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Record W2125727551 · doi:10.1172/jci44555

Loss of Gata5 in mice leads to bicuspid aortic valve

2011· article· en· W2125727551 on OpenAlexafffund
Brigitte Laforest, Grégor Andelfinger, Mona Nemer

Bibliographic record

VenueJournal of Clinical Investigation · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsUniversity of OttawaUniversité de Montréal
FundersCanadian Institutes of Health ResearchUniversity of OttawaHeart and Stroke Foundation of Canada
KeywordsBicuspid aortic valveCardiologyMedicineInternal medicineAortic valve

Abstract

fetched live from OpenAlex

Bicuspid aortic valve (BAV), the leading congenital heart disease, occurs in 1%-2% of the population. Genetic studies suggest that BAV is an autosomal-dominant disease with reduced penetrance. However, only 1 gene, NOTCH1, has been linked to cases of BAV. Here, we show that targeted deletion of Gata5 in mice leads to hypoplastic hearts and partially penetrant BAV formation. Endocardial cell-specific inactivation of Gata5 led to BAV, similar to that observed in Gata5-/- mice. In all cases, the observed BAVs resulted from fusion of the right-coronary and noncoronary leaflets, the subtype associated with the more severe valve dysfunction in humans. Neither endocardial cell proliferation nor cushion formation was altered in the absence of Gata5. Rather, defective endocardial cell differentiation, resulting from the deregulation of several components of the Notch pathway and other important endocardial cell regulators, may be the underlying mechanism of disease. The results unravel a critical cell-autonomous role for endocardial Gata5 in aortic valve formation and identify GATA5 as a potential gene responsible for congenital heart disease in humans. Mice with mutated Gata5 alleles represent unique models to dissect the mechanisms underlying degenerative aortic valve disease and to develop much-needed preventive and therapeutic interventions.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 head0.108
GPT teacher head0.407
Teacher spread0.299 · 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 designObservational
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

Citations183
Published2011
Admission routes2
Has abstractyes

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