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Sample records for signaling protein networking

  1. Protein modules and signalling networks

    NASA Astrophysics Data System (ADS)

    Pawson, Tony

    1995-02-01

    Communication between cells assumes particular importance in multicellular organisms. The growth, migration and differentiation of cells in the embryo, and their organization into specific tissues, depend on signals transmitted from one cell to another. In the adult, cell signalling orchestrates normal cellular behaviour and responses to wounding and infection. The consequences of breakdowns in this signalling underlie cancer, diabetes and disorders of the immune and cardiovascular systems. Conserved protein domains that act as key regulatory participants in many of these different signalling pathways are highlighted.

  2. Text mining for metabolic pathways, signaling cascades, and protein networks.

    PubMed

    Hoffmann, Robert; Krallinger, Martin; Andres, Eduardo; Tamames, Javier; Blaschke, Christian; Valencia, Alfonso

    2005-05-10

    The complexity of the information stored in databases and publications on metabolic and signaling pathways, the high throughput of experimental data, and the growing number of publications make it imperative to provide systems to help the researcher navigate through these interrelated information resources. Text-mining methods have started to play a key role in the creation and maintenance of links between the information stored in biological databases and its original sources in the literature. These links will be extremely useful for database updating and curation, especially if a number of technical problems can be solved satisfactorily, including the identification of protein and gene names (entities in general) and the characterization of their types of interactions. The first generation of openly accessible text-mining systems, such as iHOP (Information Hyperlinked over Proteins), provides additional functions to facilitate the reconstruction of protein interaction networks, combine database and text information, and support the scientist in the formulation of novel hypotheses. The next challenge is the generation of comprehensive information regarding the general function of signaling pathways and protein interaction networks.

  3. Autophagy signal transduction by ATG proteins: from hierarchies to networks.

    PubMed

    Wesselborg, Sebastian; Stork, Björn

    2015-12-01

    Autophagy represents an intracellular degradation process which is involved in both cellular homeostasis and disease settings. In the last two decades, the molecular machinery governing this process has been characterized in detail. To date, several key factors regulating this intracellular degradation process have been identified. The so-called autophagy-related (ATG) genes and proteins are central to this process. However, several additional molecules contribute to the outcome of an autophagic response. Several review articles describing the molecular process of autophagy have been published in the recent past. In this review article we would like to add the most recent findings to this knowledge, and to give an overview of the network character of the autophagy signaling machinery.

  4. Information theory in systems biology. Part II: protein-protein interaction and signaling networks.

    PubMed

    Mousavian, Zaynab; Díaz, José; Masoudi-Nejad, Ali

    2016-03-01

    By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed.

  5. Dynamic protein interaction networks and new structural paradigms in signaling

    PubMed Central

    Csizmok, Veronika; Follis, Ariele Viacava; Kriwacki, Richard W.; Forman-Kay, Julie D.

    2017-01-01

    Understanding signaling and other complex biological processes requires elucidating the critical roles of intrinsically disordered proteins and regions (IDPs/IDRs), which represent ~30% of the proteome and enable unique regulatory mechanisms. In this review we describe the structural heterogeneity of disordered proteins that underpins these mechanisms and the latest progress in obtaining structural descriptions of ensembles of disordered proteins that are needed for linking structure and dynamics to function. We describe the diverse interactions of IDPs that can have unusual characteristics such as “ultrasensitivity” and “regulated folding and unfolding”. We also summarize the mounting data showing that large-scale assembly and protein phase separation occurs within a variety of signaling complexes and cellular structures. In addition, we discuss efforts to therapeutically target disordered proteins with small molecules. Overall, we interpret the remodeling of disordered state ensembles due to binding and post-translational modifications within an expanded framework for allostery that provides significant insights into how disordered proteins transmit biological information. PMID:26922996

  6. Crk and CrkL adaptor proteins: networks for physiological and pathological signaling

    PubMed Central

    Birge, Raymond B; Kalodimos, Charalampos; Inagaki, Fuyuhiko; Tanaka, Shinya

    2009-01-01

    The Crk adaptor proteins (Crk and CrkL) constitute an integral part of a network of essential signal transduction pathways in humans and other organisms that act as major convergence points in tyrosine kinase signaling. Crk proteins integrate signals from a wide variety of sources, including growth factors, extracellular matrix molecules, bacterial pathogens, and apoptotic cells. Mounting evidence indicates that dysregulation of Crk proteins is associated with human diseases, including cancer and susceptibility to pathogen infections. Recent structural work has identified new and unusual insights into the regulation of Crk proteins, providing a rationale for how Crk can sense diverse signals and produce a myriad of biological responses. PMID:19426560

  7. Protein Signaling Networks from Single Cell Fluctuations and Information Theory Profiling

    PubMed Central

    Shin, Young Shik; Remacle, F.; Fan, Rong; Hwang, Kiwook; Wei, Wei; Ahmad, Habib; Levine, R.D.; Heath, James R.

    2011-01-01

    Protein signaling networks among cells play critical roles in a host of pathophysiological processes, from inflammation to tumorigenesis. We report on an approach that integrates microfluidic cell handling, in situ protein secretion profiling, and information theory to determine an extracellular protein-signaling network and the role of perturbations. We assayed 12 proteins secreted from human macrophages that were subjected to lipopolysaccharide challenge, which emulates the macrophage-based innate immune responses against Gram-negative bacteria. We characterize the fluctuations in protein secretion of single cells, and of small cell colonies (n = 2, 3,···), as a function of colony size. Measuring the fluctuations permits a validation of the conditions required for the application of a quantitative version of the Le Chatelier's principle, as derived using information theory. This principle provides a quantitative prediction of the role of perturbations and allows a characterization of a protein-protein interaction network. PMID:21575571

  8. Protein signaling networks from single cell fluctuations and information theory profiling.

    PubMed

    Shin, Young Shik; Remacle, F; Fan, Rong; Hwang, Kiwook; Wei, Wei; Ahmad, Habib; Levine, R D; Heath, James R

    2011-05-18

    Protein signaling networks among cells play critical roles in a host of pathophysiological processes, from inflammation to tumorigenesis. We report on an approach that integrates microfluidic cell handling, in situ protein secretion profiling, and information theory to determine an extracellular protein-signaling network and the role of perturbations. We assayed 12 proteins secreted from human macrophages that were subjected to lipopolysaccharide challenge, which emulates the macrophage-based innate immune responses against Gram-negative bacteria. We characterize the fluctuations in protein secretion of single cells, and of small cell colonies (n = 2, 3,···), as a function of colony size. Measuring the fluctuations permits a validation of the conditions required for the application of a quantitative version of the Le Chatelier's principle, as derived using information theory. This principle provides a quantitative prediction of the role of perturbations and allows a characterization of a protein-protein interaction network.

  9. Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction

    PubMed Central

    Saez-Rodriguez, Julio; Alexopoulos, Leonidas G; Epperlein, Jonathan; Samaga, Regina; Lauffenburger, Douglas A; Klamt, Steffen; Sorger, Peter K

    2009-01-01

    Large-scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach—implemented in the free CNO software—for turning signalling networks into logical models and calibrating the models against experimental data. When a literature-derived network of 82 proteins covering the immediate-early responses of human cells to seven cytokines was modelled, we found that training against experimental data dramatically increased predictive power, despite the crudeness of Boolean approximations, while significantly reducing the number of interactions. Thus, many interactions in literature-derived networks do not appear to be functional in the liver cells from which we collected our data. At the same time, CNO identified several new interactions that improved the match of model to data. Although missing from the starting network, these interactions have literature support. Our approach, therefore, represents a means to generate predictive, cell-type-specific models of mammalian signalling from generic protein signalling networks. PMID:19953085

  10. Predicting Pharmacodynamic Drug-Drug Interactions through Signaling Propagation Interference on Protein-Protein Interaction Networks

    PubMed Central

    Park, Kyunghyun; Kim, Docyong; Ha, Suhyun; Lee, Doheon

    2015-01-01

    As pharmacodynamic drug-drug interactions (PD DDIs) could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI) networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw. PMID:26469276

  11. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

    PubMed Central

    Teschendorff, Andrew E.; Banerji, Christopher R. S.; Severini, Simone; Kuehn, Reimer; Sollich, Peter

    2015-01-01

    One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology. PMID:25919796

  12. Screening of cell cycle fusion proteins to identify kinase signaling networks.

    PubMed

    Trojanowsky, Michelle; Vidovic, Dusica; Simanski, Scott; Penas, Clara; Schurer, Stephan; Ayad, Nagi G

    2015-01-01

    Kinase signaling networks are well-established mediators of cell cycle transitions. However, how kinases interact with the ubiquitin proteasome system (UPS) to elicit protein turnover is not fully understood. We sought a means of identifying kinase-substrate interactions to better understand signaling pathways controlling protein degradation. Our prior studies used a luciferase fusion protein to uncover kinase networks controlling protein turnover. In this study, we utilized a similar approach to identify pathways controlling the cell cycle protein p27(Kip1). We generated a p27(Kip1)-luciferase fusion and expressed it in cells incubated with compounds from a library of pharmacologically active compounds. We then compared the relative effects of the compounds on p27(Kip1)-luciferase fusion stabilization. This was combined with in silico kinome profiling to identify potential kinases inhibited by each compound. This approach effectively uncovered known kinases regulating p27(Kip1) turnover. Collectively, our studies suggest that this parallel screening approach is robust and can be applied to fully understand kinase-ubiquitin pathway interactions.

  13. Temporal regulation of EGF signaling networks by the scaffold protein Shc1

    PubMed Central

    Zheng, Yong; Zhang, Cunjie; Croucher, David R.; Soliman, Mohamed A.; St-Denis, Nicole; Pasculescu, Adrian; Taylor, Lorne; Tate, Stephen A.; Hardy, Rod W.; Colwill, Karen; Dai, Anna Yue; Bagshaw, Rick; Dennis, James W.; Gingras, Anne-Claude; Daly, Roger J.; Pawson, Tony

    2016-01-01

    Cell-surface receptors frequently employ scaffold proteins to recruit cytoplasmic targets, but the rationale for this is uncertain. Activated receptor tyrosine kinases, for example, engage scaffolds such as Shc1 that contain phosphotyrosine (pTyr) binding (PTB) domains. Using quantitative mass spectrometry, we find that Shc1 responds to epidermal growth factor (EGF) stimulation through multiple waves of distinct phosphorylation events and protein interactions. Following stimulation, Shc1 rapidly binds a group of proteins that activate pro-mitogenic/survival pathways dependent on recruitment of the Grb2 adaptor to Shc1 pTyr sites. Akt-mediated feedback phosphorylation of Shc1 Ser29 then recruits the Ptpn12 tyrosine phosphatase. This is followed by a sub-network of proteins involved in cytoskeletal reorganization, trafficking and signal termination that binds Shc1 with delayed kinetics, largely through the SgK269 pseudokinase/adaptor protein. Ptpn12 acts as a switch to convert Shc1 from pTyr/Grb2-based signaling to SgK269-mediated pathways that regulate cell invasion and morphogenesis. The Shc1 scaffold therefore directs the temporal flow of signaling information following EGF stimulation. PMID:23846654

  14. A large scale Huntingtin protein interaction network implicates Rho GTPase signaling pathways in Huntington disease.

    PubMed

    Tourette, Cendrine; Li, Biao; Bell, Russell; O'Hare, Shannon; Kaltenbach, Linda S; Mooney, Sean D; Hughes, Robert E

    2014-03-07

    Huntington disease (HD) is an inherited neurodegenerative disease caused by a CAG expansion in the HTT gene. Using yeast two-hybrid methods, we identified a large set of proteins that interact with huntingtin (HTT)-interacting proteins. This network, composed of HTT-interacting proteins (HIPs) and proteins interacting with these primary nodes, contains 3235 interactions among 2141 highly interconnected proteins. Analysis of functional annotations of these proteins indicates that primary and secondary HIPs are enriched in pathways implicated in HD, including mammalian target of rapamycin, Rho GTPase signaling, and oxidative stress response. To validate roles for HIPs in mutant HTT toxicity, we show that the Rho GTPase signaling components, BAIAP2, EZR, PIK3R1, PAK2, and RAC1, are modifiers of mutant HTT toxicity. We also demonstrate that Htt co-localizes with BAIAP2 in filopodia and that mutant HTT interferes with filopodial dynamics. These data indicate that HTT is involved directly in membrane dynamics, cell attachment, and motility. Furthermore, they implicate dysregulation in these pathways as pathological mechanisms in HD.

  15. Phosphoproteome reveals an atlas of protein signaling networks during osteoblast adhesion.

    PubMed

    Milani, Renato; Ferreira, Carmen V; Granjeiro, José M; Paredes-Gamero, Edgar J; Silva, Rodrigo A; Justo, Giselle Z; Nader, Helena B; Galembeck, Eduardo; Peppelenbosch, Maikel P; Aoyama, Hiroshi; Zambuzzi, Willian F

    2010-04-01

    Cell adhesion on surfaces is a fundamental process in the emerging biomaterials field and developmental events as well. However, the mechanisms regulating this biological process in osteoblasts are not fully understood. Reversible phosphorylation catalyzed by kinases is probably the most important regulatory mechanism in eukaryotes. Therefore, the goal of this study is to assess osteoblast adhesion through a molecular prism under a peptide array technology, revealing essential signaling proteins governing adhesion-related events. First, we showed that there are main morphological changes on osteoblast shape during adhesion up to 3 h. Second, besides classical proteins activated upon integrin activation, our results showed a novel network involving signaling proteins such as Rap1A, PKA, PKC, and GSK3beta during osteoblast adhesion on polystyrene. Third, these proteins were grouped in different signaling cascades including focal adhesion establishment, cytoskeleton rearrangement, and cell-cycle arrest. We have thus provided evidence that a global phosphorylation screening is able to yield a systems-oriented look at osteoblast adhesion, providing new insights for understanding of bone formation and improvement of cell-substratum interactions. Altogether, these statements are necessary means for further intervention and development of new approaches for the progress of tissue engineering.

  16. APG: an Active Protein-Gene network model to quantify regulatory signals in complex biological systems.

    PubMed

    Wang, Jiguang; Sun, Yidan; Zheng, Si; Zhang, Xiang-Sun; Zhou, Huarong; Chen, Luonan

    2013-01-01

    Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information.

  17. The relationship between the hierarchical position of proteins in the human signal transduction network and their rate of evolution

    PubMed Central

    2012-01-01

    Background Proteins evolve at disparate rates, as a result of the action of different types and strengths of evolutionary forces. An open question in evolutionary biology is what factors are responsible for this variability. In general, proteins whose function has a great impact on organisms’ fitness are expected to evolve under stronger selective pressures. In biosynthetic pathways, upstream genes usually evolve under higher levels of selective constraint than those acting at the downstream part, as a result of their higher hierarchical position. Similar observations have been made in transcriptional regulatory networks, whose upstream elements appear to be more essential and subject to selection. Less well understood is, however, how selective pressures distribute along signal transduction pathways. Results Here, I combine comparative genomics and directed protein interaction data to study the distribution of evolutionary forces across the human signal transduction network. Surprisingly, no evidence was found for higher levels of selective constraint at the upstream network genes (those occupying more hierarchical positions). On the contrary, purifying selection was found to act more strongly on genes acting at the downstream part of the network, which seems to be due to downstream genes being more highly and broadly expressed, performing certain functions and, in particular, encoding proteins that are more highly connected in the protein–protein interaction network. When the effect of these confounding factors is discounted, upstream and downstream genes evolve at similar rates. The trends found in the overall signaling network are exemplified by analysis of the distribution of purifying selection along the mammalian Ras signaling pathway, showing that upstream and downstream genes evolve at similar rates. Conclusions These results indicate that the upstream/downstream position of proteins in the signal transduction network has, in general, no direct effect

  18. Using transgenic modulation of protein synthesis and accumulation to probe protein signaling networks in Arabidopsis thaliana

    PubMed Central

    Warnasooriya, Sankalpi N

    2011-01-01

    Deployment of new model species in the plant biology community requires the development and/or improvement of numerous genetic tools. Sequencing of the Arabidopsis thaliana genome opened up a new challenge of assigning biological function to each gene. As many genes exhibit spatiotemporal or other conditional regulation of biological processes, probing for gene function necessitates applications that can be geared toward temporal, spatial and quantitative functional analysis in vivo. The continuing quest to establish new platforms to examine plant gene function has resulted in the availability of numerous genomic and proteomic tools. Classical and more recent genome-wide experimental approaches include conventional mutagenesis, tagged DNA insertional mutagenesis, ectopic expression of transgenes, activation tagging, RNA interference and two-component transactivation systems. The utilization of these molecular tools has resulted in conclusive evidence for the existence of many genes, and expanded knowledge on gene structure and function. This review covers several molecular tools that have become increasingly useful in basic plant research. We discuss their advantages and limitations for probing cellular protein function while emphasizing the contributions made to lay the fundamental groundwork for genetic manipulation of crops using plant biotechnology. PMID:21862868

  19. Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network.

    PubMed

    Al-Anzi, Bader; Arpp, Patrick; Gerges, Sherif; Ormerod, Christopher; Olsman, Noah; Zinn, Kai

    2015-05-01

    An approach combining genetic, proteomic, computational, and physiological analysis was used to define a protein network that regulates fat storage in budding yeast (Saccharomyces cerevisiae). A computational analysis of this network shows that it is not scale-free, and is best approximated by the Watts-Strogatz model, which generates "small-world" networks with high clustering and short path lengths. The network is also modular, containing energy level sensing proteins that connect to four output processes: autophagy, fatty acid synthesis, mRNA processing, and MAP kinase signaling. The importance of each protein to network function is dependent on its Katz centrality score, which is related both to the protein's position within a module and to the module's relationship to the network as a whole. The network is also divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We used a combination of genetics and pharmacology to simultaneously block output from multiple network nodes. The phenotypic results of this blockage define patterns of communication among distant network nodes, and these patterns are consistent with the Watts-Strogatz model.

  20. Mechanosensitive Molecular Networks Involved in Transducing Resistance Exercise-Signals into Muscle Protein Accretion

    PubMed Central

    Rindom, Emil; Vissing, Kristian

    2016-01-01

    Loss of skeletal muscle myofibrillar protein with disease and/or inactivity can severely deteriorate muscle strength and function. Strategies to counteract wasting of muscle myofibrillar protein are therefore desirable and invite for considerations on the potential superiority of specific modes of resistance exercise and/or the adequacy of low load resistance exercise regimens as well as underlying mechanisms. In this regard, delineation of the potentially mechanosensitive molecular mechanisms underlying muscle protein synthesis (MPS), may contribute to an understanding on how differentiated resistance exercise can transduce a mechanical signal into stimulation of muscle accretion. Recent findings suggest specific upstream exercise-induced mechano-sensitive myocellular signaling pathways to converge on mammalian target of rapamycin complex 1 (mTORC1), to influence MPS. This may e.g. implicate mechanical activation of signaling through a diacylglycerol kinase (DGKζ)-phosphatidic acid (PA) axis or implicate integrin deformation to signal through a Focal adhesion kinase (FAK)-Tuberous Sclerosis Complex 2 (TSC2)-Ras homolog enriched in brain (Rheb) axis. Moreover, since initiation of translation is reliant on mRNA, it is also relevant to consider potentially mechanosensitive signaling pathways involved in muscle myofibrillar gene transcription and whether some of these pathways converge with those affecting mTORC1 activation for MPS. In this regard, recent findings suggest how mechanical stress may implicate integrin deformation and/or actin dynamics to signal through a Ras homolog gene family member A protein (RhoA)-striated muscle activator of Rho signaling (STARS) axis or implicate deformation of Notch to affect Bone Morphogenetic Protein (BMP) signaling through a small mother of decapentaplegic (Smad) axis. PMID:27909410

  1. Mechanosensitive Molecular Networks Involved in Transducing Resistance Exercise-Signals into Muscle Protein Accretion.

    PubMed

    Rindom, Emil; Vissing, Kristian

    2016-01-01

    Loss of skeletal muscle myofibrillar protein with disease and/or inactivity can severely deteriorate muscle strength and function. Strategies to counteract wasting of muscle myofibrillar protein are therefore desirable and invite for considerations on the potential superiority of specific modes of resistance exercise and/or the adequacy of low load resistance exercise regimens as well as underlying mechanisms. In this regard, delineation of the potentially mechanosensitive molecular mechanisms underlying muscle protein synthesis (MPS), may contribute to an understanding on how differentiated resistance exercise can transduce a mechanical signal into stimulation of muscle accretion. Recent findings suggest specific upstream exercise-induced mechano-sensitive myocellular signaling pathways to converge on mammalian target of rapamycin complex 1 (mTORC1), to influence MPS. This may e.g. implicate mechanical activation of signaling through a diacylglycerol kinase (DGKζ)-phosphatidic acid (PA) axis or implicate integrin deformation to signal through a Focal adhesion kinase (FAK)-Tuberous Sclerosis Complex 2 (TSC2)-Ras homolog enriched in brain (Rheb) axis. Moreover, since initiation of translation is reliant on mRNA, it is also relevant to consider potentially mechanosensitive signaling pathways involved in muscle myofibrillar gene transcription and whether some of these pathways converge with those affecting mTORC1 activation for MPS. In this regard, recent findings suggest how mechanical stress may implicate integrin deformation and/or actin dynamics to signal through a Ras homolog gene family member A protein (RhoA)-striated muscle activator of Rho signaling (STARS) axis or implicate deformation of Notch to affect Bone Morphogenetic Protein (BMP) signaling through a small mother of decapentaplegic (Smad) axis.

  2. Molecular signal networks and regulating mechanisms of the unfolded protein response*

    PubMed Central

    Gong, Jing; Wang, Xing-zhi; Wang, Tao; Chen, Jiao-jiao; Xie, Xiao-yuan; Hu, Hui; Yu, Fang; Liu, Hui-lin; Jiang, Xing-yan; Fan, Han-dong

    2017-01-01

    Within the cell, several mechanisms exist to maintain homeostasis of the endoplasmic reticulum (ER). One of the primary mechanisms is the unfolded protein response (UPR). In this review, we primarily focus on the latest signal webs and regulation mechanisms of the UPR. The relationships among ER stress, apoptosis, and cancer are also discussed. Under the normal state, binding immunoglobulin protein (BiP) interacts with the three sensors (protein kinase RNA-like ER kinase (PERK), activating transcription factor 6 (ATF6), and inositol-requiring enzyme 1α (IRE1α)). Under ER stress, misfolded proteins interact with BiP, resulting in the release of BiP from the sensors. Subsequently, the three sensors dimerize and autophosphorylate to promote the signal cascades of ER stress. ER stress includes a series of positive and negative feedback signals, such as those regulating the stabilization of the sensors/BiP complex, activating and inactivating the sensors by autophosphorylation and dephosphorylation, activating specific transcription factors to enable selective transcription, and augmenting the ability to refold and export. Apart from the three basic pathways, vascular endothelial growth factor (VEGF)-VEGF receptor (VEGFR)-phospholipase C-γ (PLCγ)-mammalian target of rapamycin complex 1 (mTORC1) pathway, induced only in solid tumors, can also activate ATF6 and PERK signal cascades, and IRE1α also can be activated by activated RAC-alpha serine/threonine-protein kinase (AKT). A moderate UPR functions as a pro-survival signal to return the cell to its state of homeostasis. However, persistent ER stress will induce cells to undergo apoptosis in response to increasing reactive oxygen species (ROS), Ca2+ in the cytoplasmic matrix, and other apoptosis signal cascades, such as c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription 3 (STAT3), and P38, when cellular damage exceeds the capacity of this adaptive response. PMID:28070992

  3. Experimental and Computational Analysis of a Large Protein Network That Controls Fat Storage Reveals the Design Principles of a Signaling Network

    PubMed Central

    Al-Anzi, Bader; Zinn, Kai

    2015-01-01

    An approach combining genetic, proteomic, computational, and physiological analysis was used to define a protein network that regulates fat storage in budding yeast (Saccharomyces cerevisiae). A computational analysis of this network shows that it is not scale-free, and is best approximated by the Watts-Strogatz model, which generates “small-world” networks with high clustering and short path lengths. The network is also modular, containing energy level sensing proteins that connect to four output processes: autophagy, fatty acid synthesis, mRNA processing, and MAP kinase signaling. The importance of each protein to network function is dependent on its Katz centrality score, which is related both to the protein’s position within a module and to the module’s relationship to the network as a whole. The network is also divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We used a combination of genetics and pharmacology to simultaneously block output from multiple network nodes. The phenotypic results of this blockage define patterns of communication among distant network nodes, and these patterns are consistent with the Watts-Strogatz model. PMID:26020510

  4. A Large Scale Huntingtin Protein Interaction Network Implicates Rho GTPase Signaling Pathways in Huntington Disease*♦

    PubMed Central

    Tourette, Cendrine; Li, Biao; Bell, Russell; O'Hare, Shannon; Kaltenbach, Linda S.; Mooney, Sean D.; Hughes, Robert E.

    2014-01-01

    Huntington disease (HD) is an inherited neurodegenerative disease caused by a CAG expansion in the HTT gene. Using yeast two-hybrid methods, we identified a large set of proteins that interact with huntingtin (HTT)-interacting proteins. This network, composed of HTT-interacting proteins (HIPs) and proteins interacting with these primary nodes, contains 3235 interactions among 2141 highly interconnected proteins. Analysis of functional annotations of these proteins indicates that primary and secondary HIPs are enriched in pathways implicated in HD, including mammalian target of rapamycin, Rho GTPase signaling, and oxidative stress response. To validate roles for HIPs in mutant HTT toxicity, we show that the Rho GTPase signaling components, BAIAP2, EZR, PIK3R1, PAK2, and RAC1, are modifiers of mutant HTT toxicity. We also demonstrate that Htt co-localizes with BAIAP2 in filopodia and that mutant HTT interferes with filopodial dynamics. These data indicate that HTT is involved directly in membrane dynamics, cell attachment, and motility. Furthermore, they implicate dysregulation in these pathways as pathological mechanisms in HD. PMID:24407293

  5. A Signaling Network Controlling Androgenic Repression of c-Fos Protein in Prostate Adenocarcinoma Cells*

    PubMed Central

    Shankar, Eswar; Song, Kyung; Corum, Sarah L.; Bane, Kara L.; Wang, Hui; Kao, Hung-Ying; Danielpour, David

    2016-01-01

    The transcription factor c-Fos controls many important cellular processes, including cell growth and apoptosis. c-Fos expression is rapidly elevated in the prostate upon castration-mediated androgen withdrawal through an undefined mechanism. Here we show that androgens (5α-dihydrotestosterone and R1881) suppress c-Fos protein and mRNA expression induced by 12-O-tetradecanoylphorbol-13-acetate (TPA) or EGF in human prostate cancer (PCa) cell lines. Such suppression transpires through a transcriptional mechanism, predominantly at the proximal serum response element of the c-fos promoter. We show that androgen signaling suppresses TPA-induced c-Fos expression through repressing a PKC/MEK/ERK/ELK-1 signaling pathway. Moreover, our results support the hypothesis that p38MAPK, PI3K, and PKCδ are involved in the androgenic regulation of c-Fos through controlling MEK/ERK. Stable silencing of c-Fos and PKCδ with shRNAs suggests that R1881 promotes cell death induced by low-dose TPA through a mechanism that is dependent on both PKCδ and loss of c-Fos expression. Reciprocally, loss of either PKCδ or c-Fos activates p38MAPK while suppressing the activation of ERK1/2. We also provide the first demonstration that R1881 permits cell death induced by low-dose TPA in the LNCaP androgen-dependent PCa cell line and that TPA-induced cell death is independent of exogenous androgen in the castration-resistant variants of LNCaP, C4-2 and C4-2B. Acquisition of androgen-independent killing by TPA correlates with activation of p38MAPK, suppression of ERK1/2, and loss of c-Fos. These results provide new insights into androgenic control of c-Fos and use of PKC inhibitors in PCa therapy. PMID:26786102

  6. The synthetic genetic network around PKC1 identifies novel modulators and components of protein kinase C signaling in Saccharomyces cerevisiae.

    PubMed

    Krause, Sue A; Xu, Hong; Gray, Joseph V

    2008-11-01

    Budding yeast Saccharomyces cerevisiae contains one protein kinase C (PKC) isozyme encoded by the essential gene PKC1. Pkc1 is activated by the small GTPase Rho1 and plays a central role in the cell wall integrity (CWI) signaling pathway. This pathway acts primarily to remodel the cell surface throughout the normal life cycle and upon various environmental stresses. The pathway is heavily branched, with multiple nonessential branches feeding into and out of the central essential Rho1-Pkc1 module. In an attempt to identify novel components and modifiers of CWI signaling, we determined the synthetic lethal genetic network around PKC1 by using dominant-negative synthetic genetic array analysis. The resulting mutants are hypersensitive to lowered Pkc1 activity. The corresponding 21 nonessential genes are closely related to CWI function: 14 behave in a chemical-genetic epistasis test as acting in the pathway, and 6 of these genes encode known components. Twelve of the 21 null mutants display elevated CWI reporter activity, consistent with the idea that the pathway is activated by and compensates for loss of the gene products. Four of the 21 mutants display low CWI reporter activity, consistent with the idea that the pathway is compromised in these mutants. One of the latter group of mutants lacks Ack1(Ydl203c), an uncharacterized SEL-1 domain-containing protein that we find modulates pathway activity. Epistasis analysis places Ack1 upstream of Pkc1 in the CWI pathway and dependent on the upstream Rho1 GTP exchange factors Rom2 and Tus1. Overall, the synthetic genetic network around PKC1 directly and efficiently identifies known and novel components of PKC signaling in yeast.

  7. Neural Network Communications Signal Processing

    DTIC Science & Technology

    1994-08-01

    This final technical report describes the research and development- results of the Neural Network Communications Signal Processing (NNCSP) Program...The objectives of the NNCSP program are to: (1) develop and implement a neural network and communications signal processing simulation system for the...purpose of exploring the applicability of neural network technology to communications signal processing; (2) demonstrate several configurations of the

  8. Regulation patterns in signaling networks of cancer

    PubMed Central

    2010-01-01

    Background Formation of cellular malignancy results from the disruption of fine tuned signaling homeostasis for proliferation, accompanied by mal-functional signals for differentiation, cell cycle and apoptosis. We wanted to observe central signaling characteristics on a global view of malignant cells which have evolved to selfishness and independence in comparison to their non-malignant counterparts that fulfill well defined tasks in their sample. Results We investigated the regulation of signaling networks with twenty microarray datasets from eleven different tumor types and their corresponding non-malignant tissue samples. Proteins were represented by their coding genes and regulatory distances were defined by correlating the gene-regulation between neighboring proteins in the network (high correlation = small distance). In cancer cells we observed shorter pathways, larger extension of the networks, a lower signaling frequency of central proteins and links and a higher information content of the network. Proteins of high signaling frequency were enriched with cancer mutations. These proteins showed motifs of regulatory integration in normal cells which was disrupted in tumor cells. Conclusion Our global analysis revealed a distinct formation of signaling-regulation in cancer cells when compared to cells of normal samples. From these cancer-specific regulation patterns novel signaling motifs are proposed. PMID:21110851

  9. Integrated transcriptomic and proteomic analysis identifies protein kinase CK2 as a key signaling node in an inflammatory cytokine network in ovarian cancer cells

    PubMed Central

    Kulbe, Hagen; Iorio, Francesco; Chakravarty, Probir; Milagre, Carla S.; Moore, Robert; Thompson, Richard G.; Everitt, Gemma; Canosa, Monica; Montoya, Alexander; Drygin, Denis; Braicu, Ioana; Sehouli, Jalid; Saez-Rodriguez, Julio; Cutillas, Pedro R.; Balkwill, Frances R.

    2016-01-01

    We previously showed how key pathways in cancer-related inflammation and Notch signaling are part of an autocrine malignant cell network in ovarian cancer. This network, which we named the “TNF network”, has paracrine actions within the tumor microenvironment, influencing angiogenesis and the immune cell infiltrate. The aim of this study was to identify critical regulators in the signaling pathways of the TNF network in ovarian cancer cells that might be therapeutic targets. To achieve our aim, we used a systems biology approach, combining data from phospho-proteomic mass spectrometry and gene expression array analysis. Among the potential therapeutic kinase targets identified was the protein kinase Casein kinase II (CK2). Knockdown of CK2 expression in malignant cells by siRNA or treatment with the specific CK2 inhibitor CX-4945 significantly decreased Notch signaling and reduced constitutive cytokine release in ovarian cancer cell lines that expressed the TNF network as well as malignant cells isolated from high grade serous ovarian cancer ascites. The expression of the same cytokines was also inhibited after treatment with CX-4945 in a 3D organotypic model. CK2 inhibition was associated with concomitant inhibition of proliferative activity, reduced angiogenesis and experimental peritoneal ovarian tumor growth. In conclusion, we have identified kinases, particularly CK2, associated with the TNF network that may play a central role in sustaining the cytokine network and/or mediating its effects in ovarian cancer. PMID:26871292

  10. Role of serine threonine protein phosphatase type 5 (PP5) in the regulation of stress induced signaling networks and cancer

    PubMed Central

    Golden, Teresa; Swingle, Mark; Honkanen, Richard E.

    2008-01-01

    Although the aberrant actions of protein kinases have long been known to contribute to tumor promotion and carcinogenesis, roles for proteins phosphatases in the development of human cancer have only emerged in the last decade. In this review, we discuss the data obtained from studies examining the biological and pathological roles of a serine/threonine protein phosphate, PP5, which suggest that PP5 is a potentially important regulator of both hormone- and stress-induced networks that enable a cell to respond appropriately to genomic stress. PMID:18253812

  11. Aroclor 1254, a developmental neurotoxicant, alters energy metabolism- and intracellular signaling-associated protein networks in rat cerebellum and hippocampus

    SciTech Connect

    Kodavanti, Prasada Rao S.; Osorio, Cristina; Royland, Joyce E.; Ramabhadran, Ram; Alzate, Oscar

    2011-11-15

    The vast literature on the mode of action of polychlorinated biphenyls (PCBs) indicates that PCBs are a unique model for understanding the mechanisms of toxicity of environmental mixtures of persistent chemicals. PCBs have been shown to adversely affect psychomotor function and learning and memory in humans. Although the molecular mechanisms for PCB effects are unclear, several studies indicate that the disruption of Ca{sup 2+}-mediated signal transduction plays significant roles in PCB-induced developmental neurotoxicity. Culminating events in signal transduction pathways include the regulation of gene and protein expression, which affects the growth and function of the nervous system. Our previous studies showed changes in gene expression related to signal transduction and neuronal growth. In this study, protein expression following developmental exposure to PCB is examined. Pregnant rats (Long Evans) were dosed with 0.0 or 6.0 mg/kg/day of Aroclor-1254 from gestation day 6 through postnatal day (PND) 21, and the cerebellum and hippocampus from PND14 animals were analyzed to determine Aroclor 1254-induced differential protein expression. Two proteins were found to be differentially expressed in the cerebellum following PCB exposure while 18 proteins were differentially expressed in the hippocampus. These proteins are related to energy metabolism in mitochondria (ATP synthase, sub unit {beta} (ATP5B), creatine kinase, and malate dehydrogenase), calcium signaling (voltage-dependent anion-selective channel protein 1 (VDAC1) and ryanodine receptor type II (RyR2)), and growth of the nervous system (dihydropyrimidinase-related protein 4 (DPYSL4), valosin-containing protein (VCP)). Results suggest that Aroclor 1254-like persistent chemicals may alter energy metabolism and intracellular signaling, which might result in developmental neurotoxicity. -- Highlights: Black-Right-Pointing-Pointer We performed brain proteomic analysis of rats exposed to the neurotoxicant

  12. Quantitative Analysis of Protein Phosphorylations and Interactions by Multi-Colour IP-FCM as an Input for Kinetic Modelling of Signalling Networks

    PubMed Central

    Deswal, Sumit; Schulze, Anna K.; Höfer, Thomas; Schamel, Wolfgang W. A.

    2011-01-01

    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

  13. Modelling protein functional domains in signal transduction using Maude

    NASA Technical Reports Server (NTRS)

    Sriram, M. G.

    2003-01-01

    Modelling of protein-protein interactions in signal transduction is receiving increased attention in computational biology. This paper describes recent research in the application of Maude, a symbolic language founded on rewriting logic, to the modelling of functional domains within signalling proteins. Protein functional domains (PFDs) are a critical focus of modern signal transduction research. In general, Maude models can simulate biological signalling networks and produce specific testable hypotheses at various levels of abstraction. Developing symbolic models of signalling proteins containing functional domains is important because of the potential to generate analyses of complex signalling networks based on structure-function relationships.

  14. DLG5 connects cell polarity and Hippo signaling protein networks by linking PAR-1 with MST1/2.

    PubMed

    Kwan, Julian; Sczaniecka, Anna; Arash, Emad Heidary; Nguyen, Liem; Chen, Chia-Chun; Ratkovic, Srdjana; Klezovitch, Olga; Attisano, Liliana; McNeill, Helen; Emili, Andrew; Vasioukhin, Valeri

    2016-12-15

    Disruption of apical-basal polarity is implicated in developmental disorders and cancer; however, the mechanisms connecting cell polarity proteins with intracellular signaling pathways are largely unknown. We determined previously that membrane-associated guanylate kinase (MAGUK) protein discs large homolog 5 (DLG5) functions in cell polarity and regulates cellular proliferation and differentiation via undefined mechanisms. We report here that DLG5 functions as an evolutionarily conserved scaffold and negative regulator of Hippo signaling, which controls organ size through the modulation of cell proliferation and differentiation. Affinity purification/mass spectrometry revealed a critical role of DLG5 in the formation of protein assemblies containing core Hippo kinases mammalian ste20 homologs 1/2 (MST1/2) and Par-1 polarity proteins microtubule affinity-regulating kinases 1/2/3 (MARK1/2/3). Consistent with this finding, Hippo signaling is markedly hyperactive in mammalian Dlg5(-/-) tissues and cells in vivo and ex vivo and in Drosophila upon dlg5 knockdown. Conditional deletion of Mst1/2 fully rescued the phenotypes of brain-specific Dlg5 knockout mice. Dlg5 also interacts genetically with Hippo effectors Yap1/Taz Mechanistically, we show that DLG5 inhibits the association between MST1/2 and large tumor suppressor homologs 1/2 (LATS1/2), uses its scaffolding function to link MST1/2 with MARK3, and inhibits MST1/2 kinase activity. These data reveal a direct connection between cell polarity proteins and Hippo, which is essential for proper development of multicellular organisms.

  15. Defining a Modular Signalling Network from the Fly Interactome

    PubMed Central

    Baudot, Anaïs; Angelelli, Jean-Baptiste; Guénoche, Alain; Jacq, Bernard; Brun, Christine

    2008-01-01

    Background Signalling pathways relay information by transmitting signals from cell surface receptors to intracellular effectors that eventually activate the transcription of target genes. Since signalling pathways involve several types of molecular interactions including protein-protein interactions, we postulated that investigating their organization in the context of the global protein-protein interaction network could provide a new integrated view of signalling mechanisms. Results Using a graph-theory based method to analyse the fly protein-protein interaction network, we found that each signalling pathway is organized in two to three different signalling modules. These modules contain canonical proteins of the signalling pathways, known regulators as well as other proteins thereby predicted to participate to the signalling mechanisms. Connections between the signalling modules are prominent as compared to the other network's modules and interactions within and between signalling modules are among the more central routes of the interaction network. Conclusion Altogether, these modules form an interactome sub-network devoted to signalling with particular topological properties: modularity, density and centrality. This finding reflects the integration of the signalling system into cell functioning and its important role connecting and coordinating different biological processes at the level of the interactome. PMID:18489752

  16. ZASP Interacts with the Mechanosensing Protein Ankrd2 and p53 in the Signalling Network of Striated Muscle

    PubMed Central

    Martinelli, Valentina C.; Kyle, W. Buck; Kojic, Snezana; Vitulo, Nicola; Li, Zhaohui; Belgrano, Anna; Maiuri, Paolo; Banks, Lawrence; Vatta, Matteo; Valle, Giorgio; Faulkner, Georgine

    2014-01-01

    ZASP is a cytoskeletal PDZ-LIM protein predominantly expressed in striated muscle. It forms multiprotein complexes and plays a pivotal role in the structural integrity of sarcomeres. Mutations in the ZASP protein are associated with myofibrillar myopathy, left ventricular non-compaction and dilated cardiomyopathy. The ablation of its murine homologue Cypher results in neonatal lethality. ZASP has several alternatively spliced isoforms, in this paper we clarify the nomenclature of its human isoforms as well as their dynamics and expression pattern in striated muscle. Interaction is demonstrated between ZASP and two new binding partners both of which have roles in signalling, regulation of gene expression and muscle differentiation; the mechanosensing protein Ankrd2 and the tumour suppressor protein p53. These proteins and ZASP form a triple complex that appears to facilitate poly-SUMOylation of p53. We also show the importance of two of its functional domains, the ZM-motif and the PDZ domain. The PDZ domain can bind directly to both Ankrd2 and p53 indicating that there is no competition between it and p53 for the same binding site on Ankrd2. However there is competition for this binding site between p53 and a region of the ZASP protein lacking the PDZ domain, but containing the ZM-motif. ZASP is negative regulator of p53 in transactivation experiments with the p53-responsive promoters, MDM2 and BAX. Mutations in the ZASP ZM-motif induce modification in protein turnover. In fact, two mutants, A165V and A171T, were not able to bind Ankrd2 and bound only poorly to alpha-actinin2. This is important since the A165V mutation is responsible for zaspopathy, a well characterized autosomal dominant distal myopathy. Although the mechanism by which this mutant causes disease is still unknown, this is the first indication of how a ZASP disease associated mutant protein differs from that of the wild type ZASP protein. PMID:24647531

  17. Soft Cysteine Signaling Network: The Functional Significance of Cysteine in Protein Function and the Soft Acids/Bases Thiol Chemistry That Facilitates Cysteine Modification.

    PubMed

    Wible, Ryan S; Sutter, Thomas R

    2017-03-20

    The unique biophysical and electronic properties of cysteine make this molecule one of the most biologically critical amino acids in the proteome. The defining sulfur atom in cysteine is much larger than the oxygen and nitrogen atoms more commonly found in the other amino acids. As a result of its size, the valence electrons of sulfur are highly polarizable. Unique protein microenvironments favor the polarization of sulfur, thus increasing the overt reactivity of cysteine. Here, we provide a brief overview of the endogenous generation of reactive oxygen and electrophilic species and specific examples of enzymes and transcription factors in which the oxidation or covalent modification of cysteine in those proteins modulates their function. The perspective concludes with a discussion of cysteine chemistry and biophysics, the hard and soft acids and bases model, and the proposal of the Soft Cysteine Signaling Network: a hypothesis proposing the existence of a complex signaling network governed by layered chemical reactivity and cross-talk in which the chemical modification of reactive cysteine in biological networks triggers the reorganization of intracellular biochemistry to mitigate spikes in endogenous or exogenous oxidative or electrophilic stress.

  18. A Parkinson's disease gene regulatory network identifies the signaling protein RGS2 as a modulator of LRRK2 activity and neuronal toxicity.

    PubMed

    Dusonchet, Julien; Li, Hu; Guillily, Maria; Liu, Min; Stafa, Klodjan; Derada Troletti, Claudio; Boon, Joon Y; Saha, Shamol; Glauser, Liliane; Mamais, Adamantios; Citro, Allison; Youmans, Katherine L; Liu, LiQun; Schneider, Bernard L; Aebischer, Patrick; Yue, Zhenyu; Bandopadhyay, Rina; Glicksman, Marcie A; Moore, Darren J; Collins, James J; Wolozin, Benjamin

    2014-09-15

    Mutations in LRRK2 are one of the primary genetic causes of Parkinson's disease (PD). LRRK2 contains a kinase and a GTPase domain, and familial PD mutations affect both enzymatic activities. However, the signaling mechanisms regulating LRRK2 and the pathogenic effects of familial mutations remain unknown. Identifying the signaling proteins that regulate LRRK2 function and toxicity remains a critical goal for the development of effective therapeutic strategies. In this study, we apply systems biology tools to human PD brain and blood transcriptomes to reverse-engineer a LRRK2-centered gene regulatory network. This network identifies several putative master regulators of LRRK2 function. In particular, the signaling gene RGS2, which encodes for a GTPase-activating protein (GAP), is a key regulatory hub connecting the familial PD-associated genes DJ-1 and PINK1 with LRRK2 in the network. RGS2 expression levels are reduced in the striata of LRRK2 and sporadic PD patients. We identify RGS2 as a novel interacting partner of LRRK2 in vivo. RGS2 regulates both the GTPase and kinase activities of LRRK2. We show in mammalian neurons that RGS2 regulates LRRK2 function in the control of neuronal process length. RGS2 is also protective against neuronal toxicity of the most prevalent mutation in LRRK2, G2019S. We find that RGS2 regulates LRRK2 function and neuronal toxicity through its effects on kinase activity and independently of GTPase activity, which reveals a novel mode of action for GAP proteins. This work identifies RGS2 as a promising target for interfering with neurodegeneration due to LRRK2 mutations in PD patients.

  19. Crowdsourcing network inference: the DREAM predictive signaling network challenge.

    PubMed

    Prill, Robert J; Saez-Rodriguez, Julio; Alexopoulos, Leonidas G; Sorger, Peter K; Stolovitzky, Gustavo

    2011-08-30

    Computational analyses of systematic measurements on the states and activities of signaling proteins (as captured by phosphoproteomic data, for example) have the potential to uncover uncharacterized protein-protein interactions and to identify the subset that are important for cellular response to specific biological stimuli. However, inferring mechanistically plausible protein signaling networks (PSNs) from phosphoproteomics data is a difficult task, owing in part to the lack of sufficiently comprehensive experimental measurements, the inherent limitations of network inference algorithms, and a lack of standards for assessing the accuracy of inferred PSNs. A case study in which 12 research groups inferred PSNs from a phosphoproteomics data set demonstrates an assessment of inferred PSNs on the basis of the accuracy of their predictions. The concurrent prediction of the same previously unreported signaling interactions by different participating teams suggests relevant validation experiments and establishes a framework for combining PSNs inferred by multiple research groups into a composite PSN. We conclude that crowdsourcing the construction of PSNs-that is, outsourcing the task to the interested community-may be an effective strategy for network inference.

  20. Signaling networks in joint development.

    PubMed

    Salva, Joanna E; Merrill, Amy E

    2017-04-01

    Here we review studies identifying regulatory networks responsible for synovial, cartilaginous, and fibrous joint development. Synovial joints, characterized by the fluid-filled synovial space between the bones, are found in high-mobility regions and are the most common type of joint. Cartilaginous joints such as the intervertebral disc unite adjacent bones through either a hyaline cartilage or a fibrocartilage intermediate. Fibrous joints, which include the cranial sutures, form a direct union between bones through fibrous connective tissue. We describe how the distinct morphologic and histogenic characteristics of these joint classes are established during embryonic development. Collectively, these studies reveal that despite the heterogeneity of joint strength and mobility, joint development throughout the skeleton utilizes common signaling networks via long-range morphogen gradients and direct cell-cell contact. This suggests that different joint types represent specialized variants of homologous developmental modules. Identifying the unifying aspects of the signaling networks between joint classes allows a more complete understanding of the signaling code for joint formation, which is critical to improving strategies for joint regeneration and repair. Developmental Dynamics 246:262-274, 2017. © 2016 Wiley Periodicals, Inc.

  1. Protein interaction networks from literature mining

    NASA Astrophysics Data System (ADS)

    Ihara, Sigeo

    2005-03-01

    The ability to accurately predict and understand physiological changes in the biological network system in response to disease or drug therapeutics is of crucial importance in life science. The extensive amount of gene expression data generated from even a single microarray experiment often proves difficult to fully interpret and comprehend the biological significance. An increasing knowledge of protein interactions stored in the PubMed database, as well as the advancement of natural language processing, however, makes it possible to construct protein interaction networks from the gene expression information that are essential for understanding the biological meaning. From the in house literature mining system we have developed, the protein interaction network for humans was constructed. By analysis based on the graph-theoretical characterization of the total interaction network in literature, we found that the network is scale-free and semantic long-ranged interactions (i.e. inhibit, induce) between proteins dominate in the total interaction network, reducing the degree exponent. Interaction networks generated based on scientific text in which the interaction event is ambiguously described result in disconnected networks. In contrast interaction networks based on text in which the interaction events are clearly stated result in strongly connected networks. The results of protein-protein interaction networks obtained in real applications from microarray experiments are discussed: For example, comparisons of the gene expression data indicative of either a good or a poor prognosis for acute lymphoblastic leukemia with MLL rearrangements, using our system, showed newly discovered signaling cross-talk.

  2. Protein N-glycosylation in oral cancer: dysregulated cellular networks among DPAGT1, E-cadherin adhesion and canonical Wnt signaling.

    PubMed

    Varelas, Xaralabos; Bouchie, Meghan P; Kukuruzinska, Maria A

    2014-07-01

    N-Linked glycosylation (N-glycosylation) of proteins has long been associated with oncogenesis, but not until recently have the molecular mechanisms underlying this relationship begun to be unraveled. Here, we review studies describing how dysregulation of the N-glycosylation-regulating gene, DPAGT1, drives oral cancer. DPAGT1 encodes the first and rate-limiting enzyme in the assembly of the lipid-linked oligosaccharide precursor in the endoplasmic reticulum and thus mediates N-glycosylation of many cancer-related proteins. DPAGT1 controls N-glycosylation of E-cadherin, the major epithelial cell-cell adhesion receptor and a tumor suppressor, thereby affecting intercellular adhesion and cytoskeletal dynamics. DPAGT1 also regulates and is regulated by Wnt/β-catenin signaling, impacting the balance between proliferation and adhesion in homeostatic tissues. Thus, aberrant induction of DPAGT1 promotes a positive feedback network with Wnt/β-catenin that represses E-cadherin-based adhesion and drives tumorigenic phenotypes. Further, modification of receptor tyrosine kinases (RTKs) with N-glycans is known to control their surface presentation via the galectin lattice, and thus increased DPAGT1 expression likely contributes to abnormal activation of RTKs in oral cancer. Collectively, these studies suggest that dysregulation of the DPAGT1/Wnt/E-cadherin network underlies the etiology and pathogenesis of oral cancer.

  3. Neural Network Classification of Cerebral Embolic Signals

    DTIC Science & Technology

    2007-11-02

    application of new signal processing techniques to the analysis and classification of embolic signals. We applied a Wavelet Neural Network algorithm...to approximate the embolic signals, with the parameters of the wavelet nodes being used to train a Neural Network to classify these signals as resulting from normal flow, or from gaseous or solid emboli.

  4. Signal Processing for Optical Networks

    DTIC Science & Technology

    2007-11-02

    ONLY (Leave Blank) 2. REPORT DATE 5 /1/98 3. REPORT TYPE AND DATES COVERED Final 9/30/95 - 1/1/98 4. TITLE AND SUBTITLE Signal Processing...for Optical Networks: 6. AUTHORS Dennis M. Healy Jr. 5 . FUNDING NUMBERS G (Grant) F1960 93 0567- 7. PERFORMING ORGANIZATION NAME(S) AND...NUMBER 9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) AFOSR/PKA 110 Duncan Avenue, Room Bl 15 Boiling, AFB DC 20332- 8050 Monitor

  5. Signal Approximation with a Wavelet Neural Network

    DTIC Science & Technology

    1992-12-01

    specialized electronic devices like the Intel Electronically Trainable Analog Neural Network (ETANN) chip. The WNN representation allows the...accurately approximated with a WNN trained with irregularly sampled data. Signal approximation, Wavelet neural network .

  6. Noise-processing by signaling networks.

    PubMed

    Kontogeorgaki, Styliani; Sánchez-García, Rubén J; Ewing, Rob M; Zygalakis, Konstantinos C; MacArthur, Ben D

    2017-04-03

    Signaling networks mediate environmental information to the cell nucleus. To perform this task effectively they must be able to integrate multiple stimuli and distinguish persistent signals from transient environmental fluctuations. However, the ways in which signaling networks process environmental noise are not well understood. Here we outline a mathematical framework that relates a network's structure to its capacity to process noise, and use this framework to dissect the noise-processing ability of signaling networks. We find that complex networks that are dense in directed paths are poor noise processors, while those that are sparse and strongly directional process noise well. These results suggest that while cross-talk between signaling pathways may increase the ability of signaling networks to integrate multiple stimuli, too much cross-talk may compromise the ability of the network to distinguish signal from noise. To illustrate these general results we consider the structure of the signalling network that maintains pluripotency in mouse embryonic stem cells, and find an incoherent feedforward loop structure involving Stat3, Tfcp2l1, Esrrb, Klf2 and Klf4 is particularly important for noise-processing. Taken together these results suggest that noise-processing is an important function of signaling networks and they may be structured in part to optimize this task.

  7. DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

    SciTech Connect

    MASLOV,S.SNEPPEN,K.

    2003-11-17

    Complex networks appear in biology on many different levels: (1) All biochemical reactions taking place in a single cell constitute its metabolic network, where nodes are individual metabolites, and edges are metabolic reactions converting them to each other. (2) Virtually every one of these reactions is catalyzed by an enzyme and the specificity of this catalytic function is ensured by the key and lock principle of its physical interaction with the substrate. Often the functional enzyme is formed by several mutually interacting proteins. Thus the structure of the metabolic network is shaped by the network of physical interactions of cell's proteins with their substrates and each other. (3) The abundance and the level of activity of each of the proteins in the physical interaction network in turn is controlled by the regulatory network of the cell. Such regulatory network includes all of the multiple mechanisms in which proteins in the cell control each other including transcriptional and translational regulation, regulation of mRNA editing and its transport out of the nucleus, specific targeting of individual proteins for degradation, modification of their activity e.g. by phosphorylation/dephosphorylation or allosteric regulation, etc. To get some idea about the complexity and interconnectedness of protein-protein regulations in baker's yeast Saccharomyces Cerevisiae in Fig. 1 we show a part of the regulatory network corresponding to positive or negative regulations that regulatory proteins exert on each other. (4) On yet higher level individual cells of a multicellular organism exchange signals with each other. This gives rise to several new networks such as e.g. nervous, hormonal, and immune systems of animals. The intercellular signaling network stages the development of a multicellular organism from the fertilized egg. (5) Finally, on the grandest scale, the interactions between individual species in ecosystems determine their food webs. An interesting

  8. Lethality and centrality in protein networks

    NASA Astrophysics Data System (ADS)

    Jeong, H.; Mason, S. P.; Barabási, A.-L.; Oltvai, Z. N.

    2001-05-01

    Proteins are traditionally identified on the basis of their individual actions as catalysts, signalling molecules, or building blocks in cells and microorganisms. But our post-genomic view is expanding the protein's role into an element in a network of protein-protein interactions as well, in which it has a contextual or cellular function within functional modules. Here we provide quantitative support for this idea by demonstrating that the phenotypic consequence of a single gene deletion in the yeast Saccharomyces cerevisiae is affected to a large extent by the topological position of its protein product in the complex hierarchical web of molecular interactions.

  9. Optimal Prediction by Cellular Signaling Networks

    NASA Astrophysics Data System (ADS)

    Becker, Nils B.; Mugler, Andrew; ten Wolde, Pieter Rein

    2015-12-01

    Living cells can enhance their fitness by anticipating environmental change. We study how accurately linear signaling networks in cells can predict future signals. We find that maximal predictive power results from a combination of input-noise suppression, linear extrapolation, and selective readout of correlated past signal values. Single-layer networks generate exponential response kernels, which suffice to predict Markovian signals optimally. Multilayer networks allow oscillatory kernels that can optimally predict non-Markovian signals. At low noise, these kernels exploit the signal derivative for extrapolation, while at high noise, they capitalize on signal values in the past that are strongly correlated with the future signal. We show how the common motifs of negative feedback and incoherent feed-forward can implement these optimal response functions. Simulations reveal that E. coli can reliably predict concentration changes for chemotaxis, and that the integration time of its response kernel arises from a trade-off between rapid response and noise suppression.

  10. Prediction of N-terminal protein sorting signals.

    PubMed

    Claros, M G; Brunak, S; von Heijne, G

    1997-06-01

    Recently, neural networks have been applied to a widening range of problems in molecular biology. An area particularly suited to neural-network methods is the identification of protein sorting signals and the prediction of their cleavage sites, as these functional units are encoded by local, linear sequences of amino acids rather than global 3D structures.

  11. Neural Networks Applied to Signal Processing

    DTIC Science & Technology

    1989-09-01

    DTIC FILE COpy NAVAL POSTGRADUATE SCHOOL . Monterey, California Lf 0 (0 V’ STATES 4 THESIS NEURAL NETWORKS APPLIED TO SIGNAL PROCESSING by Mark D...FUNDING NUMBERS PROGRAM PROJECT TASK WORK UNIT ELEMENT NO NO NO ACCESSION NO. 11. TITLE (Include Security Classification) NEURAL NETWORKS APPLIED TO...for public release; distribution is unlimited Neural Networks Applied to Signal Processing by Mark D. Baehre Captain, United States Army B.S., United

  12. Modulation of signaling pathways by RNA virus capsid proteins.

    PubMed

    Urbanowski, Matthew D; Ilkow, Carolina S; Hobman, Tom C

    2008-07-01

    Capsid proteins are structural components of virus particles. They are nucleic acid-binding proteins whose main recognized function is to package viral genomes into protective structures called nucleocapsids. Research over the last 10 years indicates that in addition to their role as genome guardians, viral capsid proteins modulate host cell signaling networks. Disruption or alteration of intracellular signaling pathways by viral capsids may benefit replication of the virus by affecting innate immunity and in some cases, may underlie disease progression. In this review, we describe how the capsid proteins from medically relevant RNA viruses interact with host cell signaling pathways.

  13. The protein folding network

    NASA Astrophysics Data System (ADS)

    Rao, Francesco; Caflisch, Amedeo

    2004-03-01

    Networks are everywhere. The conformation space of a 20-residue antiparallel beta-sheet peptide [1], sampled by molecular dynamics simulations, is mapped to a network. Conformations are nodes of the network, and the transitions between them are links. As previously found for the World-Wide Web as well as for social and biological networks , the conformation space contains highly connected hubs like the native state which is the most populated free energy basin. Furthermore, the network shows a hierarchical modularity [2] which is consistent with the funnel mechanism of folding [3] and is not observed for a random heteropolymer lacking a native state. Here we show that the conformation space network describes the free energy landscape without requiring projections into arbitrarily chosen reaction coordinates. The network analysis provides a basis for understanding the heterogeneity of the folding transition state and the existence of multiple pathways. [1] P. Ferrara and A. Caflisch, Folding simulations of a three-stranded antiparallel beta-sheet peptide, PNAS 97, 10780-10785 (2000). [2] Ravasz, E. and Barabási, A. L. Hierarchical organization in complex networks. Phys. Rev. E 67, 026112 (2003). [3] Dill, K. and Chan, H From Levinthal to pathways to funnels. Nature Struct. Biol. 4, 10-19 (1997)

  14. Allele-specific suppression of a defective trans-Golgi network (TGN) localization signal in Kex2p identifies three genes involved in localization of TGN transmembrane proteins.

    PubMed Central

    Redding, K; Brickner, J H; Marschall, L G; Nichols, J W; Fuller, R S

    1996-01-01

    Kex2 protease (Kex2p) and Ste13 dipeptidyl aminopeptidase (Ste13p) are required in Saccharomyces cerevisiae for maturation of the alpha-mating factor in a late Golgi compartment, most likely the yeast trans-Golgi network (TGN). Previous studies identified a TGN localization signal (TLS) in the C-terminal cytosolic tail of Kex2p consisting of Tyr-713 and contextual sequences. Further analysis of the Kex2p TLS revealed similarity to the Ste13p TLS. Mutation of the Kex2p TLS results in transport of Kex2p to the vacuole by default. When expression of a GAL1 promoter-driven KEX2 gene is shut off in MAT(alpha) cells, the TGN becomes depleted of Kex2p, resulting in a gradual decline in mating competence which is greatly accelerated by TLS mutations. To identify the genes involved in localization of Kex2p, we isolated second-site suppressors of the rapid loss of mating competence observed upon shutting off expression of a TLS mutant form of Kex2p (Y713A). Seven of 58 suppressors were allele specific, suppressing point mutations at Tyr-713 but not deletions of the TLS or entire C-terminal cytosolic tail. By linkage analysis, the allele-specific suppressors defined three genetic loci, SOI1, S0I2, and S0I3. Pulse-chase analysis demonstrated that these suppressors increased net TGN retention of both Y713A Kex2p and a Ste13p-Pho8p fusion protein containing a point mutation in the Ste13p TLS. SOI1 suppressor alleles reduced the efficiency of localization of wild-type Kex2p to the TGN, implying an impaired ability to discriminate between the normal TLS and a mutant TLS. soi1 mutants also exhibited a recessive defect in vacuolar protein sorting. Suppressor alleles of S0I2 were dominant. These results suggest that the SOI1 and S0I2 genes encode regulators or components of the TLS recognition machinery. PMID:8887651

  15. Nonparametric Simulation of Signal Transduction Networks with Semi-Synchronized Update

    PubMed Central

    Nassiri, Isar; Masoudi-Nejad, Ali; Jalili, Mahdi; Moeini, Ali

    2012-01-01

    Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process. PMID:22737250

  16. Calcium/calmodulin-mediated signal network in plants

    NASA Technical Reports Server (NTRS)

    Yang, Tianbao; Poovaiah, B. W.

