strategy were investigated for potential pathogenicity in known IEI genes. The remaining candidate DNVs are listed in Figure 1—source data 2. These DNVs were prioritised and systematically evaluated using variant and gene level metrics, encompassing database allele frequencies (including DNV counts in other datasets via denovo-db), nucleotide conservation, pathogenicity prediction scores, functional information and possible involvement in the immune system based on mouse knockout models, pathway-based annotation (i.e. Gene Ontology terms), and literature studies (Karczewski et al., 2020; Wiel et al., 2019; Stephenson et al., 2019; Turner et al., 2017). Prioritised candidate DNVs were visually inspected using the Integrative Genomics Viewer (IGV) and/or Alamut Visual Software version 2.13 (SOPHiA GENETICS, Saint Sulpice, Switzerland) to investigate biases that would give rise to false-positive variant calls. In addition, splice site DNVs were analysed using the SpliceAI prediction score (Jaganathan et al., 2019) and the Alamut Visual Software, which has incorporated splicing prediction tools such as SpliceSiteFinder-like, MaxEntScan, NNSPLICE, GeneSplicer and ESE tools.

FBXW11 functional validation experiments

Epstein–Barr virus (EBV)-B cell lines

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Venous blood was drawn from patient 53 and collected in lithium heparin tubes. Epstein-Barr virus (EBV)-transformed B cell lines were created following established procedures (Neitzel, 1986). EBV-transformed lymphoblastoid cell lines (EBV-LCLs) from the patient and a healthy control were grown at 37 °C and 7.5% CO2 in RPMI 1640 medium (Dutch Modification, Gibco; Thermo Fisher Scientific, Inc, Waltham, MA, United States) containing 15% foetal calf serum (FCS; Sigma-Aldrich, St Louis, MO, United States), 1% 10,000 U/μl penicillin and 10,000  μg/μl streptomycin (Sigma-Aldrich), and 2% HEPES (Sigma-Aldrich). The EBV-LCLs were cultured at a concentration of 10×106 in 150 cm2 culture flasks (Corning, Corning, NY, United States) and treated with or without cycloheximide at 0.1% (20mL/20 mL medium; Sigma-Aldrich) for four hours. Cell pellets were then spun down, washed with PBS, snap-frozen in liquid nitrogen and stored at -80 °C.

RNA splicing effect

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RNA was isolated from the EBV-B cell pellets using the RNeasy Mini isolation kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Subsequently, cDNA was synthesised from RNA with the iScript cDNA Synthesis Kit (Bio-Rad, Hercules, CA, United States). A primer set was designed (Primer3web, version 4.1.0) to span exon 11–13 of FBXW11, with the following sequences: Forward 5’-GAGAGCCGGAATCAGAGGTG-3’; Reverse 5’-GAATTGGTCCGATGCATCCG-3’. Subsequently, RT-PCR was performed using the AmpliTaq Gold 360 Master Mix (Life Technologies, Carlsbad, CA, United States). The amplified PCR products and Orange G ladder were electrophoresed on a 2% agarose gel with GelRed, and the resulting bands were cut out and analysed with Sanger sequencing.

Ex vivo peripheral mononuclear blood cell (PBMC) experiments

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Venous blood was drawn and collected in EDTA tubes. Immune cell isolation was conducted as described elsewhere (Oosting et al., 2016). In brief, PBMCs were obtained from blood by differential density centrifugation, diluted 1:1 in pyrogen-free saline over Cytiva Ficoll-Paque Plus (Sigma-Aldrich). Cells were washed twice in saline and suspended in cell culture medium (Roswell Park Memorial Institute (RPMI) 1640, Gibco) supplemented with gentamicin, 50 mg/mL; L-glutamine, 2 mM; and pyruvate, 1 mM. Ex vivo PBMC stimulations were performed with 5×105 cells/well in round-bottom 96-well plates (Greiner Bio-One, Kremsmünster, Austria) for 24 hr in the presence of 10% human pool serum at 37 °C and 5% carbon dioxide. For cytokine production measurements, cells were treated with Candida albicans yeast (UC820 heat-killed, 1×106 /mL), lipopolysaccharide (LPS, 10 ng/mL), Staphylococcus aureus (ATCC25923 heat-killed, 1×106 /mL) or TLR3 ligand Poly I:C (10 µg/mL) or left untreated in regular RPMI medium. After the incubation period and centrifugation, supernatants were collected and stored at -20 °C until the measurement using enzyme-linked immunosorbent assay (ELISA).

For flow cytometry experiments, PBMCs were cultured in U-bottom plates at a final concentration of 1×106 cells in 200 µL per well containing culture medium supplemented with 5% FCS (Sigma-Aldrich) at 37 °C and 5% carbon dioxide. Subsequently, cells were stimulated with phorbol 12-myristate 13-acetate (PMA, 12.5  ng/mL, Sigma-Aldrich) and ionomycin (500  ng/mL, Sigma-Aldrich) in duplicate for 30 min.

Flow cytometry

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PBMC suspensions were transferred to a V-bottom plate while pooling the duplicates. Following centrifugation for 2.5 min, cell surface markers were stained in the dark for 30 min at 4 °C with a monoclonal antibody mix containing anti-CD3-ECD (1:25; Beckman Coulter, Brea, CA, United States), anti-CD4-BV510 (1:50; BD Bioscience, Franklin Lakes, NJ, United States), anti-CD8-APC Alexa Fluor 700 (1:400; Beckman Coulter), and anti-CD14-FITC (1:50; Dako; Agilent Technologies). Subsequently, cells were washed twice with flow cytometry buffer (FCM buffer, 0.2% BSA in PBS) and fixed (BD Biosciences Cytofix, 554655) for 10 min at 37 °C. Next, cells were washed and permeabilised with perm buffer IV (1:10 diluted with PBS, BD Biosciences Phosflow, 560746) for 20 min on ice in the dark. Cells were then stained intracellularly with anti-NF-κB p65 (pS529)-PE antibody (1:50; eBioscience; Thermo Fisher Scientific, Inc, Waltham, MA, United States) for 20 min at 4 °C. After washing the cells twice in FCM-buffer, the suspensions were measured on a Beckman Coulter Navios EX Flow Cytometer using Navios System Software. Cell immunophenotypes were analysed using Kaluza Analysis Software version 2.1 (Beckman Coulter). The mean fluorescent intensities (MFIs) were calculated using the median pNF-κB p65 expression levels within the gated immune cell populations of interest.

Cytokine measurements

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Levels of cytokines IL-1β, IL-6 and TNFα were determined in supernatants of stimulated PBMC cultures according to the instructions of the manufacturer (Duoset ELISA; R&D Systems, Minneapolis, MN, United States).

Results

Cohort characteristics

This retrospective cohort study systematically re-analysed patient-parent trio whole exome sequencing (WES) data of 123 patients with suspected, sporadic inborn errors of immunity (IEI) with the aim to identify candidate de novo single-nucleotide variants (SNVs) or small insertion-deletions (indels) (Figure 1). The selected IEI patients had a median age of 9 years (IQR 2–17), and two-thirds of the cases were below 18 years of age (Table 1). The sex distribution among patients was roughly equal. Classification of IEI phenotypes according to the International Union of Immunological Societies (IUIS) indicated that most cases presented with autoinflammatory syndromes, followed by immune dysregulation and combined, predominantly syndromal immunodeficiencies (Bousfiha et al., 2020). Eight patients remained unclassified due to limited clinical data.

Table 1
Patient cohort characteristics.
CharacteristicTotal N=123
Demographics
Age*, median (IQR) y9 (2-17)
 <18 y, %67.4
 >18 y, %33.6
Sex ratio, M:F50.4:49.6
Distribution of clinical phenotypes
 Severe combined immunodeficiency, n (%)9 (7.3)
 Suspected SCID (low TRECs), n5
 Other, n4
Combined immunodeficiency, n (%)22 (17.9)
 Syndromal, n20
 Non-syndromal, n2
Primary antibody deficiency, n (%)14 (11.4)
 CVID, n14
 Agammaglobulinemia, n0
 Other, n0
Immune dysregulation, n (%)20 (16.3)
 HLH/EBV, n5
 Autoimmunity, n15
Autoinflammatory syndrome, n (%)22 (17.9)
 Periodic fever syndrome, n19
 Interferonopathy, n0
 Other, n3
Phagocyte defect, n (%)5 (4.1)
 Functional defect, n1
 Neutropenia/other, n4
Innate/intrinsic immune defect, n (%)16 (13.0)
 Bacterial/parasitic, n2
 MSMD/Viral, n7
 Other, n7
Complement deficiencies, n (%)0 (0.0)
Bone marrow failure, n (%)10 (8.1)
Phenocopies of PIDs, n (%)0 (0.0)
Unclassified, n (%)5 (4.1)
  1. Abbreviations: IQR = interquartile range; SCID = severe combined immunodeficiency; TREC = T cell receptor excision circle; CVID = common variable immunodeficiency; HLH = haemophagocytic lymphohistiocytosis; EBV = Epstein-Barr virus; MSMD = Mendelian susceptibility to mycobacterial disease; PID = primary immunodeficiency.

  2. *

    The age at the time of genetic testing is indicated, since the age of onset has not been documented for all cases.

  3. Categorization of phenotypes is based on the IUIS classification of 2019 (14).

Table 1—source data 1

List of patient-parent trios with variants identified in genes outside the diagnostic IEI gene panel, or classified as risk factors, carriership or variants of uncertain significance.

