Abstract
Background: Systemic autoinflammatory phenotypes can clinically overlap with Familial Mediterranean Fever and other monogenic autoinflammatory diseases, yet some cases remain unclassified in the absence of pathogenic variants in MEFV or other related genes. In this study, we aimed to investigate the genetic and molecular basis of an unclassified autoinflammatory phenotype in a two-generation family comprising four affected members and one unaffected member. Methods: We performed whole-exome sequencing in all family members and prioritized variants according to rarity, predicted functional impact, segregation pattern, and biological relevance to inflammatory pathways. Downstream molecular analyses were performed using PBMCs from affected individuals and the unaffected family member. Results: Whole-exome sequencing identified a novel splice-site variant in PSMB10 (NM_002801; c.56+1G>A) affecting the canonical splice donor site. The variant segregated with the autoinflammatory phenotype and was associated with reduced full-length PSMB10 transcript levels in patient-derived PBMCs, supporting a predicted loss-of-function effect. Although pathogenic variants in PSMB10 have previously been implicated in proteasome-associated autoinflammatory syndrome (PRAAS), the clinical presentation in this family differed from the classical PRAAS phenotype. Molecular analyses showed altered immunoproteasome-related gene expression and a severity-associated interferon-related response. Severely affected individuals showed increased expression of interferon-related genes, including ISG15, IFI35, and SIGLEC1, whereas mildly affected individuals showed lower or reduced expression patterns. Notably, the extent of these molecular alterations differed among family members and broadly reflected the observed clinical heterogeneity. Conclusions: We report a novel PSMB10 splice-site variant as a strong potential contributor to an unclassified autoinflammatory disease, with a predicted disruption of canonical splicing and loss of protein function. Our findings expand the clinical spectrum of immunoproteasome-associated disorders and suggest that immunoproteasome-related dysregulation and variable interferon responses may contribute to disease severity. The intrafamilial variability observed in this family suggests that additional genetic or immunogenetic factors may modify disease expression, even in autoinflammatory disorders that appear to follow a monogenic inheritance pattern.
Keywords: exome sequencing, RNA splice sites, hereditary autoinflammatory diseases, interferon type I, gene expression, family
1. Introduction
Genetic autoinflammatory diseases are characterized by recurrent episodes of fever and systemic inflammation, often accompanied by serositis, rash, urticaria, lymphadenopathy, arthritis, and elevated acute-phase responses. These disorders primarily result from inherited defects in innate immune regulation. In contrast to autoimmune diseases, autoinflammatory conditions occur in the absence of disease-specific autoantibodies or autoreactive T cells. Although most monogenic autoinflammatory diseases present during childhood, delayed or adult-onset presentations may also occur [1]. Familial Mediterranean Fever (FMF) is the prototypic and most common monogenic autoinflammatory disease [2,3], particularly in populations of Mediterranean origin. FMF is not restricted to the Turkish population; it is also common among Armenian, Arab, and Jewish communities [4]. It has been reported, although less frequently, in Italian cohorts [5], reflecting the shared genetic ancestry and geographic distribution of MEFV founder variants across the Mediterranean basin [3]. Other monogenic autoinflammatory diseases include Tumor Necrosis Factor Receptor-Associated Periodic Syndrome (TRAPS), Mevalonate Kinase Deficiency/Hyperimmunoglobulinemia D Syndrome (MKD/HIDS), Cryopyrin-Associated Periodic Syndromes (CAPS), Blau syndrome, Deficiency of the Interleukin-1 Receptor Antagonist (DIRA), Deficiency of the Interleukin-36 Receptor Antagonist (DITRA), Pyogenic Arthritis, Pyoderma gangrenosum, and Acne syndrome (PAPA), Majeed syndrome, and Chronic Atypical Neutrophilic Dermatosis with Lipodystrophy and Elevated Temperature/Proteasome-Associated Autoinflammatory Syndrome (CANDLE/PRAAS) represent less frequent entities within this spectrum [6,7,8].
A substantial proportion of patients with suspected systemic autoinflammatory diseases lack identifiable pathogenic variants in known disease genes, which may reflect overlapping clinical presentations, atypical or adult-onset disease, incomplete genomic assessment, and the contribution of yet-unidentified genes or modifier mechanisms [9,10,11,12]. In some instances, diagnostic certainty is supported by the integration of genetic findings, while the clarification of underlying molecular pathways facilitates the optimization of therapeutic strategies [13].
In regions with high FMF prevalence, founder effects, consanguinity, and population-specific genetic heterogeneity may complicate both clinical and molecular diagnosis [14,15]. FMF-like phenotypes without biallelic pathogenic MEFV variants may reflect overlap syndromes or alternative monogenic etiologies, highlighting the need for broader molecular evaluation in unresolved cases [16]. In this context, PSMB10 has emerged as a relevant candidate gene at the interface between proteasome-associated autoinflammation and Familial Mediterranean Fever (FMF)-like phenotypic overlap [17].
PSMB10 encodes the immunoproteasome β2i subunit, and pathogenic variants in this gene have been linked to proteasome-associated autoinflammatory syndromes [17,18]. Disruption of PSMB10 function may compromise immunoproteasome maturation and proteolytic activity, leading to impaired proteostasis, accumulation of proteotoxic substrates, and dysregulated type I interferon-related innate immune signaling [18,19]. Cellular protein homeostasis and innate immune signaling therefore depend heavily on the precision of the ubiquitin–proteasome system [19].
Proteasome-associated autoinflammatory diseases have been linked to impaired protein clearance, immune dysregulation, and type I interferon-related inflammatory responses [18,20]. These observations provide a rationale for evaluating immunoproteasome regulation and selected IFN-related molecular responses in unresolved autoinflammatory phenotypes.
In this study, we used whole-exome sequencing to investigate a family with multiple cases of undiagnosed autoinflammatory disease. The clinical presentation showed overlap with FMF and other monogenic autoinflammatory disorders; however, the affected individuals lacked pathogenic variants in established autoinflammatory genes, including MEFV, NLRP3, TNFRSF1A, MVK, NOD2, NLRC4, PSTPIP1, LPIN2, IL1RN, IL36RN, TNFAIP3, NLRP12, and PLCG2. The affected individuals also showed inadequate responses to conventional treatment. We identified the PSMB10 variant c.56+1G>A (rs201451622) and evaluated its predicted effect on disruption of the canonical splice donor site, transcript-level expression, together with immunoproteasome and selected IFN-related molecular profiles in affected family members. Among the IFN-related markers evaluated, the most notable differences were observed in SIGLEC1, IFI35, and ISG15, particularly in the more severely affected individuals.
