Comparative RNA-Seq Analysis Reveals That Regulatory Network of Maize Root Development Controls the Expression of Genes in Response to N Stress
Bibliographic record
Abstract
Nitrogen (N) is an essential nutrient for plants, and it directly affects grain yield and protein content in cereal crops. Plant root systems are not only critical for anchorage in the soil, but also for N acquisition. Therefore, genes controlling root development might also affect N uptake by plants. In this study, the responses of nitrogen on root architecture of mutant rtcs and wild-type of maize were investigated by morphological and physiological analysis. Subsequently, we performed a comparative RNA-Seq analysis to compare gene expression profiles between mutant rtcs roots and wild-type roots under different N conditions. We identified 786 co-modulated differentially expressed genes (DEGs) related to root development. These genes participated in various metabolic processes. A co-expression cluster analysis and a cis-regulatory motifs analysis revealed the importance of the AP2-EREBP transcription factor family in the rtcs-dependent regulatory network. Some genotype-specific DEGs contained at least one LBD motif in their promoter region. Further analyses of the differences in gene transcript levels between rtcs and wild-type under different N conditions revealed 403 co-modulated DEGs with distinct functions. A comparative analysis revealed that the regulatory network controlling root development also controlled gene expression in response to N-deficiency. Several AP2-EREBP family members involved in multiple hormone signaling pathways were among the DEGs. These transcription factors might play important roles in the rtcs-dependent regulatory network related to root development and the N-deficiency response. Genes encoding the nitrate transporters NRT2-1, NAR2.1, NAR2.2, and NAR2.3 showed much higher transcript levels in rtcs than in wild-type under normal-N conditions. This result indicated that the LBD gene family mainly functions as transcriptional repressors, as noted in other studies. In summary, using a comparative RNA-Seq-based approach, we identified DEGs related to root development that also participated in the N-deficiency response in maize. These findings will increase our understanding of the molecular regulatory networks controlling root development and N-stress responses.
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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".