Weighted Gene Co-Expression Analysis Network-Based Analysis on the Candidate Pathways and Hub Genes in Eggplant Bacterial Wilt-Resistance: A Plant Research Study

Peng, Jiechun; Wang, Peng; Fang, Huarong; Zheng, Jieming; Chen, Zhong; Yang, Yan; Yu, Weiwu

doi:10.3390/ijms222413279

Cited by 18 publications

(12 citation statements)

References 54 publications

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“…WGCNA OF A. MUELLERI RESISTANCE TO SOFT ROT RESISTANCE. WGCNA uncovers key genes or metabolic pathways by aggregating highly related gene groups in the transcriptome and associating them with sample characteristics (Langfelder and Horvath 2008;Peng et al 2021;Wang et al 2022). The five target modules of interest selected in our study were expressed significantly after inoculation with Pcc KEGG pathways enriched in a-linolenic acid metabolism, phenylpropane biosynthesis, plant hormone signal transduction, and plant-pathogen interaction pathways.…”

Section: Discussionmentioning

confidence: 99%

Weighted Gene Coexpression Network Analysis of Candidate Pathways and Genes in Soft Rot Resistance of Amorphophallus

Gao

Wei

Liu

et al. 2022

J. Amer. Soc. Hort. Sci.

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Amorphophallus species are one of the main economic crops in the mountainous areas of southwest China. However, soft rot disease (Pectobacterium carotovorum ssp. carotovorum) is devastating for this crop. This study explored the Amorphophallus resistance mechanism against soft rot disease by analyzing transcriptome data using a weighted gene coexpression network analysis. The RNA sequencing of plants infected for 0, 12, 24, and 48 hours produced a total of 52.25 Gb of clean reads. A total of 29,096 genes were divided into 34 modules. Six modules of interest with the highest correlation with the target traits were selected to elucidate the resistance genes and pathways. The selected modules were enriched in the α-linolenic acid metabolism, phenylpropane biosynthesis, plant hormone signal transduction, and plant pathogen interaction pathways. Ultimately, AmBGLU, AmCAML, AmCDPK, AmLOX, and AmRBOHD were identified as genes of interest in the four significantly related metabolic pathways for real-time fluorescence quantitative polymerase chain reaction verification. The determination of salicylic acid (SA) and jasmonic acid (JA) in Amorphophallus muelleri and Amorphophallus konjac that suffered from soft rot disease showed that SA and JA were involved in the A. muelleri and A. konjac defense response against soft rot disease. Methyl jasmonate treatment delayed the onset of A. konjac soft rot disease. This study provides a reference for the interaction between Amorphophallus species and soft rot disease and the breeding of broad-spectrum and specific Amorphophallus cultivars that are resistant to soft rot disease.

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Section: Discussionmentioning

confidence: 99%

Weighted Gene Coexpression Network Analysis of Candidate Pathways and Genes in Soft Rot Resistance of Amorphophallus

Gao

Wei

Liu

et al. 2022

J. Amer. Soc. Hort. Sci.

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“…WGCNA is an efficient method to reveal the correlation between gene modules and phenotypic traits [ 11 , 12 ]. In this study, only two gene modules (MEpurple and MEdarkturquoise) significantly associated with S. scitamineum infection ( p < 0.01) were identified from WGCNA ( Figure 3 , Table 2 ), while other modules were not.…”

Section: Discussionmentioning

confidence: 99%

“…Weighted gene co-expression network analysis (WGCNA) is an efficient method for mining modules and hub genes related to traits of interest [ 10 ] and has been successfully used to construct gene co-expression networks in different species [ 11 , 12 , 13 ]. In this report, a WGCNA approach was used to identify modules and hub genes associated with S. scitamineum infection.…”

Section: Introductionmentioning

confidence: 99%

Identification of Gene Modules and Hub Genes Associated with Sporisorium scitamineum Infection Using Weighted Gene Co-Expression Network Analysis

