OBJECTIVE—Islet-reactive CD8+ T-cells play a key role in the pathogenesis of type 1 diabetes in the NOD mouse. The predominant T-cell specificities change over time, but whether similar shifts also occur after clinical diagnosis and insulin treatment in type 1 diabetic patients is unknown.

RESEARCH DESIGN AND METHODS—We took advantage of a recently validated islet-specific CD8+ T-cell γ-interferon enzyme-linked immunospot (ISL8Spot) assay to follow responses against preproinsulin (PPI), GAD, insulinoma-associated protein 2 (IA-2), and islet-specific glucose-6-phosphatase catalytic subunit-related protein (IGRP) epitopes in 15 HLA-A2+ adult type 1 diabetic patients close to diagnosis and at a second time point 7–16 months later.

RESULTS—CD8+ T-cell reactivities were less frequent at follow-up, as 28.6% of responses tested positive at type 1 diabetes diagnosis vs. 13.2% after a median of 11 months (P = 0.003). While GAD and IA-2 autoantibody (aAb) titers were unchanged in 75% of cases, the fraction of patients responding to PPI and/or GAD epitopes by ISL8Spot decreased from 60–67 to 20% (P < 0.02). The previously subdominant IA-2206–214 and IGRP265–273 peptides were newly targeted, thus becoming the immunodominant epitopes.

CONCLUSIONS—Shifts both in frequency and in immunodominance of CD8+ T-cell responses occur more rapidly than do changes in aAb titers. These different kinetics may suggest complementary clinical applications for T-cell and aAb measurements.

CD8+ T-cells have recently emerged as crucial actors in the pathogenesis of type 1 diabetes in the NOD mouse (1,2). Three different CD8+ pathogenic clones specific for proinsulinB15–23 (3), islet-specific glucose-6-phosphatase catalytic subunit-related protein (IGRP)206–214 (4), and dystrophia myotonica kinase138–146 (5) have been described. These specificities are highly immunodominant in NOD mice, as T-cells recognizing them are abundantly present in the early insulitis infiltrates (6,7). Moreover, the rise in circulating IGRP206–214-specific CD8+ T-cells can predict impending type 1 diabetes onset in NOD mice (8).

More recent data suggest that there could be a hierarchy of spreading from one reactivity to another, as NOD mice made tolerant to proinsulin are protected from diabetes and do not develop IGRP-specific CD8+ responses, whereas IGRP-tolerant mice still become diabetic and mount proinsulin-specific responses (9). The initiating role of proinsulin is further supported by the fact that proinsulin knockout mice reconstituted with a hormonally active proinsulin transgene carrying a single tyrosine to alanine mutation at position B16 are completely protected from type 1 diabetes (10,11). This substitution affects both the proinsulinB15–23 epitope and an overlapping immunodominant proinsulinB9–23 CD4+ epitope (12,13).

Mirroring these mouse data, CD8+ T-cells have recently received increasing attention for human type 1 diabetes as well (1419) because their detection could provide new autoimmune markers of β-cell aggression. Development of such markers is important to complement autoantibody (aAb) readouts. Indeed, aAbs can separate type 1 diabetic from healthy subjects with high sensitivity and specificity, making them indispensable tools for diagnostic classification and type 1 diabetes prediction. However, the utility of aAbs for immune monitoring purposes is limited by the fact that they do not reflect tolerance restoration following therapeutic manipulation (20,21).

To be clinically applicable, T-cell assays should be developed that can be easily transferred into routine laboratory testing. The first validation step for such assays is to test their diagnostic sensitivity and specificity in separating type 1 diabetic from healthy subjects. For CD4+ T-cells, the cellular immunoblotting technique of Brooks-Worrell et al. (22) was the assay showing the best performance (23). In many other instances, β-cell–specific CD4+ T-cells were detected in all individuals irrespective of disease status (24,25) although probably characterized by different phenotypes (2628).

For CD8+ T-cells, we have recently proposed an islet-specific CD8+ T-cell γ-interferon (IFN-γ) ELISpot (ISL8Spot) assay (18,29). The ISL8Spot employs HLA-A2–restricted β-cell epitopes derived from preproinsulin (PPI) (30), GAD, insulinoma-associated protein 2 (IA-2) (17), and IGRP (31), which have been mostly identified by proteasome digestion and DNA immunization strategies (32). Using a single IFN-γ secretion readout on unfractionated peripheral blood mononuclear cells (PBMCs), this assay detects and quantifies β-cell–reactive CD8+ T-cells directly ex vivo (18). β-cell epitope-specific CD8+ T-cells were thus found at an average frequency of 0.008%, ranging from 0.0008 to 0.08% of total PBMCs. The presence versus absence of these cells allowed discrimination of type 1 diabetic vs. healthy subjects with high sensitivity and specificity (18).

The natural history of CD8+ T-cell responses after type 1 diabetes clinical onset and start of insulin treatment has not been explored. We know from the NOD mouse that the proinsulinB15–23-specific fraction is highly immunodominant already at 5 weeks (i.e., in the prediabetic period) but rapidly disappears, becoming negligible by 20 weeks (i.e., after type 1 diabetes onset). In parallel, the IGRP206–214-specific fraction increases in number, while undergoing an avidity maturation process that selects the clonotypes of higher avidity (6,8). Whether similar shifts in epitope targeting also apply to human type 1 diabetes is unknown. We therefore used the ISL8Spot assay to measure CD8+ T-cell responses against different β-cell epitopes at type 1 diabetes clinical onset and after some months of follow-up. Our results reveal a fast waning of most ISL8Spot responses, which contrasts with the steady titers of GAD and IA-2 aAbs. Few new T-cell reactivities appear that focus on previously subdominant IA-2 and IGRP epitopes.

Peptides.

The HLA-A2–restricted β-cell epitopes used (Table 1) have previously been described (17,18,31). A viral peptide pool of Flu MP58–66 (GILGFVFTL), Epstein-Barr virus BMLF280–288 (GLCTLVAML), cytomegalovirus pp65495–503 (NLVPMVATV), and phytohemagglutinin (1 μg/ml; Sigma, Lyon, France) were used as positive controls. HIV gag77–85 (SLYNTVATL) and DMSO diluent were included as negative controls. All peptides were used at 10 μmol/l and were >80% pure (Schafer-N, Copenhagen, Denmark). Responses against these 9- to 10-amino acid–long epitopes originate from CD8+ T-cells, as previously shown (18).

Study subjects.

