Reticulon 3 regulates very low density lipoprotein secretion by controlling very low density lipoprotein transport vesicle biogenesis
Bibliographic record
Abstract
Secretion of very low density lipoprotein (VLDL) by the liver is an important physiological process; however, the rate of VLDL secretion is determined by its transport from the endoplasmic reticulum (ER) to the Golgi. This transport event is facilitated by a specialized ER-derived vesicle, the VLDL transport vesicle (VTV). We have reported earlier a detailed VTV proteome, which revealed that reticulon 3 (RTN3) is uniquely present in the VTV. Our immunoblotting and electron microscopic data demonstrate that RTN3 is enriched in the VTV; however, other ER-derived vesicles do not contain RTN3. Co-immunoprecipitation data coupled with confocal microscopic analyses strongly suggest that RTN3 interacts with VLDL core protein, apoB100, at the ER level. Our data show that either blocking of RTN3 using specific antibodies or RTN3 knockdown resulted in significant reduction in VTV biogenesis from hepatic ER membranes. Additionally, VLDL secretion from hepatocytes was significantly decreased when RTN3 was silenced by RTN3 siRNA. We conclude that RTN3 regulates VLDL secretion by controlling VTV-mediated ER-to-Golgi transport of nascent VLDL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".