Gene coexpression network analysis combined with metabonomics reveals the resistance responses to powdery mildew in Tibetan hulless barley

Abstract

Powdery mildew is a fungal disease that represents a ubiquitous threat to crop plants. Transcriptomic and metabolomic analyses were used to identify molecular and physiological changes in Tibetan hulless barley in response to powdery mildew. There were 3418 genes and 405 metabolites differentially expressed between the complete resistance cultivar G7 and the sensitive cultivar Z13. Weighted gene coexpression network analysis was carried out, and the differentially expressed genes were enriched in five and four major network modules in G7 and Z13, respectively. Further analyses showed that phytohormones, photosynthesis, phenylpropanoid biosynthesis, and flavonoid biosynthesis pathways were altered during Qingke-Blumeria graminis (DC.) f.sp. hordei (Bgh) interaction. Comparative analyses showed a correspondence between gene expression and metabolite profiles, and the activated defenses resulted in changes of metabolites involved in plant defense response, such as phytohormones, lipids, flavone and flavonoids, phenolamides, and phenylpropanoids. This study enabled the identification of Bgh responsive genes and provided new insights into the dynamic physiological changes that occur in Qingke during response to powdery mildew. These findings greatly improve our understanding of the mechanisms of induced defense response in Qingke and will provide new clues for the development of resistant Tibetan hulless barley varieties.

Comparative analysis of powdery mildew resistant and susceptible cultivated cucumber (Cucumis sativus L.) varieties to reveal the metabolic responses to Sphaerotheca fuliginea infection

“…Increasing in S. fuliginea infection-induced flavonoid contents might play an esstential role in formation of resistant quality of BK2. In Tibetan hulless barley, gene co-expression combined with metabonomic analysis reveals the resistance responses to PM infection [ 43 ]. Expression of the flavonoid pathway-related genes, such as 4CL and CHS , was considered to be associated with suppression of induced resistance in cucumber [ 18 ].…”

Combined De Novo Transcriptome and Metabolome Analysis of Common Bean Response to Fusarium oxysporum f. sp. phaseoli Infection

“…A combination of UPLC-MS/MS detection platform, self-built database, and multivariate statistical analysis was used to study the differences in metabolome between CK-24 and FO-24. It offers a platform to detect a great diversity of metabolites as previously reported in tomato [29], Prunus mira [30], and hulless barley [31][32][33]. In total, 754 metabolites were successfully detected in both sample types (Table S5).…”

Network Analysis Combining Proteomics and Metabolomics Reveals New Insights Into Early Responses of Eucalyptus grandis During Rust Infection

“…Since the early molecular events can mostly define either incompatible or compatible interactions of plants and fungal pathogens, the use of multi-omics strategies offer an ideal approach to understand mechanisms underlying plant disease resistance and susceptibility, and consequently, has the potential for the discovery of new molecules or genes to be used to enhance molecular breeding and/or gene-editing programs (Yuan et al, 2018). Despite omics experiments yield hypothetical results concerning the up-and down-regulation of molecules, since they can reflect other secondary effects, researchers have been applied these technologies to uncover the molecular mechanisms involved in plant-pathogen interactions (Cueto-Ginzo et al, 2016;Chong et al, 2018;Pang et al, 2018;Chen et al, 2019;Jia et al, 2020).…”

“…Envisioning new strategies for enhancing plant disease resistance, one of the principal purposes of plant-pathogen studies is to understand how plants modulate genes, transcripts, proteins and metabolites to physiologically adapt and respond to pathogen invasion. Hence, researchers have used network analyses to group co-expressed genes, integrate omics datasets and reveal new insights into plant physiology (Walley et al, 2013;Ployet et al, 2019), as well as plant response to stress (El-Sharkawy et al, 2015;Yuan et al, 2018;Budzinski et al, 2019;Song et al, 2019).…”