Comparative genome and transcriptome analyses reveal adaptations to opportunistic infections in woody plant degrading pathogens of Botryosphaeriaceae
Bibliographic record
Abstract
Botryosphaeriaceae are an important fungal family that cause woody plant diseases worldwide. Recent studies have established a correlation between environmental factors and disease expression; however, less is known about factors that trigger these diseases. The current study reports on the 43.3 Mb de novo genome of Lasiodiplodia theobromae and five other genomes of Botryosphaeriaceae pathogens. Botryosphaeriaceous genomes showed an expansion of gene families associated with cell wall degradation, nutrient uptake, secondary metabolism and membrane transport, which contribute to adaptations for wood degradation. Transcriptome analysis revealed that genes involved in carbohydrate catabolism, pectin, starch and sucrose metabolism, and pentose and glucuronate interconversion pathways were induced during infection. Furthermore, genes in carbohydrate-binding modules, lysine motif domain and the glycosyl hydrolase gene families were induced by high temperature. Among these genes, overexpression of two selected putative lignocellulase genes led to increased virulence in the transformants. These results demonstrate the importance of high temperatures in opportunistic infections. This study also presents a set of Botryosphaeriaceae-specific effectors responsible for the identification of virulence-related pathogen-associated molecular patterns and demonstrates their active participation in suppressing hypersensitive responses. Together, these findings significantly expand our understanding of the determinants of pathogenicity or virulence in Botryosphaeriaceae and provide new insights for developing management strategies against them.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".