EXPath: a database of comparative expression analysis inferring metabolic pathways for plants.
ABSTRACT: In general, the expression of gene alters conditionally to catalyze a specific metabolic pathway. Microarray-based datasets have been massively produced to monitor gene expression levels in parallel with numerous experimental treatments. Although several studies facilitated the linkage of gene expression data and metabolic pathways, none of them are amassed for plants. Moreover, advanced analysis such as pathways enrichment or how genes express under different conditions is not rendered.Therefore, EXPath was developed to not only comprehensively congregate the public microarray expression data from over 1000 samples in biotic stress, abiotic stress, and hormone secretion but also allow the usage of this abundant resource for coexpression analysis and differentially expression genes (DEGs) identification, finally inferring the enriched KEGG pathways and gene ontology (GO) terms of three model plants: Arabidopsis thaliana, Oryza sativa, and Zea mays. Users can access the gene expression patterns of interest under various conditions via five main functions (Gene Search, Pathway Search, DEGs Search, Pathways/GO Enrichment, and Coexpression analysis) in EXPath, which are presented by a user-friendly interface and valuable for further research.In conclusion, EXPath, freely available at http://expath.itps.ncku.edu.tw, is a database resource that collects and utilizes gene expression profiles derived from microarray platforms under various conditions to infer metabolic pathways for plants.
Project description:BACKGROUND:Gene alterations are crucial to the molecular pathogenesis of pancreatic cancer. The present study was designed to identify the potential candidate genes in the pancreatic carcinogenesis. METHODS:Gene Expression Omnibus database (GEO) datasets of pancreatic cancer tissue were retrieval and the differentially expressed genes (DEGs) from individual microarray data were merged. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) networks, and gene coexpression analysis were performed. RESULTS:Three GEO datasets, including 74 pancreatic cancer samples and 55 controls samples were selected. A total of 2325 DEGs were identified, including 1383 upregulated and 942 downregulated genes. The GO terms for molecular functions, biological processes, and cellular component were protein binding, small molecule metabolic process, and integral to membrane, respectively. The most significant pathway in KEGG analysis was metabolic pathways. PPI network analysis indicated that the significant hub genes including cytochrome P450, family 2, subfamily E, polypeptide 1 (CYP2E1), mitogen-activated protein kinase 3 (MAPK3), and phospholipase C, gamma 1 (PLCG1). Gene coexpression network analysis identified 4 major modules, and the potassium channel tetramerization domain containing 10 (KCTD10), kin of IRRE like (KIRREL), dipeptidyl-peptidase 10 (DPP10), and unc-80 homolog (UNC80) were the hub gene of each modules, respectively. CONCLUSION:Our integrative analysis provides a comprehensive view of gene expression patterns associated with the pancreatic carcinogenesis.
Project description:Patients with lymphoma are at high risk of developing venous thromboembolism (VTE). The purpose of the present study was to identify the target gene associated with VTE for patients with lymphoma. Microarray data was downloaded from the gene expression omnibus database (GSE17078), which comprised the control group, 27 normal blood outgrowth endothelial cell (BOEC) samples, and the case group, 3 BOEC samples of venous thrombosis with protein C deficiency. Differentially expressed genes (DEGs) were identified by the Limma package of R. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed via the database for annotation, visualization and integrated discovery. Differentially coexpressed pairs were identified by the DCGL package of R. The subsequent protein-protein interaction (PPI) networks and gene coexpression networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins database, and were visualized by Cytoscape software. A total of 110 DEGs were obtained, including 73 upregulated and 37 downregulated genes. GO and KEGG pathway enrichment analyses identified 132 significant GO terms and 9 significant KEGG pathways. In total, 97 PPI pairs for PPI network and 309 differential coexpression pairs for the gene coexpression network were obtained. Additionally, the connective tissue growth factor (CTGF) gene was closely connected with other genes in the two networks. A total of 2 KEGG pathways were associated with VTE and CTGF may be the target gene of VTE in patients with lymphoma. The present study may identify the molecular mechanism of VTE, but additional clinical study is required to validate the results.
