Cross-Kingdom Gene Coexpression Analysis Using a <i>Stemphylium botryosum–Lens ervoides</i> System Revealed Plasticity of Intercommunication Between the Pathogen Secretome and the Host Immune Systems
Bibliographic record
Abstract
Necrotrophic pathogens are responsible for significant declines in crop yield and quality worldwide. During the infection process, a pathogen releases a series of secretory proteins to counteract the plant immune system, and this interaction of pathogen and host molecules determines whether the pathogen will successfully invade the host plant tissues. In this study, we adopted co-transcriptomic approaches to analyze the Lens ervoides–Stemphylium botryosum system, with a focus on 1,216 fungal genes coding for secretory proteins and 8,810 disease-responsive genes of the host 48, 96, and 144 h postinoculation, captured in two F 9 recombinant inbred lines (RILs) displaying contrasting disease responses. By constructing in planta gene coexpression networks (GCNs) for S. botryosum, we found that the pathogen tended to co-upregulate genes regulating cell wall degradation enzymes, effectors, oxidoreductases, and peptidases to a much higher degree in the susceptible host LR-66-577 than in the resistant RIL LR-66-637, indicating that the promotion of these digestive enzymes and toxins increased S. botryosum virulence. Construction of cross-kingdom GCNs between pathogen and plant for the two RILs revealed that the co-upregulation of these fungal digestive enzymes and toxins simultaneously promoted a series of defense responses such as redox change, expression of membrane-related genes and serine/threonine kinase, and stress and disease responses in the susceptible RIL which was not observed in the resistant RIL, indicating that these activities exacerbated susceptibility to S. botryosum. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".