The Guanine Nucleotide Exchange Factor CNrasGEF Regulates Melanogenesis and Cell Survival in Melanoma Cells
Bibliographic record
Abstract
cAMP-dependent Ras activation has been demonstrated in numerous cell types, particularly of neuronal (including melanoma cells) and endocrine origin, but the Ras activator involved has not been identified. In B16 melanoma cells, cAMP activates the Ras/Erk pathway, leading initially to stimulation but subsequently to long term (>24-h) inhibition of melanogenesis (dendrite extension and melanin production). Here we identify CNrasGEF as the Ras guanine nucleotide exchange factor (GEF) involved. We demonstrate that CNrasGEF is expressed endogenously in B16 melanoma cells and that cAMP-mediated activation of Ras and Erk1/2 in these cells can be augmented by CNrasGEF overexpression and reduced by its knockdown by RNA interference. Moreover, we show that CNrasGEF participates in the regulation of melanogenesis. Knockdown of CNrasGEF leads to increased dendrite extension and melanin production observed approximately 50 h after forskolin/isobutylmethylxanthine treatment, suggesting that CNrasGEF inhibits melanogenesis in the long term. Independently, we find that overexpression of CNrasGEF leads to apoptosis, whereas its knockdown by RNAi enhances cell proliferation, independent of cAMP. Collectively, these results suggest that CNrasGEF regulates melanogenesis but that it also has a distinct role in regulating cell proliferation/apoptosis.
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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".