Interaction of 14-3-3ζ with Microtubule-Associated Protein Tau within Alzheimer’s Disease Neurofibrillary Tangles
Bibliographic record
Abstract
Alzheimer's disease (AD) is characterized by the presence of abnormal, straight filaments and paired helical filaments (PHFs) that are coated with amorphous aggregates. When PHFs are treated with alkali, they untwist and form filaments with a ribbonlike morphology. Tau protein is the major component of all of these ultrastructures. 14-3-3ζ is present in NFTs and is significantly upregulated in AD brain. The molecular basis of the association of 14-3-3ζ within NFTs and the pathological significance of its association are not known. In this study, we have found that 14-3-3ζ is copurified and co-immunoprecipitates with tau from NFTs of AD brain extract. In vitro, tau binds to both phosphorylated and nonphosphorylated tau. When incubated with 14-3-3ζ, tau forms amorphous aggregates, single-stranded, straight filaments, ribbonlike filaments, and PHF-like filaments, all of which resemble the corresponding ultrastructures found in AD brain. Immuno-electron microscopy determined that both tau and 14-3-3ζ are present in these ultrastructures and that they are formed in an incubation time-dependent manner. Amorphous aggregates are formed first. As the incubation time increases, the size of amorphous aggregates increases and they are incorporated into single-stranded filaments. Single-stranded filaments laterally associate to form double-stranded, ribbonlike, and PHF-like filaments. Both tau and phosphorylated tau aggregate in a similar manner when they are incubated with 14-3-3ζ. Our data suggest that 14-3-3ζ has a role in the fibrillization of tau in AD brain, and that tau phosphorylation does not affect 14-3-3ζ-induced tau aggregation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".