MicroRNA‐337‐3p controls hepatobiliary gene expression and transcriptional dynamics during hepatic cell differentiation
Bibliographic record
Abstract
Transcriptional networks control the differentiation of the hepatocyte and cholangiocyte lineages from embryonic liver progenitor cells and their subsequent maturation to the adult phenotype. However, how relative levels of hepatocyte and cholangiocyte gene expression are determined during differentiation remains poorly understood. Here, we identify microRNA (miR)-337-3p as a regulator of liver development. miR-337-3p stimulates expression of cholangiocyte genes and represses hepatocyte genes in undifferentiated progenitor cells in vitro and in embryonic mouse livers. Beyond the stage of lineage segregation, miR-337-3p controls the transcriptional network dynamics of developing hepatocytes and balances both cholangiocyte populations that constitute the ductal plate. miR-337-3p requires Notch and transforming growth factor-β signaling and exerts a biphasic control on the hepatocyte transcription factor hepatocyte nuclear factor 4α by modulating its activation and repression. With the help of an experimentally validated mathematical model, we show that this biphasic control results from an incoherent feedforward loop between miR-337-3p and hepatocyte nuclear factor 4α. CONCLUSION: Our results identify miR-337-3p as a regulator of liver development and highlight how tight quantitative control of hepatic cell differentiation is exerted through specific gene regulatory network motifs. (Hepatology 2018;67:313-327).
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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".