Activation of Peroxisome Proliferator-Activated Receptors α and δ Synergizes with Inflammatory Signals to Enhance Adoptive Cell Therapy
Bibliographic record
Abstract
Abstract Memory CD8+ T cells (Tmem) are superior mediators of adoptive cell therapy (ACT) compared with effector CD8+ T cells (Teff) due to increased persistence in vivo. Underpinning Tmem survival is a shift in cellular metabolism away from aerobic glycolysis towards fatty acid oxidation (FAO). Here we investigated the impact of the peroxisome proliferator-activated receptor (PPAR) agonist GW501516 (GW), an agent known to boost FAO in other tissues, on CD8+ T-cell metabolism, function, and efficacy in a murine ACT model. Via activation of both PPARα and PPARδ/β, GW treatment increased expression of carnitine palmitoyl transferase 1a, the rate-limiting enzyme of FAO, in activated CD8+ T cells. Using a metabolomics approach, we demonstrated that GW increased the abundance of multiple different acylcarnitines, consistent with enhanced FAO. T cells activated in the presence of GW and inflammatory signals, either mature dendritic cells or IL12, also demonstrated enhanced production of IFNγ and expression of T-bet. Despite high expression of T-bet, a characteristic of short-lived effector cells, GW-treated cells demonstrated enhanced persistence in vivo and superior efficacy in a model of ACT. Collectively, these data identify combined PPARα and PPARδ/β agonists as attractive candidates for further studies and rapid translation into clinical trials of ACT. Significance: Dual activation of peroxisome proliferator-activated receptors α and δ improves the efficacy of adoptive cell therapy by reprogramming T-cell metabolism and cytokine expression.
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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 | 0.000 | 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 teacher head, 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".