Characterization of Immunity-Inducing Rhizobacteria Highlights Diversity in Plant–Microbe Interactions
Bibliographic record
Abstract
The narrow region of soil surrounding roots (rhizosphere) contains an astonishing diversity of microorganisms. Some rhizosphere bacteria can improve plant health and immunity, via direct competition with pathogens or by establishing heightened immunity in aboveground tissues, a phenomenon known as induced systemic resistance (ISR). To identify novel immunity-activating bacterial strains, we established a screening method from a library of agricultural soil-derived culturable bacteria, using Solanum lycopersicum (tomato) and the fungal pathogen Botrytis cinerea, the causal agent of gray mold disease. Here, we report the establishment of a screening method and the identification of 13 immunity-inducing strains in tomato plants, including a strain of Chitinophaga arvensicola. A detailed characterization of a subset of five strains, belonging to the species Bacillus velezensis, Paenibacillus peoriae, and Pseudomonas parafulva, revealed that only two of them triggered canonical ISR in Arabidopsis, indicating plant host specificity, alternative modes of action, or both. Furthermore, some of the strains displayed direct anti-microbial activity, and two strains became endophytic. We also found the requirement of the lipid-binding protein DIR1, which is an important factor for systemic acquired resistance, for ISR establishment in Arabidopsis, indicating a possible convergence of systemic acquired resistance and ISR signaling. Finally, we found that P. parafulva TP18m promoted root development as well as enhanced immunity. Taken together, we have established a screening system for immunity-inducing bacteria and identified taxonomically diverse bacterial strains that may be useful for agricultural application. Our characterization revealed diverse features for each strain, which highlights the complexity of the bacteria–host interactions in the rhizosphere. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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 | 0.001 | 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".