GLP-1 Receptor Signaling Differentially Modifies the Outcomes of Sterile vs Viral Pulmonary Inflammation in Male Mice
Bibliographic record
Abstract
A number of disease states, including type 2 diabetes (T2D), are associated with an increased risk of pulmonary infection. Glucagon-like peptide-1 (GLP-1) receptor agonists are used to treat T2D and exert anti-inflammatory actions through a single, well-defined GLP-1 receptor (GLP-1R). Although highly expressed in the lung, little is known about the role of the GLP-1R in the context of pulmonary inflammation. Here we examined the consequences of gain or loss of GLP-1R activity in infectious and noninfectious lung inflammation. We studied wild-type mice treated with a GLP-1R agonist, and Glp1r-/- mice, in the setting of bleomycin-induced noninfectious lung injury and influenza virus infection. Loss of the GLP-1R attenuated the severity of bleomycin-induced lung injury, whereas activation of GLP-1R signaling increased pulmonary inflammation via the sympathetic nervous system. In contrast, GLP-1R agonism reduced the pathogen load in mice with experimental influenza virus infection in association with increased expression of intracellular interferon-inducible GTPases. Notably, the GLP-1 receptor agonist liraglutide improved the survival rate after influenza virus infection. Our results reveal context-dependent roles for the GLP-1 system in the response to lung injury. Notably, the therapeutic response of GLP-1R agonism in the setting of experimental influenza virus infection may have relevance for ongoing studies of GLP-1R agonism in people with T2D susceptible to viral lung injury.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".