Tracking the Antibody Immunome in Type 1 Diabetes Using Protein

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Tracking the Antibody Immunome in Type 1 Diabetes Using Protein Arrays Xiaofang Bian, Clive Wasserfall, Garrick Wallstrom, Jie Wang, Haoyu Wang, Kristi Barker, Desmond Schatz, Mark Atkinson, Ji Qiu, and Joshua LaBaer J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00354 • Publication Date (Web): 03 Oct 2016 Downloaded from http://pubs.acs.org on October 4, 2016

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Journal of Proteome Research

Tracking the Antibody Immunome in Type 1 Diabetes Using Protein Arrays 1

Xiaofang Bian, 2Clive Wasserfall, 1Garrick Wallstrom, 1Jie Wang, 1Haoyu Wang, 1Kristi

Barker, 3Desmond Schatz, 2Mark Atkinson, 1Ji Qiu*, 1Joshua LaBaer*

1

The Virginia G. Piper Center for Personalized Diagnostics, Biodesign Institute, Arizona State

University, Tempe, AZ 85287, USA 2

Department of Pathology, Immunology and Laboratory Medicine, College of Medicine,

University of Florida, Gainesville, FL 32603, USA 3

Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL 30607,

USA

*Correspondence Authors: Joshua LaBaer, M.D., Ph.D, Virginia G. Piper Chair of Personalized Diagnostics, The Biodesign Institute at Arizona State University, 1001 S. McAllister Ave. PO Box 876401, Tempe, AZ 85287-6401; Email: [email protected]; Phone: (480) 965-2805; FAX: (480) 965-3051;

Ji Qiu, Ph.D, Associate Research Professor, The Biodesign Institute at Arizona State University, 1001 S. McAllister Ave. PO Box 876401, Tempe, AZ 85287-6401; Email: [email protected]; Phone: (480) 727-7483

1 ACS Paragon Plus Environment


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Abstract We performed an unbiased proteome-scale profiling of humoral autoimmunity in recent-onset type 1 diabetes (T1D) patients and non-diabetic controls against ~10,000 human proteins using a Nucleic Acid Programmable Protein Array (NAPPA) platform, complemented by a knowledgebased selection of proteins from genes enriched in human pancreas.

Although the global

response was similar between cases and controls, we identified and then validated six specific novel T1D-associated autoantibodies (AAbs) with sensitivities that ranged from 16% to 27% at 95% specificity. These included AAbs against PTPRN2, MLH1, MTIF3, PPIL2, NUP50 (from NAPPA screening) and QRFPR (by targeted ELISA). Immunohistochemistry demonstrated that NUP50 protein behaved differently in islet cells, where it stained both nucleus and cytoplasm, compared with only nuclear staining in exocrine pancreas. Conversely, PPIL2 staining was absent in islet cells, despite its presence in exocrine cells. The combination of anti-PTPRN2, MLH1, -PPIL2 and -QRFPR had an AUC of 0.74 and 37.5% sensitivity at 95% specificity. These data indicates that these markers behave independently and support the use of unbiased screening to find biomarkers, because the majority was not predicted based on predicted abundance. Our study enriches the knowledge of the “autoantibody-ome” in unprecedented breadth and width.

KEYWORDS: type 1 diabetes (T1D), autoantibody (AAb), biomarkers, protein array, Nucleic Acid programmable protein array (NAPPA)


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INTRODUCTION The rapid rise in the incidence of type 1 diabetes (T1D) poses a challenge to identify a means to prevent this disease.1 By the time of clinical diagnosis, between 60 to 90% of pancreatic beta cells are destroyed.2 Whereas cellular immune responses appear to play a dominant role in the autoimmune destruction of beta cells, the specific molecular targets of the cellular immune system remain unknown. Antibodies against self-proteins (i.e., autoantibodies (AAbs)) are also produced during the natural history of disease, including the stage prior to disease diagnosis, when they can be utilized to identify those at increased risk for the disorder. Given the links between the humoral and cellular immune systems, it is possible that some of the AAb targets may also be targets of activated immune cells.3 Predicting increased risk is an important feature for identifying individuals for recruitment to T1D prevention trials.4 To date, AAbs to insulin, GAD65, IA-2 and ZnT8 have been identified as the major T1D-associated AAbs.5-8 A number of minor AAbs (i.e., present in lower frequencies in T1D patients) have been discovered by various approaches, discussed below.9-13 The number of T1D-associated AAbs may be much higher than those currently known.14 In support of this notion, some T1D individuals whose serum antibodies stain islet cells are nevertheless negative for the four major T1D-associated AAbs15 indicating the existence of additional AAbs targeting other proteins in pancreatic islet beta cells. There have only been a few studies seeking to profile the antibody repertoire in T1D patients at the proteome-scale. Previous AAb biomarkers were discovered based on either the known understanding of T1D pathogenesis (e.g., IAA) using radioimmunoassay (RIA) or immunoprecipation of autoantigens from cell lysates using patient sera.5-7 ZnT8 antibody (ZnT8A) was identified as a major T1D AAb by analyzing pancreatic gene expression profiles followed by RIA.8



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The advent of proteomics opened new avenues to search for novel AAbs in T1D. Protein microarrays provide an ideal tool to profile global antibody responses to a large number of proteins in a high-throughput manner. Our cell-free protein array platform named Nucleic Acid Programmable Protein Array (NAPPA) was used previously to screen 6,000 human proteins and identified one minor T1D-associated AAb.16 Two novel AAbs were discovered by conventional recombinant protein arrays.17 Although these results were encouraging, the samples used in these earlier studies were not ideal because they included older patients, some of whom had T1D for years. In our old study, for example, the age range was wide (17.6±10.4, with a median age of 17), and subjects had T1D for longer durations (>6 years).16 In the discovery phase of the study published by Koo et al., the subjects were 42±16 years old.17 As T1D is primarily a juvenile disease, samples from subjects with younger ages taken close to the time of disease onset will better represent the T1D population and thus are more suitable to study T1D-specific AAbs. In addition, a new study could benefit from significant technical improvements that have ensued in the intervening years.

