Protein body formation in leaves of <i><scp>N</scp>icotiana benthamiana</i>: a concentration‐dependent mechanism influenced by the presence of fusion tags
Bibliographic record
Abstract
Protein bodies (PBs) are endoplasmic reticulum (ER) derived organelles originally found in seeds whose function is to accumulate seed storage proteins. It has been shown that PB formation is not limited to seeds and green fluorescent protein (GFP) fused to either elastin-like polypeptide (ELP) or hydrophobin (HFBI) fusion tags induce the formation of PBs in leaves of N. benthamiana. In this study, we compared the ELP- and HFBI-induced PBs and showed that ELP-induced PBs are larger than HFBI-induced PBs. The size of ELP- and HFBI-induced PBs increased over time along with the accumulation levels of their fused protein. Our results show that PB formation is a concentration-dependent mechanism in which proteins accumulating at levels higher than 0.2% of total soluble protein are capable of inducing PBs in vivo. Our results show that the presence of fusion tags is not necessary for the formation of PBs, but affects the distribution pattern and size of PBs. This was confirmed by PBs induced by fluorescent proteins as well as fungal xylanases. We noticed that in the process of PB formation, secretory and ER-resident molecules are passively sequestered into the lumen of PBs. We propose to use this property of PBs as a tool to increase the accumulation levels of erythropoietin and human interleukin-10 by co-expression with PB-inducing proteins.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".