Association of autoantibodies with Ku and DNA repair proteins in connective tissue diseases
Bibliographic record
Abstract
OBJECTIVE: To analyse the autoimmune response to DNA damage response factors in systemic autoimmune rheumatic disease (SARD) patients and to determine their association with autoantibodies to Ku antigen. METHODS: We have screened the serum of 239 patients suffering from SARD, including systemic lupus erythematosus, systemic sclerosis and rheumatoid arthritis to detect the occurrence of autoantibodies to Ku and four other DNA damage response factors that form macromolecular complexes with Ku using an immunoprecipitation assay. RESULTS: We identified samples positive for autoantibodies to Ku (20.5%), DNA-dependent protein kinase catalytic subunit (DNA-PKcs, 8.4%) and poly(ADP-ribose) polymerase (5.9%), and report for the first time autoantibodies directed against two additional DNA repair proteins, Werner (6.3%) and Mre11 (9.6%). Remarkably, we found a striking correlation between the production of antibodies to Ku and the other four Ku-binding factors. Sixty-five percent of anti-Ku-positive sera were found to contain at least one of the four anti-DNA repair antibodies vs only 10% of the anti-Ku-negative sera. CONCLUSION: Our results suggest that the autoantibodies directed against Ku are elicited by macromolecular protein complexes containing Ku and the associated DNA damage proteins. The presence of autoantibodies directed against macromolecular complexes known to play roles in the DNA damage response provides evidence that B-cell responses to latent or persistent DNA damage may be present at the onset or during the development of autoimmunity in certain SARDs.
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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 | 0.001 |
| 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".