Neuronal expression of a single‐chain variable fragment antibody against Aβ oligomers protects synapses and rescues memory in Alzheimer models
Bibliographic record
Abstract
Abstract Background Alzheimer's disease (AD) is the main cause of dementia in the elderly and is characterized by abnormal accumulation of the beta‐amyloid peptide (Aß) in the brain. Considerable evidence has shown that soluble Abeta oligomers (AßOs) are the main neurotoxins involved in synaptic dysfunction and the memory loss. Method We evaluated the therapeutic potential of NUsc1, a single chain variable Fragment (scFv) antibody isolated from a screen to identify scFv’s targeting AβOs. Using an adeno‐associated virus derived vector (AAV) we analyzed its protective effects both in vitro and in vivo models of AD. AAV‐NUsc1 capability to induce NUsc1 expression and secretion in humans was tested using human brain slices in culture. Result We here show that recombinant NUsc1, a single‐chain variable fragment (scFv) antibody that selectively targets a subset of neurotoxic AβOs, prevented AβO‐induced inhibition of long‐term potentiation in hippocampal slices, and blocked memory impairment induced by intracerebroventricular infusion of AβOs in mice. We next developed an adeno‐associated virus vector to drive neuronal expression of NUsc1 (AAV‐NUsc1) as a novel therapeutic approach for AD. Importantly, transduction by AAV‐NUsc1 induced NUsc1 expression and secretion in adult human brain slices. In primary hippocampal cultures, transduction by AAV‐NUsc1 reduced AβO binding to neurons and prevented AβO‐induced loss of dendritic spines. Remarkably, AAV‐NUsc1 prevented memory impairment caused by AβO infusion in mice and reversed memory deficits in APPswe/PS1ΔE9 AD mice. Conclusion AAV‐NUsc1 represents a potential tool for gene therapy in AD by using an AAV vector to induce in vivo sustained expression of a single chain variable fragment antibody to neutralize AβOs.
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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 | 0.000 | 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".