Abstract
The presence or absence of autoantibodies against citrullinated proteins (ACPAs) distinguishes two main groups of rheumatoid arthritis (RA) patients with different etiologies, prognoses, disease severities, and, presumably, disease pathogenesis. The heterogeneous responses of RA patients to various biologics, even among ACPA-positive patients, emphasize the need for further stratification of the patients. We used high-density protein array technology for fingerprinting of ACPA reactivity. Identification of the proteome recognized by ACPAs may be a step to stratify RA patients according to immune reactivity. Pooled plasma samples from 10 anti-CCP-negative and 15 anti-CCP-positive RA patients were assessed for ACPA content using a modified protein microarray containing 1631 different natively folded proteins citrullinated in situ by protein arginine deiminases (PADs) 2 and PAD4. IgG antibodies from anti-CCP-positive RA plasma showed high-intensity binding to 87 proteins citrullinated by PAD2 and 99 proteins citrullinated by PAD4 without binding significantly to the corresponding native proteins. Curiously, the binding of IgG antibodies in anti-CCP-negative plasma was also enhanced by PAD2- and PAD4-mediated citrullination of 29 and 26 proteins, respectively. For only four proteins, significantly more ACPA binding occurred after citrullination with PAD2 compared to citrullination with PAD4, while the opposite was true for one protein. We demonstrate that PAD2 and PAD4 are equally efficient in generating citrullinated autoantigens recognized by ACPAs. Patterns of proteins recognized by ACPAs may serve as a future diagnostic tool for further subtyping of RA patients.
Subject terms: Biomarkers, Rheumatology
Introduction
Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic inflammation of the joints and synovial tissue inflammation, leading to pain, swelling, bone erosion, and disability1. Approximately two-thirds of RA patients produce anti-citrullinated protein antibodies (ACPAs)2,3. These autoantibodies may be present years before the onset of clinical symptoms, underlining their possible involvement in the pathogenesis of early RA and may serve as early biomarkers4,5. Anti-CCP-positive and anti-CCP-negative RA can be regarded as two disease entities with different predisposing factors, etiology, disease severity, prognosis, and presumably pathogenesis6,7.
The conversion of peptidyl-arginine into peptidyl-citrulline, commonly known as protein citrullination or arginine deimination, is a posttranslational modification of proteins catalyzed by protein arginine deiminases (PADs). In mammals, five isoforms of PAD differ in tissue distribution and localization within cells: PAD1–4 and PAD68. In particular, PAD2 and PAD4 are relevant to RA due to their expression in macrophages and neutrophils present in the synovial membrane of RA patients9. Their efficiency, relative to each other, in generating citrullinated neoepitopes recognized by ACPAs is not clear10–12. Citrullination is central to multiple regulatory cellular functions, such as cell differentiation, apoptosis, gene regulation, and inflammation13. Evidence is accumulating for a central role of citrullination in the pathogenesis of several diseases in addition to RA, including multiple sclerosis, Alzheimer’s disease, and cancers14–16.
The clinical presentation of RA varies considerably from patient to patient. In addition, the patients respond differently to various drugs. There is therefore an urgent need for the identification of new diagnostic tools to aid in patient stratification for precision medicine17,18. Characterization of ACPA autoantibody reactivity may provide new insight in this respect19–21. We have previously investigated the reactivity of autoantibodies against native proteins in RA patients and healthy subjects, demonstrating insignificant levels of autoantibody reactivity in especially healthy subjects for which intensities were significantly lower than both anti-CCP-positive and anti-CCP-negative RA patient samples against unmodified proteins20. Utilizing the high-throughput capacity of protein microarrays, we performed an exploratory study quantifying the binding of autoantibodies in plasma pools from well-characterized RA patients to more than 1600 different human proteins in citrullinated and non-citrullinated forms.
Results
We examined the binding of IgG antibodies in pools of plasma from anti-CCP-positive and anti-CCP-negative RA patients to microarrays containing 1631 human proteins in native form or citrullinated on-slide using PAD2 or PAD4 as catalysts. Figure 1 shows the staining intensity of individual arrays. Data from Fig. 1C,F have previously been published20. The PAD enzyme efficiency was tested on fibrinogen and is shown in Supplementary Dataset 1.
Figure 1.
Imaging of autoantibody binding to citrullinated and non-citrullinated protein arrays. Pooled plasma from 15 anti-CCP-positive RA patients or 10 anti-CCP-negative RA patients was diluted 1:200 and added to microarray plates containing 1631 human proteins that had been citrullinated by PAD2 or PAD4 or kept in native form. The binding of IgG antibodies was visualized using Cy3-labelled rabbit anti-human IgG antibodies. (A) Slide with proteins citrullinated by PAD2 and incubated with anti-CCP-positive plasma. (B) Proteins citrullinated by PAD4 incubated with anti-CCP-positive plasma. (C) Native proteins incubated with anti-CCP-positive plasma. (D) Proteins citrullinated by PAD2 incubated with anti-CCP-negative plasma. (E) Proteins citrullinated by PAD4 incubated with anti-CCP negative plasma (F). Native proteins incubated with anti-CCP-negative plasma. (C,F) have previously been published20.
Broad reactivity by low-intensity autoantibodies
We observed low reactivity of IgG antibodies against a large number of citrullinated proteins but not against the corresponding native proteins. After incubation with a pool of anti-CCP-positive plasma, 632 proteins showed more than twofold higher binding of IgG after citrullination with PAD2 than in their native form, and the corresponding number was 629 proteins after citrullination with PAD4, suggesting that these proteins were recognized by ACPAs (Fig. 2, Supplementary Dataset 2).
Figure 2.
Quantitative analysis of arrayed proteins recognized by autoantibodies. Bar chart showing the number of proteins recognized by autoantibodies to a higher degree than native proteins. Plasma from anti-CCP-positive or anti-CCP-negative RA patients was incubated with Immunome protein microarray slides containing native proteins or proteins citrullinated by PAD2 or PAD4. The number above the bars indicates the number of proteins recognized by autoantibodies under the given conditions (defined as more than two-fold differences in fluorescence intensity compared to native proteins, an intraprotein CV < 15, and a P value < 0.05). Specific targets for autoantibodies are shown in Supplementary Dataset 2.
Surprisingly, citrullination also enhanced the binding of IgG autoantibodies to a significant number of proteins when the array was incubated with the anti-CCP-negative plasma pool. This was true for 408 proteins after citrullination with PAD2 and 133 proteins after citrullination with PAD4 (Fig. 2, Supplementary Dataset 2).
PAD2 and PAD4 showed strikingly similar efficiency in generating epitopes recognized by ACPAs. Thus, only one protein, minichromosome maintenance complex-binding protein (C10orf119), was targeted to a higher degree (PAD2/PAD4 ratio: 2.7) by antibodies from anti-CCP-positive plasma when citrullinated by PAD2 compared to PAD4, while only four proteins were targeted to a higher degree when citrullinated by PAD4 (PAD4/PAD2 ratio: approximately 2): PC4 and SFRS1-interacting protein (PSIP1), nucleosome assembly protein 1-like 1 (NAP1L1), protein S100-P (S100P), and nucleosome assembly protein 1-like 4 (NAP1L4).
