Next-Generation Sequencing Analysis in Greek Patients with Predominantly Antibody Deficiencies
by
, , , , ,
Achilleas P. Galanopoulos
1,2,†, 
Sofia Raftopoulou
1,†,
Styliani Sarrou
1
,
Alexia Matziri
2,
Stamatia Papoutsopoulou
3,
Grigorios Stratakos
4,
Varvara A. Mouchtouri
2,
Martin Hölzer
5,
Christos Hadjichristodoulou
2
,
Fani Kalala
1 and
Matthaios Speletas
1,* 1
Department of Immunology & Histocompatibility, Faculty of Medicine, University of Thessaly, 41500 Larisa, Greece
2
Laboratory of Hygiene and Epidemiology, Faculty of Medicine, University of Thessaly, 41222 Larisa, Greece
3
Department of Biochemistry and Biotechnology, Faculty of Life Sciences, University of Thessaly, 41500 Larisa, Greece
4
Interventional Pulmonology Unit, 1st Respiratory Department of National and Kapodistrian University of Athens, Sotiria Chest Diseases Hospital, 11527 Athens, Greece
5
Bioinformatics and Translational Research, Genome Competence Center, Robert Koch Institute, Nordufer 20, 13353 Berlin, Germany
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Immuno 2025, 5(3), 27; https://doi.org/10.3390/immuno5030027
Submission received: 4 April 2025
/
Revised: 1 July 2025
/
Accepted: 10 July 2025
/
Published: 16 July 2025
Abstract
Predominantly antibody deficiencies (PADs) are the most prevalent types of inherited errors of immunity (IEI) and are characterized by a broad range of clinical manifestations, such as recurrent infections, autoimmunity, lymphoproliferation, atopy and malignancy. The aim of this study was to identify genetic defects associated with PADs in order to improve diagnosis and personalized care. Twenty patients (male/female: 12/8, median age of disease onset: 16.5 years, range: 1–50) were analyzed by next-generation sequencing (NGS) using a custom panel of 30 genes associated with PADs and their possible disease phenotype. The detected variants were classified according to the American College of Medical Genetics and Genomics (ACMG) guidelines and inheritance, and the penetrance patterns were evaluated by PCR–Sanger sequencing. Novel and rare mutations associated with the phenotype of common variable immunodeficiency (CVID) in genes encoding the transcription factors NFKB1, NFKB2 and IKZF1/IKAROS were identified. Alphafold3 protein structure prediction was utilized to perform a comprehensive visualization strategy and further delineate the mutation-bearing domains and elucidate their potential impact on protein function. This study highlights the value of genetic testing in PADs and will guide further research and improvement in diagnosis and treatment.
1. Introduction
Inborn errors of immunity (IEI) are disorders of the immune system, characterized by a broad range of clinical manifestations, including recurrent infections, autoimmunity, autoinflammation, atopy, and malignancy [1]. Among the IEI, the primary antibody deficiencies (PADs) represent the most common subgroup, comprising approximately 65% of cases (alone or in combination with other immune system defects). They are characterized by ineffective immunoglobulin production resulting in inadequate immune responses [2]. The primary challenge that PAD patients deal with is the remarkable mis- and under-diagnosis of their disease, leading to severe and irreversible complications [3,4]. Common variable immunodeficiency (CVID) is the most frequent PAD in humans with clinical significance [5]. CVID is a heterogeneous group of disorders, sharing common clinical and laboratory findings, including hypogammaglobulinemia and recurrent infections, along with an increased incidence of autoimmune manifestations, lymphoid hyperplasia and neoplastic diseases, especially those of the lymphoid tissue [6].
Although genetic defects have been identified in several CVID patients, the exact cause and pattern of the genetic inheritance remains unclear in most cases [7]. As a result, CVID is still considered a rarely monogenic disorder. Mutations in key genes involved in the terminal stages of B-cell development have been implicated in the pathogenesis of CVID [8]. Previous research has demonstrated a robust correlation between alterations in transcription factors involved in immune regulation, such as nuclear factor NF-kappa-B2 (encoded by the NFKB2 gene) and IKAROS (encoded by the IKZF1 gene), and the development of immunodeficiency [9,10]. In addition, defects in genes encoding immune-related receptors, such as TNFRSF13C (encoding the B-cell receptor BAFFR) and TNFRSF13B (encoding the B-cell receptor TACI), have been strongly connected to the pathogenesis of CVID [11,12,13]. These receptors play a crucial role in the activation, differentiation, and survival of B-cells [14]. Moreover, genetic variants in the adaptors, regulators, and signaling molecules involved in the immune response and regulation have also been described in association with the development of antibody deficiencies [15,16]. Therefore, it is deduced that homozygous or compound heterozygous defects in one or more immune or regulatory genes may contribute to CVID’s pathogenesis, affecting the clinical phenotype and the prognosis of the disease [17,18].
Next-generation sequencing (NGS) has become essential for the diagnosis and understanding of genetic disorders due to its ability to rapidly and accurately analyze multiple genes simultaneously [19]. By identifying both common and rare genetic defects, this technology provides crucial insights into the underlying causes of various genetic disorders. NGS also facilitates personalized treatment approaches by uncovering patient-specific mutations, enabling targeted therapies [20]. Furthermore, NGS is valuable in uncovering new gene–disease associations, improving the classification, prognosis, and clinical management of genetic diseases, including rare and complex disorders [21]. In recent years, the application of NGS technology in PAD patients has resulted in the discovery of new pathogenic defects and the detailed characterization of several IEI diseases [22,23].
The aim of this study was to develop an accurate diagnostic procedure based on a novel custom gene panel that could facilitate the discovery of genetic defects involved in the pathogenesis of PAD. The design of the gene panel was based on the literature and experimental findings from our and other laboratories, and the analysis was based on an NGS approach. We identified, among others, three pathogenic (and likely pathogenic) variants located in genes that encode transcription factors important for the function of immune system, such as NF-kB and IKAROS.
2. Materials and Methods
2.1. Patients
A total of 20 patients (male/female: 12/8, median age of disease onset: 16.5 years, range: 1–50) with a diagnosis of IEI and a PAD phenotype were enrolled in this study. Among them, 17 patients (male/female: 10/7) fulfilled the classical diagnostic criteria for CVID [24]. Two male patients displayed combined IgA and IgG subclass deficiencies, and a female patient had known CTLA4-mediated immune dysregulation syndrome. An overview of the demographic and clinical characteristics of all the patients is presented in Table 1.
All the patients had already been analyzed for TNFRSF13B/TACI defects; one (#18 in Table 1) displayed biallelic defects (p.C104R/p.I87N), four (#4, #15, #16, and #19 in Table 1) were heterozygous for the p.C104R mutation, one (#8 in Table 1) was heterozygous for the p.I87N mutation and one (#5 in Table 1) carried the p.E236X defected, also in a heterozygous state, as we recently reported [25]. Moreover, we enrolled a patient (#9 in Table 1) with a known heterozygous CTLA4 defect (p.Υ89X) that was initially reported by us previously [26], as well as another patient (#14 in Table 1) who displayed an IKZF1 defect (p.H191Y) that was identified after whole exome sequencing analysis. The above patients were considered positive controls in our experiments.
Written informed consent was obtained from all the participants or an accompanying relative for the few patients whose consent was not legally applicable (e.g., children). The study was designed according to the Helsinki II Declaration ethics and approved by the ethical committee of the Faculty of Medicine, University of Thessaly, Greece (No. 3105/19.6.2020).
2.2. Library Preparation and Sequencing
Genomic DNA was extracted from peripheral blood using the QIAamp DNA Blood Mini Kit (QIAGEN, Hilden, Germany). The concentration of the extracted DNA was measured using the Qubit 4 Fluorometer (Thermo Fisher, Waltham, MA, USA). The 260 nm/280 nm ratio of absorbance was evaluated using a NanoDrop 2000/2000c Spectrophotometer (Thermo Fisher, Waltham, MA, USA) to assess the purity of the extracted DNA.
A custom panel of 30 genes was used for the NGS analysis. The design of the gene panel was based on the literature and experimental findings from our and other laboratories in our region. The genes are listed in Table 2. The selection was based on their association with the CVID subtypes, according to the OMIM classification [27], as well as their role in the pathogenesis and phenotype of common IEI and other pathologic conditions.
