A peptide inhibitor of Tau-SH3 interactions ameliorates amyloid-β toxicity
Bibliographic record
Abstract
The microtubule-associated protein Tau is strongly implicated in Alzheimer's disease (AD) and aggregates into neurofibrillary tangles in AD. Genetic reduction of Tau is protective in several animal models of AD and cell culture models of amyloid-β (Aβ) toxicity, making it an exciting therapeutic target for treating AD. A variety of evidence indicates that Tau's interactions with Fyn kinase and other SH3 domain-containing proteins, which bind to PxxP motifs in Tau's proline-rich domain, may contribute to AD deficits and Aβ toxicity. Thus, we sought to determine if inhibiting Tau-SH3 interactions ameliorates Aβ toxicity. We developed a peptide inhibitor of Tau-SH3 interactions and a proximity ligation assay (PLA)-based target engagement assay. Then, we used membrane trafficking and neurite degeneration assays to determine if inhibiting Tau-SH3 interactions ameliorated Aβ oligomer (Aβo)-induced toxicity in primary hippocampal neurons from rats. We verified that Tau reduction ameliorated Aβo toxicity in neurons. Using PLA, we identified a peptide inhibitor that reduced Tau-SH3 interactions in HEK-293 cells and primary neurons. This peptide reduced Tau phosphorylation by Fyn without affecting Fyn's kinase activity state. In primary neurons, endogenous Tau-Fyn interaction was present primarily in neurites and was reduced by the peptide inhibitor, from which we inferred target engagement. Reducing Tau-SH3 interactions in neurons ameliorated Aβo toxicity by multiple outcome measures, namely Aβo-induced membrane trafficking abnormalities and neurite degeneration. Our results indicate that Tau-SH3 interactions are critical for Aβo toxicity and that inhibiting them is a promising therapeutic target for 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) | 0.001 | 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 | 0.000 | 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".