scholarly journals Weighted gene coexpression correlation network analysis reveals a potential molecular regulatory mechanism of anthocyanin accumulation under different storage temperatures in ‘Friar’ plum

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xueling Li ◽  
Yudou Cheng ◽  
Meng Wang ◽  
Sujuan Cui ◽  
Junfeng Guan
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Network Analysis ◽  
Correlation Network ◽  
Anthocyanin Accumulation ◽  
Structural Genes ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Sucrose Biosynthesis ◽  
Storage Temperatures

Abstract Background Flesh is prone to accumulate more anthocyanin in postharvest ‘Friar’ plum (Prunus salicina Lindl.) fruit stored at an intermediate temperature. However, little is known about the molecular mechanism of anthocyanin accumulation regulated by storage temperature in postharvest plum fruit. Results To reveal the potential molecular regulation mechanism of anthocyanin accumulation in postharvest ‘Friar’ plum fruit stored at different temperatures (0 °C, 10 °C and 25 °C), the fruit quality, metabolite profile and transcriptome of its flesh were investigated. Compared to the plum fruit stored at 0 °C and 25 °C, the fruit stored at 10 °C showed lower fruit firmness after 14 days and reduced the soluble solids content after 21 days of storage. The metabolite analysis indicated that the fruit stored at 10 °C had higher contents of anthocyanins (pelargonidin-3-O-glucoside, cyanidin-3-O-glucoside, cyanidin-3-O-rutinoside and quercetin-3-O-rutinose), quercetin and sucrose in the flesh. According to the results of weighted gene coexpression correlation network analysis (WGCNA), the turquoise module was positively correlated with the content of anthocyanin components, and flavanone 3-hydroxylase (F3H) and chalcone synthase (CHS) were considered hub genes. Moreover, MYB family transcription factor APL (APL), MYB10 transcription factor (MYB10), ethylene-responsive transcription factor WIN1 (WIN1), basic leucine zipper 43-like (bZIP43) and transcription factor bHLH111-like isoform X2 (bHLH111) were closely related to these hub genes. Further qRT–PCR analysis verified that these transcription factors were specifically more highly expressed in plum flesh stored at 10 °C, and their expression profiles were significantly positively correlated with the structural genes of anthocyanin synthesis as well as the content of anthocyanin components. In addition, the sucrose biosynthesis-associated gene sucrose synthase (SS) was upregulated at 10 °C, which was also closely related to the anthocyanin content of plum fruit stored at 10 °C. Conclusions The present results suggest that the transcription factors APL, MYB10, WIN1, bZIP43 and bHLH111 may participate in the accumulation of anthocyanin in ‘Friar’ plum flesh during intermediate storage temperatures by regulating the expression of anthocyanin biosynthetic structural genes. In addition, the SS gene may play a role in anthocyanin accumulation in plum flesh by regulating sucrose biosynthesis.

scholarly journals The potential role of chemotaxis and the complement system in the formation and progression of thoracic aortic aneurysms inferred from the weighted gene coexpression network analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chuxiang Lei ◽  
Dan Yang ◽  
Wenlin Chen ◽  
Haoxuan Kan ◽  
Fang Xu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Clinical Significance ◽  
Immune Cells ◽  
Complement System ◽  
Aortic Wall ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
The Complement System ◽  
Coexpression Network Analysis

Abstract Background Thoracic aortic aneurysm (TAA) can be life-threatening due to the progressive weakening and dilatation of the aortic wall. Once the aortic wall has ruptured, no effective pharmaceutical therapies are available. However, studies on TAA at the gene expression level are limited. Our study aimed to identify the driver genes and critical pathways of TAA through gene coexpression networks. Methods We analyzed the genetic data of TAA patients from a public database by weighted gene coexpression network analysis (WGCNA). Modules with clinical significance were identified, and the differentially expressed genes (DEGs) were intersected with the genes in these modules. Gene Ontology and pathway enrichment analyses were performed. Finally, hub genes that might be driving factors of TAA were identified. Furthermore, we evaluated the diagnostic accuracy of these genes and analyzed the composition of immune cells using the CIBERSORT algorithm. Results We identified 256 DEGs and two modules with clinical significance. The immune response, including leukocyte adhesion, mononuclear cell proliferation and T cell activation, was identified by functional enrichment analysis. CX3CR1, C3, and C3AR1 were the top 3 hub genes in the module correlated with TAA, and the areas under the curve (AUCs) by receiver operating characteristic (ROC) analysis of all the hub genes exceeded 0.7. Finally, we found that the proportions of infiltrating immune cells in TAA and normal tissues were different, especially in terms of macrophages and natural killer (NK) cells. Conclusion Chemotaxis and the complement system were identified as crucial pathways in TAA, and macrophages with interactive immune cells may regulate this pathological process.