We also found but few differences between IgG antibody binding to PAD2- and PAD4-citrullinated proteins when the protein array was incubated with the anti-CCP negative plasma pool. Three proteins were targeted to a higher degree when citrullinated by PAD2 than by PAD4 (PAD2/PAD4 ratio: approximately 3): caspase recruitment domain-containing protein 9 (CARD9), 3-phosphoinositide-dependent protein kinase 1 (PDPK1), and protein kinase C and casein kinase substrate in neurons protein 3 (PACSIN3). Only one protein was targeted to a lower degree when citrullinated by PAD2: protein CBFA2T3 (ratio PAD2-citrullinated/PAD4-citrullinated: approximately 0.3).
We did not identify native proteins that were targeted by autoantibodies to a higher degree in the native form than in the citrullinated form. We found a total of 844 citrullinated proteins recognized by ACPAs from RA patients. A list of all identified antigens can be found in Supplementary Dataset 2.
Binding pattern of autoantibodies from anti-CCP-positive patients
Many of the proteins identified as targets for ACPAs above showed low-intensity staining for IgG antibodies. Proteins that are autoantigens in vivo can be expected to show staining with high intensity; however, to identify proteins that may be autoantigens in vivo, we limited the analysis to include only proteins with z scores > 2 (Tables 1, 2).
Table 1.
Autoantigens targeted differentially by ACPAs after different citrullination procedures.
| Protein | Intra- or extracellular | Anti-CCP positive pool: Ratio: PAD2-citrullinated/native proteins Fold difference |
Anti-CCP positive pool: Ratio: PAD4-citrullinated/native proteins Fold difference |
Anti-CCP positive pool: Ratio: PAD2-citrullinated /PAD4-citrullinated proteins Fold difference |
Anti-CCP negative pool: Ratio: PAD2-citrullinated/native proteins Fold difference |
Anti-CCP negative pool: Ratio: PAD4-citrullinated/native proteins Fold difference |
Anti-CCP negative pool: Ratio: PAD2-citrullinated/PAD4-citrullinated proteins Fold difference |
|---|---|---|---|---|---|---|---|
| ACAT2 | Intracellular | 2.2 | 3 | – | – | – | – |
| AFF4 | Intracellular | 5.7 | – | – | – | – | – |
| APPL1 | Intracellular | – | – | – | 7.2 | – | – |
| BAG3 | Intracellular | – | – | – | 2.7 | 2.6 | – |
| C1orf174 | Intracellular | - | 6.6 | – | – | – | – |
| C21orf2 | Intracellular, membrane | 2.4 | 3 | – | – | – | – |
| C21orf33 | Intracellular | 2.9 | 4.5 | – | 2.1 | 2.1 | – |
| CAMK2G | Intracellular, membrane | 2.4 | 3.4 | – | – | – | – |
| CAMKK2 | Intracellular | 3.2 | – | – | – | – | – |
| CARD9 | Intracellular | - | – | – | 4.4 | - | 3 |
| CARHSP1 | Intracellular | 2.2 | 3.1 | – | – | – | – |
| CASS4 | Intracellular | 15.4 | – | – | – | – | |
| CBFA2T3 | Intracellular | 3.3 | 4.3 | – | – | 2.9 | 0.3 |
| CCM2 | Intracellular | 3.3 | 4.3 | – | – | – | – |
| CD96 | Membrane | – | – | – | 2.8 | 2.4 | – |
| CEACAM1 | – | – | – | – | 2.2 | – | |
| CNN1 | Intracellular | 3.3 | 4.6 | – | – | – | – |
| COMMD3 | Both | 3 | 3.8 | – | – | – | – |
| CRISP2 | Extracellular, secreted | 2 | 2.8 | – | – | – | – |
| CRYAB | 2.2 | 2.1 | – | – | – | – | |
| CT47A1 | Intracellular | 2 | 3 | – | 4.1 | 2.2 | – |
| CXorf48 | Secreted | 3.1 | 3.4 | – | – | – | – |
| DCBLD2 | Membrane | – | – | – | 2.5 | 2.1 | – |
| DMRTB1 | Intracellular | 2.5 | 3.5 | – | – | – | – |
| DUSP6 | Intracellular | 3.1 | 4 | – | – | – | – |
| E6 | Intracellular | 2.8 | 3.9 | – | – | – | – |
| EEF1D | Intracellular | 2.6 | 3.5 | – | 2 | 2.1 | – |
| EEF1G | Both | 2.5 | 3.2 | – | – | – | – |
| ENO2 | Both | – | 3.6 | – | – | – | – |
| ESRRG | Intracellular | 2.3 | 3.3 | – | – | – | – |
| ETS2 | Intracellular | 4.1 | – | – | – | – | – |
| FGFR1_ext | Intracellular, secreted | – | 3.5 | – | – | – | – |
| FOXI1 | Intracellular | 2.1 | 3.1 | – | – | – | – |
| FOXR2 | Intracellular | 2.7 | 2.9 | – | – | – | – |
| FTHL17 | 2.1 | 2.8 | – | – | – | – | |
| GFAP | Intracellular | 2 | 2.7 | – | 2.1 | – | – |
| GGPS1 | 2.3 | 2.8 | – | – | – | – | |
| GNAO1 | Intracellular, membrane | 2.6 | 3.5 | – | – | – | – |
| GSTT1 | Intracellular | 2.1 | 2.6 | – | – | 4.6 | – |
| HAGHL | Intracellular | 2.5 | 3.1 | – | – | – | – |
| HDAC1 | Intracellular | 2.3 | 3.2 | – | – | – | – |
| HDAC3 | Intracellular | 2.2 | 2.9 | – | – | – | – |
| HRAS | Intracellular, membrane | 3.5 | 4.3 | – | – | – | – |
| HSF1 | Intracellular | – | 3.7 | – | – | – | – |
| ID1 | Intracellular, secreted | 3.1 | 4.1 | – | – | – | – |
