GAR22β regulates cell migration, sperm motility, and axoneme structure
Bibliographic record
Abstract
Spatiotemporal cytoskeleton remodeling is pivotal for cell adhesion and migration. Here we investigated the function of Gas2-related protein on chromosome 22 (GAR22β), a poorly characterized protein that interacts with actin and microtubules. Primary and immortalized GAR22β(-/-) Sertoli cells moved faster than wild-type cells. In addition, GAR22β(-/-) cells showed a more prominent focal adhesion turnover. GAR22β overexpression or its reexpression in GAR22β(-/-) cells reduced cell motility and focal adhesion turnover. GAR22β-actin interaction was stronger than GAR22β-microtubule interaction, resulting in GAR22β localization and dynamics that mirrored those of the actin cytoskeleton. Mechanistically, GAR22β interacted with the regulator of microtubule dynamics end-binding protein 1 (EB1) via a novel noncanonical amino acid sequence, and this GAR22β-EB1 interaction was required for the ability of GAR22β to modulate cell motility. We found that GAR22β is highly expressed in mouse testes, and its absence resulted in reduced spermatozoa generation, lower actin levels in testes, and impaired motility and ultrastructural disorganization of spermatozoa. Collectively our findings identify GAR22β as a novel regulator of cell adhesion and migration and provide a foundation for understanding the molecular basis of diverse cytoskeleton-dependent processes.
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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) | 0.001 | 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".