ACC deaminase‐producing rhizosphere bacteria modulate plant responses to flooding
Bibliographic record
Abstract
Summary Flooding events are predicted to increase over the coming decades, calling for a better understanding of plant responses to submergence. Specific root‐associated microbes alter plant hormonal balance, affecting plant growth and stress tolerance. We hypothesized that the presence of such microbes may modulate plant responses to submergence. We tested whether root‐associated bacteria producing the enzyme ACC (1‐aminocyclopropane‐1‐carboxylate) deaminase affect submergence responses in Rumex palustris, a flood‐tolerant riparian plant. Ethylene is a key plant hormone regulating flood‐associated acclimations, and ACC deaminase activity of bacteria may decrease ethylene levels in the plant. Rumex palustris plants were inoculated with Pseudomonas putida UW4 or an isogenic mutant lacking ACC deaminase, and subsequently exposed to complete submergence. Submergence triggered ethylene‐mediated responses, including an increase in leaf elongation and shoot fresh weight. Flood responses, including post‐submergence ethylene production, were reduced in plants inoculated with ACC deaminase‐producing wild type bacteria, as compared to plants inoculated with the ACC deaminase‐negative mutant. Synthesis. We demonstrate that root‐associated bacteria can alter plant response to environmental stress by altering plant hormonal balance. Plant–microbe interactions may thus be an overseen driver of plant life‐history strategies that should be taken into account when assessing plant ecological adaptations such as abiotic stress resistance.
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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".