The Role of the Bacterial Muramyl Dipeptide in the Regulation of GLP-1 and Glycemia
Bibliographic record
Abstract
The host's intestinal microbiota contributes to endocrine and metabolic responses, but a dysbiosis in this environment can lead to obesity and insulin resistance. Recent work has demonstrated a role for microbial metabolites in the regulation of gut hormones, including the metabolic hormone, glucagon-like peptide-1 (GLP-1). Muramyl dipeptide (MDP) is a bacterial cell wall component which has been shown to improve insulin sensitivity and glucose tolerance in diet-induced obese mice by acting through the nucleotide oligomerization domain 2 (NOD2) receptor. The purpose of this study was to understand the effects of MDP on GLP-1 secretion and glucose regulation. We hypothesized that MDP enhances glucose tolerance by inducing intestinal GLP-1 secretion through NOD2 activation. First, we observed a significant increase in GLP-1 secretion when murine and human L-cells were treated with a fatty acid MDP derivative (L18-MDP). Importantly, we demonstrated the expression of the NOD2 receptor in mouse intestine and in L-cells. In mice, two intraperitoneal injections of MDP (5 mg/kg body weight) caused a significant increase in fasting total GLP-1 in chow-fed mice, however this did not lead to an improvement in oral glucose tolerance. When mice were exposed to a high-fat diet, they eventually lost this MDP-induced GLP-1 release. Finally, we demonstrated in L-cells that hyperglycemic conditions reduce the mRNA expression of NOD2 and GLP-1. Together these findings suggest MDP may play a role in enhancing GLP-1 during normal glycemic conditions but loses its ability to do so in hyperglycemia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".