Different Protein Kinase C Isoforms Determine Growth Factor Specificity in Neuronal Cells
Bibliographic record
Abstract
Although mitogenic and differentiating factors often activate a number of common signaling pathways, the mechanisms leading to their distinct cellular outcomes have not been elucidated. In a previous report, we demonstrated that mitogen-activated protein (MAP) kinase (ERK) activation by the neurogenic agents fibroblast growth factor (FGF) and nerve growth factor is dependent on protein kinase Cdelta (PKCdelta), whereas MAP kinase activation in response to the mitogen epidermal growth factor (EGF) is independent of PKCdelta in rat hippocampal (H19-7) and pheochromocytoma (PC12) cells. We now show that EGF activates MAP kinase through a PKCzeta-dependent pathway involving phosphatidylinositol 3-kinase and PDK1 in H19-7 cells. PKCzeta, like PKCdelta, acts upstream of MEK, and PKCzeta can potentiate Raf-1 activation by EGF. Inhibition of PKCzeta also blocks EGF-induced DNA synthesis as monitored by bromodeoxyuridine incorporation in H19-7 cells. Finally, in embryonic rat brain hippocampal cell cultures, inhibitors of PKCzeta or PKCdelta suppress MAP kinase activation by EGF or FGF, respectively, indicating that these factors activate distinct signaling pathways in primary as well as immortalized neural cells. Taken together, these results implicate different PKC isoforms as determinants of growth factor signaling specificity within the same cell. Furthermore, these data provide a mechanism whereby different growth factors can differentially activate a common signaling intermediate and thereby generate biological diversity.
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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 | 0.001 | 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".