Abstract A023: Nicotinamide Phosphoribosyltransferase (NAMPT) is required for trained macrophage-mediated antitumor immunity
Bibliographic record
Abstract
Abstract Trained immunity or innate immune memory plays an active role in suppressing tumour growth and metastasis, however the specific metabolic mechanisms underlying such memory response-mediated antitumor activity have not been fully elucidated. Here, we show that the Nicotinamide Phosphoribosyltransferase/Nicotinamide Adenine Dinucleotide (NAMPT/NAD+) axis is required for the establishment of immune memory in macrophages against tumour. Chemical inhibition or genetic knockout of NAMPT in macrophages blocked trained immunity in vitro and in vivo, which could be rescued by NMN, an NAD+ precursor and a direct metabolite of NAMPT. Further studies revealed that myeloid-specific knockout of Nampt in mice significantly impaired the antitumor effect induced by in vivo β-glucan training, accompanied with decreased infiltration of intratumoral M1 macrophages. Interestingly, NAMPT deficiency did not impair β-glucan-induced interferon signalling or Akt/mTOR/HIF-1α signalling pathway, but markedly reduced histone methylation and acetylation levels, suggesting that NAMPT/NAD+ axis regulates trained immunity in macrophages via potential epigenetic mechanisms. Collectively, our study identifies that NAMPT/NAD+ metabolic axis-regulated trained immunity is a critical mechanism in early immune surveillance that suppresses tumour development via epigenetic regulation. Citation Format: Huan Jin, Yongxiang Liu, Xiaojun Xia. Nicotinamide Phosphoribosyltransferase (NAMPT) is required for trained macrophage-mediated antitumor immunity [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A023.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; both teacher heads agree on what is shown here.
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".