The TNFα/TNFR2 axis mediates natural killer cell proliferation by promoting aerobic glycolysis
Bibliographic record
Abstract
Natural killer (NK) cells are predominant innate lymphocytes that initiate the early immune response during infection. NK cells undergo a metabolic switch to fuel augmented proliferation and activation following infection. Tumor necrosis factor-alpha (TNFα) is a well-known inflammatory cytokine that enhances NK cell function; however, the mechanism underlying NK cell proliferation in response to TNFα is not well established. Here, we demonstrated that upon infection/inflammation, NK cells upregulate the expression of TNF receptor 2 (TNFR2), which is associated with increased proliferation, metabolic activity, and effector function. Notably, IL-18 can induce TNFR2 expression in NK cells, augmenting their sensitivity toward TNFα. Mechanistically, TNFα-TNFR2 signaling upregulates the expression of CD25 (IL-2Rα) and nutrient transporters in NK cells, leading to a metabolic switch toward aerobic glycolysis. Transcriptomic analysis revealed significantly reduced expression levels of genes involved in cellular metabolism and proliferation in NK cells from TNFR2 KO mice. Accordingly, our data affirmed that genetic ablation of TNFR2 curtails CD25 upregulation and TNFα-induced glycolysis, leading to impaired NK cell proliferation and antiviral function during MCMV infection in vivo. Collectively, our results delineate the crucial role of the TNFα-TNFR2 axis in NK cell proliferation, glycolysis, and effector function.
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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".