The table displays information on inherited single nucleotide variants and small insertion-deletions or copy number variants that were identified after diagnostic in silico gene panel and/or exome-wide analysis prior to the systematic DNV analysis in this study.

https://cdn.elifesciences.org/articles/78469/elife-78469-table1-data1-v2.zip

Reported genetic variants after diagnostic whole exome sequencing

Potential disease-causing SNVs and/or copy number variants (CNVs) were reported in 36 index patients after diagnostic WES (Table 2). Twenty-four patients were carriers of recessive disease alleles, previously characterised risk factors, variants of uncertain significance (VUS) or (likely) pathogenic variants affecting established disease genes other than those associated with IEI (Table 2). Of note, three of these patients carried de novo CNVs that met the diagnostic quality criteria. Patient 21 was diagnosed with an autoinflammatory disorder and carried a 17q terminal deletion of uncertain clinical significance. Overlapping CNVs have been previously reported in DECIPHER (Firth et al., 2009). A patient with non-syndromal combined immunodeficiency carried a de novo CNV involved in the Chromosome 22q11.2 microduplication syndrome (patient 69, OMIM #608363). Another de novo CNV was identified in patient 115 who was diagnosed with severe combined immunodeficiency. This young female carried a large duplication in Xq of uncertain clinical significance.

Table 2
Genetic findings after routine diagnostic panel analysis.

Genetic variants reported after routine diagnostic whole exome sequencing analysis of the 123 patients included in this cohort of inborn errors of immunity.

Total cases in which a genetic variant was reported, n (%)36 (29.3)Patient nr.
(Likely) pathogenic mutation, n (%)18 (14.6)
Within IEI gene panel, n (%)12 (9.8)All patients listed in Table 3
Beyond IEI gene panel, n (%)6 (4.9)1, 3, 40, 69, 85, 103 (Table 1—source data 1)
Other variants, n (%)19 (15.4)Table 1—source data 1
Risk factor, n (%)6 (4.9)21, 44, 55, 56, 68, 112
Carriership recessive allele, n (%)7 (5.7)3, 7, 16, 23, 32, 44, 76
Variant of unknown significance, n (%)9 (7.3)6, 21, 23, 45, 54, 80, 100, 101, 115

In 12 patients, (likely) pathogenic SNVs were identified in known IEI genes that (partially) explain the patient’s immunological phenotype (Table 2, details shown in Table 3). While most variants were inherited, one patient with Muckle-Wells syndrome (patient 59) carried a de novo missense variant in NLRP3 (NM_001079821.2:c.1049C>T p.(Thr350Met)). This variant has previously been described in patients with Muckle-Wells syndrome (Dodé et al., 2002; Jiménez-Treviño et al., 2013). Consequently, the NLRP3 de novo variant (DNV) was classified as pathogenic (Richards et al., 2015; Wallis et al., 2013).

Table 3
Patients with previously reported single nucleotide variants, small insertion-deletions, or copy number variants that may (partially) explain the patient’s immunological phenotype.

Listed variants were identified prior to the research-based systematic re-analysis of the current study following diagnostic gene panel analysis for inborn errors of immunity.

Patient nr.SexAge range at samplingPhenotype (IUIS classification)VariantMutational mechanismACMG classificationClinVar accessionComments
10F0–5Immune dysregulation, HLH/EBVAP3B1 Chr5(GRCh37):g.77563371del NM_003664.4:c.177del p.(Lys59fs)AR (ch) LoFPathogenicVCV000224763Hermansky-Pudlak syndrome 2 (OMIM #608233)
AP3B1 Chr5(GRCh37):g.77423980_77423983del NM_003664.4:c.1839_1842del p.(Asp613fs)PathogenicVCV000224764
12F11–15CID, syndromalFAS Chr10(GRCh37):g.90774167_90774186dup NM_000043.6:c.968_987dup p.(Glu330fs)AD (htz) LoFPathogenicVCV000016509Autoimmune lymphoproliferative syndrome, type IA (OMIM #601859)
seq[GRCh37] del(16)(p11.2p11.2) NC_000016.9:g.(29469093_29624260)_(30199846_30208282)delAD (htz) LoFPathogenic-16 p11.2 deletion syndrome (OMIM #611913)
26F0–5Bone marrow failureDHFR Chr5(GRCh37):g.79950248C>T NM_000791.3:c.61G>A p.(Gly21Arg)AR (hmz) LoFLikely pathogenic-Megaloblastic anaemia due to dihydrofolate reductase deficiency (OMIM #613839)
Affected sibling carries equal variant
59M6–10Autoinflammatory disorderNLRP3 Chr1(GRCh37):g.247587794C>T NM_001079821.2:c.1049C>T p.(Thr350Met)AD (htz) LoFPathogenic-Muckle-Wells syndrome (OMIM #191900)
De novo SNV
61M0–5CID, syndromalMKL1 Chr22(GRCh37):g.40815086dup NM_020831.4:c.1356dup p.(Val453Argfs)AR (hmz) LoFLikely pathogenic-Immunodeficiency 66 (OMIM #618847)
Affected sibling carries equal variant
77F0–5CID, syndromalALOXE3 Chr17(GRCh37):g.8006708G>A NM_021628.2:c.1889C>T p.(Pro630Leu)AR (hmz) LoFPathogenic-Congenital ichthyosis 3 (OMIM #606545)
91F0–5Suspected SCID (low TRECs)FOXN1 Chr17(GRCh37):g.26857765A>G NM_003593.2:c.831–2A>G p.?AD (htz) LoFLikely pathogenic-T-cell lymphopenia, infantile, with or without nail dystrophy (OMIM #618806)
102F11–15Immune dysregulation, autoimmunity and othersCD55 Chr1(GRCh37):g.207497984dup NM_001300902.1:c.367dup p.(Thr123fs)AR (hmz) LoFPathogenic-Complement hyperactivation, angiopathic thrombosis, and protein-losing enteropathy (OMIM #226300)
PET117 Chr20(GRCh37):g.18122927C>T NM_001164811.1:c.172C>T p.(Gln58*)AR (hmz) LoFLikely pathogenicVCV000981504Mitochondrial complex IV deficiency, nuclear type 19 (OMIM #619063)
105M31–35Defects in intrinsic and innate immunity, MSMD and viral infectionTLR7 ChrX(GRCh37):g.12905756_12905759del NM_016562.3:c.2129_2132del p.(Gln710fs)XLR (hemi) LoFPathogenicVCV000977232Immunodeficiency 74, COVID19-related (OMIM #301051)
Affected sibling carries equal variant
114M6–10Immune dysregulation, autoimmunity and othersLRBA Chr4(GRCh37):g.151835415del NM_006726.4:c.1093del p.(Tyr365fs)AR (hmz) LoFPathogenic-Common variable immunodeficiency 8 (OMIM #614700)
120M11–15Congenital defect of phagocyte, functional defectsNCF1 Chr7(GRCh37):g.74191615_74191616del NM_000265.5:c.75_76del p.(Tyr26fs)AR (hmz) LoFPathogenicVCV000002249Chronic granulomatous disease 1 (OMIM #233700)
122M0–5Suspected SCID (low TRECs)FOXN1 Chr17(GRCh37):g.26851540del NM_003593.2.1:c.143del p.(Cys48fs)AD (htz) LoFPathogenic-T-cell lymphopenia, infantile, with or without nail dystrophy (OMIM #618806)
  1. Abbreviations: IUIS = International Union of Immunological societies; ACMG = American College of Medical Genetics and Genomics; HLH = haemophagocytic lymphohistiocytosis; EBV = Epstein-Barr virus; OMIM = Online Mendelian Inheritance in Man; (S)CID = (severe) combined immunodeficiency; TREC = T cell receptor excision circle; MSMD = Mendelian susceptibility to mycobacterial disease; AR = autosomal recessive; AD = autosomal dominant; XLR = X-linked recessive; ch = compound heterozygous; htz = heterozygous; hmz = homozygous; hemi = hemizygous; LoF = loss-of-function; SNV = single nucleotide variant.

Table 4
Identification of 13 heterozygous, rare and non-synonymous candidate de novo variants.

The 124 non-synonymous candidate de novo variants were systematically evaluated based on the potential to be damaging to gene and protein function and the possible involvement in the patient’s immunological phenotype.

