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Record W4310599157 · doi:10.1126/sciadv.add6172

GATA3 mediates nonclassical β-catenin signaling in skeletal cell fate determination and ectopic chondrogenesis

2022· article· en· W4310599157 on OpenAlexaff
Takamitsu Maruyama, Daigaku Hasegawa, Tomáš Valenta, Jody J. Haigh, Maxime Bouchard, Konrad Basler, Wei Hsu

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsMcGill UniversityResearch Institute in Oncology and HematologyCancerCare Manitoba
FundersNational Institute of Dental and Craniofacial Research
KeywordsChondrogenesisCell biologyEctopic expressionGATA3Cell fate determinationCateninChemistryWnt signaling pathwayBiologySignal transductionBiochemistryStem cellCell cultureTranscription factorGenetics

Abstract

fetched live from OpenAlex

Skeletal precursors are mesenchymal in origin and can give rise to distinct sublineages. Their lineage commitment is modulated by various signaling pathways. The importance of Wnt signaling in skeletal lineage commitment has been implicated by the study of β-catenin-deficient mouse models. Ectopic chondrogenesis caused by the loss of β-catenin leads to a long-standing belief in canonical Wnt signaling that determines skeletal cell fate. As β-catenin has other functions, it remains unclear whether skeletogenic lineage commitment is solely orchestrated by canonical Wnt signaling. The study of the Wnt secretion regulator Gpr177/Wntless also raises concerns about current knowledge. Here, we show that skeletal cell fate is determined by β-catenin but independent of LEF/TCF transcription. Genomic and bioinformatic analyses further identify GATA3 as a mediator for the alternative signaling effects. GATA3 alone is sufficient to promote ectopic cartilage formation, demonstrating its essential role in mediating nonclassical β-catenin signaling in skeletogenic lineage specification.

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 score0.044
Threshold uncertainty score0.282

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 head0.009
GPT teacher head0.290
Teacher spread0.281 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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