Characterization of immunity-inducing rhizobacteria highlights diversity in plant-microbe interactions
Bibliographic record
Abstract
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). We screened a bacterial library from agricultural soils to identify strains that, after root treatment, induce immunity in Solanum lycopersicum (tomato) against the fungal pathogen Botrytis cinerea . Here, we report the establishment of a screening method and characterization of a subset of five strains, belonging to the species Bacillus velezensis, Paenibacillus peoriae and Pseudomonas parafulva , that induced systemic resistance in tomato. However interestingly, only two of them triggered canonical ISR in Arabidopsis, indicating plant host specificity and/or alternative modes of actions. Furthermore, some of the strains displayed direct anti-microbial activity. We also found the requirement of the lipid-binding protein DIR1 in ISR establishment, indicating a possible convergence of SAR and ISR signaling. Finally, we found that P. parafulva TP18m, also displayed strong effects on root development. Taken together, we have identified taxonomically diverse immunity-inducing bacteria. Our characterization revealed diverse features, highlighting the complexity of bacteria- host interaction in the rhizosphere. Highlight We identified taxonomically diverse rhizobacteria that induce systemic resistance in tomato plants to Botrytis after application to the root. These bacteria display diverse modes of action to improve plant health.
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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 | 0.001 |
| 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".