Engineering plant oils as high‐value industrial feedstocks for biorefining: the need for underpinning cell biology research
Bibliographic record
Abstract
Plant oils represent renewable sources of long-chain hydrocarbons that can be used as both fuel and chemical feedstocks, and genetic engineering offers an opportunity to create further high-value specialty oils for specific industrial uses. While many genes have been identified for the production of industrially important fatty acids, expression of these genes in transgenic plants has routinely resulted in a low accumulation of the desired fatty acids, indicating that significantly more knowledge of seed oil production is required before any future rational engineering designs are attempted. Here, we provide an overview of the cellular features of fatty acid desaturases, the so-called diverged desaturases, and diacylglycerol acyltransferases, three sets of enzymes that play a central role in determining the types and amounts of fatty acids that are present in seed oil, and as such, the final application and value of the oil. Recent studies of the intracellular trafficking, assembly and regulation of these enzymes have provided new insights to the mechanisms of storage oil production, and suggest that the compartmentalization of enzyme activities within specific regions or subdomains of the ER may be essential for both the synthesis of novel fatty acid structures and the channeling of these important fatty acids into seed storage oils.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".