| IGHG1 | Both | 2.9 | 3.8 | – | 3.1 | 2.8 | – |
| IKZF1 | Intracellular | – | – | – | 6.4 | – | – |
| IL1A | Both | 2.4 | 3.4 | – | – | – | – |
| IMPDH1 | Intracellular | 3 | – | – | – | – | – |
| IRF5 | Intracellular | 29.5 | 34.7 | – | – | – | – |
| ITPK1 | Intracellular | 2.4 | 3.4 | – | – | – | – |
| KCNIP3 | Intracellular, membrane | 3.4 | 4.7 | – | – | – | – |
| KRT8 | Intracellular | 2.9 | 3.5 | – | – | – | – |
| LCK | Intracellular, membrane | – | – | – | 4.2 | 3.1 | – |
| LDHB | 2.4 | 2.4 | – | – | – | – | |
| LEPREL4 | Intracellular | – | 2.7 | – | – | – | – |
| LIMS1 | Membrane | – | – | – | 2.8 | 3.3 | – |
| MAGEA10 | Intracellular | 2.6 | 3.4 | – | – | – | – |
| MAGEB1 | Intracellular | 5.7 | 10.1 | – | 3.5 | 3.3 | – |
| MAPK8_tv1 | Intracellular | 2.7 | 3.9 | – | – | – | – |
| MAPK9 | Intracellular | 2.9 | 3.7 | – | – | – | – |
| MAPKAPK3 | Intracellular | – | – | – | 4.5 | – | – |
| MEF2C | Intracellular | 4.9 | 5.6 | – | – | – | – |
| MIF | 2.3 | 2.4 | – | – | – | – | |
| MKNK1 | Intracellular, membrane | 2.8 | 2.8 | – | – | – | – |
| MLANA | Intracellular, membrane | 2.9 | 4.3 | – | – | – | – |
| MNAT1 | Intracellular | – | – | – | 3.2 | 3.1 | – |
| MOBKL2A | Intracellular | 2.6 | 3.3 | – | – | – | – |
| MPZL2 | Membrane | 2.4 | 3.1 | – | – | – | – |
| MX1 | Intracellular | 2.4 | 3.2 | – | – | – | – |
| MYCBP | 2.1 | 2.7 | – | – | – | – | |
| NAP1L3 | Intracellular | – | 3.2 | – | 2.6 | 2.8 | – |
| NEUROD1 | – | 2.7 | – | – | – | – | |
| NME4 | Intracellular | 2.4 | 3.4 | – | – | – | – |
| NPM1 | Intracellular | – | 5.7 | – | – | – | – |
| NR2E3 | Intracellular | 2.5 | 2.8 | – | – | – | – |
| NRBF2 | 2.2 | 2.8 | – | – | – | – | |
| PACSIN3 | Intracellular, membrane | – | – | – | 4.5 | – | 2.5 |
| PCBD | Intracellular | 2.3 | 2.9 | – | – | – | – |
| PDCL3 | Intracellular | – | – | – | 3.3 | 2.2 | – |
| PDPK1 | Intracellular, membrane | – | – | – | 5.5 | – | 2.8 |
| PKLR | Intracellular, secreted | 2.7 | 3 | – | – | – | – |
| POU2AF1 | Intracellular | 3.2 | 4.2 | – | – | – | – |
| PPP1R2P9 | Predicted: intracellular | 4.4 | 5.4 | – | – | – | – |
| PRC1 | Intracellular | 4.1 | 4.8 | – | – | – | – |
| PRKAR1A | Intracellular, membrane | 2.6 | 2.5 | – | – | – | – |
| PSME2 | Both | – | 3.8 | – | – | – | – |
| PTPN20A | Intracellular | 2.5 | 3.8 | – | 10.9 | 5.5 | – |
| PYCR1 | 2.6 | 2.4 | – | – | – | – | |
| RBKS | Intracellular | 2.5 | 2.8 | – | – | – | – |
| RBM46 | Intracellular | 2.2 | 2.6 | – | – | – | – |
| RBPJ | 2 | – | – | – | 2 | – | |
| RNF7 | Intracellular | – | 3.8 | – | – | – | – |
| RPLP1 | Intracellular | 2.5 | 3.4 | – | – | – | – |
| RPS6KA1 | Intracellular | – | – | – | 3.6 | – | – |
| RPS6KB1 | Intracellular | – | – | – | 12.2 | 13.4 | – |
| RQCD1 | Intracellular | – | 3.9 | – | – | – | – |
| RUFY1 | Intracellular | 2.7 | 3.3 | – | – | – | – |
| SDCCAG8 | Intracellular | 2.6 | 2.5 | – | 2.6 | 2.5 | – |
| SEPT9a | Intracellular | – | 9.4 | – | – | – | – |
| SGSM3 | 2.1 | 2.5 | – | – | – | – | |
| SH3GL1 | Intracellular | 6.5 | 5.8 | – | 3 | 2.3 | – |
| SKAP1 | Intracellular, membrane | 3.3 | 3.9 | – | 4.5 | 2.8 | – |
| SPANXN1 | Predicted: intracellular | – | 3.8 | – | – | – | – |
| SPATA25 | Intracellular | 2.2 | 2.7 | – | – | – | – |
| SSBP4 | Intracellular | 3.1 | 2.9 | – | – | – | – |
| SSNA1 | Intracellular | – | 2.2 | – | – | – | – |
| SSX2 | Intracellular | 3.1 | 3.8 | – | – | – | – |
| STAT1 | Intracellular | 3.3 | 4 | – | – | – | – |
| STAU1 | Intracellular | – | 20 | – | – | – | – |
| STK3 | Intracellular | 3.7 | 4.3 | – | – | – | – |
| TACC1 | Intracellular | 4.7 | 4.9 | – | – | – | – |
| TBC1D2 | Intracellular | 2.7 | 3.4 | – | – | – | – |
| TEX101 | Intracellular, membrane, and secreted | 2.6 | 3.5 | – | – | – | – |
| TFG | Intracellular | 3.1 | 4 | – | – | – | – |
| TKT | Intracellular, secreted | – | 3.3 | – | – | – | – |
| TROVE2 | Intracellular | – | – | – | – | 3.6 | – |
| TSC22D1 | Intracellular | 2.2 | 2.6 | – | – | – | – |
| UCKL1 | Intracellular | 4.4 | – | – | – | – | – |
| USP10 | Intracellular | – | – | – | 7.7 | 4 | – |
| VDR | Intracellular | 2.9 | 2.8 | – | – | – | – |
| VIM | Intracellular | – | 2.6 | – | – | – | – |
| ZHX2 | Intracellular | – | 4.1 | – | – | – | – |
| ZNF207 | Intracellular | – | – | – | 4.4 | 3.5 | – |
| ZNF496 | Intracellular | – | – | – | 2.7 | 2.3 | – |
| ZNHIT3 | Intracellular | – | 3.2 | – | – | – | – |
Requirements for inclusion in the table were z score > 2, CV < 15, CI-P < 0.05, fold difference > 2, and a BH corrected P value < 0.05.
