CBL-CIPK network for calcium signaling in higher plants

Science.gov (United States)

Luan, Sheng

Plants sense their environment by signaling mechanisms involving calcium. Calcium signals are encoded by a complex set of parameters and decoded by a large number of proteins including the more recently discovered CBL-CIPK network. The calcium-binding CBL proteins specifi-cally interact with a family of protein kinases CIPKs and regulate the activity and subcellular localization of these kinases, leading to the modification of kinase substrates. This represents a paradigm shift as compared to a calcium signaling mechanism from yeast and animals. One example of CBL-CIPK signaling pathways is the low-potassium response of Arabidopsis roots. When grown in low-K medium, plants develop stronger K-uptake capacity adapting to the low-K condition. Recent studies show that the increased K-uptake is caused by activation of a specific K-channel by the CBL-CIPK network. A working model for this regulatory pathway will be discussed in the context of calcium coding and decoding processes.

Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network.

Science.gov (United States)

Yang, Huiying; Ke, Yuehua; Wang, Jian; Tan, Yafang; Myeni, Sebenzile K; Li, Dong; Shi, Qinghai; Yan, Yanfeng; Chen, Hui; Guo, Zhaobiao; Yuan, Yanzhi; Yang, Xiaoming; Yang, Ruifu; Du, Zongmin

2011-11-01

A Yersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66 Y. pestis bait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted by Y. pestis were significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted by Y. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted by Y. pestis are highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance of Y. pestis to phagocytosis. Interference with TLR and MAPK signaling pathways by Y. pestis reflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting with Y. pestis (16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibit in vitro actin assembly mediated by VASP.

Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

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Masanao Sato

Full Text Available Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2. This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i the components of the network are highly interconnected; and (ii negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector

Network features and pathway analyses of a signal transduction cascade

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Ryoji Yanashima

2009-05-01

Full Text Available The scale-free and small-world network models reflect the functional units of networks. However, when we investigated the network properties of a signaling pathway using these models, no significant differences were found between the original undirected graphs and the graphs in which inactive proteins were eliminated from the gene expression data. We analyzed signaling networks by focusing on those pathways that best reflected cellular function. Therefore, our analysis of pathways started from the ligands and progressed to transcription factors and cytoskeletal proteins. We employed the Python module to assess the target network. This involved comparing the original and restricted signaling cascades as a directed graph using microarray gene expression profiles of late onset Alzheimer's disease. The most commonly used method of shortest-path analysis neglects to consider the influences of alternative pathways that can affect the activation of transcription factors or cytoskeletal proteins. We therefore introduced included k-shortest paths and k-cycles in our network analysis using the Python modules, which allowed us to attain a reasonable computational time and identify k-shortest paths. This technique reflected results found in vivo and identified pathways not found when shortest path or degree analysis was applied. Our module enabled us to comprehensively analyse the characteristics of biomolecular networks and also enabled analysis of the effects of diseases considering the feedback loop and feedforward loop control structures as an alternative path.

[Cellular adhesion signal transduction network of tumor necrosis factor-alpha induced hepatocellular carcinoma cells].

Science.gov (United States)

Zheng, Yongchang; Du, Shunda; Xu, Haifeng; Xu, Yiyao; Zhao, Haitao; Chi, Tianyi; Lu, Xin; Sang, Xinting; Mao, Yilei

2014-11-18

To systemically explore the cellular adhesion signal transduction network of tumor necrosis factor-alpha (TNF-α)-induced hepatocellular carcinoma cells with bioinformatics tools. Published microarray dataset of TNF-α-induced HepG2, human transcription factor database HTRI and human protein-protein interaction database HPRD were used to construct and analyze the signal transduction network. In the signal transduction network, MYC and SP1 were the key nodes of signaling transduction. Several genes from the network were closely related with cellular adhesion.Epidermal growth factor receptor (EGFR) is a possible key gene of effectively regulating cellular adhesion during the induction of TNF-α. EGFR is a possible key gene for TNF-α-induced metastasis of hepatocellular carcinoma.

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

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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

Stochastic effects as a force to increase the complexity of signaling networks

KAUST Repository

Kuwahara, Hiroyuki; Gao, Xin

2013-01-01

Cellular signaling networks are complex and appear to include many nonfunctional elements. Recently, it was suggested that nonfunctional interactions of proteins cause signaling noise, which, perhaps, shapes the signal transduction mechanism

Machines vs. ensembles: effective MAPK signaling through heterogeneous sets of protein complexes.

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Ryan Suderman

Full Text Available Despite the importance of intracellular signaling networks, there is currently no consensus regarding the fundamental nature of the protein complexes such networks employ. One prominent view involves stable signaling machines with well-defined quaternary structures. The combinatorial complexity of signaling networks has led to an opposing perspective, namely that signaling proceeds via heterogeneous pleiomorphic ensembles of transient complexes. Since many hypotheses regarding network function rely on how we conceptualize signaling complexes, resolving this issue is a central problem in systems biology. Unfortunately, direct experimental characterization of these complexes has proven technologically difficult, while combinatorial complexity has prevented traditional modeling methods from approaching this question. Here we employ rule-based modeling, a technique that overcomes these limitations, to construct a model of the yeast pheromone signaling network. We found that this model exhibits significant ensemble character while generating reliable responses that match experimental observations. To contrast the ensemble behavior, we constructed a model that employs hierarchical assembly pathways to produce scaffold-based signaling machines. We found that this machine model could not replicate the experimentally observed combinatorial inhibition that arises when the scaffold is overexpressed. This finding provides evidence against the hierarchical assembly of machines in the pheromone signaling network and suggests that machines and ensembles may serve distinct purposes in vivo. In some cases, e.g. core enzymatic activities like protein synthesis and degradation, machines assembled via hierarchical energy landscapes may provide functional stability for the cell. In other cases, such as signaling, ensembles may represent a form of weak linkage, facilitating variation and plasticity in network evolution. The capacity of ensembles to signal effectively

Two Chimeric Regulators of G-protein Signaling (RGS) Proteins Differentially Modulate Soybean Heterotrimeric G-protein Cycle*

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Roy Choudhury, Swarup; Westfall, Corey S.; Laborde, John P.; Bisht, Naveen C.; Jez, Joseph M.; Pandey, Sona

2012-01-01

Heterotrimeric G-proteins and the regulator of G-protein signaling (RGS) proteins, which accelerate the inherent GTPase activity of Gα proteins, are common in animals and encoded by large gene families; however, in plants G-protein signaling is thought to be more limited in scope. For example, Arabidopsis thaliana contains one Gα, one Gβ, three Gγ, and one RGS protein. Recent examination of the Glycine max (soybean) genome reveals a larger set of G-protein-related genes and raises the possibility of more intricate G-protein networks than previously observed in plants. Stopped-flow analysis of GTP-binding and GDP/GTP exchange for the four soybean Gα proteins (GmGα1–4) reveals differences in their kinetic properties. The soybean genome encodes two chimeric RGS proteins with an N-terminal seven transmembrane domain and a C-terminal RGS box. Both GmRGS interact with each of the four GmGα and regulate their GTPase activity. The GTPase-accelerating activities of GmRGS1 and -2 differ for each GmGα, suggesting more than one possible rate of the G-protein cycle initiated by each of the Gα proteins. The differential effects of GmRGS1 and GmRGS2 on GmGα1–4 result from a single valine versus alanine difference. The emerging picture suggests complex regulation of the G-protein cycle in soybean and in other plants with expanded G-protein networks. PMID:22474294

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

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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

Phosphotyrosine signaling proteins that drive oncogenesis tend to be highly interconnected.

Science.gov (United States)

Koytiger, Grigoriy; Kaushansky, Alexis; Gordus, Andrew; Rush, John; Sorger, Peter K; MacBeath, Gavin

2013-05-01

Mutation and overexpression of receptor tyrosine kinases or the proteins they regulate serve as oncogenic drivers in diverse cancers. To better understand receptor tyrosine kinase signaling and its link to oncogenesis, we used protein microarrays to systematically and quantitatively measure interactions between virtually every SH2 or PTB domain encoded in the human genome and all known sites of tyrosine phosphorylation on 40 receptor tyrosine kinases and on most of the SH2 and PTB domain-containing adaptor proteins. We found that adaptor proteins, like RTKs, have many high affinity bindings sites for other adaptor proteins. In addition, proteins that drive cancer, including both receptors and adaptor proteins, tend to be much more highly interconnected via networks of SH2 and PTB domain-mediated interactions than nononcogenic proteins. Our results suggest that network topological properties such as connectivity can be used to prioritize new drug targets in this well-studied family of signaling proteins.

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

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Aurélien Naldi

2017-03-01

Full Text Available 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.

Interrogating the architecture of protein assemblies and protein interaction networks by cross-linking mass spectrometry

NARCIS (Netherlands)

Liu, Fan; Heck, Albert J R

2015-01-01

Proteins are involved in almost all processes of the living cell. They are organized through extensive networks of interaction, by tightly bound macromolecular assemblies or more transiently via signaling nodes. Therefore, revealing the architecture of protein complexes and protein interaction

NSP-CAS Protein Complexes: Emerging Signaling Modules in Cancer.

Science.gov (United States)

Wallez, Yann; Mace, Peter D; Pasquale, Elena B; Riedl, Stefan J

2012-05-01

The CAS (CRK-associated substrate) family of adaptor proteins comprises 4 members, which share a conserved modular domain structure that enables multiple protein-protein interactions, leading to the assembly of intracellular signaling platforms. Besides their physiological role in signal transduction downstream of a variety of cell surface receptors, CAS proteins are also critical for oncogenic transformation and cancer cell malignancy through associations with a variety of regulatory proteins and downstream effectors. Among the regulatory partners, the 3 recently identified adaptor proteins constituting the NSP (novel SH2-containing protein) family avidly bind to the conserved carboxy-terminal focal adhesion-targeting (FAT) domain of CAS proteins. NSP proteins use an anomalous nucleotide exchange factor domain that lacks catalytic activity to form NSP-CAS signaling modules. Additionally, the NSP SH2 domain can link NSP-CAS signaling assemblies to tyrosine-phosphorylated cell surface receptors. NSP proteins can potentiate CAS function by affecting key CAS attributes such as expression levels, phosphorylation state, and subcellular localization, leading to effects on cell adhesion, migration, and invasion as well as cell growth. The consequences of these activities are well exemplified by the role that members of both families play in promoting breast cancer cell invasiveness and resistance to antiestrogens. In this review, we discuss the intriguing interplay between the NSP and CAS families, with a particular focus on cancer signaling networks.

Biophysical constraints on the computational capacity of biochemical signaling networks

Science.gov (United States)

Wang, Ching-Hao; Mehta, Pankaj

Biophysics fundamentally constrains the computations that cells can carry out. Here, we derive fundamental bounds on the computational capacity of biochemical signaling networks that utilize post-translational modifications (e.g. phosphorylation). To do so, we combine ideas from the statistical physics of disordered systems and the observation by Tony Pawson and others that the biochemistry underlying protein-protein interaction networks is combinatorial and modular. Our results indicate that the computational capacity of signaling networks is severely limited by the energetics of binding and the need to achieve specificity. We relate our results to one of the theoretical pillars of statistical learning theory, Cover's theorem, which places bounds on the computational capacity of perceptrons. PM and CHW were supported by a Simons Investigator in the Mathematical Modeling of Living Systems Grant, and NIH Grant No. 1R35GM119461 (both to PM).

Time-dependent, glucose-regulated Arabidopsis Regulator of G-protein Signaling 1 network

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Dinesh Kumar Jaiswal

2016-04-01

Full Text Available Plants lack 7-transmembrane, G-protein coupled receptors (GPCRs because the G alpha subunit of the heterotrimeric G protein complex is “self-activating”—meaning that it spontaneously exchanges bound GDP for GTP without the need of a GPCR. In lieu of GPCRs, most plants have a seven transmembrane receptor-like regulator of G-protein signaling (RGS protein, a component of the complex that keeps G-protein signaling in its non-activated state. The addition of glucose physically uncouples AtRGS1 from the complex through specific endocytosis leaving the activated G protein at the plasma membrane. The complement of proteins in the AtRGS1/G-protein complex over time from glucose-induced endocytosis was profiled by immunoprecipitation coupled to mass spectrometry (IP-MS. A total of 119 proteins in the AtRGS1 complex were identified. Several known interactors of the complex were identified, thus validating the approach, but the vast majority (93/119 were not known previously. AtRGS1 protein interactions were dynamically modulated by d-glucose. At low glucose levels, the AtRGS1 complex is comprised of proteins involved in transport, stress and metabolism. After glucose application, the AtRGS1 complex rapidly sheds many of these proteins and recruits other proteins involved in vesicular trafficking and signal transduction. The profile of the AtRGS1 components answers several questions about the type of coat protein and vesicular trafficking GTPases used in AtRGS1 endocytosis and the function of endocytic AtRGS1.

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

International Nuclear Information System (INIS)

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

2013-01-01

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. (paper)

Mechanosensitive molecular networks involved in transducing resistance exercise-signals into muscle protein accretion

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Emil Rindom

2016-11-01

Full Text Available 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 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 2TSC2-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 how it may implicate deformation of Notch to affect Bone Morphogenetic Protein (BMP signaling through a small mother of decapentaplegic (Smad axis.

Towards the systematic discovery of signal transduction networks using phosphorylation dynamics data

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Yachie Nozomu

2010-05-01

Full Text Available Abstract Background Phosphorylation is a ubiquitous and fundamental regulatory mechanism that controls signal transduction in living cells. The number of identified phosphoproteins and their phosphosites is rapidly increasing as a result of recent mass spectrometry-based approaches. Results We analyzed time-course phosphoproteome data obtained previously by liquid chromatography mass spectrometry with the stable isotope labeling using amino acids in cell culture (SILAC method. This provides the relative phosphorylation activities of digested peptides at each of five time points after stimulating HeLa cells with epidermal growth factor (EGF. We initially calculated the correlations between the phosphorylation dynamics patterns of every pair of peptides and connected the strongly correlated pairs to construct a network. We found that peptides extracted from the same intracellular fraction (nucleus vs. cytoplasm tended to be close together within this phosphorylation dynamics-based network. The network was then analyzed using graph theory and compared with five known signal-transduction pathways. The dynamics-based network was correlated with known signaling pathways in the NetPath and Phospho.ELM databases, and especially with the EGF receptor (EGFR signaling pathway. Although the phosphorylation patterns of many proteins were drastically changed by the EGF stimulation, our results suggest that only EGFR signaling transduction was both strongly activated and precisely controlled. Conclusions The construction of a phosphorylation dynamics-based network provides a useful overview of condition-specific intracellular signal transduction using quantitative time-course phosphoproteome data under specific experimental conditions. Detailed prediction of signal transduction based on phosphoproteome dynamics remains challenging. However, since the phosphorylation profiles of kinase-substrate pairs on the specific pathway were localized in the dynamics

DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

Energy Technology Data Exchange (ETDEWEB)

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

Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle

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Ram Prahlad T

2008-08-01

Full Text Available Abstract Background In systems biology the experimentalist is presented with a selection of software for analyzing dynamic properties of signaling networks. These tools either assume that the network is in steady-state or require highly parameterized models of the network of interest. For biologists interested in assessing how signal propagates through a network under specific conditions, the first class of methods does not provide sufficiently detailed results and the second class requires models which may not be easily and accurately constructed. A tool that is able to characterize the dynamics of a signaling network using an unparameterized model of the network would allow biologists to quickly obtain insights into a signaling network's behavior. Results We introduce PathwayOracle, an integrated suite of software tools for computationally inferring and analyzing structural and dynamic properties of a signaling network. The feature which differentiates PathwayOracle from other tools is a method that can predict the response of a signaling network to various experimental conditions and stimuli using only the connectivity of the signaling network. Thus signaling models are relatively easy to build. The method allows for tracking signal flow in a network and comparison of signal flows under different experimental conditions. In addition, PathwayOracle includes tools for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental analysis – loading and superimposing experimental data, such as microarray intensities, on the network model. Conclusion PathwayOracle provides an integrated environment in which both structural and dynamic analysis of a signaling network can be quickly conducted and visualized along side experimental results. By using the signaling network connectivity, analyses and predictions can be performed quickly using relatively easily constructed signaling network models

Identification of Top-ranked Proteins within a Directional Protein Interaction Network using the PageRank Algorithm: Applications in Humans and Plants.

