Degron peptide targeting Ataxin-2 mitigates neurodegeneration and neuroinflammation Progression in a TDP-43 Mouse Model of ALS
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease marked by progressive degeneration of upper and lower motor neurons, accompanied by neuroinflammation and TDP-43 proteinopathy, leading to muscle atrophy and paralysis. Ataxin-2 has been identified as a key modulator of TDP-43 toxicity, and its reduction has been shown to alleviate neurodegeneration and improve survival in ALS models, making it a promising therapeutic target for modifying disease progression. In this study, we developed lipid-modified degron peptides targeting Ataxin-2 for proteasomal degradation. We performed high-density peptide screening followed by 3D structure modeling to identify the optimal Ataxin-2 binding sequence. In vitro experiments demonstrated that degron peptides induce dose- and time-dependent degradation of Ataxin-2 in primary cultured neurons. To enhance peptide stability and tissue penetration, we employed lipidation strategies incorporating C20 and C16 fatty acid modifications, which significantly improved degron peptide efficacy in vivo. In TAR4/4 ALS mice, lipid-modified Ataxin-2-targeting degron peptides ameliorated motor neuron loss, improved motor function, and prolonged survival. Interestingly, despite these therapeutic benefits, TDP-43 aggregation was not significantly reduced, suggesting that Ataxin-2 depletion exerts effects through mechanisms beyond direct TDP-43 modulation. However, our findings showed a significant reduction in neuroinflammation in TAR4/4 ALS mice following peptide treatment. These results not only establish lipid-modified degron peptides as a viable therapeutic strategy for ALS but also provide a broader framework for targeting disease-relevant proteins implicated in neurodegeneration. This study paves the way for developing precision-targeted therapeutics with enhanced stability, bioavailability, and efficacy for ALS and other neurodegenerative 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) | 0.001 | 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 | 0.001 | 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".