We investigated several phenotypic and functional parameters of T cell-mediated immunity in a large series of common variable immunodeficiency (CVID) patients. We demonstrated that the vast majority of CVID patients presented multiple T cell abnormalities intimately related among them, the severity of which was reflected in a parallel loss of CD4+ naive T cells. A strong correlation between the number of CD4+ naive T cells and clinical features was observed, supporting the subgrouping of patients according to their number of naive CD4+ T lymphocytes. A reduced thymic output and disrupted CD4+ and CD8+ TCR repertoires paralleled the contraction of CD4+ naive T cell pools. The evaluation of activation markers and cytokine production indicated a strong T cell activation that was significantly related to the increased levels of T cell turnover and apoptosis. Finally, discrete genetic profiles could be demonstrated in groups of patients showing extremely diverse T cell subset composition and function. Naive CD4+ T cell levels were significantly associated with the switched memory B cell-based classification, although the concordance between the respective subgroups did not exceed 58.8%. In conclusion, our data highlight the key role played by the T cell compartment in the pathogenesis of CVID, pointing to the need to consider this aspect for classification of this disease.

Common variable immunodeficiency (CVID)4 represents a largely heterogeneous group of immunologic disorders characterized by low serum Ig concentrations, defective specific Ab production, and increased susceptibility to bacterial infections (1). CVID patients also frequently develop splenomegaly, granulomatous lesions, and autoimmune diseases (2). The association of CVID with the development of malignancies, mainly non-Hodgkin’s lymphoma of the gastrointestinal tract, has been also documented (3). CVID has a prevalence of ∼1 in 30,000 and is thus the second most frequent primary immunodeficiency in humans (4). The 20-year survival rate after the diagnosis of CVID is 64% for male patients and 67% for female patients compared with 92–94% in the general population (5). Initial family studies suggested the presence of at least two susceptibility loci within the MHC on the short arm of chromosome 6 (6). Recently, it has been demonstrated that the homozygous loss of ICOS (7, 8) and mutations of transmembrane activator and calcium modulator ligand interactor (9, 10) can result in CVID, although both of the defects account for only 5–10% of the cases.

Clinically overt CVID necessitates long-term Ig replacement and antimicrobial therapy, but treated patients still show a higher mortality rate than the general population. This result contrasts with an exclusive role played by the defective Ig production in determining the course of the disease. A more complete explanation would encompass the T cell abnormalities in CVID pathogenesis. In fact, a number of T cell defects have been demonstrated in the past in a still undefined proportion of CVID patients, including decreased lymphocyte proliferation to mitogens and Ags (2), increased T cell apoptosis (11), impaired cytokine production (12, 13), absent generation of Ag-primed T cells after prophylactic vaccination (14, 15), and reduced expression of CD40L on activated T cells (16, 17). This complex scenario of T cell deficiency probably takes a major part in influencing the clinical outcome of CVID patients. In this regard, it has been reported that poor T cell function at the time of the diagnosis is associated with death at an earlier age (2).

Attempts to classify CVID patients have been based on the assessment of Ig synthesis in vitro and phenotypic subsets of peripheral blood B cells (18, 19, 20, 21). To date, a comprehensive evaluation of the T cell deficiency is still lacking and CVID patients remain to be subdivided into homogeneous subgroups according to their T cell abnormalities.

Therefore, it has been our interest to perform, in a large number of CVID patients, a wide panel of immunological investigations aiming specifically to a quantitative and qualitative evaluation of the T cell compartment. Genetic assays have been also performed to detect any possible link between phenotype and discrete genetic profiles.

This study includes 60 patients admitted and studied from January 1983 to June 2004 at the Division of Clinical Immunology located at the University of Rome “La Sapienza.” All of them have a well-documented CVID diagnosis according to the criteria established by the European Society for Immunodeficiencies/Pan-American Group for Immunodeficiency group of experts (1). The age range was 15–78 years and there were 29 females and 31 males. All patients were receiving regular i.v. Ig substitution therapy and were free of any serious infections when tested. As control subjects, we recruited 30 age-matched healthy donors. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki. Demographic, clinical, and laboratory characteristics of patients investigated are reported in Table I. According to the classification described by Warnatz et al. (20), CVID patients were segregated into two groups based on the flow cytometric quantification of class-switched memory B cells (CD19+CD27+IgMIgD): CVID group I comprises patients with class-switched memory B cells below 0.4% of total PBL, and CVID group II includes all patients with normal numbers of switched memory B cells (>0.4%). Group I can be subdivided according to increased (Ia) or normal (Ib) numbers of CD19+CD21 immature B cells.

Table I.

