The impact of cytokine responses in the intra- and extracellular signaling network of a traumatic injury.

Investigations of cellular responses involved in injury and repair processes have generated valuable information contributing to the advancement of wound healing and treatments. Intra- and extracellular regulators of healing mechanisms, such as cytokines, signaling proteins, and growth factors, have been described to possess significant roles in facilitating optimal recovery. This study explored a collection of 30 spatiotemporal responses comprised of cytokines (IL-1α, IL-1ß, IL-2, IL-6, TNF-α, MIP-1α), intracellular proteins (Akt, c-Jun, CREB, ERK1/2, JNK, MEK1, p38, p53, p90RSK), phosphorylated proteins (p-Akt, p-c-Jun, p-CREB, p-ERK1/2, p-GSK-3α/ß, p-HSP27, p-IκBα, p-JNK, p-MEK1, p-p38, p-p70S6K, p-p90RSK, p-STAT2, p-STAT3), and a protease (Caspase-3), measured in skeletal muscle tissue following a traumatic injury (rodent Gustilo IIIB fracture). To optimize the analysis of context-specific data sets, a network centrality parameter approach was used to assess the impact of each response in relation to all other measured responses. This approach identified proteins that were substantially amplified and potentially central in the wound healing network by evaluation of their corresponding centrality parameter, radiality. Network analysis allowed us to distinguish the progression of healing that occurred at certain time points and regions of injury. Notably, new tissue formation was proposed to occur by 168 h post-injury in severely injured tissue, while tissue 1-cm away from the site of injury that experienced relatively minor injury appeared to exhibit signs of new tissue formation as early as 24 h post-injury. In particular, hallmarks of inflammation, cytokines IL-1ß, IL-6, and IL-2, appear to have a pronounced impact at earlier time points (0-24 h post-injury), while intracellular proteins involved in cell proliferation, differentiation, or proteolysis (c-Jun, CREB, JNK, p38, p-c-Jun; p-MEK1, p-p38, p-STAT3) are more significant at later times (24-168 h). Overall, this study demonstrates the feasibility of a network analysis approach to extract significant information and also offers a spatiotemporal visualization of the intra- and extracellular signaling responses that regulate healing mechanisms.

Proteomics-based network analysis characterizes biological processes and pathways activated by preconditioned mesenchymal stem cells in cardiac repair mechanisms.

BACKGROUND: We have demonstrated that intramyocardial delivery of human mesenchymal stem cells preconditioned with a hyaluronan mixed ester of butyric and retinoic acid (MSCp+) is more effective in preventing the decay of regional myocardial contractility in a swine model of myocardial infarction (MI). However, the understanding of the role of MSCp+ in proteomic remodeling of cardiac infarcted tissue is not complete. We therefore sought to perform a comprehensive analysis of the proteome of infarct remote (RZ) and border zone (BZ) of pigs treated with MSCp+ or unconditioned stem cells. METHODS: Heart tissues were analyzed by MudPIT and differentially expressed proteins were selected by a label-free approach based on spectral counting. Protein profiles were evaluated by using PPI networks and their topological analysis. RESULTS: The proteomic remodeling was largely prevented in MSCp+ group. Extracellular proteins involved in fibrosis were down-regulated, while energetic pathways were globally up-regulated. Cardioprotectant pathways involved in the production of keto acid metabolites were also activated. Additionally, we found that new hub proteins support the cardioprotective phenotype characterizing the left ventricular BZ treated with MSCp+. In fact, the up-regulation of angiogenic proteins NCL and RAC1 can be explained by the increase of capillary density induced by MSCp+. CONCLUSIONS: Our results show that angiogenic pathways appear to be uniquely positioned to integrate signaling with energetic pathways involving cardiac repair. GENERAL SIGNIFICANCE: Our findings prompt the use of proteomics-based network analysis to optimize new approaches preventing the post-ischemic proteomic remodeling that may underlie the limited self-repair ability of adult heart.

Protein tyrosine phosphatase receptor type γ is a JAK phosphatase and negatively regulates leukocyte integrin activation.

