Methionine regulates antitumor function of CD8 <sup>+</sup> T cells through polyamine synthesis
Bibliographic record
Abstract
Abstract Methionine is an essential amino acid critical for T cell activation. While methionine restriction (MR) combined with immune checkpoint blockade has been shown to enhance T cell function, the impact of methionine on adoptive T cell therapies is largely unexplored. Here, we examined the functionality of T cells under MR and pharmaceutical inhibition of the methionine cycle (MAT2Ai), using primary T cells and a murine adoptive T cell therapy model. In vitro , transient MR or MAT2Ai treatment increased interferon gamma (IFNγ) expression in CD8 + T cells, whereas sustained MR led to the upregulation of T cell exhaustion-associated markers. Mechanistically, transient MR suppressed the polyamine synthesis pathway, and supplementation with polyamines reversed MR-induced IFNγ expression. Genetic ablation of s-adenosylmethionine decarboxylase, an enzyme in the polyamine synthesis pathway, recapitulated the effect of MR, indicating that transient MR enhances T cell function by inhibiting polyamine synthesis. Despite this, transient MR treatment of ovalbumin (OVA)-specific (OT-I) CD8 + T cells prior to adoptive transfer did not improve antitumor efficacy against EG7-OVA tumors in vivo . In contrast, sustained dietary MR accelerated EG7-OVA tumor growth in mice treated with OT-I T cells, demonstrating that methionine availability is essential for the activity of adoptively transferred T cells. These findings suggest that enhancing methionine availability in the tumor microenvironment may improve the efficacy of adoptive T cell therapies.
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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) | 0.001 | 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".