Exploring the Panax ginseng Meyer soil metagenome to uncover antagonistic bacteria against ginseng root rot disease
Bibliographic record
Abstract
Background Ginseng, renowned for its health benefits, is often cultivated with pesticides, which contradicts its health-enhancing properties. To address this, we identified Bacillus velezensis ARRI17 through a 5-year monitoring of ginseng yield on a national scale and comparative metagenome analysis. ARRI17 is a biocontrol agent that enhances ginseng growth and disease resistance under authentic field conditions. Methods We identified ARRI17 through metagenomic analysis of soil samples collected from ginseng fields classified as high-yield (3.54 ± 0.46 kg per 1.62 m 2 ) or low-yield (0.9 ± 0.21 kg per 1.62 m 2 ), based on comparisons to the national 5-year average yield of 2.13 ± 0.35 kg per 1.62 m 2 . The biocontrol efficacy of ARRI17 was validated under laboratory conditions and field trials. Additionally, we analyzed the genomic and physiological characteristics of ARRI17 to clarify its antifungal mechanisms and adaptability to diverse environments. Results ARRI17 exhibited strong inhibitory activity against multiple ginseng fungal pathogens, including Ilyonectria mors-panacis , in both controlled and field conditions. The application of ARRI17 improved ginseng growth parameters and reduced disease incidence in infested soil. Genomic analysis revealed that ARRI17 produces antimicrobial compounds, such as Iturin A, confirmed by HPLC. Furthermore, ARRI17 naturally thrived in rice straw compost, a traditional biofertilizer used in ginseng cultivation, suggesting its long-term presence and compatibility with standard ginseng farming practices. Conclusion Bacillus velezensis ARRI17 is an effective biocontrol agent that promotes ginseng growth and enhances disease resistance. Its natural compatibility with traditional farming practices, especially its presence with rice straw compost, positions ARRI17 as a promising and sustainable alternative.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".