Regulation of signal transduction networks depends on protein kinase and phosphatase activities. Protein tyrosine kinases of the JAK family have been shown to regulate integrin affinity modulation by chemokines and mediated homing to secondary lymphoid organs of human T lymphocytes. However, the role of protein tyrosine phosphatases in leukocyte recruitment is still elusive. In this study, we address this issue by focusing on protein tyrosine phosphatase receptor type γ (PTPRG), a tyrosine phosphatase highly expressed in human primary monocytes. We developed a novel methodology to study the signaling role of receptor type tyrosine phosphatases and found that activated PTPRG blocks chemoattractant-induced ß2 integrin activation. Specifically, triggering of LFA-1 to high-affinity state is prevented by PTPRG activation. High-throughput phosphoproteomics and computational analyses show that PTPRG activation affects the phosphorylation state of at least 31 signaling proteins. Deeper examination shows that JAKs are critically involved in integrin-mediated monocyte adhesion and that PTPRG activation leads to JAK2 dephosphorylation on the critical 1007-1008 phosphotyrosine residues, implying JAK2 inhibition and thus explaining the antiadhesive role of PTPRG. Overall, the data validate a new approach to study receptor tyrosine phosphatases and show that, by targeting JAKs, PTPRG downmodulates the rapid activation of integrin affinity in human monocytes, thus emerging as a potential novel critical regulator of leukocyte trafficking.

An approach to investigate intracellular protein network responses.

Modern toxicological evaluations have evolved to consider toxicity as a perturbation of biological pathways or networks. As such, toxicity testing approaches are shifting from common end point evaluations to pathway based approaches, where the degree of perturbation of select biological pathways is monitored. These new approaches are greatly increasing the data available to toxicologists, but methods of analyses to determine the inter-relationships between potentially affected pathways are needed to fully understand the consequences of exposure. An approach to construct dose-response curves that use graph theory to describe network perturbations among three disparate mitogen-activated protein kinase (MAPK) pathways is presented. Mitochondrial stress was induced in human hepatocytes (HepG2) by exposing the cells to increasing doses of the complex I inhibitor, deguelin. The relative phosphorylation responses of proteins involved in the regulation of the stress response were measured. Graph theory was applied to the phosphorylation data to obtain parameters describing the network perturbations at each individual dose tested. The graph theory results depicted the dynamic nature of the relationship between p38, JNK, and ERK1/2 under conditions of mitochondrial stress and revealed shifts in the relationships between these MAPK pathways at low doses. The inter-relationship, or crosstalk, among these 3 traditionally linear MAPK cascades was further probed by coexposing cells to deguelin plus SB202190 (JNK and p38 inhibitor) or deguelin plus SB202474 (JNK inhibitor). The cells exposed to deguelin plus SB202474 resulted in significantly decreased viability, which could be visualized and attributed to the decrease of ERK1/2 network centrality. The approach presented here allows for the construction and visualization of dose-response curves that describe network perturbations induced by chemical stress, which provides an informative and sensitive means of assessing toxicological effects on biological systems.

Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data.

MOTIVATION: Metabolomics is a rapidly evolving field that holds promise to provide insights into genotype-phenotype relationships in cancers, diabetes and other complex diseases. One of the major informatics challenges is providing tools that link metabolite data with other types of high-throughput molecular data (e.g. transcriptomics, proteomics), and incorporate prior knowledge of pathways and molecular interactions. RESULTS: We describe a new, substantially redesigned version of our tool Metscape that allows users to enter experimental data for metabolites, genes and pathways and display them in the context of relevant metabolic networks. Metscape 2 uses an internal relational database that integrates data from KEGG and EHMN databases. The new version of the tool allows users to identify enriched pathways from expression profiling data, build and analyze the networks of genes and metabolites, and visualize changes in the gene/metabolite data. We demonstrate the applications of Metscape to annotate molecular pathways for human and mouse metabolites implicated in the pathogenesis of sepsis-induced acute lung injury, for the analysis of gene expression and metabolite data from pancreatic ductal adenocarcinoma, and for identification of the candidate metabolites involved in cancer and inflammation. AVAILABILITY: Metscape is part of the National Institutes of Health-supported National Center for Integrative Biomedical Informatics (NCIBI) suite of tools, freely available at http://metscape.ncibi.org. It can be downloaded from http://cytoscape.org or installed via Cytoscape plugin manager. CONTACT: metscape-help@umich.edu; akarnovs@umich.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Analysing omics data sets with weighted nodes networks (WNNets).

