Pathogen invasion increases the abundance of predatory protists and their prey associations in the plant microbiome
Bibliographic record
Abstract
Soil and plant-associated protistan communities play a key role in shaping bacterial and fungal communities, primarily through their function as top-down predators. However, our understanding of how pathogen invasion influences these protistan communities and their relationships with bacterial and fungal communities remains limited. Here, we studied the protistan communities along the soil-plant continuum of healthy chilli peppers and those affected by Fusarium wilt disease (FWD), and integrated bacterial and fungal community data from our previous research. Our research showed that FWD was associated with a significant enrichment of phagotrophic protists in roots, and also increased the proportion and connectivity of these protists (especially Cercozoa and Ciliophora) in both intra- and inter-kingdom networks. Furthermore, the microbiome of diseased plants not only showed a higher relative abundance of functional genes related to bacterial anti-predator responses than healthy plants, but also contained a greater abundance of metagenome-assembled genomes with functional traits involved in this response. The increased microbial inter-kingdom associations between bacteria and protists, coupled with the notable bacterial anti-predator feedback in the microbiome of diseased plants, suggest that FWD may catalyse the associations between protists and their microbial prey. These findings highlight the potential role of predatory protists in influencing microbial assembly and functionality through top-down forces under pathogenic stress.
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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 | 0.001 |
| 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".