Liu

et al. 2022

JoF

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Sporisorium scitamineum is a biotrophic fungus responsible for sugarcane smut disease. To investigate the key genes involved in S. scitamineum infection, we conducted RNA sequencing of sugarcane sprouts inoculated with S. scitamineum teliospores. A weighted gene co-expression network analysis (WGCNA) showed that two co-expressed gene modules, MEdarkturquoise and MEpurple—containing 66 and 208 genes, respectively—were associated with S. scitamineum infection. The genes in these two modules were further studied using Gene Ontology (GO) enrichment analysis, pathogen-host interaction (PHI) database BLASTp, and small secreted cysteine-rich proteins (SCRPs) prediction. The top ten hub genes in each module were identified using the Cytohubba plugin. The GO enrichment analysis found that endoplasmic reticulum-related and catabolism-related genes were expressed during S. scitamineum infection. A total of 83 genes had homologs in the PHI database, 62 of which correlated with pathogen virulence. A total of 21 proteins had the characteristics of small secreted cysteine-rich proteins (SCRPs), a common source of fungal effectors. The top ten hub genes in each module were identified, and seven were annotated as Mig1-Mig1 protein, glycosyl hydrolase, beta-N-acetylglucosaminidase, secreted chorismate mutase, collagen, mRNA export factor, and pleckstrin homology domain protein, while the remaining three were unknown. Two SCRPs—SPSC_06609 and SPSC_04676—and three proteins—SPSC_01958, SPSC_02155, and SPSC_00940—identified in the PHI database were also among the top ten hub genes in the MEdarkturquoise and MEpurple modules, suggesting that they may play important roles in S. scitamineum infection. A S. scitamineum infection model was postulated based on current findings. These findings help to deepen the current understanding of early events in S. scitamineum infection.

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“…RNA-seq results showed that eggplant resistance to bacterial wilt involved in cell death pathways, cell receptor signaling pathways ( Chen et al, 2018 ). Peng et al (2021) showed that the MAPK signaling pathway, plant pathogen interaction, and glutathione metabolism were co-enriched in roots and stems after inoculation with R. solanacearum . Further research showed that SmRPP13L4 positively regulated the resistance of eggplant to bacterial wilt ( Peng et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Transcriptome and metabolome response of eggplant against Ralstonia solanacearum infection

Xiao

Lin

Feng

et al. 2023

PeerJ

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Bacterial wilt is a soil-borne disease that represents ubiquitous threat to Solanaceae crops. The whole-root transcriptomes and metabolomes of bacterial wilt-resistant eggplant were studied to understand the response of eggplant to bacterial wilt. A total of 2,896 differentially expressed genes and 63 differences in metabolites were identified after inoculation with Ralstonia solanacearum. Further analysis showed that the biosynthesis pathways for phytohormones, phenylpropanoids, and flavonoids were altered in eggplant after inoculation with R. solanacearum. The results of metabolomes also showed that phytohormones played a key role in eggplant response to bacterial wilt. Integrated analyses of the transcriptomic and metabolic datasets indicated that jasmonic acid (JA) content and gene involved in the JA signaling pathway increased in response to bacterial wilt. These findings remarkably improve our understanding of the mechanisms of induced defense response in eggplant and will provide insights intothe development of disease-resistant varieties of eggplant.

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Weighted Gene Co-Expression Analysis Network-Based Analysis on the Candidate Pathways and Hub Genes in Eggplant Bacterial Wilt-Resistance: A Plant Research Study

Cited by 18 publications

References 54 publications

Weighted Gene Coexpression Network Analysis of Candidate Pathways and Genes in Soft Rot Resistance of Amorphophallus

Weighted Gene Coexpression Network Analysis of Candidate Pathways and Genes in Soft Rot Resistance of Amorphophallus

Identification of Gene Modules and Hub Genes Associated with Sporisorium scitamineum Infection Using Weighted Gene Co-Expression Network Analysis

Transcriptome and metabolome response of eggplant against Ralstonia solanacearum infection

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