TRANSPATH(R): an information resource for storing and visualizing signaling pathways and their pathological aberrations

Krull, Mathias; Pistor, Susanne; Voss, Nico; Kel, Alexander; Reuter, Ingmar; Kronenberg, Deborah; Michael, Holger; Schwarzer, Knut; Потапов, А. П.; Choi, Claudia; Kel-Margoulis, Olga V.; Wingender, Edgar

doi:10.1093/nar/gkj107

Cited by 149 publications

(135 citation statements)

References 25 publications

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“…The master-regulator search uses the TRANSPATH ® database (http://genexplain.com/transpath) [15]. A comprehensive signal transduction network of human cells is built by the network analysis module of the BioUML platform using reactions annotated in TRANSPATH ® .…”

Section: Finding Master Regulators In Networkmentioning

confidence: 99%

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Master-regulators driving resistance of non-small cell lung cancer cells to p53 reactivator Nutlin-3

Boyarskikh

Pintus²,

Mandrik³

et al. 2017

Virtual Biology

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SummaryThe tumor suppressor protein p53 is one of the most important targets in various cancers including non-small cell lung cancer (NSCLC). The small molecule Nutlin-3 reactivates p53 in cancer cells by interacting with the complex between p53 and its repressor Mdm-2, which leads to increased apoptosis of cancer cells. Nevertheless, clinical and experimental studies of Nutlin-3 have shown that in some cases cancer cells are not sensitive to this compound. In this paper we studied possible mechanisms of such a resistance of cancer cells to Nutlin-3 through increased activity of specific pro-survival pathways that appeared to be more active in the resistant cells, as compared to the cells sensitive to the treatment by this compound. Using genome-wide gene expression profiling we compared several NSCLC cell lines. Nutlin-3 resistant cell lines showed a significantly different gene expression in comparison with sensitive cell lines before and after treatment by Nutlin-3.Using original bioinformatics approaches we analyzed the revealed gene expression patterns and identified complexes of transcription factors specifically regulating different expression of genes in the resistant cell lines. Analysis of the signal transduction network upstream from transcription factors allowed us to identify potential master-regulators responsible for 2 maintaining this resistant state. Among them the most promising was mTOR acting in the context of the activated PI3K pathway. With the help of chemical inhibitors, we were able to validate these findings experimentally. The Nutlin-3 resistant cell lines showed the highest sensitivity to the inhibitor of mTOR with PI3K, which lead to the rapid death of these cells.

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Section: Finding Master Regulators In Networkmentioning

confidence: 99%

“…The master-regulator search uses the TRANSPATH ® database (http://genexplain.com/transpath) [15] and is implemented in the workflow "From genome to target" as a last step. The main algorithm of the master regulator search has been described earlier [10].…”

Section: Finding Master Regulators In Network Upstream Of Tfsmentioning

confidence: 99%

Master-regulators driving resistance of non-small cell lung cancer cells to p53 reactivator Nutlin-3

Boyarskikh

Pintus²,

Mandrik³

et al. 2017

Virtual Biology

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“…We have implemented a broad range of importers for widely used databases (RefSeq [12], KEGG [13], BioCyc [14], TransPath [15], among others) and for standard data exchange formats (e.g. PSI-MIF level 1 [16]).…”

Section: Introductionmentioning

confidence: 99%

“…• Sequence databases: SwissProt [17], RefSeq [12] • Pathway databases: KEGG [13], BioCyc [14], TransPath [15] • Protein interaction databases: DIP [18], MINT [19], IntAct [20], HPRD [21] • Transcription factor databases: TransFac [22] • Protein annotation databases: InterPro [23], CAP [24] Based on the Java class library of BN++ we developed BiNA, a graphical user interface (GUI) and network visualizer that enables the users to carry out complex queries of the BN++ warehouse using an intuitive interface without requiring any knowledge about BioCore, the database internals, or SQL. Using sophisticated graph layout algorithms query results can be easily visualized as graphs or networks in a visually appealing manner.…”