Science.gov (United States)

Li, Xiu-Qing; Xing, Tim; Du, Donglei

2016-01-01

Somatic mutation of signal transduction genes or key nodes of the cellular protein network can cause severe diseases in humans but can sometimes genetically improve plants, likely because growth is determinate in animals but indeterminate in plants. This article reviews protein networks; human protein ranking; the mitogen-activated protein kinase (MAPK) and insulin (phospho- inositide 3kinase [PI3K]/phosphatase and tensin homolog [PTEN]/protein kinase B [AKT]) signaling pathways; human diseases caused by somatic mutations to the PI3K/PTEN/ AKT pathway; use of the MAPK pathway in plant molecular breeding; and protein domain evolution. Casitas B-lineage lymphoma (CBL), PTEN, MAPK1 and PIK3CA are among PIK3CA the top-ranked proteins in directional rankings. Eight proteins (ACVR1, CDC42, RAC1, RAF1, RHOA, TGFBR1, TRAF2, and TRAF6) are ranked in the top 50 key players in both signal emission and signal reception and in interaction with many other proteins. Top-ranked proteins likely have major impacts on the network function. Such proteins are targets for drug discovery, because their mutations are implicated in various cancers and overgrowth syndromes. Appropriately managing food intake may help reduce the growth of tumors or malformation of tissues. The role of the protein kinase C/ fatty acid synthase pathway in fat deposition in PTEN/PI3K patients should be investigated. Both the MAPK and insulin signaling pathways exist in plants, and MAPK pathway engineering can improve plant tolerance to biotic and abiotic stresses such as salinity.

A conserved mammalian protein interaction network.

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Åsa Pérez-Bercoff

Full Text Available Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.

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

Science.gov (United States)

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

Evolution of a G protein-coupled receptor response by mutations in regulatory network interactions

DEFF Research Database (Denmark)

Di Roberto, Raphaël B; Chang, Belinda; Trusina, Ala

2016-01-01

All cellular functions depend on the concerted action of multiple proteins organized in complex networks. To understand how selection acts on protein networks, we used the yeast mating receptor Ste2, a pheromone-activated G protein-coupled receptor, as a model system. In Saccharomyces cerevisiae......, Ste2 is a hub in a network of interactions controlling both signal transduction and signal suppression. Through laboratory evolution, we obtained 21 mutant receptors sensitive to the pheromone of a related yeast species and investigated the molecular mechanisms behind this newfound sensitivity. While...... demonstrate that a new receptor-ligand pair can evolve through network-altering mutations independently of receptor-ligand binding, and suggest a potential role for such mutations in disease....

Signal Processing and Neural Network Simulator

Science.gov (United States)

Tebbe, Dennis L.; Billhartz, Thomas J.; Doner, John R.; Kraft, Timothy T.

1995-04-01

The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAIC's Industrial Strength Neural Networks (ISNN) Software into SPW.

Quantitative Analysis of Signaling Networks across Differentially Embedded Tumors Highlights Interpatient Heterogeneity in Human Glioblastoma

Science.gov (United States)

2015-01-01

Glioblastoma multiforme (GBM) is the most aggressive malignant primary brain tumor, with a dismal mean survival even with the current standard of care. Although in vitro cell systems can provide mechanistic insight into the regulatory networks governing GBM cell proliferation and migration, clinical samples provide a more physiologically relevant view of oncogenic signaling networks. However, clinical samples are not widely available and may be embedded for histopathologic analysis. With the goal of accurately identifying activated signaling networks in GBM tumor samples, we investigated the impact of embedding in optimal cutting temperature (OCT) compound followed by flash freezing in LN2 vs immediate flash freezing (iFF) in LN2 on protein expression and phosphorylation-mediated signaling networks. Quantitative proteomic and phosphoproteomic analysis of 8 pairs of tumor specimens revealed minimal impact of the different sample processing strategies and highlighted the large interpatient heterogeneity present in these tumors. Correlation analyses of the differentially processed tumor sections identified activated signaling networks present in selected tumors and revealed the differential expression of transcription, translation, and degradation associated proteins. This study demonstrates the capability of quantitative mass spectrometry for identification of in vivo oncogenic signaling networks from human tumor specimens that were either OCT-embedded or immediately flash-frozen. PMID:24927040

Molecular signaling involving intrinsically disordered proteins in prostate cancer

Directory of Open Access Journals (Sweden)

Anna Russo

2016-01-01

Full Text Available 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.

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

Science.gov (United States)

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

2013-01-01

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. PMID:23758320

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

Science.gov (United States)

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.

Experimental and computational tools for analysis of signaling networks in primary cells

DEFF Research Database (Denmark)

Schoof, Erwin M; Linding, Rune

2014-01-01

Cellular information processing in signaling networks forms the basis of responses to environmental stimuli. At any given time, cells receive multiple simultaneous input cues, which are processed and integrated to determine cellular responses such as migration, proliferation, apoptosis, or differ......Cellular information processing in signaling networks forms the basis of responses to environmental stimuli. At any given time, cells receive multiple simultaneous input cues, which are processed and integrated to determine cellular responses such as migration, proliferation, apoptosis......; this information is critical when trying to elucidate key proteins involved in specific cellular responses. Here, methods to generate high-quality quantitative phosphorylation data from cell lysates originating from primary cells, and how to analyze the generated data to construct quantitative signaling network...

Construction and Deciphering of Human Phosphorylation-Mediated Signaling Transduction Networks.

Science.gov (United States)

Zhang, Menghuan; Li, Hong; He, Ying; Sun, Han; Xia, Li; Wang, Lishun; Sun, Bo; Ma, Liangxiao; Zhang, Guoqing; Li, Jing; Li, Yixue; Xie, Lu

2015-07-02

Protein phosphorylation is the most abundant reversible covalent modification. Human protein kinases participate in almost all biological pathways, and approximately half of the kinases are associated with disease. PhoSigNet was designed to store and display human phosphorylation-mediated signal transduction networks, with additional information related to cancer. It contains 11 976 experimentally validated directed edges and 216 871 phosphorylation sites. Moreover, 3491 differentially expressed proteins in human cancer from dbDEPC, 18 907 human cancer variation sites from CanProVar, and 388 hyperphosphorylation sites from PhosphoSitePlus were collected as annotation information. Compared with other phosphorylation-related databases, PhoSigNet not only takes the kinase-substrate regulatory relationship pairs into account, but also extends regulatory relationships up- and downstream (e.g., from ligand to receptor, from G protein to kinase, and from transcription factor to targets). Furthermore, PhoSigNet allows the user to investigate the impact of phosphorylation modifications on cancer. By using one set of in-house time series phosphoproteomics data, the reconstruction of a conditional and dynamic phosphorylation-mediated signaling network was exemplified. We expect PhoSigNet to be a useful database and analysis platform benefiting both proteomics and cancer studies.

Signal-regulated systems and networks

CSIR Research Space (South Africa)

Van Zyl, TL

2010-07-01

Full Text Available The article presents the use of signal regulatory networks (SRNs), a biologically inspired model based on gene regulatory networks. SRNs are a way of understanding a class of self-organizing IT systems, signal-regulated systems (SRSs). This article...

Spatial Organization in Protein Kinase A Signaling Emerged at the Base of Animal Evolution

NARCIS (Netherlands)

Peng, Mao; Aye, Thin Thin; Snel, Berend; Van Breukelen, Bas; Scholten, Arjen; Heck, Albert J R

2015-01-01

In phosphorylation-directed signaling, spatial and temporal control is organized by complex interaction networks that diligently direct kinases toward distinct substrates to fine-tune specificity. How these protein networks originate and evolve into complex regulatory machineries are among the most

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

International Nuclear Information System (INIS)

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

2015-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Ba, Qian [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Li, Junyang; Huang, Chao [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Li, Jingquan; Chu, Ruiai [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wu, Yongning, E-mail: wuyongning@cfsa.net.cn [Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wang, Hui, E-mail: huiwang@sibs.ac.cn [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); School of Life Science and Technology, ShanghaiTech University, Shanghai (China)

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.

Novel links in the plant TOR kinase signaling network.

Science.gov (United States)

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. Copyright © 2015 Elsevier Ltd. All rights reserved.

Functional modules by relating protein interaction networks and gene expression.

Science.gov (United States)

Tornow, Sabine; Mewes, H W

2003-11-01

Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships.

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

Science.gov (United States)

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. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

Science.gov (United States)

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.

Neuron-Like Networks Between Ribosomal Proteins Within the Ribosome

Science.gov (United States)

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.

Barcoding of GPCR trafficking and signaling through the various trafficking roadmaps by compartmentalized signaling networks.

Science.gov (United States)

Bahouth, Suleiman W; Nooh, Mohammed M

2017-08-01

Proper signaling by G protein coupled receptors (GPCR) is dependent on the specific repertoire of transducing, enzymatic and regulatory kinases and phosphatases that shape its signaling output. Activation and signaling of the GPCR through its cognate G protein is impacted by G protein-coupled receptor kinase (GRK)-imprinted "barcodes" that recruit β-arrestins to regulate subsequent desensitization, biased signaling and endocytosis of the GPCR. The outcome of agonist-internalized GPCR in endosomes is also regulated by sequence motifs or "barcodes" within the GPCR that mediate its recycling to the plasma membrane or retention and eventual degradation as well as its subsequent signaling in endosomes. Given the vast number of diverse sequences in GPCR, several trafficking mechanisms for endosomal GPCR have been described. The majority of recycling GPCR, are sorted out of endosomes in a "sequence-dependent pathway" anchored around a type-1 PDZ-binding module found in their C-tails. For a subset of these GPCR, a second "barcode" imprinted onto specific GPCR serine/threonine residues by compartmentalized kinase networks was required for their efficient recycling through the "sequence-dependent pathway". Mutating the serine/threonine residues involved, produced dramatic effects on GPCR trafficking, indicating that they played a major role in setting the trafficking itinerary of these GPCR. While endosomal SNX27, retromer/WASH complexes and actin were required for efficient sorting and budding of all these GPCR, additional proteins were required for GPCR sorting via the second "barcode". Here we will review recent developments in GPCR trafficking in general and the human β 1 -adrenergic receptor in particular across the various trafficking roadmaps. In addition, we will discuss the role of GPCR trafficking in regulating endosomal GPCR signaling, which promote biochemical and physiological effects that are distinct from those generated by the GPCR signal transduction

Detection of protein complex from protein-protein interaction network using Markov clustering

International Nuclear Information System (INIS)

Ochieng, P J; Kusuma, W A; Haryanto, T

2017-01-01

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks. (paper)

Perturbation Biology: Inferring Signaling Networks in Cellular Systems

Science.gov (United States)

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. PMID:24367245

CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms.

Science.gov (United States)

Terfve, Camille; Cokelaer, Thomas; Henriques, David; MacNamara, Aidan; Goncalves, Emanuel; Morris, Melody K; van Iersel, Martijn; Lauffenburger, Douglas A; Saez-Rodriguez, Julio

2012-10-18

Cells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce. Here we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities. Models generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.

The SH2 Domain–Containing Proteins in 21 Species Establish the Provenance and Scope of Phosphotyrosine Signaling in Eukaryotes

Science.gov (United States)

Liu, Bernard A.; Shah, Eshana; Jablonowski, Karl; Stergachis, Andrew; Engelmann, Brett; Nash, Piers D.

2014-01-01

The Src homology 2 (SH2) domains are participants in metazoan signal transduction, acting as primary mediators for regulated protein-protein interactions with tyrosine-phosphorylated substrates. Here, we describe the origin and evolution of SH2 domain proteins by means of sequence analysis from 21 eukaryotic organisms from the basal unicellular eukaryotes, where SH2 domains first appeared, through the multicellular animals and increasingly complex metazoans. On the basis of our results, SH2 domains and phosphotyrosine signaling emerged in the early Unikonta, and the numbers of SH2 domains expanded in the choanoflagellate and metazoan lineages with the development of tyrosine kinases, leading to rapid elaboration of phosphotyrosine signaling in early multicellular animals. Our results also indicated that SH2 domains coevolved and the number of the domains expanded alongside protein tyrosine kinases and tyrosine phosphatases, thereby coupling phosphotyrosine signaling to downstream signaling networks. Gene duplication combined with domain gain or loss produced novel SH2-containing proteins that function within phosphotyrosine signaling, which likely have contributed to diversity and complexity in metazoans. We found that intra- and intermolecular interactions within and between SH2 domain proteins increased in prevalence along with organismal complexity and may function to generate more highly connected and robust phosphotyrosine signaling networks. PMID:22155787

Protein complex prediction in large ontology attributed protein-protein interaction networks.

Science.gov (United States)

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

2013-01-01

Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

Identification and quantitation of signal molecule-dependent protein phosphorylation

KAUST Repository

Groen, Arnoud J.

2013-09-03

Phosphoproteomics is a fast-growing field that aims at characterizing phosphorylated proteins in a cell or a tissue at a given time. Phosphorylation of proteins is an important regulatory mechanism in many cellular processes. Gel-free phosphoproteome technique involving enrichment of phosphopeptide coupled with mass spectrometry has proven to be invaluable to detect and characterize phosphorylated proteins. In this chapter, a gel-free quantitative approach involving 15N metabolic labelling in combination with phosphopeptide enrichment by titanium dioxide (TiO2) and their identification by MS is described. This workflow can be used to gain insights into the role of signalling molecules such as cyclic nucleotides on regulatory networks through the identification and quantification of responsive phospho(proteins). © Springer Science+Business Media New York 2013.

