De novo transcriptome assembly and characterization of nine tissues of Lonicera japonica to identify potential candidate genes involved in chlorogenic acid, luteolosides, and secoiridoid biosynthesis pathways
Bibliographic record
Abstract
Lonicera japonica is one of the most important medicinal plants with applications in traditional Chinese and Japanese medicine for thousands of years. Extensive studies on the constituents of L. japonica extracts have revealed an accumulation of pharmaceutically active metabolite classes, such as chlorogenic acid, luteolin and other flavonoids, and secoiridoids, which impart characteristic medicinal properties. Despite being a rich source of pharmaceutically active metabolites, little is known about the biosynthetic enzymes involved, and their expression profile across different tissues of L. japonica. In this study, we performed de novo transcriptome assembly for L. japonica, representing transcripts from nine different tissues. A total of 22 Gbps clean RNA-seq reads from nine tissues of L. japonica were used, resulting in 243,185 unigenes, with 99,938 unigenes annotated based on a homology search using blastx against the NCBI-nr protein database. Unsupervised principal component analysis and correlation studies using transcript expression data from all nine tissues of L. japonica showed relationships between tissues, explaining their association at different developmental stages. Homologs for all genes associated with chlorogenic acid, luteolin, and secoiridoid biosynthesis pathways were identified in the L. japonica transcriptome assembly. Expression of unigenes associated with chlorogenic acid was enriched in stems and leaf-2, unigenes from luteolin were enriched in stems and flowers, while unigenes from secoiridoid metabolic pathways were enriched in leaf-1 and shoot apex. Our results showed that different tissues of L. japonica are enriched with sets of unigenes associated with specific pharmaceutically important metabolic pathways and, therefore, possess unique medicinal properties. The present study will serve as a resource for future attempts for functional characterization of enzyme coding genes within key metabolic processes.
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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 | 0.001 |
| 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".