020. IDENTIFICATION OF TARGET ANTIGENS FOR ANTI-ENDOTHELIAL CELL ANTIBODIES IN PATIENTS WITH TAKAYASU’S ARTERITIS USING PROTEOMICS
Bibliographic record
Abstract
Background: The mechanisms of the blood vessel injury in Takayasu’s arteritis (TAK), a systemic vasculitis characterized by inflammation of large- and medium-sized arteries, remains unknown. TAK shares common clinical and histologic findings with giant cell arteritis (GCA), another form of large- vessel vasculitis. Anti-endothelial cell antibodies (AECA) are detected frequently in rheumatic diseases such as vasculitis. We hypothesized that autoimmunity to blood vessel antigens plays a role in the pathology of TAK and, therefore, used proteomics to discover target antigens for AECA in TAK. Methods: We studied serum samples from 26 patients with TAK, 41 patients with GCA, and 17 healthy controls. We separated proteins extracted from human aortic endothelial cells (HAEC) by two- dimensional electrophoresis and transferred them onto membranes. We performed western blotting using serum from patients with TAK and healthy donors to detect antigens that were positive in TAK but not in healthy donors. We next identified the detected proteins by peptide mass finger-printing. IgG antibodies bound to antigens were detected using ELISA. The differences of serum autoantibody levels and the frequency of the autoantibodies between the groups were compared by Mann-Whitney U test and by Fisher’s exact test, respectively. Results: We successfully identified 78 proteins from 23 protein spots that were candidate targets of AECA in TAK. Antibodies appeared to target proteins with specific functions, e.g., redox-related proteins (29%), apoptosis-related proteins (28%), muscle-related proteins (23%), ATP-related proteins (19%), calcium-related proteins (19%), and coagulation- or fibrinolysis- related proteins (8%). One of the 78 proteins identified was stress-induced-phosphoprotein 1 (STIP1), a co-chaperone protein. The figure shows the mean OD±SD of IgG autoantibodies against STIP1 (anti- STIP1) was 0.177±0.125, 0.093±0.122 and 0.094±0.057 in TAK, GCA and sex- and age-matched healthy donors, respectively. There were statistically significant increased levels of anti-STIP in TAK compared with GCA (P < 0.001) and healthy donors (P < 0.005). Anti-STIP1 were detected in 27% of patients with TAK, in 5% of patients with GCA and in 6% of healthy donors. More patients with TAK had anti- STIP1 than did patients with GCA (P = 0.022). Conclusion: IgG autoantibodies to proteins in the proteome of HAEC are present in the serum of patients with TAK, implying that these autoantibodies may play a pathophysiologic role in the inflammation of blood vessels that is a key feature of TAK. Disclosures: This work was supported by JSPS KAKENHI Grant Number JP21591273. This work was also supported by R01-AR-060604 from the National Institute of Arthritis and Musculoskeletal Disorders. The Vasculitis Clinical Research Consortium (VCRC) (U54 AR057319) is part of the Rare Diseases Clinical Research Network (RDCRN), an initiative of the Office of Rare Diseases Research (ORDR), National Center for Advancing Translational Science (NCATS). The VCRC is funded through collaboration between NCATS, and the National Institute of Arthritis and Musculoskeletal and Skin Diseases, and has received funding from the National Center for Research Resources (U54 RR019497).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".