ACAT2 acetyl-CoA acetyltransferase, AFF4 AF4/FMR2 family member 4, APPL1 DCC-interacting protein 13-alpha, BAG3 BAG family molecular chaperone regulator 3, C1orf174 UPF0688 protein C1orf174, C21orf2 protein C21orf2, C21orf33 ES1 protein homolog, CAMK2B calcium/calmodulin-dependent protein kinase type II subunit beta, CAMK2G calcium/calmodulin-dependent protein kinase type II subunit gamma, CAMKK2 calcium/calmodulin-dependent protein kinase type II subunit beta, CARD9 caspase recruitment domain-containing protein 9, CARHSP1 calcium-regulated heat-stable protein 1, CASS4 Cas scaffolding protein family member 4, CBFA2T3 protein CBFA2T3, CCM2 cerebral cavernous malformations 2 protein, CD96 T-cell surface protein tactile, CEACAM1 carcinoembryonic antigen-related cell adhesion molecule 1, CNN1 Calponin-1, COMMD3 COMM domain-containing protein 3, CRADD death-domain containing protein CRADD, CRISP2 cysteine-rich secretory protein 2, CRYAB alpha-crystallin B chain, CT47A1 cancer/testis antigen 47A, CXorf48 cancer/testis antigen 55, DCBLD2 discoidin, CUB and LCCL domain containing protein 2, DMRTB1 doublesex- and mab-3-related transcription factor B1, DUSP6 dual specificity protein phosphatase 6, E6 protein E6, EEF1D elongation factor 1-delta, EEF1G elongation factor 1-gamma, ENO2 gamma-enolase, ESRRG estrogen-related receptor gamma, ETS2 protein C-ets-2, FGFR1_ext fibroblast growth factor receptor 1, FOXI1 forkhead box protein I1, FOXR2 forkhead box protein R2, FTHL17 ferritin heavy polypeptide-like 17, GFAP glial fibrillary acidic protein, GGPS1 geranylgeranyl pyrophosphate synthase, GNAO1 guanine nucleotide-binding protein G(o) subunit alpha, GSTT1 glutathione S-transferase theta-1, HAGHL hydroxyacylglutathione hydrolase-like protein, HDAC1 histone deacetylase 1, HDAC3 histone deacetylase 3, HRAS GTPase HRas, HSF1 heat shock factor protein 1, ID1 DNA-binding protein inhibitor ID-1, IFI35 interferon-induced 35 kDa protein, IGHG1 immunoglobulin heavy constant gamma 1, IKZF1 DNA-binding protein Ikaros, IL1A Interleukin-1 alpha, IMPDH1 Inosine-5’-monophosphate dehydrogenase 1, IRF5 interferon regulatory factor 5, ITPK1 inositol-tetrakisphosphate 1-kinase, KCNIP3 calsenilin, KRT8 keratin, type II cytoskeletal 8, LCK tyrosine-protein kinase Lck, LDHB L-lactate dehydrogenase B chain, LEPREL4 endoplasmic reticulum protein SC65, LIMS1 LIM and senescent cell antigen-like-containing domain protein 1, MAGEA10 melanoma-associated antigen 10, MAGEB1 melanoma-associated antigen B1, MAPK8_tv1 mitogen-activated protein kinase 8, MAPK9 mitogen-activated protein kinase 9, MAPKAPK3 MAP kinase-activated protein kinase 3, MEF2C myocyte-specific enhancer factor 2C, MIF acrophage migration inhibitory factor, MKNK1 MAP kinase-interacting serine/threonine-protein kinase 1, MLANA melanoma antigen recognized by T-cells 1, MNAT1 CDK-activating kinase assembly factor MAT1, MOBKL2A MOB kinase activator 3A, MPZL2 myelin protein zero-like protein 2, MX1 interferon-induced GTP-binding protein Mx1, MYCBP c-Myc-binding protein, NAP1L3 nucleosome assembly protein 1-like 3, NEUROD1 neurogenic differentiation factor 1, NME4 nucleoside diphosphate kinase, NPM1 nucleophosmin, NR2E3 photoreceptor-specific nuclear receptor, NRBF2 nuclear receptor-binding factor 2, PACSIN3 protein kinase C and casein kinase substrate in neurons protein 3, PCBD Pterin-4-alpha-carbinolamine dehydratase, PDCL3 Phosducin-like protein 3, PDPK1 3-phosphoinositide-dependent protein kinase 1, PKLR pyruvate kinase PKLR, POU2AF1 POU domain class 2-associating factor 1, PPP1R2P9 protein phosphatase inhibitor 2 family member C, PRC1 protein regulator of cytokinesis 1, PRKAR1A cAMP-dependent protein kinase type-I alpha regulatory subunit, PSME2 proteasome activator complex subunit 2, PTPN20 tyrosine-protein phosphatase non-receptor type 20, PYCR1 pyrroline-5-carboxylate reductase 1, mitochondrial, RBKS ribokinase, RBM46 probably RNA-binding protein 46, RBPJ recombining binding protein suppressor of hairless, RNF7 RING-box protein 2, RPLP1 60S acidic ribosomal protein P1, RPS6KA1 ribosomal protein S6 kinase alpha-1, RPS6KB1 ribosomal protein S6 kinase beta-1, RQCD1 CCR4-NOT transcription complex subunit 9, RUFY1 RUN and FYVE domain-containing protein 1, SDCCAG8 serologically defined colon cancer antigen 8, SEPT9a Septin-9a, SGSM3 small G protein signaling modulator 3, SH3GL1 endophilin-A2, SKAP1 Src kinase-associated phosphoprotein 1, SPANXN1 sperm protein, SPATA25 spermatogenesis-associated protein 25, SSBP4 single-stranded DNA-binding protein 4, SSNA1 Sjoegren syndrome nuclear autoantigen 1, SSX2 protein SSX2, STAT1 signal transducer and activator of transcription 1-alpha/beta, STAU1 double-stranded RNA-binding protein Staufen homolog 1, STK3 serine/threonine-protein kinase 3, TACC1 transforming acidic coiled-coil-containing protein 1, TBC1D2 TBC1 domain family member 2A, TEX101 testis-expressed protein 101, TFG protein TFG, TKT transketolase, TROVE 2 60 kDa SS-A/Ro ribonucleoprotein, TSC22D1 TSC22 domain family protein 1, UCKL1 uridine-cytidine kinase like 1, USP10 ubiquitin carboxyl-terminal hydrolase 10, VDR vitamin D3 receptor, VIM vimentin, ZHX2 zinc fingers and homeoboxes protein 2, ZNF207 BUB3-interacting and GLEBS motifc containing protein, ZNF496 zinc finger protein 496 , ZNHIT3 zinc finger HIT domain-containing protein 3.
–: did not fulfill the inclusion criteria as stated above.
Table 2.