Demographic and clinical characteristics of the study populationa

Patient Nos.CD4+ Naive T Cells (%)CD19+ B Cells (%)IgM/D+ CD27 Naive % BIgM/D+ CD27+ Memory % BIgM/D CD27+ Switched % BCD21 % BWarnatz’ ClassificationSexAge (year)Age at Onset (year)IgG (mg/dl)IgA (mg/dl)IgM (mg/dl)SplenomegalyADOverall Clinical Severity
5.2 0.7 0.05 27 Ia 61 44 286 17 Yes (+++) Yes 10 
ND ND ND ND ND 42 32 218 11 Yes (+++) No 11 
2.8 0.1 0.02 55 Ia 51 13 310 26 Yes (+++) Yes 13 
1.8 0.1 0.01 36 Ia 55 38 114 15 12 Yes (++) No 10 
1.7 0.2 0.01 40 Ia 44 40 88 12 99 Yes (++) No 10 
12 ND ND ND ND ND 43 20 64 85 77 Yes (+) No 
1.3 0.1 0.01 23 Ia 34 19 100 Yes (+++) No 11 
40 39.6 0.1 0.04 Ib 54 15 200 11 Yes (+++) No 15 
4.9 0.06 0.01 Ib 46 463 42 32 Yes (+++) No 12 
10 1.1 0.1 0.01 50 Ia 49 188 Yes (+++) No 14 
11 2.9 0.06 0.01 43 Ia 50 37 20 67 Yes (+++) No 10 
12 ND ND ND ND ND 36 18 100 Yes (+) Yes 
13 1.1 0.3 0.04 11 Ib 58 215 19 27 Yes (++) No 10 
14 10 10 7.3 0.45 10 Ib 51 45 220 10 11 Yes (++) Yes 11 
15 10 1.4 0.6 0.01 13 Ib 54 55 180 12 22 Yes (+) No 
16 10 ND ND ND ND ND 61 200 Yes (+++) Yes 12 
17 11 23 12 10.6 0.02 24 Ia 54 35 564 Yes (++) No 
18 12 14 12 1.7 0.07 Ib 57 15 300 28 Yes (+++) No 10 
19 13 1.7 0.12 12 Ib 56 22 92 Yes (+++) No 15 
20 15 16 11.8 0.48 Ib 30 56 16 No  No 
21 17 22 9.7 12 0.1 11 Ib 48 451 16 Yes (++) Yes 
22 17 4.3 3.7 0.8 II 42 24 601 12 Yes (+) No 
23 17 12 ND ND ND ND ND 40 133 Yes (+) No 10 
24 17 16 12 3.7 0.3 Ib 75 68 75 20 28 Yes (+++) No 
25 18 11 ND ND ND ND ND 31 21 194 22 18 Yes (+) Yes 
26 19 0.5 0.1 26 Ia 42 24 132 Yes (+) No 
27 20 ND ND ND ND ND 75 40 300 Yes (+) No 
28 20 12 10 1.3 0.04 Ib 52 289 60 Yes (++) Yes 14 
29 21 19 0.36 Ib 61 400 22 No  No 
30 22 0.7 0.52 12 II 76 250 No  No 
31 24 1.7 0.06 0.06 Ib 36 21 65 Yes (+) No 
32 24 10 8.7 1.2 0.1 Ib 66 49 398 87 42 No  No 
33 25 1.2 1.1 0.02 0.04 38 Ia 33 10 150 Yes (+) No 10 
34 25 4.5 0.6 0.8 II 50 71 49 Yes (+) No 
35 26 1.5 0.3 12 Ib 55 10 310 11 17 Yes (+) No 
36 26 3.2 0.4 0.2 Ib 22 574 20 50 No  No 
37 26 16 11 0.3 Ib 63 35 300 Yes (+) No 
38 27 1.6 0.3 Ib 45 96 Yes (+) Yes 
39 28 0.5 0.6 II 15 20 350 10 20 No  No 
40 28 2.6 0.8 0.5 II 78 250 No  No 
41 30 ND ND ND ND ND 49 206 11 Yes (+) No 
42 31 2.7 0.2 0.4 Ib 55 319 24 25 Yes (+) Yes 
43 31 1.5 0.8 0.2 Ib 45 354 Yes (+) No 
44 32 0.7 0.06 15 Ib 56 46 220 31 91 Yes (+) No 10 
45 33 15 10 II 26 50 Yes (+) Yes 12 
46 35 0.9 0.9 II 24 250 No  No 
47 36 15 2.2 11 II 40 96 10 No  No 
48 36 1.6 0.2 14 Ib 24 153 20 15 No  No 
49 37 3.6 0.9 15 II 50 40 228 18 No  No 
50 38 0.1 0.02 12 Ib 36 19 Yes (+) No 15 
51 40 11 ND ND ND ND ND 33 12 143 82 No  No 
52 40 1.9 2.5 0.5 0.2 II 60 60 403 33 35 No  No 
53 41 17 ND ND ND ND ND 39 35 411 74 36 No  No 
54 42 0.4 0.1 14 Ib 28 19 59 No  No 
55 43 ND ND ND ND ND 41 259 35 Yes (++) No 
56 44 3.5 0.7 0.5 14 II 33 20 218 18 No  No 
57 48 14 ND ND ND ND ND 34 20 85 12 No  No 
58 52 1.7 1.2 1.6 II 45 256 No  No 
59 57 15 12 0.45 II 32 29 No  No 
60 72 1.3 1.2 0.02 0.03 10 Ib 42 50 No  No 
Patient Nos.CD4+ Naive T Cells (%)CD19+ B Cells (%)IgM/D+ CD27 Naive % BIgM/D+ CD27+ Memory % BIgM/D CD27+ Switched % BCD21 % BWarnatz’ ClassificationSexAge (year)Age at Onset (year)IgG (mg/dl)IgA (mg/dl)IgM (mg/dl)SplenomegalyADOverall Clinical Severity
5.2 0.7 0.05 27 Ia 61 44 286 17 Yes (+++) Yes 10 
ND ND ND ND ND 42 32 218 11 Yes (+++) No 11 
2.8 0.1 0.02 55 Ia 51 13 310 26 Yes (+++) Yes 13 
1.8 0.1 0.01 36 Ia 55 38 114 15 12 Yes (++) No 10 
1.7 0.2 0.01 40 Ia 44 40 88 12 99 Yes (++) No 10 
12 ND ND ND ND ND 43 20 64 85 77 Yes (+) No 
1.3 0.1 0.01 23 Ia 34 19 100 Yes (+++) No 11 
40 39.6 0.1 0.04 Ib 54 15 200 11 Yes (+++) No 15 
4.9 0.06 0.01 Ib 46 463 42 32 Yes (+++) No 12 
10 1.1 0.1 0.01 50 Ia 49 188 Yes (+++) No 14 
11 2.9 0.06 0.01 43 Ia 50 37 20 67 Yes (+++) No 10 
12 ND ND ND ND ND 36 18 100 Yes (+) Yes 
13 1.1 0.3 0.04 11 Ib 58 215 19 27 Yes (++) No 10 
14 10 10 7.3 0.45 10 Ib 51 45 220 10 11 Yes (++) Yes 11 
15 10 1.4 0.6 0.01 13 Ib 54 55 180 12 22 Yes (+) No 
16 10 ND ND ND ND ND 61 200 Yes (+++) Yes 12 
17 11 23 12 10.6 0.02 24 Ia 54 35 564 Yes (++) No 
18 12 14 12 1.7 0.07 Ib 57 15 300 28 Yes (+++) No 10 
19 13 1.7 0.12 12 Ib 56 22 92 Yes (+++) No 15 
20 15 16 11.8 0.48 Ib 30 56 16 No  No 
21 17 22 9.7 12 0.1 11 Ib 48 451 16 Yes (++) Yes 
22 17 4.3 3.7 0.8 II 42 24 601 12 Yes (+) No 
23 17 12 ND ND ND ND ND 40 133 Yes (+) No 10 
24 17 16 12 3.7 0.3 Ib 75 68 75 20 28 Yes (+++) No 
25 18 11 ND ND ND ND ND 31 21 194 22 18 Yes (+) Yes 
26 19 0.5 0.1 26 Ia 42 24 132 Yes (+) No 
27 20 ND ND ND ND ND 75 40 300 Yes (+) No 
28 20 12 10 1.3 0.04 Ib 52 289 60 Yes (++) Yes 14 
29 21 19 0.36 Ib 61 400 22 No  No 
30 22 0.7 0.52 12 II 76 250 No  No 
31 24 1.7 0.06 0.06 Ib 36 21 65 Yes (+) No 
32 24 10 8.7 1.2 0.1 Ib 66 49 398 87 42 No  No 
33 25 1.2 1.1 0.02 0.04 38 Ia 33 10 150 Yes (+) No 10 
34 25 4.5 0.6 0.8 II 50 71 49 Yes (+) No 
35 26 1.5 0.3 12 Ib 55 10 310 11 17 Yes (+) No 
36 26 3.2 0.4 0.2 Ib 22 574 20 50 No  No 
37 26 16 11 0.3 Ib 63 35 300 Yes (+) No 
38 27 1.6 0.3 Ib 45 96 Yes (+) Yes 
39 28 0.5 0.6 II 15 20 350 10 20 No  No 
40 28 2.6 0.8 0.5 II 78 250 No  No 
41 30 ND ND ND ND ND 49 206 11 Yes (+) No 
42 31 2.7 0.2 0.4 Ib 55 319 24 25 Yes (+) Yes 
43 31 1.5 0.8 0.2 Ib 45 354 Yes (+) No 
44 32 0.7 0.06 15 Ib 56 46 220 31 91 Yes (+) No 10 
45 33 15 10 II 26 50 Yes (+) Yes 12 
46 35 0.9 0.9 II 24 250 No  No 
47 36 15 2.2 11 II 40 96 10 No  No 
48 36 1.6 0.2 14 Ib 24 153 20 15 No  No 
49 37 3.6 0.9 15 II 50 40 228 18 No  No 
50 38 0.1 0.02 12 Ib 36 19 Yes (+) No 15 
51 40 11 ND ND ND ND ND 33 12 143 82 No  No 
52 40 1.9 2.5 0.5 0.2 II 60 60 403 33 35 No  No 
53 41 17 ND ND ND ND ND 39 35 411 74 36 No  No 
54 42 0.4 0.1 14 Ib 28 19 59 No  No 
55 43 ND ND ND ND ND 41 259 35 Yes (++) No 
56 44 3.5 0.7 0.5 14 II 33 20 218 18 No  No 
57 48 14 ND ND ND ND ND 34 20 85 12 No  No 
58 52 1.7 1.2 1.6 II 45 256 No  No 
59 57 15 12 0.45 II 32 29 No  No 
60 72 1.3 1.2 0.02 0.03 10 Ib 42 50 No  No 
a

CVID patients are stratified according to the percentage of peripheral CD4+ naive (CD45RA+CD62L+) T cells. Serum Ig levels were at time of diagnosis. Splenomegaly was verified by computerised tomography scanner or ultrasonographic examination (+, ++, +++, >12 <15 cm, 15–18 cm, and >18 cm in craniocaudal length, respectively). The overall clinical severity of the disease was quantified on the basis of the following criteria evaluated within the last 5 years: 1) history of severe respiratory tract infections (SRT1 elevated numbers of leukocytes and C-reactive protein, body temperature higher than 38.5°C, and antibiotic therapy); 2) use of antibiotics; 3) patients’ judgment of severity; 4) physicians’ judgment of severity. A score ranging from 1 (less severe) to 4 (more severe) was assigned to each of these parameters. For the “history of infections” parameter the scores were as follows: 1, <2 SRT1 per year; 2, ≥3 and ≤4 SRTI per year; 3, ≥5 and ≤6 SRTI per year; 4, >6 SRTI per year. For the “use of antibiotic” parameter, the scores were as follows: 1, ≤2 times per year; 2, ≥3 and ≤4 times per year; 3, ≥5 and ≤12 times per year; 4, >12 times per year. The sum of the scores obtained for each parameter was used to define as overall clinical severity score that was divided into tertiles. AD, Autoimmune diseases; F, female; M, male.

