The stress sensor <scp>GCN2</scp> differentially controls ribosome biogenesis in colon cancer according to the nutritional context
Bibliographic record
Abstract
Nutrient availability is a key determinant of tumor cell behavior. While nutrient-rich conditions favor proliferation and tumor growth, scarcity, and particularly glutamine starvation, promotes cell dedifferentiation and chemoresistance. Here, linking ribosome biogenesis plasticity with tumor cell fate, we uncover that the amino acid sensor general control non-derepressible 2 (GCN2; also known as eIF-2-alpha kinase 4) represses the expression of the precursor of ribosomal RNA (rRNA), 47S, under metabolic stress. We show that blockade of GCN2 triggers cell death by an irremediable nucleolar stress and subsequent TP53-mediated apoptosis in patient-derived models of colon adenocarcinoma (COAD). In nutrient-rich conditions, a cell-autonomous GCN2 activity supports cell proliferation by stimulating 47S rRNA transcription, independently of the canonical integrated stress response (ISR) axis. Impairment of GCN2 activity prevents nuclear translocation of methionyl-tRNA synthetase (MetRS), resulting in nucleolar stress, mTORC1 inhibition and, ultimately, autophagy induction. Inhibition of the GCN2-MetRS axis drastically improves the cytotoxicity of RNA polymerase I (RNA pol I) inhibitors, including the first-line chemotherapy oxaliplatin, on patient-derived COAD tumoroids. Our data thus reveal that GCN2 differentially controls ribosome biogenesis according to the nutritional context. Furthermore, pharmacological co-inhibition of the two GCN2 branches and RNA pol I activity may represent a valuable strategy for elimination of proliferative and metabolically stressed COAD cells.
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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".