Multiomics-based characterization of specialized metabolites biosynthesis in <i>Cornus Officinalis</i>
Bibliographic record
Abstract
Cornus officinalis, an important traditional medicinal plant, is used as major constituents of tonics, analgesics, and diuretics. While several studies have focused on its characteristic bioactive compounds, little is known on their biosynthesis. In this study, we performed LC-QTOF-MS-based metabolome and RNA-seq-based transcriptome profiling for seven tissues of C. officinalis. Untargeted metabolome analysis assigned chemical identities to 1,215 metabolites and showed tissue-specific accumulation for specialized metabolites with medicinal properties. De novo transcriptome assembly established for C. officinalis showed 96% of transcriptome completeness. Co-expression analysis identified candidate genes involved in the biosynthesis of iridoids, triterpenoids, and gallotannins, the major group of bioactive metabolites identified in C. officinalis. Integrative omics analysis identified 45 cytochrome P450s genes correlated with iridoids accumulation in C. officinalis. Network-based integration of genes assigned to iridoids biosynthesis pathways with these candidate CYPs further identified seven promising CYPs associated with iridoids' metabolism. This study provides a valuable resource for further investigation of specialized metabolites' biosynthesis in C. officinalis.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 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".