Surface phenotyping and cytokine intracellular staining of PBL were performed with quadruple combinations of mAbs as described before (22). For B cell surface phenotyping, a mixture of the following Abs at optimal concentrations was used: CD27 FITC or CD21 FITC, anti-IgD PE, anti-IgM PerCP, and CD19 allophycocyanin (BD Immunocytometry Systems). Apoptosis level was quantified by a double staining flow cytometry method using FITC-conjugated annexin V/propidium iodide (PI) apoptosis detection kit (Marine Biological Laboratory) according to the manufacturer’s protocol. Reported data referred to both early (AV+/PI cells, still alive) and late (AV+/PI+ cells, dead cells) apoptotic cells, and were calculated within the CD4 and CD8 subsets.

For Ki-67 intracellular staining, cryopreserved cells, were thawed, fixed, and stained after permeabilization with Ki-67-PE mAb (BD Immunocytometry Systems).

Analyses were done with a FACSCalibur flow cytometer and the CellQuest software (BD Immunocytometry Systems).

To perform the CDR3 spectratyping, CD4+ and CD8+ T cells were separated by using CD4 and CD8 MicroBeads and MACS columns according to the manufacturer’s protocols (Miltenyi Biotec). Total mRNA was extracted directly from 1 × 106 bead-coated cells using TRIzol-LS Reagent (Invitrogen Life Technologies) and Microcarrier (Molecular Research Center). cDNA samples obtained by retrotranscription of total RNA were amplified by using a TCRB C1/C2-specific primer and a set of 24 TCRBV-specific primers as described previously (22).

For array hybridization, the Clontech Atlas Human Hematology/Immunology Array, composed of 406 human cDNAs, was used. Arrays were scanned using a Bio-Rad PhosphorImager and acquired by the Quantity One software, version 14 (Bio-Rad). Data analysis, quantification, and comparison were done using the AtlasImage 2.7 software (BD Clontech). The experiments were performed in duplicate, using the same RNA preparation. A mixture of equal amounts of RNA from negatively selected T lymphocytes purified from 10 different healthy controls or from 10 randomly selected CVID patients belonging to group I and III was used. Intensity values were normalized using the global normalization mode (sum method) allowed by the software. The expression levels of the housekeeping genes spotted on the arrays were also used to validate the normalization procedure. A 2-fold expression variation (up or down) between patients and healthy donors was the minimum requirement for a gene to be selected.

Quantitative RT-PCR analysis for host cell factor 1 (HCFC1), CD9 Ag, LFA3, and cyclophilin A were conducted on the LightCycler instrument using LightCycler RNA amplification kit SYBR Green I (Roche). The primer sequences for HCF1 were as follows: forward 5′-GACGAACTGCACGTGTACAAC-3′ and reverse 5′-GACGAACTGCACGTGTACAAC-3′; for CD9, forward 5′-GACTCTCAGACCAAGAGCATC-3′ and reverse 5′-CTTTGATGGCATCAGGACAGG-3′; for LFA-3, forward 5′-CCATGTACCAAGCAATGTGCC-3′ and reverse 5′-GTCTTGAATGACCGCTGCTTG-3′; for cyclophilin A, forward 5′-TGGTCAACCCCACCGTGTTC-3′ and reverse 5′-GCCATCCAACCACTCAGTC-3′. All of the above-mentioned primer sequences were obtained from the National Center for Biotechnology Information database. RT-PCR was performed in 20 μl of reaction mixture containing 300 ng of total RNA, 6 mM MgCl2, 2 μl of each primer (0.5 μM), 40 μl of LightCycler RT-PCR mix SYBR Green I, 0.4 μl of enzyme mix, and ddH2O in a glass capillary. Reverse transcription was performed at 55°C for 10 min. The cycle program was set at 1 cycle of denaturation at 95°C for 30 s; 40 cycles of 95°C for 0 s, annealing at 55°C for 5 s, and extension at 72°C for 18 s; 1 cycle of cooling at 40°C for 30 s. Real-time quantitative RT-PCR for human cyclophilin A gene was performed on each sample as an internal control for equivalence of template. PCR amplification dynamics were monitored on-line by fluorescence signal acquisition. Each RT-PCR amplification was repeated in triplicate. Melting curves and subsequent agarose gel electrophoresis analysis were performed to verify the specificity of amplified DNA fragments. Quantitative analysis of target genes expression was done using the comparative crossing point method (23).

Groups were compared using the Fisher’s exact test for categorical variables. Regarding continuous variables, the Kruskal-Wallis test was used for comparing several groups and the two-tailed Mann-Whitney U test for comparing two groups. Spearman’s rank correlation coefficients were calculated for naive CD4+ T cell values and overall clinical severity scores and grade of splenomegaly. Due to the nonnormality of the distribution, naive CD4+ T cell values were divided into tertiles. Initially, the univariate association between overall clinical severity (severe vs mild) and naive CD4+ T cell level was examined by logistic regression. Subsequently, multivariable logistic regression analysis was performed for examining the association between overall clinical severity and naive CD4+ T cell level, controlling for variables thought a priori to be potential confounders, such as age, gender, Ig levels (tertiles), and total number of CD4+ T cells (tertiles of absolute numbers). Similarly, multiple logistic regression was also used for examining the association between splenomegaly and naive CD4+ T cell levels. Odds ratio (OR) and 95% confidence intervals (CIs) were estimated using the regression coefficients, and their SE was obtained from the logistic regression analyses.

Statistical analysis was performed using the computer package STATA-9.0 Statistical Software for Windows (Stata).

As indicated in Fig. 1, frequency and absolute counts of CD4+ T cells within the PBL population of CVID patients were significantly reduced in comparison to healthy controls (p < 0.0001). A great interpatient variability was observed with frequencies ranging from 18% to 65% and absolute counts from 34 cells/μl to 1632 cells/μl (normal ranges, 39–57% and 603-1646 cells/μl, respectively). Differently from CD4+ T cells, CD8+ T cells were significantly increased in CVID patients but only as a percentage (p < 0.0001). A decreased distribution of CD4+ and CD8+ naive T cells was also detected (p < 0.0001). A high degree of variability was observed for CD4+ naive T cells with frequencies ranging between 3% and 72% and absolute counts from 2 cells/μl to 1,175 cells/μl. Table II illustrates the characteristics of the study sample when subclassified by naive CD4+ T cell levels. Three homogeneous subgroups were identified according to this parameter, each including one-third of cases (tertiles). Patients with CD4+ naive T cells falling in the lower, intermediate, and upper tertiles were defined as belonging to group I, II, and III, respectively. All of the other variables investigated were then analyzed with respect to the patients’ subgroups defined by the number of naive CD4+ T cells. Spearman’s rank correlation coefficients showed a strong negative correlation for naive CD4+ T cell values and overall clinical severity scores (r = −0.68) and for naive CD4+ values and levels of splenomegaly (r = −0.76). Overall clinical severity scores showed a weaker correlation with the total number of CD4+ T cells as percentages (r = −0.31) and as absolute numbers (r = −0.26). Naive CD4+ T cell levels were strongly associated with clinical severity at univariate analysis: the likelihood of having a severe disease increased 7.7-fold for a unit decrease in naive CD4+ T cell level, going from high to low to very low levels (OR = 7.65; 95% CI, 2.8 to 21.1; p < 0.001).

FIGURE 1.