A human HeLa cell lysate-based system has now been

developed to replace rabbit reticulocyte lysate for the transcription and translation of plasmid cDNA into proteins, which greatly improves the protein yield and activity on the array,18 thus further increasing the sensitivity in detecting antibody reactivity. Moreover, the number of human genes available for testing has nearly doubled from 6,000 to 10,000. In the present study, we aimed to profile the global antibody repertoire to search for new T1D-associated AAbs using an improved NAPPA array-based approach18 in recently diagnosed T1D patients and with many more human proteins than our previous study, about 50% of the human proteome.16,19 To validate the findings, we also planned to perform ELISA on the top candidates from the arrays as well as 126 proteins whose genes are highly expressed in human


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pancreas.

The protein level of identified T1D-associated AAb targets in pancreas were

subsequently evaluated by immunohistochemistry (IHC) staining.



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EXPERIMENTAL SECTION Study samples All samples were collected with written informed consent under the guidelines of the Institutional Review Boards (IRB) at the University of Florida. T1D samples were obtained from recent-onset patients within three months of diagnosis according to the American Diabetes Association (ADA) criteria. Peripheral blood samples were drawn from the antecubital vein and serum was prepared before freezing as aliquots at -80oC. Control samples were prepared in the identical fashion and selected to be age/gender matched to the patients. Controls had no T1D family history and were considered to be at low risk of T1D with absence of known T1Dassociated AAbs (except for two individuals who were positive for GADA and one who was positive for IA-2A). Neither patients nor controls were known to have any other underlying autoimmune disease. Characteristics of study subjects including age, gender and status of known T1D-associated AAbs (GADA, IA-2A) were presented as median, range and percentage (Table 1). GADA and IA-2A were measured by commercial ELISA kits (Kronus Inc, Star, ID， respectively). IAA and ZnT8A data were not available for these samples, as IAA assays have notoriously low sensitivity, and ZnT8A positivity usually overlaps with GADA or IA-2A positivity. One individual has no age or gender information. Samples for newly diagnosed juvenile patients and especially matched healthy juvenile controls were precious. Due to the limitation in clinical sample availability, sample set 4 is a subset of sample set 2. Sample set 3 is the combination of sample set 1 and sample set 2 (missing 4 pairs of samples due to sample depletion). The detailed characteristics of each sample set are shown in Table S-1. Samples were randomized without any biases between different sets and the gender and age distribution of each serum set was very similar with no significant difference.


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Study design This study combined both unbiased array-based and targeted knowledge-based approaches to search for novel AAbs in T1D (Figure 1). In the unbiased approach, we profiled AAbs against 10,000 unique human proteins displayed on protein microarrays using sample set 1 (40 cases and 40 controls) on 5 arrays sets during the discovery stage. We selected 39 antigens based on antibody reactivity in at least 2 samples from cases more than those from controls for verification by ELISA in sample set 1. 19 antigens had >10% sensitivity at 95% specificity during the verification stage. With only 39 antigens in this verification set, the risk of false discovery was not significant. They were tested in an independent set of sample set 2 (60 cases and 60 controls) and further confirmed in sample set 3 (96 cases and 96 controls) by ELISA. In the knowledge-based approach, 126 pancreas enriched genes (Table S-2) were selected by combining literature search8,13,20 and bioinformatics analysis of gene expression data. AAbs to these proteins were profiled in sample set 4 (46 cases and 46 controls) by ELISA. 14 antigens showed >10% sensitivity at 98% specificity and confirmed in sample set 3.

NAPPA production and quality assessment 10,000 human genes obtained from DNASU (http://dnasu.asu.edu/DNASU/)21 were printed on 5 array sets (GST1, GST2, GST3, GST4 and Flag).22 Plasmids encoding human proteins have either appended a C-terminal glutathione S-transferase (GST) or N-terminal Flag fusion tag in frame with the protein.23 Proteins were expressed from DNA by human in vitro transcription and translation (IVTT) HeLa cell lysate-based protein expression systems. Proteins expressed with fusion tag can be easily captured in situ on glass slides by the co-printed anti-tag antibodies.24 Genes encoding known T1D-associated autoantigens (Insulin, IA-2, GAD65 and ZnT8) were



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printed as positive controls for serum profiling. DNA printing and protein display on NAPPA were quality assured as described.16

Profiling of AAbs on NAPPA As described,19 printed slides were blocked in SuperBlock Buffer (Thermo Fisher Scientific, Waltham, MA) at room temperature (RT) for 1 hour (hr) on the shaker. Then slides were rinsed five times with deionized (DI) water and placed in a metal slide rack (Amazon, Seattle, WA) for drying by centrifugation at 1000 rpm for 3 min at RT. 160 µL human HeLa cell lysate-based protein expression system (Thermo Fisher scientific, Rockford, IL) was injected into HybriWell (Grace BIO-LABS, Bend, OR) sealed slides and incubated in the oven (EchoTherm, Carlsbad, CA) at 30°C for 1.5 hrs for protein expression and 15°C for 30 min for protein capture. Expressed slides were washed 10 times with DI water and dried by centrifugation at 4°C, 1000 rpm for 3 min.

Slides were placed in the hybridization chambers of HS 4800™ Pro

hybridization station (Tecan, Männedorf, Switzerland) and programmed with 1 hr blocking with 5% milk-PBST (0.2% Tween), 16 hrs of 160 µL 1:50 diluted serum at 4°C followed by 1 hr incubation of 160 µL 1:500 diluted Alexa Fluor 647® Goat Anti-Human IgG (Jackson ImmunoResearch Laboratories, West Grove, PA). Slides were washed, dried and scanned by Tecan scanner under consistent settings.

A pooled sample was prepared by mixing equal

volumes of samples from 40 patients and 40 controls. The pooled sample was run as technical replicate on every serum screening day.

