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Record W1858691702 · doi:10.1002/jcb.25309

The Glutamine-Alanine Repeat Domain of TCERG1 is Required for the Inhibition of the Growth Arrest Activity of C/EBPα

2015· article· en· W1858691702 on OpenAlexafffund
Nicholas J. Miller, Kaitlyn Schick, Nikolai A. Timchenko, Emmett Harrison, William J. Roesler

Bibliographic record

VenueJournal of Cellular Biochemistry · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGlutamineAlanineChemistryDomain (mathematical analysis)BiochemistryCell biologyBiologyAmino acidMathematics

Abstract

fetched live from OpenAlex

TCERG1 was characterized previously as a repressor of the transcription factor C/EBPα through a mechanism that involved relocalization of TCERG1 from nuclear speckles to pericentromeric regions. The inhibitory activity as well as the relocalization activity has been demonstrated to lie in the amino terminal half of the protein, which contains several discrete motifs including an imperfect glutamine-alanine (QA) repeat. In the present study, we showed that deletion of this domain completely abrogated the ability of TCERG1 to inhibit the growth arrest activity of C/EBPα. Moreover, the QA repeat deletion mutant of TCERG1 lost the ability to be relocalized from nuclear speckles to pericentromeric regions, and caused an increase in the average size of individual speckles. We also showed that deletion of the QA repeat abrogated the complex formation between TCERG1 and C/EBPα. Examination of mutants with varying numbers of QA repeats indicated that a minimal number of repeats are required for inhibitory activity as well as relocalization ability. These data contribute to our overall understanding of how TCERG1 can have gene-specific effects in addition to its more general roles in coordinating transcription elongation and splicing.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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