Analysis of Centranthera grandiflora Benth Transcriptome Explores Genes of Catalpol, Acteoside and Azafrin Biosynthesis
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are a major cause of health loss in the world. Prevention and treatment of this disease by traditional Chinese medicine is a promising method. Centranthera grandiflora Benth is a high-value medicinal herb in the prevention and treatment of CVDs; its main medicinal components include iridoid glycosides, phenylethanoid glycosides, and azafrin in roots. However, biosynthetic pathways of these components and their regulatory mechanisms are unknown. Furthermore, there are no genomic resources of this herb. In this article, we provide sequence and transcript abundance data for the root, stem, and leaf transcriptome of C. grandiflora Benth obtained by the Illumina Hiseq2000. More than 438 million clean reads were obtained from root, stem, and leaf libraries, which produced 153,198 unigenes. Based on databases annotation, a total of 557, 213, and 161 unigenes were annotated to catalpol, acteoside, and azafrin biosynthetic pathways, respectively. Differentially expressed gene analysis identified 14,875 unigenes differentially enriched between leaf and root with 8,054 upregulated genes and 6,821 downregulated genes. Candidate MYB transcription factors involved in catalpol, acteoside, and azafrin biosynthesis were also predicated. This work is the first transcriptome analysis in C. grandiflora Benth which will aid the deciphering of biosynthesis pathways and regulatory mechanisms of active components.
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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 | 0.001 |
| 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) | 0.001 | 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".