Strong antibody reactivity sometimes resulted in

saturated signals of the local spot with diffusion to the neighboring spots. The presence of this diffusion was defined as a ring.19,25 Differences in ring counts between T1D cases and healthy controls were used for selecting candidates for ELISA verification. 19,25


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Rapid Antigenic Protein in situ Display (RAPID) ELISA As described,26 96-well ELISA plates (Corning Life Sciences, Salt Lake City, UT) were coated with 50 µL 10 µg/mL anti-GST antibody (GE Healthcare, Pittsburgh, PA) in coating buffer (0.5 M carbonate bicarbonate buffer, pH 9.6) overnight at 4°C. On the next day, coated plates were washed five times with 100 µL PBST and blocked with 100 µL 5% milk-PBST for 1.5 hrs. Meanwhile, 40 ng/µL protein encoding plasmid was expressed in the human HeLa cell lysatebased protein expression system at 30°C for 1.5 hrs in the oven. Expressed proteins were diluted in milk-PBST at 1:50. 50 µL diluted antigen was captured in each well at RT for 1 hr on a shaker at 500 rpm. Plates were washed 5X with PBST. Each well was incubated with 50 µL 1:200 diluted serum at RT for 1 hr, washed again and incubated with 50 µL 1:10,000 diluted HRP labeled Goat Anti-Human IgG (Jackson ImmunoResearch Laboratories, West Grove, PA) at RT for 1 hr. Plates were incubated on a shaker at 500 rpm. Finally, the plates were washed and incubated with 50 µL 1-Step Ultra TMB - ELISA Substrate for 10 min (Thermo Fisher scientific, Waltham, MA) for detection and 50 µL 2M sulfuric acid to stop the reaction. OD450 was measured by Envision® Multilabel Reader (PerkinElmer, Waltham, MA).

Immunohistochemistry (IHC) staining Sections (5 µm) of formalin fixed paraffin embedded pancreas tissue of a donor without diabetes (6007, nPOD, www.jdrfnpod.org) were processed with antigen retrieval pH6 protocols as standard protocols. Primary antibodies anti-MLH1 diluted 1:20 (Abcam, ab14206), anti-PPIL2 diluted 1:100 (Sigma-Aldrich, HPA035344), anti-NUP50 diluted 1:100 (Sigma-Aldrich, HPA047162), anti-MTIF3 diluted 1:100 (Sigma-Aldrich, HPA039791), anti-QRFPR diluted



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1:20 (Sigma-Aldrich, HPA014300) were incubated on the relevant slides followed by anti-mouse or anti-rabbit HRP secondary antibodies. Visualization was with DAB as the HRP substrate (DAKO, Carpinteria, CA) and Hematoxylin as nuclear counterstain. Stained sections were scanned at 200X magnification using an Aperio CS scanner (Leica/Aperio, Vista, CA).

Statistical Analysis The ELISA relative absorbance of each plasma sample–antigen reaction was calculated using the OD450 of the expressed antigens divided by the median OD450 of all antigens measured for that sample.27 The median value was used to normalize systematic background of each plasma sample. A non-linear change-point regression classifier was used to construct biomarker panels. Specifically, the classifier score was the aggregate sum of individual marker scores, each of which was a change point function of the RAPID ELISA measure of antibody reactivity. A receiver operating characteristic (ROC) curve was constructed using predicted scores that were calculated under leave-one-out cross-validation. Heatmaps were generated in MultiExperiment Viewer version 4.9 (http://www.tm4.org/mev.html). Graphs and plots were drawn in GraphPad Prism 6.


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RESULTS AND DISCUSSION Profiling global antibody response in T1D on NAPPA Quality control of serum screening on protein arrays was assessed daily as previously described (Figure S-1).16 Antibody reactivity to known T1D-associated autoantigens (IA-2, GAD65 and ZnT8) was visually distinguishable from other antigens which showed no reactivity on the same array. The representative array images from each array set were shown in Figure 2. Note that the locations of positive control features vary in five array sets based on the composition of the set of proteins on each array. The samples from healthy controls did not show responses to any of these known T1D antigens (Figure 2), demonstrating the validity of our array platform in profiling serological AAbs in T1D. Forty (40) T1D cases and 40 healthy controls were screened on our human protein arrays with ~10,000 proteins.19 We had not detected strong reactivity to IA2 or GAD65 in our previous study.16 However, we observed strong reactivity to them in this study (Figure 2). We believe the use of human translation machinery and chaperones in the HeLa cell lysate-based in vitro expression system, which we have previously demonstrated gives much higher yield and quality of protein,18,28 may have contributed to better detection with AAbs on our array platform. We first investigated whether there were a general difference in immune responses between cases and controls. Strong antibody reactivity resulted in saturated signals of the local spot with diffusion to the neighboring spots. The presence of this diffusion was defined as a ring.19,25 The number of samples with evident rings for a particular protein was compared between cases and controls. The median number of proteins showing AAb responses was 40 and 38 for cases and controls, respectively, with no statistical difference between these two groups (Figure S-2A). Overall, there were a total of 1,204 antigens showing positivity in at least one case or control (Figure S-2B). The number dropped to 558 for antigens showing positivity in at 11 ACS Paragon Plus Environment


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least two cases or controls. When analyzing cases and controls separately, 798 antigens showed reactivity in at least one case and 287 in at least two cases. 805 antigens showed reactivity in at least one control and 302 in at least two controls. There was no difference in the AAb responses among cases ≤12 and >12 years, either (Figure S-2C). To the best of our knowledge, this is the first comprehensive analysis of the global humoral immune response against AAb targets in recent-onset T1D patients; specifically, we tested around 10,000 unique human proteins, including ~400 proteins whose genes are enriched in pancreas, using NAPPA.16 We note that proteins on NAPPA may lack some native posttranslational modifications (PTMs) that might be important for some antibody-antigen interactions. We have observed some degree of phosphorylation from the expression lysate, but most PTMs have not been thoroughly studied. We are working to accommodate PTMs on NAPPA such as citrullination and glycosylation for future studies29. The emergence of AAbs against self-antigens is a hallmark in T1D progression to full disease.30 It is natural to believe that there might be a general increase in the humoral immune response in T1D patients relative to healthy children. However, we observed no evidence of an increase in general immune reactivity among cases compared with controls (Figure S-2), in the number of AAbs (Figure S-2). The lack of difference was most likely not due to the incomplete representation of human proteins on our platform, as our arrays carried a representative protein for about ~1/2 of the proteome. Genetic pre-disposition, together with environmental triggering events,31,32 leads to T1D development. Based on our data, it seems that, whatever the triggering events might be, the number of autoantigens that can break immune-tolerance and elicit antigen immune responses is limited. In other words, there is no system wide increase of the humoral immune response to autoantigens in recent-onset T1D patients, rather the effects of the disease


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appear to be specific for certain antigens. It is probably safe to say that the system wide difference might be even smaller during the pre-symptomatic period, highlighting the importance of testing all human proteins individually to identify the few that have elicited antibody responses during T1D development. As more antibody targets in T1D are identified, we may acquire a better understanding of the disease mechanisms.

