HGF-, EGF-, and Dexamethasone-Induced Gene Expression Patterns During Formation of Tissue in Hepatic Organoid Cultures
Bibliographic record
Abstract
Corticosteroids, hepatocyte growth factor (HGF), and epidermal growth factor (EGF) play important roles in hepatic biology. We have previously shown that these molecules are required for formation of tissue with specific histology in complex organoid cultures. Dexamethasone suppresses growth and induces hepatocyte maturation; HGF and EGF are needed for formation of the nonepithelial elements. All three are needed for formation of the biliary epithelium. The gene expression patterns by which corticosteroids, HGF, and EGF mediate their effects in hepatic tissue formation are distinct. These patterns affect many gene families and are described in detail. In terms of main findings, dexamethasone induces expression of both HNF4 and C/EBPalpha, essential transcription factors for hepatocyte differentiation. It suppresses hepatocyte growth by suppressing many molecules associated with growth in liver and other tissues, including IL-6, CXC-chemokine receptor, amphiregulin, COX-2, HIF, etc. HGF and EGF induce all members of the TGF-beta family. They also induced multiple CNS-related genes, probably associated with stellate cells. Dexamethasone, as well as HGF and EGF, induces expression of HNF6-beta, associated with biliary epithelium formation. Combined addition of all three molecules is associated with mature histology in which hepatocyte and biliary lineages are separate and HNF4 is expressed only in hepatocyte nuclei. In conclusion, the results provide new and surprising information on the gene expression alterations by which corticosteroids, HGF, and EGF exert their effects on formation of hepatic tissue. The results underscore the usefulness of the organoid cultures for generating information on histogenesis, which cannot be obtained by other culture or whole animal models.
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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) | 0.001 | 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 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".