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Record W1515050147 · doi:10.1002/dvdy.23954

Expression of ski can act as a negative feedback mechanism on retinoic acid signaling

2013· article· en· W1515050147 on OpenAlexafffund
Meaghan A. Melling, Charlotte R.C. Friendship, Trevor G. Shepherd, Thomas A. Drysdale

Bibliographic record

VenueDevelopmental Dynamics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTGF-β signaling in diseases
Canadian institutionsChildren’s Health Research InstituteWestern University
FundersCanadian Institutes of Health ResearchForskningsrådet om Hälsa, Arbetsliv och Välfärd
KeywordsRetinoic acidRetinoic acid-inducible orphan G protein-coupled receptorBiologyRetinoic acid receptor betaRetinoic acid receptor gammaCell biologySignal transductionTretinoinRetinoic acid receptorRetinoic acid receptor alphaPsychological repressionRetinoid X receptor gammaGene expressionBiochemistryGene

Abstract

fetched live from OpenAlex

BACKGROUND: Retinoic acid signaling is essential for many aspects of early development in vertebrates. To control the levels of signaling, several retinoic acid target genes have been identified that act to suppress retinoic acid signaling in a negative feedback loop. The nuclear protein Ski has been extensively studied for its ability to suppress transforming growth factor-beta (TGF-β) signaling but has also been implicated in the repression of retinoic acid signaling. RESULTS: We demonstrate that ski expression is up-regulated in response to retinoic acid in both early Xenopus embryos and in human cell lines. Blocking retinoic acid signaling using a retinoic acid antagonist results in a corresponding decrease in the levels of ski mRNA. Finally, overexpression of SKI in human cells results in reduced levels of CYP26A1 mRNA, a known target of retinoic acid signaling. CONCLUSIONS: Our results, coupled with the known ability of Ski to repress retinoic acid signaling, demonstrate that Ski expression is a novel negative feedback mechanism acting on retinoic acid signaling.

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 score0.009
Threshold uncertainty score0.851

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 head0.216
Teacher spread0.211 · 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

Citations6
Published2013
Admission routes2
Has abstractyes

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