All Trans Retinoic Acid, Transforming Growth Factor β and Prostaglandin E2 in Mouse Plasma Synergize with Basophil-Secreted Interleukin-4 to M2 Polarize Murine Macrophages
Bibliographic record
Abstract
In previous studies we found that macrophages (MФs) from SH2-containing inositol-5'-phosphatase (SHIP) deficient mice are M2 polarized while their wild type (WT) counterparts are M1 polarized and that this difference in MФ phenotype can be recapitulated during in vitro derivation from bone marrow if mouse plasma (MP), but not fetal calf serum, is added to standard M-CSF-containing cultures. In the current study we investigated the mechanism by which MP skews SHIP-/- but not +/+ MФs to an M2 phenotype. Our results suggest that SHIP-/- basophils constitutively secrete higher levels of IL-4 than SHIP+/+ basophils and this higher level of IL-4 is sufficient to skew both SHIP+/+ and SHIP-/- MФs to an M2 phenotype, but only when MP is present to increase the sensitivity of the MФs to this level of IL-4. MP increases the IL-4 sensitivity of both SHIP+/+ and -/- MФs not by increasing cell surface IL-4 or CD36 receptor levels, but by triggering the activation of Erk and Akt and the production of ROS, all of which play a critical role in sensitizing MФs to IL-4-induced M2 skewing. Studies to identify the factor(s) in MP responsible for promoting IL-4-induced M2 skewing suggests that all-trans retinoic acid (ATRA), TGFβ and prostaglandin E2 (PGE2) all play a role. Taken together, these results indicate that basophil-secreted IL-4 plays an essential role in M2 skewing and that ATRA, TGFβ and PGE2 within MP collaborate to dramatically promote M2 skewing by acting directly on MФs to increase their sensitivity to IL-4.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".