The role of mRNA and protein sorting in seed storage protein synthesis, transport, and deposition
Bibliographic record
Abstract
Rice synthesizes and accumulates high levels of 2 distinct classes of seed storage proteins and sorts them to separate intracellular compartments, making it an ideal model system for studying the mechanisms of storage protein synthesis, transport, and deposition. In rice, RNA localization dictates the initial site of storage protein synthesis on specific subdomains of the cortical endoplasmic reticulum (ER), and there is a direct relation between the RNA localization site and the final destination of the encoded protein within the endomembrane system. Current data support the existence of 3 parallel RNA localization pathways leading from the nucleus to the actively synthesizing cortical ER. Additional pathways may exist for the synthesis of cytoplasmic and nuclear-encoded proteins targeted to organelles, the latter located in a stratified arrangement in developing endosperm cells. The study of rice mutants, which accumulate unprocessed glutelin precursors, indicates that these multiple pathways prevent nonproductive interactions between different classes of storage proteins that would otherwise disrupt protein sorting. Indeed, it appears that the prevention of disruptive interactions between different classes of storage proteins plays a key role in their biosynthesis in rice. In addition to highlighting the unique features of the plant endomembrane system and describing the relation between RNA and protein localization, this minireview will attempt to address a number of questions raised by recent studies on these processes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".