HLA-A2+ new-onset adult (aged >16 years) type 1 diabetic patients with acute onset of symptoms requiring permanent insulin treatment from the time of diagnosis (33) were recruited from the Diabetes Registry of the Province of Turin, Italy, and from the GOFEDI Network, France (see appendix for a detailed list of participating centers). All patients had metabolically controlled disease and were free of recent (<2 weeks) infectious or inflammatory conditions at the time of blood draw. Parallel recruitment of Caucasian healthy control subjects took place at the same institutions. Available subjects wishing to continue the study were subsequently drawn and tested a second time under identical conditions. All subjects gave informed consent, and the study was approved by the relevant ethics committees.

Blood processing.

All blood samples were shipped overnight at room temperature. Rapid HLA-A2 screening was performed with the BB7.2 monoclonal antibody, followed by subtyping using the Olerup SSP HLA*02 kit (GenoVision/Qiagen, Vienna, Austria) to verify the presence of the HLA-A*0201 allele (>95% of HLA-A2+ individuals). PBMCs were isolated by density gradient centrifugation using lymphocyte separation medium (PAA, Les Mureaux, France), and immediately used or stored frozen (10% DMSO in pooled human male AB serum). The second testing was performed under the same condition (i.e., fresh or frozen PBMCs) of the first testing.

Islet antibodies.

Serum GAD, IA-2, and insulin (auto)antibodies were measured by radioligand binding assays, following protocols previously evaluated within the Diabetes Antibody Standardization Program (lab no. 137) (34). Sensitivities in the 2005 Diabetes Antibody Standardization Program were 84% for anti-GAD, 76% for anti–IA-2 and 28% for anti-insulin, where the specificity was set at 95%. Changes >33% between two aAb titer determinations were considered significant.

ISL8Spot assay.

Ninety-six–well polyvinylidine fluoride plates (Millipore, Saint-Quentin-en-Yvelines, France) were coated overnight with an anti–IFN-γ antibody (U-CyTech, Utrecht, the Netherlands). Plates were subsequently blocked with RPMI plus 10% human serum (PAA) and peptides added (10 μmol/l final concentration) in triplicate wells along with recombinant human interleukin-2 (0.5 units/ml; R&D Systems, Lille, France), as previously described (18). PBMCs were seeded at 3 × 105 cells/well and cultured for 20–24 h. Following PBMC removal, IFN-γ secretion was visualized with a biotin-conjugated anti–IFN-γ antibody (U-CyTech), alkaline phosphatase-conjugated ExtrAvidin, and Sigmafast 5-bromo-4-chloro-3-indolyl phosphate/nitro blue tetrazolium (BCIP/NBT) tablets (both from Sigma). Spots were counted using an AID reader (Strassberg, Germany) and means of triplicate wells calculated. All ISL8Spot readouts are expressed as spot-forming cells (SFCs)/106 PBMCs.

The cutoff for a positive response was set at 3 SDs above the average basal reactivity (i.e., reactivity against HIV gag77–85 and DMSO diluent alone). This was chosen as the cutoff allowing for the best diagnostic sensitivity (i.e., highest number of positive responses to β-cell epitopes in type 1 diabetic patients) and specificity (i.e., lowest number of positive responses in healthy controls), as determined by receiver-operator characteristics analysis (18).

T-cell clone dilution experiments.

A flu MP58–66-specific HLA-A2–restricted T-cell clone was serially diluted into PBMCs from three different HLA-A2+ donors showing no response to the MP58–66 peptide and displaying different levels of basal reactivity in the absence of stimuli. These cell mixtures were challenged in the presence of 10 μmol/l flu MP58–66 peptide using the same ELISpot format as that described above.

Statistical analysis.

Values are expressed as means ± SD or medians (range) according to their distribution. Comparisons between proportions were made with the χ2 test and Fisher's exact test when appropriate. Comparisons of means between two groups were carried out with Student's t test for normal distributed variances or with the Mann-Whitney U test for non-normal variances. P < 0.05 was considered of statistical significance.

Assay validation.

The reproducibility of our ELISpot technique has previously been reported (18). Variability was found to be of 14.1% intra-assay, 4.2% at the analytical interassay level (i.e., using thawed PBMC samples frozen on the same occasion), and 9.2% at the preanalytical and analytical level (i.e., using separate blood draws from the same donor). Another problem encountered in a longitudinal setting is that the basal reactivity level (i.e., spontaneous IFN-γ production in the absence of stimuli) can be different not only among different subjects but also within the same individual when analyzed at different time points. To address whether these variations could affect the detection sensitivity of the technique, we took HLA-A2+ healthy donors displaying different IFN-γ background levels that demonstrated no response to the Flu MP58–66 epitope, mixed their PBMCs with serially diluted numbers of a flu MP58–66-specific clone, and tested these mixtures in ELISpot against the MP58–66 peptide. As shown in Fig. 1, background levels more than 10-fold different (7.3, 21.7, and 85.5 SFC/106 PBMCs, respectively) did not significantly affect the detection sensitivity of the system (i.e., the basal-subtracted net signal) over a wide range of frequencies. Moreover, all counts were above the basal ±3 SD positive cutoff value calculated from the respective backgrounds.

Most CD8+ T-cell responses are short-lived after type 1 diabetes onset.

We considered β-cell epitopes that we previously defined as targets of CD8+ T-cells in 19–50% of adult new-onset type 1 diabetic patients but not of age-matched healthy control subjects (17,18). These epitopes were derived from four different β-cell Ags, namely PPI, GAD, IA-2, and IGRP. A complete list is presented in Table 1.

Fifteen new-onset type 1 diabetic patients were available for follow-up within a median of 11 months (range 7–16) (Table 2). Their mean age at diagnosis was 27.6 ± 8.4 years, and their median type 1 diabetes duration at the time of the first blood draw was 13 days (2–180). Fifteen age-matched healthy control subjects (mean age 28.8 ± 5.9) were also tested twice within a median follow-up period of 14 months (12–26).

Results expressed as number of SFC/106 PBMCs are summarized in Fig. 2. All healthy control subjects were negative at follow-up, including two individuals (i.e., H08 and H09) previously found to be weakly positive for 1–2 epitopes. The number of type 1 diabetic patients positive for T-cell responses dropped from 80% (12 of 15) at diagnosis to 53% (8 of 15) during follow-up (P = 0.12). Moreover, while positive patients at the time of diagnosis recognized multiple epitopes (median reactivities/patient 2 [range 0–6]), there were mostly single reactivities that persisted during follow-up (median 1 [range 0–3], P = 0.05). Overall, the number of positive responses out of the total tested decreased from 28.6% (34 of 119) to 13.2% (16 of 121) (P = 0.003).

