The nuclear autoantigen CENP‐B displays cytokine‐like activities toward vascular smooth muscle cells
Bibliographic record
Abstract
OBJECTIVE: A growing number of intracellular autoantigenic polypeptides have been found to play a second biologic role when they are present in the extracellular medium. We undertook this study to determine whether the CENP-B nuclear autoantigen could be added to this set of bifunctional molecules. METHODS: Purified CENP-B or CENP-B released from apoptotic cells was tested for surface binding to a number of human cell types by cell-based enzyme-linked immunosorbent assay, flow cytometry, and indirect immunofluorescence. The biologic effects of CENP-B on the migration, interleukin secretion, and signaling pathways of its specific target cells were evaluated. RESULTS: CENP-B was found to bind specifically to the surface of human pulmonary artery smooth muscle cells (SMCs) and not to fibroblasts or endothelial cells (ECs). Furthermore, CENP-B bound preferentially to SMCs of the contractile type rather than to SMCs of the synthetic type. Binding of CENP-B to SMCs stimulated their migration during in vitro wound healing assays, as well as their secretion of interleukins 6 and 8. The mechanism by which CENP-B mediated these effects involved the focal adhesion kinase, Src, ERK-1/2, and p38 MAPK pathways. Finally, CENP-B released from apoptotic ECs was found to bind to SMCs, thus indicating a plausible in vivo source of extracellular CENP-B. CONCLUSION: These novel biologic roles of the nuclear autoantigen CENP-B open up a new perspective for studying the pathogenic role of anti-CENP-B autoantibodies.
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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) | 0.001 | 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) | 0.001 | 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".