A Forward Genetic Screen in <i>Sclerotinia sclerotiorum</i> Revealed the Transcriptional Regulation of Its Sclerotial Melanization Pathway
Bibliographic record
Abstract
Most plant fungal pathogens that cause worldwide crop losses are understudied, due to various technical challenges. With the increasing availability of sequenced whole genomes of these non-model fungi, effective genetic analysis methods are highly desirable. Here, we describe a newly developed pipeline, which combines forward genetic screening with high-throughput next-generation sequencing to enable quick gene discovery. We applied this pipeline in the notorious soilborne phytopathogen Sclerotinia sclerotiorum and identified 32 mutants with various developmental and growth deficiencies. Detailed molecular studies of three melanization-deficient mutants provide a proof of concept for the effectiveness of our method. A master transcription factor was found to regulate melanization of sclerotia through the DHN (1,8-dihydroxynaphthalene) melanin biosynthesis pathway. In addition, these mutants revealed that sclerotial melanization is important for sclerotia survival under abiotic stresses, sclerotial surface structure, and sexual reproduction. Foreseeably, this pipeline can be applied to facilitate efficient in-depth studies of other non-model fungal species in the future. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .
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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".