scholarly journals Weighted gene coexpression network analysis-based identification of key modules and hub genes associated with drought sensitivity in rice

2020 ◽  
Author(s):  
Baiyang Yu ◽  
Jianbin Liu ◽  
Di Wu ◽  
Ying Liu ◽  
Weijian Cen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Drought Resistance ◽  
Differentially Expressed ◽  
Chromosome 1 ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Go Terms ◽  
Coexpression Network Analysis

Abstract Background: Drought stress is an adverse factor with deleterious effects on several aspects of rice growth. However, the mechanism underlying drought resistance in rice remains unclear. To understand the molecular mechanism of the drought response in rice, drought-sensitive CSSL (Chromosome Single-substitution Segment Line) PY6 was used to map QTLs of sensitive phenotypes and to reveal the impact of the QTLs on transcriptional profiling.Results: The QTL dss-1 was mapped onto the short arm of chromosome 1 of rice. According to transcriptomic analysis, the identified differentially expressed genes (DEGs) exhibited a downregulated pattern and were mainly enriched in photosynthesis-related GO terms, indicating that photosynthesis was greatly inhibited under drought. Further, according to weighted gene coexpression network analysis (WGCNA), specific gene modules (designating a group of genes with a similar expression pattern) were strongly correlated with H2O2 (4 modules) and MDA (3 modules), respectively. Likewise, GO analysis revealed that the photosynthesis-related GO terms were consistently overrepresented in H2O2-correlated modules. Functional annotation of the differentially expressed hub genes (DEHGs) in the H2O2 and MDA-correlated modules revealed cross-talk between abiotic and biotic stress responses for these genes, which were annotated as encoding WRKYs and PR family proteins, were notably differentially expressed between PY6 and PR403.Conclusions: We speculated that drought-induced photosynthetic inhibition leads to H2O2 and MDA accumulation, which can then trigger the reprogramming of the rice transcriptome, including the hub genes involved in ROS scavenging, to prevent oxidative stress damage. Our results shed light on and provide deep insight into the drought resistance mechanism in rice.

scholarly journals Weighted gene coexpression network analysis-based identification of key modules and hub genes associated with drought sensitivity in rice 

2020 ◽  
Author(s):  
Baiyang Yu ◽  
Jianbin Liu ◽  
Di Wu ◽  
Ying Liu ◽  
Weijian Cen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Drought Resistance ◽  
Differentially Expressed ◽  
Chromosome 1 ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Go Terms ◽  
Coexpression Network Analysis

Abstract Background: Drought stress is an adverse factor with deleterious effects on several aspects of rice growth. However, the mechanism underlying drought resistance in rice remains unclear. To understand the molecular mechanism of the drought response in rice, drought-sensitive CSSL (Chromosome Single-substitution Segment Line) PY6 was used to map QTLs of sensitive phenotypes and to reveal the impact of the QTLs on transcriptional profiling. Results: The QTL dss-1 was mapped onto the short arm of chromosome 1 of rice. According to transcriptomic analysis, the identified differentially expressed genes (DEGs) exhibited a downregulated pattern and were mainly enriched in photosynthesis-related GO terms, indicating that photosynthesis was greatly inhibited under drought. Further, according to weighted gene coexpression network analysis (WGCNA), specific gene modules (designating a group of genes with a similar expression pattern) were strongly correlated with H2O2 (4 modules) and MDA (3 modules), respectively. Likewise, GO analysis revealed that the photosynthesis-related GO terms were consistently overrepresented in H2O2-correlated modules. Functional annotation of the differentially expressed hub genes (DEHGs) in the H2O2 and MDA-correlated modules revealed cross-talk between abiotic and biotic stress responses for these genes, which were annotated as encoding WRKYs and PR family proteins, were notably differentially expressed between PY6 and PR403.Conclusions: We speculated that drought-induced photosynthetic inhibition leads to H2O2 and MDA accumulation, which can then trigger the reprogramming of the rice transcriptome, including the hub genes involved in ROS scavenging, to prevent oxidative stress damage. Our results shed light on and provide deep insight into the drought resistance mechanism in rice.

scholarly journals Weighted gene coexpression network analysis-based identification of key modules and hub genes associated with drought sensitivity in rice

