Characterization of protein-interaction networks in tumors

Platzer, Alexander; Perco, Paul; Lukas, Arno; Mayer, Bernd

doi:10.1186/1471-2105-8-224

Cited by 103 publications

(53 citation statements)

References 49 publications

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“…This measure favors nodes that join communities such as dense subnetworks, rather than nodes that lie inside a community, and has been shown to characterize essential proteins (Platzer et al, 2007). All calculated network parameters and rankings are listed in Data Sheet 2, Tables S15, S16 or can be recalculated from the Cytoscape CI_EI.cys (Data Sheet 3) file available at http://www.picb.ac.cn/ClinicalGenomicNTW/RAmultiomic.html.…”

Section: Methodsmentioning

confidence: 99%

Multi-omic landscape of rheumatoid arthritis: re-evaluation of drug adverse effects

Tieri

Zhou

Zhu

et al. 2014

Front. Cell Dev. Biol.

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Objective: To provide a frame to estimate the systemic impact (side/adverse events) of (novel) therapeutic targets by taking into consideration drugs potential on the numerous districts involved in rheumatoid arthritis (RA) from the inflammatory and immune response to the gut-intestinal (GI) microbiome.Methods: We curated the collection of molecules from high-throughput screens of diverse (multi-omic) biochemical origin, experimentally associated to RA. Starting from such collection we generated RA-related protein-protein interaction (PPI) networks (interactomes) based on experimental PPI data. Pharmacological treatment simulation, topological and functional analyses were further run to gain insight into the proteins most affected by therapy and by multi-omic modeling.Results: Simulation on the administration of MTX results in the activation of expected (apoptosis) and adverse (nitrogenous metabolism alteration) effects. Growth factor receptor-bound protein 2 (GRB2) and Interleukin-1 Receptor Associated Kinase-4 (IRAK4, already an RA target) emerge as relevant nodes. The former controls the activation of inflammatory, proliferative and degenerative pathways in host and pathogens. The latter controls immune alterations and blocks innate response to pathogens.Conclusions: This multi-omic map properly recollects in a single analytical picture known, yet complex, information like the adverse/side effects of MTX, and provides a reliable platform for in silico hypothesis testing or recommendation on novel therapies. These results can support the development of RA translational research in the design of validation experiments and clinical trials, as such we identify GRB2 as a robust potential new target for RA for its ability to control both synovial degeneracy and dysbiosis, and, conversely, warn on the usage of IRAK4-inhibitors recently promoted, as this involves potential adverse effects in the form of impaired innate response to pathogens.

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Section: Methodsmentioning

confidence: 99%

Multi-omic landscape of rheumatoid arthritis: re-evaluation of drug adverse effects

Tieri

Zhou

Zhu

et al. 2014

Front. Cell Dev. Biol.

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“…The subgraph centrality has been previously applied to the identification of essential proteins in proteomic maps (Estrada, 2006a;2006b;Zotenko et al, 2008;Lin et al, 2008;Gursoy et al, 2008) and the characterization of malignant tissues (Platzer et al, 2007). It has also been applied to the study of weighted graphs to account for the degree of folding of protein chains (see for instance Estrada, 2002;2004) as well as to describe the molecular structure of drug-like and environmentally relevant organic compounds (for a review see Estrada and Uriarte, 2001).…”

Section: Study Of Protein-protein Interaction Networkmentioning

confidence: 99%

Generalized walks-based centrality measures for complex biological networks

Estrada

2010

Journal of Theoretical Biology

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A strategy for zooming in and out the topological environment of a node in a complex network is developed. This approach is applied here to generalize the subgraph centrality of nodes in complex networks. In this case the zooming in strategy is based on the use of some known matrix functions which allow focusing locally on the environment of a node. When a zooming out strategy is applied new matrix functions are introduced, which give a more global picture of the topological surrounds of a node. These indices permit a modulation of the scales at which the environment of a node influences its centrality. We apply them to the study of 10 protein-protein interaction (PPI) networks. We illustrate the similarities and differences between the generalized subgraph centrality indices as well as among them and some classical centrality measures. We show here that the use of centrality indices based on the zooming in strategy identifies a larger number of essential proteins in the yeast PPI network than any of the other centrality measures studied.

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“…In our proposed method, for the creation of training and testing datasets, k-fold cross validation method has been used [18]. In each iteration of k-fold cross validation, training samples are divided into thermophile, hyper-thermophile and mesophile classes based on their class label.…”

Section: Methodsmentioning

confidence: 99%