Classically Activated Macrophages Use Stable Microtubules for Matrix Metalloproteinase-9 (MMP-9) Secretion
Bibliographic record
Abstract
As major effector cells of the innate immune response, macrophages must adeptly migrate from blood to infected tissues. Endothelial transmigration is accomplished by matrix metalloproteinase (MMP)-induced degradation of basement membrane and extracellular matrix components. The classical activation of macrophages with LPS and IFN-γ causes enhanced microtubule (MT) stabilization and secretion of MMPs. Macrophages up-regulate MMP-9 expression and secretion upon immunological challenge and require its activity for migration during the inflammatory response. However, the dynamics of MMP-9 production and intracellular distribution as well as the mechanisms responsible for its trafficking are unknown. Using immunofluorescent imaging, we localized intracellular MMP-9 to small Golgi-derived cytoplasmic vesicles that contained calreticulin and protein-disulfide isomerase in activated RAW 264.7 macrophages. We demonstrated vesicular organelles of MMP-9 aligned along stable subsets of MTs and showed that selective modulation of MT dynamics contributes to the enhanced trafficking of MMP-9 extracellularly. We found a Rab3D-dependent association of MMP-9 vesicles with the molecular motor kinesin, whose association with the MT network was greatly enhanced after macrophage activation. Finally, we implicated kinesin 5B and 3B isoforms in the effective trafficking of MMP-9 extracellularly.
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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) | 0.001 | 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".