Regulation of Erythropoietin-induced STAT Serine Phosphorylation by Distinct Mitogen-activated Protein Kinases
Bibliographic record
Abstract
The STAT proteins are a family of latent transcription factors that are activated by a wide variety of cytokines. Upon receptor engagement, STATs become tyrosine phosphorylated, translocate to the nucleus, and induce expression of target genes. In addition to tyrosine phosphorylation, maximal activation of some STAT proteins requires serine phosphorylation within the transactivation domain. Here we focus on STAT phosphorylation after engagement of the erythropoietin receptor (EPO-R). In Ba/F3-EPO-R cells, EPO induces tyrosine and serine phosphorylation of STAT1, STAT3, STAT5A, and STAT5B. Identical regions of the EPO-R couple to both tyrosine and serine phosphorylation of each cognate STAT protein. A proximal region of the EPO-R lacking cytoplasmic tyrosines couples to STAT1 and STAT3 phosphorylation as well as ERK and p38(HOG) activation, but not JNK/SAPK. STAT1 serine phosphorylation was perturbed by inhibition of ERK and p38 pathways, whereas only inhibition of ERK activation blocked STAT3 serine phosphorylation in response to EPO. STAT5A/B phosphorylation is downstream of EPO-R Tyr(343), however, STAT5A/B serine phosphorylation is unaffected by either ERK or p38 inhibition. Physiological responses induced by EPO may depend on regulation of serine phosphorylation of the STAT molecules by p38(HOG) and the ERK family of kinases as well as additional serine/threonine kinases.
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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".