PPARα and fatty acid oxidation mediate glucocorticoid resistance in chronic lymphocytic leukemia
Bibliographic record
Abstract
High-dose glucocorticoids (GCs) can be a useful treatment for aggressive forms of chronic lymphocytic leukemia (CLL). However, their mechanism of action is not well understood, and resistance to GCs is inevitable. In a minimal, serum-free culture system, the synthetic GC dexamethasone (DEX) was found to decrease the metabolic activity of CLL cells, indicated by down-regulation of pyruvate kinase M2 (PKM2) expression and activity, decreased levels of pyruvate and its metabolites, and loss of mitochondrial membrane potential. This metabolic restriction was associated with decreased size and death of some of the tumor cells in the population. Concomitant plasma membrane damage increased killing of CLL cells by DEX. However, the nuclear receptor peroxisome proliferator activated receptor α (PPARα), which regulates fatty acid oxidation, was also increased by DEX, and adipocyte-derived lipids, lipoproteins, and propionic acid protected CLL cells from DEX. PPARα and fatty acid oxidation enzyme inhibitors increased DEX-mediated killing of CLL cells in vitro and clearance of CLL xenografts in vivo. These findings suggest that GCs prevent tumor cells from generating the energy needed to repair membrane damage, fatty acid oxidation is a mechanism of resistance to GC-mediated cytotoxicity, and PPARα inhibition is a strategy to improve the therapeutic efficacy of GCs.
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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".