BACKGROUNDAlthough biomedical ontologies have standardized the representation of gene products across species and databases, a method for determining the functional similarities of gene products has not yet been developed.

METHODSWe proposed a new semantic similarity measure based on Gene Ontology that considers the semantic influences from all of the ancestor terms in a graph. Our measure was compared with Resnik's measure in two applications, which were based on the association of the measure used with the gene co-expression and the protein-protein interactions.

RESULTSThe results showed a considerable association between the semantic similarity and the expression correlation and between the semantic similarity and the protein-protein interactions, and our measure performed the best overall.

CONCLUSIONThese results revealed the potential value of our newly proposed semantic similarity measure in studying the functional relevance of gene products.

No abstract available.
Gene Ontology* ; Uterine Cervical Neoplasms*

The Gene Set network viewer (GSnet) visualizes the functional enrichment of a given gene set with a protein interaction network and is implemented as a plug-in for the Cytoscape platform. The functional enrichment of a given gene set is calculated using a hypergeometric test based on the Gene Ontology annotation. The protein interaction network is estimated using public data. Set operations allow a complex protein interaction network to be decomposed into a functionally-enriched module of interest. GSnet provides a new framework for gene set analysis by integrating a priori knowledge of a biological network with functional enrichment analysis.
Gene Ontology ; Microarray Analysis ; Protein Interaction Maps

This study aims to predict the material basis and mechanism of Dachengqi Decoction in the treatment of sepsis based on network pharmacology. The chemical constituents and targets of Dachengqi Decoction were retrieved from TCMSP, UniPot and DrugBank and the targets for the treatment of sepsis from OMIM and GeneCards. The potential targets of Dachengqi Decoction for the treatment of sepsis were screened by OmicShare. STRING database and Cytoscape 3.7.2 were used to construct the Chinese medicinal-active component-target-disease, active component-key target-key pathway, and protein-protein interaction(PPT) networks. The gene ontology(GO) term enrichment analysis and Kyoto encyclopedia of genes and genomes(KEGG) pathway enrichment analysis were performed by DAVID(P<0.05). Finally, the animal experiment was conducted to verify some targets and pathways. A total of 40 active components and 157 targets of the Dachengqi Decoction, 2 407 targets for the treatment of sepsis, and 91 common targets of the prescription and the disease were also obtained. The key targets were prostaglandin G/H synthase 2(PTGS2), prostaglandin G/H synthase 1(PTGS1), protein kinase cAMP-dependent catalytic-α(PRKACA), coagulation factor 2 receptor(F2 R), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic gamma subunit(PIK3 CG), dipeptidyl peptidase 4(DPP4), etc. A total of 533 terms and 125 pathways were obtained for the 91 targets. The main terms were the response to drug, negative regulation of apoptotic process, positive regulation of nitric oxide biosynthetic process and lipopolysaccharide-mediated signaling pathway, and the pathways included pathways in cancer, hepatitis B, and phosphatidylinositol 3-kinase and protein kinase B(PI3 K/Akt) signaling pathway. The animal experiment confirmed that Dachengqi Decoction can down-regulate inflammatory cytokines interleukin-1β(IL-1β), IL-6 and tumor necrosis factor α(TNF-α)(P<0.01). It could also reduce the wet/dry weight ratio of lung tissue, the level of myeloperoxidase(MPO) and the phosphorylation of PI3 K and Akt(P<0.01). These results indicated that Dchengqi Decoction could act on inflammation-related targets and improve sepsis by inhibiting PI3 K/Akt signaling pathway. The animal experiment supported the predictions of network pharmacology. Dachengqi Decoction intervenes sepsis via multiple components, multiple targets, and multiple pathways. The result lays a foundation for further research on the mechanism of Dachengqi Decoction in the treatment of sepsis.
Animals ; Drugs, Chinese Herbal ; Gene Ontology ; Plant Extracts ; Sepsis/genetics*

Proteomic tools were used to identify the key proteins that might be associated with bronchial asthma(BA). Firstly, the serum samples from healthy adults and asthmatic patients were collected. Tandem Mass Tag~(TM)(TMT), which removes high-abundance structures and nonspecific proteins, was employed to identify the differentially expressed proteins between asthmatic patients and healthy adults. Gene Ontology(GO) annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis were carried out for the differentially expressed proteins. The core proteins in the asthma group were screened out by protein-protein interaction(PPI) analysis. Then the core proteins were verified by Western blot for 3 patients with bronchial asthma and 3 healthy adults. A total of 778 differentially expressed proteins were screened out, among which 32 proteins contained quantitative information, including 18 up-regulated proteins and 14 down-regulated proteins. The differentially expressed proteins were enriched in 28 KEGG signaling pathways. The PPI analysis showed that 10 proteins(GDN, 1433 Z, VWF, HEMO, CERU, A1 AT, TSP1, G3 P, IBP7, and KPYM) might be involved in the pathogenesis of bronchial asthma. Compared with those in healthy adults, the expression levels of SLC25 A4, SVEP1, and KRT25 in the sera of asthmatic patients were up-regulated(P<0.05). Therefore, it is hypothesized that a variety of immune signaling pathways and differentially expressed proteins play a role in the pathogenesis of BA, which provides potential target information for the treatment of BA.
Adult ; Humans ; Proteomics ; Gene Ontology ; Proteins ; Disease Susceptibility ; Asthma/genetics*

