LRRK2 Phosphorylates Neuronal Elav RNA-Binding Proteins to Regulate Phenotypes Relevant to Parkinson’s Disease
Bibliographic record
Abstract
Abstract Parkinson’s disease (PD) is characterized by accumulation of α -synuclein and the loss of dopaminergic neurons. Mutations which cause an increase in the kinase activity of Leucine-Rich-Repeat Kinase-2 (LRRK2) are a major inherited cause of PD. Research continues to determine which targets LRRK2 phosphorylates to cause disease. Polymorphisms in the locus of ELAVL4, an RNA-binding protein are a risk-factor for Parkinson’s disease and an ELAV family member was identified in Drosophila as required for pathology instigated by human mutant LRRK2. We discovered that three neuronal ELAVs including ELAVL4 (also known as HuD) are phosphorylated by LRRK2. This controls binding of neuronal ELAVs to mRNA and their post- transcriptional regulation of mRNA abundance and splicing in neuronal cell lines and the mouse midbrain. LRRK2 G2019S functionally inhibits neuronal ELAVs effects on mRNA abundance, while enhancing their effects on mRNA splicing. The combination of LRRK2 G2019S and ELAVL4 -/- causes accumulation of LRRK2 and α -synuclein, loss of dopaminergic neurons and motor deficits. Targets of neuronal ELAVs are also selectively misregulated in cells and tissues of PD patients. Together, this suggests that misregulation of neuronal ELAVs, triggered by LRRK2 mutations may contribute to the characteristic pathology of Parkinson’s disease. Brief Summary LRRK2, a kinase linked to Parkinson’s disease, phosphorylates the neuronal ELAV RNA-binding proteins to aggravate key hallmarks of Parkinson’s disease including accumulation of α -synuclein and motor deficits in mice.
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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) | 0.002 | 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".