Patient nr.SexAge range at samplingPhenotype (IUIS classification)De novo variantGnomAD AF in %in-house AF in %PhyloPCADDVarMapMetaDomeCoding DNV in denovo-db (protein effect)LOEUFFunctionLiteratureComments
Missense SNVs
1M11–15SCIDPSMB10 Chr16(GRCh37):
g.67968809C>T NM_002801.3:
c.601G>A p.(Gly201Arg)
00532Likely deleteriousNeutral-1.37Immuno- and thymoproteasome subunitHomozygous Psmb10 variant in mice causes SCID and systemic autoinflammation (Treise et al., 2018). Homozygous PSMB10 variant in humans cause PRAAS, no immunodeficiency (Sarrabay et al., 2020).Revertant somatic mosaicism (VAF: 39.7%). Additional inherited SNV and partial somatic UPD16 (Table 1—source data 1).
9M6–10Predominantly antibody deficiency, hypogamma-globulinemiaRPL27A Chr11(GRCh37):
g.8707228T>C NM_000990.4:
c.322T>C p.(Tyr108His)
0.00320.00417.427.4Likely deleteriousIntolerant-0.39Ribosomal subunitRibosomopathies may include immunological defects (Khan et al., 2011).
27M11–15Autoinflammatory disorderTAOK2 Chr16(GRCh37):
g.29997683C>T NM_016151.3:
c.2090C>T p.(Ala697Val)
004.822.5Possibly deleteriousSlightly intolerant6 (4 mis)0.24Serine/threonine-protein kinase (p38 MAPK pathway)Homozygous TAOK2 variant causes abnormal T cell activation in two patients with inflammatory bowel disease (Molho-Pessach et al., 2017).
28F16–20Predominantly antibody deficiency, hypogamma-globulinemiaKCTD9 Chr8(GRCh37):
g.25292997C>T NM_017634.3:
c.695G>A p.(Arg232His)
00.00825.832Likely deleteriousIntolerant-0.52Substrate-specific adapterInvolved in NK cell activation (Chen et al., 2013).
52M11–15Predominantly antibody deficiency, hypogamma-globulinemiaSCRIB Chr8(GRCh37):
g.144874432C>T NM_182706.4:
c.4472G>A p.(Arg1491Gln)
0.003204.229.9Possibly deleteriousIntolerant5 (4 mis)0.31Scaffold proteinInvolved in uropod and immunological synapse formation, and ROS production by antigen-presenting cells (Barreda et al., 2020).
58F21–25UnclassifiedCTCF Chr16(GRCh37):
g.67645905G>T NM_006565.4:
c.833G>T p.(Arg278Leu)
009.724.7Possibly deleteriousHighly intolerant12 (11 mis)0.15Transcriptional insulatorCTCF variants cause neurodevelopmental disorders, sometimes associated with recurrent infections and minor facial dysmorphisms (Konrad et al., 2019).Published (Konrad et al., 2019).
75F6–10Bone marrow failureFUBP1 Chr1(GRCh37):
g.78435621A>C NM_001303433.1:
c.199T>G p.(Leu67Val)
002.624.8Possibly deleteriousIntolerant1 (0 mis)0.12Transcriptional regulator that binds FUSE upstream of the c-myc promoterEssential for long-term repopulating hematopoietic stem cell renewal (Rabenhorst et al., 2015). Fubp1 KO mice show cerebral hyperplasia, pulmonary hypoplasia, pale livers, hypoplastic spleen, thymus, and bone marrow, cardiac hypertrophy, placental distress, and small size (Zhou et al., 2016).
118F0–5Immune dysregulation, autoimmunity and othersRUNX3 Chr1(GRCh37):
g.25256227C>T NM_004350.2:
c.133G>A p.(Gly45Arg)
002.418Possibly deleteriousSlightly tolerant1 (1 mis)0.42Transcriptional regulatorRUNX3 regulates CD8+T cell thymocyte development, maturation of cytotoxic CD8+T cells and the function of innate lymphoid cells 3 via stimulation of RORγt (Ebihara et al., 2015). Runx3 KO mice spontaneously develop inflammatory bowel disease and gastric lesions (Brenner et al., 2004).
Frameshift SNVs
49M26–30Predominantly antibody deficiency, hypogamma-globulinemiaDDX1 Chr2(GRCh37):
g.15769802dup NM_004939.2:
c.1952dup p.(Trp652fs)
00----4 (0 fs)0.28RNA helicasePart of a dsRNA sensor that activates the NF-κB pathway and type I interferon responses (Zhang et al., 2011).
78F6–10CID, syndromalKMT2C Chr7(GRCh37):
g.151860074del NM_170606.2:
c.10588del p.(Ser3530Leufs*3)
00----19 (4 fs)0.12Histone methyltransferaseKMT2C de novo variant causes Kleefstra syndrome 2, sometimes associated with recurrent respiratory infections (Koemans et al., 2017).
Small in-frame indel
108M21–25Bone marrow failureNSD2 Chr4(GRCh37):
g.1959681_1959687delins
TTTTTCT NM_133330.2:
c.2903_2909delins
TTTTTCT p.
(Arg968_Arg970delinsLeuPheLeu)
------0.12Histone methyltransferaseNSD2 de novo LoF variant causes mild Wolf-Hirschhorn syndrome (Barrie et al., 2019). Unclear role in immunity.Postzygotic mosaicism (VAF 29%).
Patient nr.SexAge range at samplingPhenotype (IUIS classification)De novo variantGnomAD AF in %in-house AF in %PhyloPCADDSpliceAI Acceptor GainSpliceAI Acceptor LossCoding DNV in denovo-db (protein effect)LOEUFFunctionLiteratureComments
Splice site SNVs
53F11–15Autoinflammatory disorderFBXW11 Chr5(GRCh37):
g.171295802T>C NM_012300.2:
c.1468–2A>G p.?
007.9340.01340.98622 (0 ss)0.31Component of SCF (SKP1-CUL1-F-box) E3 ubiquitin ligase complexInvolved in the regulation of NF-κB signalling (Wang et al., 2018).
119F11–15Autoinflammatory disorderRELA Chr11(GRCh37):
g.65423234C>T NM_021975.3:
c.959–1G>A p.?
003.5340.79680.9991-0.18Transcription factor p65 (NF-κB subunit)Heterozygous RELA variant causes chronic mucocutaneous ulceration (Badran et al., 2017).
  1. Abbreviations: IUIS = International Union of Immunological Societies; GnomAD = Genome Aggregation Database; AF = allele frequency; CADD = Combined Annotation Dependent Depletion; DNV = de novo variant; LOEUF = loss-of-function observed/expected upper bound fraction; SNV = single nucleotide variant; indel = insertion-deletion; (S)CID = severe combined immunodeficiency; NA = not applicable; mis = missense; fs = frameshift; ss = splice site; MAPK = mitogen-activated protein kinase; FUSE = far upstream element; PRAAS = proteasome-associated autoinflammatory syndrome; NK = natural killer; ROS = reactive oxygen species; KO = knockout; dsRNA = double-stranded RNA; NF-κB = nuclear factor kappa-light-chain-enhancer of activated B cells; LoF = loss-of-function; VAF = variant allele fraction; UPD16 = uniparental disomy of chromosome 16.

Overall, routine diagnostic WES analysis provided a likely molecular diagnosis for (part) of the phenotype in 18 patients (14.6%) based on established mutational mechanisms and disease associations (Table 2).

Rare, non-synonymous de novo variants in novel IEI candidate genes

Next, re-analysis was performed on WES data of all 123 sporadic IEI cases and their parents to systematically identify and interpret DNVs in novel IEI genes. Automated DNV filtering retained a total of 172 candidate DNVs that were rare (148 DNVs were absent from GnomAD genomes) and located in either exonic or splice site regions (the complete list can be found in Figure 1—source data 2). The total number of candidate DNVs among patients ranged between zero and six (Figure 1—figure supplement 1). Moreover, the average number of candidate DNVs was comparable to recent literature (Figure 1—source data 3). Of these candidate DNVs, 124 were non-synonymous and therefore expected to exert an effect on protein function (Figure 1—source data 2). Two pairs of patients carried candidate DNVs in the same gene, GIGYF1 (patients 49 and 83) and MAP3K10 (patients 98 and 118). However, these patients did not share phenotypic features and the function of the proteins encoded by these genes could not be linked to the respective patient phenotype.

Subsequently, all non-synonymous candidate DNVs were systematically evaluated based on information on variant and gene level metrics, leading to the selection of 14 candidate DNVs potentially causing IEI (Tables 3 and 4), including the above-mentioned variant in the known IEI gene NLRP3. The 13 novel IEI candidate DNVs were found in patients with different IEI phenotypes, although three subtypes reoccurred: predominantly antibody deficiency (hypogammaglobulinemia), autoinflammatory disorder and bone marrow failure. Candidate DNVs that were considered most promising based on variant and gene level metrics are presented in more detail in the following paragraphs.

A patient with an autoinflammatory phenotype characterised by mucocutaneous ulceration of mouth and genital area carried a DNV in RELA that was located in the canonical splice acceptor site preceding exon 10 (patient 119, Table 4). The guanine to adenine change was predicted to compromise the splice acceptor site by transferring it to the first guanine of exon 10, leading to an out-of-frame exon. The resulting frameshift was therefore assumed to cause a reduction in functional RelA protein by nonsense-mediated decay. RelA is also known as p65 and is critically involved in nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) heterodimer formation and consequent activation of NF-κB-mediated proinflammatory signalling. Although RELA has already been reported as an IEI gene in a previous IUIS classification (Tangye et al., 2020), it was not yet listed in the IEI in silico gene panel of our Department of Human Genetics (Radboudumc, 2021), because evidence was considered insufficient at the time.

In addition, a private de novo missense variant in PSMB10 was found in a patient with Omenn syndrome with severe combined immunodeficiency (SCID), ectodermal dysplasia, alopecia, hypodontia and anonychia (patient 1, Table 4). The clinical phenotype of this patient has been previously reported (D’hauw et al., 2008). The DNV was predicted to be pathogenic based on the majority of variant and gene level metrics. In additional data that was available from a single-nucleotide polymorphism (SNP) micro-array, it was shown that the genomic location of PSMB10 was spanned by a partial somatic uniparental disomy of chromosome 16 (UPD16) (manuscript in preparation). PSMB10 encodes the β2i-subunit of the immuno- and thymoproteasome and mutations leading to a loss of PSMB10 protein function have been associated with severe immunological defects (Treise et al., 2018; Sarrabay et al., 2020). Furthermore, another candidate DNV was identified in a patient with common variable immunodeficiency (CVID) due to a B cell maturation defect, auto-immune cytopenia, polyclonal T cell large granular lymphocytes in the bone marrow, recurrent viral infections, psoriasis and alopecia areata, (patient 49, Table 4). This frameshift variant in DDX1 was predicted to cause loss of protein function. DDX1 encodes a RNA helicase, which is part of a double-stranded RNA sensor that activates the NF-κB pathway and type I interferon responses (Zhang et al., 2011). Moreover, DDX1 is involved in the regulation of hematopoietic stem and progenitor cell homeostasis (Zhang et al., 2011; Wang et al., 2021).

