Alkaloid production and response to natural adverse conditions in <i>Peganum harmala</i>: <i>in silico</i> transcriptome analyses
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Bibliographic record
Abstract
<i>Peganum harmala</i> 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 <i>P. harmala</i> including 42656 coding sequences was created by merging <i>de novo</i> assembled transcriptomes. Around 35000 transcripts were annotated with more than 90% resemblance to three closely related species of <i>Citrus</i>, 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 <i>P. harmala</i> 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 <i>P. harmala</i> 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. <i>P. harmala</i> 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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it