Virulence Determinants Are Unevenly Distributed Within <i>Streptomyces</i> Species and Strains Causing Potato Common Scab in the Province of Quebec, Canada
Bibliographic record
Abstract
Potato common scab is an important bacterial disease afflicting potatoes around the world. Better knowledge of the local Streptomyces spp. populations causing this disease is key to developing durable control strategies. In this study, we isolated 230 Streptomyces strains from scab-infected potato tubers harvested from commercial potato fields located across the province of Quebec, Canada. The genetic diversity of this collection was first studied using repetitive element-based PCR fingerprinting, and the genomes of 36 representative strains were sequenced using PacBio’s sequencing technology. This enabled us to identify the strains to the species level, to study the distribution of previously characterized virulence-associated genes and clusters, and to explore the repertoires of putative plant cell wall-degrading enzymes. In parallel, the virulence of the 36 strains was evaluated using a potato tuber slice assay. The diversity was higher than previously reported, as 11 phytopathogenic species were found across the province. Among them, S. scabiei and S. acidiscabies were the most abundant as well as the most virulent. Strains belonging to these two species harbored numerous virulence determinants, including the thaxtomin biosynthetic gene cluster. By contrast, most weakly virulent strains lacked this cluster but harbored at least one known virulence determinant. The results obtained suggest that a higher number of virulence-associated genes and clusters in the genome of phytopathogenic Streptomyces spp. are associated with greater virulence. This study contributes to increasing the publicly available genomic resources of scab-causing Streptomyces spp. and expands our knowledge on the diversity and virulence of this important bacterial pathogen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".