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

Science.gov (United States)

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

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

Science.gov (United States)

Hoke, Glenn D; Ramos, Corrine; Hoke, Nicholas N; Crossland, Mary C; Shawler, Lisa G; Boykin, Joseph V

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.

Cytoprophet: a Cytoscape plug-in for protein and domain interaction networks inference.

Science.gov (United States)

Morcos, Faruck; Lamanna, Charles; Sikora, Marcin; Izaguirre, Jesús

2008-10-01

Cytoprophet is a software tool that allows prediction and visualization of protein and domain interaction networks. It is implemented as a plug-in of Cytoscape, an open source software framework for analysis and visualization of molecular networks. Cytoprophet implements three algorithms that predict new potential physical interactions using the domain composition of proteins and experimental assays. The algorithms for protein and domain interaction inference include maximum likelihood estimation (MLE) using expectation maximization (EM); the set cover approach maximum specificity set cover (MSSC) and the sum-product algorithm (SPA). After accepting an input set of proteins with Uniprot ID/Accession numbers and a selected prediction algorithm, Cytoprophet draws a network of potential interactions with probability scores and GO distances as edge attributes. A network of domain interactions between the domains of the initial protein list can also be generated. Cytoprophet was designed to take advantage of the visual capabilities of Cytoscape and be simple to use. An example of inference in a signaling network of myxobacterium Myxococcus xanthus is presented and available at Cytoprophet's website. http://cytoprophet.cse.nd.edu.

Spectral affinity in protein networks.

Science.gov (United States)

Voevodski, Konstantin; Teng, Shang-Hua; Xia, Yu

2009-11-29

Protein-protein interaction (PPI) networks enable us to better understand the functional organization of the proteome. We can learn a lot about a particular protein by querying its neighborhood in a PPI network to find proteins with similar function. A spectral approach that considers random walks between nodes of interest is particularly useful in evaluating closeness in PPI networks. Spectral measures of closeness are more robust to noise in the data and are more precise than simpler methods based on edge density and shortest path length. We develop a novel affinity measure for pairs of proteins in PPI networks, which uses personalized PageRank, a random walk based method used in context-sensitive search on the Web. Our measure of closeness, which we call PageRank Affinity, is proportional to the number of times the smaller-degree protein is visited in a random walk that restarts at the larger-degree protein. PageRank considers paths of all lengths in a network, therefore PageRank Affinity is a precise measure that is robust to noise in the data. PageRank Affinity is also provably related to cluster co-membership, making it a meaningful measure. In our experiments on protein networks we find that our measure is better at predicting co-complex membership and finding functionally related proteins than other commonly used measures of closeness. Moreover, our experiments indicate that PageRank Affinity is very resilient to noise in the network. In addition, based on our method we build a tool that quickly finds nodes closest to a queried protein in any protein network, and easily scales to much larger biological networks. We define a meaningful way to assess the closeness of two proteins in a PPI network, and show that our closeness measure is more biologically significant than other commonly used methods. We also develop a tool, accessible at http://xialab.bu.edu/resources/pnns, that allows the user to quickly find nodes closest to a queried vertex in any protein

Design principles of nuclear receptor signaling: how complex networking improves signal transduction

Science.gov (United States)

Kolodkin, Alexey N; Bruggeman, Frank J; Plant, Nick; Moné, Martijn J; Bakker, Barbara M; Campbell, Moray J; van Leeuwen, Johannes P T M; Carlberg, Carsten; Snoep, Jacky L; Westerhoff, Hans V

2010-01-01

The topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of ‘design' aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic models of increasing complexity, calculations show how these features correspond to potentially important design principles, e.g.: (i) cytosolic ‘nuclear' receptor may shuttle signal molecules to the nucleus, (ii) the active export of NRs may ensure that there is sufficient receptor protein to capture ligand at the cytoplasmic membrane, (iii) a three conveyor belts design dissipating GTP-free energy, greatly aids response, (iv) the active export of importins may prevent sequestration of NRs by importins in the nucleus and (v) the unspecific nature of the nuclear pore may ensure signal-flux robustness. In addition, the models developed are suitable for implementation in specific cases of NR-mediated signaling, to predict individual receptor functions and differential sensitivity toward physiological and pharmacological ligands. PMID:21179018

Systematic differences in signal emitting and receiving revealed by PageRank analysis of a human protein interactome.

Directory of Open Access Journals (Sweden)

Donglei Du

Full Text Available Most protein PageRank studies do not use signal flow direction information in protein interactions because this information was not readily available in large protein databases until recently. Therefore, four questions have yet to be answered: A What is the general difference between signal emitting and receiving in a protein interactome? B Which proteins are among the top ranked in directional ranking? C Are high ranked proteins more evolutionarily conserved than low ranked ones? D Do proteins with similar ranking tend to have similar subcellular locations? In this study, we address these questions using the forward, reverse, and non-directional PageRank approaches to rank an information-directional network of human proteins and study their evolutionary conservation. The forward ranking gives credit to information receivers, reverse ranking to information emitters, and non-directional ranking mainly to the number of interactions. The protein lists generated by the forward and non-directional rankings are highly correlated, but those by the reverse and non-directional rankings are not. The results suggest that the signal emitting/receiving system is characterized by key-emittings and relatively even receivings in the human protein interactome. Signaling pathway proteins are frequent in top ranked ones. Eight proteins are both informational top emitters and top receivers. Top ranked proteins, except a few species-related novel-function ones, are evolutionarily well conserved. Protein-subunit ranking position reflects subunit function. These results demonstrate the usefulness of different PageRank approaches in characterizing protein networks and provide insights to protein interaction in the cell.

Systematic differences in signal emitting and receiving revealed by PageRank analysis of a human protein interactome.

Science.gov (United States)

Du, Donglei; Lee, Connie F; Li, Xiu-Qing

2012-01-01

Most protein PageRank studies do not use signal flow direction information in protein interactions because this information was not readily available in large protein databases until recently. Therefore, four questions have yet to be answered: A) What is the general difference between signal emitting and receiving in a protein interactome? B) Which proteins are among the top ranked in directional ranking? C) Are high ranked proteins more evolutionarily conserved than low ranked ones? D) Do proteins with similar ranking tend to have similar subcellular locations? In this study, we address these questions using the forward, reverse, and non-directional PageRank approaches to rank an information-directional network of human proteins and study their evolutionary conservation. The forward ranking gives credit to information receivers, reverse ranking to information emitters, and non-directional ranking mainly to the number of interactions. The protein lists generated by the forward and non-directional rankings are highly correlated, but those by the reverse and non-directional rankings are not. The results suggest that the signal emitting/receiving system is characterized by key-emittings and relatively even receivings in the human protein interactome. Signaling pathway proteins are frequent in top ranked ones. Eight proteins are both informational top emitters and top receivers. Top ranked proteins, except a few species-related novel-function ones, are evolutionarily well conserved. Protein-subunit ranking position reflects subunit function. These results demonstrate the usefulness of different PageRank approaches in characterizing protein networks and provide insights to protein interaction in the cell.

Organization of signal flow in directed networks

International Nuclear Information System (INIS)

Bányai, M; Bazsó, F; Négyessy, L

2011-01-01

Confining an answer to the question of whether and how the coherent operation of network elements is determined by the network structure is the topic of our work. We map the structure of signal flow in directed networks by analysing the degree of edge convergence and the overlap between the in- and output sets of an edge. Definitions of convergence degree and overlap are based on the shortest paths, thus they encapsulate global network properties. Using the defining notions of convergence degree and overlapping set we clarify the meaning of network causality and demonstrate the crucial role of chordless circles. In real-world networks the flow representation distinguishes nodes according to their signal transmitting, processing and control properties. The analysis of real-world networks in terms of flow representation was in accordance with the known functional properties of the network nodes. It is shown that nodes with different signal processing, transmitting and control properties are randomly connected at the global scale, while local connectivity patterns depart from randomness. The grouping of network nodes according to their signal flow properties was unrelated to the network's community structure. We present evidence that the signal flow properties of small-world-like, real-world networks cannot be reconstructed by algorithms used to generate small-world networks. Convergence degree values were calculated for regular oriented trees, and the probability density function for networks grown with the preferential attachment mechanism. For Erdos–Rényi graphs we calculated the probability density function of both convergence degrees and overlaps

Transduction motif analysis of gastric cancer based on a human signaling network

Energy Technology Data Exchange (ETDEWEB)

Liu, G.; Li, D.Z.; Jiang, C.S.; Wang, W. [Fuzhou General Hospital of Nanjing Command, Department of Gastroenterology, Fuzhou, China, Department of Gastroenterology, Fuzhou General Hospital of Nanjing Command, Fuzhou (China)

2014-04-04

To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR, MAPK14, BCL2L1, KRT18, PTPN6, CASP3, TGFBR2, AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

Mathematical Modelling Plant Signalling Networks

KAUST Repository

Muraro, D.

2013-01-01

During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.

Protein sensing by nanofluidic crystal and its signal enhancement

Science.gov (United States)

Sang, Jianming; Du, Hongtan; Wang, Wei; Chu, Ming; Wang, Yuedan; Li, Haichao; Alice Zhang, Haixia; Wu, Wengang; Li, Zhihong

2013-01-01

Nanofluidics has a unique property that ionic conductance across a nanometer-sized confined space is strongly affected by the space surface charge density, which can be utilized to construct electrical read-out biosensor. Based on this principle, this work demonstrated a novel protein sensor along with a sandwich signal enhancement approach. Nanoparticles with designed aptamer onside are assembled in a suspended micropore to form a 3-dimensional network of nanometer-sized interstices, named as nanofluidic crystal hereafter, as the basic sensing unit. Proteins captured by aptamers will change the surface charge density of nanoparticles and thereby can be detected by monitoring the ionic conductance across this nanofluidic crystal. Another aptamer can further enlarge the variations of the surface charge density by forming a sandwich structure (capturing aptamer/protein/signal enhancement aptamer) and the read-out conductance as well. The preliminary experimental results indicated that human α-thrombin was successfully detected by the corresponding aptamer modified nanofluidic crystal with the limit of detection of 5 nM (0.18 μg/ml) and the read-out signal was enhanced up to 3 folds by using another thrombin aptamer. Being easy to graft probe, facile and low-cost to prepare the nano-device, and having an electrical read-out, the present nanofluidic crystal scheme is a promising and universal strategy for protein sensing. PMID:24404017

Spectral affinity in protein networks

Directory of Open Access Journals (Sweden)

Teng Shang-Hua

2009-11-01

Full Text Available Abstract Background Protein-protein interaction (PPI networks enable us to better understand the functional organization of the proteome. We can learn a lot about a particular protein by querying its neighborhood in a PPI network to find proteins with similar function. A spectral approach that considers random walks between nodes of interest is particularly useful in evaluating closeness in PPI networks. Spectral measures of closeness are more robust to noise in the data and are more precise than simpler methods based on edge density and shortest path length. Results We develop a novel affinity measure for pairs of proteins in PPI networks, which uses personalized PageRank, a random walk based method used in context-sensitive search on the Web. Our measure of closeness, which we call PageRank Affinity, is proportional to the number of times the smaller-degree protein is visited in a random walk that restarts at the larger-degree protein. PageRank considers paths of all lengths in a network, therefore PageRank Affinity is a precise measure that is robust to noise in the data. PageRank Affinity is also provably related to cluster co-membership, making it a meaningful measure. In our experiments on protein networks we find that our measure is better at predicting co-complex membership and finding functionally related proteins than other commonly used measures of closeness. Moreover, our experiments indicate that PageRank Affinity is very resilient to noise in the network. In addition, based on our method we build a tool that quickly finds nodes closest to a queried protein in any protein network, and easily scales to much larger biological networks. Conclusion We define a meaningful way to assess the closeness of two proteins in a PPI network, and show that our closeness measure is more biologically significant than other commonly used methods. We also develop a tool, accessible at http://xialab.bu.edu/resources/pnns, that allows the user to

The Role of Unfolded Protein Response and Mitogen-Activated Protein Kinase Signaling in Neurodegenerative Diseases with Special Focus on Prion Diseases

Directory of Open Access Journals (Sweden)

Lifeng Yang

2017-05-01

Full Text Available Prion diseases are neurodegenerative pathologies characterized by the accumulation of a protease-resistant form of the cellular prion protein named prion protein scrapie (PrPSc in the brain. PrPSc accumulation in the endoplasmic reticulum (ER result in a dysregulated calcium (Ca2+ homeostasis and subsequent initiation of unfolded protein response (UPR leading to neuronal dysfunction and apoptosis. The molecular mechanisms for the transition between adaptation to ER stress and ER stress-induced apoptosis are still unclear. Mitogen-activated protein kinases (MAPKs are serine/threonine protein kinases that rule the signaling of many extracellular stimuli from plasma membrane to the nucleus. However the identification of numerous points of cross talk between the UPR and MAPK signaling pathways may contribute to our understanding of the consequences of ER stress in prion diseases. Indeed the MAPK signaling network is known to regulate cell cycle progression and cell survival or death responses following a variety of stresses including misfolded protein response stress. In this article, we review the UPR signaling in prion diseases and discuss the triad of MAPK signaling pathways. We also describe the role played by MAPK signaling cascades in Alzheimer’s (AD and Parkinson’s disease (PD. We will also overview the mechanisms of cell death and the role of MAPK signaling in prion disease progression and highlight potential avenues for therapeutic intervention.

A Global Protein Kinase and Phosphatase Interaction Network in Yeast

Science.gov (United States)

Breitkreutz, Ashton; Choi, Hyungwon; Sharom, Jeffrey R.; Boucher, Lorrie; Neduva, Victor; Larsen, Brett; Lin, Zhen-Yuan; Breitkreutz, Bobby-Joe; Stark, Chris; Liu, Guomin; Ahn, Jessica; Dewar-Darch, Danielle; Reguly, Teresa; Tang, Xiaojing; Almeida, Ricardo; Qin, Zhaohui Steve; Pawson, Tony; Gingras, Anne-Claude; Nesvizhskii, Alexey I.; Tyers, Mike

2011-01-01

The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses. PMID:20489023

Expression Patterns and Identified Protein-Protein Interactions Suggest That Cassava CBL-CIPK Signal Networks Function in Responses to Abiotic Stresses.

