Peptide aptamer targeting Aβ–PrP–Fyn axis reduces Alzheimer’s disease pathologies in 5XFAD transgenic mouse model
Bibliographic record
Abstract
Abstract Currently, no effective therapeutics exist for the treatment of incurable neurodegenerative diseases such as Alzheimer’s disease (AD). The cellular prion protein (PrPC) acts as a high-affinity receptor for amyloid beta oligomers (AβO), a main neurotoxic species mediating AD pathology. The interaction of AβO with PrPC subsequently activates Fyn tyrosine kinase and neuroinflammation. Herein, we used our previously developed peptide aptamer 8 (PA8) binding to PrPC as a therapeutic to target the AβO–PrP–Fyn axis and prevent its associated pathologies. Our in vitro results indicated that PA8 prevents the binding of AβO with PrPC and reduces AβO-induced neurotoxicity in mouse neuroblastoma N2a cells and primary hippocampal neurons. Next, we performed in vivo experiments using the transgenic 5XFAD mouse model of AD. The 5XFAD mice were treated with PA8 and its scaffold protein thioredoxin A (Trx) at a 14.4 µg/day dosage for 12 weeks by intraventricular infusion through Alzet® osmotic pumps. We observed that treatment with PA8 improves learning and memory functions of 5XFAD mice as compared to Trx-treated 5XFAD mice. We found that PA8 treatment significantly reduces AβO levels and Aβ plaques in the brain tissue of 5XFAD mice. Interestingly, PA8 significantly reduces AβO–PrP interaction and its downstream signaling such as phosphorylation of Fyn kinase, reactive gliosis as well as apoptotic neurodegeneration in the 5XFAD mice compared to Trx-treated 5XFAD mice. Collectively, our results demonstrate that treatment with PA8 targeting the AβO–PrP–Fyn axis is a promising and novel approach to prevent and treat AD.
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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) | 0.001 | 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".