Citrullinated autoantigens targeted by autoantibodies.
| Proteins | Intra- or extracellular | PAD2-citrullinated proteins Ratio: anti-CCP positive/anti-CCP negative |
PAD4-citrullinated proteins: Ratio: anti-CCP positive/anti-CCP negatives |
|---|---|---|---|
| ACAT2 | Intracellular | 9.2 | 12.7 |
| AFF4 | Intracellular | 2.7 | – |
| ALDOA | Intracellular, secreted | 9.1 | 6 |
| C1orf174 | Intracellular | – | 6.9 |
| C21orf2 | Intracellular, membrane | 3.9 | 4.4 |
| C21orf33 | Intracellular | 2.1 | 3.3 |
| CAMK2G | Intracellular, membrane | 3.7 | 4.8 |
| CAMKK2 | Intracellular | 4.3 | – |
| CARHSP1 | Intracellular | 12.5 | 13.3 |
| CASS4 | Intracellular | 4.5 | – |
| CBFA2T3 | Intracellular | 2.6 | – |
| CCM2 | Intracellular | 3.1 | 5.1 |
| CNN1 | Intracellular | 5.8 | 7.8 |
| COMMD3 | Both | 6 | 9.7 |
| CRISP2 | Extracellular, secreted | 3 | 4.2 |
| CRYAB | Intracellular, membrane | 6 | 4.3 |
| CT47A1 | Intracellular | 0.4 | – |
| CXorf48 | Secreted | 5.5 | 5.9 |
| DMRTB1 | Intracellular | 4.8 | 6.1 |
| DUSP6 | Intracellular | 4.9 | 6.7 |
| E6 | Intracellular | 7 | 12.4 |
| EEF1D | Intracellular | 3 | 3.9 |
| EEF1G | Both | 6.1 | 7.2 |
| ENO2 | Both | – | 8.1 |
| ESRRG | Intracellular | 7.3 | 9 |
| ETS2 | Intracellular | 3.8 | – |
| FGFR1_ext | Intracellular, secreted | – | 3.9 |
| FOXI1 | Intracellular | 2.3 | 3.2 |
| FOXR2 | Intracellular | 5.9 | 5.8 |
| FTHL17 | Intracellular | 4.5 | 5 |
| GFAP | Intracellular | 2 | 3.9 |
| GGPS1 | Intracellular | 6.4 | 7.4 |
| GNAO1 | Intracellular, membrane | 3.3 | 5 |
| HAGHL | Intracellular | 2.5 | 3.3 |
| HDAC1 | Intracellular | 3 | 4.3 |
| HDAC3 | Intracellular | 3.7 | 5.7 |
| HRAS | Intracellular, membrane | 3.8 | 5.6 |
| HSF1 | Intracellular | – | 9.6 |
| ID1 | Intracellular, secreted | 5.7 | 6.9 |
| IL1A | Both | 3.7 | 4.6 |
| ILF2 | Intracellular | 4.3 | 4 |
| IMPDH1 | Intracellular | 5.4 | – |
| IRF5 | Intracellular | 5.1 | 6.9 |
| ITPK1 | Intracellular | 3.4 | 4.6 |
| KCNIP3 | Intracellular, membrane | 7.2 | 10.3 |
| KRT15 | Both, exosome | 3.2 | 2.3 |
| KRT19 | Both, exosome | 3.3 | – |
| KRT8 | Intracellular | 9.9 | 12.4 |
| LDHB | Intracellular | 6.8 | 6.8 |
| LEPREL4 | Intracellular | 3.3 | 5 |
| MAGEA10 | Intracellular | 6.1 | 7.5 |
| MAGEB1 | Intracellular | 2.8 | 5.2 |
| MAPK8_tv1 | Intracellular | 5.6 | 11.2 |
| MAPK9 | Intracellular | 3.9 | 6.6 |
| MEF2C | Intracellular | 4 | 6.9 |
| MIF | Both | 10.9 | 7.7 |
| MKNK1 | Intracellular, membrane | 5.5 | 5.6 |
| MLANA | Intracellular, membrane | 6 | 8 |
| MOBKL2A | Intracellular | 7.4 | 11.7 |
| MPZL2 | Membrane | 4.4 | 5 |
| MX1 | Intracellular | 4.4 | 6.8 |
| MYCBP | Intracellular | 9.2 | 9.6 |
| NEUROD1 | Intracellular | – | 5.8 |
| NME4 | Intracellular | 6.4 | 6.8 |
| NPM1 | Intracellular | – | 5.6 |
| NR2E3 | Intracellular | 5.7 | 6.5 |
| NRBF2 | Intracellular | 7 | 7.6 |
| NUBP2 | Intracellular | 5.2 | 3.5 |
| ODC1 | Intracellular | 6.5 | 5.8 |
| PCBD | Intracellular | 7.3 | 9.1 |
| PKLR | Intracellular, secreted | 4.1 | 4.8 |
| POU2AF1 | Intracellular | 4.6 | 6.9 |
| PPP1R2P9 | Predicted: intracellular | 4.5 | 5.9 |
| PRC1 | Intracellular | 8.6 | 8.1 |
| PRKAR1A | Intracellular, membrane | 7.8 | 6.9 |
| PSME2 | Intracellular | – | 8.2 |
| PYCR1 | Intracellular | 7.5 | 5.9 |
| RBKS | Intracellular | 7.4 | 9.1 |
| RBM46 | Intracellular | 8.1 | 8.1 |
| RNF7 | Intracellular | – | 5 |
| RPLP1 | Intracellular | 4.4 | 5 |
| RQCD1 | Intracellular | – | 7.1 |
| RUFY1 | Intracellular | 4.8 | 5.5 |
| SEPT9a | Intracellular | – | 6.6 |
| SGSM3 | Intracellular, membrane | 4.1 | 4.5 |
| SH3GL1 | Intracellular | 3.7 | 4.4 |
| SPANXN1 | Predicted: intracellular | – | 6.5 |
| SPANXN2 | Intracellular | 3.1 | 2.6 |
| SPATA25 | Intracellular | 4.7 | 4.8 |
| SSBP4 | Intracellular | 8 | 7.9 |
| SSNA1 | Intracellular | – | 11.1 |
| SSX2 | Intracellular | 5.8 | 7.1 |
| STAT1 | Intracellular | 5.6 | 6.3 |
| STAU1 | Intracellular | – | 6.3 |
| STK3 | Intracellular | 4.9 | 7.3 |
| TACC1 | Intracellular | 3.8 | 3.8 |
| TBC1D2 | Intracellular | 4.1 | 5 |
| TEX101 | Intracellular, membrane, and secreted | 4.7 | – |
| TFG | Intracellular | 5.1 | 7.1 |
| TKT | Intracellular, secreted | – | 5.8 |
| TPM1 | Intracellular, membrane | – | 4.1 |
| TSC22D1 | Intracellular | 5 | 5.6 |
| TSPY3 | Intracellular | 6.4 | 4 |
| UCKL1 | Intracellular | 3.9 | – |
| VDR | Intracellular | 6.9 | 7.6 |
| VIM | Intracellular | 4.2 | 4.8 |
| ZHX2 | Intracellular | – | 5.2 |
| ZNHIT3 | Intracellular | – | 5.3 |
Results are listed as fold differences calculated from the relative fluorescence unit value and are statistically significant.
Requirements for inclusion in the table were Z-score > 2, CV < 15, CI-P < 0.05, fold difference > 2, and a BH corrected P value < 0.05.