RAPID ELISA as an immunoassay for individual AAb Nonetheless, we wondered if nonspecific responses were masking individual AAb that showed disease-specific reactivity between patients and controls. Indeed, based on the human proteome array data, we identified 39 proteins that demonstrated an immune response in at least two more cases than in controls and selected them for further verification. We relied on RAPID ELISA as a fast companion immunoassay that allowed us to test many clinical samples against a smaller number of antigens.26 The same plasmid used for screening can be directly used in RAPID ELISA without further configuration. For internal comparisons, we used 96 cases and 96 controls, which had been previously measured for IA-2 antibody (IA-2A) and GAD65 antibody (GADA) using the commercial assay (sample set 3, table 1). We compared the performance of RAPID ELISA with the commercial assay on IA-2A and GADA. Both assays correlated well. The commercial assay showed 64.6% sensitivity at 99.0% specificity for IA-2A and 85.4% sensitivity at 97.9% specificity for GADA. Our in-house RAPID ELISA obtained 69.8% and 66.7% sensitivity at 95% specificity for IA-2A and GADA, respectively (Figure S-3A). Although both assays were reliably negative for the control group, the RAPID ELISA assays detected some patients missed by the commercial assay and vice versa (Figure S-3B), suggesting that the two methods were comparable and partially complementary.



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In addition, we compared the sero-reactivity to IA-2 by RAPID ELISA and NAPPA in sample set 1. The relative absorbance of RAPID ELISA and normalized signal intensity of NAPPA agreed well (Figure S-4A).

We further compared RAPID ELISA with the Luciferase

Immunoprecipitation Systems (LIPS) assay which was used in our previous study.16 The relative absorbance of RAPID ELISA and luminescent signal of LIPS assay matched well, too (Figure S4B). RAPID ELISA also has high intra-assay and inter-assay reproducibility (data not shown). The high sensitivity in detecting serum AAbs, the large overlap of sero-positivity with commercial assay, NAPPA and LIPS assay proved RAPID ELISA as a superb individual immunoassay to validate AAbs in this study. In the verification study, 19 out of these 39 candidate antigens had greater than 10% sensitivity at 95% specificity by RAPID ELISA and were selected for further validation (Table 2).

Knowledge-based approach It is generally believed that proteins highly expressed / uniquely expressed in β cells are more likely to be a T1D autoantigen. Insulin is highly expressed in pancreatic β cells that regulate metabolic hemostasis. GAD65 is expressed primarily in the cytosol of neuroendocrine cells that are distributed in the islet and neurons. IA-2 is localized at the membrane of insulin secretory granules.33 Notably, the latest major autoantigen ZnT8 was identified by testing candidate proteins whose genes were highly expressed in pancreas.8 To take advantage of the ease of assaying reactivity by RAPID ELISA, we tested AAbs to 126 pancreas-enriched proteins, selected by literature search and bioinformatics analysis, in 46 cases and 46 matched controls


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(sample set 4, table 1). There were 14 candidate AAbs that had >10% sensitivity at 98% specificity and were further selected for validation (Table 2).

Validation of novel AAbs discovered from two approaches We validated the performance of 19 antigens discovered with the arrays in an independent sample set (sample set 2, table 1), and further tested these 19 antigens plus an additional 14 antigens by the knowledge-based approach in sample set 3 (Table 1). From the array-based approach, AAbs to ten antigens showed >10% sensitivity at 95% specificity (Table 2). Five AAbs (anti-PTPRN2, anti-MLH1, anti-MTIF3, anti-PPIL2 and antiNUP50) showed >15% sensitivity at 95% specificity (Table 2, Figure 3). The identification of PTPRN2 as the best performer on protein arrays besides IA-2, GAD65 and ZnT8 demonstrates the validity of our discovery platform. PTPRN2, which shares 74% sequence similarity to IA-2 in the cytoplasmic domain,33,34 has been reported to be a T1D AAb biomarker with lower sensitivity than IA-2. NUP50 is a nuclear pore complex protein, which might play a role in the glucose transport pathway (Table S3). MTIF3 is a mitochondrial translation initiation factor and was implicated in obesity-related traits in the French population.35 MLH1, a DNA mismatch repair protein, is frequently mutated or deleted in cancer.36 Its role here is less clear. PPIL2 has ubiquitin ligase activity. From the knowledge-based approach, 7 out of 14 candidates showed >10% sensitivity at 95% specificity. However, only one (QRFPR, G-protein coupled receptor GPR103) achieved >15% sensitivity at 95% specificity (Table 2, Figure 3). QRFPR is a seven transmembrane domain protein, which functions as the ligand receptor of RFamide peptide. The binding of



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RFamide peptide with QRFPR promotes the survival of pancreatic β cells and human pancreatic islets, but different peptides exert opposite effects on insulin secretion.37 We also identified AAbs to a dozen of other interesting antigen proteins by the knowledge-based approach (Table 2), albeit with somewhat lower sensitivity. SNX6 is on the membrane of cytoplasmic vesicles and involved in intracellular trafficking. SYTL4 participates in exocytosis.38 IGRP/G6PC2 is an enzyme that hydrolyzes glucose-6-phosphate to glucose in the endoplasmic reticulum.39 It is also the target of pathogenic CD8+ T cells in T1D.40 This is the first time describing it as an AAb target. There are two previous reports on T1D AAbs using protein arrays, including one from Koo et al. and one from our group.16,17 We did not observe performance similar to previous reports for any of the three AAbs (Figure S-5). Differences in the ages of study subjects and/or the interval from disease onset to sample collection may have contributed to the discrepancies in AAb performance. In this study, we used samples from recent-onset patients who were 15.0±6.5 years old with a median age of 13. Cases and controls were pair-matched. In our previous paper,16 the subjects were older (17.6±10.4, with a median age of 17) who had T1D for longer durations (>6 years). In Koo et al.,17 the subjects in the discovery phase were 42±16 years old with recent-onset T1D. As T1D is primarily a juvenile disease, we believe that this current sample set represents the best approach to study T1D-specific AAbs. Nonetheless, we note that before using any autoantibody in clinical studies, further validation is necessary.

