scholarly journals An Actinidia chinensis (kiwifruit) eFP browser and network analysis of transcription factors

2020 ◽  
Author(s):  
Lara Brian ◽  
Ben Warren ◽  
Peter McAtee ◽  
Jessica Rodrigues ◽  
Niels Nieuwenhuizen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Fruit Development ◽  
Transcriptional Control ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Actinidia Chinensis ◽  
Knox Gene ◽  
Hub Genes ◽  
Flower And Fruit Development

Abstract BackgroundTranscriptomic studies combined with a well annotated genome have laid the foundations for new understanding of molecular processes. Tools which visualise gene expression patterns have further added to these resources. The manual annotation of the Actinidia chinensis (kiwifruit) genome has resulted in a high quality set of 33,044 genes. Here we investigate gene expression patterns in diverse tissues, visualised in an Electronic Fluorescent Pictograph (eFP) browser, to study the relationship of transcription factor (TF) expression using network analysis. ResultsSixty-one samples covering diverse tissues at different developmental time points were selected for RNAseq analysis and an eFP browser was generated to visualise this dataset. 2,839 TFs representing 57 different classes were identified and named. Network analysis of the TF expression patterns separated TFs into 14 different modules. Two modules consisting of 237 TFs were correlated with floral bud and flower development, a further two modules containing 160 TFs were associated with fruit development and maturation. A single module of 480 TFs was associated with ethylene-induced fruit ripening. Three “hub” genes correlated with flower and fruit development consisted of a HAF-like gene central to gynoecium development, an ERF and a DOF gene. Maturing and ripening hub genes included a KNOX gene that was associated with seed maturation, and a GRAS-like TF.ConclusionsThis study provides an insight into the complexity of the transcriptional control of flower and fruit development, as well as providing a new resource to the plant community. The eFP browser is provided in an accessible format that allows researchers to download and work internally.

scholarly journals A gene expression atlas for kiwifruit (Actinidia chinensis) and network analysis of transcription factors

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lara Brian ◽  
Ben Warren ◽  
Peter McAtee ◽  
Jessica Rodrigues ◽  
Niels Nieuwenhuizen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Fruit Development ◽  
Transcriptional Control ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Actinidia Chinensis ◽  
Knox Gene ◽  
Hub Genes ◽  
Flower And Fruit Development

Abstract Background Transcriptomic studies combined with a well annotated genome have laid the foundations for new understanding of molecular processes. Tools which visualise gene expression patterns have further added to these resources. The manual annotation of the Actinidia chinensis (kiwifruit) genome has resulted in a high quality set of 33,044 genes. Here we investigate gene expression patterns in diverse tissues, visualised in an Electronic Fluorescent Pictograph (eFP) browser, to study the relationship of transcription factor (TF) expression using network analysis. Results Sixty-one samples covering diverse tissues at different developmental time points were selected for RNA-seq analysis and an eFP browser was generated to visualise this dataset. 2839 TFs representing 57 different classes were identified and named. Network analysis of the TF expression patterns separated TFs into 14 different modules. Two modules consisting of 237 TFs were correlated with floral bud and flower development, a further two modules containing 160 TFs were associated with fruit development and maturation. A single module of 480 TFs was associated with ethylene-induced fruit ripening. Three “hub” genes correlated with flower and fruit development consisted of a HAF-like gene central to gynoecium development, an ERF and a DOF gene. Maturing and ripening hub genes included a KNOX gene that was associated with seed maturation, and a GRAS-like TF. Conclusions This study provides an insight into the complexity of the transcriptional control of flower and fruit development, as well as providing a new resource to the plant community. The Actinidia eFP browser is provided in an accessible format that allows researchers to download and work internally.