scholarly journals Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis

2021 ◽  
Vol 7 ◽  
Author(s):  
Tao Yan ◽  
Shijie Zhu ◽  
Miao Zhu ◽  
Chunsheng Wang ◽  
Changfa Guo
Keyword(s):  
Atrial Fibrillation ◽  
Network Analysis ◽  
Molecular Mechanism ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Correlation Network ◽  
Hub Genes ◽  
Qrt Pcr ◽  
Gene Correlation

Background: Atrial fibrillation (AF) is the most common tachyarrhythmia in the clinic, leading to high morbidity and mortality. Although many studies on AF have been conducted, the molecular mechanism of AF has not been fully elucidated. This study was designed to explore the molecular mechanism of AF using integrative bioinformatics analysis and provide new insights into the pathophysiology of AF.Methods: The GSE115574 dataset was downloaded, and Cibersort was applied to estimate the relative expression of 22 kinds of immune cells. Differentially expressed genes (DEGs) were identified through the limma package in R language. Weighted gene correlation network analysis (WGCNA) was performed to cluster DEGs into different modules and explore relationships between modules and immune cell types. Functional enrichment analysis was performed on DEGs in the significant module, and hub genes were identified based on the protein-protein interaction (PPI) network. Hub genes were then verified using quantitative real-time polymerase chain reaction (qRT-PCR).Results: A total of 2,350 DEGs were identified and clustered into eleven modules using WGCNA. The magenta module with 246 genes was identified as the key module associated with M1 macrophages with the highest correlation coefficient. Three hub genes (CTSS, CSF2RB, and NCF2) were identified. The results verified using three other datasets and qRT-PCR demonstrated that the expression levels of these three genes in patients with AF were significantly higher than those in patients with SR, which were consistent with the bioinformatic analysis.Conclusion: Three novel genes identified using comprehensive bioinformatics analysis may play crucial roles in the pathophysiological mechanism in AF, which provide potential therapeutic targets and new insights into the treatment and early detection of AF.

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 Expression of Adhesive Pili and the Collagen-Binding Adhesin Ace Is Activated by ArgR Family Transcription Factors inEnterococcus faecalis

2018 ◽  
Vol 200 (18) ◽  
Author(s):  
Dawn A. Manias ◽  
Gary M. Dunny
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Enterococcus Faecalis ◽  
Opportunistic Infections ◽  
Structural Genes ◽  
Gene Encoding ◽  
Collagen Binding ◽  
Content Type ◽  
Surface Adhesins

ABSTRACTIt was shown previously that the disruption of theahrCgene encoding a predicted ArgR family transcription factor results in a severe defect in biofilm formationin vitro, as well as a significant attenuation of virulence ofEnterococcus faecalisstrain OG1RF in multiple experimental infection models. Using transcriptome sequencing (RNA-seq), we observedahrC-dependent changes in the expression of more than 20 genes. AhrC-repressed genes included predicted determinants of arginine catabolism and several other metabolic genes and predicted transporters, while AhrC-activated genes included determinants involved in the production of surface protein adhesins. Most notably, the structural and regulatory genes of theebplocus encoding adhesive pili were positively regulated, as well as theacegene, encoding a collagen-binding adhesin. UsinglacZtranscription reporter fusions, we determined thatahrCand a secondargRtranscription factor gene,argR2, both function to activate the expression ofebpR, which directly activates the transcription of the pilus structural genes. Our data suggest that in the wild-typeE. faecalis, the low levels of EbpR limit the expression of pili and that biofilm biomass is also limited by the amount of pili expressed by the bacteria. The expression ofaceis similarly enhanced by AhrC and ArgR2, butaceexpression is not dependent on EbpR. Our results demonstrate the existence of novel regulatory cascades controlled by a pair of ArgR family transcription factors that might function as a heteromeric protein complex.IMPORTANCECell surface adhesins play critical roles in the formation of biofilms, host colonization, and the pathogenesis of opportunistic infections byEnterococcus faecalis. Here, we present new results showing that the expression of two major enterococcal surface adhesins,ebppili, and the collagen-binding protein Ace is positively regulated at the transcription level by twoargRfamily transcription factors, AhrC and ArgR2. In the case of pili, the direct target of regulation is theebpRgene, previously shown to activate the transcription of the pilus structural genes, while the activation ofacetranscription appears to be directly impacted by the two ArgR proteins. These transcription factors may represent new targets for blocking enterococcal infections.

