The PI3K pathway drives the maturation of mast cells via microphthalmia transcription factor
Bibliographic record
Abstract
Mast cell maturation is poorly understood. We show that enhanced PI3K activation results in accelerated maturation of mast cells by inducing the expression of microphthalmia transcription factor (Mitf). Conversely, loss of PI3K activation reduces the maturation of mast cells by inhibiting the activation of AKT, leading to reduced Mitf but enhanced Gata-2 expression and accumulation of Gr1(+)Mac1(+) myeloid cells as opposed to mast cells. Consistently, overexpression of Mitf accelerates the maturation of mast cells, whereas Gata-2 overexpression mimics the loss of the PI3K phenotype. Expressing the full-length or the src homology 3- or BCR homology domain-deleted or shorter splice variant of the p85α regulatory subunit of PI3K or activated AKT or Mitf in p85α-deficient cells restores the maturation but not growth. Although deficiency of both SHIP and p85α rescues the maturation of SHIP(-/-) and p85α(-/-) mast cells and expression of Mitf; in vivo, mast cells are rescued in some, but not all tissues, due in part to defective KIT signaling, which is dependent on an intact src homology 3 and BCR homology domain of p85α. Thus, p85α-induced maturation, and growth and survival signals, in mast cells can be uncoupled.
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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".