Leucine-rich repeat kinase-2 (LRRK2) modulates microglial phenotype and dopaminergic neurodegeneration
Bibliographic record
Abstract
Leucine-rich repeat kinase 2 (LRRK2) is a common gene implicated in Parkinson's disease and many inflammatory processes. Thus, we assessed the role of LRRK2 in the context of endotoxin (lipopolysaccharide, LPS)-induced inflammation of the substantia nigra together with the environmental toxicant, paraquat, that has been implicated in PD. Here we found that LRRK2 ablation prevented the loss of dopaminergic neurons and behavioral deficits (motor) induced by LPS priming followed by paraquat exposure. The LRRK2 ablation also provoked a phenotypic shift in LPS-primed microglia cells. The LRRK2 deficiency reduced their "activated" morphology and upregulation of the inflammatory phagocytic regulator, WAVE2 (critical for actin remodeling), while the chemokine receptor, CX3CR1, was elevated in isolated CD11b+ myeloid cells. Furthermore, LRRK2 knockout attenuated the signs of oxidative stress and morphological changes induced in primary microglia by LPS treatment. However, induced WAVE2 expression together with LPS exposure in microglia overcame the inhibitory effects of LRRK2 knockout, suggesting WAVE2 may be acting downstream of LRRK2. Neither WAVE2 nor did LRRK2 knockout influence LPS-induced cytokine elevations in the microglia. We are the first to show the importance of LRRK2 in neurodegenerative and inflammatory processes in this multi-hit toxin model of PD. These data are consistent with the proposition that LRRK2 and WAVE2 are useful therapeutic targets for PD or other conditions with a prominent neuroinflammatory component.
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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) | 0.001 | 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".