    2003-01-01

    Various extracellular stimuli elicit specific calcium signatures that can be recognized by different calcium sensors. Calmodulin, the predominant calcium receptor, is one of the best-characterized calcium sensors in eukaryotes. In recent years, completion of the Arabidopsis genome project and advances in functional genomics have helped to identify and characterize numerous calmodulin-binding proteins in plants. There are some similarities in Ca(2+)/calmodulin-mediated signaling in plants and animals. However, plants possess multiple calmodulin genes and many calmodulin target proteins, including unique protein kinases and transcription factors. Some of these proteins are likely to act as "hubs" during calcium signal transduction. Hence, a better understanding of the function of these calmodulin target proteins should help in deciphering the Ca(2+)/calmodulin-mediated signal network and its role in plant growth, development and response to environmental stimuli.

  17. Identification of Topological Network Modules in Perturbed Protein Interaction Networks

    PubMed Central

    Sardiu, Mihaela E.; Gilmore, Joshua M.; Groppe, Brad; Florens, Laurence; Washburn, Michael P.

    2017-01-01

    Biological networks consist of functional modules, however detecting and characterizing such modules in networks remains challenging. Perturbing networks is one strategy for identifying modules. Here we used an advanced mathematical approach named topological data analysis (TDA) to interrogate two perturbed networks. In one, we disrupted the S. cerevisiae INO80 protein interaction network by isolating complexes after protein complex components were deleted from the genome. In the second, we reanalyzed previously published data demonstrating the disruption of the human Sin3 network with a histone deacetylase inhibitor. Here we show that disrupted networks contained topological network modules (TNMs) with shared properties that mapped onto distinct locations in networks. We define TMNs as proteins that occupy close network positions depending on their coordinates in a topological space. TNMs provide new insight into networks by capturing proteins from different categories including proteins within a complex, proteins with shared biological functions, and proteins disrupted across networks. PMID:28272416

  18. Identification of Topological Network Modules in Perturbed Protein Interaction Networks.

    PubMed

    Sardiu, Mihaela E; Gilmore, Joshua M; Groppe, Brad; Florens, Laurence; Washburn, Michael P

    2017-03-08

    Biological networks consist of functional modules, however detecting and characterizing such modules in networks remains challenging. Perturbing networks is one strategy for identifying modules. Here we used an advanced mathematical approach named topological data analysis (TDA) to interrogate two perturbed networks. In one, we disrupted the S. cerevisiae INO80 protein interaction network by isolating complexes after protein complex components were deleted from the genome. In the second, we reanalyzed previously published data demonstrating the disruption of the human Sin3 network with a histone deacetylase inhibitor. Here we show that disrupted networks contained topological network modules (TNMs) with shared properties that mapped onto distinct locations in networks. We define TMNs as proteins that occupy close network positions depending on their coordinates in a topological space. TNMs provide new insight into networks by capturing proteins from different categories including proteins within a complex, proteins with shared biological functions, and proteins disrupted across networks.

  19. Controlling allosteric networks in proteins

    NASA Astrophysics Data System (ADS)

    Dokholyan, Nikolay

    2013-03-01

    We present a novel methodology based on graph theory and discrete molecular dynamics simulations for delineating allosteric pathways in proteins. We use this methodology to uncover the structural mechanisms responsible for coupling of distal sites on proteins and utilize it for allosteric modulation of proteins. We will present examples where inference of allosteric networks and its rewiring allows us to ``rescue'' cystic fibrosis transmembrane conductance regulator (CFTR), a protein associated with fatal genetic disease cystic fibrosis. We also use our methodology to control protein function allosterically. We design a novel protein domain that can be inserted into identified allosteric site of target protein. Using a drug that binds to our domain, we alter the function of the target protein. We successfully tested this methodology in vitro, in living cells and in zebrafish. We further demonstrate transferability of our allosteric modulation methodology to other systems and extend it to become ligh-activatable.

  20. The nuclear retention signal of HPV16 L2 protein is essential for incoming viral genome to transverse the trans-Golgi network

    SciTech Connect

    DiGiuseppe, Stephen; Bienkowska-Haba, Malgorzata; Hilbig, Lydia; Sapp, Martin

    2014-06-15

    The Human papillomavirus (HPV) capsid is composed of the major and minor capsid proteins, L1 and L2, respectively. Infectious entry requires a complex series of conformational changes in both proteins that lead to uptake and allow uncoating to occur. During entry, the capsid is disassembled and host cyclophilins dissociate L1 protein from the L2/DNA complex. Herein, we describe a mutant HPV16 L2 protein (HPV16 L2-R302/5A) that traffics pseudogenome to the trans-Golgi network (TGN) but fails to egress. Our data provide further evidence that HPV16 traffics through the TGN and demonstrates that L2 is essential for TGN egress. Furthermore, we show that cyclophilin activity is required for the L2/DNA complex to be transported to the TGN which is accompanied by a reduced L1 protein levels. - Highlights: • mNLS mutant HPV16 L2 protein traffics pseudogenome to the TGN but fails to egress. • Cyclophilin activity is required for trafficking of the L2/DNA complex to the TGN. • Majority of L1 protein is shed from the L2/DNA complex prior to reaching the TGN.

  1. Pathway illuminated: visualizing protein kinase C signaling.

    PubMed

    Violin, Jonathan D; Newton, Alexandra C

    2003-12-01

    Protein kinase C has been at the center of cell signaling since the discovery 25 years ago that it transduces signals that promote phospholipid hydrolysis. In recent years, the use of genetically encoded fluorescent reporters has enabled studies of the regulation of protein kinase C signaling in living cells. Advances in imaging techniques have unveiled unprecedented detail of the signal processing mechanics of protein kinase C, from the second messengers calcium and diacylglycerol that regulate protein kinase C activity, to the locations and kinetics of different protein kinase C isozymes, to the spatial and temporal dynamics of substrate phosphorylation by this key enzyme. This review discusses how fluorescence imaging studies have illuminated the fidelity with which protein kinase C transduces rapidly changing extracellular information into intracellular phosphorylation signals.

  2. Heterotrimeric G protein-coupled signaling in plants.

    PubMed

    Urano, Daisuke; Jones, Alan M

    2014-01-01

    Investigators studying G protein-coupled signaling--often called the best-understood pathway in the world owing to intense research in medical fields--have adopted plants as a new model to explore the plasticity and evolution of G signaling. Much research on plant G signaling has not disappointed. Although plant cells have most of the core elements found in animal G signaling, differences in network architecture and intrinsic properties of plant G protein elements make G signaling in plant cells distinct from the animal paradigm. In contrast to animal G proteins, plant G proteins are self-activating, and therefore regulation of G activation in plants occurs at the deactivation step. The self-activating property also means that plant G proteins do not need and therefore do not have typical animal G protein-coupled receptors. Targets of activated plant G proteins, also known as effectors, are unlike effectors in animal cells. The simpler repertoire of G signal elements in Arabidopsis makes G signaling easier to manipulate in a multicellular context.

  3. Noise decomposition of intracellular biochemical signaling networks using nonequivalent reporters

    PubMed Central

    Rhee, Alex; Cheong, Raymond; Levchenko, Andre

    2014-01-01

    Experimental measurements of biochemical noise have primarily focused on sources of noise at the gene expression level due to limitations of existing noise decomposition techniques. Here, we introduce a mathematical framework that extends classical extrinsic–intrinsic noise analysis and enables mapping of noise within upstream signaling networks free of such restrictions. The framework applies to systems for which the responses of interest are linearly correlated on average, although the framework can be easily generalized to the nonlinear case. Interestingly, despite the high degree of complexity and nonlinearity of most mammalian signaling networks, three distinct tumor necrosis factor (TNF) signaling network branches displayed linearly correlated responses, in both wild-type and perturbed versions of the network, across multiple orders of magnitude of ligand concentration. Using the noise mapping analysis, we find that the c-Jun N-terminal kinase (JNK) pathway generates higher noise than the NF-κB pathway, whereas the activation of c-Jun adds a greater amount of noise than the activation of ATF-2. In addition, we find that the A20 protein can suppress noise in the activation of ATF-2 by separately inhibiting the TNF receptor complex and JNK pathway through a negative feedback mechanism. These results, easily scalable to larger and more complex networks, pave the way toward assessing how noise propagates through cellular signaling pathways and create a foundation on which we can further investigate the relationship between signaling system architecture and biological noise. PMID:25404303

  4. PLC signal attenuation in branched networks

    SciTech Connect

    Durbak, D.W.; Stewart, J.R. )

    1990-04-01

    The application of power line carrier (PLC) to utility transmission systems can provide a reliable means of communication over short and long distances. However, PLC performance is dependent upon an adequate signal-to-noise ratio at receivers. The calculation of path attenuation of signals on the transmission line can be complicated, especially in branched networks. The calculation method described here is based on the construction of an impedance matrix using multi-phase, long line pi-equivalents for transmission lines. The method can predict PLC attenuation for a variety of network topologies and is demonstrated for two cases.

  5. Sentra, a database of signal transduction proteins.

    SciTech Connect

    Maltsev, N.; Marland, E.; Yu, G. X.; Bhatnagar, S.; Lusk, R.; Mathematics and Computer Science

    2002-01-01

    Sentra (http://www-wit.mcs.anl.gov/sentra) is a database of signal transduction proteins with the emphasis on microbial signal transduction. The database was updated to include classes of signal transduction systems modulated by either phosphorylation or methylation reactions such as PAS proteins and serine/threonine kinases, as well as the classical two-component histidine kinases and methyl-accepting chemotaxis proteins. Currently, Sentra contains signal transduction proteins from 43 completely sequenced prokaryotic genomes as well as sequences from SWISS-PROT and TrEMBL. Signal transduction proteins are annotated with information describing conserved domains, paralogous and orthologous sequences, and conserved chromosomal gene clusters. The newly developed user interface supports flexible search capabilities and extensive visualization of the data.

  6. SENTRA, a database of signal transduction proteins.

    SciTech Connect

    D'Souza, M.; Romine, M. F.; Maltsev, N.; Mathematics and Computer Science; PNNL

    2000-01-01

    SENTRA, available via URL http://wit.mcs.anl.gov/WIT2/Sentra/, is a database of proteins associated with microbial signal transduction. The database currently includes the classical two-component signal transduction pathway proteins and methyl-accepting chemotaxis proteins, but will be expanded to also include other classes of signal transduction systems that are modulated by phosphorylation or methylation reactions. Although the majority of database entries are from prokaryotic systems, eukaroytic proteins with bacterial-like signal transduction domains are also included. Currently SENTRA contains signal transduction proteins in 34 complete and almost completely sequenced prokaryotic genomes, as well as sequences from 243 organisms available in public databases (SWISS-PROT and EMBL). The analysis was carried out within the framework of the WIT2 system, which is designed and implemented to support genetic sequence analysis and comparative analysis of sequenced genomes.

  7. Gene transcriptional networks integrate microenvironmental signals in human breast cancer.

    PubMed

    Xu, Ren; Mao, Jian-Hua

    2011-04-01

    A significant amount of evidence shows that microenvironmental signals generated from extracellular matrix (ECM) molecules, soluble factors, and cell-cell adhesion complexes cooperate at the extra- and intracellular level. This synergetic action of microenvironmental cues is crucial for normal mammary gland development and breast malignancy. To explore how the microenvironmental genes coordinate in human breast cancer at the genome level, we have performed gene co-expression network analysis in three independent microarray datasets and identified two microenvironment networks in human breast cancer tissues. Network I represents crosstalk and cooperation of ECM microenvironment and soluble factors during breast malignancy. The correlated expression of cytokines, chemokines, and cell adhesion proteins in Network II implicates the coordinated action of these molecules in modulating the immune response in breast cancer tissues. These results suggest that microenvironmental cues are integrated with gene transcriptional networks to promote breast cancer development.

  8. The low energy signaling network.

    PubMed

    Tomé, Filipa; Nägele, Thomas; Adamo, Mattia; Garg, Abhroop; Marco-Llorca, Carles; Nukarinen, Ella; Pedrotti, Lorenzo; Peviani, Alessia; Simeunovic, Andrea; Tatkiewicz, Anna; Tomar, Monika; Gamm, Magdalena

    2014-01-01

    Stress impacts negatively on plant growth and crop productivity, caicultural production worldwide. Throughout their life, plants are often confronted with multiple types of stress that affect overall cellular energy status and activate energy-saving responses. The resulting low energy syndrome (LES) includes transcriptional, translational, and metabolic reprogramming and is essential for stress adaptation. The conserved kinases sucrose-non-fermenting-1-related protein kinase-1 (SnRK1) and target of rapamycin (TOR) play central roles in the regulation of LES in response to stress conditions, affecting cellular processes and leading to growth arrest and metabolic reprogramming. We review the current understanding of how TOR and SnRK1 are involved in regulating the response of plants to low energy conditions. The central role in the regulation of cellular processes, the reprogramming of metabolism, and the phenotypic consequences of these two kinases will be discussed in light of current knowledge and potential future developments.

  9. The low energy signaling network

    PubMed Central

    Tomé, Filipa; Nägele, Thomas; Adamo, Mattia; Garg, Abhroop; Marco-llorca, Carles; Nukarinen, Ella; Pedrotti, Lorenzo; Peviani, Alessia; Simeunovic, Andrea; Tatkiewicz, Anna; Tomar, Monika; Gamm, Magdalena

    2014-01-01

    Stress impacts negatively on plant growth and crop productivity, caicultural production worldwide. Throughout their life, plants are often confronted with multiple types of stress that affect overall cellular energy status and activate energy-saving responses. The resulting low energy syndrome (LES) includes transcriptional, translational, and metabolic reprogramming and is essential for stress adaptation. The conserved kinases sucrose-non-fermenting-1-related protein kinase-1 (SnRK1) and target of rapamycin (TOR) play central roles in the regulation of LES in response to stress conditions, affecting cellular processes and leading to growth arrest and metabolic reprogramming. We review the current understanding of how TOR and SnRK1 are involved in regulating the response of plants to low energy conditions. The central role in the regulation of cellular processes, the reprogramming of metabolism, and the phenotypic consequences of these two kinases will be discussed in light of current knowledge and potential future developments. PMID:25101105

  10. Modeling Protein Expression and Protein Signaling Pathways

    PubMed Central

    Telesca, Donatello; Müller, Peter; Kornblau, Steven M.; Suchard, Marc A.; Ji, Yuan

    2015-01-01

    High-throughput functional proteomic technologies provide a way to quantify the expression of proteins of interest. Statistical inference centers on identifying the activation state of proteins and their patterns of molecular interaction formalized as dependence structure. Inference on dependence structure is particularly important when proteins are selected because they are part of a common molecular pathway. In that case, inference on dependence structure reveals properties of the underlying pathway. We propose a probability model that represents molecular interactions at the level of hidden binary latent variables that can be interpreted as indicators for active versus inactive states of the proteins. The proposed approach exploits available expert knowledge about the target pathway to define an informative prior on the hidden conditional dependence structure. An important feature of this prior is that it provides an instrument to explicitly anchor the model space to a set of interactions of interest, favoring a local search approach to model determination. We apply our model to reverse-phase protein array data from a study on acute myeloid leukemia. Our inference identifies relevant subpathways in relation to the unfolding of the biological process under study. PMID:26246646

  11. Integrating protein-protein interaction networks with phenotypes reveals signs of interactions

    PubMed Central

    Vinayagam, Arunachalam; Zirin, Jonathan; Roesel, Charles; Hu, Yanhui; Yilmazel, Bahar; Samsonova, Anastasia A.; Neumüller, Ralph A.; Mohr, Stephanie E.; Perrimon, Norbert

    2013-01-01

    A major objective of systems biology is to organize molecular interactions as networks and to characterize information-flow within networks. We describe a computational framework to integrate protein-protein interaction (PPI) networks and genetic screens to predict the “signs” of interactions (i.e. activation/inhibition relationships). We constructed a Drosophila melanogaster signed PPI network, consisting of 6,125 signed PPIs connecting 3,352 proteins that can be used to identify positive and negative regulators of signaling pathways and protein complexes. We identified an unexpected role for the metabolic enzymes Enolase and Aldo-keto reductase as positive and negative regulators of proteolysis, respectively. Characterization of the activation/inhibition relationships between physically interacting proteins within signaling pathways will impact our understanding of many biological functions, including signal transduction and mechanisms of disease. PMID:24240319

  12. Roles for Regulator of G Protein Signaling Proteins in Synaptic Signaling and Plasticity

    PubMed Central

    Gerber, Kyle J.; Squires, Katherine E.

    2016-01-01

    The regulator of G protein signaling (RGS) family of proteins serves critical roles in G protein-coupled receptor (GPCR) and heterotrimeric G protein signal transduction. RGS proteins are best understood as negative regulators of GPCR/G protein signaling. They achieve this by acting as GTPase activating proteins (GAPs) for Gα subunits and accelerating the turnoff of G protein signaling. Many RGS proteins also bind additional signaling partners that either regulate their functions or enable them to regulate other important signaling events. At neuronal synapses, GPCRs, G proteins, and RGS proteins work in coordination to regulate key aspects of neurotransmitter release, synaptic transmission, and synaptic plasticity, which are necessary for central nervous system physiology and behavior. Accumulating evidence has revealed key roles for specific RGS proteins in multiple signaling pathways at neuronal synapses, regulating both pre- and postsynaptic signaling events and synaptic plasticity. Here, we review and highlight the current knowledge of specific RGS proteins (RGS2, RGS4, RGS7, RGS9-2, and RGS14) that have been clearly demonstrated to serve critical roles in modulating synaptic signaling and plasticity throughout the brain, and we consider their potential as future therapeutic targets. PMID:26655302

  13. Reconstruction and signal propagation analysis of the Syk signaling network in breast cancer cells.

    PubMed

    Naldi, Aurélien; Larive, Romain M; Czerwinska, Urszula; Urbach, Serge; Montcourrier, Philippe; Roy, Christian; Solassol, Jérôme; Freiss, Gilles; Coopman, Peter J; Radulescu, Ovidiu

    2017-03-01

    The ability to build in-depth cell signaling networks from vast experimental data is a key objective of computational biology. The spleen tyrosine kinase (Syk) protein, a well-characterized key player in immune cell signaling, was surprisingly first shown by our group to exhibit an onco-suppressive function in mammary epithelial cells and corroborated by many other studies, but the molecular mechanisms of this function remain largely unsolved. Based on existing proteomic data, we report here the generation of an interaction-based network of signaling pathways controlled by Syk in breast cancer cells. Pathway enrichment of the Syk targets previously identified by quantitative phospho-proteomics indicated that Syk is engaged in cell adhesion, motility, growth and death. Using the components and interactions of these pathways, we bootstrapped the reconstruction of a comprehensive network covering Syk signaling in breast cancer cells. To generate in silico hypotheses on Syk signaling propagation, we developed a method allowing to rank paths between Syk and its targets. We first annotated the network according to experimental datasets. We then combined shortest path computation with random walk processes to estimate the importance of individual interactions and selected biologically relevant pathways in the network. Molecular and cell biology experiments allowed to distinguish candidate mechanisms that underlie the impact of Syk on the regulation of cortactin and ezrin, both involved in actin-mediated cell adhesion and motility. The Syk network was further completed with the results of our biological validation experiments. The resulting Syk signaling sub-networks can be explored via an online visualization platform.

  14. Reconstruction and signal propagation analysis of the Syk signaling network in breast cancer cells

    PubMed Central

    Urbach, Serge; Montcourrier, Philippe; Roy, Christian; Solassol, Jérôme; Freiss, Gilles; Radulescu, Ovidiu

    2017-01-01

    The ability to build in-depth cell signaling networks from vast experimental data is a key objective of computational biology. The spleen tyrosine kinase (Syk) protein, a well-characterized key player in immune cell signaling, was surprisingly first shown by our group to exhibit an onco-suppressive function in mammary epithelial cells and corroborated by many other studies, but the molecular mechanisms of this function remain largely unsolved. Based on existing proteomic data, we report here the generation of an interaction-based network of signaling pathways controlled by Syk in breast cancer cells. Pathway enrichment of the Syk targets previously identified by quantitative phospho-proteomics indicated that Syk is engaged in cell adhesion, motility, growth and death. Using the components and interactions of these pathways, we bootstrapped the reconstruction of a comprehensive network covering Syk signaling in breast cancer cells. To generate in silico hypotheses on Syk signaling propagation, we developed a method allowing to rank paths between Syk and its targets. We first annotated the network according to experimental datasets. We then combined shortest path computation with random walk processes to estimate the importance of individual interactions and selected biologically relevant pathways in the network. Molecular and cell biology experiments allowed to distinguish candidate mechanisms that underlie the impact of Syk on the regulation of cortactin and ezrin, both involved in actin-mediated cell adhesion and motility. The Syk network was further completed with the results of our biological validation experiments. The resulting Syk signaling sub-networks can be explored via an online visualization platform. PMID:28306714

  15. Novel links in the plant TOR kinase signaling network.

    PubMed

    Xiong, Yan; Sheen, Jen

    2015-12-01

    Nutrient and energy sensing and signaling mechanisms constitute the most ancient and fundamental regulatory networks to control growth and development in all life forms. The target of rapamycin (TOR) protein kinase is modulated by diverse nutrient, energy, hormone and stress inputs and plays a central role in regulating cell proliferation, growth, metabolism and stress responses from yeasts to plants and animals. Recent chemical, genetic, genomic and metabolomic analyses have enabled significant progress toward molecular understanding of the TOR signaling network in multicellular plants. This review discusses the applications of new chemical tools to probe plant TOR functions and highlights recent findings and predictions on TOR-mediate biological processes. Special focus is placed on novel and evolutionarily conserved TOR kinase effectors as positive and negative signaling regulators that control transcription, translation and metabolism to support cell proliferation, growth and maintenance from embryogenesis to senescence in the plant system.

  16. Systematic Prediction of Scaffold Proteins Reveals New Design Principles in Scaffold-Mediated Signal Transduction

    PubMed Central

    Hu, Jianfei; Neiswinger, Johnathan; Zhang, Jin; Zhu, Heng; Qian, Jiang

    2015-01-01

    Scaffold proteins play a crucial role in facilitating signal transduction in eukaryotes by bringing together multiple signaling components. In this study, we performed a systematic analysis of scaffold proteins in signal transduction by integrating protein-protein interaction and kinase-substrate relationship networks. We predicted 212 scaffold proteins that are involved in 605 distinct signaling pathways. The computational prediction was validated using a protein microarray-based approach. The predicted scaffold proteins showed several interesting characteristics, as we expected from the functionality of scaffold proteins. We found that the scaffold proteins are likely to interact with each other, which is consistent with previous finding that scaffold proteins tend to form homodimers and heterodimers. Interestingly, a single scaffold protein can be involved in multiple signaling pathways by interacting with other scaffold protein partners. Furthermore, we propose two possible regulatory mechanisms by which the activity of scaffold proteins is coordinated with their associated pathways through phosphorylation process. PMID:26393507

  17. Qualitative networks: a symbolic approach to analyze biological signaling networks

    PubMed Central

    Schaub, Marc A; Henzinger, Thomas A; Fisher, Jasmin

    2007-01-01

    Background A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico. Results We consider networks in which elements range over a small finite domain allowing more flexibility than Boolean values, and add target functions that allow to model a rich set of behaviors. We propose a symbolic algorithm for analyzing the steady state of these networks, allowing us to scale up to a system consisting of 144 elements and state spaces of approximately 1086 states. We illustrate the usefulness of this approach through a model of the interaction between the Notch and the Wnt signaling pathways in mammalian skin, and its extensive analysis. Conclusion We introduce an approach for constructing computational models of biological systems that extends the framework of Boolean networks and uses formal verification methods for the analysis of the model. This approach can scale to multicellular models of complex pathways, and is therefore a useful tool for the analysis of complex biological systems. The hypotheses formulated during in-silico testing suggest new avenues to explore experimentally. Hence, this approach has the potential to efficiently complement experimental studies in biology. PMID:17408511

  18. Regulators of G protein signalling proteins in the human myometrium.

    PubMed

    Ladds, Graham; Zervou, Sevasti; Vatish, Manu; Thornton, Steven; Davey, John

    2009-05-21

    The contractile state of the human myometrium is controlled by extracellular signals that promote relaxation or contraction. Many of these signals function through G protein-coupled receptors at the cell surface, stimulating heterotrimeric G proteins and leading to changes in the activity of effector proteins responsible for bringing about the response. G proteins can interact with multiple receptors and many different effectors and are key players in the response. Regulators of G protein signalling (RGS) proteins are GTPase activating proteins for heterotrimeric G proteins and help terminate the signal. Little is known about the function of RGS proteins in human myometrium and we have therefore analysed transcript levels for RGS proteins at various stages of pregnancy (non-pregnant, preterm, term non-labouring, term labouring). RGS2 and RGS5 were the most abundantly expressed isolates in each of the patient groups. The levels of RGS4 and RGS16 (and to a lesser extent RGS2 and RGS14) increased in term labouring samples relative to the other groups. Yeast two-hybrid analysis and co-immunoprecipitation in myometrial cells revealed that both RGS2 and RGS5 interact directly with the cytoplasmic tail of the oxytocin receptor, suggesting they might help regulate signalling through this receptor.

  19. Signal dispersion within a hippocampal neural network

    NASA Technical Reports Server (NTRS)

    Horowitz, J. M.; Mates, J. W. B.

    1975-01-01

    A model network is described, representing two neural populations coupled so that one population is inhibited by activity it excites in the other. Parameters and operations within the model represent EPSPs, IPSPs, neural thresholds, conduction delays, background activity and spatial and temporal dispersion of signals passing from one population to the other. Simulations of single-shock and pulse-train driving of the network are presented for various parameter values. Neuronal events from 100 to 300 msec following stimulation are given special consideration in model calculations.

  20. Computation of signal delays in RC networks

    NASA Technical Reports Server (NTRS)

    Hidalgo, Juan Carlos; Narendran, Paliath; Chaiken, Seth

    1993-01-01

    A model for signal delay computation in RC networks is presented. The strength of the paradigm is its generality and simplicity. The definition of delay is applicable to RC meshes with potential resistive attenuating paths to ground. The algorithms can also be applied to undriven circuits (static charge sharing) and circuits with initial charge. To compute the delays, each node in the network is explored locally to derive a system of sparse linear equations. The solutions of the system are delay values based on the Elmore time constant at each point in the circuit.

  1. Pathway logic modeling of protein functional domains in signal transduction.

    PubMed

    Talcott, C; Eker, S; Knapp, M; Lincoln, P; Laderoute, K

    2004-01-01

    Protein functional domains (PFDs) are consensus sequences within signaling molecules that recognize and assemble other signaling components into complexes. Here we describe the application of an approach called Pathway Logic to the symbolic modeling signal transduction networks at the level of PFDs. These models are developed using Maude, a symbolic language founded on rewriting logic. Models can be queried (analyzed) using the execution, search and model-checking tools of Maude. We show how signal transduction processes can be modeled using Maude at very different levels of abstraction involving either an overall state of a protein or its PFDs and their interactions. The key insight for the latter is our algebraic representation of binding interactions as a graph.

  2. TSLP Signaling Network Revealed by SILAC-Based Phosphoproteomics*

    PubMed Central

    Zhong, Jun; Kim, Min-Sik; Chaerkady, Raghothama; Wu, Xinyan; Huang, Tai-Chung; Getnet, Derese; Mitchell, Christopher J.; Palapetta, Shyam M.; Sharma, Jyoti; O'Meally, Robert N.; Cole, Robert N.; Yoda, Akinori; Moritz, Albrecht; Loriaux, Marc M.; Rush, John; Weinstock, David M.; Tyner, Jeffrey W.; Pandey, Akhilesh

    2012-01-01

    Thymic stromal lymphopoietin (TSLP) is a cytokine that plays diverse roles in the regulation of immune responses. TSLP requires a heterodimeric receptor complex consisting of IL-7 receptor α subunit and its unique TSLP receptor (gene symbol CRLF2) to transmit signals in cells. Abnormal TSLP signaling (e.g. overexpression of TSLP or its unique receptor TSLPR) contributes to the development of a number of diseases including asthma and leukemia. However, a detailed understanding of the signaling pathways activated by TSLP remains elusive. In this study, we performed a global quantitative phosphoproteomic analysis of the TSLP signaling network using stable isotope labeling by amino acids in cell culture. By employing titanium dioxide in addition to antiphosphotyrosine antibodies as enrichment methods, we identified 4164 phosphopeptides on 1670 phosphoproteins. Using stable isotope labeling by amino acids in cell culture-based quantitation, we determined that the phosphorylation status of 226 proteins was modulated by TSLP stimulation. Our analysis identified activation of several members of the Src and Tec families of kinases including Btk, Lyn, and Tec by TSLP for the first time. In addition, we report TSLP-induced phosphorylation of protein phosphatases such as Ptpn6 (SHP-1) and Ptpn11 (Shp2), which has also not been reported previously. Co-immunoprecipitation assays showed that Shp2 binds to the adapter protein Gab2 in a TSLP-dependent manner. This is the first demonstration of an inducible protein complex in TSLP signaling. A kinase inhibitor screen revealed that pharmacological inhibition of PI-3 kinase, Jak family kinases, Src family kinases or Btk suppressed TSLP-dependent cellular proliferation making them candidate therapeutic targets in diseases resulting from aberrant TSLP signaling. Our study is the first phosphoproteomic analysis of the TSLP signaling pathway that greatly expands our understanding of TSLP signaling and provides novel therapeutic targets

  3. Prediction of oncogenic interactions and cancer-related signaling networks based on network topology.

    PubMed

    Acencio, Marcio Luis; Bovolenta, Luiz Augusto; Camilo, Esther; Lemke, Ney

    2013-01-01

    Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research

  4. The last enzyme of the de novo purine synthesis pathway 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase (ATIC) plays a central role in insulin signaling and the Golgi/endosomes protein network.

    PubMed

    Boutchueng-Djidjou, Martial; Collard-Simard, Gabriel; Fortier, Suzanne; Hébert, Sébastien S; Kelly, Isabelle; Landry, Christian R; Faure, Robert L

    2015-04-01

    Insulin is internalized with its cognate receptor into the endosomal apparatus rapidly after binding to hepatocytes. We performed a bioinformatic screen of Golgi/endosome hepatic protein fractions and found that ATIC, which is a rate-limiting enzyme in the de novo purine biosynthesis pathway, and PTPLAD1 are associated with insulin receptor (IR) internalization. The IR interactome (IRGEN) connects ATIC to AMPK within the Golgi/endosome protein network (GEN). Forty-five percent of the IR Golgi/endosome protein network have common heritable variants associated with type 2 diabetes, including ATIC and AMPK. We show that PTPLAD1 and AMPK are rapidly compartmentalized within the plasma membrane (PM) and Golgi/endosome fractions after insulin stimulation and that ATIC later accumulates in the Golgi/endosome fraction. Using an in vitro reconstitution system and siRNA-mediated partial knockdown of ATIC and PTPLAD1 in HEK293 cells, we show that both ATIC and PTPLAD1 affect IR tyrosine phosphorylation and endocytosis. We further show that insulin stimulation and ATIC knockdown readily increase the level of AMPK-Thr172 phosphorylation in IR complexes. We observed that IR internalization was markedly decreased after AMPKα2 knockdown, and treatment with the ATIC substrate AICAR, which is an allosteric activator of AMPK, increased IR endocytosis in cultured cells and in the liver. These results suggest the presence of a signaling mechanism that senses adenylate synthesis, ATP levels, and IR activation states and that acts in regulating IR autophosphorylation and endocytosis.

  5. A signaling network in phenylephrine-induced benign prostatic hyperplasia.

    PubMed

    Kim, Jayoung; Yanagihara, Yutaka; Kikugawa, Tadahiko; Ji, Mihee; Tanji, Nozomu; Masayoshi, Yokoyama; Freeman, Michael R

    2009-08-01

    Benign prostatic hyperplasia (BPH) is an age-related disease of unknown etiology characterized by prostatic enlargement and coinciding with distinctive alterations in tissue histomorphology. To identify the molecular mechanisms underlying the development of BPH, we conducted a DNA microarray study using a previously described animal model in which chronic alpha(1)-adrenergic stimulation by repeated administration of phenylephrine evokes histomorphological changes in the rat prostate that resemble human BPH. Bioinformatic tools were applied to microarray data obtained from prostate tissue to construct a network model of potentially relevant signal transduction pathways. Significant involvement of inflammatory pathways was demonstrable, including evidence for activation of a TGF-beta signaling cascade. The heterodimeric protein clusterin (apolipoprotein J) was also identified as a prominent node in the network. Responsiveness of TGF-beta signaling and clusterin gene and protein expression were confirmed independently of the microarray data, verifying some components of the model. This is the first attempt to develop a comprehensive molecular network for histological BPH induced by adrenergic activation. The study also implicated clusterin as a novel biochemical target for therapy.

  6. Acoustic signal propagation characterization of conduit networks

    NASA Astrophysics Data System (ADS)

    Khan, Muhammad Safeer

    Analysis of acoustic signal propagation in conduit networks has been an important area of research in acoustics. One major aspect of analyzing conduit networks as acoustic channels is that a propagating signal suffers frequency dependent attenuation due to thermo-viscous boundary layer effects and the presence of impedance mismatches such as side branches. The signal attenuation due to side branches is strongly influenced by their numbers and dimensions such as diameter and length. Newly developed applications for condition based monitoring of underground conduit networks involve measurement of acoustic signal attenuation through tests in the field. In many cases the exact installation layout of the field measurement location may not be accessible or actual installation may differ from the documented layout. The lack of exact knowledge of numbers and lengths of side branches, therefore, introduces uncertainty in the measurements of attenuation and contributes to the random variable error between measured results and those predicted from theoretical models. There are other random processes in and around conduit networks in the field that also affect the propagation of an acoustic signal. These random processes include but are not limited to the presence of strong temperature and humidity gradients within the conduits, blockages of variable sizes and types, effects of aging such as cracks, bends, sags and holes, ambient noise variations and presence of variable layer of water. It is reasonable to consider that the random processes contributing to the error in the measured attenuation are independent and arbitrarily distributed. The error, contributed by a large number of independent sources of arbitrary probability distributions, is best described by an approximately normal probability distribution in accordance with the central limit theorem. Using an analytical approach to model the attenuating effect of each of the random variable sources can be very complex and

  7. Integrated signaling network involving calcium, nitric oxide, and active oxygen species but not mitogen-activated protein kinases in BcPG1-elicited grapevine defenses.

    PubMed

    Vandelle, Elodie; Poinssot, Benoît; Wendehenne, David; Bentéjac, Marc; Alain, Pugin

    2006-04-01

    We have already reported the identification of the endopolygalacturonase 1 (BcPG1) from Botrytis cinerea as a potent elicitor of defense responses in grapevine, independently of its enzymatic activity. The aim of the present study is the analysis of the signaling pathways triggered by BcPG1 in grapevine cells. Our data indicate that BcPG1 induces a Ca2+ entry from the apoplasm, which triggers a phosphorylation-dependent nitric oxide (NO) production via an enzyme probably related to a NO synthase. Then NO is involved in (i) cytosolic calcium homeostasis, by activating Ca2+ release from internal stores and regulating Ca2+ fluxes across the plasma membrane, (ii) plasma membrane potential variation, (iii) the activation of active oxygen species (AOS) production, and (iv) defense gene expression, including phenylalanine ammonia lyase and stilbene synthase, which encode enzymes responsible for phytoalexin biosynthesis. Interestingly enough, mitogen-activated protein kinase (MAPK) activation is independent of this regulation pathway that closely connects Ca2+, NO, and AOS.

  8. Plasma membrane regulates Ras signaling networks

    PubMed Central

    Chavan, Tanmay Sanjeev; Muratcioglu, Serena; Marszalek, Richard; Jang, Hyunbum; Keskin, Ozlem; Gursoy, Attila; Nussinov, Ruth; Gaponenko, Vadim

    2015-01-01

    Ras GTPases activate more than 20 signaling pathways, regulating such essential cellular functions as proliferation, survival, and migration. How Ras proteins control their signaling diversity is still a mystery. Several pieces of evidence suggest that the plasma membrane plays a critical role. Among these are: (1) selective recruitment of Ras and its effectors to particular localities allowing access to Ras regulators and effectors; (2) specific membrane-induced conformational changes promoting Ras functional diversity; and (3) oligomerization of membrane-anchored Ras to recruit and activate Raf. Taken together, the membrane does not only attract and retain Ras but also is a key regulator of Ras signaling. This can already be gleaned from the large variability in the sequences of Ras membrane targeting domains, suggesting that localization, environment and orientation are important factors in optimizing the function of Ras isoforms. PMID:27054048

  9. Plasma membrane regulates Ras signaling networks.

    PubMed

    Chavan, Tanmay Sanjeev; Muratcioglu, Serena; Marszalek, Richard; Jang, Hyunbum; Keskin, Ozlem; Gursoy, Attila; Nussinov, Ruth; Gaponenko, Vadim

    2015-01-01

    Ras GTPases activate more than 20 signaling pathways, regulating such essential cellular functions as proliferation, survival, and migration. How Ras proteins control their signaling diversity is still a mystery. Several pieces of evidence suggest that the plasma membrane plays a critical role. Among these are: (1) selective recruitment of Ras and its effectors to particular localities allowing access to Ras regulators and effectors; (2) specific membrane-induced conformational changes promoting Ras functional diversity; and (3) oligomerization of membrane-anchored Ras to recruit and activate Raf. Taken together, the membrane does not only attract and retain Ras but also is a key regulator of Ras signaling. This can already be gleaned from the large variability in the sequences of Ras membrane targeting domains, suggesting that localization, environment and orientation are important factors in optimizing the function of Ras isoforms.

  10. SNAVI: Desktop application for analysis and visualization of large-scale signaling networks

    PubMed Central

    Ma'ayan, Avi; Jenkins, Sherry L; Webb, Ryan L; Berger, Seth I; Purushothaman, Sudarshan P; Abul-Husn, Noura S; Posner, Jeremy M; Flores, Tony; Iyengar, Ravi

    2009-01-01

    Background Studies of cellular signaling indicate that signal transduction pathways combine to form large networks of interactions. Viewing protein-protein and ligand-protein interactions as graphs (networks), where biomolecules are represented as nodes and their interactions are represented as links, is a promising approach for integrating experimental results from different sources to achieve a systematic understanding of the molecular mechanisms driving cell phenotype. The emergence of large-scale signaling networks provides an opportunity for topological statistical analysis while visualization of such networks represents a challenge. Results SNAVI is Windows-based desktop application that implements standard network analysis methods to compute the clustering, connectivity distribution, and detection of network motifs, as well as provides means to visualize networks and network motifs. SNAVI is capable of generating linked web pages from network datasets loaded in text format. SNAVI can also create networks from lists of gene or protein names. Conclusion SNAVI is a useful tool for analyzing, visualizing and sharing cell signaling data. SNAVI is open source free software. The installation may be downloaded from: . The source code can be accessed from: PMID:19154595

  11. Deciphering signaling networks in osteosarcoma pathobiology

    PubMed Central

    Adamopoulos, Christos; Gargalionis, Antonios N; Basdra, Efthimia K

    2016-01-01

    Osteosarcoma is the most frequent type of primary bone tumors among children and adolescents. During the past years, little progress has been made regarding prognosis of osteosarcoma patients, especially for those with metastatic disease. Genomic instability and gene alterations are common, but current data do not reveal a consistent and repeatable pattern of osteosarcoma development, thus paralleling the tumor's high heterogeneity. Critical signal transduction pathways have been implicated in osteosarcoma pathobiology and are being evaluated as therapeutic targets, including receptor activator for nuclear factor-κB (RANK), Wnt, Notch, phosphatidylinositol 3-kinase/Akt/mammalian target of rapamycin, and mechanotransduction pathways. Herein, we recapitulate and discuss recent advances in the context of molecular mechanisms and signaling networks that contribute to osteosarcoma progression and metastasis, towards patient-tailored and novel-targeted treatments. PMID:27190271

  12. Cleavage of Signal Regulatory Protein α (SIRPα) Enhances Inflammatory Signaling.

    PubMed

    Londino, James D; Gulick, Dexter; Isenberg, Jeffrey S; Mallampalli, Rama K

    2015-12-25

    Signal regulatory protein α (SIRPα) is a membrane glycoprotein immunoreceptor abundant in cells of monocyte lineage. SIRPα ligation by a broadly expressed transmembrane protein, CD47, results in phosphorylation of the cytoplasmic immunoreceptor tyrosine-based inhibitory motifs, resulting in the inhibition of NF-κB signaling in macrophages. Here we observed that proteolysis of SIRPα during inflammation is regulated by a disintegrin and metalloproteinase domain-containing protein 10 (ADAM10), resulting in the generation of a membrane-associated cleavage fragment in both THP-1 monocytes and human lung epithelia. We mapped a charge-dependent putative cleavage site near the membrane-proximal domain necessary for ADAM10-mediated cleavage. In addition, a secondary proteolytic cleavage within the membrane-associated SIRPα fragment by γ-secretase was identified. Ectopic expression of a SIRPα mutant plasmid encoding a proteolytically resistant form in HeLa cells inhibited activation of the NF-κB pathway and suppressed STAT1 phosphorylation in response to TNFα to a greater extent than expression of wild-type SIRPα. Conversely, overexpression of plasmids encoding the proteolytically cleaved SIRPα fragments in cells resulted in enhanced STAT-1 and NF-κB pathway activation. Thus, the data suggest that combinatorial actions of ADAM10 and γ-secretase on SIRPα cleavage promote inflammatory signaling.

  13. Cleavage of Signal Regulatory Protein α (SIRPα) Enhances Inflammatory Signaling*

    PubMed Central

    Londino, James D.; Gulick, Dexter; Isenberg, Jeffrey S.; Mallampalli, Rama K.

    2015-01-01

    Signal regulatory protein α (SIRPα) is a membrane glycoprotein immunoreceptor abundant in cells of monocyte lineage. SIRPα ligation by a broadly expressed transmembrane protein, CD47, results in phosphorylation of the cytoplasmic immunoreceptor tyrosine-based inhibitory motifs, resulting in the inhibition of NF-κB signaling in macrophages. Here we observed that proteolysis of SIRPα during inflammation is regulated by a disintegrin and metalloproteinase domain-containing protein 10 (ADAM10), resulting in the generation of a membrane-associated cleavage fragment in both THP-1 monocytes and human lung epithelia. We mapped a charge-dependent putative cleavage site near the membrane-proximal domain necessary for ADAM10-mediated cleavage. In addition, a secondary proteolytic cleavage within the membrane-associated SIRPα fragment by γ-secretase was identified. Ectopic expression of a SIRPα mutant plasmid encoding a proteolytically resistant form in HeLa cells inhibited activation of the NF-κB pathway and suppressed STAT1 phosphorylation in response to TNFα to a greater extent than expression of wild-type SIRPα. Conversely, overexpression of plasmids encoding the proteolytically cleaved SIRPα fragments in cells resulted in enhanced STAT-1 and NF-κB pathway activation. Thus, the data suggest that combinatorial actions of ADAM10 and γ-secretase on SIRPα cleavage promote inflammatory signaling. PMID:26534964

  14. Pathway connectivity and signaling coordination in the yeast stress-activated signaling network

    PubMed Central

    Chasman, Deborah; Ho, Yi-Hsuan; Berry, David B; Nemec, Corey M; MacGilvray, Matthew E; Hose, James; Merrill, Anna E; Lee, M Violet; Will, Jessica L; Coon, Joshua J; Ansari, Aseem Z; Craven, Mark; Gasch, Audrey P

    2014-01-01

    Stressed cells coordinate a multi-faceted response spanning many levels of physiology. Yet knowledge of the complete stress-activated regulatory network as well as design principles for signal integration remains incomplete. We developed an experimental and computational approach to integrate available protein interaction data with gene fitness contributions, mutant transcriptome profiles, and phospho-proteome changes in cells responding to salt stress, to infer the salt-responsive signaling network in yeast. The inferred subnetwork presented many novel predictions by implicating new regulators, uncovering unrecognized crosstalk between known pathways, and pointing to previously unknown ‘hubs’ of signal integration. We exploited these predictions to show that Cdc14 phosphatase is a central hub in the network and that modification of RNA polymerase II coordinates induction of stress-defense genes with reduction of growth-related transcripts. We find that the orthologous human network is enriched for cancer-causing genes, underscoring the importance of the subnetwork's predictions in understanding stress biology. PMID:25411400

  15. Bigger, better, faster: principles and models of AKAP anchoring protein signaling.

    PubMed

    Greenwald, Eric C; Saucerman, Jeffrey J

    2011-11-01

    A kinase anchoring proteins (AKAPs) bind multiple signaling proteins and have subcellular targeting domains that allow them to greatly impact cellular signaling. AKAPs localize, specify, amplify, and accelerate signal transduction within the cell by bringing signaling proteins together in space and time. AKAPs also organize higher-order network motifs such as feed forward and feedback loops that may create complex network responses, including adaptation, oscillation, and ultrasensitivity. Computational models have begun to provide an insight into how AKAPs regulate signaling dynamics and cardiovascular pathophysiology. Models of mitogen-activated protein kinase and epidermal growth factor receptor scaffolds have revealed additional design principles and new methods for representing signaling scaffolds mathematically. Coupling computational modeling with quantitative experimental approaches will be increasingly necessary for dissecting the diverse information processing functions performed by AKAP signaling complexes.

  16. Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer

    PubMed Central

    CHEN, CHEN; SHEN, HONG; ZHANG, LI-GUO; LIU, JIAN; CAO, XIAO-GE; YAO, AN-LIANG; KANG, SHAO-SAN; GAO, WEI-XING; HAN, HUI; CAO, FENG-HONG; LI, ZHI-GUO

    2016-01-01

    Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa. PMID:27121963

  17. Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer.

    PubMed

    Chen, Chen; Shen, Hong; Zhang, Li-Guo; Liu, Jian; Cao, Xiao-Ge; Yao, An-Liang; Kang, Shao-San; Gao, Wei-Xing; Han, Hui; Cao, Feng-Hong; Li, Zhi-Guo

    2016-06-01

    Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa.

  18. Phosphatase Specificity and Pathway Insulation in Signaling Networks

    PubMed Central

    Rowland, Michael A.; Harrison, Brian; Deeds, Eric J.

    2015-01-01

    Phosphatases play an important role in cellular signaling networks by regulating the phosphorylation state of proteins. Phosphatases are classically considered to be promiscuous, acting on tens to hundreds of different substrates. We recently demonstrated that a shared phosphatase can couple the responses of two proteins to incoming signals, even if those two substrates are from otherwise isolated areas of the network. This finding raises a potential paradox: if phosphatases are indeed highly promiscuous, how do cells insulate themselves against unwanted crosstalk? Here, we use mathematical models to explore three possible insulation mechanisms. One approach involves evolving phosphatase KM values that are large enough to prevent saturation by the phosphatase’s substrates. Although this is an effective method for generating isolation, the phosphatase becomes a highly inefficient enzyme, which prevents the system from achieving switch-like responses and can result in slow response kinetics. We also explore the idea that substrate degradation can serve as an effective phosphatase. Assuming that degradation is unsaturatable, this mechanism could insulate substrates from crosstalk, but it would also preclude ultrasensitive responses and would require very high substrate turnover to achieve rapid dephosphorylation kinetics. Finally, we show that adaptor subunits, such as those found on phosphatases like PP2A, can provide effective insulation against phosphatase crosstalk, but only if their binding to substrates is uncoupled from their binding to the catalytic core. Analysis of the interaction network of PP2A’s adaptor domains reveals that although its adaptors may isolate subsets of targets from one another, there is still a strong potential for phosphatase crosstalk within those subsets. Understanding how phosphatase crosstalk and the insulation mechanisms described here impact the function and evolution of signaling networks represents a major challenge for

  19. Glycosphingolipid–Protein Interaction in Signal Transduction

    PubMed Central

    Russo, Domenico; Parashuraman, Seetharaman; D’Angelo, Giovanni

    2016-01-01

    Glycosphingolipids (GSLs) are a class of ceramide-based glycolipids essential for embryo development in mammals. The synthesis of specific GSLs depends on the expression of distinctive sets of GSL synthesizing enzymes that is tightly regulated during development. Several reports have described how cell surface receptors can be kept in a resting state or activate alternative signalling events as a consequence of their interaction with GSLs. Specific GSLs, indeed, interface with specific protein domains that are found in signalling molecules and which act as GSL sensors to modify signalling responses. The regulation exerted by GSLs on signal transduction is orthogonal to the ligand–receptor axis, as it usually does not directly interfere with the ligand binding to receptors. Due to their properties of adjustable production and orthogonal action on receptors, GSLs add a new dimension to the control of the signalling in development. GSLs can, indeed, dynamically influence progenitor cell response to morphogenetic stimuli, resulting in alternative differentiation fates. Here, we review the available literature on GSL–protein interactions and their effects on cell signalling and development. PMID:27754465

  20. Neural Networks for Signal Processing and Control

    NASA Astrophysics Data System (ADS)

    Hesselroth, Ted Daniel

    cortex by the application of lateral interactions during the learning phase. The organization of the mature network is compared to that found in the macaque monkey by several analytical tests. The capacity of the network to process images is investigated. By a method of reconstructing the input images in terms of V1 activities, the simulations show that images can be faithfully represented in V1 by the proposed network. The signal-to-noise ratio of the image is improved by the representation, and compression ratios of well over two-hundred are possible. Lateral interactions between V1 neurons sharpen their orientational tuning. We further study the dynamics of the processing, showing that the rate of decrease of the error of the reconstruction is maximized for the receptive fields used. Lastly, we employ a Fokker-Planck equation for a more detailed prediction of the error value vs. time. The Fokker-Planck equation for an underdamped system with a driving force is derived, yielding an energy-dependent diffusion coefficient which is the integral of the spectral densities of the force and the velocity of the system. The theory is applied to correlated noise activation and resonant activation. Simulation results for the error of the network vs time are compared to the solution of the Fokker-Planck equation.

  1. Collective Calcium Signaling of Defective Multicellular Networks

    NASA Astrophysics Data System (ADS)

    Potter, Garrett; Sun, Bo

    2015-03-01

    A communicating multicellular network processes environmental cues into collective cellular dynamics. We have previously demonstrated that, when excited by extracellular ATP, fibroblast monolayers generate correlated calcium dynamics modulated by both the stimuli and gap junction communication between the cells. However, just as a well-connected neural network may be compromised by abnormal neurons, a tissue monolayer can also be defective with cancer cells, which typically have down regulated gap junctions. To understand the collective cellular dynamics in a defective multicellular network we have studied the calcium signaling of co-cultured breast cancer cells and fibroblast cells in various concentrations of ATP delivered through microfluidic devices. Our results demonstrate that cancer cells respond faster, generate singular spikes, and are more synchronous across all stimuli concentrations. Additionally, fibroblast cells exhibit persistent calcium oscillations that increase in regularity with greater stimuli. To interpret these results we quantitatively analyzed the immunostaining of purigenic receptors and gap junction channels. The results confirm our hypothesis that collective dynamics are mainly determined by the availability of gap junction communications.

  2. MSAT signalling and network management architectures

    NASA Technical Reports Server (NTRS)

    Garland, Peter; Keelty, J. Malcolm

    1989-01-01

    Spar Aerospace has been active in the design and definition of Mobile Satellite Systems since the mid 1970's. In work sponsored by the Canadian Department of Communications, various payload configurations have evolved. In addressing the payload configuration, the requirements of the mobile user, the service provider and the satellite operator have always been the most important consideration. The current Spar 11 beam satellite design is reviewed, and its capabilities to provide flexibility and potential for network growth within the WARC87 allocations are explored. To enable the full capabilities of the payload to be realized, a large amount of ground based Switching and Network Management infrastructure will be required, when space segment becomes available. Early indications were that a single custom designed Demand Assignment Multiple Access (DAMA) switch should be implemented to provide efficient use of the space segment. As MSAT has evolved into a multiple service concept, supporting many service providers, this architecture should be reviewed. Some possible signalling and Network Management solutions are explored.

  3. Perturbation biology: inferring signaling networks in cellular systems.

    PubMed

    Molinelli, Evan J; Korkut, Anil; Wang, Weiqing; Miller, Martin L; Gauthier, Nicholas P; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B; Pratilas, Christine A; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

    2013-01-01

    We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.

  4. Inferring network mechanisms: the Drosophila melanogaster protein interaction network.

    PubMed

    Middendorf, Manuel; Ziv, Etay; Wiggins, Chris H

    2005-03-01

    Naturally occurring networks exhibit quantitative features revealing underlying growth mechanisms. Numerous network mechanisms have recently been proposed to reproduce specific properties such as degree distributions or clustering coefficients. We present a method for inferring the mechanism most accurately capturing a given network topology, exploiting discriminative tools from machine learning. The Drosophila melanogaster protein network is confidently and robustly (to noise and training data subsampling) classified as a duplication-mutation-complementation network over preferential attachment, small-world, and a duplication-mutation mechanism without complementation. Systematic classification, rather than statistical study of specific properties, provides a discriminative approach to understand the design of complex networks.

  5. The Roles of NDR Protein Kinases in Hippo Signalling

    PubMed Central

    Hergovich, Alexander

    2016-01-01

    The Hippo tumour suppressor pathway has emerged as a critical regulator of tissue growth through controlling cellular processes such as cell proliferation, death, differentiation and stemness. Traditionally, the core cassette of the Hippo pathway includes the MST1/2 protein kinases, the LATS1/2 protein kinases, and the MOB1 scaffold signal transducer, which together regulate the transcriptional co-activator functions of the proto-oncoproteins YAP and TAZ through LATS1/2-mediated phosphorylation of YAP/TAZ. Recent research has identified additional kinases, such as NDR1/2 (also known as STK38/STK38L) and MAP4Ks, which should be considered as novel members of the Hippo core cassette. While these efforts helped to expand our understanding of Hippo core signalling, they also began to provide insights into the complexity and redundancy of the Hippo signalling network. Here, we focus on summarising our current knowledge of the regulation and functions of mammalian NDR kinases, discussing parallels between the NDR pathways in Drosophila and mammals. Initially, we provide a general overview of the cellular functions of NDR kinases in cell cycle progression, centrosome biology, apoptosis, autophagy, DNA damage signalling, immunology and neurobiology. Finally, we put particular emphasis on discussing NDR1/2 as YAP kinases downstream of MST1/2 and MOB1 signalling in Hippo signalling. PMID:27213455

  6. Lethality and entropy of protein interaction networks.

    PubMed

    Manke, Thomas; Demetrius, Lloyd; Vingron, Martin

    2005-01-01

    We characterize protein interaction networks in terms of network entropy. This approach suggests a ranking principle, which strongly correlates with elements of functional importance, such as lethal proteins. Our combined analysis of protein interaction networks and functional profiles in single cellular yeast and multi-cellular worm shows that proteins with large contribution to network entropy are preferentially lethal. While entropy is inherently a dynamical concept, the present analysis incorporates only structural information. Our result therefore highlights the importance of topological features, which appear as correlates of an underlying dynamical property, and which in turn determine functional traits. We argue that network entropy is a natural extension of previously studied observables, such as pathway multiplicity and centrality. It is also applicable to networks in which the processes can be quantified and therefore serves as a link to study questions of structural and dynamical robustness in a unified way.

  7. Neural network-based sensor signal accelerator.

    SciTech Connect

    Vogt, M. C.

    2000-10-16

    A strategy has been developed to computationally accelerate the response time of a generic electronic sensor. The strategy can be deployed as an algorithm in a control system or as a physical interface (on an embedded microcontroller) between a slower responding external sensor and a higher-speed control system. Optional code implementations are available to adjust algorithm performance when computational capability is limited. In one option, the actual sensor signal can be sampled at the slower rate with adaptive linear neural networks predicting the sensor's future output and interpolating intermediate synthetic output values. In another option, a synchronized collection of predictors sequentially controls the corresponding synthetic output voltage. Error is adaptively corrected in both options. The core strategy has been demonstrated with automotive oxygen sensor data. A prototype interface device is under construction. The response speed increase afforded by this strategy could greatly offset the cost of developing a replacement sensor with a faster physical response time.

  8. Hepatitis C virus infection protein network.

    PubMed

    de Chassey, B; Navratil, V; Tafforeau, L; Hiet, M S; Aublin-Gex, A; Agaugué, S; Meiffren, G; Pradezynski, F; Faria, B F; Chantier, T; Le Breton, M; Pellet, J; Davoust, N; Mangeot, P E; Chaboud, A; Penin, F; Jacob, Y; Vidalain, P O; Vidal, M; André, P; Rabourdin-Combe, C; Lotteau, V

    2008-01-01

    A proteome-wide mapping of interactions between hepatitis C virus (HCV) and human proteins was performed to provide a comprehensive view of the cellular infection. A total of 314 protein-protein interactions between HCV and human proteins was identified by yeast two-hybrid and 170 by literature mining. Integration of this data set into a reconstructed human interactome showed that cellular proteins interacting with HCV are enriched in highly central and interconnected proteins. A global analysis on the basis of functional annotation highlighted the enrichment of cellular pathways targeted by HCV. A network of proteins associated with frequent clinical disorders of chronically infected patients was constructed by connecting the insulin, Jak/STAT and TGFbeta pathways with cellular proteins targeted by HCV. CORE protein appeared as a major perturbator of this network. Focal adhesion was identified as a new function affected by HCV, mainly by NS3 and NS5A proteins.

  9. Speciation network in Laurasiatheria: retrophylogenomic signals.

    PubMed

    Doronina, Liliya; Churakov, Gennady; Kuritzin, Andrej; Shi, Jingjing; Baertsch, Robert; Clawson, Hiram; Schmitz, Juergen

    2017-03-15

    Rapid species radiation due to adaptive changes or occupation of new ecospaces challenges our understanding of ancestral speciation and the relationships of modern species. At the molecular level, rapid radiation with successive speciations over short time periods - too short to fix polymorphic alleles - is described as incomplete lineage sorting. Incomplete lineage sorting leads to random fixation of genetic markers and hence random signals of relationships in phylogenetic reconstructions. The situation is further complicated when you consider that the genome is a mosaic of ancestral and modern incompletely sorted sequence blocks that leads to reconstructed affiliations to one or the other relatives depending on the fixation of their shared ancestral polymorphic alleles. The laurasiatherian relationships among Chiroptera, Perissodactyla, Cetartiodactyla, and Carnivora present a prime example for such enigmatic affiliations. We performed whole-genome screenings for phylogenetically diagnostic retrotransposon insertions involving the representatives bat (Chiroptera), horse (Perissodactyla), cow (Cetartiodactyla), and dog (Carnivora), and extracted among 162 thousand preselected cases 102 virtually noise-free, phylogenetically informative retroelements to draw a complete picture of the highly complex evolutionary relations within Laurasiatheria. All possible evolutionary scenarios received considerable retrotransposon support, leaving us with a network of affiliations. However, the Cetartiodactyla-Carnivora relationship as well as the basal position of Chiroptera and an ancestral laurasiatherian hybridization process did exhibit some very clear, distinct signals. The significant accordance of retrotransposon presence/absence patterns and flanking nucleotide changes suggest an important influence of mosaic genome structures in the reconstruction of species histories.

  10. Mitogen-activated protein kinase signal transduction and DNA repair network are involved in aluminum-induced DNA damage and adaptive response in root cells of Allium cepa L.

    PubMed Central

    Panda, Brahma B.; Achary, V. Mohan M.

    2014-01-01

    In the current study, we studied the role of signal transduction in aluminum (Al3+)-induced DNA damage and adaptive response in root cells of Allium cepa L. The root cells in planta were treated with Al3+ (800 μM) for 3 h without or with 2 h pre-treatment of inhibitors of mitogen-activated protein kinase (MAPK), and protein phosphatase. Also, root cells in planta were conditioned with Al3+ (10 μM) for 2 h and then subjected to genotoxic challenge of ethyl methane sulfonate (EMS; 5 mM) for 3 h without or with the pre-treatment of the aforementioned inhibitors as well as the inhibitors of translation, transcription, DNA replication and repair. At the end of treatments, roots cells were assayed for cell death and/or DNA damage. The results revealed that Al3+ (800 μM)-induced significant DNA damage and cell death. On the other hand, conditioning with low dose of Al3+ induced adaptive response conferring protection of root cells from genotoxic stress caused by EMS-challenge. Pre-treatment of roots cells with the chosen inhibitors prior to Al3+-conditioning prevented or reduced the adaptive response to EMS genotoxicity. The results of this study suggested the involvement of MAPK and DNA repair network underlying Al-induced DNA damage and adaptive response to genotoxic stress in root cells of A. cepa. PMID:24926302

  11. Molecular signaling involving intrinsically disordered proteins in prostate cancer

    PubMed Central

    Russo, Anna; Manna, Sara La; Novellino, Ettore; Malfitano, Anna Maria; Marasco, Daniela

    2016-01-01

    Investigations on cellular protein interaction networks (PINs) reveal that proteins that constitute hubs in a PIN are notably enriched in Intrinsically Disordered Proteins (IDPs) compared to proteins that constitute edges, highlighting the role of IDPs in signaling pathways. Most IDPs rapidly undergo disorder-to-order transitions upon binding to their biological targets to perform their function. Conformational dynamics enables IDPs to be versatile and to interact with a broad range of interactors under normal physiological conditions where their expression is tightly modulated. IDPs are involved in many cellular processes such as cellular signaling, transcriptional regulation, and splicing; thus, their high-specificity/low-affinity interactions play crucial roles in many human diseases including cancer. Prostate cancer (PCa) is one of the leading causes of cancer-related mortality in men worldwide. Therefore, identifying molecular mechanisms of the oncogenic signaling pathways that are involved in prostate carcinogenesis is crucial. In this review, we focus on the aspects of cellular pathways leading to PCa in which IDPs exert a primary role. PMID:27212129

  12. Hepatitis C virus infection protein network

    PubMed Central

    de Chassey, B; Navratil, V; Tafforeau, L; Hiet, M S; Aublin-Gex, A; Agaugué, S; Meiffren, G; Pradezynski, F; Faria, B F; Chantier, T; Le Breton, M; Pellet, J; Davoust, N; Mangeot, P E; Chaboud, A; Penin, F; Jacob, Y; Vidalain, P O; Vidal, M; André, P; Rabourdin-Combe, C; Lotteau, V

    2008-01-01

    A proteome-wide mapping of interactions between hepatitis C virus (HCV) and human proteins was performed to provide a comprehensive view of the cellular infection. A total of 314 protein–protein interactions between HCV and human proteins was identified by yeast two-hybrid and 170 by literature mining. Integration of this data set into a reconstructed human interactome showed that cellular proteins interacting with HCV are enriched in highly central and interconnected proteins. A global analysis on the basis of functional annotation highlighted the enrichment of cellular pathways targeted by HCV. A network of proteins associated with frequent clinical disorders of chronically infected patients was constructed by connecting the insulin, Jak/STAT and TGFβ pathways with cellular proteins targeted by HCV. CORE protein appeared as a major perturbator of this network. Focal adhesion was identified as a new function affected by HCV, mainly by NS3 and NS5A proteins. PMID:18985028

  13. Nonlinear signaling on biological networks: The role of stochasticity and spectral clustering

    NASA Astrophysics Data System (ADS)

    Hernandez-Hernandez, Gonzalo; Myers, Jesse; Alvarez-Lacalle, Enrique; Shiferaw, Yohannes

    2017-03-01

    Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector. However, at high coupling, transitions are insensitive to network structure since the network can be activated by stochastic transitions of a few nodes. Thus this work identifies important features of biological signaling networks that may underlie their biological

  14. Prion protein induced signaling cascades in monocytes

    SciTech Connect

    Krebs, Bjarne; Dorner-Ciossek, Cornelia; Vassallo, Neville; Herms, Jochen; Kretzschmar, Hans A. . E-mail: Hans.Kretzschmar@med.uni-muenchen.de

    2006-02-03

    Prion proteins play a central role in transmission and pathogenesis of transmissible spongiform encephalopathies. The cellular prion protein (PrP{sup C}), whose physiological function remains elusive, is anchored to the surface of a variety of cell types including neurons and cells of the lymphoreticular system. In this study, we investigated the response of a mouse monocyte/macrophage cell line to exposure with PrP{sup C} fusion proteins synthesized with a human Fc-tag. PrP{sup C} fusion proteins showed an attachment to the surface of monocyte/macrophages in nanomolar concentrations. This was accompanied by an increase of cellular tyrosine phosphorylation as a result of activated signaling pathways. Detailed investigations exhibited activation of downstream pathways through a stimulation with PrP fusion proteins, which include phosphorylation of ERK{sub 1,2} and Akt kinase. Macrophages opsonize and present antigenic structures, contact lymphocytes, and deliver cytokines. The findings reported here may become the basis of understanding the molecular function of PrP{sup C} in monocytes and macrophages.

