A recent discovery effort resulted in identification of novel splice variant and secretory granule antigens within the HLA class I peptidome of human islets and documentation of their recognition by CD8+ T cells from peripheral blood and human islets. In the current study, we applied a systematic discovery process to identify novel CD4+ T cell epitopes derived from these candidate antigens. We predicted 145 potential epitopes spanning unique splice junctions and within conventional secretory granule antigens and measured their in vitro binding to DRB1*04:01. We generated HLA class II tetramers for the 35 peptides with detectable binding and used these to assess immunogenicity and isolate T cell clones. Tetramers corresponding to peptides with verified immunogenicity were then used to label T cells specific for these putative epitopes in peripheral blood. T cells that recognize distinct epitopes derived from a cyclin I splice variant, neuroendocrine convertase 2, and urocortin-3 were detected at frequencies that were similar to those of an immunodominant proinsulin epitope. Cells specific for these novel epitopes predominantly exhibited a Th1-like surface phenotype. Among the three epitopes, responses to the cyclin I peptide exhibited a distinct memory profile. Responses to neuroendocrine convertase 2 were detected among pancreatic infiltrating T cells. These results further establish the contribution of unconventional antigens to the loss of tolerance in autoimmune diabetes.

Type 1 diabetes is a chronic disease in which pancreatic β-cells are selectively destroyed through autoimmune attack. Although CD8+ T cells dominate the pancreatic immune infiltrates (1), the strong genetic association of type 1 diabetes with susceptible HLA class II alleles and the diagnostic importance of islet-specific antibodies both point to a significant role for CD4+ T cell responses in this disease (24). Numerous studies have sought to define the antigens and epitopes recognized by autoreactive T cells in subjects with type 1 diabetes (5). In general, published studies of CD4+ and CD8+ T cell responses have focused on a limited number of “classical” islet antigens, such as preproinsulin (PPI), GAD (GAD65), islet antigen (IA)-2, and zinc transporter 8 (ZnT8), all of which are known to be targeted by autoantibodies. Other work has identified CD4+ T cell epitopes within additional islet-associated proteins that are not standard antibody targets, such as islet-specific glucose-6-phosphatase catalytic subunit-related protein (IGRP) and islet amyloid polypeptide (IAPP) (6,7). T cells that recognize epitopes derived from all of these antigens have been detected in the peripheral blood of subjects with type 1 diabetes with use of HLA class II tetramers (8), and, in some cases, those specificities have been observed among islet-infiltrating T cells (9,10). However, questions remain about the importance and relative immunodominance of this wide array of conventional epitopes. Furthermore, the epitope specificity for the majority of islet-infiltrating T cells that have been cataloged remains unknown (9), leaving open the possibility that additional disease-relevant antigens are yet to be discovered.

It is increasingly appreciated that, as part of the disease process, pancreatic β-cells undergo inflammatory and metabolic insults, which trigger endoplasmic reticulum stress in β-cells (11,12). These insults have been shown to promote the formation of neoepitopes, including enzymatically modified antigens (7,13,14), oxidized antigens (15,16), hybrid peptide sequences (17), and novel polypeptides formed through translation of alternative open reading frames (18). The formation of such neoepitopes has the potential to increase immune recognition of β-cells, eliciting additional inflammation and creating a feed forward loop of dysfunction and destruction (19). Furthermore, it was recently shown that alternative mRNA splicing represents an additional pathway for generating novel antigenic sequences that are underrepresented in the thymus and regarded as nonself. The idea that neoepitope-specific T cells more readily escape negative selection remains an open question, but a recent study demonstrated that a T cell receptor that recognizes a deamidated GAD65 peptide was more readily selected than a T cell receptor that recognizes the corresponding wild-type peptide (20). Recent work demonstrates that a distinct alternative mRNA splicing signature can be induced in β-cells by inflammatory stress (21). Combining a rich HLA class I peptidomics data set obtained from the ECN90 β-cell line with RNA-sequencing data from primary human islets (either untreated or exposed to inflammatory cytokines) led to the identification of tissue-specific mRNA splice variants and other novel secretory granule antigens that are present within human islets (22,23). CD8+ T cells that recognize unique peptide sequences from these proteins were detectable within peripheral blood and pancreatic infiltrates by tetramer staining. Importantly, pancreas-infiltrating T cells reactive to three of these novel epitopes were observed to be enriched in individuals with type 1 diabetes (23). CD8+ T cells reactive to the murine orthologs of granule antigens were also observed in the islet infiltrates of prediabetic NOD mice and were diabetogenic on in vivo transfer into NOD.scid mice, with a potency equivalent to that of PPI-reactive CD8+ T cells.

In light of these compelling findings, we sought to investigate CD4+ T cell responses directed against unique splice variant and secretory granule protein–derived peptides. We screened a library of peptides to identify novel CD4+ T cell epitopes restricted by the prevalent type 1 diabetes–susceptible HLA-DRB1*04:01 allele. We then applied a systematic approach to identify novel immunogenic peptides. We next used the corresponding HLA class II tetramers to evaluate the frequency and phenotype of T cells reactive to these novel epitopes in the peripheral blood of subjects with type 1 diabetes and HLA-matched control subjects. Collectively, our work supports the relevance of these novel antigens and epitopes in HLA-DRB1*04:01+ subjects with type 1 diabetes.

Human Subjects

The study was approved by Benaroya Research Institute’s institutional review board (protocol no. IRB07109). All samples were obtained under approved research protocols and with written informed consent. For epitope-defining studies, peripheral blood was collected from 15 individuals with type 1 diabetes (Supplementary Table 1), immediately processed, and used for in vitro T cell assays. Peripheral blood was also collected and cryopreserved from 10 additional individuals with type 1 diabetes (Supplementary Table 2) and 8 age-matched healthy control subjects (Supplementary Table 3). Cryopreserved samples were thawed in batches for direct tetramer staining. All subjects carried at least one HLA-DRB1*04:01 allele. Subject characteristics are summarized in Supplementary Tables 1–3. Live pancreas slices from Network for Pancreatic Organ donors with Diabetes (nPOD) donor 6472 (Supplementary Table 4) were distributed through the nPOD Pancreas Slice Program under approved research protocols after written consent was obtained for research from organ donor families.

Epitope Prediction and Peptide Synthesis

Putative epitopes from splice variant junction regions and secretory granule proteins were selected with use of a prediction algorithm as previously described (24,25). Briefly, we calculated motif scores by multiplying coefficients corresponding to each anchor residue for all possible core 9-mers within each unique junction region and peptides spanning each secretory granule protein of interest. All peptides with plausible binding motifs were synthesized (Sigma-Aldrich, St. Louis, MO). A more comprehensive peptide set was synthesized for the secretory granule protein proprotein convertase subtilisin/kexin type 2 (PCSK2). As summarized in Supplementary Table 5, a total of 145 peptides were synthesized. All peptides were dissolved in DMSO to a stock concentration of 20 mg/mL and used for binding assays. Those with detectable binding were subsequently used for tetramer production and T cell studies.

Peptide Binding to HLA-DRB1*04:01

Binding was assessed through a competition assay, as previously described (26). Briefly, increasing concentrations of each peptide were incubated in competition with a biotinylated reference influenza hemagglutinin peptide (HA306-318) at 0.02 μmol/L in wells coated with DR0401 protein. After washing, residual biotin–HA306-318 was detected with use of europium-conjugated streptavidin (PerkinElmer, Waltham, MA) and quantified with a VICTOR2 D time-resolved fluorometer (PerkinElmer). Curves were simulated with Prism software (version 7.0; GraphPad Software, San Diego, CA) and IC50 values calculated as the concentration needed to displace 50% of the reference peptide.

