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A Specialized Mechanism of MicroRNA‐mediated Translation in Quiescence

2016· article· en· W4389008365 on OpenAlexaff
Shobha Vasudevan, Syed I. A. Bukhari, Samuel S. Truesdell, Sooncheol Lee, Swapna Kollu, Anthony Classon, Myriam Boukhali, Esha Jain, Akiko Yanagiya, Ruslan Sadyrev, Wilhelm Haas

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill University
Fundersnot available
KeywordsTranslation (biology)microRNABiologyEIF4ECell biologyMessenger RNAUntranslated regionRepressorDownregulation and upregulationProtein biosynthesisPolyadenylationTranslational regulationEukaryotic translationMolecular biologyGene expressionGeneticsGene

Abstract

fetched live from OpenAlex

Quiescence (G0) represents an assortment of reversible, cell cycle‐arrested states, associated with cancer persistence as well as development. G0 involves selective mRNA expression while decreasing canonical translation. The alternative translation mechanisms in G0 remain to be uncovered. Our data show that microRNAs, regulatory, non‐coding RNAs that target distinct mRNAs to alter gene expression, are important in G0, where they can associate with alternative translation factors to regulate specific mRNA translation. One subset of transcripts expressed in G0 includes specific mRNAs recruited by an FXR1a‐associated microRNP (microRNA‐protein complex) for translation activation in G0 mammalian cells and G0‐like immature Xenopus laevis oocytes. MicroRNPs predominantly mediate repression and downregulation; however, FXR1a‐microRNP lacks the conventional microRNP repressor, GW182, and instead, contains a specific RNA binding protein isoform, FXR1a. FXR1a promotes translation and is overexpressed and associated with poor prognosis in certain cancers. Activation is dependent on target recruitment by FXR1a‐microRNP in the nucleus, indicating compartmentalized mRNA recruitment for selective target activation. Our data reveal that microRNA‐mediated activation requires target mRNAs with unadenylated/shortened poly(A) tails. Polyadenylated mRNAs are repressed, potentially due to the role of PABP in enhancing microRNA‐mediated downregulation and in canonical translation that is impaired in these conditions. Consistently, PARN deadenylase is required for microRNA‐mediated activation. Low mTOR activity in G0 activates the cap complex inhibitor, eIF4E‐BP, and thereby, impairs canonical translation; instead, as previously shown, interaction of PARN with 5′ mRNA caps is increased in G0. The target mRNA 3′‐UTR recruits FXR1a‐microRNP, which associates with PARN and with p97, a homolog of the translation factor eIF4G, which recruits eIF3, and thereby, 40S ribosome subunits but lacks cap complex‐ and PABP‐interacting domains. These data reveal a specialized translation mechanism that is important for G0 maintenance and chemoresistance, where FXR1a‐microRNP connects specific, poly(A) shortened mRNAs to the ribosome through interactions with alternative factors, PARN and p97, in these conditions of reduced canonical translation. Support or Funding Information Cancer Research Institute, The Leukemia & Lymphoma Society, MGH funds & NIGMS (SV). Fund for Medical Discovery postdoctoral fellowship (SL).

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 score0.003

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.019
GPT teacher head0.267
Teacher spread0.248 · 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
Published2016
Admission routes1
Has abstractyes

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