Transcriptome analysis implicates secondary metabolite production, redox reactions, and programmed cell death during allorecognition in <i>Cryphonectria parasitica</i>
Bibliographic record
Abstract
The underlying molecular mechanisms of programmed cell death associated with fungal allorecognition, a form of innate immunity, remain largely unknown. In this study, transcriptome analysis was used to infer mechanisms activated during barrage formation in vic3-incompatible strains of Cryphonectria parasitica, the chestnut blight fungus. Pronounced differential expression occurred in barraging strains of genes involved in mating pheromone (mf2-1, mf2-2), secondary metabolite production, detoxification (including oxidative stress), apoptosis-related, RNA interference, and HET-domain genes. Evidence for secondary metabolite production and reactive oxygen species (ROS) accumulation is supported through UPLC-HRMS analysis and cytological staining, respectively. Differential expression of mating-related genes and HET-domain genes was further examined by RT-qPCR of incompatible interactions involving each of the six vegetative incompatibility (vic) loci in C. parasitica and revealed distinct recognition process networks. We infer that vegetative incompatibility in C. parasitica activates defence reactions that involve secondary metabolism, resulting in increased toxicity of the extra- and intracellular environment. Accumulation of ROS (and other potential toxins) may result in detoxification failure and activation of apoptosis, sporulation, and the expression of associated pheromone genes. The incompatible reaction leaves abundant traces of a process-specific metabolome as conidiation is initiated.
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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".