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Illuminating drug action by network integration of disease genes: a case study of myocardial infarction.

ABSTRACT: Drug discovery has produced many successful therapeutic agents; however, most of these drugs were developed without a deep understanding of the system-wide mechanisms of action responsible for their indications. Gene-disease associations produced by molecular and genetic studies of complex diseases provide great opportunities for a system-level understanding of drug activity. In this study, we focused on acute myocardial infarction (MI) and conducted an integrative network analysis to illuminate drug actions. We integrated MI drugs, MI drug interactors, drug targets, and MI disease genes into the human interactome and showed that MI drug targets are significantly proximate to MI disease proteins. We then constructed a bipartite network of MI-related drug targets and MI disease proteins and derived 12 drug-target-disease (DTD) modules. We assessed the biological relevance of these modules and demonstrated the benefits of incorporating disease genes. The results indicate that DTD modules provide insights into the mechanisms of action of MI drugs and the cardiovascular (side) effects of non-MI drugs.

SUBMITTER: Wang RS

PROVIDER: S-EPMC4846559 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Illuminating drug action by network integration of disease genes: a case study of myocardial infarction.

Wang Rui-Sheng RS Loscalzo Joseph J

Molecular bioSystems 20160401 5

Drug discovery has produced many successful therapeutic agents; however, most of these drugs were developed without a deep understanding of the system-wide mechanisms of action responsible for their indications. Gene-disease associations produced by molecular and genetic studies of complex diseases provide great opportunities for a system-level understanding of drug activity. In this study, we focused on acute myocardial infarction (MI) and conducted an integrative network analysis to illuminate ...[more]

PMID: 27004607

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Project description:BackgroundAcute myocardial infarction (AMI) has two clinical characteristics: high missed diagnosis and dysfunction of leukocytes. Transcriptional RNA on leukocytes is closely related to the course evolution of AMI patients. We hypothesized that transcriptional RNA in leukocytes might provide potential diagnostic value for AMI. Integration machine learning (IML) was first used to explore AMI discrimination genes. The following clinical study was performed to validate the results.MethodsA total of four AMI microarrays (derived from the Gene Expression Omnibus) were included in bioanalysis (220 sample size). Then, the clinical validation was finished with 20 AMI and 20 stable coronary artery disease patients (SCAD). At a ratio of 5:2, GSE59867 was included in the training set, while GSE60993, GSE62646, and GSE48060 were included in the testing set. IML was explicitly proposed in this research, which is composed of six machine learning algorithms, including support vector machine (SVM), neural network (NN), random forest (RF), gradient boosting machine (GBM), decision trees (DT), and least absolute shrinkage and selection operator (LASSO). IML had two functions in this research: filtered optimized variables and predicted the categorized value. Finally, The RNA of the recruited patients was analyzed to verify the results of IML.ResultsThirty-nine differentially expressed genes (DEGs) were identified between controls and AMI individuals from the training sets. Among the thirty-nine DEGs, IML was used to process the predicted classification model and identify potential candidate genes with overall normalized weights > 1. Finally, two genes (AQP9 and SOCS3) show their diagnosis value with the area under the curve (AUC) > 0.9 in both the training and testing sets. The clinical study verified the significance of AQP9 and SOCS3. Notably, more stenotic coronary arteries or severe Killip classification indicated higher levels of these two genes, especially SOCS3. These two genes correlated with two immune cell types, monocytes and neutrophils.ConclusionAQP9 and SOCS3 in leukocytes may be conducive to identifying AMI patients with SCAD patients. AQP9 and SOCS3 are closely associated with monocytes and neutrophils, which might contribute to advancing AMI diagnosis and shed light on novel genetic markers. Multiple clinical characteristics, multicenter, and large-sample relevant trials are still needed to confirm its clinical value.

Project description:BACKGROUND:Systematic approach for drug discovery is an emerging discipline in systems biology research area. It aims at integrating interaction data and experimental data to elucidate diseases and also raises new issues in drug discovery for cancer treatment. However, drug target discovery is still at a trial-and-error experimental stage and it is a challenging task to develop a prediction model that can systematically detect possible drug targets to deal with complex diseases. METHODS:We integrate gene expression, disease genes and interaction networks to identify the effective drug targets which have a strong influence on disease genes using network flow approach. In the experiments, we adopt the microarray dataset containing 62 prostate cancer samples and 41 normal samples, 108 known prostate cancer genes and 322 approved drug targets treated in human extracted from DrugBank database to be candidate proteins as our test data. Using our method, we prioritize the candidate proteins and validate them to the known prostate cancer drug targets. RESULTS:We successfully identify potential drug targets which are strongly related to the well known drugs for prostate cancer treatment and also discover more potential drug targets which raise the attention to biologists at present. We denote that it is hard to discover drug targets based only on differential expression changes due to the fact that those genes used to be drug targets may not always have significant expression changes. Comparing to previous methods that depend on the network topology attributes, they turn out that the genes having potential as drug targets are weakly correlated to critical points in a network. In comparison with previous methods, our results have highest mean average precision and also rank the position of the truly drug targets higher. It thereby verifies the effectiveness of our method. CONCLUSIONS:Our method does not know the real ideal routes in the disease network but it tries to find the feasible flow to give a strong influence to the disease genes through possible paths. We successfully formulate the identification of drug target prediction as a maximum flow problem on biological networks and discover potential drug targets in an accurate manner.

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Illuminating drug action by network integration of disease genes: a case study of myocardial infarction.

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Illuminating drug action by network integration of disease genes: a case study of myocardial infarction.

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