Preparation of HLA Class II Protein and Tetramer Reagents

DR0401 protein was purified from insect cell cultures as previously described (27,28). Monomers were loaded with 0.2 mg/mL peptide at 37°C for 72 h in the presence of 0.2 mg/mL n-Dodecyl β-D-maltoside and 1 mmol/L Pefabloc (Sigma-Aldrich). Individual peptide-loaded monomers for all candidate epitopes were conjugated into tetramers with R-PE streptavidin (Invitrogen, Waltham, MA) at a molar ratio of 8:1 and used to stain in vitro expanded T cells. For direct ex vivo staining, R-PE streptavidin–conjugated CCNI-00819–37 (CCNI-008 p4, HTATPLDFLHIMDSSQLIH), phycoerythrin (PE)-CF594 streptavidin (BD)–conjugated PCSK2243–257 (PCSK2 p23, QPFMTDIIEASSISH), PE-Cy5 streptavidin (BD)–conjugated UCN358–72 (UCN3 p5, SFHYLRSRDASSGEE), PE-Cy7 streptavidin (SouthernBiotech, Birmingham, AL)–conjugated PPI76–90 (SLQPLALEGSLQKRG), and BV421 streptavidin (BioLegend, San Diego, CA)–conjugated influenza MP97–116 (MP p54, VKLYRKLKREITFHGAKEIS) tetramers were prepared to provide multicolor, single tube tetramer staining.

In Vitro Tetramer Assays and T Cell Clone Isolation

Peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll underlay, counted, and either frozen in 7% DMSO and subsequently rethawed or immediately used for assays. PBMCs were suspended in T cell medium (RPMI, 10% pooled human serum, 1% penicillin-streptomycin, 1% l-glutamine) at 4 × 106 cells/mL and stimulated with peptides (20 µg/mL total) in 48-well plates for 14 days, with addition of medium and IL-2 starting on day 7. Cells were stained with individual PE-labeled tetramers for 75 min at 37°C, followed by CD3 BV510 (BioLegend), CD4 BUV395 (BD Biosciences, Franklin Lakes, NJ), CD25 FITC (BioLegend), and a combination of CD14 PerCP-Cy5.5 (Thermo Fisher Scientific, Waltham, MA), CD19 PerCP-Cy5.5 (BioLegend), and Viaprobe (BD Biosciences) for 15 min at 4°C, run on an LSR II (BD), and analyzed with FlowJo (Tree Star, Ashland, OR). Clones were isolated by sorting of single tetramer-positive CD4+ T cells with a FACSAria (BD) and expanded in 96-well plates in the presence of 1 × 105 irradiated PBMCs and 2 µg/mL phytohemagglutinin (PHA) (Remel, Lenexa, KS), with addition of media and IL-2 starting on day 10.

T Cell Clone Maintenance and Characterization

Clones specific for each immunogenic peptide were maintained in RPMI medium supplemented with 10% human serum and restimulated as needed with 2 µg/mL PHA. For assessment of specificity, T cell clones were restained with HLA class II tetramers or assayed for assessment of peptide-specific proliferation as follows: 104 T cells/well were plated with 1 × 105 irradiated DR0401+ PBMCs and stimulated in triplicate with 10 µg/mL peptide. After incubation for 48 h at 37°C and pulsing with medium containing 3[H]-thymidine (1 µCi/well), incorporation was measured 18 h later with a scintillation counter.

Ex Vivo Tetramer Staining

Ex vivo tetramer staining and enrichment were accomplished with use of previously published protocols (29,30). A total of 20 million PBMCs were thawed and treated with dasatinib for 10 min at 37°C. PBMCs were then stained with 4.5 µL CCNI-PE, 8 µL PCSK2-PE-CF594, 4.5 µL UCN3-PE-Cy5, 4.5 µL PPI76–90–PE–Cy7, and 4.5 µL influenza MP97–116–BV421–labeled tetramers at room temperature for 90 min. Cells were washed and enriched as previously described (29,30), through incubation with anti-PE and anti-Myc magnetic beads (Miltenyi Biotec, Bergisch Gladbach, Germany) (an Myc tag was incorporated in the influenza MP97–116 tetramer) at 4°C for 20 min and washed again, and a 1/100th fraction was saved for antibody staining (“Pre”). The other fraction was passed through an MS Column (Miltenyi Biotec). Bound or PE-, PE-Cy5–, PE-CF594–, PE-Cy7–, or BV421-labeled cells were flushed and collected. Both enriched (Post) and nonenriched (Pre) fractions were labeled with SYTOX Green, anti–CD4-V500 (BD), anti–CD45RA-AF700 (BD), anti–CXCR3-BV785 (BioLegend), anti–CCR4-BV605 (BioLegend), anti–CCR7-BV650 (BD), anti–CD14-FITC (BioLegend), and anti-CD19-FITC (BioLegend). Samples were run on a BD LSR II flow cytometer, and data were analyzed with FlowJo software, version 10. Single T cells (defined as a SYTOX negative and CD4+/CD14/CD19) were gated to exclude cells that were positive for more than one tetramer fluorophore. Each tetramer-positive T-cell population was then gated to determine the number of positive events and analyzed for surface receptor expression. The frequency (F) of epitope-specific T cells per million CD4+ T cells was calculated as follows: F = (1,000,000 × tetramer-positive events from enriched tube)/(100 × number of CD4+ T cells from the Pre fraction). T-cell lineages were first assigned based on CD45RA and CCR7 expression: naive (CD45RA+CCR7+), central memory (CD45RACCR7+), effector memory (CD45RACCR7), and terminal effectors (CD45RA+CCR7). The memory cell populations were pooled and subdivided into Th1-like (CXCR3+, CCR4, and CCR6), Th2-like (CXCR3, CCR4+, and CCR6), Th17-like (CXCR3, CCR4+, and CCR6+), Th1/17-like (CXCR3+, CCR4, and CCR6+), Th1/2-like (CXCR3+, CCR4+, and CCR6), or Th1*-like (CXCR3+, CCR4+, and CCR6+) lineages (3133).

Islet-Derived T-Cell Assays

Cell lines from islet-infiltrating T cells were recovered and expanded from nPOD donor 6472 (see Supplementary Table 4). For generation of lines, 150 µm live slices of pancreas were digested with 1 mg/mL collagenase-P (Sigma-Aldrich) in Hanks’ balanced salt solution with Mg2+/Ca2+ (Thermo Fisher Scientific) for 10 min in a 37°C water bath with vigorous shaking. Digestion was confirmed by visual inspection. Digested tissue was washed and placed in medium (AIM-V supplemented with 2 mmol/L l-glutamine, 5 mmol/L HEPES, 100 units/mL penicillin, and 100 μg/mL streptomycin, 0.1 mmol/L of each nonessential amino acid, and 1 mmol/L sodium pyruvate (all from Lonza, Walkersville, MD) with 5% heat-inactivated human male AB serum (Access Biologicals, Vista, CA). Individual islets were handpicked and placed in culture with irradiated PBMCs as feeders, a blocking anti-Fas antibody (1 µg/mL; eBiosciences, now Applied Biosystems, Santa Clara, CA); anti–PD-1 antibody (1 µg/mL; BD Biosciences), and mifepristone (100 nmol/L; Invitrogen). After 12 days of culture, T cell lines were collected and further expanded in culture and then phenotyped by flow cytometry for T cell subsets. Epstein-Barr virus-transformed lymphoblastoid B-cell lines (B-LCL) were generated from autologous splenocytes. Responsiveness was assessed as previously described (17). Briefly, irradiated autologous B-LCL (generated as previously described [9]), were plated (20,000/well) after pulsing with peptides at 50 µg/mL, no peptide, or DMSO volume control and then cultured with T cells (50,000/well) and anti-CD28 (5 µg/mL) for costimulation. After 48 h of coculture, responses were detected by IFN-γ ELISA. The islet T cell line previously reactive with PCSK2 peptide was restimulated with autologous B-LCL pulsed with the peptide and expanded in culture. At rest, the islet T cell line (30,000 viable cells per well) was challenged with autologous B-LCL pulsed with individual peptides and tested for cytokine secretion by IFN-γ ImmunoSpot (all reagents from CTL) after 24 h of coculture.

