Bidirectional crosstalk via IL‐6, PGE<sub>2</sub>and PGD<sub>2</sub>between murine myofibroblasts and alternatively activated macrophages enhances anti‐inflammatory phenotype in both cells
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Alternatively activated macrophages (AAMs) are important cells in the resolution of inflammation and tissue repair. We examined the impact of myofibroblasts, a vital cell in wound healing and tissue repair, on the development and function of AAMs. EXPERIMENTAL APPROACH: The interaction between AAMs and myofibroblasts was tested using conditioned medium from murine dermal myofibroblasts and bone marrow-derived macrophages. AAMs were differentiated with IL-4 and IL-13. KEY RESULTS: Conditioned medium from myofibroblasts enhanced the expression of AAM markers, arginase 1 and Ym1 (chitinase-3-like 3) and the spontaneous production of IL-10, while suppressing LPS-induced nitric oxide production. IL-6 from the myofibroblasts contributed to the amplification of the AAM phenotype; the selective COX-2 inhibitor, NS-398, significantly reduced the ability of myofibroblasts to promote an AAM phenotype. Pharmacological analyses indicated that myofibroblast-derived IL-6 enhanced arginase activity and spontaneous IL-10 output, while PGE2 , via the EP4 receptor, enhanced arginase expression and LPS-evoked IL-10 production. PGD2 suppressed LPS-evoked nitric oxide via the DP1 receptor. Reciprocally, conditioned medium from macrophages treated with IL-4 + IL-13 and myofibroblast conditioned medium components, but not macrophages given IL-4 + IL-13 only, reduced myofibroblast migration, the expression of COX-2, and the production of PGE2 and PGD2 . CONCLUSIONS AND IMPLICATIONS: These findings define mechanisms by which myofibroblasts enhance an AAM phenotype, which can promote wound healing directly, and/or via feedback communication to the myofibroblast, subsequently down-regulating its capacity to promote AAM function. This is an important homeostatic regulatory pathway in wound healing that can also limit unwanted fibrosis.
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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 | 0.001 |
| 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".