Use of insertional mutagenesis to tag putative parasitic fitness genes in the Dutch elm disease fungus <i>Ophiostoma novo-ulmi</i> subsp. <i>novo-ulmi</i>
Bibliographic record
Abstract
We used insertional mutagenesis to produce genetically tagged mutants of the Dutch elm disease fungus Ophiostoma novo-ulmi subsp. novo-ulmi. We first optimized transformation of O. novo-ulmi protoplasts by the restriction enzyme mediated integration method. A concentration of 80 U of HindIII with 108 fungal protoplasts and 5 microg of plasmid DNA was the most efficient for generating a high number of O. novo-ulmi mutants carrying a single insertion in their genome. Mycelium- and yeast-like growth kinetics of 24 O. novo-ulmi mutants were evaluated in vitro. Flanking sequences were successfully recovered in 8% of the transformants analyzed. Some mutant phenotypes appeared to result from gene disruption events, whereas others likely involved modifications of noncoding regions. Several nuclear loci that control vegetative growth and could potentially impact parasitic fitness were successfully tagged.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".