Kinase inhibition of G2019S-LRRK2 restores autolysosome formation and function to reduce endogenous alpha-synuclein intracellular inclusions
Bibliographic record
Abstract
Abstract The Parkinson’s disease (PD)-associated kinase Leucine-Rich Repeat Kinase 2 (LRRK2) is a potent modulator of autophagy and impacts on lysosome biology and function, but unclarity exists on the precise mechanics of its role and the direction of this modulation. LRRK2 is also involved in the degradation of pathological alpha-synuclein, with pathogenic mutations precipitating neuropathology in cellular and animal models of PD, and most LRRK2 familial cases manifesting with Lewy neuropathology. Defects in autophagic processing and lysosomal degradation of alpha-synuclein have been postulated to underlie its accumulation and onset of neuropathology. Thus, it is critical to reconcile these independent pieces of information to obtain a comprehensive knowledge on LRRK2-associated pathology that could also be generalized to the idiopathic disease. Here, we report a focused investigation on the role of PD-causing G2019S-LRRK2 in the autophagy-lysosome pathway in a recombinant cell line model. Initially, we evaluated the effect of LRRK2 expression on autophagy-related transcriptome. Then, we found that G2019S-LRRK2 leads to accumulation of autophagosomes with no net effect on autophagy induction. This is linked to abnormalities in lysosome morphology and proteolytic activity that are associated with a decrease in the successful formation of autolysosomes. Despite some of these features being shared by WT-LRRK2, alpha-synuclein intracellular inclusions are specifically found in G2019S-LRRK2 cells. Pharmacological kinase inhibition is capable of rescuing defects in the autophagy-lysosome pathway and reducing the number of inclusions. Notably, this effect is prevented by upstream blockade of autophagosome-lysosome fusion events, highlighting this step of the process as critical for alpha-synuclein clearance.
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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) | 0.001 | 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 | 0.001 |
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".