De Novo RNA Sequencing and Expression Analysis of Aconitum carmichaelii to Analyze Key Genes Involved in the Biosynthesis of Diterpene Alkaloids
Bibliographic record
Abstract
Aconitum carmichaelii is an important medicinal herb used widely in China, Japan, India, Korea, and other Asian countries. While extensive research on the characterization of metabolic extracts of A. carmichaelii has shown accumulation of numerous bioactive metabolites including aconitine and aconitine-type diterpene alkaloids, its biosynthetic pathway remains largely unknown. Biosynthesis of these secondary metabolites is tightly controlled and mostly occurs in a tissue-specific manner; therefore, transcriptome analysis across multiple tissues is an attractive method to identify the molecular components involved for further functional characterization. In order to understand the biosynthesis of secondary metabolites, Illumina-based deep transcriptome profiling and analysis was performed for four tissues (flower, bud, leaf, and root) of A. carmichaelii, resulting in 5.5 Gbps clean RNA-seq reads assembled into 128,183 unigenes. Unigenes annotated as possible rate-determining steps of an aconitine-type biosynthetic pathway were highly expressed in the root, in accordance with previous reports describing the root as the accumulation site for these metabolites. We also identified 21 unigenes annotated as cytochrome P450s and highly expressed in roots, which represent candidate unigenes involved in the diversification of secondary metabolites. Comparative transcriptome analysis of A. carmichaelii with A. heterophyllum identified 20,232 orthogroups, representing 30,633 unigenes of A. carmichaelii, gene ontology enrichment analysis of which revealed essential biological process together with a secondary metabolic process to be highly enriched. Unigenes identified in this study are strong candidates for aconitine-type diterpene alkaloid biosynthesis, and will serve as useful resources for further validation studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".