Glucagon-like peptide-1 receptor signaling deficiency exacerbates hematopoietic stem cell graft rejection in mice
Bibliographic record
Abstract
Graft failure (GF) following hematopoietic stem cell transplantation (HSCT) remains a major complication particularly in the setting of human leukocyte antigen (HLA)-mismatched grafts where residual host lymphocytes can drive immune-mediated rejection. While strategies to mitigate GF have been explored, such as intensified conditioning or donor T cell supplementation, these approaches carry significant risks, including increased toxicity and graft-versus-host disease (GVHD). Recent studies have highlighted the glucagon-like peptide-1 receptor (GLP1R) as a critical regulator of immune homeostasis, yet its role in HSC engraftment remains unexplored. Here, we demonstrated that GLP1R deficiency in recipient mice leads to a profound increase in GF following MHC-mismatched allogeneic HSCT. Although GLP1R knockout (GLP1RKO) and wild-type (WT) mice exhibited comparable survival and engraftment following syngeneic or minor antigen-mismatched transplants, GLP1RKO mice undergoing MHC-mismatched HSCT experienced significantly greater weight loss, earlier mortality, and reduced donor chimerism. Histologic and cytokine analyses confirmed that this phenotype is not driven by GVHD, but rather by early graft rejection. Depletion of CD90+ recipient T cells prior to transplantation rescued engraftment in GLP1RKO mice, further supporting a model in which GLP1R signaling restrains host lymphocyte-mediated graft rejection. These findings identify GLP1R as a novel regulator of allogeneic HSC engraftment and suggest that GLP1R agonists, widely used for metabolic disorders, may have therapeutic potential in preventing HSC graft rejection. Given the lack of targeted interventions for HSC graft rejection, further studies are warranted to investigate GLP1R-directed therapies in the context of allogeneic HSCT.
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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 | 0.001 |
| 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) | 0.002 | 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".