Characterization of <i>Heterobasidion occidentale</i> transcriptomes reveals candidate genes and <scp>DNA</scp> polymorphisms for virulence variations
Bibliographic record
Abstract
Characterization of genes involved in differentiation of pathogen species and isolates with variations of virulence traits provides valuable information to control tree diseases for meeting the challenges of sustainable forest health and phytosanitary trade issues. Lack of genetic knowledge and genomic resources hinders novel gene discovery, molecular mechanism studies and development of diagnostic tools in the management of forest pathogens. Here, we report on transcriptome profiling of Heterobasidion occidentale isolates with contrasting virulence levels. Comparative transcriptomic analysis identified orthologous groups exclusive to H. occidentale and its isolates, revealing biological processes involved in the differentiation of isolates. Further bioinformatics analyses identified an H. occidentale secretome, CYPome and other candidate effectors, from which genes with species- and isolate-specific expression were characterized. A large proportion of differentially expressed genes were revealed to have putative activities as cell wall modification enzymes and transcription factors, suggesting their potential roles in virulence and fungal pathogenesis. Next, large numbers of simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) were detected, including more than 14 000 interisolate non-synonymous SNPs. These polymorphic loci and species/isolate-specific genes may contribute to virulence variations and provide ideal DNA markers for development of diagnostic tools and investigation of genetic diversity.
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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".