Effects of Bacterial ACC Deaminase on<i>Brassica napus</i>Gene Expression
Bibliographic record
Abstract
Plants in association with plant growth-promoting rhizobacteria can benefit from lower plant ethylene levels through the action of the bacterial enzyme 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase. This enzyme cleaves the immediate biosynthetic precursor of ethylene, ACC. Ethylene is responsible for many aspects of plant growth and development but, under stressful conditions, it exacerbates stress symptoms. The ACC deaminase-containing bacterium Pseudomonas putida UW4 is a potent plant growth-promoting strain and, as such, was used to elaborate the detailed role of bacterial ACC deaminase in Brassica napus (canola) plant growth promotion. Transcriptional changes in bacterially treated canola plants were investigated with the use of an Arabidopsis thaliana oligonucleotide microarray. A heterologous approach was necessary because there are few tools available at present to measure global expression changes in nonmodel organisms, specifically with the sensitivity of microarrays. The results indicate that the transcription of genes involved in plant hormone regulation, secondary metabolism, and stress response was altered in plants by the presence of the bacterium, whereas the upregulation of genes for auxin response factors and the downregulation of stress response genes was observed only in the presence of bacterial ACC deaminase. These results support the suggestion that there is a direct link between ethylene and the auxin response, which has been suggested from physiological studies, and provide more evidence for the stress-reducing benefits of ACC deaminase-expressing plant growth-promoting bacteria.
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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 | 0.001 |
| 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".