Integration of Small RNA and Transcriptome Sequencing Reveal the Roles of miR395 and ATP Sulfurylase in Developing Seeds of Chinese Kale
Bibliographic record
Abstract
Seed development is closely related to plant production and reproduction, and MicroRNAs (miRNA) is widely involved in plant development including seed development. Chinese kale, as a Brassicaceae vegetable, mainly depends on seed for proper reproduction. In the present study, Chinese kale seed and silique at different stages were selected to establish small RNA (sRNA) libraries including silique wall sRNA libraries at torpedo-embryo stage (PC), silique wall sRNA libraries at cotyledonary-embryo stage (PD), seed sRNA libraries at torpedo-embryo stage (SC), and seed sRNA libraries at cotyledonary-embryo stage (SD). The results showed that miRNA expressed differentially in the seeds and corresponding siliques at different stages. To further clarify the functional mode of miRNA in the process of seed development, Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis was performed on target genes of the differentially expressed miRNAs, and these target genes were mainly enriched in plant hormone signal transduction, primary and secondary metabolic pathways. After joint analysis with the transcriptome change of the corresponding period, miR156-SPL10/SPL11, miR395-APS3, and miR397-LAC2/LAC11 modules were identified to be directly involved in the development of Chinese kale seeds. What’s more, modified 5′RLM-RACE and Agrobacteria-mediated Chinese kale transient transformation suggest miR395b_2 is involved in sulfur metabolism during seed development by regulating its target gene APS3.
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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 | 0.000 | 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".