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Record W2157757731 · doi:10.1084/jem.20150384

CNOT3 contributes to early B cell development by controlling <i>Igh</i> rearrangement and <i>p53</i> mRNA stability

2015· article· en· W2157757731 on OpenAlexafffund
Takeshi Inoue, Masahiro Morita, Atsushi Hijikata, Y. Fukuda, Shungo Adachi, Kyoichi Isono, Tomokatsu Ikawa, Hiroshi Kawamoto, Haruhiko Koseki, Tohru Natsume, Taro Fukao, Osamu Ohara, Tadashi Yamamoto, Tomohiro Kurosaki

Bibliographic record

VenueThe Journal of Experimental Medicine · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsBiologyMolecular biologyImmunoglobulin heavy chainGermlineGene rearrangementMessenger RNAB cellCell biologyGeneAntibodyGenetics

Abstract

fetched live from OpenAlex

The CCR4-NOT deadenylase complex plays crucial roles in mRNA decay and translational repression induced by poly(A) tail shortening. Although the in vitro activities of each component of this complex have been well characterized, its in vivo role in immune cells remains unclear. Here we show that mice lacking the CNOT3 subunit of this complex, specifically in B cells, have a developmental block at the pro- to pre-B cell transition. CNOT3 regulated generation of germline transcripts in the VH region of the immunoglobulin heavy chain (Igh) locus, compaction of the locus, and subsequent Igh gene rearrangement and destabilized tumor suppressor p53 mRNA. The developmental defect in the absence of CNOT3 could be partially rescued by ablation of p53 or introduction of a pre-rearranged Igh transgene. Thus, our data suggest that the CCR4-NOT complex regulates B cell differentiation by controlling Igh rearrangement and destabilizing p53 mRNA.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.285
Teacher spread0.262 · 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

Citations57
Published2015
Admission routes2
Has abstractyes

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