Statistics

All statistical analysis was performed with GraphPad Prism 9.2.0. The tests used (as appropriate) included unpaired t tests, Wilcoxon signed rank test, Mann-Whitney U tests, and Kruskal-Wallis test with Dunn multiple comparison test. P values <0.05 were considered significant. Marginal P values ≤0.1 are also provided, when applicable. Biological sex was considered as a variable, but no significant associations were observed.

Data and Resource Availability

The data sets generated or analyzed during the current study are available from the corresponding author on reasonable request.

Identification of Immunogenic Splice Variant and Secretory Granule Peptides

Through combined human islet transcriptomics and β-cell HLA peptidomics data sets, we previously identified a set of candidate antigens consisting of 10 mRNA splice antigens (cyclin I splice variant CCNI-008, GAD2 splice variant GAD2-003, guanine nucleotide binding protein variants GNAS-002 and GNAS-036, islet amyloid polypeptide variant IAPP-002, protein tyrosine phosphatase receptor type N variant PTPRN-021, receptor-type tyrosine-protein phosphatase N2 variant PTPRN2-005, RNA exonuclease 2 variant REXO2-020, secretogranin-5 variant SCG5-009, and solute carrier family 30 member 8 variant SLC30A8-002), peptide splice antigens (an IAPP/IAPP cis-spliced antigen and a neuropeptide Y [NPY]/SCG5 trans-spliced antigen), and three conventional secretory granule proteins (SCG5, urocortin-3 [UCN3], and PCSK2) (23). To facilitate efficient screening of the large number of possible peptides from these candidate proteins, we applied a previously described algorithm to identify peptides with predicted HLA-DRB1*04:01 binding motifs (24,25). To predict possible motifs, we considered each unique splice junction within CCNI-008, GAD2-003, GNAS-002, GNAS-036, IAPP-002, PTPRN-021, PTPRN2-005, REXO2-020, SCG5-009, and SLC30A8-002 and the full sequences of SCG5, UCN3, PCSK2, cis-spliced IAPP/IAPP, and trans-spliced NPY/SCG5 (also including N-terminal and C-terminal flanking residues to generate a minimum peptide length of 15). We identified a total of 145 candidate peptides (Supplementary Table 3). These were synthesized and tested for their ability to bind HLA-DRB1*04:01. In total, 35 peptides had detectable binding to HLA-DRB1*04:01 (Supplementary Table 3). The reduced fraction of peptides with confirmed binding was expected because we chose a lower cutoff value than used in our previous study (34). For these 35 peptides, we next assessed their immunogenicity by stimulating CD4+ T cells from PBMCs of HLA-DRB1*04:01+ subjects with type 1 diabetes and staining with each of the corresponding HLA class II tetramers after 2 weeks of in vitro expansion. Among the peptides tested, CCNI-00819–37 (CCNI), PCSK2243–257 (PCSK2), and UCN358–72 (UCN3) were determined to be immunogenic, based on their ability to elicit a detectable population of tetramer-positive CD4+ T cells after expanding T cells from subjects with type 1 diabetes (Fig. 1A). In performing T cell expansions in a total of 15 donors with type 1 diabetes (Supplementary Table 1), 5 showed positive staining for CCNI, 4 showed positive staining for PCSK2, and 8 showed positive staining for UCN3. In addition, we were able to isolate T cell clones for these epitopes (by sorting and expanding single tetramer–positive T cells), which proliferated robustly in response to their cognate peptide but not to an irrelevant peptide (Fig. 1B). The sequences and predicted minimal motifs for these peptides are summarized in Table 1. Among these three peptides, the CCNI splice variant peptide is a neoepitope with a sequence that differs substantially from the corresponding conventional CCNI sequence (sharing only 57.9% identity), resulting in a distinct predicted binding register (Table 2). The PCSK2 and UCN3 peptides are conventional epitopes. The remaining 32 peptides were not advanced for further study either because they failed to elicit measurable responses in more than one subject (29 peptides) or because the tetramer staining pattern was atypical and could not be confirmed by isolating a peptide-responsive T cell clone (3 peptides).

Figure 1

Recognition of novel epitopes by T cells from subjects with type 1 diabetes. A: T cells that recognize epitopes derived from splice variants and novel secretory granule antigens could be expanded from peripheral blood after peptide-specific expansion and detected by HLA class II tetramer staining. Representative results from one individual donor per epitope are shown (out of 15 subjects with T1D, Supplementary Table 1). B: Epitope-specific T cell clones were isolated by sorting and expanding single tetramer–positive T cells. Each clone proliferated robustly (stimulation index >100) in response to 10 µg/mL of its cognate peptide and exhibited negligible proliferation (stimulation index <2) in response to 10 µg/mL of an irrelevant peptide. Data are represented as stimulation index values, which we calculated by normalizing the proliferation of each clone based on [3H]-thymidine incorporation of unstimulated wells. Irrel., Irrelevant; Rel., Relevant; Tmr, Tetramer.

Figure 1

Recognition of novel epitopes by T cells from subjects with type 1 diabetes. A: T cells that recognize epitopes derived from splice variants and novel secretory granule antigens could be expanded from peripheral blood after peptide-specific expansion and detected by HLA class II tetramer staining. Representative results from one individual donor per epitope are shown (out of 15 subjects with T1D, Supplementary Table 1). B: Epitope-specific T cell clones were isolated by sorting and expanding single tetramer–positive T cells. Each clone proliferated robustly (stimulation index >100) in response to 10 µg/mL of its cognate peptide and exhibited negligible proliferation (stimulation index <2) in response to 10 µg/mL of an irrelevant peptide. Data are represented as stimulation index values, which we calculated by normalizing the proliferation of each clone based on [3H]-thymidine incorporation of unstimulated wells. Irrel., Irrelevant; Rel., Relevant; Tmr, Tetramer.

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Table 1

Sequences and binding affinities of splice variant and secretory granule epitopes

PeptideAmino acid sequenceaProtein sourceIC50 (µmol/L)bResponse ratec
CCNI-00819–37 HTATPLDFLHIMDSSQLIH Cyclin I splice variant 7.2 90% 
PCSK2243–257 QPFMTDIIEASSISH Neuroendocrine convertase 2 0.07 60% 
UCN358–72 SFHYLRSRDASSGEE Urocortin-3 0.94 100% 
PeptideAmino acid sequenceaProtein sourceIC50 (µmol/L)bResponse ratec
CCNI-00819–37 HTATPLDFLHIMDSSQLIH Cyclin I splice variant 7.2 90% 
PCSK2243–257 QPFMTDIIEASSISH Neuroendocrine convertase 2 0.07 60% 
UCN358–72 SFHYLRSRDASSGEE Urocortin-3 0.94 100% 
a

The best predicted minimal epitope is shown in boldface type. Secondary motifs are underlined.

b

IC50 represents the peptide concentration that displaces one-half of the reference peptide.

c

Ex vivo responses were examined in a total of 10 subjects with established type 1 diabetes. The response rate (percentage of subjects with a detectable population of tetramer-positive T cells) is listed for each novel epitope.

Table 2

Sequence comparison of splice variant and nonvariant CCNI

PeptideAmino acid sequencea
CCNI-00819–37 HTATPLDFLHIMDSSQLIH 
CCNI143–161 HTATPLDFLHIFHAIAVST 
PeptideAmino acid sequencea
CCNI-00819–37 HTATPLDFLHIMDSSQLIH 
CCNI143–161 HTATPLDFLHIFHAIAVST 
a

The best predicted minimal epitope is underlined. Conserved amino acids appear in boldface type.

