Identification and characterization of two bisabolene synthases from linear glandular trichomes of sunflower (Helianthus annuus L., Asteraceae)
Bibliographic record
Abstract
Sunflower is known to produce a variety of bisabolene-type sesquiterpenes and accumulates these substances in trichomes of leaves, stems and flowering parts. A bioinformatics approach was used to identify the enzyme responsible for the initial step in the biosynthesis of these compounds from its precursor farnesyl pyrophosphate. Based on sequence similarity with a known bisabolene synthases from Arabidopsis thaliana AtTPS12, candidate genes of Helianthus were searched in EST-database and used to design specific primers. PCR experiments identified two candidates in the RNA pool of linear glandular trichomes of sunflower. Their sequences contained the typical motifs of sesquiterpene synthases and their expression in yeast functionally characterized them as bisabolene synthases. Spectroscopic analysis identified the stereochemistry of the product of both enzymes as (Z)-γ-bisabolene. The origin of the two sunflower bisabolene synthase genes from the transcripts of linear trichomes indicates that they may be involved in the synthesis of sesquiterpenes produced in these trichomes. Comparison of the amino acid sequences of the sunflower bisabolene synthases showed high similarity with sesquiterpene synthases from other Asteracean species and indicated putative evolutionary origin from a β-farnesene synthase.
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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) | 0.001 | 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".