Neuropeptide-inducible upregulation of proteasome activity precedes nuclear factor kappa B activation in androgen-independent prostate cancer cells

Background

Upregulation of nuclear factor kappa B (NF??B) activity and neuroendocrine differentiation are two mechanisms known to be involved in prostate cancer (PC) progression to castration resistance. We have observed that major components of these pathways, including NF??B, proteasome, neutral endopeptidase (NEP) and endothelin 1 (ET-1), exhibit an inverse and mirror image pattern in androgen-dependent (AD) and -independent (AI) states in vitro.

Methods

We have now investigated for evidence of a direct mechanistic connection between these pathways with the use of immunocytochemistry (ICC), western blot analysis, electrophoretic mobility shift assay (EMSA) and proteasome activity assessment.

Results

Neuropeptide (NP) stimulation induced nuclear translocation of NF??B in a dose-dependent manner in AI cells, also evident as reduced total inhibitor ??B (I??B) levels and increased DNA binding in EMSA. These effects were preceded by increased 20?S proteasome activity at lower doses and at earlier times and were at least partially reversed under conditions of NP deprivation induced by specific NP receptor inhibitors, as well as NF??B, I??B kinase (IKK) and proteasome inhibitors. AD cells showed no appreciable nuclear translocation upon NP stimulation, with less intense DNA binding signal on EMSA.

Conclusions

Our results support evidence for a direct mechanistic connection between the NPs and NF??B/proteasome signaling pathways, with a distinct NP-induced profile in the more aggressive AI cancer state.     

A transcriptional analysis of carotenoid, chlorophyll and plastidial isoprenoid biosynthesis genes during development and osmotic stress responses in Arabidopsis thaliana

Background

Multiple pathway databases are available that describe the human metabolic network and have proven their usefulness in many applications, ranging from the analysis and interpretation of high-throughput data to their use as a reference repository. However, so far the various human metabolic networks described by these databases have not been systematically compared and contrasted, nor has the extent to which they differ been quantified. For a researcher using these databases for particular analyses of human metabolism, it is crucial to know the extent of the differences in content and their underlying causes. Moreover, the outcomes of such a comparison are important for ongoing integration efforts.

Results

We compared the genes, EC numbers and reactions of five frequently used human metabolic pathway databases. The overlap is surprisingly low, especially on reaction level, where the databases agree on 3% of the 6968 reactions they have combined. Even for the well-established tricarboxylic acid cycle the databases agree on only 5 out of the 30 reactions in total. We identified the main causes for the lack of overlap. Importantly, the databases are partly complementary. Other explanations include the number of steps a conversion is described in and the number of possible alternative substrates listed. Missing metabolite identifiers and ambiguous names for metabolites also affect the comparison.

Conclusions

Our results show that each of the five networks compared provides us with a valuable piece of the puzzle of the complete reconstruction of the human metabolic network. To enable integration of the networks, next to a need for standardizing the metabolite names and identifiers, the conceptual differences between the databases should be resolved. Considerable manual intervention is required to reach the ultimate goal of a unified and biologically accurate model for studying the systems biology of human metabolism. Our comparison provides a stepping stone for such an endeavor.     

cPath: open source software for collecting, storing, and querying biological pathways

Background  

Biological pathways, including metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks, are currently represented in over 220 diverse databases. These data are crucial for the study of specific biological processes, including human diseases. Standard exchange formats for pathway information, such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection of this data for biological research, but mechanisms for common storage and communication are required.     

Decision tree modeling predicts effects of inhibiting contractility signaling on cell motility

Background

Mathematical modelling of cellular networks is an integral part of Systems Biology and requires appropriate software tools. An important class of methods in Systems Biology deals with structural or topological (parameter-free) analysis of cellular networks. So far, software tools providing such methods for both mass-flow (metabolic) as well as signal-flow (signalling and regulatory) networks are lacking.