The amplification of the coding regions in the 30 targeted genes was carried out using an amplicon-based approach following the QIAseq Targeted DNA Panel protocol (QI-Agen, Hilden, Germany) according to the manufacturer’s instructions. More specifically, genomic DNA samples were fragmented, end-repaired, and A-tailed. The DNA fragments were then ligated at their 5′ ends with a sequencing platform-specific adapter containing a unique 12-base entirely random sequence, commonly called a “Unique Molecular Identifier” (UMI) and an i7 index. Subsequently, target enrichment occurred using a multiplex PCR using 1064 region-specific primers, followed by a universal PCR to amplify the library and integrate a second platform-specific adapter containing the i5 index. The combination of i7 and i5 adapters was unique for every sample. The gene panel was designed through GRCh37 human genome assembly (hg19).
The libraries were purified by QIAseq Beads (QIAGEN, Hilden, Germany) to effectively remove the adapter primers. Quality control and size distribution calculations were performed with the QIAxcel Advanced System (QIAgen, Hilden, Germany) according to the manufacturer’s instructions. The accurate quantification of the library molecules was performed via real-time PCR using the QIAseq Library Quant Assay Kit (QIAgen, Hilden, Germany) according to the manufacturer’s instructions.
Sequencing was performed on the MiSeq platform (Illumina, San Diego, CA, USA) using the MiSeq Reagent kit v2 Nano (Illumina, California, USA) according to the manufacturer’s instructions. The libraries were diluted to 4 nM, combined in equimolar amounts, and sequenced with a sequencing length of 2 × 150 bp. Additionally, library preparation for several samples was carried out using the updated version of the QIAseq Targeted DNA Panel protocol, named QIAseq Targeted DNA Pro (QIAGEN, Hilden, Germany). This revised protocol incorporates enhancements such as repositioning read 1 to the UMI side, implementing a multiplex PCR with 999 target-specific primers, utilizing unique dual indexes (instead of combinatorial), and eliminating the need for a custom read 1 sequencing primer. Moreover, the updated gene panel was designed based on the GRCh38 human genome assembly (hg38). We used this protocol for the library construction in samples #17, #18 and #19.
2.3. Bioinformatic and Genetic Analysis
After sequencing, the raw reads (FASTQ files) were analyzed through QIAGEN’s Geneglobe pipeline, based on the smCounter v2 variant caller [28], which also included UMI processing. In summary, the 12-base UMIs were identified and clustered in UMI read groups. Small reads that lacked endogenous sequences were trimmed and the remainder were mapped onto the human reference genome (hg19 version for the QIAseq Targeted DNA Panel protocol libraries and the hg38 for the Pro version ones) using the BWA-MEM 0.7.9a-r786 aligner [29]. Primers were identified and masked, variant calling took place through smCounter v2, and variant annotation was performed with SnpEff and SnpSift [30].
The output files contained information regarding the overall statistics of the variant calling, variant annotation, filters for regions with high susceptibility to artifacts (e.g., low complexity regions like homopolymers and microsatellites, regions close to primers, etc.), and variant quality control score, reflecting the probability that a variant is caused by a background error.
Furthermore, we filtered the data to keep only information of high confidence. Specifically, we used a sequencing depth threshold of 10 unique UMIs supporting each position. Variants with a variant UMI level allele fraction (VMF, variant allele frequency) lower than 0.3 were excluded from the analysis. Subsequently, variants with 0.3 < VMF < 0.7 were evaluated as showing heterozygosity and VMF values > 0.9 were evaluated as exhibiting homozygosity. The analysis concerned variants with an smCounter quality control score higher than 6, which is equivalent to the probability of less than one false positive per megabase.
After the filtering process and annotation, the variants were evaluated regarding their impact on the phenotype. The pathogenicity bioinformatic tools SIFT [31] Polyphen2 [32] and MutationTaster [33] were used. In addition, the ClinVar database (https://www.ncbi.nlm.nih.gov/clinvar/, accessed on 1 April 2025 was used to investigate submissions regarding the detected variants in association with immunodeficiency-related clinical manifestations. Finally, the Franklin genoox database (https://franklin.genoox.com) was accessed to retain the variant-associated information regarding population frequencies, and each variant was classified according to the American College of Medical Genetics (ACMG) guidelines (Franklin ACMG Classification system).
Pathogenic and likely pathogenic variants were analyzed by investigating the structural changes. AlphaFold [34] was used to create AI folding predictions for wild-type (wt) and mutated protein molecules, following visualization with PyMOL (https://www.pymol.org/). In particular, proteins with the nonsense mutation wt were aligned with the mutant proteins in PyMOL to highlight the structural changes and potentially missing protein domains.
2.4. Sanger Sequencing Validation and Family Testing
All the pathogenic/likely pathogenic variants and novel mutations were confirmed by PCR–Sanger sequencing. For all the PCR reactions, a total of 100 to 200 ng of DNA was amplified in a 30 μL reaction mixture using 62.5 mM of deoxynucleotide triphosphate, 500 pmol of each primer, 1.5 mM of MgCl2, and 0.8 U of DFS-Taq DNA polymerase (Bioron GmbH, Romerberg, Germany) in a buffer supplied by the manufacturer. The PCR primers were retrieved from the Thermo Fisher (Thermo Fisher, Waltham, MA, USA) repository (primer sequences displayed in Table 3). The PCR conditions were calculated using the Primerblast and Oligo tools. Product purification was performed using the Purelink Quick Gel Extraction Kit/QiAquick Gel Extraction Kit (QIAGEN, Hilden, Germany) according to the manufacturer’s instructions. The purified products were sequenced using an ABI Prism 310 genetic analyzer (Applied Biosystems, Foster City, CA, USA).
In cases where pathogenic or likely pathogenic variants were identified, the available family members were also screened to study the inheritance pattern and assess the impact/penetrance of the mutation.
3. Results
3.1. Genetic Defects Identified in the Study
Genetic variants, in at least one patient, were detected across 25 out of the 30 genes studied (Figure 1). No variants were identified in the IL21, PIK3CD, PRKCD, RAG2, or TAPA-1 (CD81) genes. In total, 103 variants were detected and classified according to the ACMG guidelines: three pathogenic, six likely pathogenic, 12 variants of uncertain significance (VUS), five likely benign, and 77 benign (Figure 1). The pathogenicity predictions via the bioinformatic tools (SIFT, Polyphen2 and Mutation Taster) for the missense and stop-gainedvariants classified as pathogenic, likely pathogenic, or VUS are presented in Table S1. A descriptive interpretation of all the detected variants, their classification (ACMG guidelines), and their allele frequency in the European population is displayed in Table S2. The pathogenic, likely pathogenic variants and VUS detection for each patient are visualized in Figure 2, are listed in Table S3 and are presented in the following sections.
3.2. Pathogenic/Likely Pathogenic Variants Detected in Transcription Factors Involved in Immune System
3.2.1. NFKB2
A heterozygous nonsense mutation was detected in exon 11 of the NFKB2 gene (NM_001322934.2: c.937C>T/p.R313X) in a female patient displaying recurrent respiratory and urinary infections, arthralgias, and eczema (#6 in Table 1). This variant, classified as pathogenic based on the ACMG guidelines, has an allele frequency of 0.0001% in the European population and is confirmed as pathogenic in ClinVar (variant index: 620553). Screening family members for the specific mutation revealed that her daughter carries the same mutation and presents with selective IgA deficiency (sIgAD), exhibiting chronic urticaria and hypothyroidism. In contrast, her son, who does not carry the variant, displays immunoglobulin levels within the normal range and is healthy (Figure 3B, family tree). The structural 3D interpretation of the wt NFKB2 aligned with the mutated one (NFKB2: p.R313X) is visualized in Figure 3C and the Sanger sequencing chromographs from the family confirmation testing are displayed in Figure 3A.
3.2.2. NFKB1
A novel heterozygous nonsense mutation was identified in exon 11 of the NFKB1 gene (NM_003998.4: c.1050C>G/p.Y350X) in a male CVID patient with recurrent respiratory infections and Evans syndrome (#13 in Table 1). This variant, not yet reported in ClinVar, is classified as likely pathogenic based on the ACMG standards. Family testing revealed that neither his mother nor his brother carry this mutation (Figure 4B, family tree). The structural 3D interpretation of the wt NFKB1 aligned with the mutated one (NFKB1:p.Y350X) is visualized in Figure 4C and the Sanger sequencing chromographs ascertained through family testing are displayed in Figure 4A.
