Genome-scale mining of root-preferential genes from maize and characterization of their promoter activity
Bibliographic record
Abstract
BACKGROUND: Modification of root architecture and improvement of root resistance to stresses can increase crop productivity. Functional analyses of root-specific genes are necessary for root system improvement, and root-specific promoters enable research into the regulation of root development and genetic manipulation of root traits. Maize is an important crop species; however, little systematic mining of root-specific genes and promoters has been performed to date. RESULTS: Genomic-scale mining based on microarray data sets followed by transcript detection resulted in the identification of 222 root-specific genes. Gene Ontology enrichment analyses revealed that these 222 root-specific genes were mainly involved in responses to chemical, biotic, and abiotic stresses. Of the 222 genes, 33 were verified by quantitative reverse transcription polymerase chain reaction, and 31 showed root-preferential activity. About 2 kb upstream 5 of the 31 identified root-preferential genes were cloned from the maize genome as putative promoters and named p8463, p5023, p1534, p8531 and p6629. GUS staining of transgenic maize-derived promoter-GUS constructs revealed that the five promoters drove GUS expression in a root-preferential manner. CONCLUSIONS: We mined root-preferential genes and their promoters in maize and verified p8463, p5023, p1534, p8531 and p6629 as root-preferential promoters. Our research enables the identification of other tissue-specific genes and promoters in maize and other species. In addition, the five promoters may enable enhancement of target gene(s) of maize in a root-preferential manner to generate novel maize cultivars with resistance to water, fertilizer constraints, or biotic stresses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".