2. Materials and Methods
2.1. Participants and Study Approval
A non-consanguineous Turkish family with suspected autosomal dominant inheritance, comprising three affected siblings, their affected father, and an unaffected mother, was enrolled in the study. All affected family members were followed at Istanbul University-Cerrahpasa for suspected autoinflammatory disease. Written informed consent was obtained from all participants prior to enrollment. All study procedures were conducted in accordance with the ethical principles of the institutional research committee and the 1964 Declaration of Helsinki, including its subsequent amendments. The study protocol was approved by the Health and Engineering Sciences Human Research Ethics Committee of Istanbul Technical University (approval no. MBG-2/2017).
2.2. Sample Collection and Preparation
Genomic DNA was extracted from whole blood samples using the Mammalian Blood DNA Isolation Kit (Roche Diagnostics, Mannheim, Germany) according to the manufacturer’s instructions. The remaining blood samples were processed for whole-blood RNA extraction and peripheral blood mononuclear cell (PBMC) isolation. For PBMC isolation, 20 mL of venous blood was subjected to density-gradient centrifugation using Lymphocyte Separation Medium (Capricorn Scientific, Ebsdorfergrund, Germany).
2.3. Whole Exome Sequencing and Analysis
Whole exome sequencing (WES) was performed for the four affected patients and the unaffected relative using the Twist Human Core Exome and RefSeq kit on the Illumina platform with paired-end reads (Macrogen Europe, Amsterdam, The Netherlands). Raw paired-end reads were aligned to the GRCh38 human reference assembly with BWA-MEM v0.7.17 [21] using default parameters; the resulting alignments were coordinate-sorted, and PCR duplicates were flagged and removed with samtools markdup v1.19 [22]. The deduplicated, coordinate-sorted BAM files were per-sample variant-called with DeepVariant v1.5.0 [23] and annotated with OpenCRAVAT 3.1.1 [24], integrating CADD v1.6 [25], gnomAD v3.1.2 [26], ClinVar, and Human Phenotype Ontology mappings [27].
HLA class I (HLA-A, -B, -C, and the non-classical -E, -F, -G) and class II (HLA-DRB1, -DRB3/4/5, -DQA1, -DQB1, -DPA1, -DPB1) typing was performed directly from paired-end whole-exome FASTQ reads using HLA-HD v1.7.1 [28] against the IPD-IMGT/HLA reference database, returning 4-digit (2-field) allele assignments per locus.
2.4. In Silico Splice Site Analysis
The transcript-level consequences of PSMB10 NM_002801.3: c.56+1G>A were predicted using SpliceAI v1.3.1 [29], Pangolin v1.0.1 [30], and SAI-10k-calc v1.1 [31]. The intrinsic strength of the affected splice site was quantified using MaxEntScan v1.1, a maximum-entropy model of human splice-site sequences that accounts for dependencies between both adjacent and non-adjacent positions and returns a log-odds score in bits [32].
2.5. RNA Isolation and Quantitative Real-Time PCR Analysis
Total RNA was extracted from whole blood with Trizol Reagent (Thermo Fisher Scientific, Waltham, MA, USA) and cDNA was synthesized from 1 µg of total RNA using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA) following the manufacturer’s instructions. qPCR was performed to assess gene expression for PSMB10 Exon1/Exon2 Junction, PSMB10, PSMB8, ISG15, IFI35 and SIGLEC1, and analyzed for relative gene expression by the 2−∆∆Ct method [33].
Two complementary PSMB10 qRT-PCR assays were used. The exon 1–exon 2 assay was designed to quantify the canonical exon 1–exon 2 splice product, whereas the total PSMB10 assay targeted a shared exonic region and was used as a broader measure of PSMB10 transcript abundance. Accordingly, the exon 1–exon 2 assay was used to assess the abundance of the normally spliced transcript rather than to directly characterize the identity of any aberrant splice product.
Primers used in this study are given below:
| Primer Name | Direction | Sequence 5′→3′ |
| hPSMB10 | Forward | CCTGGCGGTGCTAGAAGAC |
| hPSMB10 | Reverse | GATCACACATGCGTCCACATT |
| hPSMB8 | Forward | GCAGGCTGTACTATCTGCGAA |
| hPSMB8 | Reverse | AGAGCCGAGTCCCATGTTCAT |
| hISG15 | Forward | CGCAGATCACCCAGAAGATCG |
| hISG15 | Reverse | TTCGTCGCATTTGTCCACCA |
| hIFI35 | Forward | AACAAAAGGAGCACACGATCA |
| hIFI35 | Reverse | CTCCGTTCCTAGTCTTGCCAA |
| hSIGLEC1 | Forward | ATGGGGTACGCCTCCAAAC |
| hSIGLEC1 | Reverse | GTGCCTCATTGGGTGTGTTG |
| hGAPDH | Forward | TGCACCACCAACTGCTTAGC |
| hGAPDH | Reverse | GGCATGGACTGTGGTCATGAG |
| PSMB10-Exon-1/2 Junction | Forward | CTGCCAAAGAAATGCATCATTG |
| PSMB10-Exon-1/2 Junction | Reverse | GACGCGTTCCAATGATGCATT |
2.6. Statistics
Gene expression measurements were obtained from a single biological sample per individual (four samples as independent healthy controls and one sample per family member). Each qPCR assay was performed in duplicate (two technical replicates). For each gene analyzed, the expression level of every family member was compared individually with the control baseline using nested t-tests or one-way ANOVA, as indicated in the figure legends, in GraphPad Prism 10 software. In these analyses, individual subjects were treated as the experimental unit; the models therefore incorporate intra-assay technical variation (duplicates) across all samples and inter-individual biological variation among the control subjects. p-values < 0.05 were considered statistically significant. When appropriate, Dunn’s multiple-comparisons post hoc test was applied.
3. Results
3.1. Clinical Characterization of the Family
The study included a two-generation family with four clinically affected individuals and one unaffected family member. The pedigree in Figure 1 showed a dominantly segregating autoinflammatory phenotype through the paternal line. The affected father and all three affected offspring carried the PSMB10 c.56+1G>A variant, whereas the unaffected mother was variant-negative.
Figure 1.
Pedigree of the Turkish family with four affected individuals showing a paternal segregation pattern. Squares = male, circles = female; filled = clinically affected, open = unaffected [34]. The asterisk (*) marks heterozygous carriers of PSMB10 c.56+1G>A.