Another frameshift DNV in KMT2C was carried by a patient with a syndromal combined immunodeficiency characterised by recurrent ear infections, developmental delay, low-average intelligence level and facial dysmorphism (patient 78, Table 4). The variant was predicted to lead to a loss-of-function (LoF) of the KMT2C protein, which acts as a histone methyltransferase in the regulation of chromatin organisation.

Lastly, a DNV affecting FBXW11 was identified in a patient with an autoinflammatory disorder characterised by recurrent periodic fever and severe headaches (patient 53, Table 4). FBXW11 encodes a component of SCF (SKP1-CUL1-F-box) E3 ubiquitin ligase complex, TrCP2, that is involved in the regulation of NF-κB signalling through the ubiquitination of several of its components (Wang et al., 2018; Kanarek and Ben-Neriah, 2012). An important function of both the TrCP1 and TrCP2 isoforms is the regulation of IκBα degradation, leading to subsequent activation of NF-κB and release of pro-inflammatory cytokines (Kim et al., 2015; Yaron et al., 1998). The identified DNV affected the canonical splice acceptor site preceding exon 12 (NM_012300.2:c.1468-2A>G) and was predicted to lead to skipping of exon 12 based on splicing prediction by the Alamut Visual Software and to be deleterious by all utilised in silico prediction tools. The predicted RNA splicing defect leading to an in-frame, shortened RNA transcript was confirmed in Epstein-Barr virus (EBV) transformed B cells from the patient (Figure 2—figure supplement 1).

The other candidate DNVs will not be described in detail here, as there is insufficient evidence to suggest pathogenicity or a genotype-phenotype relationship. Future discovery of cases with DNVs in the presented genes and overlapping clinical phenotypes could encourage further in-depth research into the possible mutational mechanisms.

Functional validation of FBXW11 de novo variant

In addition to systematic DNV analysis, we have selected the candidate DNV in FBXW11 for functional validation as part of this study to provide further evidence for a causal genotype-phenotype relationship (patient 53, Table 4). As such, the putative effects on NF-κB signalling and the downstream production of pro-inflammatory cytokines were investigated in peripheral blood mononuclear cells (PBMC) extracted from the patient and a healthy control. In unstimulated PBMC of the patient showed higher levels of phosphorylated NF-kB p65 compared to the control. Ex vivo stimulation of these PBMC with phorbol 12-myristate 13-acetate (PMA) and ionomycin for 30 min led to higher NF-κB activation, reflected by p65 phosphorylation fluorescence intensity measured by flow cytometry, as compared to the healthy control (Figure 2, panel A). The greatest difference was observed in the lymphocyte subset, particularly in CD8 +T cells (Figure 2, panel A). Subsequently, the downstream production of the cytokines IL-1β, IL-6, and TNFα was investigated. The patient-derived PBMC produced more IL-1β upon in vitro stimulation with the heat-killed pathogens Candida albicans and Staphylococcus aureus, the TLR4 agonist lipopolysaccharides (LPS) and the TLR3 ligand Poly I:C after 24 hr, as compared to the healthy control (Figure 2, panel B). This trend was not observed for the production of IL-6 and TNFα (Figure 2, panels C and D). These results indicate that the FBXW11 DNV leads to a splicing defect with skipping of exon 12, resulting in a shorter transcript and increased NF-κB signalling and downstream IL-1β production.

Figure 2 with 1 supplement see all
NF-κB signalling and production of innate cytokines upon ex vivo PBMC stimulation.

Panel A shows the median fluorescence intensity expression levels of pNF-κB p65 (S529) in peripheral blood CD14 +monocytes and CD8 +T cells from a healthy control (blue) and patient 53 (red), in the absence (baseline) or presence of phorbol 12-myristate 13-acetate and ionomycin stimulation, with the absolute values indicated in the lower right corner. Panels B, C, and D display the production of IL-1β, IL-6, and TNFα, respectively, after ex vivo stimulation for 24 hr.

Discussion

We investigated the potential benefit of trio-based whole exome sequencing (WES) over routine single WES analysis in a retrospective cohort of 123 patients with suspected, sporadic inborn errors of immunity (IEI). Systematic analysis of de novo SNVs and small insertion-deletions (indels) led to the identification of 14 candidate de novo variants (DNVs), of which two were in known IEI genes and classified as pathogenic (NLRP3, RELA). Of the 12 variants in potentially novel candidate genes for IEI, four were considered to be most likely pathogenic (PSMB10, DDX1, KMT2C, FBXW11) based on gene and variant level metrics. Additionally, we have provided functional evidence that the FBXW11 splice site DNV led to skipping of exon 12 resulting in the transcription of an altered protein product and subsequent downstream activation of NF-κB signalling with higher IL-1β production capacity.

We have performed a systematic DNV analysis in patients with a suspected, sporadic IEI. On average, these patients carried 1.4 DNVs in coding regions, a rate comparable to other, larger studies, indicating that DNV enrichment or depletion in IEI patients is unlikely (Kaplanis et al., 2020). Based on gene and variant level information, 14 DNVs (11.4%) were considered potential disease-causing candidates. Six of the candidate DNVs (4.9%) were considered likely or possibly pathogenic variants, while the consequence of the other eight DNVs (6.5%) was uncertain.

Two DNVs were in IEI genes (NLRP3, RELA) listed in the most recent IUIS classification and were classified as pathogenic (Richards et al., 2015; Wallis et al., 2013; Tangye et al., 2020). The heterozygous NLRP3 variant in patient 59 (p.Thr350Met) with Muckle-Wells syndrome had been reported in patients with a similar phenotype (Dodé et al., 2002; Jiménez-Treviño et al., 2013). Moreover, the canonical splice site DNV affecting RELA in patient 119 with mucocutaneous ulceration was predicted to lead to a loss of the splice acceptor site and a subsequent frameshift. Heterozygous loss-of-function (LoF) mutations causing RelA haploinsufficiency have been reported as a cause of chronic mucocutaneous ulceration and familial Behçet’s disease (Badran et al., 2017; Adeeb et al., 2021). Badran et al. reported a family of four affected family members with mucocutaneous ulceration harbouring a mutation in the canonical donor splice site of exon 6 (NM_021975:c.559+1 G>A), likely leading to a premature stop codon and haploinsufficiency (Badran et al., 2017). The DNV in RELA was not picked up in our diagnostic in silico IEI gene panel (Radboudumc, 2021), because evidence was considered insufficient at the time. Based on the matching phenotype and similar mutational mechanism this DNV has now been classified as pathogenic, which could potentially carry implications for therapy with anti-tumour necrosis factor alpha (TNFα) inhibitors (Adeeb et al., 2021).

Moreover, DNVs in the potentially novel IEI genes PSMB10, DDX1, KMT2C, and FBXW11 were considered the most promising candidate DNVs based on the predicted variant effect and immunological function of the respective gene. The private missense DNV in PSMB10 was found in a patient with clinically diagnosed Omenn syndrome and showed high scores for pathogenicity. The presumed deleterious effect was further supported by the extremely rare occurrence of revertant mosaicism in this patient (unpublished data), that is, somatic and recurrent uniparental disomy 16q overlapping the PSMB10 locus, suggesting a strong (cellular) effect of this variant. A homozygous missense variant in PSMB10 has been shown previously in a 3-year-old Algerian female with autoinflammatory signs suggestive of proteasome-associated autoinflammatory syndrome (PRAAS), leading to disturbed formation of the 20S proteasome (Sarrabay et al., 2020). In addition, it has been shown in mice that another homozygous PSMB10 variant (p.Gly170Trp) could induce severe combined immunodeficiency (SCID) and systemic autoinflammation, while heterozygous mice only had a T cell defect (Treise et al., 2018). PRAAS is predominantly caused by autosomal recessive or digenic heterozygous mutations in proteasome subunit genes or their chaperone proteins, although heterozygous (de novo) mutations have also been shown to underlie PRAAS (Sarrabay et al., 2020; Agarwal et al., 2010; Brehm et al., 2015). A de novo missense variant in the b1i-subunit PSMB9 (NM_002800; c.467 G>A; p.G156D) was found in three unrelated infants with a type I interferonopathy with immunodeficiency (PRAAS-ID). This variant resulted in impaired maturation and activity of the immunoproteasome in patient-derived B lymphoblastoid cell lines (Kanazawa et al., 2021; Kataoka et al., 2021), a phenotype that was mirrored in mice (Kanazawa et al., 2021). It is interesting to consider that the PSMB10 DNV could act through a similar autosomal dominant mutational mechanism by affecting the formation of the immunoproteasome, as has been shown in mice harbouring a mutation affecting an amino acid in close proximity to the identified DNV in the patient (Treise et al., 2018). However, the functional consequence and pathogenic relevance of the candidate DNV in PSMB10 remain to be confirmed.

In addition, a de novo frameshift variant in the highly intolerant DDX1 (pLI 0.994) was identified in a patient with hypogammaglobulinemia, hematopoietic cell lineage abnormalities and recurrent infections. Although DDX1 plays a role in NF-κB signalling, type I interferon responses and the regulation of hematopoietic stem and progenitor cell homeostasis (Zhang et al., 2011; Wang et al., 2021), a causal genotype-phenotype relationship remains unclear. Furthermore, the de novo frameshift variant in KMT2C was detected in a patient with combined immunodeficiency and a neurodevelopmental phenotype, displaying partial phenotypic overlap with Kleefstra syndrome type 2 that has already been associated with de novo LoF mutations in KMT2C (Koemans et al., 2017). Two of the six individuals described by Koemans et al. were reported to have recurrent respiratory infections (Koemans et al., 2017). The occurrence of immunological symptoms in patients with mutations in chromatin-regulating genes is increasingly being recognised in the field of intellectual disability (ID) (Ehrlich et al., 2008; Hoffman et al., 2005). Therefore, more in-depth characterisation of patients with KMT2C mutations and predominant ID phenotypes might indicate (mild) immunological phenotypes that overlap with the phenotype of our patient, in support of pathogenicity of the observed DNV.

