Measuring mitochondrial protein turnover in a human midbrain organoid Parkinson's model by mass spectrometry
Bibliographic record
Abstract
Parkinson’s Disease (PD) is a currently incurable neurodegenerative disorder that manifests in the elderly through motor symptoms of bradykinesia, rigidity and tremor. Those symptoms are caused by a dopamine (DA) deficit, which leads to ineffective neural motor function. Intriguingly, certain DA neuronal populations involved in the disease will die whilst others nearby that are very similar will survive. One of the prevalent theories explaining this selective death is the mitochondrial stress hypothesis, where affected neurons are more susceptible to mitochondrial damage. Therefore, understanding the mechanisms of mitochondrial quality control in these PD-associated neural populations is critical. PINK1, a mitochondrial-targeted kinase, and Parkin, a ubiquitin ligase, are two proteins implicated with early-onset PD. Previous studies have found that the turnover, or rate of degradation, of mitochondrial proteins in Drosophila is slowed down by mutations in Parkin and PINK1. Whether the loss of Parkin or PINK1 in mammals have similar effects on mitochondrial proteins has yet to be confirmed. The objective of this thesis research project was to measure protein turnover in a human midbrain induced pluripotent stem cell organoids (IPSC) model using stable isotope labeling of amino acid in cell culture (SILAC) and mass spectrometry proteomics. Sample preparation and data acquisition protocols were optimized on a wild-type and Parkin knock-out (KO) human IPSC organoid model, and their proteome compared to mouse brain proteomes. Then, organoids were incubated in a medium supplemented with deuterium(D3)-labeled leucine over a 28-day time course, which revealed progressive incorporation of the label. Various data acquisition and analysis pipelines were evaluated and compared with regards to proteome coverage and SILAC quantification. Future experiments are planned to increase biological replicates in the organoid model and within a mouse Parkin KO model while continuing to improve our mass spectrometry methods and proteomic software analysis
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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 | 0.000 | 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) | 0.000 | 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 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".