Novel Epitope-Specific CD4+ T Cells Are Detectable in Subjects with Diabetes

We next asked whether T cells that recognize these three novel epitopes are detectable in the peripheral blood of individuals with type 1 diabetes (Supplementary Table 1). We directly assessed the frequency and surface phenotype of epitope-specific T cells using a multiplex HLA class II tetramer staining approach that allows ex vivo enrichment and detection of multiple tetramer specificities in a single peripheral blood sample and costaining with cell surface marker antibodies (10). The flow cytometry panel included tetramers corresponding to three novel immunogenic peptides: CCNI, PCSK2, and UCN3 plus a PPI76–90 reference tetramer that was previously reported as an immunodominant self-epitope (8). Each of the three novel tetramers was effective at labeling a distinct population of T cells (Fig. 2A shows a positive staining result for each tetramer), albeit with lower fluorescence intensity for CCNI. Staining above the limit of detection (previously reported as 1 cell per million for direct HLA class II tetramer staining [35]) was observed in 9 of 10 subjects for CCNI, 6 of 10 subjects for PCKS2, 10 of 10 subjects for UCN3, and 10 of 10 subjects for PPI (Fig. 2B). Individual subjects had variable frequencies of T cells specific for these epitopes (Fig. 2C). For example, subjects T1D no. 1 and no. 6 had comparatively high frequencies of PPI- and PCSK2-specific T cells and lower frequencies of CCNI- and UCN3-specific T cells, whereas T1D no. 3 had high frequencies of CCNI- and PCSK2-specific T cells and lower frequencies of UCN3- and PPI-specific T cells.

Figure 2

T cells specific for novel epitopes have variable frequencies in subjects with type 1 diabetes. T cells specific for the novel epitopes identified were directly enumerated in peripheral blood using a magnetic enrichment procedure. A: HLA class II tetramers were used to enumerate CD4+ T cells for three novel epitopes plus a reference PPI epitope. Cells were gated based on size, viability, and lack of CD14/CD19 expression and CD4 expression and then displayed on a CD4 vs. tetramer (PE, PE-CF594, PE-Cy5, or PE-Cy7 labeled). A typical result, representative of 10 subjects with T1D examined (Supplementary Table 2), is shown. Each upper panel shows a precolumn fraction, used to determine the total number of CD4+ T cells in the unmanipulated sample and to set a threshold for positive tetramer staining. Each lower panel shows the corresponding enriched (postcolumn) fraction, used to determine the total number of epitope-specific CD4+ T cells in the sample. B: Summary of ex vivo frequencies for each epitope measured in the peripheral blood of 10 subjects with established type 1 diabetes (Supplementary Table 2). The dotted line indicates the previously reported limit of detection for direct tetramer staining. CCNI and PPI were more frequent (5.6 and 5.3 cells per million, respectively) than PCSK2 and UCN3 (3.9 and 3.6 cells per million). C: A heat map of the same frequency data reveals unique patterns of reactivity in different subjects with type 1 diabetes. Post, enriched fractions; Pre, nonenriched fractions; Tmr, Tetramer.

Figure 2

T cells specific for novel epitopes have variable frequencies in subjects with type 1 diabetes. T cells specific for the novel epitopes identified were directly enumerated in peripheral blood using a magnetic enrichment procedure. A: HLA class II tetramers were used to enumerate CD4+ T cells for three novel epitopes plus a reference PPI epitope. Cells were gated based on size, viability, and lack of CD14/CD19 expression and CD4 expression and then displayed on a CD4 vs. tetramer (PE, PE-CF594, PE-Cy5, or PE-Cy7 labeled). A typical result, representative of 10 subjects with T1D examined (Supplementary Table 2), is shown. Each upper panel shows a precolumn fraction, used to determine the total number of CD4+ T cells in the unmanipulated sample and to set a threshold for positive tetramer staining. Each lower panel shows the corresponding enriched (postcolumn) fraction, used to determine the total number of epitope-specific CD4+ T cells in the sample. B: Summary of ex vivo frequencies for each epitope measured in the peripheral blood of 10 subjects with established type 1 diabetes (Supplementary Table 2). The dotted line indicates the previously reported limit of detection for direct tetramer staining. CCNI and PPI were more frequent (5.6 and 5.3 cells per million, respectively) than PCSK2 and UCN3 (3.9 and 3.6 cells per million). C: A heat map of the same frequency data reveals unique patterns of reactivity in different subjects with type 1 diabetes. Post, enriched fractions; Pre, nonenriched fractions; Tmr, Tetramer.

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T Cells Specific for Novel Epitopes Have Distinct Phenotypes

To draw inferences about T cell lineages, we used cell surface marker expression to characterize tetramer-positive T cells that recognized these novel specificities. The cell surface markers present in our panel included CD45RA and CCR7, which we used to classify T cells as naive, central memory (TCM), effector memory (TEM), and terminal effectors (TEMRA) (Fig. 3A). To draw inferences about the cell subset distribution for T cells that recognized these novel specificities, we used CXCR3, CCR4, CXCR5, and CCR6 expression to define T helper subsets using established lineage definitions: Th1-like (CXCR3+, CCR4, and CCR6), Th2-like (CXCR3, CCR4+, and CCR6), Th17-like (CXCR3, CCR4+, and CCR6+), Th1/17-like (CXCR3+, CCR4, and CCR6+), Th1/2-like (CXCR3+, CCR4+, and CCR6), or Th1*-like (CXCR3+, CCR4+, and CCR6+) lineages (3133) (Fig. 3B). For each novel epitope and PPI, approximately one-half of the T cells exhibited an apparently naive phenotype. We observed apparent differences in the memory distribution of CCNI-specific T cells as compared with PCSK2, UCN3, and PPI. In particular, CCNI had a distinct memory profile, with a significantly lower proportion of naive cells than UCN3 and that trended lower than PCSK2 (Fig. 3C). Subset analysis suggested that CCNI-, PCSK2-, and PPI-specific memory T cells all exhibited a predominantly Th1-like surface phenotype. In contrast, UCN3 had a distinct cell subset distribution, with a significantly lower proportion of Th1-like cells than CCNI (P = 0.04) and proportion of Th2-like cells that was significantly higher than PCSK2 (P = 0.03) and trended higher than CCNI (P = 0.1) (Fig. 3C). These results suggest that UCN3-specific T cells may exhibit a different functional phenotype than CCNI-, PCSK2-, and PPI-specific T cells.

Figure 3

T cells specific for novel epitopes exhibit different phenotypes. Epitope-specific CD4+ T cells in subjects with T1D (Supplementary Table 2) were phenotyped based on their cell surface expression of lineage markers. A: CD45RA and CCR7 surface staining was used to classify T cells as naive (CD45RA+CCR7+), central memory (CD45RACCR7+), effector memory (CD45RACCR7), or terminal effectors (CD45RA+CCR7). This allowed comparison of the memory distributions for CCNI, UCN3, PCSK2, and PPI (as a reference epitope). B: Chemokine receptors were used to subdivide memory T cells into Th1-like (CXCR3+, CCR4, and CCR6), Th2-like (CXCR3, CCR4+, and CCR6), Th17-like (CXCR3+, CCR4, and CCR6+), Th1/17-like (CXCR3+, CCR4, and CCR6+), Th1/2-like (CXCR3+, CCR4+, and CCR6), or Th1*-like (CXCR3+, CCR4+, and CCR6+) states. This allowed comparison of the cell subset distributions for CCNI, UCN3, PCSK2, and PPI (as a reference epitope). C: CCNI had a distinct memory profile, with a significantly lower proportion of naive cells than UCN3 (P = 0.04) and trended lower than PCSK2 (P = 0.07). UCN3 had a distinct cell subset distribution, with a significantly lower proportion of Th1-like cells than CCNI (P = 0.04) and proportion of Th2-like cells that was significantly higher than PCSK2 (P = 0.03) and trended higher than CCNI (P = 0.1). CM, central memory; EM, effector memory.