Another candidate DNV in a potentially novel IEI gene was identified in the highly conserved FBXW11 (pLI 0.976). This DNV affected the canonical splice acceptor site preceding exon 12 and was shown to create a splice defect leading to exon skipping with a shortened transcript that retained expression at the RNA level. Exon 12 encodes component 7 of the WD40 repeat domain (WD7), which is involved in substrate recognition (Skaar et al., 2013). De novo missense and nonsense variants in FBXW11 have been previously described in patients with a neurodevelopmental syndrome with abnormalities of the digits, jaw and eyes (Holt et al., 2019). These variants were located in WD1, WD4, and WD6 and have been shown to compromise substrate recognition or binding of the Wnt and Hedgehog signalling developmental pathways. We hypothesised a specific functional effect on NF-κB signalling in our patient with a distinct autoinflammatory phenotype. In peripheral blood mononuclear cells (PBMC) extracted from the patient, we demonstrated that the phosphorylation of the NF-κB subunit p65 was constitutively higher in monocytes and CD8+ T cells as compared to a healthy control, which suggests a functional effect of the FBXW11 variant. This effect is further substantiated by the observation of increased p65 phosphorylation and downstream production of IL-1β after stimulation with pathogens and a TLR3 ligand in the patient. However, a note of caution should be made regarding n=1 studies, as we cannot exclude that the difference is due to normal inter-individual biological variability. These results suggest that NF-κB signalling was aberrantly increased in the patient, a mechanism that has been shown to be involved in the pathogenesis of other monogenic autoinflammatory disorders known as relopathies (van der Made et al., 2020). However, the exact mutational mechanism that explains the different phenotype compared to the previous cases with neurodevelopmental disease remains unclear. The most likely explanation would be a differential effect on the specific functions of the SCF complex that could be cell-type dependent rather than a difference in tissue-specific isoform expression, since the DNV affects all three high quality protein-coding transcripts that produce the most abundant isoforms (Consortium, 2020). Further experiments addressing the effect of this DNV on IκBα degradation, substrate recognition and TrCP protein abundance should be undertaken to provide conclusive evidence.

To our knowledge, two other cohort studies have systematically performed trio-based sequencing in IEI patients as part of their study design, although patients were not pre-selected based on sporadic phenotypes (Stray-Pedersen et al., 2017; Simon et al., 2020). Stray-Pedersen et al. conducted a large international cohort study to investigate the benefit of WES in IEI patients from 278 families, which included 39 patient-parent trios (Stray-Pedersen et al., 2017). The authors reported a molecular diagnosis in 40% of the patients, including 15 (13.6%) de novo mutations, of which 4 were identified by trio-based analysis and 11 after segregation analysis. The additional value of trio-based sequencing is indicated by the higher detection rate compared to that of the single cases followed by segregation analysis of candidate variants (44 vs 36%), as well as the discovery of potentially novel IEI genes or expansion of the immunological phenotype. Furthermore, Simon et al. performed WES in a cohort of 106 IEI patients with a consanguineous background, including 26 patient-parent trios (Simon et al., 2020). A molecular diagnosis was established in 70% of the patients, including 13 (17.6%) de novo mutations, although it is unclear whether these variants were identified through trio-based sequencing or the segregation analysis that was performed for each variant. The authors conclude that trio-based sequencing does not lead to additional diagnostic benefit, although it should be argued that this is also not expected in a cohort of predominantly consanguineous patients (62.2%) with a higher a priori chance of autosomal recessive (AR) disease.

Multiple studies have highlighted the potential benefits of routine trio-based sequencing in IEI patients over single WES (Meyts et al., 2016; Arts et al., 2019; Vorsteveld et al., 2021; Chinn et al., 2020). These advantages apply mostly to patients with sporadic, severe phenotypes in particular, as has been shown for other rare diseases such as neurodevelopmental disorders (Kaplanis et al., 2020). Trio-based sequencing constitutes an unbiased way to identify rare, coding DNVs that are by definition strong candidate variants. It could therefore improve candidate variant prioritisation both during in silico gene panel analysis as part of routine diagnostics, as well as for research-based exome-wide analysis. Furthermore, targeted DNV analysis could improve the detection of somatic variants, which is especially relevant in the field of monogenic autoinflammatory disorders (van der Made et al., 2022). Somatic variants can be successfully identified by trio-based WES (de Koning et al., 2015). However, this specific DNV subtype can be missed during routine analysis especially if the variant allele fraction (VAF) is below the set threshold during standard variant filtering, which is not required to filter out false-positive variants for a condensed set of potential DNVs. In this study, no candidate DNVs with a VAF below the set threshold of 20% were found in established IEI genes. Another advantage of trio-based sequencing is that it provides direct segregation of inherited variants and enables determination of autosomal recessive compound heterozygosity or X-linked recessive disease as the causative disease mechanism.

Based on the results of this study as well as evidence from other studies including those from other rare disease fields, we suggest that trio-based sequencing should be part of the routine evaluation of patients with a sporadic IEI phenotype (Box 1). An exome-wide analysis should be conducted to identify potentially novel disease genes in cases with a negative diagnostic WES result in whom a strong clinical suspicion for an underlying monogenic cause remains. Thus far, the relative proportion of DNVs among IEI patients with a genetic diagnosis, estimated to be around 6–14%, seems modest compared to other rare disease fields (i.e. >80% in neurodevelopmental disorders (NDDs)) (Brunet et al., 2021). There are several explanations for this difference that suggest that the true contribution of DNVs is higher than currently appreciated. Most importantly, much more experience has been gained with DNV assessment in the field of NDDs. Despite a steep increase in the total diagnostic rate (Vissers et al., 2010; de Ligt et al., 2012; Deciphering Developmental Disorders Study, 2017) and the identification of 285 developmental disorder (DD)-associated DNVs, modelling suggests that more than 1000 DD-associated genes still remain to be discovered (Kaplanis et al., 2020). As more trio-based sequencing data will be generated from suspected IEI patients, the field should undertake larger-scale analyses that leverage existing statistical models from the field of NDDs/DDs, including models for gene/exon level enrichment and the identification of gain-of-function nucleotide clusters (Kaplanis et al., 2020). Moreover, there is still a bias towards AR disease genes in the IEI field, while this imbalance is shifting with the discovery of an increasing number of autosomal dominant (AD) disease genes (van der Made et al., 2020). Trio-based sequencing could accelerate the discovery of mutations in novel AD IEI genes.

Box 1.

Proposed indications for trio-based sequencing in patients with inborn errors of immunity.

  1. Clinical features with a high a priori chance for a causative pre- or post-zygotic de novo variant (DNV)

    1. Sporadic and ultra-rare

    2. Early-onset (infancy/childhood)

    3. Severe symptoms, often involving organs other than the immune system

  2. Clinical features with a high a priori chance for a causative somatic DNV acquired during life

    1. Late-onset (adolescence/adulthood)

    2. Severe symptoms, often involving signs of autoinflammation, immune dysregulation and/or bone marrow abnormalities

    3. Evidence for immune cell- or bone marrow lineage-specific dysfunction (i.e. myeloid cells [Beck et al., 2020], lymphoid cells [Wolach et al., 2005])

Inborn errors of immunity constitute a large group of heterogeneous disorders with differences in the expected contribution of DNVs. The a priori probability for the identification of a DNV will be highest in patients with early-onset, severe phenotypes, such as the combined immunodeficiencies (CID), especially CIDs with syndromic features, and patients with autoinflammatory syndromes and/or immune dysregulation with autoimmunity (Box 1). Although most of the reported genes underlying CIDs follow AR inheritance patterns, many genes following AD and X-linked (dominant) inheritance patterns have been described in recent years (Tangye et al., 2020). The genes affected in these disorders possess high intolerance for loss-of-function mutations and essential biological functions. As expected, the DNVs in this category reported to date act through mechanisms of haploinsufficiency (i.e. RELA, pLI 0.999), dominant-negative interference (i.e. IKZF1, pLI 0.999 Kuehn et al., 2016; STAT3, pLI 1.000 Holland et al., 2007) or complete deficiency in hemizygotic males (i.e. WAS, pLI 0.999 Howard et al., 2016; IL2RG, pLI 0.992 Moya-Quiles et al., 2014). Some heterozygous DNVs can also cause CID through hypermorphic effects at protein level (i.e. RAC2, pLI 0.966 Hsu et al., 2019). Trio-based sequencing should also be considered in patients with sporadic autoinflammatory syndromes and/or autoimmunity, even when presenting at an adult age that could suggest somatic de novo mutations. In these patients, various pathogenic DNVs in different genes have already been described, originating both from the germline (PLCG2, STAT1) and soma (i.e. NLRP3, UBA1, TLR8) (Liu et al., 2011; Mensa-Vilaro et al., 2016; Aluri et al., 2021; Beck et al., 2020; van der Made et al., 2022; Zhou et al., 2012). These genes do not necessarily have high constraint for LoF mutations, but they possess nucleotide clusters that are highly conserved and intolerant to variation, encoding protein domains with important regulatory functions.