OBJECTIVE: To screen differentially expressed gene (DEG) related to myelodysplastic syndrome (MDS) based on Gene Expression Omnibus (GEO) database, and explore the core genes and pathogenesis of MDS by analyzing the biological functions and related signaling pathways of DEG. METHODS: The expression profiles of GSE4619, GSE19429, GSE58831 including MDS patients and normal controls were downloaded from GEO database. The gene expression analysis tool (GEO2R) of GEO database was used to screen DEG according to | log FC (fold change) |≥1 and P<0.01. David online database was used to annotate gene ontology function (GO). Metascape online database was used to enrich and analyze differential genes in Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction network (PPI) was constructed by using STRING database. CytoHubba and Mcode plug-ins of Cytoscape were used to analyze the key gene clusters and hub genes. R language was used to diagnose hub genes and draw the ROC curve. GSEA enrichment analysis was performed on GSE19429 according to the expression of LEF1. RESULTS: A total of 74 co-DEG were identified, including 14 up-regulated genes and 60 down regulated genes. GO enrichment analysis indicated that BP of down regulated genes was mainly enriched in the transcription and regulation of RNA polymerase II promoter, negative regulation of cell proliferation, and immune response. CC of down regulated genes was mainly enriched in the nucleus, transcription factor complexes, and adhesion spots. MF was mainly enriched in protein binding, DNA binding, and β-catenin binding. KEGG pathway was enriched in primary immunodeficiency, Hippo signaling pathway, cAMP signaling pathway, transcriptional mis-regulation in cancer and hematopoietic cell lineage. BP of up-regulated genes was mainly enriched in type I interferon signaling pathway and viral response. CC was mainly enriched in cytoplasm. MF was mainly enriched in RNA binding. Ten hub genes and three important gene clusters were screened by STRING database and Cytoscape software. The functions of the three key gene clusters were closely related to immune regulation. ROC analysis showed that the hub genes had a good diagnostic significance for MDS. GSEA analysis indicated that LEF1 may affect the normal function of hematopoietic stem cells by regulating inflammatory reaction, which further revealed the pathogenesis of MDS. CONCLUSION Bioinformatics can effectively screen the core genes and key signaling pathways of MDS, which provides a new strategy for the diagnosis and treatment of MDS.
Computational Biology ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Gene Ontology ; Humans ; Myelodysplastic Syndromes/genetics*

BACKGROUNDAsthma is a heterogeneous disease for which a strong genetic basis has been firmly established. Until now no studies have been undertaken to systemically explore the network of asthma-related genes using an internally developed literature-based discovery approach. This study was to explore asthma-related genes by using literature-based mining and network centrality analysis.

METHODSLiterature involving asthma-related genes were searched in PubMed from 2001 to 2011. Integration of natural language processing with network centrality analysis was used to identify asthma susceptibility genes and their interaction network. Asthma susceptibility genes were classified into three functional groups by gene ontology (GO) analysis and the key genes were confirmed by establishing asthma-related networks and pathways.

RESULTSThree hundred and twenty-six genes related with asthma such as IGHE (IgE), interleukin (IL)-4, 5, 6, 10, 13, 17A, and tumor necrosis factor (TNF)-alpha were identified. GO analysis indicated some biological processes (developmental processes, signal transduction, death, etc.), cellular components (non-structural extracellular, plasma membrane and extracellular matrix), and molecular functions (signal transduction activity) that were involved in asthma. Furthermore, 22 asthma-related pathways such as the Toll-like receptor signaling pathway, hematopoietic cell lineage, JAK-STAT signaling pathway, chemokine signaling pathway, and cytokine-cytokine receptor interaction, and 17 hub genes, such as JAK3, CCR1-3, CCR5-7, CCR8, were found.

CONCLUSIONSOur study provides a remarkably detailed and comprehensive picture of asthma susceptibility genes and their interacting network. Further identification of these genes and molecular pathways may play a prominent role in establishing rational therapeutic approaches for asthma.

