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SignaLink 2 - a signaling pathway resource with multi-layered regulatory networks

BackgroundSignaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor.DescriptionWe developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download page enables the analysis of any user-specified part of the signaling network. Compared to other signaling resources, distinctive features of SignaLink 2 are the following: 1) it involves experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; 2) combines manual curation with large-scale datasets; 3) provides confidence scores for each interaction; 4) operates a customizable download page with multiple file formats (e.g., BioPAX, Cytoscape, SBML). Non-profit users can access SignaLink 2 free of charge at http://SignaLink.org.ConclusionsWith SignaLink 2 as a single resource, users can effectively analyze signaling pathways, scaffold proteins, modifier enzymes, transcription factors and miRNAs that are important in the regulation of signaling processes. This integrated resource allows the systems-level examination of how cross-talks and signaling flow are regulated, as well as provide data for cross-species comparisons and drug discovery analyses.

The NRF2-related interactome and regulome contain multifunctional proteins and fine-tuned autoregulatory loops

NRF2 is a well‐known, master transcription factor (TF) of oxidative and xenobiotic stress responses. Recent studies uncovered an even wider regulatory role of NRF2 influencing carcinogenesis, inflammation and neurodegeneration. Prompted by these advances here we present a systems‐level resource for NRF2 interactome and regulome that includes 289 protein–protein, 7469 TF–DNA and 85 miRNA interactions. As systems‐level examples of NRF2‐related signaling we identified regulatory loops of NRF2 interacting proteins (e.g., JNK1 and CBP) and a fine‐tuned regulatory system, where 35 TFs regulated by NRF2 influence 63 miRNAs that down‐regulate NRF2. The presented network and the uncovered regulatory loops may facilitate the development of efficient, NRF2‐based therapeutic agents.

Complex regulation of autophagy in cancer - Integrated approaches to discover the networks that hold a double-edged sword

Autophagy, a highly regulated self-degradation process of eukaryotic cells, is a context-dependent tumor-suppressing mechanism that can also promote tumor cell survival upon stress and treatment resistance. Because of this ambiguity, autophagy is considered as a double-edged sword in oncology, making anti-cancer therapeutic approaches highly challenging. In this review, we present how systems-level knowledge on autophagy regulation can help to develop new strategies and efficiently select novel anti-cancer drug targets. We focus on the protein interactors and transcriptional/post-transcriptional regulators of autophagy as the protein and regulatory networks significantly influence the activity of core autophagy proteins during tumor progression. We list several network resources to identify interactors and regulators of autophagy proteins. As in silico analysis of such networks often necessitates experimental validation, we briefly summarize tractable model organisms to examine the role of autophagy in cancer. We also discuss fluorescence techniques for high-throughput monitoring of autophagy in humans. Finally, the challenges of pharmacological modulation of autophagy are reviewed. We suggest network-based concepts to overcome these difficulties. We point out that a context-dependent modulation of autophagy would be favored in anti-cancer therapy, where autophagy is stimulated in normal cells, while inhibited only in stressed cancer cells. To achieve this goal, we introduce the concept of regulo-network drugs targeting specific transcription factors or miRNA families identified with network analysis. The effect of regulo-network drugs propagates indirectly through transcriptional or post-transcriptional regulation of autophagy proteins, and, as a multi-directional intervention tool, they can both activate and inhibit specific proteins in the same time. The future identification and validation of such regulo-network drug targets may serve as novel intervention points, where autophagy can be effectively modulated in cancer therapy.

OmniPath: guidelines and gateway for literature-curated signaling pathway resources

Supplementary Results • S13 Sup. Results 1 • Classification of the resources • S13 Sup. Results 2 • Literature curated signaling resources • S13 Sup. Results 3 • Uses of the different pathway resources • S17 Sup. Results 4 • Benchmarking pathway resources • S18 Sup. Results 4.1 • Consistency among literature curated resources • S18 Sup. Results 4.2 • Analysing occurrence of high-throughput interactions • S18 Sup. Results 4.3 • What do the PubMed IDs tell us? • S20 Sup. Results 4.4 • Biological properties of the proteins and interactions covered • S20 Sup. Results 5 • The combined network: Omnipath and pypath • S25

Autophagy Regulatory Network - a systems-level bioinformatics resource for studying the mechanism and regulation of autophagy.

