Contribution of the GABA shunt to hypoxia-induced alanine accumulation in roots of Arabidopsis thaliana
Bibliographic record
Abstract
When subjected to low oxygen stress, plants accumulate alanine and gamma-aminobutyric acid (GABA). To investigate the function of GABA metabolism under hypoxia and its contribution to alanine accumulation, we studied the genes that encode the two key enzymes of the GABA shunt, glutamate decarboxylase (GAD) and GABA transaminase (GABA-T). Among the five homologous GAD genes found in Arabidopsis thaliana, GAD1 expression was predominantly found in roots, while GAD2 expression was evident in all organs. Expression of the other three GAD genes was generally weak. In response to hypoxia, transcriptional induction was observed for GAD4 only. For GABA-T1, its expression was detected in all organs, but there was no significant transcriptional change under hypoxic conditions. Moreover, we have isolated and characterized Arabidopsis mutants defective in GAD1 and GABA-T1. In gad1 mutants, GAD activity was significantly reduced in roots but was not affected in shoots. In the gaba-t1 mutant, GABA-T activity was decreased to negligible levels in both shoots and roots. These mutants were phenotypically normal under normal growth conditions except for the reduced seed production of the pop2 mutants as described previously. However, metabolite analysis revealed significant changes in GABA content in gad1 and gaba-t1 mutants. The levels of alanine under hypoxic conditions were also affected in the roots of gad1 and gaba-t1 mutants. The partial inhibition of the hypoxia-induced alanine accumulation in roots of these mutants suggests that the GABA shunt is, in part, responsible for the alanine accumulation under hypoxia.
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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) | 0.000 | 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) | 0.001 | 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".