Comparative transcriptome expression analysis in susceptible and resistant potato (Solanum tuberosum) cultivars to common scab (Streptomyces scabies) revealed immune priming responses in the incompatible interaction
Bibliographic record
Abstract
Common scab disease in potato has become a widespread issue in major potato production areas, leading to increasing economic losses. Varietal resistance is seen as a viable and long-term scab management strategy. However, the genes and mechanisms of varietal resistance are unknown. In the current study, a comparative RNA transcriptome sequencing and differential gene signaling and priming sensitization studies were conducted in two potato cultivars that differ by their response to common scab (Streptomyces scabies), for unraveling the genes and pathways potentially involved in resistance within this pathosystem. We report on a consistent and contrasted gene expression pattern from 1,064 annotated genes differentiating a resistant (Hindenburg) and a susceptible (Green Mountain) cultivars, and identified a set of 273 co-regulated differentially expressed genes in 34 pathways that more likely reflect the genetic differences of the cultivars and metabolic mechanisms involved in the scab pathogenesis and resistance. The data suggest that comparative transcriptomic phenotyping can be used to predict scab lesion phenotype in breeding lines using mature potato tuber. The study also showed that the resistant cultivar, Hindenburg, has developed and maintained a capacity to sense and prime itself for persistent response to scab disease over time, and suggests an immune priming reaction as a mechanism for induced-resistance in scab resistant potato cultivars. The set of genes identified, described, and discussed in the study paves the foundation for detailed characterizations towards tailoring and designing procedures for targeted gene knockout through gene editing and phenotypic evaluation.
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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 | 0.000 |
| 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".