  15. ATR inhibition rewires cellular signaling networks induced by replication stress.

    PubMed

    Wagner, Sebastian A; Oehler, Hannah; Voigt, Andrea; Dalic, Denis; Freiwald, Anja; Serve, Hubert; Beli, Petra

    2016-02-01

    The slowing down or stalling of replication forks is commonly known as replication stress and arises from multiple causes such as DNA lesions, nucleotide depletion, RNA-DNA hybrids, and oncogene activation. The ataxia telangiectasia and Rad3-related kinase (ATR) plays an essential role in the cellular response to replication stress and inhibition of ATR has emerged as therapeutic strategy for the treatment of cancers that exhibit high levels of replication stress. However, the cellular signaling induced by replication stress and the substrate spectrum of ATR has not been systematically investigated. In this study, we employed quantitative MS-based proteomics to define the cellular signaling after nucleotide depletion-induced replication stress and replication fork collapse following ATR inhibition. We demonstrate that replication stress results in increased phosphorylation of a subset of proteins, many of which are involved in RNA splicing and transcription and have previously not been associated with the cellular replication stress response. Furthermore, our data reveal the ATR-dependent phosphorylation following replication stress and discover novel putative ATR target sites on MCM6, TOPBP1, RAD51AP1, and PSMD4. We establish that ATR inhibition rewires cellular signaling networks induced by replication stress and leads to the activation of the ATM-driven double-strand break repair signaling.

  16. NAPS: Network Analysis of Protein Structures

    PubMed Central

    Chakrabarty, Broto; Parekh, Nita

    2016-01-01

    Traditionally, protein structures have been analysed by the secondary structure architecture and fold arrangement. An alternative approach that has shown promise is modelling proteins as a network of non-covalent interactions between amino acid residues. The network representation of proteins provide a systems approach to topological analysis of complex three-dimensional structures irrespective of secondary structure and fold type and provide insights into structure-function relationship. We have developed a web server for network based analysis of protein structures, NAPS, that facilitates quantitative and qualitative (visual) analysis of residue–residue interactions in: single chains, protein complex, modelled protein structures and trajectories (e.g. from molecular dynamics simulations). The user can specify atom type for network construction, distance range (in Å) and minimal amino acid separation along the sequence. NAPS provides users selection of node(s) and its neighbourhood based on centrality measures, physicochemical properties of amino acids or cluster of well-connected residues (k-cliques) for further analysis. Visual analysis of interacting domains and protein chains, and shortest path lengths between pair of residues are additional features that aid in functional analysis. NAPS support various analyses and visualization views for identifying functional residues, provide insight into mechanisms of protein folding, domain-domain and protein–protein interactions for understanding communication within and between proteins. URL:http://bioinf.iiit.ac.in/NAPS/. PMID:27151201

  17. Protein-protein interaction network of celiac disease

    PubMed Central

    Zamanian Azodi, Mona; Peyvandi, Hassan; Rostami-Nejad, Mohammad; Safaei, Akram; Rostami, Kamran; Vafaee, Reza; Heidari, Mohammadhossein; Hosseini, Mostafa; Zali, Mohammad Reza

    2016-01-01

    Aim: The aim of this study is to investigate the Protein-Protein Interaction Network of Celiac Disease. Background: Celiac disease (CD) is an autoimmune disease with susceptibility of individuals to gluten of wheat, rye and barley. Understanding the molecular mechanisms and involved pathway may lead to the development of drug target discovery. The protein interaction network is one of the supportive fields to discover the pathogenesis biomarkers for celiac disease. Material and methods: In the present study, we collected the articles that focused on the proteomic data in celiac disease. According to the gene expression investigations of these articles, 31 candidate proteins were selected for this study. The networks of related differentially expressed protein were explored using Cytoscape 3.3 and the PPI analysis methods such as MCODE and ClueGO. Results: According to the network analysis Ubiquitin C, Heat shock protein 90kDa alpha (cytosolic and Grp94); class A, B and 1 member, Heat shock 70kDa protein, and protein 5 (glucose-regulated protein, 78kDa), T-complex, Chaperon in containing TCP1; subunit 7 (beta) and subunit 4 (delta) and subunit 2 (beta), have been introduced as hub-bottlnecks proteins. HSP90AA1, MKKS, EZR, HSPA14, APOB and CAD have been determined as seed proteins. Conclusion: Chaperons have a bold presentation in curtail area in network therefore these key proteins beside the other hub-bottlneck proteins may be a suitable candidates biomarker panel for diagnosis, prognosis and treatment processes in celiac disease. PMID:27895852

  18. Dopamine D1 signaling organizes network dynamics underlying working memory

    PubMed Central

    Roffman, Joshua L.; Tanner, Alexandra S.; Eryilmaz, Hamdi; Rodriguez-Thompson, Anais; Silverstein, Noah J.; Ho, New Fei; Nitenson, Adam Z.; Chonde, Daniel B.; Greve, Douglas N.; Abi-Dargham, Anissa; Buckner, Randy L.; Manoach, Dara S.; Rosen, Bruce R.; Hooker, Jacob M.; Catana, Ciprian

    2016-01-01

    Local prefrontal dopamine signaling supports working memory by tuning pyramidal neurons to task-relevant stimuli. Enabled by simultaneous positron emission tomography–magnetic resonance imaging (PET-MRI), we determined whether neuromodulatory effects of dopamine scale to the level of cortical networks and coordinate their interplay during working memory. Among network territories, mean cortical D1 receptor densities differed substantially but were strongly interrelated, suggesting cross-network regulation. Indeed, mean cortical D1 density predicted working memory–emergent decoupling of the frontoparietal and default networks, which respectively manage task-related and internal stimuli. In contrast, striatal D1 predicted opposing effects within these two networks but no between-network effects. These findings specifically link cortical dopamine signaling to network crosstalk that redirects cognitive resources to working memory, echoing neuromodulatory effects of D1 signaling on the level of cortical microcircuits. PMID:27386561

  19. Interaction networks: from protein functions to drug discovery. A review.

    PubMed

    Chautard, E; Thierry-Mieg, N; Ricard-Blum, S

    2009-06-01

    Most genes, proteins and other components carry out their functions within a complex network of interactions and a single molecule can affect a wide range of other cell components. A global, integrative, approach has been developed for several years, including protein-protein interaction networks (interactomes). In this review, we describe the high-throughput methods used to identify new interactions and to build large interaction datasets. The minimum information required for reporting a molecular interaction experiment (MIMIx) has been defined as a standard for storing data in publicly available interaction databases. Several examples of interaction networks from molecular machines (proteasome) or organelles (phagosome, mitochondrion) to whole organisms (viruses, bacteria, yeast, fly, and worm) are given and attempts to cover the entire human interaction network are discussed. The methods used to perform the topological analysis of interaction networks and to extract biological information from them are presented. These investigations have provided clues on protein functions, signalling and metabolic pathways, and physiological processes, unraveled the molecular basis of some diseases (cancer, infectious diseases), and will be very useful to identify new therapeutic targets and for drug discovery. A major challenge is now to integrate data from different sources (interactome, transcriptome, phenome, localization) to switch from static to dynamic interaction networks. The merging of a viral interactome and the human interactome has been used to simulate viral infection, paving the way for future studies aiming at providing molecular basis of human diseases.

  20. Evolution of protein-protein interaction networks in yeast.

    PubMed

    Schoenrock, Andrew; Burnside, Daniel; Moteshareie, Houman; Pitre, Sylvain; Hooshyar, Mohsen; Green, James R; Golshani, Ashkan; Dehne, Frank; Wong, Alex

    2017-01-01

    Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms.

  1. Characterization and modeling of protein protein interaction networks

    NASA Astrophysics Data System (ADS)

    Colizza, Vittoria; Flammini, Alessandro; Maritan, Amos; Vespignani, Alessandro

    2005-07-01

    The recent availability of high-throughput gene expression and proteomics techniques has created an unprecedented opportunity for a comprehensive study of the structure and dynamics of many biological networks. Global proteomic interaction data, in particular, are synthetically represented as undirected networks exhibiting features far from the random paradigm which has dominated past effort in network theory. This evidence, along with the advances in the theory of complex networks, has triggered an intense research activity aimed at exploiting the evolutionary and biological significance of the resulting network's topology. Here we present a review of the results obtained in the characterization and modeling of the yeast Saccharomyces Cerevisiae protein interaction networks obtained with different experimental techniques. We provide a comparative assessment of the topological properties and discuss possible biases in interaction networks obtained with different techniques. We report on dynamical models based on duplication mechanisms that cast the protein interaction networks in the family of dynamically growing complex networks. Finally, we discuss various results and analysis correlating the networks’ topology with the biological function of proteins.

  2. Signal Destruction Tunes the Zone of Activation in Spatially Distributed Signaling Networks.

    PubMed

    Silva, Kalinga Pavan; Chellamuthu, Prithiviraj; Boedicker, James Q

    2017-03-14

    Diverse microbial communities coordinate group behaviors through signal exchange, such as the exchange of acyl-homoserine lactones (AHLs) by Gram-negative bacteria. Cellular communication is prone to interference by neighboring microbes. One mechanism of interference is signal destruction through the production of an enzyme that cleaves the signaling molecule. Here we examine the ability of one such interference enzyme, AiiA, to modulate signal propagation in a spatially distributed system of bacteria. We have developed an experimental assay to measure signal transduction and implement a theoretical model of signaling dynamics to predict how the system responds to interference. We show that titration of an interfering strain into a signaling network tunes the spatial range of activation over the centimeter length scale, quantifying the robustness of the signaling network to signal destruction and demonstrating the ability to program systems-level responses of spatially heterogeneous cellular networks.

  3. Bio-basis function neural networks in protein data mining.

    PubMed

    Yang, Zheng Rong; Hamer, Rebecca

    2007-01-01

    Accurately identifying functional sites in proteins is one of the most important topics in bioinformatics and systems biology. In bioinformatics, identifying protease cleavage sites in protein sequences can aid drug/inhibitor design. In systems biology, post-translational protein-protein interaction activity is one of the major components for analyzing signaling pathway activities. Determining functional sites using laboratory experiments are normally time consuming and expensive. Computer programs have therefore been widely used for this kind of task. Mining protein sequence data using computer programs covers two major issues: 1) discovering how amino acid specificity affects functional sites and 2) discovering what amino acid specificity is. Both need a proper coding mechanism prior to using a proper machine learning algorithm. The development of the bio-basis function neural network (BBFNN) has made a new way for protein sequence data mining. The bio-basis function used in BBFNN is biologically sound in well coding biological information in protein sequences, i.e. well measuring the similarity between protein sequences. BBFNN has therefore been outperforming conventional neural networks in many subjects of protein sequence data mining from protease cleavage site prediction to disordered protein identification. This review focuses on the variants of BBFNN and their applications in mining protein sequence data.

  4. The Robustness of a Signaling Complex to Domain Rearrangements Facilitates Network Evolution

    PubMed Central

    Sato, Paloma M.; Yoganathan, Kogulan; Jung, Jae H.; Peisajovich, Sergio G.

    2014-01-01

    The rearrangement of protein domains is known to have key roles in the evolution of signaling networks and, consequently, is a major tool used to synthetically rewire networks. However, natural mutational events leading to the creation of proteins with novel domain combinations, such as in frame fusions followed by domain loss, retrotranspositions, or translocations, to name a few, often simultaneously replace pre-existing genes. Thus, while proteins with new domain combinations may establish novel network connections, it is not clear how the concomitant deletions are tolerated. We investigated the mechanisms that enable signaling networks to tolerate domain rearrangement-mediated gene replacements. Using as a model system the yeast mitogen activated protein kinase (MAPK)-mediated mating pathway, we analyzed 92 domain-rearrangement events affecting 11 genes. Our results indicate that, while domain rearrangement events that result in the loss of catalytic activities within the signaling complex are not tolerated, domain rearrangements can drastically alter protein interactions without impairing function. This suggests that signaling complexes can maintain function even when some components are recruited to alternative sites within the complex. Furthermore, we also found that the ability of the complex to tolerate changes in interaction partners does not depend on long disordered linkers that often connect domains. Taken together, our results suggest that some signaling complexes are dynamic ensembles with loose spatial constraints that could be easily re-shaped by evolution and, therefore, are ideal targets for cellular engineering. PMID:25490747

  5. Implications of mitogen-activated protein kinase signaling in glioma.

    PubMed

    Pandey, Vimal; Bhaskara, Vasantha Kumar; Babu, Phanithi Prakash

    2016-02-01

    Gliomas are the most common primary central nervous system tumors. Gliomas originate from astrocytes, oligodendrocytes, and neural stem cells or their precursors. According to WHO classification, gliomas are classified into four different malignant grades ranging from grade I to grade IV based on histopathological features and related molecular aberrations. The induction and maintenance of these tumors can be attributed largely to aberrant signaling networks. In this regard, the mitogen-activated protein kinase (MAPK) network has been widely studied and is reported to be severely altered in glial tumors. Mutations in MAPK pathways most frequently affect RAS and B-RAF in the ERK, c-Jun N-terminal kinase (JNK), and p38 pathways leading to malignant transformation. Also, it is linked to both inherited and sequential accumulations of mutations that control receptor tyrosine kinase (RTK)-activated signal transduction pathways, cell cycle growth arrest pathways, and nonresponsive cell death pathways. Genetic alterations that modulate RTK signaling can also alter several downstream pathways, including RAS-mediated MAP kinases along with JNK pathways, which ultimately regulate cell proliferation and cell death. The present review focuses on recent literature regarding important deregulations in the RTK-activated MAPK pathway during gliomagenesis and progression.

  6. Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction

    NASA Astrophysics Data System (ADS)

    Yeger-Lotem, Esti; Sattath, Shmuel; Kashtan, Nadav; Itzkovitz, Shalev; Milo, Ron; Pinter, Ron Y.; Alon, Uri; Margalit, Hanah

    2004-04-01

    Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.

  7. Heat dissipation guides activation in signaling proteins

    PubMed Central

    Weber, Jeffrey K.; Shukla, Diwakar; Pande, Vijay S.

    2015-01-01

    Life is fundamentally a nonequilibrium phenomenon. At the expense of dissipated energy, living things perform irreversible processes that allow them to propagate and reproduce. Within cells, evolution has designed nanoscale machines to do meaningful work with energy harnessed from a continuous flux of heat and particles. As dictated by the Second Law of Thermodynamics and its fluctuation theorem corollaries, irreversibility in nonequilibrium processes can be quantified in terms of how much entropy such dynamics produce. In this work, we seek to address a fundamental question linking biology and nonequilibrium physics: can the evolved dissipative pathways that facilitate biomolecular function be identified by their extent of entropy production in general relaxation processes? We here synthesize massive molecular dynamics simulations, Markov state models (MSMs), and nonequilibrium statistical mechanical theory to probe dissipation in two key classes of signaling proteins: kinases and G-protein–coupled receptors (GPCRs). Applying machinery from large deviation theory, we use MSMs constructed from protein simulations to generate dynamics conforming to positive levels of entropy production. We note the emergence of an array of peaks in the dynamical response (transient analogs of phase transitions) that draw the proteins between distinct levels of dissipation, and we see that the binding of ATP and agonist molecules modifies the observed dissipative landscapes. Overall, we find that dissipation is tightly coupled to activation in these signaling systems: dominant entropy-producing trajectories become localized near important barriers along known biological activation pathways. We go on to classify an array of equilibrium and nonequilibrium molecular switches that harmonize to promote functional dynamics. PMID:26240354

  8. Network component analysis: reconstruction of regulatory signals in biological systems.

    PubMed

    Liao, James C; Boscolo, Riccardo; Yang, Young-Lyeol; Tran, Linh My; Sabatti, Chiara; Roychowdhury, Vwani P

    2003-12-23

    High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component analysis, ignore the underlying network structures and provide decompositions based purely on a priori statistical constraints on the computed component signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. Here, we develop a method, called network component analysis, for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. The a priori network structure information is first tested for compliance with a set of identifiability criteria. For networks that satisfy the criteria, the signals from the regulatory nodes and their strengths of influence on each output node can be faithfully reconstructed. This method is first validated experimentally by using the absorbance spectra of a network of various hemoglobin species. The method is then applied to microarray data generated from yeast Saccharamyces cerevisiae and the activities of various transcription factors during cell cycle are reconstructed by using recently discovered connectivity information for the underlying transcriptional regulatory networks.

  9. Teaching resources. Introduction: Overview of pathways and networks and GPCR signaling.

    PubMed

    Iyengar, Ravi

    2005-02-08

    This Teaching Resource provides the overview to the course "Cell Signaling Systems: A Course for Graduate Students" and lays out the general principles that can be deduced from the current understanding of the organization of cell signaling pathways and networks and how information flows through these pathways and networks. In addition, the lecture provides an overview of G protein-coupled receptor (GPCR) signaling. A description of the lecture, along with a set of slides (http://stke.sciencemag.org/cgi/content/full/sigtrans;2005/270/tr4/DC1) used to present this information, is provided.

  10. The N and C Termini of ZO-1 Are Surrounded by Distinct Proteins and Functional Protein Networks*

    PubMed Central

    Van Itallie, Christina M.; Aponte, Angel; Tietgens, Amber Jean; Gucek, Marjan; Fredriksson, Karin; Anderson, James Melvin

    2013-01-01

    The proteins and functional protein networks of the tight junction remain incompletely defined. Among the currently known proteins are barrier-forming proteins like occludin and the claudin family; scaffolding proteins like ZO-1; and some cytoskeletal, signaling, and cell polarity proteins. To define a more complete list of proteins and infer their functional implications, we identified the proteins that are within molecular dimensions of ZO-1 by fusing biotin ligase to either its N or C terminus, expressing these fusion proteins in Madin-Darby canine kidney epithelial cells, and purifying and identifying the resulting biotinylated proteins by mass spectrometry. Of a predicted proteome of ∼9000, we identified more than 400 proteins tagged by biotin ligase fused to ZO-1, with both identical and distinct proteins near the N- and C-terminal ends. Those proximal to the N terminus were enriched in transmembrane tight junction proteins, and those proximal to the C terminus were enriched in cytoskeletal proteins. We also identified many unexpected but easily rationalized proteins and verified partial colocalization of three of these proteins with ZO-1 as examples. In addition, functional networks of interacting proteins were tagged, such as the basolateral but not apical polarity network. These results provide a rich inventory of proteins and potential novel insights into functions and protein networks that should catalyze further understanding of tight junction biology. Unexpectedly, the technique demonstrates high spatial resolution, which could be generally applied to defining other subcellular protein compartmentalization. PMID:23553632

  11. Geometric de-noising of protein-protein interaction networks.

    PubMed

    Kuchaiev, Oleksii; Rasajski, Marija; Higham, Desmond J; Przulj, Natasa

    2009-08-01

    Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H), tandem affinity purification (TAP) and other high-throughput methods for protein-protein interaction (PPI) detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise.We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.

  12. Radar signal categorization using a neural network

    NASA Technical Reports Server (NTRS)

    Anderson, James A.; Gately, Michael T.; Penz, P. Andrew; Collins, Dean R.

    1991-01-01

    Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications.

  13. Hub-activated signal transmission in complex networks

    NASA Astrophysics Data System (ADS)

    Jahnke, Sven; Memmesheimer, Raoul-Martin; Timme, Marc

    2014-03-01

    A wide range of networked systems exhibit highly connected nodes (hubs) as prominent structural elements. The functional roles of hubs in the collective nonlinear dynamics of many such networks, however, are not well understood. Here, we propose that hubs in neural circuits may activate local signal transmission along sequences of specific subnetworks. Intriguingly, in contrast to previous suggestions of the functional roles of hubs, here, not the hubs themselves, but nonhub subnetworks transfer the signals. The core mechanism relies on hubs and nonhubs providing activating feedback to each other. It may, thus, induce the propagation of specific pulse and rate signals in neuronal and other communication networks.

  14. Bio-inspired signal transduction with heterogeneous networks of nanoscillators

    NASA Astrophysics Data System (ADS)

    Cervera, Javier; Manzanares, José A.; Mafé, Salvador

    2012-02-01

    Networks of single-electron transistors mimic some of the essential properties of neuron populations, because weak electrical signals trigger network oscillations with a frequency proportional to the input signal. Input potentials representing the pixel gray level of a grayscale image can then be converted into rhythms and the image can be recovered from these rhythms. Networks of non-identical nanoscillators complete the noisy transduction more reliably than identical ones. These results are important for signal processing schemes and could support recent studies suggesting that neuronal variability enhances the processing of biological information.

  15. Regulator of G protein signaling proteins differentially modulate signaling of μ and δ opioid receptors

    PubMed Central

    Xie, Zhihua; Li, Zhisong; Guo, Lei; Ye, Caiying; Li, Juan; Yu, Xiaoli; Yang, Huifen; Wang, Yulin; Chen, Chongguang; Zhang, Dechang; Liu-Chen, Lee-Yuan

    2009-01-01

    Effects of regulator of G protein signaling (RGS) proteins on μ and δ opioid receptors were investigated in HEK293 cells. Co-expression of RGS1, RGS2, RGS4, RGS9, RGS10 or RGS19 (Gα-interacting protein (GAIP)) significantly reduced [Tyr-D-Ala-Gly-N-methyl-Phe-Gly-ol]-Enkephalin (DAMGO)-induced inhibition of adenylyl cyclase (AC) mediated by μ opioid receptor, but only RGS9 decreased the effects of [Tyr-D-Pen-Gly-p-Chloro-Phe-D-Pen]-Enkephalin (DPDPE) mediated by δ opioid receptor. When C-tails of the receptors were exchanged (μ/δC and δ/μC chimeras), RGS proteins decreased δ/μC-mediated AC inhibition, but none had significant effects on that via μ/δC receptor. Thus, the C-terminal domains of the receptors are critical for the differential effects of RGS proteins, which may be due to differences in receptor - G protein - RGS protein interactions in signaling complexes. PMID:17433292

  16. An extended signal control strategy for urban network traffic flow

    NASA Astrophysics Data System (ADS)

    Yan, Fei; Tian, Fuli; Shi, Zhongke

    2016-03-01

    Traffic flow patterns are in general repeated on a daily or weekly basis. To improve the traffic conditions by using the inherent repeatability of traffic flow, a novel signal control strategy for urban networks was developed via iterative learning control (ILC) approach. Rigorous analysis shows that the proposed learning control method can guarantee the asymptotic convergence. The impacts of the ILC-based signal control strategy on the macroscopic fundamental diagram (MFD) were analyzed by simulations on a test road network. The results show that the proposed ILC strategy can evenly distribute the accumulation in the network and improve the network mobility.

  17. Interface-Resolved Network of Protein-Protein Interactions

    PubMed Central

    Johnson, Margaret E.; Hummer, Gerhard

    2013-01-01

    We define an interface-interaction network (IIN) to capture the specificity and competition between protein-protein interactions (PPI). This new type of network represents interactions between individual interfaces used in functional protein binding and thereby contains the detail necessary to describe the competition and cooperation between any pair of binding partners. Here we establish a general framework for the construction of IINs that merges computational structure-based interface assignment with careful curation of available literature. To complement limited structural data, the inclusion of biochemical data is critical for achieving the accuracy and completeness necessary to analyze the specificity and competition between the protein interactions. Firstly, this procedure provides a means to clarify the information content of existing data on purported protein interactions and to remove indirect and spurious interactions. Secondly, the IIN we have constructed here for proteins involved in clathrin-mediated endocytosis (CME) exhibits distinctive topological properties. In contrast to PPI networks with their global and relatively dense connectivity, the fragmentation of the IIN into distinctive network modules suggests that different functional pressures act on the evolution of its topology. Large modules in the IIN are formed by interfaces sharing specificity for certain domain types, such as SH3 domains distributed across different proteins. The shared and distinct specificity of an interface is necessary for effective negative and positive design of highly selective binding targets. Lastly, the organization of detailed structural data in a network format allows one to identify pathways of specific binding interactions and thereby predict effects of mutations at specific surfaces on a protein and of specific binding inhibitors, as we explore in several examples. Overall, the endocytosis IIN is remarkably complex and rich in features masked in the coarser

  18. Information routing driven by background chatter in a signaling network.

    PubMed

    Domedel-Puig, Núria; Rué, Pau; Pons, Antonio J; García-Ojalvo, Jordi

    2011-12-01

    Living systems are capable of processing multiple sources of information simultaneously. This is true even at the cellular level, where not only coexisting signals stimulate the cell, but also the presence of fluctuating conditions is significant. When information is received by a cell signaling network via one specific input, the existence of other stimuli can provide a background activity -or chatter- that may affect signal transmission through the network and, therefore, the response of the cell. Here we study the modulation of information processing by chatter in the signaling network of a human cell, specifically, in a Boolean model of the signal transduction network of a fibroblast. We observe that the level of external chatter shapes the response of the system to information carrying signals in a nontrivial manner, modulates the activity levels of the network outputs, and effectively determines the paths of information flow. Our results show that the interactions and node dynamics, far from being random, confer versatility to the signaling network and allow transitions between different information-processing scenarios.

  19. Predicting disease-related proteins based on clique backbone in protein-protein interaction network.

    PubMed

    Yang, Lei; Zhao, Xudong; Tang, Xianglong

    2014-01-01

    Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.

  20. Analysis and logical modeling of biological signaling transduction networks

    NASA Astrophysics Data System (ADS)

    Sun, Zhongyao

    The study of network theory and its application span across a multitude of seemingly disparate fields of science and technology: computer science, biology, social science, linguistics, etc. It is the intrinsic similarities embedded in the entities and the way they interact with one another in these systems that link them together. In this dissertation, I present from both the aspect of theoretical analysis and the aspect of application three projects, which primarily focus on signal transduction networks in biology. In these projects, I assembled a network model through extensively perusing literature, performed model-based simulations and validation, analyzed network topology, and proposed a novel network measure. The application of network modeling to the system of stomatal opening in plants revealed a fundamental question about the process that has been left unanswered in decades. The novel measure of the redundancy of signal transduction networks with Boolean dynamics by calculating its maximum node-independent elementary signaling mode set accurately predicts the effect of single node knockout in such signaling processes. The three projects as an organic whole advance the understanding of a real system as well as the behavior of such network models, giving me an opportunity to take a glimpse at the dazzling facets of the immense world of network science.

  1. Integrative Signaling Networks of Membrane Guanylate Cyclases: Biochemistry and Physiology

    PubMed Central

    Sharma, Rameshwar K.; Duda, Teresa; Makino, Clint L.

    2016-01-01

    This monograph presents a historical perspective of cornerstone developments on the biochemistry and physiology of mammalian membrane guanylate cyclases (MGCs), highlighting contributions made by the authors and their collaborators. Upon resolution of early contentious studies, cyclic GMP emerged alongside cyclic AMP, as an important intracellular second messenger for hormonal signaling. However, the two signaling pathways differ in significant ways. In the cyclic AMP pathway, hormone binding to a G protein coupled receptor leads to stimulation or inhibition of an adenylate cyclase, whereas the cyclic GMP pathway dispenses with intermediaries; hormone binds to an MGC to affect its activity. Although the cyclic GMP pathway is direct, it is by no means simple. The modular design of the molecule incorporates regulation by ATP binding and phosphorylation. MGCs can form complexes with Ca2+-sensing subunits that either increase or decrease cyclic GMP synthesis, depending on subunit identity. In some systems, co-expression of two Ca2+ sensors, GCAP1 and S100B with ROS-GC1 confers bimodal signaling marked by increases in cyclic GMP synthesis when intracellular Ca2+ concentration rises or falls. Some MGCs monitor or are modulated by carbon dioxide via its conversion to bicarbonate. One MGC even functions as a thermosensor as well as a chemosensor; activity reaches a maximum with a mild drop in temperature. The complexity afforded by these multiple limbs of operation enables MGC networks to perform transductions traditionally reserved for G protein coupled receptors and Transient Receptor Potential (TRP) ion channels and to serve a diverse array of functions, including control over cardiac vasculature, smooth muscle relaxation, blood pressure regulation, cellular growth, sensory transductions, neural plasticity and memory. PMID:27695398

  2. Elementary signaling modes predict the essentiality of signal transduction network components

    PubMed Central

    2011-01-01

    Background Understanding how signals propagate through signaling pathways and networks is a central goal in systems biology. Quantitative dynamic models help to achieve this understanding, but are difficult to construct and validate because of the scarcity of known mechanistic details and kinetic parameters. Structural and qualitative analysis is emerging as a feasible and useful alternative for interpreting signal transduction. Results In this work, we present an integrative computational method for evaluating the essentiality of components in signaling networks. This approach expands an existing signaling network to a richer representation that incorporates the positive or negative nature of interactions and the synergistic behaviors among multiple components. Our method simulates both knockout and constitutive activation of components as node disruptions, and takes into account the possible cascading effects of a node's disruption. We introduce the concept of elementary signaling mode (ESM), as the minimal set of nodes that can perform signal transduction independently. Our method ranks the importance of signaling components by the effects of their perturbation on the ESMs of the network. Validation on several signaling networks describing the immune response of mammals to bacteria, guard cell abscisic acid signaling in plants, and T cell receptor signaling shows that this method can effectively uncover the essentiality of components mediating a signal transduction process and results in strong agreement with the results of Boolean (logical) dynamic models and experimental observations. Conclusions This integrative method is an efficient procedure for exploratory analysis of large signaling and regulatory networks where dynamic modeling or experimental tests are impractical. Its results serve as testable predictions, provide insights into signal transduction and regulatory mechanisms and can guide targeted computational or experimental follow-up studies. The

  3. Characterization of essential proteins based on network topology in proteins interaction networks

    NASA Astrophysics Data System (ADS)

    Bakar, Sakhinah Abu; Taheri, Javid; Zomaya, Albert Y.

    2014-06-01

    The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/sub-graph in the network.

  4. Cellular prion protein transduces neuroprotective signals

    PubMed Central

    Chiarini, Luciana B.; Freitas, Adriana R.O.; Zanata, Silvio M.; Brentani, Ricardo R.; Martins, Vilma R.; Linden, Rafael

    2002-01-01

    To test for a role for the cellular prion protein (PrPc) in cell death, we used a PrPc-binding peptide. Retinal explants from neonatal rats or mice were kept in vitro for 24 h, and anisomycin (ANI) was used to induce apoptosis. The peptide activated both cAMP/protein kinase A (PKA) and Erk pathways, and partially prevented cell death induced by ANI in explants from wild-type rodents, but not from PrPc-null mice. Neuroprotection was abolished by treatment with phosphatidylinositol-specific phospholipase C, with human peptide 106–126, with certain antibodies to PrPc or with a PKA inhibitor, but not with a MEK/Erk inhibitor. In contrast, antibodies to PrPc that increased cAMP also induced neuroprotection. Thus, engagement of PrPc transduces neuroprotective signals through a cAMP/PKA-dependent pathway. PrPc may function as a trophic receptor, the activation of which leads to a neuroprotective state. PMID:12093733

  5. Neural network analysis of oxygenation signals in infants during sleep.

    PubMed

    Taktak, A F; Simpson, S; Patel, S; Meyer, G

    2000-08-01

    The use of artificial neural networks (ANNs) to interpret sleep monitoring signals is described. Recordings from ten infants with apparent life threatening episodes were assigned into training feedforward R-PROP networks. In order to separate good signal from artefact, 60 second time frames of SaO2 and TcPO2 signals were processed and the mean and standard deviation values were used as inputs to the networks. Intra-human errors were minimized using this method whilst inter-human errors remained significant. To decrease the latter, the number of hidden units was increased to eight. Sensitivity figures of the SaO2 network were 0.93 and 0.9 for the training and test sets respectively whilst the specificity figures were 0.7 and 0.65 respectively. For the TcPO2 signals the above figures were 0.92, 0.85, 0.77 and 0.61 respectively.

  6. Information transduction capacity of noisy biochemical signaling networks

    PubMed Central

    Cheong, Raymond; Rhee, Alex; Wang, Chiaochun Joanne; Nemenman, Ilya; Levchenko, Andre

    2014-01-01

    Molecular noise restricts the ability of an individual cell to resolve input signals of different strengths and gather information about the external environment. Transmitting information through complex signaling networks with redundancies can overcome this limitation. We developed an integrative theoretical and experimental framework, based on the formalism of information theory, to quantitatively predict and measure the amount of information transduced by molecular and cellular networks. Analyzing tumor necrosis factor (TNF) signaling revealed that individual TNF signaling pathways transduce information sufficient for accurate binary decisions, and an upstream bottleneck limits the information gained via multiple pathways together. Negative feedback to this bottleneck could both alleviate and enhance its limiting effect, despite decreasing noise. Bottlenecks likewise constrain information attained by networks signaling through multiple genes or cells. PMID:21921160

  7. Enhanced photoacoustic signal from DNA assembled gold nanoparticle networks

    NASA Astrophysics Data System (ADS)

    Buchkremer, A.; Beckmann, M. F.; Linn, M.; Ruff, J.; Rosencrantz, R. R.; von Plessen, G.; Schmitz, G.; Simon, U.

    2014-12-01

    We report an experimental finding of photoacoustic signal enhancement from finite sized DNA-gold nanoparticle networks. We synthesized DNA-functionalized hollow and solid gold nanospheres (AuNS) to form finite sized networks, which were characterized by means of optical extinction spectroscopy, dynamic light scattering, and scanning electron microscopy in transmission mode. It is shown that the signal amplification scales with network size for networks comprising either hollow or solid AuNS as well as networks consisting of both types of nanoparticles. The laser intensities applied in our multispectral setup (λ = 650 nm, 850 nm, 905 nm) were low enough to maintain the structural integrity of the networks. This reflects that the binding and recognition properties of the temperature-sensitive cross-linking DNA-molecules are retained.

  8. Signal Classification Using The Mean Separator Neural Network

    DTIC Science & Technology

    2000-03-01

    and investigated. One modification involved input data preconditioning prior to neural network processing. A second modification incorporated...decision-making capacity. More data is not needed; enhanced information and knowledge are essential. This study built upon the Mean Separator Neural ... Network (MSNN) signal classification tool originally proposed by Duzenli (1998) and modified it for increased robustness. MSNN variants were developed

  9. Parameter space exploration within dynamic simulations of signaling networks.

    PubMed

    De Ambrosi, Cristina; Barla, Annalisa; Tortolina, Lorenzo; Castagnino, Nicoletta; Pesenti, Raffaele; Verri, Alessandro; Ballestrero, Alberto; Patrone, Franco; Parodi, Silvio

    2013-02-01

    We started offering an introduction to very basic aspects of molecular biology, for the reader coming from computer sciences, information technology, mathematics. Similarly we offered a minimum of information about pathways and networks in graph theory, for a reader coming from the bio-medical sector. At the crossover about the two different types of expertise, we offered some definition about Systems Biology. The core of the article deals with a Molecular Interaction Map (MIM), a network of biochemical interactions involved in a small signaling-network sub-region relevant in breast cancer. We explored robustness/sensitivity to random perturbations. It turns out that our MIM is a non-isomorphic directed graph. For non physiological directions of propagation of the signal the network is quite resistant to perturbations. The opposite happens for biologically significant directions of signal propagation. In these cases we can have no signal attenuation, and even signal amplification. Signal propagation along a given pathway is highly unidirectional, with the exception of signal-feedbacks, that again have a specific biological role and significance. In conclusion, even a relatively small network like our present MIM reveals the preponderance of specific biological functions over unspecific isomorphic behaviors. This is perhaps the consequence of hundreds of millions of years of biological evolution.

  10. Signal propagation through feedforward neuronal networks with different operational modes

    NASA Astrophysics Data System (ADS)

    Li, Jie; Liu, Feng; Xu, Ding; Wang, Wei

    2009-02-01

    How neuronal activity is propagated across multiple layers of neurons is a fundamental issue in neuroscience. Using numerical simulations, we explored how the operational mode of neurons —coincidence detector or temporal integrator— could affect the propagation of rate signals through a 10-layer feedforward network with sparse connectivity. Our study was based on two kinds of neuron models. The Hodgkin-Huxley (HH) neuron can function as a coincidence detector, while the leaky integrate-and-fire (LIF) neuron can act as a temporal integrator. When white noise is afferent to the input layer, rate signals can be stably propagated through both networks, while neurons in deeper layers fire synchronously in the absence of background noise; but the underlying mechanism for the development of synchrony is different. When an aperiodic signal is presented, the network of HH neurons can represent the temporal structure of the signal in firing rate. Meanwhile, synchrony is well developed and is resistant to background noise. In contrast, rate signals are somewhat distorted during the propagation through the network of LIF neurons, and only weak synchrony occurs in deeper layers. That is, coincidence detectors have a performance advantage over temporal integrators in propagating rate signals. Therefore, given weak synaptic conductance and sparse connectivity between layers in both networks, synchrony does greatly subserve the propagation of rate signals with fidelity, and coincidence detection could be of considerable functional significance in cortical processing.

  11. Neuron-Like Networks Between Ribosomal Proteins Within the Ribosome

    NASA Astrophysics Data System (ADS)

    Poirot, Olivier; Timsit, Youri

    2016-05-01

    From brain to the World Wide Web, information-processing networks share common scale invariant properties. Here, we reveal the existence of neural-like networks at a molecular scale within the ribosome. We show that with their extensions, ribosomal proteins form complex assortative interaction networks through which they communicate through tiny interfaces. The analysis of the crystal structures of 50S eubacterial particles reveals that most of these interfaces involve key phylogenetically conserved residues. The systematic observation of interactions between basic and aromatic amino acids at the interfaces and along the extension provides new structural insights that may contribute to decipher the molecular mechanisms of signal transmission within or between the ribosomal proteins. Similar to neurons interacting through “molecular synapses”, ribosomal proteins form a network that suggest an analogy with a simple molecular brain in which the “sensory-proteins” innervate the functional ribosomal sites, while the “inter-proteins” interconnect them into circuits suitable to process the information flow that circulates during protein synthesis. It is likely that these circuits have evolved to coordinate both the complex macromolecular motions and the binding of the multiple factors during translation. This opens new perspectives on nanoscale information transfer and processing.

  12. Finding undetected protein associations in cell signaling by belief propagation

    PubMed Central

    Bailly-Bechet, M.; Borgs, C.; Braunstein, A.; Chayes, J.; Dagkessamanskaia, A.; François, J.-M.; Zecchina, R.

    2011-01-01

    External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High-throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partners. Some of them may be detected using data coming from the integration of a protein–protein interaction network and mRNA expression profiles. This inference problem can be mapped onto the problem of finding appropriate optimal connected subgraphs of a network defined by these datasets. The optimization procedure turns out to be computationally intractable in general. Here we present a new distributed algorithm for this task, inspired from statistical physics, and apply this scheme to alpha factor and drug perturbations data in yeast. We identify the role of the COS8 protein, a member of a gene family of previously unknown function, and validate the results by genetic experiments. The algorithm we present is specially suited for very large datasets, can run in parallel, and can be adapted to other problems in systems biology. On renowned benchmarks it outperforms other algorithms in the field. PMID:21187432

  13. Spectral reconstruction of protein contact networks

    NASA Astrophysics Data System (ADS)

    Maiorino, Enrico; Rizzi, Antonello; Sadeghian, Alireza; Giuliani, Alessandro

    2017-04-01

    In this work, we present a method for generating an adjacency matrix encoding a typical protein contact network. This work constitutes a follow-up to our recent work (Livi et al., 2015), whose aim was to estimate the relative contribution of different topological features in discovering of the unique properties of protein structures. We perform a genetic algorithm based optimization in order to modify the matrices generated with the procedures explained in (Livi et al., 2015). Our objective here is to minimize the distance with respect to a target spectral density, which is elaborated using the normalized graph Laplacian representation of graphs. Such a target density is obtained by averaging the kernel-estimated densities of a class of experimental protein maps having different dimensions. This is possible given the bounded-domain property of the normalized Laplacian spectrum. By exploiting genetic operators designed for this specific problem and an exponentially-weighted objective function, we are able to reconstruct adjacency matrices representing networks of varying size whose spectral density is indistinguishable from the target. The topological features of the optimized networks are then compared to the real protein contact networks and they show an increased similarity with respect to the starting networks. Subsequently, the statistical properties of the spectra of the newly generated matrices are analyzed by employing tools borrowed from random matrix theory. The nearest neighbors spacing distribution of the spectra of the generated networks indicates that also the (short-range) correlations of the Laplacian eigenvalues are compatible with those of real proteins.

  14. The community structure of human cellular signaling network.

    PubMed

    Diao, Yuanbo; Li, Menglong; Feng, Zinan; Yin, Jiajian; Pan, Yi

    2007-08-21

    Living cell is highly responsive to specific chemicals in its environment, such as hormones and molecules in food or aromas. The reason is ascribed to the existence of widespread and diverse signal transduction pathways, between which crosstalks usually exist, thus constitute a complex signaling network. Evidently, knowledge of topology characteristic of this network could contribute a lot to the understanding of diverse cellular behaviors and life phenomena thus come into being. In this presentation, signal transduction data is extracted from KEGG to construct a cellular signaling network of Homo sapiens, which has 931 nodes and 6798 links in total. Computing the degree distribution, we find it is not a random network, but a scale-free network following a power-law of P(K) approximately K(-gamma), with gamma approximately equal to 2.2. Among three graph partition algorithms, the Guimera's simulated annealing method is chosen to study the details of topology structure and other properties of this cellular signaling network, as it shows the best performance. To reveal the underlying biological implications, further investigation is conducted on ad hoc community and sketch map of individual community is drawn accordingly. The involved experiment data can be found in the supplementary material.

  15. Heart Failure Protein May Signal Early Brain Damage

    MedlinePlus

    ... 162447.html Heart Failure Protein May Signal Early Brain Damage Higher levels indicated potential trouble, study showed ... a specific heart disease protein are associated with brain damage, a new study suggests. N-terminal Pro- ...

  16. Structural permeability of complex networks to control signals

    PubMed Central

    Lo Iudice, Francesco; Garofalo, Franco; Sorrentino, Francesco

    2015-01-01

    Many biological, social and technological systems can be described as complex networks. The goal of affecting their behaviour has motivated recent work focusing on the relationship between the network structure and its propensity to be controlled. While this work has provided insight into several relevant problems, a comprehensive approach to address partial and complete controllability of networks is still lacking. Here, we bridge this gap by developing a framework to maximize the diffusion of the control signals through a network, while taking into account physical and economic constraints that inevitably arise in applications. This approach allows us to introduce the network permeability, a unified metric of the propensity of a network to be controllable. The analysis of the permeability of several synthetic and real networks enables us to extract some structural features that deepen our quantitative understanding of the ease with which specific controllability requirements can be met. PMID:26391186

  17. Telesonar Signaling and Seaweb Underwater Wireless Networks

    DTIC Science & Technology

    2001-04-01

    personnel and mobile undersea systems as network nodes, contributing to this effort are Bob Creber, Chris Fletcher, The annual Seaweb and Sublink...Oceanographic deployable devices that will augment high-value space Partnership Program (NOPP), Jim Eckman (ONR and naval platforms. Distributed system

  18. Network measures for protein folding state discrimination

    PubMed Central

    Menichetti, Giulia; Fariselli, Piero; Remondini, Daniel

    2016-01-01

    Proteins fold using a two-state or multi-state kinetic mechanisms, but up to now there is not a first-principle model to explain this different behavior. We exploit the network properties of protein structures by introducing novel observables to address the problem of classifying the different types of folding kinetics. These observables display a plain physical meaning, in terms of vibrational modes, possible configurations compatible with the native protein structure, and folding cooperativity. The relevance of these observables is supported by a classification performance up to 90%, even with simple classifiers such as discriminant analysis. PMID:27464796

  19. Emerging Roles of Protein Deamidation in Innate Immune Signaling

    PubMed Central

    Zhao, Jun; Li, Junhua; Xu, Simin

    2016-01-01

    Protein deamidation has been considered a nonenzymatic process associated with protein functional decay or “aging.” Recent studies implicate protein deamidation in regulating signal transduction in fundamental biological processes, such as innate immune responses. Work investigating gammaherpesviruses and bacterial pathogens indicates that microbial pathogens deploy deamidases or enzyme-deficient homologues (pseudoenzymes) to induce deamidation of key signaling components and evade host immune responses. Here, we review studies on protein deamidation in innate immune signaling and present several imminent questions concerning the roles of protein deamidation in infection and immunity. PMID:26889032

  20. The Evolutionary Dynamics of Protein-Protein Interaction Networks Inferred from the Reconstruction of Ancient Networks

    PubMed Central

    Rattei, Thomas; Makse, Hernán A.

    2013-01-01

    Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. A number of theoretical models have been developed to explain both the network formation and the current structure. Favored are models based on duplication and divergence of genes, as they most closely represent the biological foundation of network evolution. However, studies are often based on simulated instead of empirical data or they cover only single organisms. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today's PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI

  1. Network-Based Protein Biomarker Discovery Platforms

    PubMed Central

    Kim, Minhyung

    2016-01-01

    The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms. PMID:27103885

  2. Towards blueprints for network architecture, biophysical dynamics and signal transduction.

    PubMed

    Coombes, Stephen; Doiron, Brent; Josić, Kresimir; Shea-Brown, Eric

    2006-12-15

    We review mathematical aspects of biophysical dynamics, signal transduction and network architecture that have been used to uncover functionally significant relations between the dynamics of single neurons and the networks they compose. We focus on examples that combine insights from these three areas to expand our understanding of systems neuroscience. These range from single neuron coding to models of decision making and electrosensory discrimination by networks and populations and also coincidence detection in pairs of dendrites and dynamics of large networks of excitable dendritic spines. We conclude by describing some of the challenges that lie ahead as the applied mathematics community seeks to provide the tools which will ultimately underpin systems neuroscience.

  3. Connecting G protein signaling to chemoattractant-mediated cell polarity and cytoskeletal reorganization.

    PubMed

    Liu, Youtao; Lacal, Jesus; Firtel, Richard A; Kortholt, Arjan

    2016-10-07

    The directional movement towards extracellular chemical gradients, a process called chemotaxis, is an important property of cells. Central to eukaryotic chemotaxis is the molecular mechanism by which chemoattractant-mediated activation of G-protein coupled receptors (GPCRs) induces symmetry breaking in the activated downstream signaling pathways. Studies with mainly Dictyostelium and mammalian neutrophils as experimental systems have shown that chemotaxis is mediated by a complex network of signaling pathways. Recently, several labs have used extensive and efficient proteomic approaches to further unravel this dynamic signaling network. Together these studies showed the critical role of the interplay between heterotrimeric G-protein subunits and monomeric G proteins in regulating cytoskeletal rearrangements during chemotaxis. Here we highlight how these proteomic studies have provided greater insight into the mechanisms by which the heterotrimeric G protein cycle is regulated, how heterotrimeric G proteins-induced symmetry breaking is mediated through small G protein signaling, and how symmetry breaking in G protein signaling subsequently induces cytoskeleton rearrangements and cell migration.

  4. Protein-protein interaction network analysis of cirrhosis liver disease

    PubMed Central

    Safaei, Akram; Rezaei Tavirani, Mostafa; Arefi Oskouei, Afsaneh; Zamanian Azodi, Mona; Mohebbi, Seyed Reza; Nikzamir, Abdol Rahim

    2016-01-01

    Aim: Evaluation of biological characteristics of 13 identified proteins of patients with cirrhotic liver disease is the main aim of this research. Background: In clinical usage, liver biopsy remains the gold standard for diagnosis of hepatic fibrosis. Evaluation and confirmation of liver fibrosis stages and severity of chronic diseases require a precise and noninvasive biomarkers. Since the early detection of cirrhosis is a clinical problem, achieving a sensitive, specific and predictive novel method based on biomarkers is an important task. Methods: Essential analysis, such as gene ontology (GO) enrichment and protein-protein interactions (PPI) was undergone EXPASy, STRING Database and DAVID Bioinformatics Resources query. Results: Based on GO analysis, most of proteins are located in the endoplasmic reticulum lumen, intracellular organelle lumen, membrane-enclosed lumen, and extracellular region. The relevant molecular functions are actin binding, metal ion binding, cation binding and ion binding. Cell adhesion, biological adhesion, cellular amino acid derivative, metabolic process and homeostatic process are the related processes. Protein-protein interaction network analysis introduced five proteins (fibroblast growth factor receptor 4, tropomyosin 4, tropomyosin 2 (beta), lectin, Lectin galactoside-binding soluble 3 binding protein and apolipoprotein A-I) as hub and bottleneck proteins. Conclusion: Our result indicates that regulation of lipid metabolism and cell survival are important biological processes involved in cirrhosis disease. More investigation of above mentioned proteins will provide a better understanding of cirrhosis disease. PMID:27099671

  5. Predicting the fission yeast protein interaction network.

    PubMed

    Pancaldi, Vera; Saraç, Omer S; Rallis, Charalampos; McLean, Janel R; Převorovský, Martin; Gould, Kathleen; Beyer, Andreas; Bähler, Jürg

    2012-04-01

    A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein-protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70-80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (www.bahlerlab.info/PInt).

  6. The Role of Inhibitory G Proteins and Regulators of G Protein Signaling in the in vivo Control of Heart Rate and Predisposition to Cardiac Arrhythmias

    PubMed Central

    Ang, Richard; Opel, Aaisha; Tinker, Andrew

    2012-01-01

    Inhibitory heterotrimeric G proteins and the control of heart rate. The activation of cell signaling pathways involving inhibitory heterotrimeric G proteins acts to slow the heart rate via modulation of ion channels. A large number of Regulators of G protein signalings (RGSs) can act as GTPase accelerating proteins to inhibitory G proteins and thus it is important to understand the network of RGS\\G-protein interaction. We will review our recent findings on in vivo heart rate control in mice with global genetic deletion of various inhibitory G protein alpha subunits. We will discuss potential central and peripheral contributions to the phenotype and the controversies in the literature. PMID:22783193

  7. Plant gravitropic signal transduction: A network analysis leads to gene discovery

    NASA Astrophysics Data System (ADS)

    Wyatt, Sarah

    Gravity plays a fundamental role in plant growth and development. Although a significant body of research has helped define the events of gravity perception, the role of the plant growth regulator auxin, and the mechanisms resulting in the gravity response, the events of signal transduction, those that link the biophysical action of perception to a biochemical signal that results in auxin redistribution, those that regulate the gravitropic effects on plant growth, remain, for the most part, a “black box.” Using a cold affect, dubbed the gravity persistent signal (GPS) response, we developed a mutant screen to specifically identify components of the signal transduction pathway. Cloning of the GPS genes have identified new proteins involved in gravitropic signaling. We have further exploited the GPS response using a multi-faceted approach including gene expression microarrays, proteomics analysis, and bioinformatics analysis and continued mutant analysis to identified additional genes, physiological and biochemical processes. Gene expression data provided the foundation of a regulatory network for gravitropic signaling. Based on these gene expression data and related data sets/information from the literature/repositories, we constructed a gravitropic signaling network for Arabidopsis inflorescence stems. To generate the network, both a dynamic Bayesian network approach and a time-lagged correlation coefficient approach were used. The dynamic Bayesian network added existing information of protein-protein interaction while the time-lagged correlation coefficient allowed incorporation of temporal regulation and thus could incorporate the time-course metric from the data set. Thus the methods complemented each other and provided us with a more comprehensive evaluation of connections. Each method generated a list of possible interactions associated with a statistical significance value. The two networks were then overlaid to generate a more rigorous, intersected

  8. Ras and Nox: linked signaling networks?

    PubMed Central

    Wu, Ru Feng; Terada, Lance S.

    2009-01-01

    Both Ras and Nox represent ancient gene families which control a broad range of cellular responses. Both families mediate signals governing motility, differentiation, and proliferation, and both inhabit overlapping subcellular microdomains. Yet little is known of the precise functional relationship between these two ubiquitous families. In this review, we examine the interface where these two large fields meet. PMID:19501154

  9. Designed Proteins To Modulate Cellular Networks

    PubMed Central

    Cortajarena, Aitziber L.; Liu, Tina Y.; Hochstrasser, Mark; Regan, Lynne

    2012-01-01

    A major challenge of protein design is to create useful new proteins that interact specifically with biological targets in living cells. Such binding modules have many potential applications, including the targeted perturbation of protein networks. As a general approach to create such modules, we designed a library with approximately 109 different binding specificities based on a small 3-tetratricopeptide repeat (TPR) motif framework. We employed a novel strategy, based on split GFP reassembly, to screen the library for modules with the desired binding specificity. Using this approach, we identified modules that bind tightly and specifically to Dss1, a small human protein that interacts with the tumor suppressor protein BRCA2. We showed that these modules also bind the yeast homologue of Dss1, Sem1. Furthermore, we demonstrated that these modules inhibit Sem1 activity in yeast. This strategy will be generally applicable to make novel genetically encoded tools for systems/synthetic biology applications. PMID:20020775

  10. Toponomics: studying protein-protein interactions and protein networks in intact tissue.

    PubMed

    Pierre, Sandra; Scholich, Klaus

    2010-04-01

    The function of a protein is determined on several levels including the genome, transcriptome, proteome, and the recently introduced toponome. The toponome describes the topology of all proteins, protein complexes and protein networks which constitute and influence the microenvironment of a given protein. It has long been known that cellular function or dysfunction of proteins strongly depends on their microenvironment and even small changes in protein arrangements can dramatically alter their activity/function. Thus, deciphering the topology of the multi-dimensional networks which control normal and disease-related pathways will give a better understanding of the mechanisms underlying disease development. While various powerful proteomic tools allow simultaneous quantification of proteins, only a limited number of techniques are available to visualize protein networks in intact cells and tissues. This review discusses a novel approach to map and decipher functional molecular networks of proteins in intact cells or tissues. Multi-epitope-ligand-cartography (MELC) is an imaging technology that identifies and quantifies protein networks at the subcellular level of morphologically-intact specimens. This immunohistochemistry-based method allows serial visualization and biomathematical analysis of up to 100 cellular components using fluorescence-labelled tags. The resulting toponome maps, simultaneously ranging from the subcellular to the supracellular scale, have the potential to provide the basis for a mathematical description of the dynamic topology of protein networks, and will complement current proteomic data to enhance the understanding of physiological and pathophysiological cell functions.

  11. An integrated signal transduction network of macrophage migration inhibitory factor.

    PubMed

    Subbannayya, Tejaswini; Variar, Prathyaksha; Advani, Jayshree; Nair, Bipin; Shankar, Subramanian; Gowda, Harsha; Saussez, Sven; Chatterjee, Aditi; Prasad, T S Keshava

    2016-06-01

    Macrophage migration inhibitory factor (MIF) is a glycosylated multi-functional protein that acts as an enzyme as well as a cytokine. MIF mediates its actions through a cell surface class II major histocompatibility chaperone, CD74 and co-receptors such as CD44, CXCR2, CXCR4 or CXCR7. MIF has been implicated in the pathogenesis of several acute and chronic inflammatory diseases. Although MIF is a molecule of biomedical importance, a public resource of MIF signaling pathway is currently lacking. In view of this, we carried out detailed data mining and documentation of the signaling events pertaining to MIF from published literature and developed an integrated reaction map of MIF signaling. This resulted in the cataloguing of 68 molecules belonging to MIF signaling pathway, which includes 24 protein-protein interactions, 44 post-translational modifications, 11 protein translocation events and 8 activation/inhibition events. In addition, 65 gene regulation events at the mRNA levels induced by MIF signaling have also been catalogued. This signaling pathway has been integrated into NetPath ( http://www.netpath.org ), a freely available human signaling pathway resource developed previously by our group. The MIF pathway data is freely available online in various community standard data exchange formats. We expect that data on signaling events and a detailed signaling map of MIF will provide the scientific community with an improved platform to facilitate further molecular as well as biomedical investigations on MIF.

  12. A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks.

    PubMed

    Samarasinghe, S; Ling, H

    2017-02-04

    In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced

  13. Predicting the Fission Yeast Protein Interaction Network

    PubMed Central

    Pancaldi, Vera; Saraç, Ömer S.; Rallis, Charalampos; McLean, Janel R.; Převorovský, Martin; Gould, Kathleen; Beyer, Andreas; Bähler, Jürg

    2012-01-01

    A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein–protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70–80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (www.bahlerlab.info/PInt). PMID:22540037

  14. A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions.

    PubMed

    Birlutiu, Adriana; d'Alché-Buc, Florence; Heskes, Tom

    2015-01-01

    Computational methods for predicting protein-protein interactions are important tools that can complement high-throughput technologies and guide biologists in designing new laboratory experiments. The proteins and the interactions between them can be described by a network which is characterized by several topological properties. Information about proteins and interactions between them, in combination with knowledge about topological properties of the network, can be used for developing computational methods that can accurately predict unknown protein-protein interactions. This paper presents a supervised learning framework based on Bayesian inference for combining two types of information: i) network topology information, and ii) information related to proteins and the interactions between them. The motivation of our model is that by combining these two types of information one can achieve a better accuracy in predicting protein-protein interactions, than by using models constructed from these two types of information independently.

  15. Characterization of Radar Signals Using Neural Networks

    DTIC Science & Technology

    1990-12-01

    can take hours (23:466) (14: 4 ). Once trained, these networks usually provide high accuracy for pattern classification while -requiring relatively short...parameters. Even with the use of high speed computers, this task is computationally intense- and can take several seconds to properly-classify the radarbignal...N aWE= -- . dn)lo0(1-y) (B.108) W"L N,"i - _n= 4 Thus aCE 1 N a OwK, -N _[dn_ -log(y) (1 - d,)w log(1 - Y)] (B.109) Taking the derivative provides

  16. Paradigms and paradox in the ethylene signaling pathway and interaction network.

    PubMed

    Zhao, Qiong; Guo, Hong-Wei

    2011-07-01

    Phytohormone ethylene plays pivotal roles in plant response to developmental and environmental signals. During the past few years, the emerging evidence has led us to a new understanding of the signaling mechanisms and regulatory networks of the ethylene action. In this review, we focus on the major advances made in the past three years, particularly the findings leading to new paradigms and the observations under debate. With the recent demonstration of the regulation of the protein stability of numerous key signaling components including EIN3, EIL1, EIN2, ETR2, EBF1/EBF2, and ETP1/ETP2, we highlight proteasome-dependent protein degradation as an essential regulatory mechanism that is widely adopted in the ethylene signaling pathway. We also discuss the implication of the negative feedback mechanism in the ethylene signaling pathway in light of ethylene-induced ETR2 and EBF2 gene expression. Meanwhile, we summarize the controversy on the involvement of MKK9-MPK3/6 cascade in the ethylene signaling versus biosynthesis pathway, and discuss the possible role of this MAPK module in the ethylene action. Finally, we describe the complex interactions between ethylene and other signaling pathways including auxin, light, and plant innate immunity, and propose that EIN3/EIL1 act as a convergence point in the ethylene-initiated signaling network.

  17. Regulation of the BMP Signaling-Responsive Transcriptional Network in the Drosophila Embryo

    PubMed Central

    Saunders, Abbie; Wilcockson, Scott G.; Zeef, Leo A. H.; Donaldson, Ian J.; Ashe, Hilary L.

