The Parkinson’s Disease-associated mutation LRRK2 G2019S impacts neutrophil effector functions and the response to intestinal infection in mice 9106
Bibliographic record
Abstract
Abstract Description Parkinson’s Disease (PD) is a progressive, neurodegenerative disorder. Emerging evidence suggests involvement of the gut-brain axis and the immune system in PD pathogenesis. The leucine-rich repeat kinase 2 gene (LRRK2) is expressed in immune cells and mutations in this gene are associated with increased risk for PD and for Inflammatory Bowel Disease. LRRK2 Gly2019Ser (G2019S), located within the kinase domain, is the most common PD-associated mutation and increases LRRK2 kinase activity. Using single cell RNA sequencing, we demonstrate that LRRK2 G2019S mutation promotes a dysregulated gene expression profile, especially within neutrophil, monocyte and γδ T cell populations, following Citrobacter rodentium infection in mice. Transcriptionally, LRRK2 G2019S neutrophils have a greater pro- inflammatory type I and II IFN response compared to those of WT mice. This is accompanied by an increase in neutrophil numbers in the lamina propria in LRRK2 G2019S mice during infection. We also uncover cell-intrinsic functional defects in LRRK2 G2019S neutrophils, including increased chemotaxis, degranulation and neutrophil extracellular trap formation. Increased neutrophil infiltration is associated with an upregulation in Th17 immune responses, which may together contribute to the observed increase in colon pathology during infection. These findings increase our understanding of the role of PD-associated genes in immune cells and their contribution to immune dysregulation. Funding Sources Supported by CIHR, The Michael J. Fox Foundation for Parkinson’s Research (MJFF) and the Aligning Science Across Parkinson’s (ASAP) initiative. Topic Categories Neuroimmunology (NEUR)
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".