MOTS-c Promotes Glycolysis via AMPK–HIF-1α–PFKFB3 Pathway to Ameliorate Cardiopulmonary Bypass–induced Lung Injury
Bibliographic record
Abstract
Abstract Cardiopulmonary bypass (CPB) is essential during cardiac surgery but frequently leads to lung ischemia–reperfusion injury (LIRI), a significant contributor to postoperative complications. We investigated the protective effects of mitochondrial open reading frame of the 12S ribosomal RNA type C (MOTS-c), a mitochondrial-derived peptide, against LIRI-induced acute lung injury (ALI), emphasizing glycolytic reprogramming and ferroptosis in pulmonary microvascular endothelial cells. We hypothesized that MOTS-c exerts its protective effects by regulating glycolysis and suppressing ferroptosis via metabolic signaling pathways. We conducted a prospective, controlled trial involving 107 patients undergoing CPB, evaluating plasma concentrations of MOTS-c and inflammatory markers. MOTS-c concentrations were significantly reduced in patients with ALI. In vivo and in vitro experiments demonstrated that MOTS-c pretreatment alleviated LIRI by enhancing glycolytic flux, reducing oxidative stress, and suppressing ferroptosis in pulmonary microvascular endothelial cells. In particular, MOTS-c reinstated the expression of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3), an essential glycolytic enzyme, thus preserving cellular energy homeostasis and diminishing lipid peroxidation. The findings further emphasize the involvement of the AMPK (AMP-activated protein kinase)–hypoxia inducible factor-1α (HIF-1α) signaling pathway in the protective benefits facilitated by MOTS-c. MOTS-c elevated phosphorylated AMPKα and HIF-1α expression, indicating a vital function for these pathways in enhancing glycolysis and antioxidant defenses. Genetic and pharmacological inhibition of PFKFB3 abrogated the protective effects of MOTS-c, thereby confirming the essential role of PFKFB3-mediated glycolysis in alleviating LIRI. Our research indicates that MOTS-c could serve as a potential therapeutic agent for the prevention or treatment of LIRI-induced ALI by enhancing glycolysis, suppressing ferroptosis, and activating the AMPK–HIF-1α pathway. Future study should explore the clinical application of MOTS-c, potentially improving outcomes for patients undergoing high-risk cardiac operations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".