Differential gene expression analysis reveals transcription factors that control flower initiation and development in Cannabis sativa
Bibliographic record
Abstract
RNA-Seq and differential gene expression analysis were employed to identify transcription factors (TF) that are specifically expressed in developing buds, and potentially control flower timing and organ development in C. sativa . We found 199 TFs that were upregulated and 140 TFs that were downregulated in floral buds compared to young leaves. Of these, 16 exhibited strong homology to MADS Box type TFs that control flower timing and pattern development in other plants. For proof of concept, we assessed the in planta roles of two SEPALLATA-type TFs through the stable overexpression of these genes in Arabidopsis thaliana plants. Transgenic plants overexpressing csSEP3 grew at a slower rate compared to wild-type plants during the early growth stages, but they caught up to wild-type plants after 20 days of growth. On the other hand, transgenic plants overexpressing csSEP1 exhibited stunted growth, and produced floral buds about 10 days earlier than the wild-type controls. At the time of bolting, these plants had 8.5 ± 0.2 rosette leaves, while transgenic plants overexpressing csSEP3 and the wild-type plants had 31.8 ± 3.9 and 38.4 ± 2.9 leaves, respectively. Flower pattern development was not affected in either of the transgenic plants. Not surprisingly, production of the floral scent constituents (monoterpenes) was not affected in either transgenic plant. The differentially expressed genes (in particular TFs) highlighted in this study represent a valuable resource for further investigating the molecular basis of flower development in C. sativa .
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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 | 0.001 |
| 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 | 0.001 |
| 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".