VEGFA mRNA-LNP promotes biliary epithelial cell-to-hepatocyte conversion in acute and chronic liver diseases and reverses steatosis and fibrosis
Bibliographic record
Abstract
The liver is known for its remarkable regenerative ability through proliferation of hepatocytes. Yet, during chronic injury or severe hepatocyte death, proliferation of hepatocytes is exhausted. To overcome this hurdle, we propose vascular-endothelial-growth-factor A (VEGFA) as a therapeutic means to accelerate biliary epithelial cell (BEC)-to-hepatocyte conversion. Investigation in zebrafish establishes that blocking VEGF receptors abrogates BEC-driven liver repair, while VEGFA overexpression promotes it. Delivery of VEGFA via non-integrative and safe nucleoside-modified mRNA encapsulated into lipid-nanoparticles (mRNA-LNP) in acutely or chronically injured mouse livers induces robust BEC-to-hepatocyte conversion and reversion of steatosis and fibrosis. In human and murine diseased livers, we further identified VEGFA-receptor KDR-expressing BECs associated with KDR-expressing cell-derived hepatocytes. This defines KDR-expressing cells, most likely being BECs, as facultative progenitors. This study reveals novel therapeutic benefits of VEGFA delivered via nucleoside-modified mRNA-LNP, whose safety is widely validated with COVID-19 vaccines, for harnessing BEC-driven repair to potentially treat liver diseases. Highlights: Complementary mouse and zebrafish models of liver injury demonstrate the therapeutic impact of VEGFA-KDR axis activation to harness BEC-driven liver regeneration.VEGFA mRNA LNPs restore two key features of the chronic liver disease in humans such as steatosis and fibrosis.Identification in human cirrhotic ESLD livers of KDR-expressing BECs adjacent to clusters of KDR+ hepatocytes suggesting their BEC origin.KDR-expressing BECs may represent facultative adult progenitor cells, a unique BEC population that has yet been uncovered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".