Comprehensive αβ T‐Cell Receptor Repertoire Analysis Reveals a Unique CD8+ TCR Landscape in DOCK8‐Deficient Patients

Ceren Bozkurt; Gökhan Cildir; Umran Aba; Rahmi Kutay Erdogan; Nicholas I Warnock, I; Chung Hoow Kok; Asena Pinar Sefer; Sule Haskologlu; Sidem Didar Tekeoglu; Gülşah Merve Kılınç; Canberk Ipsir; Tugba Arikoglu; Aylin Kont Ozhan; Saliha Esenboga; Ahmet Özen; Elif Karakoç‐Aydiner; Sevgi Bilgiç Eltan; Çigdem Aydogmus; Candan Islamoglu; Kübra Baskın; Betul Karaatmaca; Ayse Metin; Deniz Çagdaş; Figen Dogu; Aydan Ikinciogullari; Safa Baris; Baran Erman

doi:10.1111/all.16580

. 2025 May 10;80(9):2519–2530. doi: 10.1111/all.16580

Comprehensive αβ T‐Cell Receptor Repertoire Analysis Reveals a Unique CD8+ TCR Landscape in DOCK8‐Deficient Patients

Ceren Bozkurt ^1,², Gökhan Cildir ³, Umran Aba ^1,², Rahmi Kutay Erdogan ¹, Nicholas I Warnock I ^3,⁴, Chung Hoow Kok ^3,^4,⁵, Asena Pinar Sefer ⁶, Sule Haskologlu ⁷, Sidem Didar Tekeoglu ^1,², Gülşah Merve Kılınç ⁸, Canberk Ipsir ^1,², Tugba Arikoglu ⁹, Aylin Kont Ozhan ⁹, Saliha Esenboga ¹⁰, Ahmet Özen ¹¹, Elif Karakoç‐Aydiner ¹¹, Sevgi Bilgiç Eltan ¹¹, Çigdem Aydogmus ¹², Candan Islamoglu ⁷, Kübra Baskın ¹³, Betul Karaatmaca ¹⁴, Ayse Metin ¹⁴, Deniz Çagdaş ^10,^15,¹⁶, Figen Dogu ⁷, Aydan Ikinciogullari ⁷, Safa Baris ¹¹, Baran Erman ^1,^17,^✉

^¹Can Sucak Research Laboratory for Translational Immunology, Hacettepe University, Ankara, Türkiye

^²Department of Pediatric Immunology, Institute of Child Health, Hacettepe University, Ankara, Türkiye

^³Centre for Cancer Biology, University of South Australia and SA Pathology, Adelaide, South Australia, Australia

^⁴Data and Bioinformatics Innovation, Department of Genetics and Molecular Pathology, SA Pathology, Adelaide, South Australia, Australia

^⁵Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia

^⁶Division of Pediatric Allergy and Immunology, School of Medicine, Recep Tayyip Erdogan University, Rize, Türkiye

^⁷Department of Pediatric Immunology and Allergy, Ankara University Faculty of Medicine, Ankara, Türkiye

^⁸Department of Bioinformatics, Graduate School of Health Sciences, Hacettepe University, Ankara, Türkiye

^⁹Department of Pediatric Allergy and Immunology, Faculty of Medicine, Mersin University, Mersin, Türkiye

^¹⁰Ihsan Dogramaci Childrens Hospital, Hacettepe University Faculty of Medicine, Ankara, Türkiye

^¹¹Department of Pediatric Allergy and Immunology, Jeffrey Modell Diagnostic and Research Center for Primary Immunodeficiencies, the Isil Berat Barlan Center for Translational Medicine, Faculty of Medicine, Marmara University, Istanbul, Türkiye

^¹²Department of Pediatric Allergy and Clinical Immunology, University of Health Sciences, Istanbul Basaksehir Cam and Sakura City Hospital, Istanbul, Türkiye

^¹³Department of Pediatric Immunology and Allergy, Gazi University Faculty of Medicine, Ankara, Türkiye

^¹⁴Department of Pediatric Allergy and Immunology, University of Health Sciences, Ankara Bilkent City Hospital, Ankara, Türkiye

^¹⁵Department of Pediatric Immunology, Pediatric Basic Sciences, Institute of Child Health, Hacettepe University, Ankara, Türkiye

^¹⁶Division of Pediatric Immunology, Department of Pediatrics, Hacettepe University Faculty of Medicine, Ankara, Türkiye

^¹⁷Institute of Child Health, Hacettepe University, Ankara, Türkiye

Correspondence: Baran Erman (baranerman@gmail.com)

^✉

Corresponding author.

PMCID: PMC12444818 PMID: 40346988

ABSTRACT

Background

Dedicator of cytokinesis protein 8 (DOCK8) is a guanine nucleotide exchange factor highly expressed in, and critical for, the function of various innate and adaptive immune cells. DOCK8 deficiency leads to combined immunodeficiency characterized by susceptibility to infections, autoimmunity, and a severe Th2‐type immune response. While dysfunction in various T cell subsets has been implicated in these phenotypes, a comprehensive analysis of the T‐cell receptor (TCR) repertoire in these patients has not yet been documented. This study investigates the αβ TCR repertoire in DOCK8‐deficient patients to identify features related to disease pathogenesis and explore the potential role of TCR repertoire alterations in disease development.

Methods

We compared immune repertoire profiles determined by high‐throughput TCR sequencing of circulating CD4+ and CD8+ T cells from patients with DOCK8 deficiency (n = 10) to healthy controls (n = 7) and patients with ataxia‐telangiectasia (AT) (n = 5).

