Changes in the Sclerotinia sclerotiorum transcriptome during infection of Brassica napus
Bibliographic record
Abstract
BACKGROUND: Sclerotinia sclerotiorum causes stem rot in Brassica napus, which leads to lodging and severe yield losses. Although recent studies have explored significant progress in the characterization of individual S. sclerotiorum pathogenicity factors, a gap exists in profiling gene expression throughout the course of S. sclerotiorum infection on a host plant. In this study, RNA-Seq analysis was performed with focus on the events occurring through the early (1 h) to the middle (48 h) stages of infection. RESULTS: Transcript analysis revealed the temporal pattern and amplitude of the deployment of genes associated with aspects of pathogenicity or virulence during the course of S. sclerotiorum infection on Brassica napus. These genes were categorized into eight functional groups: hydrolytic enzymes, secondary metabolites, detoxification, signaling, development, secreted effectors, oxalic acid and reactive oxygen species production. The induction patterns of nearly all of these genes agreed with their predicted functions. Principal component analysis delineated gene expression patterns that signified transitions between pathogenic phases, namely host penetration, ramification and necrotic stages, and provided evidence for the occurrence of a brief biotrophic phase soon after host penetration. CONCLUSIONS: The current observations support the notion that S. sclerotiorum deploys an array of factors and complex strategies to facilitate host colonization and mitigate host defenses. This investigation provides a broad overview of the sequential expression of virulence/pathogenicity-associated genes during infection of B. napus by S. sclerotiorum and provides information for further characterization of genes involved in the S. sclerotiorum-host plant interactions.
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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".