Current trends in biomedical research indicate data integration as a fundamental step towards precision medicine. In this context, network models allow representing and analysing complex biological processes. However, although effective in unveiling network properties, these models fail in considering the individual, biochemical variations occurring at molecular level. As a consequence, the analysis of these models partially loses its predictive power. To overcome these limitations, Weighted Nodes Networks (WNNets) were developed. WNNets allow to easily and effectively weigh nodes using experimental information from multiple conditions. In this study, the characteristics of WNNets were described and a proteomics data set was modelled and analysed. Results suggested that degree, an established centrality index, may offer a novel perspective about the functional role of nodes in WNNets. Indeed, degree allowed retrieving significant differences between experimental conditions, highlighting relevant proteins, and provided a novel interpretation for degree itself, opening new perspectives in experimental data modelling and analysis. Overall, WNNets may be used to model any high-throughput experimental data set requiring weighted nodes. Finally, improving the power of the analysis by using centralities such as betweenness may provide further biological insights and unveil novel, interesting characteristics of WNNets.

Analyzing biological network parameters with CentiScaPe.

SUMMARY: The increasing availability of large network datasets along with the progresses in experimental high-throughput technologies have prompted the need for tools allowing easy integration of experimental data with data derived form network computational analysis. In order to enrich experimental data with network topological parameters, we have developed the Cytoscape plug-in CentiScaPe. The plug-in computes several network centrality parameters and allows the user to analyze existing relationships between experimental data provided by the users and node centrality values computed by the plug-in. CentiScaPe allows identifying network nodes that are relevant from both experimental and topological viewpoints. CentiScaPe also provides a Boolean logic-based tool that allows easy characterization of nodes whose topological relevance depends on more than one centrality. Finally, different graphic outputs and the included description of biological significance for each computed centrality facilitate the analysis by the end users not expert in graph theory, thus allowing easy node categorization and experimental prioritization. AVAILABILITY: CentiScaPe can be downloaded via the Cytoscape web site: http://chianti.ucsd.edu/cyto_web/plugins/index.php. Tutorial, centrality descriptions and example data are available at: http://profs.sci.univr.it/ approximately scardoni/centiscape/centiscapepage.php CONTACT: giovanni.scardoni@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of microRNAs implicated in the late differentiation stages of normal B cells suggests a central role for miRNA targets ZEB1 and TP53.

In the late B cell differentiation stages, miRNAs expression modifications promoting or inhibiting key pathways are only partially defined. We isolated 29 CD19+ human B cell samples at different stages of differentiation: B cells from peripheral blood; naïve, germinal center (GC) and subepithelial (SE) B cells from tonsils. SE cells were further split in activated and resting B cell. The miRNA expression profile of these B cells was assessed by microarray analysis and selected miRNAs were validated by quantitative RT-PCR and in situ hybridization on normal tonsils. The comparison of all samples showed changes in 107 miRNAs in total. Among 48 miRNAs differentially expressed in naïve, GC and SE cells, we identified 8 miRNAs: mir-323, mir-138, mir-9*, mir-211, mir-149, mir-373, mir-135a and mir-184, strictly specific to follicular cells that had never been implicated before in late stages of B cell development. Moreover, we unveiled 34 miRNAs able to discriminate between CD5- activated B cells and resting B cells. The miRNAs profile of CD5- resting B cells showed a higher similarity to naïve CD5+ than CD5- activated B cells. Finally, network analysis on shortest paths connecting gene targets suggested ZEB1 and TP53 as key miRNA targets during the follicular differentiation pathway. These data confirm and extend our knowledge on the miRNAs-related regulatory pathways involved in the late B cell maturation.

Creating, generating and comparing random network models with NetworkRandomizer.

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. We have developed an app for the Cytoscape platform which allows the creation of randomized networks and the randomization of existing, real networks. Since there is a lack of tools for generating and randomizing networks, our app helps researchers to exploit different, well known random network models which could be used as a benchmark for validating real datasets. We also propose a novel methodology for creating random weighted networks starting from experimental data. Finally the app provides a statistical tool which compares real versus random attributes, in order to validate all the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

Finding the shortest path with PesCa: a tool for network reconstruction.

