GLP-1R signaling does not modify the severity of experimental graft versus host disease
Bibliographic record
Abstract
OBJECTIVE: Glucagon-like peptide-1 (GLP-1) reduces systemic and gut inflammation. Here we assessed whether gain or loss of GLP-1 receptor (GLP-1R) signaling modifies the extent of gut injury and inflammation in experimental murine acute graft vs. host disease (aGvHD). METHODS: Allogeneic hematopoietic cell transplantation (HCT) was performed using bone marrow and splenocytes from BALB/c donors to induce aGvHD in C57BL/6 recipients or vice versa. Chimerism was determined by flow cytometry analysis of immune cell compartments. Inflammation was assessed by histological scoring of gut mucosal damage and by measuring circulating cytokine levels. qPCR was used to quantify gene expression in small intestine immune cells and tissues. The gut microbiome was assessed by 16S rRNA sequencing. RESULTS: Allogeneic chimerism was greater than 90% in peripheral blood and in the gut epithelial compartment. Levels of Glp1r mRNA transcripts were induced in the ileum of both vehicle- and semaglutide-treated allogeneic mice, reflecting that allogeneic T cells homing to the gut express a functional GLP-1R. Nevertheless, semaglutide did not attenuate the severity of systemic cytokine induction, gut injury or inflammation, or the extent of aGvHD in the gut mucosa. Loss of GLP-1R signaling in donor cells had limited effects on overall microbial diversity during acute GvHD, and semaglutide-treated mice exhibited modest changes in proportions of microbial species. CONCLUSIONS: Although gut T cells express a functional GLP-1R, GLP-1R signaling has no meaningful impact on systemic or intestinal inflammation or microbiota composition in mice with experimental aGvHD, highlighting that the anti-inflammatory actions of GLP-1 medicines are highly context-dependent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".