Flow cytometric analysis of total CD4+ and CD8+ and naive T cell subsets from CVID patients and age-matched healthy controls. Data are represented as box plots displaying medians, 25th and 75th percentiles as boxes, and 10th and 90th percentiles as whiskers. The distribution of naive T cells was evaluated by using mAbs directed toward the CD45RA and CD62L molecules. Differences between patients and controls were compared by the Mann-Whitney U test.

FIGURE 1.

Flow cytometric analysis of total CD4+ and CD8+ and naive T cell subsets from CVID patients and age-matched healthy controls. Data are represented as box plots displaying medians, 25th and 75th percentiles as boxes, and 10th and 90th percentiles as whiskers. The distribution of naive T cells was evaluated by using mAbs directed toward the CD45RA and CD62L molecules. Differences between patients and controls were compared by the Mann-Whitney U test.

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Table II.

Characteristics of the study sample by naive CD4+ T cell levels

Group I (<15%) (no. of patients = 20)Naive CD4+ T Cell Levels Group II (16–29%) (no. of patients = 20)Group III (at least 30%) (no. of patients = 20)pa
n(%)n(%)n(%)
Gender        
 Male 40.0 40.0 15 75.0 0.04 
 Female 12 60.0 12 60.0 25.0  
Age (year)        
 Mean 49.3  50.3  39.6   
 Median (range) 51 (30–61) 49 (15–78) 39.5 (24–60) 0.02b 
Overall clinical severityc        
 Mild 5.3 36.8 15 79.0 <0.001 
 Severe 10 52.6 11 57.9 10.5  
 Very severe 42.1 5.3 10.5  
Splenomegaly        
 No 5.0 30.0 13 65.0 <0.001 
 Grade 1 15.0 11 55.0 30.0  
 Grade 2 25.0 10.0 5.0  
 Grade 3 11 55.0 5.0 0.0  
IgG tertiles        
 Lower tertile 35.0 25.0 40.0 0.5 
 Intermediate tertile 40.0 25.0 35.0  
 Upper tertile 25.0 10 50.0 25.0  
IgA tertiles        
 Lower tertile 12 60.0 11 55.0 12 60.0 0.9 
 Intermediate tertile 10.0 15.0 5.0  
 Upper tertile 30.0 30.0 35.0  
IgM tertiles        
 Lower tertile 40.0 45.0 25.0 0.8 
 Intermediate tertile 30.0 25.0 35.0  
 Upper tertile 30.0 30.0 40.0  
CD4+ T cells (cells/Microliter)        
 Mean 581.4  539  813.3   
 Median (range) 497 (34–1652) 526 (278–970) 659 (404–1635) 0.02b 
CD4+ T cells (%)        
 Mean 33.4  35.4  39.3   
 Median (range) 30 (18–58) 33.5 (21–65) 39 (19–54) 0.14b 
Group I (<15%) (no. of patients = 20)Naive CD4+ T Cell Levels Group II (16–29%) (no. of patients = 20)Group III (at least 30%) (no. of patients = 20)pa
n(%)n(%)n(%)
Gender        
 Male 40.0 40.0 15 75.0 0.04 
 Female 12 60.0 12 60.0 25.0  
Age (year)        
 Mean 49.3  50.3  39.6   
 Median (range) 51 (30–61) 49 (15–78) 39.5 (24–60) 0.02b 
Overall clinical severityc        
 Mild 5.3 36.8 15 79.0 <0.001 
 Severe 10 52.6 11 57.9 10.5  
 Very severe 42.1 5.3 10.5  
Splenomegaly        
 No 5.0 30.0 13 65.0 <0.001 
 Grade 1 15.0 11 55.0 30.0  
 Grade 2 25.0 10.0 5.0  
 Grade 3 11 55.0 5.0 0.0  
IgG tertiles        
 Lower tertile 35.0 25.0 40.0 0.5 
 Intermediate tertile 40.0 25.0 35.0  
 Upper tertile 25.0 10 50.0 25.0  
IgA tertiles        
 Lower tertile 12 60.0 11 55.0 12 60.0 0.9 
 Intermediate tertile 10.0 15.0 5.0  
 Upper tertile 30.0 30.0 35.0  
IgM tertiles        
 Lower tertile 40.0 45.0 25.0 0.8 
 Intermediate tertile 30.0 25.0 35.0  
 Upper tertile 30.0 30.0 40.0  
CD4+ T cells (cells/Microliter)        
 Mean 581.4  539  813.3   
 Median (range) 497 (34–1652) 526 (278–970) 659 (404–1635) 0.02b 
CD4+ T cells (%)        
 Mean 33.4  35.4  39.3   
 Median (range) 30 (18–58) 33.5 (21–65) 39 (19–54) 0.14b 
a

Fisher’s exact test.

b

Kruskal-Wallis test.

c

Overall clinical severity: mild = lower tertile (score <7); severe = intermediate tertile (score 7–10); very severe = upper tertile (score at least 10).

As shown in Table III, multivariate analysis confirmed the significant association between naive CD4+ T cell levels and clinical severity independently of age, gender, total number of CD4+ T cells, and Ig levels. The only other variable associated with disease severity was the IgA level.

Table III.

Multiple logistic regression ORs for the association between overall clinical severity (severe vs mild disease) and naive CD4+ T cell levels, controlling for potential confounders (gender, age, Ig levels, and total number of CD4+ T cells)a

OR(95% CI)p
Naive CD4+ level (from higher to lower levels)b 7.46 (2.1 to 26.5) 0.002 
Gender    
 Male   
 Female 3.13 (0.6 to 15.1) 0.2 
Age (year) 1.0 (0.9 to 1.1) 0.9 
IgG tertiles (from lower to higher tertiles)c 0.69 (0.2 to 2.0) 0.5 
IgM tertiles (from lower to higher tertiles)c 2.31 (0.8 to 7.1) 0.1 
IgA tertiles (from lower to higher tertiles)c 0.35 (0.1 to 0.9) 0.046 
CD4+ T cells (absolute numbers in tertiles) 0.79 (0.3 to 2.2) 0.6 
OR(95% CI)p
Naive CD4+ level (from higher to lower levels)b 7.46 (2.1 to 26.5) 0.002 
Gender    
 Male   
 Female 3.13 (0.6 to 15.1) 0.2 
Age (year) 1.0 (0.9 to 1.1) 0.9 
IgG tertiles (from lower to higher tertiles)c 0.69 (0.2 to 2.0) 0.5 
IgM tertiles (from lower to higher tertiles)c 2.31 (0.8 to 7.1) 0.1 
IgA tertiles (from lower to higher tertiles)c 0.35 (0.1 to 0.9) 0.046 
CD4+ T cells (absolute numbers in tertiles) 0.79 (0.3 to 2.2) 0.6 
a

The severe and very severe categories of the overall clinical severity were grouped together defining them as severe diseases.

b

For unit decrease in naive CD4+ T-cell level, going from high to low to very low levels.

c

For unit increase in Ig levels going from lower to intermediate to upper tertiles.

Moreover, multiple logistic regression analysis was performed examining the association between splenomegaly and naive CD4+ T cell levels, including gender, age, and Ig levels in the model. Lower naive CD4+ T cell levels were significantly associated with an increased likelihood of splenomegaly (OR = 4.78; 95% CI, 1.8 to 12.9; p = 0.002 for a unit decrease in naive CD4+ T cell levels, going from high to low to very low levels).

Naive CD4+ T cell levels were also shown to be significantly associated with the Warnatz classification groups (Fisher’s exact test; p < 0.001) (20). In particular, among patients in the lowest naive category, i.e., group I, 50% belonged to the Warnatz group Ia and 50% to group Ib; among patients in the intermediate naive category, i.e., group II, the majority of patients (58.8%) belonged to Warnatz group Ib, 29.4% to group II, and 11.7% to group Ia; among patients with values of CD4+ naive T cells >30%, i.e., group III, 53.3% belonged to Warnatz group II and 46.7% to group Ib.

