Exogenous LRRK2G2019S induces parkinsonian-like pathology in a nonhuman primate
Bibliographic record
Abstract
Parkinson's disease (PD) is the second most prevalent neurodegenerative disease among the elderly. To understand its pathogenesis and to test therapies, animal models that faithfully reproduce key pathological PD hallmarks are needed. As a prelude to developing a model of PD, we tested the tropism, efficacy, biodistribution, and transcriptional effect of canine adenovirus type 2 (CAV-2) vectors in the brain of Microcebus murinus, a nonhuman primate that naturally develops neurodegenerative lesions. We show that introducing helper-dependent (HD) CAV-2 vectors results in long-term, neuron-specific expression at the injection site and in afferent nuclei. Although HD CAV-2 vector injection induced a modest transcriptional response, no significant adaptive immune response was generated. We then generated and tested HD CAV-2 vectors expressing leucine-rich repeat kinase 2 (LRRK2) and LRRK2 carrying a G2019S mutation (LRRK2G2019S), which is linked to sporadic and familial autosomal dominant forms of PD. We show that HD-LRRK2G2019S expression induced parkinsonian-like motor symptoms and histological features in less than 4 months.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".