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Depletion of the Intracellular Akt Kinase, and Its Downstream Signaling, Mediates Synergy between the Proteasome Inhibitor MG-132 and the Heat Shock Protein 90 Inhibitor 17-AAG in the Multiple Myeloma Cell Line U266.

2004· article· en· W2979861224 on OpenAlexaff
Christopher Maisel, Nizar J. Bahlis, Yanling Miao, Lili Liu, Stanton L. Gerson

Bibliographic record

VenueBlood · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsProtein kinase BGeldanamycinProteasome inhibitorHeat shock proteinHsp90 inhibitorApoptosisProteasomeKinaseAnnexin A5AnnexinChemistryInhibitor of apoptosisMolecular biologyBortezomibHsp90BiologyProgrammed cell deathBiochemistryImmunologyMultiple myeloma

Abstract

fetched live from OpenAlex

Abstract While proteasome inhibitors are effective therapy for multiple myeloma (MM), their efficacy could be improved by synergistic targeting of apoptosis. The intracellular serine/threonine kinase Akt has been demonstrated to have a central role in MM cell growth, survival, and drug resistance. Akt is activated by extracellular cytokines such as IGF-1 and IL-6, and contributes to MM resistance by ameliorating the apoptotic effects of proteasome inhibition. Akt requires chaperone proteins for proper stability and function, including the 90 kD molecule Heat Shock Protein 90 (HSP-90). HSP-90 function is abrogated by geldanamycin and its derivative, 17-allylamino-17-demethoxygeldanamycin (17-AAG). In the U266 MM cell line, the IC50 of the proteasome inhibitor MG-132 and 17-AAG was 100 nM and 800 nM, respectively. Following exposure of U266 MM cells to either drug alone, or the combination at a fixed-ratio of their IC50s (1:8), apoptosis was determined by Annexin V staining and FACScan analysis. Synergy analysis was performed using Calcusyn (Biosoft, Cambridge, UK). We found that the combination index (CI) was synergistic (CI<1) throughout the dose range, with a CI = 0.449 ± 0.298 at the combination IC50 (highly significant). For example, the apoptotic effect of 50 nM MG-132 and 400 nM 17-AAG was 6 ±2 % and 23 ±3 %, respectively, whereas the 50:400 nM combination produced apoptosis in 68 ± 2 % of the cells. To analyze effects on Akt and its substrates, we incubated U266 MM cells with MG-132 (50 nM), 17-AAG (400 nM), or the combination. We harvested lysates after zero, two, six, and 24 hours incubation, and Western blot analysis was performed. Co-incubation with MG-132 and 17-AAG, but not either alone, depleted Akt by 24 hours post-therapy. Co-treatment also produced significantly greater upregulation of HSP-90 and HSP-70 than 17-AAG alone, thus demonstrating greater functional inhibition of Akt. The combination also demonstrated the greatest abrogation of Akt-mediated effects on mitochondrial apoptosis: Co-treatment produced the greatest expression of BAD, decreased BCL-XL expression, reduced phosphorylation of GSK-3, and produced the greatest activation of caspase 3. Monotherapy with 17-AAG upregulated HSP-90 and HSP-70, reduced BCL-XL expression, and activated caspase 9. MG-132 monotherapy produced none of these effects. These findings demonstrate that synergy between proteasome inhibitors and 17-AAG is mediated by Akt depletion and abrogation of Akt signaling, predominantly by MG-132 augmentation of 17-AAG-mediated decay of Akt. Down-regulation of Akt-mediated resistance allows dual-apoptotic signaling and synergistic effect of combination therapy. These findings demonstrate a mechanistic rationale for utilizing heat shock protein inhibitors in combination with proteasome inhibitors as therapy for MM.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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
Published2004
Admission routes1
Has abstractyes

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