Deletion of the Toll-Like Receptor 5 Gene Per Se Does Not Determine the Gut Microbiome Profile That Induces Metabolic Syndrome: Environment Trumps Genotype
Bibliographic record
Abstract
Over the past decade, emerging evidence has linked alterations in the gut microbial composition to a wide range of diseases including obesity, type 2 diabetes, and cardiovascular disease. Toll-like receptors (TLRs) are the major mediators for the interactions between gut microbiota and host innate immune system, which is involved in the localization and structuring of host gut microbiota. A previous study found that TLR5 deficient mice (TLR5KO1) had altered gut microbial composition which led to the development of metabolic syndrome including hyperlipidemia, hypertension, insulin resistance and increased adiposity. In the current study, a second TLR5-deficient mouse model was studied (TLR5KO2). TLR5 deficient mice did not manifest metabolic abnormalities related to the metabolic syndrome compared with littermate controls maintained on normal chow or after feeding a high fat diet. Analysis of the gut microbial composition of littermate TLR5KO2 and wild type mice revealed no significant difference in the overall microbiota structure between genotypes. However, the TLR5KO2 microbiota was distinctly different from that previously reported for TLR5KO1 mice with metabolic syndrome. We conclude that an altered composition of the microbiota in a given environment can result in metabolic syndrome, but it is not a consequence of TLR5 deficiency per se.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".