The SH2 Inositol 5-Phosphatase Ship1 Is Recruited in an SH2-dependent Manner to the Erythropoietin Receptor
Bibliographic record
Abstract
Ship1 (SH2 inositol 5-phosphatase 1) has been shown to be a target of tyrosine phosphorylation downstream of cytokine and immunoregulatory receptors. In addition to its catalytic activity on phosphatidylinositol substrates, it can serve as an adaptor protein in binding Shc and Grb2. Erythropoietin (EPO), the primary regulator of erythropoiesis, has been shown to activate the tyrosine phosphorylation of Shc, resulting in recruitment of Grb2. However, the mechanism by which the erythropoietin receptor (EPO-R) recruits Shc remains unknown. EPO activates the tyrosine phosphorylation of Ship1, resulting in the interdependent recruitment of Shc and Grb2. Ship1 is recruited to the EPO-R in an SH2-dependent manner. Utilizing a panel of EPO-R deletion and tyrosine mutants, we have discovered remarkable redundancy in Ship1 recruitment. EPO-R Tyr(401) appears to be a major site of Ship1 binding; however, Tyr(429) and Tyr(431) can also serve to recruit Ship1. In addition, we have shown that EPO stimulates the formation of a ternary complex consisting of Ship1, Shc, and Grb2. Ship1 may modulate several discrete signal transduction pathways. EPO-dependent activation of ERK1/2 and protein kinase B (PKB)/Akt was examined utilizing a panel of EPO-R deletion mutants. Activation of ERK1/2 was observed in EPO-RDelta99, which retains only the most proximal tyrosine, Tyr(343). In contrast, EPO-dependent PKB activation was observed in EPO-RDelta43, but not in EPO-RDelta99. It appears that EPO-dependent PKB activation is downstream of a region that indirectly couples to phosphatidylinositol 3-kinase.
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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) | 0.002 | 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".