Autophagy is a complex cellular process having multiple roles, depending on tissue, physiological, or pathological conditions. Major post-translational regulators of autophagy are well known, however, they have not yet been collected comprehensively. The precise and context-dependent regulation of autophagy necessitates additional regulators, including transcriptional and post-transcriptional components that are listed in various datasets. Prompted by the lack of systems-level autophagy-related information, we manually collected the literature and integrated external resources to gain a high coverage autophagy database. We developed an online resource, Autophagy Regulatory Network (ARN; http://autophagy-regulation.org), to provide an integrated and systems-level database for autophagy research. ARN contains manually curated, imported, and predicted interactions of autophagy components (1,485 proteins with 4,013 interactions) in humans. We listed 413 transcription factors and 386 miRNAs that could regulate autophagy components or their protein regulators. We also connected the above-mentioned autophagy components and regulators with signaling pathways from the SignaLink 2 resource. The user-friendly website of ARN allows researchers without computational background to search, browse, and download the database. The database can be downloaded in SQL, CSV, BioPAX, SBML, PSI-MI, and in a Cytoscape CYS file formats. ARN has the potential to facilitate the experimental validation of novel autophagy components and regulators. In addition, ARN helps the investigation of transcription factors, miRNAs and signaling pathways implicated in the control of the autophagic pathway. The list of such known and predicted regulators could be important in pharmacological attempts against cancer and neurodegenerative diseases.

NRF2-ome: An Integrated Web Resource to Discover Protein Interaction and Regulatory Networks of NRF2

NRF2 is the master transcriptional regulator of oxidative and xenobiotic stress responses. NRF2 has important roles in carcinogenesis, inflammation, and neurodegenerative diseases. We developed an online resource, NRF2-ome, to provide an integrated and systems-level database for NRF2. The database contains manually curated and predicted interactions of NRF2 as well as data from external interaction databases. We integrated NRF2 interactome with NRF2 target genes, NRF2 regulating TFs, and miRNAs. We connected NRF2-ome to signaling pathways to allow mapping upstream NRF2 regulatory components that could directly or indirectly influence NRF2 activity totaling 35,967 protein-protein and signaling interactions. The user-friendly website allows researchers without computational background to search, browse, and download the database. The database can be downloaded in SQL, CSV, BioPAX, SBML, PSI-MI, and in a Cytoscape CYS file formats. We illustrated the applicability of the website by suggesting a posttranscriptional negative feedback of NRF2 by MAFG protein and raised the possibility of a connection between NRF2 and the JAK/STAT pathway through STAT1 and STAT3. NRF2-ome can also be used as an evaluation tool to help researchers and drug developers to understand the hidden regulatory mechanisms in the complex network of NRF2.

The orchestra of lipid-transfer proteins at the crossroads between metabolism and signaling.

Within the eukaryotic cell, more than 1000 species of lipids define a series of membranes essential for cell function. Tightly controlled systems of lipid transport underlie the proper spatiotemporal distribution of membrane lipids, the coordination of spatially separated lipid metabolic pathways, and lipid signaling mediated by soluble proteins that may be localized some distance away from membranes. Alongside the well-established vesicular transport of lipids, non-vesicular transport mediated by a group of proteins referred to as lipid-transfer proteins (LTPs) is emerging as a key mechanism of lipid transport in a broad range of biological processes. More than a hundred LTPs exist in humans and these can be divided into at least ten protein families. LTPs are widely distributed in tissues, organelles and membrane contact sites (MCSs), as well as in the extracellular space. They all possess a soluble and globular domain that encapsulates a lipid monomer and they specifically bind and transport a wide range of lipids. Here, we present the most recent discoveries in the functions and physiological roles of LTPs, which have expanded the playground of lipids into the aqueous spaces of cells.

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.

Benchmark and integration of resources for the estimation of human transcription factor activities

Prediction of transcription factor (TF) activities from the gene expression of their targets (i.e. TF regulon) is becoming a widely-used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and datasets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are: (i) manually curated repositories, (ii) interactions derived from ChIP-seq binding data, (iii) in silico prediction of TF binding on gene promoters, and (iv) reverse-engineered regulons from large gene expression datasets. However, it is not known how these different sources of regulons affect the TF activity estimations, and thereby downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark datasets. We assembled a collection of TF-target interactions among 1,541 TFs, and evaluated how the different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities or mode of interaction with the chromatin, affect the predictions of TF activity changes. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies with a confidence score, as a resource for enhanced prediction of TF activities. Financial support This work was supported by Open Targets (grant number OTAR016) and the JRC for Computational Biomedicine which was partially funded by Bayer AG.