Performance of panels of novel AAbs We asked whether we could combine AAbs into panels to improve the performance of known T1D-associated AAbs using data from the ELISA assay. The combination of anti-QRFPR, anti-


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MLH1, anti-PPIL2 and anti-PTPRN2 had an AUC of 0.74 and 37.5% sensitivity at 95% specificity. When we combined this panel with ZnT8A, it showed an AUC of 0.81 and 55.2% sensitivity at 95% specificity compared with an AUC of 0.62 and 38.3% sensitivity at 95% specificity for ZnT8A alone (Figure 4). The addition of anti-QRFPR to IA-2A also improved the AUC from 0.81 to 0.86. However, anti-QRFPR did not help to improve the performance of GADA (Figure S-6).

IHC staining of novel autoantigens We profiled the protein expression level of five novel autoantigens using human pancreatic tissue sections from a healthy individual (6007 nPOD) to determine whether they were enriched or altered in the islets. PTPRN2 was not tested because it is already known to be islet-specific.33 Two of the other four antigens discovered on the arrays showed unusual islet expression (Figure 5). Although NUP50 was ubiquitously expressed in the pancreas, it showed dramatically higher expression level in the islets. More interestingly, its subcellular localization was different in islet cells, where it was localized in both cytoplasm and nucleus compared to only nuclear localization in the exocrine glandular cells. The appearance of cytoplasmic staining for this nuclear pore complex protein may suggest a novel role in islet cells and possibly different isoforms. The protein product of PPIL2 was apparently absent in the islets but high elsewhere in the pancreas (Figure 5). One possibility is that PPIL2 is expressed at lower levels in these cells. Alternatively, the gene may be expressed, but translation may be inhibited, either by a miRNA or some other post-transcriptional regulators. A third alternative is that PPIL2 may undergo some sort of post-translational modification in islet cells that make it a good antigen in T1D but cause it to lose or obscure the epitope targeted by the antibody used to stain the tissue. QRFPR, the



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antigen discovered from the knowledge-based approach, was also highly expressed in the islets, which is consistent with an earlier study and the Human Protein Atlas (HPA).37 Further studies for both NUP50 and PPIL2 will are warranted to elucidate the relation between the proteins pancreatic/cellular localization and the antigenicity. Taken together these findings may guide future studies into mechanistic aspects of T1D autoimmunity as well as prognostication. In addition their contribution to risk prediction, these new AAbs may contribute to tools that can stratify patients with different molecular subtypes and/or clinical courses.

Our

observation that different patients responded to different antigens reflects the heterogeneous nature of the disease (e.g., different rates of progression, etiological factors, genetic backgrounds).41,42 Although clinical outcomes data were not available for these samples, it will be helpful in the future to determine if responses to certain antigens or groups of antigens associate with different clinical courses. These novel AAbs may also help us understand the process of autoimmune destruction and identify antigens involved in cellular immune response. Although a number of new AAbs were identified and validated in this retrospective study, there would be significant value in a future systematic study using longitudinal samples collected before and after “seroconversion” to clinical T1D, which would enhance our understanding of the natural history and mechanisms leading to diabetes specific autoimmunity.


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CONCLUSIONS This is the most comprehensive study of its kind, having profiled more than ½ of the human proteome to identify novel AAbs in T1D. It extends the existing list of specific AAbs from previous studies.13,16,17,43,44 At the individual AAb levels, we identified AAbs to six minor T1D autoantigens including AAbs against PTPRN2, MLH1, MTIF3, PPIL2, NUP50 (from NAPPA screening) and QRFPR (by targeted ELISA). The combination of anti-PTPRN2, -MLH1, PPIL2 and -QRFPR had an AUC of 0.74 and 37.5% sensitivity at 95% specificity. Immunohistochemistry demonstrated that NUP50 protein behaved differently in islet cells, where it stained both nucleus and cytoplasm, compared with only nuclear staining in exocrine pancreas. Conversely, PPIL2 staining was absent in islet cells, despite its presence in exocrine cells. We have demonstrated the value of an unbiased data-driven immunoproteomics study in the identification of T1D-specific AAbs.



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AUTHOR INFORMATION Corresponding Author *Complete Address: Virginia G. Piper Chair of Personalized Diagnostics, The Biodesign Institute at Arizona State University, 1001 S. McAllister Ave. PO Box 876401, Tempe, AZ 85287-6401; Email: [email protected]; Phone: +001 480 965 2805; FAX: +001 480 965 3051. Author Contributions: X.B. 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. X.B. made the study plan, performed serum screening, finished validation work and wrote the manuscript. C.H.W did the IHC staining of top candidate autoantigens on human pancreas tissue samples. G.W. is responsible for data analysis. J.P. performed bioinformatics analysis of pancreas enriched genes. J.W. is involved in NAPPA array construction and quality assurance.

H.W. and K.B. helped in ELISA validation

experiments. D.S. and M.A. provided serum samples, valuable discussions and reviewed/edited the manuscript. J.Q. and J.L. contributed to experiment design, data generation and analysis and reviewed/edited manuscript.

All authors have given approval to the final version of the

manuscript. Notes The authors declare no conflict of interest. ACKOWLEDGEMENTS This study was supported by the Juvenile Diabetes Research Foundation (JDRF): 5-2005-1170, 17-2007-1045 and 6-2012-513.


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SUPPORTING INFORMATION The Supporting Information is available free of charge on the ACS Publications website at DOI: (to be provided). Supplemental Figures

Figure S-1. Quality of DNA staining, protein display and serum profiling on NAPPA: (A) Representative slide images of DNA staining, protein display and serum profiling. DNA staining is pesudo-colored in green, protein display and serum profiling is pseudo-colored in rainbow color (Blue, yellow and red indicates low, medium and high signal intensity); (B) Scatter plot of signal intensities of two protein display slides on the left and two serum profiling slides on the right. Figure S-2. Ring counts of sero-positivity between 40 T1D cases and 40 healthy controls: (A) Number of ring counts of sero-positivity between case and control groups; (B) Venn diagram of ring counts of antigens showed sero-reactivity in as least one case or one control; (C) Venn diagram of ring counts of antigens showed sero-reactivity in as least two cases or two controls; (D) Number of ring counts of sero-positivity between cases ≤12 and >12 years. Figure S-3. Reactivity to IA-2 and GAD65 measured by a commercial assay and RAPID ELISA in sample set 3: (A) Scatter plots with jitter of reactivity to IA-2 and GAD65 by commercial assay and RAPOID ELISA; (B) Venn diagram shows the overlap of individuals with reactivity to IA-2 or GAD65 in commercial assay and RAPID ELISA. T1DM: recent-onset T1D patients; HC: healthy controls. Figure S-4. Comparison of RAPID ELISA with NAPPA and LIPS assay: (A) Bar plots of seroreactivity to IA-2 measured by RAPID ELISA and NAPPA in sample set 1; (B) Bar plots of sero-reactivity to NUP50 measured by RAPID ELISA and LIPS Assay in sample set 2. Red represents patient samples. Green represents control samples. 21 ACS Paragon Plus Environment


