Metabolomic Modeling To Monitor Host Responsiveness to Gut Microbiota Manipulation in the BTBR <sup>T+tf/j</sup> Mouse
Bibliographic record
Abstract
The microbiota, the entirety of microorganisms residing in the gut, is increasingly recognized as an environmental factor in the maintenance of health and the development of disease. The objective of this analysis was to model in vivo interactions between gut microbiota and both serum and liver metabolites. Different genotypic models (C57BL/6 and BTBR(T+tf/j) mice) were studied in combination with significant dietary manipulations (chow vs ketogenic diets) to perturb the gut microbiota. Diet rather than genotype was the primary driver of microbial changes, with the ketogenic diet diminishing total bacterial levels. Fecal but not cecal microbiota profiles were associated with the serum and liver metabolomes. Modeling metabolome-microbiota interactions showed fecal Clostridium leptum to have the greatest impact on host metabolism, significantly correlating with 10 circulating metabolites, including 5 metabolites that did not correlate with any other microbes. C. leptum correlated negatively with serum ketones and positively with glucose and glutamine. Interestingly, microbial groups most strongly correlated with host metabolism were those modulating gut barrier function, the primary site of microbe-host interactions. These results show very robust relationships and provide a basis for future work wherein the compositional and functional associations of the microbiome can be modeled in the context of the metabolome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".