Transcriptome Profiling of Khat (Catha edulis) and Ephedra sinica Reveals Gene Candidates Potentially Involved in Amphetamine-Type Alkaloid Biosynthesis
Bibliographic record
Abstract
Amphetamine analogues are produced by plants in the genus Ephedra and by khat (Catha edulis), and include the widely used decongestants and appetite suppressants (1S,2S)-pseudoephedrine and (1R,2S)-ephedrine. The production of these metabolites, which derive from L-phenylalanine, involves a multi-step pathway partially mapped out at the biochemical level using knowledge of benzoic acid metabolism established in other plants, and direct evidence using khat and Ephedra species as model systems. Despite the commercial importance of amphetamine-type alkaloids, only a single step in their biosynthesis has been elucidated at the molecular level. We have employed Illumina next-generation sequencing technology, paired with Trinity and Velvet-Oases assembly platforms, to establish data-mining frameworks for Ephedra sinica and khat plants. Sequence libraries representing a combined 200,000 unigenes were subjected to an annotation pipeline involving direct searches against public databases. Annotations included the assignment of Gene Ontology (GO) terms used to allocate unigenes to functional categories. As part of our functional genomics program aimed at novel gene discovery, the databases were mined for enzyme candidates putatively involved in alkaloid biosynthesis. Queries used for mining included enzymes with established roles in benzoic acid metabolism, as well as enzymes catalyzing reactions similar to those predicted for amphetamine alkaloid metabolism. Gene candidates were evaluated based on phylogenetic relationships, FPKM-based expression data, and mechanistic considerations. Establishment of expansive sequence resources is a critical step toward pathway characterization, a goal with both academic and industrial implications.
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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) | 0.000 | 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) | 0.001 | 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".