Optogenetic control of kinesins -1, -2, -3 and dynein reveals their specific roles in vesicular transport
Bibliographic record
Abstract
Each cargo in a cell employs a unique set of motor proteins for its transport. Often multiple types of kinesins are bound to the same cargo. It is puzzling why several types of motors are required for robust transport. To dissect the roles of each type of motor, we developed optogenetic inhibitors of kinesin-1, -2, -3 and dynein. This system allows us to control the activity of the endogenous set of motor proteins that are bound to intracellular cargoes. We examined the effect of optogenetic inhibition of kinesins-1, -2, and -3 and dynein on the transport of early endosomes, late endosomes, and lysosomes. While kinesin-1, kinesin-3, and dynein transport vesicles at all stages of endocytosis, kinesin-2 primarily drives late endosomes and lysosomes. In agreement with previous studies, sustained inhibition of either kinesins or dynein results in reduced motility in both directions. However, transient, optogenetic inhibition of kinesin-1 or dynein causes both early and late endosomes to move more processively by relieving competition with opposing motors. In contrast, optogenetic inhibition of kinesin-2 reduces the motility of late endosomes and lysosomes, and inhibition of kinesin-3 reduces the motility of endosomes and lysosomes. These results suggest that the directionality of transport is likely controlled through regulating kinesin-1 and dynein activity. On vesicles transported by several kinesin and dynein motors, motility can be directed by modulating the activity of a single type of motor on the cargo.
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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) | 0.001 | 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".