Histone H2B as a functionally important plasminogen receptor on macrophages
Bibliographic record
Abstract
Plasminogen (Plg) facilitates inflammatory cell recruitment, a function that depends upon its binding to Plg receptors (Plg-Rs). However, the Plg-Rs that are critical for cell migration are not well defined. Three previously characterized Plg-Rs (alpha-enolase, annexin 2, and p11) and a recently identified Plg-R (histone H2B [H2B]) were assessed for their contribution to Plg binding and function on macrophages. Two murine macrophage cell lines (RAW 264.7 and J774A.1) and mouse peritoneal macrophages induced by thioglycollate were analyzed. All 4 Plg-Rs were present on the surface of these cells and showed enhanced expression on the thioglycollate-induced macrophages compared with peripheral blood monocytes. Using blocking Fab fragments to each Plg-R, H2B supported approximately 50% of the Plg binding capacity, whereas the other Plg-Rs contributed less than 25%. Anti-H2B Fab also demonstrated a major role of this Plg-R in plasmin generation and matrix invasion. When mice were treated intravenously with anti-H2B Fab, peritoneal macrophage recruitment in response to thioglycollate was reduced by approximately 45% at 24, 48, and 72 hours, with no effect on blood monocyte levels. Taken together, these data suggest that multiple Plg-Rs do contribute to Plg binding to macrophages, and among these, H2B plays a very prominent and functionally important role.
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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) | 0.000 | 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) | 0.002 | 0.001 |
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".