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: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.

Project description:Alzheimer's disease (AD) is a progressive neurodegenerative disease. The study of blood-based biomarkers has lasted for a long time in AD, because it supports the concept that peripheral changes are involved in AD pathology. But it is still unclear how peripheral blood is involved in the temporal characteristic molecular mechanisms of AD from aging to mild cognitive impairment (MCI) and which cells are responsible for the molecular mechanisms. The main purpose of our study is to gain a systematic and comprehensive understanding of temporal characteristic networks of peripheral blood in AD using whole blood samples with 329 case-control samples, including 104 normal elderly control subjects (CTL), 80 MCI who are susceptible to AD, and 145 AD, by the weighted gene co-expression network analysis (WGCNA). The module-trait relationships were constructed and module preservation was validated with independent datasets GSE63061, GSE97760, GSE18309, GSE29378, GSE28146, and GSE29652. Our results indicate that the down-regulated protein modification and ubiquitin degradation systems, and the up-regulated insulin resistance both play a major role in MCI, while the up-regulated inflammatory cascade dominates in AD, which is mainly mediated by monocytes, macrophages. Although there is mixed activation of M1 and M2 macrophages in all stages of AD, the immune neutral state or M2 polarization may predominate in MCI, and M1 polarization may predominate in AD. Moreover, we found that TRPV2, NDUFV1, ATF4, HSPA8, STAT3 and LUC7L3 may mediate the pathological changes in MCI, while SIRPA, LAMP-2, NDUFB5, HSPA8, STAT3 and FPR2 may mediate the conversion from MCI-AD or the pathological changes in AD, which provide a basis for the treatment based on the peripheral blood system. In addition, we also found that the combined diagnosis based on a panel of genes from the red, blue, and brown modules have a moderate diagnostic effect on distinguishing MCI and AD from CTL, suggesting that those panels of genes may be used for detection of MCI and prediction of this conversion from MCI to AD. Our research emphasizes that pathological changes, based on temporal characteristics of peripheral blood, provide a theoretical basis for targeted peripheral treatment based on appropriate times and identified several diagnostic markers.

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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