    2016-01-01

    The BMP signaling pathway has a conserved role in dorsal-ventral axis patterning during embryonic development. In Drosophila, graded BMP signaling is transduced by the Mad transcription factor and opposed by the Brinker repressor. In this study, using the Drosophila embryo as a model, we combine RNA-seq with Mad and Brinker ChIP-seq to decipher the BMP-responsive transcriptional network underpinning differentiation of the dorsal ectoderm during dorsal-ventral axis patterning. We identify multiple new BMP target genes, including positive and negative regulators of EGF signaling. Manipulation of EGF signaling levels by loss- and gain-of-function studies reveals that EGF signaling negatively regulates embryonic BMP-responsive transcription. Therefore, the BMP gene network has a self-regulating property in that it establishes a balance between its activity and that of the antagonistic EGF signaling pathway to facilitate correct patterning. In terms of BMP-dependent transcription, we identify key roles for the Zelda and Zerknüllt transcription factors in establishing the resulting expression domain, and find widespread binding of insulator proteins to the Mad and Brinker-bound genomic regions. Analysis of embryos lacking the BEAF-32 insulator protein shows reduced transcription of a peak BMP target gene and a reduction in the number of amnioserosa cells, the fate specified by peak BMP signaling. We incorporate our findings into a model for Mad-dependent activation, and discuss its relevance to BMP signal interpretation in vertebrates. PMID:27379389

  18. Regulation of the BMP Signaling-Responsive Transcriptional Network in the Drosophila Embryo.

    PubMed

    Deignan, Lisa; Pinheiro, Marco T; Sutcliffe, Catherine; Saunders, Abbie; Wilcockson, Scott G; Zeef, Leo A H; Donaldson, Ian J; Ashe, Hilary L

    2016-07-01

    The BMP signaling pathway has a conserved role in dorsal-ventral axis patterning during embryonic development. In Drosophila, graded BMP signaling is transduced by the Mad transcription factor and opposed by the Brinker repressor. In this study, using the Drosophila embryo as a model, we combine RNA-seq with Mad and Brinker ChIP-seq to decipher the BMP-responsive transcriptional network underpinning differentiation of the dorsal ectoderm during dorsal-ventral axis patterning. We identify multiple new BMP target genes, including positive and negative regulators of EGF signaling. Manipulation of EGF signaling levels by loss- and gain-of-function studies reveals that EGF signaling negatively regulates embryonic BMP-responsive transcription. Therefore, the BMP gene network has a self-regulating property in that it establishes a balance between its activity and that of the antagonistic EGF signaling pathway to facilitate correct patterning. In terms of BMP-dependent transcription, we identify key roles for the Zelda and Zerknüllt transcription factors in establishing the resulting expression domain, and find widespread binding of insulator proteins to the Mad and Brinker-bound genomic regions. Analysis of embryos lacking the BEAF-32 insulator protein shows reduced transcription of a peak BMP target gene and a reduction in the number of amnioserosa cells, the fate specified by peak BMP signaling. We incorporate our findings into a model for Mad-dependent activation, and discuss its relevance to BMP signal interpretation in vertebrates.

  19. Phospho-tyrosine dependent protein–protein interaction network

    PubMed Central

    Grossmann, Arndt; Benlasfer, Nouhad; Birth, Petra; Hegele, Anna; Wachsmuth, Franziska; Apelt, Luise; Stelzl, Ulrich

    2015-01-01

    Post-translational protein modifications, such as tyrosine phosphorylation, regulate protein–protein interactions (PPIs) critical for signal processing and cellular phenotypes. We extended an established yeast two-hybrid system employing human protein kinases for the analyses of phospho-tyrosine (pY)-dependent PPIs in a direct experimental, large-scale approach. We identified 292 mostly novel pY-dependent PPIs which showed high specificity with respect to kinases and interacting proteins and validated a large fraction in co-immunoprecipitation experiments from mammalian cells. About one-sixth of the interactions are mediated by known linear sequence binding motifs while the majority of pY-PPIs are mediated by other linear epitopes or governed by alternative recognition modes. Network analysis revealed that pY-mediated recognition events are tied to a highly connected protein module dedicated to signaling and cell growth pathways related to cancer. Using binding assays, protein complementation and phenotypic readouts to characterize the pY-dependent interactions of TSPAN2 (tetraspanin 2) and GRB2 or PIK3R3 (p55γ), we exemplarily provide evidence that the two pY-dependent PPIs dictate cellular cancer phenotypes. PMID:25814554

  20. Efficient Mobility Management Signalling in Network Mobility Supported PMIPV6.

    PubMed

    Samuelraj, Ananthi Jebaseeli; Jayapal, Sundararajan

    2015-01-01

    Proxy Mobile IPV6 (PMIPV6) is a network based mobility management protocol which supports node's mobility without the contribution from the respective mobile node. PMIPV6 is initially designed to support individual node mobility and it should be enhanced to support mobile network movement. NEMO-BSP is an existing protocol to support network mobility (NEMO) in PMIPV6 network. Due to the underlying differences in basic protocols, NEMO-BSP cannot be directly applied to PMIPV6 network. Mobility management signaling and data structures used for individual node's mobility should be modified to support group nodes' mobility management efficiently. Though a lot of research work is in progress to implement mobile network movement in PMIPV6, it is not yet standardized and each suffers with different shortcomings. This research work proposes modifications in NEMO-BSP and PMIPV6 to achieve NEMO support in PMIPV6. It mainly concentrates on optimizing the number and size of mobility signaling exchanged while mobile network or mobile network node changes its access point.

  1. Efficient Mobility Management Signalling in Network Mobility Supported PMIPV6

    PubMed Central

    Jebaseeli Samuelraj, Ananthi; Jayapal, Sundararajan

    2015-01-01

    Proxy Mobile IPV6 (PMIPV6) is a network based mobility management protocol which supports node's mobility without the contribution from the respective mobile node. PMIPV6 is initially designed to support individual node mobility and it should be enhanced to support mobile network movement. NEMO-BSP is an existing protocol to support network mobility (NEMO) in PMIPV6 network. Due to the underlying differences in basic protocols, NEMO-BSP cannot be directly applied to PMIPV6 network. Mobility management signaling and data structures used for individual node's mobility should be modified to support group nodes' mobility management efficiently. Though a lot of research work is in progress to implement mobile network movement in PMIPV6, it is not yet standardized and each suffers with different shortcomings. This research work proposes modifications in NEMO-BSP and PMIPV6 to achieve NEMO support in PMIPV6. It mainly concentrates on optimizing the number and size of mobility signaling exchanged while mobile network or mobile network node changes its access point. PMID:26366431

  2. Integrated module and gene-specific regulatory inference implicates upstream signaling networks.

    PubMed

    Roy, Sushmita; Lagree, Stephen; Hou, Zhonggang; Thomson, James A; Stewart, Ron; Gasch, Audrey P

    2013-01-01

    Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development.

  3. Integrated Module and Gene-Specific Regulatory Inference Implicates Upstream Signaling Networks

    PubMed Central

    Roy, Sushmita; Lagree, Stephen; Hou, Zhonggang; Thomson, James A.; Stewart, Ron; Gasch, Audrey P.

    2013-01-01

    Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development. PMID:24146602

  4. Mammalian circadian signaling networks and therapeutic targets.

    PubMed

    Liu, Andrew C; Lewis, Warren G; Kay, Steve A

    2007-10-01

    Virtually all cells in the body have an intracellular clockwork based on a negative feedback mechanism. The circadian timekeeping system in mammals is a hierarchical multi-oscillator network, with the suprachiasmatic nuclei (SCN) acting as the central pacemaker. The SCN synchronizes to daily light-dark cycles and coordinates rhythmic physiology and behavior. Synchronization in the SCN and at the organismal level is a key feature of the circadian clock system. In particular, intercellular coupling in the SCN synchronizes neuron oscillators and confers robustness against perturbations. Recent advances in our knowledge of and ability to manipulate circadian rhythms make available cell-based clock models, which lack strong coupling and are ideal for target discovery and chemical biology.

  5. Distributed Estimation for Vector Signal in Linear Coherent Sensor Networks

    NASA Astrophysics Data System (ADS)

    Wu, Chien-Hsien; Lin, Ching-An

    We introduce the distributed estimation of a random vector signal in wireless sensor networks that follow coherent multiple access channel model. We adopt the linear minimum mean squared error fusion rule. The problem of interest is to design linear coding matrices for those sensors in the network so as to minimize mean squared error of the estimated vector signal under a total power constraint. We show that the problem can be formulated as a convex optimization problem and we obtain closed form expressions of the coding matrices. Numerical results are used to illustrate the performance of the proposed method.

  6. Role of signal peptides in targeting of proteins in cyanobacteria.

    PubMed Central

    Mackle, M M; Zilinskas, B A

    1994-01-01

    Proteins of cyanobacteria may be transported across one of two membrane systems: the typical eubacterial cell envelope (consisting of an inner membrane, periplasmic space, and an outer membrane) and the photosynthetic thylakoids. To investigate the role of signal peptides in targeting in cyanobacteria, Synechococcus sp. strain PCC 7942 was transformed with vectors carrying the chloramphenicol acetyltransferase reporter gene fused to coding sequences for one of four different signal peptides. These included signal peptides of two proteins of periplasmic space origin (one from Escherichia coli and the other from Synechococcus sp. strain PCC 7942) and two other signal peptides of proteins located in the thylakoid lumen (one from a cyanobacterium and the other from a higher plant). The location of the gene fusion products expressed in Synechococcus sp. strain PCC 7942 was determined by a chloramphenicol acetyltransferase enzyme-linked immunosorbent assay of subcellular fractions. The distribution pattern for gene fusions with periplasmic signal peptides was different from that of gene fusions with thylakoid lumen signal peptides. Primary sequence analysis revealed conserved features in the thylakoid lumen signal peptides that were absent from the periplasmic signal peptides. These results suggest the importance of the signal peptide in protein targeting in cyanobacteria and point to the presence of signal peptide features conserved between chloroplasts and cyanobacteria for targeting of proteins to the thylakoid lumen. Images PMID:8144451

  7. Construction of a protein-protein interaction network of Wilms' tumor and pathway prediction of molecular complexes.

    PubMed

    Teng, W J; Zhou, C; Liu, L J; Cao, X J; Zhuang, J; Liu, G X; Sun, C G

    2016-05-23

    Wilms' tumor (WT), or nephroblastoma, is the most common malignant renal cancer that affects the pediatric population. Great progress has been achieved in the treatment of WT, but it cannot be cured at present. Nonetheless, a protein-protein interaction network of WT should provide some new ideas and methods. The purpose of this study was to analyze the protein-protein interaction network of WT. We screened the confirmed disease-related genes using the Online Mendelian Inheritance in Man database, created a protein-protein interaction network based on biological function in the Cytoscape software, and detected molecular complexes and relevant pathways that may be included in the network. The results showed that the protein-protein interaction network of WT contains 654 nodes, 1544 edges, and 5 molecular complexes. Among them, complex 1 is predicted to be related to the Jak-STAT signaling pathway, regulation of hematopoiesis by cytokines, cytokine-cytokine receptor interaction, cytokine and inflammatory responses, and hematopoietic cell lineage pathways. Molecular complex 4 shows a correlation of WT with colorectal cancer and the ErbB signaling pathway. The proposed method can provide the bioinformatic foundation for further elucidation of the mechanisms of WT development.

  8. Signal processing techniques for synchronization of wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Lee, Jaehan; Wu, Yik-Chung; Chaudhari, Qasim; Qaraqe, Khalid; Serpedin, Erchin

    2010-11-01

    Clock synchronization is a critical component in wireless sensor networks, as it provides a common time frame to different nodes. It supports functions such as fusing voice and video data from different sensor nodes, time-based channel sharing, and sleep wake-up scheduling, etc. Early studies on clock synchronization for wireless sensor networks mainly focus on protocol design. However, clock synchronization problem is inherently related to parameter estimation, and recently, studies of clock synchronization from the signal processing viewpoint started to emerge. In this article, a survey of latest advances on clock synchronization is provided by adopting a signal processing viewpoint. We demonstrate that many existing and intuitive clock synchronization protocols can be interpreted by common statistical signal processing methods. Furthermore, the use of advanced signal processing techniques for deriving optimal clock synchronization algorithms under challenging scenarios will be illustrated.

  9. Integrating In Silico Resources to Map a Signaling Network

    PubMed Central

    Liu, Hanqing; Beck, Tim N.; Golemis, Erica A.; Serebriiskii, Ilya G.

    2013-01-01

    The abundance of publicly available life science databases offer a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol to building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature. PMID:24233784

  10. Beyond 'furballs' and 'dumpling soups' - towards a molecular architecture of signaling complexes and networks.

    PubMed

    Lewitzky, Marc; Simister, Philip C; Feller, Stephan M

    2012-08-14

    The molecular architectures of intracellular signaling networks are largely unknown. Understanding their design principles and mechanisms of processing information is essential to grasp the molecular basis of virtually all biological processes. This is particularly challenging for human pathologies like cancers, as essentially each tumor is a unique disease with vastly deranged signaling networks. However, even in normal cells we know almost nothing. A few 'signalosomes', like the COP9 and the TCR signaling complexes have been described, but detailed structural information on their architectures is largely lacking. Similarly, many growth factor receptors, for example EGF receptor, insulin receptor and c-Met, signal via huge protein complexes built on large platform proteins (Gab, Irs/Dok, p130Cas[BCAR1], Frs families etc.), which are structurally not well understood. Subsequent higher order processing events remain even more enigmatic. We discuss here methods that can be employed to study signaling architectures, and the importance of too often neglected features like macromolecular crowding, intrinsic disorder in proteins and the sophisticated cellular infrastructures, which need to be carefully considered in order to develop a more mature understanding of cellular signal processing.

  11. Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case

    PubMed Central

    Di Paola, Luisa; Liang, Zhongjie; Giuliani, Alessandro

    2017-01-01

    The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM) and Protein Contact Network (PCN) are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb) was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology. PMID:28243596

  12. Dynamics of a sensory signaling network in a unicellular eukaryote.

    PubMed

    Foster, Kenneth W; Josef, Keith; Saranak, Jureepan; Tuck, Ned

    2006-01-01

    The processing components and the dynamic signaling network that an individual cell uses to do signal integration and make decisions based on multiple sensory inputs are being identified in a well studied free-swimming unicellular green algal model organism, Chlamydomonas. It has many sensory photoreceptors and measurable behavior associated with its orienting and swimming with respect to light sources in its environment. Study of the dynamics of the beating of its two steering cilia reveals their complex specialization.

  13. Deciphering Supramolecular Structures with Protein-Protein Interaction Network Modeling

    PubMed Central

    Tsuji, Toshiyuki; Yoda, Takao; Shirai, Tsuyoshi

    2015-01-01

    Many biological molecules are assembled into supramolecules that are essential to perform complicated functions in the cell. However, experimental information about the structures of supramolecules is not sufficient at this point. We developed a method of predicting and modeling the structures of supramolecules in a biological network by combining structural data of the Protein Data Bank (PDB) and interaction data in IntAct databases. Templates for binary complexes in IntAct were extracted from PDB. Modeling was attempted by assembling binary complexes with superposed shared subunits. A total of 3,197 models were constructed, and 1,306 (41% of the total) contained at least one subunit absent from experimental structures. The models also suggested 970 (25% of the total) experimentally undetected subunit interfaces, and 41 human disease-related amino acid variants were mapped onto these model-suggested interfaces. The models demonstrated that protein-protein interaction network modeling is useful to fill the information gap between biological networks and structures. PMID:26549015

  14. Dystrophin complex functions as a scaffold for signalling proteins.

    PubMed

    Constantin, Bruno

    2014-02-01

    Dystrophin is a 427kDa sub-membrane cytoskeletal protein, associated with the inner surface membrane and incorporated in a large macromolecular complex of proteins, the dystrophin-associated protein complex (DAPC). In addition to dystrophin the DAPC is composed of dystroglycans, sarcoglycans, sarcospan, dystrobrevins and syntrophin. This complex is thought to play a structural role in ensuring membrane stability and force transduction during muscle contraction. The multiple binding sites and domains present in the DAPC confer the scaffold of various signalling and channel proteins, which may implicate the DAPC in regulation of signalling processes. The DAPC is thought for instance to anchor a variety of signalling molecules near their sites of action. The dystroglycan complex may participate in the transduction of extracellular-mediated signals to the muscle cytoskeleton, and β-dystroglycan was shown to be involved in MAPK and Rac1 small GTPase signalling. More generally, dystroglycan is view as a cell surface receptor for extracellular matrix proteins. The adaptor proteins syntrophin contribute to recruit and regulate various signalling proteins such as ion channels, into a macromolecular complex. Although dystrophin and dystroglycan can be directly involved in signalling pathways, syntrophins play a central role in organizing signalplex anchored to the dystrophin scaffold. The dystrophin associated complex, can bind up to four syntrophin through binding domains of dystrophin and dystrobrevin, allowing the scaffold of multiple signalling proteins in close proximity. Multiple interactions mediated by PH and PDZ domains of syntrophin also contribute to build a complete signalplex which may include ion channels, such as voltage-gated sodium channels or TRPC cation channels, together with, trimeric G protein, G protein-coupled receptor, plasma membrane calcium pump, and NOS, to enable efficient and regulated signal transduction and ion transport. This article is part

  15. Ultrasensitive response motifs: basic amplifiers in molecular signalling networks

    PubMed Central

    Zhang, Qiang; Bhattacharya, Sudin; Andersen, Melvin E.

    2013-01-01

    Multi-component signal transduction pathways and gene regulatory circuits underpin integrated cellular responses to perturbations. A recurring set of network motifs serve as the basic building blocks of these molecular signalling networks. This review focuses on ultrasensitive response motifs (URMs) that amplify small percentage changes in the input signal into larger percentage changes in the output response. URMs generally possess a sigmoid input–output relationship that is steeper than the Michaelis–Menten type of response and is often approximated by the Hill function. Six types of URMs can be commonly found in intracellular molecular networks and each has a distinct kinetic mechanism for signal amplification. These URMs are: (i) positive cooperative binding, (ii) homo-multimerization, (iii) multistep signalling, (iv) molecular titration, (v) zero-order covalent modification cycle and (vi) positive feedback. Multiple URMs can be combined to generate highly switch-like responses. Serving as basic signal amplifiers, these URMs are essential for molecular circuits to produce complex nonlinear dynamics, including multistability, robust adaptation and oscillation. These dynamic properties are in turn responsible for higher-level cellular behaviours, such as cell fate determination, homeostasis and biological rhythm. PMID:23615029

  16. Adenylate Kinase and AMP Signaling Networks: Metabolic Monitoring, Signal Communication and Body Energy Sensing

    PubMed Central

    Dzeja, Petras; Terzic, Andre

    2009-01-01

    Adenylate kinase and downstream AMP signaling is an integrated metabolic monitoring system which reads the cellular energy state in order to tune and report signals to metabolic sensors. A network of adenylate kinase isoforms (AK1-AK7) are distributed throughout intracellular compartments, interstitial space and body fluids to regulate energetic and metabolic signaling circuits, securing efficient cell energy economy, signal communication and stress response. The dynamics of adenylate kinase-catalyzed phosphotransfer regulates multiple intracellular and extracellular energy-dependent and nucleotide signaling processes, including excitation-contraction coupling, hormone secretion, cell and ciliary motility, nuclear transport, energetics of cell cycle, DNA synthesis and repair, and developmental programming. Metabolomic analyses indicate that cellular, interstitial and blood AMP levels are potential metabolic signals associated with vital functions including body energy sensing, sleep, hibernation and food intake. Either low or excess AMP signaling has been linked to human disease such as diabetes, obesity and hypertrophic cardiomyopathy. Recent studies indicate that derangements in adenylate kinase-mediated energetic signaling due to mutations in AK1, AK2 or AK7 isoforms are associated with hemolytic anemia, reticular dysgenesis and ciliary dyskinesia. Moreover, hormonal, food and antidiabetic drug actions are frequently coupled to alterations of cellular AMP levels and associated signaling. Thus, by monitoring energy state and generating and distributing AMP metabolic signals adenylate kinase represents a unique hub within the cellular homeostatic network. PMID:19468337

  17. RegPhos: a system to explore the protein kinase–substrate phosphorylation network in humans

    PubMed Central

    Lee, Tzong-Yi; Bo-Kai Hsu, Justin; Chang, Wen-Chi; Huang, Hsien-Da

    2011-01-01

    Protein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. With the increasing number of experimental phosphorylation sites that has been identified by mass spectrometry-based proteomics, the desire to explore the networks of protein kinases and substrates is motivated. Manning et al. have identified 518 human kinase genes, which provide a starting point for comprehensive analysis of protein phosphorylation networks. In this study, a knowledgebase is developed to integrate experimentally verified protein phosphorylation data and protein–protein interaction data for constructing the protein kinase–substrate phosphorylation networks in human. A total of 21 110 experimental verified phosphorylation sites within 5092 human proteins are collected. However, only 4138 phosphorylation sites (∼20%) have the annotation of catalytic kinases from public domain. In order to fully investigate how protein kinases regulate the intracellular processes, a published kinase-specific phosphorylation site prediction tool, named KinasePhos is incorporated for assigning the potential kinase. The web-based system, RegPhos, can let users input a group of human proteins; consequently, the phosphorylation network associated with the protein subcellular localization can be explored. Additionally, time-coursed microarray expression data is subsequently used to represent the degree of similarity in the expression profiles of network members. A case study demonstrates that the proposed scheme not only identify the correct network of insulin signaling but also detect a novel signaling pathway that may cross-talk with insulin signaling network. This effective system is now freely available at http://RegPhos.mbc.nctu.edu.tw. PMID:21037261

  18. Identification of essential proteins based on ranking edge-weights in protein-protein interaction networks.

    PubMed

    Wang, Yan; Sun, Huiyan; Du, Wei; Blanzieri, Enrico; Viero, Gabriella; Xu, Ying; Liang, Yanchun

    2014-01-01

    Essential proteins are those that are indispensable to cellular survival and development. Existing methods for essential protein identification generally rely on knock-out experiments and/or the relative density of their interactions (edges) with other proteins in a Protein-Protein Interaction (PPI) network. Here, we present a computational method, called EW, to first rank protein-protein interactions in terms of their Edge Weights, and then identify sub-PPI-networks consisting of only the highly-ranked edges and predict their proteins as essential proteins. We have applied this method to publicly-available PPI data on Saccharomyces cerevisiae (Yeast) and Escherichia coli (E. coli) for essential protein identification, and demonstrated that EW achieves better performance than the state-of-the-art methods in terms of the precision-recall and Jackknife measures. The highly-ranked protein-protein interactions by our prediction tend to be biologically significant in both the Yeast and E. coli PPI networks. Further analyses on systematically perturbed Yeast and E. coli PPI networks through randomly deleting edges demonstrate that the proposed method is robust and the top-ranked edges tend to be more associated with known essential proteins than the lowly-ranked edges.

  19. Single-Molecule Protein Conformational Dynamics in Cell Signaling

    SciTech Connect

    Lu, H PETER.

    2004-08-22

    We have demonstrated the application of single-molecule imaging and ultrafast spectroscopy to probe protein conformational dynamics in solution and in lipid bilayers. Dynamic protein-protein interactions involve significant conformational motions that initiate chain reactions leading to specific cellular responses. We have carried out a single molecule study of dynamic protein-protein interactions in a GTPase intracellular signaling protein Cdc42 in complex with a downstream effector protein, WASP. We were able to probe hydrophobic interactions significant to Cdc42/WASP recognition. Single molecule fluorescence intensity and polarization measurements have revealed the dynamic and inhomogeneous nature of protein-protein interactions within the Cdc42/WASP complex that is characterized by structured distributions of conformational fluctuation rates. Conducting a single-molecule fluorescence anisotropy study of calmodulin (CaM), a regulatory protein for calcium-dependent cell signaling, we were able to probe CaM conformational dynamics at a wide time scale. In this study, CaM contains a site-specifically inserted tetra-cysteine motif that reacted with FlAsH, a biarsenic fluorescein derivative that can be rotationally locked to the host protein. The study provided direct characterization of the nanosecond motions of CaM tethered to a biologically compatible surface under physiological buffer solution. The unique technical approaches are applicable of studying single-molecule dynamics of protein conformational motions and protein-protein interactions at a wide time range without the signal convolution of probe-dye molecule motions

  20. CD25 and CD69 induction by α4β1 outside-in signalling requires TCR early signalling complex proteins.

    PubMed

    Cimo, Ann-Marie; Ahmed, Zamal; McIntyre, Bradley W; Lewis, Dorothy E; Ladbury, John E

    2013-08-15

    Distinct signalling pathways producing diverse cellular outcomes can utilize similar subsets of proteins. For example, proteins from the TCR (T-cell receptor) ESC (early signalling complex) are also involved in interferon-α receptor signalling. Defining the mechanism for how these proteins function within a given pathway is important in understanding the integration and communication of signalling networks with one another. We investigated the contributions of the TCR ESC proteins Lck (lymphocyte-specific kinase), ZAP-70 (ζ-chain-associated protein of 70 kDa), Vav1, SLP-76 [SH2 (Src homology 2)-domain-containing leukocyte protein of 76 kDa] and LAT (linker for activation of T-cells) to integrin outside-in signalling in human T-cells. Lck, ZAP-70, SLP-76, Vav1 and LAT were activated by α4β1 outside-in signalling, but in a manner different from TCR signalling. TCR stimulation recruits ESC proteins to activate the mitogen-activated protein kinase ERK (extracellular-signal-regulated kinase). α4β1 outside-in-mediated ERK activation did not require TCR ESC proteins. However, α4β1 outside-in signalling induced CD25 and co-stimulated CD69 and this was dependent on TCR ESC proteins. TCR and α4β1 outside-in signalling are integrated through the common use of TCR ESC proteins; however, these proteins display functionally distinct roles in these pathways. These novel insights into the cross-talk between integrin outside-in and TCR signalling pathways are highly relevant to the development of therapeutic strategies to overcome disease associated with T-cell deregulation.

  1. LocSigDB: a database of protein localization signals.

    PubMed

    Negi, Simarjeet; Pandey, Sanjit; Srinivasan, Satish M; Mohammed, Akram; Guda, Chittibabu

    2015-01-01

    LocSigDB (http://genome.unmc.edu/LocSigDB/) is a manually curated database of experimental protein localization signals for eight distinct subcellular locations; primarily in a eukaryotic cell with brief coverage of bacterial proteins. Proteins must be localized at their appropriate subcellular compartment to perform their desired function. Mislocalization of proteins to unintended locations is a causative factor for many human diseases; therefore, collection of known sorting signals will help support many important areas of biomedical research. By performing an extensive literature study, we compiled a collection of 533 experimentally determined localization signals, along with the proteins that harbor such signals. Each signal in the LocSigDB is annotated with its localization, source, PubMed references and is linked to the proteins in UniProt database along with the organism information that contain the same amino acid pattern as the given signal. From LocSigDB webserver, users can download the whole database or browse/search for data using an intuitive query interface. To date, LocSigDB is the most comprehensive compendium of protein localization signals for eight distinct subcellular locations. Database URL: http://genome.unmc.edu/LocSigDB/

  2. BAR domain proteins regulate Rho GTPase signaling

    PubMed Central

    Aspenström, Pontus

    2014-01-01

    BAR proteins comprise a heterogeneous group of multi-domain proteins with diverse biological functions. The common denominator is the Bin-Amphiphysin-Rvs (BAR) domain that not only confers targeting to lipid bilayers, but also provides scaffolding to mold lipid membranes into concave or convex surfaces. This function of BAR proteins is an important determinant in the dynamic reconstruction of membrane vesicles, as well as of the plasma membrane. Several BAR proteins function as linkers between cytoskeletal regulation and membrane dynamics. These links are provided by direct interactions between BAR proteins and actin-nucleation-promoting factors of the Wiskott-Aldrich syndrome protein family and the Diaphanous-related formins. The Rho GTPases are key factors for orchestration of this intricate interplay. This review describes how BAR proteins regulate the activity of Rho GTPases, as well as how Rho GTPases regulate the function of BAR proteins. This mutual collaboration is a central factor in the regulation of vital cellular processes, such as cell migration, cytokinesis, intracellular transport, endocytosis, and exocytosis. PMID:25483303

  3. Subsurface Event Detection and Classification Using Wireless Signal Networks

    PubMed Central

    Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T.

    2012-01-01

    Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

  4. Discrete dynamic modeling of T cell survival signaling networks

    NASA Astrophysics Data System (ADS)

    Zhang, Ranran

    2009-03-01

    Biochemistry-based frameworks are often not applicable for the modeling of heterogeneous regulatory systems that are sparsely documented in terms of quantitative information. As an alternative, qualitative models assuming a small set of discrete states are gaining acceptance. This talk will present a discrete dynamic model of the signaling network responsible for the survival and long-term competence of cytotoxic T cells in the blood cancer T-LGL leukemia. We integrated the signaling pathways involved in normal T cell activation and the known deregulations of survival signaling in leukemic T-LGL, and formulated the regulation of each network element as a Boolean (logic) rule. Our model suggests that the persistence of two signals is sufficient to reproduce all known deregulations in leukemic T-LGL. It also indicates the nodes whose inactivity is necessary and sufficient for the reversal of the T-LGL state. We have experimentally validated several model predictions, including: (i) Inhibiting PDGF signaling induces apoptosis in leukemic T-LGL. (ii) Sphingosine kinase 1 and NFκB are essential for the long-term survival of T cells in T-LGL leukemia. (iii) T box expressed in T cells (T-bet) is constitutively activated in the T-LGL state. The model has identified potential therapeutic targets for T-LGL leukemia and can be used for generating long-term competent CTL necessary for tumor and cancer vaccine development. The success of this model, and of other discrete dynamic models, suggests that the organization of signaling networks has an determining role in their dynamics. Reference: R. Zhang, M. V. Shah, J. Yang, S. B. Nyland, X. Liu, J. K. Yun, R. Albert, T. P. Loughran, Jr., Network Model of Survival Signaling in LGL Leukemia, PNAS 105, 16308-16313 (2008).

  5. Signal-Response Modeling of Partial Hormone Feedback Networks

    PubMed Central

    Johnson, Michael L.; Veldhuis, Paula P.; Evans, William S.

    2009-01-01

    Background Endocrine feedback control networks are typically complex and contain multiple hormones, pools, and compartments. The hormones themselves commonly interact via multiple pathways and targets within the networks, and a complete description of such relationships may involve hundreds of parameters. In addition, it is often difficult, if not impossible, to collect experimental data pertaining to every component within the network. Therefore, the complete simultaneous analysis of such networks is challenging. Nevertheless, an understanding of these networks is critical for furthering our knowledge of hormonal regulation in both physiologic and pathophysiologic conditions. Methods We propose a novel approach for the analysis of dose-response relationships of subsets of hormonal feedback networks. The algorithm and signal-response quantification (SRQuant) software is based on convolution integrals, and tests whether several discretely measured input signals can be individually delayed, spread in time, transformed, combined, and discretely convolved with an elimination function to predict the time course of the concentration of an output hormone. Signal-response quantification is applied to examples from the endocrine literature to demonstrate its applicability to the analysis of the different endocrine networks. Results In one example, SRQuant determines the dose-response relationship by which one hormone regulates another, highlighting its advantages over other traditional methods. In a second example, for the first time (to the best of our knowledge), we show that the secretion of glucagon may be jointly controlled by the β and the δ cells. Conclusion We have developed a novel convolution integral-based approach, algorithm, and software (SRQuant) for the analysis of dose-response relationships within subsets of complex endocrine feedback control networks. PMID:20046649

  6. Data-driven quantification of the robustness and sensitivity of cell signaling networks

    NASA Astrophysics Data System (ADS)

    Mukherjee, Sayak; Seok, Sang-Cheol; Vieland, Veronica J.; Das, Jayajit

    2013-12-01

    Robustness and sensitivity of responses generated by cell signaling networks has been associated with survival and evolvability of organisms. However, existing methods analyzing robustness and sensitivity of signaling networks ignore the experimentally observed cell-to-cell variations of protein abundances and cell functions or contain ad hoc assumptions. We propose and apply a data-driven maximum entropy based method to quantify robustness and sensitivity of Escherichia coli (E. coli) chemotaxis signaling network. Our analysis correctly rank orders different models of E. coli chemotaxis based on their robustness and suggests that parameters regulating cell signaling are evolutionary selected to vary in individual cells according to their abilities to perturb cell functions. Furthermore, predictions from our approach regarding distribution of protein abundances and properties of chemotactic responses in individual cells based on cell population averaged data are in excellent agreement with their experimental counterparts. Our approach is general and can be used to evaluate robustness as well as generate predictions of single cell properties based on population averaged experimental data in a wide range of cell signaling systems.

  7. Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies

    PubMed Central

    Kolch, Walter; Kholodenko, Boris N.; Ambrosi, Cristina De; Barla, Annalisa; Biganzoli, Elia M.; Nencioni, Alessio; Patrone, Franco; Ballestrero, Alberto; Zoppoli, Gabriele; Verri, Alessandro; Parodi, Silvio

    2015-01-01

    The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis. We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions. Starting from an initial “physiologic condition”, the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model. Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal. PMID:25671297

  8. Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies.

    PubMed

    Tortolina, Lorenzo; Duffy, David J; Maffei, Massimo; Castagnino, Nicoletta; Carmody, Aimée M; Kolch, Walter; Kholodenko, Boris N; De Ambrosi, Cristina; Barla, Annalisa; Biganzoli, Elia M; Nencioni, Alessio; Patrone, Franco; Ballestrero, Alberto; Zoppoli, Gabriele; Verri, Alessandro; Parodi, Silvio

    2015-03-10

    The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis.We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions.Starting from an initial "physiologic condition", the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model.Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal.

  9. Neural network approach to classification of infrasound signals

    NASA Astrophysics Data System (ADS)

    Lee, Dong-Chang

    As part of the International Monitoring Systems of the Preparatory Commissions for the Comprehensive Nuclear Test-Ban Treaty Organization, the Infrasound Group at the University of Alaska Fairbanks maintains and operates two infrasound stations to monitor global nuclear activity. In addition, the group specializes in detecting and classifying the man-made and naturally produced signals recorded at both stations by computing various characterization parameters (e.g. mean of the cross correlation maxima, trace velocity, direction of arrival, and planarity values) using the in-house developed weighted least-squares algorithm. Classifying commonly observed low-frequency (0.015--0.1 Hz) signals at out stations, namely mountain associated waves and high trace-velocity signals, using traditional approach (e.g. analysis of power spectral density) presents a problem. Such signals can be separated statistically by setting a window to the trace-velocity estimate for each signal types, and the feasibility of such technique is demonstrated by displaying and comparing various summary plots (e.g. universal, seasonal and azimuthal variations) produced by analyzing infrasound data (2004--2007) from the Fairbanks and Antarctic arrays. Such plots with the availability of magnetic activity information (from the College International Geophysical Observatory located at Fairbanks, Alaska) leads to possible physical sources of the two signal types. Throughout this thesis a newly developed robust algorithm (sum of squares of variance ratios) with improved detection quality (under low signal to noise ratios) over two well-known detection algorithms (mean of the cross correlation maxima and Fisher Statistics) are investigated for its efficacy as a new detector. A neural network is examined for its ability to automatically classify the two signals described above against clutter (spurious signals with common characteristics). Four identical perceptron networks are trained and validated (with

  10. Protein interaction network for Alzheimer's disease using computational approach.

    PubMed

    Srinivasa Rao, V; Srinivas, K; Kumar, G N Sunand; Sujin, G N

    2013-01-01

    Alzheimer's disease (AD) is the most common form of dementia. It is the sixth leading cause of death in old age people. Despite recent advances in the field of drug design, the medical treatment for the disease is purely symptomatic and hardly effective. Thus there is a need to understand the molecular mechanism behind the disease in order to improve the drug aspects of the disease. We provided two contributions in the field of proteomics in drug design. First, we have constructed a protein-protein interaction network for Alzheimer's disease reviewed proteins with 1412 interactions predicted among 969 proteins. Second, the disease proteins were given confidence scores to prioritize and then analyzed for their homology nature with respect to paralogs and homologs. The homology persisted with the mouse giving a basis for drug design phase. The method will create a new drug design technique in the field of bioinformatics by linking drug design process with protein-protein interactions via signal pathways. This method can be improvised for other diseases in future.

  11. Regulation, Signaling, and Physiological Functions of G-Proteins.

    PubMed

    Syrovatkina, Viktoriya; Alegre, Kamela O; Dey, Raja; Huang, Xin-Yun

    2016-09-25

    Heterotrimeric guanine-nucleotide-binding regulatory proteins (G-proteins) mainly relay the information from G-protein-coupled receptors (GPCRs) on the plasma membrane to the inside of cells to regulate various biochemical functions. Depending on the targeted cell types, tissues, and organs, these signals modulate diverse physiological functions. The basic schemes of heterotrimeric G-proteins have been outlined. In this review, we briefly summarize what is known about the regulation, signaling, and physiological functions of G-proteins. We then focus on a few less explored areas such as the regulation of G-proteins by non-GPCRs and the physiological functions of G-proteins that cannot be easily explained by the known G-protein signaling pathways. There are new signaling pathways and physiological functions for G-proteins to be discovered and further interrogated. With the advancements in structural and computational biological techniques, we are closer to having a better understanding of how G-proteins are regulated and of the specificity of G-protein interactions with their regulators.

  12. Using Protein Motion to Read, Write, and Erase Ubiquitin Signals*

    PubMed Central

    Phillips, Aaron H.; Corn, Jacob E.

    2015-01-01

    Eukaryotes use a tiny protein called ubiquitin to send a variety of signals, most often by post-translationally attaching ubiquitins to substrate proteins and to each other, thereby forming polyubiquitin chains. A combination of biophysical, biochemical, and biological studies has shown that complex macromolecular dynamics are central to many aspects of ubiquitin signaling. This review focuses on how equilibrium fluctuations and coordinated motions of ubiquitin itself, the ubiquitin conjugation machinery, and deubiquitinating enzymes enable activity and regulation on many levels, with implications for how such a tiny protein can send so many signals. PMID:26354440

  13. Topological and functional properties of the small GTPases protein interaction network.

    PubMed

    Delprato, Anna

    2012-01-01

    Small GTP binding proteins of the Ras superfamily (Ras, Rho, Rab, Arf, and Ran) regulate key cellular processes such as signal transduction, cell proliferation, cell motility, and vesicle transport. A great deal of experimental evidence supports the existence of signaling cascades and feedback loops within and among the small GTPase subfamilies suggesting that these proteins function in a coordinated and cooperative manner. The interplay occurs largely through association with bi-partite regulatory and effector proteins but can also occur through the active form of the small GTPases themselves. In order to understand the connectivity of the small GTPases signaling routes, a systems-level approach that analyzes data describing direct and indirect interactions was used to construct the small GTPases protein interaction network. The data were curated from the Search Tool for the Retrieval of Interacting Genes (STRING) database and include only experimentally validated interactions. The network method enables the conceptualization of the overall structure as well as the underlying organization of the protein-protein interactions. The interaction network described here is comprised of 778 nodes and 1943 edges and has a scale-free topology. Rac1, Cdc42, RhoA, and HRas are identified as the hubs. Ten sub-network motifs are also identified in this study with themes in apoptosis, cell growth/proliferation, vesicle traffic, cell adhesion/junction dynamics, the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase response, transcription regulation, receptor-mediated endocytosis, gene silencing, and growth factor signaling. Bottleneck proteins that bridge signaling paths and proteins that overlap in multiple small GTPase networks are described along with the functional annotation of all proteins in the network.

  14. SSL: Signal Similarity-Based Localization for Ocean Sensor Networks

    PubMed Central

    Chen, Pengpeng; Ma, Honglu; Gao, Shouwan; Huang, Yan

    2015-01-01

    Nowadays, wireless sensor networks are often deployed on the sea surface for ocean scientific monitoring. One of the important challenges is to localize the nodes’ positions. Existing localization schemes can be roughly divided into two types: range-based and range-free. The range-based localization approaches heavily depend on extra hardware capabilities, while range-free ones often suffer from poor accuracy and low scalability, far from the practical ocean monitoring applications. In response to the above limitations, this paper proposes a novel signal similarity-based localization (SSL) technology, which localizes the nodes’ positions by fully utilizing the similarity of received signal strength and the open-air characteristics of the sea surface. In the localization process, we first estimate the relative distance between neighboring nodes through comparing the similarity of received signal strength and then calculate the relative distance for non-neighboring nodes with the shortest path algorithm. After that, the nodes’ relative relation map of the whole network can be obtained. Given at least three anchors, the physical locations of nodes can be finally determined based on the multi-dimensional scaling (MDS) technology. The design is evaluated by two types of ocean experiments: a zonal network and a non-regular network using 28 nodes. Results show that the proposed design improves the localization accuracy compared to typical connectivity-based approaches and also confirm its effectiveness for large-scale ocean sensor networks. PMID:26610520

  15. Tetramer formation in Arabidopsis MADS domain proteins: analysis of a protein-protein interaction network

    PubMed Central

    2014-01-01

    Background MADS domain proteins are transcription factors that coordinate several important developmental processes in plants. These proteins interact with other MADS domain proteins to form dimers, and it has been proposed that they are able to associate as tetrameric complexes that regulate transcription of target genes. Whether the formation of functional tetramers is a widespread property of plant MADS domain proteins, or it is specific to few of these transcriptional regulators remains unclear. Results We analyzed the structure of the network of physical interactions among MADS domain proteins in Arabidopsis thaliana. We determined the abundance of subgraphs that represent the connection pattern expected for a MADS domain protein heterotetramer. These subgraphs were significantly more abundant in the MADS domain protein interaction network than in randomized analogous networks. Importantly, these subgraphs are not significantly frequent in a protein interaction network of TCP plant transcription factors, when compared to expectation by chance. In addition, we found that MADS domain proteins in tetramer-like subgraphs are more likely to be expressed jointly than proteins in other subgraphs. This effect is mainly due to proteins in the monophyletic MIKC clade, as there is no association between tetramer-like subgraphs and co-expression for proteins outside this clade. Conclusions Our results support that the tendency to form functional tetramers is widespread in the MADS domain protein-protein interaction network. Our observations also suggest that this trend is prevalent, or perhaps exclusive, for proteins in the MIKC clade. Because it is possible to retrodict several experimental results from our analyses, our work can be an important aid to make new predictions and facilitates experimental research on plant MADS domain proteins. PMID:24468197

  16. Choline-releasing glycerophosphodiesterase EDI3 links the tumor metabolome to signaling network activities

    PubMed Central

    Marchan, Rosemarie; Lesjak, Michaela S.; Stewart, Joanna D.; Winter, Roland; Seeliger, Janine; Hengstler, Jan G.

    2012-01-01

    Recently, EDI3 was identified as a key factor for choline metabolism that controls tumor cell migration and is associated with metastasis in endometrial carcinomas. EDI3 cleaves glycerophosphocholine (GPC) to form choline and glycerol-3-phosphate (G3P). Choline is then further metabolized to phosphatidylcholine (PtdC), the major lipid in membranes and a key player in membrane-mediated cell signaling. The second product, G3P, is a precursor molecule for several lipids with central roles in signaling, for example lysophosphatidic acid (LPA), phosphatidic acid (PA) and diacylglycerol (DAG). LPA activates intracellular signaling pathways by binding to specific LPA receptors, including membrane-bound G protein-coupled receptors and the intracellular nuclear receptor, PPARγ. Conversely, PA and DAG mediate signaling by acting as lipid anchors that bind and activate several signaling proteins. For example, binding of GTPases and PKC to PA and DAG, respectively, increases the activation of signaling networks, mediating processes such as migration, adhesion, proliferation or anti-apoptosis—all relevant for tumor development. We present a concept by which EDI3 either directly generates signaling molecules or provides “membrane anchors” for downstream signaling factors. As a result, EDI3 links choline metabolism to signaling activities resulting in a more malignant phenotype. PMID:23114620

  17. Signal bi-amplification in networks of unidirectionally coupled MEMS

    NASA Astrophysics Data System (ADS)

    Tchakui, Murielle Vanessa; Woafo, Paul; Colet, Pere

    2016-01-01

    The purpose of this paper is to analyze the propagation and the amplification of an input signal in networks of unidirectionally coupled micro-electro-mechanical systems (MEMS). Two types of external excitations are considered: sinusoidal and stochastic signals. We show that sinusoidal signals are amplified up to a saturation level which depends on the transmission rate and despite MEMS being nonlinear the sinusoidal shape is well preserved if the number of MEMS is not too large. However, increasing the number of MEMS, there is an instability that leads to chaotic behavior and which is triggered by the amplification of the harmonics generated by the nonlinearities. We also show that for stochastic input signals, the MEMS array acts as a band-pass filter and after just a few elements the signal has a narrow power spectra.

  18. Yeast two-hybrid analysis of jasmonate signaling proteins.

    PubMed

    Cuéllar, Amparo Pérez; Pauwels, Laurens; De Clercq, Rebecca; Goossens, Alain

    2013-01-01

    Protein-protein interaction studies are crucial to unravel how jasmonate (JA) signals are transduced. Among the different techniques available, yeast two-hybrid (Y2H) is commonly used within the JA research community to identify proteins belonging to the core JA signaling module. The technique is based on the reconstitution of a transcriptional activator that drives the reporter gene expression upon protein-protein interactions. The method is sensitive and straightforward and can be adapted for different approaches. In this chapter, we provide a detailed protocol to perform targeted Y2H assays to test known proteins and/or protein domains for direct interaction in a pairwise manner and present the possibility to study ternary protein complexes through Y3H.

  19. Reverse engineering GTPase programming languages with reconstituted signaling networks.

    PubMed

    Coyle, Scott M

    2016-07-02

    The Ras superfamily GTPases represent one of the most prolific signaling currencies used in Eukaryotes. With these remarkable molecules, evolution has built GTPase networks that control diverse cellular processes such as growth, morphology, motility and trafficking. (1-4) Our knowledge of the individual players that underlie the function of these networks is deep; decades of biochemical and structural data has provided a mechanistic understanding of the molecules that turn GTPases ON and OFF, as well as how those GTPase states signal by controlling the assembly of downstream effectors. However, we know less about how these different activities work together as a system to specify complex dynamic signaling outcomes. Decoding this molecular "programming language" would help us understand how different species and cell types have used the same GTPase machinery in different ways to accomplish different tasks, and would also provide new insights as to how mutations to these networks can cause disease. We recently developed a bead-based microscopy assay to watch reconstituted H-Ras signaling systems at work under arbitrary configurations of regulators and effectors. (5) Here we highlight key observations and insights from this study and propose extensions to our method to further study this and other GTPase signaling systems.

  20. Optimal Signal Processing in Small Stochastic Biochemical Networks

    PubMed Central

    Ziv, Etay; Nemenman, Ilya; Wiggins, Chris H.

    2007-01-01

    We quantify the influence of the topology of a transcriptional regulatory network on its ability to process environmental signals. By posing the problem in terms of information theory, we do this without specifying the function performed by the network. Specifically, we study the maximum mutual information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species, each under the control of one regulator. We find that a generic network, constrained to low molecule numbers and reasonable response times, can transduce more information than a simple binary switch and, in fact, manages to achieve close to the optimal information transmission fidelity. These high-information solutions are robust to tenfold changes in most of the networks' biochemical parameters; moreover they are easier to achieve in networks containing cycles with an odd number of negative regulators (overall negative feedback) due to their decreased molecular noise (a result which we derive analytically). Finally, we demonstrate that a single circuit can support multiple high-information solutions. These findings suggest a potential resolution of the “cross-talk” phenomenon as well as the previously unexplained observation that transcription factors that undergo proteolysis are more likely to be auto-repressive. PMID:17957259

  1. SoxB1-driven transcriptional network underlies neural-specific interpretation of morphogen signals.

    PubMed

    Oosterveen, Tony; Kurdija, Sanja; Ensterö, Mats; Uhde, Christopher W; Bergsland, Maria; Sandberg, Magnus; Sandberg, Rickard; Muhr, Jonas; Ericson, Johan

    2013-04-30

    The reiterative deployment of a small cadre of morphogen signals underlies patterning and growth of most tissues during embyogenesis, but how such inductive events result in tissue-specific responses remains poorly understood. By characterizing cis-regulatory modules (CRMs) associated with genes regulated by Sonic hedgehog (Shh), retinoids, or bone morphogenetic proteins in the CNS, we provide evidence that the neural-specific interpretation of morphogen signaling reflects a direct integration of these pathways with SoxB1 proteins at the CRM level. Moreover, expression of SoxB1 proteins in the limb bud confers on mesodermal cells the potential to activate neural-specific target genes upon Shh, retinoid, or bone morphogenetic protein signaling, and the collocation of binding sites for SoxB1 and morphogen-mediatory transcription factors in CRMs faithfully predicts neural-specific gene activity. Thus, an unexpectedly simple transcriptional paradigm appears to conceptually explain the neural-specific interpretation of pleiotropic signaling during vertebrate development. Importantly, genes induced in a SoxB1-dependent manner appear to constitute repressive gene regulatory networks that are directly interlinked at the CRM level to constrain the regional expression of patterning genes. Accordingly, not only does the topology of SoxB1-driven gene regulatory networks provide a tissue-specific mode of gene activation, but it also determines the spatial expression pattern of target genes within the developing neural tube.

  2. Attractor Structures of Signaling Networks: Consequences of Different Conformational Barcode Dynamics and Their Relations to Network-Based Drug Design.

    PubMed

    Szalay, Kristóf Z; Nussinov, Ruth; Csermely, Peter

    2014-06-01

    Conformational barcodes tag functional sites of proteins and are decoded by interacting molecules transmitting the incoming signal. Conformational barcodes are modified by all co-occurring allosteric events induced by post-translational modifications, pathogen, drug binding, etc. We argue that fuzziness (plasticity) of conformational barcodes may be increased by disordered protein structures, by integrative plasticity of multi-phosphorylation events, by increased intracellular water content (decreased molecular crowding) and by increased action of molecular chaperones. This leads to increased plasticity of signaling and cellular networks. Increased plasticity is both substantiated by and inducing an increased noise level. Using the versatile network dynamics tool, Turbine (www.turbine.linkgroup.hu), here we show that the 10 % noise level expected in cellular systems shifts a cancer-related signaling network of human cells from its proliferative attractors to its largest, apoptotic attractor representing their health-preserving response in the carcinogen containing and tumor suppressor deficient environment modeled in our study. Thus, fuzzy conformational barcodes may not only make the cellular system more plastic, and therefore more adaptable, but may also stabilize the complex system allowing better access to its largest attractor.

  3. Signalling network construction for modelling plant defence response.

    PubMed

    Miljkovic, Dragana; Stare, Tjaša; Mozetič, Igor; Podpečan, Vid; Petek, Marko; Witek, Kamil; Dermastia, Marina; Lavrač, Nada; Gruden, Kristina

    2012-01-01

    Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2) triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be utilised for

  4. Protein Structure Network-based Drug Design.

    PubMed

    Liang, Zhongjie; Hu, Guang

    2016-01-01

    Although structure-based drug design (SBDD) has become an indispensable tool in drug discovery for a long time, it continues to pose major challenges to date. With the advancement of "omics" techniques, systems biology has enriched SBDD into a new era, called polypharmacology, in which multi-targets drug or drug combination is designed to fight complex diseases. As a preliminary tool in systems biology, protein structure networks (PSNs) treat a protein as a set of residues linked by edges corresponding to the intramolecular interactions existing in folded structures between the residues. The PSN offers a computationally efficient tool to study the structure and function of proteins, and thus may facilitate structurebased drug design. Herein, we provide an overview of recent advances in PSNs, from predicting functionally important residues, to charactering protein-protein interactions and allosteric communication paths. Furthermore, we discuss potential pharmacological applications of PSN concepts and tools, and highlight the application to two families of drug targets, GPCRs and Hsp90. Although the application of PSNs as a framework for computer-aided drug discovery has been limited to date, we put forward the potential utility value in the near future and propose the PSNs could also serve as a new tool for polypharmacology research.

  5. Signal Transduction Pathways of TNAP: Molecular Network Analyses.

    PubMed

    Négyessy, László; Györffy, Balázs; Hanics, János; Bányai, Mihály; Fonta, Caroline; Bazsó, Fülöp

    2015-01-01

    Despite the growing body of evidence pointing on the involvement of tissue non-specific alkaline phosphatase (TNAP) in brain function and diseases like epilepsy and Alzheimer's disease, our understanding about the role of TNAP in the regulation of neurotransmission is severely limited. The aim of our study was to integrate the fragmented knowledge into a comprehensive view regarding neuronal functions of TNAP using objective tools. As a model we used the signal transduction molecular network of a pyramidal neuron after complementing with TNAP related data and performed the analysis using graph theoretic tools. The analyses show that TNAP is in the crossroad of numerous pathways and therefore is one of the key players of the neuronal signal transduction network. Through many of its connections, most notably with molecules of the purinergic system, TNAP serves as a controller by funnelling signal flow towards a subset of molecules. TNAP also appears as the source of signal to be spread via interactions with molecules involved among others in neurodegeneration. Cluster analyses identified TNAP as part of the second messenger signalling cascade. However, TNAP also forms connections with other functional groups involved in neuronal signal transduction. The results indicate the distinct ways of involvement of TNAP in multiple neuronal functions and diseases.

  6. Serotonin signaling mediates protein valuation and aging

    PubMed Central

    Ro, Jennifer; Pak, Gloria; Malec, Paige A; Lyu, Yang; Allison, David B; Kennedy, Robert T; Pletcher, Scott D

    2016-01-01

    Research into how protein restriction improves organismal health and lengthens lifespan has largely focused on cell-autonomous processes. In certain instances, however, nutrient effects on lifespan are independent of consumption, leading us to test the hypothesis that central, cell non-autonomous processes are important protein restriction regulators. We characterized a transient feeding preference for dietary protein after modest starvation in the fruit fly, Drosophila melanogaster, and identified tryptophan hydroxylase (Trh), serotonin receptor 2a (5HT2a), and the solute carrier 7-family amino acid transporter, JhI-21, as required for this preference through their role in establishing protein value. Disruption of any one of these genes increased lifespan up to 90% independent of food intake suggesting the perceived value of dietary protein is a critical determinant of its effect on lifespan. Evolutionarily conserved neuromodulatory systems that define neural states of nutrient demand and reward are therefore sufficient to control aging and physiology independent of food consumption. DOI: http://dx.doi.org/10.7554/eLife.16843.001 PMID:27572262

  7. Brain Network Analysis from High-Resolution EEG Signals

    NASA Astrophysics Data System (ADS)

    de Vico Fallani, Fabrizio; Babiloni, Fabio

    Over the last decade, there has been a growing interest in the detection of the functional connectivity in the brain from different neuroelectromagnetic and hemodynamic signals recorded by several neuro-imaging devices such as the functional Magnetic Resonance Imaging (fMRI) scanner, electroencephalography (EEG) and magnetoencephalography (MEG) apparatus. Many methods have been proposed and discussed in the literature with the aim of estimating the functional relationships among different cerebral structures. However, the necessity of an objective comprehension of the network composed by the functional links of different brain regions is assuming an essential role in the Neuroscience. Consequently, there is a wide interest in the development and validation of mathematical tools that are appropriate to spot significant features that could describe concisely the structure of the estimated cerebral networks. The extraction of salient characteristics from brain connectivity patterns is an open challenging topic, since often the estimated cerebral networks have a relative large size and complex structure. Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach seems relevant and useful as firstly demonstrated on a set of anatomical brain networks. In those studies, the authors have employed two characteristic measures, the average shortest path L and the clustering index C, to extract respectively the global and local properties of the network structure. They have found that anatomical brain networks exhibit many local connections (i.e. a high C) and few random long distance connections (i.e. a low L). These values identify a particular model that interpolate between a regular

  8. Construction of ontology augmented networks for protein complex prediction.

    PubMed

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

    2013-01-01

    Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

  9. [Analysis of signal peptides of the secreted proteins in Agrobacterium tumefaciens C58].

    PubMed

    Fan, Cheng-Ming; Li, Cheng-Yun; Zhao, Ming-Fu; He, Yue-Qiu

    2005-08-01

    The 4554 ORFs of Agrobacterium tumefaciens C58 Cereon were used for the prediction of signal peptides by the network tools, such as SignalP3.0, LipoP1.0, TMHMM2.0 and TargetP1.01. Total 203 signal peptides with conserved amino residues are found, among them, 158 are secretary types, 9 are RR-motif types, 28 are SignalPase II types and 8 are bacteriocin-pheromone types. However, only two signal peptides from the secreted proteins, AGR-C-1878p and AGR-C-1880p have the same amino sequences, showing the signal peptides of the strain are highly variable.

  10. Control of protein signaling using a computationally designed GTPase/GEF orthogonal pair.

    PubMed

    Kapp, Gregory T; Liu, Sen; Stein, Amelie; Wong, Derek T; Reményi, Attila; Yeh, Brian J; Fraser, James S; Taunton, Jack; Lim, Wendell A; Kortemme, Tanja

    2012-04-03

    Signaling pathways depend on regulatory protein-protein interactions; controlling these interactions in cells has important applications for reengineering biological functions. As many regulatory proteins are modular, considerable progress in engineering signaling circuits has been made by recombining commonly occurring domains. Our ability to predictably engineer cellular functions, however, is constrained by complex crosstalk observed in naturally occurring domains. Here we demonstrate a strategy for improving and simplifying protein network engineering: using computational design to create orthogonal (non-crossreacting) protein-protein interfaces. We validated the design of the interface between a key signaling protein, the GTPase Cdc42, and its activator, Intersectin, biochemically and by solving the crystal structure of the engineered complex. The designed GTPase (orthoCdc42) is activated exclusively by its engineered cognate partner (orthoIntersectin), but maintains the ability to interface with other GTPase signaling circuit components in vitro. In mammalian cells, orthoCdc42 activity can be regulated by orthoIntersectin, but not wild-type Intersectin, showing that the designed interaction can trigger complex processes. Computational design of protein interfaces thus promises to provide specific components that facilitate the predictable engineering of cellular functions.

  11. EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT

    PubMed Central

    Choudhary, Kumari Sonal; Rohatgi, Neha; Briem, Eirikur; Gudjonsson, Thorarinn; Gudmundsson, Steinn; Rolfsson, Ottar

    2016-01-01

    Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend. PMID:27253373

  12. Network Analysis of Neurodegenerative Disease Highlights a Role of Toll-Like Receptor Signaling

    PubMed Central

    Nguyen, Thanh-Phuong; Morine, Melissa J.

    2014-01-01

    Despite significant advances in the study of the molecular mechanisms altered in the development and progression of neurodegenerative diseases (NDs), the etiology is still enigmatic and the distinctions between diseases are not always entirely clear. We present an efficient computational method based on protein-protein interaction network (PPI) to model the functional network of NDs. The aim of this work is fourfold: (i) reconstruction of a PPI network relating to the NDs, (ii) construction of an association network between diseases based on proximity in the disease PPI network, (iii) quantification of disease associations, and (iv) inference of potential molecular mechanism involved in the diseases. The functional links of diseases not only showed overlap with the traditional classification in clinical settings, but also offered new insight into connections between diseases with limited clinical overlap. To gain an expanded view of the molecular mechanisms involved in NDs, both direct and indirect connector proteins were investigated. The method uncovered molecular relationships that are in common apparently distinct diseases and provided important insight into the molecular networks implicated in disease pathogenesis. In particular, the current analysis highlighted the Toll-like receptor signaling pathway as a potential candidate pathway to be targeted by therapy in neurodegeneration. PMID:24551850

  13. Next generation of network medicine: interdisciplinary signaling approaches.

    PubMed

    Korcsmaros, Tamas; Schneider, Maria Victoria; Superti-Furga, Giulio

    2017-02-20

    In the last decade, network approaches have transformed our understanding of biological systems. Network analyses and visualizations have allowed us to identify essential molecules and modules in biological systems, and improved our understanding of how changes in cellular processes can lead to complex diseases, such as cancer, infectious and neurodegenerative diseases. "Network medicine" involves unbiased large-scale network-based analyses of diverse data describing interactions between genes, diseases, phenotypes, drug targets, drug transport, drug side-effects, disease trajectories and more. In terms of drug discovery, network medicine exploits our understanding of the network connectivity and signaling system dynamics to help identify optimal, often novel, drug targets. Contrary to initial expectations, however, network approaches have not yet delivered a revolution in molecular medicine. In this review, we propose that a key reason for the limited impact, so far, of network medicine is a lack of quantitative multi-disciplinary studies involving scientists from different backgrounds. To support this argument, we present existing approaches from structural biology, 'omics' technologies (e.g., genomics, proteomics, lipidomics) and computational modeling that point towards how multi-disciplinary efforts allow for important new insights. We also highlight some breakthrough studies as examples of the potential of these approaches, and suggest ways to make greater use of the power of interdisciplinarity. This review reflects discussions held at an interdisciplinary signaling workshop which facilitated knowledge exchange from experts from several different fields, including in silico modelers, computational biologists, biochemists, geneticists, molecular and cell biologists as well as cancer biologists and pharmacologists.

  14. Ca2+ signaling and intracellular Ca2+ binding proteins.

    PubMed

    Niki, I; Yokokura, H; Sudo, T; Kato, M; Hidaka, H

    1996-10-01

    Changes in cytosolic Ca2+ concentrations evoke a wide range of cellular responses and intracellular Ca(2+)-binding proteins are the key molecules to transduce Ca2+ signaling via enzymatic reactions or modulation of protein/protein interations (Fig.1). The EF hand proteins, like calmodulin and S100 proteins, are considered to exert Ca(2+)-dependent actions in the nucleus or the cytoplasm. The Ca2+/phospholipid binding proteins are classified into two groups, the annexins and the C2 region proteins. These proteins, distributed mainly in the cytoplasm, translocate to the plasma membrane in response to an increase in cytosolic Ca2+ and function in the vicinity of the membrane. Ca2+ storage proteins in the endoplasmic or sarcoplasmic reticulum provide the high Ca2+ capacity of the Ca2+ store sites, which regulate intracellular Ca2+ distribution. The variety and complexity of Ca2+ signaling result from the cooperative actions of specific Ca(2+)-binding proteins. This review describes biochemical properties of intracellular Ca(2+)-binding proteins and their proposed roles in mediating Ca2+ signaling.