Science.gov (United States)

Mo, Chunyan; Wan, Shumin; Xia, Youquan; Ren, Ning; Zhou, Yang; Jiang, Xingyu

2018-01-01

Cassava is an energy crop that is tolerant of multiple abiotic stresses. It has been reported that the interaction between Calcineurin B-like (CBL) protein and CBL-interacting protein kinase (CIPK) is implicated in plant development and responses to various stresses. However, little is known about their functions in cassava. Herein, 8 CBL ( MeCBL ) and 26 CIPK ( MeCIPK ) genes were isolated from cassava by genome searching and cloning of cDNA sequences of Arabidopsis CBL s and CIPK s. Reverse-transcriptase polymerase chain reaction (RT-PCR) analysis showed that the expression levels of MeCBL and MeCIPK genes were different in different tissues throughout the life cycle. The expression patterns of 7 CBL and 26 CIPK genes in response to NaCl, PEG, heat and cold stresses were analyzed by quantitative real-time PCR (qRT-PCR), and it was found that the expression of each was induced by multiple stimuli. Furthermore, we found that many pairs of CBLs and CIPKs could interact with each other via investigating the interactions between 8 CBL and 25 CIPK proteins using a yeast two-hybrid system. Yeast cells co-transformed with cassava MeCIPK24, MeCBL10 , and Na + /H + antiporter MeSOS1 genes exhibited higher salt tolerance compared to those with one or two genes. These results suggest that the cassava CBL-CIPK signal network might play key roles in response to abiotic stresses.

Expression Patterns and Identified Protein-Protein Interactions Suggest That Cassava CBL-CIPK Signal Networks Function in Responses to Abiotic Stresses

Directory of Open Access Journals (Sweden)

Chunyan Mo

2018-03-01

Full Text Available Cassava is an energy crop that is tolerant of multiple abiotic stresses. It has been reported that the interaction between Calcineurin B-like (CBL protein and CBL-interacting protein kinase (CIPK is implicated in plant development and responses to various stresses. However, little is known about their functions in cassava. Herein, 8 CBL (MeCBL and 26 CIPK (MeCIPK genes were isolated from cassava by genome searching and cloning of cDNA sequences of Arabidopsis CBLs and CIPKs. Reverse-transcriptase polymerase chain reaction (RT-PCR analysis showed that the expression levels of MeCBL and MeCIPK genes were different in different tissues throughout the life cycle. The expression patterns of 7 CBL and 26 CIPK genes in response to NaCl, PEG, heat and cold stresses were analyzed by quantitative real-time PCR (qRT-PCR, and it was found that the expression of each was induced by multiple stimuli. Furthermore, we found that many pairs of CBLs and CIPKs could interact with each other via investigating the interactions between 8 CBL and 25 CIPK proteins using a yeast two-hybrid system. Yeast cells co-transformed with cassava MeCIPK24, MeCBL10, and Na+/H+ antiporter MeSOS1 genes exhibited higher salt tolerance compared to those with one or two genes. These results suggest that the cassava CBL-CIPK signal network might play key roles in response to abiotic stresses.

Discovery of nitrate-CPK-NLP signalling in central nutrient-growth networks

Science.gov (United States)

Liu, Kun-hsiang; Niu, Yajie; Konishi, Mineko; Wu, Yue; Du, Hao; Sun Chung, Hoo; Li, Lei; Boudsocq, Marie; McCormack, Matthew; Maekawa, Shugo; Ishida, Tetsuya; Zhang, Chao; Shokat, Kevan; Yanagisawa, Shuichi; Sheen, Jen

2018-01-01

Nutrient signalling integrates and coordinates gene expression, metabolism and growth. However, its primary molecular mechanisms remain incompletely understood in plants and animals. Here we report novel Ca2+ signalling triggered by nitrate with live imaging of an ultrasensitive biosensor in Arabidopsis leaves and roots. A nitrate-sensitized and targeted functional genomic screen identifies subgroup III Ca2+-sensor protein kinases (CPKs) as master regulators orchestrating primary nitrate responses. A chemical switch with the engineered CPK10(M141G) kinase enables conditional analyses of cpk10,30,32 to define comprehensive nitrate-associated regulatory and developmental programs, circumventing embryo lethality. Nitrate-CPK signalling phosphorylates conserved NIN-LIKE PROTEIN (NLP) transcription factors (TFs) to specify reprogramming of gene sets for downstream TFs, transporters, N-assimilation, C/N-metabolism, redox, signalling, hormones, and proliferation. Conditional cpk10,30,32 and nlp7 similarly impair nitrate-stimulated system-wide shoot growth and root establishment. The nutrient-coupled Ca2+ signalling network integrates transcriptome and cellular metabolism with shoot-root coordination and developmental plasticity in shaping organ biomass and architecture. PMID:28489820

Intelligent sensor networks the integration of sensor networks, signal processing and machine learning

CERN Document Server

Hu, Fei

2012-01-01

Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, incl

The role of exon shuffling in shaping protein-protein interaction networks

Directory of Open Access Journals (Sweden)

França Gustavo S

2010-12-01

Full Text Available Abstract Background Physical protein-protein interaction (PPI is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks. Results We tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Cryptococcus neoformans and Arabidopsis thaliana. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains, self-interacting (able to interact with another copy of themselves and abundant in the genomes presents a stronger signal for exon shuffling. Conclusions Exon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion.

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

Science.gov (United States)

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

2013-01-01

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. PMID:23530221

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

Science.gov (United States)

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.

Unified Alignment of Protein-Protein Interaction Networks.

Science.gov (United States)

Malod-Dognin, Noël; Ban, Kristina; Pržulj, Nataša

2017-04-19

Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.

SH2 Domains Serve as Lipid-Binding Modules for pTyr-Signaling Proteins.

Science.gov (United States)

Park, Mi-Jeong; Sheng, Ren; Silkov, Antonina; Jung, Da-Jung; Wang, Zhi-Gang; Xin, Yao; Kim, Hyunjin; Thiagarajan-Rosenkranz, Pallavi; Song, Seohyeon; Yoon, Youngdae; Nam, Wonhee; Kim, Ilshin; Kim, Eui; Lee, Dong-Gyu; Chen, Yong; Singaram, Indira; Wang, Li; Jang, Myoung Ho; Hwang, Cheol-Sang; Honig, Barry; Ryu, Sungho; Lorieau, Justin; Kim, You-Me; Cho, Wonhwa

2016-04-07

The Src-homology 2 (SH2) domain is a protein interaction domain that directs myriad phosphotyrosine (pY)-signaling pathways. Genome-wide screening of human SH2 domains reveals that ∼90% of SH2 domains bind plasma membrane lipids and many have high phosphoinositide specificity. They bind lipids using surface cationic patches separate from pY-binding pockets, thus binding lipids and the pY motif independently. The patches form grooves for specific lipid headgroup recognition or flat surfaces for non-specific membrane binding and both types of interaction are important for cellular function and regulation of SH2 domain-containing proteins. Cellular studies with ZAP70 showed that multiple lipids bind its C-terminal SH2 domain in a spatiotemporally specific manner and thereby exert exquisite spatiotemporal control over its protein binding and signaling activities in T cells. Collectively, this study reveals how lipids control SH2 domain-mediated cellular protein-protein interaction networks and suggest a new strategy for therapeutic modulation of pY-signaling pathways. Copyright © 2016 Elsevier Inc. All rights reserved.

Social multimedia signals a signal processing approach to social network phenomena

CERN Document Server

Roy, Suman Deb

2014-01-01

This book provides a comprehensive coverage of the state-of-the-art in understanding media popularity and trends in online social networks through social multimedia signals. With insights from the study of popularity and sharing patterns of online media, trend spread in social media, social network analysis for multimedia and visualizing diffusion of media in online social networks. In particular, the book will address the following important issues: Understanding social network phenomena from a signal processing point of view; The existence and popularity of multimedia as shared and social me

Discrete dynamic modeling of cellular signaling networks.

Science.gov (United States)

Albert, Réka; Wang, Rui-Sheng

2009-01-01

Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

Global Optimization for Transport Network Expansion and Signal Setting

OpenAIRE

Liu, Haoxiang; Wang, David Z. W.; Yue, Hao

2015-01-01

This paper proposes a model to address an urban transport planning problem involving combined network design and signal setting in a saturated network. Conventional transport planning models usually deal with the network design problem and signal setting problem separately. However, the fact that network capacity design and capacity allocation determined by network signal setting combine to govern the transport network performance requires the optimal transport planning to consider the two pr...

A scored human protein-protein interaction network to catalyze genomic interpretation

DEFF Research Database (Denmark)

Li, Taibo; Wernersson, Rasmus; Hansen, Rasmus B

2017-01-01

Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap,......Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (In...

Neural networks in signal processing

International Nuclear Information System (INIS)

Govil, R.

2000-01-01

Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)

Protein conservation and variation suggest mechanisms of cell type-specific modulation of signaling pathways.

Directory of Open Access Journals (Sweden)

Martin H Schaefer

2014-06-01

Full Text Available Many proteins and signaling pathways are present in most cell types and tissues and yet perform specialized functions. To elucidate mechanisms by which these ubiquitous pathways are modulated, we overlaid information about cross-cell line protein abundance and variability, and evolutionary conservation onto functional pathway components and topological layers in the pathway hierarchy. We found that the input (receptors and the output (transcription factors layers evolve more rapidly than proteins in the intermediary transmission layer. In contrast, protein expression variability decreases from the input to the output layer. We observed that the differences in protein variability between the input and transmission layer can be attributed to both the network position and the tendency of variable proteins to physically interact with constitutively expressed proteins. Differences in protein expression variability and conservation are also accompanied by the tendency of conserved and constitutively expressed proteins to acquire somatic mutations, while germline mutations tend to occur in cell type-specific proteins. Thus, conserved core proteins in the transmission layer could perform a fundamental role in most cell types and are therefore less tolerant to germline mutations. In summary, we propose that the core signal transmission machinery is largely modulated by a variable input layer through physical protein interactions. We hypothesize that the bow-tie organization of cellular signaling on the level of protein abundance variability contributes to the specificity of the signal response in different cell types.

Human cancer protein-protein interaction network: a structural perspective.

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Gozde Kar

2009-12-01

Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network. The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%. We illustrate the interface related affinity properties of two cancer-related hub

Signaling in large-scale neural networks

DEFF Research Database (Denmark)

Berg, Rune W; Hounsgaard, Jørn

2009-01-01

We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this m......We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages...... of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons....

Information processing in network architecture of genome controlled signal transduction circuit. A proposed theoretical explanation.

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Chakraborty, Chiranjib; Sarkar, Bimal Kumar; Patel, Pratiksha; Agoramoorthy, Govindasamy

2012-01-01

In this paper, Shannon information theory has been applied to elaborate cell signaling. It is proposed that in the cellular network architecture, four components viz. source (DNA), transmitter (mRNA), receiver (protein) and destination (another protein) are involved. The message transmits from source (DNA) to transmitter (mRNA) and then passes through a noisy channel reaching finally the receiver (protein). The protein synthesis process is here considered as the noisy channel. Ultimately, signal is transmitted from receiver to destination (another protein). The genome network architecture elements were compared with genetic alphabet L = {A, C, G, T} with a biophysical model based on the popular Shannon information theory. This study found the channel capacity as maximum for zero error (sigma = 0) and at this condition, transition matrix becomes a unit matrix with rank 4. The transition matrix will be erroneous and finally at sigma = 1 channel capacity will be localized maxima with a value of 0.415 due to the increased value at sigma. On the other hand, minima exists at sigma = 0.75, where all transition probabilities become 0.25 and uncertainty will be maximum resulting in channel capacity with the minima value of zero.

Neurodegeneration in Alzheimer Disease: Role of Amyloid Precursor Protein and Presenilin 1 Intracellular Signaling

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Mario Nizzari

2012-01-01

Full Text Available Alzheimer disease (AD is a heterogeneous neurodegenerative disorder characterized by (1 progressive loss of synapses and neurons, (2 intracellular neurofibrillary tangles, composed of hyperphosphorylated Tau protein, and (3 amyloid plaques. Genetically, AD is linked to mutations in few proteins amyloid precursor protein (APP and presenilin 1 and 2 (PS1 and PS2. The molecular mechanisms underlying neurodegeneration in AD as well as the physiological function of APP are not yet known. A recent theory has proposed that APP and PS1 modulate intracellular signals to induce cell-cycle abnormalities responsible for neuronal death and possibly amyloid deposition. This hypothesis is supported by the presence of a complex network of proteins, clearly involved in the regulation of signal transduction mechanisms that interact with both APP and PS1. In this review we discuss the significance of novel finding related to cell-signaling events modulated by APP and PS1 in the development of neurodegeneration.

Control of striatal signaling by G protein regulators

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Keqiang eXie

2011-08-01

Full Text Available Signaling via heterotrimeric G proteins plays a crucial role in modulating the responses of striatal neurons that ultimately shape core behaviors mediated by the basal ganglia circuitry, such as reward valuation, habit formation and movement coordination. Activation of G-protein-coupled receptors (GPCRs by extracellular signals activates heterotrimeric G proteins by promoting the binding of GTP to their α subunits. G proteins exert their effects by influencing the activity of key effector proteins in this region, including ion channels, second messenger enzymes and protein kinases. Striatal neurons express a staggering number of GPCRs whose activation results in the engagement of downstream signaling pathways and cellular responses with unique profiles but common molecular mechanisms. Studies over the last decade have revealed that the extent and duration of GPCR signaling are controlled by a conserved protein family named Regulator of G protein Signaling (RGS. RGS proteins accelerate GTP hydrolysis by the α subunits of G proteins, thus promoting deactivation of GPCR signaling. In this review, we discuss the progress made in understanding the roles of RGS proteins in controlling striatal G protein signaling and providing integration and selectivity of signal transmission. We review evidence on the formation of a macromolecular complex between RGS proteins and other components of striatal signaling pathways, their molecular regulatory mechanisms and impacts on GPCR signaling in the striatum obtained from biochemical studies and experiments involving genetic mouse models. Special emphasis is placed on RGS9-2, a member of the RGS family that is highly enriched in the striatum and plays critical roles in drug addiction and motor control.

The protist, Monosiga brevicollis, has a tyrosine kinase signaling network more elaborate and diverse than found in any known metazoan.

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Manning, Gerard; Young, Susan L; Miller, W Todd; Zhai, Yufeng

2008-07-15

Tyrosine kinase signaling has long been considered a hallmark of intercellular communication, unique to multicellular animals. Our genomic analysis of the unicellular choanoflagellate Monosiga brevicollis discovers a remarkable count of 128 tyrosine kinases, 38 tyrosine phosphatases, and 123 phosphotyrosine (pTyr)-binding SH2 proteins, all higher counts than seen in any metazoan. This elaborate signaling network shows little orthology to metazoan counterparts yet displays many innovations reminiscent of metazoans. These include extracellular domains structurally related to those of metazoan receptor kinases, alternative methods for membrane anchoring and phosphotyrosine interaction in cytoplasmic kinases, and domain combinations that link kinases to small GTPase signaling and transcription. These proteins also display a wealth of combinations of known signaling domains. This uniquely divergent and elaborate signaling network illuminates the early evolution of pTyr signaling, explores innovative ways to traverse the cellular signaling circuitry, and shows extensive convergent evolution, highlighting pervasive constraints on pTyr signaling.

