Rab43 regulates the sorting of a subset of membrane protein cargo through the medial Golgi
Bibliographic record
Abstract
To evaluate the role of cytoplasmic domains of membrane-spanning proteins in directing trafficking through the secretory pathway, we generated fluorescently tagged VSV G tsO45 with either the native G tail (G) or a cytoplasmic tail derived from the chicken AE1-4 anion exchanger (G(AE)). We previously showed that these two proteins progressed through the Golgi with distinct kinetics. To investigate the basis for the differential sorting of G and G(AE), we analyzed the role of several Golgi-associated small GTP-binding proteins and found that Rab43 differentially regulated their transport through the Golgi. We show that the expression of GFP-Rab43 arrested the anterograde transport of G(AE) in a Rab43-positive medial Golgi compartment. GFP-Rab43 expression also inhibited the acquisition of endoH-resistant sugars and the surface delivery of G(AE), as well as the surface delivery of the AE1-4 anion exchanger. In contrast, GFP-Rab43 expression did not affect the glycosylation or surface delivery of G. Unexpectedly, down-regulation of endogenous Rab43 using small interfering RNA resulted in an increase in the accumulation of G(AE) on the cell surface while having minimal effect on the surface levels of G. Our data demonstrate that Rab43 regulates the sorting of a subset of membrane-spanning cargo as they progress through the medial Golgi.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".