Distinct mechanisms controlling rough and smooth endoplasmic reticulum-mitochondria contacts
Bibliographic record
Abstract
Gp78 (also known as AMFR), an endoplasmic-reticulum (ER)-associated protein degradation (ERAD) E3 ubiquitin ligase, localizes to mitochondria-associated ER and targets the mitofusin (Mfn1 and Mfn2) mitochondrial fusion proteins for degradation. Gp78 is also the cell surface receptor for autocrine motility factor (AMF), which prevents Gp78-dependent mitofusin degradation. Gp78 ubiquitin ligase activity promotes ER-mitochondria association and ER-mitochondria Ca(2+) coupling, processes that are reversed by AMF. Electron microscopy of HT-1080 fibrosarcoma cancer cells identified both smooth ER (SER; ∼8 nm) and wider (∼50-60 nm) rough ER (RER)-mitochondria contacts. Both short hairpin RNA (shRNA)-mediated knockdown of Gp78 (shGp78) and AMF treatment selectively reduced the extent of RER-mitochondria contacts without impacting on SER--mitochondria contacts. Concomitant small interfering RNA (siRNA)-mediated knockdown of Mfn1 increased SER-mitochondria contacts in both control and shGp78 cells, whereas knockdown of Mfn2 increased RER-mitochondria contacts selectively in shGp78 HT-1080 cells. The mitofusins therefore inhibit ER-mitochondria interaction. Regulation of close SER-mitochondria contacts by Mfn1 and of RER-mitochondria contacts by AMF-sensitive Gp78-mediated degradation of Mfn2 define new mechanisms that regulate ER-mitochondria interactions.
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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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".