Project description:We describe a computationally efficient statistical framework for estimating networks of coexpressed genes. This framework exploits first-order conditional independence relationships among gene-expression measurements to estimate patterns of association. We use this approach to estimate a coexpression network from microarray gene-expression measurements from Saccharomyces cerevisiae. We demonstrate the biological utility of this approach by showing that a large number of metabolic pathways are coherently represented in the estimated network. We describe a complementary unsupervised graph search algorithm for discovering locally distinct subgraphs of a large weighted graph. We apply this algorithm to our coexpression network model and show that subgraphs found using this approach correspond to particular biological processes or contain representatives of distinct gene families.
Project description:Hepatitis B virus-associated acute liver failure (HBV-ALF) is a rare but life-threatening syndrome that carried a high morbidity and mortality. Our study aimed to explore the possible molecular mechanisms of HBV-ALF by means of bioinformatics analysis. In this study, genes expression microarray datasets of HBV-ALF from Gene Expression Omnibus were collected, and then we identified differentially expressed genes (DEGs) by the limma package in R. After functional enrichment analysis, we constructed the protein-protein interaction (PPI) network by the Search Tool for the Retrieval of Interacting Genes online database and weighted genes coexpression network by the WGCNA package in R. Subsequently, we picked out the hub genes among the DEGs. A total of 423 DEGs with 198 upregulated genes and 225 downregulated genes were identified between HBV-ALF and normal samples. The upregulated genes were mainly enriched in immune response, and the downregulated genes were mainly enriched in complement and coagulation cascades. Orosomucoid 1 (ORM1), orosomucoid 2 (ORM2), plasminogen (PLG), and aldehyde oxidase 1 (AOX1) were picked out as the hub genes that with a high degree in both PPI network and weighted genes coexpression network. The weighted genes coexpression network analysis found out 3 of the 5 modules that upregulated genes enriched in were closely related to immune system. The downregulated genes enriched in only one module, and the genes in this module majorly enriched in the complement and coagulation cascades pathway. In conclusion, 4 genes (ORM1, ORM2, PLG, and AOX1) with immune response and the complement and coagulation cascades pathway may take part in the pathogenesis of HBV-ALF, and these candidate genes and pathways could be therapeutic targets for HBV-ALF.
Project description:Aim:The incidence of Alzheimer's disease (AD) has been increasing in recent years, but there exists no cure and the pathological mechanisms are not fully understood. This study aimed to find out the pathogenesis of learning and memory impairment, new biomarkers, potential therapeutic targets, and drugs for AD. Methods:We downloaded the microarray data of entorhinal cortex (EC) and hippocampus (HIP) of AD and controls from Gene Expression Omnibus (GEO) database, and then the differentially expressed genes (DEGs) in EC and HIP regions were analyzed for functional and pathway enrichment. Furthermore, we utilized the DEGs to construct coexpression networks to identify hub genes and discover the small molecules which were capable of reversing the gene expression profile of AD. Finally, we also analyzed microarray and RNA-seq dataset of blood samples to find the biomarkers related to gene expression in brain. Results:We found some functional hub genes, such as ErbB2, ErbB4, OCT3, MIF, CDK13, and GPI. According to GO and KEGG pathway enrichment, several pathways were significantly dysregulated in EC and HIP. CTSD and VCAM1 were dysregulated significantly in blood, EC, and HIP, which were potential biomarkers for AD. Target genes of four microRNAs had similar GO_terms distribution with DEGs in EC and HIP. In addtion, small molecules were screened out for AD treatment. Conclusion:These biological pathways and DEGs or hub genes will be useful to elucidate AD pathogenesis and identify novel biomarkers or drug targets for developing improved diagnostics and therapeutics against AD.
