Antagonistic Roles of the ERK and p38 Pathways in Chemically Induced Erythroid Differentiation of Murine Erythroleukemia Cells.
Bibliographic record
Abstract
Abstract Murine erythroleukemia (MEL) cells provide a valuable model to study the molecular events leading to erythroid differentiation. Maturing erythroid cells synthesize large quantitities of hemoglobin, a process requiring the coordinated synthesis of heme and globin. Here, we investigated the role of the ERK and p38 mitogen-activated protein kinase (MAPK) signaling pathways in chemically induced differentiating MEL cells. We showed that treatment of DMSO- or HMBA-induced MEL cells with the MEK1/2 inhibitor UO126, blocking the ERK pathway, results in the increase of beta-globin and 5-aminolevulinate synthase-2 (ALAS-2) transcript levels. We found that addition of the p38 inhibitor SB203580 has the opposite effect, leading to decreased beta-globin and ALAS-2 mRNA levels. The regulation on the transcript level correlated with increased or decreased hemoglobin levels in the presence of MEK1/2 and p38 inhibitors, respectively. Our results suggest that the ERK and p38 pathways play antagonistic roles in DMSO- and HMBA induced erythroid differentiation in MEL cells. This data also provide a novel link between MAPK signaling and the regulation of intracellular heme levels, as ERK and p38 MAPK signaling cascades appear to differentially regulate one of the key enzymes of the heme biosynthesis pathway.
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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".