Peroxisome Proliferator-Activated Receptor γ-Mediated NF-κB Activation and Apoptosis in Pre-B Cells
Bibliographic record
Abstract
The role of peroxisome proliferator-activated receptor gamma (PPARgamma) in adipocyte physiology has been exploited for the treatment of diabetes. The expression of PPARgamma in lymphoid organs and its modulation of macrophage inflammatory responses, T cell proliferation and cytokine production, and B cell proliferation also implicate it in immune regulation. Despite significant human exposure to PPARgamma agonists, little is known about the consequences of PPARgamma activation in the developing immune system. Here, well-characterized models of B lymphopoiesis were used to investigate the effects of PPARgamma ligands on nontransformed pro/pre-B (BU-11) and transformed immature B (WEHI-231) cell development. Treatment of BU-11, WEHI-231, or primary bone marrow B cells with PPARgamma agonists (ciglitazone and GW347845X) resulted in rapid apoptosis. A role for PPARgamma and its dimerization partner, retinoid X receptor (RXR)alpha, in death signaling was supported by 1) the expression of RXRalpha mRNA and cytosolic PPARgamma protein, 2) agonist-induced binding of PPARgamma to a PPRE, and 3) synergistic increases in apoptosis following cotreatment with PPARgamma agonists and 9-cis-retinoic acid, an RXRalpha agonist. PPARgamma agonists activated NF-kappaB (p50, Rel A, c-Rel) binding to the upstream kappaB regulatory element site of c-myc. Only doses of agonists that induced apoptosis stimulated NF-kappaB-DNA binding. Cotreatment with 9-cis-retinoic acid and PPARgamma agonists decreased the dose required to activate NF-kappaB. These data suggest that activation of PPARgamma-RXR initiates a potent apoptotic signaling cascade in B cells, potentially through NF-kappaB activation. These results have implications for the nominal role of the PPARgamma in B cell development and for the use of PPARgamma agonists as immunomodulatory therapeutics.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".