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Record W2075224571 · doi:10.1038/ncomms1036

Divergent transcriptional activities determine limb identity

2010· article· en· W2075224571 on OpenAlexafffund
Jean‐François Ouimette, Marisol Lavertu Jolin, Aurore L’honoré, Anthony J. Gifuni, Jacques Drouin

Bibliographic record

VenueNature Communications · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsMontreal Clinical Research Institute
FundersCanadian Institutes of Health Research
KeywordsRepressorLimb developmentTranscription factorBiologyHindlimbForelimbActivator (genetics)EffectorCell biologyGeneticsGeneNeuroscienceAnatomy

Abstract

fetched live from OpenAlex

Limbs develop using a common genetic programme despite widely differing morphologies. This programme is modulated by limb-restricted regulators such as hindlimb (HL) transcription factors Pitx1 and Tbx4 and the forelimb (FL) Tbx5. Both Tbx factors have been implicated in limb patterning and growth, but their relative activities and underlying mechanisms remain unclear. In this paper, we show that Tbx4 and Tbx5 harbour conserved and divergent transcriptional regulatory domains that account for their roles in limb development. In particular, both factors share an activator domain and the ability to stimulate limb growth. However, we find that Tbx4 is the primary effector of HL identity for both skeletal and muscle development; this activity relies on a repressor domain that is inactivated by a human TBX4 small-patella syndrome mutation. We propose that limb identity is largely achieved by default in FL, whereas a specific repressor activity unique to Tbx4 determines HL identity. The embryonic development of forelimbs and hindlimbs is regulated by transcription factors, including Pitx1, Tbx4 and Tbx5. In this study, the contributions of Tbx4 and Tbx5 to limb identity are further delineated, resulting in the demonstration of a role for Tbx4 in hindlimb skeletal and muscle patterning.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.332

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 science0.0010.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.341
Teacher spread0.320 · 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

Citations57
Published2010
Admission routes2
Has abstractyes

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