3.2.3. IKZF1
A heterozygous missense variant was identified in exon 5 of the IKZF1 gene (NM_006060.6: c.427C>T/p.R143W) in a female patient displaying recurrent respiratory infections and hypothyroidism (#3 in Table 1). Classified as likely pathogenic by the ACMG criteria, this variant has an allele frequency of 0.0002% in Europeans and is reported in ClinVar as likely pathogenic (variant index: 2907372). The variant was characterized as deleterious/probably damaging by the in silico predictors, and it was also found in the patient’s mother and grandmother (Figure 5B, family tree). The mother suffers from IgG4 deficiency, displaying an episode of autoimmune thrombocytopenia during pregnancy, while the grandmother does not show a CVID phenotype. The structural 3D interpretation of the variant location (between zinc-finger domains 1 and 2) regarding the superposition of the wt molecule with the mutated one (IKZF1: p.R143W) is visualized in Figure 5C and the family testing chromographs from the Sanger sequencing are displayed in Figure 5A.
3.2.4. LRBA
A nonsense heterozygous mutation was identified in exon 17 of the LRBA gene (NM_001364905.1: c.2149C>T/p.Q717X) in a female patient with recurrent respiratory infections and CVID-associated interstitial lung disease (CVID-ILD) (#17 in Table 1). Classified as pathogenic by the ACMG criteria, this variant is considered likely pathogenic in ClinVar (variant index: 2637021). No relevant records regarding LRBA:p.Q717X were found in the gnomAD–exomes catalogue regarding the European population. The structural 3D interpretation of the wt LRBA aligned with the mutated one is visualized in Figure S1.
3.3. Variants of Unknown Significance
We identified several rare variants of unknown significance (VUS), all in conditions of heterozygosity, that may be linked to the patients’ clinical manifestations. We detected NFKB2-p.A567V and IKZF1-p.T244S among the immune-related transcription factors. For the immune-related receptors, the variants identified include p.P151L and p.S1016L in CR2, p.I64V and p.P79T in MS4A1 (encoding CD20).. Additionally, we found VUS in the adaptors, regulators, and signaling molecules involved in the immune response, including p.L100V and p.R123S in BLK, p.H202D in NPAT, p.R251Q in MYD88, and c.2843-587C>T in PLCG2. Lastly, a notable variant, p.R1683Q, was detected in the vesicular trafficking anchor protein LRBA.
4. Discussion
NGS has transformed sequencing technologies by allowing high-throughput sequencing at progressively lower costs, making it widely accessible for both clinical and research applications [22]. In this study, we implemented an NGS-based approach using a custom gene panel tailored to identify rare pathogenic variants in a Greek PAD cohort. The novel NGS method we developed successfully detected all the described mutations of patients utilized as positive controls in the study, demonstrating its effectiveness. Additionally, several pathogenic defects that appear to be associated with the disease pathogenesis and/or phenotype were identified.
We detected three rare mutations associated with transcription factors known to be involved in the development and function of the immune system. NFKB2-p.R313X was found in a female CVID patient and her daughter who suffered from sIgAD. This nonsense mutation (NM_001322934.2: c.937C>T/p.R313X) is situated in the C-terminal region of the Rel homology domain (RHD), a critical domain of the p52/p100 protein, essential for DNA binding and dimerization [35]. A stop codon at this specific site results in a truncated molecule of 312 amino acids (WT: 900aa) lacking the transcription factor’s nuclear localization signal (NLS) and ankyrin repeats (ANK repeats). These domains are vital for retaining the protein in the cytoplasm and preventing its premature processing into p52 [36]. A similar nonsense mutation in NFKB2 (c.809 G>A, p.W270X) has been previously reported in a Hispanic family, resulting in increased mRNA decay with no mutant protein expression, ultimately leading to NFKB2 haploinsufficiency. That study highlighted that true NFKB2 haploinsufficiency, driven by mutant mRNA decay and protein instability, is linked to immunodeficiency, though with variable clinical penetrance [37]. Mutations in the NFKB2 gene have been strongly associated with early-onset CVID, often accompanied by features such as central adrenal insufficiency, ectodermal dysplasia, and, in some cases, autoimmunity [38]. Such mutations are inherited in an autosomal dominant manner and typically function in a dominant-negative fashion, disrupting the non-canonical NF-κB signaling pathway [39]. This pathway is critical for various immune processes, including B-cell survival, differentiation into plasma cells, isotype class switching, and dendritic cell activation [40].
The presence of the NFKB2-p.R313X nonsense mutation in the daughter of the CVID patient, who also has sIgAD, suggests that this defect displays a high penetrance and highlights its pathogenic potential. Based on these observations, we have already recommended close medical monitoring to mitigate the risk of developing more severe immune-related complications associated with this variant.
In addition, NFKB1 heterozygous damaging variants, inherited in an autosomal dominant manner, represent the most common monogenic cause of CVID [41]. Mutations that cause p50 haploinsufficiency have been associated with a range of clinical presentations, from isolated antibody deficiency to more complex, multiorgan autoinflammatory conditions, with antibody deficiency being the most frequent finding among affected individuals [42]. In line with these findings, we identified a novel NFKB1 nonsense mutation (NM_003998.4: c.1050C>G/p.Y350X) in patient #13 (Table 1). This variant resides in the C-terminal portion of the RHD and is classified as likely pathogenic regarding the ACMG criteria in the Franklin database, while there are no related data in ClinVar. Similar to the aforementioned mutation in NFKB2 (p.R313X), this variant results in a stop codon resulting in a tancated protein of 349aa (WT: 969aa), which excludes important domains of the p50/p100 protein, such as the NLS and ANK repeats. The patient’s unaffected brother and mother tested negative for this defect. However, his father, who passed away from lung cancer, could not be tested, leaving us unable to confirm whether the variant was inherited paternally or represents a de novo defect.
Furthermore, a heterozygous missense variant (NM_006060.6: c.427C>T/p.R143W) of the IKZF1 gene was identified in a patient with recurrent infections, thyroid disease, and hypogammaglobulinemia (#3 in Table 1). Family testing revealed that the variant was also carried by the patient’s unaffected mother and grandmother. IKAROS, encoded by IKZF1, functions as a zinc-finger transcription factor expressed in hematopoietic cells [10,43,44]. It binds to regulatory gene regions, associates with chromatin remodeling complexes, facilitating conversion to pericentromeric heterochromatin (PC-HC) [45,46] and plays a crucial role in early B-cell development, as demonstrated in murine models [44,47]. The identified variant causes the substitution of arginine 143, a highly hydrophilic, positively charged amino acid, with tryptophan, which is hydrophobic. It is situated between zinc-fingers ZF1 and ZF2 of IKAROS (Figure 5C). The p.R143W mutation was also reported in a structural analysis study [48]. It is highlighted that the W143 mutant side chain may interfere with F154, likely disrupting the backbone hydrogen bonds (H-bonds) between F145 and F154, destabilizing the ZF2 β-hairpin, which is essential for Zn ion coordination and stabilizing the loop containing N159, a site known for dominant-negative mutations in IKZF1. Transient transfection studies show that the p.R143W mutation partially interferes with PC targeting and DNA binding [48].
However, the IKZF1-R143W variant was detected in relatives with no CVID phenotype, implying that this variant alone does not directly cause CVID. In line with our observations, a German family study reported an association of the p.R143W with CVID and highlighted that the phenotype may involve contributions from additional variants [10]. Interestingly, our patient (#3 in Table 1) harbors three additional heterozygous variants, classified as VUS based on the ACMG criteria (Figure 2): CR2-p.P151L, MYD88-p.R251Q, and PLCG2:c.2843-587C>T. These variants may collectively influence the patient’s clinical phenotype and align with our future objectives for functional testing. This case highlights the potential polygenic nature of CVID, suggesting that mutations across multiple genes could contribute to the disease’s heterogeneity and variability in clinical manifestations.