Numerous studies have reported that genes with similar expression patterns are co-regulated. From gene expression data, we have assumed that genes having similar expression pattern would share similar transcription factor binding sites (TFBSs). These function as the binding regions for transcription factors (TFs) and thereby regulate gene expression. In this context, various analysis tools have been developed. However, they have shortcomings in the combined analysis of expression patterns and significant TFBSs and in the functional analysis of target genes of significantly overrepresented putative regulators. In this study, we present a web-based A Functional Clustering Analysis Tool for Predicted Transcription Regulatory Elements and Gene Ontology Terms (FCAnalyzer). This system integrates microarray clustering data with similar expression patterns, and TFBS data in each cluster. FCAnalyzer is designed to perform two independent clustering procedures. The first process clusters gene expression profiles using the K-means clustering method, and the second process clusters predicted TFBSs in the upstream region of previously clustered genes using the hierarchical biclustering method for simultaneous grouping of genes and samples. This system offers retrieved information for predicted TFBSs in each cluster using Match(TM) in the TRANSFAC database. We used gene ontology term analysis for functional annotation of genes in the same cluster. We also provide the user with a combinatorial TFBS analysis of TFBS pairs. The enrichment of TFBS analysis and GO term analysis is statistically by the calculation of P values based on Fisher's exact test, hypergeometric distribution and Bonferroni correction. FCAnalyzer is a web-based, user-friendly functional clustering analysis system that facilitates the transcriptional regulatory analysis of co-expressed genes. This system presents the analyses of clustered genes, significant TFBSs, significantly enriched TFBS combinations, their target genes and TFBS-TF pairs.
Binding Sites ; Cluster Analysis* ; Gene Expression ; Gene Ontology* ; Transcription Factors ; Transcriptome

BACKGROUND: Age-related macular degeneration (AMD) is the leading cause of vision loss worldwide. However, the mechanisms involved in the development and progression of AMD are poorly delineated. We aimed to explore the critical genes involved in the progression of AMD. METHODS: The differentially expressed genes (DEGs) in AMD retinal pigment epithelial (RPE)/choroid tissues were identified using the microarray datasets GSE99248 and GSE125564, which were downloaded from the gene expression omnibus database. The overlapping DEGs from the two datasets were screened to identify DEG-related biological pathways using gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The hub genes were identified from these DEGs through protein-protein interaction network analyses. The expression levels of hub genes were evaluated by quantitative real-time polymerase chain reaction following the induction of senescence in ARPE-19 with FK866. Following the identification of AMD-related key genes, the potential small molecule compounds targeting the key genes were predicted by PharmacoDB. Finally, a microRNA-gene interaction network was constructed. RESULTS: Microarray analyses identified 174 DEGs in the AMD RPE compared to the healthy RPE samples. These DEGs were primarily enriched in the pathways involved in the regulation of DNA replication, cell cycle, and proteasome-mediated protein polyubiquitination. Among the top ten hub genes, HSP90AA1, CHEK1, PSMA4, PSMD4, and PSMD8 were upregulated in the senescent ARPE-19 cells. Additionally, the drugs targeting HSP90AA1, CHEK1, and PSMA4 were identified. We hypothesize that Hsa-miR-16-5p might target four out of the five key DEGs in the AMD RPE. CONCLUSIONS Based on our findings, HSP90AA1 is likely to be a central gene controlling the DNA replication and proteasome-mediated polyubiquitination during the RPE senescence observed in the progression of AMD. Targeting HSP90AA1, CHEK1, PSMA4, PSMD4, and/or PSMD8 genes through specific miRNAs or small molecules might potentially alleviate the progression of AMD through attenuating RPE senescence.
DNA Replication ; Gene Expression Profiling ; Gene Ontology ; Humans ; Macular Degeneration/genetics* ; Proteasome Endopeptidase Complex

OBJECTIVE: To investigate a Met-controlled allosteric module (AM) of neural generation as a potential therapeutic target for brain ischemia. METHODS: We selected Markov clustering algorithm (MCL) to mine functional modules in the related target networks. According to the topological similarity, one functional module was predicted in the modules of baicalin (BA), jasminoidin (JA), cholic acid (CA), compared with I/R model modules. This functional module included three genes: Inppl1, Met and Dapk3 (IMD). By gene ontology enrichment analysis, biological process related to this functional module was obtained. This functional module participated in generation of neurons. Western blotting was applied to present the compound-dependent regulation of IMD. Co-immunoprecipitation was used to reveal the relationship among the three members. We used IF to determine the number of newborn neurons between compound treatment group and ischemia/reperfusion group. The expressions of vascular endothelial growth factor (VEGF) and matrix metalloproteinase 9 (MMP-9) were supposed to show the changing circumstances for neural generation under cerebral ischemia. RESULTS: Significant reduction in infarction volume and pathological changes were shown in the compound treatment groups compared with the I/R model group (P<0.05). Three nodes in one novel module of IMD were found to exert diverse compound-dependent ischemic-specific excitatory regulatory activities. An anti-ischemic excitatory allosteric module (AM CONCLUSIONS AM
Animals ; Brain Ischemia/drug therapy* ; Gene Ontology ; Gene Regulatory Networks ; Rodentia ; Vascular Endothelial Growth Factor A