RNA-Seq Analysis of Spatiotemporal Gene Expression Patterns During Fruit Development Revealed Reference Genes for Transcript Normalization in Plums

2015 ◽  
Vol 33 (6) ◽  
pp. 1634-1649 ◽  
Author(s):  
Ho-Youn Kim ◽  
Prasenjit Saha ◽  
Macarena Farcuh ◽  
Bosheng Li ◽  
Avi Sadka ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fruit Development ◽  
Reference Genes ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Rna Seq

scholarly journals Weighted gene co-expression network analysis to identify key modules and hub genes associated with paucigranulocytic asthma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Min Li ◽  
Wenye Zhu ◽  
Chu Wang ◽  
Yuanyuan Zheng ◽  
Shibo Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Immune Status ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes

Abstract Background Asthma is a heterogeneous disease that can be divided into four inflammatory phenotypes: eosinophilic asthma (EA), neutrophilic asthma (NA), mixed granulocytic asthma (MGA), and paucigranulocytic asthma (PGA). While research has mainly focused on EA and NA, the understanding of PGA is limited. In this study, we aimed to identify underlying mechanisms and hub genes of PGA. Methods Based on the dataset from Gene Expression Omnibus(GEO), weighted gene coexpression network analysis (WGCNA), differentially expressed genes (DEGs) analysis and protein–protein interaction (PPI) network analysis were conducted to construct a gene network and to identify key gene modules and hub genes. Functional enrichment analyses were performed to investigate the biological process, pathways and immune status of PGA. The hub genes were validated in a separate dataset. Results Compared to non-PGA, PGA had a different gene expression pattern, in which 449 genes were differentially expressed. One gene module significantly associated with PGA was identified. Intersection between the differentially expressed genes (DEGs) and the genes from the module that were most relevant to PGA were mainly enriched in inflammation and immune response regulation. The single sample Gene Set Enrichment Analysis (ssGSEA) suggested a decreased immune infiltration and function in PGA. Finally six hub genes of PGA were identified, including ADCY2, CXCL1, FPRL1, GPR109B, GPR109A and ADCY3, which were validated in a separate dataset of GSE137268. Conclusions Our study characterized distinct gene expression patterns, biological processes and immune status of PGA and identified hub genes, which may improve the understanding of underlying mechanism and provide potential therapeutic targets for PGA.

scholarly journals In Silico Analysis of the Gene Expression Patterns between Aldosterone-Producing Adenoma and Nonfunctional Adrenocortical Adenoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yongfa Dai ◽  
Jing Li ◽  
Hong Wen ◽  
Jie Liu ◽  
Jianling Li
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Primary Aldosteronism ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Pathophysiological Mechanism ◽  
Adrenocortical Adenoma ◽  
Hub Genes ◽  
Aldosterone Producing Adenoma

Primary aldosteronism is the most common form of secondary hypertension, and aldosteronoma makes up a significant proportion of primary aldosteronism cases. Aldosteronoma is also called aldosterone-producing adenoma (APA). Although there have been many studies about APA, the pathogenesis of this disease is not yet fully understood. In this study, we aimed to find out the difference of gene expression patterns between APA and nonfunctional adrenocortical adenoma (NFAA) using a weighted gene coexpression network (WGCNA) and differentially expressed gene (DEG) analysis; only the genes that meet the corresponding standards of both methods were defined as real hub genes and then used for further analysis. Twenty-nine real hub genes were found out, most of which were enriched in the phospholipid metabolic process. WISP2, S100A10, SSTR5-AS1, SLC29A1, APOC1, and SLITRK4 are six real hub genes with the same gene expression pattern between the combined and validation datasets, three of which indirectly or directly participate in lipid metabolism including WISP2, S100A10, and APOC1. According to the gene expression pattern of DEGs, we speculated five candidate drugs with potential therapeutic value for APA, one of which is cycloheximide, an inhibitor for phospholipid biosynthesis. All the evidence suggests that phospholipid metabolism may be an important pathophysiological mechanism for APA. Our study provides a new perspective regarding the pathophysiological mechanism of APA and offers some small molecules that may possibly be effective drugs against APA.

