Drug Effects Viewed from a Signal Transduction Network Perspective

Fliri, Anton; Loging, William; Volkmann, Robert A.

doi:10.1021/jm901001p

Cited by 27 publications

(20 citation statements)

References 35 publications

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“…Such systems modeling approaches may be used to define putative drug targets in HCC and improve efficacy of current therapies [35], [36]. First, it was confirmed that the topological properties (betweeneess, clustering coefficients, assortativity, diameter, and modularity) of the Conserved-Net are significantly non-random, thus more likely to reflect biological node importance [37].…”

Section: Discussionmentioning

confidence: 84%

Functional and Topological Properties in Hepatocellular Carcinoma Transcriptome

et al. 2012

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Hepatocellular carcinoma (HCC) is a leading cause of global cancer mortality. However, little is known about the precise molecular mechanisms involved in tumor formation and pathogenesis. The primary goal of this study was to elucidate genome-wide molecular networks involved in development of HCC with multiple etiologies by exploring high quality microarray data. We undertook a comparative network analysis across 264 human microarray profiles monitoring transcript changes in healthy liver, liver cirrhosis, and HCC with viral and alcoholic etiologies. Gene co-expression profiling was used to derive a consensus gene relevance network of HCC progression that consisted of 798 genes and 2,012 links. The HCC interactome was further confirmed to be phenotype-specific and non-random. Additionally, we confirmed that co-expressed genes are more likely to share biological function, but not sub-cellular localization. Analysis of individual HCC genes revealed that they are topologically central in a human protein-protein interaction network. We used quantitative RT-PCR in a cohort of normal liver tissue (n = 8), hepatitis C virus (HCV)-induced chronic liver disease (n = 9), and HCC (n = 7) to validate co-expressions of several well-connected genes, namely ASPM, CDKN3, NEK2, RACGAP1, and TOP2A. We show that HCC is a heterogeneous disorder, underpinned by complex cross talk between immune response, cell cycle, and mRNA translation pathways. Our work provides a systems-wide resource for deeper understanding of molecular mechanisms in HCC progression and may be used further to define novel targets for efficient treatment or diagnosis of this disease.

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Section: Discussionmentioning

confidence: 84%

Functional and Topological Properties in Hepatocellular Carcinoma Transcriptome

et al. 2012

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“…The available evidences showed that molecular synergisms can be emerged from different aspects, for example, protein complexes in cell-regulatory systems [42], crosstalk [43-47] and feedback control in the structures of signal pathways [48,49], stimuli-influenced number of molecules (e.g. number of activated enzymes, receptors, channels or transcription factors) [50,51] and gene expression profile [52] in signal transduction process. Thus, from the network target perspective, we can gain a comprehensive understanding of drug synergistic mechanisms on the basis of complex biological systems.…”

Section: Resultsmentioning

confidence: 99%

Network target for screening synergistic drug combinations with application to traditional Chinese medicine

2011

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BackgroundMulticomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge.ResultsIn this work, we proposed for the first time a “network target”-based paradigm instead of the traditional "single target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network target-based Identification of Multicomponent Synergy) to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological network as a therapeutic target and assumes that the relationship among agents can be transferred to network interactions among the molecular level entities (targets or responsive gene products) of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM) as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a network target perspective.ConclusionsNIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The network target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the development of multicomponent therapeutics as well as the modernization of TCM.

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“…However, the direct connection between the targeting of metabolic and signaling pathways by drugs and the adverse drug reactions that they cause has so far not been systematically studied and is only known for individual cases [16], [17], [18], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Network Neighbors of Drug Targets Contribute to Drug Side-Effect Similarity

et al. 2011

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In pharmacology, it is essential to identify the molecular mechanisms of drug action in order to understand adverse side effects. These adverse side effects have been used to infer whether two drugs share a target protein. However, side-effect similarity of drugs could also be caused by their target proteins being close in a molecular network, which as such could cause similar downstream effects. In this study, we investigated the proportion of side-effect similarities that is due to targets that are close in the network compared to shared drug targets. We found that only a minor fraction of side-effect similarities (5.8 %) are caused by drugs targeting proteins close in the network, compared to side-effect similarities caused by overlapping drug targets (64%). Moreover, these targets that cause similar side effects are more often in a linear part of the network, having two or less interactions, than drug targets in general. Based on the examples, we gained novel insight into the molecular mechanisms of side effects associated with several drug targets. Looking forward, such analyses will be extremely useful in the process of drug development to better understand adverse side effects.

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Drug Effects Viewed from a Signal Transduction Network Perspective

Cited by 27 publications

References 35 publications

Functional and Topological Properties in Hepatocellular Carcinoma Transcriptome

Functional and Topological Properties in Hepatocellular Carcinoma Transcriptome

Network target for screening synergistic drug combinations with application to traditional Chinese medicine

Network Neighbors of Drug Targets Contribute to Drug Side-Effect Similarity

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