Although all affected family members showed a multisystem inflammatory phenotype, the clinical presentation was heterogeneous within the family. The clinical characteristics of the five-member family are summarized in Table 1. The two affected siblings (P2 and P3) with detailed clinical data had childhood-onset disease characterized by recurrent fever, mucocutaneous findings, sicca symptoms, Raynaud phenomenon, arthritis/arthralgia, pleuritic chest pain, dyspnea, abdominal pain, and diarrhea. However, the severity and spectrum of organ involvement varied among affected individuals. Additional variable features included suspected uveitis, epilepsy, proteinuria, thyroid disease, recurrent ovarian torsion, congenital cataract, and severe weight loss in one affected sibling. The affected brother (P4) had a milder or less extensively documented phenotype, mainly characterized by intermittent abdominal pain and diarrhea, whereas the father (P1) had limited reported systemic manifestations. Prior clinical evaluations led to overlapping diagnoses, including FMF, juvenile/rheumatoid arthritis, Systemic Lupus Erythematosus (SLE), vasculitis/Henoch-Schonlein Purpura (HSP), and unclassified autoinflammatory disease. This familial clustering and intrafamilial clinical variability provided the rationale for performing whole-exome sequencing to identify shared candidate genetic variants segregating with the autoinflammatory phenotype.
Table 1.
Demographics and multi-system clinical phenotype of five-member Turkish family segregating the immunoproteasome PSMB10 splice-donor variant c.56+1G>A. The asterisk (*) indicates affected individuals; PC = mother/unaffected family control. “+” = feature explicitly documented as present; “−” = feature explicitly documented as absent; “†” = intermittent elevation; “NR” = not reported (no inference); ”dx” = Diagnosis; “—”, not applicable. PRUNED CONVENTION: phenotype rows for which no family member is documented as present have been removed for clarity (3 rows: fatigue/malaise, Interstitial Lung Disease (ILD) screening, Chronic Obstructive Pulmonary Disease (COPD)-targeted bronchodilator). Demographics and genetic-test rows with results are retained even where some individuals are NR. The table is strictly derived from the primary clinical source records and annotated with verified Human Phenotype Ontology (HPO) identifiers [27].
| Characteristic (HPO ID) | P1 * | PC | P2 * | P3 * | P4 * |
|---|---|---|---|---|---|
| Demographics | |||||
| Sex | M | F | F | F | M |
| Age at first evaluation (years) | NR | NR | 23 | 24 | NR |
| Age at last evaluation (years) | NR | NR | 27 | 28 | NR |
| Height (m) | NR | NR | 1.60 | 1.56 | NR |
| Weight (kg) | NR | NR | 92 | 98 | NR |
| Body Mass Index (BMI, kg/m2) | NR | NR | 35.9 | 40.3 | NR |
| Geographic origin | Kastamonu | Sivas | Kastamonu | Kastamonu | NR |
| Consanguinity | No | No | No | No | No |
| Age at symptom onset (years) | NR | NR | + (7–8) | + (7–8) | NR |
| First medical evaluation (year) | NR | NR | + (2015) | + (2015) | NR |
| Lifestyle Factors | |||||
| Smoking History | NR | NR | No | No | NR |
| Alcohol consumption | NR | NR | No | No | NR |
| Constitutional symptoms | |||||
| Recurrent fever (HP:0001954) | NR | NR | + | + | NR |
| Cachexia/severe weight loss (HP:0004326) | NR | NR | + (35 kg/5 months) | NR | NR |
| Mucocutaneous involvement | |||||
| Malar rash (HP:0025300) | NR | NR | + | + | NR |
| Cutaneous photosensitivity (HP:0000992) | NR | NR | NR | + | NR |
| Recurrent aphthous stomatitis (HP:0011463) | NR | NR | + | + | NR |
| Alopecia (HP:0002293) | NR | NR | + | + | NR |
| Erythema nodosum (HP:0012219) | NR | NR | + (childhood) | + (childhood) | NR |
| Sicca syndrome (Sjögren spectrum) | |||||
| Keratoconjunctivitis sicca, Schirmer-positive (HP:0001097) | NR | NR | + | + | NR |
| Xerostomia (HP:0000217) | NR | NR | + | + | NR |
| Dysphagia (HP:0002015) | NR | NR | NR | + | NR |
| Vasculopathy | |||||
| Raynaud phenomenon (HP:0030880) | NR | NR | + | + | NR |
| Musculoskeletal involvement | |||||
| Polyarthritis (HP:0005764) ± joint stiffness (HP:0001387) | NR | NR | + (R ankle, R wrist, MCP) | + (knee, L wrist, MCP) | NR |
| Inflammatory low back pain (HP:0003418) | NR | NR | + | + | NR |
| Arthralgia (HP:0002829) | NR | NR | + (childhood) | + (childhood) | NR |
| Cardiovascular and pulmonary involvement | |||||
| Vascular occlusive disease | + | + | NR | NR | NR |
| Arterial hypertension (HP:0000822) | + | + | NR | NR | + (Holter monitoring) |
| Cardiac arrhythmia evaluation (HP:0011675) | NR | NR | NR | NR | + (Holter monitoring) |
| Chronic obstructive pulmonary disease (COPD) (HP:0006510) | + | NR | NR | NR | NR |
| Pleurisy/pleuritic chest pain (HP:0001875) | NR | NR | + | + | NR |
| Dyspnea (HP:0002094) | NR | NR | + | + | NR |
| Gastrointestinal involvement | |||||
| Recurrent abdominal pain (HP:0002574) | NR | NR | + (1–2 flares/month) | + | + (intermittent) |
| Chronic diarrhea (HP:0002028) | NR | NR | + (1–month, 15–20 stools/day) | + (2–3 day) | + (intermittent) |
| Nausea (HP:0002018) | NR | NR | + | + | NR |
| Ocular (inflammatory and structural) | |||||
| Uveitis (HP:0000554)—suspected | NR | NR | NR | + | NR |
| Congenital cataract (HP:0000519) | NR | NR | + (operated) | NR | NR |
| Neurological involvement | |||||
| Generalized epilepsy (HP:0002197) | NR | NR | NR | + (3-years) | NR |
| Syncope (HP:0001279)—pain-triggered | NR | NR | NR | + | NR |
| Endocrine and metabolic | |||||
| Obesity | NR | NR | + (BMI 35.9 kg/m2) | + (BMI 40.3 kg/m2) | NR |
| Insulin resistance | NR | NR | + | NR | NR |
| Polycystic Ovary syndrome (PCOS) | - | NR | + | NR | - |