Results

Herein we introduce CellNetAnalyzer, a toolbox for MATLAB facilitating, in an interactive and visual manner, a comprehensive structural analysis of metabolic, signalling and regulatory networks. The particular strengths of CellNetAnalyzer are methods for functional network analysis, i.e. for characterising functional states, for detecting functional dependencies, for identifying intervention strategies, or for giving qualitative predictions on the effects of perturbations. CellNetAnalyzer extends its predecessor FluxAnalyzer (originally developed for metabolic network and pathway analysis) by a new modelling framework for examining signal-flow networks. Two of the novel methods implemented in CellNetAnalyzer are discussed in more detail regarding algorithmic issues and applications: the computation and analysis (i) of shortest positive and shortest negative paths and circuits in interaction graphs and (ii) of minimal intervention sets in logical networks.

Conclusion

CellNetAnalyzer provides a single suite to perform structural and qualitative analysis of both mass-flow- and signal-flow-based cellular networks in a user-friendly environment. It provides a large toolbox with various, partially unique, functions and algorithms for functional network analysis.CellNetAnalyzer is freely available for academic use.     

Quantitative analysis of protein phosphorylations and interactions by multi-colour IP-FCM as an input for kinetic modelling of signalling networks

Background

To understand complex biological signalling mechanisms, mathematical modelling of signal transduction pathways has been applied successfully in last few years. However, precise quantitative measurements of signal transduction events such as activation-dependent phosphorylation of proteins, remains one bottleneck to this success.

Methodology/Principal Findings

We use multi-colour immunoprecipitation measured by flow cytometry (IP-FCM) for studying signal transduction events to unrivalled precision. In this method, antibody-coupled latex beads capture the protein of interest from cellular lysates and are then stained with differently fluorescent-labelled antibodies to quantify the amount of the immunoprecipitated protein, of an interaction partner and of phosphorylation sites. The fluorescence signals are measured by FCM. Combining this procedure with beads containing defined amounts of a fluorophore allows retrieving absolute numbers of stained proteins, and not only relative values. Using IP-FCM we derived multidimensional data on the membrane-proximal T-cell antigen receptor (TCR-CD3) signalling network, including the recruitment of the kinase ZAP70 to the TCR-CD3 and subsequent ZAP70 activation by phosphorylation in the murine T-cell hybridoma and primary murine T cells. Counter-intuitively, these data showed that cell stimulation by pervanadate led to a transient decrease of the phospho-ZAP70/ZAP70 ratio at the TCR. A mechanistic mathematical model of the underlying processes demonstrated that an initial massive recruitment of non-phosphorylated ZAP70 was responsible for this behaviour. Further, the model predicted a temporal order of multisite phosphorylation of ZAP70 (with Y319 phosphorylation preceding phosphorylation at Y493) that we subsequently verified experimentally.

Conclusions/Significance

The quantitative data sets generated by IP-FCM are one order of magnitude more precise than Western blot data. This accuracy allowed us to gain unequalled insight into the dynamics of the TCR-CD3-ZAP70 signalling network.     

Hybrid cells derived from breast epithelial cell/breast cancer cell fusion events show a differential RAF-AKT crosstalk

Background

The biological phenomenon of cell fusion has been linked to several characteristics of tumour progression, including an enhanced metastatogenic capacity and an enhanced drug resistance of hybrid cells. We demonstrated recently that M13SV1-EGFP-Neo breast epithelial cells exhibiting stem cell characteristics spontaneously fused with MDA-MB-435-Hyg breast cancer cells, thereby giving rise to stable M13MDA435 hybrid cells, which are characterised by a unique gene expression profile and migratory behaviour. Here we investigated the involvement of the PLC-??/??1, PI3K/AKT and RAS-RAF-ERK signal transduction cascades in the EGF and SDF-1?? induced migration of two M13MDA435 hybrid cell clones in comparison to their parental cells.

