RNA Sequencing of LX-2 Cells Treated with TGF-β1 Identifies Genes Associated with Early Hepatic Stellate Cell Activation
Bibliographic record
Abstract
Abstract Hepatic stellate cells (HSCs) are liver-resident myofibroblast precursors. In chronic liver disease, HSC-derived myofibroblasts are responsible for the production of collagen and, as such, are generally associated with diseases characterised by hepatic fibrosis. HSCs undergo a process of "activation" in response to tissue damage or pathogen invasion, thereby assuming roles in extracellular matrix synthesis, wound healing, and pathogen defence. The process of HSC activation and collagen production is most commonly driven by the cytokine transforming growth factor-β1 (TGF-β1). To investigate the transcriptional impact of TGF-β1 signalling on liver myofibroblasts, RNA sequencing was used to quantitate the biological changes observed 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 known roles in influencing HSC activity, as well as those involved in fibrotic responses in other cell lines and tissues. Interestingly, genes with no currently characterised role in either HSC activation or fibrotic processes were also identified, including CIITA and SERPINB2. Further in silico analysis revealed the prominent signalling pathways downstream of TGF-β1 in LX-2 cells, which will be useful for improving the understanding of how this cytokine influences broader signalling pathways in the activation of HSCs. 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".