As indicated in Fig. 2, C and E, the frequency of effector memory cells were significantly increased in all three groups of patients in both CD4 and CD8 subsets, whereas central memory cells were increased for CD4+ T cells in groups I and II and for CD8+ T cells only in group I (p < 0.01). Absolute counts of memory subsets in CD4+ T cells showed a decrease of central memory cells in all patients (p < 0.01), whereas no significant differences in comparison to controls were detected for the effector memory cells (Fig. 2,D). Differently, in CD8+ T cells, a sustained increase in the absolute count of central memory cells was detected in group I (p = 0.0124), whereas the effector memory cells were increased in all patients (p < 0.02) (Fig. 2 F).

FIGURE 2.

Flow cytometric analysis of CD4+ (A, C, D, G, and H) and CD8+ (B, E, F, I, and L) T cell subsets of CVID patients and age-matched healthy controls. Data are represented as mean values ± SD. The naive subset is defined as CD45RA+CD62L+, whereas the remaining cells comprise the memory subsets (CD45RACD62L+, central memory subset; CD45RACD62L and CD45RA+CD62L, effector memory subsets) (4748 ). ∗, Statistical difference compared with control values (p < 0.05; Mann-Whitney U test).

FIGURE 2.

Flow cytometric analysis of CD4+ (A, C, D, G, and H) and CD8+ (B, E, F, I, and L) T cell subsets of CVID patients and age-matched healthy controls. Data are represented as mean values ± SD. The naive subset is defined as CD45RA+CD62L+, whereas the remaining cells comprise the memory subsets (CD45RACD62L+, central memory subset; CD45RACD62L and CD45RA+CD62L, effector memory subsets) (4748 ). ∗, Statistical difference compared with control values (p < 0.05; Mann-Whitney U test).

Close modal

Increased frequencies of CD4+ and CD8+ CD95- and HLA-DR-expressing T cells could be demonstrated in all three groups of patients (p < 0.01) (Fig. 2, G and I). However, in group III, the level of T cell activation was less prominent than that observed in group I and II of patients (III vs I and II; p < 0.01). Minor differences were detected in the expression of CCR5 and significantly increased in CD4+ and CD8+ T cells only in group I (p = 0.0007 and p = 0.0010, respectively).

The percentages of CD95-, CCR5-, and HLA-DR-expressing T cells negatively correlated with the frequency of naive T cells in both CD4 and CD8 subsets (r > 0.6 and p < 0.0001 for all analyzed populations). The increased frequency of HLA-DR+, CD95+, and CCR5+CD4+ T cells was not seen in association with a parallel increase in their absolute count, with the exception of CD4+HLA-DR+ cells in group I (p = 0.0139) (Fig. 2,H). A different trend was observed in the CD8 subset. Here, high absolute counts were detected for both CD95+ and HLA-DR+ T cells in all CVID patients (p < 0.01 for both markers), whereas no meaningful differences were observed for CCR5+ T cell count with the exception of group I (p = 0.0102) (Fig. 2 L).

Thymic output was evaluated by measuring the expression of CD31 molecules that allows us to distinguish postthymically expanded naive CD4+ T cells (centralnaive CD4+ T cells) from true recent thymic emigrants (thymicnaive CD4+ T cells) (24). In healthy control individuals, the percentage of CD31-expressing CD4+ T cells declined with age (Fig. 3). Among CVID patients this physiological, age-related decline of CD31-expressing CD4+ T cells was no longer detectable in group I and II. In fact, in these patients, the production of new thymicnaive CD4+ T cells was already deeply impaired during early adulthood (p = 0.006, p < 0.001, and p = 0.19 for groups I, II, and III vs controls). We also evaluated the TCR excision circle content (25) of CD4+ T cells in several CVID patients studied here, finding a good correlation with the CD31 data (data not shown).

FIGURE 3.

Frequencies of CD31-expressing CD4+ T cells during aging in both CVID patients and age-matched control donors. Thymic output was evaluated by measuring the expression of CD31 molecules that allows us to distinguish postthymically expanded naive CD4+ T cells (centralnaive CD4+ T cells) from true recent thymic emigrants (thymicnaive CD4+ T cells) (24 ). Peripheral blood T cells were stained with anti-CD4, anti-CD45RA, anti-CD62L, and anti-CD31 mAbs and examined by flow cytometry.

FIGURE 3.

Frequencies of CD31-expressing CD4+ T cells during aging in both CVID patients and age-matched control donors. Thymic output was evaluated by measuring the expression of CD31 molecules that allows us to distinguish postthymically expanded naive CD4+ T cells (centralnaive CD4+ T cells) from true recent thymic emigrants (thymicnaive CD4+ T cells) (24 ). Peripheral blood T cells were stained with anti-CD4, anti-CD45RA, anti-CD62L, and anti-CD31 mAbs and examined by flow cytometry.

Close modal

Anomalies in the relative TCRBV usage were investigated by using a panel of β-chain variable region (BV) subfamily specific mAbs covering ∼60–70% of T cells expressing TCR in healthy controls (Fig. 4, A and B). Within the CD4 subset significant expansions were observed, among the 22 TCRBV genes investigated, mainly in patients belonging to group I. These expansions (n = 96) clustered preferentially to certain TCRBV genes: expansions of BV16, BV14, and BV 21.3 were detected in 77, 61, and 50%, respectively, of patients belonging to group I. The number of expanded CD4 BV families dramatically decreased to 14 in group II and to 8 in group III, demonstrating a significant association between the level of CD4+ naive cells and the restriction of TCR repertoires (p < 0.001). Expanded BV families were also observed within the CD8 subset; however, differently from CD4+ T cells, they were not confined to groups I and II, but were also observed in group III (47, 38, and 27 expansions for group I, II, and III, respectively; p = 0.186).

FIGURE 4.

Diversity of TCRBV repertoires studied by flow cytometric and CDR3 spectratyping analyses in CVID patients. Flow cytometric analyses were done with BV-specific mAbs in CD4+ (A) and CD8+ (B) T cells. Values >3 SD above the mean of controls were used to establish skewed use of BV gene families by CD4+ and CD8+ T cells. Data are shown as percentages of patients showing expansion of TCRBV families. CDR3 spectratyping were performed in CD4+ (C) and CD8+ (D) T cells from 14 representative CVID patients (9 from group I, 3 from group II, and 2 from group III). To analyze the level of TCRBV perturbation, the CDR3 spectratypes were first translated into probability distributions as function of the area under the profile for each CDR3 length. A control profile, representing the nonperturbed repertoire, was determined for each BV by calculating the average distribution of the corresponding CD4 and CD8 profiles from healthy donors. The extent of perturbation for each CDR3 fragment was then calculated by the difference between the sample’s distribution and the control’s distribution. Finally, the TCR repertoire perturbation per BV family was defined as the sum of the absolute values of the differences between each sample’s CDR3 length and the corresponding control distribution. Values of BV perturbation greater than the sum of the SDs calculated in normal blood donors for each CDR3 profile were considered abnormal (▪). □, Values within normal range. Patient nos. 1–18, group I; patient nos. 22–33, group II; patient nos. 52–59, group III.

FIGURE 4.

Diversity of TCRBV repertoires studied by flow cytometric and CDR3 spectratyping analyses in CVID patients. Flow cytometric analyses were done with BV-specific mAbs in CD4+ (A) and CD8+ (B) T cells. Values >3 SD above the mean of controls were used to establish skewed use of BV gene families by CD4+ and CD8+ T cells. Data are shown as percentages of patients showing expansion of TCRBV families. CDR3 spectratyping were performed in CD4+ (C) and CD8+ (D) T cells from 14 representative CVID patients (9 from group I, 3 from group II, and 2 from group III). To analyze the level of TCRBV perturbation, the CDR3 spectratypes were first translated into probability distributions as function of the area under the profile for each CDR3 length. A control profile, representing the nonperturbed repertoire, was determined for each BV by calculating the average distribution of the corresponding CD4 and CD8 profiles from healthy donors. The extent of perturbation for each CDR3 fragment was then calculated by the difference between the sample’s distribution and the control’s distribution. Finally, the TCR repertoire perturbation per BV family was defined as the sum of the absolute values of the differences between each sample’s CDR3 length and the corresponding control distribution. Values of BV perturbation greater than the sum of the SDs calculated in normal blood donors for each CDR3 profile were considered abnormal (▪). □, Values within normal range. Patient nos. 1–18, group I; patient nos. 22–33, group II; patient nos. 52–59, group III.

