Transcriptome analysis to identify genes involved in lignan, sesquiterpenoid and triterpenoid biosynthesis in medicinal plant Kadsura heteroclita
Bibliographic record
Abstract
Stems and roots of Kadsura plant species were the significant ingredients of traditional Chinese medicine. Kadsura heteroclita is one of the popular medicinal plants used in Tujia and Yao nationalities of China. Antioxidant compounds like lignan, sesquiterpenoid and triterpenoid are the major active components of K. hetroclita. Mass cultivation and bio-manufacturing strategies were being proposed to meet the increasing demand of Kadsura species plant parts. Therefore, it is important to reveal the molecular networks involved in biosynthesis of these highly efficient medicinal compounds. Here, transcriptomes of roots, stems and leaves in K. heteroclite seedling were sequenced by Hiseq2000 and unigenes involved in biosynthesis of lignan, sesquiterpenoid and triterpenoid biosynthesis were mined. As a result, 472 million clean reads were obtained which after aligning resulted in 160,248 transcripts and 98,005 genes. 191 and 279 unigenes were expected to be involved in biosynthesis of lignan, sesquiterpenoid and triterpenoid biosynthetic pathways respectively. Lignan, sesquiterpenoid and triterpenoid biosynthesis pathway genes were highly significant and differentially upregulated in roots and stems and downregulated in leaves. Also, genes encoding for MYB and bHLH transcription factors possibly involved in regulation of lignan, sesquiterpenoid and triterpenoid biosynthesis were discovered. These results provide the fundamental genomic resources for dissecting of biosynthetic pathways of the active components in K. hetroclita.
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.
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) | 0.001 | 0.001 |
| 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) | 0.002 | 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".