Regulatory modules controlling early shade avoidance response in maize seedlings
Bibliographic record
Abstract
BACKGROUND: Optimization of shade avoidance response (SAR) is crucial for enhancing crop yield in high-density planting conditions in modern agriculture, but a comprehensive study of the regulatory network of SAR is still lacking in monocot crops. RESULTS: In this study, the genome-wide early responses in maize seedlings to the simulated shade (low red/far-red ratio) and also to far-red light treatment were transcriptionally profiled. The two processes were predominantly mediated by phytochrome B and phytochrome A, respectively. Clustering of differentially transcribed genes (DTGs) along with functional enrichment analysis identified important biological processes regulated in response to both treatments. Co-expression network analysis identified two transcription factor modules as potentially pivotal regulators of SAR and de-etiolation, respectively. A comprehensive cross-species comparison of orthologous DTG pairs between maize and Arabidopsis in SAR was also conducted, with emphasis on regulatory circuits controlling accelerated flowering and elongated growth, two physiological hallmarks of SAR. Moreover, it was found that the genome-wide distribution of DTGs in SAR and de-etiolation both biased toward the maize1 subgenome, and this was associated with differential retention of various cis-elements between the two subgenomes. CONCLUSIONS: The results provide the first transcriptional picture for the early dynamics of maize phytochrome signaling. Candidate genes with regulatory functions involved in maize shade avoidance response have been identified, offering a starting point for further functional genomics investigation of maize adaptation to heavily shaded field conditions.
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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 | 0.000 | 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".