Transcriptome analysis of metabolic pathways associated with oil accumulation in developing seed kernels of Styrax tonkinensis, a woody biodiesel species
Bibliographic record
Abstract
BACKGROUND: Styrax tonkinensis (Pierre) Craib ex Hartwich has great potential as a woody biodiesel species having seed kernels with high oil content, excellent fatty acid composition and good fuel properties. However, no transcriptome information is available on the molecular regulatory mechanism of oil accumulation in developing S. tonkinensis kernels. RESULTS: The dynamic patterns of oil content and fatty acid composition at 11 time points from 50 to 150 days after flowering (DAF) were analyzed. The percent oil content showed an up-down-up pattern, with yield and degree of unsaturation peaking on or after 140 DAF. Four time points (50, 70, 100, and 130 DAF) were selected for Illumina transcriptome sequencing. Approximately 73 million high quality clean reads were generated, and then assembled into 168,207 unigenes with a mean length of 854 bp. There were 5916 genes that were differentially expressed between different time points. These differentially expressed genes were grouped into 9 clusters based on their expression patterns. Expression patterns of a subset of 12 unigenes were confirmed by qRT-PCR. Based on their functional annotation through the Basic Local Alignment Search Tool and publicly available protein databases, specific unigenes encoding key enzymes, transmembrane transporters, and transcription factors associated with oil accumulation were determined. Three main patterns of expression were evident. Most unigenes peaked at 70 DAF, coincident with a rapid increase in oil content during kernel development. Unigenes with high expression at 50 DAF were associated with plastid formation and earlier stages of oil synthesis, including pyruvate and acetyl-CoA formation. Unigenes associated with triacylglycerol biosynthesis and oil body development peaked at 100 or 130 DAF. CONCLUSIONS: Transcriptome changes during oil accumulation show a distinct temporal trend with few abrupt transitions. Expression profiles suggest that acetyl-CoA formation for oil biosynthesis is both directly from pyruvate and indirectly via acetaldehyde, and indicate that the main carbon source for fatty acid biosynthesis is triosephosphate originating from phosphohexose outside the plastid. Different sn-glycerol-3-phosphate acyltransferases are implicated in diacylglycerol biosynthesis at early versus late stages of oil accumulation. Triacylglycerol biosynthesis may be accomplished by both diacylglycerol and by phospholipid:diacylglycerol acyltransferases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".