The transcriptional landscape of Rhizoctonia solani AG1-IA during infection of soybean as defined by RNA-seq
Bibliographic record
Abstract
Rhizoctonia solani Kühn infects most plant families and can cause significant agricultural yield losses worldwide; however, plant resistance to this disease is rare and short-lived, and therefore poorly understood, resulting in the use of chemical pesticides for its control. Understanding the functional responses of this pathogen during host infection can help elucidate the molecular mechanisms that are necessary for successful host invasion. Using the pathosystem model soybean-R. solani anastomosis group AG1-IA, we examined the global transcriptional responses of R. solani during early and late infection stages of soybean by applying an RNA-seq approach. Approximately, 148 million clean paired-end reads, representing 93% of R. solani AG1-IA genes, were obtained from the sequenced libraries. Analysis of R. solani AG1-IA transcripts during soybean invasion revealed that most genes were similarly expressed during early and late infection stages, and only 11% and 15% of the expressed genes were differentially expressed during early and late infection stages, respectively. Analyses of the differentially expressed genes (DEGs) revealed shifts in molecular pathways involved in antibiotics biosynthesis, amino acid and carbohydrate metabolism, as well as pathways involved in antioxidant production. Furthermore, several KEGG pathways were unique to each time point, particularly the up-regulation of genes related to toxin degradation (e.g., nicotinate and nicotinamid metabolism) at onset of necrosis, and those linked to synthesis of anti-microbial compounds and pyridoxine (vitamin B6) biosynthesis 24 h.p.o. of necrosis. These results suggest that particular genes or pathways are required for either invasion or disease development. Overall, this study provides the first insights into R. solani AG1-IA transcriptome responses to soybean invasion providing beneficial information for future targeted control methods of this successful pathogen.
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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) | 0.001 | 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".