| Diabetes Mellitus | + | NR | NR | NR | NR |
| Primary hypothyroidism (HP:0000821) | NR | + | + (post-RAI) | + | NR |
| Differentiated thyroid carcinoma (HP:0002890) | NR | NR | + (total thyroidectomy + RAI) | NR | NR |
| Metabolic and biochemical laboratory findings | |||||
| Total cholesterol (mg/dL) | NR | NR | 198.2 | 185 | NR |
| LDL cholesterol (mg/dL) | NR | NR | 143.7 | 110 | NR |
| HDL cholesterol (mg/dL) | NR | NR | 52.2 | 51 | NR |
| Triglycerides (mg/dL) | NR | NR | 77.5 | 146 | NR |
| Glucose (mg/dL) | NR | NR | 68 | 76 | NR |
| Creatinine (mg/dL) | NR | NR | 0.56 | 0.78 | NR |
| AST (IU/L) | NR | NR | 65 † | 17.6 | NR |
| ALT (IU/L) | NR | NR | 104 † | 15 | NR |
| Reproductive/gynecological | |||||
| Recurrent ovarian torsion (HP:0008733) | — | NR | + (2017-19) | NR | — |
| Renal involvement | |||||
| Proteinuria (HP:0000093) | NR | NR | NR | + (2017) | NR |
| Infectious history | |||||
| Mycobacterial infection (HP:0032279), renal localization | NR | NR | NR | + (renal TBC) | NR |
| PPD-positive/latent TBC (HP:0032279) | NR | NR | + (latent) | + (active) | NR |
| Autoantibodies | |||||
| Antinuclear antibody (ANA) positivity (HP:0003493) | NR | NR | + | + | NR |
| Working clinical diagnoses (chronological) | |||||
| Henoch-Schönlein purpura (HSP) (HP:0010748)—suspected, 2015 | NR | NR | NR | + | NR |
| Vasculitis (HP:0002633)—initial differential | NR | NR | NR | + | NR |
| Familial Mediterranean fever (clinical dx; OMIM #249100) | NR | NR | + | + | NR |
| Juvenile idiopathic/rheumatoid arthritis (HP:0030308) | NR | NR | + (differential) | + (RA dx) | NR |
| Systemic lupus erythematosus (HP:0002725) | NR | NR | + | + | NR |
| PRAAS5 (PSMB10 c.56+1G>A AD; OMIM #619175) | + | − | + | + | + |
| Current and historical pharmacotherapy | |||||
| Rituximab—Mabthera® | NR | NR | + | NR | NR |
| Hydroxychloroquine—Plaquenil® | NR | NR | + | NR | NR |
| Methotrexate (MTX) | NR | NR | + | NR | NR |
| Abatacept—Orencia® | NR | NR | + | NR | NR |
| Azathioprine—Imuran® | NR | NR | + | NR | NR |
| Mycophenolate mofetil—CellCept® | NR | NR | + | NR | NR |
| Tacrolimus | NR | NR | + | NR | NR |
| Colchicine | NR | NR | + (intermittent) | + (offered; self-discontinued) | NR |
| Sulfasalazine—Salozopyrin® (historical) | NR | NR | + | + | NR |
| Indomethacin—Endol® (historical) | NR | NR | + | + | NR |
| Anti-tuberculosis therapy | NR | NR | + (INH irregular) | + (R + E + H × 1.5 years) | NR |
| Levothyroxine replacement | NR | + | +(150 μg/day) | + (150 μg/day) | NR |
| Antihypertensive therapy | + | + | NR | NR | + (workup) |
| Levetiracetam (Keppra®)—historical | NR | NR | NR | + (self-discontinued) | NR |
| Most Recent Medication | |||||
| Upadacitinib | NR | NR | + (15 mg/day) | + (15 mg/day) | NR |
| Quetiapine | NR | NR | NR | + (300 mg/day) | NR |
| Venlafaxine | NR | NR | NR | + (150 mg/day) | NR |
| Orlistat | NR | NR | NR | + (120 mg/day) | NR |
| Surgical and procedural history | |||||
| Congenital cataract surgery | NR | NR | + | NR | NR |
| Total thyroidectomy + RAI | NR | NR | + | NR | NR |
| Ovarian de-torsion surgery (2017-19) | — | NR | + (recurrent) | NR | — |
| Hospitalization for inflammatory exacerbations | NR | NR | + (multiple) | + (multiple) | NR |
3.2. Genetics: Identification of a Segregating PSMB10 Variant
Whole-exome sequencing generated 696,351 distinct variants. After sequential filtering for high-confidence DeepVariant PASS calls, functional consequences, rarity, predicted deleteriousness, adequate read depth, and compatibility with an autosomal-dominant inheritance model, six candidate loci remained within the curated inflammation/immunity gene panel. Cross-referencing these candidates with OMIM Morbid Map and the IUIS inborn errors of immunity classification identified PSMB10 as the only gene with an established autoinflammatory disease association (Figure 2a). The qualifying variant was a heterozygous splice-donor variant, PSMB10 NM_002801.3: c.56+1G>A (rs201451622), with a high predicted deleteriousness score (CADD PHRED 34.0) and low population frequency in gnomAD v3.1.2 (global allele frequency 2.80 × 10−4). IGV-style alignments of each patient at the variant locus (chr16:67,936,693) (Figure 2b).
Figure 2.
Variant identification and segregation patterns of PSMB10 c.56+1G>A. (a) Variant filtering workflow for whole-exome sequencing analysis. Sequential filtering of the joint-called family whole-exome variant set identified a single candidate variant, PSMB10 (NM_002801.3: c.56+1G>A), after quality, functional, population frequency, pathogenicity, segregation, gene panel, and disease annotation filtering. (b) Read-level evidence at the variant locus. IGV-style alignments at chr16:67,936,693 (BWA-MEM v0.7.17, samtools markdup v1.19) show heterozygous carriage in P1*, P2*, P3* and P4*, whereas PC shows reference-only reads, supporting paternal autosomal-dominant transmission. The red dashed line marks the variant column; alternate allele reads are shown in grey.
Further analysis for HLA is summarized in Table 2 and identified a paternal HLA haplotype that was shared by the affected father and all three affected children but absent in the unaffected mother. Five alleles showed this strict segregation pattern: HLA-B*44:02, HLA-C*16:04, HLA-G*01:04, HLA-DPB1*02:01, and HLA-DMA*01:02. Additional paternal alleles, including HLA-B*35:01, HLA-C*04:01, HLA-DRB1*01:01, HLA-DQA1*01:01, HLA-DQB1*05:01, and HLA-DPB1*23:01, were present only in the affected father and were not transmitted to the affected children, which may contribute to immunogenetic heterogeneity among patients. However, the functional relevance of these alleles needs validation.
Table 2.
