Alkaloid production and response to natural adverse conditions in <i>Peganum harmala</i>: <i>in silico</i> transcriptome analyses
Bibliographic record
Abstract
Peganum harmala is a valuable wild plant that grows and survives under adverse conditions and produces pharmaceutical alkaloid metabolites. Using different assemblers to develop a transcriptome improves the quality of assembled transcriptome. In this study, a concrete and accurate method for detecting stress-responsive transcripts by comparing stress-related gene ontology (GO) terms and public domains was designed. An integrated transcriptome for P. harmala including 42656 coding sequences was created by merging de novo assembled transcriptomes. Around 35000 transcripts were annotated with more than 90% resemblance to three closely related species of Citrus, which confirmed the robustness of the assembled transcriptome; 4853 stress-responsive transcripts were identified. CYP82 involved in alkaloid biosynthesis showed a higher number of transcripts in P. harmala than in other plants, indicating its diverse alkaloid biosynthesis attributes. Transcription factors (TFs) and regulatory elements with 3887 transcripts comprised 9% of the transcriptome. Among the TFs of the integrated transcriptome, cystein2/histidine2 (C2H2) and WD40 repeat families were the most abundant. The Kyoto Encyclopedia of Genes and Genomes (KEGG) MAPK (mitogen-activated protein kinase) signaling map and the plant hormone signal transduction map showed the highest assigned genes to these pathways, suggesting their potential stress resistance. The P. harmala whole-transcriptome survey provides important resources and paves the way for functional and comparative genomic studies on this plant to discover stress-tolerance-related markers and response mechanisms in stress physiology, phytochemistry, ecology, biodiversity, and evolution. P. harmala can be a potential model for studying adverse environmental cues and metabolite biosynthesis and a major source for the production of various alkaloids.
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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 | 0.000 | 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 | 0.001 |
| 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".