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 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 | 0.001 | 0.001 |
| 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 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".