Inflammation and gut microbiota-induced hepatic lipogenesis drives metabolic syndrome in TLR5 deficient mice (HUM1P.310)
Bibliographic record
Abstract
Abstract The gut microbiota plays a key role in host metabolism. Toll-Like Receptor 5 (TLR5), a flagellin receptor, is required for gut microbiota homeostasis. Accordingly, TLR5 deficient (T5KO) mice are prone to develop microbiota-dependent spontaneous colitis and metabolic syndrome. Herein, we investigated whether metabolic syndrome in T5KO mice correlates with hepatic dyslipidemia and inflammation. T5KO mice displayed elevated neutral lipids with substantial enrichment of C18:1 (n9) relative to wild-type littermates. Oleate enrichment of hepatic lipids was microbiota-dependent. Analyzing cecal contents via 1H NMR-based metabolomics revealed that T5KO mice exhibited elevated short chain fatty acids (SCFA) with a concomitant increase in colonic SCFA receptors and hepatic pro-inflammatory genes and lipogenic enzymes including stearoyl-CoA desaturase1 (SCD1). SCFA treatment further aggravated metabolic syndrome in T5KO mice. Interestingly, deletion of hepatic SCD1 not only prevented hepatic neutral lipid oleate enrichment but also ameliorated hepatic inflammation and metabolic syndrome in T5KO mice. Collectively, these results underscore the key role of the gut microbiota-liver axis in the pathogenesis of metabolic diseases.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".