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Record W2107529783 · doi:10.1158/0008-5472.can-09-4072

The Antidepressant Sertraline Inhibits Translation Initiation by Curtailing Mammalian Target of Rapamycin Signaling

2010· article· en· W2107529783 on OpenAlexafffund
Chen‐Ju Lin, Françis Robert, Rami Sukarieh, Stephen W. Michnick, Jerry Pelletier

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsUniversité de MontréalMcGill University
FundersCanadian Institutes of Health Research
KeywordsSertralinePI3K/AKT/mTOR pathwayTensinPharmacologyCancer researchAntidepressantPhosphorylationPhosphataseChemistrymTORC1Signal transductionCell biologyPTENBiologyEndocrinology

Abstract

fetched live from OpenAlex

Sertraline, a selective serotonin reuptake inhibitor, is a widely used antidepressant agent. Here, we show that sertraline also exhibits antiproliferative activity. Exposure to sertraline leads to a concentration-dependent decrease in protein synthesis. Moreover, polysome profile analysis of sertraline-treated cells shows a reduction in polysome content and a concomitant increase in 80S ribosomes. The inhibition in translation caused by sertraline is associated with decreased levels of the eukaryotic initiation factor (eIF) 4F complex, altered localization of eIF4E, and increased eIF2alpha phosphorylation. The latter event leads to increased REDD1 expression, which in turn impinges on the mammalian target of rapamycin (mTOR) pathway by affecting TSC1/2 signaling. Sertraline also independently targets the mTOR signaling pathway downstream of Rheb. In the Emu-myc murine lymphoma model where carcinogenesis is driven by phosphatase and tensin homologue (PTEN) inactivation, sertraline is able to enhance chemosensitivity to doxorubicin. Our results indicate that sertraline exerts antiproliferative activity by targeting the mTOR signaling pathway in a REDD1-dependent manner.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.028
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.372
Teacher spread0.329 · 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 teacher head, 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

Citations68
Published2010
Admission routes2
Has abstractyes

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