AAV-mediated neuronal expression of an scFv antibody selective for Aβ oligomers protects synapses and rescues memory in Alzheimer models
Bibliographic record
Abstract
The accumulation of soluble oligomers of the amyloid-β peptide (AβOs) in the brain has been implicated in synapse failure and memory impairment in Alzheimer's disease. Here, we initially show that treatment with NUsc1, a single-chain variable-fragment antibody (scFv) that selectively targets a subpopulation of AβOs and shows minimal reactivity to Aβ monomers and fibrils, prevents the inhibition of long-term potentiation in hippocampal slices and memory impairment induced by AβOs in mice. As a therapeutic approach for intracerebral antibody delivery, we developed an adeno-associated virus vector to drive neuronal expression of NUsc1 (AAV-NUsc1) within the brain. Transduction by AAV-NUsc1 induced NUsc1 expression and secretion in adult human brain slices and inhibited AβO binding to neurons and AβO-induced loss of dendritic spines in primary rat hippocampal cultures. Treatment of mice with AAV-NUsc1 prevented memory impairment induced by AβOs and, remarkably, reversed memory deficits in aged APPswe/PS1ΔE9 Alzheimer's disease model mice. These results support the feasibility of immunotherapy using viral vector-mediated gene delivery of NUsc1 or other AβO-specific single-chain antibodies as a potential therapeutic approach in Alzheimer's disease.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".