Leucine-rich repeat kinase 2 (LRRK2) regulates α-synuclein clearance in microglia
Bibliographic record
Abstract
BACKGROUND: α-Synuclein (αSYN) has been genetically implicated in familial and sporadic Parkinson's disease (PD), and is associated with disease susceptibility, progression and pathology. Excess amounts of αSYN are toxic to neurons. In the brain, microglial αSYN clearance is closely related to neuronal survival. Leucine-rich repeat kinase 2 (LRRK2) is the one of the other genes implicated in familial and sporadic PD. While LRRK2 is known to be expressed in microglia, its true function remains to be elucidated. In this study, we investigated αSYN clearance by microglia isolated from LRRK2-knockout (KO) mice. RESULTS: In LRRK2-KO microglia, αSYN was taken up in larger amounts and cleared from the supernatant more effectively than for microglia isolated from wild-type (WT) mice. This higher clearance ability of LRRK2-KO microglia was thought to be due to an increase of Rab5-positive endosomes, but not Rab7- or Rab11-positive endosomes. Increased engagement between Rab5 and dynamin 1 was also observed in LRRK2-KO microglia. CONCLUSION: LRRK2 negatively regulates the clearance of αSYN accompanied by down-regulation of the endocytosis pathway. Our findings reveal a new functional role of LRRK2 in microglia and offer a new insight into the mechanism of PD pathogenesis.
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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 | 0.000 |
| 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".