The master energy sensor AMPK-1α regulates the expression of various autophagic and mitochondrial respiratory elements in T cell memory
Bibliographic record
Abstract
AMPK-1α is a master energy sensor that phosphorylates more than 100 client proteins involved in almost all branches of cellular metabolism. We recently demonstrated that weak mTORC1 (mTORC1Weak) signaling promotes the differentiation of pro-survival cytokine IL-7- or IL-15-stimulated CD8+ memory T (IL-7/TM and IL-15/TM) cells derived from ovalbumin (OVA)-specific T-cell receptor transgenic OTI mice by upregulating the expression of AMPK-1α and another twelve autophagic and metabolic regulators, including ULK1, ATG7, LC3II, SIRT1, PGC1α, CPT1α, AQP9, Complex I, LAL, OPA1, Bcl6, and TFAM. To investigate the potential role of AMPK-1α in controlling the abundance of these proteins, we genetically engineered AMPK knockout (KO)/OTI mice, and then prepared and subjected IL-7/TM or IL-15/TM cells from these animals to western blot analyses. Interestingly, we found that their steady-state levels were all significantly down-regulated in both IL-7/TM and IL-15/TM cells upon the loss of AMPK-1α expression. Thus, our data suggest that AMPK-1α indeed regulates these 12 downstream targets, a possibility that is further substantiated by the fact that a modern chemical genetic screen previously identified ULK1, ATG7, LC3II, SERT1, and PGC1α as AMPK-1α substrates. Taken together, our data establishes that the master energy sensor AMPK-1α controls the expression of seven additional regulatory markers, which are all critical to cellular metabolism and whose identification may impact the development of AMPK-1α-targeted therapeutics for treating metabolic disorders and cancer diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".