Early‐immediate gene Egr1 is associated with <scp>TGFβ1</scp> regulation of epigenetic reader Bromodomain‐containing protein 4 via the canonical Smad3 signaling in hepatic stellate cells in vitro and in vivo
Bibliographic record
Abstract
Abstract Upon chronic damage to the liver, multiple cytokines stimulate hepatic stellate cells (HSCs), causing the alterations of gene expression profiles and thus leading to HSC activation, a key step in liver fibrogenesis. Activated HSCs are the dominant contributors to liver fibrosis. Bromodomain containing protein 4 (BrD4), an important epigenetic reader, was demonstrated to concentrate on hundreds of enhancers associated with genes involved in multiple profibrotic pathways, thereby directing HSC activation and the fibrotic responses. The present studies were designed to examine the effect of transforming growth factor beta‐1 (TGFβ1), the most potent pro‐fibrotic cytokine, on BrD4 expression in HSCs and, if so, elucidated the underlying mechanisms in vitro and in vivo. The experiments employed the heterogeneous TGFβ1 knockout (TGFβ1 +/− ) mice, gene knockdown in vivo, and a model of thioacetamide (TAA)‐induced liver injury. The results revealed that TGFβ1 enhanced BrD4 expression in HSCs, which was mediated, at least, by Smad3 signaling and early‐immediate gene Egr1 (early growth response‐1). TGFβ1‐induced Smad3 signaling increased Egr1 expression and promoted Egr1 binding to BrD4 promoter at a site around −111 bp, promoting BrD4 expression. Egr1 knockdown reduced BrD4 expression in HSCs in a mouse model of TAA‐induced liver injury and lessened liver fibrosis. Double fluorescence staining demonstrated a strong increase in BrD4 expression in activated HSCs in fibrotic areas of the human livers, paralleling the upregulation of p‐Smad3 and Egr1. This research suggested novel molecular events underlying the roles of the master pro‐fibrotic cytokine TGFβ1 in HSC activation and liver fibrogenesis.
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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) | 0.002 | 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".