Close modal

Because the evaluation of TCRBV usage with mAbs does not allow an exclusion of reduced diversity of the TCR repertoire even in the presence of a normal distribution of individual BV families, we looked for further restrictions of repertoires by performing the more informative CDR3 spectratyping, namely the quantitative analysis of the CDR3s with different sizes generated by the random insertion/deletion of nucleotides during V(D)J rearrangement. Nine patients from group I, three from group II, and two from group III were randomly assigned to perform the CDR3 spectratyping.

Mathematical analysis of the deviation of patients’ histograms from the normal distribution revealed altered patterns of selected CD4 BV families in most patients of group I (Fig. 4 C). These oligoclonal expansions were mainly detected in BV families described above as preferentially expanded on the basis of cytofluorimetric analysis. CDR3 perturbations were negatively correlated with the CD4 counts (r = −0.690; p = 0.0131). Low levels of CDR3 perturbation were observed in the remaining five patients assigned to group II and III in agreement with their milder clinical courses. The sequencing of CDR3 regions, performed in selected patients, confirmed the oligoclonal nature of peaks detected by CDR3 spectratyping (data not shown).

By contrast, massive alterations of CDR3 profiles were observed in CD8+ T cells of all the 14 patients studied by spectratyping (Fig. 4 D). Partially at variance with CD4+ T cells, the exceptionally high levels of CD8 CDR3 perturbation were not confined to group I but were also observed in groups II and III. In CD8+ T cells, disrupted CDR3 profiles were detected in virtually all BV family investigated irrespective of the relative CD8 BV usage demonstrated by cytofluorometry.

Fig. 5 illustrates all three patient groups and controls for the peripheral distribution of CD4+ and CD8+ IFN-γ-, IL-2-, IL-4-, and IL-10-producing T cells. Within the CD4+ T cell subset, the production of IFN-γ was significantly increased only in group I (p = 0.0016), whereas IL-4 was within normal range in all CVID patients. The percentage of CD4+ IFN-γ-producing cells was positively related to that of memory/effector cells (r = 0.716; p < 0.0001) as well as to their activation state (r > 0.7 and p = < 0.0001 vs CD95-, CCR5-, and HLA-DR-expressing CD4+ T cells). Differently, no significant correlation between the level of IL-4 and the memory/activated CD4+ T cell subsets was detected. When CD8+ T cells were considered, high levels of IFN-γ were detected in groups I and II (p = 0.0040 and p = 0.0240, respectively), whereas IL-4 production was increased in all three groups (p = 0.0162, p = 0.0140, p = 0.0336, for group I, II, and III, respectively). A positive correlation between IFN-γ production and the memory/effector and activated CD8 subsets was detected (r > 0.6 and p < 0.001 for all cases). A mild correlation between the IL-4 production and the memory/effector CD8+ T cell subset was also observed (r = 0.382; p = 0.0450). A significantly reduced production of IL-10 (p < 0.05) was detected in all subgroups of patients in CD4+ and CD8+ T cells, although no significant correlation between the composition of the T cell subsets and the distribution of IL-10-expressing T cells could be observed. No differences between CVID and controls could be observed at the level of IL-2 production in both CD4+ and CD8+ T cells.

FIGURE 5.

Flow cytometric analysis of cytokine production in CVID patients and age-matched controls. Cytokine production was studied in PBLs stimulated with ionomycin and PMA in the presence of brefeldin A (49 ). Data are represented as box plots displaying medians, 25th and 75th percentiles as boxes, and 10th and 90th percentiles as whiskers. Differences between patients and healthy controls were compared by the Mann-Whitney U test.

FIGURE 5.

Flow cytometric analysis of cytokine production in CVID patients and age-matched controls. Cytokine production was studied in PBLs stimulated with ionomycin and PMA in the presence of brefeldin A (49 ). Data are represented as box plots displaying medians, 25th and 75th percentiles as boxes, and 10th and 90th percentiles as whiskers. Differences between patients and healthy controls were compared by the Mann-Whitney U test.

Close modal

Proliferation of CD4+ and CD8+ T lymphocytes was evaluated by measuring the Ki-67 nuclear Ag expression (Fig. 6, A and B, for CD4+ and CD8+ T cells, respectively). In CVID patients, the percentage of both Ki-67+ CD4+ and CD8+ T cells was significantly increased in all three groups as compared with healthy controls (p < 0.05). Significant differences in the percentage of Ki-67 expression could also be detected in the naive and CD27+ memory subsets in CVID patients belonging to group I and II for CD4+ T cells and to group I for CD8+ T cells, as compared with healthy controls (p < 0.05). Naive T cells showed a 7- (CD8) to 15- (CD4) fold elevation in Ki-67+ T cell percentage, whereas memory and effector T cells showed an increase up to 4-fold. In CD4+ T cells, the expression of Ki-67 Ag negatively correlated with the percentage of both naive (r = −0.7; p < 0.0001) and CD27 memory (r = −0.434; p = 0.02) subsets and positively with the percentage of CD27+ memory T cells (r = 0.445; p = 0.02). In contrast, in CD8+ T cells, a significant correlation could be demonstrated only between the percentage of Ki-67 expression and that of the CD27 memory T cell subset (r = 0.471; p = 0.0132).

FIGURE 6.

Percentage of Ki-67+ T lymphocytes within the CD4+ and CD8+ peripheral blood T cells of CVID patients and age-matched healthy controls. Ki-67 Ag expression was measured in CD4+ (A) and CD8+ (B) naive (CD45ROCD27high), and CD27+ memory (CD45RO+/CD27+), CD27 memory (CD45RO+/CD27) cells, and CD27 effector (CD45RO/CD27) T cells (35 ). Data are represented as mean values ± SD. An asterisk indicates statistical difference compared with control values (Mann-Whitney U test).

FIGURE 6.

Percentage of Ki-67+ T lymphocytes within the CD4+ and CD8+ peripheral blood T cells of CVID patients and age-matched healthy controls. Ki-67 Ag expression was measured in CD4+ (A) and CD8+ (B) naive (CD45ROCD27high), and CD27+ memory (CD45RO+/CD27+), CD27 memory (CD45RO+/CD27) cells, and CD27 effector (CD45RO/CD27) T cells (35 ). Data are represented as mean values ± SD. An asterisk indicates statistical difference compared with control values (Mann-Whitney U test).

Close modal

The percentage of total lymphocytes expressing annexin V was significantly increased in all three groups of CVID patients (p < 0.0040, p = 0.0050, and p = 0.0056 for group I, II, and III, respectively) (Fig. 7,A). However, the percentage of apoptotic lymphocytes was massively increased in group I, as compared to groups II and III (p < 0.01), and negatively correlated with the number of CD4+ naive cells (r = −0.855; p < 0.0001). A more detailed analysis of lymphocyte population confirmed, within both the CD4 and CD8 subsets, significant levels of apoptosis in all three groups of CVID patients (Fig. 7, B and C). Also, in this instance, apoptosis was maximally increased in group I vs groups II and III (for CD4+ T cells, p < 0.01 and for CD8+ T cells, p < 0.05). The percentage of CD4+ apoptotic cells were related to their activation state (r > 0.7 and p < 0.0001 for CD95-, CCR5-, HLA-DR-, and IFN-γ-expressing CD8+ T cells). A positive correlation was also detected between the percentage of CD4+ apoptotic cells and that of Ki-67+CD4+ T cells (r = 0.635; p = 0.0006). Similarly to that observed in CD4+ T cells, a positive correlation between the rate of CD8+ T cell apoptosis and activation/proliferation markers could be demonstrated (r > 0.5; p < 0.001).

FIGURE 7.

