Identification of full-length genes involved in the biosynthesis of β-caryophyllene and lupeol from the leaf transcriptome of <i>Ayapana triplinervis</i>
Bibliographic record
Abstract
β-Caryophyllene possesses potential anticancer properties against various cancers, including breast, colon, and lung cancer. Therefore, the essential oil of Ayapana triplinervis, which is rich in β-caryophyllene, can be a potential herbal remedy for treating cancer. However, molecular and genomic studies on A. triplinervis are still sparse . In this study, we obtained 14.7 Gb of RNA-Seq data from A. triplinervis leaf RNA and assembled 137 554 transcripts with an N50 value of 1437 bp. We annotated 72 436 (52.7%) transcripts and mapped 10 640 transcripts to 156 biochemical pathways. Among them, 218 were related to terpenoid backbone biosynthesis, while 27 were linked to sesquiterpenoid and triterpenoid pathways. Ninety-four transcripts were annotated in the β-caryophyllene and lupeol pathways. From these transcripts, for the first time, we identified 25 full-length genes encoding all the 17 enzymes involved in β-caryophyllene biosynthesis and an additional five genes involved in lupeol biosynthesis. These genes will be useful for the metabolic engineering of β-caryophyllene and lupeol biosynthesis, not just in A. triplinervis but also in other species.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".