Moreover, a nonsense mutation in the LRBA gene that encodes the lipopolysaccharide-responsive beige-like anchor protein was detected. The LRBA-p.Q717X variant was found in the condition of heterozygosity in one patient in our cohort. LRBA plays a critical role in immune regulation by anchoring CTLA-4 to the cellular membrane, thus ensuring sufficient CTLA-4 activity to downregulate the immune responses effectively [49]. The detected mutation results in a truncated LRBA protein of 716aa (WT: 2852aa), missing the BEACH (beige and Chediak–Higashi) and WD40 domains, as illustrated in the 3D protein structure interpretation in Figure S1. The BEACH domain is essential for intracellular vesicle trafficking and immune signaling, while the WD40 domain facilitates protein–protein interactions critical for cellular processes, including autophagy and immune regulation [15]. The absence of these domains likely disrupts the regulatory function of LRBA in maintaining immune homeostasis, which may contribute to the observed clinical phenotype in this patient. Although the variant is present in a heterozygous state and does not fully meet the criteria required to define an LRBA deficiency phenotype, its potential contribution to immune dysregulation and the patient’s clinical manifestations cannot be excluded.
Additional genetic variations were identified in several genes, including CR2, MS4A1, MYD88 and LRBA, all present in heterozygous states. These variants are categorized as VUS according to the ACMG guidelines and are depicted in Figure 1. While deficiencies associated with variants in these genes typically follow an autosomal recessive inheritance pattern, in our study they are found in a heterozygous state. However, we deem that these variants might contribute to the phenotypic expression of PADs in affected individuals.
Our study faced certain limitations, including the limited number of patients and the constraints in terms of analyzing the inheritance patterns in some cases due to deceased relatives or missing data, limiting our conclusions on pathogenicity and variant penetrance.
5. Conclusions
This study underscores the critical role of NGS and comprehensive genetic testing in identifying rare pathogenic mutations within immune-related genes in patients presenting with PAD symptoms. Family testing proved particularly informative, suggesting the need for closer monitoring of affected individuals. Moreover, genetic analysis illustrated the phenotypic heterogeneity of CVID, suggesting that additional genetic or epigenetic factors may modulate the disease presentation beyond single-gene mutations, emphasizing the need for further research into these complex interactions.
By enabling the rapid and comprehensive analysis of genetic variations, NGS minimizes the risk of PAD underdiagnosis, a persistent challenge in detecting rare immune disorders. Moreover, our research aims to extend beyond genetic identification to functional testing of these variants to elucidate their exact role in disease mechanisms and potential therapeutic targeting. This ongoing work will further clarify the genetic landscape of PADs and support precision medicine approaches for affected individuals and families.
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/immuno5030027/s1, Figure S1: Structural 3D interpretation of the wt LRBA aligned with the mutated c.2149C>T/p.Q717X; Table S1: Pathogenicity predictions via the bioinformatic tools (SIFT, Polyphen2, and Mutation Taster) for the missense variants classified as pathogenic, likely pathogenic, or VUS.; Table S2: All the detected variants, their classification and their allele frequency in the European population. Table S3: Clinical and demographic characteristics of the patients in accordance with the detected variants (VUS, Likely-pathogenic and Pathogenic ones).
Author Contributions
Conceptualization, M.S.; methodology, A.P.G., S.R., S.S., A.M. and S.P.; software, A.P.G. and M.H.; validation, S.S., S.P., G.S., V.A.M., M.H., C.H., F.K. and M.S.; formal analysis, A.P.G. and S.R.; investigation, S.R., S.S., G.S., F.K. and M.S.; data curation, A.P.G., S.R., A.M., F.K., G.S., V.A.M. and C.H.; writing—original draft preparation, A.P.G., S.R. and S.S.; writing—review and editing, S.S., S.P., M.H., F.K. and M.S.; visualization, A.P.G. and M.H.; supervision, M.S. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supported by the standard budget provided by the Research Committee of the University of Thessaly to the Laboratory of Immunology and Histocompatibility of Medical School of the University of Thessaly.
Institutional Review Board Statement
This study was designed according to the Helsinki II Declaration ethics and approved by the ethical committee of the Faculty of Medicine, University of Thessaly, Greece (No. 3105/19.6.2020).
Informed Consent Statement
Written informed consent was obtained from all participants or an accompanying relative for the few patients whose consent was not legally applicable (e.g., children).
Data Availability Statement
All data being analyzed in this manuscript are available upon request to the corresponding author.
Acknowledgments
The authors gratefully thank Evangelia S. Gramoustianou for her excellent technical assistance.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Notarangelo, L.D.; Bacchetta, R.; Casanova, J.-L.; Su, H.C. Human Inborn Errors of Immunity: An Expanding Universe. Sci. Immunol. 2020, 5, eabb1662. [Google Scholar] [CrossRef] [PubMed]
- Geha, R.S.; Notarangelo, L.D.; Casanova, J.-L.; Chapel, H.; Conley, M.E.; Fischer, A.; Hammarström, L.; Nonoyama, S.; Ochs, H.D.; Puck, J.M.; et al. International Union of Immunological Societies Primary Immunodeficiency Diseases Classification Committee. Primary Immunodeficiency Diseases: An Update from the International Union of Immunological Societies Primary Immunodeficiency Diseases Classification Committee. J. Allergy Clin. Immunol. 2007, 120, 776–794. [Google Scholar] [CrossRef] [PubMed]
- Meyts, I.; Bousfiha, A.; Duff, C.; Singh, S.; Lau, Y.L.; Condino-Neto, A.; Bezrodnik, L.; Ali, A.; Adeli, M.; Drabwell, J. Primary Immunodeficiencies: A Decade of Progress and a Promising Future. Front. Immunol. 2020, 11, 625753. [Google Scholar] [CrossRef] [PubMed]
- Kapousouzi, A.; Kalala, F.; Sarrou, S.; Farmaki, E.; Antonakos, N.; Kakkas, I.; Kourakli, A.; Labropoulou, V.; Kelaidi, C.; Tsiouma, G.; et al. A Nationwide Study of the Delayed Diagnosis and the Clinical Manifestations of Predominantly Antibody Deficiencies and CTLA4-Mediated Immune Dysregulation Syndrome in Greece. Medicina 2024, 60, 782. [Google Scholar] [CrossRef] [PubMed]
