RhoGDI in RBL-2H3 cells acts as a negative regulator of Rho GTPase signaling to inhibit granule exocytosis
Bibliographic record
Abstract
Mast cells are hematopoietic-derived immune cells that possess numerous cytoplasmic granules containing immune mediators such as cytokines and histamine. Antigen stimulation triggers mast cell granule exocytosis, releasing granule contents in a process known as degranulation. We have shown that Rho GTPase signaling is an essential component of granule exocytosis, however, the proteins that regulate Rho GTPases during this process are not well defined. Here we examined the role of Rho guanine-nucleotide dissociation inhibitors (RhoGDIs) in regulating Rho GTPase signaling using RBL-2H3 cells as a mast cell model. We found that RBL-2H3 cells express two RhoGDI isoforms which are primarily localized to the cytosol. Knockdown of RhoGDI1 and RhoGDI2 greatly reduced the levels of all Rho GTPases tested: RhoA, RhoG, Rac1, Rac2, and Cdc42. The reduction in Rho GTPase levels was accompanied by an increase in their membrane-localized fraction and an elevation in the levels of active Rho GTPases. All RhoGDI knockdown strains had altered resting cell morphology, although each strain was activation competent when stimulated. Live cell imaging revealed that the RhoGDI1/2 double knockdown (DKD) strain maintained its activated state for prolonged periods of time compared to the other strains. Only the RhoGDI1/2 DKD strain showed a significant increase in granule exocytosis. Conversely, RhoGDI overexpression in RBL-2H3 cells did not noticeably affect Rho GTPases or degranulation. Based on these results, RhoGDIs act as negative regulators of Rho GTPases during mast cell degranulation, and inhibit exocytosis by sequestering Rho GTPases in the cytosol.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".