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Record W1972047327 · doi:10.1073/pnas.1412107111

Miz-1 regulates translation of <i>Trp53</i> via ribosomal protein L22 in cells undergoing V(D)J recombination

2014· article· en· W1972047327 on OpenAlexafffund
Marissa Rashkovan, Charles Vadnais, Julie Ross, Mathieu Gigoux, Woong‐Kyung Suh, Wei Gu, Christian Kosan, Tarik Möröy

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsMcGill UniversityUniversité de MontréalMontreal Clinical Research Institute
FundersCanadian Institutes of Health ResearchJulius-Maximilians-Universität WürzburgGovernment of Canada
KeywordsRibosomal proteinSuppressorBiologyRecombinationTranscription (linguistics)Translation (biology)Cell biologyMolecular biologyProtein biosynthesisDNARibosomal RNAMessenger RNAGeneticsGeneRNARibosome

Abstract

fetched live from OpenAlex

To be effective, the adaptive immune response requires a large repertoire of antigen receptors, which are generated through V(D)J recombination in lymphoid precursors. These precursors must be protected from DNA damage-induced cell death, however, because V(D)J recombination generates double-strand breaks and may activate p53. Here we show that the BTB/POZ domain protein Miz-1 restricts p53-dependent induction of apoptosis in both pro-B and DN3a pre-T cells that actively rearrange antigen receptor genes. Miz-1 exerts this function by directly activating the gene for ribosomal protein L22 (Rpl22), which binds to p53 mRNA and negatively regulates its translation. This mechanism limits p53 expression levels and thus contains its apoptosis-inducing functions in lymphocytes, precisely at differentiation stages in which V(D)J recombination occurs.

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.250
Teacher spread0.229 · 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

Citations34
Published2014
Admission routes2
Has abstractyes

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Same venueProceedings of the National Academy of SciencesSame topicImmune Cell Function and InteractionFrench-language works237,207