Development of a high throughput luciferase reporter gene system for screening activators and repressors of human collagen Iα2 gene expression
Bibliographic record
Abstract
Fibrosis, which is characterized by the excessive production of matrix proteins, occurs in multiple tissues and is associated with increased morbidity and mortality. Despite its significant negative impact on patient outcomes, therapies targeted to treat fibrosis are currently lacking. Screening for inhibitors of the expression of collagen, the primary component of fibrotic lesions, represents an option for the identification of novel lead compounds for therapeutic development with potentially fewer off-target effects compared with the targeting of multifunctional cell signaling pathways. Here we report on the generation of a stable luciferase reporter system using a fibroblast cell line, which can be used for rapidly screening both activators and repressors of human collagen COL1A2 gene transcription in a high throughput setting. This in vitro screening tool was validated using known agonists (scleraxis, TGF-β, angiotensin II, CTGF) and antagonists (TNF-α, pirfenidone) of COL1A2 gene expression. The COL1A2-luc NIH-3T3 fibroblast system provides a useful and effective screen for potential lead compounds with pro- or anti-fibrotic properties.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".