HLA Allele genotypes of a five-member Turkish family. P1 affected father, P2–P4 affected children, PC unaffected mother. Alleles shared among affected individuals are given in purple. Alleles that are present only in affected father are given in green. Alleles that are shared among unaffected mother and patients are not highlighted.
| Locus | Class | PC-A1 | PC-A2 | P1-A1 | P1-A2 | P2-A1 | P2-A2 | P3-A1 | P3-A2 | P4-A1 | P4-A2 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| HLA-B | Class I | B*07:02:01 | B*18:01:01 | B*44:02:01 | B*35:01:01 | B*18:01:01 | B*44:02:01 | B*07:02:01 | B*44:02:01 | B*18:01:01 | B*44:02:01 |
| HLA-C | Class I | C*12:03:01 | C*07:02:01 | C*16:04:01 | C*04:01:01 | C*16:04:01 | C*12:03:01 | C*07:02:01 | C*16:04:01 | C*16:04:01 | C*12:03:01 |
| HLA-G | Class Ib | G*01:01:01 | - | G*01:01:01 | G*01:04:01 | G*01:04:01 | G*01:01:01 | G*01:01:01 | G*01:04:01 | G*01:01:01 | G*01:04:01 |
| DRB1 | Class II—DR | DRB1*15:01:01 | DRB1*11:04:01 | DRB1*11:04:01 | DRB1*01:01:01 | DRB1*11:04:01 | - | DRB1*15:01:01 | DRB1*11:04:01 | DRB1*11:04:01 | - |
| DQA1 | Class II—DQ | DQA1*01:02:01 | DQA1*05:05:01 | DQA1*01:01:01 | DQA1*05:05:01 | DQA1*05:05:01 | - | DQA1*01:02:01 | DQA1*05:05:01 | DQA1*05:05:01 | - |
| DQB1 | Class II—DQ | DQB1*03:01:01 | DQB1*06:02:01 | DQB1*05:01:01 | DQB1*03:01:01 | DQB1*03:01:01 | - | DQB1*06:02:01 | DQB1*03:01:01 | DQB1*03:01:01 | - |
| DPB1 | Class II—DP | DPB1*04:02:01 | DPB1*04:01:01 | DPB1*23:01 | DPB1*02:01:02 | DPB1*04:02:01 | DPB1*02:01:02 | DPB1*04:01:01 | DPB1*02:01:02 | DPB1*04:02:01 | DPB1*02:01:02 |
| DMA | Class II—DM | DMA*01:01:01 | - | DMA*01:01:01 | DMA*01:02 | DMA*01:01:01 | DMA*01:02 | DMA*01:01:01 | DMA*01:02 | DMA*01:01:01 | DMA*01:02 |
3.3. In Silico Analysis of the PSMB10 c.56+1G>A Variant
Following variant identification and segregation analysis, we further performed in silico analyses to assess the consequences of the PSMB10 c.56+1G>A variant at the transcript and protein level. In silico splicing analysis using SpliceAI demonstrated a Donor Loss with a Δ score of 1.00 at position +1 bp, predicting complete abolition of the canonical splice donor site at the intron 1 boundary, accompanied by an Acceptor Loss with a Δ score of 0.61 at −208 bp [29]. Pangolin independently corroborated these findings with a Splice Loss score of 0.78 at the same +1 bp position [30]. MaxEntScan [32] scored the reference intron 1 donor (AAG|GTGAAG) at 5.73 bits, whereas c.56+1G>A reduced it to −2.45 bits, an 8.18-bit (143%) loss, well beyond the 15% damaging threshold, predicting highly functional donor loss. No de novo splice donor site was created; cryptic sites at c.56 + 85 (4.51 bits) and c.56 + 144 (6.25 bits) remain possible but uncertain. The SAI-10k-calc algorithm [31] predicted intron 1 retention of a 208 bp coding sequence as the primary splicing consequence, resulting in a frameshift [30]. Complete or partial intron retention introduces the same premature TGA at codon 20 (Figure 3a). Supporting evolutionary constraint and functional impact, the variant received a CADD score of 34 and a PhyloP score of 8.85, reflecting high nucleotide conservation at this position [35] (Figure 3a). The disrupted donor is used by eight of eleven Ensembl PSMB10 transcripts, including MANE Select NM_002801.4.
Figure 3.
In silico analysis of PSMB10 c.56+1G>A variant. (a) Predicted splicing consequence of PSMB10 c.56+1G>A together with the MaxEntScan 5’ splice site scores of the canonical splicing, predicted main consequence (intron 1 retention), and two other possible cryptic donor sites. (b) Linear domain architecture of the PSMB10 precursor. (c) Three-dimensional structure of the mature PSMB10 catalytic subunit based on the human immunoproteasome 20S X-ray structure (PDB 6E5B, 2.77 Å).
At the protein level, domain mapping showed that the canonical PSMB10 precursor consists of an N-terminal propeptide followed by the mature β2i/LMP10 catalytic subunit (Figure 3b). This premature stop lies entirely within the autocatalytically excised propeptide region, upstream of the catalytic Thr40 nucleophile. Consequently, the predicted translated product in carriers is limited to a 19-residue N-terminal peptide, MLKPALEPRGGFSFENCQR, while the mature β2i/LMP10 catalytic β-sandwich domain is not produced (Figure 3b,c). Structural visualization further supported the prediction that the truncation occurs due to termination at codon 20; the mature β-sandwich fold and Thr40 active site are not produced (Figure 3c). Collectively, these computational analyses provide concordant evidence that c.56+1G>A disrupts the canonical PSMB10 splice-donor site. Intron 1 retention represents the primary predicted aberrant transcript model and is predicted to result in a frameshift, generation of a premature termination codon, and consequent loss of normal protein function. However, computational analyses alone cannot exclude the possibility of additional aberrant splice products or establish their relative abundance in vivo.
3.4. Transcript-Level and Immunoproteasome-Related Gene Expression Analysis in Patient-Derived PBMCs
To provide experimental support for the predicted transcript-level consequence of the c.56+1G>A variant, we performed q-RT PCR to measure the abundance of the canonical PSMB10 exon 1-exon 2 splice product in patient-derived peripheral blood mononuclear cells (PBMCs) isolated from the patients and a healthy control. Primers spanning the PSMB10 exon 1–exon 2 junction were used, with each reaction assayed in duplicate (two technical replicates) using a single biological sample per individual. Given the limited number of individuals analyzed, the statistical comparisons presented below, including those in Section 3.5, should be regarded as exploratory rather than definitive. Affected individuals demonstrated reduced abundance of canonical exon1–exon2 product compared with the healthy control (Figure 3a and Figure 4a). This finding is consistent with the predicted effect of the c.56+1G>A variant on impaired formation or stability predictions of the normally spliced PSMB10 transcript. However, the assay also does not determine the identity of the aberrant transcript generated following disruption of the canonical donor site. Accordingly, intron 1 retention and its predicted downstream consequences, including frameshift formation, generation of a premature termination codon, and predicted/possible nonsense-mediated mRNA decay (NMD), remain computationally inferred rather than experimentally demonstrated.
Figure 4.
