Gene expression analyses on Dickeya solani strains of diverse virulence levels unveil important pathogenicity factors for this species
Bibliographic record
Abstract
Dickeya solani causes soft rot and blackleg mainly on potato crops. High pathogenicity of this species results from efficient production of plant cell wall-degrading enzymes, especially pectate lyases, potent root colonization, and fast vascular movement. Despite genomic homogeneity, variations in virulence-related phenotypes suggest differences in the gene expression patterns between diverse strains. Therefore, the methylomes and transcriptomes of two strains (virulent IFB0099 and low virulent IFB0223), differing in tissue maceration capacity and virulence factors production, have been studied. Methylation analysis revealed no significant differences. However, the analysis of transcriptomes, studied under both non-induced and induced by polygalacturonic acid conditions (in order to mimic diverse stages of plant infection process), unveiled higher expression of pectate lyases (pelD, pelE, pelL), pectin esterase (pemA), proteases (prtE, prtD) and Vfm-associated quorum-sensing genes (vfmC, vfmD, vfmE) in IFB0099 strain compared to IFB0223. Additionally, the genes related to the secretion system II (T2SS) (gspJ, nipE) displayed higher induction of expression in IFB0099. Furthermore, IFB0099 showed more elevated expression of genes involved in flagella formation, which coincides with enhanced motility and pathogenicity of this strain compared to IFB0223. To sum up, differential expression analysis of genes important for the virulence of D. solani indicated candidate genes, which might be crucial for the pathogenicity of this species.
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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".