The Energy Sensor AMPKα1 Is Critical in Rapamycin-Inhibition of mTORC1-S6K-Induced T-cell Memory
Bibliographic record
Abstract
Energy sensors mTORC1 and AMPKα1 regulate T-cell metabolism and differentiation, while rapamycin (Rapa)-inhibition of mTORC1 (RIM) promotes T-cell memory. However, the underlying pathway and the role of AMPKα1 in Rapa-induced T-cell memory remain elusive. Using genetic and pharmaceutical tools, we demonstrate that Rapa promotes T-cell memory in mice in vivo post Listeria monocytogenesis rLmOVA infection and in vitro transition of effector T (TE) to memory T (TM) cells. IL-2- and IL-2+Rapa-stimulated T [IL-2/T and IL-2(Rapa+)/T] cells, when transferred into mice, differentiate into short-term IL-7R−CD62L−KLRG1+ TE and long-lived IL-7R+CD62L+KLRG1− TM cells, respectively. To assess the underlying pathways, we performed Western blotting, confocal microscopy and Seahorse-assay analyses using IL-2/T and IL-2(Rapa+)/T-cells. We determined that IL-2(Rapa+)/T-cells activate transcription FOXO1, TCF1 and Eomes and metabolic pAMPKα1(T172), pULK1(S555) and ATG7 molecules and promote mitochondrial biogenesis and fatty-acid oxidation (FAO). We found that rapamycin-treated AMPKα-deficient AMPKα1-KO IL-2(Rapa+)/TM cells up-regulate transcription factor HIF-1α and induce a metabolic switch from FAO to glycolysis. Interestingly, despite the rapamycin treatment, AMPKα-deficient TM cells lost their cell survival capacity. Taken together, our data indicate that rapamycin promotes T-cell memory via transcriptional FOXO1-TCF1-Eomes programs and AMPKα1-ULK1-ATG7 metabolic axis, and that AMPKα1 plays a critical role in RIM-induced T-cell memory.
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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 | 0.001 |
| 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".