Targeting of anti-citrullinated protein/peptide antibodies in rheumatoid arthritis using peptides mimicking endogenously citrullinated fibrinogen antigens
Bibliographic record
Abstract
INTRODUCTION: We have previously identified endogenously citrullinated peptides derived from fibrinogen in rheumatoid arthritis (RA) synovial tissues. In this study, we have investigated the auto-antigenicity of four of those citrullinated peptides, and explored their feasibility to target anti-citrullinated protein/peptide antibodies (ACPA). METHODS: The autoantigenic potential of the fibrinogen peptides was investigated by screening 927 serum samples from the Epidemiological Investigation of RA (EIRA) cohort on a peptide microarray based on the ImmunoCAP ISAC® system. In order to assay for ACPA blocking, two independent pools of purified ACPA were incubated with the respective targeting peptide prior to binding to cyclic citrullinated peptide (CCP)2 using the CCPlus® ELISA kit. RESULTS: Two peptides derived from the fibrinogen α chain, Arg573Cit (563-583) and Arg591Cit (580-600), referred to as Cit573 and Cit591, and two peptides from the fibrinogen β chain, Arg72Cit (62-81) and Arg74Cit (62-81) (Cit72 and Cit74), displayed 65%, 15%, 35%, and 53% of immune reactivity among CCP2-positive RA sera, respectively. In CCP2-negative RA sera, a positive reactivity was detected in 5% (Cit573), 6% (Cit591), 8% (Cit72), and 4% (Cit74). In the competition assay, Cit573 and Cit591 peptides reduced ACPA binding to CCP2 by a maximum of 84% and 63% respectively. An additive effect was observed when these peptides were combined. In contrast, Cit74 and Cit72 were less effective. Cyclization of the peptide structure containing Cit573 significantly increased the blocking efficiency. CONCLUSIONS: Here we demonstrate extensive autoantibody reactivity against in vivo citrullinated fibrinogen epitopes, and further show the potential use of these peptides for antagonizing ACPA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".