Figure 3

T cells specific for novel epitopes exhibit different phenotypes. Epitope-specific CD4+ T cells in subjects with T1D (Supplementary Table 2) were phenotyped based on their cell surface expression of lineage markers. A: CD45RA and CCR7 surface staining was used to classify T cells as naive (CD45RA+CCR7+), central memory (CD45RACCR7+), effector memory (CD45RACCR7), or terminal effectors (CD45RA+CCR7). This allowed comparison of the memory distributions for CCNI, UCN3, PCSK2, and PPI (as a reference epitope). B: Chemokine receptors were used to subdivide memory T cells into Th1-like (CXCR3+, CCR4, and CCR6), Th2-like (CXCR3, CCR4+, and CCR6), Th17-like (CXCR3+, CCR4, and CCR6+), Th1/17-like (CXCR3+, CCR4, and CCR6+), Th1/2-like (CXCR3+, CCR4+, and CCR6), or Th1*-like (CXCR3+, CCR4+, and CCR6+) states. This allowed comparison of the cell subset distributions for CCNI, UCN3, PCSK2, and PPI (as a reference epitope). C: CCNI had a distinct memory profile, with a significantly lower proportion of naive cells than UCN3 (P = 0.04) and trended lower than PCSK2 (P = 0.07). UCN3 had a distinct cell subset distribution, with a significantly lower proportion of Th1-like cells than CCNI (P = 0.04) and proportion of Th2-like cells that was significantly higher than PCSK2 (P = 0.03) and trended higher than CCNI (P = 0.1). CM, central memory; EM, effector memory.

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T Cells Specific for Novel Epitopes Are Present at Higher Frequencies in Subjects With Diabetes

To further assess the relevance of T cells that recognize these novel epitopes, we assessed their frequency in the peripheral blood of age- and HLA-matched control subjects (demographics in Supplementary Table 2). As expected, some T cells that recognized these epitopes were present in control subjects. However, the combined frequency of novel epitope–reactive T cells in subjects with type 1 diabetes was significantly higher than in control subjects (P = 0.0003) (Fig. 4A). Considering T cell frequencies for individual epitopes, frequencies trended toward being higher in subjects with type 1 diabetes than in control subjects for PCSK2 and UCN3 (Fig. 4B), but differences only reached statistical significance for CCNI (P = 0.02, Šídák multiple comparisons test). Using CD45RA and CCR7, we classified novel epitope–reactive T cells as naive or memory and compared the ratio (the combined percentage of all memory subsets divided by the percentage that were naive) in subjects with type 1 diabetes and control subjects (Fig. 4C). Although subjects with type 1 diabetes showed a very modest trend toward having a higher proportion of epitope-specific memory T cells, there was no significant difference (P = 0.565). We next used CXCR3, CCR4, and CCD6 to compare the surface phenotype of novel epitope–reactive T cells in subjects with type 1 diabetes and control subjects (Fig. 4D). Unlike subjects with type 1 diabetes, the predominant phenotype in healthy control subjects was Th2 like (CCR4 single positive). Thus, subjects with established type 1 diabetes had significantly greater numbers of novel epitope–reactive CD4+ T cells and control subjects lacked the Th1-like bias seen for some specificities in subjects with type 1 diabetes.

Figure 4

T cells specific for novel epitopes are more frequent in subjects with type 1 diabetes and exhibit phenotype differences. HLA class II tetramers were used to characterize T cells specific for the three novel epitopes in 10 HLA-matched control subjects (Supplementary Table 3). A: Combining all three epitopes (CCNI, PCSK2, and UCN3) total frequencies were significantly higher (P = 0.0003, Mann Whitney test) in subjects with type 1 diabetes (black circles) than in HLA-matched controls (white circles). B. In examining individual epitopes, individual frequencies were significantly higher in subjects with type 1 diabetes (black circles, taken from Fig. 2B but shown here on a log axis to more effectively depict the low frequencies seen in control subjects) than in healthy control subjects (white circles) only for CCNI (P = 0.021). C: With combination of all three epitopes (CCNI, PCSK2, and UCN3) the proportion of T cells that had a memory phenotype (calculated as a ratio of the combined percentage of all memory subsets and percentage that were naive) was not significantly different (P = 0.565, Mann Whitney U test) in subjects with type 1 diabetes (black circles) than in HLA-matched control subjects (white circles). D: Chemokine receptors were used to subdivide memory T cells from healthy control subjects into Th1-like, Th2-like, Th17-like, Th1/17-like, Th1/2-like, or Th1*-like states (just as in Fig. 3B for subjects with T1D). Control subjects lacked the Th1-like bias seen for some specificities in subjects with type 1 diabetes.

Figure 4

T cells specific for novel epitopes are more frequent in subjects with type 1 diabetes and exhibit phenotype differences. HLA class II tetramers were used to characterize T cells specific for the three novel epitopes in 10 HLA-matched control subjects (Supplementary Table 3). A: Combining all three epitopes (CCNI, PCSK2, and UCN3) total frequencies were significantly higher (P = 0.0003, Mann Whitney test) in subjects with type 1 diabetes (black circles) than in HLA-matched controls (white circles). B. In examining individual epitopes, individual frequencies were significantly higher in subjects with type 1 diabetes (black circles, taken from Fig. 2B but shown here on a log axis to more effectively depict the low frequencies seen in control subjects) than in healthy control subjects (white circles) only for CCNI (P = 0.021). C: With combination of all three epitopes (CCNI, PCSK2, and UCN3) the proportion of T cells that had a memory phenotype (calculated as a ratio of the combined percentage of all memory subsets and percentage that were naive) was not significantly different (P = 0.565, Mann Whitney U test) in subjects with type 1 diabetes (black circles) than in HLA-matched control subjects (white circles). D: Chemokine receptors were used to subdivide memory T cells from healthy control subjects into Th1-like, Th2-like, Th17-like, Th1/17-like, Th1/2-like, or Th1*-like states (just as in Fig. 3B for subjects with T1D). Control subjects lacked the Th1-like bias seen for some specificities in subjects with type 1 diabetes.

Close modal

Cells That Recognize Novel Epitopes Are Present Among Islet-Infiltrating T Cells

To further support the relevance of these novel epitopes, we next evaluated whether their cognate T cells are present within human islets by probing the specificity of T cell lines from nPOD donor 6472 (Supplementary Table 4). Multiple T cell lines were expanded as delineated in Research Design and Methods. We tested the reactivity of these lines toward CCNI-008, PCSK2, and UCN3 by assaying for IFN-γ secretion stimulated by each peptide. Islet-derived CD4+ T cell line AG (isolated and expanded from nPOD donor 6472) recognized PCSK2 in the context of autologous splenocyte-derived B-LCL by IFN-γ secretion (Fig. 5A) and by enzyme-linked immunosorbent spot (ELISpot) (Fig. 5B). The donor was positive for HLA-DRB1*04:04 but not HLA-DRB1*04:01, indicating that PCSK2 can apparently be presented by both of these HLA-DR4 subtypes. These observations establish that CD4+ T cells that recognize PCSK2 are present among the islet-infiltrating T cells of this subject, further supporting the relevance of these newly discovered epitopes to disease.

Figure 5

PCSK2-specific CD4+ T cells infiltrate the islets of a donor with type 1 diabetes. An islet-derived CD4+ T cell line for a donor with T1D recognizes PCSK2. T cells were isolated and expanded from live, vibratome slices of pancreas tissue from nPOD donor 6472. A: Irradiated autologous B-LCL pulsed with peptides at 50 µg/mL, no peptide, or DMSO volume control and then cultured with T cells and anti-CD28 (5 µg/mL) for costimulation for 48 h. Supernatants were collected and responses detected with a standard IFN-γ ELISA. The data shown represent one of four similar experiments. B: The islet T cell line previously cultured with PCSK3 peptide was restimulated (with peptide-pulsed autologous B-LCL as antigen-presenting cells), expanded, and then retested by IFNγ ELISpot for reactivity to the peptides. Tumor necrosis factor-α was not detected by ELISpot. The data shown represent one of two similar experiments.

