Metabolic Reprogramming Supports IFN-γ Production by CD56bright NK Cells
Bibliographic record
Abstract
Human NK cells can be classified into phenotypically and functionally distinct subsets based on levels of CD56 receptor. CD56(dim) cells are generally considered more cytotoxic, whereas the CD56(bright) cells are potent producers of IFN-γ. In this study, we define the metabolic changes that occur in peripheral blood NK cells in response to cytokine. Metabolic analysis showed that NK cells upregulate glycolysis and oxidative phosphorylation in response to either IL-2 or IL-12/15 cytokine combinations. Despite the fact that both these cytokine combinations robustly upregulated mammalian Target of Rapamycin Complex 1 in human NK cells, only the IL-2-induced metabolic changes were sensitive to mammalian Target of Rapamycin Complex 1 inhibition by rapamycin. Interestingly, we found that CD56(bright) cells were more metabolically active compared with CD56(dim) cells. They preferentially upregulated nutrient receptors and also differed substantially in terms of their glucose metabolism. CD56(bright) cells expressed high levels of the glucose uptake receptor, Glut1 (in the absence of any cytokine), and had higher rates of glucose uptake compared with CD56(dim) cells. Elevated levels of oxidative phosphorylation were required to support both cytotoxicity and IFN-γ production in all NK cells. Finally, although elevated glycolysis was not required directly for NK cell degranulation, limiting the rate of glycolysis significantly impaired IFN-γ production by the CD56(bright) subset of cells. Overall, we have defined CD56(bright) NK cells to be more metabolically active than CD56(dim) cells, which supports their production of large amounts of IFN-γ during an immune response.
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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 | 0.001 |
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".