<i>Pseudomonas fluorescens</i> LBUM223 Increases Potato Yield and Reduces Common Scab Symptoms in the Field
Bibliographic record
Abstract
Common scab of potato, caused by pathogenic Streptomyces spp., is an important disease not efficiently controlled by current methods. We previously demonstrated that Pseudomonas fluorescens LBUM223 reduces common scab development under controlled conditions through phenazine-1-carboxylic (PCA) production, leading to reduced thaxtomin A production by the pathogen, a key pathogenicity and virulence factor. Here, we aimed at determining if LBUM223 is able to increase potato yield and control common scab under field conditions, while characterizing the biocontrol mechanisms involved. We investigated if a reduction in pathogen soil populations, activation of induced systemic resistance in potato, and/or changes in txtA gene expression, involved in thaxtomin A biosynthesis in pathogenic Streptomyces spp. were involved in common scab control by LBUM223. Common scab symptoms were significantly reduced and total tuber weight increased by 46% using biweekly applications of LBUM223. LBUM223 did not reduce pathogen soil populations, nor was potato systemic defense-related gene expression significantly altered between treatments. However, a significant down-regulation of txtA expression occurred in the geocaulosphere. This is the first demonstration that a Pseudomonas strain can directly alter the transcriptional activity of a key pathogenesis gene in a plant pathogen under field conditions, contributing to disease control.
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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 | 0.001 |
| 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".