The impact on nitrogen-efficient phenotypes when aspartate aminotransferase is expressed tissue-specifically in Brassica napus
Bibliographic record
Abstract
Aspartate aminotransferase (AAT) catalyzes a reversible transamination reaction, producing aspartate and 2-oxoglutarate from glutamate and oxaloacetate, in various cellular plant compartments. Previous work in our lab had shown that a similar aminotransferase enzyme, alanine aminotransferase (AlaAT), produced nitrogen use efficient (NUE) phenotypes when over-expressed in canola (Brassica napus) under the salt-stress inducible promoter, btg-26. Given the similarities between these two enzymes and their roles in plant metabolism, it was hypothesized that over-expression of AAT could also produce an NUE phenotype in canola. Transgenic Brassica napus lines over-expressing AAT from Medicago sativa were produced and analyzed for NUE phenotypes under both high and low nitrogen conditions. While several lines showed promising increases in biomass under the various fertilizer regimes, these alterations could not be reliably replicated and increases in expression of the transgene detected via RT-PCR did not translate into significant increases in AAT activity in plant tissues. Transgenic Brassica napus lines over-expressing AAT do not display NUE phenotypes similar to those plants over-expressing AlaAT. Although this work produced a negative result, it is important to compare the NUE phenotype produced by over-expression of AlaAT and AAT, and differences in metabolism between AAT vs AlaAT over-expressing lines which may be used to deduce changes in plant N metabolism important for NUE in cereal crops.
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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 | 0.001 |
| 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".