Combined transcriptome, metabolome, and miRNA analysis reveals the genetic regulatory network of sweet corn pericarp thickness
Bibliographic record
Abstract
The thickness of the pericarp is a complex characteristic that determines sweet corn’s palatability. However, the molecular mechanisms in forming pericarp thickness differences have not been clarified. In this study, we conducted transcriptomics, miRNA analysis, and metabolomics to explore the underlying molecular mechanisms. Scanning electron microscopy (SEM) revealed that the disparity in pericarp thickness is primarily due to variations in the number of cell layers. Our combined multi-omics analysis discovered 6,054 differentially expressed genes (DEGs), 73 differentially expressed miRNAs, 113 differentially accumulated metabolites (DAMs), and several key miRNAs, such as zma-miR164 , zma-miR166 , zma-miR827 , and zma-miR171b were identified, which modulate the expression of different transcription factors and regulate the signal transduction of various plant hormones, thereby influencing pericarp thickness. Additionally, our integrated transcriptomic and metabolomic analysis revealed that genes and metabolites involved in plant hormone signal transduction and phenylpropanoids biosynthesis pathway play a significant role in regulating pericarp growth and development. Furthermore, we observed that in the thick pericarp line (M08), the content of cytokinins was significantly reduced, while phenylpropanoid compounds such as 5-O-feruloylquinic acid glucoside, berberine, scopoletin, sinapic alcohol, sinapic acid and 3-O-feruloylquinic acid glucoside accumulated considerably. These findings provide valuable theoretical support and genetic resources.
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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 | 0.001 |
| 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".