Rapid and sustained differentiation of disease-suppressive phyllosphere microbiomes in tomato following experimental microbiome selection
Bibliographic record
Abstract
BACKGROUND: Microbial-based treatments to protect plants against phytopathogens typically focus on soil-borne disease or the aboveground application of one or a few biocontrol microorganisms. However, diverse microbiomes may provide unique benefits to phytoprotection in the phyllosphere, by restricting pathogen access to niche space and/or through multiple forms of direct antagonism. We previously showed that successive experimental passaging of phyllosphere microbiomes along with the phytopathogen Pseudomonas syringae pv. tomato (Pto), which causes bacterial speck in tomato, led to the development of a disease suppressive microbial community. Here, we used amplicon sequencing to assess bacterial and fungal composition at the end of each passage, as well as shotgun metagenomics at key passages based on observed disease-suppressive phenotypes, to assess differences in functional potential between suppressive and non-suppressive communities. RESULTS: Bacterial composition changed and diversity declined quickly due to passaging and remained low, particularly in treatments with Pto present, whereas fungal diversity did not. Pseudomonas and Xanthomonas populations were particularily enriched in disease-suppressive microbiomes compared to conducive microbiomes. The relative abundance of Pseudomonas syringae group gemonosp. 3 (the clade to which the introduced pathogen belongs) in shotgun metagenomic data was similar to what we observed for Pseudomonas ASVs in the 16S rRNA gene dataset. We also observed an increase in the abundance of genes associated with microbial antagonism at Passage 4, corresponding to the highest observed disease severity. CONCLUSIONS: Taxonomic richness and evenness were low within samples, with clustering occurring for suppressive or non-suppressive microbiomes. The relative abundance of genes associated with antagonism was higher for disease-suppressive phyllosphere microbiomes. This work is an important step towards understanding the microbe-microbe interactions within disease-suppressive phyllosphere communities.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".