NOD2 in hepatocytes engages a liver-gut axis to protect against steatosis, fibrosis, and gut dysbiosis during fatty liver disease in mice
Bibliographic record
Abstract
Obesity promotes nonalcoholic fatty liver disease (NAFLD). The intestinal microbiota contributes to NAFLD progression through a gut-to-liver pathway that promotes inflammation and fibrosis. Gut microbiota-derived factors can travel to the liver and activate immune responses in liver resident cells to promote inflammation and NAFLD. Little is known about bacterial sensors or immune responses that can protect against NAFLD. We tested whether the bacterial cell wall sensor nucleotide-binding oligomerization domain-containing (NOD)2 protects against diet-induced NAFLD in mice. Whole body deletion of NOD2 exacerbated liver steatosis and fibrosis in mice fed a NAFLD-promoting diet. Mice with a hepatocyte-specific deletion of NOD2 ( Nod2−/−HKO) also had higher liver steatosis and fibrosis compared with littermate wild-type mice (WT) fed a NAFLD-promoting diet. Hepatocyte-specific NOD2 deletion altered the composition of the gut microbiome. Nod2−/−HKO mice had increased relative abundance of Clostridiales and lower Erysipelotrichaceae among other changes in cecal bacteria compared with littermate WT mice. Hepatocyte-specific NOD2 deletion altered a transcriptional program of liver inflammation, metabolism, and fibrosis. Nod2−/−HKO mice had higher levels of transcripts involved in lipid and cholesterol metabolism. Nod2−/−HKO mice had higher transcript levels of transforming growth factor-β and collagen isoforms, which coincided with higher levels of liver collagen compared with WT mice. These data show that bacterial cell wall sensing within hepatocytes can engage retrograde cross-talk from the liver to the gut, where liver immunity communicates with the gut to influence the intestinal host-microbe relationship during diet-induced NAFLD, and NOD2 within the hepatocyte confers protection from liver steatosis and fibrosis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".