MétaCan
Menu
Back to cohort
Record W2559690752 · doi:10.1093/nar/gkw1161

ING1 regulates rRNA levels by altering nucleolar chromatin structure and mTOR localization

2016· article· en· W2559690752 on OpenAlexafffund
Uma Karthika Rajarajacholan, Subhash Thalappilly, Karl Riabowol

Bibliographic record

VenueNucleic Acids Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchNutrition Obesity Research Center, University of North Carolina
KeywordsBiologyNucleolusChromatinTranscription (linguistics)HistoneTranscriptional regulationEpigeneticsHeterochromatinPI3K/AKT/mTOR pathwayCell biologyTranscription factorMolecular biologyGeneticsGeneSignal transduction

Abstract

fetched live from OpenAlex

Epigenetic, transcriptional and signaling processes in the nucleolus regulate rRNA transcription and cell growth. We report here that the tumor suppressor ING1b binds rDNA, regulates rDNA chromatin modifications and affects nucleolar localization of mTOR to modulate rRNA levels. ING1 represses rDNA transcription by recruiting HDAC1 to rDNA loci, increasing its association with the NoRC complex and deacetylating the histone H3K9 and H3K27 marks of active transcription. Loss of ING1 enhances nucleolar localization of phospho-mTOR and its association with Raptor and GβL, even during rapamycin treatment. ING1 inhibits rDNA transcription by inhibiting UBF activity and its interaction with mTOR. Regulation of rDNA heterochromatin and rRNA synthesis by ING1 is also apparent during normal cell growth and during cell stress. Moreover, this function was also important during PMA induced differentiation of THP1 cells, since knocking down ING1 affected the process by inhibiting rRNA transcriptional repression. These observations show that ING1 regulates the nucleolar epigenome and rDNA transcription suggesting that regulation of protein synthesis might serve as the basis for ING1 function as a type II tumor suppressor.

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 score0.107
Threshold uncertainty score0.308

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 head0.026
GPT teacher head0.314
Teacher spread0.288 · 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

Citations20
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueNucleic Acids ResearchSame topicRNA modifications and cancerFrench-language works237,207