Introduction

Inborn errors of immunity or primary immunodeficiencies (PIDs) represent a diverse group of disorders characterized by increased susceptibility to infections, malignancy, allergy, and immune dysregulation [1]. While these diseases occur at a frequency of approximately 1 in 10,000 in the general population, their prevalence is higher in societies with elevated rates of consanguinity, such as Türkiye [2,3,4]. The genetic pleiotropy and heterogeneity observed in IEI contribute to the broad range of clinical manifestations associated with these disorders [5]. The majority of IEI cases are monogenic diseases with autosomal recessive inheritance patterns [5]. Therefore, comprehensive genetic diagnosis is vital for effective management of patients with IEI. In the past decade, NGS methods have revolutionized genetic screening, greatly enhancing the diagnostic capabilities for IEI [6]. This progress has led to an unprecedented increase in the identification of genes causing immunodeficiencies, with approximately 500 genetic defects associated with immunodeficiency currently recognized [7].

Founded in 2018 in memory of Can Sucak, who suffered from ZAP70 deficiency, the Candan Bişeyler Foundation (CSCBF) actively supports research in the field of IEI and raises awareness in Türkiye. The “Hacettepe University Can Sucak Research Laboratory for Translational Immunology” is dedicated to providing genetic diagnosis for immunodeficiency patients and conducting advanced functional research in a comprehensive manner throughout the country. This study presents the results of a comprehensive investigation into the genetic diagnosis of an extensive cohort of IEI patients from a specialized immune deficiency research center in Türkiye.

Methods

Study Participants

Patients diagnosed with IEI based on clinical and laboratory characteristics between 2020 and 2023 were included in the study. These patients were recruited from multiple clinical immunology centers in Türkiye. Blood samples were collected from the patients following the guidelines and approval of the local Ethics Committee of Hacettepe University. Informed consent forms were obtained from the participants or their parents. The study's workflow is illustrated in Fig. 1.

Fig. 1
figure 1

Schematic workflow of the study

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Whole Exome Sequencing and Variant Analysis

Genomic DNA was isolated from peripheral blood samples using a DNA isolation kit (GeneAll). The NGS exome library was prepared utilizing the Illumina Nextera DNA Prep with Enrichment Kit. Sequencing was carried out on the Illumina NextSeq 550 platform, generating 150-bp paired-end reads. Mapping, variant calling, and annotation were performed using SEQ Platform v8 (Genomize). Copy number variation (CNV) analysis was conducted using SEQ Platform as well.

To identify causative variants, we employed a filtering strategy that involved screening all variants identified from the WES data. Our focus was on exonic and splice site variants, excluding synonymous variants, and we specifically looked for rare variants with a minor allele frequency of less than 1% in different strategic gene groups. Initially, we examined rare variants in known IEI genes (approximately 500), followed by potential candidate genes predicted by the human gene connectome [8]. Finally, we assessed variants across the entire set of genes (Supplementary Figure 2A).

Sanger Sequencing

To validate the identified variants, we conducted Sanger sequencing using standard protocols [9].

RT-qPCR

RT-qPCR was utilized to validate the effects of structural variants. Total RNA was isolated from peripheral blood mononuclear cells (PBMCs) obtained from both patients and healthy controls using the NucleoSpin RNA Plus Kit (Macherey-Nagel). Subsequently, cDNA was synthesized using the iScript cDNA synthesis kit (Bio-Rad). RT-qPCR was carried out on the CFX Connect System (Bio-Rad) using the iTaq Universal SYBR Green Supermix (Bio-Rad) [10].

Results

Technical Output of the Sequencing Data

The results of the WES data showed a total number of reads ranging from 21.7 to 77.6 million (median: 46.1) (Supplementary Figure 2B). The average depth of coverage varied between 24.5 and 134.2 (median: 64.1) (Supplementary Figure 2C). The target regions (exons and splice regions) were covered at a depth of 20X from 89.02% to 99.91%, and at a depth of 50X from 68.13% to 99.65% (Supplementary Figure 2D).