Assessment of PSMB10/PSMB8 Expression in patients. (a–c) Total RNA was extracted from healthy and patient human PBMC cells and analyzed by qRT-PCR for (a) PSMB10 exon1/2 splice junction, (b) PSMB10, and (c) PSMB8, with GAPDH used as the reference gene. Each bar represents one individual assayed in duplicate (two technical replicates). Relative fold change was calculated per subject: in (a) values are shown relative to a healthy control and statistical significance was determined by one-way ANOVA followed by Dunnett’s post hoc test, evaluating each subject against the baseline; in (b) and (c), values of each family member (n = 1) are shown relative to healthy control group (n = 4 independent individuals) and statistical significance was determined by nested t-test. Statistical details are provided in Methods, Statistics. Data are presented as mean ± SEM; p-value of <0.05 was considered statistically significant. * p < 0.05, ** p < 0.01; ns: not significant. HC: healthy control; C1–4: healthy control group; PC: unaffected patient control; P1–P4: affected individuals.
Immunoproteasomes play a critical role in maintaining cellular proteostasis and are functionally linked to autophagy pathways [36,37]. PSMB10 encodes the β2i catalytic subunit of the immunoproteasome, a specialized form of the proteasome responsible for degradation of ubiquitinated proteins and regulation of immune and inflammatory responses [37].
To evaluate the impact of the identified PSMB10 variant on cellular degradative pathways, we assessed the expression levels of PSMB10 together with PSMB8, which encodes the β5i catalytic subunit of the immunoproteasome and is the best-characterized immunoproteasome gene in proteasome-associated autoinflammatory disease, within the patient cohort [18,38]. Analysis of each patient with respect to healthy controls (pool, n = 4) revealed that PSMB10 mRNA levels were significantly upregulated (in more severely affected patients 2 and 3 (2 fold ** p < 0.01; 2.1 fold * p < 0.05 respectively), whereas others (mild phenotypes) (0.3, 0.2 fold respectively with * p < 0.05) and unaffected patient control was downregulated (0.2 fold, * p < 0.05.) (Figure 4b). The severity associated variaton and this opposite expression pattern, combined with the reduced abundance of canonical full-length PSMB10 shown in Figure 4a, suggests that the change in total expression is not a global compensatory response to the splicing defect, but might instead correlate with the inflammatory status of the individuals.
Similar trend was observed for PSMB8, with increased expression levels for patient P2 (1.86 fold, * p < 0.05) and P3 (even if P3 could not reach to significance 1.6 fold, p ~ 0.08), whereas others exhibited lower expression levels with respect to healthy controls (P1: 0.24, P2: 0.19, P3: 0.15 fold with * p < 0.05) (Figure 4c). While the reason for the different expression levels of immunoproteasome subunits (PSMB10 and PSMB8) among patients that carry the same variation remains unclear, our results might indicate that immunoproteasome-related pathways are differentially regulated among patients according to the severity of the phenotype.
3.5. Interferon-Related Gene Expression Analysis in Patient-Derived PBMCs
Because proteasome dysfunction has been associated with type I interferon responses [18,20], we next explored whether patients carrying the PSMB10 variant with q-RT PCR in patient-derived peripheral blood mononuclear cells (PBMCs) isolated from the patients (P1–P4), an unaffected patient control, and a healthy control group (n = 4). Primers specific for ISG15, IFI35 and SIGLEC1 were used, with each reaction assayed in duplicate (two technical replicates) using a single biological sample per individual. Analysis of IFN-signature-related genes revealed patient-specific patterns rather than a general and uniform interferonopathy-like response across all affected individuals, as shown in Figure 5.
Figure 5.
Interferon-related gene expression analysis. (a–c) Total RNA was extracted from healthy and patient human PBMC cells and analyzed by qRT-PCR for (a) ISG15, (b) IFI35, and (c) SIGLEC1, with GAPDH used as the reference gene. Each bar represents one individual assayed in duplicate (two technical replicates). Relative fold change was calculated per subject; values of each family member (n = 1) are shown relative to healthy control pool (n = 4 independent individuals). Each family member (n = 1) was compared with the control group (n = 4 individuals), and statistical significance was determined by nested t-test. Statistical details are provided in methods, statistics. Data are presented as mean ± SEM; p < 0.05 was considered statistically significant. * p < 0.05, ** p < 0.01, *** p < 0.001; ns: not significant. C1–4: healthy control group, PC: unaffected patient control, P1–P4: affected individuals.
ISG15 expression was increased in P3 (Figure 5a), IFI35 was elevated in P2 (Figure 5b), and SIGLEC1 was increased in both P2 and P3 (Figure 5c). In contrast, P4 showed lower expression of all three genes, while P1 showed a similar overall reduction trend. The remaining evaluated IFN-related genes did not show consistent alterations across affected individuals. Overall, IFN-related gene expression varied among affected individuals, with the most pronounced changes observed in the clinically more severely affected individuals.
4. Discussion
In this study, we describe a two-generation Turkish family with a dominantly segregating autoinflammatory phenotype associated with a heterozygous splice-donor variant in PSMB10 (c.56+1G>A). HLA typing revealed a paternally inherited haplotype that was shared by the affected father and all three affected offspring but absent in the unaffected mother (Table 2). This observation suggests that the affected individuals share an additional paternal immunogenetic background together with the shared PSMB10 variant, raising the possibility that this haplotype may contribute to phenotypic variability as a modifier. However, given the small number of affected individuals and the absence of functional validation, any inference about a modifier effect on disease expression remains hypothesis-generating. Therefore, we note this shared immunogenetic background as an observation of potential interest, while acknowledging that its contribution to immune heterogeneity or clinical variability cannot be determined within a single pedigree. Further evaluation in additional unrelated PSMB10-associated families, together with functional studies, will be required before any causal modifier effect can be inferred.
Computational analyses consistently predicted disruption of the canonical PSMB10 exon 1 donor site. Consistent with this prediction, the exon 1–exon 2 assay showed reduced abundance of the canonical splice product in variant carriers, providing transcript-level evidence of altered PSMB10 processing. Although intron 1 retention is our primary predicted model, we identified two other potential but weaker cryptic donor sites; therefore, alternative splicing products cannot be completely excluded. Computational predictions suggest the introduction of an early PTC at around codon 20 and destruction of the aberrant transcripts by nonsense-mediated mRNA decay (NMD). However, NMD efficiency can be variable and context-dependent [39]. If those transcripts escape from the surveillance system, they can give rise to truncated proteins with dominant-negative or gain-of-function potential [40]. PSMB10 variants have been reported to disrupt proteasome assembly [19]; this is structurally unlikely for the C56+1 G>A variant. The predicted product is 19 amino acid residue N terminal peptide, upstream of catalytic Thr40, which is too short to be incorporated into the 20S particle. Therefore, even if it escapes from NMD, continued translation would yield a severely truncated product that would be unlikely to assemble into an immune–proteasome complex.
