Sestrin2 Modulates AMPK Subunit Expression and Its Response to Ionizing Radiation in Breast Cancer Cells
Bibliographic record
Abstract
BACKGROUND: The sestrin family of stress-responsive genes (SESN1-3) are suggested to be involved in regulation of metabolism and aging through modulation of the AMPK-mTOR pathway. AMP-activated protein kinase (AMPK) is an effector of the tumour suppressor LKB1, which regulates energy homeostasis, cell polarity, and the cell cycle. SESN1/2 can interact directly with AMPK in response to stress to maintain genomic integrity and suppress tumorigenesis. Ionizing radiation (IR), a widely used cancer therapy, is known to increase sestrin expression, and acutely activate AMPK. However, the regulation of AMPK expression by sestrins in response to IR has not been studied in depth. METHODS AND FINDINGS: Through immunoprecipitation we observed that SESN2 directly interacted with the AMPKα1β1γ1 trimer and its upstream regulator LKB1 in MCF7 breast cancer cells. SESN2 overexpression was achieved using a Flag-tagged SESN2 expression vector or a stably-integrated tetracycline-inducible system, which also increased AMPKα1 and AMPKβ1 subunit phosphorylation, and co-localized with phosphorylated AMPKα-Thr127 in the cytoplasm. Furthermore, enhanced SESN2 expression increased protein levels of LKB1 and AMPKα1β1γ1, as well as mRNA levels of LKB1, AMPKα1, and AMPKβ1. Treatment of MCF7 cells with IR elevated AMPK expression and activity, but this effect was attenuated in the presence of SESN2 siRNA. In addition, elevated SESN2 inhibited IR-induced mTOR signalling and sensitized MCF7 cells to IR through an AMPK-dependent mechanism. CONCLUSIONS: Our results suggest that in breast cancer cells SESN2 is associated with AMPK, it is involved in regulation of basal and IR-induced expression and activation of this enzyme, and it mediates sensitization of cancer cells to IR.
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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".