The types and numbers of kinesins and dyneins transporting endocytic cargoes modulate their motility and response to tau
Bibliographic record
Abstract
Organelles and vesicular cargoes are transported by teams of kinesin and dynein motors along microtubules. We isolated endocytic organelles from cells at different stages of maturation and reconstituted their motility along microtubules in vitro. We asked how the sets of motors transporting a cargo determine its motility and response to the microtubule-associated protein tau. Here, we find that phagosomes move in both directions along microtubules, but the directional bias changes during maturation. Early phagosomes exhibit retrograde-biased transport while late phagosomes are directionally unbiased. Correspondingly, early and late phagosomes are bound by different numbers and combinations of kinesins-1, -2, -3, and dynein. Tau stabilizes microtubules and directs transport within neurons. While single-molecule studies show that tau differentially regulates the motility of kinesins and dynein in vitro, less is known about its role in modulating the trafficking of endogenous cargoes transported by their native teams of motors. Previous studies showed that tau preferentially inhibits kinesin motors, which biases late phagosome transport towards the microtubule minus-end. Here, we show that tau strongly inhibits long-range, dynein-mediated motility of early phagosomes. Tau reduces forces generated by teams of dynein motors on early phagosomes and accelerates dynein unbinding under load. Thus, cargoes differentially respond to tau, where dynein complexes on early phagosomes are more sensitive to tau inhibition than those on late phagosomes. Mathematical modeling further explains how small changes in the number of kinesins and dynein on cargoes impact the net directionality but also that cargoes with different sets of motors respond differently to tau.
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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".