Anti-DNA topoisomerase I autoantibodies bind directly to the cell surface of fibroblasts in patients with systemic sclerosis
Bibliographic record
Abstract
Fibroblasts play a crucial role in the development of systemic sclerosis (SSc), and antifibroblast antibodies (AFAs) capable of inducing a proinflammatory phenotype in fibroblasts have been detected in SSc sera. In the present study, we examined the prevalence of AFAs in SSc and other diseases, and the possible correlation between AFAs and known antinuclear antibody specificities in SSc patients. Sera from 99 patients with SSc, from 123 patients with other autoimmune and nonautoimmune diseases, and from 30 age-matched and gender-matched healthy controls were examined. The AFA prevalence was assessed by flow cytometry and further characterized by indirect immunofluorescence, ELISA and immunoblotting. Anti-topoisomerase I (anti-topo I) from SSc sera was purified by affinity chromatography on topoisomerase I. AFAs were more common in SSc patients (26%) than in any other diseases studied. The presence of AFAs was significantly associated with pulmonary involvement and death. SSc AFA-positive sera bound to all human and rodent fibroblasts tested but not to human primary endothelial or smooth muscle cells. All SSc AFAs strongly reacted with topoisomerase I by ELISA and immunoblotting. The binding intensity of SSc AFAs correlated strongly with reactivity against topoisomerase I on immunoblots of fibroblast extracts, and with the immunofluorescent pattern typical of anti-topo I on permeabilized cells. Total IgGs and affinity-purified anti-topo I from AFA-positive SSc sera were found to react with the surface of unpermeabilized fibroblasts by flow cytometry and by immunofluorescence and confocal microscopy. This is the first report establishing that AFAs in SSc are strongly correlated with anti-topo I and, furthermore, that anti-topo I itself displays AFA activity by reacting with determinants at the fibroblast surface.
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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 | 0.002 | 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) | 0.002 | 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".