Virally encoded single-chain antibody fragments targeting alpha-synuclein protect against motor impairments and neuropathology in a mouse model of synucleinopathy
Bibliographic record
Abstract
Abstract Parkinson’s disease (PD) is a neurodegenerative disorder mainly characterized by the loss of dopaminergic neurons from the substantia nigra. Affected neurons exhibit intracellular aggregates primarily composed of misfolded and phosphorylated alpha-synuclein (aSyn). In pathological conditions, this presynaptic protein has been shown to be transmitted from cell to cell in a prion-like manner, which contributes to the progression of the disease. Single-chain variable fragments (scFvs) are small polypeptides derived from the binding domains of antibodies that are less immunogenic and have better tissue penetration compared to full antibodies. In this work, we aimed to demonstrate the potential of extracellular scFvs to slow down the propagation of pathological aSyn in an in vivo model of synucleinopathy. We generated scFvs that target aSyn, and tested two of them in a PD mouse model consisting of transgenic M83 mice injected with human aSyn pre-formed fibrils (PFFs). The sequence encoding each anti-aSyn scFv was cloned in a self-complementary AAV2 viral vector, and purified particles were administered intravenously. CNS expression of either scFv protected against the development of paralysis and limb weakness, in addition to significantly reducing pathologic aggregates of phosphorylated aSyn in the brain. Moreover, in vitro results in human iPSCs-derived dopaminergic neurons suggest that the scFvs can mitigate aSyn spreading by preventing its internalization. Overall, our findings demonstrate that single-chain antibody fragments exhibit strong therapeutic potential in a preclinical mouse model. Thus, our minimally invasive, gene-mediated immunotherapy approach has the potential to serve as an effective treatment for halting the progression of Lewy body diseases.
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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".