Patients

Our study involved a total of 303 individuals who were clinically diagnosed with IEI. These participants were recruited from 21 separate clinical immunology centers and they were selected after assessments with their clinicians. Especially, patients truly exhibited severe phenotypes of immunodeficiency were admitted to the study. However, six patients were excluded from the current analysis as they exhibited potential novel IEI-associated genes, pending further investigation through functional studies. Therefore, the analysis in this study includes 297 patients.

Among the included patients, there were 145 males and 152 females, representing a relatively balanced gender distribution. The age range of the participants varied from three months to 42 years, with a median age of nine years. The majority of the cohort consisted of pediatric patients (n=252), while a smaller subset comprised adult patients (n=45). A notable observation in our study was the high consanguinity rate, with 64.6% (192 out of 297 cases) of patients demonstrating consanguineous relationships within their families. The distribution of clinical diagnoses, classified according to the International Union of Immunological Societies (IUIS) classification, included 27 cases of Severe Combined Immunodeficiency (SCID), 105 cases of Combined Immunodeficiency (CID), 64 cases of Primary Antibody Deficiency (PAD), 49 cases of Primary Immune Regulatory Disorder (PIRD), 22 cases of congenital anomalies affecting phagocyte number/function, 17 cases of disorders of intrinsic and innate immunity, 10 cases of autoinflammatory disorders, and 3 cases of other classified IEI. These other cases potentially involve bone marrow failure or complement deficiencies, as illustrated in Fig. 2A.

Fig. 2
figure 2

Patient and variant characteristics. A Distribution of the patients based on their clinical diagnosis. B Diagnostic yield of the patients. C Number of the detected variants and their distribution across different IEI genes. D Types of detected variants and their novelty. E Distribution of zygosity. F Number of diagnosis in patient groups

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Results of Genetic Diagnosis and the Profile of Disease-Causing Variants

In our cohort, a genetic diagnosis was established in 122 out of the 297 patients examined, with a total of 127 potential genetic variants identified. This yielded a diagnostic rate of 41.1%. Among the 193 patients with consanguineous parents, causative genetic defects were identified in 95 individuals, resulting in a diagnostic rate of 49.7%. On the other hand, among the 106 patients from non-consanguineous parents, 28 individuals (25.7%) received a genetic diagnosis. The diagnostic rate was higher in pediatric patients, with 44.4% (112 out of 252) receiving a genetic diagnosis, compared to the adult group, which had a lower rate of 22% (10 out of 45) (Fig. 2B). Details of all identified genetic variants and their associated clinical features are presented in Table 1, Table 2 and Supplementary Table 3. In addition, variant characteristics including American College of Medical Genetics (ACMG) criteria and pathogenicity prediction scores were given in Supplementary Table 1). Overall, a total of 127 likely causative genetic anomalies were identified across 64 known IEI genes, as depicted in Fig. 2C. Among these genetic variants, 75 had been previously reported in public databases, while 52 were novel findings reported in this study (Fig. 2D). The variants consisted of 92 homozygous, 27 heterozygous, and 8 hemizygous mutations (Fig. 2E). The spectrum of variant types included 69 missense mutations, 24 nonsense mutations, 22 insertion/deletions (indels), 9 essential splice site variations, and 3 copy number variations (Figure 2D). CNV analysis was performed on 57 subjects using a strategy that incorporated samples with comparable mean read depths. The implications of the CNVs were validated through capillary sequencing or quantitative PCR (qPCR). The causality of monoallelic variants was evaluated based on clinical and laboratory features of the patients, literature associations, or different functional analyses (Supplementary Table 2). The diagnostic rates across different disease categories were as follows: Severe Combined Immunodeficiency (SCID) had a diagnostic rate of 100%, congenital anomalies affecting phagocyte number/function at 68.1%, autoinflammatory disorders at 50%, Primary Immune Regulatory Disorder (PIRD) at 46.9%, intrinsic and innate immunity defects at 41.1%, Combined Immunodeficiency (CID) at 32.3%, other forms of IEI at 33.3%, and Primary Antibody Deficiency (PAD) at 15.6%, and (Fig. 2F).

