Degradation of splicing factor SRSF3 contributes to progressive liver disease
Bibliographic record
Abstract
Serine rich splicing factor 3 (SRSF3) plays a critical role in liver function and its loss promotes chronic liver damage and regeneration. As a consequence, genetic deletion of SRSF3 in hepatocytes caused progressive liver disease and ultimately led to hepatocellular carcinoma. Here we show that SRSF3 is decreased in human liver samples with non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), or cirrhosis that was associated with alterations in RNA splicing of known SRSF3 target genes. Hepatic SRSF3 expression was similarly decreased and RNA splicing dysregulated in mouse models of NAFLD and NASH. We showed that palmitic acid-induced oxidative stress caused conjugation of the ubiquitin like NEDD8 protein to SRSF3 and proteasome mediated degradation. SRSF3 was selectively neddylated at lysine11 and mutation of this residue (SRSF3-K11R) was sufficient to prevent both SRSF3 degradation and alterations in RNA splicing. Finally prevention of SRSF3 degradation in vivo partially protected mice from hepatic steatosis, fibrosis and inflammation. These results highlight a neddylation-dependent mechanism regulating gene expression in the liver that is disrupted in early metabolic liver disease and may contribute to the progression to NASH, cirrhosis and ultimately hepatocellular carcinoma.
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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".