Rho inhibition recruits DCC to the neuronal plasma membrane and enhances axon chemoattraction to netrin 1
Bibliographic record
Abstract
Molecular cues, such as netrin 1, guide axons by influencing growth cone motility. Rho GTPases are a family of intracellular proteins that regulate the cytoskeleton, substrate adhesion and vesicle trafficking. Activation of the RhoA subfamily of Rho GTPases is essential for chemorepellent axon guidance; however, their role during axonal chemoattraction is unclear. Here, we show that netrin 1, through its receptor DCC, inhibits RhoA in embryonic spinal commissural neurons. To determine whether netrin 1-mediated chemoattraction requires Rho function, we inhibited Rho signaling and assayed axon outgrowth and turning towards netrin 1. Additionally, we examined two important mechanisms that influence the guidance of axons to netrin 1: substrate adhesion and transport of the netrin receptor DCC to the plasma membrane. We found that inhibiting Rho signaling increased plasma membrane DCC and adhesion to substrate-bound netrin 1, and also enhanced netrin 1-mediated axon outgrowth and chemoattractive axon turning. Conversely, overexpression of RhoA or constitutively active RhoA inhibited axonal responses to netrin 1. These findings provide evidence that Rho signaling reduces axonal chemoattraction to netrin 1 by limiting the amount of plasma membrane DCC at the growth cone, and suggest that netrin 1-mediated inhibition of RhoA activates a positive-feedback mechanism that facilitates chemoattraction to netrin 1. Notably, these findings also have relevance for CNS regeneration research. Inhibiting RhoA promotes axon regeneration by disrupting inhibitory responses to myelin and the glial scar. By contrast, we demonstrate that axon chemoattraction to netrin 1 is not only maintained but enhanced, suggesting that this might facilitate directing regenerating axons to appropriate targets.
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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 | 0.001 |
| 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".