Results

Analysis of the migratory behaviour by using the three-dimensional collagen matrix migration assay showed that M13SV1-EGFP-Neo cells as well as M13MDA435 hybrid cells, but not the breast cancer cell line, responded to EGF stimulation with an increased locomotory activity. By contrast, SDF-1?? solely stimulated the migration of M13SV1-EGFP-Neo cells, whereas the migratory activity of the other cell lines was blocked. Analysis of signal transduction cascades revealed a putative differential RAF-AKT crosstalk in M13MDA435-1 and -3 hybrid cell clones. The PI3K inhibitor Ly294002 effectively blocked the EGF induced migration of M13MDA435-3 hybrid cells, whereas the EGF induced locomotion of M13MDA435-1 hybrid cells was markedly increased. Analysis of RAF-1 S259 phosphorylation, being a major mediator of the negative regulation of RAF-1 by AKT, showed decreased pRAF-1 S259 levels in LY294002 treated M13MDA435-1 hybrid cells. By contrast, pRAF-1 S259 levels remained unaltered in the other cell lines. Inhibition of PI3K/AKT signalling by Ly294002 relieves the AKT mediated phosphorylation of RAF-1, thereby restoring MAPK signalling.

Conclusions

Here we show that hybrid cells could evolve exhibiting a differential active RAF-AKT crosstalk. Because PI3K/AKT signalling has been chosen as a target for anti-cancer therapies our data might point to a possible severe side effect of AKT targeted cancer therapies. Inhibition of PI3K/AKT signalling in RAF-AKT crosstalk positive cancer (hybrid) cells could result in a progression of these cells. Thus, not only the receptor (activation) status, but also the activation of signal transduction molecules should be analysed thoroughly prior to therapy.     

Extending pathways and processes using molecular interaction networks to analyse cancer genome data

Background  

Cellular processes and pathways, whose deregulation may contribute to the development of cancers, are often represented as cascades of proteins transmitting a signal from the cell surface to the nucleus. However, recent functional genomic experiments have identified thousands of interactions for the signalling canonical proteins, challenging the traditional view of pathways as independent functional entities. Combining information from pathway databases and interaction networks obtained from functional genomic experiments is therefore a promising strategy to obtain more robust pathway and process representations, facilitating the study of cancer-related pathways.     

Towards an understating of signal transduction protein interaction networks

Protein network analysis has witnessed a number of advancements in the past for understanding molecular characteristics for important network topologies in biological systems. The signaling pathway regulates cell cycle progression and anti-apoptotic molecules. This pathway is also involved in maintaining cell survival by modulating the activity of apoptosis through RAS, P13K, AKT and BAD activities. The importance of protein-protein interactions to improve usability of the interactome by scoring and ranking interaction data for proteins in signal transduction networks is illustrated using available data and resources.     

Schizophrenia Gene Networks and Pathways and Their Applications for Novel Candidate Gene Selection

Background

Schizophrenia (SZ) is a heritable, complex mental disorder. We have seen limited success in finding causal genes for schizophrenia from numerous conventional studies. Protein interaction network and pathway-based analysis may provide us an alternative and effective approach to investigating the molecular mechanisms of schizophrenia.

Methodology/Principal Findings

We selected a list of schizophrenia candidate genes (SZGenes) using a multi-dimensional evidence-based approach. The global network properties of proteins encoded by these SZGenes were explored in the context of the human protein interactome while local network properties were investigated by comparing SZ-specific and cancer-specific networks that were extracted from the human interactome. Relative to cancer genes, we observed that SZGenes tend to have an intermediate degree and an intermediate efficiency on a perturbation spreading throughout the human interactome. This suggested that schizophrenia might have different pathological mechanisms from cancer even though both are complex diseases. We conducted pathway analysis using Ingenuity System and constructed the first schizophrenia molecular network (SMN) based on protein interaction networks, pathways and literature survey. We identified 24 pathways overrepresented in SZGenes and examined their interactions and crosstalk. We observed that these pathways were related to neurodevelopment, immune system, and retinoic X receptor (RXR). Our examination of SMN revealed that schizophrenia is a dynamic process caused by dysregulation of the multiple pathways. Finally, we applied the network/pathway approach to identify novel candidate genes, some of which could be verified by experiments.