Results

Different diversity analyses revealed a restricted TRA and TRB repertoire in both CD4+ and CD8+ T cells from DOCK8‐deficient patients, with the restriction being more pronounced in CD8+ T cells. Skewed usage of individual variable (V) and joining (J) genes and potentially self‐reactive CD8+ T cell clones, as determined by hydrophobicity and cysteine indices, were identified in DOCK8‐deficient patients.

Conclusion

Our study represents the most comprehensive immune repertoire analysis in DOCK8 deficiency. The identification of a significantly restricted αβ TCR repertoire, along with the detection of potentially autoreactive clones, highlights the crucial role of immune repertoire profiling in elucidating the pathogenesis of DOCK8 deficiency.

Keywords: autoimmunity, DOCK8 deficiency, immune repertoire sequencing, T‐cell receptor repertoire

T cell receptor repertoire analysis by high‐throughput immune repertoire RNA‐sequencing in the patients with DOCK8 deficiency. Comprehensive comparisons revealed a restricted TCR repertoire diversity in the patients. Additional assessments showed potential auto‐reactive CD8+ T cell clones in the patients.Abbreviations: DOCK8, dedicator of cytokinesis protein 8; TCR, T cell receptor; TRA, TCR alpha; TRB, TCR beta.

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1. Introduction

DOCK8, a member of the DOCK family of guanine nucleotide exchange factors, plays a critical role in immune cell function. It regulates survival, actin cytoskeleton dynamics, and migration in lymphocytes, natural killer (NK) cells, and dendritic cells (DCs) [1]. DOCK8 deficiency is an inborn error of immunity (IEI) characterized by a wide spectrum of clinical features, including recurrent infections, atopy (allergic predisposition), autoimmunity, and an increased risk of cancer [2, 3]. Patients often experience elevated serum IgE levels and prominent skin manifestations, frequently linked to viral infections [4]. Loss‐of‐function mutations in the DOCK8 gene cause this debilitating condition, and hematopoietic stem cell transplantation (HSCT) remains the definitive treatment due to the significant morbidity and mortality [5].

T‐cell abnormalities are recognized as central contributors to the clinical symptoms of DOCK8 deficiency. Patients exhibit T‐cell lymphopenia, affecting both CD4+ and CD8+ T cell populations [3, 6]. Additionally, research suggests a skewed polarization of naïve T cells toward the Th2 (helper T cell type 2) phenotype, known for its role in allergic responses [7]. Furthermore, reduced numbers and impaired function of regulatory T cells (Tregs), essential for maintaining immune tolerance, have been well documented in DOCK8 deficiency [8]. These defects likely underlie the high susceptibility to viral infections and atopy observed in these patients. Despite the crucial role of TCR‐mediated antigen recognition in immune responses, a comprehensive characterization of the TCR alpha (TRA) and beta (TRB) chains in CD4+ and CD8+ T cells from DOCK8‐deficient individuals has been lacking. This gap in knowledge hinders our understanding of how DOCK8 deficiency impacts T cell development and function.

Immune repertoire sequencing (IR‐seq) enables detailed examination of TCR and B cell receptor (BCR) repertoires, revealing the breadth and specificity of antigen recognition. This technology allows researchers to map TCR and BCR diversity, track clonal expansions (expansion of specific T or B cell populations), and assess the impact of genetic mutations on immune cell populations [9, 10]. In the context of IEI, such as DOCK8 deficiency, IR‐seq provides a valuable tool for understanding how genetic defects influence immune repertoire dynamics [11, 12, 13, 14, 15], revealing skewed or reduced TCR diversity, the presence of clonally expanded T cell populations, and the functional impact of the deficiency on immune surveillance and response [16]. TCR repertoire analysis can also identify potentially autoreactive T cell clones, which can initiate self‐directed immune attacks. Two complementary indices, the hydrophobicity index and the cysteine index [17, 18] have been proposed as indicators of such autoreactivity, reflecting distinct tolerance mechanisms. The hydrophobicity index is thought to reflect defects in peripheral tolerance (the suppression of self‐reactive T cells outside the thymus), while the cysteine index is considered a more specific marker of impaired cortical thymic tolerance, where T cells undergo maturation. Notably, TCRαβ + CD8αα + intraepithelial lymphocytes (CD8αα + αβ IELs), a specialized T cell subset in the gut epithelium, and their thymic precursors exhibit an increased cysteine index [18]. Conversely, regulatory T cells (Tregs) have been associated with a higher hydrophobicity index [17, 19].

While restricted TCRβ repertoires and skewed BCR repertoires have been previously reported in some DOCK8‐deficient patients [12, 20, 21], our study represents the first comprehensive high‐throughput analysis investigating the T cell repertoire across multiple parameters. This detailed immunological profiling, including analysis of autoreactive potential, can provide critical insights into the pathophysiology of DOCK8 deficiency, revealing how specific genetic alterations disrupt normal immune function and contribute to the clinical manifestations of the disease.

2. Methods

2.1. Study Participants

Peripheral blood samples were obtained from patients with genetically diagnosed DOCK8 or ataxia‐telangiectasia mutated (ATM) deficiency and from healthy controls with approval from the Hacettepe University Ethics Committee (GO 21/801). Informed consent was obtained from all participants.

2.2. αβ T‐Cell Repertoire Sequencing

CD4+ and CD8+ T cells were enriched from peripheral blood mononuclear cells (PBMCs) by negative magnetic separation using the MojoSort Human CD4 and CD8 T‐cell Isolation Kit (BioLegend). The purity of the isolated cells was confirmed by flow cytometry (> 95%). Total RNA was isolated from the purified T cells using the NucleoSpin RNA Plus Kit (Macherey‐Nagel). TRA and TRB libraries were prepared from 50 ng of total RNA using the SMARTer Human TCR α/β Profiling Kit (Takara) according to the manufacturer's instructions. Library validation was performed by amplicon size measurement using the Agilent 2100 Bioanalyzer with the DNA 1000 kit. The resulting libraries were sequenced on the Illumina MiSeq platform using 2 × 300 bp paired‐end sequencing. Immune profiling data were generated from FASTQ files using the Cogent NGS Immune Profiler Software (Takara). Preprocessing included splitting reads by matching the read sequence to different receptor chains and excluding short reads (< 30 bp). Reads were then grouped into molecular identifiers (MIGs) using unique molecular identifiers (UMIs). The process was completed by aligning reads to V(D)J sequences and performing clonotype calling.

