Development of a novel human triculture model of non-alcoholic fatty liver disease and identification of berberine as ameliorating steatosis, oxidative stress and fibrosis
Bibliographic record
Abstract
Objectives: Non-alcoholic fatty liver disease (NAFLD) and its progression to non-alcoholic steatohepatitis (NASH) and hepatocarcinoma is a serious and growing problem. However, the development of new therapies is severely hindered by a lack of high-throughput assays for drug testing. Methods: We have developed a simple transwell assay comprised of HepG2 hepatocytes, hepatic LX-2 stellate cells, and differentiated THP-1 cells. The cells were incubated with an activating mixture containing the NASH-associated risk factors, glucose, insulin, free fatty acids (FFAs), and lipopolysaccharide (LPS) for 72 h. We compared different combinations of culture conditions to obtain a model system that recapitulates the main features of NAFLD/NASH, i.e., increased steatosis, reactive oxygen species (ROS), secretion of pro-inflammatory cytokines/chemokines, and presence of fibrosis. To confirm the usefulness of the optimized model system, we screened for compounds that inhibit steatosis in the hepatocytes and evaluated the most effective compound in the triculture model system. Results: The activating mixture stimulated HepG2 cells in this triculture to accumulate more fat and produce higher levels of reactive oxygen species (ROS) than HepG2 cells in monocultures. As well, higher levels of inflammatory cytokines and chemokines (IL-8, IL-6, MIP-1α, etc. ) were produced in this triculture compared to monocultures. In addition, in all LX-2 monocultures and cocultures, exposure to the activating mixture increased markers of fibrosis. A major strength of our triculture system is that it makes possible the simultaneous monitoring of 4 main features of NASH, i.e., steatosis, oxidative stress, inflammation and fibrosis. Screening potential modulators that may reduce steatosis in HepG2 cells revealed the protective effects of the isoalkaloid, berberine. Tested using this novel triculture assay, treatment with 5 µM berberine decreased steatosis and ROS in HepG2 hepatocytes, reduced inflammatory cytokine production and inhibited collagen production from LX-2 cells. Conclusion: This simple triculture model recapitulates the main features of NAFLD/NASH and should be useful for high-throughput preclinical drug discovery. In this model, berberine showed promising results in decreasing steatosis and ROS and protection against fibrosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".