Conclusions/Significance

This study provides the first comprehensive review of the network and pathway characteristics of schizophrenia candidate genes. Our preliminary results suggest that this systems biology approach might prove promising for selection of candidate genes for complex diseases. Our findings have important implications for the molecular mechanisms for schizophrenia and, potentially, other psychiatric disorders.     

Signalogs: orthology-based identification of novel signaling pathway components in three metazoans

Background

Uncovering novel components of signal transduction pathways and their interactions within species is a central task in current biological research. Orthology alignment and functional genomics approaches allow the effective identification of signaling proteins by cross-species data integration. Recently, functional annotation of orthologs was transferred across organisms to predict novel roles for proteins. Despite the wide use of these methods, annotation of complete signaling pathways has not yet been transferred systematically between species.

Principal Findings

Here we introduce the concept of ‘signalog’ to describe potential novel signaling function of a protein on the basis of the known signaling role(s) of its ortholog(s). To identify signalogs on genomic scale, we systematically transferred signaling pathway annotations among three animal species, the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and humans. Using orthology data from InParanoid and signaling pathway information from the SignaLink database, we predict 88 worm, 92 fly, and 73 human novel signaling components. Furthermore, we developed an on-line tool and an interactive orthology network viewer to allow users to predict and visualize components of orthologous pathways. We verified the novelty of the predicted signalogs by literature search and comparison to known pathway annotations. In C. elegans, 6 out of the predicted novel Notch pathway members were validated experimentally. Our approach predicts signaling roles for 19 human orthodisease proteins and 5 known drug targets, and suggests 14 novel drug target candidates.

Conclusions

Orthology-based pathway membership prediction between species enables the identification of novel signaling pathway components that we referred to as signalogs. Signalogs can be used to build a comprehensive signaling network in a given species. Such networks may increase the biomedical utilization of C. elegans and D. melanogaster. In humans, signalogs may identify novel drug targets and new signaling mechanisms for approved drugs.     

Predicting new molecular targets for rhein using network pharmacology

Background

Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.

Methods

In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.

Results

Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.

Conclusions

Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.     

Gli2a protein localization reveals a role for Iguana/DZIP1 in primary ciliogenesis and a dependence of Hedgehog signal transduction on primary cilia in the zebrafish

Background  

In mammalian cells, the integrity of the primary cilium is critical for proper regulation of the Hedgehog (Hh) signal transduction pathway. Whether or not this dependence on the primary cilium is a universal feature of vertebrate Hedgehog signalling has remained contentious due, in part, to the apparent divergence of the intracellular transduction pathway between mammals and teleost fish.     

Reconciling taxonomy and phylogenetic inference: formalism and algorithms for describing discord and inferring taxonomic roots

Background

Understanding the evolution of biological networks can provide insight into how their modular structure arises and how they are affected by environmental changes. One approach to studying the evolution of these networks is to reconstruct plausible common ancestors of present-day networks, allowing us to analyze how the topological properties change over time and to posit mechanisms that drive the networks?? evolution. Further, putative ancestral networks can be used to help solve other difficult problems in computational biology, such as network alignment.

Results

We introduce a combinatorial framework for encoding network histories, and we give a fast procedure that, given a set of gene duplication histories, in practice finds network histories with close to the minimum number of interaction gain or loss events to explain the observed present-day networks. In contrast to previous studies, our method does not require knowing the relative ordering of unrelated duplication events. Results on simulated histories and real biological networks both suggest that common ancestral networks can be accurately reconstructed using this parsimony approach. A software package implementing our method is available under the Apache 2.0 license at http://cbcb.umd.edu/kingsford-group/parana.

Conclusions

Our parsimony-based approach to ancestral network reconstruction is both efficient and accurate. We show that considering a larger set of potential ancestral interactions by not assuming a relative ordering of unrelated duplication events can lead to improved ancestral network inference.