Disease associated mutations in tau encode for changes in aggregate structure conformation
Bibliographic record
Abstract
Abstract The accumulation of tau aggregates is associated with neurodegenerative diseases collectively known as tauopathies. Tau aggregates isolated from different tauopathies such as Alzheimer’s disease, corticobasal degeneration and progressive supranuclear palsy have distinct cryo-electron microscopy structures with respect to their packed fibril cores. To understand the mechanisms by which tau can be sensitized to form distinct aggregate conformations, we created a panel of tau variants encoding for individual disease-associated missense mutations in full-length 0N4R tau (wild-type and 36 mutants). We developed a high-throughput protein purification platform for direct comparison of tau variants in biochemical assays. Structural analysis of the protease-resistant core of tau aggregates formed in vitro reveals that mutations can promote aggregate core packing distinct from that produced by WT tau. Comparing aggregate structure changes with aggregation kinetic parameters for tau mutants revealed no clear linkage between these two aggregation properties. We also found that tau mutation-dependent alterations of tau aggregate structure are not readily explained by current tau fibril structure data. This is the first study to show the broad potential of tau mutations to alter the packed core structures contained within aggregated tau and sheds new insights into the molecular mechanisms underlying the formation of tau aggregate structures that may drive their associated pathology in disease.
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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".