Fueling the future; plant genetic engineering for sustainable biodiesel production
Bibliographic record
Abstract
Biodiesel has huge potentials as a green and technologically feasible alternative to fossil diesel. However, biodiesel production from edible oil crops has been widely criticized while nonedible oil plants are associated with some serious disadvantages, such as high cost, low oil yield, and unsuitable oil composition. The next generation sequencing (NGS), omics technologies, and genetic engineering have opened new paths toward achieving high performance-oil plants varieties for commercial biodiesel production. The intent of the present review paper is to review and critically discuss the recent genetic and metabolic engineering strategies developed to overcome the shortcoming faced in nonedible plants, including Jatropha curcas and Camelina sativa, as emerging platforms for biodiesel production. These strategies have been looked into three different categories. Through the first strategy aimed at enhancing oil content, the key genes involved in triacylglycerols (TAGs) biosynthesis pathway (e.g., diacylglycerol acyltransferase (DGAT), acetyl-CoA carboxylase (ACCase), and glycerol‐3‐phosphate dehydrogenase (GPD1)), genes affecting seed size and plant growth (e.g., transcription factors (WRI1), auxin response factor 19 (ARF19), leafy cotyledon1 (LEC1), purple acid phosphatase 2 (PAP2), G-protein c subunit 3 (AGG3), and flowering locus T (FT)), as well as genes involved in TAGs degradation (e.g., sugar-dependent protein 1 triacylglycerol lipase (SDP1)) have been deliberated. While through the second strategy targeting enhanced oil composition, suppression of the genes involved in the biosynthesis of linoleic acids (e.g., fatty acid desaturase (FAD2), fatty acid elongase (FAE1), acyl-ACP thioesterase (FATB), and ketoacyl-ACP synthase II (KASII)), suppression of the genes encoding toxic metabolites (curcin precursor and casbene synthase (JcCASA)), and finally, engineering the genes responsible for the production of unusual TAGs (e.g., Acetyl-TAGs and hydroxylated fatty acids (HFA)) have been debated. In addition to those, enhancing tolerance to biotic (pest and disease) and abiotic (drought, salinity, freezing, and heavy metals) stresses as another important genetic engineering strategy to facilitate the cultivation of nonedible oil plants under conditions unsuitable for food crops has been addressed. Finally, the challenges faced prior to successful commercialization of the resultant GM oil plants such have been presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".