Multi-Omics Analysis Decodes Biosynthesis of Specialized Metabolites Constituting the Therapeutic Terrains of Magnolia obovata
Bibliographic record
Abstract
Magnolia obovata is renowned for its unique bioactive constituents with medicinal properties traditionally used to treat digestive disorders, anxiety, and respiratory conditions. This study aimed to establish a comprehensive omics resource through untargeted metabolome and transcriptome profiling to explore biosynthesis of pharmacologically active compounds of M. obovata using seven tissues: young leaf, mature leaf, stem, bark, central cylinder, floral bud, and pistil. Untargeted metabolomic analysis identified 6733 mass features across seven tissues and captured chemo-diversity and its tissue-specificity in M. obovata. Through a combination of cheminformatics and manual screening approach, we confirmed the identities of 105 metabolites, including neolignans, such as honokiol and magnolol, which were found to be spatially accumulated in the bark tissue. RNA sequencing generated a comprehensive transcriptome resource, and expression analysis revealed significant tissue-specific expression patterns. Omics dataset integration identified T12 transcript module from WGCNA being correlated with the biosynthesis of magnolol and honokiol in M. obovata. Notably, phylogenetic analysis using transcripts from T12 module identified two laccase (Mo_LAC1 and Mo_LAC2) and three dirigent proteins from the DIR-b/d subfamily as potential candidate genes involved in neolignan biosynthesis. This research established omics resources of M. obovata and laid the groundwork for future studies aimed at optimizing and further understanding the biosynthesis of metabolites of therapeutic potential.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".