The MEK/ERK Pathway Promotes NOTCH Signalling in Pancreatic Cancer Cells
Bibliographic record
Abstract
Activation of the NOTCH receptors relies on their intracellular proteolysis by the gamma-secretase complex. This cleavage liberates the NOTCH intracellular domain (NIC) thereby allowing the translocation of NIC towards the nucleus to assemble into a transcriptional platform. Little information is available regarding the regulatory steps operating on NIC following its release from the transmembrane receptor up to its association with transcriptional partners. Interfering with these regulatory steps might potentially influences the nuclear outcome of NOTCH signalling. Herein, we exploited a reliable model to study the molecular events occurring subsequent to NOTCH1 cleavage. In pancreatic cancer cells, pulse of NOTCH1 activation led to increased expression of NOTCH target genes namely HES1 and c-MYC. We uncovered that, upon its release, the NOTCH1 intracellular domain, NIC1, undergoes a series of post-translational modifications that include phosphorylation. Most interestingly, we found that activation of the MEK/ERK pathway promotes HES1 expression. Inhibition of the gamma-secretase complex prevented the MEK/ERK-induced HES1 expression suggesting a NOTCH-dependent mechanism. Finally, higher levels of NIC1 were found associated with its transcriptional partners [CBF1, Su(H) and LAG-1] (CSL) and MASTERMIND-LIKE 1 (MAML1) upon MEK/ERK activation providing a potential mechanism whereby the MEK/ERK pathway promotes expression of NOTCH target genes. For the first time, our data exposed a signalling pathway, namely the MEK/ERK pathway that positively impacts on NOTCH nuclear outcome.
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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 | 0.000 |
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".