Inhibition of LRRK2 kinase activity rescues deficits in striatal dopamine dynamics in VPS35 p.D620N knock-in mice
Bibliographic record
Abstract
Abstract Dysregulation of dopamine neurotransmission profoundly affects motor, motivation and learning behaviors, and is often observed during the prodromal phase of Parkinson’s disease (PD). However, the mechanism underlying these pathophysiological changes remains to be elucidated. Mutations in vacuolar protein sorting 35 ( VPS35 ) and leucine-rich repeat kinase 2 ( LRRK2 ) both lead to autosomal dominant PD, and VPS35 and LRRK2 may physically interact to govern the trafficking of synaptic cargos within the endo-lysosomal network in a kinase-dependent manner. To better understand the functional role of VPS35 and LRRK2 on dopamine physiology, we examined Vps35 haploinsufficient (Haplo) and Vps35 p.D620N knock-in (VKI) mice and how their behavior, dopamine kinetics and biochemistry are influenced by LRRK2 kinase inhibitors. We found Vps35 p.D620N significantly elevates LRRK2-mediated phosphorylation of Rab10, Rab12 and Rab29. In contrast, Vps35 haploinsufficiency reduces phosphorylation of Rab12. While striatal dopamine transporter (DAT) expression and function is similarly impaired in both VKI and Haplo mice, that physiology is normalized in VKI by treatment with the LRRK2 kinase inhibitor, MLi-2. As a corollary, VKI animals show a significant increase in amphetamine induced hyperlocomotion, compared to Haplo mice, that is also abolished by MLi-2. Taken together, these data show Vps35 p.D620N confers a gain-of-function with respect to LRRK2 kinase activation, and VPS35 and LRRK2 functionally interact to regulate DAT trafficking and striatal dopamine neurotransmission.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".