This explorative study has a number of limitations. First, the sample size precludes a reliable estimation of the prevalence of DNVs among patients with sporadic IEIs. Furthermore, the strict diagnostic rate of both inherited variants and (likely) pathogenic DNVs in our cohort is limited compared to other studies. It has been previously reported that the diagnostic yield of WES for IEI patients varies widely from 10 to 79% (Vorsteveld et al., 2021). This study reports (likely) pathogenic variants in 22 cases (17.9%), of which 10 (8.1%) received a definitive molecular diagnosis for their immunological phenotype. In addition to inherent technical shortcomings of WES, including uneven coverage of coding regions and GC bias and also the inability to explore the non-coding space (Meyts et al., 2016), the most likely explanation for a relatively low diagnostic yield in our study is the patient selection and the primary focus on DNVs, which constitute only a fraction of disease-causing variants. We excluded patients with suspected inherited disease but chose not to apply any other selection criteria in order to study a representative cross-section of suspected IEI patients in our centre in whom WES was performed. As a result, patients were included even if the a priori chance of an IEI was limited but to be ruled out in the differential diagnosis (i.e. new-born screening shows low T cell receptor excision circles (TRECs)). Moreover, compared to other cohorts, the percentage of patients with syndromal CIDs, autoinflammatory syndromes and immune dysregulation was relatively high and could influence the generalisability of our results. Lastly, the functional effect of most candidate DNVs were not evaluated. As DNVs have a high chance of being deleterious, functional experiments should always be attempted to validate the predicted effect. The candidate DNVs in potentially novel IEI genes were shared on GeneMatcher in order to find similar cases that could motivate further investigation into the underlying mechanisms (Acuna-Hidalgo et al., 2016; Sobreira et al., 2015).

In conclusion, we applied trio-based WES in a retrospective cohort of 123 patients with suspected, sporadic IEI, leading to the identification of 14 DNVs with a possible or likely chance of pathogenicity. Amongst the candidate DNVs in potentially novel IEI genes, additional functional evidence was provided in support of a pathogenic role for the DNV in FBXW11 in a patient with an autoinflammatory phenotype. We advocate the structural implementation of trio-based sequencing in the diagnostic evaluation of patients with sporadic IEI. With decreasing costs of exome sequencing, this approach could improve the diagnostic rate of IEI and advance IEI gene discovery.

Data availability

The code used to filter DNA sequencing data for candidate de novo mutations (DNMs) and to generate output files is provided in Figure 1—source code 1. Source data linked to Figure 1—figure supplement 1 is provided as an additional, numerical data file. Source data for candidate DNM evaluation is provided in Figure 1—source data 2. Source data linked to Figure 2—figure supplement 1A is an uncropped, raw gel image used to create this figure. Source data linked to Figure 2B–D is provided as an additional, numerical data file. Raw DNA sequencing data of patients are not publicly available as it is confidential human subject data that would compromise anonymity. Researchers that are interested to access the sequencing data of our cohort are advised to contact the corresponding author, A Hoischen (alexander.hoischen@radboudumc.nl). Anonymized subject data will be shared on request from qualified investigators for the purposes of replicating procedures and results, and for other non-commercial research purposes within the limits of participants' consent. Any data sharing will also require evaluation of the request by the regional Arnhem and Nijmegen Ethics Committee and the signature of a data transfer agreement (DTA).

References

Decision letter

  1. Tony Yuen
    Reviewing Editor; Icahn School of Medicine at Mount Sinai, United States
  2. Mone Zaidi
    Senior Editor; Icahn School of Medicine at Mount Sinai, United States

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Trio-based whole exome sequencing in patients with suspected sporadic inborn errors of immunity: a retrospective cohort study" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Mone Zaidi as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) As commented by Reviewer 1, please check the filtering strategy for DNV calls to filter false positives.

2) The novel identified candidate variants should be confirmed by Sanger sequencing or some other methods/tools (CNVs). This will give a sense of any false variant calls in this type of analysis.

Reviewer #1 (Recommendations for the authors):

Genomics analysis of rare diseases, including IEI, is valuable to the field despite the limited sample size. However, the current manuscript seems to focus on describing affected genes (though a functional experiment on one candidate). I think the paper will be more suitable for readers in clinical immunology or medical genetics.

Reviewer #2 (Recommendations for the authors):

1. Page 5, line 28, "<20% variant allele frequency". Do the authors mean Variant Allele Fraction? The filtering pipeline excluded somatic variation below 20% VAF, which is understandable as decreasing the VAF cutoff introduces a lot of noise; nevertheless, in some diseases, somatic variations are found at 5-20% VAF. This could be commented on in the Discussion.

2. Page 11, line 13: the wording in this sentence is too strong. Finding a heterozygous pathogenic or a likely pathogenic variant in recessively inherited disease-associated genes does not constitute " a likely molecular diagnosis." Unless there is evidence that at least some of these pathogenic and LP variants affect the same pathway and have a synergistic effect.

3. What is the percentage of patients with a confirmed molecular diagnosis? I can count only 6 patients in the table 3 (10,12, 59,91,105,122). Please correct me if I am wrong.

4. Finding small CNV's (for example 1-2 exon deletions) is hard due to a high false-positive rate. If only one tool was used (i.e., CoNIFER), did the authors use any additional method to confirm these de novo CNVs in patients 21, 69, and 115? It is unclear to me whether any of these CNVs have been previously identified/reported in some databases.

5. During the variant filtering step, only de novo nonsynonymous coding and splice region variants were kept. The authors mentioned analyzing splice-site DNVs specifically using the Alamut software, which provides a few prediction tools. The authors may consider using other algorithms, such as SpliceAI, which appears to be highly reliable for prioritizing variants. (PMID: 30661751)

6. Table 4, I question the pathogenicity predictions regarding the KMT2C frameshift variant. The CADD score is only 14.7, and according to UCSC as well as polyphen-2, this residue is not conserved between species (Serine to Histidine between human and mouse).

7. Page 17, line 9: although most proteasome-associate diseases are recessively inherited, there are examples of a dominantly inherited mutation in PSMB9. (PMID 33727065 and 34819510)

8. Page 21, line 13: are there different isoforms of FBXW11? Cell-specific expression of various isoforms can potentially explain how LoF variants in this gene can be associated with a neurodevelopmental phenotype in some patients and an immunological disease in other patients. (e.g., CDC42-associated diseases, UBA1-associated diseases).

9. Page 24, line 10: there is a typo in this sentence " soma", it should be "somatic".

10. The authors may discuss other possible explanations for the lower-than-expected diagnostic rate in this cohort and similar cohorts of patients with suspected monogenic immunological diseases. Possible explanations are uneven coverage of coding regions (especially exon 1 tends to be poorly covered), the fact that only protein-coding regions are captured by WES, somatic mutations in the early post-zygotic stage (PMID: 32750333), or some other explanations. Reduced penetrance is another factor, which is not pertinent to this study, as this study focuses on the identification of de novo variants. Expectedly, the diagnostic rate will be higher when working with patients with recessively inherited PID and from founder populations and this may explain the diagnostic rate of 75% in some studies.

11. Some sentences are repetitive in the Result section and Discussion e.g., the discussion on the PSMB10, RELA, and NLRP3 variants.

https://doi.org/10.7554/eLife.78469.sa1

Author response

Essential revisions:

1) As commented by Reviewer 1, please check the filtering strategy for DNV calls to filter false positives.

We agree with Reviewer 1 that filtering for rare variants, even more so for DNVs, is prone to falsepositive findings. Our filtering strategy is in accordance with other DNV filtering strategies in literature [1-3]. While this filtering strategy minimizes the likelihood for false-positive findings, it ensures that only a fraction of true positives are missed. A very low variant allele frequency should be permitted, especially for variants in genes known to cause inborn errors of immunity. DNVs in these genes that are found in presumed healthy individuals in population databases might still be pathogenic due to decreased penetrance, adult-onset development of the disease or postzygotic occurrence. Apart from standard filters on variant allele frequency and quality, we have applied several approaches to minimize the risk of false-positive DNVs:

  • To minimize the contribution of local genetic variation and sequencing artefacts to potential false-positive variant calls, we have filtered variants both on the Dutch population database (GoNL) and our in-house database (containing >60,000 exomes) variant allele frequencies.

  • Additionally, the automated DNV calling is based on >2% alternate allele reads in either parent, meaning that the DNVs are high confidence calls.

  • Variants were further filtered based on an annotated function in the immune system, which increased the likelihood of finding true-positive DNVs.

  • The final 14 candidate DNVs and variants reported by the diagnostic department were inspected visually with the Integrative Genomics Viewer (IGV) and/or Alamut Visual software to exclude false-positive variants. We have added Author response images 117 , our final 14 candidate DNVs, for the inspection by the Editors and reviewers, and could add this information to the manuscript supplement if judged to be helpful.

Author response image 1
Patient 1: PSMB10 Chr16(GRCh37):g.67968809C>T NM_002801.3:c.601G>A p.(Gly201Arg).

Score <500, but validated by an independent test (Sanger sequencing).

Author response image 2
Patient 9: RPL27A Chr11(GRCh37):g.8707228T>C NM_000990.4:c.322T>C p.(Tyr108His).

Validated by an independent test (identified by single and trio-based WES).

Author response image 3
Patient 21: seq[GRCh37] del(17)(q25.3qter) NC_000017.10:g.(80544076_80544938)_qterdel.

Validated by independent test (identified by two different enrichment kits: Agilent Technologies and Twist Bioscience).

Author response image 4
Patient 27: TAOK2 Chr16(GRCh37):g.29997683C>T NM_016151.3:c.2090C>T p.(Ala697Val).
Author response image 5
Patient 28: KCTD9 Chr8(GRCh37):g.25292997C>T NM_017634.3:c.695G>A p.(Arg232His).

Score <500, but validated by an independent test (Sanger sequencing).

