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Record W3117443003 · doi:10.1073/pnas.2007322118

GATA6 is a regulator of sinus node development and heart rhythm

2020· article· en· W3117443003 on OpenAlexafffund
Lara Gharibeh, Abir Yamak, Jamieson Whitcomb, Aizhu Lu, Mathieu Joyal, Hiba Komati, Wenbin Liang, Céline Fiset, Mona Nemer

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsUniversité de MontréalMontreal Heart InstituteUniversity of Ottawa
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsGATA6RegulatorSinoatrial nodeHaploinsufficiencyBiologyCardiologyInternal medicineMedicineGeneticsTranscription factorPhenotypeGeneHeart rate

Abstract

fetched live from OpenAlex

The sinus node (SAN) is the primary pacemaker of the human heart, and abnormalities in its structure or function cause sick sinus syndrome, the most common reason for electronic pacemaker implantation. Here we report that transcription factor GATA6, whose mutations in humans are linked to arrhythmia, is highly expressed in the SAN and its haploinsufficiency in mice results in hypoplastic SANs and rhythm abnormalities. Cell-specific deletion reveals a requirement for GATA6 in various SAN lineages. Mechanistically, GATA6 directly activates key regulators of the SAN genetic program in conduction and nonconduction cells, such as TBX3 and EDN1, respectively. The data identify GATA6 as an important regulator of the SAN and provide a molecular basis for understanding the conduction abnormalities associated with GATA6 mutations in humans. They also suggest that GATA6 may be a potential modifier of the cardiac pacemaker.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.046
GPT teacher head0.317
Teacher spread0.271 · 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

Citations35
Published2020
Admission routes2
Has abstractyes

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Same venueProceedings of the National Academy of SciencesSame topicCongenital heart defects researchFrench-language works237,207