Flow cytometric analysis of spontaneous apoptosis in CVID patients and age-matched healthy controls. Spontaneous apoptosis was evaluated in PBLs (A), CD4+ (B), and CD8+ (C) T cells after 48-h cultures. Annexin V-positive cells include both cells in the early (FITC-conjugated annexin V single-positive cells) and later stages of apoptosis (FITC-conjugated annexin V and PI double-positive cells). Data are represented as box plots displaying medians, 25th and 75th percentiles as boxes, and 10th and 90th percentiles as whiskers. Differences between patients and controls were compared by the Mann-Whitney U test.

FIGURE 7.

Flow cytometric analysis of spontaneous apoptosis in CVID patients and age-matched healthy controls. Spontaneous apoptosis was evaluated in PBLs (A), CD4+ (B), and CD8+ (C) T cells after 48-h cultures. Annexin V-positive cells include both cells in the early (FITC-conjugated annexin V single-positive cells) and later stages of apoptosis (FITC-conjugated annexin V and PI double-positive cells). Data are represented as box plots displaying medians, 25th and 75th percentiles as boxes, and 10th and 90th percentiles as whiskers. Differences between patients and controls were compared by the Mann-Whitney U test.

Close modal

To find a possible link between the above described T cell defects and the genotype of CVID patients, we studied the expression of 406 immunologic and hematologic potentially relevant genes. Differences in gene expression (>2-fold up- or down-regulated) for CVID group I and III vs control group are listed in Table IV for group I (top) and III (bottom), respectively. Prominently enriched among the genes up-regulated in CVID patients of group I were many genes known to be induced during lymphocyte activation such as genes encoding cell adhesion molecules, signal transducers, and transcription activators. Moreover, some of the genes overexpressed in group I, i.e., LFA-1A, LFA-3, VLA-4, STAT1, and STAT2 were down-regulated in group III, whereas genes down-regulated in group I such as AF-17 and Ikaros were up-regulated in group III. Adjacent to these sets of differentially expressed genes, another series of genes including ICAM-1, CREB1, BTG1, NK4, LCP1, and BAT2 could be detected, the expression patterns of which were similar in both group I and III.

Table IV.

Selected genes with differential expression in CVID patients of group I and III as compared to healthy controls

Group I
NameAccession Nos.Ratio
Increased   
 Adhesion/migration   
  CD9 Ag M38690 11:46 
  ICAM-1 J03132 2:74 
  LFA-1A Y00796 2:43 
  T cell-specific RANTES protein M21121 2:21 
  LFA-3 Y00636 2:16 
  VLA-4 L12002 2:01 
 Regulation of transcription/signal transduction/intracellular signaling cascade   
  CREB1 M34356 5:28 
  Activating transcription factor 4 D90209 3:01 
  STAT2 U18671 2:83 
  STAT1 αβ M97935 2:11 
Group I
NameAccession Nos.Ratio
Increased   
 Adhesion/migration   
  CD9 Ag M38690 11:46 
  ICAM-1 J03132 2:74 
  LFA-1A Y00796 2:43 
  T cell-specific RANTES protein M21121 2:21 
  LFA-3 Y00636 2:16 
  VLA-4 L12002 2:01 
 Regulation of transcription/signal transduction/intracellular signaling cascade   
  CREB1 M34356 5:28 
  Activating transcription factor 4 D90209 3:01 
  STAT2 U18671 2:83 
  STAT1 αβ M97935 2:11 
NameAccession Nos.Ratio
Decreased   
 Oncogenes and tumor suppressors   
  Tyrosine-protein kinase lyn M16038 0:38 
  AF-17 protein U07932 0:40 
  B cell translocation gene 1 protein (BTG1) X61123 0:47 
 Regulation of transcription/signal transduction/intracellular signaling cascade   
  HCFC1 L20010 0:07 
  Zinc finger protein, subfamily 1A, 1 (Ikaros) U40462 0:35 
  JAK3 U09607 0:40 
 Other immune system proteins   
  NK cells protein 4 precursor (NK4) M59807 0:14 
  Lymphocyte cytosolic protein 1 (LCP1) M22300 0:43 
  HLA-B-associated transcript 2 (BAT2) M33509 0:48 
NameAccession Nos.Ratio
Decreased   
 Oncogenes and tumor suppressors   
  Tyrosine-protein kinase lyn M16038 0:38 
  AF-17 protein U07932 0:40 
  B cell translocation gene 1 protein (BTG1) X61123 0:47 
 Regulation of transcription/signal transduction/intracellular signaling cascade   
  HCFC1 L20010 0:07 
  Zinc finger protein, subfamily 1A, 1 (Ikaros) U40462 0:35 
  JAK3 U09607 0:40 
 Other immune system proteins   
  NK cells protein 4 precursor (NK4) M59807 0:14 
  Lymphocyte cytosolic protein 1 (LCP1) M22300 0:43 
  HLA-B-associated transcript 2 (BAT2) M33509 0:48 
Group III
NameAccession Nos.Ratio
Increased   
 Adhesion/migration   
  ICAM-1 J03132 3:22 
 Oncogenes and tumor suppressors   
  AF-17 protein U07932 1:86 
 Regulation of transcription/signal transduction/intracellular signaling cascade   
  Ikaros U40462 2:00 
  CREB1 M34356 1:89 
 Other immune system proteins   
  FGFR1 X66945 2:90 
Group III
NameAccession Nos.Ratio
Increased   
 Adhesion/migration   
  ICAM-1 J03132 3:22 
 Oncogenes and tumor suppressors   
  AF-17 protein U07932 1:86 
 Regulation of transcription/signal transduction/intracellular signaling cascade   
  Ikaros U40462 2:00 
  CREB1 M34356 1:89 
 Other immune system proteins   
  FGFR1 X66945 2:90 
NameAccession Nos.Ratio
Decreased   
 Adhesion/migration   
  LFA-3 Y00636 0:004 
  VLA-4 L12002 0:41 
  LFA-1A Y00796 0:47 
 Oncogenes and tumor suppressors   
  BTG1 X61123 0:40 
 Regulation of transcription/signal transduction/intracellular signaling cascade   
  HCFC1 L20010 0:07 
  Nucleophosmin 1 M23613 0:21 
  STAT1 αβ M97935 0:28 
  STAT2 U18671 0:29 
 Other immune system proteins   
  LCP1 M22300 0:10 
  NK4 M59807 0:17 
  BAT2 M33509 0:17 
NameAccession Nos.Ratio
Decreased   
 Adhesion/migration   
  LFA-3 Y00636 0:004 
  VLA-4 L12002 0:41 
  LFA-1A Y00796 0:47 
 Oncogenes and tumor suppressors   
  BTG1 X61123 0:40 
 Regulation of transcription/signal transduction/intracellular signaling cascade   
  HCFC1 L20010 0:07 
  Nucleophosmin 1 M23613 0:21 
  STAT1 αβ M97935 0:28 
  STAT2 U18671 0:29 
 Other immune system proteins   
  LCP1 M22300 0:10 
  NK4 M59807 0:17 
  BAT2 M33509 0:17 

To validate these macroarray data, we analyzed, by quantitative real-time RT-PCR, some of the genes whose expression showed extreme variations among the two groups of patients, i.e., HCF1, LFA-3, and CD9. The results demonstrated a reasonable agreement between the data obtained using these two different techniques. In particular, in groups I and III, the expression of HCFC1 was 12.6 ± 2.3 and 21 ± 13%, the expression of CD9 was 166 ± 3 and 84 ± 4%, and the expression of LFA3 was 116 ± 21 and 19 ± 11%, respectively, in comparison to controls normalized to 100%.

Despite intense investigation, the etiology as well as the pathogenesis of CVID, years after its initial recognition, remain largely unknown. A major obstacle in characterizing the molecular events leading to CVID is certainly the great heterogeneity of the disease, which is implied by defining it as a variable. This obstacle has also hampered the fulfillment of a commonly accepted approach to classify subgroups of cases with CVID. Achieving classification systems has therefore become one of the main goals for people involved in study of CVID (26), who need to precisely define homogeneous subpopulations of patients. It is well known that patients with CVID may present a vast array of T cell abnormalities, but, to date, a comprehensive evaluation of the T cell deficiency is lacking. In this study, we demonstrate that CVID constantly associates with variable degrees of T cell dysfunction. Multiple T cell abnormalities are simultaneously detected in affected individuals intimately related among them, and whose severity reflects in a parallel loss of CD4+ naive T cells. Due to its strong correlation with the severity of the disease, we propose that the level of CD4+ naive T cell should be considered as a parameter essential to classify CVID and possibly as a valuable prognostic marker.