Author response image 6
Patient 49: DDX1 Chr2(GRCh37):g.15769802dup NM_004939.2:c.1952dup p.(Trp652fs).
Author response image 7
Patient 52: SCRIB Chr8(GRCh37):g.144874432C>T NM_182706.4:c.4472G>A p.(Arg1491Gln).

Score <500 and not validated by an independent test.

Author response image 8
Patient 53: FBXW11 Chr5(GRCh37):g.171295802T>C NM_012300.2:c.1468-2A>G p.?.
Author response image 9
Patient 58: CTCF Chr16(GRCh37):g.67645905G>T NM_006565.4:c.833G>T p.(Arg278Leu).
Author response image 10
Patient 59: NLRP3 Chr1(GRCh37):g.247587794C>T NM_001079821.2:c.1049C>T p.(Thr350Met).

Score <500, but validated by an independent test (identified prior to trio-based WES in a different hospital).

Author response image 11
Patient 59: NLRP3 Chr1(GRCh37):g.Patient 69: seq[GRCh37] dup(22)(q11.21q11.21) NC_000022.10:g.(18775421_18893960)_(21414845_21576183)dup.
Author response image 12
Patient 75: FUBP1 Chr1(GRCh37):g.78435621A>C NM_001303433.1:c.199T>G p.(Leu67Val).

Validated by an independent test (identified by single and trio-based WES).

Author response image 13
Patient 78: KMT2C Chr7(GRCh37):g.151860074del NM_170606.2:c.10588del p.(Ser3530Leufs*3).
Author response image 14
Patient 108: NSD2 Chr4(GRCh37):g.1959681_1959687delinsTTTTTCT NM_133330.2:c.2903_2909delinsTTTTTCT p.(Arg968_Arg970delinsLeuPheLeu).

Validated by independent test (WES on different tissue (skin biopsy)).

Author response image 15
Patient 115: seq[GRCh37] dup(X)(q13).
Author response image 16
Patient 118: RUNX3 Chr1(GRCh37):g.25256227C>T NM_004350.2:c.133G>A p.(Gly45Arg).
Author response image 17
Patient 119: RELA Chr11(GRCh37):g.65423234C>T NM_021975.3:c.959-1G>A p.?.

Integrative Genomics Viewer (IGV) and/or Alamut Visual software screenshots of the 14 final candidate de novo variants and 3 de novo copy number variants

In conclusion, we are convinced that the used DNV filtering strategy already minimizes the chance of false-positives; and average number of DNVs are in line with recent literature (Figure 1 – source data 3; [4]). Nevertheless, we have adjusted the filtering strategy according to the reviewer’s suggestion by increasing the variation reads from 5 to 10, as this stricter cut-off is also mentioned by some studies in literature [3, 5, 6]. This reduced the total amount of rare DNVs to 172 (previously 187). In addition, we have clarified in the text that our analysis includes 24 variants that were present at very low allele frequencies in population databases (page 13 line 4). We have updated the text (page 5 line 7-8 and 30, page 6 lines 6-8, page 13 lines 3-5 and 8, page 15 line 1, page 23 line 12), and Figure 1 with corresponding table supplement 2 and 3 as well as figure supplement 1.

2) The novel identified candidate variants should be confirmed by Sanger sequencing or some other methods/tools (CNVs). This will give a sense of any false variant calls in this type of analysis.

We agree that for this type of analysis it is very important to keep the possibility of false-positive variant calls as limited as possible. However, the use of Sanger sequencing to confirm SNV candidate variants is no longer standard of practice – in our nor other laboratories as large validation studies preceded the current effort. SNVs called in next-generation sequencing techniques such as whole exome sequencing have been shown to reach a concordance of 99.965% with Sanger sequencing, indicating a very limited chance on false-positives [7]. To further support the absence of any type of bias that would give rise to false-positive DNVs (low quality base calls, read end artifacts, strand bias artifacts, erroneous alignment to low complexity regions, and paralogous alignment of reads) [8], we have added IGV and/or Alamut screenshots of the 14 final 14 DNVs, remaining as the most promising variants, at the end of this rebuttal. Furthermore, our diagnostics department follows standard practice guidelines (ISO15189) that dictate the performance of validations using other techniques if quality scores are <500, as the chances of a false-positive variant call is very low if this score exceeds 500. According to these guidelines, the DNVs identified in patient 1, 28, 52 and 59 would require validation for diagnostic reporting. For three of these patients (1, 28 and 59) as well as patients 9, 75 and 108 independent tests such as re-sequencing, sequencing of other tissue samples or Sanger sequencing were performed and confirmed the presence of the identified DNVs. All other DNVs fulfil diagnostic quality criteria and would be shared in diagnostic reports without any further validation.

Finally, the de novo copy number variants (CNVs) of patient 21, 69 and 115, were also reported as high confident calls by the diagnostic division of our department according to standard operating procedures, please refer to the attached IGV and/or Alamut screenshots. The high quality scores were most importantly based on the large CNV sizes: patient 21 del17q 500kb, patient 69 dup22q 2.5MB and patient 115 dupXq 400-500kb. Moreover, the CNV in patient 21 was identified by two different enrichment kits, increasing the likelihood that this CNV is a true-positive. Lastly, the CNV in patient 69 is involved in a recurrent microduplication syndrome (Chromosome 22q11.2 micro-duplication syndrome, OMIM #608363) with well-established breakpoints in low-copy repeat elements mediated by nonallelic homologous recombination, which makes true nature of this CNV very likely.

Reviewer #1 (Recommendations for the authors):

Genomics analysis of rare diseases, including IEI, is valuable to the field despite the limited sample size. However, the current manuscript seems to focus on describing affected genes (though a functional experiment on one candidate). I think the paper will be more suitable for readers in clinical immunology or medical genetics.

We thank the reviewer for the overall positive assessment of our work. We leave it up to the expertise of the Editor to choose the most appropriate research area within eLife that would reach this readership.

Reviewer #2 (Recommendations for the authors):

1. Page 5, line 28, "<20% variant allele frequency". Do the authors mean Variant Allele Fraction? The filtering pipeline excluded somatic variation below 20% VAF, which is understandable as decreasing the VAF cutoff introduces a lot of noise; nevertheless, in some diseases, somatic variations are found at 5-20% VAF. This could be commented on in the Discussion.

We indeed meant to express “variant allele fraction”, which has now been corrected in the revised manuscript. Moreover, the reviewer is right in pointing out that somatic variants could be missed with a VAF cut-off of 20%. However, we have checked that no filtered DNVs with a VAF below 20% were missed in established inborn errors of immunity genes. We have added a sentence specifying this in the Materials and methods (page 5 line 31 – page 6 line 1) and Discussion section (page 24 lines 20-21).

2. Page 11, line 13: the wording in this sentence is too strong. Finding a heterozygous pathogenic or a likely pathogenic variant in recessively inherited disease-associated genes does not constitute " a likely molecular diagnosis." Unless there is evidence that at least some of these pathogenic and LP variants affect the same pathway and have a synergistic effect.

We think this might be based on a misunderstanding, as we do not suggest that the finding of a heterozygous pathogenic or likely pathogenic variant in a recessive disease gene would constitute a diagnosis, but merely carriership. We have updated Table 2 to make it more comprehensible.

3. What is the percentage of patients with a confirmed molecular diagnosis? I can count only 6 patients in the table 3 (10,12, 59,91,105,122). Please correct me if I am wrong.

This too might be based on a misunderstanding. We have rephrased the corresponding sentence in the revised manuscript to increase comprehensibility (page 12 lines 19-21) and we have additionally updated Table 2.

4. Finding small CNV's (for example 1-2 exon deletions) is hard due to a high false-positive rate. If only one tool was used (i.e., CoNIFER), did the authors use any additional method to confirm these de novo CNVs in patients 21, 69, and 115? It is unclear to me whether any of these CNVs have been previously identified/reported in some databases.

As elaborated in our response on page 2, recommendation 2, the de novo CNVs in patients 21, 69 and 115 were all based on high-confidence calls. Due to the high quality scores, the CNVs were not validated by additional sequencing approaches. The CNVs were also manually inspected, please refer to the attached IGV and/or Alamut screenshots. Moreover, the CNV in patient 69 had been previously reported as a cause for 22q11.2 microduplication syndrome (page 12 lines 8-10, Table 2 – source data 1). The CNV identified in patient 21 was encountered in DECIPHER, a reference to which has been added to the revised manuscript (page 12 lines 7-8, Table 2 – source data 1). The CNV of patient 115 was not identified in any database (page 12 lines 10-12). However, it was previously described in another case of our hospital, although the phenotypic overlap was unclear.

5. During the variant filtering step, only de novo nonsynonymous coding and splice region variants were kept. The authors mentioned analyzing splice-site DNVs specifically using the Alamut software, which provides a few prediction tools. The authors may consider using other algorithms, such as SpliceAI, which appears to be highly reliable for prioritizing variants. (PMID: 30661751)

We thank the reviewer for this useful comment. We have added the SpliceAI scores to Table 3 and now mention its use in the Materials and methods section (page 6 line 9).

6. Table 4, I question the pathogenicity predictions regarding the KMT2C frameshift variant. The CADD score is only 14.7, and according to UCSC as well as polyphen-2, this residue is not conserved between species (Serine to Histidine between human and mouse).

Since variant-specific metrics are not useful to assess the functional impact of predicted loss-offunction variants, we have removed these metrics from Table 4 for the predicted loss-of-function variants. In contrast, gene-specific metrics are much more informative. As stated in Table 4, the LOEUF score of KMT2C is 0.12, which indicates a strong constraint against loss-of-function mutations.

