Ablation of Akt2 Induces Autophagy through Cell Cycle Arrest, the Downregulation of p70S6K, and the Deregulation of Mitochondria in MDA-MB231 Cells
Bibliographic record
Abstract
BACKGROUND: Akt/PKB is a promising anticancer therapeutic target, since abnormally elevated Akt activity is directly correlated to tumor development, progression, poor prognosis and resistance to cancer therapies. Currently, the unique role of each Akt isoform and their relevance to human breast cancer are poorly understood. METHODOLOGY/PRINCIPAL FINDINGS: We previously found that Akt1, 2 and 3 are localized at specific subcellular compartments (the cytoplasm, mitochondria and nucleus, respectively), raising the possibility that each isoform may have unique functions and employ different regulation mechanisms. By systematically studying Akt-ablated MDA-MB231 breast cancer cells with isoform-specific siRNA, we here show that Akt2 is the most relevant isoform to cell proliferation and survival in our cancer model. Prolonged ablation of Akt2 with siRNA resulted in cell-cycle arrest in G0/G1 by downregulating Cdk2 and cyclin D, and upregulating p27. The analysis of the Akt downstream signaling pathways suggested that Akt2 specifically targets and activates the p70S6K signaling pathway. We also found that Akt2 ablation initially resulted in an increase in the mitochondrial volume concomitantly with the upregulation of PGC-1α, a regulator of mitochondrial biogenesis. Prolonged ablation of Akt2, but not Akt1 or Akt3, eventually led to cell death by autophagy of the mitochondria (i.e., mitophagy). CONCLUSIONS/SIGNIFICANCE: Collectively, our data demonstrates that Akt2 augments cell proliferation by facilitating cell cycle progression through the upregulation of the cell cycle engine, and protects a cell from pathological autophagy by modulating mitochondrial homeostasis. Our data, thus, raises the possibility that Akt2 can be an effective anticancer target for the control of (breast) cancer.
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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".