Regulation of Fibronectin Gene Expression by Scleraxis
Bibliographic record
Abstract
The glycoprotein fibronectin is a key component of the extracellular matrix (ECM), linking myriad ECM components such as collagen to one another as well as to cell surface integrins and contributing to basal tissue structure. Up‐regulation of fibronectin occurs in response to injury, and increased fibronectin expression is an important feature of pathological cardiac fibrosis, however the molecular regulators of this process are incompletely defined. We previously found that the transcription factor scleraxis regulates the expression of numerous ECM components, and that scleraxis is up‐regulated by pro‐fibrotic stimuli, thus our objective was to determine the effect of scleraxis on fibronectin expression. Transfection of NIH‐3T3 fibroblasts with scleraxis resulted in a three‐fold increase in fibronectin gene expression. In silico analysis of the human fibronectin gene promoter sequence revealed two putative E‐box sequences to which scleraxis may bind. Using chromatin immunoprecipitation assays, we confirmed the occupancy of these sites by scleraxis in human cardiac myofibroblasts, and demonstrated the ability of scleraxis to bind these E‐boxes using electrophoretic mobility shift assays. By luciferase gene reporter experiments, we found that scleraxis transactivates the fibronectin gene promoter. Site‐directed mutagenesis of single E‐boxes significantly reduced promoter transactivation, with complete abrogation of scleraxis‐mediated reporter activity when both E‐boxes were mutated. Our data demonstrates that scleraxis is a potent transcriptional regulator of fibronectin gene expression, reveals the mechanism by which this regulation is achieved, and suggests that scleraxis‐mediated fibronectin expression may contribute to fibrosis. Support or Funding Information Supported by the Canadian Institutes of Health Research (MOP‐136862 to MPC).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".