Fat-rich diet reprograms intrapulmonary neutrophils to boost tissue-specific antitumor immunity
Bibliographic record
Abstract
Abstract Neutrophils adapt to tissue-specific signals and exert innate defense functions against infections and malignancies. Fat-rich diet (FRD), such as high-fat diet (HFD) and ketogenic diet (KD), has complex impacts on immunity. However, whether and how FRD shapes tissue-specific functions of neutrophils remain unclear. Here we show that both isocaloric HFD- and KD-fed mice demonstrate enhanced neutrophil-mediated pulmonary tumor resistance than chow diet-fed mice. Intrapulmonary but not systemic neutrophils in FRD-fed mice bear enhanced potential of reactive oxygen species (ROS) production and ROS-dependent tumor cytotoxicity. Mechanistically, FRD-induced increased serum saturated fatty acids and cholesterol stimulate lung vascular endothelial cells (LVECs), which reprogram intrapulmonary neutrophils via contact- and intercellular adhesion molecule-1 (ICAM-1)-dependent mechanisms. Analysis on human lung single-cell RNA sequencing data showed that intensified cell adhesion and priming signals from human LVECs are associated with enhanced antitumor functions in intrapulmonary neutrophils. Our findings highlight the roles of dietary fats in shaping neutrophil functions in a tissue-specific manner. Dietary intervention targeting tissue-specific reprogramming of neutrophils therefore represents a potential strategy against malignancies in the lungs.
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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".