Abstract 222: SCARA5 and Suprabasin are Hub Genes of Co-expression Network Modules Associated with Peripheral Vein Graft Patency

2015 ◽  
Vol 35 (suppl_1) ◽  
Author(s):  
Richard D Kenagy ◽  
Mete Civelek ◽  
Shinsuke Kikuchi ◽  
Lihua Chen ◽  
Anthony Grieff ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Cell Lines ◽  
Vein Graft ◽  
Expression Patterns ◽  
Collagen Gel ◽  
Separate Analysis ◽  
Hub Genes ◽  
Graft Outcome ◽  
Collagen Gel Contraction

Introduction: About 30% of vein grafts fail because of intimal hyperplasia or negative remodeling. We have reported that vein graft cells from patients that develop stenosis proliferate more than cells from patients that maintain patent grafts. We have now analyzed gene expression of the same cell lines using a systems biology approach to cluster genes into modules based on their co-expression patterns and to correlate the results with graft outcome, growth data from our prior study, and with new studies of migration and matrix remodeling. Methods: RNA from 4 hour serum- or PDGF-BB-stimulated cells (13 non-stenotic and 7 stenotic cell lines) was used for microarray analysis of gene expression followed by weighted gene co-expression network analysis. Cell migration in microchemotaxis chambers in response to PDGF-BB and cell-mediated collagen gel contraction in response to serum were also determined. Gene function in growth or collagen gel contraction was determined using siRNA to inhibit gene expression. Results: Neither migration nor collagen gel contraction were correlated with graft outcome. While 1,188 and 1,340 genes were differentially expressed in response to serum and PDGF, respectively, graft outcome was not correlated with expression of any single gene. Network analysis revealed one module each from the separate analysis of the PDGF and serum data sets, which were called “Yellow” and “Skyblue” respectively, that were correlated with later graft stenosis (P=.005 and .02, respectively). Yellow was also associated with increased cell growth, and Skyblue was also associated with inhibition of collagen gel contraction. The hub genes for Yellow and Skyblue (i.e. the gene most correlated with other genes in the module), SCARA5 and SBSN, respectively, were tested for effects on proliferation and collagen contraction. SCARA5, but not SBSN, inhibited proliferation, and SBSN, but not SCARA5, inhibited collagen gel contraction. Conclusion: Using weighted gene co-expression network analysis of cultured vein graft cell gene expression, we have discovered a small number of genes of interest in vein graft failure. Further experiments are needed to discriminate the roles these genes play in vein graft healing starting with the module hub genes SCARA5 and SBSN.

scholarly journals Gene Co-expression Networks Identifies Common Hub Genes Between Cutaneous Sarcoidosis and Discoid Lupus Erythematosus

2020 ◽  
Vol 7 ◽  
Author(s):  
Melissa A. Nickles ◽  
Kai Huang ◽  
Yi-Shin Chang ◽  
Maria M. Tsoukas ◽  
Nadera J. Sweiss ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lupus Erythematosus ◽  
Expression Patterns ◽  
Therapeutic Targets ◽  
Gene Expression Patterns ◽  
Discoid Lupus Erythematosus ◽  
Cutaneous Sarcoidosis ◽  
Hub Genes ◽  
Common Gene ◽  
Gene Similarity