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Figure S-5. Scatter plots with jitter plots of reported AAb biomarkers: (A) DYRK2; (B) UBE2L3 and EEF1A1. T1DM: recent-onset T1D patients; HC: healthy controls. Figure S-6. ROC analysis of AAb panel: (A) AAb complements to IA-2A; (B) AAb complements to GADA.

Supplemental Tables Table S-1. The detailed characteristics of each sample set. NA: sample depleted. BD means below detection. Table S-2. Genes enriched in pancreas. PMID: Pubmed ID. Table S-3. Characteristic of AAb targets (from Uniprot).


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


REFERNCES 1.

2. 3. 4. 5.

6.

7. 8.

9.

10.

11.

12.

13.

14. 15.

16.

Onkamo P, Vaananen S, Karvonen M, Tuomilehto J. Worldwide increase in incidence of Type I diabetes--the analysis of the data on published incidence trends. Diabetologia. 1999;42(12):1395-1403. Atkinson MA. The pathogenesis and natural history of type 1 diabetes. Cold Spring Harb Perspect Med. 2012;2(11). Roep BO, Peakman M. Antigen Targets of Type 1 Diabetes Autoimmunity. Cold Spring Harb Perspect Med. 2012;2(4). Marino E, Silveira PA, Stolp J, Grey ST. B cell-directed therapies in type 1 diabetes. Trends Immunol. 2011;32(6):287-294. Baekkeskov S, Aanstoot HJ, Christgau S, et al. Identification of the 64k Autoantigen in Insulin-Dependent Diabetes as the Gaba-Synthesizing Enzyme Glutamic-Acid Decarboxylase. Nature. 1990;347(6289):151-156. Bonifacio E, Lampasona V, Genovese S, Ferrari M, Bosi E. Identification of ProteinTyrosine Phosphatase-Like Ia2 (Islet-Cell Antigen-512) as the Insulin-Dependent Diabetes-Related 37/40k-Autoantigen and a Target of Islet-Cell Antibodies. J Immunol. 1995;155(11):5419-5426. Palmer JP, Asplin CM, Clemons P, et al. Insulin-Antibodies in Insulin-Dependent Diabetics before Insulin-Treatment. Science. 1983;222(4630):1337-1339. Wenzlau JM, Juhl K, Yu LP, et al. The cation efflux transporter ZnT8 (Slc30A8) is a major autoantigen in human type 1 diabetes. P Natl Acad Sci USA. 2007;104(43):1704017045. de Graeff-Meeder ER, Rijkers GT, Voorhorst-Ogink MM, et al. Antibodies to human HSP60 in patients with juvenile chronic arthritis, diabetes mellitus, and cystic fibrosis. Pediatr Res. 1993;34(4):424-428. Pietropaolo M, Castano L, Babu S, et al. Islet cell autoantigen 69 kD (ICA69). Molecular cloning and characterization of a novel diabetes-associated autoantigen. J Clin Invest. 1993;92(1):359-371. Kasimiotis H, Myers MA, Argentaro A, et al. Sex-determining region Y-related protein SOX13 is a diabetes autoantigen expressed in pancreatic islets. Diabetes. 2000;49(4):555-561. Winnock F, Christie MR, Batstra MR, et al. Autoantibodies to a 38-kDa glycosylated islet cell membrane-associated antigen in (pre)type 1 diabetes: association with IA-2 and islet cell autoantibodies. Diabetes Care. 2001;24(7):1181-1186. Hirai H, Miura J, Hu YF, et al. Selective screening of secretory vesicle-associated proteins for autoantigens in type 1 diabetes: VAMP2 and NPY are new minor autoantigens. Clin Immunol. 2008;127(3):366-374. Winter WE, Schatz DA. Autoimmune markers in diabetes. Clin Chem. 2011;57(2):168175. Wang J MD, Babu S, Yu J, Barker J, Klingensmith G, Rewers M, Eisenbarth GS, Yu L. Prevalence of autoantibody-negative diabetes is not rare at all ages and increases with older age and obesity. J Clin Endocrinol Metab. 2007;92(1):88-92. Miersch S, Bian X, Wallstrom G, et al. Serological autoantibody profiling of type 1 diabetes by protein arrays. J Proteomics. 2013;94:486-496.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

17.

18. 19. 20.

21.

22. 23. 24. 25.

26. 27.

28.

29.

30. 31. 32. 33.

34.

