Identification of differentially expressed miRNAs and their target genes in response to brassinolide treatment on flowering of tree peony (<i>Paeonia ostii</i>)
Bibliographic record
Abstract
Tree peony is a famous flower plant in China, but the short and concentrated flowering period limits its ornamental value and economic value. Brassinolide (BR) plays an important role in plant growth and development including flowering. There have been a large number of reports on the molecular aspects of the flowering process, but the genetic mechanism that was responsible for miRNA-guided regulation of tree peony is almost unclear. In this study, the leaves of tree peony cultivar, ‘Feng Dan’, were sprayed with different concentrations of BR, and the obvious bloom delay was found at the treatment with BR 50 μg/L. The small RNA sequencing and transcriptome sequencing were performed on the petals of tree peony under an untreated control (CK) and the treatment with BR 50 μg/L during four consecutive flowering development stages. A total of 22 known miRNAs belonging to 12 families were identified and 84 novel miRNAs were predicted. Combined with transcriptome data, a total of 376 target genes were predicted for the 18 differentially expressed known miRNAs and 177 target genes were predicted for the 23 differentially expressed novel miRNAs. Additionally, the potential miRNAs and their target genes were identified, including miR156b targeting SPL, miR172a_4 targeting AP2 and four novel miRNAs targeting SPA1, and revealed that they might affect the flowering time in tree peony. Collectively, these results would provide a theoretical basis for further analysis of miRNA-guided regulation on flowering period in tree peony.
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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".