Germ-free mice exhibit profound gut microbiota-dependent alterations of intestinal endocannabinoidome signaling
Bibliographic record
Abstract
The gut microbiota is a unique ecosystem of microorganisms interacting with the host through several biochemical mechanisms. The endocannabinoidome (eCBome), a complex signaling system including the endocannabinoid system, approximately 50 receptors and metabolic enzymes, and more than 20 lipid mediators with important physiopathologic functions, modulates gastrointestinal tract function and may mediate host cell-microbe communications there. Germ-free (GF) mice, which lack an intestinal microbiome and so differ drastically from conventionally raised (CR) mice, offer a unique opportunity to explore the eCBome in a microbe-free model and in the presence of a reintroduced functional gut microbiome through fecal microbiota transplant (FMT). We aimed to gain direct evidence for a link between the microbiome and eCBome systems by investigating eCBome alterations in the gut in GF mice before and after FMT. Basal eCBome gene expression and lipid profiles were measured in various segments of the intestine of GF and CR mice at juvenile and adult ages using targeted quantitative PCR transcriptomics and LC-MS/MS lipidomics. GF mice exhibited age-dependent modifications in intestinal eCBome gene expression and lipid mediator levels. FMT from CR donor mice to age-matched GF male mice reversed several of these alterations, particularly in the ileum and jejunum, after only 1 week, demonstrating that the gut microbiome directly impacts the host eCBome and providing a cause-effect relationship between the presence or absence of intestinal microbes and eCBome signaling. These results open the way to new studies investigating the mechanisms through which intestinal microorganisms exploit eCBome signaling to exert some of their physiopathologic functions.
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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 | 0.001 |
| 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) | 0.002 | 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".