  15. Protein phosphorylation and its role in archaeal signal transduction

    PubMed Central

    Esser, Dominik; Hoffmann, Lena; Pham, Trong Khoa; Bräsen, Christopher; Qiu, Wen; Wright, Phillip C.; Albers, Sonja-Verena; Siebers, Bettina

    2016-01-01

    Reversible protein phosphorylation is the main mechanism of signal transduction that enables cells to rapidly respond to environmental changes by controlling the functional properties of proteins in response to external stimuli. However, whereas signal transduction is well studied in Eukaryotes and Bacteria, the knowledge in Archaea is still rather scarce. Archaea are special with regard to protein phosphorylation, due to the fact that the two best studied phyla, the Euryarchaeota and Crenarchaeaota, seem to exhibit fundamental differences in regulatory systems. Euryarchaeota (e.g. halophiles, methanogens, thermophiles), like Bacteria and Eukaryotes, rely on bacterial-type two-component signal transduction systems (phosphorylation on His and Asp), as well as on the protein phosphorylation on Ser, Thr and Tyr by Hanks-type protein kinases. Instead, Crenarchaeota (e.g. acidophiles and (hyper)thermophiles) only depend on Hanks-type protein phosphorylation. In this review, the current knowledge of reversible protein phosphorylation in Archaea is presented. It combines results from identified phosphoproteins, biochemical characterization of protein kinases and protein phosphatases as well as target enzymes and first insights into archaeal signal transduction by biochemical, genetic and polyomic studies. PMID:27476079

  16. Discriminating lysosomal membrane protein types using dynamic neural network.

    PubMed

    Tripathi, Vijay; Gupta, Dwijendra Kumar

    2014-01-01

    This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.

  17. Signal peptides are allosteric activators of the protein translocase

    PubMed Central

    Gouridis, Giorgos; Karamanou, Spyridoula; Gelis, Ioannis; Kalodimos, Charalampos G.; Economou, Anastassios

    2010-01-01

    Extra-cytoplasmic polypeptides are usually synthesized as “preproteins” carrying aminoterminal, cleavable signal peptides1 and secreted across membranes by translocases. The main bacterial translocase comprises the SecYEG protein-conducting channel and the peripheral ATPase motor SecA2,3. Most proteins destined for the periplasm and beyond are exported post-translationally by SecA2,3. Preprotein targeting to SecA is thought to involve signal peptides4 and chaperones like SecB5,6. Here we reveal that signal peptides have a novel role beyond targeting: they are essential allosteric activators of the translocase. Upon docking on their binding groove on SecA, signal peptides act in trans to drive three successive states: first, “triggering” that drives the translocase to a lower activation energy state; then “trapping” that engages non-native preprotein mature domains docked with high affinity on the secretion apparatus and, finally, “secretion” during which trapped mature domains undergo multiple turnovers of translocation in segments7. A significant contribution by mature domains renders signal peptides less critical in bacterial secretory protein targeting than currently assumed. Rather, it is their function as allosteric activators of the translocase that renders signal peptides essential for protein secretion. A role for signal peptides and targeting sequences as allosteric activators may be universal in protein translocases. PMID:19924216

  18. Signal Processing in Periodically Forced Gradient Frequency Neural Networks.

    PubMed

    Kim, Ji Chul; Large, Edward W

    2015-01-01

    Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing.

  19. Signal Processing in Periodically Forced Gradient Frequency Neural Networks

    PubMed Central

    Kim, Ji Chul; Large, Edward W.

    2015-01-01

    Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing. PMID:26733858

  20. Programs for control of an analog-signal switching network

    SciTech Connect

    D'Ottavio, T.; Enriquez, R.; Katz, R.; Skelly, J.

    1989-01-01

    A suite of programs has been developed to control the network of analog-signal switching multiplexers in the AGS complex. The software is driven by a relational database which describes the architecture of the multiplexer tree and the set of available analog signals. Signals are routed through a three-layer multiplexer tree, to be made available at four consoles each with three 4-trace oscilloscopes. A menu-structured operator interface program is available at each console, to accept requests to route any available analog signal to any of that console's 12 oscilloscope traces. A common routing-server program provides automatic routing-server program provides automatic routing of requested signals through the layers of multiplexers, maintaining a reservation database to denote free and in-use trunks. Expansion of the analog signal system is easily accommodated in software by adding new signals, trunks, multiplexers, or consoles to the database. Programmatic control of the triggering signals for each of the oscilloscopes is also provided. 3 refs., 4 figs., 3 tabs.

  1. PACRG, a protein linked to ciliary motility, mediates cellular signaling.

    PubMed

    Loucks, Catrina M; Bialas, Nathan J; Dekkers, Martijn P J; Walker, Denise S; Grundy, Laura J; Li, Chunmei; Inglis, P Nick; Kida, Katarzyna; Schafer, William R; Blacque, Oliver E; Jansen, Gert; Leroux, Michel R

    2016-07-01

    Cilia are microtubule-based organelles that project from nearly all mammalian cell types. Motile cilia generate fluid flow, whereas nonmotile (primary) cilia are required for sensory physiology and modulate various signal transduction pathways. Here we investigate the nonmotile ciliary signaling roles of parkin coregulated gene (PACRG), a protein linked to ciliary motility. PACRG is associated with the protofilament ribbon, a structure believed to dictate the regular arrangement of motility-associated ciliary components. Roles for protofilament ribbon-associated proteins in nonmotile cilia and cellular signaling have not been investigated. We show that PACRG localizes to a small subset of nonmotile cilia in Caenorhabditis elegans, suggesting an evolutionary adaptation for mediating specific sensory/signaling functions. We find that it influences a learning behavior known as gustatory plasticity, in which it is functionally coupled to heterotrimeric G-protein signaling. We also demonstrate that PACRG promotes longevity in C. elegans by acting upstream of the lifespan-promoting FOXO transcription factor DAF-16 and likely upstream of insulin/IGF signaling. Our findings establish previously unrecognized sensory/signaling functions for PACRG and point to a role for this protein in promoting longevity. Furthermore, our work suggests additional ciliary motility-signaling connections, since EFHC1 (EF-hand containing 1), a potential PACRG interaction partner similarly associated with the protofilament ribbon and ciliary motility, also positively regulates lifespan.

  2. PACRG, a protein linked to ciliary motility, mediates cellular signaling

    PubMed Central

    Loucks, Catrina M.; Bialas, Nathan J.; Dekkers, Martijn P. J.; Walker, Denise S.; Grundy, Laura J.; Li, Chunmei; Inglis, P. Nick; Kida, Katarzyna; Schafer, William R.; Blacque, Oliver E.; Jansen, Gert; Leroux, Michel R.

    2016-01-01

    Cilia are microtubule-based organelles that project from nearly all mammalian cell types. Motile cilia generate fluid flow, whereas nonmotile (primary) cilia are required for sensory physiology and modulate various signal transduction pathways. Here we investigate the nonmotile ciliary signaling roles of parkin coregulated gene (PACRG), a protein linked to ciliary motility. PACRG is associated with the protofilament ribbon, a structure believed to dictate the regular arrangement of motility-associated ciliary components. Roles for protofilament ribbon–associated proteins in nonmotile cilia and cellular signaling have not been investigated. We show that PACRG localizes to a small subset of nonmotile cilia in Caenorhabditis elegans, suggesting an evolutionary adaptation for mediating specific sensory/signaling functions. We find that it influences a learning behavior known as gustatory plasticity, in which it is functionally coupled to heterotrimeric G-protein signaling. We also demonstrate that PACRG promotes longevity in C. elegans by acting upstream of the lifespan-promoting FOXO transcription factor DAF-16 and likely upstream of insulin/IGF signaling. Our findings establish previously unrecognized sensory/signaling functions for PACRG and point to a role for this protein in promoting longevity. Furthermore, our work suggests additional ciliary motility-signaling connections, since EFHC1 (EF-hand containing 1), a potential PACRG interaction partner similarly associated with the protofilament ribbon and ciliary motility, also positively regulates lifespan. PMID:27193298

  3. STITCH: interaction networks of chemicals and proteins

    PubMed Central

    Kuhn, Michael; von Mering, Christian; Campillos, Monica; Jensen, Lars Juhl; Bork, Peer

    2008-01-01

    The knowledge about interactions between proteins and small molecules is essential for the understanding of molecular and cellular functions. However, information on such interactions is widely dispersed across numerous databases and the literature. To facilitate access to this data, STITCH (‘search tool for interactions of chemicals’) integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug–target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. Our database contains interaction information for over 68 000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database. STITCH is available at http://stitch.embl.de/ PMID:18084021

  4. Signaling networks during development: the case of asymmetric cell division in the Drosophila nervous system.

    PubMed

    Carmena, Ana

    2008-09-01

    Remarkable progress in genetics and molecular biology has made possible the sequencing of the genomes from numerous species. In the post-genomic era, technical developments in the fields of proteomics and bioinformatics are poised to further catapult our understanding of protein structure, function and organization into complex signaling networks. One of the greatest challenges in the field now is to unravel the functional signaling networks and their spatio-temporal regulation in living cells. Here, the need for such in vivo system-wide level approach is illustrated in relation to the mechanisms that underlie the biological process of asymmetric cell division. Genomic, post-genomic and live imaging techniques are reviewed in light of the huge impact they are having on this field for the discovering of new proteins and for the in vivo analysis of asymmetric cell division. The proteins, signals and the emerging networking of functional connections that is arising between them during this process in the Drosophila nervous system will be also discussed.

  5. A Comprehensive Statistical Model for Cell Signaling and Protein Activity Inference

    PubMed Central

    Yörük, Erdem; Ochs, Michael F.; Geman, Donald; Younes, Laurent

    2010-01-01

    Protein signaling networks play a central role in transcriptional regulation and the etiology of many diseases. Statistical methods, particularly Bayesian networks, have been widely used to model cell signaling, mostly for model organisms and with focus on uncovering connectivity rather than inferring aberrations. Extensions to mammalian systems have not yielded compelling results, due likely to greatly increased complexity and limited proteomic measurements in vivo. In this study, we propose a comprehensive statistical model that is anchored to a predefined core topology, has a limited complexity due to parameter sharing and uses micorarray data of mRNA transcripts as the only observable components of signaling. Specifically, we account for cell heterogeneity and a multi-level process, representing signaling as a Bayesian network at the cell level, modeling measurements as ensemble averages at the tissue level and incorporating patient-to-patient differences at the population level. Motivated by the goal of identifying individual protein abnormalities as potential therapeutical targets, we applied our method to the RAS-RAF network using a breast cancer study with 118 patients. We demonstrated rigorous statistical inference, established reproducibility through simulations and the ability to recover receptor status from available microarray data. PMID:20855924

  6. Regulator of G-protein signaling (RGS) proteins in cancer biology.

    PubMed

    Hurst, Jillian H; Hooks, Shelley B

    2009-11-15

    The regulator of G-protein signaling (RGS) family is a diverse group of multifunctional proteins that regulate cellular signaling events downstream of G-protein coupled receptors (GPCRs). In recent years, GPCRs have been linked to the initiation and progression of multiple cancers; thus, regulators of GPCR signaling are also likely to be important to the pathophysiology of cancer. This review highlights recent studies detailing changes in RGS transcript expression during oncogenesis, single nucleotide polymorphisms in RGS proteins linked to lung and bladder cancers, and specific roles for RGS proteins in multiple cancer types.

  7. N-myristoylated proteins, key components in intracellular signal transduction systems enabling rapid and flexible cell responses

    PubMed Central

    HAYASHI, Nobuhiro; TITANI, Koiti

    2010-01-01

    N-myristoylation, one of the co- or post-translational modifications of proteins, has so far been regarded as necessary for anchoring of proteins to membranes. Recently, we have revealed that Nα-myristoylation of several brain proteins unambiguously regulates certain protein–protein interactions that may affect signaling pathways in brain. Comparison of the amino acid sequences of myristoylated proteins including those in other organs suggests that this regulation is involved in signaling pathways not only in brain but also in other organs. Thus, it has been shown that myristoylated proteins in cells regulate the signal transduction between membranes and cytoplasmic fractions. An algorithm we have developed to identify myristoylated proteins in cells predicts the presence of hundreds of myristoylated proteins. Interestingly, a large portion of the myristoylated proteins thought to take part in signal transduction between membranes and cytoplasmic fractions are included in the predicted myristoylated proteins. If the proteins functionally regulated by myristoylation, a posttranslational protein modification, were understood as cross-talk points within the intracellular signal transduction system, known signaling pathways could thus be linked to each other, and a novel map of this intracellular network could be constructed. On the basis of our recent results, this review will highlight the multifunctional aspects of protein N-myristoylation in brain. PMID:20467215

  8. Modularity and functional plasticity of scaffold proteins as p(l)acemakers in cell signaling.

    PubMed

    Pan, Catherine Qiurong; Sudol, Marius; Sheetz, Michael; Low, Boon Chuan

    2012-11-01

    Cells coordinate and integrate various functional modules that control their dynamics, intracellular trafficking, metabolism and gene expression. Such capacity is mediated by specific scaffold proteins that tether multiple components of signaling pathways at plasma membrane, Golgi apparatus, mitochondria, endoplasmic reticulum, nucleus and in more specialized subcellular structures such as focal adhesions, cell-cell junctions, endosomes, vesicles and synapses. Scaffold proteins act as "pacemakers" as well as "placemakers" that regulate the temporal, spatial and kinetic aspects of protein complex assembly by modulating the local concentrations, proximity, subcellular dispositions and biochemical properties of the target proteins through the intricate use of their modular protein domains. These regulatory mechanisms allow them to gate the specificity, integration and crosstalk of different signaling modules. In addition to acting as physical platforms for protein assembly, many professional scaffold proteins can also directly modify the properties of their targets while they themselves can be regulated by post-translational modifications and/or mechanical forces. Furthermore, multiple scaffold proteins can form alliances of higher-order regulatory networks. Here, we highlight the emerging themes of scaffold proteins by analyzing their common and distinctive mechanisms of action and regulation, which underlie their functional plasticity in cell signaling. Understanding these mechanisms in the context of space, time and force should have ramifications for human physiology and for developing new therapeutic approaches to control pathological states and diseases.

  9. Homologs of the Hh signalling network in C. elegans.

    PubMed

    Bürglin, Thomas R; Kuwabara, Patricia E

    2006-01-28

    In Drosophila and vertebrates, Hedgehog (Hh) signalling is mediated by a cascade of genes, which play essential roles in cell proliferation and survival, and in patterning of the embryo, limb buds and organs. In C. elegans, this pathway has undergone considerable evolutionary divergence; genes encoding homologues of key pathway members, including Hh, Smoothened, Cos2, Fused and Suppressor of Fused, are absent. Surprisingly, over sixty proteins (i.e. WRT, GRD, GRL, and QUA), encoded by a set of genes collectively referred to as the Hh-related genes, and two co-orthologs (PTC-1,-3) of fly Patched, a Hh receptor, are present in C. elegans. Several of the Hh-related proteins are bipartite and all can potentially generate peptides with signalling activity, although none of these peptides shares obvious sequence similarity with Hh. In addition, the ptc-related (ptr) genes, which are present in a single copy in Drosophila and vertebrates and encode proteins closely related to Patched, have undergone an expansion in number in nematodes. A number of functions, including roles in molting, have been attributed to the C. elegans Hh-related, PTC and PTR proteins; most of these functions involve processes that are associated with the trafficking of proteins, sterols or sterol-modified proteins. Genes encoding other components of the Hh signalling pathway are also found in C. elegans, but their functions remain to be elucidated.

  10. An Improved Network Strain Filter for Detecting Transient Deformation Signals

    NASA Astrophysics Data System (ADS)

    Ohtani, R.; McGuire, J.; Segall, P.

    2008-12-01

    We have developed a tool to detect transient signals such as aseismic fault slip and magmatic intrusion automatically from large-scale (principally GPS) geodetic arrays, referred to as a Network Strain Filter (NSF). The NSF is capable of detecting transient signals in large data sets which may be difficult to identify by visual inspection of individual time series. The underlying principle is to exploit the spatially coherent nature of tectonic signals. The NSF models GPS displacement time series as a sum of contributions from tectonic transients, steady motion due to secular deformation, site-specific local benchmark motion, reference frame errors, and white noise. Transient deformation is represented by a spatial wavelet basis with time varying coefficients estimated using Kalman filtering techniques. A "hyperparameter" is also estimated to constrain the amount of temporal smoothness of the tectonic deformation. As station distribution is irregular and wavelets have local support (non-zero only over a localized domain), the design matrix is generally ill-conditioned. We investigate two strategies for regularizing the problem. The first is explicit spatial smoothing of the transient deformation. The second is to simply exclude wavelet bases that don't span some minimum number of stations. In this case, the smallest wavelet scale is determined such that the residual variance is consistent with the a priori errors of the data. Similarly the degree of spatial smoothing is determined by a priori knowledge of the data errors. To test the performance of the NSF, we carried out numerical tests using the southern California Integrated GPS Network (SCIGN) station distribution with synthetic transients of variable signal to noise ratio. We tested a six-year-long time series with a slow slip event with a duration of three years. Due to the long duration of the transient event, the contributions from secular motion and benchmark wobble make it difficult to identify the

  11. Dissecting Arabidopsis G beta signal transduction on the protein surface

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The heterotrimeric G protein complex provides signal amplification and target specificity. The Arabidopsis Gbeta subunit of this complex (AGB1) interacts with and modulates the activity of target cytoplasmic proteins. This specificity resides in the structure of the interface between AGB1 and its ta...

  12. Distributed Signal Processing for Wireless EEG Sensor Networks.

    PubMed

    Bertrand, Alexander

    2015-11-01

    Inspired by ongoing evolutions in the field of wireless body area networks (WBANs), this tutorial paper presents a conceptual and exploratory study of wireless electroencephalography (EEG) sensor networks (WESNs), with an emphasis on distributed signal processing aspects. A WESN is conceived as a modular neuromonitoring platform for high-density EEG recordings, in which each node is equipped with an electrode array, a signal processing unit, and facilities for wireless communication. We first address the advantages of such a modular approach, and we explain how distributed signal processing algorithms make WESNs more power-efficient, in particular by avoiding data centralization. We provide an overview of distributed signal processing algorithms that are potentially applicable in WESNs, and for illustration purposes, we also provide a more detailed case study of a distributed eye blink artifact removal algorithm. Finally, we study the power efficiency of these distributed algorithms in comparison to their centralized counterparts in which all the raw sensor signals are centralized in a near-end or far-end fusion center.

  13. Protein-spanning water networks and implications for prediction of protein-protein interactions mediated through hydrophobic effects.

    PubMed

    Cui, Di; Ou, Shuching; Patel, Sandeep

    2014-12-01

    Hydrophobic effects, often conflated with hydrophobic forces, are implicated as major determinants in biological association and self-assembly processes. Protein-protein interactions involved in signaling pathways in living systems are a prime example where hydrophobic effects have profound implications. In the context of protein-protein interactions, a priori knowledge of relevant binding interfaces (i.e., clusters of residues involved directly with binding interactions) is difficult. In the case of hydrophobically mediated interactions, use of hydropathy-based methods relying on single residue hydrophobicity properties are routinely and widely used to predict propensities for such residues to be present in hydrophobic interfaces. However, recent studies suggest that consideration of hydrophobicity for single residues on a protein surface require accounting of the local environment dictated by neighboring residues and local water. In this study, we use a method derived from percolation theory to evaluate spanning water networks in the first hydration shells of a series of small proteins. We use residue-based water density and single-linkage clustering methods to predict hydrophobic regions of proteins; these regions are putatively involved in binding interactions. We find that this simple method is able to predict with sufficient accuracy and coverage the binding interface residues of a series of proteins. The approach is competitive with automated servers. The results of this study highlight the importance of accounting of local environment in determining the hydrophobic nature of individual residues on protein surfaces.

  14. G proteins: critical control points for transmembrane signals.

    PubMed Central

    Neer, E. J.

    1994-01-01

    Heterotrimeric GTP-binding proteins (G proteins) that are made up of alpha and beta gamma subunits couple many kinds of cell-surface receptors to intracellular effector enzymes or ion channels. Every cell contains several types of receptors, G proteins, and effectors. The specificity with which G protein subunits interact with receptors and effectors defines the range of responses a cell is able to make to an external signal. Thus, the G proteins act as a critical control point that determines whether a signal spreads through several pathways or is focused to a single pathway. In this review, I will summarize some features of the structure and function of mammalian G protein subunits, discuss the role of both alpha and beta gamma subunits in regulation of effectors, the role of the beta gamma subunit in macromolecular assembly, and the mechanisms that might make some responses extremely specific and others rather diffuse. PMID:8142895

  15. Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases

    PubMed Central

    Lin, Peng-Lin; Yu, Ya-Wen

    2016-01-01

    Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn’s disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn’s disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net. PMID:27622767

  16. Evolution of biomolecular networks: lessons from metabolic and protein interactions.

    PubMed

    Yamada, Takuji; Bork, Peer

    2009-11-01

    Despite only becoming popular at the beginning of this decade, biomolecular networks are now frameworks that facilitate many discoveries in molecular biology. The nodes of these networks are usually proteins (specifically enzymes in metabolic networks), whereas the links (or edges) are their interactions with other molecules. These networks are made up of protein-protein interactions or enzyme-enzyme interactions through shared metabolites in the case of metabolic networks. Evolutionary analysis has revealed that changes in the nodes and links in protein-protein interaction and metabolic networks are subject to different selection pressures owing to distinct topological features. However, many evolutionary constraints can be uncovered only if temporal and spatial aspects are included in the network analysis.

  17. Systematic interactome mapping and genetic perturbation analysis of a C. elegans TGF-beta signaling network.

    PubMed

    Tewari, Muneesh; Hu, Patrick J; Ahn, Jin Sook; Ayivi-Guedehoussou, Nono; Vidalain, Pierre-Olivier; Li, Siming; Milstein, Stuart; Armstrong, Chris M; Boxem, Mike; Butler, Maurice D; Busiguina, Svetlana; Rual, Jean-François; Ibarrola, Nieves; Chaklos, Sabrina T; Bertin, Nicolas; Vaglio, Philippe; Edgley, Mark L; King, Kevin V; Albert, Patrice S; Vandenhaute, Jean; Pandey, Akhilesh; Riddle, Donald L; Ruvkun, Gary; Vidal, Marc

    2004-02-27

    To initiate a system-level analysis of C. elegans DAF-7/TGF-beta signaling, we combined interactome mapping with single and double genetic perturbations. Yeast two-hybrid (Y2H) screens starting with known DAF-7/TGF-beta pathway components defined a network of 71 interactions among 59 proteins. Coaffinity purification (co-AP) assays in mammalian cells confirmed the overall quality of this network. Systematic perturbations of the network using RNAi, both in wild-type and daf-7/TGF-beta pathway mutant animals, identified nine DAF-7/TGF-beta signaling modifiers, seven of which are conserved in humans. We show that one of these has functional homology to human SNO/SKI oncoproteins and that mutations at the corresponding genetic locus daf-5 confer defects in DAF-7/TGF-beta signaling. Our results reveal substantial molecular complexity in DAF-7/TGF-beta signal transduction. Integrating interactome maps with systematic genetic perturbations may be useful for developing a systems biology approach to this and other signaling modules.

  18. An integrative C. elegans protein-protein interaction network with reliability assessment based on a probabilistic graphical model.

    PubMed

    Huang, Xiao-Tai; Zhu, Yuan; Chan, Leanne Lai Hang; Zhao, Zhongying; Yan, Hong

    2016-01-01

    In Caenorhabditis elegans, a large number of protein-protein interactions (PPIs) are identified by different experiments. However, a comprehensive weighted PPI network, which is essential for signaling pathway inference, is not yet available in this model organism. Therefore, we firstly construct an integrative PPI network in C. elegans with 12,951 interactions involving 5039 proteins from seven molecular interaction databases. Then, a reliability score based on a probabilistic graphical model (RSPGM) is proposed to assess PPIs. It assumes that the random number of interactions between two proteins comes from the Bernoulli distribution to avoid multi-links. The main parameter of the RSPGM score contains a few latent variables which can be considered as several common properties between two proteins. Validations on high-confidence yeast datasets show that RSPGM provides more accurate evaluation than other approaches, and the PPIs in the reconstructed PPI network have higher biological relevance than that in the original network in terms of gene ontology, gene expression, essentiality and the prediction of known protein complexes. Furthermore, this weighted integrative PPI network in C. elegans is employed on inferring interaction path of the canonical Wnt/β-catenin pathway as well. Most genes on the inferred interaction path have been validated to be Wnt pathway components. Therefore, RSPGM is essential and effective for evaluating PPIs and inferring interaction path. Finally, the PPI network with RSPGM scores can be queried and visualized on a user interactive website, which is freely available at .

  19. Photoacoustic analysis of proteins: volumetric signals and fluorescence quantum yields.

    PubMed Central

    Kurian, E; Prendergast, F G; Small, J R

    1997-01-01

    A series of proteins has been examined using time-resolved, pulsed-laser volumetric photoacoustic spectroscopy. Photoacoustic waveforms were collected to measure heat release for calculation of fluorescence quantum yields, and to explore the possibility of photoinduced nonthermal volume changes occurring in these protein samples. The proteins studied were the green fluorescent protein (GFP); intestinal fatty acid binding protein (IFABP), and adipocyte lipid-binding protein (ALBP), each labeled noncovalently with 1-anilinonaphthalene-8-sulfonate (1,8-ANS) and covalently with 6-acryloyl-2-(dimethylamino)naphthalene (acrylodan); and acrylodan-labeled IFABP and ALBP with added oleic acid. Of this group of proteins, only the ALBP labeled with 1,8-ANS showed significant nonthermal volume changes at the beta = 0 temperature (approximately 3.8 degrees C) for the buffer used (10 mM Tris-HCI, pH 7.5) (beta is the thermal cubic volumetric expansion coefficient). For all of the proteins except for acrylodan-labeled IFABP, the fluorescence quantum yields calculated assuming simple energy conservation were anomalously high, i.e., the apparent heat signals were lower than those predicted from independent fluorescence measurements. The consistent anomalies suggest that the low photoacoustic signals may be characteristic of fluorophores buried in proteins, and that photoacoustic signals derive in part from the microenvironment of the absorbing chromophore. Images FIGURE 1 PMID:9199809

  20. Topological, functional, and dynamic properties of the protein interaction networks rewired by benzo(a)pyrene.

    PubMed

    Ba, Qian; Li, Junyang; Huang, Chao; Li, Jingquan; Chu, Ruiai; Wu, Yongning; Wang, Hui

    2015-03-01

    Benzo(a)pyrene is a common environmental and foodborne pollutant that has been identified as a human carcinogen. Although the carcinogenicity of benzo(a)pyrene has been extensively reported, its precise molecular mechanisms and the influence on system-level protein networks are not well understood. To investigate the system-level influence of benzo(a)pyrene on protein interactions and regulatory networks, a benzo(a)pyrene-rewired protein interaction network was constructed based on 769 key proteins derived from more than 500 literature reports. The protein interaction network rewired by benzo(a)pyrene was a scale-free, highly-connected biological system. Ten modules were identified, and 25 signaling pathways were enriched, most of which belong to the human diseases category, especially cancer and infectious disease. In addition, two lung-specific and two liver-specific pathways were identified. Three pathways were specific in short and medium-term networks (<48h), and five pathways were enriched only in the medium-term network (6h-48h). Finally, the expression of linker genes in the network was validated by Western blotting. These findings establish the overall, tissue- and time-specific benzo(a)pyrene-rewired protein interaction networks and provide insights into the biological effects and molecular mechanisms of action of benzo(a)pyrene.

  1. The Signaling State of Orange Carotenoid Protein

    PubMed Central

    Maksimov, Eugene G.; Shirshin, Evgeny A.; Sluchanko, Nikolai N.; Zlenko, Dmitry V.; Parshina, Evgenia Y.; Tsoraev, Georgy V.; Klementiev, Konstantin E.; Budylin, Gleb S.; Schmitt, Franz-Josef; Friedrich, Thomas; Fadeev, Victor V.; Paschenko, Vladimir Z.; Rubin, Andrew B.

    2015-01-01

    Orange carotenoid protein (OCP) is the photoactive protein that is responsible for high light tolerance in cyanobacteria. We studied the kinetics of the OCP photocycle by monitoring changes in its absorption spectrum, intrinsic fluorescence, and fluorescence of the Nile red dye bound to OCP. It was demonstrated that all of these three methods provide the same kinetic parameters of the photocycle, namely, the kinetics of OCP relaxation in darkness was biexponential with a ratio of two components equal to 2:1 independently of temperature. Whereas the changes of the absorption spectrum of OCP characterize the geometry and environment of its chromophore, the intrinsic fluorescence of OCP reveals changes in its tertiary structure, and the fluorescence properties of Nile red indicate the exposure of hydrophobic surface areas of OCP to the solvent following the photocycle. The results of molecular-dynamics studies indicated the presence of two metastable conformations of 3′-hydroxyechinenone, which is consistent with characteristic changes in the Raman spectra. We conclude that rotation of the β-ionylidene ring in the C-terminal domain of OCP could be one of the first conformational rearrangements that occur during photoactivation. The obtained results suggest that the photoactivated form of OCP represents a molten globule-like state that is characterized by increased mobility of tertiary structure elements and solvent accessibility. PMID:26244741

  2. Protein function prediction using neighbor relativity in protein-protein interaction network.

    PubMed

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

    There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network.

  3. Wnt signaling: role in LTP, neural networks and memory.

    PubMed

    Oliva, Carolina A; Vargas, Jessica Y; Inestrosa, Nibaldo C

    2013-06-01

    Wnt components are key regulators of a variety of developmental processes, including embryonic patterning, cell specification, and cell polarity. The Wnt signaling pathway participates in the development of the central nervous system and growing evidence indicates that Wnts also regulates the function of the adult nervous system. In fact, most of the key components including Wnts and Frizzled receptors are expressed in the adult brain. Wnt ligands have been implicated in the regulation of synaptic assembly as well as in neurotransmission and synaptic plasticity. Deregulation of Wnt signaling has been associated with several pathologies, and more recently has been related to neurodegenerative diseases and to mental and mood disorders. In this review, we focus our attention on the Wnt signaling cascade in postnatal life and we review in detail the presence of Wnt signaling components in pre- and postsynaptic regions. Due to the important role of Wnt proteins in wiring neural circuits, we discuss recent findings about the role of Wnt pathways both in basal spontaneous activities as well as in activity-dependent processes that underlie synaptic plasticity. Finally, we review the role of Wnt in vivo and we finish with the most recent data in literature that involves the effect of components of the Wnt signaling pathway in neurological and mental disorders, including a special emphasis on in vivo studies that relate behavioral abnormalities to deficiencies in Wnt signaling, as well as the data that support a neuroprotective role of Wnt proteins in relation to the pathogenesis of Alzheimer's disease.

  4. G protein-coupled receptors provide survival signals in prostate cancer.

    PubMed

    Yowell, Charles W; Daaka, Yehia

    2002-12-01

    Prostate cancer is the leading cause for noncutaneous cancer-related deaths among men in the United States. The disease is biologically characterized as being either androgen dependent or androgen independent. Whereas androgen-dependent prostate cancer can be successfully treated with androgen ablative therapy, to date no cure exists for androgen-independent disease. Mechanisms involved in the progression of prostate cancer to androgen independence are not known. Here we present evidence that in addition to growth factor receptor tyrosine kinases, G protein- coupled receptors can mediate survival signals in prostate cancer cells. The G protein- coupled receptors exert their effects by activating multiple intracellular signal transduction networks that promote prostate cancer cell survival, including the activation of c-Jun N-terminal kinase, protein kinase B (Akt) and nuclear factor-kB. Prostate-expressed G protein- coupled receptors and their downstream effectors may prove to be effective targets in the treatment of advanced prostate cancer.

  5. An information-based network approach for protein classification

    PubMed Central

    Wan, Xiaogeng; Zhao, Xin; Yau, Stephen S. T.

    2017-01-01

    Protein classification is one of the critical problems in bioinformatics. Early studies used geometric distances and polygenetic-tree to classify proteins. These methods use binary trees to present protein classification. In this paper, we propose a new protein classification method, whereby theories of information and networks are used to classify the multivariate relationships of proteins. In this study, protein universe is modeled as an undirected network, where proteins are classified according to their connections. Our method is unsupervised, multivariate, and alignment-free. It can be applied to the classification of both protein sequences and structures. Nine examples are used to demonstrate the efficiency of our new method. PMID:28350835

  6. RNAi screening reveals a large signaling network controlling the Golgi apparatus in human cells.

    PubMed

    Chia, Joanne; Goh, Germaine; Racine, Victor; Ng, Susanne; Kumar, Pankaj; Bard, Frederic

    2012-01-01

    The Golgi apparatus has many important physiological functions, including sorting of secretory cargo and biosynthesis of complex glycans. These functions depend on the intricate and compartmentalized organization of the Golgi apparatus. To investigate the mechanisms that regulate Golgi architecture, we developed a quantitative morphological assay using three different Golgi compartment markers and quantitative image analysis, and performed a kinome- and phosphatome-wide RNAi screen in HeLa cells. Depletion of 159 signaling genes, nearly 20% of genes assayed, induced strong and varied perturbations in Golgi morphology. Using bioinformatics data, a large regulatory network could be constructed. Specific subnetworks are involved in phosphoinositides regulation, acto-myosin dynamics and mitogen activated protein kinase signaling. Most gene depletion also affected Golgi functions, in particular glycan biosynthesis, suggesting that signaling cascades can control glycosylation directly at the Golgi level. Our results provide a genetic overview of the signaling pathways that control the Golgi apparatus in human cells.

  7. Protein Expression Signatures for Inhibition of Epidermal Growth Factor Receptor-mediated Signaling*

    PubMed Central

    Myers, Matthew V.; Manning, H. Charles; Coffey, Robert J.; Liebler, Daniel C.

    2012-01-01

    Analysis of cellular signaling networks typically involves targeted measurements of phosphorylated protein intermediates. However, phosphoproteomic analyses usually require affinity enrichment of phosphopeptides and can be complicated by artifactual changes in phosphorylation caused by uncontrolled preanalytical variables, particularly in the analysis of tissue specimens. We asked whether changes in protein expression, which are more stable and easily analyzed, could reflect network stimulation and inhibition. We employed this approach to analyze stimulation and inhibition of the epidermal growth factor receptor (EGFR) by EGF and selective EGFR inhibitors. Shotgun analysis of proteomes from proliferating A431 cells, EGF-stimulated cells, and cells co-treated with the EGFR inhibitors cetuximab or gefitinib identified groups of differentially expressed proteins. Comparisons of these protein groups identified 13 proteins whose EGF-induced expression changes were reversed by both EGFR inhibitors. Targeted multiple reaction monitoring analysis verified differential expression of 12 of these proteins, which comprise a candidate EGFR inhibition signature. We then tested these 12 proteins by multiple reaction monitoring analysis in three other models: 1) a comparison of DiFi (EGFR inhibitor-sensitive) and HCT116 (EGFR-insensitive) cell lines, 2) in formalin-fixed, paraffin-embedded mouse xenograft DiFi and HCT116 tumors, and 3) in tissue biopsies from a patient with the gastric hyperproliferative disorder Ménétrier's disease who was treated with cetuximab. Of the proteins in the candidate signature, a core group, including c-Jun, Jagged-1, and Claudin 4, were decreased by EGFR inhibitors in all three models. Although the goal of these studies was not to validate a clinically useful EGFR inhibition signature, the results confirm the hypothesis that clinically used EGFR inhibitors generate characteristic protein expression changes. This work further outlines a prototypical

  8. Living ordered neural networks as model systems for signal processing

    NASA Astrophysics Data System (ADS)

    Villard, C.; Amblard, P. O.; Becq, G.; Gory-Fauré, S.; Brocard, J.; Roth, S.

    2007-06-01

    Neural circuit architecture is a fundamental characteristic of the brain, and how architecture is bound to biological functions is still an open question. Some neuronal geometries seen in the retina or the cochlea are intriguing: information is processed in parallel by several entities like in "pooling" networks which have recently drawn the attention of signal processing scientists. These systems indeed exhibit the noise-enhanced processing effect, which is also actively discussed in the neuroscience community at the neuron scale. The aim of our project is to use in-vitro ordered neuron networks as living paradigms to test ideas coming from the computational science. The different technological bolts that have to be solved are enumerated and the first results are presented. A neuron is a polarised cell, with an excitatory axon and a receiving dendritic tree. We present how soma confinement and axon differentiation can be induced by surface functionalization techniques. The recording of large neuron networks, ordered or not, is also detailed and biological signals shown. The main difficulty to access neural noise in the case of weakly connected networks grown on micro electrode arrays is explained. This open the door to a new detection technology suitable for sub-cellular analysis and stimulation, whose development will constitute the next step of this project.

  9. Atypical Diabetic Foot Ulcer Keratinocyte Protein Signaling Correlates with Impaired Wound Healing

    PubMed Central

    Hoke, Glenn D.; Ramos, Corrine; Hoke, Nicholas N.; Crossland, Mary C.; Shawler, Lisa G.

    2016-01-01

    Diabetes mellitus is associated with chronic diabetic foot ulcers (DFUs) and wound infections often resulting in lower extremity amputations. The protein signaling architecture of the mechanisms responsible for impaired DFU healing has not been characterized. In this preliminary clinical study, the intracellular levels of proteins involved in signal transduction networks relevant to wound healing were non-biasedly measured using reverse-phase protein arrays (RPPA) in keratinocytes isolated from DFU wound biopsies. RPPA allows for the simultaneous documentation and assessment of the signaling pathways active in each DFU. Thus, RPPA provides for the accurate mapping of wound healing pathways associated with apoptosis, proliferation, senescence, survival, and angiogenesis. From the study data, we have identified potential diagnostic, or predictive, biomarkers for DFU wound healing derived from the ratios of quantified signaling protein expressions within interconnected pathways. These biomarkers may allow physicians to personalize therapeutic strategies for DFU management on an individual basis based upon the signaling architecture present in each wound. Additionally, we have identified altered, interconnected signaling pathways within DFU keratinocytes that may help guide the development of therapeutics to modulate these dysregulated pathways, many of which parallel the therapeutic targets which are the hallmarks of molecular therapies for treating cancer. PMID:27840833

  10. Global multiple protein-protein interaction network alignment by combining pairwise network alignments

    PubMed Central

    2015-01-01

    Background A wealth of protein interaction data has become available in recent years, creating an urgent need for powerful analysis techniques. In this context, the problem of finding biologically meaningful correspondences between different protein-protein interaction networks (PPIN) is of particular interest. The PPIN of a species can be compared with that of other species through the process of PPIN alignment. Such an alignment can provide insight into basic problems like species evolution and network component function determination, as well as translational problems such as target identification and elucidation of mechanisms of disease spread. Furthermore, multiple PPINs can be aligned simultaneously, expanding the analytical implications of the result. While there are several pairwise network alignment algorithms, few methods are capable of multiple network alignment. Results We propose SMAL, a MNA algorithm based on the philosophy of scaffold-based alignment. SMAL is capable of converting results from any global pairwise alignment algorithms into a MNA in linear time. Using this method, we have built multiple network alignments based on combining pairwise alignments from a number of publicly available (pairwise) network aligners. We tested SMAL using PPINs of eight species derived from the IntAct repository and employed a number of measures to evaluate performance. Additionally, as part of our experimental investigations, we compared the effectiveness of SMAL while aligning up to eight input PPINs, and examined the effect of scaffold network choice on the alignments. Conclusions A key advantage of SMAL lies in its ability to create MNAs through the use of pairwise network aligners for which native MNA implementations do not exist. Experiments indicate that the performance of SMAL was comparable to that of the native MNA implementation of established methods such as IsoRankN and SMETANA. However, in terms of computational time, SMAL was significantly faster

  11. Random Deep Belief Networks for Recognizing Emotions from Speech Signals

    PubMed Central

    Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

    2017-01-01

    Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition. PMID:28356908

  12. Protein-protein interaction network-based detection of functionally similar proteins within species.

    PubMed

    Song, Baoxing; Wang, Fen; Guo, Yang; Sang, Qing; Liu, Min; Li, Dengyun; Fang, Wei; Zhang, Deli

    2012-07-01

    Although functionally similar proteins across species have been widely studied, functionally similar proteins within species showing low sequence similarity have not been examined in detail. Identification of these proteins is of significant importance for understanding biological functions, evolution of protein families, progression of co-evolution, and convergent evolution and others which cannot be obtained by detection of functionally similar proteins across species. Here, we explored a method of detecting functionally similar proteins within species based on graph theory. After denoting protein-protein interaction networks using graphs, we split the graphs into subgraphs using the 1-hop method. Proteins with functional similarities in a species were detected using a method of modified shortest path to compare these subgraphs and to find the eligible optimal results. Using seven protein-protein interaction networks and this method, some functionally similar proteins with low sequence similarity that cannot detected by sequence alignment were identified. By analyzing the results, we found that, sometimes, it is difficult to separate homologous from convergent evolution. Evaluation of the performance of our method by gene ontology term overlap showed that the precision of our method was excellent.

  13. Analysis of membrane proteins in metagenomics: networks of correlated environmental features and protein families.

    PubMed

    Patel, Prianka V; Gianoulis, Tara A; Bjornson, Robert D; Yip, Kevin Y; Engelman, Donald M; Gerstein, Mark B

    2010-07-01

    Recent metagenomics studies have begun to sample the genomic diversity among disparate habitats and relate this variation to features of the environment. Membrane proteins are an intuitive, but thus far overlooked, choice in this type of analysis as they directly interact with the environment, receiving signals from the outside and transporting nutrients. Using global ocean sampling (GOS) data, we found nearly approximately 900,000 membrane proteins in large-scale metagenomic sequence, approximately a fifth of which are completely novel, suggesting a large space of hitherto unexplored protein diversity. Using GPS coordinates for the GOS sites, we extracted additional environmental features via interpolation from the World Ocean Database, the National Center for Ecological Analysis and Synthesis, and empirical models of dust occurrence. This allowed us to study membrane protein variation in terms of natural features, such as phosphate and nitrate concentrations, and also in terms of human impacts, such as pollution and climate change. We show that there is widespread variation in membrane protein content across marine sites, which is correlated with changes in both oceanographic variables and human factors. Furthermore, using these data, we developed an approach, protein families and environment features network (PEN), to quantify and visualize the correlations. PEN identifies small groups of covarying environmental features and membrane protein families, which we call "bimodules." Using this approach, we find that the affinity of phosphate transporters is related to the concentration of phosphate and that the occurrence of iron transporters is connected to the amount of shipping, pollution, and iron-containing dust.

  14. Rap G protein signal in normal and disordered lymphohematopoiesis.

    PubMed

    Minato, Nagahiro

    2013-09-10

    Rap proteins (Rap1, Rap2a, b, c) are small molecular weight GTPases of the Ras family. Rap G proteins mediate diverse cellular events such as cell adhesion, proliferation, and gene activation through various signaling pathways. Activation of Rap signal is regulated tightly by several specific regulatory proteins including guanine nucleotide exchange factors and GTPase-activating proteins. Beyond cell biological studies, increasing attempts have been made in the past decade to define the roles of Rap signal in specific functions of normal tissue systems as well as in cancer. In the immune and hematopoietic systems, Rap signal plays crucial roles in the development and function of essentially all lineages of lymphocytes and hematopoietic cells, and importantly, deregulated Rap signal may lead to unique pathological conditions depending on the affected cell types, including various types of leukemia and autoimmunity. The phenotypical studies have unveiled novel, even unexpected functional aspects of Rap signal in cells from a variety of tissues, providing potentially important clues for controlling human diseases, including malignancy.

  15. Rap G protein signal in normal and disordered lymphohematopoiesis

    SciTech Connect

    Minato, Nagahiro

    2013-09-10

    Rap proteins (Rap1, Rap2a, b, c) are small molecular weight GTPases of the Ras family. Rap G proteins mediate diverse cellular events such as cell adhesion, proliferation, and gene activation through various signaling pathways. Activation of Rap signal is regulated tightly by several specific regulatory proteins including guanine nucleotide exchange factors and GTPase-activating proteins. Beyond cell biological studies, increasing attempts have been made in the past decade to define the roles of Rap signal in specific functions of normal tissue systems as well as in cancer. In the immune and hematopoietic systems, Rap signal plays crucial roles in the development and function of essentially all lineages of lymphocytes and hematopoietic cells, and importantly, deregulated Rap signal may lead to unique pathological conditions depending on the affected cell types, including various types of leukemia and autoimmunity. The phenotypical studies have unveiled novel, even unexpected functional aspects of Rap signal in cells from a variety of tissues, providing potentially important clues for controlling human diseases, including malignancy.

  16. Computational models of signalling networks for non-linear control.

    PubMed

    Fuente, Luis A; Lones, Michael A; Turner, Alexander P; Stepney, Susan; Caves, Leo S; Tyrrell, Andy M

    2013-05-01

    Artificial signalling networks (ASNs) are a computational approach inspired by the signalling processes inside cells that decode outside environmental information. Using evolutionary algorithms to induce complex behaviours, we show how chaotic dynamics in a conservative dynamical system can be controlled. Such dynamics are of particular interest as they mimic the inherent complexity of non-linear physical systems in the real world. Considering the main biological interpretations of cellular signalling, in which complex behaviours and robust cellular responses emerge from the interaction of multiple pathways, we introduce two ASN representations: a stand-alone ASN and a coupled ASN. In particular we note how sophisticated cellular communication mechanisms can lead to effective controllers, where complicated problems can be divided into smaller and independent tasks.

  17. Rapid Exact Signal Scanning With Deep Convolutional Neural Networks

    NASA Astrophysics Data System (ADS)

    Thom, Markus; Gritschneder, Franz

    2017-03-01

    A rigorous formulation of the dynamics of a signal processing scheme aimed at dense signal scanning without any loss in accuracy is introduced and analyzed. Related methods proposed in the recent past lack a satisfactory analysis of whether they actually fulfill any exactness constraints. This is improved through an exact characterization of the requirements for a sound sliding window approach. The tools developed in this paper are especially beneficial if Convolutional Neural Networks are employed, but can also be used as a more general framework to validate related approaches to signal scanning. The proposed theory helps to eliminate redundant computations and renders special case treatment unnecessary, resulting in a dramatic boost in efficiency particularly on massively parallel processors. This is demonstrated both theoretically in a computational complexity analysis and empirically on modern parallel processors.

  18. Hormone signaling through protein destruction: a lesson from plants.

    PubMed

    Tan, Xu; Zheng, Ning

    2009-02-01

    Ubiquitin-dependent protein degradation has emerged as a major pathway regulating eukaryotic biology. By employing a variety of ubiquitin ligases to target specific cellular proteins, the ubiquitin-proteasome system controls physiological processes in a highly regulated fashion. Recent studies on a plant hormone auxin have unveiled a novel paradigm of signal transduction in which ubiquitin ligases function as hormone receptors. Perceived by the F-box protein subunit of the SCF(TIR1) ubiquitin ligase, auxin directly promotes the recruitment of a family of transcriptional repressors for ubiquitination, thereby activating extensive transcriptional programs. Structural studies have revealed that auxin functions through a "molecular glue" mechanism to enhance protein-protein interactions with the assistance of another small molecule cofactor, inositol hexakisphosphate. Given the extensive repertoire of similar ubiquitin ligases in eukaryotic cells, this novel and widely adopted hormone-signaling mechanism in plants may also exist in other organisms.

  19. Revisiting Apoplastic Auxin Signaling Mediated by AUXIN BINDING PROTEIN 1.

    PubMed

    Feng, Mingxiao; Kim, Jae-Yean

    2015-10-01

    It has been suggested that AUXIN BINDING PROTEIN 1 (ABP1) functions as an apoplastic auxin receptor, and is known to be involved in the post-transcriptional process, and largely independent of the already well-known SKP-cullin-F-box-transport inhibitor response (TIR1) /auxin signaling F-box (AFB) (SCF(TIR1/AFB)) pathway. In the past 10 years, several key components downstream of ABP1 have been reported. After perceiving the auxin signal, ABP1 interacts, directly or indirectly, with plasma membrane (PM)-localized transmembrane proteins, transmembrane kinase (TMK) or SPIKE1 (SPK1), or other unidentified proteins, which transfer the signal into the cell to the Rho of plants (ROP). ROPs interact with their effectors, such as the ROP interactive CRIB motif-containing protein (RIC), to regulate the endocytosis/exocytosis of the auxin efflux carrier PIN-FORMED (PIN) proteins to mediate polar auxin transport across the PM. Additionally, ABP1 is a negative regulator of the traditional SCF(TIR1/AFB) auxin signaling pathway. However, Gao et al. (2015) very recently reported that ABP1 is not a key component in auxin signaling, and the famous abp1-1 and abp1-5 mutant Arabidopsis lines are being called into question because of possible additional mutantion sites, making it necessary to reevaluate ABP1. In this review, we will provide a brief overview of the history of ABP1 research.

  20. Topological, functional, and dynamic properties of the protein interaction networks rewired by benzo(a)pyrene

    SciTech Connect

    Ba, Qian; Li, Junyang; Huang, Chao; Li, Jingquan; Chu, Ruiai; Wu, Yongning; Wang, Hui

    2015-03-01

    Benzo(a)pyrene is a common environmental and foodborne pollutant that has been identified as a human carcinogen. Although the carcinogenicity of benzo(a)pyrene has been extensively reported, its precise molecular mechanisms and the influence on system-level protein networks are not well understood. To investigate the system-level influence of benzo(a)pyrene on protein interactions and regulatory networks, a benzo(a)pyrene-rewired protein interaction network was constructed based on 769 key proteins derived from more than 500 literature reports. The protein interaction network rewired by benzo(a)pyrene was a scale-free, highly-connected biological system. Ten modules were identified, and 25 signaling pathways were enriched, most of which belong to the human diseases category, especially cancer and infectious disease. In addition, two lung-specific and two liver-specific pathways were identified. Three pathways were specific in short and medium-term networks (< 48 h), and five pathways were enriched only in the medium-term network (6 h–48 h). Finally, the expression of linker genes in the network was validated by Western blotting. These findings establish the overall, tissue- and time-specific benzo(a)pyrene-rewired protein interaction networks and provide insights into the biological effects and molecular mechanisms of action of benzo(a)pyrene. - Highlights: • Benzo(a)pyrene induced scale-free, highly-connected protein interaction networks. • 25 signaling pathways were enriched through modular analysis. • Tissue- and time-specific pathways were identified.

  1. Hypoxia induces a phase transition within a kinase signaling network in cancer cells.

    PubMed

    Wei, Wei; Shi, Qihui; Remacle, Francoise; Qin, Lidong; Shackelford, David B; Shin, Young Shik; Mischel, Paul S; Levine, R D; Heath, James R

    2013-04-09

    Hypoxia is a near-universal feature of cancer, promoting glycolysis, cellular proliferation, and angiogenesis. The molecular mechanisms of hypoxic signaling have been intensively studied, but the impact of changes in oxygen partial pressure (pO2) on the state of signaling networks is less clear. In a glioblastoma multiforme (GBM) cancer cell model, we examined the response of signaling networks to targeted pathway inhibition between 21% and 1% pO2. We used a microchip technology that facilitates quantification of a panel of functional proteins from statistical numbers of single cells. We find that near 1.5% pO2, the signaling network associated with mammalian target of rapamycin (mTOR) complex 1 (mTORC1)--a critical component of hypoxic signaling and a compelling cancer drug target--is deregulated in a manner such that it will be unresponsive to mTOR kinase inhibitors near 1.5% pO2, but will respond at higher or lower pO2 values. These predictions were validated through experiments on bulk GBM cell line cultures and on neurosphere cultures of a human-origin GBM xenograft tumor. We attempt to understand this behavior through the use of a quantitative version of Le Chatelier's principle, as well as through a steady-state kinetic model of protein interactions, both of which indicate that hypoxia can influence mTORC1 signaling as a switch. The Le Chatelier approach also indicates that this switch may be thought of as a type of phase transition. Our analysis indicates that certain biologically complex cell behaviors may be understood using fundamental, thermodynamics-motivated principles.

  2. Regulation of Bone Morphogenetic Protein Signaling by ADP-ribosylation*

    PubMed Central

    Watanabe, Yukihide; Papoutsoglou, Panagiotis; Maturi, Varun; Tsubakihara, Yutaro; Hottiger, Michael O.; Heldin, Carl-Henrik; Moustakas, Aristidis

    2016-01-01

    We previously established a mechanism of negative regulation of transforming growth factor β signaling mediated by the nuclear ADP-ribosylating enzyme poly-(ADP-ribose) polymerase 1 (PARP1) and the deribosylating enzyme poly-(ADP-ribose) glycohydrolase (PARG), which dynamically regulate ADP-ribosylation of Smad3 and Smad4, two central signaling proteins of the pathway. Here we demonstrate that the bone morphogenetic protein (BMP) pathway can also be regulated by the opposing actions of PARP1 and PARG. PARG positively contributes to BMP signaling and forms physical complexes with Smad5 and Smad4. The positive role PARG plays during BMP signaling can be neutralized by PARP1, as demonstrated by experiments where PARG and PARP1 are simultaneously silenced. In contrast to PARG, ectopic expression of PARP1 suppresses BMP signaling, whereas silencing of endogenous PARP1 enhances signaling and BMP-induced differentiation. The two major Smad proteins of the BMP pathway, Smad1 and Smad5, interact with PARP1 and can be ADP-ribosylated in vitro, whereas PARG causes deribosylation. The overall outcome of this mode of regulation of BMP signal transduction provides a fine-tuning mechanism based on the two major enzymes that control cellular ADP-ribosylation. PMID:27129221

  3. G proteins as regulators in ethylene-mediated hypoxia signaling.

    PubMed

    Steffens, Bianka; Sauter, Margret

    2010-04-01

    Waterlogging or flooding are frequently or constitutively encountered by many plant species. The resulting reduction in endogenous O2 concentration poses a severe threat. Numerous adaptations at the anatomical, morphological and metabolic level help plants to either escape low oxygen conditions or to endure them. Formation of aerenchyma or rapid shoot elongation are escape responses, as is the formation of adventitious roots. The metabolic shift from aerobic respiration to anaerobic fermentation contributes to a basal energy supply at low oxygen conditions. Ethylene plays a central role in hypoxic stress signaling, and G proteins have been recognized as crucial signal transducers in various hypoxic signaling pathways. The programmed death of parenchyma cells that results in hypoxia-induced aerenchyma formation is an ethylene response. In maize, aerenchyma are induced in the absence of ethylene when G proteins are constitutively activated. Similarly, ethylene induced death of epidermal cells that cover adventitious roots at the stem node of rice is strictly dependent on heterotrimeric G protein activity. Knock down of the unique Gα gene RGA1 in rice prevents epidermal cell death. Finally, in Arabidopsis, induction of alcohol dehydrogenase with resulting increased plant survival relies on the balanced activities of a small Rop G protein and its deactivating protein RopGAP4. Identifying the general mechanisms of G protein signaling in hypoxia adaptation of plants is one of the tasks ahead.

  4. The protein-protein interaction network of the human Sirtuin family.

    PubMed

    Sharma, Ankush; Costantini, Susan; Colonna, Giovanni

    2013-10-01

    Protein-protein interaction networks are useful for studying human diseases and to look for possible health care through a holistic approach. Networks are playing an increasing and important role in the understanding of physiological processes such as homeostasis, signaling, spatial and temporal organizations, and pathological conditions. In this article we show the complex system of interactions determined by human Sirtuins (Sirt) largely involved in many metabolic processes as well as in different diseases. The Sirtuin family consists of seven homologous Sirt-s having structurally similar cores but different terminal segments, being rather variable in length and/or intrinsically disordered. Many studies have determined their cellular location as well as biological functions although molecular mechanisms through which they act are actually little known therefore, the aim of this work was to define, explore and understand the Sirtuin-related human interactome. As a first step, we have integrated the experimentally determined protein-protein interactions of the Sirtuin-family as well as their first and second neighbors to a Sirtuin-related sub-interactome. Our data showed that the second-neighbor network of Sirtuins encompasses 25% of the entire human interactome, and exhibits a scale-free degree distribution and interconnectedness among top degree nodes. Moreover, the Sirtuin sub interactome showed a modular structure around the core comprising mixed functions. Finally, we extracted from the Sirtuin sub-interactome subnets related to cancer, aging and post-translational modifications for information on key nodes and topological space of the subnets in the Sirt family network.

  5. 5'-AMP-activated protein kinase signaling in Caenorhabditis elegans.

    PubMed

    Beale, Elmus G

    2008-01-01

    5'-AMP-activated protein kinase (AMPK) has been called "the metabolic master switch" because of its central role in regulating fuel homeostasis. AMPK, a heterotrimeric serine/threonine protein kinase composed of alpha, beta, and gamma subunits, is activated by upstream kinases and by 5'-AMP in response to various nutritional and stress signals. Downstream effects include regulation of metabolism, protein synthesis, cell growth, and mediation of the actions of a number of hormones, including leptin. However, AMPK research represents a young and growing field; hence, there are many unanswered questions regarding the control and action of AMPK. This review presents evidence for the existence of AMPK signaling pathways in Caenorhabditis elegans, a genetically tractable model organism that has yet to be fully exploited to elucidate AMPK signaling mechanisms.

  6. G-protein-coupled receptors, Hedgehog signaling and primary cilia.

    PubMed

    Mukhopadhyay, Saikat; Rohatgi, Rajat

    2014-09-01

    The Hedgehog (Hh) pathway has become an important model to study the cell biology of primary cilia, and reciprocally, the study of ciliary processes provides an opportunity to solve longstanding mysteries in the mechanism of vertebrate Hh signal transduction. The cilium is emerging as an unique compartment for G-protein-coupled receptor (GPCR) signaling in many systems. Two members of the GPCR family, Smoothened and Gpr161, play important roles in the Hh pathway. We review the current understanding of how these proteins may function to regulate Hh signaling and also highlight some of the critical unanswered questions being tackled by the field. Uncovering GPCR-regulated mechanisms important in Hh signaling may provide therapeutic strategies against the Hh pathway that plays important roles in development, regeneration and cancer.

  7. Bone Morphogenetic Protein (BMP) signaling in development and human diseases

    PubMed Central

    Wang, Richard N.; Green, Jordan; Wang, Zhongliang; Deng, Youlin; Qiao, Min; Peabody, Michael; Zhang, Qian; Ye, Jixing; Yan, Zhengjian; Denduluri, Sahitya; Idowu, Olumuyiwa; Li, Melissa; Shen, Christine; Hu, Alan; Haydon, Rex C.; Kang, Richard; Mok, James; Lee, Michael J.; Luu, Hue L.; Shi, Lewis L.

    2014-01-01

    Bone Morphogenetic Proteins (BMPs) are a group of signaling molecules that belongs to the Transforming Growth Factor-β (TGF-β) superfamily of proteins. Initially discovered for their ability to induce bone formation, BMPs are now known to play crucial roles in all organ systems. BMPs are important in embryogenesis and development, and also in maintenance of adult tissue homeostasis. Mouse knockout models of various components of the BMP signaling pathway result in embryonic lethality or marked defects, highlighting the essential functions of BMPs. In this review, we first outline the basic aspects of BMP signaling and then focus on genetically manipulated mouse knockout models that have helped elucidate the role of BMPs in development. A significant portion of this review is devoted to the prominent human pathologies associated with dysregulated BMP signaling. PMID:25401122

  8. An Approach to Spatiotemporally Resolve Protein Interaction Networks in Living Cells.

    PubMed

    Lobingier, Braden T; Hüttenhain, Ruth; Eichel, Kelsie; Miller, Kenneth B; Ting, Alice Y; von Zastrow, Mark; Krogan, Nevan J

    2017-04-06

    Cells operate through protein interaction networks organized in space and time. Here, we describe an approach to resolve both dimensions simultaneously by using proximity labeling mediated by engineered ascorbic acid peroxidase (APEX). APEX has been used to capture entire organelle proteomes with high temporal resolution, but its breadth of labeling is generally thought to preclude the higher spatial resolution necessary to interrogate specific protein networks. We provide a solution to this problem by combining quantitative proteomics with a system of spatial references. As proof of principle, we apply this approach to interrogate proteins engaged by G-protein-coupled receptors as they dynamically signal and traffic in response to ligand-induced activation. The method resolves known binding partners, as well as previously unidentified network components. Validating its utility as a discovery pipeline, we establish that two of these proteins promote ubiquitin-linked receptor downregulation after prolonged activation.

  9. Detecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networks.

    PubMed

    Wu, Hao; Gao, Lin; Dong, Jihua; Yang, Xiaofei

    2014-01-01

    In this paper, we present a novel rough-fuzzy clustering (RFC) method to detect overlapping protein complexes in protein-protein interaction (PPI) networks. RFC focuses on fuzzy relation model rather than graph model by integrating fuzzy sets and rough sets, employs the upper and lower approximations of rough sets to deal with overlapping complexes, and calculates the number of complexes automatically. Fuzzy relation between proteins is established and then transformed into fuzzy equivalence relation. Non-overlapping complexes correspond to equivalence classes satisfying certain equivalence relation. To obtain overlapping complexes, we calculate the similarity between one protein and each complex, and then determine whether the protein belongs to one or multiple complexes by computing the ratio of each similarity to maximum similarity. To validate RFC quantitatively, we test it in Gavin, Collins, Krogan and BioGRID datasets. Experiment results show that there is a good correspondence to reference complexes in MIPS and SGD databases. Then we compare RFC with several previous methods, including ClusterONE, CMC, MCL, GCE, OSLOM and CFinder. Results show the precision, sensitivity and separation are 32.4%, 42.9% and 81.9% higher than mean of the five methods in four weighted networks, and are 0.5%, 11.2% and 66.1% higher than mean of the six methods in five unweighted networks. Our method RFC works well for protein complexes detection and provides a new insight of network division, and it can also be applied to identify overlapping community structure in social networks and LFR benchmark networks.

