Global transcriptomics and metabolomics driven approaches to study the host and pathogen responses during Rhizoctonia solani-soybean interactions
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».