Alanine aminotransferase catalyses the breakdown of alanine after hypoxia in <i>Arabidopsis thaliana</i>
Bibliographic record
Abstract
Alanine aminotransferase (AlaAT) catalyses the reversible transfer of an amino group from glutamate to pyruvate to form 2-oxoglutarate and alanine. The regulation of AlaAT in several plant species has been studied in response to low-oxygen stress, light and nitrogen application. In this study, induction of Arabidopsis AlaAT1 and AlaAT2 during hypoxia was observed at the transcriptional level, and an increase in enzyme activity was detected in hypoxically treated roots. In addition, the tissue-specific expression of AlaAT1 and AlaAT2 was analysed using promoter:GUS fusions. The GUS staining patterns indicated that both AlaAT genes are expressed predominantly in vascular tissues. We manipulated AlaAT expression to determine the relative importance of this enzyme in low-oxygen stress tolerance and nitrogen metabolism. This was done by analysing T-DNA mutants and over-expressing barley AlaAT in Arabidopsis. The AlaAT1 knockout mutant (alaat1-1) showed a dramatic reduction in AlaAT activity, suggesting that AlaAT1 is the major AlaAT isozyme in Arabidopsis. Over-expression of barley AlaAT significantly increased the AlaAT activity in the transgenic plants. These plants were analysed for metabolic changes over a period of hypoxic stress and during their subsequent recovery. The results showed that alaat1-1 plants accumulate more alanine than wild-type plants during the early phase of hypoxia, and the decline in accumulated alanine was delayed in the alaat1-1 line during the post-hypoxia recovery period. When alanine was supplied as the nitrogen source, alaat1-1 plants utilized alanine less efficiently than wild-type plants did. These results indicate that the primary role of AlaAT1 is to break down alanine when it is in excess. Therefore, AlaAT appears to be crucial for the rapid conversion of alanine to pyruvate during recovery from low-oxygen stress.
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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) | 0.001 | 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".