2.3. Single‐Cell RNA and TCR Sequencing

PBMCs from patient P7 and two healthy control subjects were analyzed using single‐cell RNA sequencing (scRNA‐seq). Briefly, cryopreserved PBMCs were thawed, washed with RPMI 1640 medium, and resuspended in sterile PBS containing 0.04% bovine serum albumin (BSA). One million cells were filtered through a cell strainer, and live cells were isolated by negative magnetic selection using a Dead Cell Removal Kit (BioLegend). Subsequently, scRNA‐seq was performed using the Chromium Single Cell Controller and Chromium Next GEM Single Cell 5′ Kit v2 (10× Genomics) according to the manufacturer's protocol. TCR sequences were amplified using the Chromium Single Cell V(D)J Reagent Kit (10× Genomics). Sequencing was performed on the Illumina NovaSeq platform using a 75‐bp paired‐end configuration. TCR analysis was performed using scRepertoire [22].

2.4. Bioinformatics Analyses of TCR Repertoires

Data analyses were performed using the Immunarch package (https://github.com/immunomind/immunarch) within R Studio and the Cogent NGS Immune Viewer Tool (Takara). The specific functions used are detailed in Table S1. Pathology‐associated TRB sequences were identified by comparison with records from two publicly available, manually curated databases: McPAS‐TCR and TCRdb [23, 24]. Hydrophobicity and cysteine indices were calculated using previously described code [25]. Proximal and distal TRAV/TRAJ associations were determined with a computational strategy based on PROMIDISα analyses published by Berland et al. [26].

2.5. Statistical Analysis

Statistical analyses were performed using GraphPad Prism software (version 10.0.2). Specific statistical tests used for each analysis are detailed in the figure legends.

3. Results

3.1. Restricted TCR Repertoire in Patients With DOCK8 Deficiency

To comprehensively characterize the αβ TCR repertoire in DOCK8 deficiency, we recruited 10 previously undescribed patients with genetically confirmed DOCK8 deficiency (age range: 3–11 years), 7 age‐matched pediatric healthy controls, and 5 patients with ataxia‐telangiectasia (AT; age range: 5–10 years), a well‐established model of restricted T cell repertoire. Clinical and laboratory findings for the DOCK8 patients are presented in Table 1 and Table S2. Except for patient 3 (P3), who had a biallelic missense variant, all other patients had biallelic deletions, frameshift, or nonsense mutations in the DOCK8 gene. While some variants have been previously reported, others are novel to this study (Table 1). ATM variants are detailed in Table S3.

TABLE 1.

DOCK8 variants and laboratory findings of the patients.

