Dynamic transport of SNARE proteins in the Golgi apparatus
Bibliographic record
Abstract
Localization of a membrane protein in a subcellular compartment can be achieved by its retention in the compartment or by its continuous transport toward this compartment. Previous results have suggested that specific enzymes are localized in the Golgi apparatus at least in part by selective retention and exclusion from transport vesicles. However, the function of some Golgi SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptor) proteins is not compatible with their exclusion from transport vesicles. To help understand the mechanism accounting for the localization of SNARE proteins in the Golgi apparatus, we analyzed their lateral distribution in the Golgi cisternae and their incorporation into transport vesicles. According to our results, all SNARE proteins are efficiently incorporated into transport vesicles, indicating that the localization of SNARE proteins in the Golgi apparatus is not based on a static retention mechanism. Detailed analysis suggested that incorporation into transport vesicles was more efficient for SNARE proteins restricted to the cis face of the Golgi as compared with SNAREs present at the trans face. Furthermore, overexpression of a cis-Golgi SNARE protein altered concomitantly its incorporation in transport vesicles and its intra-Golgi localization. These observations suggest that, contrary to resident Golgi enzymes, SNARE proteins are localized in the Golgi apparatus as the result of a dynamic transport equilibrium.
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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 | 0.001 | 0.001 |
| 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 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".