mTORC1-Dependent Metabolic Reprogramming Is a Prerequisite for NK Cell Effector Function
Bibliographic record
Abstract
The mammalian target of rapamycin complex 1 (mTORC1) is a key regulator of cellular metabolism and also has fundamental roles in controlling immune responses. Emerging evidence suggests that these two functions of mTORC1 are integrally linked. However, little is known regarding mTORC1 function in controlling the metabolism and function of NK cells, lymphocytes that play key roles in antiviral and antitumor immunity. This study investigated the hypothesis that mTORC1-controlled metabolism underpins normal NK cell proinflammatory function. We demonstrate that mTORC1 is robustly stimulated in NK cells activated in vivo and in vitro. This mTORC1 activity is required for the production of the key NK cell effector molecules IFN-γ, which is important in delivering antimicrobial and immunoregulatory functions, and granzyme B, a critical component of NK cell cytotoxic granules. The data reveal that NK cells undergo dramatic metabolic reprogramming upon activation, upregulating rates of glucose uptake and glycolysis, and that mTORC1 activity is essential for attaining this elevated glycolytic state. Directly limiting the rate of glycolysis is sufficient to inhibit IFN-γ production and granzyme B expression. This study provides the highly novel insight that mTORC1-mediated metabolic reprogramming of NK cells is a prerequisite for the acquisition of normal effector functions.
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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 | 0.001 |
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".