020. IDENTIFICATION OF TARGET ANTIGENS FOR ANTI-ENDOTHELIAL CELL ANTIBODIES IN PATIENTS WITH TAKAYASU’S ARTERITIS USING PROTEOMICS
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».