Dissecting the Biology of the Fungal Wheat Pathogen <i>Zymoseptoria tritici</i>: A Laboratory Workflow
Bibliographic record
Abstract
The fungus Zymoseptoria tritici is one of the most devastating pathogens of wheat. Aside from its importance as a disease-causing agent, this species has emerged as a powerful model system for evolutionary genetic studies of crop-infecting fungal pathogens. Z. tritici exhibits exceptionally high levels of genetic and phenotypic diversity as well as morphological plasticity, which can make experimental studies and comparability of results obtained in different laboratories, e.g., from infection assays, challenging. Therefore, standardized experimental methods are crucial for research on Z. tritici biology and the interaction of this fungus with its wheat host. Here, we describe a suite of well-tested and optimized protocols ranging from isolation of Z. tritici field specimens to analyses of virulence assays under controlled conditions. Several biological and technical aspects of working with Z. tritici under laboratory conditions are considered and carefully described in each protocol. © 2020 The Authors. Basic Protocol 1: Purification of Z. tritici field isolates from leaf material Basic Protocol 2: Molecular identification of Z. tritici isolates Support Protocol 1: Rapid extraction of Z. tritici genomic DNA Support Protocol 2: Extraction of high-quality Z. tritici genomic DNA Basic Protocol 3: In vitro culture and long-term storage of Z. tritici isolates Basic Protocol 4: Analysis of Z. tritici virulence in wheat Support Protocol 3: Preparation of Z. tritici inoculum.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.018 |
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".