NEpiC: a network-assisted algorithm for epigenetic studies using mean and variance combined signals.

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Ruan, Peifeng; Shen, Jing; Santella, Regina M; Zhou, Shuigeng; Wang, Shuang

2016-09-19

DNA methylation plays an important role in many biological processes. Existing epigenome-wide association studies (EWAS) have successfully identified aberrantly methylated genes in many diseases and disorders with most studies focusing on analysing methylation sites one at a time. Incorporating prior biological information such as biological networks has been proven to be powerful in identifying disease-associated genes in both gene expression studies and genome-wide association studies (GWAS) but has been under studied in EWAS. Although recent studies have noticed that there are differences in methylation variation in different groups, only a few existing methods consider variance signals in DNA methylation studies. Here, we present a network-assisted algorithm, NEpiC, that combines both mean and variance signals in searching for differentially methylated sub-networks using the protein-protein interaction (PPI) network. In simulation studies, we demonstrate the power gain from using both the prior biological information and variance signals compared to using either of the two or neither information. Applications to several DNA methylation datasets from the Cancer Genome Atlas (TCGA) project and DNA methylation data on hepatocellular carcinoma (HCC) from the Columbia University Medical Center (CUMC) suggest that the proposed NEpiC algorithm identifies more cancer-related genes and generates better replication results. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

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Xiaomin Wang

2011-01-01

Full Text Available With the availability of more and more genome-scale protein-protein interaction (PPI networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods.

Reconstruction of periodic signals using neural networks

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José Danilo Rairán Antolines

2014-01-01

Full Text Available In this paper, we reconstruct a periodic signal by using two neural networks. The first network is trained to approximate the period of a signal, and the second network estimates the corresponding coefficients of the signal's Fourier expansion. The reconstruction strategy consists in minimizing the mean-square error via backpro-pagation algorithms over a single neuron with a sine transfer function. Additionally, this paper presents mathematical proof about the quality of the approximation as well as a first modification of the algorithm, which requires less data to reach the same estimation; thus making the algorithm suitable for real-time implementations.

A fuzzy neural network for sensor signal estimation

International Nuclear Information System (INIS)

Na, Man Gyun

2000-01-01

In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique. Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors

Coordination of the recruitment of the FANCD2 and PALB2 Fanconi anemia proteins by an ubiquitin signaling network.

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Bick, Gregory; Zhang, Fan; Meetei, A Ruhikanta; Andreassen, Paul R

2017-06-01

Fanconi anemia (FA) is a chromosome instability syndrome and the 20 identified FA proteins are organized into two main arms which are thought to function at distinct steps in the repair of DNA interstrand crosslinks (ICLs). These two arms include the upstream FA pathway, which culminates in the monoubiquitination of FANCD2 and FANCI, and downstream breast cancer (BRCA)-associated proteins that interact in protein complexes. How, and whether, these two groups of FA proteins are integrated is unclear. Here, we show that FANCD2 and PALB2, as indicators of the upstream and downstream arms, respectively, colocalize independently of each other in response to DNA damage induced by mitomycin C (MMC). We also show that ubiquitin chains are induced by MMC and colocalize with both FANCD2 and PALB2. Our finding that the RNF8 E3 ligase has a role in recruiting FANCD2 and PALB2 also provides support for the hypothesis that the two branches of the FA-BRCA pathway are coordinated by ubiquitin signaling. Interestingly, we find that the RNF8 partner, MDC1, as well as the ubiquitin-binding protein, RAP80, specifically recruit PALB2, while a different ubiquitin-binding protein, FAAP20, functions only in the recruitment of FANCD2. Thus, FANCD2 and PALB2 are not recruited in a single linear pathway, rather we define how their localization is coordinated and integrated by a network of ubiquitin-related proteins. We propose that such regulation may enable upstream and downstream FA proteins to act at distinct steps in the repair of ICLs.

Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.

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Fröhlich, Holger; Bahamondez, Gloria; Götschel, Frank; Korf, Ulrike

2015-01-01

Aberrant activation of sonic Hegdehog (SHH) signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy cells, which are presumable a model system for medulloblastoma, a highly malignant brain tumor that predominately occurs in children. Currently ongoing are several clinical trials for different solid cancers, which are designed to validate the clinical benefits of targeting the SHH in combination with other pathways. This has motivated us to investigate interactions between EGFR and SHH dependent signaling in greater depth. To our knowledge, there is no mathematical model describing the interplay between EGFR and SHH dependent signaling in medulloblastoma so far. Here we come up with a fully probabilistic approach using Dynamic Bayesian Networks (DBNs). To build our model, we made use of literature based knowledge describing SHH and EGFR signaling and integrated gene expression (Illumina) and cellular location dependent time series protein expression data (Reverse Phase Protein Arrays). We validated our model by sub-sampling training data and making Bayesian predictions on the left out test data. Our predictions focusing on key transcription factors and p70S6K, showed a high level of concordance with experimental data. Furthermore, the stability of our model was tested by a parametric bootstrap approach. Stable network features were in agreement with published data. Altogether we believe that our model improved our understanding of the interplay between two highly oncogenic signaling pathways in Daoy cells. This may open new perspectives for the future therapy of Hedghog/EGF-dependent solid tumors.

Processing of seismic signals from a seismometer network

International Nuclear Information System (INIS)

Key, F.A.; Warburton, P.J.

1983-08-01

A description is given of the Seismometer Network Analysis Computer (SNAC) which processes short period data from a network of seismometers (UKNET). The nine stations of the network are distributed throughout the UK and their outputs are transmitted to a control laboratory (Blacknest) where SNAC monitors the data for seismic signals. The computer gives an estimate of the source location of the detected signals and stores the waveforms. The detection logic is designed to maintain high sensitivity without excessive ''false alarms''. It is demonstrated that the system is able to detect seismic signals at an amplitude level consistent with a network of single stations and, within the limitations of signal onset time measurements made by machine, can locate the source of the seismic disturbance. (author)

Identification and analysis of signaling networks potentially involved in breast carcinoma metastasis to the brain.

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Feng Li

Full Text Available Brain is a common site of breast cancer metastasis associated with significant neurologic morbidity, decreased quality of life, and greatly shortened survival. However, the molecular and cellular mechanisms underpinning brain colonization by breast carcinoma cells are poorly understood. Here, we used 2D-DIGE (Difference in Gel Electrophoresis proteomic analysis followed by LC-tandem mass spectrometry to identify the proteins differentially expressed in brain-targeting breast carcinoma cells (MB231-Br compared with parental MDA-MB-231 cell line. Between the two cell lines, we identified 12 proteins consistently exhibiting greater than 2-fold (p<0.05 difference in expression, which were associated by the Ingenuity Pathway Analysis (IPA with two major signaling networks involving TNFα/TGFβ-, NFκB-, HSP-70-, TP53-, and IFNγ-associated pathways. Remarkably, highly related networks were revealed by the IPA analysis of a list of 19 brain-metastasis-associated proteins identified recently by the group of Dr. A. Sierra using MDA-MB-435-based experimental system (Martin et al., J Proteome Res 2008 7:908-20, or a 17-gene classifier associated with breast cancer brain relapse reported by the group of Dr. J. Massague based on a microarray analysis of clinically annotated breast tumors from 368 patients (Bos et al., Nature 2009 459: 1005-9. These findings, showing that different experimental systems and approaches (2D-DIGE proteomics used on brain targeting cell lines or gene expression analysis of patient samples with documented brain relapse yield highly related signaling networks, suggest strongly that these signaling networks could be essential for a successful colonization of the brain by metastatic breast carcinoma cells.

Neuronal Functions of Activators of G Protein Signaling

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Man K. Tse

2012-05-01

Full Text Available G protein-coupled receptors (GPCRs are one of the most important gateways for signal transduction across the plasma membrane. Over the past decade, several classes of alternative regulators of G protein signaling have been identified and reported to activate the G proteins independent of the GPCRs. One group of such regulators is the activator of G protein signaling (AGS family which comprises of AGS1-10. They have entirely different activation mechanisms for G proteins as compared to the classic model of GPCR-mediated signaling and confer upon cells new avenues of signal transduction. As GPCRs are widely expressed in our nervous system, it is believed that the AGS family plays a major role in modulating the G protein signaling in neurons. In this article, we will review the current knowledge on AGS proteins in relation to their potential roles in neuronal regulations.

Oligomeric protein structure networks: insights into protein-protein interactions

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Brinda KV

2005-12-01

Full Text Available Abstract Background Protein-protein association is essential for a variety of cellular processes and hence a large number of investigations are being carried out to understand the principles of protein-protein interactions. In this study, oligomeric protein structures are viewed from a network perspective to obtain new insights into protein association. Structure graphs of proteins have been constructed from a non-redundant set of protein oligomer crystal structures by considering amino acid residues as nodes and the edges are based on the strength of the non-covalent interactions between the residues. The analysis of such networks has been carried out in terms of amino acid clusters and hubs (highly connected residues with special emphasis to protein interfaces. Results A variety of interactions such as hydrogen bond, salt bridges, aromatic and hydrophobic interactions, which occur at the interfaces are identified in a consolidated manner as amino acid clusters at the interface, from this study. Moreover, the characterization of the highly connected hub-forming residues at the interfaces and their comparison with the hubs from the non-interface regions and the non-hubs in the interface regions show that there is a predominance of charged interactions at the interfaces. Further, strong and weak interfaces are identified on the basis of the interaction strength between amino acid residues and the sizes of the interface clusters, which also show that many protein interfaces are stronger than their monomeric protein cores. The interface strengths evaluated based on the interface clusters and hubs also correlate well with experimentally determined dissociation constants for known complexes. Finally, the interface hubs identified using the present method correlate very well with experimentally determined hotspots in the interfaces of protein complexes obtained from the Alanine Scanning Energetics database (ASEdb. A few predictions of interface hot

Protein Translation and Signaling in Human Eosinophils

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Stephane Esnault

2017-09-01

Full Text Available We have recently reported that, unlike IL-5 and GM-CSF, IL-3 induces increased translation of a subset of mRNAs. In addition, we have demonstrated that Pin1 controls the activity of mRNA binding proteins, leading to enhanced mRNA stability, GM-CSF protein production and prolonged eosinophil (EOS survival. In this review, discussion will include an overview of cap-dependent protein translation and its regulation by intracellular signaling pathways. We will address the more general process of mRNA post-transcriptional regulation, especially regarding mRNA binding proteins, which are critical effectors of protein translation. Furthermore, we will focus on (1 the roles of IL-3-driven sustained signaling on enhanced protein translation in EOS, (2 the mechanisms regulating mRNA binding proteins activity in EOS, and (3 the potential targeting of IL-3 signaling and the signaling leading to mRNA binding activity changes to identify therapeutic targets to treat EOS-associated diseases.

Pharmacodynamic Assay Panel for Monitoring Phospho-Signaling Networks | Office of Cancer Clinical Proteomics Research

Science.gov (United States)

The DNA damage response (DDR) is a highly regulated signal transduction network that orchestrates the temporal and spatial organization of protein complexes required to repair (or tolerate) DNA damage (e.g., nucleotide excision repair, base excision repair, homologous recombination, non-homologous end joining, post-replication repair).

Neuroplasticity pathways and protein-interaction networks are modulated by vortioxetine in rodents

DEFF Research Database (Denmark)

Waller, Jessica A.; Nygaard, Sara Holm; Li, Yan

2017-01-01

species and sexes, different brain regions, and in response to distinct routes of administration and regimens. Conclusions: A recurring theme, based on the present study as well as previous findings, is that networks related to synaptic plasticity, synaptic transmission, signal transduction...... and rat in response to distinct treatment regimens and in different brain regions. Furthermore, analysis of complexes of physically-interacting proteins reveal that biomarkers involved in transcriptional regulation, neurodevelopment, neuroplasticity, and endocytosis are modulated by vortioxetine....... A subsequent qPCR study examining the expression of targets in the protein-protein interactome space in response to chronic vortioxetine treatment over a range of doses provides further biological validation that vortioxetine engages neuroplasticity networks. Thus, the same biology is regulated in different...

Analysis of hepatocellular carcinoma and metastatic hepatic carcinoma via functional modules in a protein-protein interaction network

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Jun Pan

2014-01-01

Full Text Available Introduction: This study aims to identify protein clusters with potential functional relevance in the pathogenesis of hepatocellular carcinoma (HCC and metastatic hepatic carcinoma using network analysis. Materials and Methods: We used human protein interaction data to build a protein-protein interaction network with Cytoscape and then derived functional clusters using MCODE. Combining the gene expression profiles, we calculated the functional scores for the clusters and selected statistically significant clusters. Meanwhile, Gene Ontology was used to assess the functionality of these clusters. Finally, a support vector machine was trained on the gold standard data sets. Results: The differentially expressed genes of HCC were mainly involved in metabolic and signaling processes. We acquired 13 significant modules from the gene expression profiles. The area under the curve value based on the differentially expressed modules were 98.31%, which outweighed the classification with DEGs. Conclusions: Differentially expressed modules are valuable to screen biomarkers combined with functional modules.

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

Energy Technology Data Exchange (ETDEWEB)

Yang, Chin-Rang [National Inst. of Health (NIH), Bethesda, MD (United States). National Heart, Lung and Blood Inst.

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.

Anchoring Proteins as Regulators of Signaling Pathways

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Perino, Alessia; Ghigo, Alessandra; Scott, John D.; Hirsch, Emilio

2012-01-01

Spatial and temporal organization of signal transduction is coordinated through the segregation of signaling enzymes in selected cellular compartments. This highly evolved regulatory mechanism ensures the activation of selected enzymes only in the vicinity of their target proteins. In this context, cAMP-responsive triggering of protein kinase A is modulated by a family of scaffold proteins referred to as A-kinase anchoring proteins. A-kinase anchoring proteins form the core of multiprotein complexes and enable simultaneous but segregated cAMP signaling events to occur in defined cellular compartments. In this review we will focus on the description of A-kinase anchoring protein function in the regulation of cardiac physiopathology. PMID:22859670

RMOD: a tool for regulatory motif detection in signaling network.

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Jinki Kim

Full Text Available Regulatory motifs are patterns of activation and inhibition that appear repeatedly in various signaling networks and that show specific regulatory properties. However, the network structures of regulatory motifs are highly diverse and complex, rendering their identification difficult. Here, we present a RMOD, a web-based system for the identification of regulatory motifs and their properties in signaling networks. RMOD finds various network structures of regulatory motifs by compressing the signaling network and detecting the compressed forms of regulatory motifs. To apply it into a large-scale signaling network, it adopts a new subgraph search algorithm using a novel data structure called path-tree, which is a tree structure composed of isomorphic graphs of query regulatory motifs. This algorithm was evaluated using various sizes of signaling networks generated from the integration of various human signaling pathways and it showed that the speed and scalability of this algorithm outperforms those of other algorithms. RMOD includes interactive analysis and auxiliary tools that make it possible to manipulate the whole processes from building signaling network and query regulatory motifs to analyzing regulatory motifs with graphical illustration and summarized descriptions. As a result, RMOD provides an integrated view of the regulatory motifs and mechanism underlying their regulatory motif activities within the signaling network. RMOD is freely accessible online at the following URL: http://pks.kaist.ac.kr/rmod.

