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Record W2058130691 · doi:10.1074/jbc.m411522200

Dominant-negative Retinoic Acid Receptors Elicit Epidermal Defects through a Non-canonical Pathway

2004· article· en· W2058130691 on OpenAlexaff
Chang Feng Chen, David Lohnes

Bibliographic record

VenueJournal of Biological Chemistry · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsMcGill UniversityUniversité de MontréalMontreal Clinical Research Institute
Fundersnot available
KeywordsRetinoic acidReceptorChemistryNon canonicalCell biologyBiologyPsychologyInternal medicineBiochemistryMedicineGene

Abstract

fetched live from OpenAlex

Previous work has shown that a dominant-negative retinoic acid receptor alpha (dnRARalpha), expressed under the K14 promoter, causes severe epidermal defects. Similar defects are, however, not seen in RARalphagamma double null mutant mice, which lack the entire complement of RARs expressed in the epidermis. To investigate the mechanism of action of these dominant-negative receptors, dnRARalpha or a DNA binding-deficient variant, dnRARalpha(DBD), were targeted to the basal epidermis. Expression of either receptor type led to similar epidermal phenotypes suggesting that both RAR mutants acted through a common mechanism. The epidermal phenotype was reminiscent of defects seen in p63(-/-) mice. Consistent with this, reduced p63 expression was observed in transgenic offspring expressing either RAR mutant, suggesting that down-regulation of p63 might underlie the effects of these receptors on epidermal development. By contrast, expression of p63 in the epidermis of RARalphagamma(-/-) offspring was unaffected, indicating that RARs were not essential for p63 expression. These findings suggest that dnRARs may impact on epidermal development through one or more non-canonical pathways, which are independent of receptor-DNA interaction.

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)0.001
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 score0.010
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.001
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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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

Citations19
Published2004
Admission routes1
Has abstractyes

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