Prosteatotic and Protective Components in a Unique Model of Fatty Liver: Gut Microbiota and Suppressed Complement System
Bibliographic record
Abstract
Goose can develop severe hepatic steatosis without overt injury, thus it may serve as a unique model for uncovering how steatosis-related injury is prevented. To identify the markedly prosteatotic and protective mechanisms, we performed an integrated analysis of liver transcriptomes and gut microbial metagenomes using samples collected from overfed and normally-fed geese at different time points. The results indicated that the fatty liver transcriptome, initially featuring a 'metabolism' pathway, was later joined by 'cell growth and death' and 'immune diseases' pathways. Gut microbiota played a synergistic role in the liver response as microbial and hepatic genes affected by overfeeding shared multiple pathways. Remarkably, the complement system, an inflammatory component, was comprehensively suppressed in fatty liver, which was partially due to increased blood lactic acid from enriched Lactobacillus. Data from in vitro studies suggested that lactic acid suppressed TNFα via the HNF1α/C5 pathway. In conclusion, gut microbes and their hosts respond to excess energy influx as an organic whole, severe steatosis and related tolerance of goose liver may be partially attributable to gut microbiotic products and suppressed complement system, and lactic acid from gut microbiota participates in the suppression of hepatic TNFα/inflammation through the HNF1α/C5 pathway.
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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 | 0.001 |
| 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".