We evaluated its possible association with immunoproteasome regulation and interferon-related responses. The family comprised five individuals: an unaffected mother who does not carry the variant; an affected father and a son who carry the variant, but both have a milder phenotype; and two sisters who carry the variant and have a more severe phenotype. Therefore, we assessed each affected individual separately because of the variable phenotypes. Our findings suggest that individuals with a more severe phenotype show more notable alterations in these pathways compared with mildly affected family members. Although the family was initially investigated in the context of an unclassified autoinflammatory phenotype overlapping with known monogenic autoinflammatory disorders, the present findings should be interpreted within the broader framework of proteasome-associated immune dysregulation rather than as evidence for a specific FMF-like PSMB10-associated syndrome. Previously reported PSMB10-associated disorders have primarily been discussed in relation to proteasome-associated autoinflammatory syndromes, particularly PRAAS, immunoproteasome dysfunction, immune dysregulation, and type I-interferon-related inflammation [17,18,20,41]. Therefore, our findings may expand the emerging spectrum of PSMB10-associated autoinflammatory disease, although the FMF-like features should be interpreted as clinical overlap rather than a defining characteristic of PSMB10-related disease.
This expansion is also geographic: FMF-like autoinflammatory phenotypes and the diagnostic challenges they pose, including MEFV-negative or atypical presentations requiring broader molecular evaluation, are well documented across Mediterranean-basin populations. FMF is highly prevalent among Armenian, Arab, and Jewish communities, and substantial case clusters have also been reported in Italian cohorts, reflecting shared genetic ancestry and the population-specific distribution of MEFV founder variants across the basin [3,4,5]. FMF-like phenotypes without biallelic pathogenic MEFV variants may reflect overlap syndromes or alternative monogenic etiologies [16], and family-based whole-exome sequencing strategies similar to ours have successfully resolved such unclassified presentations in FMF-prevalent populations: in Turkish families with clinical FMF, segregation-based exome analysis has identified pathogenic variants beyond the classical autoinflammatory disease panel, including a homozygous MVK mutation and a novel heterozygous TNFRSF1A mutation [13], and targeted next-generation sequencing panels have demonstrated diagnostic utility in the clinical suspicion of systemic autoinflammatory disease [12]. To our knowledge, no heterozygous PSMB10 splice-site variant that segregates in an autosomal dominant pattern has been reported by other groups, neither in any of these populations nor in any autoinflammatory context; our preliminary findings were first presented at the 56th European Society of Human Genetics Conference [42]. The PSMB10-associated conditions described to date have largely presented as classical CANDLE/PRAAS, as proteasome-related immune dysregulation with T lymphopenia, or as de novo dominant disease, rather than as a dominantly segregating, FMF-overlapping presentation [17,18,19].
Catalytic subunits of immunoproteasomes are important for their role in degradation of ubiquitinylated proteins and consequently maintaining intracellular protein homeostasis [37,41]. PSMB10 and PSMB8 encode catalytic immunoproteasome subunits, β2i and β5i respectively; pathogenic variants in both genes have been linked to proteasome-associated autoinflammatory syndromes and to impaired protein homeostasis [17,18,38,41].
PSMB10 and PSMB8 expression differed markedly among affected family members, with higher total transcript levels observed in the more severely affected individuals and lower levels in the mildly affected individuals (Figure 4b,c). Although the mechanism underlying this difference remains unclear, this specific pattern might indicate a potential relationship between immunoproteasome-related gene expression and clinical severity.
While the exon 1/exon 2 junction assay shows a reduced abundance of the canonical splice product in variant carriers, overall PSMB10 levels were elevated in severe cases; this reduction in exon 1–exon 2 signal in variant carriers suggests the disruption of normal PSMB10 transcript processing but does not directly identify the aberrant RNA species. The phenotypic difference among patients suggests that a global cell-autonomous compensatory mechanism is unlikely. Instead, the increased expression of immunoproteasome-associated genes in more severe patients may reflect an inflammation-driven transcriptional response associated with impaired canonical splicing and altered proteasome biology. Thus, the increased total expression of PSMB10 does not necessarily indicate restored transcription; rather, it may reflect a combination of altered transcription and disease-associated transcriptional regulation. Collectively, whereas the reduced expression observed in mildly affected variant carriers suggests that the PSMB10 variant alone may not be sufficient to drive immunoproteasome upregulation, additional factors, such as disease activity, cellular inflammatory state, or modifier genetic background, may influence the transcriptional response, which requires further investigation [18,20,37]. Therefore, these expression findings should be interpreted as an observed association with clinical severity rather than direct evidence of altered proteasome function.
Proteasome-associated autoinflammatory diseases link impaired protein clearance with immune dysregulation and type I interferon-related inflammatory responses [18,20]. However, the clinical and molecular consequences of proteasome gene variants may be heterogeneous and limited, and context-dependent interferon-stimulated gene expression can be observed in some cases [43,44,45]. In this context, previous studies suggest that incomplete protein clearance and proteotoxic stress may engage cellular stress-response and innate immune pathways, providing a possible link between proteasome impairment and selective IFN-related transcriptional changes without necessarily inducing a generalized interferonopathy-like response [46,47]. These observations provided a rationale for evaluating IFN-related gene expression changes in this family.
Evaluation of IFN-related genes revealed limited but notable and context-dependent differences, mostly in the more severely affected patients. SIGLEC1, IFI35, and ISG15 showed an overall reduced expression pattern in milder phenotypes, including significant downregulation in one patient. Severely affected patients showed concomitant upregulation of ISG15, and IFI35 expression levels were elevated in one individual of each, and SIGLEC1 expression levels were elevated in both. This distribution might suggest heterogeneous activation of inflammatory pathways rather than a generalized interferon response across all affected individuals. Although there is some activation in certain signature-related genes, it is not strong (not significant) and/or consistent among affected individuals. Given the small sample size and the limited number of IFN-related genes showing expression differences, these data should be interpreted cautiously and do not establish a causal relationship between IFN-related gene expression and clinical severity. Furthermore, although SIGLEC1 has been reported as a useful marker of type I IFN activity [48], the present data do not directly support a functional hierarchy among SIGLEC1, IFI35, and ISG15. Therefore, these changes should be interpreted as exploratory molecular findings that may arise secondary to broader PSMB10-associated immunoproteasome dysregulation, patient-specific inflammatory status, and possible epistatic interactions involving unknown genetic or epigenetic modifiers, rather than as direct evidence of a primary interferonopathy [18,20].
