Altered endocannabinoidome bioactive lipid levels accompany reduced DNBS-induced colonic inflammation in germ-free mice
Bibliographic record
Abstract
BACKGROUND: Gut microbiota are involved in the onset and development of chronic intestinal inflammation. The recently described endocannabinoidome (eCBome), a diverse and complex system of bioactive lipid mediators, has been reported to play a role in various physio-pathological processes such as inflammation, immune responses and energy metabolism. The eCBome and the gut microbiome (miBIome) are closely linked and form the eCBome - miBIome axis, which may be of special relevance to colitis. METHODS: Colitis was induced in conventionally raised (CR), antibiotic-treated (ABX) and germ-free (GF) mice with dinitrobenzene sulfonic acid (DNBS). Inflammation was assessed by Disease Activity Index (DAI) score, body weight change, colon weight-length ratio, myeloperoxidase (MPO) activity and cytokine gene expression. Colonic eCBome lipid mediator concentrations were measured by HPLC-MS /MS. RESULTS: GF mice showed increased levels of anti-inflammatory eCBome lipids (LEA, OEA, DHEA and 13- HODE-EA) in the healthy state and higher MPO activity. DNBS elicited reduced inflammation in GF mice, having lower colon weight/length ratios and lower expression levels of Il1b, Il6, Tnfa and neutrophil markers compared to one or both of the other DNBS-treated groups. Il10 expression was also lower and the levels of several N-acyl ethanolamines and 13-HODE-EA levels were higher in DNBS-treated GF mice than in CR and ABX mice. The levels of these eCBome lipids negatively correlated with measures of colitis and inflammation. CONCLUSIONS: These results suggest that the depletion of the gut microbiota and subsequent differential development of the gut immune system in GF mice is followed by a compensatory effect on eCBome lipid mediators, which may explain, in part, the observed lower susceptibility of GF mice to develop DNBS-induced colitis.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".