ALDOA fructose-biphosphate aldolase A, ILF2 interleukin enhancer-binding factor 2, KRT15 keratin, type I cytoskeletal 15, KRT19 keratin, type I cytoskeletal 19, NUBP2 cytosolic Fe-S cluster assembly factor NUBP2, ODC1 ornithine decarboxylase, PSME3 proteasome activator complex subunit 3, SPANXN2 sperm protein associated with the nucleus on the X chromosome N2, SSB lupus La protein, TPM1 tropomyosin alpha-1 chain, TSPY3 testis-specific Y-encoded protein 3, VEGFB vascular endothelial growth factor B. The remaining names are listed in the footer of Table 1.
–: did not fulfill the inclusion criteria as stated above.
After incubation of the arrayed proteins with the anti-CCP-positive plasma pool, two well-established autoantigens in RA fulfilled this criterion: vimentin and keratin 8. For both proteins, the binding of IgG autoantibodies increased markedly when they were citrullinated by PAD4 compared to native proteins, while the same only applied to keratin 8 after citrullination with PAD2 (Table 1). Irrespective of whether PAD2 or PAD4 was used for citrullination, more antibody binding was observed after incubation with the anti-CCP-positive plasma pool than after incubation with the anti-CCP-negative plasma pool (Table 2).
The proteins that showed the greatest increase in autoantibody capture after citrullination with PAD2 compared to native proteins were interferon-induced 35 kDa protein (IRF5; 29.5-fold increase after citrullination), cas scaffolding protein family member 4 (CASS4; 15.4-fold increase), and endophilin-A2 (SH3GL1; 6.5-fold increase). The proteins with the greatest change in autoantibody capture after citrullination with PAD4 were IRF5 (34.7-fold increase), double-stranded RNA-binding protein Staufen homolog 1 (STAU1; 20.0-fold increase), and melanoma-associated antigen B1 (MAGEB1; 10.1-fold increase).
Binding pattern of autoantibodies from anti-CCP-negative patients
We next examined the binding of autoantibodies contained in the plasma pool from anti-CCP-negative patients to citrullinated and native proteins. Even after exclusion of proteins with z scores < 2, we identified several proteins that showed increased IgG autoantibody binding after citrullination. This applied to 29 proteins after citrullination by PAD2 and 26 proteins after citrullination by PAD4 (Table 1).
Comparison between anti-CCP-positive and anti-CCP-negative plasma
Finally, we examined the binding of IgG autoantibodies to the protein array after incubation with the anti-CCP-positive versus the anti-CCP-negative plasma pool (Table 2). When PAD2 was used for citrullination, 91 proteins showed more than twofold higher binding of autoantibodies when incubated with anti-CCP-positive plasma than with anti-CCP-negative plasma. After citrullination with PAD4, the corresponding number was 98 proteins. The most significant differences were observed for calcium-regulated heat-stable protein 1 (CARHSP1, ratio anti-CCP-positive plasma/anti-CCP-negative: 12.5), macrophage migration inhibitory factor (MIF; ratio 10.9), and keratin type II cytoskeletal 8 (KRT8; ratio 9.9) when PAD2 was used for citrullination. When PAD4 was used for citrullination, the greatest anti-CCP-positive/anti-CCP-negative ratios were observed for calcium-regulated heat-stable protein 1 (CARHSP1, ratio 13.3), acetyl-CoA acetyltransferase (ACAT2, ratio: 12.7), and protein E6 (E6, ratio 12.4).
Discussion
We performed a high-throughput high-density protein microarray analysis on pools of plasma from 15 anti-CCP-positive and 10 anti-CCP-negative RA patients to identify proteins recognized by IgG autoantibodies before and after citrullination. The method proved successful, and we provide here a list of 844 out of 1631 arrayed proteins that were recognized by autoantibodies after citrullination, i.e. recognized by ACPAs. To our knowledge, this is the largest number of proteins identified as potential targets of ACPAs to date. Previous studies have used other types of citrullinated protein arrays to investigate ACPA reactivity but have focused on a single or fewer citrullinated proteins, usually on known RA antigens such as vimentin, fibrinogen, and alpha-enolase or used processed sample material11,22–29. This is the first investigation of autoantibody reactivity against citrullinated proteins on the KREX protein array platform using pure plasma samples from RA patients that we are aware of. Although we demonstrate here that more than 800 proteins can be recognized by ACPAs, they are not necessarily autoantigens in vivo, where several requirements must be met for citrullination to occur: the protein must localize to the same compartment as PAD2 or PAD4 and requirements to pH level, reducing conditions and calcium concentration should be met30–33. More research is needed to clarify which of the proteins shown to bind ACPAs under the optimal conditions used here in vitro also do so in vivo and elaboratory validation experiments are critical in this regard.
Per se, the high numbers of identified ACPA targets suggest that PAD enzymes are promiscuous in generating citrullinated neoepitopes recognized by ACPAs. On the other hand, approximately half of the proteins in the protein array used here were not recognized by ACPA, suggesting either that those proteins lack surface-exposed arginine residues or that they lack citrullination motifs for PADs.
Many of the abovementioned proteins that bound IgG autoantibodies showed low staining intensity. ACPAs appear to consist of a pool of either specific or cross-reactive antibodies, and it can be speculated that the low staining intensity is a result of cross-reactive antibodies34. The literature on monoclonal ACPAs shows extensive cross-reactivity, especially if glycine is present in the + 1 position of citrulline35,36. This fact may be important to consider in any multiplex ACPA assay using on-array citrullination so that there is complete control of which epitopes are citrullinated and which are not. Proteins that are potential autoantigens in vivo are likely to have relatively high affinity and/or concentrations, so in an effort to narrow down the list to potential genuine autoantigens, we implemented an additional filtration using z score (cut-off > 2) that excluded low-intensity antigens. Approximately 100 proteins in anti-CCP-positive plasma were identified. Among them was vimentin, a well-known autoantigen in RA. Other proteins showed strong increases in IgG binding intensity after citrullination, e.g., IRF5, CASS4, SH3GL1, and STAU1. Further studies are needed to determine whether the citrullinated forms of these proteins contain T-cell epitopes in addition to being targeted by ACPAs.
Interestingly, we also identified a rather large number of proteins recognized by autoantibodies from anti-CCP-negative individuals. This has been shown several times before and may demonstrate a subgroup of RA patients not identified using traditional serological testing37–39. Furthermore, this supports the conclusion of Wagner and colleagues that the commonly used commercial anti-CCP assays fail to identify some ACPA-positive RA patients (at least 10% in the authors setup)40. ACPA-positive and ACPA-negative RA have quite different pathogenesis6,7, and when future treatment targeting ACPA-positive RA specifically (e.g., PAD inhibitors) becomes available, protein arrays such as the one employed here may discriminate ACPA-positive and ACPA-negative RA better than the anti-CCP test. Another explanation why we identify several proteins recognized by autoantibodies from anti-CCP negative patients may be due to the implication of citrullination and not the citrullinated epitope. As already mentioned, the citrullination process results in conformational changes of the proteins which may lead to recognition of unmodified proteins from the anti-CCP negative patient pool and not necessarily recognition of the citrullinated epitope.