Those patients with detectable reactivities could be divided in two categories: on one side, patients already positive at diagnosis (i.e., patients P04, P09, P10, P11, P14, and P15) in whom at least one of the original epitopes was still recognized, while new ones sometimes emerged (i.e., P04, P11, and P14). On the other side, patients whose responses were all negative at diagnosis and who developed scattered reactivities at follow-up (1–2 epitopes recognized; patients P06 and P08). CD8+ T-cell responses against a pool of viral epitopes were included as internal controls. They decreased or disappeared only in two patients (P08 and P11), remaining unchanged in all other instances (13 of 15 [87%]).

CD8+ T-cell responses shift toward IA-2206–214 and IGRP265–273 epitopes after type 1 diabetes onset.

A simplified graphical representation of these data are provided in Fig. 3, where only CD8+ T-cell responses testing positive either at diagnosis or follow-up are represented. Such responses are displayed as absent (<3 SDs above the mean basal reactivity), low (3–4 SDs), intermediate (4–5 SDs), or high (>5 SDs), following the quantitative ranking reported in Fig. 2.

Of the total positive T-cell responses to islet epitopes observed at diagnosis, the majority (26 of 42 [62%]) dropped to nondetectable levels, while few of them (8 of 42 [19%]) remained detectable at follow-up. Only in five patients (i.e., P04, P06, P08, P11, and P14) did new epitopes become targeted (8 of 42 [19%]). Except for patient P04 who recognized proinsulinB18–27, in all other instances (7 of 8 [87.5%]) the newly targeted epitopes were IA-2206–214 and IGRP265–273. These two epitopes were recognized together in three of four patients.

While the number of patients responding to epitopes derived from PPI and GAD significantly decreased (60 to 20% for PPI and 67 to 20% for GAD; P < 0.02) (Fig. 4A), IA-2206–214 became the major immunodominant epitope detected at follow-up. This was true both in terms of IA-2206–214–responding patients (5 of 14 [36%]) (Fig. 4B) and of frequency of IA-2206–214 among all targeted epitopes (5 of 16 [31%]) (Fig. 5). Second in order came IGRP265–273, which was recognized by 27% (3 of 11) of patients (Fig. 4B), thus constituting 19% (3 of 16) all reactivities detected (Fig. 5). Another 20% of patients and 19% of reactivities were covered at follow-up by GAD114–123 and proinsulinB18–27, which were the two immunodominant epitopes at diagnosis (53 and 36% prevalence and 23 and 14% relative frequency, respectively). Thus, four epitopes (i.e., the newly immunodominant IA-2206–214 and IGRP265–273 along with GAD114–123 and proinsulinB18–27) accounted for 88% of all reactivities detected at follow-up. Three epitopes (proinsulinB10–18, GAD536–545, and IGRP228–236) were instead no longer recognized. This focusing of T-cell responses toward more selected epitopes contrasted with the situation at diagnosis (Fig. 4B and Fig. 5) where, besides GAD114–123 and proinsulinB18–27 emerging as the immunodominant specificities, the other seven epitopes were evenly ranked (13–29% prevalence; 6–12% of total reactivities).

aAb titers remain stable in face of fading CD8+ T-cell responses.

A sharp dichotomy was observed when comparing the progression of aAb titers and T-cell responses (Table 2). While the fraction of positive ISL8Spot responses decreased from 28.6 to 13.2% (P = 0.003), that of positive GAD or IA-2 aAb responses did not change (63.3 vs. 60.7%; P = 0.83). Even the titers of GAD and IA-2 aAbs remained nearly identical in the majority of cases (21 of 28 [75.0%]). A decrease in titers was registered only in 17.9% (5 of 28) of case subjects, while both GAD and IA-2 further rose in only one patient.

The picture was different when we compared (auto)antibody and T-cell responses against PPI. In line with their adult onset type 1 diabetes, all but two (13 of 15 [86.7%]) patients were negative for insulin autoantibodies at diagnosis (Table 2). Following initiation of insulin therapy, insulin autoantibodies titers became positive in most patients, with the exception of P03 and P10. P10 was also one of three patients for whom proinsulin-specific T-cell responses were still detectable at follow-up.

Here, we describe the progression of β-cell–directed CD8+ T-cell responses after type 1 diabetes clinical onset and initiation of insulin therapy. As the aim of this study was the longitudinal evaluation of type 1 diabetic patients rather than their comparison with healthy control subjects, the fact that these two groups were matched only with respect to age does not bias our conclusions. In this longitudinal setting, it is instead essential to distinguish between changes in T-cell responses due to the variability of the ISL8Spot technique from those reflecting true biological fluctuations. In this respect, the 9.2% interassay variability registered when analyzing separate blood draws from the same individual (18) is reassuring. Although some fluctuations in the ELISpot background noise are unavoidable, most of these fluctuations were shown to have a minimal impact on the final basal-subtracted signal obtained (18). Moreover, detection of a serially diluted clone over different basal backgrounds was also similar. Three main conclusions can thus be drawn: 1) most CD8+ T-cell responses are short-lived after clinical onset, 2) some new reactivities appear, which preferentially target the previously subdominant IA-2206–214 and IGRP265–273 epitopes, and 3) GAD and IA-2 aAb titers display less fluctuations.

There are several hypotheses which, without being mutually exclusive, could explain the rapid disappearance of most β-cell–specific IFN-γ–producing CD8+ T-cells after type 1 diabetes diagnosis. The simplest explanation is that these T-cells are no longer detected just because they are no longer present at sufficient frequencies. They may have been deleted or suppressed as part of the natural blunting of (auto)immune responses. Although some β-cells are thought to survive for a long time after type 1 diabetes onset, the increasing paucity of islet Ags may doom most of these T-cells to die for lack of Ag stimulation. These cells may also become undetectable because of a change in their recirculation or homing behavior. Alternatively, β-cell–specific T-cells may have stopped secreting IFN-γ, changing their phenotype to a different one. There are indeed examples in the setting of both chronic virus infections (35) and of neoplastic diseases (36), where activated virus- or tumor-specific CD8+ T-cells persist but lose their effector function (i.e., cytotoxicity and cytokine production).

