Role of Hepatic-Specific Transcription Factors and Polycomb Repressive Complex 2 during Induction of Fibroblasts to Hepatic Fate
Bibliographic record
Abstract
Direct reprogramming using defined sets of transcription factors (TFs) is a recent strategy for generating induced hepatocytes (iHeps) from fibroblasts for use in regenerative medicine and drug development. Comprehensive studies detailing the regulatory role of TFs during this reprogramming process could help increase its efficiency. This study aimed to find the TFs with the greatest influences on the generation of iHeps from fibroblasts, and to further understand their roles in the regulation of the gene expression program. Here, we used systems biology approaches to analyze high quality expression data sets in combination with TF-binding sites data and protein-protein interactions data during the direct reprogramming of fibroblasts to iHeps. Our results revealed two main patterns for differentially expressed genes (DEGs): up-regulated genes were categorized as hepatic-specific pattern, and down-regulated genes were categorized as mesoderm- and fibroblast-specific pattern. Interestingly, hepatic-specific genes co-expressed and were regulated by hepatic-specific TFs, specifically Hnf4a and Foxa2. Conversely, the mesoderm- and fibroblast-specific pattern was mainly silenced by polycomb repressive complex 2 (PRC2) members, including Suz12, Mtf2, Ezh2, and Jarid2. Independent analysis of both the gene and core regulatory network of DE-TFs showed significant roles for Hnf4a, Foxa2, and PRC2 members in the regulation of the gene expression program and in biological processes during the direct conversion process. Altogether, using systems biology approaches, we clarified the role of Hnf4a and Foxa2 as hepatic-specific TFs, and for the first time, introduced the PRC2 complex as the main regulator that favors the direct reprogramming process in cooperation with hepatic-specific factors.
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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) | 0.000 | 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 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".