2020 ◽  
Author(s):  
Baiyang Yu ◽  
Jianbin Liu ◽  
Di Wu ◽  
Ying Liu ◽  
Weijian Cen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Drought Resistance ◽  
Differentially Expressed ◽  
Chromosome 1 ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Go Terms ◽  
Coexpression Network Analysis

Abstract Background: Drought stress is an adverse factor with deleterious effects on several aspects of rice growth. However, the mechanism underlying drought resistance in rice remains unclear. To understand the molecular mechanism of the drought response in rice, drought-sensitive CSSL (Chromosome Single-substitution Segment Line) PY6 was used to map QTLs of sensitive phenotypes and to reveal the impact of the QTLs on transcriptional profiling.Results: The QTL dss-1 was mapped onto the short arm of chromosome 1 of rice. According to transcriptomic analysis, the identified differentially expressed genes (DEGs) exhibited a downregulated pattern and were mainly enriched in photosynthesis-related GO terms, indicating that photosynthesis was greatly inhibited under drought. Further, according to weighted gene coexpression network analysis (WGCNA), specific gene modules (designating a group of genes with a similar expression pattern) were strongly correlated with H2O2 (4 modules) and MDA (3 modules), respectively. Likewise, GO analysis revealed that the photosynthesis-related GO terms were consistently overrepresented in H2O2-correlated modules. Functional annotation of the differentially expressed hub genes (DEHGs) in the H2O2 and MDA-correlated modules revealed cross-talk between abiotic and biotic stress responses for these genes, which were annotated as encoding WRKYs and PR family proteins, were notably differentially expressed between PY6 and PR403.Conclusions: We speculated that drought-induced photosynthetic inhibition leads to H2O2 and MDA accumulation, which can then trigger the reprogramming of the rice transcriptome, including the hub genes involved in ROS scavenging, to prevent oxidative stress damage. Our results shed light on and provide deep insight into the drought resistance mechanism in rice.

scholarly journals Weighted gene coexpression network analysis-based identification of key modules and hub genes associated with drought sensitivity in rice

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Baiyang Yu ◽  
Jianbin Liu ◽  
Di Wu ◽  
Ying Liu ◽  
Weijian Cen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Drought Resistance ◽  
Differentially Expressed ◽  
Chromosome 1 ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Go Terms ◽  
Coexpression Network Analysis

Abstract Background Drought stress is an adverse factor with deleterious effects on several aspects of rice growth. However, the mechanism underlying drought resistance in rice remains unclear. To understand the molecular mechanism of the drought response in rice, drought-sensitive CSSL (Chromosome Single-substitution Segment Line) PY6 was used to map QTLs of sensitive phenotypes and to reveal the impact of the QTLs on transcriptional profiling. Results The QTL dss-1 was mapped onto the short arm of chromosome 1 of rice. According to transcriptomic analysis, the identified differentially expressed genes (DEGs) exhibited a downregulated pattern and were mainly enriched in photosynthesis-related GO terms, indicating that photosynthesis was greatly inhibited under drought. Further, according to weighted gene coexpression network analysis (WGCNA), specific gene modules (designating a group of genes with a similar expression pattern) were strongly correlated with H2O2 (4 modules) and MDA (3 modules), respectively. Likewise, GO analysis revealed that the photosynthesis-related GO terms were consistently overrepresented in H2O2-correlated modules. Functional annotation of the differentially expressed hub genes (DEHGs) in the H2O2 and MDA-correlated modules revealed cross-talk between abiotic and biotic stress responses for these genes, which were annotated as encoding WRKYs and PR family proteins, were notably differentially expressed between PY6 and PR403. Conclusions We speculated that drought-induced photosynthetic inhibition leads to H2O2 and MDA accumulation, which can then trigger the reprogramming of the rice transcriptome, including the hub genes involved in ROS scavenging, to prevent oxidative stress damage. Our results shed light on and provide deep insight into the drought resistance mechanism in rice.

scholarly journals Weighted Gene Coexpression Network Analysis-Based Identification of Key Modules and Hub Genes that Regulate the Drought Stress Response in the Rice Drought-Sensitive Line PY6

