Spermidine metabolism regulates leukemia stem and progenitor cell function through KAT7 expression in patient-derived mouse models
Bibliographic record
Abstract
Acute myeloid leukemia (AML) is a devastating disease initiated and maintained by a rare subset of cells called leukemia stem cells (LSCs). LSCs are responsible for driving disease relapse, making the development of new therapeutic strategies to target LSCs urgently needed. The use of mass spectrometry-based metabolomics profiling has enabled the discovery of unique and targetable metabolic properties in LSCs. However, we do not have a comprehensive understanding of metabolite differences between LSCs and their normal counterparts, hematopoietic stem and progenitor cells (HSPCs). In this study, we used an unbiased mass spectrometry-based metabolomics analysis to define differences in metabolites between primary human LSCs and HSPCs, which revealed that LSCs have a distinct metabolome. Spermidine was the most enriched metabolite in LSCs compared with HSPCs. Pharmacological reduction of spermidine concentrations decreased LSC function but spared normal HSPCs. Polyamine depletion also decreased leukemic burden in patient-derived xenografts. Mechanistically, spermidine depletion induced LSC myeloid differentiation by decreasing eIF5A-dependent protein synthesis, resulting in reduced expression of a select subset of proteins. KAT7, a histone acetyltransferase, was one of the top candidates identified to be down-regulated by spermidine depletion. Overexpression of KAT7 partially rescued polyamine depletion-induced decreased colony-forming ability, demonstrating that loss of KAT7 is an essential part of the mechanism by which spermidine depletion targets AML clonogenic potential. Together, we identified and mechanistically dissected a metabolic vulnerability of LSCs that has the potential to be rapidly translated into clinical trials to improve outcomes for patients with AML.
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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 | 0.001 | 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) | 0.002 | 0.001 |
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".