Isolation and characterization of novel soil- and plant-associated bacteria with multiple phytohormone-degrading activities using a targeted methodology
Bibliographic record
Abstract
Ethylene (ET), salicylic acid (SA) and indole-3-acetic acid (IAA) are important phytohormones regulating plant growth and development, as well as plant-microbe interactions. Plant growth-promoting bacteria (PGPB) naturally associate with plants and facilitate plant growth through a variety of mechanisms, including the ability to modulate the concentrations of these phytohormones in planta. Importantly, the wide presence of phytohormone degradation mechanisms amongst symbiotic and other soil- and plant-associated bacteria indicates that the ability to modulate phytohormone concentrations plays an important role in bacterial colonization and plant-growth promotion abilities. Obtaining phytohormone-degrading bacteria is therefore key for the development of novel solutions aiming to increase plant growth and protection. In this paper, we report an optimized targeted methodology and the consequent isolation of novel soil- and plant-associated bacteria, including rhizospheric, endophytic and phyllospheric strains, with the ability to degrade the phytohormones, SA and IAA, as well as the ET precursor, 1-aminocyclopropane-1-carboxylic acid (ACC). By using an optimized targeted methodology, we rapidly isolated diverse soil- and plant-associated bacteria presenting phytohormone-degrading abilities from several plants, plant tissues and environments, without the need for prior extensive and laborious isolation and maintenance of large numbers of isolates. The developed methodology facilitates PGPB research, especially in developing countries. Here, we also report, for the first time, the isolation of bacterial strains able to concomitantly catabolize three phytohormones (SA, IAA and ACC). Ultimately, the described targeted methodology and the novel phytohormone-degrading bacteria obtained in this work may be useful tools for future plant-microbe interaction studies, and in the development of new inoculant formulations for agriculture and biotechnology.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".