Proteomic Profiling of Mitochondrial-Derived Vesicles in Brain Reveals Enrichment of Respiratory Complex Sub-assemblies and Small TIM Chaperones
Bibliographic record
Abstract
The generation of mitochondrial-derived vesicles (MDVs) is implicated in a plethora of vital cell functions, from mitochondrial quality control to peroxisomal biogenesis. The discovery of distinct subtypes of MDVs has revealed the selective inclusion of mitochondrial cargo in response to varying stimuli. However, the true scope and variety of MDVs is currently unclear, and unbiased approaches have yet to be used to understand their biology. Furthermore, as mitochondrial dysfunction has been implicated in many neurodegenerative diseases, it is essential to understand MDV pathways in the nervous system. To address this, we sought to identify the cargo in brain MDVs. We used an in vitro budding assay and proteomic approach to identify proteins selectively enriched in MDVs. 72 proteins were identified as MDV-enriched, of which 31% were OXPHOS proteins. Interestingly, the OXPHOS proteins localized to specific modules of the respiratory complexes, hinting at the inclusion of sub-assemblies in MDVs. Small TIM chaperones were also highly enriched in MDVs, linking mitochondrial chaperone-mediated protein transport to MDV formation. As the two Parkinson’s disease genes PINK1 and Parkin have been previously implicated in MDV biogenesis in response to oxidative stress, we compared the MDV proteomes from the brains of wild-type mice with those of PINK1 -/- and Parkin -/- mice. No significant difference was found, suggesting that PINK1- and Parkin-dependent MDVs make up a small proportion of all MDVs in the brain. Our findings demonstrate a previously uncovered landscape of MDV complexity and provide a foundation from which further novel MDV functions can be discovered. Data are available via ProteomeXchange with identifier PXD020197.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".