Table 1 Details of the variants detected in the study
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Table 2 Clinical features of the patients associated with detected gene defects
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Discussion

Advancements in NGS, with WES at the forefront, have been instrumental in the diagnostic processes of IEI by pinpointing causative genetic aberrations [72]. Genetic diagnosis now routinely assists in the delineation of IEI, underscoring its significance in the strategic management of patient treatments. Literature suggests a wide-ranging diagnostic yield for targeted and exome sequencing, from 10% to 70%, across various IEI patient groups [23, 58, 68, 73,74,75,76,77,78,79] . In this study, out of the 127 causative genetic defects in 122 patients, we identified 52 novel IEI-causing variants. We also discovered novel and very rare gene variants in NFATC2, CHUK, and PIK3CG genes, which have limited reported cases in the literature [80,81,82,83].

Among the 297 patients evaluated, a genetic etiology was confirmed in 122 individuals, resulting in a diagnostic yield of 41.1%. Diagnostic success exhibited pronounced variation among the different IEI subtypes: cases of SCID reached a 100% genetic identification rate, whereas CID and PID manifested lower diagnostic rates of 31% and 45%, respectively. Within the PAD cohort, genetic causality was determined in a mere 15.6% of cases (10 patients). This notably diminished diagnostic yield in Primary Antibody Deficiencies is in concordance with prior regional studies conducted by Fırtına S et al. [84]. In contrast, patients with probable Mendelian susceptibility to mycobacterial diseases and chronic granulomatous disease (CGD) demonstrated significantly higher diagnostic rates, with near-complete success in CGD patients.

The discrepancies in diagnostic success among IEI subtypes are primarily attributed to the complex nature of these disorders rather than limitations of WES. Factors such as the specific type of immunodeficiency, diverse clinical presentations, patient medical histories, and environmental influences affect the probability of achieving a genetic diagnosis [72]. Other factors include variable gene penetrance, the distinction between monogenic and polygenic influences, and various environmental considerations such as pathogenic exposures and age at presentation [85, 86]. Consanguinity plays a significant role in genetic diagnosis, as most IEI cases have autosomal recessive inheritance. Consanguineous populations or those from isolated regions with distinct phenotypes have reported higher diagnostic yields [87]. In our study, the consanguinity rate was 64.6%, and a diagnosis was made in 49.7% of those cases. We found 27 heterozygous variants in 21 unrelated patients, which can provide insights into the impact of heterozygous variants on protein function and aid in the search for novel IEI genes.

Currently, approximately 500 genetic etiologies leading to IEI are known [7]. Although the use of NGS, particularly WES, is increasing, it has limitations. Exome sequencing focuses on coding regions and essential splice sites, making it challenging to detect structural variations [72] and the use of short-read sequencing as in our study makes it difficult to map reads to repeated sequences, and pseudogenes [88]. Long-read sequencing (LRS) technologies both for exome or genome, have the capacity to enhance the detection of genetic variations and regions that are challenging to analyze with existing short-read NGS techniques [88,89,90]. However, the cost and complexity of analyzing large datasets pose challenges for WGS. In our study, we only identified three structural variants in 57 patients. Nevertheless, studies have shown the effectiveness of WGS in detecting both CNVs and coding variants [91, 92]. Reducing the cost of WGS and developing user-friendly bioinformatic tools may make it a routine diagnostic approach for IEI screening.

In conclusion, our findings highlight the limited success of WES in the genetic investigation of presumed IEI. The prospective adoption of WGS could enhance diagnostic yields, potentially surpassing WES in clinical examinations. With our substantial study cohort and diverse clinical presentations, the genetic variations we have identified will significantly contribute to the diagnosis of future IEI cases and guide the development of optimized NGS panels for these conditions.