The Critical Role of IL-15–PI3K–mTOR Pathway in Natural Killer Cell Effector Functions
Bibliographic record
Abstract
Natural killer (NK) cells were so named for their uniqueness in killing certain tumor and virus-infected cells without prior sensitization. Their functions are modulated in vivo by several soluble immune mediators; interleukin-15 (IL-15) being the most potent among them in enabling NK cell homeostasis, maturation, and activation. During microbial infections, NK cells stimulated with IL-15 display enhanced cytokine responses. This priming effect has previously been shown with respect to increased IFN-γ production in NK cells upon IL-12 and IL-15/IL-2 co-stimulation. In this study, we explored if this effect of IL-15 priming can be extended to various other cytokines and observed enhanced NK cell responses to stimulation with IL-4, IL-21, IFN-α, and IL-2 in addition to IL-12. Notably, we also observed elevated IFN-γ production in primed NK cells upon stimulation through the Ly49H activation receptor. Currently, the fundamental processes required for priming and whether these signaling pathways work collaboratively or independently for NK cell functions are poorly understood. To identify the key signaling events for NK cell priming, we examined IL-15 effects on NK cells in which the pathways emanating from IL-15 receptor activation were blocked with specific inhibitors. Our results demonstrate that the PI3K-AKT-mTOR pathway is critical for cytokine responses in IL-15 primed NK cells. Furthermore, this pathway is also implicated in a broad range of IL-15-induced NK cell effector functions such as proliferation and cytotoxicity. Likewise, NK cells from mice treated with rapamycin to block the mTOR pathway displayed defects in proliferation, and IFN-γ and granzyme B productions resulting in elevated viral burdens upon murine cytomegalovirus infection. Taken together, our data demonstrate the requirement of PI3K-mTOR pathway for enhanced NK cell functions by IL-15, thereby coupling the metabolic sensor mTOR to NK cell anti-viral responses.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".