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Record W2921262733 · doi:10.1080/15384101.2019.1593708

Ribosomal protein RPL22/eL22 regulates the cell cycle by acting as an inhibitor of the CDK4-cyclin D complex

2019· article· en· W2921262733 on OpenAlexafffund
Neylen Del Toro, Ana Fernández Ruiz, Lian Mignacca, Paloma Kalegari, Marie‐Camille Rowell, Sebastian Igelmann, Emmanuelle Saint‐Germain, Mehdi Benfdil, Stéphane Lopes‐Paciencia, Léa Brakier‐Gingras, Véronique Bourdeau, Gerardo Ferbeyre, Frédéric Lessard

Bibliographic record

VenueCell Cycle · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health ResearchInstitute of Cancer ResearchCanadian Cancer Society
KeywordsBiologySenescenceRibosomal proteinCyclin D1Cell cycleCell biologyCyclinCyclin DRetinoblastoma proteinKinaseCell cycle checkpointCell Cycle ProteinRibosomal protein s6RibosomeProtein kinase ACellBiochemistryProtein phosphorylationRNAGene

Abstract

fetched live from OpenAlex

Senescence is a tumor suppressor program characterized by a stable growth arrest while maintaining cell viability. Senescence-associated ribogenesis defects (SARD) have been shown to regulate senescence through the ability of the ribosomal protein S14 (RPS14 or uS11) to bind and inhibit the cyclin-dependent kinase 4 (CDK4). Here we report another ribosomal protein that binds and inhibits CDK4 in senescent cells: L22 (RPL22 or eL22). Enforcing the expression of RPL22/eL22 is sufficient to induce an RB and p53-dependent cellular senescent phenotype in human fibroblasts. Mechanistically, RPL22/eL22 can interact with and inhibit CDK4-Cyclin D1 to decrease RB phosphorylation both in vitro and in cells. Briefly, we show that ribosome-free RPL22/eL22 causes a cell cycle arrest which could be relevant during situations of nucleolar stress such as cellular senescence or the response to cancer chemotherapy.

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 score0.025
Threshold uncertainty score0.370

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 head0.006
GPT teacher head0.227
Teacher spread0.220 · 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

Citations28
Published2019
Admission routes2
Has abstractyes

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