The Eole of LRRK2 G2019S Mutation in Neuroinflammatory Processes and Its Relevance for Parkinson's Disease
Bibliographic record
Abstract
Pa ki so s disease (PD) has been linked to a variety of environmental and immunological challenges involving both oxidative and inflammatory processes, however, genetics also clearly plays a role in vulnerability to such insults.Indeed, the Gly-2019-Ser (G2019S) leucine rich repeat kinase (LRRK2) mutation is the most common mutation enriched compared to controls in both familial and sporadic Pa ki so s patie ts.Our current study focuses on the interplay between the G2019S genetic mutation and exposure to the bacterial endotoxin, lipopolysaccharide (LPS).In the current study mice were centrally infused with either vehicle or one of two doses of LPS (2 or 10ug) in order to determine whether an inflammatory based insult would produce augmented pathology in G2019S mutant mice.Infusion of LPS into the substantia nigra pars compacta (SNc; the region containing dopamine neurons which degenerate in PD) produced a (non-statistically significant) trend towards a dose-dependent reduction in SNc dopamine neurons in both G2019S mutants and their wild type littermates.Although few motor deficits were apparent (likely a reflection of the modest changes in dopamine neurons), the G2019S mutants did display a basal difference in gait.As would be expected, LPS infusion also induced marked sickness behaviour along with weight loss; however, G2019S mutant mice showed a slower recovery of weight days following the infusion.Interestingly, the lower of the two LPS doses (2 µg) increased microglial activation and this effect was only apparent in the G2019S mutant mice.Overall, the present results do not support a major impact of G2019S LRRK2 mutation in increasing vulnerability to the neurodegenerative or motor effects of the immunogenic insult, LPS.That said, LRRK2 G2019S mice might have increased vulnerability to non-motor co-morbid or pro-inflammatory effects of immune challenges along with displaying subtle basal motor deficits.iii
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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 | 0.001 | 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".