MYC and EGR1 synergize to trigger tumor cell death by controlling NOXA and BIM transcription upon treatment with the proteasome inhibitor bortezomib
Bibliographic record
Abstract
The c-MYC (MYC afterward) oncogene is well known for driving numerous oncogenic programs. However, MYC can also induce apoptosis and this function of MYC warrants further clarification. We report here that a clinically relevant proteasome inhibitor significantly increases MYC protein levels and that endogenous MYC is necessary for the induction of apoptosis. This kind of MYC-induced cell death is mediated by enhanced expression of the pro-apoptotic BCL2 family members NOXA and BIM. Quantitative promoter-scanning chromatin immunoprecipitations (qChIP) further revealed binding of MYC to the promoters of NOXA and BIM upon proteasome inhibition, correlating with increased transcription. Both promoters are further characterized by the presence of tri-methylated lysine 4 of histone H3, marking active chromatin. We provide evidence that in our apoptosis models cell death occurs independently of p53 or ARF. Furthermore, we demonstrate that recruitment of MYC to the NOXA as well as to the BIM gene promoters depends on MYC's interaction with the zinc finger transcription factor EGR1 and an EGR1-binding site in both promoters. Our study uncovers a novel molecular mechanism by showing that the functional cooperation of MYC with EGR1 is required for bortezomib-induced cell death. This observation may be important for novel therapeutic strategies engaging the inherent pro-death function of MYC.
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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".