Proteomics characterization of mitochondrial‐derived vesicles under oxidative stress
Bibliographic record
Abstract
Mitochondria share attributes of vesicular transport with their bacterial ancestors given their ability to form mitochondrial-derived vesicles (MDVs). MDVs are involved in mitochondrial quality control and their formation is enhanced with stress and may, therefore, play a potential role in mitochondrial-cellular communication. However, MDV proteomic cargo has remained mostly undefined. In this study, we strategically used an in vitro MDV budding/reconstitution assay on cardiac mitochondria, followed by graded oxidative stress, to identify and characterize the MDV proteome. Our results confirmed previously identified cardiac MDV markers, while also revealing a complete map of the MDV proteome, paving the way to a better understanding of the role of MDVs. The oxidative stress vulnerability of proteins directed the cargo loading of MDVs, which was enhanced by antimycin A (Ant-A). Among OXPHOS complexes, complexes III and V were found to be Ant-A-sensitive. Proteins from metabolic pathways such as the TCA cycle and fatty acid metabolism, along with Fe-S cluster, antioxidant response proteins, and autophagy were also found to be Ant-A sensitive. Intriguingly, proteins containing hyper-reactive cysteine residues, metabolic redox switches, including professional redox enzymes and those that mediate iron metabolism, were found to be components of MDV cargo with Ant-A sensitivity. Last, we revealed a possible contribution of MDVs to the formation of extracellular vesicles, which may indicate mitochondrial stress. In conclusion, our study provides an MDV proteomics signature that delineates MDV cargo selectivity and hints at the potential for MDVs and their novel protein cargo to serve as vital biomarkers during mitochondrial stress and related pathologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".