hSef Inhibits PC-12 Cell Differentiation by Interfering with Ras-Mitogen-activated Protein Kinase MAPK Signaling
Bibliographic record
Abstract
Growth factor signaling by receptor tyrosine kinases regulates several cell fates, such as proliferation and differentiation. Sef was genetically identified as a negative regulator of fibroblast growth factor (FGF) signaling. Using bioinformatic methods and rapid amplification of cDNA ends-PCR, we isolated both the mouse and the human Sef genes, which encoded the Sef protein and Sef-S isoform that was generated through alternative splicing. We provide evidence that the Sef gene products were located mainly on the cell membrane. Co-immunoprecipitation and immunostaining experiments indicate that hSef interacts with FGFR1 and FGFR2 but not FGFR3. Our results demonstrated that stably expressed hSef strongly inhibits FGF2- or nerve growth factor-induced PC-12 cell differentiation. The intracellular domain of hSef is necessary for the inhibitory effect on FGF2-induced PC-12 cell differentiation. Furthermore, our data suggested Sef exerted the negative effect on FGF2-induced PC-12 cell differentiation through the prevention of Ras-mitogen-activated protein kinase signaling, possibly functioning upstream of the Ras molecule. These findings suggest that Sef may play an important role in the regulation of PC-12 cell differentiation.
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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".