2308-PUB: A Protective Role of H2S from Obesity-Associated Metabolic Stress in GLP-1 Regulation
Bibliographic record
Abstract
Background: Obesity is a risk factor in the development of a variety of diseases including cardiovascular disease and type 2 diabetes. Circulating fatty acids (including palmitate) and protein glycation are both increased in obesity and diabetes and are known to cause metabolic stress. This includes the impairment of the glucoregulatory hormone glucagon-like peptide-1 (GLP-1) secreted from intestinal L-cells. Recently, the gastrointestinal microbiome has been implicated in the regulation of GLP-1. We believe that the bacterial metabolite hydrogen sulfide (H2S) can play a protective role in L-cell metabolic stress. Methods: Our study was conducted on both human NCI H716 and mouse GLUTag GLP-1 secreting L-cells. L-cell stress was assessed using the total reactive oxygen species (ROS) dye DCF-DA. ROS levels were examined during incubation with palmitate (PA), glycated albumin (GA), and the bacterial gas donor GYY4137(H2S). Next, GLP-1 secretion was measured after treating cells with PA and GA, along with H2S using a GLP-1 ELISA. Results: We have demonstrated that elements of the obese physiological environment: PA and GA, cause an increase of reactive oxygen species (ROS) in L-cells. Specifically, 500μm PA and 200μg/mL GA caused a 75% and 50% increase in ROS generation, respectively. Furthermore, these treatments led to a 25% reduction of GLP-1 secretion in human L-cells. Finally, the microbial gas H₂S reduced basal and PA-induced ROS by nearly half. Conclusion: The microbial gas H2S reduces metabolic stress in GLP-1 cells. This study will lay the foundation for future work exploring how bacterial products such as prebiotics and probiotics may be used as a treatment for complications associated with obesity and diabetes. Disclosure A. Mezouari: None. J. Gagnon: None. R. Nangia: None. Funding Natural Sciences and Engineering Research Council of Canada (RGPIN-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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".