Analysis of Centranthera grandiflora Benth Transcriptome Explores Genes of Catalpol, Acteoside and Azafrin Biosynthesis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it