Proteinase-Activated Receptor-2 Exerts Protective and Pathogenic Cell Type-Specific Effects in Alzheimer’s Disease
Bibliographic record
Abstract
The proteinase-activated receptors (PARs) are a novel family of G protein-coupled receptors, and their effects in neurodegenerative diseases remain uncertain. Alzheimer's disease (AD) is a neurodegenerative disorder defined by misfolded protein accumulation with concurrent neuroinflammation and neuronal death. We report suppression of proteinase-activated receptor-2 (PAR2) expression in neurons of brains from AD patients, whereas PAR2 expression was increased in proximate glial cells, together with up-regulation of proinflammatory cytokines and chemokines and reduced IL-4 expression (p < 0.05). Glial PAR2 activation increased expression of formyl peptide receptor-2 (p < 0.01), a cognate receptor for a fibrillar 42-aa form of beta-amyloid (Abeta(1-42)), enhanced microglia-mediated proinflammatory responses, and suppressed astrocytic IL-4 expression, resulting in neuronal death (p < 0.05). Conversely, neuronal PAR2 activation protected human neurons against the toxic effects of Abeta(1-42) (p < 0.05), a key component of AD neuropathogenesis. Amyloid precursor protein-transgenic mice, displayed glial fibrillary acidic protein and IL-4 induction (p < 0.05) in the absence of proinflammatory gene up-regulation and neuronal injury, whereas PAR2 was up-regulated at this early stage of disease progression. PAR2-deficient mice, after hippocampal Abeta(1-42) implantation, exhibited enhanced IL-4 induction and less neuroinflammation (p < 0.05), together with improved neurobehavioral outcomes (p < 0.05). Thus, PAR2 exerted protective properties in neurons, but its activation in glia was pathogenic with secretion of neurotoxic factors and suppression of astrocytic anti-inflammatory mechanisms contributing to Abeta(1-42)-mediated neurodegeneration.
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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 | 0.000 | 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) | 0.001 | 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".