Axonal Mechanotransduction Drives Cytoskeletal Responses to Physiological Mechanical Forces
Bibliographic record
Abstract
Abstract Axons experience strong mechanical forces due to animal movement. While these forces serve as sensory cues in mechanosensory neurons, their impact on other neuron types remains poorly defined. Here, we uncover signaling that controls an axonal cytoskeletal response to external physiological forces and plays a key role in axonal integrity. Live imaging of microtubules at single-polymer resolution in a C. elegans motor neuron reveals local oscillatory movements that fine-tune polymer positioning. Combining cell-specific chemogenetic silencing with targeted degradation alleles to distinguish neuron-intrinsic from extrinsic regulators of these movements, we find that they are driven by muscle contractions and require the mechanosensitive protein Talin, the small GTPase RhoA, and actomyosin activity in the axon. Genetic perturbation of the axon’s ability to buffer tension by disrupting the spectrin-based membrane-associated skeleton leads to RhoA hyperactivation, actomyosin relocalization to foci at microtubule ends, and converts local oscillations into processive bidirectional movements. This results in large gaps between microtubules, disrupting coverage of the axon and leading to its breakage and degeneration. Notably, hyperpolarizing muscle or degrading components of the mechanotransduction signaling pathway in the axon rescues cytoskeletal defects in spectrin-deficient axons. These results identify mechanisms of an axonal cytoskeletal response to physiological forces and highlight the importance of force-buffering and mechanotransduction signaling for axonal integrity.
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.
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".