Cardioprotective GLP-1 metabolite prevents ischemic cardiac injury by inhibiting mitochondrial trifunctional protein-α
Bibliographic record
Abstract
Mechanisms mediating the cardioprotective actions of glucagon-like peptide 1 (GLP-1) were unknown. Here, we show in both ex vivo and in vivo models of ischemic injury that treatment with GLP-1(28-36), a neutral endopeptidase-generated (NEP-generated) metabolite of GLP-1, was as cardioprotective as GLP-1 and was abolished by scrambling its amino acid sequence. GLP-1(28-36) enters human coronary artery endothelial cells (caECs) through macropinocytosis and acts directly on mouse and human coronary artery smooth muscle cells (caSMCs) and caECs, resulting in soluble adenylyl cyclase Adcy10-dependent (sAC-dependent) increases in cAMP, activation of protein kinase A, and cytoprotection from oxidative injury. GLP-1(28-36) modulates sAC by increasing intracellular ATP levels, with accompanying cAMP accumulation lost in sAC-/- cells. We identify mitochondrial trifunctional protein-α (MTPα) as a binding partner of GLP-1(28-36) and demonstrate that the ability of GLP-1(28-36) to shift substrate utilization from oxygen-consuming fatty acid metabolism toward oxygen-sparing glycolysis and glucose oxidation and to increase cAMP levels is dependent on MTPα. NEP inhibition with sacubitril blunted the ability of GLP-1 to increase cAMP levels in coronary vascular cells in vitro. GLP-1(28-36) is a small peptide that targets novel molecular (MTPα and sAC) and cellular (caSMC and caEC) mechanisms in myocardial ischemic injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".