- Bayry, J.; Hermine, O.; Webster, D.A.; Lévy, Y.; Kaveri, S.V. Common Variable Immunodeficiency: The Immune System in Chaos. Trends Mol. Med. 2005, 11, 370–376. [Google Scholar] [CrossRef] [PubMed]
- Gereige, J.D.; Maglione, P.J. Current Understanding and Recent Developments in Common Variable Immunodeficiency Associated Autoimmunity. Front. Immunol. 2019, 10, 2753. [Google Scholar] [CrossRef] [PubMed]
- Yazdani, R.; Habibi, S.; Sharifi, L.; Azizi, G.; Abolhassani, H.; Olbrich, P.; Aghamohammadi, A. Common Variable Immunodeficiency: Epidemiology, Pathogenesis, Clinical Manifestations, Diagnosis, Classification, and Management. J. Investig. Allergol. Clin. Immunol. 2020, 30, 14–34. [Google Scholar] [CrossRef] [PubMed]
- Abolhassani, H.; Hammarström, L.; Cunningham-Rundles, C. Current Genetic Landscape in Common Variable Immune Deficiency. Blood 2020, 135, 656–667. [Google Scholar] [CrossRef] [PubMed]
- Chen, K.; Coonrod, E.M.; Kumánovics, A.; Franks, Z.F.; Durtschi, J.D.; Margraf, R.L.; Wu, W.; Heikal, N.M.; Augustine, N.H.; Ridge, P.G.; et al. Germline Mutations in NFKB2 Implicate the Noncanonical NF-κB Pathway in the Pathogenesis of Common Variable Immunodeficiency. Am. J. Hum. Genet. 2013, 93, 812–824. [Google Scholar] [CrossRef] [PubMed]
- Eskandarian, Z.; Fliegauf, M.; Bulashevska, A.; Proietti, M.; Hague, R.; Smulski, C.R.; Schubert, D.; Warnatz, K.; Grimbacher, B. Assessing the Functional Relevance of Variants in the IKAROS Family Zinc Finger Protein 1 (IKZF1) in a Cohort of Patients With Primary Immunodeficiency. Front. Immunol. 2019, 10, 568. [Google Scholar] [CrossRef] [PubMed]
- Salzer, U.; Chapel, H.M.; Webster, A.D.B.; Pan-Hammarström, Q.; Schmitt-Graeff, A.; Schlesier, M.; Peter, H.H.; Rockstroh, J.K.; Schneider, P.; Schäffer, A.A.; et al. Mutations in TNFRSF13B Encoding TACI Are Associated with Common Variable Immunodeficiency in Humans. Nat. Genet. 2005, 37, 820–828. [Google Scholar] [CrossRef] [PubMed]
- Poodt, A.E.J.; Driessen, G.J.A.; de Klein, A.; van Dongen, J.J.M.; van der Burg, M.; de Vries, E. TACI Mutations and Disease Susceptibility in Patients with Common Variable Immunodeficiency. Clin. Exp. Immunol. 2009, 156, 35–39. [Google Scholar] [CrossRef] [PubMed]
- Block, V.; Sevdali, E.; Recher, M.; Abolhassani, H.; Hammarstrom, L.; Smulski, C.R.; Baronio, M.; Plebani, A.; Proietti, M.; Speletas, M.; et al. CVID-Associated B Cell Activating Factor Receptor Variants Change Receptor Oligomerization, Ligand Binding, and Signaling Responses. J. Clin. Immunol. 2023, 43, 391–405. [Google Scholar] [CrossRef] [PubMed]
- Sevdali, E.; Block, V.; Lataretu, M.; Li, H.; Smulski, C.R.; Briem, J.-S.; Heitz, Y.; Fischer, B.; Ramirez, N.-J.; Grimbacher, B.; et al. BAFFR Activates PI3K/AKT Signaling in Human Naive but Not in Switched Memory B Cells through Direct Interactions with B Cell Antigen Receptors. Cell Rep. 2022, 39, 111019. [Google Scholar] [CrossRef] [PubMed]
- Lopez-Herrera, G.; Tampella, G.; Pan-Hammarström, Q.; Herholz, P.; Trujillo-Vargas, C.M.; Phadwal, K.; Simon, A.K.; Moutschen, M.; Etzioni, A.; Mory, A.; et al. Deleterious Mutations in LRBA Are Associated with a Syndrome of Immune Deficiency and Autoimmunity. Am. J. Hum. Genet. 2012, 90, 986–1001. [Google Scholar] [CrossRef] [PubMed]
- Martín-Nalda, A.; Fortuny, C.; Rey, L.; Bunney, T.D.; Alsina, L.; Esteve-Solé, A.; Bull, D.; Anton, M.C.; Basagaña, M.; Casals, F.; et al. Severe Autoinflammatory Manifestations and Antibody Deficiency Due to Novel Hypermorphic PLCG2 Mutations. J. Clin. Immunol. 2020, 40, 987–1000. [Google Scholar] [CrossRef] [PubMed]
- Speletas, M.; Mamara, A.; Papadopoulou-Alataki, E.; Iordanakis, G.; Liadaki, K.; Bardaka, F.; Kanariou, M.; Germenis, A.E. TNFRSF13B/TACI Alterations in Greek Patients with Antibody Deficiencies. J. Clin. Immunol. 2011, 31, 550–559. [Google Scholar] [CrossRef] [PubMed]
- Dieudonné, Y.; Guffroy, A.; Vollmer, O.; Carapito, R.; Korganow, A.-S. IKZF1 Loss-of-Function Variant Causes Autoimmunity and Severe Familial Antiphospholipid Syndrome. J. Clin. Immunol. 2019, 39, 353–357. [Google Scholar] [CrossRef] [PubMed]
- Adams, D.R.; Eng, C.M. Next-Generation Sequencing to Diagnose Suspected Genetic Disorders. N. Engl. J. Med. 2018, 379, 1353–1362. [Google Scholar] [CrossRef] [PubMed]
- Ruiz-Garcia, E.; Vega, H.A.-d.l. (Eds.) Translational Research and Onco-Omics Applications in the Era of Cancer Personal Genomics; Springer: Cham, Switzerland, 2019; ISBN 978-3-030-24100-1. [Google Scholar] [CrossRef]
- Vinkšel, M.; Writzl, K.; Maver, A.; Peterlin, B. Improving Diagnostics of Rare Genetic Diseases with NGS Approaches. J. Community Genet. 2021, 12, 247–256. [Google Scholar] [CrossRef] [PubMed]
- Vorsteveld, E.E.; Hoischen, A.; van der Made, C.I. Next-Generation Sequencing in the Field of Primary Immunodeficiencies: Current Yield, Challenges, and Future Perspectives. Clin. Rev. Allergy Immunol. 2021, 61, 212–225. [Google Scholar] [CrossRef] [PubMed]
- Rudilla, F.; Franco-Jarava, C.; Martínez-Gallo, M.; Garcia-Prat, M.; Martín-Nalda, A.; Rivière, J.; Aguiló-Cucurull, A.; Mongay, L.; Vidal, F.; Solanich, X.; et al. Expanding the Clinical and Genetic Spectra of Primary Immunodeficiency-Related Disorders With Clinical Exome Sequencing: Expected and Unexpected Findings. Front. Immunol. 2019, 10, 2325. [Google Scholar] [CrossRef] [PubMed]
- Diagnosis Criteria—ESID. Available online: https://esid.org/working-parties/registry-working-party/diagnosis-criteria/ (accessed on 1 April 2025).
- Kakkas, I.; Tsinti, G.; Kalala, F.; Farmaki, E.; Kourakli, A.; Kapousouzi, A.; Dimou, M.; Kalaitzidou, V.; Sevdali, E.; Peristeri, A.-M.; et al. TACI Mutations in Primary Antibody Deficiencies: A Nationwide Study in Greece. Medicina 2021, 57, 827. [Google Scholar] [CrossRef] [PubMed]
- Sic, H.; Speletas, M.; Cornacchione, V.; Seidl, M.; Beibel, M.; Linghu, B.; Yang, F.; Sevdali, E.; Germenis, A.E.; Oakeley, E.J.; et al. An Activating Janus Kinase-3 Mutation Is Associated with Cytotoxic T Lymphocyte Antigen-4-Dependent Immune Dysregulation Syndrome. Front. Immunol. 2017, 8, 1824. [Google Scholar] [CrossRef] [PubMed]
- Online Mendelian Inheritance in Man, OMIM®. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University. (Baltimore, MD). Available online: https://omim.org/ (accessed on 1 April 2025).
