Comparative secretome analysis of <i><scp>F</scp>usarium graminearum</i> and two of its non‐pathogenic mutants upon deoxynivalenol induction in vitro
Bibliographic record
Abstract
To understand early events in plant-pathogen interactions, it is necessary to explore the pathogen secretome to identify secreted proteins that help orchestrate pathology. The secretome can be obtained from pathogens grown in vitro, and then characterized using standard proteomic approaches based on protein extraction and subsequent identification of tryptic peptides by LC-MS. A subset of the secretome is composed of proteins whose presence is required to initiate infection and their removal from the secretome would result in pathogens with reduced or no virulence. We present here comparative secretome from Fusarium graminearum. This filamentous fungus causes Fusarium head blight on wheat, a serious cereal disease found in many cereal-growing regions. Affected grain is contaminated with mycotoxins and cannot be used for food or feed. We used label-free quantitative MS to compare the secretomes of wild-type with two nonpathogenic deletion mutants of F. graminearum, Δtri6, and Δtri10. These mutations in mycotoxin-regulating transcription factors revealed a subset of 29 proteins whose relative abundance was affected in their secretomes, as measured by spectral counting. Proteins that decreased in abundance are potential candidate virulence factors and these included cell wall-degrading enzymes, metabolic enzymes, pathogenesis-related proteins, and proteins of unknown function.
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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) | 0.000 | 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 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".