A third explanation is that the epitope specificity of the CD8+ T-cell population may have shifted toward different targets. Although it is possible that other unknown specificities become prevalent, an epitope focusing phenomenon was observed. Indeed, only six of the nine original epitopes were still targeted at follow-up. Furthermore, a shift in the epitopes preferentially targeted by CD8+ T-cells was observed, as two previously subdominant IA-2 and IGRP epitopes became targeted by 27–36% of patients, while the previously immunodominant responses against proinsulin and GAD epitopes became less prominent. IA-2206–214 and IGRP265–273 (but not IGRP228–236) were also the two epitopes newly targeted in the vast majority (87.5%) of case subjects. These results are relevant in light of the recent debate as to whether there is a hierarchy of epitope spreading in the type 1 diabetes pathogenic process. Data in the NOD mouse suggest that proinsulin may stand at the source of this spreading (10,11), while IGRP would lie downstream in the cascade (9). Our human data are consistent with this model. Indeed, proinsulin- and GAD-specific responses decreased, while IA-2206–214–specific and, to a lesser extent, IGRP265–273-specific T-cells became immunodominant. While the decreasing proinsulin-specific responses and the increasing of IGRP265–273 reactivity are reminiscent of NOD mouse data (6), the finding of a similar phenomenon for GAD and IA-2 is unparalleled. This is not surprising, since GAD and IA-2 are thought to be important target Ags in human type 1 diabetes (15,17,26,3739) but not in the NOD mouse (4043). Consideration of additional epitopes poorly recognized at type 1 diabetes onset (17,18) may reveal further specificities becoming immunodominant at later time points. It is also interesting that, similarly to the overlapping proinsulinB9–23 and proinsulinB15–23 mouse epitopes, the immunodominant human CD8+ T-cell epitopes at diagnosis (i.e., GAD114–123) and at follow-up (i.e., IA-2206–214) overlap with the DR4-restricted epitopes GAD115–127 (44,45) and IA-2206–221 (46).

The observed shift in epitope specificity could be due either to the emergence of novel clonotypes or to an avidity maturation of preexisting ones. Indeed, if IA-2206–214–specific CD8+ T-cells undergo avidity maturation, this would lower the ISL8Spot detection threshold and thus increase their measured frequencies. Alternatively, the measured frequencies could increase as a result of higher precursor numbers following de novo generation and/or expansion. The study of epitope-specific CD8+ clones obtained at different time points will clarify this issue.

Another important question is whether the observed changes in CD8+ T-cell responses are part of the type 1 diabetes natural history or, rather, reflect an immunological effect induced by insulin therapy. Indeed, daily insulin treatment in these patients may have modulated T-cell responses. Consistent with this hypothesis, proinsulin-specific responses were frequently undetected at follow-up (12 of 15 patients [80%]), a trend which contrasted with the opposite behavior of insulin antibody responses, which rose in most (10 of 12 [83%]) instances. The increase in insulin Ab titers could also contribute to this shift in T-cell responses, as Abs could change the efficiency of presentation of proinsulin-derived T-cell epitopes (47,48). Besides the possibility of a direct effect of proinsulin as an Ag on its cognate T-cells, an indirect tolerogenic effect could also be mediated by the hormonal activity of proinsulin. Insulin treatment can indeed reduce the functional pressure on the β-cell, which may translate to reduced β-cell apoptosis and/or reduced β-cell Ag release.

The slower changes observed for aAb titers compared with T-cell responses have important implications. Autoreactive memory B-cells are long-lived and can keep replenishing the plasma cell compartment without further need for Ag-specific stimulation (49). It is not completely clear whether the same rules apply to CD8+ T-cell memory (50). The efficient B-cell homeostasis may make it possible for the aAb titers to be maintained longer than T-cell responses. The faster dynamics of CD8+ T-cell responses could make their measurement more suitable to promptly reflect the autoimmune modifications of the disease. In this scenario, aAb and T-cell measurements could find complementary clinical applications. We hypothesize that the slow changes of aAb titers may make them more suitable for predicting the “ifs” of type 1 diabetes (i.e., 5-year probability to develop disease in at-risk subjects). Conversely, the faster kinetics of T-cell responses may be less useful to predict long-term odds but better suited to reflect more proximal events, i.e., the “whens” of type 1 diabetes once disease onset, remission, or relapse are approaching.

Another important application for T-cell assays will be for the etiologic classification of borderline cases. One such example is patient P03, whose diagnosis of type 1 diabetes could be questioned, having ketoacidosis and insulin dependency at diagnosis but being seronegative and genetically protected (DR15+ with no HLA class II susceptibility allele). Nonetheless, his robust CD8+ T-cell responses at diagnosis hint at an autoimmune component for his diabetes.

The disappearance of most CD8+ T-cell responses following type 1 diabetes onset and initiation of insulin therapy also has important implications for the application of T-cell assays in the clinics. There is quite some parallelism between human and NOD mouse data, as in both cases T-cell responses significantly wane after clinical onset (8). If this human/mouse parallel also holds true for the prediabetic period, we may find the zenith of T-cell responses before diabetes onset. Longitudinal follow-up of at-risk subjects will shed further light on the time course of these events.

The following colleagues and Institutions contributed to the study with patient recruitment:

Piedmont Study Group for Diabetes Epidemiology, Italy.

Coordinators, G. Novelli and G. Bruno: S. Cianciosi, Avigliana; A. Perrino, Carmagnola; C. Giorda and E. Imperiale, Chieri; A. Chiambretti and R. Fornengo, Chivasso; V. Trinelli and D. Gallo, Ciriè-Lanzo; A. Caccavale, Collegno; F. Ottenga, Cuneo; R. Autino and P. Modina, Cuognè; L. Gurioli and L. Costa-Laia, Ivrea; C. Marengo and M. Comoglio, Moncalieri; T. Mahagna, Nichelino; M. Trovati and F. Cavalot, San Luigi Hospital, Orbassano; A. Ozzello and P. Gennari, Pinerolo-Pomaretto-Torre Pellice; S. Bologna and D. D'Avanzo, Rivoli; S. Davì and M. Dore, Susa; S. Martelli and E. Megale, Giovanni Bosco Hospital, Turin; S. Gamba and A. Blatto, Maria Vittoria Hospital, Turin; P. Griseri and C. Matteoda, Martini Hospital, Turin; A. Grassi and A. Mormile, Mauriziano Hospital, Turin; E. Pisu, G. Grassi, V. Martina, V. Inglese, and R. Quadri, Molinette Hospital, Turin; G. Petraroli and L. Corgiat-Mansin, Ophthalmologic Hospital, Turin; F. Cerutti and C. Sacchetti, Regina Margherita Pediatric Hospital, Turin; A. Clerico and L. Richiardi, Valdese Hospital, Turin; and G. Bendinelli and A. Bogazzi, Venaria.

GOFEDI (Groupe Ouest-France pour l'Etude du Diabète Insulino-dépendant), France.