- Xu, C.; Gu, X.; Padmanabhan, R.; Wu, Z.; Peng, Q.; DiCarlo, J.; Wang, Y. smCounter2: An Accurate Low-Frequency Variant Caller for Targeted Sequencing Data with Unique Molecular Identifiers. Bioinformatics 2019, 35, 1299–1309. [Google Scholar] [CrossRef] [PubMed]
- Li, H.; Durbin, R. Fast and Accurate Short Read Alignment with Burrows-Wheeler Transform. Bioinformatics 2009, 25, 1754–1760. [Google Scholar] [CrossRef] [PubMed]
- Cingolani, P. Variant Annotation and Functional Prediction: SnpEff. Methods Mol. Biol. 2022, 2493, 289–314. [Google Scholar] [CrossRef] [PubMed]
- Ng, P.C.; Henikoff, S. SIFT: Predicting Amino Acid Changes That Affect Protein Function. Nucleic Acids Res. 2003, 31, 3812–3814. [Google Scholar] [CrossRef] [PubMed]
- Adzhubei, I.; Jordan, D.M.; Sunyaev, S.R. Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2. Curr. Protoc. Hum. Genet. 2013, 76, 7–20. [Google Scholar] [CrossRef] [PubMed]
- Schwarz, J.M.; Rödelsperger, C.; Schuelke, M.; Seelow, D. MutationTaster Evaluates Disease-Causing Potential of Sequence Alterations. Nat. Methods 2010, 7, 575–576. [Google Scholar] [CrossRef] [PubMed]
- Abramson, J.; Adler, J.; Dunger, J.; Evans, R.; Green, T.; Pritzel, A.; Ronneberger, O.; Willmore, L.; Ballard, A.J.; Bambrick, J.; et al. Accurate Structure Prediction of Biomolecular Interactions with AlphaFold 3. Nature 2024, 630, 493–500. [Google Scholar] [CrossRef] [PubMed]
- Oeckinghaus, A.; Ghosh, S. The NF-kappaB Family of Transcription Factors and Its Regulation. Cold Spring Harb. Perspect. Biol. 2009, 1, a000034. [Google Scholar] [CrossRef] [PubMed]
- Yang, H.-T.; Wang, Y.; Zhao, X.; Demissie, E.; Papoutsopoulou, S.; Mambole, A.; O’Garra, A.; Tomczak, M.F.; Erdman, S.E.; Fox, J.G.; et al. NF-κB1 Inhibits TLR-Induced IFN-β Production in Macrophages through TPL-2-Dependent ERK Activation. J. Immunol. 2011, 186, 1989–1996. [Google Scholar] [CrossRef] [PubMed]
- Kuehn, H.S.; Bernasconi, A.; Niemela, J.E.; Almejun, M.B.; Gallego, W.A.F.; Goel, S.; Stoddard, J.L.; Sánchez, R.G.P.; Franco, C.A.A.; Oleastro, M.; et al. A Nonsense N -Terminus NFKB2 Mutation Leading to Haploinsufficiency in a Patient with a Predominantly Antibody Deficiency. J. Clin. Immunol. 2020, 40, 1093–1101. [Google Scholar] [CrossRef] [PubMed]
- Aird, A.; Lagos, M.; Vargas-Hernández, A.; Posey, J.E.; Coban-Akdemir, Z.; Jhangiani, S.; Mace, E.M.; Reyes, A.; King, A.; Cavagnaro, F.; et al. Novel Heterozygous Mutation in NFKB2 Is Associated With Early Onset CVID and a Functional Defect in NK Cells Complicated by Disseminated CMV Infection and Severe Nephrotic Syndrome. Front. Pediatr. 2019, 7, 303. [Google Scholar] [CrossRef] [PubMed]
- Tuijnenburg, P.; Lango Allen, H.; de Bree, G.J.; Savic, S.; Jansen, M.H.; Stockdale, C.; Simeoni, I.; Ten Berge, I.J.M.; van Leeuwen, E.M.M.; NIHR BioResource; et al. Pathogenic NFKB2 Variant in the Ankyrin Repeat Domain (R635X) Causes a Variable Antibody Deficiency. Clin. Immunol. 2019, 203, 23–27. [Google Scholar] [CrossRef] [PubMed]
- Sun, S.-C. Non-Canonical NF-κB Signaling Pathway. Cell Res. 2011, 21, 71–85. [Google Scholar] [CrossRef] [PubMed]
- Fliegauf, M.; Krüger, R.; Steiner, S.; Hanitsch, L.G.; Büchel, S.; Wahn, V.; von Bernuth, H.; Grimbacher, B. A Pathogenic Missense Variant in NFKB1 Causes Common Variable Immunodeficiency Due to Detrimental Protein Damage. Front. Immunol. 2021, 12, 621503. [Google Scholar] [CrossRef] [PubMed]
- Fliegauf, M.; Bryant, V.L.; Frede, N.; Slade, C.; Woon, S.-T.; Lehnert, K.; Winzer, S.; Bulashevska, A.; Scerri, T.; Leung, E.; et al. Haploinsufficiency of the NF-κB1 Subunit P50 in Common Variable Immunodeficiency. Am. J. Hum. Genet. 2015, 97, 389–403. [Google Scholar] [CrossRef] [PubMed]
- John, L.B.; Ward, A.C. The Ikaros Gene Family: Transcriptional Regulators of Hematopoiesis and Immunity. Mol. Immunol. 2011, 48, 1272–1278. [Google Scholar] [CrossRef] [PubMed]
- Wang, J.H.; Nichogiannopoulou, A.; Wu, L.; Sun, L.; Sharpe, A.H.; Bigby, M.; Georgopoulos, K. Selective Defects in the Development of the Fetal and Adult Lymphoid System in Mice with an Ikaros Null Mutation. Immunity 1996, 5, 537–549. [Google Scholar] [CrossRef] [PubMed]
- Cobb, B.S.; Morales-Alcelay, S.; Kleiger, G.; Brown, K.E.; Fisher, A.G.; Smale, S.T. Targeting of Ikaros to Pericentromeric Heterochromatin by Direct DNA Binding. Genes. Dev. 2000, 14, 2146–2160. [Google Scholar] [CrossRef] [PubMed]
- Papathanasiou, P.; Perkins, A.C.; Cobb, B.S.; Ferrini, R.; Sridharan, R.; Hoyne, G.F.; Nelms, K.A.; Smale, S.T.; Goodnow, C.C. Widespread Failure of Hematolymphoid Differentiation Caused by a Recessive Niche-Filling Allele of the Ikaros Transcription Factor. Immunity 2003, 19, 131–144. [Google Scholar] [CrossRef] [PubMed]
- Georgopoulos, K.; Moore, D.D.; Derfler, B. Ikaros, an Early Lymphoid-Specific Transcription Factor and a Putative Mediator for T Cell Commitment. Science 1992, 258, 808–812. [Google Scholar] [CrossRef] [PubMed]
- Yilmaz, E.; Kuehn, H.S.; Odakir, E.; Niemela, J.E.; Ozcan, A.; Eken, A.; Rohlfs, M.; Cansever, M.; Gok, V.; Aydin, F.; et al. Common Variable Immunodeficiency, Autoimmune Hemolytic Anemia, and Pancytopenia Associated with a Defect in IKAROS. J. Pediatr. Hematol. Oncol. 2021, 43, e351–e357. [Google Scholar] [CrossRef] [PubMed]
- Ezen, E.; Çatak, M.C.; Çatak, F.B.; Piepoli, S.; Zahedimaram, P.; Ultanır, E.; Barış, S.; Erman, B. Structure and Function of the LRBA Protein. TJI 2024, 12 (Suppl. 1), 37–46. [Google Scholar] [CrossRef]
Figure 1.
Overall demonstration of the variants via a circular plot. The outermost layer displays the gene names, while the next layer uses a heatmap format to show the number of variants detected per gene, with the colors corresponding to their ACMG classification based on Franklin genoox. The third layer consists of a bar plot representing the allele frequency of each variant (using gnomAD–exomes data for the European non-Finnish population), and the innermost layer is a bar plot indicating the number of patients in which each variant was identified.
Figure 1.
Overall demonstration of the variants via a circular plot. The outermost layer displays the gene names, while the next layer uses a heatmap format to show the number of variants detected per gene, with the colors corresponding to their ACMG classification based on Franklin genoox. The third layer consists of a bar plot representing the allele frequency of each variant (using gnomAD–exomes data for the European non-Finnish population), and the innermost layer is a bar plot indicating the number of patients in which each variant was identified.
Figure 2.
The heatmap displays the detection of pathogenic, likely pathogenic, and variants of unknown significance (VUS) in the patients in the study. The x-axis represents the patients, identified by their study IDs, while the y-axis lists the various genetic variants grouped by their gene of origin. The color coding indicates the ACMG classification as determined by Franklin genoox.
Figure 2.
The heatmap displays the detection of pathogenic, likely pathogenic, and variants of unknown significance (VUS) in the patients in the study. The x-axis represents the patients, identified by their study IDs, while the y-axis lists the various genetic variants grouped by their gene of origin. The color coding indicates the ACMG classification as determined by Franklin genoox.
Figure 3.
Genetic analysis, family testing and 3D visualization for the NFKB2-pR313X defect. (A) Sanger sequencing chromatograms for the proband and tested relatives. (B) Family tree illustrating the genetic testing results for the proband (indicated by arrow) and relatives. (C) Three-dimensional protein visualization of the wt NFKB2 (red) vs. mutated (blue) protein: the artificial intelligence (AI)-driven protein folding predictions generated with Alphafold3 for both the wild-type and mutated protein molecules. Using PyMOL for visualization, the wild-type protein (red) was aligned with the mutated protein (blue) to emphasize the structural alterations and potential loss of protein domains. The ANK repeats and nuclear localization signal of NFKB2 are additionally highlighted.
Figure 3.
Genetic analysis, family testing and 3D visualization for the NFKB2-pR313X defect. (A) Sanger sequencing chromatograms for the proband and tested relatives. (B) Family tree illustrating the genetic testing results for the proband (indicated by arrow) and relatives. (C) Three-dimensional protein visualization of the wt NFKB2 (red) vs. mutated (blue) protein: the artificial intelligence (AI)-driven protein folding predictions generated with Alphafold3 for both the wild-type and mutated protein molecules. Using PyMOL for visualization, the wild-type protein (red) was aligned with the mutated protein (blue) to emphasize the structural alterations and potential loss of protein domains. The ANK repeats and nuclear localization signal of NFKB2 are additionally highlighted.
