Autoantigenic properties of the aminoacyl tRNA synthetase family in idiopathic inflammatory myopathies
Bibliographic record
Abstract
ABSTRACT Objectives Autoantibodies are thought to play a key role in the pathogenesis of idiopathic inflammatory myopathies (IIM). However, up to 40% of IIM patients, even those with clinical manifestations of anti-synthetase syndrome (ASSD), test seronegative to all known myositis-specific autoantibodies (MSAs). We hypothesized the existence of new potential autoantigens among human cytoplasmic aminoacyl tRNA synthetases (aaRS) in patients with IIM. Methods Plasma samples and clinical data from 217 patients with, 50 patients with ASSD, 165 without, and two with unknown ASSD status were included retrospectively, as well as serum from 156 age/sex-matched population controls. Samples were screened using a multiplex bead array assay for presence of autoantibodies against a panel of 118 recombinant protein variants, representing 33 myositis-related proteins, including all 19 cytoplasmic aaRS. Results We identified reactivity towards 16 aaRS in 72 of the 217 patients. Twelve patients displayed reactivity against nine novel aaRS. The novel autoantibody specificities were detected in four patients previously seronegative for MSAs and in eight with previously detected MSAs. We also confirmed reactivity to four of the most common aaRS (Jo1, PL12, PL7, and EJ (n=45)) and identified patients positive for anti-Zo, -KS, and -HA (n=10) that were not previously tested. A low frequency of anti-aaRS autoantibodies was detected in controls. Conclusion Our results suggest that most, if not all, cytoplasmic aaRS may become autoantigenic. Autoantibodies against new aaRS may be found in plasma of patients previously classified as seronegative with potential high clinical relevance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".