Aggregation promoting sequences rather than phosphorylation are essential for Tau-mediated toxicity in <i>Drosophila</i>
Bibliographic record
Abstract
Abstract Background Disease-modifying therapies for tauopathies like Alzheimer’s disease have targeted Tau hyperphosphorylation and aggregation, as both pathological manifestations are implicated in Tau-mediated toxicity. However, the relative contributions of these pathology-linked changes to Tau neurotoxicity remain unclear. Methods Leveraging the genetic tractability of Drosophila , we generated multiple inducible human Tau transgenes with altered phosphorylation status and/or aggregation propensity. Their individual and combined impact was tested in vivo by quantifying Tau accumulation and neurodegenerative phenotypes in the aging fly nervous system. Results We report that phospho-mimicking Tau (hTau2N4R E14 ) induced profound neurodegeneration, supporting a neurotoxic role for phosphorylation. However, when we rendered hTau2N4R E14 aggregation incompetent, by deleting the 306 VQIVYK 311 motif in the microtubule-binding region, neurotoxicity was abolished. Moreover, a peptide inhibitor targeting this motif efficaciously reduced Tau toxicity in aging Drosophila . Conclusion Neurodegeneration mediated by Tau hyperphosphorylation is gated via at least one aggregation-mediating motif on the protein. This highlights the primacy of blocking Tau aggregation in therapy, perhaps without the need to clear phosphorylated species.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".