Network analysis is of growing interest in several fields ranging from economics to biology. Several methods have been developed to investigate different properties of physical networks abstracted as graphs, including quantification of specific topological properties, contextual data enrichment, simulation of pathway dynamics and visual representation. In this context, the PesCa app for the Cytoscape network analysis environment is specifically designed to help researchers infer and manipulate networks based on the shortest path principle. PesCa offers different algorithms allowing network reconstruction and analysis starting from a list of genes, proteins and in general a set of interconnected nodes. The app is useful in the early stage of network analysis, i.e. to create networks or generate clusters based on shortest path computation, but can also help further investigations and, in general, it is suitable for every situation requiring the connection of a set of nodes that apparently do not share links, such as isolated nodes in sub-networks. Overall, the plugin enhances the ability of discovering interesting and not obvious relations between high dimensional sets of interacting objects.

Node interference and robustness: performing virtual knock-out experiments on biological networks: the case of leukocyte integrin activation network.

The increasing availability of large network datasets derived from high-throughput experiments requires the development of tools to extract relevant information from biological networks, and the development of computational methods capable of detecting qualitative and quantitative changes in the topological properties of biological networks is of critical relevance. We introduce the notions of node interference and robustness as measures of the reciprocal influence between nodes within a network. We examine the theoretical significance of these new, centrality-based, measures by characterizing the topological relationships between nodes and groups of nodes. Node interference analysis allows topologically determining the context of functional influence of single nodes. Conversely, the node robustness analysis allows topologically identifying the nodes having the highest functional influence on a specific node. A new Cytoscape plug-in calculating these measures was developed and applied to a protein-protein interaction network specifically regulating integrin activation in human primary leukocytes. Notably, the functional effects of compounds inhibiting important protein kinases, such as SRC, HCK, FGR and JAK2, are predicted by the interference and robustness analysis, are in agreement with previous studies and are confirmed by laboratory experiments. The interference and robustness notions can be applied to a variety of different contexts, including, for instance, the identification of potential side effects of drugs or the characterization of the consequences of genes deletion, duplication or of proteins degradation, opening new perspectives in biological network analysis.

Biological network analysis with CentiScaPe: centralities and experimental dataset integration.

The growing dimension and complexity of the available experimental data generating biological networks have increased the need for tools that help in categorizing nodes by their topological relevance. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes used for the identification of the most important nodes in a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be integrated with data set from lab experiments, like expression or phosphorylation levels for each protein represented in the network. Our app opens new perspectives in the analysis of biological networks, since the integration of topological analysis with lab experimental data enhance the predictive power of the bioinformatics analysis.

Computational identification of phospho-tyrosine sub-networks related to acanthocyte generation in neuroacanthocytosis.

Acanthocytes, abnormal thorny red blood cells (RBC), are one of the biological hallmarks of neuroacanthocytosis syndromes (NA), a group of rare hereditary neurodegenerative disorders. Since RBCs are easily accessible, the study of acanthocytes in NA may provide insights into potential mechanisms of neurodegeneration. Previous studies have shown that changes in RBC membrane protein phosphorylation state affect RBC membrane mechanical stability and morphology. Here, we coupled tyrosine-phosphoproteomic analysis to topological network analysis. We aimed to predict signaling sub-networks possibly involved in the generation of acanthocytes in patients affected by the two core NA disorders, namely McLeod syndrome (MLS, XK-related, Xk protein) and chorea-acanthocytosis (ChAc, VPS13A-related, chorein protein). The experimentally determined phosphoproteomic data-sets allowed us to relate the subsequent network analysis to the pathogenetic background. To reduce the network complexity, we combined several algorithms of topological network analysis including cluster determination by shortest path analysis, protein categorization based on centrality indexes, along with annotation-based node filtering. We first identified XK- and VPS13A-related protein-protein interaction networks by identifying all the interactomic shortest paths linking Xk and chorein to the corresponding set of proteins whose tyrosine phosphorylation was altered in patients. These networks include the most likely paths of functional influence of Xk and chorein on phosphorylated proteins. We further refined the analysis by extracting restricted sets of highly interacting signaling proteins representing a common molecular background bridging the generation of acanthocytes in MLS and ChAc. The final analysis pointed to a novel, very restricted, signaling module of 14 highly interconnected kinases, whose alteration is possibly involved in generation of acanthocytes in MLS and ChAc.