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 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) | 0.000 | 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".