Figure 5

PCSK2-specific CD4+ T cells infiltrate the islets of a donor with type 1 diabetes. An islet-derived CD4+ T cell line for a donor with T1D recognizes PCSK2. T cells were isolated and expanded from live, vibratome slices of pancreas tissue from nPOD donor 6472. A: Irradiated autologous B-LCL pulsed with peptides at 50 µg/mL, no peptide, or DMSO volume control and then cultured with T cells and anti-CD28 (5 µg/mL) for costimulation for 48 h. Supernatants were collected and responses detected with a standard IFN-γ ELISA. The data shown represent one of four similar experiments. B: The islet T cell line previously cultured with PCSK3 peptide was restimulated (with peptide-pulsed autologous B-LCL as antigen-presenting cells), expanded, and then retested by IFNγ ELISpot for reactivity to the peptides. Tumor necrosis factor-α was not detected by ELISpot. The data shown represent one of two similar experiments.

Close modal

During the development of type 1 diabetes, there is a failure of self-tolerance that is most clearly manifested by the appearance of islet autoantibodies but is also accompanied by loss of T cell tolerance to insulin and other islet-specific antigens (8,3639). The sequential acquisition of additional islet autoantibodies suggests mounting waves of autoimmune attack that encompass new antigen specificities (36,40). As a consequence of innate and adaptive immune activation and subsequent metabolic dysregulation, pancreatic β-cells experience diverse stressors (4145). These have been shown to promote the formation of neoepitopes through enzymatic and nonenzymatic posttranslational modification of proteins and defective ribosomal initiation (7,13,16,18,4547). Such neoepitopes are thought to represent a profound challenge to central and peripheral tolerance mechanisms (19,48). Indeed, T cells that recognize citrullinated, deamidated, and hybrid peptide neoepitopes are present at higher frequencies in subjects with type 1 diabetes and are also present among islet-infiltrating T cells and in pancreatic lymph nodes in this setting (7,9,10,17,49).

The induction of altered transcripts and splice variants leads to the generation of neoepitopes (and epitopes from previously understudied secretory granule proteins) that were previously shown to be recognized by CD8+ T cells (22,23). In this study, we sought to identify novel epitopes derived from those secretory granule proteins and stress-associated splice variant proteins and to explore CD4+ T cell responses directed against these epitopes. Using a systematic approach, we predicted peptides likely to bind to DR0401, assessed their in vitro binding and immunogenicity, and verified their validity by isolating peptide-responsive T cell clones from subjects with type 1 diabetes. These efforts led us to identify three novel epitopes. One of these epitopes spanned a unique junction from cyclin I splice variant CCNI-008. The other epitopes originated from PCSK2 and UCN3, which are both conventional secretory granule proteins, but which have not previously been appreciated as CD4+ T cell targets. Previously identified CD8+ T cell epitopes from these antigens were restricted by HLA-A2 (CCNI-00814–22, PCSK230–38, and UCN31–9) (22) and HLA-A3 (UCN346–56) (23). The murine orthologs PCSK2 and UCN3 included epitopes (restricted by Kd) that were found in the islet-infiltrating T cells of prediabetic NOD mice (23) (mRNA splice epitopes were not investigated in mice, as splicing rules display significant interspecies differences). The novel CCNI-008 epitope that was recognized by CD4+ T cells (CCNI-00819–37) overlaps with the CD8+ T cell epitope CCNI-00814–22, raising the possibility of linked recognition. The novel PCSK2 and UCN3 CD4+ T cell epitopes (PCSK2243–257 UCN358–72) did not overlap with the previous CD8+ T cell epitopes.

Through direct analysis with HLA class II tetramers, we observed detectable populations of T cells that recognized these peptides in the peripheral blood of subjects with type 1 diabetes that, in aggregate, were present at significantly higher frequencies than in HLA-matched control subjects. In considering the individual epitopes separately, only CCNI-specific T cells were present at significantly higher frequencies. Notably, T cells specific for this splice variant–derived CCNI peptide had the highest overall frequencies and exhibited a distinct memory profile (increased proportions of TCM and TEM) compared with the other specificities examined. As a whole, these observations suggest that T cells that recognize this splice variant–derived epitope might be more effectively primed and expanded than T cells that recognize the epitopes derived from conventional secretory granule antigens. Notably, T cells that recognize the PCSK2 epitope were present among the islet-infiltrating T cells of an nPOD donor, supporting the relevance of this specificity. In accord with this, CCNI- and PCSK2-specific T cells predominantly exhibited a Th1-like phenotype, whereas a significant proportion of T cells specific for the UCN3 epitope exhibited a Th2-like surface phenotype. Based on the paradigm that Th1 cells have a key role in the pathogenesis of type 1 diabetes, as supported by the foundational work of Katz et al. (50), this observation suggests a pathogenic role for these cells in type 1 diabetes.

T cells that recognize these novel epitopes were also present in HLA-matched control subjects, albeit at lower frequencies and lacking the Th1-like phenotype that was predominant in subjects with type 1 diabetes. In line with previous reports on islet-reactive CD8+ T cells (22,23,51), a notable proportion of these novel autoreactive CD4+ T cells were naive in peripheral blood, supporting the possibility that the fraction actively engaged in the autoimmune process may be preferentially sequestered in the pancreas target organ (52,53). These observations indicate incomplete tolerance, consistent with other studies suggesting that having a disease-susceptible HLA is sufficient to cause the selection of a potentially autoreactive T cell repertoire (51,54,55). The observation that self-reactive T cells were not completely limited to a naive phenotype in healthy donors indicates that limited T cell expansion can occur in the absence of autoimmune disease. This is in accord with the concept of “benign” islet autoimmunity, which postulates that self-reactive T cells are present in all individuals and that contributing behavior on the part of pancreatic β-cells is required to potentiate disease progression (52,53).

Our study does have limitations. Notably, although we observed diverse frequencies of T cells specific for novel T cell epitopes, our study was not adequately powered to correlate these differences with clinical parameters. Furthermore, our study was limited to peptides with predicted binding to DR0401 and derived from targets that were selected based on HLA class I peptidome data. Although the known β-cell antigens targeted by CD4+ and CD8+ T cells have a high degree of overlap (5), the approach we followed would omit any self-antigens that are uniquely presented to CD4+ T cells. In addition, given that HLA binding predictions are imperfect, there could be epitopes present among the peptides we chose not to synthesize. Since our methods relied on HLA tetramer staining, which preferentially detects high-affinity T-cell receptors, it is possible that there are additional low-affinity specificities that we failed to discover. Also, it is likely that additional epitopes derived from the unique splice variants and secretory granule proteins that we studied are recognized in human subjects with type 1 diabetes in the context of other disease-associated HLA molecules, such as DQ0302 (DQ8). We have developed a method for predicting DQ8-binding peptides, which we plan to use in our ongoing studies.

Collectively, our findings introduce UCN3 as a potentially relevant CD4+ T cell antigen and strongly suggest that the CCNI splice variant and PCSK2 are important T cell targets in type 1 diabetes. Among these, CCNI-specific T cells appear to be more extensively expanded in the circulation of subjects with type 1 diabetes. We can speculate that pathogenic CD4+ T cells that recognize this tissue-specific splice junction epitope may not be effectively controlled by central and peripheral tolerance mechanisms. Our data support the hypothesis that stress-related changes that occur in human β-cells can generate neoepitopes, eliciting inflammatory T cell responses, which amplify autoimmunity and promote the progression of type 1 diabetes.

This article contains supplementary material online at https://doi.org/10.2337/figshare.21264837.

Acknowledgments. The authors thank Virginia M. Green for copy editing and proofreading the manuscript and Cynthia Cousens-Jacobs for administrative support (both at Benaroya Research Institute).

