Expression of Nutrient Transporters on NK Cells during Murine Cytomegalovirus Infection is MyD88-Dependent
Bibliographic record
Abstract
Abstract Natural killer (NK) cells are the predominant innate lymphocytes that provide the first line of defence without prior sensitization. In the inflammatory milieu, NK cells modify their metabolism to cope with the high energy demand required to support their proliferation, activation, and functional plasticity. This metabolic reprogramming is usually accompanied by the upregulated expression of nutrient transporters on the cell surface, leading to increased nutrient uptake needed for robust proliferation. The members of interleukin-1 family of inflammatory cytokines are critical in activating NK cells during infection; however, their function in NK cell metabolism is not fully elucidated. Previously, IL-18 has been shown to upregulate the expression of solute carrier transmembrane proteins and thereby induces a robust metabolic boost in NK cells. However, we demonstrated that IL-18 signaling is dispensable during viral infection in vivo whereas the expression of nutrient transporters on NK cells is primarily regulated by MyD88-pathway since NK cells from Myd88−/− mice displayed significantly reduced expression of nutrient receptors. Moreover, we identified that IL-33, another cytokine employing MyD88 signaling, can induce the expression of nutrient receptor but requires a sequential exposure to IL-12. Furthermore, signaling through NK cells activating receptor, Ly49H, can also promote the expression of nutrient transporters. In summary, our findings revealed multiple pathways that induce the expression of nutrient transporters on NK cells while highlighting the imperative role of MyD88 in NK cell metabolism during infection.
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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 | 0.001 |
| 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".