Project description:Next generation sequencing (NGS) has become the mainstream approach for monitoring gene expression levels in parallel with various experimental treatments. Unfortunately, there is no systematical webserver to comprehensively perform further analysis based on the huge amount of preliminary data that is obtained after finishing the process of gene annotation. Therefore, a user-friendly and effective system is required to mine important genes and regulatory pathways under specific conditions from high-throughput transcriptome data. EXPath Tool (available at: http://expathtool.itps.ncku.edu.tw/) was developed for the pathway annotation and comparative analysis of user-customized gene expression profiles derived from microarray or NGS platforms under various conditions to infer metabolic pathways for all organisms in the KEGG database. EXPath Tool contains several functions: access the gene expression patterns and the candidates of co-expression genes; dissect differentially expressed genes (DEGs) between two conditions (DEGs search), functional grouping with pathway and GO (Pathway/GO enrichment analysis), and correlation networks (co-expression analysis), and view the expression patterns of genes involved in specific pathways to infer the effects of the treatment. Additionally, the effectively of EXPath Tool has been performed by a case study on IAA-responsive genes. The results demonstrated that critical hub genes under IAA treatment could be efficiently identified.
Project description:The mechanism of winter survival for perennials involves multiple levels of gene regulation, especially cold resistance. Agropyron mongolicum is one important perennial grass species, but there is little information regarding its overwintering mechanism. We performed a comprehensive transcriptomics study to evaluate global gene expression profiles regarding the winter survival of Agropyron mongolicum. A genome-wide gene expression analysis involving four different periods was identified. Twenty-eight coexpression modules with distinct patterns were performed for transcriptome profiling. Furthermore, differentially expressed genes (DEGs) and their functional characterization were defined using a genome database such as NT, NR, COG, and KEGG.A total of 79.6% of the unigenes were characterized to be involved in 136 metabolic pathways. In addition, the expression level of ABA receptor genes, regulation of transcription factors, and a coexpression network analysis were conducted using transcriptome data. We found that ABA receptors regulated downstream gene expression by activating bZIP and NAC transcription factors to improve cold resistance and winter survival.This study provides comprehensive transcriptome data for the characterization of overwintering-related gene expression profiles in A. mongolicum. Genomics resources can help provide a better understanding of the overwintering mechanism for perennial gramineae species. By analyzing genome-wide expression patterns for the four key stages of tiller bud development, the functional characteristics of the DEGs were identified that participated in various metabolic pathways and have been shown to be strongly associated with cold tolerance. These results can be further exploited to determine the mechanism of overwintering in perennial gramineae species.
Project description:The aim of the present study was to screen potential key genes associated with osteoporotic fracture healing. The microarray data from the Gene Expression Omnibus database accession number GSE51686, were downloaded and used to identify differentially expressed genes (DEGs) in fracture callus tissue samples obtained from the femora of type I collagen (Col1a1)?kringle containing transmembrane protein 2 (Krm2) mice and low density lipoprotein receptor?related protein 5?/? (Lrp5?/?) transgenic mice of osteoporosis compared with those in wild?type (WT) mice. Enrichment analysis was performed to reveal the DEG function. In addition, protein?protein interactions (PPIs) of DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes database. The coexpression associations between hub genes in the PPI network were investigated, and a coexpression network was constructed. A total of 841 DEGs (335 upregulated and 506 downregulated) were identified in the Col1a1?Krm2 vs. the WT group, and 50 DEGs (16 upregulated and 34 downregulated) were identified in the Lrp5?/? vs. the WT group. The DEGs in Col1a1?Krm2 mice were primarily associated with immunity and cell adhesion (GO: 0007155) functions. By contrast, the DEGs in Lrp5?/? mice were significantly associated with muscle system process (GO: 0003012) and regulation of transcription (GO: 0006355). In addition, a series of DEGs demonstrated a higher score in the PPI network, and were observed to be coexpressed in the coexpression network, and included thrombospondin 2 (Thbs2), syndecan 2 (Sdc2), FK506 binding protein 10 (Fkbp10), 2'?5'-oligoadneylate synthase?like protein 2 (Oasl2), interferon induced protein with tetratricopeptide repeats (Ifit) 1 and Ifit2. Thbs2 and Sdc2 were significantly correlated with extracellular matrix?receptor interactions. The results suggest that Thbs2, Sdc2, Fkbp10, Oasl2, Ifit1 and Ifit2 may serve important roles during the fracture healing process in osteoporosis. In addition, this is the first study to demonstrate that Sdc2, Fkbp10, Oasl2, Ifit1 and Ifit2 may be associated with osteoporotic fracture healing.
