MEK1/2 Inhibition Promotes Macrophage Reparative Properties
Bibliographic record
Abstract
Abstract Macrophages have important functional roles in regulating the timely promotion and resolution of inflammation. Although many of the intracellular signaling pathways involved in the proinflammatory responses of macrophages are well characterized, the components that regulate macrophage reparative properties are less well understood. We identified the MEK1/2 pathway as a key regulator of macrophage reparative properties. Pharmacological inhibition of the MEK1/2 pathway by a MEK1/2 inhibitor (MEKi) significantly increased expression of IL-4/IL-13 (M2)-responsive genes in murine bone marrow–derived and alveolar macrophages. Deletion of the MEK1 gene using LysMCre+/+Mek1fl/fl macrophages as an alternate approach yielded similar results. MEKi enhanced STAT6 phosphorylation, and MEKi-induced changes in M2 polarization were dependent on STAT6. In addition, MEKi treatment significantly increased murine and human macrophage efferocytosis of apoptotic cells, independent of macrophage polarization and STAT6. These phenotypes were associated with increased gene and protein expression of Mertk, Tyro3, and Abca1, three proteins that promote macrophage efferocytosis. We also studied the effects of MEKi on in vivo macrophage efferocytosis and polarization. MEKi-treated mice had increased efferocytosis of apoptotic polymorphonuclear leukocytes instilled into the peritoneum. Furthermore, administration of MEKi after LPS-induced lung injury led to improved recovery of weight, fewer neutrophils in the alveolar compartment, and greater macrophage M2 polarization. Collectively, these results show that MEK1/2 inhibition is capable of promoting the reparative properties of murine and human macrophages. These studies suggest that the MEK1/2 pathway may be a therapeutic target to promote the resolution of inflammation via modulation of macrophage functions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".