Coordinator, L. Chaillous: P.H. Ducluzeau and V. Rohmer, Angers; M. Dolz, V. Kerlan, and E. Sonnet, Brest; B. Charbonnel, Nantes; R. Marechaud, Poitiers; and P. Lecomte, Tours.

FIG. 1.

ELISpot detection of epitope-specific signals over different background noise levels. Flu MP58–66-specific T-cells were serially diluted into PBMCs displaying different background noise values as indicated. The number of MP58–66-specific T-cells plated is shown on the x-axis, while the y-axis displays the SFC counted per well after basal background subtraction. All counts were above the basal ±3 SD cutoff value for a positive response, as calculated from the respective background noise levels. A representative experiment of two performed is shown.

FIG. 1.

ELISpot detection of epitope-specific signals over different background noise levels. Flu MP58–66-specific T-cells were serially diluted into PBMCs displaying different background noise values as indicated. The number of MP58–66-specific T-cells plated is shown on the x-axis, while the y-axis displays the SFC counted per well after basal background subtraction. All counts were above the basal ±3 SD cutoff value for a positive response, as calculated from the respective background noise levels. A representative experiment of two performed is shown.

FIG. 2.

Summary of HLA-A2+ type 1 diabetic patients (n = 15) and healthy control subjects (n = 15) assayed for β-cell reactivities by ISL8Spot close to diagnosis (0 months) and at follow-up (7–14 months, as indicated). All values, including basal ±n SD cutoffs, are expressed as SFC/106 PBMCs and are basal subtracted. Unsubtracted basal values (reactivities to DMSO and gag77–85) are shown in the last row of each column. Reactivities are ranked as low (between 3 and 4 SD, in yellow), intermediate (between 4 and 5 SD, in orange), and high (>5 SD, in red). ++++, off-scale ELISpot reading.

FIG. 2.

Summary of HLA-A2+ type 1 diabetic patients (n = 15) and healthy control subjects (n = 15) assayed for β-cell reactivities by ISL8Spot close to diagnosis (0 months) and at follow-up (7–14 months, as indicated). All values, including basal ±n SD cutoffs, are expressed as SFC/106 PBMCs and are basal subtracted. Unsubtracted basal values (reactivities to DMSO and gag77–85) are shown in the last row of each column. Reactivities are ranked as low (between 3 and 4 SD, in yellow), intermediate (between 4 and 5 SD, in orange), and high (>5 SD, in red). ++++, off-scale ELISpot reading.

FIG. 3.

Longitudinal follow-up of ISL8Spot responses against β-cell epitopes in type 1 diabetic patients. Patients previously tested by ISL8Spot close to diagnosis (Fig. 2) (0 months here) were reassayed after 7–14 months follow-up, as indicated. Reactivities testing positive at either time point are depicted and ranked as absent (<3 SD above basal), low (3–4 SD), medium (4–5 SD), and high (>5 SD) as in Fig. 2. Responses against a pool of viral epitopes are included as positive controls.

FIG. 3.

Longitudinal follow-up of ISL8Spot responses against β-cell epitopes in type 1 diabetic patients. Patients previously tested by ISL8Spot close to diagnosis (Fig. 2) (0 months here) were reassayed after 7–14 months follow-up, as indicated. Reactivities testing positive at either time point are depicted and ranked as absent (<3 SD above basal), low (3–4 SD), medium (4–5 SD), and high (>5 SD) as in Fig. 2. Responses against a pool of viral epitopes are included as positive controls.

FIG. 4.

A: Cumulative prevalence of CD8+ T-cell responses against epitopes derived from PPI, GAD, IA-2, and IGRP in type 1 diabetic patients at diagnosis and at follow-up. *P < 0.02. B: Prevalence of single epitope specificities in type 1 diabetic patients at diagnosis and at follow-up. **P < 0.05.

FIG. 4.

A: Cumulative prevalence of CD8+ T-cell responses against epitopes derived from PPI, GAD, IA-2, and IGRP in type 1 diabetic patients at diagnosis and at follow-up. *P < 0.02. B: Prevalence of single epitope specificities in type 1 diabetic patients at diagnosis and at follow-up. **P < 0.05.

FIG. 5.

Relative distribution of epitope specificities. The percent prevalence of each epitope out of all epitopes recognized at diagnosis (n = 34) and at follow-up (n = 16) is shown.

FIG. 5.

Relative distribution of epitope specificities. The percent prevalence of each epitope out of all epitopes recognized at diagnosis (n = 34) and at follow-up (n = 16) is shown.

TABLE 1

The HLA-A2–restricted β-cell epitope panel used in the ISL8Spot assay

NameSequence
PPI2–10 ALWMRLLPL 
PIB10–18 (PPI34–42) HLVEALYLV 
PIB18–27 (PPI42–51) VCGERGFFYT 
PIA12–20 (PPI101–109) SLYQLENYC 
GAD65114–123 VMNILLQYVV 
GAD65536–545 RMMEYGTTMV 
IA-2206–214 VIVMLTPLV 
IGRP228–236 LNIDLLWSV 
IGRP265–273 VLFGLGFAI 
NameSequence
PPI2–10 ALWMRLLPL 
PIB10–18 (PPI34–42) HLVEALYLV 
PIB18–27 (PPI42–51) VCGERGFFYT 
PIA12–20 (PPI101–109) SLYQLENYC 
GAD65114–123 VMNILLQYVV 
GAD65536–545 RMMEYGTTMV 
IA-2206–214 VIVMLTPLV 
IGRP228–236 LNIDLLWSV 
IGRP265–273 VLFGLGFAI 

For PPI, aa numbering is given with respect to both the proinsulin sequence alone (aa B1-A21) and the complete PPI sequence (aa 1–110).

