Comprehensive Profiling of the Rheumatoid Arthritis Antibody Repertoire
Bibliographic record
Abstract
OBJECTIVE: Autoantibodies against citrullinated proteins are found in 64-89% of rheumatoid arthritis (RA) patients, with 88-99% specificity. This study was undertaken to create an unbiased, comprehensive profile of serum antibodies against the human proteome, including the citrullinome and the homocitrullinome, in RA patients, using a high-density peptide array. METHODS: Our high-density peptide array, consisting of >4.6 million peptides, contained the entire annotated human proteome. The 20,246 proteins were represented as overlapping 16-mer peptides. In addition to native peptides, citrullinated and homocitrullinated peptides were included, as substitutions for arginine and lysine, and provided a comprehensive screen against all possible epitopes. Twenty-six serum samples (from 8 controls and 18 RA patients) were profiled on the high-density peptide array. Using RA-specific epitopes, we constructed an 8-epitope diagnostic biomarker on a Gyrolab xPlore instrument with a cohort of 92 serum samples (from 29 controls and 63 RA patients). The diagnostic biomarker was further validated with an independent cohort of 181 serum samples (from 54 controls and 127 RA patients). RESULTS: In the initial cohort the diagnostic performance of the 8-epitope biomarker yielded 96.6% specificity and 92.1% sensitivity. The overall diagnostic performance in the validation cohort was 94.4% specificity and 85% sensitivity. In both cohorts, the performance of the 8-epitope diagnostic biomarker compared favorably against the Abnova cyclic citrullinated peptide 2 (CCP2) assay. Using data from the peptide array, we identified novel RA-specific epitopes and formed the basis of a new RA diagnostic assay. CONCLUSION: Comprehensive antibody profiling using a high-density peptide array not only identified novel RA-specific epitopes but also allowed us to construct a novel diagnostic biomarker that is as specific as and more sensitive than the Abnova CCP2 assay.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".