Transcriptome and metabolome profiling of Narcissus pseudonarcissus ‘King Alfred’ reveal components of Amaryllidaceae alkaloid metabolism
Bibliographic record
Abstract
Amaryllidaceae alkaloids (AAs) represent a diverse class of plant specialized metabolites and many display potent pharmacological activities. The AA metabolic pathway is poorly understood and resources are minimal. To enable AA pathway elucidation and novel biosynthetic enzymes discovery, we generated comprehensive metabolomic and corresponding transcriptomic datasets from different tissues of Narcissus pseudonarcissus 'King Alfred'. In this study, we performed untargeted UPLC-QTOF-MS metabolite analysis from different tissues, which generated exhaustive list of compounds, including several AAs, most predominant and diverse in bulbs. RNA sequencing of N. pseudonarcissus 'King Alfred' bulbs yielded 195,347 transcripts, after assembly. Top expressed genes belong to process like metabolism, survival, and defense including alkaloid biosynthetic genes. The transcriptome contained complete sequences for all proposed genes encoding AA-biosynthetic enzymes such as tyrosine decarboxylase (TYDC1 and TYDC2), phenylalanine ammonia-lyase (PAL1 and PAL2) and phenolic acids hydroxylases (C4H and C3H) to name a few. Furthermore, transcriptome data were validated using RT-qPCR analysis and expression study in different tissues of N. pseudonarcissus 'King Alfred' was performed. Here, we present the first comprehensive metabolome and transcriptome study from N. pseudonarcissus 'King Alfred' providing invaluable resources for metabolic engineering and biotechnological applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".