Killer Cell Lectin-like Receptor G1 Inhibits NK Cell Function through Activation of Adenosine 5′-Monophosphate–Activated Protein Kinase
Bibliographic record
Abstract
NK cells are the first line of defense against infected and transformed cells. Defective NK cell activity was shown to increase susceptibility for viral infections and reduce tumor immune-surveillance. With age, the incidence of infectious diseases and malignancy rises dramatically, suggesting that impaired NK cell function might contribute to disease in these individuals. We found an increased frequency of NK cells with high expression of the inhibitory killer cell lectin-like receptor G1 (KLRG1) in individuals >70 y. The role of KLRG1 in ageing is not known, and the mechanism of KLRG1-induced inhibition of NK cell function is not fully understood. We report that NK cells with high KLRG1 expression spontaneously activate the metabolic sensor AMP-activated protein kinase (AMPK) and that activation of AMPK negatively regulates NK cell function. Pre-existing AMPK activity is further amplified by ligation of KLRG1 in these cells, which leads to internalization of the receptor and allows interaction with AMPK. We show that KLRG1 activates AMPK by preventing its inhibitory dephosphorylation by protein phosphatase-2C rather than inducing de novo kinase activation. Finally, inhibition of KLRG1 or AMPK prevented KLRG1-induced activation of AMPK and reductions in NK cell cytotoxicity, cytokine secretion, proliferation, and telomerase expression. This novel signaling pathway links metabolic sensing, effector function, and cell differentiation with inhibitory receptor signaling that may be exploited to enhance NK cell activity during ageing.
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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 | 0.000 |
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".