Weighted Gene Co-Expression Network Analysis Uncovers Critical Genes and Pathways Involved in Soybean Response to Soybean Mosaic Virus
Background: Soybean mosaic virus (SMV) is a globally prevalent and detrimental virus that belongs to the Potyvirus genus. Pathogenic viruses of this genus are typically linear in shape, with dimensions ranging between 630 and 750 nm, and are composed of single-stranded RNA and proteins. We have deve...
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2024-10-01
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| author | Hanhan Zhu Ruiqiong Li Yaoyao Fang Xue Zhao Weili Teng Haiyan Li Yingpeng Han |
| author_facet | Hanhan Zhu Ruiqiong Li Yaoyao Fang Xue Zhao Weili Teng Haiyan Li Yingpeng Han |
| author_sort | Hanhan Zhu |
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| description | Background: Soybean mosaic virus (SMV) is a globally prevalent and detrimental virus that belongs to the Potyvirus genus. Pathogenic viruses of this genus are typically linear in shape, with dimensions ranging between 630 and 750 nm, and are composed of single-stranded RNA and proteins. We have developed an SMV-resistant soybean line, Dongnong 93-046, which has no significant changes in disease resistance identification in the adult plants and has neat grains with no obvious brown or black markings. To explore the defense mechanisms of soybean against SMV, we performed comparative transcriptomic sequencing of the leaves between the Dongnong 93-046 inoculated with SMV at 8 h (T) and the non-inoculated control (C) on the HiSeq2000 platform. In addition, we performed non-targeted metabolomic sequencing of leaves from the treatment and control groups. Results: We identified a total of 41,189 differentially expressed genes (DEGs). A total of 9809 differentially expressed genes (DEGs) met the criteria of |Log2FC (Fold Change)| ≥ 1 and adjusted <i>p</i>-value ≤ 0.001. Among the 41,189 DEGs identified, 9196 exhibited FPKM values greater than 10. KEGG pathway enrichment analysis of the 9809 DEGs revealed significant enrichment of genes involved in resistance-related pathways such as plant–pathogen interaction, linoleic acid metabolism, mitogen-activated protein kinase (MAPK) signaling pathway, and plant hormone signaling transduction. Functional analysis using MapMan software identified multiple DEGs that were associated with pathways such as jasmonate synthesis and phenylpropanoid biosynthesis. Weighted gene co-expression network analysis (WGCNA) using the differential metabolites and the 9196 DEGs revealed a strong correlation between gene clusters within the Turquoise module and the content of jasmonate-related metabolites. Further functional enrichment analysis of the 894 genes within the gene clusters showed a significant and repeated enrichment of pathways related to plant–pathogen interaction, linoleic acid metabolism, and plant hormone signaling transduction. Subsequent focused pathway analysis identified key genes involved in plant hormone signaling transduction pathways, such as the jasmonate ZIM domain protein <i>Glyma.16G010000</i>, the gene <i>Glyma.01G235600</i> encoding the essential diterpene reductase required for jasmonate synthesis in the jasmonate biosynthesis pathway, and the transcription factor <i>Glyma.02G232600</i> involved in the plant–pathogen interaction pathway, among others. This study provides a theoretical framework for understanding the resistance mechanism of soybean cultivar Dongnong 93-046 against the <i>SMV N1</i> strain, offers potential gene resources for breeding soybean varieties with resistance to SMV, and paves the way for new strategies to control SMV infection, enhance resistance, and improve crop yield and quality. |
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| institution | Kabale University |
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| spelling | doaj-art-6220cf7cd18e4d8c9ff9304e81349d252024-11-26T17:44:02ZengMDPI AGAgronomy2073-43952024-10-011411245510.3390/agronomy14112455Weighted Gene Co-Expression Network Analysis Uncovers Critical Genes and Pathways Involved in Soybean Response to Soybean Mosaic VirusHanhan Zhu0Ruiqiong Li1Yaoyao Fang2Xue Zhao3Weili Teng4Haiyan Li5Yingpeng Han6Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin 150030, ChinaKey Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin 150030, ChinaKey Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin 150030, ChinaKey Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin 