Dataset Information

Network characteristic analysis of ADR-related proteins and identification of ADR-ADR associations.

ABSTRACT: Adverse drug reactions (ADRs) are caused by interactions between drugs or their metabolites and specific proteins. Knowledge of these proteins is important for facilitating mechanistic research of ADRs and new drug discovery. Here, we identified 41 network modules from an ADR-protein network; analysed the function of each module; revealed the potential accompanying actions of the ADRs and the new ADR-related proteins (ADRPs) to a unique ADR and studied the characteristics of composition, subcellular location and tissue distribution of these ADRPs by comparing them with drug-related proteins (DRPs). The results indicated that ADRs are mainly caused by risk drug-related proteins (RDRPs) and that drug off-target effects are a secondary cause. Biological processes that enzymes involve are the main reason for the occurrence of ADRs. However, drug-related transporters have a higher risk of inducing ADRs than drug-related enzymes do, and ADRPs locating in the cell membrane tend to induce multiple ADRs.

SUBMITTER: Chen X

PROVIDER: S-EPMC3638162 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Network characteristic analysis of ADR-related proteins and identification of ADR-ADR associations.

Chen Xiujie X Liu Xiangqiong X Jia Xiaodong X Tan Fujian F Yang Ruizhi R Chen Sheng S Liu Lei L Wang Yunfeng Y Chen Yuelong Y

Scientific reports 20130101

Adverse drug reactions (ADRs) are caused by interactions between drugs or their metabolites and specific proteins. Knowledge of these proteins is important for facilitating mechanistic research of ADRs and new drug discovery. Here, we identified 41 network modules from an ADR-protein network; analysed the function of each module; revealed the potential accompanying actions of the ADRs and the new ADR-related proteins (ADRPs) to a unique ADR and studied the characteristics of composition, subcell ...[more]

PMID: 23625301

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Project description:BackgroundGallbladder carcinoma (GBC) is a highly malignant tumor with a poor overall prognosis. This study aimed to identify the characteristic microRNAs (miRNAs) of GBC and the competing endogenous RNA (ceRNA) regulatory mechanisms.MethodsThe microarray data of GBC tissue samples and normal gallbladder (NGB) tissue samples from the Gene Expression Omnibus (GEO) database was downloaded. GBC-related differentially expressed miRNAs (DE-miRNAs) were identified by inter-group differential expression analysis and weighted gene co-expression network analysis (WGCNA). Machine learning algorithms were used to screen the characteristic miRNA based on the intersect between least absolute shrinkage and selection operator (LASSO) and Support vector machine-recursive feature elimination (SVM-RFE). Based on the differential expression analysis of GEO database, the ceRNA network of characteristic miRNA was predicted and constructed. The biological functions of the ceRNA network were revealed by carrying out the gene enrichment analysis was implemented. We further screened the key genes of ceRNA network and constructed a protein-protein interaction (PPI) network, and predicted and generated the transcription factors (TFs) network of signature miRNAs. The expression of characteristic miRNA in clinical samples was verified by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 131 GBC-related DE-miRNAs were obtained. The hsa-miR-4770 was defined as characteristic miRNA for GBC. The ceRNA network containing 211 mRNAs, one miRNA, two lncRNAs, and 48 circRNAs was created. Gene enrichment analysis suggested that the downstream genes were mainly involved in actin filament organization, cell-substrate adhesion, cell-matrix adhesion, reactive oxygen species metabolic process, glutamine metabolic process and extracellular matrix (ECM)-receptor interaction pathway. 10 key genes in the network were found to be most correlated with disease, and involved in cell cycle-related processes, p53, and extrinsic apoptotic signaling pathways. The qRT-PCR result demonstrated that hsa-miR-4770 is down-regulated in GBC, and the expression trend is consistent with the public database.ConclusionsWe identified hsa-miR-4770 as the characteristic miRNA for GBC. The ceRNA network of hsa-miR-4770 may play key roles in GBC. This study provided some basis for potential pathogenesis of GBC.

Project description:BackgroundOntologies are useful in many branches of biomedical research. For instance, in the vaccine domain, the community-based Vaccine Ontology (VO) has been widely used to promote vaccine data standardization, integration, and computer-assisted reasoning. However, a major challenge in the VO has been to construct ontologies of vaccine functions, given incomplete vaccine knowledge and inconsistencies in how this knowledge is manually curated.ResultsIn this study, we show that network-based analysis of vaccine-related networks can identify underlying structural information consistent with that captured by the VO, and commonalities in the vaccine adverse events for vaccines and for diseases to produce new hypotheses about pathomechanisms involving the vaccine and the disease status. First, a vaccine-vaccine network was inferred by applying a bipartite network projection strategy to the vaccine-disease network extracted from the Semantic MEDLINE database. In total, 76 vaccines and 573 relationships were identified to construct the vaccine network. The shortest paths between all pairs of vaccines were calculated within the vaccine network. The correlation between the shortest paths of vaccine pairs and their semantic similarities in the VO was then investigated. Second, a vaccine-gene network was also constructed. In this network, 4 genes were identified as hubs interacting with at least 3 vaccines, and 4 vaccines were identified as hubs associated with at least 3 genes. These findings correlate with existing knowledge and provide new hypotheses in the fundamental interaction mechanisms involving vaccines, diseases, and genes.ConclusionsIn this study, we demonstrated that a combinatorial analysis using a literature knowledgebase, semantic technology, and ontology is able to reveal important unidentified knowledge critical to biomedical research and public health and to generate testable hypotheses for future experimental verification. As the associations from Semantic MEDLINE remain incomplete, we expect to extend this work by (1) integrating additional association databases to complement Semantic MEDLINE knowledge, (2) extending the neighbor genes of vaccine-associated genes, and (3) assigning confidence weights to different types of associations or associations from different sources.

Dataset Information

Network characteristic analysis of ADR-related proteins and identification of ADR-ADR associations.

Publications

Network characteristic analysis of ADR-related proteins and identification of ADR-ADR associations.

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