The relative efficiency of PAD2 and PAD4 in generating epitopes recognized by ACPAs has been a matter of some controversy. One study showed that at high antibody titers (1:250 and 1:1000) but not low titers (1:40 and 1:100), ACPAs preferentially bind to fibrinogen citrullinated by PAD4, while we have previously reported that PAD2 and PAD4 are equally efficient in generating epitopes for the binding of ACPAs to fibrinogen and alpha-enolase10,11. In a similar setup, we previously showed that PAD4 was the dominant isoform in generating ACPA-binding sites in histone H311. At the serum dilution used in the present study (1:200), the staining IgG ACPAs was equally intense when proteins were citrullinated by PAD2 and PAD4, except for only four proteins out of 1631 proteins.
The protein array methodology to investigate posttranslationally modified epitopes may not only be relevant for RA but may also be used in diseases where autoantibodies against other modified proteins have been shown, such as oxidized proteins in type 1 diabetes or autoantigens phosphorylated during stress-induced apoptosis in systemic lupus erythematosus5,41,42. Furthermore, it may be relevant to investigate at risk individuals to compare citrullination profiles or investigate other PAD enzymes such as the P. gingivalis PAD enzyme, which has been proposed to trigger RA even though conflicting studies exist43,44.
A limitation to the current study is that the Immunome protein arrays were not specifically enriched for antigens of particular relevance for RA, i.e. proteins that are present in joints or proteins that have previously been identified as autoantigens in RA, although numerous prominent ribonuclear proteins and other well-known autoantigens are present on the arrays. The development of focused arrays containing such proteins would be a natural next step. Another limitation is the use of plasma pools rather than individual plasma samples. Using individual samples, we could compare specific clinical phenotypes to autoantibody patterns or demonstrate the potential of subdifferentiation of patients based on their autoantibody profile45–48. This first study of its kind was merely a proof-of-concept study; it proves that further development of the technique is warranted.
Conclusion
We present a list of 844 citrullinated proteins recognized by ACPAs from RA patients. We demonstrate that PAD2 and PAD4 are equally efficient at generating binding sites for ACPAs. We present a list of approximately 100 potential autoantigens in RA, and we suggest that the pattern of autoantibody recognition may form a basis for subgrouping of anti-CCP-positive RA patients and anti-CCP-negative patients that rightfully should be considered ACPA-positive. The next steps in the development of the technique should be the production of arrays with RA-associated antigens and comparison of ACPA reactivity patterns with clinical phenotypes.
Materials and methods
Collection of patient plasma
Plasma samples were obtained from 10 anti-CCP-negative RA patients and 15 anti-CCP-positive RA patients. Patient data can be seen in Table 3. Plasma from anti-CCP-negative and anti-CCP-positive RA patients was pooled separately before protein array analysis. Individual patient response data have previously been published, and we have previously used the same patient cohorts in another study to investigate autoantibody reactivity against native autoantigens11,20. RA patients fulfilled the American College of Rheumatology and European League Against Rheumatism criteria for the diagnosis of RA49. Plasma was isolated from peripheral venous blood and drawn into BD Vacutainers containing EDTA (BD, Plymouth, UK). The use of patient samples was approved by the local ethics committee of the Institute of Rheumatology in Prague, Czech Republic, and written informed consent was obtained from all patients before initiation of the study (June 26, 2012, No. 3294/2012). All methods were performed according to relevant guidelines and regulations and in accordance with the Declaration of Helsinki.
Table 3.
Patient data from included participants.
| Anti-CCP-positive (n = 15) | Anti-CCP-negative (n = 10) | |
|---|---|---|
| Age (years) | 52.8 ± 15.6 (27–84) | 53.8 ± 17.1 (34–81) |
| Gender | 11 females and 4 males | 5 females and 5 males |
| Anti-CCP (median) | 891 mU [interquartile range 784] | 3.6 mU [interquartile range 10] |
| DAS28-ESR | 5.0 ± 1.4 | 3.7 ± 0.8 |
| DAS28-CRP | 4.7 ± 1.2 | 4 ± 1.2 |
| CRP (mg/L) | 29.4 ± 31.8 | 12.6 ± 8.9 |
| RF IgM | 91.7 ± 126.3 | 21.6 ± 19.7 |
| Treatment | 14 receiving csDMARDs and 4 also receiving bDMARDs | 7 receiving csDMARDs and 1 receiving only bDMARDs |
Sample preparation for protein array analysis
The Immunome (v1) protein microarray (Sengenics, Singapore) consists of 1631 proteins, in addition to several control proteins, spotted in quadruplicates, allowing assessment of spot-to-spot variation and across-slide variation in background intensity (Fig. 3A). The microarray consists of a variety of different proteins representing different categories, such as cancer-associated antigens, transcription factors, kinases, and other proteins involved in inflammation and cell signaling (Fig. 3B).
Figure 3.
Immunome slide design and protein categories on the protein microarray. (A) The Immunome protein microarray consists of four identical subarrays, each containing 1631 different proteins in addition to 11 control proteins (BCCP, BSA, Cy3BSA, IgA, IgG, IgM, and four different control probes for the BCCP tag acting as negative controls). The control proteins are located between the subarrays. (B) The proteins spotted on the microarray represent different protein groups, such as cancer-associated antigens, transcription factors, kinases, and signaling proteins. The number of proteins in each category is shown. BCCP biotin carboxyl carrier protein, BSA bovine serum albumin.
Each protein in the array is coupled to a biotin carboxyl carrier protein tag, which ensures correct three-dimensional folding during expression. Four slides were carefully transferred to different quadriperm chambers (Greiner BioOne, Kremsmünster, Austria) containing different citrulline reaction buffers consisting of 1.2 µg/mL recombinant human PAD2 or PAD4 (Cayman Chemicals, Ann Arbor, MI, USA); 1 mM 1,4-dithiothreitol (DTT), 10 mM CaCl, and 100 mM Tris–HCl and incubated at 37 °C for 3 h while shaking under horizontal rotation at 50 rpm (IKA, Germany, Königswinter). The slides were washed two times using cold serum albumin buffer (SAB) containing 0.1% Triton X-100 and 0.1% bovine serum albumin (BSA) in phosphate-buffered saline (PBS) and placed in a new quadriperm chamber. Additionally, two slides following the same procedure, but without the addition of PAD enzymes, were used.
Four milliliters of diluted pooled plasma (1:200) from anti-CCP-positive or anti-CCP-negative RA patients was added to the new chambers and incubated at 20 °C for 2 h at 50 rpm. The slides were washed using SAB buffer and added to a new quadriperm chamber. The binding of IgG antibodies was detected using Cy3-conjugated (GE Healthcare, Chicago, Ill, USA) polyclonal rabbit anti-human IgG (Dako, Santa Clara, CA, USA) diluted 1:1000 (v/v) in SAB buffer. The slides were covered in tinfoil and incubated at 20 °C for 2 h at 50 rpm. Finally, the slides were washed twice in SAB buffer and three times in ultrapure water followed by centrifugation at 240g for 5 min to dry the slides. Slides were stored at room temperature and scanned within 24 h.
