Myosin Va binding to neurofilaments is essential for correct myosin Va distribution and transport and neurofilament density
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The identification of molecular motors that modulate the neuronal cytoskeleton has been elusive. Here, we show that a molecular motor protein, myosin Va, is present in high proportions in the cytoskeleton of mouse CNS and peripheral nerves. Immunoelectron microscopy, coimmunoprecipitation, and blot overlay analyses demonstrate that myosin Va in axons associates with neurofilaments, and that the NF-L subunit is its major ligand. A physiological association is indicated by observations that the level of myosin Va is reduced in axons of NF-L-null mice lacking neurofilaments and increased in mice overexpressing NF-L, but unchanged in NF-H-null mice. In vivo pulse-labeled myosin Va advances along axons at slow transport rates overlapping with those of neurofilament proteins and actin, both of which coimmunoprecipitate with myosin Va. Eliminating neurofilaments from mice selectively accelerates myosin Va translocation and redistributes myosin Va to the actin-rich subaxolemma and membranous organelles. Finally, peripheral axons of dilute-lethal mice, lacking functional myosin Va, display selectively increased neurofilament number and levels of neurofilament proteins without altering axon caliber. These results identify myosin Va as a neurofilament-associated protein, and show that this association is essential to establish the normal distribution, axonal transport, and content of myosin Va, and the proper numbers of neurofilaments in axons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it