In this study we analyzed gene co-expression networks of three immune-related skin diseases: cutaneous sarcoidosis (CS), discoid lupus erythematosus (DLE), and psoriasis. We propose that investigation of gene co-expression networks may provide insights into underlying disease mechanisms. Microarray expression data from two cohorts of patients with CS, DLE, or psoriasis skin lesions were analyzed. We applied weighted gene correlation network analysis (WGCNA) to construct gene-gene similarity networks and cluster genes into modules based on similar expression profiles. A module of interest that was preserved between datasets and corresponded with case/control status was identified. This module was related to immune activation, specifically leukocyte activation, and was significantly increased in both CS lesions and DLE lesions compared to their respective controls. Protein-protein interaction (PPI) networks constructed for this module revealed seven common hub genes between CS lesions and DLE lesions: TLR1, ITGAL, TNFRSF1B, CD86, SPI1, BTK, and IL10RA. Common hub genes were highly upregulated in CS lesions and DLE lesions compared to their respective controls in a differential expression analysis. Our results indicate common gene expression patterns in the immune processes of CS and DLE, which may have indications for future therapeutic targets and serve as Th1-mediated disease biomarkers. Additionally, we identified hub genes unique to CS and DLE, which can help differentiate these diseases from one another and may serve as unique therapeutic targets and biomarkers. Notably, we find common gene expression patterns in the immune processes of CS and DLE through utilization of WGCNA.

Microarray analysis of gene expression patterns during fruit development in European pear (Pyrus communis)

2013 ◽  
Vol 164 ◽  
pp. 466-473 ◽  
Author(s):  
Kenji Nashima ◽  
Tokurou Shimizu ◽  
Chikako Nishitani ◽  
Toshiya Yamamoto ◽  
Hirokazu Takahashi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Microarray Analysis ◽  
Fruit Development ◽  
Expression Patterns ◽  
Pyrus Communis ◽  
Gene Expression Patterns ◽  
European Pear

Differential gene expression in liposarcoma: Insight into pathways for dedifferentiation?

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 10048-10048
Author(s):  
Dale Han ◽  
Gregory C Bloom ◽  
Marilyn M Bui ◽  
Steven Enkemann ◽  
Hideko Yamauchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lipid Metabolism ◽  
Network Analysis ◽  
Differential Expression ◽  
Differential Gene Expression ◽  
Gene Networks ◽  
Expression Patterns ◽  
Differential Regulation ◽  
Gene Expression Patterns ◽  
Differential Gene

10048 Background: Liposarcoma (LPS) dedifferentiation signifies conversion to a clinically aggressive phenotype, but the biologic processes required for this change have not been determined. We describe differential gene expression patterns between well-differentiated (WD) and dedifferentiated (DD) tumors to determine pathways involved in LPS dedifferentiation. Methods: From 1999 to 2006, 121 fatty tumors were resected at a single institution. Twenty tumors, consisting of atypical lipomatous tumors (ALT), WD LPS or DD LPS, were randomly selected and clinicopathologic characteristics were retrospectively reviewed. Gene expression profiling was performed on extracted RNA using the Affymetrix GeneChip platform. Differentially expressed genes were obtained and gene network analysis was done using GeneGO by MetaCore. Results: Median age was 59 years and 70% of cases were male. WD tumors, consisting of 3 ALT and 6 WD LPS, were compared with 11 DD LPS. After a median follow-up of 64 months, 7 patients had died of whom 6 had DD LPS. DD histology was associated with lower overall survival (p<0.05). Significance Analysis of Microarrays for WD tumors vs. DD LPS using a 0% false discovery rate showed differential expression of 188 genes. Network analysis of genes from WD tumors vs. DD LPS showed significant (p<0.001) differential regulation of glucose-activated transcription factor ChREBP (carbohydrate response element binding protein), a key element involved in lipogenesis, gluconeogenesis and glycolysis. There was also significant differential regulation of insulin signaling, PI3K-dependent and PKA signal transduction pathways and of amino acid, fatty acid and glucose metabolism pathways (p<0.05). These pathways, based on Gene Ontology cellular processes, mapped to gene networks primarily involved in lipid metabolism (p<0.05). Conclusions: Differential expression of genes involved in lipid metabolism networks is seen in DD LPS and changes in lipid metabolism may be associated with dedifferentiation. These differential gene expression patterns may help identify fatty tumors potentially at risk for progressing to a malignant or DD state and provide prognostic factors and therapeutic targets for patients with LPS.

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