Interleukin-18 up-regulates amino acid transporters and facilitates amino acid–induced mTORC1 activation in natural killer cells
Bibliographic record
Abstract
Upon inflammation, natural killer (NK) cells undergo metabolic changes to support their high energy demand for effector function and proliferation. The metabolic changes are usually accompanied by an increase in the expression of nutrient transporters, leading to increased nutrient uptake. Among various cytokines inducing NK cell proliferation, the mechanisms underlying the effect of interleukin (IL)-18 in promoting NK cell proliferation are not completely understood. Here, we demonstrate that IL-18 is a potent cytokine that can enhance the expression of the nutrient transporter CD98/LAT1 for amino acids independently of the mTORC1 pathway and thereby induce a dramatic metabolic change associated with increased proliferation of NK cells. Notably, treatment of IL-18-stimulated NK cells with leucine activates the metabolic sensor mTORC1, indicating that the high expression of amino acid transporters induces amino acid-driven mTORC1 activation. Inhibition of the amino acid transporter CD98/LAT1 abrogated the leucine-driven mTORC1 activation and reduced NK cell effector function. Taken together, our study identified a novel role of IL-18 in up-regulating nutrient transporters on NK cells and thereby inducing metabolic changes, including the mTORC1 activation by amino acids.
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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".