TABLE 2

Characteristics of HLA-A2+ new-onset type 1 diabetic (T1D) patients (n = 15) and age-matched healthy control subjects (n = 15)

Age at 1st draw (years)SexDiabetes duration at diagnosis (days)Follow-up time (months)HLA DRB1GAD autoantibody (AU)
IA-2 autoantibody (AU)
Insulin (auto)antibody (AU)
1st2nd1st2nd1st2nd
T1D patients            
    P01 38 180 13 04–16 2,817 2,384 13 21 2.54 4.43 
    P02 37 60 11 07–08 2,898 1,118 30 16 0.24 0.89 
    P03 43 11 11–15 60 61 11 18 0.30 0.45 
    P04 21 24 16 03–16 2,894 2,044 598 244 0.40 1.36 
    P05 23 16 04–13 58 — 18 — 0.31 21.7 
    P06 33 19 14 — 2,114 5,102 505 1,204 0.44 1.99 
    P07 16 01–01 1,441 1,413 578 315 0.29 2.30 
    P08 18 13 14 03–15 883 722 11 35 0.35 9.30 
    P09 29 26 12 03–04 2,548 2,572 1,570 1,334 0.35 2.10 
    P10 27 70 14 03–13 3,292 3,105 30 22 0.77 0.43 
    P11 24 03–13 1,602 88 2,876 3,688 0.33 11.4 
    P12 40 04–07 298 273 20 19 0.62 3.47 
    P13 18 01–04 2,352 2,105 5,542 5,255 0.40 3.84 
    P14 22 11 01–03 190 125 21 14 0.33 3.25 
    P15 25 04–11 114 87 3,638 1,872 0.58 3.05 
Healthy subjects            
    H01 25  24 13–14 — 40 — 19 — 0.23 
    H02 26  16 09–11 — 37 — 11 — 0.20 
    H03 30  26 01–16 — 41 — 15 — 0.37 
    H04 32  15 11–16 — 60 — 10 — 0.22 
    H05 33  15 04–14 — 63 — — 0.23 
    H06 27  14 07–15 — 25 — 16 — 0.26 
    H07 26  22 08–11 — 43 — 13 — 0.47 
    H08 25  22 11–15 — 48 — 14 — 0.36 
    H09 23  12 03–11 — 41 — 26 — 0.76 
    H10 27  14 01–11 — 66 — 28 — 0.20 
    H11 26  14 01–07 — 43 — 27 — 0.28 
    H12 26  14 13–15 — 70 — 14 — 0.15 
    H13 25  14 — — 76 — 20 — 0.28 
    H14 48  14 04–11 — 70 — 13 — 0.13 
    H15 33  13 07–13 — 60 — 14 — 0.49 
Age at 1st draw (years)SexDiabetes duration at diagnosis (days)Follow-up time (months)HLA DRB1GAD autoantibody (AU)
IA-2 autoantibody (AU)
Insulin (auto)antibody (AU)
1st2nd1st2nd1st2nd
T1D patients            
    P01 38 180 13 04–16 2,817 2,384 13 21 2.54 4.43 
    P02 37 60 11 07–08 2,898 1,118 30 16 0.24 0.89 
    P03 43 11 11–15 60 61 11 18 0.30 0.45 
    P04 21 24 16 03–16 2,894 2,044 598 244 0.40 1.36 
    P05 23 16 04–13 58 — 18 — 0.31 21.7 
    P06 33 19 14 — 2,114 5,102 505 1,204 0.44 1.99 
    P07 16 01–01 1,441 1,413 578 315 0.29 2.30 
    P08 18 13 14 03–15 883 722 11 35 0.35 9.30 
    P09 29 26 12 03–04 2,548 2,572 1,570 1,334 0.35 2.10 
    P10 27 70 14 03–13 3,292 3,105 30 22 0.77 0.43 
    P11 24 03–13 1,602 88 2,876 3,688 0.33 11.4 
    P12 40 04–07 298 273 20 19 0.62 3.47 
    P13 18 01–04 2,352 2,105 5,542 5,255 0.40 3.84 
    P14 22 11 01–03 190 125 21 14 0.33 3.25 
    P15 25 04–11 114 87 3,638 1,872 0.58 3.05 
Healthy subjects            
    H01 25  24 13–14 — 40 — 19 — 0.23 
    H02 26  16 09–11 — 37 — 11 — 0.20 
    H03 30  26 01–16 — 41 — 15 — 0.37 
    H04 32  15 11–16 — 60 — 10 — 0.22 
    H05 33  15 04–14 — 63 — — 0.23 
    H06 27  14 07–15 — 25 — 16 — 0.26 
    H07 26  22 08–11 — 43 — 13 — 0.47 
    H08 25  22 11–15 — 48 — 14 — 0.36 
    H09 23  12 03–11 — 41 — 26 — 0.76 
    H10 27  14 01–11 — 66 — 28 — 0.20 
    H11 26  14 01–07 — 43 — 27 — 0.28 
    H12 26  14 13–15 — 70 — 14 — 0.15 
    H13 25  14 — — 76 — 20 — 0.28 
    H14 48  14 04–11 — 70 — 13 — 0.13 
    H15 33  13 07–13 — 60 — 14 — 0.49 

Data are n. The 97.5th percentile cutoff values for (auto)antibody titers are as follows: GAD autoantibodies, 180 arbitrary units (AUs). IA-2 autoantibody, 80 AU; insulin (auto)antibodies, 0.7 AU. Positive autoantibody titers are displayed in boldface, while changes in titers >33% are italicized. —, not determined.

Published ahead of print at http://diabetes.diabetesjournals.org on 27 February 2008. DOI: 10.2337/db07-1594.

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.

See accompanying commentary, p. 1156.

This study was supported by the Juvenile Diabetes Research Foundation International grants 1-2005-39 and 20-2006-1095. R.M. was recipient of an Accueil Chercheur Etranger Fellowship of the Fondation Recherche Médicale.

This study was presented at the 9th International Congress of the Immunology of Diabetes Society and American Diabetes Association Research Symposium, Miami, Florida, 14–18 November 2007.

