Global transcriptomics and metabolomics driven approaches to study the host and pathogen responses during Rhizoctonia solani-soybean interactions
Bibliographic record
Abstract
Rhizoctonia solani anastomosis group (AG) 4 causes damping-off, stem rot, and root rot of young and adult soybean plants. Stand losses can reach 50% or greater leading to yield losses of 26 K metric tons in Canada. Recently, strains of R. solani AG1 subgroup IA have been reported to cause soybean Rhizoctonia foliar blight (RFB), with annual losses of over 1.1 M metric tons in China making it an important agricultural disease. Despite its economic importance, no studies were attempted to uncover the mechanisms of pathogenesis employed by R. solani AG1-IA or the defence mechanisms employed by soybean. Such knowledge is critical for the enhancement of breeding strategies aimed at increasing soybean resistance to R. solani and the development of targeted control methods. Global omics-driven approaches such as transcriptomics and metabolomics can provide comprehensive insights of the associated molecular responses in both the host and pathogen during host-pathogen interactions. Such knowledge is invaluable for dissecting the molecular responses of soybean and R. solani AG1-IA during RFB disease development, and the present study examined these interactions using multi-omic approaches. Significant fluctuations occurred in the soybean glycolysis pathway, the TCA cycle, photosynthesis and photosynthate production providing novel insights into identification of biomarkers and the biological correlations of candidate genes with metabolites that could be used in breeding for soybean resistance against R. solani AG1-IA. Global transcriptomics of R. solani AG1-IA during early and late infection stages was examined for the first time. RNA-seq analysis of R. solani AG1-IA during soybean invasion revealed that most genes are similarly expressed during early and late infection stages of the soybean host; however, differential expression of certain genes at the two time points suggests 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 responses to soybean invasion providing beneficial information for future targeted control methods of this successful pathogen. Application of biochar is known to increase resistance of plants against diseases, but also bears the potential to have inconsistent and contradictory results depending on the type of biochar feedstock and application rate. As such, the effect of maple bark biochar on soybean resistance to Rhizoctonia root rot and RFB diseases caused by R. solani was examined. Biochar amendment enhanced soybean susceptibility to both foliar (AG1-IA) and soilborne (AG4) strains of R. solani by modifying (i) the expression of soybean genes associated with primary and secondary metabolic pathways; and (ii) the metabolic profile of both root and foliar strains of R. solani. Biochar caused fluctuations in R. solani AG4 metabolites with increases in possible virulence-related metabolites such as mannitol, as well as perturbations in the TCA cycle and glycolysis. Furthermore, expression of several R. solani AG1-IA genes associated with carbohydrate metabolism, redox reactions and detoxification were altered, despite no contact between the biochar and the foliar pathogen. In conjunction, biochar caused general down-regulation of soybean genes, which were tightly linked with an increased susceptibility to RFB disease. The overall metabolic changes resulted in enhanced soybean susceptibility and enhanced pathogen virulence resulting in increased disease severity. Taken together, this study provides the first insight into the molecular responses of soybean to RFB caused by R. solani AG1-IA, as well as the first understanding into the molecular mechanisms employed by R. solani AG1-IA to successfully attack and invade its soybean host. Maple bark biochar proved to be insufficient for controlling different diseases caused by R. solani by decreasing soybean defenses and enhancing R. solani virulence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".