Tau directs intracellular trafficking by regulating the forces exerted by kinesin and dynein teams
Bibliographic record
Abstract
Organelles, proteins, and mRNA are transported bidirectionally along microtubules by plus-end directed kinesin and minus-end directed dynein motors. Microtubules are decorated by microtubule-associated proteins (MAPs) that organize the cytoskeleton, regulate microtubule dynamics and modulate the interaction between motor proteins and microtubules to direct intracellular transport. Tau is a neuronal MAP that stabilizes axonal microtubules and crosslinks them into bundles. Dysregulation of tau leads to a range of neurodegenerative diseases known as tauopathies including Alzheimer's disease (AD). Tau reduces the processivity of kinesin and dynein by acting as an obstacle on the microtubule. Single-molecule assays indicate that kinesin-1 is more strongly inhibited than kinesin-2 or dynein, suggesting tau might act to spatially modulate the activity of specific motors. To investigate the role of tau in regulating bidirectional transport, we isolated phagosomes driven by kinesin-1, kinesin-2, and dynein and reconstituted their motility along microtubules. We find that tau biases bidirectional motility towards the microtubule minus-end in a dose-dependent manner. Optical trapping measurements show that tau increases the magnitude and frequency of forces exerted by dynein through inhibiting opposing kinesin motors. Mathematical modeling indicates that tau controls the directional bias of intracellular cargoes through differentially tuning the processivity of kinesin-1, kinesin-2, and dynein. Taken together, these results demonstrate that tau modulates motility in a motor-specific manner to direct intracellular transport, and suggests that dysregulation of tau might contribute to neurodegeneration by disrupting the balance of plus- and minus-end directed transport.
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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) | 0.000 | 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".