2020 ◽  
Author(s):  
Baiyang Yu ◽  
Jianbin Liu ◽  
Di Wu ◽  
Ying Liu ◽  
Weijian Cen ◽  
...  
Keyword(s):  
Drought Stress ◽  
Network Analysis ◽  
Drought Resistance ◽  
Differentially Expressed ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Go Terms ◽  
Coexpression Network Analysis

Abstract Drought stress is an adverse factor with deleterious effects on several aspects of rice growth. However, the mechanism underlying drought resistance in rice remains unclear. To understand the molecular mechanism of the drought response in rice, drought-sensitive CSSL (Chromosome Single-substitution Segment Line) PY6 was used to map QTLs of sensitive phenotypes and to reveal the impact of the QTLs on transcriptional profiling. The dss-1 locus was mapped onto the short arm of chromosome 1 of rice. According to transcriptomic analysis, the identified differentially expressed genes (DEGs) exhibited a downregulated pattern and were mainly enriched in photosynthesis-related GO terms, indicating that photosynthesis was greatly inhibited under drought. Further, according to weighted gene coexpression network analysis (WGCNA), 4 and 3 modules were strongly correlated with H2O2 and MDA, respectively. Likewise, GO analysis revealed that the photosynthesis-related GO terms were consistently overrepresented in H2O2-correlated modules. Functional annotation of the DEGs in the H2O2 and MDA-correlated modules revealed cross-talk between abiotic and biotic stress responses for these genes, which were annotated as encoding WRKYs and PR family proteins, were notably differentially expressed between PY6 and PR403.We speculated that drought-induced photosynthetic inhibition leads to H2O2 and MDA accumulation, which can then trigger the reprogramming of the rice transcriptome, including the hub genes involved in ROS scavenging, to prevent oxidative stress damage. Our results shed light on and provide deep insight into the drought resistance mechanism in rice.

scholarly journals Weighted gene coexpression network analysis reveals hub genes involved in cholangiocarcinoma progression and prognosis

2019 ◽  
Vol 49 (10) ◽  
pp. 1195-1206 ◽  
Author(s):  
Aiping Tian ◽  
Ke Pu ◽  
Boxuan Li ◽  
Min Li ◽  
Xiaoguang Liu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

scholarly journals Correction to: Weighted gene coexpression network analysis-based identification of key modules and hub genes associated with drought sensitivity in rice

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Baiyang Yu ◽  
Jianbin Liu ◽  
Di Wu ◽  
Ying Liu ◽  
Weijian Cen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Drought Sensitivity ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals Identification of Candidate Genes and MicroRNAs for Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yan Li ◽  
Xiao_nan He ◽  
Chao Li ◽  
Ling Gong ◽  
Min Liu
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Microarray Data ◽  
Differentially Expressed ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Background. Identification of potential molecular targets of acute myocardial infarction is crucial to our comprehensive understanding of the disease mechanism. However, studies of gene coexpression analysis via jointing multiple microarray data of acute myocardial infarction still remain restricted. Methods. Microarray data of acute myocardial infarction (GSE48060, GSE66360, GSE97320, and GSE19339) were downloaded from Gene Expression Omnibus database. Three data sets without heterogeneity (GSE48060, GSE66360, and GSE97320) were subjected to differential expression analysis using MetaDE package. Differentially expressed genes having upper 25% variation across samples were imported in weighted gene coexpression network analysis. Functional and pathway enrichment analyses were conducted for genes in the most significant module using DAVID. The predicted microRNAs to regulate target genes in the most significant module were identified using TargetScan. Moreover, subpathway analyses using iSubpathwayMiner package and GenCLiP 2.0 were performed on hub genes with high connective weight in the most significant module. Results. A total of 1027 differentially expressed genes and 33 specific modules were screened out between acute myocardial infarction patients and control samples. Ficolin (collagen/fibrinogen domain containing) 1 (FCN1), CD14 molecule (CD14), S100 calcium binding protein A9 (S100A9), and mitochondrial aldehyde dehydrogenase 2 (ALDH2) were identified as critical target molecules; hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential regulators of the expression of the key genes in the two biggest modules. Conclusions. FCN1, CD14, S100A9, ALDH2, hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential candidate regulators in acute myocardial infarction. These findings might provide new comprehension into the underlying molecular mechanism of disease.