1.
Wong FS, Janeway CA Jr: The role of CD4 vs. CD8 T cells in IDDM.
J Autoimmun
13
:
290
–295,
1999
2.
DiLorenzo TP, Serreze DV: The good turned ugly: immunopathogenic basis for diabetogenic CD8+ T cells in NOD mice.
Immunol Rev
204
:
250
–263,
2005
3.
Wong FS, Visintin I, Wen L, Flavell RA, Janeway CA Jr.: CD8 T cell clones from young nonobese diabetic (NOD) islets can transfer rapid onset of diabetes in NOD mice in the absence of CD4 cells.
J Exp Med
183
:
67
–76,
1996
4.
Nagata M, Santamaria P, Kawamura T, Utsugi T, Yoon JW: Evidence for the role of CD8+ cytotoxic T cells in the destruction of pancreatic beta-cells in nonobese diabetic mice.
J Immunol
152
:
2042
–2050,
1994
5.
Graser RT, DiLorenzo TP, Wang F, Christianson GJ, Chapman HD, Roopenian DC, Nathenson SG, Serreze DV: Identification of a CD8 T cell that can independently mediate autoimmune diabetes development in the complete absence of CD4 T cell helper functions.
J Immunol
164
:
3913
–3918,
2000
6.
Amrani A, Verdaguer J, Serra P, Tafuro S, Tan R, Santamaria P: Progression of autoimmune diabetes driven by avidity maturation of a T-cell population.
Nature
406
:
739
–742,
2000
7.
DiLorenzo TP, Lieberman SM, Takaki T, Honda S, Chapman HD, Santamaria P, Serreze DV, Nathenson SG: During the early prediabetic period in NOD mice, the pathogenic CD8(+) T-cell population comprises multiple antigenic specificities.
Clin Immunol
105
:
332
–341,
2002
8.
Trudeau JD, Kelly-Smith C, Verchere CB, Elliott JF, Dutz JP, Finegood DT, Santamaria P, Tan R: Prediction of spontaneous autoimmune diabetes in NOD mice by quantification of autoreactive T cells in peripheral blood.
J Clin Invest
111
:
217
–223,
2003
9.
Krishnamurthy B, Dudek NL, McKenzie MD, Purcell AW, Brooks AG, Gellert S, Colman PG, Harrison LC, Lew AM, Thomas HE, Kay TW: Responses against islet antigens in NOD mice are prevented by tolerance to proinsulin but not IGRP.
J Clin Invest
116
:
3258
–3265,
2006
10.
Nakayama M, Abiru N, Moriyama H, Babaya N, Liu E, Miao D, Yu L, Wegmann DR, Hutton JC, Elliott JF, Eisenbarth GS: Prime role for an insulin epitope in the development of type 1 diabetes in NOD mice.
Nature
435
:
220
–223,
2005
11.
Nakayama M, Beilke JN, Jasinski JM, Kobayashi M, Miao D, Li M, Coulombe MG, Liu E, Elliott JF, Gill RG, Eisenbarth GS: Priming and effector dependence on insulin B:9–23 peptide in NOD islet autoimmunity.
J Clin Invest
117
:
1835
–1843,
2007
12.
Daniel D, Gill RG, Schloot N, Wegmann D: Epitope specificity, cytokine production profile and diabetogenic activity of insulin-specific T cell clones isolated from NOD mice.
Eur J Immunol
25
:
1056
–1062,
1995
13.
Daniel D, Wegmann DR: Protection of nonobese diabetic mice from diabetes by intranasal or subcutaneous administration of insulin peptide B-(9–23).
Proc Natl Acad Sci U S A
93
:
956
–960,
1996
14.
Toma A, Haddouk S, Briand JP, Camoin L, Gahery H, Connan F, Dubois-Laforgue D, Caillat-Zucman S, Guillet JG, Carel JC, Muller S, Choppin J, Boitard C: Recognition of a subregion of human proinsulin by class I-restricted T cells in type 1 diabetic patients.
Proc Natl Acad Sci U S A
102
:
10581
–10586,
2005
15.
Ouyang Q, Standifer NE, Qin H, Gottlieb P, Verchere CB, Nepom GT, Tan R, Panagiotopoulos C: Recognition of HLA class I–restricted β-cell epitopes in type 1 diabetes.
Diabetes
55
:
3068
–3074,
2006
16.
Standifer NE, Ouyang Q, Panagiotopoulos C, Verchere CB, Tan R, Greenbaum CJ, Pihoker C, Nepom GT: Identification of novel HLA-A*0201–restricted epitopes in recent-onset type 1 diabetic subjects and antibody-positive relatives.
Diabetes
55
:
3061
–3067,
2006
17.
Blancou P, Mallone R, Martinuzzi E, Severe S, Pogu S, Novelli G, Bruno G, Charbonnel B, Dolz M, Chaillous L, van Endert P, Bach JM: Immunization of HLA class I transgenic mice identifies autoantigenic epitopes eliciting dominant responses in type 1 diabetes patients.
J Immunol
178
:
7458
–7466,
2007
18.
Mallone R, Martinuzzi E, Blancou P, Novelli G, Afonso G, Dolz M, Bruno G, Chaillous L, Chatenoud L, Bach JM, van Endert P: CD8+ T-Cell responses identify β-cell autoimmunity in human type 1 diabetes.
Diabetes
56
:
613
–621,
2007
19.
Martinuzzi E, Lemonnier FA, Boitard C, Mallone R: Measurement of CD8+ T-cell responses in human type 1 diabetes.
Ann N Y Acad Sci.
In press
20.
Herold KC, Hagopian W, Auger JA, Poumian-Ruiz E, Taylor L, Donaldson D, Gitelman SE, Harlan DM, Xu D, Zivin RA, Bluestone JA: Anti-CD3 monoclonal antibody in new-onset type 1 diabetes mellitus.
N Engl J Med
346
:
1692
–1698,
2002
21.
Keymeulen B, Vandemeulebroucke E, Ziegler AG, Mathieu C, Kaufman L, Hale G, Gorus F, Goldman M, Walter M, Candon S, Schandene L, Crenier L, De BC, Seigneurin JM, De PP, Pierard D, Weets I, Rebello P, Bird P, Berrie E, Frewin M, Waldmann H, Bach JF, Pipeleers D, Chatenoud L: Insulin needs after CD3-antibody therapy in new-onset type 1 diabetes.
N Engl J Med
352
:
2598
–2608,
2005
22.
Brooks-Worrell BM, Starkebaum GA, Greenbaum C, Palmer JP: Peripheral blood mononuclear cells of insulin-dependent diabetic patients respond to multiple islet cell proteins.
J Immunol
157
:
5668
–5674,
1996
23.
Seyfert-Margolis V, Gisler TD, Asare AL, Wang RS, Dosch HM, Brooks-Worrell B, Eisenbarth GS, Palmer JP, Greenbaum CJ, Gitelman SE, Nepom GT, Bluestone JA, Herold KC: Analysis of T-cell assays to measure autoimmune responses in subjects with type 1 diabetes: results of a blinded controlled study.
Diabetes
55
:
2588
–2594,
2006
24.
Danke NA, Koelle DM, Yee C, Beheray S, Kwok WW: Autoreactive T cells in healthy individuals.
J Immunol
172
:
5967
–5972,
2004
25.
