Autoantibodies to a novel Rpp38 (Th/To) derived B-cell epitope are specific for systemic sclerosis and associate with a distinct clinical phenotype
Bibliographic record
Abstract
OBJECTIVE: Detection of antinuclear antibodies and specific autoantibodies is important in the diagnosis and classification of SSc. Several proteins of the Th/To complex, including Rpp25, Rpp38 and hPop1 are the target of autoantibodies in SSc patients. However, very little is known about the epitope distribution of this autoantigen. Consequently, we screened Rpp25, Rpp38 and hPop1 for B cell epitopes and evaluated their clinical relevance. METHODS: Serum pools with (n = 2) and without (n = 1) anti-Th/To autoantibodies were generated and used for epitope discovery. Identified biomarker candidate sequences were then utilized to synthesize synthetic, biotinylated, soluble peptides. The peptides were tested to determine reactivity with sera from SSc cohorts (n = 202) and controls (n = 159) using a chemiluminescence immunoassay. Additionally, samples were also tested for antibodies to full-length recombinant Rpp25 antibodies by chemiluminescence immunoassay. RESULTS: Several immunodominant regions were found on the three proteins. The strongest reactivity was observed with an Rpp38 peptide (aa 229-243). Autoantibodies to the Rpp38 peptide were detected in 8/149 (5.4%) limited cutaneous SSc patients, but not in any of 159 controls (P = 0.003 by two-sided Fisher's exact probability test). Although reactivity to the novel antigenic peptide was correlated with the binding to Rpp25 (rho = 0.44; P < 0.0001), subsets of patient sera either reacted strongly with Rpp25 or with the novel Rpp38-derived peptide. CONCLUSION: A novel Rpp38 epitope holds promise to increase the sensitivity in the detection of anti-Th/To autoantibodies, thus enhancing the serological diagnosis of SSc.
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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) | 0.004 | 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".