Genome-wide analysis of parasitic fitness traits in a non-model tree pathogen
Bibliographic record
Abstract
The ascomycete fungus Ophiostoma novo-ulmi is the highly aggressive pathogen responsible for the current, highly destructive, pandemic of Dutch elm disease (DED). In spite of its economic and ecological impact, O. novo-ulmi is not considered a model species, even though this fungus is easily grown in the laboratory and amenable to standard genetic and molecular investigations. The nuclear genomes of O. novo-ulmi and related species O. ulmi were recently sequenced and annotated, thus providing new opportunities for deciphering the complex basis of parasitic fitness in these pathogens. Comparative in silico analyses with genomes from other, well-characterized fungal and bacterial genomes have confirmed that the DED pathogens possess several hundred orthologues of genes encoding putative pathogenicity factors. Physiological, molecular, genomic and transcriptomic approaches are currently being used for studying yeast-mycelium dimorphism, a trait that the DED fungi share with several other pathogens of plants and mammals. The response of O. novo-ulmi to specific external stimuli suggests that oxylipins may be involved in yeast-mycelium transition. Orthologues of genes encoding enzymes involved in the biosynthesis of oxylipins are present in the nuclear genome of O. novo-ulmi. Genome-wide analyses of gene expression in the yeast and mycelial phases have revealed that over 10% of the nuclear genes are differentially expressed between these conditions. Comparisons with transcriptomes of the human pathogens Candida albicans and Histoplasma capsulatum showed that regulation patterns differed among the three species, thus highlighting the importance of developing resources for non-model organisms such as the DED fungi.
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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 | 0.001 |
| 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".