Reticulon and CLIMP-63 regulate nanodomain organization of peripheral ER tubules
Bibliographic record
Abstract
The endoplasmic reticulum (ER) is an expansive, membrane-enclosed organelle composed of smooth peripheral tubules and rough, ribosome-studded central ER sheets whose morphology is determined, in part, by the ER-shaping proteins, reticulon (RTN) and cytoskeleton-linking membrane protein 63 (CLIMP-63), respectively. Here, stimulated emission depletion (STED) super-resolution microscopy shows that reticulon4a (RTN4a) and CLIMP-63 also regulate the organization and dynamics of peripheral ER tubule nanodomains. STED imaging shows that lumenal ER monomeric oxidizing environment-optimized green fluorescent protein (ERmoxGFP), membrane Sec61βGFP, knock-in calreticulin-GFP, and antibody-labeled ER-resident proteins calnexin and derlin-1 are all localized to periodic puncta along the length of peripheral ER tubules that are not readily observable by diffraction limited confocal microscopy. RTN4a segregates away from and restricts lumenal blob length, while CLIMP-63 associates with and increases lumenal blob length. RTN4a and CLIMP-63 also regulate the nanodomain distribution of ER-resident proteins, being required for the preferential segregation of calnexin and derlin-1 puncta away from lumenal ERmoxGFP blobs. High-speed (40 ms/frame) live cell STED imaging shows that RTN4a and CLIMP-63 regulate dynamic nanoscale lumenal compartmentalization along peripheral ER tubules. RTN4a enhances and CLIMP-63 disrupts the local accumulation of lumenal ERmoxGFP at spatially defined sites along ER tubules. The ER-shaping proteins RTN and CLIMP-63 therefore regulate lumenal ER nanodomain heterogeneity, interaction with ER-resident proteins, and dynamics in peripheral ER tubules.
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 | 0.001 |
| 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".