7. Page 17, line 9: although most proteasome-associate diseases are recessively inherited, there are examples of a dominantly inherited mutation in PSMB9. (PMID 33727065 and 34819510)

We thank the reviewer and have adapted the suggestion by adding a section on the dominantly inherited PSMB9 mutation: “PRAAS is … in mice.” (page 22 lines 10-16).

8. Page 21, line 13: are there different isoforms of FBXW11? Cell-specific expression of various isoforms can potentially explain how LoF variants in this gene can be associated with a neurodevelopmental phenotype in some patients and an immunological disease in other patients. (e.g., CDC42-associated diseases, UBA1-associated diseases).

FBXW11 indeed has multiple isoforms, as can be observed in Figure 2 —figure supplement 1. It has three high quality protein-coding transcripts that produce the most abundant isoforms, and all are affected by the splice-acceptor DNV. The cell-type or tissue-specific expression does not seem to differ greatly among the isoforms (GTEx Portal, v8), but it could be that the specific functions of the SCF complex in which FBXW11 participates are cell-type specific. We have now also stated this in the discussion: “However, the exact mutational mechanism … most abundant isoforms.” (page 21 lines 19-24).

9. Page 24, line 10: there is a typo in this sentence " soma", it should be "somatic".

We have not corrected this as we actually meant to express the noun, which is ‘soma’.

10. The authors may discuss other possible explanations for the lower-than-expected diagnostic rate in this cohort and similar cohorts of patients with suspected monogenic immunological diseases. Possible explanations are uneven coverage of coding regions (especially exon 1 tends to be poorly covered), the fact that only protein-coding regions are captured by WES, somatic mutations in the early post-zygotic stage (PMID: 32750333), or some other explanations. Reduced penetrance is another factor, which is not pertinent to this study, as this study focuses on the identification of de novo variants. Expectedly, the diagnostic rate will be higher when working with patients with recessively inherited PID and from founder populations and this may explain the diagnostic rate of 75% in some studies.

We would like to thank Reviewer 2 for these suggestions. We have integrated some of these ideas into the Discussion section (page 26 lines 12-15).

11. Some sentences are repetitive in the Result section and Discussion e.g., the discussion on the PSMB10, RELA, and NLRP3 variants.

We have addressed this by reducing the respective sections in the Results section and by re-writing parts of the paragraphs in the Discussion section. Please refer to the revised manuscript.

References

1. Olfson, E., et al., Whole-exome DNA sequencing in childhood anxiety disorders identifies rare de novo damaging coding variants. Depress Anxiety, 2022. 39(6): p. 474-484.

2. Pedersen, B.S., et al., Effective variant filtering and expected candidate variant yield in studies of rare human disease. NPJ Genom Med, 2021. 6(1): p. 60.

3. Oud, M.S., et al., A de novo paradigm for male infertility. Nat Commun, 2022. 13(1): p. 154.

4. Kaplanis, J., et al., Evidence for 28 genetic disorders discovered by combining healthcare and research data. Nature, 2020. 586(7831): p. 757-762.

5. Dong, W., et al., Exome Sequencing Implicates Impaired GABA Signaling and Neuronal Ion Transport in Trigeminal Neuralgia. iScience, 2020. 23(10): p. 101552.

6. Homsy, J., et al., de novo mutations in congenital heart disease with neurodevelopmental and other congenital anomalies. Science, 2015. 350(6265): p. 1262-6.

7. Beck, T.F., et al., Systematic Evaluation of Sanger Validation of Next-Generation Sequencing Variants. Clin Chem, 2016. 62(4): p. 647-54.

8. Koboldt, D.C., Best practices for variant calling in clinical sequencing. Genome Med, 2020. 12(1): p. 91.

9. Wang, T., et al., Integrated gene analyses of de novo mutations from 46,612 trios with autism and developmental disorders. BioRxiv, 2021.

10. Hoischen, A., N. Krumm, and E.E. Eichler, Prioritization of neurodevelopmental disease genes by discovery of new mutations. Nat Neurosci, 2014. 17(6): p. 764-72.

https://doi.org/10.7554/eLife.78469.sa2

Article and author information

Author details

  1. Anne Hebert

    Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8945-015X
  2. Annet Simons

    Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  3. Janneke HM Schuurs-Hoeijmakers

    Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Hans JPM Koenen

    Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Resources, Formal analysis, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  5. Evelien Zonneveld-Huijssoon

    Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  6. Stefanie SV Henriet

    Department of Pediatric Infectious Diseases and Immunology, Amalia Children’s Hospital, Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  7. Ellen JH Schatorjé

    Department of Pediatric Rheumatology and Immunology, Amalia Children’s Hospital, Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  8. Esther PAH Hoppenreijs

    Department of Pediatric Rheumatology and Immunology, Amalia Children’s Hospital, Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  9. Erika KSM Leenders

    Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  10. Etienne JM Janssen

    Department of Clinical Genetics, Maastricht University Medical Center+, Maastricht, Netherlands
    Contribution
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    No competing interests declared
  11. Gijs WE Santen

    Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, Netherlands
    Contribution
    Resources, Writing – review and editing
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    No competing interests declared
  12. Sonja A de Munnik

    Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  13. Simon V van Reijmersdal

    Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  14. Esther van Rijssen

    Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Resources, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  15. Simone Kersten

    Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Resources, Investigation, Writing – review and editing
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    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0251-5564
  16. Mihai G Netea

    1. Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
    2. Department for Immunology and Metabolism, Life and Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
    Contribution
    Supervision, Funding acquisition, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2421-6052
  17. Ruben L Smeets

    1. Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, Nijmegen, Netherlands
    2. Department of Laboratory Medicine, Laboratory for Diagnostics, Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Resources, Formal analysis, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  18. Frank L van de Veerdonk

    Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Supervision, Funding acquisition, Writing – review and editing
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1121-4894
  19. Alexander Hoischen

    1. Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands
    2. Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    alexander.hoischen@radboudumc.nl
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8072-4476
  20. Caspar I van der Made

    1. Department of Human Genetics, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, Netherlands
    2. Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0763-4017

Funding

European Research Council (No. 833247)

  • Mihai G Netea

ZonMw (Spinoza Grant)

  • Mihai G Netea

Radboud Institute for Molecular Life Sciences (Internal grant)

  • Mihai G Netea

ZonMw (Vidi)

  • Frank L van de Veerdonk

H2020 European Research Council (HDM-FUN)

  • Frank L van de Veerdonk

H2020 European Research Council (Solve-RD (No. 779257))

  • Alexander Hoischen

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank the Bioinformatics group of the Genome Diagnostics division of the department of Human Genetics and the Radboud Genomics Technology Center of the Radboud University Medical Center for the sharing, annotation and pseudonymisation of whole exome sequencing datasets of patients and their parents included in this study. Furthermore, we acknowledge all members of the multidisciplinary immunogenetics sign-out meeting of the University Medical Centers from Nijmegen and Maastricht. The authors also acknowledge support from several funding parties. MG Netea was supported by an ERC Advanced Grant (No. 833247) and a Spinoza Grant of the Netherlands Organization for Scientific Support. This research was also part of a Radboud Institute for Molecular Life Sciences PhD grant (to M G Netea). F L van de Veerdonk was supported by a ZonMW Vidi grant and HDM-FUN EU H2020. A Hoischen was supported by the Solve-RD project of the European Union’s Horizon 2020 research and innovation programme (No. 779257). Funding This research was supported by grants from the European Union, ZonMW and the Radboud Institute for Molecular Life Sciences.

Ethics

Human subjects: Patients and their parents provided written informed consent for in silico inborn errors of immunity whole exome sequencing gene panel analysis with or without exome-wide variant analysis in line with the diagnostic procedure and clinical question, as approved by the Medical Ethics Review Committee Arnhem-Nijmegen (2011/188 and 2020-7142). This research is in compliance with the principles of the Declaration of Helsinki.

Senior Editor

  1. Mone Zaidi, Icahn School of Medicine at Mount Sinai, United States

Reviewing Editor

  1. Tony Yuen, Icahn School of Medicine at Mount Sinai, United States

Publication history

  1. Received: March 8, 2022
  2. Preprint posted: April 18, 2022 (view preprint)
  3. Accepted: October 5, 2022
  4. Accepted Manuscript published: October 17, 2022 (version 1)
  5. Version of Record published: November 4, 2022 (version 2)

Copyright

© 2022, Hebert et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Anne Hebert
  2. Annet Simons
  3. Janneke HM Schuurs-Hoeijmakers
  4. Hans JPM Koenen
  5. Evelien Zonneveld-Huijssoon
  6. Stefanie SV Henriet
  7. Ellen JH Schatorjé
  8. Esther PAH Hoppenreijs
  9. Erika KSM Leenders
  10. Etienne JM Janssen
  11. Gijs WE Santen
  12. Sonja A de Munnik
  13. Simon V van Reijmersdal
  14. Esther van Rijssen
  15. Simone Kersten
  16. Mihai G Netea
  17. Ruben L Smeets
  18. Frank L van de Veerdonk
  19. Alexander Hoischen
  20. Caspar I van der Made
(2022)
Trio-based whole exome sequencing in patients with suspected sporadic inborn errors of immunity: A retrospective cohort study
eLife 11:e78469.
https://doi.org/10.7554/eLife.78469

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    MicroRNAs (miRNA) and other components contained in extracellular vesicles may reflect the presence of a disease. Lung tissue, sputum, and sera of individuals with idiopathic pulmonary fibrosis (IPF) show alterations in miRNA expression. We designed this study to test whether urine and/or tissue derived exosomal miRNAs from individuals with IPF carry cargo that can promote fibrosis.

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    Funding:

    This work was supported in part by Lester and Sue Smith Foundation and The Samrick Family Foundation and NIH grants R21 AG060338 (SE and MKG), U01 DK119085 (IP, RS, MTC).