Section: Introductionmentioning

confidence: 99%

BN++ – A Biological Information System

Küntzer

Blum

Gerasch

et al. 2006

Journal of Integrative Bioinformatics

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SummaryRecent years have seen an explosive growth in the amount of biochemical data available. Numerous databases have been established and are being used as an essential resource by biologists around the world. The sheer amount and heterogeneity of these data poses a major challenge: data integration and, based thereupon, the integrative analysis of these data. We present BN++, the biochemical network library, a powerful software package for integrating, analyzing, and visualizing biochemical data in the context of networks. BN++ is based on a comprehensive and extensible object model (BioCore), which has been implemented as a C++ framework, a Java class library, and a relational database. The C++ framework is used to efficiently import, integrate, and analyze the data, which is stored in a data warehouse. The Java-based viewer (BiNA) provides a powerful platform-independent visualization of the data using sophisticated graph layout algorithms. Currently, the data warehouse imports and integrates data from about a dozen important databases including, among others, sequence data, metabolic and regulatory networks, and protein interaction data. We illustrate the usefulness of BN++ with a few select example applications. Availability: BN++ is open source software available from our website at www.bnplusplus. org.

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“…To deal with these problems, we have recently developed a prototype of a pathway search engine (PSE) that is capable of determining the cellular signaling response from the multitude of changes in global protein expression (23). The PSE performs non-linear analysis of proteomics data using TRANSPATHா, a high quality database focused on signal transduction (24,25), and TRANSFAC, a collection of experimental data on transcription factors (26).…”

mentioning

confidence: 99%

Proteomics and Pathway Analysis Identifies JNK Signaling as Critical for High Linear Energy Transfer Radiation-induced Apoptosis in Non-small Lung Cancer Cells

Ståhl

Fung

Adams

et al. 2009

Molecular & Cellular Proteomics

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During the past decade, we have witnessed an explosive increase in generation of large proteomics data sets, not least in cancer research. There is a growing need to extract and correctly interpret information from such data sets to generate biologically relevant hypotheses. A pathway search engine (PSE) has recently been developed as a novel tool intended to meet these requirements. Ionizing radiation (IR) is an anticancer treatment modality that triggers multiple signal transduction networks. In this work, we show that high linear energy transfer (LET) IR induces apoptosis in a non-small cell lung cancer cell line, U-1810, whereas low LET IR does not. PSE was applied to study changes in pathway status between high and low LET IR to find pathway candidates of importance for high LET-induced apoptosis. Such pathways are potential clinical targets, and they were further validated in vitro. We used an unsupervised shotgun proteomics approach where high resolution mass spectrometry coupled to nanoflow liquid chromatography determined the identity and relative abundance of expressed proteins. Based on the proteomics data, PSE suggested the JNK pathway (p ‫؍‬ 6⅐10 ؊6 ) as a key event in response to high LET IR. In addition, the Fas pathway was found to be activated (p ‫؍‬ 3⅐10 ؊5 ) and the p38 pathway was found to be deactivated (p ‫؍‬ 0.001) compared with untreated cells. Antibodybased analyses confirmed that high LET IR caused an increase in phosphorylation of JNK. Moreover pharmacological inhibition of JNK blocked high LET-induced apoptotic signaling. In contrast, neither an activation of p38 nor a role for p38 in high LET IR-induced apoptotic signaling was found. We conclude that, in contrast to conventional low LET IR, high LET IR can trigger activation of the JNK pathway, which in turn is critical for induction of apoptosis in these cells. Thus PSE predictions were largely confirmed, and PSE was proven to be a useful hypothesis-generating tool.

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TRANSPATH(R): an information resource for storing and visualizing signaling pathways and their pathological aberrations

Cited by 149 publications

References 25 publications

Master-regulators driving resistance of non-small cell lung cancer cells to p53 reactivator Nutlin-3

Master-regulators driving resistance of non-small cell lung cancer cells to p53 reactivator Nutlin-3

BN++ – A Biological Information System

Proteomics and Pathway Analysis Identifies JNK Signaling as Critical for High Linear Energy Transfer Radiation-induced Apoptosis in Non-small Lung Cancer Cells

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