Recent Advances in the Biosynthesis and Metabolic Engineering of Storage Lipids and Proteins in Seeds
Bibliographic record
Abstract
During plant seed maturation, a key developmental step is the storage of biomolecules that remain within the embryo throughout dormancy and provide the necessary metabolites to support seedling growth. Seed storage lipids and proteins are among the most valuable materials for food, industrial, and other applications. Triacylglycerol (TAG) is the major storage lipid in most terrestrial plants and is an energy-dense molecule. TAGs are composed of one glycerol backbone esterified to three fatty acid (FA) tails and are highly abundant in the seeds of oleaginous plants. Seed storage proteins (SSPs) are polymers of amino acids (AAs) for nutrient storage and have a great variety of properties and compositions. Due to a growing global population and the climate-related need for petrochemical alternatives and non-animal protein sources, the demand for plant-sourced oils and proteins is steadily increasing. As such, there are strong research interests in exploring the biosynthesis and regulation of plant storage lipids and proteins and, subsequently, in using the knowledge gained to increase their accumulation and quality. In this chapter, we outline the current understanding of seed storage lipid and protein biosynthesis in higher plants, as well as promising genetic engineering strategies for optimizing the content and composition of these storage molecules.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.012 |
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".