Rab32 uses its effector reticulon 3L to trigger autophagic degradation of mitochondria-associated membrane (MAM) proteins
Bibliographic record
Abstract
BACKGROUND: Rab32 is a small GTPase associated with multiple organelles but is particularly enriched at the endoplasmic reticulum (ER). Here, it controls targeting to mitochondria-ER contacts (MERCs), thus influencing composition of the mitochondria-associated membrane (MAM). Moreover, Rab32 regulates mitochondrial membrane dynamics via its effector dynamin-related protein 1 (Drp1). Rab32 has also been reported to induce autophagy, an essential pathway targeting intracellular components for their degradation. However, no autophagy-specific effectors have been identified for Rab32. Similarly, the identity of the intracellular membrane targeted by this small GTPase and the type of autophagy it induces are not known yet. RESULTS: To investigate the target of autophagic degradation mediated by Rab32, we tested a large panel of organellar proteins. We found that a subset of MERC proteins, including the thioredoxin-related transmembrane protein TMX1, are specifically targeted for degradation in a Rab32-dependent manner. We also identified the long isoform of reticulon-3 (RTN3L), a known ER-phagy receptor, as a Rab32 effector. CONCLUSIONS: Rab32 promotes degradation of mitochondrial-proximal ER membranes through autophagy with the help of RTN3L. We propose to call this type of selective autophagy "MAM-phagy".
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".