Protein Bodies in Leaves Exchange Contents through the Endoplasmic Reticulum
Bibliographic record
Abstract
Protein bodies (PBs) are organelles found in seeds whose main function is the storage of proteins that are used during germination for sustaining growth. PBs can also be induced to form in leaves when foreign proteins are produced at high levels in the endoplasmic reticulum (ER) and when fused to one of three tags: Zera®, elastin-like polypeptides (ELP), or hydrophobin-I (HFBI). In this study, we investigate the differences between ELP, HFBI and Zera PB formation, packing, and communication. Our results confirm the ER origin of all three fusion-tag-induced PBs. We show that secretory pathway proteins can be sequestered into all types of PBs but with different patterns, and that different fusion tags can target a specific protein to different PBs. Zera PBs are mobile and dependent on actomyosin motility similar to ELP and HFBI PBs. We show in vivo trafficking of proteins between PBs using GFP photoconversion. We also show that protein trafficking between ELP or HFBI PBs is faster and proteins travel further when compared to Zera PBs. Our results indicate that fusion-tag-induced PBs do not represent terminally stored cytosolic organelles, but that they form in, and remain part of the ER, and dynamically communicate with each other via the ER. We hypothesize that the previously documented PB mobility along the actin cytoskeleton is associated with ER movement rather than independent streaming of detached organelles.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".