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Scleraxis works synergistically with Smads to regulate collagen gene expression

2012· article· en· W1409774414 on OpenAlexafffund
Rushita A. Bagchi, Michael P. Czubryt

Bibliographic record

VenueThe FASEB Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiac Fibrosis and Remodeling
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health ResearchManitoba Health Research Council
KeywordsTransactivationMolecular biologyLuciferaseGene expressionSMADPromoterChemistryTranscription factorGeneBiologyReporter geneCell biologySignal transductionBiochemistryTransfection

Abstract

fetched live from OpenAlex

Elevated type I collagen expression in cardiac fibrosis impairs heart function. We have shown that the transcription factor scleraxis (Scx) is expressed by cardiac fibroblasts and myofibroblasts, and is sufficient to regulate human collagen Iα2 ( COLIα2 ) expression. Here we studied the interaction of Scx with the fibrotic Smad signaling pathway. We identified Scx binding sites (E‐boxes) within the COLIα2 proximal gene promoter, examined promoter occupancy by Scx using ChIP assay, and measured promoter activation by Scx and/or Smads via luciferase gene reporter assays. Scx augmented TGF‐β 1 transactivation of the promoter. Scx and Smad3 synergistically up‐regulated COLIα2 promoter transactivation. Mutation of the promoter Smad binding element significantly reduced transactivation by Scx. A similar effect of E‐box mutations on Smad3‐mediated activation was also observed, suggesting that cross‐talk in these pathways is required for maximal gene activity. Scx expression was up‐regulated by Smad3 and down‐regulated by Smad7, further supporting cross‐pathway signaling. A dominant negative Scx mutant completely abrogated COLIα2 gene expression by TGF‐β 1 . Our results indicate that Scx and Smads synergistically co‐regulate collagen gene expression, and that interference with this interaction may represent a novel avenue for anti‐fibrotic therapy development. Supported by CIHR and MHRC.

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)0.001
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 score0.127
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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

Citations0
Published2012
Admission routes2
Has abstractyes

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