2301-PUB: The Role of Muramyl Dipeptide in Glucagon-Like Peptide-1 Regulation
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. A new area a research is focused on understanding how bacterial metabolites contribute to intestinal glucagon-like peptide-1 (GLP-1) release. 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. The purpose of this study was to understand MDP’s mechanism of action in glucose regulation. We hypothesized that MDP enhances glucose tolerance by inducing intestinal GLP-1 secretion. We observed a significant increase (P<0.001, n=7) in GLP-1 secretion when mouse L-cells were treated with an MDP derivative at 10ug/ml. Using fluorescent immunohistochemistry and RT-PCR we located MDP’s receptor, nucleotide oligomerizing domain 2 (NOD2), in mouse intestine and mouse L-cells. In mice, two intraperitoneal injections of MDP (5mg/kg body weight) or PBS caused a significant increase (P=0.0009, n=15-19) in fasting total GLP-1. Understanding how bacterial products influence GLP-1 secretion and subsequent insulin release, could be translated into novel treatments options for improving glucose regulation in metabolic disease. Disclosure L. Williams: None. A. Alshehri: None. S. Mustafa: None. J. Gagnon: None. Funding Natural Sciences and Engineering Research Council of Canada (2016-05905)
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.174 | 0.065 |
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".