RalA-Exocyst Interaction Mediates GTP-dependent Exocytosis
Bibliographic record
Abstract
Many secretory cells utilize a GTP-dependent pathway, in addition to the well characterized Ca2+-dependent pathway, to trigger exocytotic secretion. However, little is currently known about the mechanism by which this may occur. Here we show the key signaling pathway that mediates GTP-dependent exocytosis. Incubation of permeabilized PC12 cells with soluble RalA GTPase, but not RhoA or Rab3A GTPases, strongly inhibited GTP-dependent exocytosis. A Ral-binding fragment from Sec5, a component of the exocyst complex, showed a similar inhibition. Point mutations in both RalA (RalA(E38R)) and the Sec5 (Sec5(T11A)) fragment, which abolish RalA-Sec5 interaction also abolished the inhibition of GTP-dependent exocytosis. Moreover, transfection with wild-type RalA, but not RalA(E38R), enhanced GTP-dependent exocytosis. In contrast the RalA and the Sec5 fragment showed no inhibition of Ca2+-dependent exocytosis, but cleavage of a SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptor) protein by Botulinum neurotoxin blocked both GTP- and Ca2+-dependent exocytosis. Our results indicate that the interaction between RalA and the exocyst complex (containing Sec5) is essential for GTP-dependent exocytosis. Furthermore, GTP- and Ca2+-dependent exocytosis use different sensors and effectors for triggering exocytosis whereas their final fusion steps are both SNARE-dependent.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".