The Role of Triacylglycerol in Plant Stress Response
Bibliographic record
Abstract
Vegetable oil is mainly composed of triacylglycerol (TAG), a storage lipid that serves as a major commodity for food and industrial purposes, as well as an alternative biofuel source. While TAG is typically not produced at significant levels in vegetative tissues, emerging evidence suggests that its accumulation in such tissues may provide one mechanism by which plants cope with abiotic stress. Different types of abiotic stress induce lipid remodeling through the action of specific lipases, which results in various alterations in membrane lipid composition. This response induces the formation of toxic lipid intermediates that cause membrane damage or cell death. However, increased levels of TAG under stress conditions are believed to function, at least in part, as a means of sequestering these toxic lipid intermediates. Moreover, the lipid droplets (LDs) in which TAG is enclosed also function as a subcellular factory to provide binding sites and substrates for the biosynthesis of bioactive compounds that protect against insects and fungi. Though our knowledge concerning the role of TAG in stress tolerance is expanding, many gaps in our understanding of the mechanisms driving these processes are still evident. In this review, we highlight progress that has been made to decipher the role of TAG in plant stress response, and we discuss possible ways in which this information could be utilized to improve crops in the future.
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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 | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".