Comparative RNA-Seq analysis reveals genes associated with masculinization in female Cannabis sativa
Bibliographic record
Abstract
MAIN CONCLUSION: Using RNA profiling, we identified several silver thiosulfate-induced genes that potentially control the masculinization of female Cannabis sativa plants. Genetically female Cannabis sativa plants normally bear female flowers, but can develop male flowers in response to environmental and developmental cues. In an attempt to elucidate the molecular elements responsible for sex expression in C. sativa plants, we developed genetically female lines producing both female and chemically-induced male flowers. Furthermore, we carried out RNA-Seq assays aimed at identifying differentially expressed genes responsible for male flower development in female plants. The results revealed over 10,500 differentially expressed genes, of which around 200 potentially control masculinization of female cannabis plants. These genes include transcription factors and other genes involved in male organ (i.e., anther and pollen) development, as well as genes involved in phytohormone signalling and male-biased phenotypes. The expressions of 15 of these genes were further validated by qPCR assay confirming similar expression patterns to that of RNA-Seq data. These genes would be useful for understanding predisposed plants producing flowers of both sex types in the same plant, and help breeders to regulate the masculinization of female plants through targeted breeding and plant biotechnology.
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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 | 0.001 | 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) | 0.002 | 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".