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Record W2556470338 · doi:10.1182/blood.v108.11.368.368

A Chemical Biology Screen Identifies Glucocorticoids as Inhibitors of C-Maf and C-Maf-Dependent Transactivation of Cyclin D2.

2006· article· en· W2556470338 on OpenAlexaff
Xinliang Mao, A. Keith Stewart, Rose Hurren, Alessandro Datti, Shi Chang, Yuanxiao Zhu, Kyle Lee, Rodger E. Tiedemann, Yanina Eberhard, Seth J. Corey, Jeffrey L. Wrana, Aaron D. Schimmer

Bibliographic record

VenueBlood · 2006
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsMount Sinai HospitalPrincess Margaret Cancer CentreOntario Institute for Cancer Research
Fundersnot available
KeywordsTransactivationCyclin D2Cyclin D1CyclinBiologyCancer researchCyclin D3Molecular biologyCell cycleCellBiochemistryTranscription factorGene

Abstract

fetched live from OpenAlex

Abstract The oncogene c-maf is frequently over-expressed in multiple myeloma cell lines and patient samples and contributes to increased cellular proliferation in part by inducing cyclin D2 expression. Therefore, small molecules that inhibit c-maf and its targets could be useful chemical probes to better understand the role and regulation of this protein. We developed a high throughput chemical screen in NIH 3T3 cells stably over-expressing the promoter of the c-maf target cyclin D2 driving firefly luciferase. From a screen of 2400 off-patent drugs and chemicals, we identified 32 compounds that preferentially reduced cyclin D2 transactivation. Of these, 24 of the 32 hits belonged to the corticosteroid family of drugs. Indeed, the screen identified 24 of the 26 corticosteroids in the library. The most potent inhibitors were glucocorticoids such as dexamethasone. Mineralocorticoids such as fludrocortisone were weak hits, reflecting their weak glucocorticoid activity. The 24 glucocorticoids identified in this screen preferentially reduced cyclin D2 transactivation in the presence of c-maf. For example, the IC50 of dexamethasone was 11 ± 0.7 nM and >50 uM in NIH 3T3 cell with and without c-maf, respectively. Given the effects of glucocorticoids on c-maf in NIH 3T3 cells, we extended our investigation to multiple myeloma cell lines and demonstrated that nanomolar concentrations of the glucocorticoid dexamethasone reduced levels of c-maf protein and its target cyclin D2 within 6 hours of treatment. C-Maf was down regulated in isogenic MM1.S but not MM1.R cells respectively sensitive and resistant to dexamethasone. We also observed reductions in another c-maf target, beta integrin. Compared to cell lines RPMI 8226 and OCIMY5 that harbor the t(14;16) c-maf translocation, the concentration of dexamethasone required to reduce c-maf was approximately 50-fold lower in cell lines such as LP1 and OPM1 that lack the translocation but over-express c-maf. While dexamethasone reduced c-maf protein, no changes in levels of c-maf mRNA were detected. In both multiple myeloma and NIH3T3 cells, dexamethasone increased the ubiquitin-dependent destruction of c-maf. Finally, we linked glucocorticoids to c-maf ubiquitination by demonstrating that dexamethasone upregulated ubiquitin C mRNA at concentrations associated with the ubiquitination of c-maf. Moreover, ectopic expression of ubiquitin cDNA recapitulated the effects of dexamethasone and reduced levels of c-maf, suggesting that increased expression of ubiquitin C by dexamethasone is functionally important for dexamethasone’s effects on c-maf levels. Conclusion: a chemical biology screen identified glucocorticoids as c-maf dependent inhibitors of cyclin D2 transactivation. Glucocorticoids reduce c-maf by promoting its ubiquitination via the upregulation of ubiquitin C mRNA. This work provides new insights into the regulation of c-maf and has identified a novel mechanism by which glucocorticoids exert an anti-myeloma effect.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.012
GPT teacher head0.282
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2006
Admission routes1
Has abstractyes

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