RNAseq Analysis Highlights Specific Transcriptome Signatures of Yeast and Mycelial Growth Phases in the Dutch Elm Disease Fungus<i>Ophiostoma novo-ulmi</i>
Bibliographic record
Abstract
Fungal dimorphism is a complex trait and our understanding of the ability of fungi to display different growth morphologies is limited to a small number of model species. Here we study a highly aggressive dimorphic fungus, the ascomycete Ophiostoma novo-ulmi, which is a model in plant pathology and the causal agent of Dutch elm disease. The two growth phases that this fungus displays, i.e., a yeast phase and mycelial phase, are thought to be involved in key steps of disease development. We used RNAseq to investigate the genome-wide gene expression profiles that are associated with yeast and mycelial growth phases in vitro. Our results show a clear molecular distinction between yeast and mycelial phase gene expression profiles. Almost 12% of the gene content is differentially expressed between the two phases, which reveals specific functions related to each growth phase. We compared O. novo-ulmi transcriptome profiles with those of two model dimorphic fungi, Candida albicans and Histoplasma capsulatum. Few orthologs showed similar expression regulation between the two growth phases, which suggests that, globally, the genes associated with these two life forms are poorly conserved. This poor conservation underscores the importance of developing specific tools for emerging model species that are distantly related to the classical ones. Taken together, our results provide insights into transcriptome regulation and molecular specificity in O. novo-ulmi and offer a new perspective for understanding fungal dimorphism.
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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".