The small GTPase Rit2 modulates LRRK2 kinase activity, is required for lysosomal function and protects against alpha-synuclein neuropathology
Bibliographic record
Abstract
In Parkinson's disease (PD) misfolded alpha-synuclein (aSyn) accumulates in the substantia nigra, where dopaminergic neurons are progressively lost. The mechanisms underlying aSyn pathology are still unclear, but they are hypothesized to involve the autophagy-lysosome pathway (ALP). LRRK2 mutations are a major cause of familial and sporadic PD, and LRRK2 kinase activity has been shown to be involved in pS129-aSyn inclusion modulation. We observed selective downregulation of the novel PD risk factor RIT2 in vitro and in vivo. Rit2 overexpression in G2019S-LRRK2 cells rescued ALP abnormalities and diminished aSyn inclusions. In vivo, viral mediated overexpression of Rit2 operated neuroprotection against AAV-A53T-aSyn. Furthermore, Rit2 overexpression prevented the A53T-aSyn-dependent increase of LRRK2 kinase activity in vivo. On the other hand, reduction of Rit2 levels leads to defects in the ALP, similar to those induced by the G2019S-LRRK2 mutation. Our data indicate that Rit2 is required for correct lysosome function, inhibits overactive LRRK2 to ameliorate ALP impairment, and counteracts aSyn aggregation and related deficits. Targeting Rit2 could represent an effective strategy to combat neuropathology in familial and idiopathic PD.
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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".