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

International Nuclear Information System (INIS)

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

2011-01-01

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 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 β (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: ► We performed brain proteomic analysis of rats exposed to the neurotoxicant, Aroclor 1254. ► Cerebellum and

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

Energy Technology Data Exchange (ETDEWEB)

Kodavanti, Prasada Rao S., E-mail: kodavanti.prasada@epa.gov [Neurotoxicology Branch, NHEERL, ORD, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina (United States); Osorio, Cristina [Systems Proteomics Center, University of North Carolina at Chapel Hill, North Carolina (United States); Program on Molecular Biology and Biotechnology, University of North Carolina at Chapel Hill, North Carolina (United States); Royland, Joyce E.; Ramabhadran, Ram [Genetic and Cellular Toxicology Branch, NHEERL, ORD, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina (United States); Alzate, Oscar [Department of Cellular and Developmental Biology, University of North Carolina at Chapel Hill, North Carolina (United States); Systems Proteomics Center, University of North Carolina at Chapel Hill, North Carolina (United States); Program on Molecular Biology and Biotechnology, University of North Carolina at Chapel Hill, North Carolina (United States)

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

Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

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Peng Liu

2015-01-01

Full Text Available A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

Scalable rule-based modelling of allosteric proteins and biochemical networks.

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Julien F Ollivier

2010-11-01

Full Text Available 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.

Influence of degree correlations on network structure and stability in protein-protein interaction networks

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Zimmer Ralf

2007-08-01

Full Text Available Abstract Background The existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs. Results For each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks. Conclusion Considerable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree

Finding undetected protein associations in cell signaling by belief propagation.

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

2011-01-11

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.

An analysis pipeline for the inference of protein-protein interaction networks

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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.

Tolerance to drought and salt stress in plants: Unraveling the signaling networks

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Dortje eGolldack

2014-04-01

Full Text Available Tolerance of plants to abiotic stressors such as drought and salinity is triggered by complex multicomponent signaling pathways to restore cellular homeostasis and promote survival. Major plant transcription factor families such as bZIP, NAC, AP2/ERF and MYB orchestrate regulatory networks underlying abiotic stress tolerance. Sucrose nonfermenting 1-related protein kinase 2 (SnRK2 and MAPK pathways contribute to initiation of stress adaptive downstream responses and promote plant growth and development. As a convergent point of multiple abiotic cues, cellular effects of environmental stresses are not only imbalances of ionic and osmotic homeostasis but also impaired photosynthesis, cellular energy depletion, and redox imbalances. Recent evidence of regulatory systems that link sensing and signaling of environmental conditions and the intracellular redox status have shed light on interfaces of stress and energy signaling. ROS (reactive oxygen species cause severe cellular damage by peroxidation and de-esterification of membrane lipids, however, current models also define a pivotal signaling function of ROS in triggering tolerance against stress. Recent research advances suggest and support a regulatory role of ROS in the cross talks of stress triggered hormonal signaling such as the abscisic acid (ABA pathway and endogenously induced redox and metabolite signals. Here, we discuss and review the versatile molecular convergence in the abiotic stress responsive signaling networks in the context of ROS and lipid derived signals and the specific role of stomatal signaling.

Regulation of protease-activated receptor 1 signaling by the adaptor protein complex 2 and R4 subfamily of regulator of G protein signaling proteins.

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Chen, Buxin; Siderovski, David P; Neubig, Richard R; Lawson, Mark A; Trejo, Joann

2014-01-17

The G protein-coupled protease-activated receptor 1 (PAR1) is irreversibly proteolytically activated by thrombin. Hence, the precise regulation of PAR1 signaling is important for proper cellular responses. In addition to desensitization, internalization and lysosomal sorting of activated PAR1 are critical for the termination of signaling. Unlike most G protein-coupled receptors, PAR1 internalization is mediated by the clathrin adaptor protein complex 2 (AP-2) and epsin-1, rather than β-arrestins. However, the function of AP-2 and epsin-1 in the regulation of PAR1 signaling is not known. Here, we report that AP-2, and not epsin-1, regulates activated PAR1-stimulated phosphoinositide hydrolysis via two different mechanisms that involve, in part, a subset of R4 subfamily of "regulator of G protein signaling" (RGS) proteins. A significantly greater increase in activated PAR1 signaling was observed in cells depleted of AP-2 using siRNA or in cells expressing a PAR1 (420)AKKAA(424) mutant with defective AP-2 binding. This effect was attributed to AP-2 modulation of PAR1 surface expression and efficiency of G protein coupling. We further found that ectopic expression of R4 subfamily members RGS2, RGS3, RGS4, and RGS5 reduced activated PAR1 wild-type signaling, whereas signaling by the PAR1 AKKAA mutant was minimally affected. Intriguingly, siRNA-mediated depletion analysis revealed a function for RGS5 in the regulation of signaling by the PAR1 wild type but not the AKKAA mutant. Moreover, activation of the PAR1 wild type, and not the AKKAA mutant, induced Gαq association with RGS3 via an AP-2-dependent mechanism. Thus, AP-2 regulates activated PAR1 signaling by altering receptor surface expression and through recruitment of RGS proteins.

DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.

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Mistry, Divya; Wise, Roger P; Dickerson, Julie A

2017-01-01

Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be

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

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Jeremy Worley

2016-02-01

Full Text Available 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.

Information flow in a network of dispersed signalers-receivers

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Halupka, Konrad

2017-11-01

I consider a stochastic model of multi-agent communication in regular network. The model describes how dispersed animals exchange information. Each agent can initiate and transfer the signal to its nearest neighbors, who may pass it farther. For an external observer of busy networks, signaling activity may appear random, even though information flow actually thrives. Only when signal initiation and transfer are at low levels do spatiotemporal autocorrelations emerge as clumping signaling activity in space and pink noise time series. Under such conditions, the costs of signaling are moderate, but the signaler can reach a large audience. I propose that real-world networks of dispersed signalers-receivers may self-organize into this state and the flow of information maintains their integrity.

Emergence of modularity and disassortativity in protein-protein interaction networks.

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Wan, Xi; Cai, Shuiming; Zhou, Jin; Liu, Zengrong

2010-12-01

In this paper, we present a simple evolution model of protein-protein interaction networks by introducing a rule of small-preference duplication of a node, meaning that the probability of a node chosen to duplicate is inversely proportional to its degree, and subsequent divergence plus nonuniform heterodimerization based on some plausible mechanisms in biology. We show that our model cannot only reproduce scale-free connectivity and small-world pattern, but also exhibit hierarchical modularity and disassortativity. After comparing the features of our model with those of real protein-protein interaction networks, we believe that our model can provide relevant insights into the mechanism underlying the evolution of protein-protein interaction networks. © 2010 American Institute of Physics.

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

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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. Copyright © 2012 Elsevier Ltd. All rights reserved.

Calcium-Oxidant Signaling Network Regulates AMP-activated Protein Kinase (AMPK) Activation upon Matrix Deprivation*

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Sundararaman, Ananthalakshmy; Amirtham, Usha; Rangarajan, Annapoorni

2016-01-01

The AMP-activated protein kinase (AMPK) has recently been implicated in anoikis resistance. However, the molecular mechanisms that activate AMPK upon matrix detachment remain unexplored. In this study, we show that AMPK activation is a rapid and sustained phenomenon upon matrix deprivation, whereas re-attachment to the matrix leads to its dephosphorylation and inactivation. Because matrix detachment leads to loss of integrin signaling, we investigated whether integrin signaling negatively regulates AMPK activation. However, modulation of focal adhesion kinase or Src, the major downstream components of integrin signaling, failed to cause a corresponding change in AMPK signaling. Further investigations revealed that the upstream AMPK kinases liver kinase B1 (LKB1) and Ca2+/calmodulin-dependent protein kinase kinase β (CaMKKβ) contribute to AMPK activation upon detachment. In LKB1-deficient cells, we found AMPK activation to be predominantly dependent on CaMKKβ. We observed no change in ATP levels under detached conditions at early time points suggesting that rapid AMPK activation upon detachment was not triggered by energy stress. We demonstrate that matrix deprivation leads to a spike in intracellular calcium as well as oxidant signaling, and both these intracellular messengers contribute to rapid AMPK activation upon detachment. We further show that endoplasmic reticulum calcium release-induced store-operated calcium entry contributes to intracellular calcium increase, leading to reactive oxygen species production, and AMPK activation. We additionally show that the LKB1/CaMKK-AMPK axis and intracellular calcium levels play a critical role in anchorage-independent cancer sphere formation. Thus, the Ca2+/reactive oxygen species-triggered LKB1/CaMKK-AMPK signaling cascade may provide a quick, adaptable switch to promote survival of metastasizing cancer cells. PMID:27226623

Protein Networks in Alzheimer's Disease

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Carlsen, Eva Meier; Rasmussen, Rune

2017-01-01

Overlap of RNA and protein networks reveals glia cells as key players for the development of symptomatic Alzheimer’s disease in humans......Overlap of RNA and protein networks reveals glia cells as key players for the development of symptomatic Alzheimer’s disease in humans...

Topology and weights in a protein domain interaction network--a novel way to predict protein interactions.

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Wuchty, Stefan

2006-05-23

While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions

SH2/SH3 signaling proteins.

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Schlessinger, J

1994-02-01

SH2 and SH3 domains are small protein modules that mediate protein-protein interactions in signal transduction pathways that are activated by protein tyrosine kinases. SH2 domains bind to short phosphotyrosine-containing sequences in growth factor receptors and other phosphoproteins. SH3 domains bind to target proteins through sequences containing proline and hydrophobic amino acids. SH2 and SH3 domain containing proteins, such as Grb2 and phospholipase C gamma, utilize these modules in order to link receptor and cytoplasmic protein tyrosine kinases to the Ras signaling pathway and to phosphatidylinositol hydrolysis, respectively. The three-dimensional structures of several SH2 and SH3 domains have been determined by NMR and X-ray crystallography, and the molecular basis of their specificity is beginning to be unveiled.

Protein Phosphatase 2A in the Regulation of Wnt Signaling, Stem Cells, and Cancer.

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Thompson, Joshua J; Williams, Christopher S

2018-02-26

Protein phosphorylation is a ubiquitous cellular process that allows for the nuanced and reversible regulation of protein activity. Protein phosphatase 2A (PP2A) is a heterotrimeric serine-threonine phosphatase-composed of a structural, regulatory, and catalytic subunit-that controls a variety of cellular events via protein dephosphorylation. While much is known about PP2A and its basic biochemistry, the diversity of its components-especially the multitude of regulatory subunits-has impeded the determination of PP2A function. As a consequence of this complexity, PP2A has been shown to both positively and negatively regulate signaling networks such as the Wnt pathway. Wnt signaling modulates major developmental processes, and is a dominant mediator of stem cell self-renewal, cell fate, and cancer stem cells. Because PP2A affects Wnt signaling both positively and negatively and at multiple levels, further understanding of this complex dynamic may ultimately provide insight into stem cell biology and how to better treat cancers that result from alterations in Wnt signaling. This review will summarize literature that implicates PP2A as a tumor suppressor, explore PP2A mutations identified in human malignancy, and focus on PP2A in the regulation of Wnt signaling and stem cells so as to better understand how aberrancy in this pathway can contribute to tumorigenesis.

An Integrative Analysis of the InR/PI3K/Akt Network Identifies the Dynamic Response to Insulin Signaling

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Arunachalam Vinayagam

2016-09-01

Full Text Available Insulin regulates an essential conserved signaling pathway affecting growth, proliferation, and metabolism. To expand our understanding of the insulin pathway, we combine biochemical, genetic, and computational approaches to build a comprehensive Drosophila InR/PI3K/Akt network. First, we map the dynamic protein-protein interaction network surrounding the insulin core pathway using bait-prey interactions connecting 566 proteins. Combining RNAi screening and phospho-specific antibodies, we find that 47% of interacting proteins affect pathway activity, and, using quantitative phosphoproteomics, we demonstrate that ∼10% of interacting proteins are regulated by insulin stimulation at the level of phosphorylation. Next, we integrate these orthogonal datasets to characterize the structure and dynamics of the insulin network at the level of protein complexes and validate our method by identifying regulatory roles for the Protein Phosphatase 2A (PP2A and Reptin-Pontin chromatin-remodeling complexes as negative and positive regulators of ribosome biogenesis, respectively. Altogether, our study represents a comprehensive resource for the study of the evolutionary conserved insulin network.

Predicting Secretory Proteins with SignalP

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Nielsen, Henrik

2017-01-01

SignalP is the currently most widely used program for prediction of signal peptides from amino acid sequences. Proteins with signal peptides are targeted to the secretory pathway, but are not necessarily secreted. After a brief introduction to the biology of signal peptides and the history...

Synchronization transmission of laser pattern signal within uncertain switched network

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Lü, Ling; Li, Chengren; Li, Gang; Sun, Ao; Yan, Zhe; Rong, Tingting; Gao, Yan

2017-06-01

We propose a new technology for synchronization transmission of laser pattern signal within uncertain network with controllable topology. In synchronization process, the connection of dynamic network can vary at all time according to different demands. Especially, we construct the Lyapunov function of network through designing a special semi-positive definite function, and the synchronization transmission of laser pattern signal within uncertain network with controllable topology can be realized perfectly, which effectively avoids the complicated calculation for solving the second largest eignvalue of the coupling matrix of the dynamic network in order to obtain the network synchronization condition. At the same time, the uncertain parameters in dynamic equations belonging to network nodes can also be identified accurately via designing the identification laws of uncertain parameters. In addition, there are not any limitations for the synchronization target of network in the new technology, in other words, the target can either be a state variable signal of an arbitrary node within the network or an exterior signal.

AP2/EREBP transcription factors are part of gene regulatory networks and integrate metabolic, hormonal and environmental signals in stress acclimation and retrograde signalling.

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Dietz, Karl-Josef; Vogel, Marc Oliver; Viehhauser, Andrea

2010-09-01

To optimize acclimation responses to environmental growth conditions, plants integrate and weigh a diversity of input signals. Signal integration within the signalling networks occurs at different sites including the level of transcription factor activation. Accumulating evidence assigns a major and diversified role in environmental signal integration to the family of APETALA 2/ethylene response element binding protein (AP2/EREBP) transcription factors. Presently, the Plant Transcription Factor Database 3.0 assigns 147 gene loci to this family in Arabidopsis thaliana, 200 in Populus trichocarpa and 163 in Oryza sativa subsp. japonica as compared to 13 to 14 in unicellular algae ( http://plntfdb.bio.uni-potsdam.de/v3.0/ ). AP2/EREBP transcription factors have been implicated in hormone, sugar and redox signalling in context of abiotic stresses such as cold and drought. This review exemplarily addresses present-day knowledge of selected AP2/EREBP with focus on a function in stress signal integration and retrograde signalling and defines AP2/EREBP-linked gene networks from transcriptional profiling-based graphical Gaussian models. The latter approach suggests highly interlinked functions of AP2/EREBPs in retrograde and stress signalling.

