A critical role of energy sensor AMPKα1 in rapamycin dependent memory CD8+ T cell survival
Bibliographic record
Abstract
Abstract Energy sensors mTORC1 and AMPKα1 regulate T-cell metabolism and differentiation, while rapamycin (Rapa), an inhibitor of mTORC1, supports T-cell memory. However, the underlying pathway, the exact mechanisms, and the role of AMPKα1 in Rapa-induced T-cell memory is not well understood. Using the Listeria monocytogenes (rLmOVA) infection model and genetic and pharmaceutical tools, we demonstrate that Rapa promotes T-cell memory in mice post-rLmOVA infection. When T-cells stimulated in vitro in the presence of IL-2 without rapamycin (IL-2/T) or with rapamycin (IL-2+Rapa /T) and transferred into mice, it differentiated into short-term effector T (TE) [IL-7R− CD62L− KLRG1+] and long-lived memory T (TM) [IL-7R+ CD62L+ KLRG1−] cells, respectively. We determined that rapamycin-treated T cells activated transcriptional factors, FOXO1, TCF1 and Eomes and metabolic pAMPKα1(T172), pULK1(S555), and ATG7 molecules and promoted mitochondrial biogenesis and oxidative phosphorylation (OXPHOS). Using Seahorse-real time metabolic analyzer, we found that rapamycin-treated AMPKα-deficient TM cells up-regulated transcription factor HIF-1α and induced a metabolic switch from OXPHOS to glycolysis. Interestingly, despite the rapamycin treatment, AMPKα-deficient TM cells lost their cell survival capacity. Altogether, our data provide a mechanistic explanation for enhanced T-cell survival after rapamycin treatment and suggest that rapamycin promotes T-cell memory via transcriptional FOXO1-TCF1-Eomes programs and AMPKα1-ULK1-ATG7 metabolic axis. And AMPKα1 plays a critical role in rapamycin-induced increased survival and metabolic switching of CTLs during the transition of effector to memory T cells. Supported by Canadian Institutes of Health Research grant (409228). Saskatchewan Health Research Foundation (SHRF) postdoctoral fellowship.
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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) | 0.002 | 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".