Decoding nitrogen uptake efficiency in maize and sorghum: insights from comparative gene regulatory networks
Bibliographic record
Abstract
Nitrogen (N) is an essential macronutrient for plant growth and yield, yet optimizing nitrogen use efficiency remains a challenge in agriculture. To better understand the regulatory basis of plant responses to N availability, we constructed a maize-specific nitrogen uptake efficiency gene regulatory network (mNUEGRN) comprising 1625 protein-DNA interactions (PDI) between 70 promoters and 301 transcription factors using enhanced yeast one-hybrid assays. We also projected a sorghum NUE GRN (spNUEGRN) based on maize orthologs and analyzed N-responsive subnetworks in both species using transcriptome profiling under N stress of early deprivation and recovery. Cross-species comparison with an existing Arabidopsis GRN revealed about 18% conserved interaction, corresponding to 11% of the mNUEGRN, particularly within the nitrate assimilation pathways. Notably, bZIP18 and bZIP30 emerged as central regulators in mNUEGRN, forming highly connected feed-forward loops (FFLs). From our time series data, we identified 19 236 and 23 864 differentially expressed genes in maize and sorghum, respectively. Gini correlation analysis uncovered 764 and 638 FFLs in mNUEGRN and spNUEGRN, respectively, of which 22 FFLs in maize and 35 in sorghum were identified in both leaf and root for each species. These FFLs may represent candidate regulatory motifs that contribute to modulating transcriptional responses under fluctuating N conditions, but their potential roles require further investigation. Together, our findings reveal evolutionarily conserved and species-specific regulatory strategies that mediate early N responsiveness, offering a foundation for engineering crops with improved NUE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".