	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10
DOCK8 variant	c.3067‐3068dupAT p.V1024Lfs*13 #	Exon 1–14 deletion	c.137G>A p.G46D	c.C4902G p.Y1634X #	c.3067‐3068dupAT p.V1024Lfs*13 #	Exon 2–26 deletion	Exon 2–26 deletion	c.3067‐3068dupAT p.V1024Lfs*13 #	c.4507C>T p. Gln1503X #	c.3067‐3068dupAT p.V1024Lfs*13 #
ALC (10⁹/l)	1.2 (1.5–2.6)	3.6 (1.5–7.6)	4.1 (1.7–5.7)	1.7 (1.5–5.2)	2.4 (1.5–5.2)	1.7 (1.5–7.6)	5.6 (1.5–5.2)	4.7 (1.5–5.2)	2.3 (1.5–5.2)	1.4 (1.5–5.2)
CD3+ T cells*	41/0.49 (57–81/1–4.9)	74/2.66 (57–81/1–4.9)	59/2.41 (58–82/1.1–4.1)	35/0.6 (55–79/1.9–3.6)	41/0.98 (55–79/1.9–3.6)	61/1.02 (57–81/1–4.9)	58/3.25 (55–79/1.9–3.6)	54/2.53 (55–79/1.9–3.6)	35/0.8 (55–79/1.9–3.6)	51/0.69 (55–79/1.9–3.6)
CD4+ T cells*	23/0.28 (24–47/0.5–2.7)	24/0.86 (24–47/0.5–2.7)	29/1.19 (26–48/0.6–2.4)	27/0.43 (26–49/0.6–2)	24/0.58 (26–49/0.6–2)	36/0.61 (24–47/0.5–2.7)	22/1.23 (26–49/0.6–2)	24/1.12 (26–49/0.6–2)	13/0.3 (26–49/0.6–2)	10/0.14 (26–49/0.6–2)
Naïve CD4+ T cells*	47/0.13 (17–40/0.3–2.4)	31/0.27 (17–40/0.3–2.4)	83/0.98 (16–40/0.4–2)	74/0.32 (20–41/0.5–1.6)	NA	54/0.33 (17–40/0.3–2.4)	77/0.95 (20–41/0.5–1.6)	54/0.6 (20–41/0.5–1.6)	58/0.17 (20–41/0.5–1.6)	NA
Memory CD4+ T cells*	49/0.14 (9–23/0.2–1)	69/0.59 (9–23/0.2–1)	17/0.2 (8–26/0.2–0.8)	25/0.1 (8–42/0.2–0.8)	NA	41/0.25 (9–23/0.2–1)	19/0.23 (8–42/0.2–0.8)	44/0.49 (8–42/0.2–0.8)	41/0.12 (8–42/0.2–0.8)	NA
CD8+ T cells*	18/0.22 (17–37/0.3–21)	38/1.37 (17–37/0.3–21)	28/1.14 (16–32/0.4–1.5)	6/0.1 (9–35/0.3–1.3)	15/0.36 (9–35/0.3–1.3)	22/0.37 (17–37/0.3–21)	31/1 (9–35/0.3–1.3)	28/1.31 (9–35/0.3–1.3)	23/0.53 (9–35/0.3–1.3)	40/0.54 (9–35/0.3–1.3)
Naïve CD8+ T cells*	NA	78/1.06 (15–32/0.4–2)	NA	67/0.07 (13–31/0.3–1.3)	NA	NA	87/0.87 (13–31/0.3–1.3)	70/0.92 (13–31/0.3–1.3)	NA	NA
Memory CD8+ T cells*	NA	21/0.29 (4–15/0.09–0.8)	NA	29/0.03 (2–10/0.06–0.5)	NA	NA	10/0.1 (2–10/0.06–0.5)	30/0.39 (2–10/0.06–0.5)	NA	NA
CD19+ B cells*	43/0.52 (10–27/0.2–2.2)	16/0.58 (10–27/0.2–2.2)	14/0.57 (10–30/0.2–1.4)	38/0.66 (11–31/0.3–1.2)	51/1.22 (11–31/0.3–1.2)	26/0.44 (10–27/0.2–2.2)	37/2.07 (11–31/0.3–1.2)	39/1.83 (11–31/0.3–1.2)	45/1.03 (11–31/0.3–1.2)	29/0.39 (11–31/0.3–1.2)
NK cells*	10/0.12 (8–28/0.2–0.9)	6/0.22 (8–28/0.2–0.9)	25/1.00 (8–30/0.2–1)	25/0.43 (5–28/0.2–1.2)	5/0.12 (5–28/0.2–1.2)	10/0.17 (8–28/0.2–0.9)	4/0.22 (5–28/0.2–1.2)	7/0.33 (5–28/0.2–1.2)	16/0.37 (5–28/0.2–1.2)	19/0.26 (5–28/0.2–1.2)
IgA (mg/dl)	288 (62–390)	236 (70–303)	10 (67–433)	6 (44–244)	187 (44–244)	166 (70–303)	62 (57–282)	96 (26–296)	246 (44–244)	6 (26–296)
IgG (mg/dl)	1080 (842–1943)	1385 (764–2134)	500 (835–2094)	964 (640–2010)	865 (640–2010)	1250 (764–2134)	1090 (745–1804)	1013 (604–1941)	1000 (640–2010)	395 (604–1941)
IgM (mg/dl)	34 (54–392)	32 (69–387)	43 (47–494)	164 (52–297)	22 (52–297)	48 (69–387)	30 (78–261)	22 (71–234)	16 (52–297)	10 (71–234)
IgE (IU/mL)	4541	5172	382	3144	6083	11,500	14,600	664	5420	> 2500

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Note: The numbers in the brackets indicate the reference values #: novel variants. Reference ranges are based on published results [27]. Bold numbers indicate aberrant values.

Abbreviations: *, % and count (10⁹/L); ALC, absolute lymphocyte count; IU, international unit; NA, not assessed; NK, natural killer cells.

To gain independent insights into helper and cytotoxic T cells, we purified circulating CD4+ and CD8+ T cells from all individuals and performed high throughput sequencing of TRA and TRB repertoires. To eliminate potential bias due to sequencing depth, we down‐sampled (except Inverse Simpson) all samples to the smallest number of total clones before analyzing and comparing TRA and TRB repertoire diversity and evenness using the Chao1, Inverse Simpson, and True Diversity indices, as well as the Gini coefficient and Hill numbers. Overall, we observed reduced diversity in both the TRA and TRB repertoires of unique complementarity‐determining region 3 (CDR3) sequences in CD4+ and CD8+ T cells from DOCK8‐deficient patients compared to healthy controls. Notably, this restriction was more evident in CD8+ T cells than in CD4+ T cells (Figures 1 and 2).

Diversity analyses of immune repertoire in CD4+ T cells of the samples. (A) Diversity of TRA and TRB immune repertoire (richness) calculated by Chao1 index. (B, C) TRA and TRB diversity analyses based on clonotype abundance. The results of Inverse Simpson and True diversity indices were given in B and C, respectively. (D) The inequality comparisons of TRA and TRB repertoires calculated by Gini coefficient index. (E) Comprehensive diversity comparisons (Hill numbers) of TRA and TRB repertoires. In TRA Gini coefficient graph bars show mean with ±standard error of mean (SEM) and statistical significance between HC‐DOCK8 and HC‐AT groups was determined by one‐way ANOVA. For the other graphs bars show median with confidence interval (CI). Statistical significance between HC‐DOCK8 and HC‐AT groups was determined by Mann–Whitney test. *p < 0.05, **p < 0.01. AT, ataxia‐telangiectasia; HC, healthy control; ns, not significant; TRA, T‐cell receptor alpha; TRB, T‐cell receptor beta. Q values represent different diversity calculations.

Diversity analyses of immune repertoire in CD8+ T cells of the samples. (A) Diversity of TRA and TRB immune repertoire (richness) calculated by Chao1 index. (B, C) TRA and TRB diversity analyses based on clonotype abundance. The results of Inverse simpson and True diversity indices were given in B and C, respectively. (D) The inequality comparisons of TRA and TRB repertoires calculated by Gini coefficient index. (E) Comprehensive diversity comparisons (Hill numbers) of TRA and TRB repertoires. Bars show median with CI. Statistical significance between HC‐DOCK8 and HC‐AT groups was determined by the Mann–Whitney test. *p < 0.05, **p < 0.01, ***p < 0.001. AT, ataxia‐telangiectasia; HC, healthy control; ns, not significant; TRA, T‐cell receptor alpha; TRB, T‐cell receptor beta. Q values represent different diversity calculations.