  10. Identification of Protein Interactions Involved in Cellular Signaling

    PubMed Central

    Westermarck, Jukka; Ivaska, Johanna; Corthals, Garry L.

    2013-01-01

    Protein-protein interactions drive biological processes. They are critical for all intra- and extracellular functions, and the technologies to analyze them are widely applied throughout the various fields of biological sciences. This study takes an in-depth view of some common principles of cellular regulation and provides a detailed account of approaches required to comprehensively map signaling protein-protein interactions in any particular cellular system or condition. We provide a critical review of the benefits and disadvantages of the yeast two-hybrid method and affinity purification coupled with mass spectrometric procedures for identification of signaling protein-protein interactions. In particular, we emphasize the quantitative and qualitative differences between tandem affinity and one-step purification (such as FLAG and Strep tag) methods. Although applicable to all types of interaction studies, a special section is devoted in this review to aspects that should be considered when attempting to identify signaling protein interactions that often are transient and weak by nature. Finally, we discuss shotgun and quantitative information that can be gleaned by MS-coupled methods for analysis of multiprotein complexes. PMID:23481661

  11. Biasing vector network analyzers using variable frequency and amplitude signals

    NASA Astrophysics Data System (ADS)

    Nobles, J. E.; Zagorodnii, V.; Hutchison, A.; Celinski, Z.

    2016-08-01

    We report the development of a test setup designed to provide a variable frequency biasing signal to a vector network analyzer (VNA). The test setup is currently used for the testing of liquid crystal (LC) based devices in the microwave region. The use of an AC bias for LC based devices minimizes the negative effects associated with ionic impurities in the media encountered with DC biasing. The test setup utilizes bias tees on the VNA test station to inject the bias signal. The square wave biasing signal is variable from 0.5 to 36.0 V peak-to-peak (VPP) with a frequency range of DC to 10 kHz. The test setup protects the VNA from transient processes, voltage spikes, and high-frequency leakage. Additionally, the signals to the VNA are fused to ½ amp and clipped to a maximum of 36 VPP based on bias tee limitations. This setup allows us to measure S-parameters as a function of both the voltage and the frequency of the applied bias signal.

  12. A Probabilistic Boolean Network Approach for the Analysis of Cancer-Specific Signalling: A Case Study of Deregulated PDGF Signalling in GIST

    PubMed Central

    Wiesinger, Monique; Bahlawane, Christelle; Haan, Serge; Sauter, Thomas

    2016-01-01

    Background Signal transduction networks are increasingly studied with mathematical modelling approaches while each of them is suited for a particular problem. For the contextualisation and analysis of signalling networks with steady-state protein data, we identified probabilistic Boolean network (PBN) as a promising framework which could capture quantitative changes of molecular changes at steady-state with a minimal parameterisation. Results and Conclusion In our case study, we successfully applied the PBN approach to model and analyse the deregulated Platelet-Derived Growth Factor (PDGF) signalling pathway in Gastrointestinal Stromal Tumour (GIST). We experimentally determined a rich and accurate dataset of steady-state profiles of selected downstream kinases of PDGF-receptor-alpha mutants in combination with inhibitor treatments. Applying the tool optPBN, we fitted a literature-derived candidate network model to the training dataset consisting of single perturbation conditions. Model analysis suggested several important crosstalk interactions. The validity of these predictions was further investigated experimentally pointing to relevant ongoing crosstalk from PI3K to MAPK signalling in tumour cells. The refined model was evaluated with a validation dataset comprising multiple perturbation conditions. The model thereby showed excellent performance allowing to quantitatively predict the combinatorial responses from the individual treatment results in this cancer setting. The established optPBN pipeline is also widely applicable to gain a better understanding of other signalling networks at steady-state in a context-specific fashion. PMID:27232499

  13. CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Luo, Jiawei; Li, Guanghui; Song, Dan; Liang, Cheng

    2014-12-01

    Discovering motifs in protein-protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

  14. Receptor component protein (RCP): a member of a multi-protein complex required for G-protein-coupled signal transduction.

    PubMed

    Prado, M A; Evans-Bain, B; Dickerson, I M

    2002-08-01

    The calcitonin-gene-related peptide (CGRP) receptor component protein (RCP) is a 148-amino-acid intracellular protein that is required for G-protein-coupled signal transduction at receptors for the neuropeptide CGRP. RCP works in conjunction with two other proteins to constitute a functional CGRP receptor: calcitonin-receptor-like receptor (CRLR) and receptor-activity-modifying protein 1 (RAMP1). CRLR has the stereotypical seven-transmembrane topology of a G-protein-coupled receptor; it requires RAMP1 for trafficking to the cell surface and for ligand specificity, and requires RCP for coupling to the cellular signal transduction pathway. We have made cell lines that expressed an antisense construct of RCP and determined that CGRP-mediated signal transduction was reduced, while CGRP binding was unaffected. Furthermore, signalling at two other endogenous G-protein-coupled receptors was unaffected, suggesting that RCP was specific for a limited subset of receptors.

  15. Cellular Signaling Pathways and Posttranslational Modifications Mediated by Nematode Effector Proteins1

    PubMed Central

    Hewezi, Tarek

    2015-01-01

    Plant-parasitic cyst and root-knot nematodes synthesize and secrete a suite of effector proteins into infected host cells and tissues. These effectors are the major virulence determinants mediating the transformation of normal root cells into specialized feeding structures. Compelling evidence indicates that these effectors directly hijack or manipulate refined host physiological processes to promote the successful parasitism of host plants. Here, we provide an update on recent progress in elucidating the molecular functions of nematode effectors. In particular, we emphasize how nematode effectors modify plant cell wall structure, mimic the activity of host proteins, alter auxin signaling, and subvert defense signaling and immune responses. In addition, we discuss the emerging evidence suggesting that nematode effectors target and recruit various components of host posttranslational machinery in order to perturb the host signaling networks required for immunity and to regulate their own activity and subcellular localization. PMID:26315856

  16. Networking in the nucleus: A spotlight on LEM-domain proteins

    PubMed Central

    Barton, Lacy J.; Soshnev, Alexey A.; Geyer, Pamela K.

    2015-01-01

    Proteins resident in the inner nuclear membrane and underlying nuclear lamina form a network that regulates nuclear functions. This review highlights a prominent family of nuclear lamina proteins that carries the LAP2-emerin-MAN1-domain (LEM-D). LEM-D proteins share an ability to bind lamins and tether repressive chromatin at the nuclear periphery. The importance of this family is underscored by findings that loss of individual LEM-D proteins causes progressive, tissue-restricted diseases, known as laminopathies. Diverse functions of LEM-D proteins are linked to interactions with unique and overlapping partners including signal transduction effectors, transcription factors and architectural proteins. Recent investigations suggest that LEM-D proteins form hubs within the nuclear lamina that integrate external signals important for tissue homeostasis and maintenance of progenitor cell populations. PMID:25863918

  17. Science Signaling Podcast for 12 July 2016: Adaptor proteins limit signaling.

    PubMed

    Wiley, H Steven; VanHook, Annalisa M

    2016-07-12

    This Podcast features an interview with Steven Wiley, senior author of a Research Article that appears in the 12 July 2016 issue of Science Signaling, about how the abundance of adaptor proteins and feedback regulators affect the flow of information downstream of the epidermal growth factor receptor (EGFR). Information flows through a signaling pathway by sequential interactions between core components of the pathway, many of which have enzymatic activity. Adaptor proteins do not directly participate in relaying the signal and do not have enzymatic activity, but are important for signaling because they facilitate interactions between the core components. Using quantitative methods, Shi et al demonstrated that core components of the EGFR pathway were highly abundant in both normal cells and cancer cells. However, adaptor proteins were present in much lower abundance in both cell types, indicating that it is the abundance of these proteins that limit signaling downstream of EGFR. The authors also found that differences in EGFR signaling between different cell types likely resulted from the variable abundance of feedback regulators.Listen to Podcast.

  18. Regulation of cardiomyocyte signaling by RGS proteins: differential selectivity towards G proteins and susceptibility to regulation.

    PubMed

    Hao, Jianming; Michalek, Christina; Zhang, Wei; Zhu, Ming; Xu, Xiaomei; Mende, Ulrike

    2006-07-01

    Many signals that regulate cardiomyocyte growth, differentiation and function are mediated via heterotrimeric G proteins, which are under the control of RGS proteins (Regulators of G protein Signaling). Several RGS proteins are expressed in the heart, but so far little is known about their function and regulation. Using adenoviral gene transfer, we conducted the first comprehensive analysis of the capacity and selectivity of the major cardiac RGS proteins (RGS2-RGS5) to regulate central G protein-mediated signaling pathways in adult ventricular myocytes (AVM). All four RGS proteins potently inhibited Gq/11-mediated phospholipase C beta stimulation and cell growth (assessed in neonatal myocytes). Importantly, RGS2 selectively inhibited Gq/11 signaling, whereas RGS3, RGS4 and RGS5 had the capacity to regulate both Gq/11 and Gi/o signaling (carbachol-induced cAMP inhibition). Gs signaling was unaffected, and, contrary to reports in other cell lines, RGS2-RGS5 did not appear to regulate adenylate cyclase directly in AVM. Since RGS proteins can be highly regulated in their expression by many different stimuli, we also tested the hypothesis that RGS expression is subject to G protein-mediated regulation in AVM and determined the specificity with which enhanced G protein signaling alters endogenous RGS expression in AVM. RGS2 mRNA and protein were markedly but transiently up-regulated by enhanced Gq/11 signaling (alpha1-adrenergic stimulation or Galphaq* overexpression), possibly by a negative feedback mechanism. In contrast, the other negative regulators of Gq/11 signaling (RGS3-RGS5) were unchanged. Endogenous RGS2 (but not RGS3-RGS5) expression was also up-regulated in cells with enhanced AC signaling (beta-adrenergic or forskolin stimulation). Taken together, these findings suggest diverse roles of RGS proteins in regulating myocyte signaling. RGS2 emerged as the only selective and highly regulated inhibitor of Gq/11 signaling that could potentially become a promising

  19. Analysis of nitrated proteins in Saccharomyces cerevisiae involved in mating signal transduction.

    PubMed

    Kang, Jeong Won; Lee, Na Young; Cho, Kyung-Cho; Lee, Min Young; Choi, Do-Young; Park, Sang-Hyun; Kim, Kwang Pyo

    2015-01-01

    Protein tyrosine nitration (PTN) is a PTM that regulates signal transduction and inflammatory responses, and is related to neurodegenerative and cardiovascular diseases. The cellular function of PTN remains unclear because the low stoichiometry of PTN limits the identification and quantification of nitrated peptides. Effective enrichment is an important aspect of PTN analysis. In this study, we analyzed the in vivo nitroproteome elicited by mating signal transduction in Saccharomyces cerevisiae using a novel chemical enrichment method followed by LC-MS/MS. Nitroproteome profiling successfully identified changes in the nitration states of 14 proteins during mating signal transduction in S. cerevisiae, making this the first reported in vivo nitroproteome in yeast. We investigated the biological functions of these nitroproteins and their relationships to mating signal transduction in S. cerevisiae using a protein-protein interaction network. Our results suggest that PTN and denitration may be involved in nonreactive nitrogen species-mediated signal transduction and can provide clues for understanding the functional roles of PTN in vivo.

  20. Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks

    PubMed Central

    2015-01-01

    Abstract Background Molecular networks are the basis of biological processes. Such networks can be decomposed into smaller modules, also known as network motifs. These motifs show interesting dynamical behaviors, in which co-operativity effects between the motif components play a critical role in human diseases. We have developed a motif-searching algorithm, which is able to identify common motif types from the cancer networks and signal transduction networks (STNs). Some of the network motifs are interconnected which can be merged together and form more complex structures, the so-called coupled motif structures (CMS). These structures exhibit mixed dynamical behavior, which may lead biological organisms to perform specific functions. Results In this study, we integrate transcription factors (TFs), microRNAs (miRNAs), miRNA targets and network motifs information to build the cancer-related TF-miRNA-motif networks (TMMN). This allows us to examine the role of network motifs in cancer formation at different levels of regulation, i.e. transcription initiation (TF → miRNA), gene-gene interaction (CMS), and post-transcriptional regulation (miRNA → target genes). Among the cancer networks and STNs we considered, it is found that there is a substantial amount of crosstalking through motif interconnections, in particular, the crosstalk between prostate cancer network and PI3K-Akt STN. Conclusions To validate the role of network motifs in cancer formation, several examples are presented which demonstrated the effectiveness of the present approach. A web-based platform has been set up which can be accessed at: http://ppi.bioinfo.asia.edu.tw/pathway/. It is very likely that our results can supply very specific CMS missing information for certain cancer types, it is an indispensable tool for cancer biology research. PMID:25707690

  1. Constitutive and ligand-induced EGFR signaling triggers distinct and mutually exclusive downstream signaling networks

    PubMed Central

    Chakraborty, Sharmistha; Li, Li; Puliyappadamba, VineshkumarThidil; Guo, Gao; Hatanpaa, Kimmo J.; Mickey, Bruce; Souza, Rhonda F.; Vo, Peggy; Herz, Joachim; Chen, Mei-Ru; Boothman, David A.; Pandita, Tej K.; Wang, David H.; Sen, Ganes C.; Habib, Amyn A.

    2014-01-01

    EGFR overexpression plays an important oncogenic role in cancer. Regular EGFR protein levels are increased in cancer cells and the receptor then becomes constitutively active. However, downstream signals generated by constitutively activated EGFR are unknown. Here we report that the overexpressed EGFR oscillates between two distinct and mutually exclusive modes of signaling. Constitutive or non-canonical EGFR signaling activates the transcription factor IRF3 leading to expression of IFI27, IFIT1, and TRAIL. Ligand-mediated activation of EGFR switches off IRF3 dependent transcription, activates canonical ERK and Akt signals, and confers sensitivity to chemotherapy and virus-induced cell death. Mechanistically, the distinct downstream signals result from a switch of EGFR associated proteins. EGFR constitutively complexes with IRF3 and TBK1 leading to TBK1 and IRF3 phosphorylation. Addition of EGF dissociates TBK1, IRF3, and EGFR leading to a loss of IRF3 activity, Shc-EGFR association and ERK activation. Finally, we provide evidence for non-canonical EGFR signaling in glioblastoma. PMID:25503978

  2. Protein Co-Expression Network Analysis (ProCoNA)

    SciTech Connect

    Gibbs, David L.; Baratt, Arie; Baric, Ralph; Kawaoka, Yoshihiro; Smith, Richard D.; Orwoll, Eric S.; Katze, Michael G.; Mcweeney, Shannon K.

    2013-06-01

    Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology. We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions). Biological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery.

  3. Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery.

    PubMed

    Murakami, Yoichi; Tripathi, Lokesh P; Prathipati, Philip; Mizuguchi, Kenji

    2017-03-29

    Protein-protein interactions (PPIs) are vital to maintaining cellular homeostasis. Several PPI dysregulations have been implicated in the etiology of various diseases and hence PPIs have emerged as promising targets for drug discovery. Surface residues and hotspot residues at the interface of PPIs form the core regions, which play a key role in modulating cellular processes such as signal transduction and are used as starting points for drug design. In this review, we briefly discuss how PPI networks (PPINs) inferred from experimentally characterized PPI data have been utilized for knowledge discovery and how in silico approaches to PPI characterization can contribute to PPIN-based biological research. Next, we describe the principles of in silico PPI prediction and survey the existing PPI and PPI site prediction servers that are useful for drug discovery. Finally, we discuss the potential of in silico PPI prediction in drug discovery.

  4. Early-warning signals of topological collapse in interbank networks

    NASA Astrophysics Data System (ADS)

    Squartini, Tiziano; van Lelyveld, Iman; Garlaschelli, Diego

    2013-11-01

    The financial crisis clearly illustrated the importance of characterizing the level of `systemic' risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998-2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear - but unpredictable - signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies.

  5. Uncovering signal transduction networks from high-throughput data by integer linear programming.

    PubMed

    Zhao, Xing-Ming; Wang, Rui-Sheng; Chen, Luonan; Aihara, Kazuyuki

    2008-05-01

    Signal transduction is an important process that transmits signals from the outside of a cell to the inside to mediate sophisticated biological responses. Effective computational models to unravel such a process by taking advantage of high-throughput genomic and proteomic data are needed to understand the essential mechanisms underlying the signaling pathways. In this article, we propose a novel method for uncovering signal transduction networks (STNs) by integrating protein interaction with gene expression data. Specifically, we formulate STN identification problem as an integer linear programming (ILP) model, which can be actually solved by a relaxed linear programming algorithm and is flexible for handling various prior information without any restriction on the network structures. The numerical results on yeast MAPK signaling pathways demonstrate that the proposed ILP model is able to uncover STNs or pathways in an efficient and accurate manner. In particular, the prediction results are found to be in high agreement with current biological knowledge and available information in literature. In addition, the proposed model is simple to be interpreted and easy to be implemented even for a large-scale system.

  6. Single-Molecule Study of Protein-Protein Interaction Dynamics in a Cell Signaling System

    SciTech Connect

    Tan, Xin; Nalbant, Perihan; Toutchkine, Alexei; Hu, Dehong; Vorpagel, Erich R.; Hahn, Klaus M.; Lu, H PETER.

    2004-01-15

    We report a combined single-molecule fluorescence and molecular dynamics (MD) simulation study of protein-protein interactions in a GTP-binding intracellular signaling protein Cdc42 in complex with a downstream effector protein WASP. A 13- kDa WASP fragment which binds only the activated GTP-loaded Cdc42 was labeled with a novel solvatochromic dye and used to probe hydrophobic interactions significant to Cdc42/WASP recognition. Our single-molecule fluorescence measurements have shown conformational fluctuations of the protein complex and suggested multiple conformational states at a wide range of time scales might be involved in protein interaction dynamics. Single-molecule experiments have revealed the dynamic disorder or protein-protein interactions within the Cdc42/WASP complex, which may be important for regulating downstream signaling events.

  7. Single-Molecule Study of Protein-Protein Interaction Dynamics in a Cell Signaling System

    SciTech Connect

    Tan, Xin; Nalbant, Perihan; Toutchkine, Alexei; Hu, Dehong; Vorpagel, Erich R.; Hahn, Klaus M.; Lu, H. Peter

    2004-01-01

    We report a study on protein-protein noncovalent interactions in an intracellular signaling protein complex, using single-molecule spectroscopy and molecular dynamics (MD) simulations. A Wiskott-Aldrich Syndrome Protein (WASP) fragment that binds only the activated intracellular signaling protein Cdc42 was labeled with a novel solvatochromic dye and used to probe hydrophobic interactions significant to Cdc42/WASP recognition. The study shows static and dynamic inhomogeneous conformational fluctuations of the protein complex that involve bound and loosely bound states. A two-coupled, two-state Markovian kinetic model is proposed for the conformational dynamics. Finally, the MD simulations explore the origin of these conformational states and associated conformational fluctuations in this protein-protein interaction system.

  8. Improved Protein Arrays for Quantitative Systems Analysis of the Dynamics of Signaling Pathway Interactions

    SciTech Connect

    YANG, CHIN-RANG

    2013-12-11

    Astronauts and workers in nuclear plants who repeatedly exposed to low doses of ionizing radiation (IR, <10 cGy) are likely to incur specific changes in signal transduction and gene expression in various tissues of their body. Remarkable advances in high throughput genomics and proteomics technologies enable researchers to broaden their focus from examining single gene/protein kinetics to better understanding global gene/protein expression profiling and biological pathway analyses, namely Systems Biology. An ultimate goal of systems biology is to develop dynamic mathematical models of interacting biological systems capable of simulating living systems in a computer. This Glue Grant is to complement Dr. Boothman’s existing DOE grant (No. DE-FG02-06ER64186) entitled “The IGF1/IGF-1R-MAPK-Secretory Clusterin (sCLU) Pathway: Mediator of a Low Dose IR-Inducible Bystander Effect” to develop sensitive and quantitative proteomic technology that suitable for low dose radiobiology researches. An improved version of quantitative protein array platform utilizing linear Quantum dot signaling for systematically measuring protein levels and phosphorylation states for systems biology modeling is presented. The signals are amplified by a confocal laser Quantum dot scanner resulting in ~1000-fold more sensitivity than traditional Western blots and show the good linearity that is impossible for the signals of HRP-amplification. Therefore this improved protein array technology is suitable to detect weak responses of low dose radiation. Software is developed to facilitate the quantitative readout of signaling network activities. Kinetics of EGFRvIII mutant signaling was analyzed to quantify cross-talks between EGFR and other signaling pathways.

  9. Deep Space Network Capabilities for Receiving Weak Probe Signals

    NASA Technical Reports Server (NTRS)

    Asmar, Sami; Johnston, Doug; Preston, Robert

    2005-01-01

    Planetary probes can encounter mission scenarios where communication is not favorable during critical maneuvers or emergencies. Launch, initial acquisition, landing, trajectory corrections, safing. Communication challenges due to sub-optimum antenna pointing or transmitted power, amplitude/frequency dynamics, etc. Prevent lock-up on signal and extraction of telemetry. Examples: loss of Mars Observer, nutation of Ulysses, Galileo antenna, Mars Pathfinder and Mars Exploration Rovers Entry, Descent, and Landing, and the Cassini Saturn Orbit Insertion. A Deep Space Network capability to handle such cases has been used successfully to receive signals to characterize the scenario. This paper will describe the capability and highlight the cases of the critical communications for the Mars rovers and Saturn Orbit Insertion and preparation radio tracking of the Huygens probe at (non-DSN) radio telescopes.

  10. Sugar signalling and antioxidant network connections in plant cells.

    PubMed

    Bolouri-Moghaddam, Mohammad Reza; Le Roy, Katrien; Xiang, Li; Rolland, Filip; Van den Ende, Wim

    2010-05-01

    Sugars play important roles as both nutrients and regulatory molecules throughout plant life. Sugar metabolism and signalling function in an intricate network with numerous hormones and reactive oxygen species (ROS) production, signalling and scavenging systems. Although hexokinase is well known to fulfil a crucial role in glucose sensing processes, a scenario is emerging in which the catalytic activity of mitochondria-associated hexokinase regulates glucose-6-phosphate and ROS levels, stimulating antioxidant defence mechanisms and the synthesis of phenolic compounds. As a new concept, it can be hypothesized that the synergistic interaction of sugars (or sugar-like compounds) and phenolic compounds forms part of an integrated redox system, quenching ROS and contributing to stress tolerance, especially in tissues or organelles with high soluble sugar concentrations.

  11. Bacterial protein signals are associated with Crohn’s disease

    PubMed Central

    Juste, Catherine; Kreil, David P; Beauvallet, Christian; Guillot, Alain; Vaca, Sebastian; Carapito, Christine; Mondot, Stanislas; Sykacek, Peter; Sokol, Harry; Blon, Florence; Lepercq, Pascale; Levenez, Florence; Valot, Benoît; Carré, Wilfrid; Loux, Valentin; Pons, Nicolas; David, Olivier; Schaeffer, Brigitte; Lepage, Patricia; Martin, Patrice; Monnet, Véronique; Seksik, Philippe; Beaugerie, Laurent; Ehrlich, S Dusko; Gibrat, Jean-François; Van Dorsselaer, Alain; Doré, Joël

    2014-01-01

    Objective No Crohn’s disease (CD) molecular maker has advanced to clinical use, and independent lines of evidence support a central role of the gut microbial community in CD. Here we explore the feasibility of extracting bacterial protein signals relevant to CD, by interrogating myriads of intestinal bacterial proteomes from a small number of patients and healthy controls. Design We first developed and validated a workflow—including extraction of microbial communities, two-dimensional difference gel electrophoresis (2D-DIGE), and LC-MS/MS—to discover protein signals from CD-associated gut microbial communities. Then we used selected reaction monitoring (SRM) to confirm a set of candidates. In parallel, we used 16S rRNA gene sequencing for an integrated analysis of gut ecosystem structure and functions. Results Our 2D-DIGE-based discovery approach revealed an imbalance of intestinal bacterial functions in CD. Many proteins, largely derived from Bacteroides species, were over-represented, while under-represented proteins were mostly from Firmicutes and some Prevotella members. Most overabundant proteins could be confirmed using SRM. They correspond to functions allowing opportunistic pathogens to colonise the mucus layers, breach the host barriers and invade the mucosae, which could still be aggravated by decreased host-derived pancreatic zymogen granule membrane protein GP2 in CD patients. Moreover, although the abundance of most protein groups reflected that of related bacterial populations, we found a specific independent regulation of bacteria-derived cell envelope proteins. Conclusions This study provides the first evidence that quantifiable bacterial protein signals are associated with CD, which can have a profound impact on future molecular diagnosis. PMID:24436141

  12. Evidence of Probabilistic Behaviour in Protein Interaction Networks

    DTIC Science & Technology

    2008-01-31

    Evidence of degree-weighted connectivity in nine PPI networks. a, Homo sapiens (human); b, Drosophila melanogaster (fruit fly); c-e, Saccharomyces...illustrates maps for the networks of Homo sapiens and Dro- sophila melanogaster, while maps for the remaining net- works are provided in Additional file 2. As...protein-protein interaction networks. a, Homo sapiens ; b, Drosophila melanogaster. Distances shown as average shortest path lengths L(k1, k2) between

  13. Guard Cell Signal Transduction Network: Advances in Understanding Abscisic Acid, CO2, and Ca2+ Signaling

    PubMed Central

    Kim, Tae-Houn; Böhmer, Maik; Hu, Honghong; Nishimura, Noriyuki; Schroeder, Julian I.

    2011-01-01

    Stomatal pores are formed by pairs of specialized epidermal guard cells and serve as major gateways for both CO2 influx into plants from the atmosphere and transpirational water loss of plants. Because they regulate stomatal pore apertures via integration of both endogenous hormonal stimuli and environmental signals, guard cells have been highly developed as a model system to dissect the dynamics and mechanisms of plant-cell signaling. The stress hormone ABA and elevated levels of CO2 activate complex signaling pathways in guard cells that are mediated by kinases/phosphatases, secondary messengers, and ion channel regulation. Recent research in guard cells has led to a new hypothesis for how plants achieve specificity in intracellular calcium signaling: CO2 and ABA enhance (prime) the calcium sensitivity of downstream calcium-signaling mechanisms. Recent progress in identification of early stomatal signaling components are reviewed here, including ABA receptors and CO2-binding response proteins, as well as systems approaches that advance our understanding of guard cell-signaling mechanisms. PMID:20192751

  14. Early signaling network in rice PRR-mediated and R-mediated immunity.

    PubMed

    Kawano, Yoji; Shimamoto, Ko

    2013-08-01

    Recent studies on plant immunity and pathogen infection have revealed sophisticated forms of plant-pathogen interaction. Considerable progress has been made recently in our understanding of the molecular mechanism underlying chitin signaling in rice. The identification and characterization of two direct substrates, OsRacGEF1 and OsRLCK185, as components in the chitin receptor complex of OsCERK1 have revealed how pattern recognition receptors transduce pathogen signals to downstream molecules in rice. In this review, we highlight these and other recent studies that have contributed to our current understanding of the signaling network in rice immunity, especially with regard to pattern recognition receptors, disease resistance (R) proteins, and their downstream targets.

  15. Natural Genetic Variation Influences Protein Abundances in C. elegans Developmental Signalling Pathways

    PubMed Central

    Singh, Kapil Dev; Roschitzki, Bernd; Snoek, L. Basten; Grossmann, Jonas; Zheng, Xue; Elvin, Mark; Kamkina, Polina; Schrimpf, Sabine P.; Poulin, Gino B.; Kammenga, Jan E.; Hengartner, Michael O.

    2016-01-01

    Complex traits, including common disease-related traits, are affected by many different genes that function in multiple pathways and networks. The apoptosis, MAPK, Notch, and Wnt signalling pathways play important roles in development and disease progression. At the moment we have a poor understanding of how allelic variation affects gene expression in these pathways at the level of translation. Here we report the effect of natural genetic variation on transcript and protein abundance involved in developmental signalling pathways in Caenorhabditis elegans. We used selected reaction monitoring to analyse proteins from the abovementioned four pathways in a set of recombinant inbred lines (RILs) generated from the wild-type strains N2 (Bristol) and CB4856 (Hawaii) to enable quantitative trait locus (QTL) mapping. About half of the cases from the 44 genes tested showed a statistically significant change in protein abundance between various strains, most of these were however very weak (below 1.3-fold change). We detected a distant QTL on the left arm of chromosome II that affected protein abundance of the phosphatidylserine receptor protein PSR-1, and two separate QTLs that influenced embryonic and ionizing radiation-induced apoptosis on chromosome IV. Our results demonstrate that natural variation in C. elegans is sufficient to cause significant changes in signalling pathways both at the gene expression (transcript and protein abundance) and phenotypic levels. PMID:26985669

  16. Protein kinase A signaling as an anti-aging target.

    PubMed

    Enns, Linda C; Ladiges, Warren

    2010-07-01

    Protein kinase A (PKA) is a multi-unit protein kinase that mediates signal transduction of G-protein-coupled receptors through its activation by adenyl cyclase (AC)-mediated cAMP. The vital importance of PKA signaling to cellular function is reflected in the widespread expression of PKA subunit genes. As one of its many functions, PKA plays a key role in the regulation of metabolism and triglyceride storage. The PKA pathway has become of great interest to the study of aging, since mutations that cause a reduction in PKA signaling have been shown to extend lifespan in yeast, and to both delay the incidence and severity of age-related disease, and to promote leanness and longevity, in mice. There is increasing interest in the potential for the inhibition or redistribution of adiposity to attenuate aging, since obesity is associated with impaired function of most organ systems, and is a strong risk factor for shortened life span. Its association with coronary heart disease, hypertension, type 2 diabetes, cancer, sleep apnea and osteoarthritis is leading to its accession as a major cause of global ill health. Therefore, gene signaling pathways such as PKA that promote adiposity are potential inhibitory targets for aging intervention. Since numerous plant compounds have been found that both prevent adipogenesis and inhibit PKA signaling, a focused investigation into their effects on biological systems and the corresponding molecular mechanisms would be of high relevance to the discovery of novel and non-toxic compounds that promote healthy aging.

  17. INHIBITOR OF APOPTOSIS PROTEINS AS INTRACELLULAR SIGNALING INTERMEDIATES

    PubMed Central

    Kocab, Andrew J.; Duckett, Colin S.

    2015-01-01

    The inhibitor of apoptosis (IAP) proteins have often been considered inhibitors of cell death due to early studies describing their ability to directly bind and inhibit caspases, the primary factors that implement apoptosis. However, a greater understanding is evolving for the vital roles played by the IAPs as transduction intermediates in a diverse set of signaling cascades that have been associated with functions ranging from the innate immune response to cell migration to cell cycle regulation. In this review, we discuss the functions of the IAPs in signaling, focusing primarily on the cellular IAP (c-IAP) proteins. The c-IAPs are important components in the TNF receptor superfamily signaling cascades, which include the activation of the NF-κB transcription factor family. Since these receptors can modulate cell proliferation and cell death, the roles of the c-IAPs in these pathways provide additional means of controlling cellular fate beyond simply inhibiting caspase activity. Additionally, IAP binding proteins, such as Smac and caspases, which have been described as having cell death-independent roles, may impact c-IAP activity in intracellular signaling. Collectively, the multifaceted functions and complex regulation of the c-IAPs illustrate the importance of the c-IAPs as intracellular signaling intermediates. PMID:26462035

  18. Inhibitor of apoptosis proteins as intracellular signaling intermediates.

    PubMed

    Kocab, Andrew J; Duckett, Colin S

    2016-01-01

    Inhibitor of apoptosis (IAP) proteins have often been considered inhibitors of cell death due to early reports that described their ability to directly bind and inhibit caspases, the primary factors that implement apoptosis. However, a greater understanding is evolving regarding the vital roles played by IAPs as transduction intermediates in a diverse set of signaling cascades associated with functions ranging from the innate immune response to cell migration to cell-cycle regulation. In this review, we discuss the functions of IAPs in signaling, focusing primarily on the cellular IAP (c-IAP) proteins. The c-IAPs are important components in tumor necrosis factor receptor superfamily signaling cascades, which include activation of the NF-κB transcription factor family. As these receptors modulate cell proliferation and cell death, the involvement of the c-IAPs in these pathways provides an additional means of controlling cellular fate beyond simply inhibiting caspase activity. Additionally, IAP-binding proteins, such as Smac and caspases, which have been described as having cell death-independent roles, may affect c-IAP activity in intracellular signaling. Collectively, the multi-faceted functions and complex regulation of the c-IAPs illustrate their importance as intracellular signaling intermediates.

  19. Security Enhancement of Wireless Sensor Networks Using Signal Intervals.

    PubMed

    Moon, Jaegeun; Jung, Im Y; Yoo, Jaesoo

    2017-04-02

    Various wireless technologies, such as RF, Bluetooth, and Zigbee, have been applied to sensor communications. However, the applications of Bluetooth-based wireless sensor networks (WSN) have a security issue. In one pairing process during Bluetooth communication, which is known as simple secure pairing (SSP), the devices are required to specify I/O capability or user interference to prevent man-in-the-middle (MITM) attacks. This study proposes an enhanced SSP in which a nonce to be transferred is converted to a corresponding signal interval. The quantization level, which is used to interpret physical signal intervals, is renewed at every connection by the transferred nonce and applied to the next nonce exchange so that the same signal intervals can represent different numbers. Even if attackers eavesdrop on the signals, they cannot understand what is being transferred because they cannot determine the quantization level. Furthermore, the proposed model does not require exchanging passkeys as data, and the devices are secure in the case of using a fixed PIN. Subsequently, the new quantization level is calculated automatically whenever the same devices attempt to connect with each other. Therefore, the pairing process can be protected from MITM attacks and be convenient for users.

  20. Role of Regulators of G Protein Signaling Proteins in Bone Physiology and Pathophysiology

    PubMed Central

    Jules, Joel; Yang, Shuying; Chen, Wei; Li, Yi-Ping

    2016-01-01

    Regulators of G protein signaling (RGS) proteins enhance the intrinsic GTPase activity of α subunits of the heterotrimeric G protein complex of G protein-coupled receptors (GPCRs) and thereby inactivate signal transduction initiated by GPCRs. The RGS family consists of nearly 37 members with a conserved RGS homology domain which is critical for their GTPase accelerating activity. RGS proteins are expressed in most tissues, including heart, lung, brain, kidney, and bone and play essential roles in many physiological and pathological processes. In skeletal development and bone homeostasis as well as in many bone disorders, RGS proteins control the functions of various GPCRs, including the parathyroid hormone receptor type 1 and calcium-sensing receptor and also regulate various critical signaling pathways, such as Wnt and calcium oscillations. This chapter will discuss the current findings on the roles of RGS proteins in regulating signaling of key GPCRs in skeletal development and bone homeostasis. We also will examine the current updates of RGS proteins’ regulation of calcium oscillations in bone physiology and highlight the roles of RGS proteins in selected bone pathological disorders. Despite the recent advances in bone and mineral research, RGS proteins remain understudied in the skeletal system. Further understanding of the roles of RGS proteins in bone should not only provide great insights into the molecular basis of various bone diseases but also generate great therapeutic drug targets for many bone diseases. PMID:26123302

  1. The yeast Sks1p kinase signaling network regulates pseudohyphal growth and glucose response.

    PubMed

    Johnson, Cole; Kweon, Hye Kyong; Sheidy, Daniel; Shively, Christian A; Mellacheruvu, Dattatreya; Nesvizhskii, Alexey I; Andrews, Philip C; Kumar, Anuj

    2014-03-01

    The yeast Saccharomyces cerevisiae undergoes a dramatic growth transition from its unicellular form to a filamentous state, marked by the formation of pseudohyphal filaments of elongated and connected cells. Yeast pseudohyphal growth is regulated by signaling pathways responsive to reductions in the availability of nitrogen and glucose, but the molecular link between pseudohyphal filamentation and glucose signaling is not fully understood. Here, we identify the glucose-responsive Sks1p kinase as a signaling protein required for pseudohyphal growth induced by nitrogen limitation and coupled nitrogen/glucose limitation. To identify the Sks1p signaling network, we applied mass spectrometry-based quantitative phosphoproteomics, profiling over 900 phosphosites for phosphorylation changes dependent upon Sks1p kinase activity. From this analysis, we report a set of novel phosphorylation sites and highlight Sks1p-dependent phosphorylation in Bud6p, Itr1p, Lrg1p, Npr3p, and Pda1p. In particular, we analyzed the Y309 and S313 phosphosites in the pyruvate dehydrogenase subunit Pda1p; these residues are required for pseudohyphal growth, and Y309A mutants exhibit phenotypes indicative of impaired aerobic respiration and decreased mitochondrial number. Epistasis studies place SKS1 downstream of the G-protein coupled receptor GPR1 and the G-protein RAS2 but upstream of or at the level of cAMP-dependent PKA. The pseudohyphal growth and glucose signaling transcription factors Flo8p, Mss11p, and Rgt1p are required to achieve wild-type SKS1 transcript levels. SKS1 is conserved, and deletion of the SKS1 ortholog SHA3 in the pathogenic fungus Candida albicans results in abnormal colony morphology. Collectively, these results identify Sks1p as an important regulator of filamentation and glucose signaling, with additional relevance towards understanding stress-responsive signaling in C. albicans.

  2. 14-3-3 Proteins in Guard Cell Signaling

    PubMed Central

    Cotelle, Valérie; Leonhardt, Nathalie

    2016-01-01

    Guard cells are specialized cells located at the leaf surface delimiting pores which control gas exchanges between the plant and the atmosphere. To optimize the CO2 uptake necessary for photosynthesis while minimizing water loss, guard cells integrate environmental signals to adjust stomatal aperture. The size of the stomatal pore is regulated by movements of the guard cells driven by variations in their volume and turgor. As guard cells perceive and transduce a wide array of environmental cues, they provide an ideal system to elucidate early events of plant signaling. Reversible protein phosphorylation events are known to play a crucial role in the regulation of stomatal movements. However, in some cases, phosphorylation alone is not sufficient to achieve complete protein regulation, but is necessary to mediate the binding of interactors that modulate protein function. Among the phosphopeptide-binding proteins, the 14-3-3 proteins are the best characterized in plants. The 14-3-3s are found as multiple isoforms in eukaryotes and have been shown to be involved in the regulation of stomatal movements. In this review, we describe the current knowledge about 14-3-3 roles in the regulation of their binding partners in guard cells: receptors, ion pumps, channels, protein kinases, and some of their substrates. Regulation of these targets by 14-3-3 proteins is discussed and related to their function in guard cells during stomatal movements in response to abiotic or biotic stresses. PMID:26858725

  3. Genome-Wide Analysis of the TORC1 and Osmotic Stress Signaling Network in Saccharomyces cerevisiae

    PubMed Central

    Worley, Jeremy; Sullivan, Arron; Luo, Xiangxia; Kaplan, Matthew E.; Capaldi, Andrew P.

    2015-01-01

    The Target of Rapamycin kinase Complex I (TORC1) is a master regulator of cell growth and metabolism in eukaryotes. Studies in yeast and human cells have shown that nitrogen/amino acid starvation signals act through Npr2/Npr3 and the small GTPases Gtr1/Gtr2 (Rags in humans) to inhibit TORC1. However, it is unclear how other stress and starvation stimuli inhibit TORC1, and/or act in parallel with the TORC1 pathway, to control cell growth. To help answer these questions, we developed a novel automated pipeline and used it to measure the expression of a TORC1-dependent ribosome biogenesis gene (NSR1) during osmotic stress in 4700 Saccharomyces cerevisiae strains from the yeast knock-out collection. This led to the identification of 440 strains with significant and reproducible defects in NSR1 repression. The cell growth control and stress response proteins deleted in these strains form a highly connected network, including 56 proteins involved in vesicle trafficking and vacuolar function; 53 proteins that act downstream of TORC1 according to a rapamycin assay—including components of the HDAC Rpd3L, Elongator, and the INO80, CAF-1 and SWI/SNF chromatin remodeling complexes; over 100 proteins involved in signaling and metabolism; and 17 proteins that directly interact with TORC1. These data provide an important resource for labs studying cell growth control and stress signaling, and demonstrate the utility of our new, and easily adaptable, method for mapping gene regulatory networks. PMID:26681516

  4. Bone morphogenetic proteins: Signaling periodontal bone regeneration and repair.

    PubMed

    Anusuya, G Sai; Kandasamy, M; Jacob Raja, S A; Sabarinathan, S; Ravishankar, P; Kandhasamy, Balu

    2016-10-01

    Bone morphogenetic proteins (BMPs) are a group of growth factors also known as cytokines and as metabologens. Originally discovered by their ability to induce the formation of bone and cartilage, BMPs are now considered to constitute a group of pivotal morphogenetic signals, orchestrating tissue architecture throughout the body. The important functioning of BMP signals in physiology is emphasized by the multitude of roles for dysregulated BMP signaling in pathological processes. A study done wherein it was found that protein extracts from bone implanted into the animals at nonbone sites induced the formation of new cartilage and bone tissue. This protein extract contained multiple factors that stimulated bone formation and was termed as "BMP." There are at least 15 different BMPs identified to date and are a part of the transforming growth factor-β super family. The most widely studied BMPs are BMP-2, BMP-3 (osteogenin), BMP-4, and BMP-7 (osteogenic protein-1). Now, any recombination type of morphogenic proteins have been synthesized, for example - recombinant human BMPs.

  5. Bone morphogenetic proteins: Signaling periodontal bone regeneration and repair

    PubMed Central

    Anusuya, G. Sai; Kandasamy, M.; Jacob Raja, S. A.; Sabarinathan, S.; Ravishankar, P.; Kandhasamy, Balu

    2016-01-01

    Bone morphogenetic proteins (BMPs) are a group of growth factors also known as cytokines and as metabologens. Originally discovered by their ability to induce the formation of bone and cartilage, BMPs are now considered to constitute a group of pivotal morphogenetic signals, orchestrating tissue architecture throughout the body. The important functioning of BMP signals in physiology is emphasized by the multitude of roles for dysregulated BMP signaling in pathological processes. A study done wherein it was found that protein extracts from bone implanted into the animals at nonbone sites induced the formation of new cartilage and bone tissue. This protein extract contained multiple factors that stimulated bone formation and was termed as “BMP.” There are at least 15 different BMPs identified to date and are a part of the transforming growth factor-β super family. The most widely studied BMPs are BMP-2, BMP-3 (osteogenin), BMP-4, and BMP-7 (osteogenic protein-1). Now, any recombination type of morphogenic proteins have been synthesized, for example - recombinant human BMPs. PMID:27829744

  6. Spatio-temporal Remodeling of Functional Membrane Microdomains Organizes the Signaling Networks of a Bacterium

    PubMed Central

    Schneider, Johannes; Klein, Teresa; Mielich-Süss, Benjamin; Koch, Gudrun; Franke, Christian; Kuipers, Oscar P.; Kovács, Ákos T.; Sauer, Markus; Lopez, Daniel

    2015-01-01

    Lipid rafts are membrane microdomains specialized in the regulation of numerous cellular processes related to membrane organization, as diverse as signal transduction, protein sorting, membrane trafficking or pathogen invasion. It has been proposed that this functional diversity would require a heterogeneous population of raft domains with varying compositions. However, a mechanism for such diversification is not known. We recently discovered that bacterial membranes organize their signal transduction pathways in functional membrane microdomains (FMMs) that are structurally and functionally similar to the eukaryotic lipid rafts. In this report, we took advantage of the tractability of the prokaryotic model Bacillus subtilis to provide evidence for the coexistence of two distinct families of FMMs in bacterial membranes, displaying a distinctive distribution of proteins specialized in different biological processes. One family of microdomains harbors the scaffolding flotillin protein FloA that selectively tethers proteins specialized in regulating cell envelope turnover and primary metabolism. A second population of microdomains containing the two scaffolding flotillins, FloA and FloT, arises exclusively at later stages of cell growth and specializes in adaptation of cells to stationary phase. Importantly, the diversification of membrane microdomains does not occur arbitrarily. We discovered that bacterial cells control the spatio-temporal remodeling of microdomains by restricting the activation of FloT expression to stationary phase. This regulation ensures a sequential assembly of functionally specialized membrane microdomains to strategically organize signaling networks at the right time during the lifespan of a bacterium. PMID:25909364

  7. Predicting protein subcellular location using digital signal processing.

    PubMed

    Pan, Yu-Xi; Li, Da-Wei; Duan, Yun; Zhang, Zhi-Zhou; Xu, Ming-Qing; Feng, Guo-Yin; He, Lin

    2005-02-01

    The biological functions of a protein are closely related to its attributes in a cell. With the rapid accumulation of newly found protein sequence data in databanks, it is highly desirable to develop an automated method for predicting the subcellular location of proteins. The establishment of such a predictor will expedite the functional determination of newly found proteins and the process of prioritizing genes and proteins identified by genomic efforts as potential molecular targets for drug design. The traditional algorithms for predicting these attributes were based solely on amino acid composition in which no sequence order effect was taken into account. To improve the prediction quality, it is necessary to incorporate such an effect. However, the number of possible patterns in protein sequences is extremely large, posing a formidable difficulty for realizing this goal. To deal with such difficulty, a well-developed tool in digital signal processing named digital Fourier transform (DFT) [1] was introduced. After being translated to a digital signal according to the hydrophobicity of each amino acid, a protein was analyzed by DFT within the frequency domain. A set of frequency spectrum parameters, thus obtained, were regarded as the factors to represent the sequence order effect. A significant improvement in prediction quality was observed by incorporating the frequency spectrum parameters with the conventional amino acid composition. One of the crucial merits of this approach is that many existing tools in mathematics and engineering can be easily applied in the predicting process. It is anticipated that digital signal processing may serve as a useful vehicle for many other protein science areas.

  8. Biomolecular Simulation of Base Excision Repair and Protein Signaling

    SciTech Connect

    Straatsma, TP; McCammon, J A; Miller, John H; Smith, Paul E; Vorpagel, Erich R; Wong, Chung F; Zacharias, Martin W

    2006-03-03

    The goal of the Biomolecular Simulation of Base Excision Repair and Protein Signaling project is to enhance our understanding of the mechanism of human polymerase-β, one of the key enzymes in base excision repair (BER) and the cell-signaling enzymes cyclic-AMP-dependent protein kinase. This work used molecular modeling and simulation studies to specifically focus on the • dynamics of DNA and damaged DNA • dynamics and energetics of base flipping in DNA • mechanism and fidelity of nucleotide insertion by BER enzyme human polymerase-β • mechanism and inhibitor design for cyclic-AMP-dependent protein kinase. Molecular dynamics simulations and electronic structure calculations have been performed using the computer resources at the Molecular Science Computing Facility at the Environmental Molecular Sciences Laboratory.

  9. Evolution: A Guide to Perturb Protein Function and Networks

    PubMed Central

    Lichtarge, Olivier; Wilkins, Angela

    2010-01-01

    Summary Protein interactions give rise to networks that control cell fate in health and disease; selective means to probe these interactions are therefore of wide interest. We discuss here Evolutionary Tracing (ET), a comparative method to identify protein functional sites and to guide experiments that selectively block, recode, or mimic their amino acid determinants. These studies suggest, in principle, a scalable approach to perturb individual links in protein networks. PMID:20444593

  10. RANK-mediated signaling network and cancer metastasis

    PubMed Central

    Chu, Chia-Yi Gina; Chung, Leland W. K.

    2014-01-01

    Cancer metastasis is highly inefficient and complex. Common features of metastatic cancer cells have been observed using cancer cell lines and genetically reconstituted mouse and human tumor xenograft models. These include cancer cell interaction with the tumor microenvironment, and the ability of cancer cells to sense extracellular stimuli and adapt to adverse growth conditions. This review summarizes the coordinated response of cancer cells to soluble growth factors, such as RANKL, by a unique forward feedback mechanism employing coordinated upregulation of RANKL and c-Met with downregulation of androgen receptor. The RANK-mediated signal network was found to drive epithelial to mesenchymal transition in prostate cancer cells, promote osteomimicry and the ability of prostate cancer cells to assume stem cell and neuroendocrine phenotypes, and confer the ability of prostate cancer cells to home to bone. Prostate cancer cells with activated RANK-mediated signal network were observed to recruit and even transform the non-tumorigenic prostate cancer cells to participate in bone and soft tissue colonization. The coordinated regulation of cancer cell invasion and metastasis by the forward feedback mechanism involving RANKL, c-Met, transcription factors and VEGF-neuropilin could offer new therapeutic opportunities to target prostate cancer bone and soft tissue metastases. PMID:24398859

  11. Neural network classification of myoelectric signal for prosthesis control.

    PubMed

    Kelly, M F; Parker, P A; Scott, R N

    1991-12-01

    An alternate approach to deriving control for multidegree of freedom prosthetic arms is considered. By analyzing a single-channel myoelectric signal (MES), we can extract information that can be used to identify different contraction patterns in the upper arm. These contraction patterns are generated by subjects without previous training and are naturally associated with specific functions. Using a set of normalized MES spectral features, we can identify contraction patterns for four arm functions, specifically extension and flexion of the elbow and pronation and supination of the forearm. Performing identification independent of signal power is advantageous because this can then be used as a means for deriving proportional rate control for a prosthesis. An artificial neural network implementation is applied in the classification task. By using three single-layer perceptron networks, the MES is classified, with the spectral representations as input features. Trials performed on five subjects with normal limbs resulted in an average classification performance level of 85% for the four functions.

  12. RANK-mediated signaling network and cancer metastasis.

    PubMed

    Chu, Gina Chia-Yi; Chung, Leland W K

    2014-09-01

    Cancer metastasis is highly inefficient and complex. Common features of metastatic cancer cells have been observed using cancer cell lines and genetically reconstituted mouse and human tumor xenograft models. These include cancer cell interaction with the tumor microenvironment and the ability of cancer cells to sense extracellular stimuli and adapt to adverse growth conditions. This review summarizes the coordinated response of cancer cells to soluble growth factors, such as RANKL, by a unique feed forward mechanism employing coordinated upregulation of RANKL and c-Met with downregulation of androgen receptor. The RANK-mediated signal network was found to drive epithelial to mesenchymal transition in prostate cancer cells, promote osteomimicry and the ability of prostate cancer cells to assume stem cell and neuroendocrine phenotypes, and confer the ability of prostate cancer cells to home to bone. Prostate cancer cells with activated RANK-mediated signal network were observed to recruit and even transform the non-tumorigenic prostate cancer cells to participate in bone and soft tissue colonization. The coordinated regulation of cancer cell invasion and metastasis by the feed forward mechanism involving RANKL, c-Met, transcription factors, and VEGF-neuropilin could offer new therapeutic opportunities to target prostate cancer bone and soft tissue metastases.

  13. Dissection of the cis-2-decenoic acid signaling network in Pseudomonas aeruginosa using microarray technique

    PubMed Central

    Rahmani-Badi, Azadeh; Sepehr, Shayesteh; Fallahi, Hossein; Heidari-Keshel, Saeed

    2015-01-01

    Many bacterial pathogens use quorum-sensing (QS) signaling to regulate the expression of factors contributing to virulence and persistence. Bacteria produce signals of different chemical classes. The signal molecule, known as diffusible signal factor (DSF), is a cis-unsaturated fatty acid that was first described in the plant pathogen Xanthomonas campestris. Previous works have shown that human pathogen, Pseudomonas aeruginosa, also synthesizes a structurally related molecule, characterized as cis-2-decenoic acid (C10: Δ2, CDA) that induces biofilm dispersal by multiple types of bacteria. Furthermore, CDA has been shown to be involved in inter-kingdom signaling that modulates fungal behavior. Therefore, an understanding of its signaling mechanism could suggest strategies for interference, with consequences for disease control. To identify the components of CDA signaling pathway in this pathogen, a comparative transcritpome analysis was conducted, in the presence and absence of CDA. A protein-protein interaction (PPI) network for differentially expressed (DE) genes with known function was then constructed by STRING and Cytoscape. In addition, the effects of CDA in combination with antimicrobial agents on the biofilm surface area and bacteria viability were evaluated using fluorescence microscopy and digital image analysis. Microarray analysis identified 666 differentially expressed genes in the presence of CDA and gene ontology (GO) analysis revealed that in P. aeruginosa, CDA mediates dispersion of biofilms through signaling pathways, including enhanced motility, metabolic activity, virulence as well as persistence at different temperatures. PPI data suggested that a cluster of five genes (PA4978, PA4979, PA4980, PA4982, PA4983) is involved in the CDA synthesis and perception. Combined treatments using both CDA and antimicrobial agents showed that following exposure of the biofilms to CDA, remaining cells on the surface were easily removed and killed by

  14. Networks of WRKY transcription factors in defense signaling.

    PubMed

    Eulgem, Thomas; Somssich, Imre E

    2007-08-01

    Members of the complex family of WRKY transcription factors have been implicated in the regulation of transcriptional reprogramming associated with plant immune responses. Recently genetic evidence directly proving their significance as positive and negative regulators of disease resistance has accumulated. WRKY genes were shown to be functionally connected forming a transcriptional network composed of positive and negative feedback loops and feed-forward modules. Within a web of partially redundant elements some WRKY factors hold central positions mediating fast and efficient activation of defense programs. A key mechanism triggering strong immune responses appears to be based on the inactivation of defense-suppressing WRKY proteins.

  15. Signalling-Dependent Interactions Between the Kinase-Coupling Protein CheW and Chemoreceptors in Living Cells

    PubMed Central

    Pedetta, Andrea; Parkinson, John S.; Studdert, Claudia A.

    2014-01-01

    Summary Chemical signals sensed on the periplasmic side of bacterial cells by transmembrane chemoreceptors are transmitted to the flagellar motors via the histidine kinase CheA, which controls the phosphorylation level of the effector protein CheY. Chemoreceptor arrays comprise remarkably stable supramolecular structures in which thousands of chemoreceptors are networked through interactions between their cytoplasmic tips, CheA, and the small coupling protein CheW. To explore the conformational changes that occur within this protein assembly during signalling, we used in vivo crosslinking methods to detect close interactions between the coupling protein CheW and the serine receptor Tsr in intact E. coli cells. We identified two signal-sensitive contacts between CheW and the cytoplasmic tip of Tsr. Our results suggest that ligand binding triggers changes in the receptor that alter its signalling contacts with CheW (and/or CheA). PMID:25060668

  16. Control of cancer-related signal transduction networks

    NASA Astrophysics Data System (ADS)

    Albert, Reka

    2013-03-01

    Intra-cellular signaling networks are crucial to the maintenance of cellular homeostasis and for cell behavior (growth, survival, apoptosis, movement). Mutations or alterations in the expression of elements of cellular signaling networks can lead to incorrect behavioral decisions that could result in tumor development and/or the promotion of cell migration and metastasis. Thus, mitigation of the cascading effects of such dysregulations is an important control objective. My group at Penn State is collaborating with wet-bench biologists to develop and validate predictive models of various biological systems. Over the years we found that discrete dynamic modeling is very useful in molding qualitative interaction information into a predictive model. We recently demonstrated the effectiveness of network-based targeted manipulations on mitigating the disease T cell large granular lymphocyte (T-LGL) leukemia. The root of this disease is the abnormal survival of T cells which, after successfully fighting an infection, should undergo programmed cell death. We synthesized the relevant network of within-T-cell interactions from the literature, integrated it with qualitative knowledge of the dysregulated (abnormal) states of several network components, and formulated a Boolean dynamic model. The model indicated that the system possesses a steady state corresponding to the normal cell death state and a T-LGL steady state corresponding to the abnormal survival state. For each node, we evaluated the restorative manipulation consisting of maintaining the node in the state that is the opposite of its T-LGL state, e.g. knocking it out if it is overexpressed in the T-LGL state. We found that such control of any of 15 nodes led to the disappearance of the T-LGL steady state, leaving cell death as the only potential outcome from any initial condition. In four additional cases the probability of reaching the T-LGL state decreased dramatically, thus these nodes are also possible control

  17. Automated Measurement and Signaling Systems for the Transactional Network

    SciTech Connect

    Piette, Mary Ann; Brown, Richard; Price, Phillip; Page, Janie; Granderson, Jessica; Riess, David; Czarnecki, Stephen; Ghatikar, Girish; Lanzisera, Steven

    2013-12-31

    The Transactional Network Project is a multi-lab activity funded by the US Department of Energy?s Building Technologies Office. The project team included staff from Lawrence Berkeley National Laboratory, Pacific Northwest National Laboratory and Oak Ridge National Laboratory. The team designed, prototyped and tested a transactional network (TN) platform to support energy, operational and financial transactions between any networked entities (equipment, organizations, buildings, grid, etc.). PNNL was responsible for the development of the TN platform, with agents for this platform developed by each of the three labs. LBNL contributed applications to measure the whole-building electric load response to various changes in building operations, particularly energy efficiency improvements and demand response events. We also provide a demand response signaling agent and an agent for cost savings analysis. LBNL and PNNL demonstrated actual transactions between packaged rooftop units and the electric grid using the platform and selected agents. This document describes the agents and applications developed by the LBNL team, and associated tests of the applications.

  18. Pythoscape: a framework for generation of large protein similarity networks.

    PubMed

    Barber, Alan E; Babbitt, Patricia C

    2012-11-01

    Pythoscape is a framework implemented in Python for processing large protein similarity networks for visualization in other software packages. Protein similarity networks are graphical representations of sequence, structural and other similarities among proteins for which pairwise all-by-all similarity connections have been calculated. Mapping of biological and other information to network nodes or edges enables hypothesis creation about sequence-structure-function relationships across sets of related proteins. Pythoscape provides several options to calculate pairwise similarities for input sequences or structures, applies filters to network edges and defines sets of similar nodes and their associated data as single nodes (termed representative nodes) for compression of network information and output data or formatted files for visualization.

  19. Evolutionary pressure on the topology of protein interface interaction networks.

    PubMed

    Johnson, Margaret E; Hummer, Gerhard

    2013-10-24

    The densely connected structure of protein-protein interaction (PPI) networks reflects the functional need of proteins to cooperate in cellular processes. However, PPI networks do not adequately capture the competition in protein binding. By contrast, the interface interaction network (IIN) studied here resolves the modular character of protein-protein binding and distinguishes between simultaneous and exclusive interactions that underlie both cooperation and competition. We show that the topology of the IIN is under evolutionary pressure, and we connect topological features of the IIN to specific biological functions. To reveal the forces shaping the network topology, we use a sequence-based computational model of interface binding along with network analysis. We find that the more fragmented structure of IINs, in contrast to the dense PPI networks, arises in large part from the competition between specific and nonspecific binding. The need to minimize nonspecific binding favors specific network motifs, including a minimal number of cliques (i.e., fully connected subgraphs) and many disconnected fragments. Validating the model, we find that these network characteristics are closely mirrored in the IIN of clathrin-mediated endocytosis. Features unexpected on the basis of our motif analysis are found to indicate either exceptional binding selectivity or important regulatory functions.

  20. Self-incompatibility in Papaver: advances in integrating the signalling network.

    PubMed

    Eaves, Deborah J; Flores-Ortiz, Carlos; Haque, Tamanna; Lin, Zongcheng; Teng, Nianjun; Franklin-Tong, Vernonica E

    2014-04-01

    Self-fertilization, which results in reduced fitness of offspring, is a common problem in hermaphrodite angiosperms. To prevent this, many plants utilize SI (self-incompatibility), which is determined by the multi-allelic S-locus, that allows discrimination between self (incompatible) and non-self (compatible) pollen by the pistil. In poppy (Papaver rhoeas), the pistil S-determinant (PrsS) is a small secreted protein which interacts with the pollen S-determinant PrpS, a ~20 kDa novel transmembrane protein. Interaction of matching pollen and pistil S-determinants results in self-recognition, initiating a Ca²⁺-dependent signalling network in incompatible pollen. This triggers several downstream events, including alterations to the cytoskeleton, phosphorylation of sPPases (soluble inorganic pyrophosphatases) and an MAPK (mitogen-activated protein kinase), increases in ROS (reactive oxygen species) and nitric oxide (NO), and activation of several caspase-like activities. This results in the inhibition of pollen tube growth, prevention of self-fertilization and ultimately PCD (programmed cell death) in incompatible pollen. The present review focuses on our current understanding of the integration of these signals with their targets in the SI/PCD network. We also discuss our recent functional expression of PrpS in Arabidopsis thaliana pollen.