Analysis and logical modeling of biological signaling transduction networks

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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.

Regulator of G-protein signaling - 5 (RGS5 is a novel repressor of hedgehog signaling.

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William M Mahoney

Full Text Available Hedgehog (Hh signaling plays fundamental roles in morphogenesis, tissue repair, and human disease. Initiation of Hh signaling is controlled by the interaction of two multipass membrane proteins, patched (Ptc and smoothened (Smo. Recent studies identify Smo as a G-protein coupled receptor (GPCR-like protein that signals through large G-protein complexes which contain the Gαi subunit. We hypothesize Regulator of G-Protein Signaling (RGS proteins, and specifically RGS5, are endogenous repressors of Hh signaling via their ability to act as GTPase activating proteins (GAPs for GTP-bound Gαi, downstream of Smo. In support of this hypothesis, we demonstrate that RGS5 over-expression inhibits sonic hedgehog (Shh-mediated signaling and osteogenesis in C3H10T1/2 cells. Conversely, signaling is potentiated by siRNA-mediated knock-down of RGS5 expression, but not RGS4 expression. Furthermore, using immuohistochemical analysis and co-immunoprecipitation (Co-IP, we demonstrate that RGS5 is present with Smo in primary cilia. This organelle is required for canonical Hh signaling in mammalian cells, and RGS5 is found in a physical complex with Smo in these cells. We therefore conclude that RGS5 is an endogenous regulator of Hh-mediated signaling and that RGS proteins are potential targets for novel therapeutics in Hh-mediated diseases.

Network Compression as a Quality Measure for Protein Interaction Networks

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Royer, Loic; Reimann, Matthias; Stewart, A. Francis; Schroeder, Michael

2012-01-01

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients. PMID:22719828

Comment on "A dynamic network model of mTOR signaling reveals TSC-independent mTORC2 regulation": building a model of the mTOR signaling network with a potentially faulty tool.

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Manning, Brendan D

2012-07-10

In their study published in Science Signaling (Research Article, 27 March 2012, DOI: 10.1126/scisignal.2002469), Dalle Pezze et al. tackle the dynamic and complex wiring of the signaling network involving the protein kinase mTOR, which exists within two distinct protein complexes (mTORC1 and mTORC2) that differ in their regulation and function. The authors use a combination of immunoblotting for specific phosphorylation events and computational modeling. The primary experimental tool employed is to monitor the autophosphorylation of mTOR on Ser(2481) in cell lysates as a surrogate for mTOR activity, which the authors conclude is a specific readout for mTORC2. However, Ser(2481) phosphorylation occurs on both mTORC1 and mTORC2 and will dynamically change as the network through which these two complexes are connected is manipulated. Therefore, models of mTOR network regulation built using this tool are inherently imperfect and open to alternative explanations. Specific issues with the main conclusion made in this study, involving the TSC1-TSC2 (tuberous sclerosis complex 1 and 2) complex and its potential regulation of mTORC2, are discussed here. A broader goal of this Letter is to clarify to other investigators the caveats of using mTOR Ser(2481) phosphorylation in cell lysates as a specific readout for either of the two mTOR complexes.

The heat-shock protein/chaperone network and multiple stress resistance.

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Jacob, Pierre; Hirt, Heribert; Bendahmane, Abdelhafid

2017-04-01

Crop yield has been greatly enhanced during the last century. However, most elite cultivars are adapted to temperate climates and are not well suited to more stressful conditions. In the context of climate change, stress resistance is a major concern. To overcome these difficulties, scientists may help breeders by providing genetic markers associated with stress resistance. However, multistress resistance cannot be obtained from the simple addition of single stress resistance traits. In the field, stresses are unpredictable and several may occur at once. Consequently, the use of single stress resistance traits is often inadequate. Although it has been historically linked with the heat stress response, the heat-shock protein (HSP)/chaperone network is a major component of multiple stress responses. Among the HSP/chaperone 'client proteins', many are primary metabolism enzymes and signal transduction components with essential roles for the proper functioning of a cell. HSPs/chaperones are controlled by the action of diverse heat-shock factors, which are recruited under stress conditions. In this review, we give an overview of the regulation of the HSP/chaperone network with a focus on Arabidopsis thaliana. We illustrate the role of HSPs/chaperones in regulating diverse signalling pathways and discuss several basic principles that should be considered for engineering multiple stress resistance in crops through the HSP/chaperone network. © 2016 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

Phosphoproteomics-based systems analysis of signal transduction networks

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Hiroko eKozuka-Hata

2012-01-01

Full Text Available Signal transduction systems coordinate complex cellular information to regulate biological events such as cell proliferation and differentiation. Although the accumulating evidence on widespread association of signaling molecules has revealed essential contribution of phosphorylation-dependent interaction networks to cellular regulation, their dynamic behavior is mostly yet to be analyzed. Recent technological advances regarding mass spectrometry-based quantitative proteomics have enabled us to describe the comprehensive status of phosphorylated molecules in a time-resolved manner. Computational analyses based on the phosphoproteome dynamics accelerate generation of novel methodologies for mathematical analysis of cellular signaling. Phosphoproteomics-based numerical modeling can be used to evaluate regulatory network elements from a statistical point of view. Integration with transcriptome dynamics also uncovers regulatory hubs at the transcriptional level. These omics-based computational methodologies, which have firstly been applied to representative signaling systems such as the epidermal growth factor receptor pathway, have now opened up a gate for systems analysis of signaling networks involved in immune response and cancer.

Multi-tasking role of the mechanosensing protein Ankrd2 in the signaling network of striated muscle.

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Anna Belgrano

Full Text Available Ankrd2 (also known as Arpp together with Ankrd1/CARP and DARP are members of the MARP mechanosensing proteins that form a complex with titin (N2A/calpain 3 protease/myopalladin. In muscle, Ankrd2 is located in the I-band of the sarcomere and moves to the nucleus of adjacent myofibers on muscle injury. In myoblasts it is predominantly in the nucleus and on differentiation shifts from the nucleus to the cytoplasm. In agreement with its role as a sensor it interacts both with sarcomeric proteins and transcription factors.Expression profiling of endogenous Ankrd2 silenced in human myotubes was undertaken to elucidate its role as an intermediary in cell signaling pathways. Silencing Ankrd2 expression altered the expression of genes involved in both intercellular communication (cytokine-cytokine receptor interaction, endocytosis, focal adhesion, tight junction, gap junction and regulation of the actin cytoskeleton and intracellular communication (calcium, insulin, MAPK, p53, TGF-β and Wnt signaling. The significance of Ankrd2 in cell signaling was strengthened by the fact that we were able to show for the first time that Nkx2.5 and p53 are upstream effectors of the Ankrd2 gene and that Ankrd1/CARP, another MARP member, can modulate the transcriptional ability of MyoD on the Ankrd2 promoter. Another novel finding was the interaction between Ankrd2 and proteins with PDZ and SH3 domains, further supporting its role in signaling. It is noteworthy that we demonstrated that transcription factors PAX6, LHX2, NFIL3 and MECP2, were able to bind both the Ankrd2 protein and its promoter indicating the presence of a regulatory feedback loop mechanism.In conclusion we demonstrate that Ankrd2 is a potent regulator in muscle cells affecting a multitude of pathways and processes.

Topology and weights in a protein domain interaction network – a novel way to predict protein interactions

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Wuchty Stefan

2006-05-01

Full Text Available Abstract Background While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. Results We consider a web of interactions between protein domains of the Protein Family database (PFAM, which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Conclusion Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we

Finding local communities in protein networks.

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Voevodski, Konstantin; Teng, Shang-Hua; Xia, Yu

2009-09-18

Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent, making our application useful for biologists who wish to

Finding local communities in protein networks

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Teng Shang-Hua

2009-09-01

Full Text Available Abstract Background Protein-protein interactions (PPIs play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. Results We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. Conclusion The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent

NAPS: Network Analysis of Protein Structures

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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

Detection test of wireless network signal strength and GPS positioning signal in underground pipeline

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Li, Li; Zhang, Yunwei; Chen, Ling

2018-03-01

In order to solve the problem of selecting positioning technology for inspection robot in underground pipeline environment, the wireless network signal strength and GPS positioning signal testing are carried out in the actual underground pipeline environment. Firstly, the strength variation of the 3G wireless network signal and Wi-Fi wireless signal provided by China Telecom and China Unicom ground base stations are tested, and the attenuation law of these wireless signals along the pipeline is analyzed quantitatively and described. Then, the receiving data of the GPS satellite signal in the pipeline are tested, and the attenuation of GPS satellite signal under underground pipeline is analyzed. The testing results may be reference for other related research which need to consider positioning in pipeline.

Sweet Taste Receptor Signaling Network: Possible Implication for Cognitive Functioning

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Menizibeya O. Welcome

2015-01-01

Full Text Available Sweet taste receptors are transmembrane protein network specialized in the transmission of information from special “sweet” molecules into the intracellular domain. These receptors can sense the taste of a range of molecules and transmit the information downstream to several acceptors, modulate cell specific functions and metabolism, and mediate cell-to-cell coupling through paracrine mechanism. Recent reports indicate that sweet taste receptors are widely distributed in the body and serves specific function relative to their localization. Due to their pleiotropic signaling properties and multisubstrate ligand affinity, sweet taste receptors are able to cooperatively bind multiple substances and mediate signaling by other receptors. Based on increasing evidence about the role of these receptors in the initiation and control of absorption and metabolism, and the pivotal role of metabolic (glucose regulation in the central nervous system functioning, we propose a possible implication of sweet taste receptor signaling in modulating cognitive functioning.

Retroactive signaling in short signaling pathways.

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Jacques-Alexandre Sepulchre

Full Text Available In biochemical signaling pathways without explicit feedback connections, the core signal transduction is usually described as a one-way communication, going from upstream to downstream in a feedforward chain or network of covalent modification cycles. In this paper we explore the possibility of a new type of signaling called retroactive signaling, offered by the recently demonstrated property of retroactivity in signaling cascades. The possibility of retroactive signaling is analysed in the simplest case of the stationary states of a bicyclic cascade of signaling cycles. In this case, we work out the conditions for which variables of the upstream cycle are affected by a change of the total amount of protein in the downstream cycle, or by a variation of the phosphatase deactivating the same protein. Particularly, we predict the characteristic ranges of the downstream protein, or of the downstream phosphatase, for which a retroactive effect can be observed on the upstream cycle variables. Next, we extend the possibility of retroactive signaling in short but nonlinear signaling pathways involving a few covalent modification cycles.

Review: Mitogen-Activated Protein kinases in nutritional signaling in Arabidopsis

KAUST Repository

Chardin, Camille; Schenk, Sebastian T.; Hirt, Heribert; Colcombet, Jean; Krapp, Anne

2017-01-01

Mitogen-Activated Protein Kinase (MAPK) cascades are functional modules widespread among eukaryotic organisms. In plants, these modules are encoded by large multigenic families and are involved in many biological processes ranging from stress responses to cellular differentiation and organ development. Furthermore, MAPK pathways are involved in the perception of environmental and physiological modifications. Interestingly, some MAPKs play a role in several signaling networks and could have an integrative function for the response of plants to their environment. In this review, we describe the classification of MAPKs and highlight some of their biochemical actions. We performed an in silico analysis of MAPK gene expression in response to nutrients supporting their involvement in nutritional signaling. While several MAPKs have been identified as players in sugar, nitrogen, phosphate, iron and potassium-related signaling pathways, their biochemical functions are yet mainly unknown. The integration of these regulatory cascades in the current understanding of nutrient signaling is discussed and potential new avenues for approaches toward plants with higher nutrient use efficiencies are evoked.

Review: Mitogen-Activated Protein kinases in nutritional signaling in Arabidopsis

KAUST Repository

Chardin, Camille

2017-04-14

Mitogen-Activated Protein Kinase (MAPK) cascades are functional modules widespread among eukaryotic organisms. In plants, these modules are encoded by large multigenic families and are involved in many biological processes ranging from stress responses to cellular differentiation and organ development. Furthermore, MAPK pathways are involved in the perception of environmental and physiological modifications. Interestingly, some MAPKs play a role in several signaling networks and could have an integrative function for the response of plants to their environment. In this review, we describe the classification of MAPKs and highlight some of their biochemical actions. We performed an in silico analysis of MAPK gene expression in response to nutrients supporting their involvement in nutritional signaling. While several MAPKs have been identified as players in sugar, nitrogen, phosphate, iron and potassium-related signaling pathways, their biochemical functions are yet mainly unknown. The integration of these regulatory cascades in the current understanding of nutrient signaling is discussed and potential new avenues for approaches toward plants with higher nutrient use efficiencies are evoked.

Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

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Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2016-10-06

Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

Network analysis of epidermal growth factor signaling using integrated genomic, proteomic and phosphorylation data.

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Katrina M Waters

Full Text Available To understand how integration of multiple data types can help decipher cellular responses at the systems level, we analyzed the mitogenic response of human mammary epithelial cells to epidermal growth factor (EGF using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies. A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot. Integration of these disparate data types showed that each contributed qualitatively different components to the observed cell response to EGF and that varying degrees of concordance in gene expression and protein abundance measurements could be linked to specific biological processes. Networks inferred from individual data types were relatively limited, whereas networks derived from the integrated data recapitulated the known major cellular responses to EGF and exhibited more highly connected signaling nodes than networks derived from any individual dataset. While cell cycle regulatory pathways were altered as anticipated, we found the most robust response to mitogenic concentrations of EGF was induction of matrix metalloprotease cascades, highlighting the importance of the EGFR system as a regulator of the extracellular environment. These results demonstrate the value of integrating multiple levels of biological information to more accurately reconstruct networks of cellular response.

Network Analysis of Epidermal Growth Factor Signaling using Integrated Genomic, Proteomic and Phosphorylation Data

Energy Technology Data Exchange (ETDEWEB)

Waters, Katrina M.; Liu, Tao; Quesenberry, Ryan D.; Willse, Alan R.; Bandyopadhyay, Somnath; Kathmann, Loel E.; Weber, Thomas J.; Smith, Richard D.; Wiley, H. S.; Thrall, Brian D.