150030, ChinaKey Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin 150030, ChinaKey Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin 150030, ChinaKey Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin 150030, ChinaBackground: Soybean mosaic virus (SMV) is a globally prevalent and detrimental virus that belongs to the Potyvirus genus. Pathogenic viruses of this genus are typically linear in shape, with dimensions ranging between 630 and 750 nm, and are composed of single-stranded RNA and proteins. We have developed an SMV-resistant soybean line, Dongnong 93-046, which has no significant changes in disease resistance identification in the adult plants and has neat grains with no obvious brown or black markings. To explore the defense mechanisms of soybean against SMV, we performed comparative transcriptomic sequencing of the leaves between the Dongnong 93-046 inoculated with SMV at 8 h (T) and the non-inoculated control (C) on the HiSeq2000 platform. In addition, we performed non-targeted metabolomic sequencing of leaves from the treatment and control groups. Results: We identified a total of 41,189 differentially expressed genes (DEGs). A total of 9809 differentially expressed genes (DEGs) met the criteria of |Log2FC (Fold Change)| ≥ 1 and adjusted <i>p</i>-value ≤ 0.001. Among the 41,189 DEGs identified, 9196 exhibited FPKM values greater than 10. KEGG pathway enrichment analysis of the 9809 DEGs revealed significant enrichment of genes involved in resistance-related pathways such as plant–pathogen interaction, linoleic acid metabolism, mitogen-activated protein kinase (MAPK) signaling pathway, and plant hormone signaling transduction. Functional analysis using MapMan software identified multiple DEGs that were associated with pathways such as jasmonate synthesis and phenylpropanoid biosynthesis. Weighted gene co-expression network analysis (WGCNA) using the differential metabolites and the 9196 DEGs revealed a strong correlation between gene clusters within the Turquoise module and the content of jasmonate-related metabolites. Further functional enrichment analysis of the 894 genes within the gene clusters showed a significant and repeated enrichment of pathways related to plant–pathogen interaction, linoleic acid metabolism, and plant hormone signaling transduction. Subsequent focused pathway analysis identified key genes involved in plant hormone signaling transduction pathways, such as the jasmonate ZIM domain protein <i>Glyma.16G010000</i>, the gene <i>Glyma.01G235600</i> encoding the essential diterpene reductase required for jasmonate synthesis in the jasmonate biosynthesis pathway, and the transcription factor <i>Glyma.02G232600</i> involved in the plant–pathogen interaction pathway, among others. This study provides a theoretical framework for understanding the resistance mechanism of soybean cultivar Dongnong 93-046 against the <i>SMV N1</i> strain, offers potential gene resources for breeding soybean varieties with resistance to SMV, and paves the way for new strategies to control SMV infection, enhance resistance, and improve crop yield and quality.https://www.mdpi.com/2073-4395/14/11/2455soybeanSMVMapManWGCNARNA-SeqRT-qPCR |
| spellingShingle | Hanhan Zhu Ruiqiong Li Yaoyao Fang Xue Zhao Weili Teng Haiyan Li Yingpeng Han Weighted Gene Co-Expression Network Analysis Uncovers Critical Genes and Pathways Involved in Soybean Response to Soybean Mosaic Virus Agronomy soybean SMV MapMan WGCNA RNA-Seq RT-qPCR |
| title | Weighted Gene Co-Expression Network Analysis Uncovers Critical Genes and Pathways Involved in Soybean Response to Soybean Mosaic Virus |
| title_full | Weighted Gene Co-Expression Network Analysis Uncovers Critical Genes and Pathways Involved in Soybean Response to Soybean Mosaic Virus |
| title_fullStr | Weighted Gene Co-Expression Network Analysis Uncovers Critical Genes and Pathways Involved in Soybean Response to Soybean Mosaic Virus |
| title_full_unstemmed | Weighted Gene Co-Expression Network Analysis Uncovers Critical Genes and Pathways Involved in Soybean Response to Soybean Mosaic Virus |
| title_short | Weighted Gene Co-Expression Network Analysis Uncovers Critical Genes and Pathways Involved in Soybean Response to Soybean Mosaic Virus |
| title_sort | weighted gene co expression network analysis uncovers critical genes and pathways involved in soybean response to soybean mosaic virus |
| topic | soybean SMV MapMan WGCNA RNA-Seq RT-qPCR |
| url | https://www.mdpi.com/2073-4395/14/11/2455 |
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