  1. Control of protein trafficking by reversible masking of transport signals

    PubMed Central

    Abraham, Omer; Gotliv, Karnit; Parnis, Anna; Boncompain, Gaelle; Perez, Franck; Cassel, Dan

    2016-01-01

    Systems that allow the control of protein traffic between subcellular compartments have been valuable in elucidating trafficking mechanisms. Most current approaches rely on ligand or light-controlled dimerization, which results in either retardation or enhancement of the transport of a reporter. We developed an alternative approach for trafficking regulation that we term “controlled unmasking of targeting elements” (CUTE). Regulated trafficking is achieved by reversible masking of the signal that directs the reporter to its target organelle, relying on the streptavidin–biotin system. The targeting signal is generated within or immediately after a 38–amino acid streptavidin-binding peptide (SBP) that is appended to the reporter. The binding of coexpressed streptavidin to SBP causes signal masking, whereas addition of biotin causes complex dissociation and triggers protein transport to the target organelle. We demonstrate the application of this approach to the control of nuclear and peroxisomal protein import and the generation of biotin-dependent trafficking through the endocytic and COPI systems. By simultaneous masking of COPI and endocytic signals, we were able to generate a synthetic pathway for efficient transport of a reporter from the plasma membrane to the endoplasmic reticulum. PMID:26941332

  2. Identifying protein complexes in protein-protein interaction networks by using clique seeds and graph entropy.

    PubMed

    Chen, Bolin; Shi, Jinhong; Zhang, Shenggui; Wu, Fang-Xiang

    2013-01-01

    The identification of protein complexes plays a key role in understanding major cellular processes and biological functions. Various computational algorithms have been proposed to identify protein complexes from protein-protein interaction (PPI) networks. In this paper, we first introduce a new seed-selection strategy for seed-growth style algorithms. Cliques rather than individual vertices are employed as initial seeds. After that, a result-modification approach is proposed based on this seed-selection strategy. Predictions generated by higher order clique seeds are employed to modify results that are generated by lower order ones. The performance of this seed-selection strategy and the result-modification approach are tested by using the entropy-based algorithm, which is currently the best seed-growth style algorithm to detect protein complexes from PPI networks. In addition, we investigate four pairs of strategies for this algorithm in order to improve its accuracy. The numerical experiments are conducted on a Saccharomyces cerevisiae PPI network. The group of best predictions consists of 1711 clusters, with the average f-score at 0.68 after removing all similar and redundant clusters. We conclude that higher order clique seeds can generate predictions with higher accuracy and that our improved entropy-based algorithm outputs more reasonable predictions than the original one.

  3. Insights into protein interaction networks reveal non-receptor kinases as significant druggable targets for psoriasis.

    PubMed

    Sundarrajan, Sudharsana; Lulu, Sajitha; Arumugam, Mohanapriya

    2015-07-25

    Psoriasis is a chronic disease of the skin characterized by hyper proliferation and inflammation of the epidermis and dermal components of the skin. T-cell-dependent inflammatory process in skin governs the pathogenesis of psoriasis. An in-silico search strategy was utilized to identify psoriatic therapeutic drug targets. The gene expression profiling of psoriatic skin identified a total of 427 differentially expressed genes (DEGs). Gene ontology investigation of DEGs identified genes involved in calcium binding, apoptosis, keratinisation, lipid transportation and homeostasis apart from immune mediated processes. The protein interaction networks identified proteins involved in various signaling mechanisms with high degree of interconnections. The gene modules derived from the main network were enriched with rich kinome. These sub-networks were dominated by the presence of non-receptor kinase family members which are major signal transmitters in immune response. The computational approach has aided in the identification of non-receptor kinases as potential targets for psoriasis drug development.

  4. In silico evidence of signaling pathways of notch mediated networks in leukemia

    PubMed Central

    Jamil, Kaiser; Jayaraman, Archana; Rao, Raghunatha; Raju, Suryanarayana

    2012-01-01

    Notch signaling plays a critical role in cell fate determination and maintenance of progenitors in many developmental systems. Notch receptors have been shown to be expressed on hematopoietic progenitor cells as well as to various degrees in peripheral blood T and B lymphocytes, monocytes, and neutrophils. Our aim was to understand the protein interaction network, using Notch1 protein name as query in STRING database and we generated a model to assess the significance of Notch1 associated proteins in Acute Lymphoblastic Leukemia (ALL). We further analyzed the expression levels of the genes encoding hub proteins, using Oncomine database, to determine their significance in leukemogenesis. Of the forty two hub genes, we observed that sixteen genes were underexpressed and eleven genes were overexpressed in T-cell Acute Lymphoblastic samples in comparison to their expression levels in normal cells. Of these, we found three novel genes which have not been reported earlier- KAT2B, PSEN1 (underexpressed) and CDH2 (overexpressed).These three identified genes may provide new insights into the abnormal hematopoietic process observed in Leukemia as these genes are involved in Notch signaling and cell adhesion processes. It is evident that experimental validation of the protein interactors in leukemic cells could help in the identification of new diagnostic markers for leukemia. PMID:24688641

  5. Recent Advances on the Role of G Protein-Coupled Receptors in Hypoxia-Mediated Signaling.

    PubMed

    Lappano, Rosamaria; Rigiracciolo, Damiano; De Marco, Paola; Avino, Silvia; Cappello, Anna Rita; Rosano, Camillo; Maggiolini, Marcello; De Francesco, Ernestina Marianna

    2016-03-01

    G protein-coupled receptors (GPCRs) are cell surface proteins mainly involved in signal transmission; however, they play a role also in several pathophysiological conditions. Chemically heterogeneous molecules like peptides, hormones, lipids, and neurotransmitters activate second messengers and induce several biological responses by binding to these seven transmembrane receptors, which are coupled to heterotrimeric G proteins. Recently, additional molecular mechanisms have been involved in GPCR-mediated signaling, leading to an intricate network of transduction pathways. In this regard, it should be mentioned that diverse GPCR family members contribute to the adaptive cell responses to low oxygen tension, which is a distinguishing feature of several illnesses like neoplastic and cardiovascular diseases. For instance, the G protein estrogen receptor, namely G protein estrogen receptor (GPER)/GPR30, has been shown to contribute to relevant biological effects induced by hypoxia via the hypoxia-inducible factor (HIF)-1α in diverse cell contexts, including cancer. Likewise, GPER has been found to modulate the biological outcome of hypoxic/ischemic stress in both cardiovascular and central nervous systems. Here, we describe the role exerted by GPCR-mediated signaling in low oxygen conditions, discussing, in particular, the involvement of GPER by a hypoxic microenvironment.

  6. Pattern recognition for electroencephalographic signals based on continuous neural networks.

    PubMed

    Alfaro-Ponce, M; Argüelles, A; Chairez, I

    2016-07-01

    This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artificial neural networks (NN) described by ordinary differential equations (ODEs). The training method for this kind of continuous NN (CNN) was developed according to the Lyapunov theory stability analysis. A parallel structure with fixed weights was proposed to perform the classification stage. The pattern recognition efficiency was validated by two methods, a generalization-regularization and a k-fold cross validation (k=5). The classifier was applied on two different databases. The first one was made up by signals collected from patients suffering of epilepsy and it is divided in five different classes. The second database was made up by 90 single EEG trials, divided in three classes. Each class corresponds to a different visual evoked potential. The pattern recognition algorithm achieved a maximum correct classification percentage of 97.2% using the information of the entire database. This value was similar to some results previously reported when this database was used for testing pattern classification. However, these results were obtained when only two classes were considered for the testing. The result reported in this study used the whole set of signals (five different classes). In comparison with similar pattern recognition methods that even considered less number of classes, the proposed CNN proved to achieve the same or even better correct classification results.

  7. The eFIP system for text mining of protein interaction networks of phosphorylated proteins.

    PubMed

    Tudor, Catalina O; Arighi, Cecilia N; Wang, Qinghua; Wu, Cathy H; Vijay-Shanker, K

    2012-01-01

    Protein phosphorylation is a central regulatory mechanism in signal transduction involved in most biological processes. Phosphorylation of a protein may lead to activation or repression of its activity, alternative subcellular location and interaction with different binding partners. Extracting this type of information from scientific literature is critical for connecting phosphorylated proteins with kinases and interaction partners, along with their functional outcomes, for knowledge discovery from phosphorylation protein networks. We have developed the Extracting Functional Impact of Phosphorylation (eFIP) text mining system, which combines several natural language processing techniques to find relevant abstracts mentioning phosphorylation of a given protein together with indications of protein-protein interactions (PPIs) and potential evidences for impact of phosphorylation on the PPIs. eFIP integrates our previously developed tools, Extracting Gene Related ABstracts (eGRAB) for document retrieval and name disambiguation, Rule-based LIterature Mining System (RLIMS-P) for Protein Phosphorylation for extraction of phosphorylation information, a PPI module to detect PPIs involving phosphorylated proteins and an impact module for relation extraction. The text mining system has been integrated into the curation workflow of the Protein Ontology (PRO) to capture knowledge about phosphorylated proteins. The eFIP web interface accepts gene/protein names or identifiers, or PubMed identifiers as input, and displays results as a ranked list of abstracts with sentence evidence and summary table, which can be exported in a spreadsheet upon result validation. As a participant in the BioCreative-2012 Interactive Text Mining track, the performance of eFIP was evaluated on document retrieval (F-measures of 78-100%), sentence-level information extraction (F-measures of 70-80%) and document ranking (normalized discounted cumulative gain measures of 93-100% and mean average

  8. Engineering modular and tunable genetic amplifiers for scaling transcriptional signals in cascaded gene networks.

    PubMed

    Wang, Baojun; Barahona, Mauricio; Buck, Martin

    2014-08-01

    Synthetic biology aims to control and reprogram signal processing pathways within living cells so as to realize repurposed, beneficial applications. Here we report the design and construction of a set of modular and gain-tunable genetic amplifiers in Escherichia coli capable of amplifying a transcriptional signal with wide tunable-gain control in cascaded gene networks. The devices are engineered using orthogonal genetic components (hrpRS, hrpV and PhrpL) from the hrp (hypersensitive response and pathogenicity) gene regulatory network in Pseudomonas syringae. The amplifiers can linearly scale up to 21-fold the transcriptional input with a large output dynamic range, yet not introducing significant time delay or significant noise during signal amplification. The set of genetic amplifiers achieves different gains and input dynamic ranges by varying the expression levels of the underlying ligand-free activator proteins in the device. As their electronic counterparts, these engineered transcriptional amplifiers can act as fundamental building blocks in the design of biological systems by predictably and dynamically modulating transcriptional signal flows to implement advanced intra- and extra-cellular control functions.

  9. Oxidative modification of proteins: an emerging mechanism of cell signaling.

    PubMed

    Wall, Stephanie B; Oh, Joo-Yeun; Diers, Anne R; Landar, Aimee

    2012-01-01

    There are a wide variety of reactive species which can affect cell function, including reactive oxygen, nitrogen, and lipid species. Some are formed endogenously through enzymatic or non-enzymatic pathways, and others are introduced through diet or environmental exposure. Many of these reactive species can interact with biomolecules and can result in oxidative post-translational modification of proteins. It is well documented that some oxidative modifications cause macromolecular damage and cell death. However, a growing body of evidence suggests that certain classes of reactive species initiate cell signaling by reacting with specific side chains of peptide residues without causing cell death. This process is generally termed "redox signaling," and its role in physiological and pathological processes is a subject of active investigation. This review will give an overview of oxidative protein modification as a mechanism of redox signaling, including types of reactive species and how they modify proteins, examples of modified proteins, and a discussion about the current concepts in this area.

  10. P(II) signal transduction proteins: nitrogen regulation and beyond.

    PubMed

    Huergo, Luciano F; Chandra, Govind; Merrick, Mike

    2013-03-01

    The P(II) proteins are one of the most widely distributed families of signal transduction proteins in nature. They are pivotal players in the control of nitrogen metabolism in bacteria and archaea, and are also found in the plastids of plants. Quite remarkably, P(II) proteins control the activities of a diverse range of enzymes, transcription factors and membrane transport proteins, and in recent years the extent of these interactions has been recognized to be much greater than heretofore described. Major advances have been made in structural studies of P(II) proteins, including the solution of the first structures of P(II) proteins complexed with their targets. We have also begun to gain insights into how the key effector molecules, 2-oxoglutarate and ATP/ADP, influence the activities of P(II) proteins. In this review, we have set out to summarize our current understanding of P(II) biology and to consider where future studies of these extraordinarily adaptable proteins might lead us.

  11. FGF21 is an endocrine signal of protein restriction.

    PubMed

    Laeger, Thomas; Henagan, Tara M; Albarado, Diana C; Redman, Leanne M; Bray, George A; Noland, Robert C; Münzberg, Heike; Hutson, Susan M; Gettys, Thomas W; Schwartz, Michael W; Morrison, Christopher D

    2014-09-01

    Enhanced fibroblast growth factor 21 (FGF21) production and circulation has been linked to the metabolic adaptation to starvation. Here, we demonstrated that hepatic FGF21 expression is induced by dietary protein restriction, but not energy restriction. Circulating FGF21 was increased 10-fold in mice and rats fed a low-protein (LP) diet. In these animals, liver Fgf21 expression was increased within 24 hours of reduced protein intake. In humans, circulating FGF21 levels increased dramatically following 28 days on a LP diet. LP-induced increases in FGF21 were associated with increased phosphorylation of eukaryotic initiation factor 2α (eIF2α) in the liver, and both baseline and LP-induced serum FGF21 levels were reduced in mice lacking the eIF2α kinase general control nonderepressible 2 (GCN2). Finally, while protein restriction altered food intake, energy expenditure, and body weight gain in WT mice, FGF21-deficient animals did not exhibit these changes in response to a LP diet. These and other data demonstrate that reduced protein intake underlies the increase in circulating FGF21 in response to starvation and a ketogenic diet and that FGF21 is required for behavioral and metabolic responses to protein restriction. FGF21 therefore represents an endocrine signal of protein restriction, which acts to coordinate metabolism and growth during periods of reduced protein intake.

  12. The Sts Proteins Target Tyrosine Phosphorylated, Ubiquitinated Proteins within TCR Signaling Pathways

    SciTech Connect

    Carpino, N.; Chen, Y; Nassar, N; Oh, H

    2009-01-01

    The T cell receptor (TCR) detects the presence of infectious pathogens and activates numerous intracellular signaling pathways. Protein tyrosine phosphorylation and ubiquitination serve as key regulatory mechanisms downstream of the TCR. Negative regulation of TCR signaling pathways is important in controlling the immune response, and the Suppressor of TCR Signaling proteins (Sts-1 and Sts-2) have been shown to function as critical negative regulators of TCR signaling. Although their mechanism of action has yet to be fully uncovered, it is known that the Sts proteins possess intrinsic phosphatase activity. Here, we demonstrate that Sts-1 and Sts-2 are instrumental in down-modulating proteins that are dually modified by both protein tyrosine phosphorylation and ubiquitination. Specifically, both naive and activated T cells derived from genetically engineered mice that lack the Sts proteins display strikingly elevated levels of tyrosine phosphorylated, ubiquitinated proteins following TCR stimulation. The accumulation of the dually modified proteins is transient, and in activated T cells but not naive T cells is significantly enhanced by co-receptor engagement. Our observations hint at a novel regulatory mechanism downstream of the T cell receptor.

  13. Application of oncoproteomics to aberrant signalling networks in changing the treatment paradigm in acute lymphoblastic leukaemia.

    PubMed

    López Villar, Elena; Wang, Xiangdong; Madero, Luis; Cho, William C

    2015-01-01

    Oncoproteomics is an important innovation in the early diagnosis, management and development of personalized treatment of acute lymphoblastic leukaemia (ALL). As inherent factors are not completely known - e.g. age or family history, radiation exposure, benzene chemical exposure, certain viral exposures such as infection with the human T-cell lymphoma/leukaemia virus-1, as well as some inherited syndromes may raise the risk of ALL - each ALL patient may modify the susceptibility of therapy. Indeed, we consider these unknown inherent factors could be explained via coupling cytogenetics plus proteomics, especially when proteins are the ones which play function within cells. Innovative proteomics to ALL therapy may help to understand the mechanism of drug resistance and toxicities, which in turn will provide some leads to improve ALL management. Most important of these are shotgun proteomic strategies to unravel ALL aberrant signalling networks. Some shotgun proteomic innovations and bioinformatic tools for ALL therapies will be discussed. As network proteins are distinctive characteristics for ALL patients, unrevealed by cytogenetics, those network proteins are currently an important source of novel therapeutic targets that emerge from shotgun proteomics. Indeed, ALL evolution can be studied for each individual patient via oncoproteomics.

  14. Networks of ProteinProtein Interactions: From Uncertainty to Molecular Details.

    PubMed

    Garcia-Garcia, Javier; Bonet, Jaume; Guney, Emre; Fornes, Oriol; Planas, Joan; Oliva, Baldo

    2012-05-01

    Proteins are the bricks and mortar of cells. The work of proteins is structural and functional, as they are the principal element of the organization of the cell architecture, but they also play a relevant role in its metabolism and regulation. To perform all these functions, proteins need to interact with each other and with other bio-molecules, either to form complexes or to recognize precise targets of their action. For instance, a particular transcription factor may activate one gene or another depending on its interactions with other proteins and not only with DNA. Hence, the ability of a protein to interact with other bio-molecules, and the partners they have at each particular time and location can be crucial to characterize the role of a protein. Proteins rarely act alone; they rather constitute a mingled network of physical interactions or other types of relationships (such as metabolic and regulatory) or signaling cascades. In this context, understanding the function of a protein implies to recognize the members of its neighborhood and to grasp how they associate, both at the systemic and atomic level. The network of physical interactions between the proteins of a system, cell or organism, is defined as the interactome. The purpose of this review is to deepen the description of interactomes at different levels of detail: from the molecular structure of complexes to the global topology of the network of interactions. The approaches and techniques applied experimentally and computationally to attain each level are depicted. The limits of each technique and its integration into a model network, the challenges and actual problems of completeness of an interactome, and the reliability of the interactions are reviewed and summarized. Finally, the application of the current knowledge of protein-protein interactions on modern network medicine and protein function annotation is also explored.

  15. NetworkAnalyst - integrative approaches for protein–protein interaction network analysis and visual exploration

    PubMed Central

    Xia, Jianguo; Benner, Maia J.; Hancock, Robert E. W.

    2014-01-01

    Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required - identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. PMID:24861621

  16. Context-based retrieval of functional modules in protein-protein interaction networks.

    PubMed

    Dobay, Maria Pamela; Stertz, Silke; Delorenzi, Mauro

    2017-03-27

    Various techniques have been developed for identifying the most probable interactants of a protein under a given biological context. In this article, we dissect the effects of the choice of the protein-protein interaction network (PPI) and the manipulation of PPI settings on the network neighborhood of the influenza A virus (IAV) network, as well as hits in genome-wide small interfering RNA screen results for IAV host factors. We investigate the potential of context filtering, which uses text mining evidence linked to PPI edges, as a complement to the edge confidence scores typically provided in PPIs for filtering, for obtaining more biologically relevant network neighborhoods. Here, we estimate the maximum performance of context filtering to isolate a Kyoto Encyclopedia of Genes and Genomes (KEGG) network Ki from a union of KEGG networks and its network neighborhood. The work gives insights on the use of human PPIs in network neighborhood approaches for functional inference.

  17. Inhibition of Protein-Protein Interactions and Signaling by Small Molecules

    NASA Astrophysics Data System (ADS)

    Freire, Ernesto

    2010-03-01

    Protein-protein interactions are at the core of cell signaling pathways as well as many bacterial and viral infection processes. As such, they define critical targets for drug development against diseases such as cancer, arthritis, obesity, AIDS and many others. Until now, the clinical inhibition of protein-protein interactions and signaling has been accomplished with the use of antibodies or soluble versions of receptor molecules. Small molecule replacements of these therapeutic agents have been extremely difficult to develop; either the necessary potency has been hard to achieve or the expected biological effect has not been obtained. In this presentation, we show that a rigorous thermodynamic approach that combines differential scanning calorimetry (DSC) and isothermal titration calorimetry (ITC) provides a unique platform for the identification and optimization of small molecular weight inhibitors of protein-protein interactions. Recent advances in the development of cell entry inhibitors of HIV-1 using this approach will be discussed.

  18. Finding the “Dark Matter” in Human and Yeast Protein Network Prediction and Modelling

    PubMed Central

    Lees, Jon G.; Reid, Adam J.; Yeats, Corin; Clegg, Andrew B.; Sanchez-Jimenez, Francisca; Orengo, Christine

    2010-01-01

    Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or “dark matter” of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case

  19. Arabidopsis protein phosphatase DBP1 nucleates a protein network with a role in regulating plant defense.

    PubMed

    Carrasco, José Luis; Castelló, María José; Naumann, Kai; Lassowskat, Ines; Navarrete-Gómez, Marisa; Scheel, Dierk; Vera, Pablo

    2014-01-01

    Arabidopsis thaliana DBP1 belongs to the plant-specific family of DNA-binding protein phosphatases. Although recently identified as a novel host factor mediating susceptibility to potyvirus, little is known about DBP1 targets and partners and the molecular mechanisms underlying its function. Analyzing changes in the phosphoproteome of a loss-of-function dbp1 mutant enabled the identification of 14-3-3λ isoform (GRF6), a previously reported DBP1 interactor, and MAP kinase (MAPK) MPK11 as components of a small protein network nucleated by DBP1, in which GRF6 stability is modulated by MPK11 through phosphorylation, while DBP1 in turn negatively regulates MPK11 activity. Interestingly, grf6 and mpk11 loss-of-function mutants showed altered response to infection by the potyvirus Plum pox virus (PPV), and the described molecular mechanism controlling GRF6 stability was recapitulated upon PPV infection. These results not only contribute to a better knowledge of the biology of DBP factors, but also of MAPK signalling in plants, with the identification of GRF6 as a likely MPK11 substrate and of DBP1 as a protein phosphatase regulating MPK11 activity, and unveils the implication of this protein module in the response to PPV infection in Arabidopsis.

  20. Protein sorting at the trans-Golgi network.

    PubMed

    Guo, Yusong; Sirkis, Daniel W; Schekman, Randy

    2014-01-01

    The trans-Golgi network (TGN) is an important cargo sorting station within the cell where newly synthesized proteins are packaged into distinct transport carriers that are targeted to various destinations. To maintain the fidelity of protein transport, elaborate protein sorting machinery is employed to mediate sorting of specific cargo proteins into distinct transport carriers. Protein sorting requires assembly of the cytosolic sorting machinery onto the TGN membrane and capture of cargo proteins. We review the cytosolic and transmembrane sorting machinery that function at the TGN and describe molecular interactions and regulatory mechanisms that enable accurate protein sorting. In addition, we highlight the importance of TGN sorting in physiology and disease.

  1. Directed disassembly of an interfacial rubisco protein network.

    PubMed

    Onaizi, Sagheer A; Malcolm, Andrew S; He, Lizhong; Middelberg, Anton P J

    2007-05-22

    We present the first study of the directed disassembly of a protein network at the air-water interface by the synergistic action of a surfactant and an enzyme. We seek to understand the fundamentals of protein network disassembly by using rubisco adsorbed at the air-water interface as a model. We propose that rubisco adsorption at the air-water interface results in the formation of a fishnet-like network of interconnected protein molecules, capable of transmitting lateral force. The mechanical properties of the rubisco network during assembly and disassembly at the air-water interface were characterized by direct measurement of laterally transmitted force through the protein network using the Cambridge interfacial tensiometer. We have shown that, when used individually, either 2 ppm of the surfactant, sodium dodecyl benzyl sulfonate (SDOBS), or 2 ppm of the enzyme, subtilisin A (SA), were insufficient to completely disassemble the rubisco network within 1 h of treatment. However, a combination of 2 ppm SDOBS and 2 ppm SA led to almost complete disassembly within 1 h. Increasing the concentration of SA in the mixture from 2 to 10 ppm, while keeping the SDOBS concentration constant, significantly decreased the time required to completely disassemble the rubisco network. Furthermore, the initial rate of network disassembly using formulations containing SDOBS was surprisingly insensitive to this increase in SA concentration. This study gives insight into the role of lateral interactions between protein molecules at interfaces in stabilizing interfacial protein networks and shows that surfactant and enzyme working in combination proves more effective at disrupting and mobilizing the interfacial protein network than the action of either agent alone.

  2. Transduction of Redox Signaling by Electrophile-Protein Reactions

    PubMed Central

    Rudolph, Tanja K.; Freeman, Bruce A.

    2014-01-01

    Over the last 50 years, the posttranslational modification (PTM) of proteins has emerged as a central mechanism for cells to regulate metabolism, growth, differentiation, cell-cell interactions, and immune responses. By influencing protein structure and function, PTM leads to a multiplication of proteome diversity. Redox-dependent PTMs, mediated by environmental and endogenously generated reactive species, induce cell signaling responses and can have toxic effects in organisms. PTMs induced by the electrophilic by-products of redox reactions most frequently occur at protein thiols; other nucleophilic amino acids serve as less favorable targets. Advances in mass spectrometry and affinity-chemistry strategies have improved the detection of electrophile-induced protein modifications both in vitro and in vivo and have revealed a high degree of amino acid and protein selectivity of electrophilic PTM. The identification of biological targets of electrophiles has motivated further study of the functional impact of various PTM reactions on specific signaling pathways and how this might affect organisms. PMID:19797270

  3. FunMod: a Cytoscape plugin for identifying functional modules in undirected protein-protein networks.

    PubMed

    Natale, Massimo; Benso, Alfredo; Di Carlo, Stefano; Ficarra, Elisa

    2014-08-01

    The characterization of the interacting behaviors of complex biological systems is a primary objective in protein-protein network analysis and computational biology. In this paper we present FunMod, an innovative Cytoscape version 2.8 plugin that is able to mine undirected protein-protein networks and to infer sub-networks of interacting proteins intimately correlated with relevant biological pathways. This plugin may enable the discovery of new pathways involved in diseases. In order to describe the role of each protein within the relevant biological pathways, FunMod computes and scores three topological features of the identified sub-networks. By integrating the results from biological pathway clustering and topological network analysis, FunMod proved to be useful for the data interpretation and the generation of new hypotheses in two case studies.

  4. Protein tagging reveals new insights into signaling in flagella.

    PubMed

    Ishikawa, Takashi

    2014-03-03

    In this issue, Oda et al. (2014. J. Cell Biol. http://dx.doi.org/10.1083/jcb.201312014) use mutant analysis, protein tagging, and cryoelectron tomography to determine the detailed location of components in flagellar radial spokes-a complex of proteins that connect the peripheral microtubule doublets to the central pair. Remarkably, this approach revealed an interaction between radial spokes and the central pair based on geometry rather than a specific signaling mechanism, highlighting the importance of studying a system in three dimensions.

  5. Predicting Protein Function via Semantic Integration of Multiple Networks.

    PubMed

    Yu, Guoxian; Fu, Guangyuan; Wang, Jun; Zhu, Hailong

    2016-01-01

    Determining the biological functions of proteins is one of the key challenges in the post-genomic era. The rapidly accumulated large volumes of proteomic and genomic data drives to develop computational models for automatically predicting protein function in large scale. Recent approaches focus on integrating multiple heterogeneous data sources and they often get better results than methods that use single data source alone. In this paper, we investigate how to integrate multiple biological data sources with the biological knowledge, i.e., Gene Ontology (GO), for protein function prediction. We propose a method, called SimNet, to Semantically integrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sources of Yeast, Human, Mouse, and Fly show that, SimNet not only achieves better (or comparable) results than other related competitive approaches, but also takes much less time. The Matlab codes of SimNet are available at https://sites.google.com/site/guoxian85/simnet.

  6. Solvated dissipative electro-elastic network model of hydrated proteins.

    PubMed

    Martin, Daniel R; Matyushov, Dmitry V

    2012-10-28

    Elastic network models coarse grain proteins into a network of residue beads connected by springs. We add dissipative dynamics to this mechanical system by applying overdamped Langevin equations of motion to normal-mode vibrations of the network. In addition, the network is made heterogeneous and softened at the protein surface by accounting for hydration of the ionized residues. Solvation changes the network Hessian in two ways. Diagonal solvation terms soften the spring constants and off-diagonal dipole-dipole terms correlate displacements of the ionized residues. The model is used to formulate the response functions of the electrostatic potential and electric field appearing in theories of redox reactions and spectroscopy. We also formulate the dielectric response of the protein and find that solvation of the surface ionized residues leads to a slow relaxation peak in the dielectric loss spectrum, about two orders of magnitude slower than the main peak of protein relaxation. Finally, the solvated network is used to formulate the allosteric response of the protein to ion binding. The global thermodynamics of ion binding is not strongly affected by the network solvation, but it dramatically enhances conformational changes in response to placing a charge at the active site of the protein.

  7. Artificial Neural Network for the Prediction of Tyrosine-Based Sorting Signal Recognition by Adaptor Complexes

    PubMed Central

    Mukherjee, Debarati; Hanna, Claudia B.; Aguilar, R. Claudio

    2012-01-01

    Sorting of transmembrane proteins to various intracellular compartments depends on specific signals present within their cytosolic domains. Among these sorting signals, the tyrosine-based motif (YXXØ) is one of the best characterized and is recognized by μ-subunits of the four clathrin-associated adaptor complexes (AP-1 to AP-4). Despite their overlap in specificity, each μ-subunit has a distinct sequence preference dependent on the nature of the X-residues. Moreover, combinations of these residues exert cooperative or inhibitory effects towards interaction with the various APs. This complexity makes it impossible to predict a priori, the specificity of a given tyrosine-signal for a particular μ-subunit. Here, we describe the results obtained with a computational approach based on the Artificial Neural Network (ANN) paradigm that addresses the issue of tyrosine-signal specificity, enabling the prediction of YXXØ-μ interactions with accuracies over 90%. Therefore, this approach constitutes a powerful tool to help predict mechanisms of intracellular protein sorting. PMID:22505811

  8. Evolution of a protein domain interaction network

    NASA Astrophysics Data System (ADS)

    Gao, Li-Feng; Shi, Jian-Jun; Guan, Shan

    2010-01-01

    In this paper, we attempt to understand complex network evolution from the underlying evolutionary relationship between biological organisms. Firstly, we construct a Pfam domain interaction network for each of the 470 completely sequenced organisms, and therefore each organism is correlated with a specific Pfam domain interaction network; secondly, we infer the evolutionary relationship of these organisms with the nearest neighbour joining method; thirdly, we use the evolutionary relationship between organisms constructed in the second step as the evolutionary course of the Pfam domain interaction network constructed in the first step. This analysis of the evolutionary course shows: (i) there is a conserved sub-network structure in network evolution; in this sub-network, nodes with lower degree prefer to maintain their connectivity invariant, and hubs tend to maintain their role as a hub is attached preferentially to new added nodes; (ii) few nodes are conserved as hubs; most of the other nodes are conserved as one with very low degree; (iii) in the course of network evolution, new nodes are added to the network either individually in most cases or as clusters with relative high clustering coefficients in a very few cases.

  9. A calcium sensor – protein kinase signaling module diversified in plants and is retained in all lineages of Bikonta species

    PubMed Central

    Beckmann, Linda; Edel, Kai H.; Batistič, Oliver; Kudla, Jörg

    2016-01-01

    Calcium (Ca2+) signaling is a universal mechanism of signal transduction and involves Ca2+ signal formation and decoding of information by Ca2+ binding proteins. Calcineurin B-like proteins (CBLs), which upon Ca2+ binding activate CBL-interacting protein kinases (CIPKs) regulate a multitude of physiological processes in plants. Here, we combine phylogenomics and functional analyses to investigate the occurrence and structural conservation of CBL and CIPK proteins in 26 species representing all major clades of eukaryotes. We demonstrate the presence of at least singular CBL-CIPK pairs in representatives of Archaeplastida, Chromalveolates and Excavates and their general absence in Opisthokonta and Amoebozoa. This denotes CBL-CIPK complexes as evolutionary ancient Ca2+ signaling modules that likely evolved in the ancestor of all Bikonta. Furthermore, we functionally characterize the CBLs and CIPK from the parabasalid human pathogen Trichomonas vaginalis. Our results reveal strict evolutionary conservation of functionally important structural features, preservation of biochemical properties and a remarkable cross-kingdom protein-protein interaction potential between CBLs and CIPKs from Arabidopsis thaliana and T. vaginalis. Together our findings suggest an ancient evolutionary origin of a functional CBL-CIPK signaling module close to the root of eukaryotic evolution and provide insights into the initial evolution of signaling networks and Ca2+ signaling specificity. PMID:27538881

  10. Correlated EEG Signals Simulation Based on Artificial Neural Networks.

    PubMed

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2016-09-30

    In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.

  11. Chemical signal amplification in two-dimensional paper networks

    PubMed Central

    Fu, Elain; Kauffman, Peter; Lutz, Barry; Yager, Paul

    2010-01-01

    Two-dimensional paper networks (2DPNs) hold great potential for extending the utility of paper-based chemical and biochemical diagnostics at a cost and ease-of-use that is comparable to conventional lateral flow strips. 2DPNs enable the automated sequential delivery of multiple reagents to a detection region with a single user activation step, and therefore have the potential to extend the processing capabilities of inexpensive paper-based assays with comparable ease of use to conventional lateral flow tests. In this communication, we used a simple 3 inlet 2DPN to perform signal amplification of a colloidal gold label using a gold enhancement solution, thus demonstrating the capability of 2DPNs to perform processes that can improve limits of detection. PMID:20706615

  12. Reconstruction of protein networks from an atlas of maize seed proteotypes

    PubMed Central

    Walley, Justin W.; Shen, Zhouxin; Sartor, Ryan; Wu, Kevin J.; Osborn, Joshua; Smith, Laurie G.; Briggs, Steven P.

    2013-01-01

    A comprehensive knowledge of proteomic states is essential for understanding biological systems. Using mass spectrometry, we mapped an atlas of developing maize seed proteotypes comprising 14,165 proteins and 18,405 phosphopeptides (from 4,511 proteins), quantified across eight tissues. We found that many of the most abundant proteins are not associated with detectable levels of their mRNAs, and we provide evidence for three potential explanations: transport of proteins between tissues; diurnal, out-of-phase accumulation of mRNAs and cognate proteins; and differential lifetimes of mRNAs compared with proteins. Likewise, many of the most abundant mRNAs were not associated with detectable levels of their proteins. Across the entire dataset, protein abundance was poorly correlated with mRNA levels and was largely independent of phosphorylation status. Comparisons between proteotypes revealed the quantitative contribution of specific proteins and phosphorylation events to the spatially and temporally regulated starch and oil biosynthetic pathways. Reconstruction of signaling networks established associations of proteins and phosphoproteins with distinct biological processes acting during seed development. Additionally, a protein kinase substrate network was reconstructed, enabling the identification of 762 potential substrates of specific protein kinases. Finally, examination of 694 transcription factors revealed remarkable constraints on patterns of expression and phosphorylation within transcription factor families. These results provide a resource for understanding seed development in a crop that is the foundation of modern agriculture. PMID:24248366

  13. Regulation of Chemokine Signal Integration by Activator of G-Protein Signaling 4 (AGS4)

    PubMed Central

    Robichaux, William G.; Branham-O’Connor, Melissa; Hwang, Il-Young; Vural, Ali; Kehrl, Johne H.

    2017-01-01

    Activator of G-protein signaling 4 (AGS4)/G-protein signaling modulator 3 (Gpsm3) contains three G-protein regulatory (GPR) motifs, each of which can bind Gαi-GDP free of Gβγ. We previously demonstrated that the AGS4-Gαi interaction is regulated by seven transmembrane-spanning receptors (7-TMR), which may reflect direct coupling of the GPR-Gαi module to the receptor analogous to canonical Gαβγ heterotrimer. We have demonstrated that the AGS4-Gαi complex is regulated by chemokine receptors in an agonist-dependent manner that is receptor-proximal. As an initial approach to investigate the functional role(s) of this regulated interaction in vivo, we analyzed leukocytes, in which AGS4/Gpsm3 is predominantly expressed, from AGS4/Gpsm3-null mice. Loss of AGS4/Gpsm3 resulted in mild but significant neutropenia and leukocytosis. Dendritic cells, T lymphocytes, and neutrophils from AGS4/Gpsm3-null mice also exhibited significant defects in chemoattractant-directed chemotaxis and extracellular signal-regulated kinase activation. An in vivo peritonitis model revealed a dramatic reduction in the ability of AGS4/Gpsm3-null neutrophils to migrate to primary sites of inflammation. Taken together, these data suggest that AGS4/Gpsm3 is required for proper chemokine signal processing in leukocytes and provide further evidence for the importance of the GPR-Gαi module in the regulation of leukocyte function. PMID:28062526

  14. Network coding based joint signaling and dynamic bandwidth allocation scheme for inter optical network unit communication in passive optical networks

    NASA Astrophysics Data System (ADS)

    Wei, Pei; Gu, Rentao; Ji, Yuefeng

    2014-06-01

    As an innovative and promising technology, network coding has been introduced to passive optical networks (PON) in recent years to support inter optical network unit (ONU) communication, yet the signaling process and dynamic bandwidth allocation (DBA) in PON with network coding (NC-PON) still need further study. Thus, we propose a joint signaling and DBA scheme for efficiently supporting differentiated services of inter ONU communication in NC-PON. In the proposed joint scheme, the signaling process lays the foundation to fulfill network coding in PON, and it can not only avoid the potential threat to downstream security in previous schemes but also be suitable for the proposed hybrid dynamic bandwidth allocation (HDBA) scheme. In HDBA, a DBA cycle is divided into two sub-cycles for applying different coding, scheduling and bandwidth allocation strategies to differentiated classes of services. Besides, as network traffic load varies, the entire upstream transmission window for all REPORT messages slides accordingly, leaving the transmission time of one or two sub-cycles to overlap with the bandwidth allocation calculation time at the optical line terminal (the OLT), so that the upstream idle time can be efficiently eliminated. Performance evaluation results validate that compared with the existing two DBA algorithms deployed in NC-PON, HDBA demonstrates the best quality of service (QoS) support in terms of delay for all classes of services, especially guarantees the end-to-end delay bound of high class services. Specifically, HDBA can eliminate queuing delay and scheduling delay of high class services, reduce those of lower class services by at least 20%, and reduce the average end-to-end delay of all services over 50%. Moreover, HDBA also achieves the maximum delay fairness between coded and uncoded lower class services, and medium delay fairness for high class services.

  15. Distinctive topologies of partner-switching signaling networks correlate with their physiological roles

    PubMed Central

    Igoshin, Oleg A.; Brody, Margaret S.; Price, Chester W.; Savageau, Michael A.

    2009-01-01

    Summary Regulatory networks controlling bacterial gene expression often evolve from common origins and share homologous proteins and similar network motifs. However, when functioning in different physiological contexts, these motifs may be re-arranged with different topologies that significantly affect network performance. Here we analyze two related signaling networks in the bacterium Bacillus subtilis in order to assess the consequences of their different topologies, with the aim of formulating design principles applicable to other systems. These two networks control the activities of the general stress response factor σB and the first sporulation-specific factor σF. Both networks have at their core a “partner-switching” mechanism, in which an anti-sigma factor forms alternate complexes either with the sigma factor, holding it inactive, or with an anti-anti-sigma factor, thereby freeing sigma. However, clear differences in network structure are apparent: the anti-sigma-factor for σF forms a long-lived, “dead-end” complex with its anti-anti-sigma factor and ADP, whereas the genes encoding σB and its network partners lie in a σB-controlled operon, resulting in positive and negative feedback loops. We constructed mathematical models of both networks and examined which features were critical for the performance of each design. The σF model predicts that the self-enhancing formation of the dead-end complex transforms the network into a largely irreversible hysteretic switch; the simulations reported here also demonstrate that hysteresis and slow turn off kinetics are the only two system properties associated with this complex formation. By contrast, the σB model predicts that the positive and negative feedback loops produce graded, reversible behavior with high regulatory capacity and fast response time. Our models demonstrate how alterations in network design result in different system properties that correlate with regulatory demands. These design

  16. Simulated Evolution of Protein-Protein Interaction Networks with Realistic Topology

    PubMed Central

    Peterson, G. Jack; Pressé, Steve; Peterson, Kristin S.; Dill, Ken A.

    2012-01-01

    We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein’s neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution. PMID:22768057

  17. Multi-label ℓ2-regularized logistic regression for predicting activation/inhibition relationships in human protein-protein interaction networks

    PubMed Central

    Mei, Suyu; Zhang, Kun

    2016-01-01

    Protein-protein interaction (PPI) networks are naturally viewed as infrastructure to infer signalling pathways. The descriptors of signal events between two interacting proteins such as upstream/downstream signal flow, activation/inhibition relationship and protein modification are indispensable for inferring signalling pathways from PPI networks. However, such descriptors are not available in most cases as most PPI networks are seldom semantically annotated. In this work, we extend ℓ2-regularized logistic regression to the scenario of multi-label learning for predicting the activation/inhibition relationships in human PPI networks. The phenomenon that both activation and inhibition relationships exist between two interacting proteins is computationally modelled by multi-label learning framework. The problem of GO (gene ontology) sparsity is tackled by introducing the homolog knowledge as independent homolog instances. ℓ2-regularized logistic regression is accordingly adopted here to penalize the homolog noise and to reduce the computational complexity of the double-sized training data. Computational results show that the proposed method achieves satisfactory multi-label learning performance and outperforms the existing phenotype correlation method on the experimental data of Drosophila melanogaster. Several predictions have been validated against recent literature. The predicted activation/inhibition relationships in human PPI networks are provided in the supplementary file for further biomedical research. PMID:27819359

  18. Diversity in the origins of proteostasis networks- a driver for protein function in evolution

    PubMed Central

    Powers, Evan T.; Balch, William E.

    2013-01-01

    Although a protein’s primary sequence largely determines its function, proteins can adopt different folding states in response to changes in the environment, some of which may be deleterious to the organism. All organisms, including Bacteria, Archaea and Eukarya, have evolved a protein homeostasis network, or proteostasis network, that consists of chaperones and folding factors, degradation components, signalling pathways and specialized compartmentalized modules that manage protein folding in response to environmental stimuli and variation. Surveying the origins of proteostasis networks reveals that they have co-evolved with the proteome to regulate the physiological state of the cell, reflecting the unique stresses that different cells or organisms experience, and that they have a key role in driving evolution by closely managing the link between the phenotype and the genotype. PMID:23463216

  19. Three-dimensional visualization of protein interaction networks.

    PubMed

    Han, Kyungsook; Byun, Yanga

    2004-03-01

    Protein interaction networks provide us with contextual information within which protein function can be interpreted and will assist many biomedical studies. We have developed a new force-directed layout algorithm for visualizing protein interactions in three-dimensional space. Our algorithm divides nodes into three groups based on their interacting properties: bi-connected sub-graph in the center, terminal nodes at the outermost region, and the rest in between them. Experimental results show that our algorithm efficiently generates a clear and aesthetically pleasing drawing of large-scale protein interaction networks and that it is an order of magnitude faster than other force-directed layouts.

  20. The ATM signaling network in development and disease

    PubMed Central

    Stracker, Travis H.; Roig, Ignasi; Knobel, Philip A.; Marjanović, Marko

    2013-01-01

    The DNA damage response (DDR) rapidly recognizes DNA lesions and initiates the appropriate cellular programs to maintain genome integrity. This includes the coordination of cell cycle checkpoints, transcription, translation, DNA repair, metabolism, and cell fate decisions, such as apoptosis or senescence (Jackson and Bartek, 2009). DNA double-strand breaks (DSBs) represent one of the most cytotoxic DNA lesions and defects in their metabolism underlie many human hereditary diseases characterized by genomic instability (Stracker and Petrini, 2011; McKinnon, 2012). Patients with hereditary defects in the DDR display defects in development, particularly affecting the central nervous system, the immune system and the germline, as well as aberrant metabolic regulation and cancer predisposition. Central to the DDR to DSBs is the ataxia-telangiectasia mutated (ATM) kinase, a master controller of signal transduction. Understanding how ATM signaling regulates various aspects of the DDR and its roles in vivo is critical for our understanding of human disease, its diagnosis and its treatment. This review will describe the general roles of ATM signaling and highlight some recent advances that have shed light on the diverse roles of ATM and related proteins in human disease. PMID:23532176

  1. Reconstruction of signaling networks regulating fungal morphogenesis by transcriptomics.

    PubMed

    Meyer, Vera; Arentshorst, Mark; Flitter, Simon J; Nitsche, Benjamin M; Kwon, Min Jin; Reynaga-Peña, Cristina G; Bartnicki-Garcia, Salomon; van den Hondel, Cees A M J J; Ram, Arthur F J

    2009-11-01

    Coordinated control of hyphal elongation and branching is essential for sustaining mycelial growth of filamentous fungi. In order to study the molecular machinery ensuring polarity control in the industrial fungus Aspergillus niger, we took advantage of the temperature-sensitive (ts) apical-branching ramosa-1 mutant. We show here that this strain serves as an excellent model system to study critical steps of polar growth control during mycelial development and report for the first time a transcriptomic fingerprint of apical branching for a filamentous fungus. This fingerprint indicates that several signal transduction pathways, including TORC2, phospholipid, calcium, and cell wall integrity signaling, concertedly act to control apical branching. We furthermore identified the genetic locus affected in the ramosa-1 mutant by complementation of the ts phenotype. Sequence analyses demonstrated that a single amino acid exchange in the RmsA protein is responsible for induced apical branching of the ramosa-1 mutant. Deletion experiments showed that the corresponding rmsA gene is essential for the growth of A. niger, and complementation analyses with Saccharomyces cerevisiae evidenced that RmsA serves as a functional equivalent of the TORC2 component Avo1p. TORC2 signaling is required for actin polarization and cell wall integrity in S. cerevisiae. Congruently, our microscopic investigations showed that polarized actin organization and chitin deposition are disturbed in the ramosa-1 mutant. The integration of the transcriptomic, genetic, and phenotypic data obtained in this study allowed us to reconstruct a model for cellular events involved in apical branching.

  2. Fluctuations in Mass-Action Equilibrium of Protein Binding Networks

    NASA Astrophysics Data System (ADS)

    Yan, Koon-Kiu; Walker, Dylan; Maslov, Sergei

    2008-12-01

    We consider two types of fluctuations in the mass-action equilibrium in protein binding networks. The first type is driven by slow changes in total concentrations of interacting proteins. The second type (spontaneous) is caused by quickly decaying thermodynamic deviations away from equilibrium. We investigate the effects of network connectivity on fluctuations by comparing them to scenarios in which the interacting pair is isolated from the network and analytically derives bounds on fluctuations. Collective effects are shown to sometimes lead to large amplification of spontaneous fluctuations. The strength of both types of fluctuations is positively correlated with the complex connectivity and negatively correlated with complex concentration. Our general findings are illustrated using a curated network of protein interactions and multiprotein complexes in baker’s yeast, with empirical protein concentrations.

  3. Early-warning signals of topological collapse in interbank networks

    PubMed Central

    Squartini, Tiziano; van Lelyveld, Iman; Garlaschelli, Diego

    2013-01-01

    The financial crisis clearly illustrated the importance of characterizing the level of ‘systemic’ risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998–2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear – but unpredictable – signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies. PMID:24285089

  4. Neural Networks for Signal Processing VII Proceeding of the 1997 IEEE Workshop

    DTIC Science & Technology

    2007-11-02

    Fu, Hsin-Chia and Xu, Y. Y. 626 Neural Networks for Engine Fault Diagnostics Dong, Dawei W., Hopfield , John J., and Unnikrishnan, K. P. 636...June 98 REPORT TYPE AND DATES COVERED Final \\^Q\\ Cf\\ - M DteC ^ TITLE AND SUBTITLE Neural Networks for Signal Processing VII Proceeding...sponsored by the Neural Networks Technical Committee of the IEEE Signal Processing Society, in cooperation with the IEEE Neural Networks Council and

  5. Controllability of protein-protein interaction phosphorylation-based networks: Participation of the hub 14-3-3 protein family

    PubMed Central

    Uhart, Marina; Flores, Gabriel; Bustos, Diego M.

    2016-01-01

    Posttranslational regulation of protein function is an ubiquitous mechanism in eukaryotic cells. Here, we analyzed biological properties of nodes and edges of a human protein-protein interaction phosphorylation-based network, especially of those nodes critical for the network controllability. We found that the minimal number of critical nodes needed to control the whole network is 29%, which is considerably lower compared to other real networks. These critical nodes are more regulated by posttranslational modifications and contain more binding domains to these modifications than other kinds of nodes in the network, suggesting an intra-group fast regulation. Also, when we analyzed the edges characteristics that connect critical and non-critical nodes, we found that the former are enriched in domain-to-eukaryotic linear motif interactions, whereas the later are enriched in domain-domain interactions. Our findings suggest a possible structure for protein-protein interaction networks with a densely interconnected and self-regulated central core, composed of critical nodes with a high participation in the controllability of the full network, and less regulated peripheral nodes. Our study offers a deeper understanding of complex network control and bridges the controllability theorems for complex networks and biological protein-protein interaction phosphorylation-based networked systems. PMID:27195976

  6. Controllability of protein-protein interaction phosphorylation-based networks: Participation of the hub 14-3-3 protein family.

    PubMed

    Uhart, Marina; Flores, Gabriel; Bustos, Diego M

    2016-05-19

    Posttranslational regulation of protein function is an ubiquitous mechanism in eukaryotic cells. Here, we analyzed biological properties of nodes and edges of a human protein-protein interaction phosphorylation-based network, especially of those nodes critical for the network controllability. We found that the minimal number of critical nodes needed to control the whole network is 29%, which is considerably lower compared to other real networks. These critical nodes are more regulated by posttranslational modifications and contain more binding domains to these modifications than other kinds of nodes in the network, suggesting an intra-group fast regulation. Also, when we analyzed the edges characteristics that connect critical and non-critical nodes, we found that the former are enriched in domain-to-eukaryotic linear motif interactions, whereas the later are enriched in domain-domain interactions. Our findings suggest a possible structure for protein-protein interaction networks with a densely interconnected and self-regulated central core, composed of critical nodes with a high participation in the controllability of the full network, and less regulated peripheral nodes. Our study offers a deeper understanding of complex network control and bridges the controllability theorems for complex networks and biological protein-protein interaction phosphorylation-based networked systems.

  7. Disease Gene Prioritization Based on Topological Similarity in Protein-Protein Interaction Networks

    NASA Astrophysics Data System (ADS)

    Erten, Sinan; Bebek, Gurkan; Koyutürk, Mehmet

    In recent years, many algorithms have been developed to narrow down the set of candidate disease genes implicated by genome wide association studies (GWAS), using knowledge on protein-protein interactions (PPIs). All of these algorithms are based on a common principle; functional association between proteins is correlated with their connectivity/proximity in the PPI network. However, recent research also reveals that networks are organized into recurrent network schemes that underlie the mechanisms of cooperation among proteins with different function, as well as the crosstalk between different cellular processes. In this paper, we hypothesize that proteins that are associated with similar diseases may exhibit patterns of "topological similarity" in PPI networks. Motivated by these observations, we introduce the notion of "topological profile", which represents the location of a protein in the network with respect to other proteins. Based on this notion, we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to develop algorithms that prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. Systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance (OMIM) database show that the proposed algorithm, Vavien, clearly outperforms state-of-the-art network based prioritization algorithms. Vavien is available as a web service at http://www.diseasegenes.org .

  8. The evolution of regulators of G protein signalling proteins as drug targets - 20 years in the making: IUPHAR Review 21.

    PubMed

    Sjögren, B

    2017-03-01

    Regulators of G protein signalling (RGS) proteins are celebrating the 20th anniversary of their discovery. The unveiling of this new family of negative regulators of G protein signalling in the mid-1990s solved a persistent conundrum in the G protein signalling field, in which the rate of deactivation of signalling cascades in vivo could not be replicated in exogenous systems. Since then, there has been tremendous advancement in the knowledge of RGS protein structure, function, regulation and their role as novel drug targets. RGS proteins play an important modulatory role through their GTPase-activating protein (GAP) activity at active, GTP-bound Gα subunits of heterotrimeric G proteins. They also possess many non-canonical functions not related to G protein signalling. Here, an update on the status of RGS proteins as drug targets is provided, highlighting advances that have led to the inclusion of RGS proteins in the IUPHAR/BPS Guide to PHARMACOLOGY database of drug targets.

  9. The GAPs, GEFs, GDIs and…now, GEMs: New kids on the heterotrimeric G protein signaling block.

    PubMed

    Ghosh, Pradipta; Rangamani, Padmini; Kufareva, Irina

    2017-03-13

    The canonical process of activation of heterotrimeric G proteins by G protein coupled receptors (GPCRs) is well studied. Recently, a rapidly emerging paradigm has revealed the existence of a new, non-canonical set of cytosolic G protein modulators, guanine exchange modulators (GEMs). Among G proteins regulators, GEMs are uniquely capable of initiating pleiotropic signals: these bifunctional modulators can activate cAMP inhibitory (Gi) proteins and inhibit cAMP-stimulatory (Gs) proteins through a single short evolutionarily conserved module. A prototypical member of the GEM family, GIV/Girdin, integrates signals downstream of a myriad of cell surface receptors, e.g., growth factor RTKs, integrins, cytokine, GPCRs, etc., and translates these signals into G protein activation or inhibition. By their pleiotropic action, GIV and other GEMs modulate several key pathways within downstream signaling network. Unlike canonical G protein signaling that is finite and is triggered directly and exclusively by GPCRs, the temporal and spatial features of non-canonical activation of G protein via GIV-family of cytosolic GEMs are unusually relaxed. GIV uses this relaxed circuitry to integrate, reinforce and compartmentalize signals downstream of both growth factors and G proteins in a way that enables it to orchestrate cellular phenotypes in a sustained manner. Mounting evidence suggests the importance of GIV and other GEMs as disease modulators and their potential to serve as therapeutic targets; however, a lot remains unknown within the layers of the proverbial onion that must be systematically peeled. This perspective summarizes the key concepts of the GEM-dependent G protein signaling paradigm and discusses the multidisciplinary approaches that are likely to revolutionize our understanding of this paradigm from the atomic level to systems biology.

  10. PADPIN: protein-protein interaction networks of angiogenesis, arteriogenesis, and inflammation in peripheral arterial disease

    PubMed Central

    Vijay, Chaitanya G.; Annex, Brian H.; Bader, Joel S.; Popel, Aleksander S.

    2015-01-01

    Peripheral arterial disease (PAD) results from an obstruction of blood flow in the arteries other than the heart, most commonly the arteries that supply the legs. The complexity of the known signaling pathways involved in PAD, including various growth factor pathways and their cross talks, suggests that analyses of high-throughput experimental data could lead to a new level of understanding of the disease as well as novel and heretofore unanticipated potential targets. Such bioinformatic analyses have not been systematically performed for PAD. We constructed global protein-protein interaction networks of angiogenesis (Angiome), immune response (Immunome), and arteriogenesis (Arteriome) using our previously developed algorithm GeneHits. The term “PADPIN” refers to the angiome, immunome, and arteriome in PAD. Here we analyze four microarray gene expression datasets from ischemic and nonischemic gastrocnemius muscles at day 3 posthindlimb ischemia (HLI) in two genetically different C57BL/6 and BALB/c mouse strains that display differential susceptibility to HLI to identify potential targets and signaling pathways in angiogenesis, immune, and arteriogenesis networks. We hypothesize that identification of the differentially expressed genes in ischemic and nonischemic muscles between the strains that recovers better (C57BL/6) vs. the strain that recovers more poorly (BALB/c) will help for the prediction of target genes in PAD. Our bioinformatics analysis identified several genes that are differentially expressed between the two mouse strains with known functions in PAD including TLR4, THBS1, and PRKAA2 and several genes with unknown functions in PAD including EphA4, TSPAN7, SLC22A4, and EIF2a. PMID:26058837

  11. Integrated Strategies to Gain a Systems-Level View of Dynamic Signaling Networks.

    PubMed

    Newman, Robert H; Zhang, Jin

    2017-01-01

    In order to survive and function properly in the face of an ever changing environment, cells must be able to sense changes in their surroundings and respond accordingly. Cells process information about their environment through complex signaling networks composed of many discrete signaling molecules. Individual pathways within these networks are often tightly integrated and highly dynamic, allowing cells to respond to a given stimulus (or, as is typically the case under physiological conditions, a combination of stimuli) in a specific and appropriate manner. However, due to the size and complexity of many cellular signaling networks, it is often difficult to predict how cellular signaling networks will respond under a particular set of conditions. Indeed, crosstalk between individual signaling pathways may lead to responses that are nonintuitive (or even counterintuitive) based on examination of the individual pathways in isolation. Therefore, to gain a more comprehensive view of cell signaling processes, it is important to understand how signaling networks behave at the systems level. This requires integrated strategies that combine quantitative experimental data with computational models. In this chapter, we first examine some of the progress that has recently been made toward understanding the systems-level regulation of cellular signaling networks, with a particular emphasis on phosphorylation-dependent signaling networks. We then discuss how genetically targetable fluorescent biosensors are being used together with computational models to gain unique insights into the spatiotemporal regulation of signaling networks within single, living cells.

  12. Differential ligand-signaling network of CCL19/CCL21-CCR7 system.

    PubMed

    Raju, Rajesh; Gadakh, Sachin; Gopal, Priyanka; George, Bijesh; Advani, Jayshree; Soman, Sowmya; Prasad, T S K; Girijadevi, Reshmi

    2015-01-01

    Chemokine (C-C motif) receptor 7 (CCR7), a class A subtype G-Protein Coupled Receptor (GPCR), is involved in the migration, activation and survival of multiple cell types including dendritic cells, T cells, eosinophils, B cells, endothelial cells and different cancer cells. Together, CCR7 signaling system has been implicated in diverse biological processes such as lymph node homeostasis, T cell activation, immune tolerance, inflammatory response and cancer metastasis. CCL19 and CCL21, the two well-characterized CCR7 ligands, have been established to be differential in their signaling through CCR7 in multiple cell types. Although the differential ligand signaling through single receptor have been suggested for many receptors including GPCRs, there exists no resource or platform to analyse them globally. Here, first of its kind, we present the cell-type-specific differential signaling network of CCL19/CCL21-CCR7 system for effective visualization and differential analysis of chemokine/GPCR signaling. Database URL: http:// www. netpath. org/ pathways? path_ id= NetPath_ 46.

  13. Chikungunya virus capsid protein contains nuclear import and export signals

    PubMed Central

    2013-01-01

    Background Chikungunya virus (CHIKV) is an alphavirus of the Togaviridae family. After autoproteolytic cleavage, the CHIKV capsid protein (CP) is involved in RNA binding and assembly of the viral particle. The monomeric CP is approximately 30 kDa in size and is small enough for passive transport through nuclear pores. Some alphaviruses are found to harbor nuclear localization signals (NLS) and transport of these proteins between cellular compartments was shown to be energy dependent. The active nuclear import of cytoplasmic proteins is mediated by karyopherins and their export by exportins. As nuclear and cytoplasmic trafficking may play a role in the life cycle of CHIKV, we have sought to identify nuclear localization and nuclear export signals in CHIKV CP in a virus-free system. Methods EGFP-fusion proteins of CHIKV CP and mutants thereof were created and used to monitor their intracellular localization. Binding of cellular proteins was confirmed in pull-down assays with purified CP using co-immuoprecipitation. Nuclear localization was demonstrated in a virus-free system using fluorescence microscopy. Results Here we show that CHIKV CP is a nuclear-cytoplasmic shuttling protein with an active NLS that binds to karyopherin α (Karα) for its nuclear translocation. We also found that the Karα4 C-terminal NLS binding site is sufficient for this interaction. We further demonstrate that CHIKV CP interacts directly with the export receptor CRM1 to transport this viral protein out of the nucleus via a nuclear export signal (NES). The CHIKV CP NES was mapped between amino acids 143 and 155 of CP. Deduced from in silico analyses we found that the NES has a mode of binding similar to the snurportin-1 CRM1 complex. Conclusions We were able to show that in a virus-free system that the CHIKV capsid protein contains both, a NLS and a NES, and that it is actively transported between the cytoplasma and the nucleus. We conclude that CHIKV CP has the ability to shuttle via

  14. Introduction of inflammatory bowel disease biomarkers panel using protein-protein interaction (PPI) network analysis

    PubMed Central

    Asadzadeh-Aghdaee, Hamid; Shahrokh, Shabnam; Norouzinia, Mohsen; Hosseini, Mostafa; Keramatinia, Aliasghar; Jamalan, Mostafa; Naghibzadeh, Bijan; Sadeghi, Ali; Jahani Sherafat, Somayeh; Zali, Mohammad Reza

    2016-01-01

    Aim: In the present study, a protein-protein interaction network construction is conducted for IBD. Background: Inflammatory bowel diseases as serious chronic gastrointestinal disorders attracted many molecular investigations. Diverse molecular information is present for IBD. However, these molecular findings are not highlighted based on interactome analysis. On the other hand, PPI network analysis is a powerful method for study of molecular interactions in the protein level that provide useful information for highlighting the desired key proteins. Methods: Cytoscape is the used software with its plug-ins for detailed analysis. Two centrality parameters including degree and betweenness are determined and the crucial proteins based on these parameters are introduced. Results: The 75 proteins among 100 initial proteins are included in the network of IBD. Seventy-five nodes and 260 edges constructed the network as a scale free network. The findings indicate that there are seven hub-bottleneck proteins in the IBD network. Conclusion: More examination revealed the essential roles of these key proteins in the integrity of the network. Finally, the indicator panel including NFKB1, CD40, TNFA, TYK2, NOD2, IL23R, and STAT3 is presented as a possible molecular index for IBD. PMID:28224022

  15. Stress Signal Network between Hypoxia and ER Stress in Chronic Kidney Disease

    PubMed Central

    Maekawa, Hiroshi; Inagi, Reiko

    2017-01-01

    Chronic kidney disease (CKD) is characterized by an irreversible decrease in kidney function and induction of various metabolic dysfunctions. Accumulated findings reveal that chronic hypoxic stress and endoplasmic reticulum (ER) stress are involved in a range of pathogenic conditions, including the progression of CKD. Because of the presence of an arteriovenous oxygen shunt, the kidney is thought to be susceptible to hypoxia. Chronic kidney hypoxia is induced by a number of pathogenic conditions, including renal ischemia, reduced peritubular capillary, and tubulointerstitial fibrosis. The ER is an organelle which helps maintain the quality of proteins through the unfolded protein response (UPR) pathway, and ER dysfunction associated with maladaptive UPR activation is named ER stress. ER stress is reported to be related to some of the effects of pathogenesis in kidney, particularly in the podocyte slit diaphragm and tubulointerstitium. Furthermore, chronic hypoxia mediates ER stress in blood vessel endothelial cells and tubulointerstitium via several mechanisms, including oxidative stress, epigenetic alteration, lipid metabolism, and the AKT pathway. In summary, a growing consensus considers that these stresses interact via complicated stress signal networks, which leads to the exacerbation of CKD (Figure 1). This stress signal network might be a target for interventions aimed at ameliorating CKD. PMID:28228736

  16. Stress Signal Network between Hypoxia and ER Stress in Chronic Kidney Disease.

    PubMed

    Maekawa, Hiroshi; Inagi, Reiko

    2017-01-01

    Chronic kidney disease (CKD) is characterized by an irreversible decrease in kidney function and induction of various metabolic dysfunctions. Accumulated findings reveal that chronic hypoxic stress and endoplasmic reticulum (ER) stress are involved in a range of pathogenic conditions, including the progression of CKD. Because of the presence of an arteriovenous oxygen shunt, the kidney is thought to be susceptible to hypoxia. Chronic kidney hypoxia is induced by a number of pathogenic conditions, including renal ischemia, reduced peritubular capillary, and tubulointerstitial fibrosis. The ER is an organelle which helps maintain the quality of proteins through the unfolded protein response (UPR) pathway, and ER dysfunction associated with maladaptive UPR activation is named ER stress. ER stress is reported to be related to some of the effects of pathogenesis in kidney, particularly in the podocyte slit diaphragm and tubulointerstitium. Furthermore, chronic hypoxia mediates ER stress in blood vessel endothelial cells and tubulointerstitium via several mechanisms, including oxidative stress, epigenetic alteration, lipid metabolism, and the AKT pathway. In summary, a growing consensus considers that these stresses interact via complicated stress signal networks, which leads to the exacerbation of CKD (Figure 1). This stress signal network might be a target for interventions aimed at ameliorating CKD.