scholarly journals Application of Weighted Gene Coexpression Network Analysis to Identify Key Modules and Hub Genes in Systemic Juvenile Idiopathic Arthritis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mi Zhou ◽  
Ruru Guo ◽  
Yong-Fei Wang ◽  
Wanling Yang ◽  
Rongxiu Li ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Network Analysis ◽  
Immune Responses ◽  
Systemic Juvenile Idiopathic Arthritis ◽  
Functional Enrichment ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Systemic juvenile idiopathic arthritis (sJIA) is a severe autoinflammatory disorder with a still not clearly defined molecular mechanism. To better understand the disease, we used scattered datasets from public domains and performed a weighted gene coexpression network analysis (WGCNA) to identify key modules and hub genes underlying sJIA pathogenesis. Two gene expression datasets, GSE7753 and GSE13501, were used to construct the WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to the genes and hub genes in the sJIA modules. Cytoscape was used to screen and visualize the hub genes. We further compared the hub genes with the genome-wide association study (GWAS) genes and used a consensus WGCNA to verify that our conclusions were conservative and reproducible across multiple independent datasets. A total of 5,414 genes were obtained for WGCNA, from which highly correlated genes were divided into 17 modules. The red module demonstrated the highest correlation with the sJIA module ( r = 0.8 , p = 3 e − 29 ), whereas the green-yellow module was found to be closely related to the non-sJIA module ( r = 0.62 , p = 1 e − 14 ). Functional enrichment analysis demonstrated that the red module was mostly enriched in the activation of immune responses, infection, nucleosomes, and erythrocytes, and the green-yellow module was mostly enriched in immune responses and inflammation. Additionally, the hub genes in the red module were highly enriched in erythrocyte differentiation, including ALAS2, AHSP, TRIM10, TRIM58, and KLF1. The hub genes from the green-yellow module were mainly associated with immune responses, as exemplified by the genes KLRB1, KLRF1, CD160, and KIRs. We identified sJIA-related modules and several hub genes that might be associated with the development of sJIA. Particularly, the modules may help understand the mechanisms of sJIA, and the hub genes may become biomarkers and therapeutic targets of sJIA in the future.

scholarly journals Identification of key gene modules and hub genes of human mantle cell lymphoma by coexpression network analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8843
Author(s):  
Dongmei Guo ◽  
Hongchun Wang ◽  
Li Sun ◽  
Shuang Liu ◽  
Shujing Du ◽  
...  
Keyword(s):  
Network Analysis ◽  
Cell Lymphoma ◽  
Enrichment Analysis ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Mantle Cell ◽  
Coexpression Network Analysis ◽  
Blue Module

Purpose Mantle cell lymphoma (MCL) is a rare and aggressive subtype of non-Hodgkin lymphoma that is incurable with standard therapies. The use of gene expression analysis has been of interest, recently, to detect biomarkers for cancer. There is a great need for systemic coexpression network analysis of MCL and this study aims to establish a gene coexpression network to forecast key genes related to the pathogenesis and prognosis of MCL. Methods The microarray dataset GSE93291 was downloaded from the Gene Expression Omnibus database. We systematically identified coexpression modules using the weighted gene coexpression network analysis method (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were performed on the modules deemed important. The protein–protein interaction networks were constructed and visualized using Cytoscape software on the basis of the STRING website; the hub genes in the top weighted network were identified. Survival data were analyzed using the Kaplan–Meier method and were compared using the log-rank test. Results Seven coexpression modules consisting of different genes were applied to 5,000 genes in the 121 human MCL samples using WGCNA software. GO and KEGG enrichment analysis identified the blue module as one of the most important modules; the most critical pathways identified were the ribosome, oxidative phosphorylation and proteasome pathways. The hub genes in the top weighted network were regarded as real hub genes (IL2RB, CD3D, RPL26L1, POLR2K, KIF11, CDC20, CCNB1, CCNA2, PUF60, SNRNP70, AKT1 and PRPF40A). Survival analysis revealed that seven genes (KIF11, CDC20, CCNB1, CCNA2, PRPF40A, CD3D and PUF60) were associated with overall survival time (p < 0.05). Conclusions The blue module may play a vital role in the pathogenesis of MCL. Five real hub genes (KIF11, CDC20, CCNB1, CCNA2 and PUF60) were identified as potential prognostic biomarkers as well as therapeutic targets with clinical utility for MCL.

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