Transcriptome analysis of nitrogen‐efficient rice over‐expressing alanine aminotransferase
Bibliographic record
Abstract
Crop plants require nitrogen for key macromolecules, such as DNA, proteins and metabolites, yet they are generally inefficient at acquiring nitrogen from the soil. Crop producers compensate for this low nitrogen utilization efficiency by applying nitrogen fertilizers. However, much of this nitrogen is unavailable to the plants as a result of microbial uptake and environmental loss of nitrogen, causing air, water and soil pollution. We engineered rice over-expressing alanine aminotransferase (AlaAT) under the control of a tissue-specific promoter that showed a strong nitrogen use efficiency phenotype. In this study, we examined the transcriptome response in roots and shoots to the over-expression of AlaAT to provide insights into the nitrogen-use-efficient phenotype of these plants. Transgenic and control rice plants were grown hydroponically and the root and shoot gene expression profiles were analysed using Affymetrix Rice GeneChip microarrays. Transcriptome analysis revealed that there was little impact on the transgenic transcriptome compared with controls, with 0.11% and 0.07% differentially regulated genes in roots and shoots, respectively. The most up-regulated transcripts, a glycine-rich cell wall (GRP) gene and a gene encoding a hypothetical protein (Os8823), were expressed in roots. Another transgenic root-specific up-regulated gene was leucine rich repeat (LRR). Genes induced in the transgenic shoots included GRP, LRR, acireductone dioxygenase (OsARD), SNF2 ATP-translocase and a putative leucine zipper transcription factor. This study provides a genome-wide view of the response to AlaAT over-expression, and elucidates some of the genes that may play a role in the nitrogen-use-efficient phenotype.
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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 | 0.001 |
| 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 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".