A Siderophore Analog of Fimsbactin from Acinetobacter Hinders Growth of the Phytopathogen Pseudomonas syringae and Induces Systemic Priming of Immunity in Arabidopsis thaliana
Bibliographic record
Abstract
Siderophores produced in soil by plant growth-promoting rhizobacteria (PGPRs) play several roles, including nutrient mobilizers and can be useful as plants defense elicitors. We investigated the role of a synthetic mixed ligand bis-catechol-mono-hydroxamate siderophore (SID) that mimics the chemical structure of a natural siderophore, fimsbactin, produced by Acinetobacter spp. in the resistance against the phytopathogen Pseudomonas syringaepv tomato DC3000 (Pst DC3000), in Arabidopsis thaliana. We first tested the antibacterial activity of SID against Pst DC3000 in vitro. After confirming that SID had antibacterial activity against Pst DC3000, we tested whether the observed in vitro activity could translate into resistance of Arabidopsis to Pst DC3000, using bacterial loads as endpoints in a plant infection model. Furthermore, using quantitative polymerase chain reaction, we explored the molecular actors involved in the resistance of Arabidopsis induced by SID. Finally, to assure that SID would not interfere with PGPRs, we tested in vitro the influence of SID on the growth of a reference PGPR, Bacillus subtilis. We report here that SID is an antibacterial agent as well as an inducer of systemic priming of resistance in A. thaliana against Pst DC3000, and that SID can, at the same time, promote growth of a PGPR.
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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".