Hepatocyte nuclear receptor SHP suppresses inflammation and fibrosis in a mouse model of nonalcoholic steatohepatitis
Bibliographic record
Abstract
Nonalcoholic fatty liver disease (NAFLD) is a burgeoning health problem worldwide, ranging from nonalcoholic fatty liver (NAFL, steatosis without hepatocellular injury) to the more aggressive nonalcoholic steatohepatitis (NASH, steatosis with ballooning, inflammation, or fibrosis). Although many studies have greatly contributed to the elucidation of NAFLD pathogenesis, the disease progression from NAFL to NASH remains incompletely understood. Nuclear receptor small heterodimer partner (Nr0b2, SHP ) is a transcriptional regulator critical for the regulation of bile acid, glucose, and lipid metabolism. Here, we show that SHP levels are decreased in the livers of patients with NASH and in diet-induced mouse NASH. Exposing primary mouse hepatocytes to palmitic acid and lipopolysaccharide in vitro , we demonstrated that the suppression of Shp expression in hepatocytes is due to c-Jun N-terminal kinase (JNK) activation, which stimulates c-Jun–mediated transcriptional repression of Shp . Interestingly, in vivo induction of hepatocyte-specific SHP in steatotic mouse liver ameliorated NASH progression by attenuating liver inflammation and fibrosis, but not steatosis. Moreover, a key mechanism linking the anti-inflammatory role of hepatocyte-specific SHP expression to inflammation involved SHP-induced suppression of NF-κB p65-mediated induction of chemokine (C–C motif) ligand 2 (CCL2), which activates macrophage proinflammatory polarization and migration. In summary, our results indicate that a JNK/SHP/NF-κB/CCL2 regulatory network controls communications between hepatocytes and macrophages and contributes to the disease progression from NAFL to NASH. Our findings may benefit the development of new management or prevention strategies for NASH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".