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Record W2102028478 · doi:10.1158/0008-5472.can-07-5876

c-Myc and eIF4F Are Components of a Feedforward Loop that Links Transcription and Translation

2008· article· en· W2102028478 on OpenAlexaff
Chen‐Ju Lin, Regina Cencic, John R. Mills, Françis Robert, Jerry Pelletier

Bibliographic record

VenueCancer Research · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiologyTranscription (linguistics)Cell biologyTranscription factorProtein biosynthesisPromoterMolecular biologyGeneticsGene expressionGene

Abstract

fetched live from OpenAlex

The Myc/Max/Mad family of transcription factors and the eukaryotic initiation factor 4F (eIF4F) complex play fundamental roles in regulating cell growth, proliferation, differentiation, and oncogenic transformation. eIF4F is involved in the recruitment of ribosomes to mRNAs and is thought to generally be the rate-limiting phase of translation. Here, we show that c-Myc directly activates transcription of the three subunits of eIF4F (eIF4E, eIF4AI, and eIF4GI). These transcriptional effects are mediated through canonical E-boxes (5'CACGTG3') present in the promoters of these genes. In addition, the c-Myc antagonist Mad1 down-regulates the expression of eIF4F subunits. We also show that MycER activation stimulates protein synthesis at the level of translation initiation. Increased eIF4F levels result in stimulation of c-Myc mRNA translation specifically, as assessed by quantitative reverse transcription-PCR. We use a murine model of lymphomagenesis to show the expression of eIF4F subunits is also up-regulated by c-Myc in vivo. Our results suggest the presence of a feedforward loop involving c-Myc and eIF4F that serves to link transcription and translation and that could contribute to the effects of c-Myc on cell proliferation and neoplastic growth.

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)0.000
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 score0.046
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.164
GPT teacher head0.375
Teacher spread0.210 · 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

Citations172
Published2008
Admission routes1
Has abstractyes

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