Protein array imaging
The intensity of the individual spots was measured using a microarray laser scanner (Innoscan 710AL, Innopsys, Carbonne, France) using Mapix software (Ver. 8.2.2, Innopsys). The scan settings were as follows: 532 nm laser with low laser power (5 V), PMT gain at 60%, 5 µm resolution, and a scan speed of 35 px/s. Spotxel (SICASYS ver. 1.7.6) was used to automatically annotate each protein on the slide. Semiautomatic array alignment was used to specify the location of each spot. The median pixel intensity for each spot was used to eliminate the effect of outliers. Background intensity levels were extracted from the intensity of the adjacent spot. The data were exported as CSV files, and further data analysis was performed in R (Ver. 1.1.456, R Core Team).
Protein array quantitation data analysis
Raw intensities were normalized using a combination of quantile and intensity-based normalization50. Based on the normalized intensity levels, a z score, percent coefficient of variation (CV%), and Chebyshev inequality precision (CI-P) were calculated for each protein. An intraprotein CV% cutoff of < 15 was applied to ensure high reproducibility between the same protein spots (n = 4) on each microarray and to demonstrate an equal degree of citrullination. A CI-P cutoff of < 0.05 was applied to ensure that the identified intensities did not belong to the negative control distribution. We applied a z score with a cutoff of > 2 to discard low RFU intensities. Two-sample t tests with Benjamini–Hochberg FDR were performed to identify any statistically significant changes between the positive spots and the corresponding spots on the other slides. Finally, ratios for the statistically significantly changed expressions were calculated, and fold differences below 2 were discarded.
Mass spectrometry sample preparation
Fibrinogen (Cayman Chemical) was incubated for 3 h at 37 °C in citrulline reaction buffer containing 1.2 µg/mL PAD2 or PAD4 (Cayman Chemicals), 1 mM DTT, 10 mM CaCl, and 100 mM Tris–HCl to citrullinate fibrinogen. Digestion of fibrinogen (Cayman Chemical) was performed using filter-aided sample preparation51. Briefly, samples were transferred to Amicon Ultra 0.5 Centrifugal filters 10 kDa (Merck Millipore, MA, USA) containing 0.5% SDC in 50 mM triethylammonium bicarbonate (TEAB) buffer were centrifuged at 14,000g for 15 min. Next, the samples were reduced and alkylated by incubating in 10 mM tris(2-carboxyethyl)phosphine hydrochloride and 50 mM chloroacetamide for 30 min at 37 °C. Samples were washed in 0.5% sodium deoxycholate in 50 mM TEAB, and each wash was followed by centrifugation at 14,000g for 15 min at 20 °C. Next, samples were digested using 1 µg trypsin/100 µg sample protein in 0.5% SDC in TEAB and incubated overnight at 37 °C. Peptides were eluted by centrifugation at 14,000g for 15 min followed by the addition of 200 µl TEAB buffer and another centrifugation step. Next, the peptides were isolated by phase separation using ethyl acetate and acidified by trifluoroacetic acid. Phase separation was repeated two times, and the aqueous phase containing the peptides was recovered. All samples were dried down and stored at − 20 °C until the time of analysis.
Ultra-performance liquid chromatography tandem mass spectrometry
Fibrinogen samples were rehydrated in 2% acetonitrile and 0.1% formic acid. Protein concentration was measured using a DeNovix spectrophotometer DS-11 FX+ (DeNovix, Wilmington, Del, USA), and 0.4 µg was loaded per sample. Peptides were separated by reverse-phase liquid chromatography on a UPLC system (Dionex RSLX, Thermo Scientific), ionized by a nanoelectrospray ion source (CaptiveSpray, Bruker Daltonics), and analyzed using a timsTOF PRO mass spectrometer (Bruker Daltonics, Bremen, Germany). Samples were injected directly onto a C18 reversed-phase column (IonOpticks, 25 cm × 75 µm ID, 1.6 µm C18) kept at 40 °C. The peptides were eluted with a constant flow rate of 400 nL/min using solvent A (0.1% formic acid in water) and solvent B (acetonitrile with 0.1% formic acid) with a total runtime of 60 min. The gradient was as follows: 0–16 min at 2% B, 16–45 min at 5% B, 45–48 at 35% B, 48–52 min at 95% B, 52–60 min at 2% B. Raw files were loaded into PEAKS (Bioinformatics Solution Inc, v. 10.5) and followed the standard analysis pipeline with the addition of citrulline as a variable modification.
Ethics approval and consent to participate
The use of patient samples was approved by the local ethics committee of the Institute of Rheumatology in Prague, Czech Republic, and written informed consent was obtained from all patients before initiation of the study (June 26, 2012, No. 3294/2012).
Supplementary Information
Acknowledgements
Department of Immunology at Rigshospitalet, Denmark is acknowledged for providing the biological samples. TP is co-financed by a Grant from the Sino-Danish Center and Aalborg University. LS is supported by research project No. 023728 of the Ministry of Health of the Czech Republic for the conceptual development of a research organization. JMB thanks the National Research Foundation for a Research Chair Grant. Figure 3 was reproduced from a figure provided by Sengenics.
Abbreviations
- RA
Rheumatoid arthritis
- ACPA
Anti-citrullinated protein antibody
- PAD
Protein arginine deiminase
- anti-CCP
Anti-cyclic citrullinated peptide
- CV%
Percent coefficient of variation
- CI-P
Chebyshev inequality precision
- BCCP
Biotin carboxyl carrier protein
Author contributions
T.P., A.S., C.N., and D.D. conceptualized the overall study. D.D. and L.S. collected samples for the study. T.P. wrote the draft version of the paper and revised it according to the coauthors' comments (C.N., A.S., D.D., and J.B.). T.P. performed the sample preparation and data analysis. M.J. contributed to the methodology. A.S. and C.N. supervised the study. All authors have approved the final version of the manuscript.
Funding
The study is part of the PROCIT study financed by the Danish Council for Independent Research (DFF-7016-00233). Moreover, the Danish National Mass Spectrometry Platform for Functional Proteomics (PRO-MS; Grant no. 5072-00007B), the Obelske family foundation, the Svend Andersen Foundation, and the SparNord foundation are acknowledged for grants to the analytical platform, enabling parts of this study.
Data availability
The microarray raw data are available from the corresponding author on reasonable request. The mass spectrometry proteomics data generated during the current study are available from the ProteomeXchange Consortium via the partner repository with the dataset identifier PXD02495552.
Competing interests
JMB is the Chief Scientific Officer of Sengenics Corporation who commercializes the Immunome arrays used in this study. TP, DD, MJ, CN, AS, and LS declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
These authors contributed equally: Claus H. Nielsen and Allan Stensballe.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-021-96675-z.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The microarray raw data are available from the corresponding author on reasonable request. The mass spectrometry proteomics data generated during the current study are available from the ProteomeXchange Consortium via the partner repository with the dataset identifier PXD02495552.