Figure 4.
Genetic analysis, family testing and 3D visualization for the NFKB1-pY350X defect. (A) Sanger sequencing chromatograms for the proband and tested relatives. (B) Family tree illustrating the genetic testing results for the proband (indicated by arrow) and relatives. (C) Three-dimensional protein visualization of the wt NFKB1 (red) vs. mutated (blue) protein: the AI-driven protein folding predictions generated with Alphafold3 for both the wild-type and mutated protein molecules. Using PyMOL for visualization, the wild-type protein (red) was aligned with the mutated protein (blue) to emphasize the structural alterations and potential loss of protein domains. The ANK repeats and nuclear localization signal of NFKB1 are additionally highlighted.
Figure 4.
Genetic analysis, family testing and 3D visualization for the NFKB1-pY350X defect. (A) Sanger sequencing chromatograms for the proband and tested relatives. (B) Family tree illustrating the genetic testing results for the proband (indicated by arrow) and relatives. (C) Three-dimensional protein visualization of the wt NFKB1 (red) vs. mutated (blue) protein: the AI-driven protein folding predictions generated with Alphafold3 for both the wild-type and mutated protein molecules. Using PyMOL for visualization, the wild-type protein (red) was aligned with the mutated protein (blue) to emphasize the structural alterations and potential loss of protein domains. The ANK repeats and nuclear localization signal of NFKB1 are additionally highlighted.
Figure 5.
Genetic analysis, family testing and 3D visualization for the IKZF1-pR143W defect. (A) Sanger sequencing chromatograms for the proband and tested relatives. (B) Family tree illustrating the genetic testing results for the proband (indicated by arrow) and relatives. (C) Three-dimensional protein visualization of the IKZF1-pR143W defect: the AI-driven protein folding predictions generated with Alphafold3 for both the wild-type and mutated protein molecules. Using PyMOL for visualization, the superposition of the wild-type with the mutated protein clearly highlights the variant’s location between zinc-finger domains 1 and 2.
Figure 5.
Genetic analysis, family testing and 3D visualization for the IKZF1-pR143W defect. (A) Sanger sequencing chromatograms for the proband and tested relatives. (B) Family tree illustrating the genetic testing results for the proband (indicated by arrow) and relatives. (C) Three-dimensional protein visualization of the IKZF1-pR143W defect: the AI-driven protein folding predictions generated with Alphafold3 for both the wild-type and mutated protein molecules. Using PyMOL for visualization, the superposition of the wild-type with the mutated protein clearly highlights the variant’s location between zinc-finger domains 1 and 2.
Table 1.
Clinical and demographic characteristics of the patients in this study.
| No. | Sex | Year of Birth | Age of Onset | Clinical Presentation | Diagnosis |
|---|---|---|---|---|---|
| 1 | F | 1980 | 4 | Recurrent respiratory infections; COPD/CRPD; splenomegaly | CVID |
| 2 | M | 1970 | 7 | Recurrent respiratory infections; COPD; bronchiectasis; meningitis | CVID |
| 3 | F | 2002 | 14 | Recurrent respiratory infections; hypothyroidism | CVID |
| 4 | M | 1962 | 38 | Recurrent respiratory and urinary infections; lymphadenopathy; granulomatous disease; CNS myelitis; lymphomas (Hodgkin and non-Hodgkin) | CVID * |
| 5 | M | 1976 | 8 | Recurrent respiratory infections; AIT; lymphoproliferation (lymphadenopathy, splenomegaly) | IgA + sGD |
| 6 | F | 1980 | 26 | Recurrent respiratory and urinary infections, arthralgias, eczema | CVID |
| 7 | M | 1997 | 4 | Recurrent respiratory infections, hypothyroidism, non-Hodgkin lymphoma | CVID * |
| 8 | M | 2004 | 15 | Recurrent AIT, splenomegaly, lymphadenopathy | CVID |
| 9 | F | 1995 | 14 | Recurrent respiratory infections, Crohn-like disease, hypothyroidism, pernicious anemia, tonsillar hypertrophy | IDS |
| 10 | M | 1975 | 20 | Recurrent malaria infections, AΙΤ, splenomegaly, Hodgkin lymphoma, recurrent, eczema | CVID * |
| 11 | F | 1958 | 18 | Recurrent respiratory infections, hypothyroidism, optic nerve atrophy | CVID ** |
| 12 | F | 1949 | 50 | Recurrent respiratory infections, granulomatous disease, hypothyroidism | CVID ** |
| 13 | M | 1976 | 32 | Recurrent respiratory infections, recurrent Evans syndrome | CVID |
| 14 | M | 1998 | 1 | Recurrent skin infections, refractory AIT | CVID |
| 15 | M | 1965 | 39 | Recurrent attacks of AIT and AHA, splenomegaly, enteropathy | CVID |
| 16 | M | 1985 | 10 | Recurrent respiratory infections, bronchiectasis, vitiligo | CVID |
| 17 | F | 1972 | 27 | Recurrent respiratory infections, CVID-ILD, AIT | CVID |
| 18 | M | 1946 | 48 | Recurrent respiratory infections, hypothyroidism, pernicious anemia, AHA, non-Hodgkin lymphoma | CVID * |
| 19 | M | 2003 | 3 | Recurrent respiratory infections, refractory AIT, splenomegaly, lymphadenopathy | IgA + sGD |
| 20 | F | 1986 | 35 | PBC, incidental diagnosis after recurrent abortions | CVID |
Abbreviations: AHA, autoimmune hemolytic anemia; AIT, autoimmune thrombocytopenia; CNS, central nervous system; COPD, chronic obstructive pulmonary disease; CRPD, chronic restrictive pulmonary disease; CVID, common variable immunodeficiency; CVID-ILD, CVID–interstitial lung disease; F, female; IDS, immune dysregulation syndrome (due to a CTLA4 defect); IgAD, IgA deficiency; IgA + IgGscD; IgA deficiency along with an IgG subclass deficiency; M, male. * Patient #4 was diagnosed with CVID 7 years after disease onset (displaying recurrent infections, splenomegaly, and granulomatous disease) when Hodgkin lymphoma developed and passed away 10 years after diagnosis due to non-Hodgkin lymphoma relapse; patient #7 displayed recurrent and severe infections early in his life, but the diagnosis of CVID was made after lymphoma diagnosis and treatment (10 years after the disease onset); patient #10 was diagnosed 13 years after disease onset, when Hodgkin disease emerged that relapsed one year after first remission and was monitored by autologous bone marrow transplantation; and patient #17 was diagnosed 20 years after disease onset when a diffuse large B-cell non-Hodgkin lymphoma along with AHA emerged. ** Patient #11 was initially diagnosed with CVID at another center, while patient #12’s sister who was initially mis-diagnosed with sarcoidosis 10 years after disease onset (the patient developed severe parasitosis after corticosteroid therapy and was finally passed away during the COVID-19 pandemic).
Table 2.
Targeted genes analyzed by next-generation sequencing in our study.