Observed diversity was estimated using the Chao1 index, a nonparametric estimator of species richness. Both TRA and TRB diversity were reduced in patients with DOCK8 deficiency compared to healthy controls (Figures 1A and 2A). This reduction was more pronounced in CD8+ T cells from DOCK8‐deficient patients, reaching a similarly low level to that observed in AT patients (Figure 2A). We then quantified the average proportional abundance of clonotypes using the Inverse Simpson and True Diversity indices. The Inverse Simpson index for both TRA and TRB repertoires was lower in CD4+ and CD8+ T cells from DOCK8‐deficient patients than in healthy controls (Figures 1B and 2B). Similarly, True Diversity was reduced in DOCK8 patients (Figures 1C and 2C). Notably, only the TRB repertoire in CD4+ T cells did not show a statistically significant difference in terms of True Diversity. As with the Chao1 index, the reduction in diversity based on the proportional abundance of clonotypes was more pronounced in CD8+ T cells from DOCK8‐deficient patients. Clonotype distribution inequality was assessed using the Gini coefficient, where a value of zero reflects perfect equality. Increased inequality was observed in DOCK8‐deficient patients, particularly in the TRA and TRB repertoires of CD8+ T cells, similar to that observed in AT patients (Figures 1D and 2D).

Hill numbers, which represent a mathematical combination of various diversity indices, were used for an overall diversity comparison. Significant differences in both TRA and TRB diversity were observed between healthy controls and patient groups. All diversity parameters for CD4+ T cells were reduced in DOCK8‐deficient patients (Figure 1E). Notably, the diversity indices for CD8+ T cells from DOCK8‐deficient patients were comparable to those of AT patients (Figure 2E). To further estimate diversity, we determined the proportions of top clonal sequences, based on the proportion of the repertoire occupied by the most frequent clonotypes. The top 50 TRA and top 100 TRB unique clonotypes occupied a larger proportion of the total repertoire in DOCK8 patients than in healthy controls (Figure S1A–D), particularly within CD8+ T cells. Repertoire overlap analysis, using Morisita's overlap index, revealed a clear distinction between healthy controls and patient groups (Figure S2A–D). The distribution of public clonotypes across samples was highly similar between the DOCK8 and AT groups, reflecting the reduced diversity observed in both patient groups compared to the greater number of shared public clonotypes in healthy controls.

TRA and TRB repertoire restriction with increased clonality was also demonstrated by V–J association diagrams (Figures S3–S6). Individual analyses of V–J joining showed a limited number of specific V–J clones and increased occupancy of relative rearrangement frequency, especially in CD8+ TRA and TRB chains in the DOCK8 patients.

While bulk TCR‐seq can infer clonal expansion from overrepresented V/J segments or CDR3 sequences, it cannot identify individual clonotypes. In contrast, single‐cell TCR sequencing (scTCR‐seq) directly links α and β TCR chains within individual cells, enabling precise clonotype identification, accurate tracking of clonal expansion, and co‐expression analysis with other genes. To validate our bulk immune repertoire findings at the single‐cell level, we performed scRNA‐seq with VDJ enrichment using the 10× Genomics platform on two healthy controls and patient P7. Although we detected only a few hundred T cells, comparison of single‐cell TCR clonotypes from CD4+ and CD8+ cells yielded results consistent with our bulk TCR‐seq data (Figure S7).

It has previously been reported that somatic reversion of DOCK8 expression was observed in some patients with DOCK8 deficiency, who are older and experience less severe disease symptoms than non‐revertant patients [21, 28]. Due to limited sample availability, we were unable to check somatic reversion of DOCK8 expression in all patients. However, no DOCK8‐expressing cells were observed in the scRNA‐seq dataset from patient P7, supporting the absence of a reversion event in this patient (Figure S8). Notably, previous studies have also shown that somatic reversion does not significantly affect the overall TCR Vβ usage [21, 28].

3.2. Skewed Variable (V) and Joining (J) Gene Usage in TCRα/β Repertoires of DOCK8‐Deficient Patients

V and J gene usage, which determines the repertoire diversity generated by V(D)J recombination in the thymus, can also provide information about clonal expansions occurring in response to antigenic stimuli in the periphery [29, 30]. We analyzed V and J gene usage in unique TRA and TRB chains of CD4+ and CD8+ T cells from both patients and healthy controls (Figure 3A–D). We observed skewed usage of TRBV18 in CD4+ T cells (Figure 3B) and TRAV24, TRAV26‐2, and TRBV5‐6 in CD8+ T cells (Figure 3C,D) in DOCK8‐deficient patients. For J genes, TRBJ1‐1 usage in CD4+ T cells (Figure 3B) and TRAJ30 usage in CD8+ T cells (Figure 3C) were more frequent in DOCK8 patients than in healthy controls. Although we observed several individual differences in V and J gene usage, gene usage correlation and principal component analysis (PCA) did not reveal global discrimination between patients and healthy controls (Figure S9A–D).

TRAV and TRAJ gene usage in DOCK8 patients and healthy controls. (A) The frequency of *TRAV* and *TRAJ* genes in CD4+ T cell repertoire sequences. (B) The frequency of *TRBV* and *TRBJ* genes in CD4+ T cell repertoire sequences. (C) The frequency of *TRAV* and *TRAJ* genes in CD8+ T cell repertoire sequences. (D) The frequency of *TRBV* and *TRBJ* genes in CD8+ T cell repertoire sequences. Bars show mean with ±SEM. Statistical significance was determined by unpaired t test. HC, healthy control; TRAJ, T‐cell receptor alpha joining; TRAV, T‐cell receptor alpha variable; TRBJ, T‐cell receptor beta joining; TRBV, T‐cell receptor beta variable.

