AMPK-dependent phosphorylation is required for transcriptional activation of TFEB/TFE3
Bibliographic record
Abstract
Abstract Increased autophagy and lysosomal activity promote tumor growth, survival and chemo-resistance. During acute starvation, autophagy is rapidly engaged by AMPK activation and mTORC1 inhibition to maintain energy homeostasis and cell survival. TFEB and TFE3 are master transcriptional regulators of autophagy and lysosomal activity and their cytoplasm/nuclear shuttling is controlled by mTORC1-dependent multisite phosphorylation. However, it is not known whether and how the transcriptional activity of TFEB or TFE3 is regulated. We show that AMPK mediates phosphorylation of TFEB and TFE3 on three serine residues, leading to TFEB/TFE3 transcriptional activity upon nutrient starvation, FLCN depletion and pharmacological manipulation of mTORC1 or AMPK. AMPK loss does not affect TFEB/TFE3 nuclear localization nor protein levels but reduces their transcriptional activity. Collectively, we show that mTORC1 specifically controls TFEB/TFE3 cytosolic retention whereas AMPK is essential for TFEB/TFE3 transcriptional activity. This dual and opposing regulation of TFEB/TFE3 by mTORC1 and AMPK is reminiscent of the regulation of another critical regulator of autophagy, ULK1. Surprisingly, we show that chemoresistance is mediated by AMPK-dependent activation of TFEB, which is abolished by pharmacological inhibition of AMPK or mutation of serine 466/467/469 to alanine residues within TFEB. Altogether, we show that AMPK is a key regulator of TFEB/TFE3 transcriptional activity, and we validate AMPK as a promising target in cancer therapy to evade chemotherapeutic resistance.
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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) | 0.003 | 0.001 |
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".