Fungal Pathogen Emergence: Investigations with an Ustilago maydis × Sporisorium reilianum Hybrid
Bibliographic record
Abstract
The emergence of new fungal pathogens threatens sustainable crop production worldwide. One mechanism by which new pathogens may arise is hybridization. To investigate hybridization, the related smut fungi, Ustilago maydis and Sporisorium reilianum, were selected because they both infect Zea mays, can hybridize, and tools are available for their analysis. The hybrid dikaryons of these fungi grew as filaments on plates but their colonization and virulence in Z. mays were reduced compared to the parental dikaryons. The anthocyanin induction caused by the hybrid dikaryon infections was distinct, suggesting its interaction with the host was different from that of the parental dikaryons. Selected virulence genes previously characterized in U. maydis and their predicted S. reilianum orthologs had altered transcript levels during hybrid infection of Z. mays. The downregulated U. maydis effectors, tin2, pit2, and cce1, and transcription factors, rbf1, hdp2, and nlt1, were constitutively expressed in the hybrid. Little impact was observed with increased effector expression; however, increased expression of rbf1 and hdp2, which regulate early pathogenic development by U. maydis, increased the hybrid’s capacity to induce symptoms including the rare induction of small leaf tumors. These results establish a base for investigating molecular aspects of smut fungal hybrid pathogen emergence.
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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 | 0.001 |
| 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".