Genetic Regulation of Secondary Metabolism and Its Association with Pharmacological Traits in Honeysuckle (Lonicera japonica)
Bibliographic record
Abstract
This study mainly introduces several important metabolic pathways such as phenolic acids, flavonoids, iridoids and volatile terpenoids, discusses some key structural genes in these pathways, summarizes the key influences played by several core transcription factors in regulating these metabolic networks, and analyzes genomic, transcriptomic and metabolomic data together. It was observed that the metabolic flow varies under different tissues, developmental stages, and environmental conditions. This further explained the accumulation mode of metabolites and their relationship with pharmacological effects. The application prospects of MAS, CRISPR/Cas gene editing, and synthetic biology in the quality improvement and targeted enhancement of active components of honeysuckle were also discussed. This study aims to establish a relatively complete “metabolism-gene-trait” association framework, providing a reference for the rapid breeding of honeysuckle and more accurate pharmacological prediction in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".