Characterization and use of the ECV304 autoantigenic citrullinome to understand anti-citrullinated protein/peptide autoantibodies in rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: Anti-citrullinated protein antibodies (ACPAs) are highly specific for rheumatoid arthritis (RA). In vivo, ACPAs target peptidyl-citrulline epitopes (cit-) in a variety of proteins (cit-prot-ACPAs) and derived peptides (cit-pept-ACPAs) generated via the peptidylarginine deiminase (PAD) isoenzymes. We aimed to identify a cell line with self-citrullination capacity, to describe its autoantigenic citrullinome, and to test it as a source of autocitrullinated proteins and peptides. METHODS: Human cell lines were screened for cit-proteins by Western blot. PAD isoenzymes were identified by RT-PCR. Autocitrullination of ECV304 was optimized, and the ECV304 autocitrullinomes immunoprecipitated by sera from three RA patients were characterized by mass spectrometry. Cit-pept-ACPAs were detected using anti-CCP2 ELISA and cit-prot-ACPAs, by an auto-cit-prot-ECV304 ELISA. Sera from 177 RA patients, 59 non-RA rheumatic disease patients and 25 non-disease controls were tested. RESULTS: Of the seven cell lines studied, only ECV304 simultaneously overexpressed PAD2 and PAD3 and its extracts reproducibly autocitrullinated self and non-self-proteins. Proteomic analysis of the cit-ECV304 products immunoprecipitated by RA sera, identified novel cit-targets: calreticulin, profilin 1, vinculin, new 14-3-3 protein family members, chaperones, and mitochondrial enzymes. The auto-cit-prot-ECV304 ELISA had a sensitivity of 50% and a specificity of 95% for RA diagnosis. CONCLUSIONS: ECV304 cells overexpress two of the PAD isoenzymes capable of citrullinating self-proteins. These autocitrullinated cells constitute a basic and clinical research tool that enable the detection of cit-prot-ACPAs with high diagnostic specificity and allow the identification of the specific cit-proteins targeted by individual RA sera.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".