Down-regulation of Stathmin Is Required for the Phenotypic Changes and Classical Activation of Macrophages
Bibliographic record
Abstract
Macrophages are important cells of innate immunity with specialized capacity for recognition and elimination of pathogens and presentation of antigens to lymphocytes for adaptive immunity. Macrophages become activated upon exposure to pro-inflammatory cytokines and pathogenic stimuli. Classical activation of macrophages with interferon-γ (IFNγ) and lipopolysaccharide (LPS) triggers a wide range of signaling events and morphological changes to induce the immune response. Our previous microtubule (MT) proteomic work revealed that the stathmin association with MTs is considerably reduced in activated macrophages, which contain significantly more stabilized MTs. Here, we show that there is a global decrease in stathmin levels, an MT catastrophe protein, in activated macrophages using both immunoblotting and immunofluorescent microscopy. This is an LPS-specific response that induces proteasome-mediated degradation of stathmin. We explored the functions of stathmin down-regulation in activated macrophages by generating a stable cell line overexpressing stathmin-GFP. We show that stathmin-GFP overexpression impacts MT stability, impairs cell spreading, and reduces activation-associated phenotypes. Furthermore, overexpressing stathmin reduces complement receptor 3-mediated phagocytosis and cellular activation, implicating a pivotal inhibitory role for stathmin in classically activated macrophages.
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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".