3.3. TRAV/TRAJ Association and CDR3 Length Distribution

The length and composition of CDR3 typically play a critical role in adaptive immune responses to nonself antigens [31, 32]. Furthermore, TRA repertoire diversity influences thymocyte recombination during lymphocyte maturation [33]. VJ rearrangement occurs first in the proximally located TRAV and TRAJ genes, followed by recombination of the more distally located genes [34]. This process can affect thymocyte lifespan [33]. Therefore, we characterized the proximal and distal usage of TRAV–TRAJ gene clusters. No significant difference was observed between DOCK8 patients and healthy control groups. On the other hand, significant differences in middle and distal associations were evident in CD4+ T cells of AT patients (Figure 4A).

Analyses of *TRAV–TRAJ* clusters and CDR3 lengths. (A) Frequencies of proximally, middle, and distally located TRAV–TRAJ associations in the samples. The graph on the left represents CD4+ T cells, while the one on the right shows CD8+ T cells. Bars show mean with ±SEM. Statistical significance was determined by unpaired t test. The schematic illustration represents chromosomal locations of *TRAV*, *TRAJ*, and *TRAC* genes. (B) The frequency of TRA and TRB CDR3 sequences with different nucleotide lengths of CD4+ T cells in unique clonotypes. (C) The frequency of TRA and TRB CDR3 sequences with different nucleotide lengths of CD8+ T cells in unique clonotypes. AT, ataxia‐telangiectasia; HC, healthy control; TRA, T‐cell receptor alpha; TRAJ, T‐cell receptor alpha joining; TRAV, T‐cell receptor alpha variable; TRB, T‐cell receptor beta.

We then determined the CDR3 length distribution of unique TRA and TRB transcripts in the analyzed samples. While the CDR3 lengths of CD4+ TRA sequences were similar between groups (Figure 4B), several differences were observed in other chain transcripts. TRA and TRB CDR3 lengths in cytotoxic T cells, ranging from 39 to 45 nucleotides, were shorter in the patient groups (Figure 4B,C). For TRB CDR3 lengths in CD4+ and CD8+ T cells, a notable decrease in the 45–51 nucleotide range was observed only in AT patients (Figure 4B,C). However, no CDR3 length abnormalities were identified that could distinguish the overall repertoire profiles of DOCK8 patients from those of healthy controls.

3.4. Potentially Self‐Reactive CD8+ T Cells in DOCK8‐Deficient Patients

Cysteine and hydrophobic residues at distinct positions within CDR3 sequences have been associated with T cell self‐reactivity [17, 18]. The frequency of TRA and TRB chains containing cysteine within two positions of the CDR3 apex can serve as a self‐reactivity index [18]. Our analysis of cysteine indices in CDR3 transcripts revealed a significant increase in the CD8+ TRB repertoire of DOCK8‐deficient patients, an increase not observed in AT patients (Figure 5B). Another indicator of potentially self‐reactive clones is the presence of hydrophobic amino acid doublets at positions 6 and 7 of TRB CDR3 sequences [17]. We also observed a notable increase in the percentage of these hydrophobic doublets in DOCK8 patients compared to healthy controls (Figure 5C). On the other hand, no statistically significant differences in these indices were observed between AT patients and healthy controls (Figure 5A–C).

Self‐reactive T cell indices and pathology associated TRB clonotypes. (A) Cysteine index of TRA and TRB repertoire of CD4+ T cells. (B) Cysteine index of TRA and TRB repertoire of CD8+ T cells. (C) Hydrophobic index of TRB repertoire of CD4+ and CD8+ T cells. (D) Frequencies of pathology associated TRB clonotypes in CD4+ and CD8+ T cells. Bars in cysteine and hydrophobic index graphs represent median with CI. Statistical significance between HC‐DOCK8 and HC‐AT groups was determined by Mann–Whitney test. *p < 0.05. AT, ataxia‐telangiectasia; HC, healthy control; TRA, T‐cell receptor alpha; TRB, T‐cell receptor beta.

3.5. Analysis of Pathology‐Associated T Lymphocytes

The phenotypic heterogeneity of DOCK8 deficiency encompasses recurrent respiratory and persistent viral skin infections, various atopic conditions, autoimmunity, and an increased risk of cancer [5]. To investigate the presence of pathology‐associated TCR clones in DOCK8 patients, we compiled a list of pathology‐associated TRB CDR3 sequences from two manually curated databases [23, 24] and calculated the frequency of unique clonotypes targeting the identified antigens. We found no significant difference in the frequency of autoimmunity‐, cancer‐, allergy‐, or pathogen (bacteria and virus)‐associated CDR3 sequences between patient and healthy control groups. However, we observed a trend toward an increased frequency of virus‐associated clonotypes in both CD4+ and CD8+ T cells from DOCK8‐deficient patients compared to healthy controls (Figure 5D).

4. Discussion

DOCK8 deficiency is associated with T‐cell lymphopenia, including reduced naïve CD8 T cells and increased exhausted CD8+ effector memory T cells [3, 35, 36], as well as disrupted Treg and NKT cell development [37, 38]. Low T‐cell receptor excision circle (TREC) numbers suggest impaired T cell production [39]. While we observed significant T‐cell lymphopenia in most of our patients, naïve and memory T cell phenotyping was limited to four patients. We characterized both the TRA and TRB repertoires of circulating CD4+ and CD8+ T cells from DOCK8 patients using high‐throughput RNA‐seq and various computational approaches, including analyses of diversity, gene usage, and CDR3 region composition. While most repertoire studies focus on TCR β chains due to their greater combinatorial diversity, the TCR antigen‐binding site comprises both α and β chains [40]. Therefore, analysis of TRA transcripts is also informative, and we thus analyzed both α and β sequences.

