Proteomic analysis of metabolic mechanisms associated with fatty acid biosynthesis during <scp><i>Styrax tonkinensis</i></scp> kernel development
Bibliographic record
Abstract
BACKGROUND: Styrax tonkinensis is a white-flowered tree with considerable potential as a feedstock source for biodiesel production from the oily seed contained within its nutlike drupes. Transcriptome changes during oil accumulation have been previously reported, but not concurrent changes in the proteome. RESULTS: Using proteomic analysis of samples collected at 50, 70, 100 and 130 days after flowering (DAF), we identified 1472 differentially expressed proteins (DEPs). Based on their expression patterns, we grouped the DEPs into nine clusters and analyzed the pathway enrichment. Proteins related to starch and sucrose metabolism were most abundant at 50 DAF. Proteins involved in fatty acid (FA) biosynthesis were mainly grouped into a cluster that peaked at 70 DAF. Proteins related to protein processing in endoplasmic reticulum had two major patterns, trending either upwards or downwards, while proteins involved in amino acid biosynthesis showed more complex relationships. We identified 42 key enzymes involved in lipid accumulation during kernel development, including the acetyl-CoA carboxylase complex (ACC) and the pyruvate dehydrogenase complex (PDC). One oil body membrane protein, oleosin, continuously increased during kernel development. CONCLUSION: A regulatory network of oil accumulation processes was built based on protein and available transcriptome expression data, which were in good temporal agreement. This analysis placed ACC and PDC in the center of the network, suggesting that the glycolytic provision of substrate plays a central regulatory role in FA biosynthesis and oil accumulation. © 2021 Society of Chemical Industry.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".