RNA-Seq Transcriptome Analysis of Rice Primary Roots Reveals the Role of Flavonoids in Regulating the Rice Primary Root Growth
Bibliographic record
Abstract
Flavonoids play important roles in root development and in its tropic responses, whereas the flavonoids-mediated changes of the global transcription levels during root growth remain unclear. Here, the global transcription changes in quercetin-treated rice primary roots were analyzed. Quercetin treatment significantly induced the inhibition of root growth and the reduction of H2O2 and O2− levels. In addition, the RNA-seq analysis revealed that there are 1243 differentially expressed genes (DEGs) identified in quercetin-treated roots, including 1032 up-regulated and 211 down-regulated genes. A gene ontology (GO) enrichment analysis showed that the enriched GO terms are mainly associated with the cell wall organization, response to oxidative stress, and response to hormone stimulus. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis showed that the enriched DEGs are involved in phenylpropanoid biosynthesis, glutathione metabolism, and plant hormone signal transduction. Moreover, the quercetin treatment led to an increase of the antioxidant enzyme activities of catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD) in rice roots. Also, the quercetin treatment altered the DR5:GUS expression pattern in the root tips. All of these data indicated that the flavonoids-mediated transcription changes of genes are related to the genes involved in cell wall remodeling, redox homeostasis, and auxin signaling, leading to a reduced cell division in the meristem zone and cell elongation in the elongation zone of roots.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".