Irrespective of the T cell defects affecting the vast majority of the patients, the only proposed classifications for CVID are actually based on functional or phenotypic characteristics of B cells. Clustering patients with a diagnosis of CVID according to their naive CD4+ T cell numbers allows the recognition of discrete subgroups that exhibit several shared abnormalities of T cell-mediated immunity. On the basis of the results presented in this study, three main groups of CVID patient may be identified. One group is characterized by marked depletion of CD4+ naive T cells, massive T cell activation, proliferation, and apoptosis, disruption of CD4+ and CD8+ TCR repertoires, and clinically characterized by severe immunodeficiency generally associated with splenomegaly; a second group is characterized by less prominent alteration of CD4+ and CD8+ T cells and less advanced immunodeficiency occasionally presenting moderate splenomegaly; and, finally, a third group of CVID patients is characterized by prevalent CD8+ T cell abnormalities, CD4+ naive T cell levels within normal range, mild clinical symptoms, and slight or absent splenomegaly. Naive CD4+ T cell levels were significantly associated with the Warnatz et al. (20) classification, based on phenotypic B cell maturation markers, although the three groups did not overlap with those assigned on the basis of naive CD4+ T cells. The correlation highlighted the presence of more severe memory B cell depletion in those CVID cases that had more severe loss of naive CD4+ lymphocytes, but the resulting assignment to subgroups did not exceed 58.8% concordance between the two systems.

In normal conditions, the size of the peripheral blood T cell compartment is regulated by the combined effects of thymic output and peripheral proliferation counterbalanced by apoptosis (27). However, differences exist in the CD4+ and CD8+ T cell regenerative pathway that account for the natural tendency of the CD4+ T cell compartment to shrink (28). Differently from reconstitution of CD8+ T cells which is rapid, independent from thymic function and occurring by peripheral expansion, reconstitution of CD4+ T cells is slower and depends on thymic output (29, 30). A first, natural obstacle to CD4+ T cell recovery is therefore the age-dependent decline of thymopoiesis. The regenerative capability of the CD4+ T cell compartment is also reduced by the proneness of CD4+ T cells to undergo apoptosis as well as by an intrinsically limited expansion of CD4+ T cells in response to antigenic stimulation (31, 32).

Evidence from mice as well as from HIV-1-infected individuals undergoing highly active antiretroviral therapy treatment, suggests that the number of newly formed naive T lymphocytes is determined by the thymic mass (33). According to this result, in normal individuals the frequency of recent thymic emigrants start to decline between 15 and 20 years of age, corresponding to the time of thymic involution. The reduced frequency of CD31+ T cells, which we found in a large part of CVID patients, indicated a remarkable impairment of thymic output, and loss of the physiological age-dependent decline was observed in controls. Although it was impossible to establish the effective duration of the disease, our data, showing a deeply reduced number of thymic emigrants even at 30–35 years of age, contrast with an effect of chronic CVID on T cell homeostasis in the naive compartment. An impaired supply of bone marrow progenitor cells could play a role in the premature exhaustion of thymic function, as suggested by the reduced maturation of hemopoietic progenitors and destruction of stromal elements we recently described in CVID (34).

Enhanced self-replication of T cells was demonstrated in CVID by increased Ki-67 nuclear Ag expression in both peripheral naive and memory cells. The mean percentage of dividing naive CD4+ T cells in CVID patients was 10-fold higher than in controls. Similarly to HIV-1 infection (35), higher increases in percentages of Ki-67+CD4+ naive T cells were observed only when the number of naive cells fell below 20%. As expected, the rate of proliferation significantly associated with the levels of T cell apoptosis and activation. These observations point to a key role exerted in CVID by T cell activation in inducing an increased cell death first, and an increased peripheral proliferation of T cells after, to compensate for the ineffective thymic output. The peripheral homeostatic expansion implies that the diversity of the T cell pool is lower than a comparably sized T cell pool derived from intrathymic maturation. A restricted TCR repertoire might result in turn in a decreased ability to counteract infections or cancer.

Only limited information is currently available on the diversity of TCR repertoire of CVID (36, 37). We detected a marked contraction of the TCRBV repertoire in most CVID patients. The significant correlation observed between the number of CD4 TCRBV expansions and the frequency of CD4+ naive T cells suggested that the impaired influx of new cells played a role in the genesis of the disrupted repertoires. Alternatively, expansion of selected BV families could represent the result of chronic antigenic stimulation inducing expansion of specific memory cells. In CVID patients, a preferential BV usage was observed for a number of CD4 BV families, among them BV16, BV14, and BV 21.3. The spectratyping of the VDJ region showed oligoclonal profiles in patients with severe depletion of CD4+ naive T cells (group I, <20% of CD4+ T cells), in striking contrast to Gaussian profiles observed in patients with less or no impairment of T cell homeostasis. Notably, the CDR3 perturbation, exceptionally high in CD4+ T cells, reached impressive levels in CD8+ T cells, even more than that observed in advanced HIV-1 infection, which invariably associates with disrupted TCR repertoires (38, 39). Interestingly, the oligoclonality of the CD8 TCRBV repertoire was not confined to clinically more compromised patients but also concerned patients with milder clinical course, although to a lesser extent.

It has been reported that i.v. Ig treatment can influence the T cell repertoires (40). However, the TCRBV analysis we performed in CVID patients naive for Ig treatment, resulted in data overlapping those observed in i.v. Ig-treated patients, allowing us to exclude a possible role of this therapeutic intervention in shaping the oligoclonal expansions (our unpublished observations).

Our data on cDNA arrays, pointed to discrete genetic profiles for the different subgroups of CVID patients. In group I of patients, the gene giving the strongest hybridization signal was CD9, a cell surface molecule belonging to the tetraspanin family. This family of proteins is involved in an astonishing variety of biologic responses including adhesion, morphology, activation, proliferation, and differentiation of B, T, and other cells (41). Other genes we found up-regulated in group I were the genes encoding for the adhesion molecules ICAM-1 and LFA-3. This finding was consistent with previous reports showing an increased expression of these molecules in a subgroup of CVID patients characterized by low CD4:CD8 ratio, increased T cell activation, and splenomegaly (42, 43, 44). Noteworthy was also the differential expression of AF-17 and Ikaros genes that we found in patients of group I and III, which could represent a useful marker for subgroup prediction and assignment.

Recent evidence also suggest a primary defect of myeloid dendritic cells (DCs) in CVID (45, 46). DCs have a crucial role in Ag presentation to T lymphocytes and instructively direct their activation and differential functional maturation. It is likely that DC defects underlie several of the immune abnormalities observed in CVID, and this result should be considered in future studies.

In conclusion, data presented in this study indicate that, far from being innocent bystanders, T cells play a key role in the pathogenesis of CVID. Therefore, a proper classification of this disease should include, in addition to functional and phenotypic characterization of B cells, the evaluation of T cell functions and numbers.

The authors have no financial conflict of interest.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1

This work was supported by grants from University of Rome, Faculty of Medicine 2001–2004 and 2004–2006 for Primary Immunodeficiencies (to F.A.); Ministero dell’Università e della Ricerca Scientifica e Technologica, projects for Primary Immunodeficiencies, 2002–2003 (to F.A.); Istituto Superiore di Sanità, 2003–2005 (to F.A.); and Istituto Superiore di Sanità, 2003–2004, Grant C3AF (to M.P.).

4

Abbreviations used in this paper: CVID, common variable immunodeficiency; PI, propidium iodide; HCFC1, host cell factor 1; OR, odds ratio; CI, confidence interval; BV, β-chain variable region; DC, dendritic cell.

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