MOTS-c attenuates lung ischemia-reperfusion injury via MYH9-Dependent nuclear translocation and transcriptional activation of antioxidant genes
Bibliographic record
Abstract
Acute respiratory distress syndrome (ARDS) following cardiopulmonary bypass (CPB) is driven by oxidative stress during lung ischemia-reperfusion injury (LIRI). Mitochondrial-derived peptide MOTS-c has emerged as a regulator of mitochondrial-nuclear communication, yet its role in CPB-induced ARDS remains unclear. Here, we identify MOTS-c as a critical mediator of endothelial protection against LIRI through MYH9-dependent nuclear translocation and transcriptional activation of antioxidant genes. In rat LIRI models, endothelial cells exhibited the most significant MOTS-c upregulation, correlating with barrier preservation and reduced oxidative stress. Mechanistically, hypoxia-reoxygenation (HR) triggered reactive oxygen species (ROS)-dependent phosphorylation of MYH9 at Ser1943 via casein kinase II subunit alpha (CK2A), enabling MOTS-c binding to MYH9-γ-Actin complexes for nuclear transport. RNA sequencing (RNA-seq) combined with chromatin immunoprecipitation sequencing (ChIP-seq) revealed direct MOTS-c interaction with promoters of antioxidant genes (e.g., HMOX1, NQO1), which harbor antioxidant response elements (AREs). Clinically, serum MOTS-c increments within 24 h post-CPB (ΔMOTS-c) outperformed traditional biomarkers in predicting ARDS incidence, with multivariate models incorporating ΔMOTS-c achieving superior discriminative power (AUC = 0.885). Exogenous MOTS-c administration in rats attenuated lung injury by reducing oxidative damage, inflammation, and mortality, recapitulating endogenous protective mechanisms. Our findings establish MOTS-c as a dual-function molecule-acting via ROS-CK2A-MYH9 signaling to activate nuclear antioxidant defenses and serving as a prognostic biomarker for CPB-related complications. This study bridges mitochondrial dynamics, nuclear transcriptional regulation, and clinical outcomes, offering novel preventive avenues for IRI-associated pathologies.
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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".