Integration of Transcriptome, Proteome, and Metabolome Provides Insights into How Calcium Enhances the Mechanical Strength of Herbaceous Peony Inflorescence Stems
Bibliographic record
Abstract
Weak stem mechanical strength severely restrains cut flowers quality and stem weakness can be alleviated by calcium (Ca) treatment, but the mechanisms underlying Ca-mediated enhancement of stem mechanical strength remain largely unknown. In this study, we performed a comparative transcriptomic, proteomic, and metabolomic analysis of herbaceous peony (Paeonia lactiflora Pall.) inflorescence stems treated with nanometer Ca carbonate (Nano-CaCO3). In total, 2643 differentially expressed genes (DEGs) and 892 differentially expressed proteins (DEPs) were detected between the Control and nano-CaCO3 treatment. Among the 892 DEPs, 152 were coregulated at both the proteomic and transcriptomic levels, and 24 DEPs related to the secondary cell wall were involved in signal transduction, energy metabolism, carbohydrate metabolism and lignin biosynthesis, most of which were upregulated after nano-CaCO3 treatment during the development of inflorescence stems. Among these four pathways, numerous differentially expressed metabolites (DEMs) related to lignin biosynthesis were identified. Furthermore, structural observations revealed the thickening of the sclerenchyma cell walls, and the main wall constitutive component lignin accumulated significantly in response to nano-CaCO3 treatment, thereby indicating that Ca can enhance the mechanical strength of the inflorescence stems by increasing the lignin accumulation. These results provided insights into how Ca treatment enhances the mechanical strength of inflorescence stems in P. lactiflora.
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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".