An Integrated Bioinformatics Analysis Reveals Divergent Evolutionary Pattern of Oil Biosynthesis in High- and Low-Oil Plants
Bibliographic record
Abstract
Seed oils provide a renewable source of food, biofuel and industrial raw materials that is important for humans. Although many genes and pathways for acyl-lipid metabolism have been identified, little is known about whether there is a specific mechanism for high-oil content in high-oil plants. Based on the distinct differences in seed oil content between four high-oil dicots (20~50%) and three low-oil grasses (<3%), comparative genome, transcriptome and differential expression analyses were used to investigate this mechanism. Among 4,051 dicot-specific soybean genes identified from 252,443 genes in the seven species, 54 genes were shown to directly participate in acyl-lipid metabolism, and 93 genes were found to be associated with acyl-lipid metabolism. Among the 93 dicot-specific genes, 42 and 27 genes, including CBM20-like SBDs and GPT2, participate in carbohydrate degradation and transport, respectively. 40 genes highly up-regulated during seed oil rapid accumulation period are mainly involved in initial fatty acid synthesis, triacylglyceride assembly and oil-body formation, for example, ACCase, PP, DGAT1, PDAT1, OLEs and STEROs, which were also found to be differentially expressed between high- and low-oil soybean accessions. Phylogenetic analysis revealed distinct differences of oleosin in patterns of gene duplication and loss between high-oil dicots and low-oil grasses. In addition, seed-specific GmGRF5, ABI5 and GmTZF4 were predicted to be candidate regulators in seed oil accumulation. This study facilitates future research on lipid biosynthesis and potential genetic improvement of seed oil content.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".