Commensal <i>Pseudomonas fluorescens</i> protect <i>Arabidopsis</i> from closely-related <i>Pseudomonas</i> pathogens in a colonization-dependent manner
Bibliographic record
Abstract
Abstract Plants form commensal associations with soil microorganisms, creating a root microbiome that provides benefits to the host including protection against pathogens. While bacteria can inhibit pathogens through production of antimicrobial compounds in vitro , it is largely unknown how microbiota contribute to pathogen protection in planta . We developed a gnotobiotic model system consisting of Arabidopsis thaliana , and an opportunistic pathogen Pseudomonas sp. N2C3, to identify mechanisms that determine the outcome of plant-pathogen-microbiome interactions in the rhizosphere. We screened 25 phylogenetically diverse Pseudomonas strains for their ability to protect against N2C3 and found that commensal strains closely related to N2C3 were more likely to protect against pathogenesis. We used a comparative genomics approach to identify unique genes in the protective strains that revealed no genes that correlate with protection, suggesting that variable regulation of components of the core Pseudomonas genome may contribute to pathogen protection. We found that commensal colonization level was highly predictive of protection and so tested deletions in genes previously shown to be required for Arabidopsis rhizosphere colonization. We identified a response regulator colR that is required for Pseudomonas protection from N2C3 and fitness in competition with N2C3 indicating that competitive exclusion may contribute to pathogen protection. We found that Pseudomonas WCS365 also protects against the agricultural pathogen Pseudomonas fuscovaginae SE-1, the causal agent of bacterial sheath brown rot of rice. This work establishes a gnotobiotic model to uncover mechanisms by which members of the microbiome can protect hosts from pathogens and informs our understanding of the use of beneficial strains for microbiome engineering in dysbiotic soil systems.
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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".