Activation of endogenous PRKN by structural derepression is linked to increased turnover of the E3 ubiquitin ligase
Bibliographic record
Abstract
Loss-of-function mutations in the PINK1 and PRKN genes are the most common cause of early-onset Parkinson disease (PD). The encoded enzymatic pair selectively identifies, labels, and targets damaged mitochondria for degradation via the macroautophagy/autophagy-lysosome system (mitophagy). This pathway is cytoprotective and efforts to activate mitophagy are pursued as therapeutic avenues to combat PD and other neurodegenerative disorders. When mitochondria are damaged, the ubiquitin kinase PINK1 accumulates and recruits PRKN from the cytosol to activate the E3 ubiquitin ligase from its auto-inhibited conformation. We have previously designed several mutations that effectively derepress the structure of PRKN and activate its enzymatic functions in vitro. However, it remained unclear how these PRKN-activating mutations would perform endogenously in cultured neurons or in vivo in the brain. Here, we gene-edited neural progenitor cells and induced pluripotent stem cells to express PRKN-activating mutations in dopaminergic cultures. All tested PRKN-activating mutations indeed enhanced the enzymatic activity of PRKN in the absence of exogenous stress, but their hyperactivity was linked to their own PINK1-dependent degradation. Strikingly, in vivo in a mouse model expressing an equivalent activating mutation, we find the same relationship between PRKN enzymatic activity and protein stability. We conclude that PRKN degradation is the consequence of its structural derepression and enzymatic activation, thus resulting only in a temporary gain of activity. Our findings imply that pharmacological activation of endogenous PRKN will lead to increased turnover and suggest that additional considerations might be necessary to achieve sustained E3 ubiquitin ligase activity for disease treatment.Abbreviations: BSA: bovine serum album, CCCP: carbonyl cyanide 3-chlorophenylhydrazone; ECL: electrochemiluminescence; EGF: epidermal growth factor; ELISA: enzyme-linked immunosorbent assay; FGF: fibroblast growth factor; iPSC: induced pluripotent stem cell; KI: knock-in; KO: knockout; MAP2: microtubule associated protein 2; MFN2: mitofusin 2; MSD: Meso Scale Discovery; mt-Keima: mitochondrial targeted Keima; NPC: neural progenitor cell; PD: Parkinson disease; PDH: pyruvate dehydrogenase; p-S65-PRKN: Serine 65 phosphorylated PRKN; p-S65-Ub: Serine 65 phosphorylated ubiquitin; REP: repressor element of PRKN; TH: tyrosine hydroxylase; TX: Triton X-100, Ub: ubiquitin; UBL: ubiquitin-like; WT: wild-type.
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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".