Project description:Type 2 diabetes mellitus (T2DM) is a metabolic disorder. Numerous proteins have been identified that are associated with the occurrence and development of T2DM. This study aimed to identify potential core genes and pathways involved in T2DM, through exhaustive bioinformatic analyses using GSE20966 microarray profiles of pancreatic ??cells obtained from healthy controls and patients with T2DM. The original microarray data were downloaded from the Gene Expression Omnibus database. Data were processed by the limma package in R software and the differentially expressed genes (DEGs) were identified. Gene Ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were carried out to identify potential biological functions and pathways of the DEGs. Key transcription factors were identified using the WEB?based GEne SeT AnaLysis Toolkit (WebGestalt) and Enrichr. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to establish a protein?protein interaction (PPI) network for the DEGs. In total, 329 DEGs were involved in T2DM, with 208 upregulated genes enriched in pancreatic secretion and the complement and coagulation cascades, and 121 downregulated genes enriched in insulin secretion, carbohydrate digestion and absorption, and the Toll?like receptor pathway. Furthermore, hepatocyte nuclear factor 1?alpha (HNF1A), signal transducer and activator of transcription 3 (STAT3) and glucocorticoid receptor (GR) were key transcription factors in T2DM. Twenty important nodes were detected in the PPI network. Finally, two core genes, serpin family G member 1 (SERPING1) and alanyl aminopeptidase, membrane (ANPEP), were shown to be associated with the development of T2DM. On the whole, the findings of this study enhance our understanding of the potential molecular mechanisms of T2DM and provide potential targets for further research.
Project description:A remarkable characteristic of pineapple is its ability to undergo floral induction in response to external ethylene stimulation. However, little information is available regarding the molecular mechanism underlying this process. In this study, the differentially expressed genes (DEGs) in plants exposed to 1.80 mL·L(-1) (T1) or 2.40 mL·L(-1) ethephon (T2) compared with Ct plants (control, cleaning water) were identified using RNA-seq and gene expression profiling. Illumina sequencing generated 65,825,224 high-quality reads that were assembled into 129,594 unigenes with an average sequence length of 1173 bp. Of these unigenes, 24,775 were assigned to specific KEGG pathways, of which metabolic pathways and biosynthesis of secondary metabolites were the most highly represented. Gene Ontology (GO) analysis of the annotated unigenes revealed that the majority were involved in metabolic and cellular processes, cell and cell part, catalytic activity and binding. Gene expression profiling analysis revealed 3788, 3062, and 758 DEGs in the comparisons of T1 with Ct, T2 with Ct, and T2 with T1, respectively. GO analysis indicated that these DEGs were predominantly annotated to metabolic and cellular processes, cell and cell part, catalytic activity, and binding. KEGG pathway analysis revealed the enrichment of several important pathways among the DEGs, including metabolic pathways, biosynthesis of secondary metabolites and plant hormone signal transduction. Thirteen DEGs were identified as candidate genes associated with the process of floral induction by ethephon, including three ERF-like genes, one ETR-like gene, one LTI-like gene, one FT-like gene, one VRN1-like gene, three FRI-like genes, one AP1-like gene, one CAL-like gene, and one AG-like gene. qPCR analysis indicated that the changes in the expression of these 13 candidate genes were consistent with the alterations in the corresponding RPKM values, confirming the accuracy and credibility of the RNA-seq and gene expression profiling results. Ethephon-mediated induction likely mimics the process of vernalization in the floral transition in pineapple by increasing LTI, FT, and VRN1 expression and promoting the up-regulation of floral meristem identity genes involved in flower development. The candidate genes screened can be used in investigations of the molecular mechanisms of the flowering pathway and of various other biological mechanisms in pineapple.