Identification of Candidate Adaxial–Abaxial-Related Genes Regulating Petal Expansion During Flower Opening in Rosa chinensis “Old Blush”
Bibliographic record
Abstract
Petal expansion is the main process by which flower opening occurs in roses (Rosa chinensis). While the regulation of leaf expansion has been extensively studied, little is known about the mechanisms controlling petal expansion. The regulation of leaf dorsoventral (adaxial–abaxial) polarity is important for blade expansion and morphogenesis, but the mechanisms involved adaxial-abaxial regulation in petals are unknown. We found that auxin, a key hormonal regulator of leaf adaxial–abaxial patterning, is unevenly distributed in rose petals. The transcriptomes of the adaxial and abaxial petal tissues were sequenced at three developmental stages during flower opening. Genes that were differentially expressed between the two tissues were filtered for those known to be involved in petal expansion and phytohormone biosynthesis, transport, and signaling, revealing potential roles in petal expansion, especially auxin pathway genes. Using a weighted gene coexpression network analysis (WGCNA), we identified two gene modules that may involve in adaxial–abaxial regulation, 21 and five hub genes have been found respectively. The qRT-PCR validation results were consistent with the RNA-seq data. Based on these findings, we propose a simple network of adaxial–abaxial related genes that regulates petal expansion in R. chinensis ‘Old Blush’. For the first time, we report the adaxial–abaxial transcriptional changes that occur during petal expansion, providing a reference for the study of the regulation of polarity in plant development.
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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 | 0.001 |
| 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".