Because diversity measurements are based on different mathematical calculations, we used a range of estimation indices. We observed a significantly reduced diversity in TRA and TRB transcripts of both CD4+ and CD8+ T cells in almost all estimations. Our repertoire results suggest that impaired T cell production, coupled with specific circulating antigenic stimuli, may contribute to increased clonality. T‐cell repertoire diversity varies between CD4+ and CD8+ cells, and between naïve and memory cells, with CD4+ and naïve cells generally exhibiting greater diversity [41]. Thus, the restricted immune repertoire in DOCK8 deficiency may also be attributable to decreased naïve cell populations, particularly CD8+ cells. This reduced diversity may also explain the increased susceptibility to infections observed in these patients. Comparison of reported pathology‐associated T cell clonotypes revealed a slight, nonsignificant increase in virus‐associated clonotypes in our patients' CD4+ and CD8+ T cells.

We then analyzed V/J gene usage and composition. Because V(D)J recombination generates repertoire diversity and clonality, gene usage patterns may reflect clonal constraints. While we observed skewed usage of individual V and J genes in patients compared to healthy controls, overall gene usage analysis did not reveal correlated skewing between the two groups. Nevertheless, these skewed V and J usages may reflect increased repertoire clonality in DOCK8 deficiency. Single‐cell sequencing or surface Vβ staining by flow cytometry could more comprehensively reveal skewed repertoires. While Jing et al. and Pillay et al. [21, 28] examined surface Vβ chain expression in DOCK8 patients, their analyses focused on differential TRBV usage in revertant T cell populations. To explore potential V–J recombination abnormalities in the thymus, we also analyzed TRAV–TRAJ transcript associations based on chromosomal location [26]. However, we found no differences in differentially located transcripts between patients and controls. This methodology, which has revealed significant proximal TRAV–TRAJ transcript abundance in IEI with V(D)J recombination defects such as Cernunnos/XLF and DNA ligase‐IV deficiency [26], may be more beneficial for patients with direct mutations affecting somatic recombination.

In our study, we observed increased cysteine and hydrophobic indices in CD8+ TRB sequences, which, along with the reported decreased number or defective function of Tregs in DOCK8 patients [38, 42], may contribute to the autoimmune features observed, particularly immune thrombocytopenia and autoimmune hemolytic anemia. Notably, our autoimmune analysis, based on known TCR sequences from several autoimmune conditions including SLE, T1D, RA, and IBD, showed no increase in these sequences in our DOCK8‐deficient patients. This suggests that the autoimmune clones in DOCK8 deficiency differ from those found in common autoimmune conditions. Furthermore, given the heterogeneity of IEIs and the variable presentation of autoimmunity, these indices may provide valuable insights into disease progression, therapy response, and personalized treatment strategies. This is particularly relevant in DOCK8 deficiency, where autoimmunity manifests with varying clinical degrees. For example, in the largest reported DOCK8‐deficient patient cohort (136 individuals), 20 patients presented with clinically apparent autoimmunity, primarily vasculitis and autoimmune hemolytic anemia [43]. However, another study detected serum autoantibodies against several cytoplasmic proteins in all examined DOCK8‐deficient patients, suggesting a potentially higher prevalence of subclinical autoimmunity [8]. This discrepancy highlights the possibility of underdiagnosis and the need for more sensitive detection methods.

Previous TCR repertoire analyses in primary immunodeficiencies, including RAG1/2 [18], WASp [44], NFKB2, and AIRE [25] deficiencies, have investigated hydrophobicity and/or cysteine indices. However, increased cysteine indices in conventional CD4+ or CD8+ T cells were not observed in those studies. We have recently reported an increase in both indices in STAT3 gain‐of‐function (GOF) disease and an increase in the hydrophobicity index in IKKα kinase‐deficient individuals; however, those analyses were performed on PBMCs, not on purified CD4+ and CD8+ T cell populations [45, 46]. To our knowledge, DOCK8 deficiency is the first reported primary immunodeficiency in which increases in both cysteine and hydrophobicity indices have been observed specifically in CD8+ T cells. Although the cysteine index was initially considered a correlative marker, a recent study demonstrated the critical role of cysteine residues at the CDR3 apex [47]. Cysteine‐cysteine‐mediated disulfide bond formation between the TCR and MHC‐bound peptide is a strong indicator of T cell deletion or agonist selection in the thymus cortex. Interestingly, the patient with the missense variant (P3) had a lower cysteine index than other patients, who had deletions, frameshift, or nonsense mutations. Although a larger cohort is needed to perform correlation analysis, this difference could be related to the variant's impact on DOCK8 expression, function, and the TCR repertoire.

One limitation of our study is that, while repertoire analyses were performed separately for CD4+ and CD8+ T cells, they do not provide information on subpopulations of T cells such as Tregs and Tfh cells. Investigation of repertoire diversity in naïve vs. memory CD8+ T cells independently would provide critical insights. This would help determine whether observed differences are mainly thymic in origin or emerge in the periphery due to persistent infections and lymphopenia. However, due to limited access to patient samples, as many patients underwent bone marrow transplantation or are deceased, further subtype‐specific analyses were not possible. Future single‐cell sequencing studies with more samples could provide a more detailed analysis of rare cell populations and TCRαβ heterodimer structure.

In summary, integrating IR‐seq into the investigation of DOCK8 deficiency allows for a comprehensive evaluation of the immune repertoire, providing new insights into the disease mechanism. By elucidating the relationship between DOCK8 function and T‐cell diversity, this approach enhances our understanding of the underlying immunological defects in affected patients.

