Fungal pathogens of cereal crops: Proteomic insights into fungal pathogenesis, host defense, and resistance
Bibliographic record
Abstract
Fungal infections of cereal crops pose a significant risk to global food security through reduced grain production and quality, as well as contamination of animal feed and human products for consumption. To combat fungal disease, we need to understand how the pathogen adapts and survives within the hostile environment of the host and how the host's defense response can be modulated for protection from disease. Such investigations offer insight into fungal pathogenesis, host immunity, the development of resistance, and mechanisms of action for currently-used control strategies. Mass spectrometry-based proteomics provides a technologically-advanced platform to define differences among fungal pathogens and their hosts at the protein level, supporting the discovery of proteins critical for disease, and uncovering novel host responses driving susceptibly or resistance of the host. In this Review, we explore the role of mass spectrometry-based proteomics in defining the intricate relationship between a pathogen and host during fungal disease of cereal crops with a focus on recent discoveries derived from the globally-devastating diseases of Fusarium head blight, Rice blast, and Powdery mildew. We highlight advances made for each of these diseases and discuss opportunities to extrapolate findings to further our fight against fungal pathogens on a global scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".