MAP7 recruits kinesin-1 to microtubules to direct organelle transport
Bibliographic record
Abstract
Microtubule-associated proteins (MAPs) play well-characterized roles in regulating microtubule polymerization, dynamics, and organization. In addition, MAPs control trans-port along microtubules by regulating the motility of kinesin and dynein. MAP7 (ensconsin, E-MAP-115) is a ubiquitous MAP that organizes the microtubule cytoskeleton in mitosis and neuronal branching. MAP7 also promotes the interaction of kinesin-1 with microtubules. We expressed and purified full-length kinesin-1 and MAP7 in Sf9 cells. In single-molecule motiity assays, MAP7 recruits kinesin-1 to microtubules, increasing the frequency of both diffusive and processive runs. Optical trapping assays on beads transported by single and teams of kinesin-1 motors indicate that MAP7 increases the relative binding rate of kinesin-1 and the number of motors simultaneously engaged in ensembles. To examine the role of MAP7 in regulating bidirectional transport, we isolated late phagosomes along with their native set of kinesin-1, kinesin-2, and dynein motors. Bidirectional cargoes exhibit a clear shift towards plus-end directed motility on MAP7-decorated microtubules due to increased forces exerted by kinesin teams. Collectively, our results indicate that MAP7 enhances kinesin-1 recruitment to microtubules and targets organelle transport to the plus end.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".