Author Contributions

C.B., G.C., U.A., R.K.E., S.D.T., C.I., and B.E. performed the experiments. N.I.W. and C.H.K. analyzed the TRAV–TRAJ clusters, hydrophobicity, and cysteine indices. G.C., G.M.K., and B.E. analyzed the repertoire sequences. A.P.S., S.H., T.A., A.K.O., S.E., A.Ö., E.K.‐A., S.B.E., Ç.A., C.I., K.B., B.K., A.M., D.Ç., F.D., A.I., and S.B. provided clinical care of the patient and clinical data. C.B., G.C., and B.E. wrote the manuscript. B.E. conceptualized and coordinated the study and provided laboratory resources. All authors critically reviewed the manuscript and agreed to its publication.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Appendix S1.

ALL-80-2519-s001.pdf^{(97.8MB, pdf)}

Acknowledgments

We thank “The Can Sucak Candan Biseyler” Foundation (CSCBF) for their support and contributions during the study. CSCBF was founded in 2018 to honor Can Sucak who lost his life due to complications of inborn errors of immunity (IEI). CSCBF supports research in the field of IEI and promotes awareness. Additionally, we would like to acknowledge The Hospital Research Foundation (THRF) in Australia supporting G.C. B.E. was supported by the Scientific and Technological Research Council of Türkiye (TUBITAK, 121S667 and 123S777) and the Scientific Research Projects Coordination Unit of Hacettepe University (THD‐2022‐20180, TKB‐2021‐19539, and THD‐2024‐21191). This work was also supported by a grant from the Marmara University Scientific Research Project Coordination Unit (ADT‐2025‐11514) to S.B.

Funding: B.E. was supported by the Scientific and Technological Research Council of Türkiye (121S667 and 123S777) and the Scientific Research Projects Coordination Unit of Hacettepe University (THD‐2022‐20180, TKB‐2021‐19539, and THD‐2024‐21191). This work was also supported by a grant from the Marmara University Scientific Research Project Coordination Unit (ADT‐2025‐11514) to S.B.

Ceren Bozkurt and Gökhan Cildir are co‐first authors.

Data Availability Statement

Raw immune repertoire sequencing data and scRNA‐seq data are available at the NCBI sequence read archive (SRA) under accession number PRJNA1207946.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix S1.

ALL-80-2519-s001.pdf^{(97.8MB, pdf)}

Data Availability Statement

Raw immune repertoire sequencing data and scRNA‐seq data are available at the NCBI sequence read archive (SRA) under accession number PRJNA1207946.

[all16580-bib-0001] 1. Janssen E., Kumari S., Tohme M., et al., “DOCK8 Enforces Immunological Tolerance by Promoting IL‐2 Signaling and Immune Synapse Formation in Tregs,” JCI Insight 2, no. 19 (2017): e94298, 10.1172/jci.insight.94298. [DOI] [PMC free article] [PubMed] [Google Scholar]

[all16580-bib-0002] 2. Engelhardt K. R., McGhee S., Winkler S., et al., “Large Deletions and Point Mutations Involving the Dedicator of Cytokinesis 8 (DOCK8) in the Autosomal‐Recessive Form of Hyper‐IgE Syndrome,” Journal of Allergy and Clinical Immunology 124, no. 6 (2009): 1289–1302, 10.1016/j.jaci.2009.10.038. [DOI] [PMC free article] [PubMed] [Google Scholar]

[all16580-bib-0003] 3. Zhang Q., Davis J. C., Lamborn I. T., et al., “Combined Immunodeficiency Associated With DOCK8 Mutations,” New England Journal of Medicine 361, no. 21 (2009): 2046–2055, 10.1056/NEJMoa0905506. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Comprehensive αβ T‐Cell Receptor Repertoire Analysis Reveals a Unique CD8+ TCR Landscape in DOCK8‐Deficient Patients

Ceren Bozkurt

Gökhan Cildir

Umran Aba

Rahmi Kutay Erdogan

Nicholas I Warnock I

Chung Hoow Kok

Asena Pinar Sefer

Sule Haskologlu

Sidem Didar Tekeoglu

Gülşah Merve Kılınç

Canberk Ipsir

Tugba Arikoglu

Aylin Kont Ozhan

Saliha Esenboga

Ahmet Özen

Elif Karakoç‐Aydiner

Sevgi Bilgiç Eltan

Çigdem Aydogmus

Candan Islamoglu

Kübra Baskın

Betul Karaatmaca

Ayse Metin

Deniz Çagdaş

Figen Dogu

Aydan Ikinciogullari

Safa Baris

Baran Erman

ABSTRACT

Background

Methods

Results

Conclusion

1. Introduction

2. Methods

2.1. Study Participants

2.2. αβ T‐Cell Repertoire Sequencing

2.3. Single‐Cell RNA and TCR Sequencing

2.4. Bioinformatics Analyses of TCR Repertoires

2.5. Statistical Analysis

3. Results

3.1. Restricted TCR Repertoire in Patients With DOCK8 Deficiency

TABLE 1.

FIGURE 1.

FIGURE 2.

3.2. Skewed Variable (V) and Joining (J) Gene Usage in TCRα/β Repertoires of DOCK8‐Deficient Patients

FIGURE 3.

3.3. TRAV/TRAJ Association and CDR3 Length Distribution

FIGURE 4.

3.4. Potentially Self‐Reactive CD8+ T Cells in DOCK8‐Deficient Patients

FIGURE 5.

3.5. Analysis of Pathology‐Associated T Lymphocytes

4. Discussion

Author Contributions

Conflicts of Interest

Supporting information

Acknowledgments

Data Availability Statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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