Prophylactic phage biocontrol prevents <i>Burkholderia gladioli</i> infection in a quantitative <i>ex planta</i> model of bacterial virulence
Bibliographic record
Abstract
ABSTRACT Agricultural crop yield losses and food destruction due to infections by phytopathogenic bacteria such as Burkholderia gladioli , which causes devastating diseases in onion, mushroom, corn, and rice crops, pose major threats to worldwide food security and cause enormous damage to the global economy. Biocontrol using bacteriophages has emerged as a promising strategy against a number of phytopathogenic species but has never been attempted against B. gladioli due to a lack of quantitative infection models and a scarcity of phages targeting this specific pathogen. In this study, we present a novel, procedurally straightforward, and highly generalizable fully quantitative ex planta maceration model and an accompanying quantitative metric, the ex planta maceration index ( x PMI). In utilizing this model to test the ex planta virulence of a panel of 12 strains of B. gladioli in Allium cepa and Agaricus bisporus , we uncover substantial temperature-, host-, and strain-dependent diversity in the virulence of this fascinating pathogenic species. Crucially, we demonstrate that Burkholderia phages KS12 and AH2, respectively, prevent and reduce infection-associated onion tissue destruction, measured through significant ( P < 0.0001) reductions in x PMI, by phytopathogenic strains of B. gladioli , thereby demonstrating the potential of agricultural phage biocontrol targeting this problematic microorganism. IMPORTANCE Agricultural crop destruction is increasing due to infections caused by bacteria such as Burkholderia gladioli, which causes plant tissue diseases in onion, mushroom, corn, and rice crops. These bacteria pose a major threat to worldwide food production, which, in turn, damages the global economy. One potential solution being investigated to prevent bacterial infections of plants is “biocontrol” using bacteriophages (or phages), which are bacterial viruses that readily infect and destroy bacterial cells. In this article, we demonstrate that Burkholderia phages KS12 and AH2 prevent or reduce infection-associated plant tissue destruction caused by strains of B. gladioli , thereby demonstrating the inherent potential of agricultural phage biocontrol.
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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) | 0.000 | 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 | 0.001 |
| 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".