Toll-Like Receptor Expression in the Blood and Brain of Patients and a Mouse Model of Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: Accumulating evidence supports a role for the immune system in the pathogenesis of Parkinson's disease. Importantly, recent preclinical studies are now suggesting a specific contribution of inflammation to the α-synuclein-induced pathology seen in this condition. METHODS: We used flow cytometry and western blots to detect toll-like receptor 2 and 4 expression in blood and brain samples of Parkinson's disease patients and mice overexpressing human α-synuclein. To further assess the effects of α-synuclein overexpression on the innate immune system, we performed a longitudinal study using Thy1.2-α-synuclein mice that expressed a bicistronic DNA construct (reporter genes luciferase and green fluorescent protein) under the transcriptional control of the murine toll-like receptor 2 promoter. RESULTS: Here, we report increases in toll-like receptors 2 and 4 expression in circulating monocytes and of toll-like receptor 4 in B cells and in the caudate/putamen of Parkinson's disease patients. Monthly bioluminescence imaging of Thy1.2-α-synuclein mice showed increasing toll-like receptor 2 expression from 10 months of age, although no change in toll-like receptor 2 and 4 expression was observed in the blood and brain of these mice at 12 months of age. Dexamethasone treatment starting at 5 months of age for 1 month significantly decreased the microglial response in the brain of these mice and promoted functional recovery as observed using a wheel-running activity test. CONCLUSION: Our results show that toll-like receptors 2 and 4 are modulated in the blood and brain of Parkinson's disease patients and that overexpression of α-synuclein leads to a progressive microglial response, the inhibition of which has a beneficial impact on some motor phenotypes of an animal model of α-synucleinopathy.
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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 | 0.000 |
| 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 | 0.000 |
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".