| No. | Gene, OMIM | Pathway Function | Associated Diseases | Clinical Manifestations | Inheritance |
|---|---|---|---|---|---|
| 1 | ICOS, 604558 | T-cell stimulation, immune response regulation | CVID | Recurrent infections | AR |
| 2 | TACI, 604907 | B-cell activation, Ab production | CVID, sIgAD | Recurrent infections, autoimmunity | AD/AR |
| 3 | CD19, 107265 | BCR signaling and B-cell development | CVID | Recurrent infections | AR |
| 4 | BAFFR, 606269 | B-cell survival and maturation | CVID, autoimmune diseases | Recurrent infections, autoimmunity | AR |
| 5 | MS4A1, 112210 | BCR signaling and B-cell development | CVID | Recurrent infections | AR |
| 6 | CD81, 186845 | T-cell development, B-cell signaling | CVID | Recurrent infections, autoimmunity | AR |
| 7 | CR2, 120650 | Complement receptor, B-cell activation | CVID, SLE | Recurrent infections, autoimmunity | AR |
| 8 | LRBA, 606453 | Vesicle trafficking, immune regulation | IDS, autoimmune diseases | Recurrent infections autoimmunity, lymphoproliferation, hypogamma | AR |
| 9 | PRKCD, 176977 | Apoptosis, BCR signaling | CVID, ALPS | Lymphoproliferation, autoimmunity | AR |
| 10 | NFKB2, 164012 | Transcription factor, immune response regulation | CVID, autoimmune diseases | Recurrent infections, autoimmunity | AD |
| 11 | IL21, 605384 | Immune responses regulation | CVID, autoimmune diseases | Recurrent infections, autoimmunity | AR |
| 12 | NFKB1, 164011 | Transcription factor, immune response regulation | CVID, autoimmune diseases | Recurrent infections, autoimmunity | AD |
| 13 | IKZF1, 603023 | Transcription factor, regulation of cell differentiation | CVID, ALL | Recurrent infections, leukemia | AD |
| 14 | CD27, 186711 | TNF receptor superfamily, T- and B-cell activation | Lymphoproliferative syndrome, combined immunodeficiency | Recurrent infections, lymphoproliferation | AR |
| 15 | RAG1, 179615 | V(D)J recombination, B- and T-cell development | SCID, Omenn syndrome | Recurrent infections, autoimmunity | AR |
| 16 | RAG2, 179616 | V(D)J recombination, B- and T-cell development | SCID, Omenn syndrome | Recurrent infections, autoimmunity | AR |
| 17 | PIK3CD, 602839 | PI3K signaling, B-cell and T-cell function | APDS | Lymphoproliferation, recurrent infections | AD, AR |
| 18 | PIK3R1, 171833 | PI3K signaling, B-cell and T-cell function | APDS | Lymphoproliferation, recurrent infections | AD, AR |
| 19 | RAC2, 602049 | Small GTPase, neutrophil function | Immunodeficiency | Recurrent infections, neutropenia | AD |
| 20 | BLK, 191305 | BCR signaling, B-cell development | SLE, MODY11 | SLE, diabetes | AD |
| 21 | PLCG2, 600220 | B-cell activation | PLAID, APLAID | Cold urticaria, autoimmunity, recurrent infections | AD |
| 22 | VAV1, 164875 | T- and B-cell activation | Myasthenia Gravis susceptibility, CVID | Recurrent infections, bone marrow failure | AD |
| 23 | IRF2BP2, 615332 | Transcriptional repressor, B-cell differentiation | CVID | Immunodeficiency | AD |
| 24 | NPAT, 601448 | Histone and cyclin E transcription, cell cycle regulation | Ataxia, lymphoma susceptibility | Ataxia, lymphomagenesis | AD |
| 25 | MYD88, 602170 | TLR signaling, NFkB activation | Immunodeficiency, WM | Pyogenic bacterial infections | AR |
| 26 | IRAK4, 606883 | TLR signaling, NFkB activation | Immunodeficiency | Recurrent bacterial infections | AR |
| 27 | TWEAK, 602695 | Apoptosis, NFkB activation | Immunodeficiency, cardiomyopathy | recurrent infections, hypogamma | AD |
| 28 | IL21R, 605383 | Immune responses regulation | Immunodeficiency | Recurrent infections | AR |
| 29 | CTLA4, 123890 | Immune checkpoint, T-cell regulation | IDS, autoimmune diseases | Recurrent infections autoimmunity, lymphoproliferation, hypogamma | AD |
| 30 | DOCK8, 611432 | Immune cell function, migration and survival | Hyper-IgE syndrome, combined immunodeficiency | Recurrent infections, eczema, skin lesions, asthma | AR |
Abbreviations: Ab, antibody; AD, autosomal dominant; ALL, acute lymphoblastic leukemia; ALPS, autoimmune lymphoproliferation syndrome; APDS; activated PI3K delta syndrome; APL, acute promyelocytic leukemia; APLAID, autoinflammation–PLCG2-associated antibody deficiency; AR, autosomal recessive; BAFFR, BAFF receptor; BCR, B-cell receptor; BLK, B lymphocyte kinase; CVID, common variable immunodeficiency; CR2, complement receptor 2; DOCK8, dedicator of cytokinesis 8; hypogamma, hypogammaglobulinemia; ICOS, inducible T-cell costimulatory; IDS, immune dysregulation syndrome; IKZF1, IKAROS family zinc finger 1; IL21, interleukin 21; IRAK4, interleukin 1 receptor associated kinase 4; IRF2BP2, interferon regulatory factor-2 binding protein; LRBA, LPS responsive beige-like anchor; MODY11, maturity-onset diabetes of the young; MYD88, myeloid differentiation primary response 88; MS4A1, membrane spanning 4-domains A1; NFkB, nuclear factor kappa B; NFKB1, nuclear factor kappa B subunit 1; NFKB2, nuclear factor kappa B subunit 2; NIS, neutrophil immunodeficiency syndrome; NPAT, nuclear protein ataxia–telangiectasia locus; OMIM, Online Mendelian Inheritance in Man; PIK3CD, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit delta; PIK3R1, phosphoinositide-3-kinase regulatory subunit 1; PLAID, PLCG2-associated antibody deficiency and immune dysregulation; PLCG2, phospholipase C gamma 2; RAC2, Rac family small GTPase 2; RAG1, recombination activating gene 1; RAG2, recombination activating gene 2; SCID, severe combined immunodeficiency; sIgAD, selective IgA deficiency; SLE, systemic lupus erythematosus; TACI, transmembrane activator and CAML interactor; TLR, Toll-like receptor; TWEAK; VAV1, Vav guanine nucleotide exchange factor 1, WM, Waldenstrom macroglobulinemia; XD, X-linked dominant.
Table 3.
Primer sequences for the amplification of regions with pathogenic/likely pathogenic variants.
Table 3.
Primer sequences for the amplification of regions with pathogenic/likely pathogenic variants.
| Gene | Target Variant | Primers | PCR Product | |
|---|---|---|---|---|
| NFKB1 | NM_003998.4: c.1050C>G, p.Y350X | F | 5′-GCCATTGTCTTCAAAACTCCAAAGTAT-3′ | 150 bp. |
| R | 5′-AACAACTGACTTACCTTTGATTTCAGGA-3′ | |||
| NFKB2 | NM_001322934.2: c.937C>T, p.R313X | F | 5′-GGGCACCAAGGACATCGAGTAT-3′ | 497 bp. |
| R | 5′-CCACATCTAGCTCTTGGGACCTTCT-3′ | |||
| IKZF1 | NM_006060.6: c.427C>T, p.R143W | F | 5′-CTCGTAGCATCGTCCTCATGT-3′ | 174 bp. |
| R | 5′-TGGCATTTGAAGGGCTTCTCC-3′ |
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Galanopoulos, A.P.; Raftopoulou, S.; Sarrou, S.; Matziri, A.; Papoutsopoulou, S.; Stratakos, G.; Mouchtouri, V.A.; Hölzer, M.; Hadjichristodoulou, C.; Kalala, F.; et al. Next-Generation Sequencing Analysis in Greek Patients with Predominantly Antibody Deficiencies. Immuno 2025, 5, 27. https://doi.org/10.3390/immuno5030027
AMA Style
Galanopoulos AP, Raftopoulou S, Sarrou S, Matziri A, Papoutsopoulou S, Stratakos G, Mouchtouri VA, Hölzer M, Hadjichristodoulou C, Kalala F, et al. Next-Generation Sequencing Analysis in Greek Patients with Predominantly Antibody Deficiencies. Immuno. 2025; 5(3):27. https://doi.org/10.3390/immuno5030027
Chicago/Turabian StyleGalanopoulos, Achilleas P., Sofia Raftopoulou, Styliani Sarrou, Alexia Matziri, Stamatia Papoutsopoulou, Grigorios Stratakos, Varvara A. Mouchtouri, Martin Hölzer, Christos Hadjichristodoulou, Fani Kalala, and et al. 2025. "Next-Generation Sequencing Analysis in Greek Patients with Predominantly Antibody Deficiencies" Immuno 5, no. 3: 27. https://doi.org/10.3390/immuno5030027
APA StyleGalanopoulos, A. P., Raftopoulou, S., Sarrou, S., Matziri, A., Papoutsopoulou, S., Stratakos, G., Mouchtouri, V. A., Hölzer, M., Hadjichristodoulou, C., Kalala, F., & Speletas, M. (2025). Next-Generation Sequencing Analysis in Greek Patients with Predominantly Antibody Deficiencies. Immuno, 5(3), 27. https://doi.org/10.3390/immuno5030027