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 Mutational Analysis of AREA, a Transcriptional Activator Mediating Nitrogen Metabolite Repression in Aspergillus nidulans and a Member of the “Streetwise” GATA Family of Transcription Factors

1998 ◽  
Vol 62 (3) ◽  
pp. 586-596 ◽  
Author(s):  
Richard A. Wilson ◽  
Herbert N. Arst
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Dna Binding ◽  
Aspergillus Nidulans ◽  
Transcriptional Activator ◽  
Structural Genes ◽  
Gata Factor ◽  
Mutational Change ◽  
Nitrogen Metabolite Repression ◽  
Gata Family

SUMMARY The transcriptional activator AREA is a member of the GATA family of transcription factors and mediates nitrogen metabolite repression in the fungus Aspergillus nidulans. The nutritional versatility of A. nidulans and its amenability to classical and reverse genetic manipulations make the AREA DNA binding domain (DBD) a useful model for analyzing GATA family DBDs, particularly as structures of two AREA-DNA complexes have been determined. The 109 extant mutant forms of the AREA DBD surveyed here constitute one of the highest totals of eukaryotic transcription factor DBD mutants, are discussed in light of the roles of individual residues, and are compared to corresponding mutant sequence changes in other fungal GATA factor DBDs. Other topics include delineation of the DBD using both homology and mutational truncation, use of frameshift reversion to detect regions of tolerance to mutational change, the finding that duplication of the DBD can apparently enhance AREA function, and use of the AREA system to analyze a vertebrate GATA factor DBD. Some major points to emerge from work on the AREA DBD are (i) tolerance to sequence change (with retention of function) is surprisingly great, (ii) mutational changes in a transcription factor can have widely differing, even opposing, effects on expression of different structural genes so that monitoring expression of one or even several structural genes can be insufficient and possibly misleading, and (iii) a mutational change altering local hydrophobic packing and DNA binding target specificity can markedly influence the behavior of mutational changes elsewhere in the DBD.

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.

Identification of potential proteases for abdominal aortic aneurysm by weighted gene coexpression network analysis

Genome ◽  
2020 ◽  
Vol 63 (11) ◽  
pp. 561-575
Author(s):  
Hui Zhang ◽  
Dan Yang ◽  
Siliang Chen ◽  
Fangda Li ◽  
Liqiang Cui ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Network Analysis ◽  
Aortic Aneurysm ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Abdominal Aortic ◽  
T Cell Regulation ◽  
Coexpression Network Analysis

Proteases are involved in the degradation of the extracellular matrix (ECM), which contributes to the formation of abdominal aortic aneurysm (AAA). To identify new disease targets in addition to the results of previous microarray studies, we performed next-generation sequencing (NGS) of the whole transcriptome of Angiotensin II-treated ApoE−/− male mice (n = 4) and control mice (n = 4) to obtain differentially expressed genes (DEGs). Identified DEGs of proteases were analyzed using weighted gene coexpression network analysis (WGCNA). RT-qPCR was conducted to validate the differential expression of selected hub genes. We found that 43 DEGs were correlated with the expression of the protease profile, and most were clustered in immune response module. Among 26 hub genes, we found that Mmp16 and Mmp17 were significantly downregulated in AAA mice, while Ctsa, Ctsc, and Ctsw were upregulated. Our functional annotation analysis of genes coexpressed with the five hub genes indicated that Ctsw and Mmp17 were involved in T cell regulation and Cell adhesion molecule pathway, respectively, and that both were involved in general regulation of the cell cycle and gene expression. Overall, our data suggest that these ectopic genes are potentially crucial to AAA formation and may act as biomarkers for the diagnosis of AAA.

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