RNA Sequencing of LX-2 Cells Treated with TGF-β1 Identifies Genes Associated with Early Hepatic Stellate Cell Activation.
Bibliographic record
Abstract
Abstract BackgroundHepatic stellate cells (HSCs) are liver-resident myofibroblast precursors responsible for the production of collagen and maintenance of the hepatic extracellular matrix (ECM). As such, they are generally associated with fibrotic liver diseases. HSCs become "activated" in response to tissue damage or pathogen invasion, a process most commonly driven by transforming growth factor-β1 (TGF-β1). Despite this, the full extent of TGF-β1 signalling in these cells is poorly understood. Clarifying the range and diversity of this signalling will further improve our understanding of the process of HSC activation.Methods and ResultsRNA sequencing was used to quantitate the transcriptomic changes induced in LX-2 cells, an activated human HSC line, following TGF-b1 treatment. In total, 5,258 genes were found to be significantly differentially expressed with a false discovery rate cut-off of < 0.1. The topmost deregulated of these genes included those with no currently characterised role in either HSC activation or fibrotic processes, including CIITA and SERPINB2. In silico analysis revealed the prominent signalling pathways downstream of TGF-β1 in LX-2 cells.ConclusionsIn this study, we describe the genes and signalling pathways significantly deregulated in LX-2 cells following TGF-β1 treatment. We identified several highly deregulated genes with no currently characterised role in HSC activation, which may represent novel mediators of fibrotic responses in HSCs or the liver macroenvironment. This work may be of use in the identification of new markers of liver fibrosis and could provide insight into prospective genes or pathways that might be targeted for the amelioration of fibrotic liver disease in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".