Transcriptional profiling of the responses to infection by the false smut fungus <i>Ustilaginoidea virens</i> in resistant and susceptible rice varieties
Bibliographic record
Abstract
Rice false smut is a severe fungal disease worldwide, but the mechanisms underlying resistance to the causal agent Ustilaginoidea virens in rice remain unknown. We performed RNA-Seq to investigate the transcriptional modulation in resistant and susceptible rice varieties for the responses to U. virens infection. In total, 1405 and 1066 differentially expressed genes (DEGs) were identified in resistant ‘IR28’ and susceptible ‘HXZ’ cultivars, respectively. Gene ontology (GO) enrichment analysis revealed a set of GO terms differentially enriched in the two rice varieties. Functional analysis of DEGs showed that a large number of genes encoding secondary metabolites, flavin-containing monooxygenases and peroxidases were differentially regulated. Classification analysis of DEGs revealed that brassinosteroids may have more important roles than salicylic acid and ethylene in response to U. virens infection; the crosstalk of other hormones such as auxin, gibberellins and jasmonates may also affect U. virens infection. Our results revealed that pattern recognition, cellular metabolic changes and hormone signalling constitute a comprehensive network to manipulate the responses of host rice plant to U. virens infection.
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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".