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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.006 | 0.002 |
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".