Yang J, Danke NA, Berger D, Reichstetter S, Reijonen H, Greenbaum C, Pihoker C, James EA, Kwok WW: Islet-specific glucose-6-phosphatase catalytic subunit-related protein-reactive CD4+ T cells in human subjects.
J Immunol
176
:
2781
–2789,
2006
26.
Viglietta V, Kent SC, Orban T, Hafler DA: GAD65-reactive T cells are activated in patients with autoimmune type 1a diabetes.
J Clin Invest
109
:
895
–903,
2002
27.
Arif S, Tree TI, Astill TP, Tremble JM, Bishop AJ, Dayan CM, Roep BO, Peakman M: Autoreactive T cell responses show proinflammatory polarization in diabetes but a regulatory phenotype in health.
J Clin Invest
113
:
451
–463,
2004
28.
Monti P, Scirpoli M, Rigamonti A, Mayr A, Jaeger A, Bonfanti R, Chiumello G, Ziegler AG, Bonifacio E: Evidence for in vivo primed and expanded autoreactive T cells as a specific feature of patients with type 1 diabetes.
J Immunol
179
:
5785
–5792,
2007
29.
Martinuzzi E, Scotto M, Enée E, Ribeil JA, van Endert P, Mallone R: Serum-free culture medium and IL-7 costimulation increase the sensitivity of ELISpot detection.
J Immunol Methods.
January 29 2008 [Epub ahead of print]
30.
Hassainya Y, Garcia-Pons F, Kratzer R, Lindo V, Greer F, Lemonnier FA, Niedermann G, van Endert PM: Identification of naturally processed HLA-A2 restricted proinsulin epitopes by reverse immunology.
Diabetes
54
:
2053
–2059,
2005
31.
Takaki T, Marron MP, Mathews CE, Guttmann ST, Bottino R, Trucco M, DiLorenzo TP, Serreze DV: HLA-A*0201-restricted T cells from humanized NOD mice recognize autoantigens of potential clinical relevance to type 1 diabetes.
J Immunol
176
:
3257
–3265,
2006
32.
van Endert P, Hassainya Y, Lindo V, Bach JM, Blancou P, Lemonnier F, Mallone R: HLA class I epitope discovery in type 1 diabetes.
Ann N Y Acad Sci
1079
:
190
–197,
2006
33.
American Diabetes Association: Diagnosis and classification of diabetes mellitus (Position Statement).
Diabetes Care
29
(Suppl. 1):
S43
–S48,
2006
34.
Bingley PJ, Bonifacio E, Mueller PW: Diabetes antibody standardization program: first assay proficiency evaluation.
Diabetes
52
:
1128
–1136,
2003
35.
Zajac AJ, Blattman JN, Murali-Krishna K, Sourdive DJ, Suresh M, Altman JD, Ahmed R: Viral immune evasion due to persistence of activated T cells without effector function.
J Exp Med
188
:
2205
–2213,
1998
36.
Monsurro V, Wang E, Yamano Y, Migueles SA, Panelli MC, Smith K, Nagorsen D, Connors M, Jacobson S, Marincola FM: Quiescent phenotype of tumor-specific CD8+ T cells following immunization.
Blood
104
:
1970
–1978,
2004
37.
Nepom GT, Lippolis JD, White FM, Masewicz S, Marto JA, Herman A, Luckey CJ, Falk B, Shabanowitz J, Hunt DF, Engelhard VH, Nepom BS: Identification and modulation of a naturally processed T cell epitope from the diabetes-associated autoantigen human glutamic acid decarboxylase 65 (hGAD65).
Proc Natl Acad Sci U S A
98
:
1763
–1768,
2001
38.
Mallone R, Kochik SA, Laughlin EM, Gersuk VH, Reijonen H, Kwok WW, Nepom GT: Differential recognition and activation thresholds in human autoreactive GAD-specific T-Cells.
Diabetes
53
:
971
–977,
2004
39.
Peakman M, Stevens EJ, Lohmann T, Narendran P, Dromey J, Alexander A, Tomlinson AJ, Trucco M, Gorga JC, Chicz RM: Naturally processed and presented epitopes of the islet cell autoantigen IA-2 eluted from HLA-DR4.
J Clin Invest
104
:
1449
–1457,
1999
40.
Jaeckel E, Klein L, Martin-Orozco N, Von BH: Normal incidence of diabetes in NOD mice tolerant to glutamic acid decarboxylase.
J Exp Med
197
:
1635
–1644,
2003
41.
Yamamoto T, Yamato E, Tashiro F, Sato T, Noso S, Ikegami H, Tamura S, Yanagawa Y, Miyazaki JI: Development of autoimmune diabetes in glutamic acid decarboxylase 65 (GAD65) knockout NOD mice.
Diabetologia
47
:
221
–224,
2003
42.
Kubosaki A, Nakamura S, Notkins AL: Dense core vesicle proteins IA-2 and IA-2beta: metabolic alterations in double knockout mice.
Diabetes
54
(Suppl. 2):
S46
–S51,
2005
43.
Bonifacio E, Atkinson M, Eisenbarth G, Serreze D, Kay TW, Lee-Chan E, Singh B: International Workshop on Lessons From Animal Models for Human Type 1 Diabetes: identification of insulin but not glutamic acid decarboxylase or IA-2 as specific autoantigens of humoral autoimmunity in nonobese diabetic mice.
Diabetes
50
:
2451
–2458,
2001
44.
Wicker LS, Chen SL, Nepom GT, Elliott JF, Freed DC, Bansal A, Zheng S, Herman A, Lernmark A, Zaller DM, Peterson LB, Rothbard JB, Cummings R, Whiteley PJ: Naturally processed T cell epitopes from human glutamic acid decarboxylase identified using mice transgenic for the type 1 diabetes-associated human MHC class II allele, DRB1*0401.
J Clin Invest
98
:
2597
–2603,
1996
45.
Patel SD, Cope AP, Congia M, Chen TT, Kim E, Fugger L, Wherrett D, Sonderstrup-McDevitt G: Identification of immunodominant T cell epitopes of human glutamic acid decarboxylase 65 by using HLA-DR(alpha1*0101, beta1*0401) transgenic mice.
Proc Natl Acad Sci U S A
94
:
8082
–8087,
1997
46.
Honeyman MC, Stone NL, Harrison LC: T-cell epitopes in type 1 diabetes autoantigen tyrosine phosphatase IA-2: potential for mimicry with rotavirus and other environmental agents.
Mol Med
4
:
231
–239,
1998
47.
Watts C, Lanzavecchia A: Suppressive effect of antibody on processing of T cell epitopes.
J Exp Med
178
:
1459
–1463,
1993
48.
Reijonen H, Daniels TL, Lernmark A, Nepom GT: GAD65-specific autoantibodies enhance the presentation of an immunodominant T-cell epitope from GAD65.
Diabetes
49
:
1621
–1626,
2000
49.
Bernasconi NL, Traggiai E, Lanzavecchia A: Maintenance of serological memory by polyclonal activation of human memory B cells.
Science
298
:
2199
–2202,
2002
50.
Prlic M, Williams MA, Bevan MJ: Requirements for CD8 T-cell priming, memory generation and maintenance.
Curr Opin Immunol
19
:
315
–319,
2007

Supplementary data