The thiazolidinedione peroxisome proliferator-activated receptor gamma (PPARγ) agonist troglitazone alters histone post-translational modifications in MCF7 breast cancer cells.
Bibliographic record
Abstract
Abstract Abstract #2140 Troglitazone (TRG), a thiazolidinedione (TZD) PPARγ ligand, is a potent antiproliferative agent in many different cancer cell lines. We have previously demonstrated in multiple drug resistant (MDR) human K562 leukemia cells that TRG, in combination with the anthracycline antibiotic Doxorubicin (DOX), is an effective anticancer agent in MDR cells. The killing of drug resistant leukemia cells was accompanied by increased global histone H3 acetylation. In this report, we investigated the effects of TRG on both histone metabolism and killing of human MCF7 breast cancer cells. MCF7 cells were treated with TRG, as well as additional TZD's, such as Rosiglitazone, Ciglitazone and Pioglitazone. In all cases, a dose-dependent increase in H3K9 acetylation, total H2AX protein levels, and cell killing were noted. Striking similarities in histone modification profiles were also noted between TRG and histone deacetylase inhibitor (HDACi) treatment. A wide spectrum of common histone post-translational modifications (PTMs) were specifically induced by TRG and HDACi treatment, as determined by mass spectrometry. These findings suggested that TRG may possess HDACi activity. In support of this, in vitro HDACi activity assays showed TRG, but not other tested TZDs, possessed HDACi-like activity. Global H3K9 acetylation was also increased by inhibition of the PI3K/AKT pathway (but not the MEK/ERK pathway), raising the possibility that TRG and HDACi's may act via inhibition of AKT. Consistent with this idea, MCF7 cells treated with TRG or HDACi's exhibit reduced phosphorylated AKT. Our data supports a mechanistic model linking TRG with inhibition of histone de-acetylation in human breast cancer cells. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2140.
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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) | 0.002 | 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".