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A High Throughput Seeking Myeloma Therapeutics Identifies Glucocorticoids as Inhibitors of c-Maf-Dependent Transactivation of Cyclin D2.

2005· article· en· W2590996367 on OpenAlexaff
Xinliang Mao, A. Keith Stewart, Alessandro Datti, Aaron D. Schimmer

Bibliographic record

VenueBlood · 2005
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsMount Sinai HospitalPrincess Margaret Cancer CentreOntario Institute for Cancer Research
Fundersnot available
KeywordsTransactivationLuciferaseCyclinCancer researchCyclin D1BiologyTranscription factorChemistryMolecular biologyCell cycleBiochemistryCellGeneTransfection

Abstract

fetched live from OpenAlex

Abstract c-Maf is a transcription factor that regulates expression of several genes including cyclin D2. c-Maf and cyclin D2 are frequently over expressed in multiple myeloma and associated with chemoresistance and poor clinical outcome. Therefore, molecules that inhibit c-Maf-dependent transactivation of cyclin D2 may be important biological tools to understand the pathogenesis of myeloma and could be therapeutically useful. To identify such compounds, we devised a high throughput screen in NIH3T3 cells. NIH3T3 cells over expressing c-Maf and the cyclin D2 promoter-driving luciferase were treated with aliquots at a final concentration of 5 μM from the LOPAC and Prestwick libraries of totally 2400 biologically active chemicals and off-patent drugs. Cyclin D2 transactivation (luciferase assay) and cell viability (MTS-based assay) were measured 24hr later. From this screen, compounds that preferentially reduced cyclin D2 transactivation over viability were identified. Hits were defined mathematically as (sample luciferase/control luciferase)/(sample MTS/control MTS) < 0.5. The Z score of the screening assay was 0.58, and the coefficient of variance was <3%, denoting a robust screen. Forty compounds that reproducibly repressed cyclin D2 transactivation were identified through this screen. To classify groups of drugs that influenced cyclin D2 transactivation, microarray software Treeview and Cluster were used to cluster the drugs into families. Thirty-one of the 40 hits from the screen were members of the glucocorticoid family and the screen identified all of the glucocorticoids found in the two libraries. To test the specificity of these hits, NIH3T3 cells over-expressing the cyclin D2 promoter-driving luciferase with or without c-Maf expression, and NIH3T3 cells expressing the RSV promoter-driving luciferase were treated with the compounds. Luciferase expression driven by the RSV promoter was reduced by one compound. Eight compounds repressed cyclin D2 transactivation independent of c-Maf expression. The 31 glucocorticoids identified in this screen preferentially reduced cyclin D2 transactivation in the presence of c-Maf. For example, the EC50 was < 1 μM for betamethasone, budesonide and dexamethasone in NIH3T3 cells over-expressing c-Maf and was > 50 μM for these glucocorticoids in the NIH3T3 cells without c-Maf expression, where the EC50 represents the concentration of the compound required to reduce luciferase activity by 50%. Glucocorticoids significantly reduced c-Maf expression by immunoblotting at 5 μM, consistent with a c-Maf-dependent inhibition of cyclin D2 transactivation. Given their effects on c-Maf and cyclin D2, we tested the selected glucocorticoids in the c-Maf over-expressing myeloma cell line 8226. At a final concentration of 5 μM, glucocorticoids betamethasone, budesonide and dexamethasone induced 30, 31, and 32% apoptosis, respectively 24 hours after treatment, but induced < 5% apoptosis in the myeloma cell line KMS12 that lacks c-maf expression. In conclusion, glucocorticoids repress c-Maf dependent transactivation of cyclin D2. These findings demonstrate a new mechanism for glucocorticoid-induced apoptosis and help explain their activity in multiple myeloma.

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 score0.011

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 head0.022
GPT teacher head0.300
Teacher spread0.278 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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