Maize transcription factor ZmEREB167 negatively regulates starch accumulation and kernel size
Bibliographic record
Abstract
Transcription factors play critical roles in the regulation of gene expression during maize kernel development. The maize endosperm, a large storage organ, accounting for nearly 90% of the dry weight of mature kernels, serves as the primary site for starch storage. In this study, we identify an endosperm-specific EREB gene, ZmEREB167, which encodes a nucleus-localized EREB protein. Knockout of ZmEREB167 significantly increases kernel size and weight, as well as starch and protein content, compared with the wild type. In situ hybridization experiments show that ZmEREB167 is highly expressed in the BETL as well as PED regions of maize kernels. Dual-luciferase assays show that ZmEREB167 exhibits transcriptionally repressor activity in maize protoplasts. Transcriptome analysis reveals that a large number of genes are up-regulated in the Zmereb167-C1 mutant compared with the wild type, including key genetic factors such as ZmMRP-1 and ZmMN1, as well as multiple transporters involved in maize endosperm development. Integration of RNA-seq and ChIP-seq results identify 68 target genes modulated by ZmEREB167. We find that ZmEREB167 directly targets OPAQUE2, ZmNRT1.1, ZmIAA12, ZmIAA19, and ZmbZIP20, repressing their expressions. Our study demonstrates that ZmEREB167 functions as a negative regulator in maize endosperm development and affects starch accumulation and kernel size.
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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) | 0.001 | 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) | 0.002 | 0.001 |
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".