Page 24 of 35

Koo BK, Chae S, Kim KM, et al. Identification of Novel Autoantibodies in Type 1 Diabetic Patients Using a High-Density Protein Microarray. Diabetes. 2014;63(9):30223032. Festa F, Rollins SM, Vattem K, et al. Robust microarray production of freshly expressed proteins in a human milieu. Proteomics Clin Appl. 2013;7(5-6):372-377. Wang J, Figueroa JD, Wallstrom G, et al. Plasma autoantibodies associated with basallike breast cancers. Cancer Epidemiol Biomarkers Prev. 2015. Lindskog C, Korsgren O, Ponten F, Eriksson JW, Johansson L, Danielsson A. Novel pancreatic beta cell-specific proteins: antibody-based proteomics for identification of new biomarker candidates. J Proteomics. 2012;75(9):2611-2620. Seiler CY, Park JG, Sharma A, et al. DNASU plasmid and PSI:Biology-Materials repositories: resources to accelerate biological research. Nucleic Acids Res. 2014;42(Database issue):D1253-1260. Miersch S, LaBaer J. Nucleic Acid programmable protein arrays: versatile tools for arraybased functional protein studies. Curr Protoc Protein Sci. 2011;Chapter 27:Unit27 22. Festa F, Steel J, Bian XF, Labaer J. High-throughput cloning and expression library creation for functional proteomics. Proteomics. 2013;13(9):1381-1399. Ramachandran N, Raphael JV, Hainsworth E, et al. Next-generation high-density selfassembling functional protein arrays. Nat Methods. 2008;5(6):535-538. Wang J, Shivakumar S, Barker K, et al. Comparative Study of Autoantibody Responses between Lung Adenocarcinoma and Benign Pulmonary Nodules. J Thorac Oncol. 2016;11(3):334-345. Bian X, Wiktor P, Kahn P, et al. Antiviral antibody profiling by high-density protein arrays. Proteomics. 2015. Wang J SS, Barker K, Tang Y, Wallstrom G, Park JG, Tsay JC, Pass HI, Rom WN, LaBaer J, Qiu J. Comparative Study of Autoantibody Responses between Lung Adenocarcinoma and Benign Pulmonary Nodules. J Thorac Oncol. 2016;11(3):334-345. Wang J, Barker K, Steel J, et al. A versatile protein microarray platform enabling antibody profiling against denatured proteins. Proteomics Clin Appl. 2013;7(5-6):378383. Karthikeyan K, Barker K, Tang Y, et al. A Contra Capture Protein Array Platform for Studying Post-translationally Modified (PTM) Auto-antigenomes. Mol Cell Proteomics. 2016;15(7):2324-2337. Atkinson MA. The Pathogenesis and Natural History of Type 1 Diabetes. Csh Perspect Biol. 2012;4(12). Regnell SE, Lernmark A. The environment and the origins of islet autoimmunity and Type 1 diabetes. Diabetic Med. 2013;30(2):155-160. Bian X, Wallstrom G, Davis A, et al. Immunoproteomic Profiling of Anti-Viral Antibodies in New-Onset Type 1 Diabetes Using Protein Arrays. Diabetes. 2015. Lu J, Li Q, Xie H, et al. Identification of a second transmembrane protein tyrosine phosphatase, IA-2 beta, as an autoantigen in insulin-dependent diabetes mellitus: Precursor of the 37-kDa tryptic fragment. P Natl Acad Sci USA. 1996;93(6):2307-2311. Kawasaki E, Hutton JC, Eisenbarth GS. Molecular cloning and characterization of the human transmembrane protein tyrosine phosphatase homologue, phogrin, an autoantigen of type 1 diabetes. Biochem Biophys Res Commun. 1996;227(2):440-447.


Page 25 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


35.

36.

37.

38.

39. 40.

41. 42.

43.

44.

Goumidi L, Cottel D, Dallongeville J, Amouyel P, Meirhaeghe A. Effects of established BMI-associated loci on obesity-related traits in a French representative population sample. BMC Genet. 2014;15:62. Bronner CE, Baker SM, Morrison PT, et al. Mutation in the DNA Mismatch Repair Gene Homolog Hmlh1 Is Associated with Hereditary Nonpolyposis Colon-Cancer. Nature. 1994;368(6468):258-261. Granata R, Settanni F, Trovato L, et al. RFamide peptides 43RFa and 26RFa both promote survival of pancreatic beta-cells and human pancreatic islets but exert opposite effects on insulin secretion. Diabetes. 2014;63(7):2380-2393. Jacobsson G BA, Scheller RH, Juntti-Berggren L, Deeney JT, Berggren PO, Meister B. Identification of synaptic proteins and their isoform mRNAs in compartments of pancreatic endocrine cells. Proc Natl Acad Sci U S A. 1994;91(26):12487-12491. Arden SD, Zahn T, Steegers S, et al. Molecular cloning of a pancreatic islet-specific glucose-6-phosphatase catalytic subunit-related protein. Diabetes. 1999;48(3):531-542. Lieberman SM, Evans AM, Han B, et al. Identification of the beta cell antigen targeted by a prevalent population of pathogenic CD8+ T cells in autoimmune diabetes. P Natl Acad Sci USA. 2003;100(14):8384-8388. Achenbach P, Warncke K, Reiter J, et al. Stratification of type 1 diabetes risk on the basis of islet autoantibody characteristics. Diabetes. 2004;53(2):384-392. Achenbach P, Hummel M, Thumer L, Boerschmann H, Hofelmann D, Ziegler AG. Characteristics of rapid vs slow progression to type 1 diabetes in multiple islet autoantibody-positive children. Diabetologia. 2013;56(7):1615-1622. Larman HB, Laserson U, Querol L, et al. PhIP-Seq characterization of autoantibodies from patients with multiple sclerosis, type 1 diabetes and rheumatoid arthritis. J Autoimmun. 2013;43:1-9. Massa O, Alessio M, Russo L, et al. Serological Proteome Analysis (SERPA) as a tool for the identification of new candidate autoantigens in type 1 diabetes. J Proteomics. 2013;82:263-273.



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TABLES Table 1. Characteristics of subjects. Array Characteristics

Based

Knowledge

Sample set 1

Sample set 2

T1DM

HC

T1DM

HC

T1DM

HC

T1DM

HC

40

40

*60

60

46

46

*96

96

15.0+6.5

15.0+6.6

14.0+7.6

14.2+7.4

14.0+8.4

14.0+8.1

14.0+7.3

14.0+7.1

Median

13

13

12

12

11

12

12

12

Male(%)

(14)35.0%

(14)35.0%

(28)47.5%

(29)48.3%

(20)43.5%

(21)45.7%

(41)43.2%

(42)43.8%

GADA positive

(36)90.0%

0

(50)83.3%

(2)3.3%

(40)87.0%

(2)4.3%

(82)85.4%

(2)2.1%

IA-2A positive

(26)65.0%

0

(40)66.7%

(1)1.7%

(31)67.4%

(1)2.2%

(62)64.6%

(1)1.0%

Number of Subjects

Sample

Based set 4

Sample

set 3

Age Mean+ SD

Gender

T1DM: recent-onset T1D patients; HC: healthy controls. One individual has no age/gender information; Sample set 4 is a subset of sample set 2; Sample set 3 is the combination of sample set 1 and 2 (missing 4 pairs of samples due to sample depletion).