Funding. This work was supported by JDRF grants 1-SRA-2020-978-S-B and 2-SRA-2014-297-Q-R to E.A.J. Work in the laboratory of S.C.K. was supported by National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, grant UC4DK116284 to S.C.K. Work in the laboratory of D.L.E. was supported by Welbio and Fonds National de la Recherche Scientifique, Belgium, grants CR-2015A-06 and CR-2019C-04, and Innovate2CureType1-Dutch Diabetes Research Fundation (DDRF). Work in the laboratory of R.M. was supported by The Leona M. and Harry B. Helmsley Charitable Trust (1901-03689), Agence Nationale de la Recherche (ANR-19-CE15-0014-01), Fondation pour la Recherche Medicale (EQU20193007831), and IdEx Université Paris 2019. D.L.E. and R.M. received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreements 115797 and 945268 (INNODIA and INNODIA HARVEST), which receive support from the EU Horizon 2020 program, the European Federation of Pharmaceutical Industries and Associations, JDRF, and The Leona M. and Harry B. Helmsley Charitable Trust. The work of C.S. was supported by JDRF under grant 3-SRA-2019-791-S-B. This research was performed with the support of nPOD (RRID:SCR_014641), a collaborative type 1 diabetes research project supported by JDRF (nPOD: 5-SRA-2018-557-Q-R) and The Leona M. and Harry B. Helmsley Charitable Trust (2018PG-T1D053, G-2108-04793). Organ procurement organizations partnering with nPOD to provide research resources are listed at https://www.jdrfnpod.org/for-partners/npod-partners/.

These funders had no role in study design, data collection and analysis, or decisions made to prepare or publish the manuscript. The content and views expressed are the responsibility of the authors and do not necessarily reflect the official view of nPOD.

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. R.M. and E.A.J. conceptualized and designed the study. R.M. and D.L.E. identified the sequences of candidate splice variant junctions and secretory granule proteins of interest. C.S. and S.L. were responsible for subject recruitment, sample collection, and clinical data collection. P.G. and D.A.-L. performed peptide binding assays and T cell studies. A.M. and S.C.K. performed islet-derived T cell experiments. P.G. and E.A.J. wrote the manuscript with assistance from all co-authors. E.A.J. obtained funding and was responsible for the entire project. E.A.J. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

