AMP-activated protein kinase (AMPK) is required for recovery from metabolic stress induced by ultrasound microbubble treatment
Bibliographic record
Abstract
Abstract Ultrasound and microbubbles (USMB) is a promising strategy for cancer therapy. USMB can induce a variety of effects on cells including transient formation of plasma membrane pores (sonoporation) and enhanced endocytosis, which enhance drug delivery, and can also lead to enhanced cell death. However, the outcomes of USMB on cell physiology are heterogeneous, in that USMB elicits cell death in a proportion of cells while exerting minimal effects on others. This suggests that mechanisms of adaptation following USMB allow some cells to survive and/or proliferate. The molecular mechanisms of adaptation to USMB-induced stress remain poorly understood, thus potentially hindering broad therapeutic applications of USMB. Herein, we used several triple negative breast cancer cells to study the effect of USMB-induced metabolite stress and the role of AMPK as a response to this stress. We found that USMB alters steady-state levels of amino acids, glycolytic intermediates, and citric acid cycle intermediates. USMB treatment acutely reduces ATP levels and stimulates AMP-activated protein kinase (AMPK) phosphorylation and activation. Further, AMPK is required to restore ATP levels in cells that survived the initial insult and support cell proliferation post-USMB treatment. These results suggest that AMPK and metabolic perturbations are likely determinants of the anti-neoplastic efficacy of USMB treatment.
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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".