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Table 2. Sensitivities of candidate AAbs at 95% specificity: (A) Array-based; (B) Knowledgebased approaches. Sen=Sensitivity, Spec=Specificity.

Verification

Validation

Confirmation

Antigen

Sen

Spec

Sen

Spec

Sen

Spec

PTPRN2

25%

95%

20%

95%

22%

95%

MLH1

15%

95%

33%

95%

27%

95%

MTIF3

15%

95%

20%

95%

25%

95%

PPIL2

20%

95%

18%

95%

19%

95%

NUP50

15%

95%

17%

95%

16%

95%

TOX4

20%

95%

12%

95%

13%

95%

FIGN

13%

95%

12%

95%

13%

95%

C9orf142

18%

95%

5%

95%

11%

95%

ZNF280D

13%

95%

8%

95%

11%

95%

HES1

10%

95%

15%

95%

11%

95%

TOX

30%

95%

7%

95%

9%

95%

CTBP1

25%

95%

8%

95%

9%

95%

POLR1D

15%

95%

7%

95%

9%

95%

TAF11

15%

95%

3%

95%

8%

95%

PHF15

15%

95%

5%

95%

7%

95%

STMN3

10%

95%

5%

95%

6%

95%

TCEAL4

13%

95%

3%

95%

5%

95%

FILIP1

10%

95%

3%

95%

4%

95%

BANK1

10%

95%

2%

95%

2%

95%

(A) Array-based approach

Discovery

Confirmation

Antigen

Sen

Spec

Sen

Spec

QRFPR

11%

98%

20%

95%

CTRC

13%

98%

15%

95%

SNX6

13%

98%

15%

95%

SYTL4

17%

98%

13%

95%

ELA2A

11%

98%

13%

95%

IGRP

11%

98%

11%

95%

PAX6

11%

98%

11%

95%

HMGN3

20%

98%

10%

95%

STXBP1

22%

98%

9%

95%

REG3G

15%

98%

9%

95%

SCG5

13%

98%

6%

95%

RCBTB2

13%

98%

5%

95%

ASB9

13%

98%

4%

95%

PPY

13%

98%

2%

95%

(B) Knowledge-based approach 27 ACS Paragon Plus Environment


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Figures Figure 1. Study design. (1) Array-based approach: i. Discovery: samples from sample set 1 were screened against 10,000 human proteins across 5 NAPPA array sets; ii. Verification: 39 candidate AAbs were verified using samples from sample set 1 by RAPID ELISA; iii. Validation: 19 candidate AAbs were validated using samples from sample set 2 by RAPID ELISA; iv. Confirmation: 19 candidate AAbs were confirmed using samples from sample set 3 by RAPID ELISA. (2) Knowledge based approach: i. Discovery: 126 pancreas enriched genes (literature, bioinformatics) were tested in samples from sample set 4 by RAPID ELISA; ii. Confirmation: 14 candidate AAbs were tested in samples from sample set 3 by RAPID ELISA. One individual has no age/gender information; Sample set 4 is a subset of sample set 2; Sample set 3 is the combination of sample set 1 and 2 (missing 4 pairs of samples due to sample depletion).

Figure 2. Representative slide images showed the antibody reactivity to the known T1Dassociated autoantigens (IA-2, GAD65 and ZnT8) on five NAPPA array sets (four printed with genes with GST tag, one printed with genes with flag tag).

Figure 3. Novel AAbs from both array and knowledge-based approaches in sample set 3 by RAPID ELISA: (A) Scatter plots with jitter; (B) Heatmap of both known and novel AAbs. T1DM: recent-onset T1D patients; HC: healthy controls.


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Figure 4. ROC analysis of AAb panel: (A) Best AAb panel constructed from six AAbs; (B) AAbs complement to ZnT8A.

Figure 5. Immunohistochemistry (IHC) staining of new autoantigens: (A) QRFPR (scale bar=2 mm); (B) QRFPR zoomed in (scale bar=200 µm); (C) NUP50 (scale bar=200 µm); (D) PPIL2 (scale bar=200 µm). The arrows point to the islets in the pancreatic tissue section.



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Figure 1. Study design. (1) Array-based approach: i. Discovery: samples from sample set 1 were screened against 10,000 human proteins across 5 NAPPA array sets; ii. Verification: 39 candidate AAbs were verified using samples from sample set 1 by RAPID ELISA; iii. Validation: 19 candidate AAbs were validated using samples from sample set 2 by RAPID ELISA; iv. Confirmation: 19 candidate AAbs were confirmed using samples from sample set 3 by RAPID ELISA. (2) Knowledge based approach: i. Discovery: 126 pancreas enriched genes (literature, bioinformatics) were tested in samples from sample set 4 by RAPID ELISA; ii. Confirmation: 14 candidate AAbs were tested in samples from sample set 3 by RAPID ELISA. One individual has no age/gender information; Sample set 4 is a subset of sample set 2; Sample set 3 is the combination of sample set 1 and 2 (missing 4 pairs of samples due to sample depletion). 187x185mm (300 x 300 DPI)

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Figure 2. Representative slide images showed the antibody reactivity to the known T1D-associated autoantigens (IA-2, GAD65 and ZnT8) on five NAPPA array sets (four printed with genes with GST tag, one printed with genes with flag tag). 177x83mm (300 x 300 DPI)



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Figure 3. Novel AAbs from both array and knowledge-based approaches in sample set 3 by RAPID ELISA: (A) Scatter plots with jitter; (B) Heatmap of both known and novel AAbs. T1DM: recent-onset T1D patients; HC: healthy controls. 135x164mm (300 x 300 DPI)


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Figure 4. ROC analysis of AAb panel: (A) Best AAb panel constructed from six AAbs; (B) AAbs complement to ZnT8A. 181x88mm (300 x 300 DPI)



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Figure 5. Immunohistochemistry (IHC) staining of new autoantigens: (A) QRFPR (scale bar=2 mm); (B) QRFPR zoomed in (scale bar=200 µm); (C) NUP50 (scale bar=200 µm); (D) PPIL2 (scale bar=200 µm). The arrows point to the islets in the pancreatic tissue section. 215x279mm (300 x 300 DPI)


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for TOC only 215x279mm (300 x 300 DPI)


Tracking the Antibody Immunome in Type 1 Diabetes Using Protein

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