The ubiquitous microtubule-associated protein 4 (MAP4) controls organelle distribution by regulating the activity of the kinesin motor
Bibliographic record
Abstract
Regulation of organelle transport by molecular motors along the cytoskeletal microtubules is central to maintaining cellular functions. Here, we show that the ubiquitous tau-related microtubule-associated protein 4 (MAP4) can bias the bidirectional transport of organelles toward the microtubule minus-ends. This is concurrent with MAP4 phosphorylation, mediated by the kinase GSK3β. We demonstrate that MAP4 achieves this bias by tethering the cargo to the microtubules, allowing it to impair the force generation of the plus-end motor kinesin-1. Consistent with this mechanism, MAP4 physically interacts with dynein and dynactin and, when phosphorylated, associates with the cargo-motor complex through its projection domain. Its phosphorylation coincides with the perinuclear accumulation of organelles, a phenotype that is rescued by abolishing the cargo-microtubule MAP4 tether or by the pharmacological inhibition of dynein, confirming the ability of kinesin to inch along, albeit inefficiently, in the presence of phosphorylated MAP4. These findings have broad biological significance because of the ubiquity of MAP4 and the involvement of GSK3β in multiple diseases, more specifically in cancer, where the MAP4-dependent redistribution of organelles may be prevalent in cancer cells, as we demonstrate here for mitochondria in lung carcinoma epithelial cells.
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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".