Uncovering crosstalk between AMPK and the ATF4-Integrated Stress Response
Bibliographic record
Abstract
The reprograming of cellular metabolism in cancer cells is directly inter-related to their capacity for uncontrolled growth. As such, targeting of mitochondrial function has been explored as a therapeutic strategy in cancer. However, inhibition of mitochondria inevitably leads to activation of cellular stress pathways including the integrated stress response (ISR). The ISR includes as a key feature, increased activity of activating transcription factor 4 (ATF4), which goes on to up-regulate a range of gene targets to restore cellular homeostasis. Inhibition of mitochondria also can impair bioenergetic and biosynthetic capacity, which might activate AMP-activated protein kinase (AMPK), a cellular energy sensor that also orchestrates metabolic homeostasis. While the ISR and AMPK both critically regulate metabolism in cancer cells, the link between these mechanisms remains unclear. To address this gap, we tested effects of various energetic stress conditions in MEF cell systems deficient in AMPK activity. To comprehensively impair metabolic networks, we used nutrient starvation treatments in combination with silencing of the protein OPA1 which maintains organisational integrity of the mitochondrial cristae. Our results suggest that AMPK may be involved in suppressing the ISR upon mitochondrial disruption. To further explore, we took the alternative approach to test a range of mitochondrial targeting drugs in wildtype or AMPK-deficient MEF. To gain insight into roles of AMPK in the ISR and related adaptive gene responses, we analysed resulting transcriptomes. These results further revealed a regulatory link between AMPK and the ATF4-ISR, showing that AMPK loss hypersensitizes the cell to energetic stress. Our work uncovering the crosstalk between AMPK and the ATF4-ISR has potential to reveal novel interactions that could suggest metabolic liabilities that may be relevant for targeting in cancer 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".