Glucagon-like peptide-1 receptor mediates the beneficial effect of liraglutide in an acute lung injury mouse model involving the thioredoxin-interacting protein
Bibliographic record
Abstract
Repurposing clinically used drugs is among the important strategies in drug discovery. Glucagon-like peptide-1 (GLP-1) and its diabetes-based drugs, such as liraglutide, possess a spectrum of extra-pancreatic functions, while GLP-1 receptor (GLP-1R) is most abundantly expressed in the lung. Recent studies have suggested that GLP-1-based drugs exert beneficial effects in chronic, as well as acute, lung injury rodent models. Here, we show that liraglutide pretreatment reduced LPS induced acute lung injury in mice. It significantly reduced lung injury score, wet/dry lung weight ratio, bronchoalveolar lavage fluid immune cell count and protein concentration, and cell apoptosis in the lung, and it was associated with reduced lung inflammatory cytokine and chemokine gene expression. Importantly, these effects were virtually absent in GLP-1R−/− mice. A well-known function of GLP-1 and GLP-based drugs in pancreatic β-cells is the attenuation of high-glucose stimulated expression of thioredoxin-interacting protein (TxNIP), a key component of inflammasome. LPS-challenged lungs showed elevated TxNIP mRNA and protein expression, which was attenuated by liraglutide treatment in a GLP-1R-dependent manner. Hence, our observations suggest that GLP-1R is essential in mediating beneficial effects of liraglutide in acute lung injury, with the inflammasome component TxNIP as a potential target.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".