Myosin-Va-interacting protein, RILPL2, controls cell shape and neuronal morphogenesis via Rac signaling
Bibliographic record
Abstract
Neuronal morphology plays an essential role in neuronal function. The establishment and maintenance of neuronal morphology is intimately linked to the actin cytoskeleton; however, the molecular mechanisms that regulate changes in neuronal morphology are poorly understood. Here we identify a novel myosin-Va (MyoVa)-interacting protein, RILPL2, which regulates cellular morphology. Overexpression of this protein in young or mature hippocampal neurons results in an increase in the number of spine-like protrusions. By contrast, knockdown of endogenous RILPL2 in neurons by short hairpin RNA (shRNA) interference results in reduced spine-like protrusions, a phenotype rescued by overexpression of an shRNA-insensitive RILPL2 mutant, suggesting a role for RILPL2 in both the establishment and maintenance of dendritic spines. Interestingly, we demonstrate that RILPL2 and the Rho GTPase Rac1 form a complex, and that RILPL2 is able to induce activation of Rac1 and its target, p21-activated kinase (Pak). Notably, both RILPL2-mediated morphological changes and activation of Rac1-Pak signaling were blocked by expression of a truncated tail form of MyoVa or MyoVa shRNA, demonstrating that MyoVa is crucial for proper RILPL2 function. This might represent a novel mechanism linking RILPL2, the motor protein MyoVa and Rac1 with neuronal structure and function.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".