The main strength of the present study is that, even though the study was performed in a single family, the presence of multiple affected individuals across two generations, together with an unaffected family member, provided a valuable opportunity for segregation analysis and intrafamilial comparison of molecular findings in relation to clinical severity. In this context, evaluation of immunoproteasome-related gene expression, IFN-related markers, and the shared immunogenetic background revealed molecular patterns that appeared more pronounced in the more severely affected individuals. Taken together, this two-generation family structure is particularly informative, despite the limitations, in the context of a rare disorder, as it allows genetic findings and molecular evaluation across several affected relatives.
Several limitations should be considered when interpreting these findings. The molecular analysis was based on a limited number of individuals, with expression studies performed using a single biological sample per subject, assayed in duplicate (two technical replicates). Consequently, this restricts the generalizability of the results. The statistical analyses should be interpreted cautiously, and the observed molecular differences should be regarded as exploratory findings that require validation in additional patients or families. In addition, the aberrant RNA species generated by the splice-site variant was not directly characterized; therefore, the predicted intron 1 retention remains computationally inferred. Moreover, significant expression differences within the IFN-related pathway were observed in only a limited number of genes and appeared to parallel clinical severity; therefore, these findings should not be regarded as evidence of a comprehensive interferon signature. Broader functional studies in larger cohorts, preferably including unrelated families or patients, will be important to extend these findings and clarify the role of PSMB10 in proteasome-associated autoinflammatory disease. Finally, the potential contribution of the shared paternal HLA background or other modifier factors cannot be determined from this family alone and remains a hypothesis for future investigation.
5. Conclusions
Overall, our findings suggest that the identified PSMB10 c.56+1G>A variant is associated with immunoproteasome-related transcript levels in patient-derived PBMCs, with the most pronounced molecular changes occurring in clinically more severely affected individuals. Together with familial segregation, concordant in silico prediction of splice-donor disruption, and reduced abundance of the canonical exon 1–exon 2 transcript, these findings support the variant as a strong candidate contributor to the autoinflammatory phenotype observed in this family.
Although these molecular alterations broadly paralleled the intrafamilial clinical heterogeneity, we emphasize that the present data are exploratory and do not establish a causal genotype–phenotype relationship. Limited and heterogeneous IFN-related gene expression changes were also observed, particularly in clinically more severe individuals. Importantly, the intrafamilial variability observed in this family suggests that autoinflammatory diseases that appear to follow a monogenic inheritance pattern may still involve a more complex genetic architecture, in which modifier variants, immunogenetic background, disease activity, and pathway-level interactions influence clinical severity and molecular responses. Further studies in additional families and larger patient cohorts, together with modifier-gene analyses and mechanistic functional assays, including direct proteasome activity assays, β2i protein-level validation, and broader transcriptomic profiling, will be important to validate these findings and conclusively establish how the PSMB10 c.56+1G>A variant contributes to proteasome-associated autoinflammatory disease.
Acknowledgments
During the preparation of this manuscript, the authors used ChatGPT and Gemini for literature summarization to review the literature. After using AI tools, all sources that were used during the review were carefully revised, and the references were controlled. The incorporation of AI tool usage mostly impacted the efficiency of the literature review process. The manuscript was written through careful review of the sources that were analyzed. The authors have reviewed and edited the content as needed and take full responsibility for the final version of this publication.
Abbreviations
The following abbreviations are used in this manuscript:
| CAMPS | CARD14-mediated psoriasis |
| CAPS | Cryopyrin-associated periodic syndromes |
| CANDLE/PRAAS | Chronic Atypical Neutrophilic Dermatosis with Lipodystrophy and Elevated Temperature/Proteasome-associated Autoinflammatory Syndrome |
| DIRA | Deficiency of the Interleukin-1 Receptor Antagonist |
| DITRA | Deficiency of the Interleukin-36 Receptor Antagonist |
| EOS | Early-onset sarcoidosis |
| FMF | Familial Mediterranean Fever |
| SLE | Systemic Lupus Erythematosus |
| HSP | Henoch-Schonlein Purpura |
| ILS | Interstitial Lung Disease |
| COPD | Chronic Obstructive Pulmonary Disease |
| RAI | Radioactive Iodine Therapy |
| HC | Healthy Control |
| IFI35 | Interferon-Induced Protein 35 |
| IFN | Interferon |
| ISG15 | Interferon-Stimulated Gene 15 |
| MEFV | Mediterranean Fever Gene |
| MKD-HIDS | Mevalonate kinase deficiency/Hyperimmunoglobulin D syndrome |
| NLRP12AD | NLRP12-associated autoinflammatory disorder |
| PC | Patient Control (unaffected family member) |
| PAPA | Pyogenic arthritis, pyoderma gangrenosum and acne syndrome |
| PBMC | Peripheral Blood Mononuclear Cells |
| PSMB8 | Proteasome 20S subunit beta 8 |
| PSMB10 | Proteasome 20S subunit beta 10 |
| TRAPS | Tumor Necrosis Factor receptor-associated periodic syndrome |
| WES | Whole-exome sequencing |
Author Contributions
Conceptualization, E.T.T., U.I.O. and A.H.O.; methodology, E.T.T. and U.I.O.; software, A.B.; validation, A.B., E.T.T. and U.I.O.; formal analysis, U.I.O., E.T.T., A.B., D.S., E.A.S., S.U. and A.H.O.; investigation, U.I.O., A.B., D.S. and E.T.T.; resources, E.T.T.; data curation, U.I.O., A.B., E.T.T., D.S., E.A.S. and S.U.; writing—original draft preparation, U.I.O. and E.T.T.; writing—review and editing, U.I.O., E.T.T., S.U., A.H.O., A.B., D.S. and E.A.S.; visualization, A.B. and U.I.O.; supervision, U.I.O., E.T.T. and A.H.O.; project administration, E.T.T.; funding acquisition, E.T.T. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Health and Engineering Sciences Human Research Ethics Committee of Istanbul Technical University (approval no. MBG-2/2017, Date: 22 May 2017).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The clinically relevant variant identified in this study has been deposited in the NCBI Linvar database under the variant accession number VCV003780496.7 (Variation ID: 3780496; with submitter accession number SCV007596971.1). The record is publicly available and can be directly accessed via the following web link: https://www.ncbi.nlm.nih.gov/clinvar/variation/3780496/ with the accession date of 6 June 2026.
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This study was supported by Acibadem Mehmet Ali Aydinlar University Scientific Research Projects Funding with Project Number 2022/0716 to ETT.
Footnotes
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The clinically relevant variant identified in this study has been deposited in the NCBI Linvar database under the variant accession number VCV003780496.7 (Variation ID: 3780496; with submitter accession number SCV007596971.1). The record is publicly available and can be directly accessed via the following web link: https://www.ncbi.nlm.nih.gov/clinvar/variation/3780496/ with the accession date of 6 June 2026.