Alterations in the LRRK2-Rab pathway in urinary extracellular vesicles as Parkinson’s disease and pharmacodynamic biomarkers
Bibliographic record
Abstract
Expression or phosphorylation levels of leucine-rich repeat kinase 2 (LRRK2) and its Rab substrates have strong potential as disease or pharmacodynamic biomarkers. The main objective of this study is therefore to assess the LRRK2-Rab pathway for use as biomarkers in human, non-human primate (NHP) and rat urine. With urine collected from human subjects and animals, we applied an ultracentrifugation based fractionation protocol to isolate small urinary extracellular vesicles (uEVs). We used western blot with antibodies directed against total and phosphorylated LRRK2, Rab8, and Rab10 to measure these LRRK2 and Rab epitopes in uEVs. We confirm the presence of LRRK2 and Rab8/10 in human and NHP uEVs, including total LRRK2 as well as phospho-LRRK2, phospho-Rab8 and phospho-Rab10. We also confirm LRRK2 and Rab expression in rodent uEVs. We quantified LRRK2 and Rab epitopes in human cohorts and found in a first cohort that pS1292-LRRK2 levels were elevated in individuals carrying the LRRK2 G2019S mutation, without significant differences between healthy and PD groups, whether for LRRK2 G2019S carriers or not. In a second cohort, we found that PD was associated to increased Rab8 levels and decreased pS910-LRRK2 and pS935-LRRK2. In animals, acute treatment with LRRK2 kinase inhibitors led to decreased pT73-Rab10. The identification of changes in Rab8 and LRRK2 phosphorylation at S910 and S935 heterologous phosphosites in uEVs of PD patients and pT73-Rab10 in inhibitor-dosed animals further reinforces the potential of the LRRK2-Rab pathway as a source of PD and pharmacodynamic biomarkers in uEVs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".