  17. S-Glutathionylation and Redox Protein Signaling in Drug Addiction.

    PubMed

    Womersley, Jacqueline S; Uys, Joachim D

    2016-01-01

    Drug addiction is a chronic relapsing disorder that comes at a high cost to individuals and society. Therefore understanding the mechanisms by which drugs exert their effects is of prime importance. Drugs of abuse increase the production of reactive oxygen and nitrogen species resulting in oxidative stress. This change in redox homeostasis increases the conjugation of glutathione to protein cysteine residues; a process called S-glutathionylation. Although traditionally regarded as a protective mechanism against irreversible protein oxidation, accumulated evidence suggests a more nuanced role for S-glutathionylation, namely as a mediator in redox-sensitive protein signaling. The reversible modification of protein thiols leading to alteration in function under different physiologic/pathologic conditions provides a mechanism whereby change in redox status can be translated into a functional response. As such, S-glutathionylation represents an understudied means of post-translational protein modification that may be important in the mechanisms underlying drug addiction. This review will discuss the evidence for S-glutathionylation as a redox-sensing mechanism and how this may be involved in the response to drug-induced oxidative stress. The function of S-glutathionylated proteins involved in neurotransmission, dendritic spine structure, and drug-induced behavioral outputs will be reviewed with specific reference to alcohol, cocaine, and heroin.

  18. S-Glutathionylation and Redox Protein Signaling in Drug Addiction

    PubMed Central

    Womersley, Jacqueline S.; Uys, Joachim D.

    2016-01-01

    Drug addiction is a chronic relapsing disorder that comes at a high cost to individuals and society. Therefore understanding the mechanisms by which drugs exert their effects is of prime importance. Drugs of abuse increase the production of reactive oxygen and nitrogen species resulting in oxidative stress. This change in redox homeostasis increases the conjugation of glutathione to protein cysteine residues; a process called S-glutathionylation. Although traditionally regarded as a protective mechanism against irreversible protein oxidation, accumulated evidence suggests a more nuanced role for S-glutathionylation, namely as a mediator in redox-sensitive protein signaling. The reversible modification of protein thiols leading to alteration in function under different physiologic/pathologic conditions provides a mechanism whereby change in redox status can be translated into a functional response. As such, S-glutathionylation represents an understudied means of post-translational protein modification that may be important in the mechanisms underlying drug addiction. This review will discuss the evidence for S-glutathionylation as a redox-sensing mechanism and how this may be involved in the response to drug-induced oxidative stress. The function of S-glutathionylated proteins involved in neurotransmission, dendritic spine structure, and drug-induced behavioral outputs will be reviewed with specific reference to alcohol, cocaine, and heroin. PMID:26809999

  19. Promotion of Bone Morphogenetic Protein Signaling by Tetraspanins and Glycosphingolipids

    PubMed Central

    Szymczak, Lindsey C.; Aydin, Taner; Yun, Sijung; Constas, Katharine; Schaeffer, Arielle; Ranjan, Sinthu; Kubba, Saad; Alam, Emad; McMahon, Devin E.; He, Jingpeng; Shwartz, Neta; Tian, Chenxi; Plavskin, Yevgeniy; Lindy, Amanda; Dad, Nimra Amir; Sheth, Sunny; Amin, Nirav M.; Zimmerman, Stephanie; Liu, Dennis; Schwarz, Erich M.; Smith, Harold; Krause, Michael W.; Liu, Jun

    2015-01-01

    Bone morphogenetic proteins (BMPs) belong to the transforming growth factor β (TGFβ) superfamily of secreted molecules. BMPs play essential roles in multiple developmental and homeostatic processes in metazoans. Malfunction of the BMP pathway can cause a variety of diseases in humans, including cancer, skeletal disorders and cardiovascular diseases. Identification of factors that ensure proper spatiotemporal control of BMP signaling is critical for understanding how this pathway is regulated. We have used a unique and sensitive genetic screen to identify the plasma membrane-localized tetraspanin TSP-21 as a key new factor in the C. elegans BMP-like “Sma/Mab” signaling pathway that controls body size and postembryonic M lineage development. We showed that TSP-21 acts in the signal-receiving cells and genetically functions at the ligand-receptor level. We further showed that TSP-21 can associate with itself and with two additional tetraspanins, TSP-12 and TSP-14, which also promote Sma/Mab signaling. TSP-12 and TSP-14 can also associate with SMA-6, the type I receptor of the Sma/Mab pathway. Finally, we found that glycosphingolipids, major components of the tetraspanin-enriched microdomains, are required for Sma/Mab signaling. Our findings suggest that the tetraspanin-enriched membrane microdomains are important for proper BMP signaling. As tetraspanins have emerged as diagnostic and prognostic markers for tumor progression, and TSP-21, TSP-12 and TSP-14 are all conserved in humans, we speculate that abnormal BMP signaling due to altered expression or function of certain tetraspanins may be a contributing factor to cancer development. PMID:25978409

  20. Differential Proteomic Analysis of Platelets Suggested Possible Signal Cascades Network in Platelets Treated with Salvianolic Acid B

    PubMed Central

    Ma, Chao; Yao, Yan; Yue, Qing-Xi; Zhou, Xin-Wen; Yang, Peng-Yuan; Wu, Wan-Ying; Guan, Shu-Hong; Jiang, Bao-Hong; Yang, Min; Liu, Xuan; Guo, De-An

    2011-01-01

    Background Salvianolic acid B (SB) is an active component isolated from Danshen, a traditional Chinese medicine widely used for the treatment of cardiovascular disorders. Previous study suggested that SB might inhibit adhesion as well as aggregation of platelets by a mechanism involving the integrin α2β1. But, the signal cascades in platelets after SB binding are still not clear. Methodology/Principal Findings In the present study, a differential proteomic analysis (two-dimensional electrophoresis) was conducted to check the protein expression profiles of rat platelets with or without treatment of SB. Proteins altered in level after SB exposure were identified by MALDI-TOF MS/MS. Treatment of SB caused regulation of 20 proteins such as heat shock-related 70 kDa protein 2 (hsp70), LIM domain protein CLP-36, copine I, peroxiredoxin-2, coronin-1 B and cytoplasmic dynein intermediate chain 2C. The regulation of SB on protein levels was confirmed by Western blotting. The signal cascades network induced by SB after its binding with integrin α2β1 was predicted. To certify the predicted network, binding affinity of SB to integrin α2β1 was checked in vitro and ex vivo in platelets. Furthermore, the effects of SB on protein levels of hsp70, coronin-1B and intracellular levels of Ca(2+) and reactive oxygen species (ROS) were checked with or without pre-treatment of platelets using antibody against integrin α2β1. Electron microscopy study confirmed that SB affected cytoskeleton structure of platelets. Conclusions/Significance Integrin α2β1 might be one of the direct target proteins of SB in platelets. The signal cascades network of SB after binding with integrin α2β1 might include regulation of intracellular Ca(2+) level, cytoskeleton-related proteins such as coronin-1B and cytoskeleton structure of platelets. PMID:21379382

  1. Protein domain networks: Scale-free mixing of positive and negative exponents

    NASA Astrophysics Data System (ADS)

    Nacher, J. C.; Hayashida, M.; Akutsu, T.

    2006-07-01

    Many biological studies have been focused on the study of proteins, since proteins are essential for most cell functions. Although proteins are unique, they share certain common properties. For example, well-defined regions within a protein can fold independently from the rest of the protein and have their own function. They are called protein domains, and served as protein building blocks. In this article, we present a theoretical model for studying the protein domain networks, where one node of the network corresponds to one protein and two proteins are connected if they contain the same domain. The resulting distribution of nodes with a given degree, k, shows not only a power-law with negative exponent γ=-1, but it resembles the superposition of two power-law functions, one with a negative exponent and another with a positive exponent β=1. We call this distribution pattern “ scale-free mixing”. To explain the emergence of this superposition of power-laws, we propose a basic model with two main components: (1) mutation and (2) duplication of domains. Precisely, duplication gives rise to complete subgraphs (i.e., cliques) on the network, thus for several values of k a large number of nodes with degree k is produced, which explains the positive power-law branch of the degree distribution. In order to compare our model with experimental data, we generate protein domain networks with data from the UniProt Knowledgebase-Swissprot database for protein sequences and using InterPro, Pfam and Smart for domain databases. Our results indicate that the signal of this scale-free mixing pattern is also observed in the experimental data and it is conserved among organisms as Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, Drosophila melanogaster, Mus musculus, and Homo sapiens.

  2. Rac GTPase signaling through the PP5 protein phosphatase

    PubMed Central

    Gentile, Saverio; Darden, Thomas; Erxleben, Christian; Romeo, Charles; Russo, Angela; Martin, Negin; Rossie, Sandra; Armstrong, David L.

    2006-01-01

    We have investigated the Rac-dependent mechanism of KCNH2 channel stimulation by thyroid hormone in a rat pituitary cell line, GH4C1, with the patch-clamp technique. Here we present physiological evidence for the protein serine/threonine phosphatase, PP5, as an effector of Rac GTPase signaling. We also propose and test a specific molecular mechanism for PP5 stimulation by Rac-GTP. Inhibition of PP5 with the microbial toxin, okadaic acid, blocked channel stimulation by thyroid hormone and by Rac, but signaling was restored by expression of a toxin-insensitive mutant of PP5, Y451A, which we engineered. PP5 is unique among protein phosphatases in that it contains an N-terminal regulatory domain with three tetratricopeptide repeats (TPR) that inhibit its activity. Expression of the TPR domain coupled to GFP blocked channel stimulation by the thyroid hormone. We also show that the published structures of the PP5 TPR domain and the TPR domain of p67, the Rac-binding subunit of NADPH oxidase, superimpose over 92 α carbons. Mutation of the PP5 TPR domain at two predicted contact points with Rac-GTP prevents the TPR domain from functioning as a dominant negative and blocks the ability of Y451A to rescue signaling in the presence of okadaic acid. PP5 stimulation by Rac provides a unique molecular mechanism for the antagonism of Rho-dependent signaling through protein kinases in many cellular processes, including metastasis, immune cell chemotaxis, and neuronal development. PMID:16549782

  3. Trans-species learning of cellular signaling systems with bimodal deep belief networks

    PubMed Central

    Chen, Lujia; Cai, Chunhui; Chen, Vicky; Lu, Xinghua

    2015-01-01

    Motivation: Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli. Results: We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These ‘deep learning’ models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems. Availability and implementation: The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers. Contact: xinghua@pitt.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25995230

  4. A Protein Complex Network of Drosophila melanogaster

    PubMed Central

    Guruharsha, K. G.; Rual, J. -F.; Zhai, B.; Mintseris, J.; Vaidya, P.; Vaidya, N.; Beekman, C.; Wong, C.; Rhee, D. Y.; Cenaj, O.; McKillip, E.; Shah, S.; Stapleton, M.; Wan, K. H.; Yu, C.; Parsa, B.; Carlson, J. W.; Chen, X.; Kapadia, B.; VijayRaghavan, K.; Gygi, S. P.; Celniker, S. E.; Obar, R. A.; Artavanis-Tsakonas, S.

    2011-01-01

    SUMMARY Determining the composition of protein complexes is an essential step towards understanding the cell as an integrated system. Using co-affinity purification coupled to mass spectrometry analysis, we examined protein associations involving nearly five thousand individual, FLAG-HA epitope-tagged Drosophila proteins. Stringent analysis of these data, based on a novel statistical framework to define individual protein-protein interactions, led to the generation of a Drosophila Protein interaction Map (DPiM) encompassing 556 protein complexes. The high quality of DPiM and its usefulness as a paradigm for metazoan proteomes is apparent from the recovery of many known complexes, significant enrichment for shared functional attributes and validation in human cells. DPiM defines potential novel members for several important protein complexes and assigns functional links to 586 protein-coding genes lacking previous experimental annotation. DPiM represents, to our knowledge, the largest metazoan protein complex map and provides a valuable resource for analysis of protein complex evolution. PMID:22036573

  5. Functional module search in protein networks based on semantic similarity improves the analysis of proteomics data.

    PubMed

    Boyanova, Desislava; Nilla, Santosh; Klau, Gunnar W; Dandekar, Thomas; Müller, Tobias; Dittrich, Marcus

    2014-07-01

    The continuously evolving field of proteomics produces increasing amounts of data while improving the quality of protein identifications. Albeit quantitative measurements are becoming more popular, many proteomic studies are still based on non-quantitative methods for protein identification. These studies result in potentially large sets of identified proteins, where the biological interpretation of proteins can be challenging. Systems biology develops innovative network-based methods, which allow an integrated analysis of these data. Here we present a novel approach, which combines prior knowledge of protein-protein interactions (PPI) with proteomics data using functional similarity measurements of interacting proteins. This integrated network analysis exactly identifies network modules with a maximal consistent functional similarity reflecting biological processes of the investigated cells. We validated our approach on small (H9N2 virus-infected gastric cells) and large (blood constituents) proteomic data sets. Using this novel algorithm, we identified characteristic functional modules in virus-infected cells, comprising key signaling proteins (e.g. the stress-related kinase RAF1) and demonstrate that this method allows a module-based functional characterization of cell types. Analysis of a large proteome data set of blood constituents resulted in clear separation of blood cells according to their developmental origin. A detailed investigation of the T-cell proteome further illustrates how the algorithm partitions large networks into functional subnetworks each representing specific cellular functions. These results demonstrate that the integrated network approach not only allows a detailed analysis of proteome networks but also yields a functional decomposition of complex proteomic data sets and thereby provides deeper insights into the underlying cellular processes of the investigated system.

  6. Functional features and protein network of human sperm-egg interaction.

    PubMed

    Sabetian, Soudabeh; Shamsir, Mohd Shahir; Abu Naser, Mohammed

    2014-12-01

    Elucidation of the sperm-egg interaction at the molecular level is one of the unresolved problems in sexual reproduction, and understanding the molecular mechanism is crucial in solving problems in infertility and failed in vitro fertilization (IVF). Many molecular interactions in the form of protein-protein interactions (PPIs) mediate the sperm-egg membrane interaction. Due to the complexity of the problem such as difficulties in analyzing in vivo membrane PPIs, many efforts have failed to comprehensively elucidate the fusion mechanism and the molecular interactions that mediate sperm-egg membrane fusion. The main purpose of this study was to reveal possible protein interactions and associated molecular function during sperm-egg interaction using a protein interaction network approach. Different databases have been used to construct the human sperm-egg interaction network. The constructed network revealed new interactions. These included CD151 and CD9 in human oocyte that interact with CD49 in sperm, and CD49 and ITGA4 in sperm that interact with CD63 and CD81, respectively, in the oocyte. These results showed that the different integrins in sperm may be involved in human sperm-egg interaction. It was also suggested that sperm ADAM2 plays a role as a protein candidate involved in sperm-egg membrane interaction by interacting with CD9 in the oocyte. Interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, and manganese ion transmembrane transport activity are the major molecular functions in sperm-egg interaction protein network. The disease association analysis indicated that sperm-egg interaction defects are also reflected in other disease networks such as cardiovascular, hematological, and breast cancer diseases. By analyzing the network, we identified the major molecular functions and disease association genes in sperm-egg interaction protein. Further experimental studies will be required to confirm the significance of these new

  7. In silico modeling of the yeast protein and protein family interaction network

    NASA Astrophysics Data System (ADS)

    Goh, K.-I.; Kahng, B.; Kim, D.

    2004-03-01

    Understanding of how protein interaction networks of living organisms have evolved or are organized can be the first stepping stone in unveiling how life works on a fundamental ground. Here we introduce an in silico ``coevolutionary'' model for the protein interaction network and the protein family network. The essential ingredient of the model includes the protein family identity and its robustness under evolution, as well as the three previously proposed: gene duplication, divergence, and mutation. This model produces a prototypical feature of complex networks in a wide range of parameter space, following the generalized Pareto distribution in connectivity. Moreover, we investigate other structural properties of our model in detail with some specific values of parameters relevant to the yeast Saccharomyces cerevisiae, showing excellent agreement with the empirical data. Our model indicates that the physical constraints encoded via the domain structure of proteins play a crucial role in protein interactions.

  8. An analysis pipeline for the inferenceof protein-protein interaction networks

    SciTech Connect

    Taylor, Ronald C.; Singhal, Mudita; Daly, Don S.; Gilmore, Jason; Cannon, Bill; Domico, Kelly; White, Amanda M.; Auberry, Deanna L; Auberry, Kenneth J; Hooker, Brian; Hurst, Gregory {Greg} B; McDermott, Jason; McDonald, W Hayes; Pelletier, Dale A; Schmoyer, Denise D; Wiley, Steven

    2009-09-01

    We present an integrated platform that is used for the reconstruction and analysis of protein-protein interaction networks inferred from Mass Spectrometry (MS) bait-prey experiment data. At the heart of this pipeline is the Software Environment for Biological Network Inference (SEBINI), an interactive environment for the deployment and testing of network inference algorithms that use high-throughput data. Among the many algorithms available in SEBINI is the Bayesian Estimator of Probabilities of Protein-Protein Associations (BEPro3) algorithm, which is used to infer interaction networks from such MS affinity isolation data. For integration, comparison and analysis of the inferred protein-protein interactions with interaction evidence obtained from multiple public sources, the pipeline connects to the Collective Analysis of Biological Interaction Networks (CABIN) software. Incorporating BEPro3 into SEBINI and automatically feeding the resulting inferred network into CABIN, we have created a structured workflow for protein-protein network inference and supplemental analysis from sets of MS bait-prey experiments.

  9. Scalable rule-based modelling of allosteric proteins and biochemical networks.

    PubMed

    Ollivier, Julien F; Shahrezaei, Vahid; Swain, Peter S

    2010-11-04

    Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds. This "regulatory complexity" causes a combinatorial increase in the number of parameters required to fit experimental data as the number of protein interactions increases. It therefore challenges the creation, updating, and re-use of biochemical models. Here, we propose a rule-based modelling framework that exploits the intrinsic modularity of protein structure to address regulatory complexity. Rather than treating proteins as "black boxes", we model their hierarchical structure and, as conformational changes, internal dynamics. By modelling the regulation of allosteric proteins through these conformational changes, we often decrease the number of parameters required to fit data, and so reduce over-fitting and improve the predictive power of a model. Our method is thermodynamically grounded, imposes detailed balance, and also includes molecular cross-talk and the background activity of enzymes. We use our Allosteric Network Compiler to examine how allostery can facilitate macromolecular assembly and how competitive ligands can change the observed cooperativity of an allosteric protein. We also develop a parsimonious model of G protein-coupled receptors that explains functional selectivity and can predict the rank order of potency of agonists acting through a receptor. Our methodology should provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology.

  10. Signal Activation and Inactivation by the Gα Helical Domain: A Long-Neglected Partner in G Protein Signaling

    PubMed Central

    Dohlman, Henrik G.; Jones, Janice C.

    2013-01-01

    Heterotrimeric guanine nucleotide–binding proteins (G proteins) are positioned at the top of many signal transduction pathways. The G protein α subunit is composed of two domains, one that resembles Ras and another that is composed entirely of α helices. Historically, most attention has focused on the Ras-like domain, but emerging evidence reveals that the helical domain is an active participant in G protein signaling. PMID:22649098

  11. Identification of G protein-coupled receptor signaling pathway proteins in marine diatoms using comparative genomics

    PubMed Central

    2013-01-01

    Background The G protein-coupled receptor (GPCR) signaling pathway plays an essential role in signal transmission and response to external stimuli in mammalian cells. Protein components of this pathway have been characterized in plants and simpler eukaryotes such as yeast, but their presence and role in unicellular photosynthetic eukaryotes have not been determined. We use a comparative genomics approach using whole genome sequences and gene expression libraries of four diatoms (Pseudo-nitzschia multiseries, Thalassiosira pseudonana, Phaeodactylum tricornutum and Fragilariopsis cylindrus) to search for evidence of GPCR signaling pathway proteins that share sequence conservation to known GPCR pathway proteins. Results The majority of the core components of GPCR signaling were well conserved in all four diatoms, with protein sequence similarity to GPCRs, human G protein α- and β-subunits and downstream effectors. There was evidence for the Gγ-subunit and thus a full heterotrimeric G protein only in T. pseudonana. Phylogenetic analysis of putative diatom GPCRs indicated similarity but deep divergence to the class C GPCRs, with branches basal to the GABAB receptor subfamily. The extracellular and intracellular regions of these putative diatom GPCR sequences exhibited large variation in sequence length, and seven of these sequences contained the necessary ligand binding domain for class C GPCR activation. Transcriptional data indicated that a number of the putative GPCR sequences are expressed in diatoms under various stress conditions in culture, and that many of the GPCR-activated signaling proteins, including the G protein, are also expressed. Conclusions The presence of sequences in all four diatoms that code for the proteins required for a functional mammalian GPCR pathway highlights the highly conserved nature of this pathway and suggests a complex signaling machinery related to environmental perception and response in these unicellular organisms. The lack of

  12. P-glycoprotein (ABCB1) inhibited network of mitochondrion transport along microtubule and BMP signal-induced cell shape in chimpanzee left cerebrum by systems-theoretical analysis.

    PubMed

    Lin, Hong; Wang, Lin; Jiang, Minghu; Huang, Juxiang; Qi, Lianxiu

    2012-10-01

    We constructed the significant low-expression P-glycoprotein (ABCB1) inhibited transport and signal network in chimpanzee compared with high-expression (fold change ≥2) the human left cerebrum in GEO data set, by using integration of gene regulatory activated and inhibited network inference method with gene ontology (GO) analysis. Our result showed that ABCB1 transport and signal upstream network RAB2A inhibited ABCB1, and downstream ABCB1-inhibited SMAD1_2, NCK2, SLC25A46, GDF10, RASGRP1, EGFR, LRPPRC, RASSF2, RASA4, CA2, CBLB, UBR5, SLC25A16, ITGB3BP, DDIT4, PDPN, RAB2A in chimpanzee left cerebrum. We obtained that the different biological processes of ABCB1 inhibited transport and signal network repressed carbon dioxide transport, ER to Golgi vesicle-mediated transport, folic acid transport, mitochondrion transport along microtubule, water transport, BMP signaling pathway, Ras protein signal transduction, transforming growth factor beta receptor signaling pathway in chimpanzee compared with the inhibited network of the human left cerebrum, as a result of inducing inhibition of mitochondrion transport along microtubule and BMP signal-induced cell shape in chimpanzee left cerebrum. Our hypothesis was verified by the same and different biological processes of ABCB1 inhibited transport and signal network of chimpanzee compared with the corresponding activated network of chimpanzee and the human left cerebrum, respectively.

  13. Crosstalk and Signaling Switches in Mitogen-Activated Protein Kinase Cascades

    PubMed Central

    Fey, Dirk; Croucher, David R.; Kolch, Walter; Kholodenko, Boris N.

    2012-01-01

    Mitogen-activated protein kinase (MAPK) cascades control cell fate decisions, such as proliferation, differentiation, and apoptosis by integrating and processing intra- and extracellular cues. However, similar MAPK kinetic profiles can be associated with opposing cellular decisions depending on cell type, signal strength, and dynamics. This implies that signaling by each individual MAPK cascade has to be considered in the context of the entire MAPK network. Here, we develop a dynamic model of feedback and crosstalk for the three major MAPK cascades; extracellular signal-regulated kinase (ERK), p38 mitogen-activated protein kinase (p38), c-Jun N-terminal kinase (JNK), and also include input from protein kinase B (AKT) signaling. Focusing on the bistable activation characteristics of the JNK pathway, this model explains how pathway crosstalk harmonizes different MAPK responses resulting in pivotal cell fate decisions. We show that JNK can switch from a transient to sustained activity due to multiple positive feedback loops. Once activated, positive feedback locks JNK in a highly active state and promotes cell death. The switch is modulated by the ERK, p38, and AKT pathways. ERK activation enhances the dual specificity phosphatase (DUSP) mediated dephosphorylation of JNK and shifts the threshold of the apoptotic switch to higher inputs. Activation of p38 restores the threshold by inhibiting ERK activity via the PP1 or PP2A phosphatases. Finally, AKT activation inhibits the JNK positive feedback, thus abrogating the apoptotic switch and allowing only proliferative signaling. Our model facilitates understanding of how cancerous deregulations disturb MAPK signal processing and provides explanations for certain drug resistances. We highlight a critical role of DUSP1 and DUSP2 expression patterns in facilitating the switching of JNK activity and show how oncogene induced ERK hyperactivity prevents the normal apoptotic switch explaining the failure of certain drugs to

  14. PROSNET: INTEGRATING HOMOLOGY WITH MOLECULAR NETWORKS FOR PROTEIN FUNCTION PREDICTION

    PubMed Central

    Wang, Sheng; Qu, Meng

    2016-01-01

    Automated annotation of protein function has become a critical task in the post-genomic era. Network-based approaches and homology-based approaches have been widely used and recently tested in large-scale community-wide assessment experiments. It is natural to integrate network data with homology information to further improve the predictive performance. However, integrating these two heterogeneous, high-dimensional and noisy datasets is non-trivial. In this work, we introduce a novel protein function prediction algorithm ProSNet. An integrated heterogeneous network is first built to include molecular networks of multiple species and link together homologous proteins across multiple species. Based on this integrated network, a dimensionality reduction algorithm is introduced to obtain compact low-dimensional vectors to encode proteins in the network. Finally, we develop machine learning classification algorithms that take the vectors as input and make predictions by transferring annotations both within each species and across different species. Extensive experiments on five major species demonstrate that our integration of homology with molecular networks substantially improves the predictive performance over existing approaches. PMID:27896959

  15. Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory.

    PubMed

    Stavrakas, Vassilis; Melas, Ioannis N; Sakellaropoulos, Theodore; Alexopoulos, Leonidas G

    2015-01-01

    Modeling of signal transduction pathways is instrumental for understanding cells' function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells' biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways' logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein-protein interaction networks and to provide meaningful biological insights.

  16. Extracellular chloride signals collagen IV network assembly during basement membrane formation

    PubMed Central

    Cummings, Christopher F.; Pedchenko, Vadim; Brown, Kyle L.; Colon, Selene; Rafi, Mohamed; Jones-Paris, Celestial; Pokydeshava, Elena; Liu, Min; Pastor-Pareja, Jose C.; Stothers, Cody; Ero-Tolliver, Isi A.; McCall, A. Scott; Vanacore, Roberto; Bhave, Gautam; Santoro, Samuel; Blackwell, Timothy S.; Zent, Roy; Pozzi, Ambra

    2016-01-01

    Basement membranes are defining features of the cellular microenvironment; however, little is known regarding their assembly outside cells. We report that extracellular Cl− ions signal the assembly of collagen IV networks outside cells by triggering a conformational switch within collagen IV noncollagenous 1 (NC1) domains. Depletion of Cl− in cell culture perturbed collagen IV networks, disrupted matrix architecture, and repositioned basement membrane proteins. Phylogenetic evidence indicates this conformational switch is a fundamental mechanism of collagen IV network assembly throughout Metazoa. Using recombinant triple helical protomers, we prove that NC1 domains direct both protomer and network assembly and show in Drosophila that NC1 architecture is critical for incorporation into basement membranes. These discoveries provide an atomic-level understanding of the dynamic interactions between extracellular Cl− and collagen IV assembly outside cells, a critical step in the assembly and organization of basement membranes that enable tissue architecture and function. Moreover, this provides a mechanistic framework for understanding the molecular pathobiology of NC1 domains. PMID:27216258

  17. Prolactin receptor and signal transduction to milk protein genes

    SciTech Connect

    Djiane, J.; Daniel, N.; Bignon, C.

    1994-06-01

    After cloning of the mammary gland prolactin (PRL) receptor cDNA, a functional assay was established using co-transfection of PRL receptor cDNA together with a milk protein promoter/chloramphenicol acetyl transferase (CAT) construct in Chinese hamster ovary (CHO) cells. Different mutants of the PRL receptor were tested in this CAT assay to delimit the domains in the receptor necessary for signal transduction to milk protein genes. In CHO cells stably transfected with PRL receptor cDNA, high numbers of PRL receptor are expressed. By metabolic labeling and immunoprecipitation, expressed PRL receptor was identified as a single species of 100 kDa. Using these cells, we analyzed the effects of PRL on intracellular free Ca{sup ++} concentration. PRL stimulates Ca{sup ++} entry and induces secondary Ca{sup ++} mobilization. The entry of Ca{sup ++} is a result of an increase in K{sup +} conductance that hyperpolarizes the membranes. We have also analyzed tyrosine phosphorylation induced by PRL. In CHO cells stably transfected with PRL receptor cDNA, PRL induced a very rapid and transient tyrosine phosphorylation of a 100-kDa protein which is most probably the PRL receptor. The same finding was obtained in mammary membranes after PRL injection to lactating rabbits. Whereas tyrosine kinase inhibitors genistein and lavendustin were without effect, PRL stimulation of milk protein gene promoters was partially inhibited by 2 {mu}M herbimycin in CHO cells co-transfected with PRL receptor cDNA and the {Beta} lactoglobulin CAT construct. Taken together these observations indicate that the cytoplasmic domain of the PRL receptor interacts with one or several tyrosine kinases, which may represent early postreceptor events necessary for PRL signal transduction to milk protein genes. 14 refs., 4 figs.

  18. G-Protein/β-Arrestin-Linked Fluctuating Network of G-Protein-Coupled Receptors for Predicting Drug Efficacy and Bias Using Short-Term Molecular Dynamics Simulation

    PubMed Central

    Ichikawa, Osamu; Fujimoto, Kazushi; Yamada, Atsushi; Okazaki, Susumu; Yamazaki, Kazuto

    2016-01-01

    The efficacy and bias of signal transduction induced by a drug at a target protein are closely associated with the benefits and side effects of the drug. In particular, partial agonist activity and G-protein/β-arrestin-biased agonist activity for the G-protein-coupled receptor (GPCR) family, the family with the most target proteins of launched drugs, are key issues in drug discovery. However, designing GPCR drugs with appropriate efficacy and bias is challenging because the dynamic mechanism of signal transduction induced by ligand—receptor interactions is complicated. Here, we identified the G-protein/β-arrestin-linked fluctuating network, which initiates large-scale conformational changes, using sub-microsecond molecular dynamics (MD) simulations of the β2-adrenergic receptor (β2AR) with a diverse collection of ligands and correlation analysis of their G protein/β-arrestin efficacy. The G-protein-linked fluctuating network extends from the ligand-binding site to the G-protein-binding site through the connector region, and the β-arrestin-linked fluctuating network consists of the NPxxY motif and adjacent regions. We confirmed that the averaged values of fluctuation in the fluctuating network detected are good quantitative indexes for explaining G protein/β-arrestin efficacy. These results indicate that short-term MD simulation is a practical method to predict the efficacy and bias of any compound for GPCRs. PMID:27187591

  19. Regulation of Nuclear Localization of Signaling Proteins by Cytokinin

    SciTech Connect

    Kieber, J.J.

    2010-05-01

    Cytokinins are a class of mitogenic plant hormones that play an important role in most aspects of plant development, including shoot and root growth, vascular and photomorphogenic development and leaf senescence. A model for cytokinin perception and signaling has emerged that is similar to bacterial two-component phosphorelays. In this model, binding of cytokinin to the extracellular domain of the Arabidopsis histidine kinase (AHKs) receptors induces autophosphorylation within the intracellular histidine-kinase domain. The phosphoryl group is subsequently transferred to cytosolic Arabidopsis histidine phosphotransfer proteins (AHPs), which have been suggested to translocate to the nucleus in response to cytokinin treatment, where they then transfer the phosphoryl group to nuclear-localized response regulators (Type-A and Type-B ARRs). We examined the effects of cytokinin on AHP subcellular localization in Arabidopsis and, contrary to expectations, the AHPs maintained a constant nuclear/cytosolic distribution following cytokinin treatment. Furthermore, mutation of the conserved phosphoacceptor histidine residue of the AHP, as well as disruption of multiple cytokinin signaling elements, did not affect the subcellular localization of the AHP proteins. Finally, we present data indicating that AHPs maintain a nuclear/cytosolic distribution by balancing active transport into and out of the nucleus. Our findings suggest that the current models indicating relocalization of AHP protein into the nucleus in response to cytokinin are incorrect. Rather, AHPs actively maintain a consistent nuclear/cytosolic distribution regardless of the status of the cytokinin response pathway.

  20. A Systems Level Analysis of Vasopressin-mediated Signaling Networks in Kidney Distal Convoluted Tubule Cells

    PubMed Central

    Cheng, Lei; Wu, Qi; Kortenoeven, Marleen L. A.; Pisitkun, Trairak; Fenton, Robert A.

    2015-01-01

    The kidney distal convoluted tubule (DCT) plays an essential role in maintaining body sodium balance and blood pressure. The major sodium reabsorption pathway in the DCT is the thiazide-sensitive NaCl cotransporter (NCC), whose functions can be modulated by the hormone vasopressin (VP) acting via uncharacterized signaling cascades. Here we use a systems biology approach centered on stable isotope labeling by amino acids in cell culture (SILAC) based quantitative phosphoproteomics of cultured mouse DCT cells to map global changes in protein phosphorylation upon acute treatment with a VP type II receptor agonist 1-desamino-8-D-arginine vasopressin (dDAVP). 6330 unique proteins, containing 12333 different phosphorylation sites were identified. 185 sites were altered in abundance following dDAVP. Basophilic motifs were preferential targets for upregulated sites upon dDAVP stimulation, whereas proline-directed motifs were prominent for downregulated sites. Kinase prediction indicated that dDAVP increased AGC and CAMK kinase families’ activities and decreased activity of CDK and MAPK families. Network analysis implicated phosphatidylinositol-4,5-bisphosphate 3-kinase or CAMKK dependent pathways in VP-mediated signaling; pharmacological inhibition of which significantly reduced dDAVP induced increases in phosphorylated NCC at an activating site. In conclusion, this study identifies unique VP signaling cascades in DCT cells that may be important for regulating blood pressure. PMID:26239621

  1. Metazoans evolved by taking domains from soluble proteins to expand intercellular communication network

    PubMed Central

    Nam, Hyun-Jun; Kim, Inhae; Bowie, James U.; Kim, Sanguk

    2015-01-01

    A central question in animal evolution is how multicellular animals evolved from unicellular ancestors. We hypothesize that membrane proteins must be key players in the development of multicellularity because they are well positioned to form the cell-cell contacts and to provide the intercellular communication required for the creation of complex organisms. Here we find that a major mechanism for the necessary increase in membrane protein complexity in the transition from non-metazoan to metazoan life was the new incorporation of domains from soluble proteins. The membrane proteins that have incorporated soluble domains in metazoans are enriched in many of the functions unique to multicellular organisms such as cell-cell adhesion, signaling, immune defense and developmental processes. They also show enhanced protein-protein interaction (PPI) network complexity and centrality, suggesting an important role in the cellular diversification found in complex organisms. Our results expose an evolutionary mechanism that contributed to the development of higher life forms. PMID:25923201

  2. Metazoans evolved by taking domains from soluble proteins to expand intercellular communication network.

    PubMed

    Nam, Hyun-Jun; Kim, Inhae; Bowie, James U; Kim, Sanguk

    2015-04-29

    A central question in animal evolution is how multicellular animals evolved from unicellular ancestors. We hypothesize that membrane proteins must be key players in the development of multicellularity because they are well positioned to form the cell-cell contacts and to provide the intercellular communication required for the creation of complex organisms. Here we find that a major mechanism for the necessary increase in membrane protein complexity in the transition from non-metazoan to metazoan life was the new incorporation of domains from soluble proteins. The membrane proteins that have incorporated soluble domains in metazoans are enriched in many of the functions unique to multicellular organisms such as cell-cell adhesion, signaling, immune defense and developmental processes. They also show enhanced protein-protein interaction (PPI) network complexity and centrality, suggesting an important role in the cellular diversification found in complex organisms. Our results expose an evolutionary mechanism that contributed to the development of higher life forms.

  3. Dynamic rheology of food protein networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Small amplitude oscillatory shear analyses (SAOSA) of samples containing protein are useful for determining the nature of the protein matrix without damaging it. The Dairy Processing and Products Research Unit of the Agricultural Research Service, USDA has pioneered the use of SAOSA in understandin...

  4. Dynamic rheology of food protein networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Small amplitude oscillatory shear analyses of samples containing protein are useful for determining the nature of the protein matrix without damaging it. Elastic modulus, viscous modulus, and loss tangent (the ratio of viscous modulus to elastic modulus) give information on the strength of the netw...

  5. Protein-protein interaction networks in the spinocerebellar ataxias

    PubMed Central

    Rubinsztein, David C

    2006-01-01

    A large yeast two-hybrid study investigating whether the proteins mutated in different forms of spinocerebellar ataxia have interacting protein partners in common suggests that some forms do share common pathways, and will provide a valuable resource for future work on these diseases. PMID:16904001

  6. Regulation of longevity by regulator of G-protein signaling protein, Loco.

    PubMed

    Lin, Yuh-Ru; Kim, Keetae; Yang, Yanfei; Ivessa, Andreas; Sadoshima, Junichi; Park, Yongkyu

    2011-06-01

    Regulator of G-protein signaling (RGS) proteins contribute to G-protein signaling pathways as activators or repressors with GTPase-activating protein (GAP) activity. To characterize whether regulation of RGS proteins influences longevity in several species, we measured stress responses and lifespan of RGS-overexpressing and RGS-lacking mutants. Reduced expression of Loco, a RGS protein of Drosophila melanogaster, resulted in a longer lifespan for both male and female flies, also exhibiting stronger resistance to three different stressors (starvation, oxidation, and heat) and higher manganese-containing superoxide dismutase (MnSOD) activity. In addition, this reduction in Loco expression increased fat content and diminished cAMP levels. In contrast, overexpression of both genomic and cDNA loco gene significantly shortened the lifespan with weaker stress resistance and lower fat content. Deletion analysis of the Loco demonstrated that its RGS domain is required for the regulation of longevity. Consistently, when expression of RGS14, mammalian homologue of Loco, was reduced in rat fibroblast cells, the resistance to oxidative stress increased with higher MnSOD expression. The changes of yeast Rgs2 expression, which shares a conserved RGS domain with the fly Loco protein, also altered lifespan and stress resistance in Saccharomyces cerevisiae. Here, we provide the first evidence that RGS proteins with GAP activity affect both stress resistance and longevity in several species.

  7. Construction of protein interaction network involved in lung adenocarcinomas using a novel algorithm

    PubMed Central

    Chen, Juan; Yang, Hai-Tao; Li, Zhu; Xu, Ning; Yu, Bo; Xu, Jun-Ping; Zhao, Pei-Ge; Wang, Yan; Zhang, Xiu-Juan; Lin, Dian-Jie

    2016-01-01

    Studies that only assess differentially-expressed (DE) genes do not contain the information required to investigate the mechanisms of diseases. A complete knowledge of all the direct and indirect interactions between proteins may act as a significant benchmark in the process of forming a comprehensive description of cellular mechanisms and functions. The results of protein interaction network studies are often inconsistent and are based on various methods. In the present study, a combined network was constructed using selected gene pairs, following the conversion and combination of the scores of gene pairs that were obtained across multiple approaches by a novel algorithm. Samples from patients with and without lung adenocarcinoma were compared, and the RankProd package was used to identify DE genes. The empirical Bayesian (EB) meta-analysis approach, the search tool for the retrieval of interacting genes/proteins database (STRING), the weighted gene coexpression network analysis (WGCNA) package and the differentially-coexpressed genes and links package (DCGL) were used for network construction. A combined network was also constructed with a novel rank-based algorithm using a combined score. The topological features of the 5 networks were analyzed and compared. A total of 941 DE genes were screened. The topological analysis indicated that the gene interaction network constructed using the WGCNA method was more likely to produce a small-world property, which has a small average shortest path length and a large clustering coefficient, whereas the combined network was confirmed to be a scale-free network. Gene pairs that were identified using the novel combined method were mostly enriched in the cell cycle and p53 signaling pathway. The present study provided a novel perspective to the network-based analysis. Each method has advantages and disadvantages. Compared with single methods, the combined algorithm used in the present study may provide a novel method to

  8. Stimulus-Dependent Inhibitor of Apoptosis Protein Expression Prolongs the Duration of B Cell Signalling

    PubMed Central

    Shinohara, Hisaaki; Inoue, Kentaro; Yumoto, Noriko; Nagashima, Takeshi; Okada-Hatakeyama, Mariko

    2016-01-01

    Different dynamic behaviours of signalling activity can induce distinct biological responses in a variety of cells. However, the molecular mechanisms that determine the dynamics of kinase activities in immune cells are not well understood. In this study, we showed that the duration of both IκB kinase (IKK) and extracellular signal-regulated kinase (ERK) activities in B cell receptor (BCR)- and CD40-signalling pathways in B cells were regulated by transcriptional feedback loops. We conducted a time-course transcriptome analysis after BCR or CD40 stimulation and identified the following four candidate genes as feedback regulators for IKK and ERK: inhibitor of apoptosis protein (IAP), TNF alpha-induced protein 3, dual-specificity phosphatase 5, and sprouty homolog 2. Quantitative experiments and mathematical modelling suggested that IAP inhibition shortened the duration of IKK and ERK activity following both BCR and CD40 pathway stimulation, indicating a positive role for IAP in B cell signalling. Furthermore, transient kinase activities induced by IAP blockage reduced the levels of delayed expression genes. Together, our findings suggest that IKK and ERK activity durations can be fine-tuned by the coordinated regulation of positive and negative transcriptional feedback and that these network properties determine the biological output of B cells. PMID:27277891

  9. Predicting protein function by frequent functional association pattern mining in protein interaction networks.

    PubMed

    Cho, Young-Rae; Zhang, Aidong

    2010-01-01

    Predicting protein function from protein interaction networks has been challenging because of the complexity of functional relationships among proteins. Most previous function prediction methods depend on the neighborhood of or the connected paths to known proteins. However, their accuracy has been limited due to the functional inconsistency of interacting proteins. In this paper, we propose a novel approach for function prediction by identifying frequent patterns of functional associations in a protein interaction network. A set of functions that a protein performs is assigned into the corresponding node as a label. A functional association pattern is then represented as a labeled subgraph. Our frequent labeled subgraph mining algorithm efficiently searches the functional association patterns that occur frequently in the network. It iteratively increases the size of frequent patterns by one node at a time by selective joining, and simplifies the network by a priori pruning. Using the yeast protein interaction network, our algorithm found more than 1400 frequent functional association patterns. The function prediction is performed by matching the subgraph, including the unknown protein, with the frequent patterns analogous to it. By leave-one-out cross validation, we show that our approach has better performance than previous link-based methods in terms of prediction accuracy. The frequent functional association patterns generated in this study might become the foundations of advanced analysis for functional behaviors of proteins in a system level.

  10. 47 CFR 73.4157 - Network signals which adversely affect affiliate broadcast service.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ....4157 Network signals which adversely affect affiliate broadcast service. See Public Notice, FCC 79-387... 47 Telecommunication 4 2010-10-01 2010-10-01 false Network signals which adversely affect affiliate broadcast service. 73.4157 Section 73.4157 Telecommunication FEDERAL COMMUNICATIONS...

  11. Mitogen Activated Protein kinase signal transduction pathways in the prostate

    PubMed Central

    Maroni, Paul D; Koul, Sweaty; Meacham, Randall B; Koul, Hari K

    2004-01-01

    The biochemistry of the mitogen activated protein kinases ERK, JNK, and p38 have been studied in prostate physiology in an attempt to elucidate novel mechanisms and pathways for the treatment of prostatic disease. We reviewed articles examining mitogen-activated protein kinases using prostate tissue or cell lines. As with other tissue types, these signaling modules are links/transmitters for important pathways in prostate cells that can result in cellular survival or apoptosis. While the activation of the ERK pathway appears to primarily result in survival, the roles of JNK and p38 are less clear. Manipulation of these pathways could have important implications for the treatment of prostate cancer and benign prostatic hypertrophy. PMID:15219238

  12. Constant change: dynamic regulation of membrane transport by calcium signalling networks keeps plants in tune with their environment.

    PubMed

    Kleist, Thomas J; Luan, Sheng

    2016-03-01

    Despite substantial variation and irregularities in their environment, plants must conform to spatiotemporal demands on the molecular composition of their cytosol. Cell membranes are the major interface between organisms and their environment and the basis for controlling the contents and intracellular organization of the cell. Membrane transport proteins (MTPs) govern the flow of molecules across membranes, and their activities are closely monitored and regulated by cell signalling networks. By continuously adjusting MTP activities, plants can mitigate the effects of environmental perturbations, but effective implementation of this strategy is reliant on precise coordination among transport systems that reside in distinct cell types and membranes. Here, we examine the role of calcium signalling in the coordination of membrane transport, with an emphasis on potassium transport. Potassium is an exceptionally abundant and mobile ion in plants, and plant potassium transport has been intensively studied for decades. Classic and recent studies have underscored the importance of calcium in plant environmental responses and membrane transport regulation. In reviewing recent advances in our understanding of the coding and decoding of calcium signals, we highlight established and emerging roles of calcium signalling in coordinating membrane transport among multiple subcellular locations and distinct transport systems in plants, drawing examples from the CBL-CIPK signalling network. By synthesizing classical studies and recent findings, we aim to provide timely insights on the role of calcium signalling networks in the modulation of membrane transport and its importance in plant environmental responses.

  13. An analysis pipeline for the inference of protein-protein interaction networks

    SciTech Connect

    Taylor, Ronald C.; Singhal, Mudita; Daly, Don S.; Gilmore, Jason M.; Cannon, William R.; Domico, Kelly O.; White, Amanda M.; Auberry, Deanna L.; Auberry, Kenneth J.; Hooker, Brian S.; Hurst, G. B.; McDermott, Jason E.; McDonald, W. H.; Pelletier, Dale A.; Schmoyer, Denise A.; Wiley, H. S.

    2009-12-01

    An analysis pipeline has been created for deployment of a novel algorithm, the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro), for use in the reconstruction of protein-protein interaction networks. We have combined the Software Environment for BIological Network Inference (SEBINI), an interactive environment for the deployment and testing of network inference algorithms that use high-throughput data, and the Collective Analysis of Biological Interaction Networks (CABIN), software that allows integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources, to allow interactions computed by BEPro to be stored, visualized, and further analyzed. Incorporating BEPro into SEBINI and automatically feeding the resulting inferred network into CABIN, we have created a structured workflow for protein-protein network inference and supplemental analysis from sets of mass spectrometry bait-prey experiment data. SEBINI demo site: https://www.emsl.pnl.gov /SEBINI/ Contact: ronald.taylor@pnl.gov. BEPro is available at http://www.pnl.gov/statistics/BEPro3/index.htm. Contact: ds.daly@pnl.gov. CABIN is available at http://www.sysbio.org/dataresources/cabin.stm. Contact: mudita.singhal@pnl.gov.

  14. Network-Assisted Investigation of Combined Causal Signals from Genome-Wide Association Studies in Schizophrenia

    PubMed Central

    Jia, Peilin; Wang, Lily; Fanous, Ayman H.; Pato, Carlos N.; Edwards, Todd L.; Zhao, Zhongming

    2012-01-01

    With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had P meta<1×10−4, including the gene HLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available. PMID:22792057

  15. Low-dose radiation affects cardiac physiology: gene networks and molecular signaling in cardiomyocytes

    PubMed Central

    Coleman, Matthew A.; Sasi, Sharath P.; Onufrak, Jillian; Natarajan, Mohan; Manickam, Krishnan; Schwab, John; Muralidharan, Sujatha; Peterson, Leif E.; Alekseyev, Yuriy O.; Yan, Xinhua

    2015-01-01

    There are 160,000 cancer patients worldwide treated with particle radiotherapy (RT). With the advent of proton, and high (H) charge (Z) and energy (E) HZE ionizing particle RT, the cardiovascular diseases risk estimates are uncertain. In addition, future deep space exploratory-type missions will expose humans to unknown but low doses of particle irradiation (IR). We examined molecular responses using transcriptome profiling in left ventricular murine cardiomyocytes isolated from mice that were exposed to 90 cGy, 1 GeV proton (1H) and 15 cGy, 1 GeV/nucleon iron (56Fe) over 28 days after exposure. Unsupervised clustering analysis of gene expression segregated samples according to the IR response and time after exposure, with 56Fe-IR showing the greatest level of gene modulation. 1H-IR showed little differential transcript modulation. Network analysis categorized the major differentially expressed genes into cell cycle, oxidative responses, and transcriptional regulation functional groups. Transcriptional networks identified key nodes regulating expression. Validation of the signal transduction network by protein analysis and gel shift assay showed that particle IR clearly regulates a long-lived signaling mechanism for ERK1/2, p38 MAPK signaling and identified NFATc4, GATA4, STAT3, and NF-κB as regulators of the response at specific time points. These data suggest that the molecular responses and gene expression to 56Fe-IR in cardiomyocytes are unique and long-lasting. Our study may have significant implications for the efforts of National Aeronautics and Space Administration to develop heart disease risk estimates for astronauts and for patients receiving conventional and particle RT via identification of specific HZE-IR molecular markers. PMID:26408534

  16. Low-dose radiation affects cardiac physiology: gene networks and molecular signaling in cardiomyocytes.

    PubMed

    Coleman, Matthew A; Sasi, Sharath P; Onufrak, Jillian; Natarajan, Mohan; Manickam, Krishnan; Schwab, John; Muralidharan, Sujatha; Peterson, Leif E; Alekseyev, Yuriy O; Yan, Xinhua; Goukassian, David A

    2015-12-01

    There are 160,000 cancer patients worldwide treated with particle radiotherapy (RT). With the advent of proton, and high (H) charge (Z) and energy (E) HZE ionizing particle RT, the cardiovascular diseases risk estimates are uncertain. In addition, future deep space exploratory-type missions will expose humans to unknown but low doses of particle irradiation (IR). We examined molecular responses using transcriptome profiling in left ventricular murine cardiomyocytes isolated from mice that were exposed to 90 cGy, 1 GeV proton ((1)H) and 15 cGy, 1 GeV/nucleon iron ((56)Fe) over 28 days after exposure. Unsupervised clustering analysis of gene expression segregated samples according to the IR response and time after exposure, with (56)Fe-IR showing the greatest level of gene modulation. (1)H-IR showed little differential transcript modulation. Network analysis categorized the major differentially expressed genes into cell cycle, oxidative responses, and transcriptional regulation functional groups. Transcriptional networks identified key nodes regulating expression. Validation of the signal transduction network by protein analysis and gel shift assay showed that particle IR clearly regulates a long-lived signaling mechanism for ERK1/2, p38 MAPK signaling and identified NFATc4, GATA4, STAT3, and NF-κB as regulators of the response at specific time points. These data suggest that the molecular responses and gene expression to (56)Fe-IR in cardiomyocytes are unique and long-lasting. Our study may have significant implications for the efforts of National Aeronautics and Space Administration to develop heart disease risk estimates for astronauts and for patients receiving conventional and particle RT via identification of specific HZE-IR molecular markers.

  17. Network Signal Processor No. 2 after removal from Columbia

    NASA Technical Reports Server (NTRS)

    1998-01-01

    Two USA employees, Tim Seymour (at left) and Danny Brown (at right), look at the network signal processor (NSP) that was responsible for postponement of the launch of STS-90 on Apr. 16. The Space Shuttle Columbia's liftoff from Launch Pad 39B was postponed 24 hours due to difficulty with NSP No. 2 on the orbiter. This device formats data and voice communications between the ground and the Space Shuttle. The unit, which is located in the orbiter's mid-deck, was removed and replaced on Apr. 16. Mission managers first noticed the problem at about 3 a.m. during normal communications systems activation prior to tanking operations. As a result, work to load the external tank with the cryogenic propellants did not begin and launch postponement was made official at about 8:15 a.m. STS-90 is slated to be the launch of Neurolab, a nearly 17-day mission to examine the effects of spaceflight on the brain, spinal cord, peripheral nerves and sensory organs in the human body.

  18. Signal processing using artificial neural network for BOTDA sensor system.

    PubMed

    Azad, Abul Kalam; Wang, Liang; Guo, Nan; Tam, Hwa-Yaw; Lu, Chao

    2016-03-21

    We experimentally demonstrate the use of artificial neural network (ANN) to process sensing signals obtained from Brillouin optical time domain analyzer (BOTDA). The distributed temperature information is extracted directly from the local Brillouin gain spectra (BGSs) along the fiber under test without the process of determination of Brillouin frequency shift (BFS) and hence conversion from BFS to temperature. Unlike our previous work for short sensing distance where ANN is trained by measured BGSs, here we employ ideal BGSs with different linewidths to train the ANN in order to take the linewidth variation due to different conditions from the training and testing phases into account, making it feasible for long distance sensing. Moreover, the performance of ANN is compared with other two techniques, Lorentzian curve fitting and cross-correlation method, and our results show that ANN has higher accuracy and larger tolerance to measurement error, especially at large frequency scanning step. We also show that the temperature extraction from BOTDA measurements employing ANN is significantly faster than the other two approaches. Hence ANN can be an excellent alternative tool to process BGSs measured by BOTDA and obtain temperature distribution along the fiber, especially when large frequency scanning step is adopted to significantly reduce the measurement time but without sacrifice of sensing accuracy.

  19. Computational models reduce complexity and accelerate insight into cardiac signaling networks.

    PubMed

    Yang, Jason H; Saucerman, Jeffrey J

    2011-01-07

    Cardiac signaling networks exhibit considerable complexity in size and connectivity. The intrinsic complexity of these networks complicates the interpretation of experimental findings. This motivates new methods for investigating the mechanisms regulating cardiac signaling networks and the consequences these networks have on cardiac physiology and disease. Next-generation experimental techniques are also generating a wealth of genomic and proteomic data that can be difficult to analyze or interpret. Computational models are poised to play a key role in addressing these challenges. Computational models have a long history in contributing to the understanding of cardiac physiology and are useful for identifying biological mechanisms, inferring multiscale consequences to cell signaling activities and reducing the complexity of large data sets. Models also integrate well with experimental studies to explain experimental observations and generate new hypotheses. Here, we review the contributions computational modeling approaches have made to the analysis of cardiac signaling networks and forecast opportunities for computational models to accelerate cardiac signaling research.

  20. Protein ranking: From local to global structure in the protein similarity network

    PubMed Central

    Weston, Jason; Elisseeff, Andre; Zhou, Dengyong; Leslie, Christina S.; Noble, William Stafford

    2004-01-01

    Biologists regularly search databases of DNA or protein sequences for evolutionary or functional relationships to a given query sequence. We describe a ranking algorithm that exploits the entire network structure of similarity relationships among proteins in a sequence database by performing a diffusion operation on a precomputed, weighted network. The resulting ranking algorithm, evaluated by using a human-curated database of protein structures, is efficient and provides significantly better rankings than a local network search algorithm such as psi-blast. PMID:15087500

  1. Cherry Valley Ducks Mitochondrial Antiviral-Signaling Protein-Mediated Signaling Pathway and Antiviral Activity Research

    PubMed Central

    Li, Ning; Hong, Tianqi; Li, Rong; Wang, Yao; Guo, Mengjiao; Cao, Zongxi; Cai, Yumei; Liu, Sidang; Chai, Tongjie; Wei, Liangmeng

    2016-01-01

    Mitochondrial antiviral-signaling protein (MAVS), an adaptor protein of retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs)-mediated signal pathway, is involved in innate immunity. In this study, Cherry Valley duck MAVS (duMAVS) was cloned from the spleen and analyzed. duMAVS was determined to have a caspase activation and recruitment domain at N-terminal, followed by a proline-rich domain and a transmembrane domain at C-terminal. Quantitative real-time PCR indicated that duMAVS was expressed in all tissues tested across a broad expression spectrum. The expression of duMAVS was significantly upregulated after infection with duck Tembusu virus (DTMUV). Overexpression of duMAVS could drive the activation of interferon (IFN)-β, nuclear factor-κB, interferon regulatory factor 7, and many downstream factors (such as Mx, PKR, OAS, and IL-8) in duck embryo fibroblast cells. What is more, RNA interference further confirmed that duMAVS was an important adaptor for IFN-β activation. The antiviral assay showed that duMAVS could suppress the various viral replications (DTMUV, novel reovirus, and duck plague virus) at early stages of infection. Overall, these results showed that the main signal pathway mediated by duMAVS and it had a broad-spectrum antiviral ability. This research will be helpful to better understanding the innate immune system of ducks. PMID:27708647

  2. Methods for Mapping of Interaction Networks Involving Membrane Proteins

    SciTech Connect

    Hooker, Brian S.; Bigelow, Diana J.; Lin, Chiann Tso

    2007-11-23

    Numerous approaches have been taken to study protein interactions, such as tagged protein complex isolation followed by mass spectrometry, yeast two-hybrid methods, fluorescence resonance energy transfer, surface plasmon resonance, site-directed mutagenesis, and crystallography. Membrane protein interactions pose significant challenges due to the need to solubilize membranes without disrupting protein-protein interactions. Traditionally, analysis of isolated protein complexes by high-resolution 2D gel electrophoresis has been the main method used to obtain an overall picture of proteome constituents and interactions. However, this method is time consuming, labor intensive, detects only abundant proteins and is not suitable for the coverage required to elucidate large interaction networks. In this review, we discuss the application of various methods to elucidate interactions involving membrane proteins. These techniques include methods for the direct isolation of single complexes or interactors as well as methods for characterization of entire subcellular and cellular interactomes.

  3. Novel insights into G protein and G protein-coupled receptor signaling in cancer.

    PubMed

    O'Hayre, Morgan; Degese, Maria S; Gutkind, J Silvio

    2014-04-01

    G protein-coupled receptors (GPCRs) play a central role in signal transmission, thereby controlling many facets of cellular function. Overwhelming evidence now implicates GPCRs, G proteins and their downstream signaling targets in cancer initiation and progression, where they can influence aberrant cell growth and survival, largely through activation of AKT/mTOR, MAPKs, and Hippo signaling pathways. GPCRs also play critical roles in the invasion and metastasis of cancer cells via activation of Rho GTPases and cytoskeletal changes, and angiogenesis to supply the tumor with nutrients and provide routes for metastasis. Lastly, GPCRs contribute to the establishment and maintenance of a permissive tumor microenvironment. Understanding GPCR involvement in cancer malignancy may help identify novel therapeutic opportunities for cancer prevention and treatment.

  4. Protein S-glutathionlyation links energy metabolism to redox signaling in mitochondria

    PubMed Central

    Mailloux, Ryan J.; Treberg, Jason R.

    2015-01-01

    At its core mitochondrial function relies on redox reactions. Electrons stripped from nutrients are used to form NADH and NADPH, electron carriers that are similar in structure but support different functions. NADH supports ATP production but also generates reactive oxygen species (ROS), superoxide (O2·-) and hydrogen peroxide (H2O2). NADH-driven ROS production is counterbalanced by NADPH which maintains antioxidants in an active state. Mitochondria rely on a redox buffering network composed of reduced glutathione (GSH) and peroxiredoxins (Prx) to quench ROS generated by nutrient metabolism. As H2O2 is quenched, NADPH is expended to reactivate antioxidant networks and reset the redox environment. Thus, the mitochondrial redox environment is in a constant state of flux reflecting changes in nutrient and ROS metabolism. Changes in redox environment can modulate protein function through oxidation of protein cysteine thiols. Typically cysteine oxidation is considered to be mediated by H2O2 which oxidizes protein thiols (SH) forming sulfenic acid (SOH). However, problems begin to emerge when one critically evaluates the regulatory function of SOH. Indeed SOH formation is slow, non-specific, and once formed SOH reacts rapidly with a variety of molecules. By contrast, protein S-glutathionylation (PGlu) reactions involve the conjugation and removal of glutathione moieties from modifiable cysteine residues. PGlu reactions are driven by fluctuations in the availability of GSH and oxidized glutathione (GSSG) and thus should be exquisitely sensitive to changes ROS flux due to shifts in the glutathione pool in response to varying H2O2 availability. Here, we propose that energy metabolism-linked redox signals originating from mitochondria are mediated indirectly by H2O2 through the GSH redox buffering network in and outside mitochondria. This proposal is based on several observations that have shown that unlike other redox modifications PGlu reactions fulfill the requisite