1.
Coppieters
KT
,
Dotta
F
,
Amirian
N
, et al
.
Demonstration of islet-autoreactive CD8 T cells in insulitic lesions from recent onset and long-term type 1 diabetes patients
.
J Exp Med
2012
;
209
:
51
60
2.
Di Lorenzo
TP
,
Peakman
M
,
Roep
BO
.
Translational mini-review series on type 1 diabetes: Systematic analysis of T cell epitopes in autoimmune diabetes
.
Clin Exp Immunol
2007
;
148
:
1
16
3.
Roep
BO
,
Peakman
M
.
Antigen targets of type 1 diabetes autoimmunity
.
Cold Spring Harb Perspect Med
2012
;
2
:
a007781
4.
Erlich
H
,
Valdes
AM
,
Noble
J
, et al.;
Type 1 Diabetes Genetics Consortium
.
HLA DR-DQ haplotypes and genotypes and type 1 diabetes risk: analysis of the type 1 diabetes genetics consortium families
.
Diabetes
2008
;
57
:
1084
1092
5.
James
EA
,
Mallone
R
,
Kent
SC
,
DiLorenzo
TP
.
T-cell epitopes and neo-epitopes in type 1 diabetes: A comprehensive update and reappraisal
.
Diabetes
2020
;
69
:
1311
1335
6.
Yang
J
,
Danke
NA
,
Berger
D
, et al
.
Islet-specific glucose-6-phosphatase catalytic subunit-related protein-reactive CD4+ T cells in human subjects
.
J Immunol
2006
;
176
:
2781
2789
7.
Marre
ML
,
McGinty
JW
,
Chow
IT
, et al
.
Modifying enzymes are elicited by ER stress, generating epitopes that are selectively recognized by CD4+ T cells in patients with type 1 diabetes
.
Diabetes
2018
;
67
:
1356
1368
8.
Blahnik
G
,
Uchtenhagen
H
,
Chow
IT
, et al
.
Analysis of pancreatic beta cell specific CD4+ T cells reveals a predominance of proinsulin specific cells
.
Cell Immunol
2019
;
335
:
68
75
9.
Babon
JA
,
DeNicola
ME
,
Blodgett
DM
, et al
.
Analysis of self-antigen specificity of islet-infiltrating T cells from human donors with type 1 diabetes
.
Nat Med
2016
;
22
:
1482
1487
10.
Michels
AW
,
Landry
LG
,
McDaniel
KA
, et al
.
Islet-derived CD4 T cells targeting proinsulin in human autoimmune diabetes
.
Diabetes
2017
;
66
:
722
734
11.
Eizirik
DL
,
Colli
ML
,
Ortis
F
.
The role of inflammation in insulitis and beta-cell loss in type 1 diabetes
.
Nat Rev Endocrinol
2009
;
5
:
219
226
12.
Marroqui
L
,
Dos Santos
RS
,
Op de Beeck
A
, et al
.
Interferon-α mediates human beta cell HLA class I overexpression, endoplasmic reticulum stress and apoptosis, three hallmarks of early human type 1 diabetes
.
Diabetologia
2017
;
60
:
656
667
13.
Buitinga
M
,
Callebaut
A
,
Marques Câmara Sodré
F
, et al
.
Inflammation-induced citrullinated glucose-regulated protein 78 elicits immune responses in human type 1 diabetes
.
Diabetes
2018
;
67
:
2337
2348
14.
McGinty
JW
,
Chow
IT
,
Greenbaum
C
,
Odegard
J
,
Kwok
WW
,
James
EA
.
Recognition of posttranslationally modified GAD65 epitopes in subjects with type 1 diabetes
.
Diabetes
2014
;
63
:
3033
3040
15.
Mannering
SI
,
Harrison
LC
,
Williamson
NA
, et al
.
The insulin A-chain epitope recognized by human T cells is posttranslationally modified
.
J Exp Med
2005
;
202
:
1191
1197
16.
Strollo
R
,
Vinci
C
,
Arshad
MH
, et al
.
Antibodies to post-translationally modified insulin in type 1 diabetes
.
Diabetologia
2015
;
58
:
2851
2860
17.
Delong
T
,
Wiles
TA
,
Baker
RL
, et al
.
Pathogenic CD4 T cells in type 1 diabetes recognize epitopes formed by peptide fusion
.
Science
2016
;
351
:
711
714
18.
Kracht
MJ
,
van Lummel
M
,
Nikolic
T
, et al
.
Autoimmunity against a defective ribosomal insulin gene product in type 1 diabetes
.
Nat Med
2017
;
23
:
501
507
19.
James
EA
,
Pietropaolo
M
,
Mamula
MJ
.
Immune recognition of β-cells: neoepitopes as key players in the loss of tolerance
.
Diabetes
2018
;
67
:
1035
1042
20.
Jing
Y
,
Kong
Y
,
McGinty
J
, et al
.
T-cell receptor/HLA humanized mice reveal reduced tolerance and increased immunogenicity of posttranslationally modified GAD65 epitope
.
Diabetes
2022
;
71
:
1012
1022
21.
Eizirik
DL
,
Sammeth
M
,
Bouckenooghe
T
, et al
.
The human pancreatic islet transcriptome: expression of candidate genes for type 1 diabetes and the impact of pro-inflammatory cytokines
.
PLoS Genet
2012
;
8
:
e1002552
22.
Gonzalez-Duque
S
,
Azoury
ME
,
Colli
ML
, et al
.
Conventional and neo-antigenic peptides presented by β cells are targeted by circulating naïve CD8+ T cells in type 1 diabetic and healthy donors
.
Cell Metab
2018
;
28
:
946
960.e6
23.
Azoury
ME
,
Tarayrah
M
,
Afonso
G
, et al
.
Peptides derived from insulin granule proteins are targeted by CD8+ T cells across MHC class I restrictions in humans and NOD mice
.
Diabetes
2020
;
69
:
2678
2690
24.
James
EA
,
Rieck
M
,
Pieper
J
, et al
.
Citrulline-specific Th1 cells are increased in rheumatoid arthritis and their frequency is influenced by disease duration and therapy
.
Arthritis Rheumatol
2014
;
66
:
1712
1722
25.
James
EA
,
Moustakas
AK
,
Bui
J
, et al
.
HLA-DR1001 presents “altered-self” peptides derived from joint-associated proteins by accepting citrulline in three of its binding pockets
.
Arthritis Rheum
2010
;
62
:
2909
2918
26.
Ettinger
RA
,
Papadopoulos
GK
,
Moustakas
AK
,
Nepom
GT
,
Kwok
WW
.
Allelic variation in key peptide-binding pockets discriminates between closely related diabetes-protective and diabetes-susceptible HLA-DQB1*06 alleles
.
J Immunol
2006
;
176
:
1988
1998
27.
Chow
IT
,
Yang
J
,
Gates
TJ
, et al
.
Assessment of CD4+ T cell responses to glutamic acid decarboxylase 65 using DQ8 tetramers reveals a pathogenic role of GAD65 121-140 and GAD65 250-266 in T1D development
.
PLoS One
2014
;
9
:
e112882
28.
Yang
J
,
Chow
IT
,
Sosinowski
T
, et al
.
Autoreactive T cells specific for insulin B:11-23 recognize a low-affinity peptide register in human subjects with autoimmune diabetes
.
Proc Natl Acad Sci USA
2014
;
111
:
14840
14845
29.
Rims
C
,
Uchtenhagen
H
,
Kaplan
MJ
, et al
.
Citrullinated aggrecan epitopes as targets of autoreactive CD4+ T cells in patients with rheumatoid arthritis
.
Arthritis Rheumatol
2019
;
71
:
518
528
30.
Uchtenhagen
H
,
Rims
C
,
Blahnik
G
, et al
.
Efficient ex vivo analysis of CD4+ T-cell responses using combinatorial HLA class II tetramer staining
.
Nat Commun
2016
;
7
:
12614
31.
Acosta-Rodriguez
EV
,
Rivino
L
,
Geginat
J
, et al
.
Surface phenotype and antigenic specificity of human interleukin 17-producing T helper memory cells
.
Nat Immunol
2007
;
8
:
639
646
32.
Becattini
S
,
Latorre
D
,
Mele
F
, et al
.
T cell immunity. Functional heterogeneity of human memory CD4+ T cell clones primed by pathogens or vaccines
.
Science
2015
;
347
:
400
406
33.
James
EA
,
Gates
TJ
,
LaFond
RE
, et al
.
Neuroinvasive West Nile infection elicits elevated and atypically polarized T cell responses that promote a pathogenic outcome
.
PLoS Pathog
2016
;
12
:
e1005375
34.
Arribas-Layton
D
,
Guyer
P
,
Delong
T
, et al
.
Hybrid insulin peptides are recognized by human T cells in the context of DRB1*04:01
.
Diabetes
2020
;
69
:
1492
1502
35.
Kwok
WW
,
Tan
V
,
Gillette
L
, et al
.
Frequency of epitope-specific naive CD4(+) T cells correlates with immunodominance in the human memory repertoire
.
J Immunol
2012
;
188
:
2537
2544
36.
Vehik
K
,
Bonifacio
E
,
Lernmark
Å
, et al.;
TEDDY Study Group
.
Hierarchical order of distinct autoantibody spreading and progression to type 1 diabetes in the TEDDY Study
.
Diabetes Care
2020
;
43
:
2066
2073
37.
Ferrannini
E
,
Mari
A
,
Nofrate
V
,
Sosenko
JM
;
DPT-1 Study Group
.
Progression to diabetes in relatives of type 1 diabetic patients: mechanisms and mode of onset
.
Diabetes
2010
;
59
:
679
685
38.
Yu
L
,
Dong
F
,
Miao
D
,
Fouts
AR
,
Wenzlau
JM
,
Steck
AK
.
Proinsulin/Insulin autoantibodies measured with electrochemiluminescent assay are the earliest indicator of prediabetic islet autoimmunity
.
Diabetes Care
2013
;
36
:
2266
2270
39.
Hummel
M
,
Bonifacio
E
,
Schmid
S
,
Walter
M
,
Knopff
A
,
Ziegler
AG
.
Brief communication: early appearance of islet autoantibodies predicts childhood type 1 diabetes in offspring of diabetic parents
.
Ann Intern Med
2004
;
140
:
882
886
40.
Ziegler
AG
,
Rewers
M
,
Simell
O
, et al
.
Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children
.
JAMA
2013
;
309
:
2473
2479
41.
Tersey
SA
,
Nishiki
Y
,
Templin
AT
, et al
.
Islet β-cell endoplasmic reticulum stress precedes the onset of type 1 diabetes in the nonobese diabetic mouse model
.
Diabetes
2012
;
61
:
818
827
42.
Syed
F
,
Evans-Molina
C
.
Nucleic acid biomarkers of β cell stress and death in type 1 diabetes
.
Curr Opin Endocrinol Diabetes Obes
2016
;
23
:
312
317
43.
Sims
EK
,
Evans-Molina
C
,
Tersey
SA
,
Eizirik
DL
,
Mirmira
RG
.
Biomarkers of islet beta cell stress and death in type 1 diabetes
.
Diabetologia
2018
;
61
:
2259
2265
44.
Marhfour
I
,
Lopez
XM
,
Lefkaditis
D
, et al
.
Expression of endoplasmic reticulum stress markers in the islets of patients with type 1 diabetes
.
Diabetologia
2012
;
55
:
2417
2420
45.
Marselli
L
,
Piron
A
,
Suleiman
M
, et al
.
Persistent or transient human β cell dysfunction induced by metabolic stress: Specific signatures and shared gene expression with type 2 diabetes
.
Cell Rep
2020
;
33
:
108466
46.
Cnop
M
,
Abdulkarim
B
,
Bottu
G
, et al
.
RNA sequencing identifies dysregulation of the human pancreatic islet transcriptome by the saturated fatty acid palmitate
.
Diabetes
2014
;
63
:
1978
1993
47.
Colli
ML
,
Ramos-Rodríguez
M
,
Nakayasu
ES
, et al
.
An integrated multi-omics approach identifies the landscape of interferon-α-mediated responses of human pancreatic beta cells
.
Nat Commun
2020
;
11
:
2584
48.
Nguyen
H
,
Guyer
P
,
Ettinger
RA
,
James
EA
.
Non-genetically encoded epitopes are relevant targets in autoimmune diabetes
.
Biomedicines
2021
;
9
:
202
49.
Wiles
TA
,
Hohenstein
A
,
Landry
LG
, et al
.
Characterization of human CD4 T cells specific for a C-peptide/C-peptide hybrid insulin peptide
.
Front Immunol
2021
;
12
:
668680
50.
Katz
JD
,
Benoist
C
,
Mathis
D
.
T helper cell subsets in insulin-dependent diabetes
.
Science
1995
;
268
:
1185
1188
51.
Culina
S
,
Lalanne
AI
,
Afonso
G
, et al.;
ImMaDiab Study Group
.
Islet-reactive CD8+ T cell frequencies in the pancreas, but not in blood, distinguish type 1 diabetic patients from healthy donors
.
Sci Immunol
2018
;
3
:
eaao4013
52.
Mallone
R
,
Eizirik
DL
.
Presumption of innocence for beta cells: why are they vulnerable autoimmune targets in type 1 diabetes?
Diabetologia
2020
;
63
:
1999
2006
53.
Carré
A
,
Richardson
SJ
,
Larger
E
,
Mallone
R
.
Presumption of guilt for T cells in type 1 diabetes: lead culprits or partners in crime depending on age of onset?
Diabetologia
2021
;
64
:
15
25
54.
Danke
NA
,
Koelle
DM
,
Yee
C
,
Beheray
S
,
Kwok
WW
.
Autoreactive T cells in healthy individuals
.
J Immunol
2004
;
172
:
5967
5972
55.
Yang
J
,
James
EA
,
Sanda
S
,
Greenbaum
C
,
Kwok
WW
.
CD4+ T cells recognize diverse epitopes within GAD65: implications for repertoire development and diabetes monitoring
.
Immunology
2013
;
138
:
269
279
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