2012-03-29

To understand how integration of multiple data types can help decipher cellular responses at the systems level, we analyzed the mitogenic response of human mammary epithelial cells to epidermal growth factor (EGF) using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies. A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot. Integration of these disparate data types showed that each contributed qualitatively different components to the observed cell response to EGF and that varying degrees of concordance in gene expression and protein abundance measurements could be linked to specific biological processes. Networks inferred from individual data types were relatively limited, whereas networks derived from the integrated data recapitulated the known major cellular responses to EGF and exhibited more highly connected signaling nodes than networks derived from any individual dataset. While cell cycle regulatory pathways were altered as anticipated, we found the most robust response to mitogenic concentrations of EGF was induction of matrix metalloprotease cascades, highlighting the importance of the EGFR system as a regulator of the extracellular environment. These results demonstrate the value of integrating multiple levels of biological information to more accurately reconstruct networks of cellular response.

3DProIN: Protein-Protein Interaction Networks and Structure Visualization.

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Li, Hui; Liu, Chunmei

2014-06-14

3DProIN is a computational tool to visualize protein-protein interaction networks in both two dimensional (2D) and three dimensional (3D) view. It models protein-protein interactions in a graph and explores the biologically relevant features of the tertiary structures of each protein in the network. Properties such as color, shape and name of each node (protein) of the network can be edited in either 2D or 3D views. 3DProIN is implemented using 3D Java and C programming languages. The internet crawl technique is also used to parse dynamically grasped protein interactions from protein data bank (PDB). It is a java applet component that is embedded in the web page and it can be used on different platforms including Linux, Mac and Window using web browsers such as Firefox, Internet Explorer, Chrome and Safari. It also was converted into a mac app and submitted to the App store as a free app. Mac users can also download the app from our website. 3DProIN is available for academic research at http://bicompute.appspot.com.

Prediction and characterization of protein-protein interaction networks in swine

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Wang Fen

2012-01-01

Full Text Available Abstract Background Studying the large-scale protein-protein interaction (PPI network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes. Results We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively. Conclusion The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/.

Dominating biological networks.

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Tijana Milenković

Full Text Available Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of "biologically central (BC" genes (i.e., their protein products, such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network.To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts. We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its "spine" that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks.

Prioritizing Signaling Information Transmission in Next Generation Networks

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Jasmina Baraković

2011-01-01

Full Text Available Next generation transport network is characterized by the use of in-band signaling, where Internet Protocol (IP packets carrying signaling or media information are mixed in transmission. Since transport resources are limited, when any segment of access or core network is congested, IP packets carrying signaling information may be discarded. As a consequence, it may be impossible to implement reachability and quality of service (QoS. Since present approaches are insufficient to completely address this problem, a novel approach is proposed, which is based on prioritizing signaling information transmission. To proof the concept, a simulation study was performed using Network Simulator version 2 (ns-2 and independently developed Session Initiation Protocol (SIP module. The obtained results were statistically processed using Statistical Package for the Social Sciences (SPSS version 15.0. Summarizing our research results, several issues are identified for future work.

All-optical network coding for DPSK signals

DEFF Research Database (Denmark)

An, Yi; Da Ros, Francesco; Peucheret, Christophe

2013-01-01

All-optical network coding for path protection is experimentally demonstrated using four-wave mixing in SOAs for10 Gbit/s NRZ-DPSK signals with error free performance. The total power penalty after two cascaded XOR stage is only 2 dB.......All-optical network coding for path protection is experimentally demonstrated using four-wave mixing in SOAs for10 Gbit/s NRZ-DPSK signals with error free performance. The total power penalty after two cascaded XOR stage is only 2 dB....

Construction of ontology augmented networks for protein complex prediction.

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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.

Discovering disease-associated genes in weighted protein-protein interaction networks

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Cui, Ying; Cai, Meng; Stanley, H. Eugene

2018-04-01

Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

Thematic minireview series: cell biology of G protein signaling.

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Dohlman, Henrik G

2015-03-13

This thematic series is on the topic of cell signaling from a cell biology perspective, with a particular focus on G proteins. G protein-coupled receptors (GPCRs, also known as seven-transmembrane receptors) are typically found at the cell surface. Upon agonist binding, these receptors will activate a GTP-binding G protein at the cytoplasmic face of the plasma membrane. Additionally, there is growing evidence that G proteins can also be activated by non-receptor binding partners, and they can signal from non-plasma membrane compartments. The production of second messengers at multiple, spatially distinct locations represents a type of signal encoding that has been largely neglected. The first minireview in the series describes biosensors that are being used to monitor G protein signaling events in live cells. The second describes the implementation of antibody-based biosensors to dissect endosome signaling by G proteins and their receptors. The third describes the function of a non-receptor, cytoplasmic activator of G protein signaling, called GIV (Girdin). Collectively, the advances described in these articles provide a deeper understanding and emerging opportunities for new pharmacology. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Modeling evolution of crosstalk in noisy signal transduction networks

Science.gov (United States)

Tareen, Ammar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

2018-02-01

Signal transduction networks can form highly interconnected systems within cells due to crosstalk between constituent pathways. To better understand the evolutionary design principles underlying such networks, we study the evolution of crosstalk for two parallel signaling pathways that arise via gene duplication. We use a sequence-based evolutionary algorithm and evolve the network based on two physically motivated fitness functions related to information transmission. We find that one fitness function leads to a high degree of crosstalk while the other leads to pathway specificity. Our results offer insights on the relationship between network architecture and information transmission for noisy biomolecular networks.

Mathematical Modelling Plant Signalling Networks

KAUST Repository

Muraro, D.; Byrne, H.M.; King, J.R.; Bennett, M.J.

2013-01-01

methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more

Fiber fault location utilizing traffic signal in optical network.

Science.gov (United States)

Zhao, Tong; Wang, Anbang; Wang, Yuncai; Zhang, Mingjiang; Chang, Xiaoming; Xiong, Lijuan; Hao, Yi

2013-10-07

We propose and experimentally demonstrate a method for fault location in optical communication network. This method utilizes the traffic signal transmitted across the network as probe signal, and then locates the fault by correlation technique. Compared with conventional techniques, our method has a simple structure and low operation expenditure, because no additional device is used, such as light source, modulator and signal generator. The correlation detection in this method overcomes the tradeoff between spatial resolution and measurement range in pulse ranging technique. Moreover, signal extraction process can improve the location result considerably. Experimental results show that we achieve a spatial resolution of 8 cm and detection range of over 23 km with -8-dBm mean launched power in optical network based on synchronous digital hierarchy protocols.

Dopamine D1 signaling organizes network dynamics underlying working memory.

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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

2016-06-01

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.

Discriminating lysosomal membrane protein types using dynamic neural network.

Science.gov (United States)

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.

JNK Signaling: Regulation and Functions Based on Complex Protein-Protein Partnerships

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Zeke, András; Misheva, Mariya

2016-01-01

SUMMARY The c-Jun N-terminal kinases (JNKs), as members of the mitogen-activated protein kinase (MAPK) family, mediate eukaryotic cell responses to a wide range of abiotic and biotic stress insults. JNKs also regulate important physiological processes, including neuronal functions, immunological actions, and embryonic development, via their impact on gene expression, cytoskeletal protein dynamics, and cell death/survival pathways. Although the JNK pathway has been under study for >20 years, its complexity is still perplexing, with multiple protein partners of JNKs underlying the diversity of actions. Here we review the current knowledge of JNK structure and isoforms as well as the partnerships of JNKs with a range of intracellular proteins. Many of these proteins are direct substrates of the JNKs. We analyzed almost 100 of these target proteins in detail within a framework of their classification based on their regulation by JNKs. Examples of these JNK substrates include a diverse assortment of nuclear transcription factors (Jun, ATF2, Myc, Elk1), cytoplasmic proteins involved in cytoskeleton regulation (DCX, Tau, WDR62) or vesicular transport (JIP1, JIP3), cell membrane receptors (BMPR2), and mitochondrial proteins (Mcl1, Bim). In addition, because upstream signaling components impact JNK activity, we critically assessed the involvement of signaling scaffolds and the roles of feedback mechanisms in the JNK pathway. Despite a clarification of many regulatory events in JNK-dependent signaling during the past decade, many other structural and mechanistic insights are just beginning to be revealed. These advances open new opportunities to understand the role of JNK signaling in diverse physiological and pathophysiological states. PMID:27466283

Analysis of network motifs in cellular regulation: Structural similarities, input-output relations and signal integration.

Science.gov (United States)

Straube, Ronny

2017-12-01

Much of the complexity of regulatory networks derives from the necessity to integrate multiple signals and to avoid malfunction due to cross-talk or harmful perturbations. Hence, one may expect that the input-output behavior of larger networks is not necessarily more complex than that of smaller network motifs which suggests that both can, under certain conditions, be described by similar equations. In this review, we illustrate this approach by discussing the similarities that exist in the steady state descriptions of a simple bimolecular reaction, covalent modification cycles and bacterial two-component systems. Interestingly, in all three systems fundamental input-output characteristics such as thresholds, ultrasensitivity or concentration robustness are described by structurally similar equations. Depending on the system the meaning of the parameters can differ ranging from protein concentrations and affinity constants to complex parameter combinations which allows for a quantitative understanding of signal integration in these systems. We argue that this approach may also be extended to larger regulatory networks. Copyright © 2017 Elsevier B.V. All rights reserved.

Application of neural networks to signal prediction in nuclear power plant

International Nuclear Information System (INIS)

Wan Joo Kim; Soon Heung Chang; Byung Ho Lee

1993-01-01

This paper describes the feasibility study of an artificial neural network for signal prediction. The purpose of signal prediction is to estimate the value of undetected next time step signal. As the prediction method, based on the idea of auto regression, a few previous signals are inputs to the artificial neural network and the signal value of next time step is estimated with the outputs of the network. The artificial neural network can be applied to the nonlinear system and answers in short time. The training algorithm is a modified backpropagation model, which can effectively reduce the training time. The target signal of the simulation is the steam generator water level, which is one of the important parameters in nuclear power plants. The simulation result shows that the predicted value follows the real trend well

In silico modeling of the yeast protein and protein family interaction network

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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.

An empirical Bayesian approach for model-based inference of cellular signaling networks

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Klinke David J

2009-11-01

Full Text Available Abstract Background A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways. Results As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies. Conclusion In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements.

Improved prediction of signal peptides: SignalP 3.0

DEFF Research Database (Denmark)

Bendtsen, Jannick Dyrløv; Nielsen, Henrik; von Heijne, G.

2004-01-01

We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the ...

Mapping the follicle-stimulating hormone-induced signalling networks

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Pauline eGloaguen

2011-10-01

Full Text Available Follicle-stimulating hormone (FSH is a central regulator of male and female reproductive function. Over the last decade, there has been a growing perception of the complexity associated with FSH-induced cellular signalling. It is now clear that the canonical Gs/cAMP/PKA pathway is not the sole mechanism that must be considered in FSH biological actions. In parallel, consistent with the emerging concept of biased agonism, several examples of ligand-mediated selective signalling pathway activation by gonadotropin receptors have been reported. In this context, it is important to gain an integrative view of the signalling pathways induced by FSH and how they interconnect to form a network. In this review, we propose a first attempt at building topological maps of various pathways known to be involved in the FSH-induced signalling network. We discuss the multiple facets of FSH-induced signalling and how they converge to the hormone integrated biological response. Despite of their incompleteness, these maps of the FSH-induced signalling network represent a first step towards gaining a system-level comprehension of this hormone’s actions, which may ultimately facilitate the discovery of novel regulatory processes and therapeutic strategies for infertilities and non-steroidal contraception.

Analysis of protein-protein interaction networks by means of annotated graph mining algorithms

NARCIS (Netherlands)

Rahmani, Hossein

2012-01-01

This thesis discusses solutions to several open problems in Protein-Protein Interaction (PPI) networks with the aid of Knowledge Discovery. PPI networks are usually represented as undirected graphs, with nodes corresponding to proteins and edges representing interactions among protein pairs. A large

High-throughput profiling of signaling networks identifies mechanism-based combination therapy to eliminate microenvironmental resistance in acute myeloid leukemia.

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Zeng, Zhihong; Liu, Wenbin; Tsao, Twee; Qiu, YiHua; Zhao, Yang; Samudio, Ismael; Sarbassov, Dos D; Kornblau, Steven M; Baggerly, Keith A; Kantarjian, Hagop M; Konopleva, Marina; Andreeff, Michael

2017-09-01

The bone marrow microenvironment is known to provide a survival advantage to residual acute myeloid leukemia cells, possibly contributing to disease recurrence. The mechanisms by which stroma in the microenvironment regulates leukemia survival remain largely unknown. Using reverse-phase protein array technology, we profiled 53 key protein molecules in 11 signaling pathways in 20 primary acute myeloid leukemia samples and two cell lines, aiming to understand stroma-mediated signaling modulation in response to the targeted agents temsirolimus (MTOR), ABT737 (BCL2/BCL-XL), and Nutlin-3a (MDM2), and to identify the effective combination therapy targeting acute myeloid leukemia in the context of the leukemia microenvironment. Stroma reprogrammed signaling networks and modified the sensitivity of acute myeloid leukemia samples to all three targeted inhibitors. Stroma activated AKT at Ser473 in the majority of samples treated with single-agent ABT737 or Nutlin-3a. This survival mechanism was partially abrogated by concomitant treatment with temsirolimus plus ABT737 or Nutlin-3a. Mapping the signaling networks revealed that combinations of two inhibitors increased the number of affected proteins in the targeted pathways and in multiple parallel signaling, translating into facilitated cell death. These results demonstrated that a mechanism-based selection of combined inhibitors can be used to guide clinical drug selection and tailor treatment regimens to eliminate microenvironment-mediated resistance in acute myeloid leukemia. Copyright© 2017 Ferrata Storti Foundation.

The T cell STAT signaling network is reprogrammed within hours of bacteremia via secondary signals1

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Hotson, Andrew N.; Hardy, Jonathan W.; Hale, Matthew B.; Contag, Christopher H.; Nolan, Garry P.

2014-01-01

The delicate balance between protective immunity and inflammatory disease is challenged during sepsis, a pathologic state characterized by aspects of both a hyper-active immune response and immunosuppression. The events driven by systemic infection by bacterial pathogens on the T cell signaling network that likely control these responses have not been illustrated in great detail. We characterized how intracellular signaling within the immune compartment is reprogrammed at the single cell level when the host is challenged with a high levels of pathogen. To accomplish this, we applied flow cytometry to measure the phosphorylation potential of key signal transduction proteins during acute bacterial challenge. We modeled the onset of sepsis by intravenous administration of avirulent strains of Listeria and E. coli to mice. Within six hours of bacterial challenge, T cells were globally restricted in their ability to respond to specific cytokine stimulations as determined by assessing the extent of STAT protein phosphorylation. Mechanisms by which this negative feedback response occurred included SOCS1 and SOCS3 gene up regulation and IL-6 induced endocystosis of the IL-6 receptor. In addition, macrophages were partially tolerized in their ability to respond to TLR agonists. Thus, in contrast to the view that there is a wholesale immune activation during sepsis, one immediate host response to blood borne bacteria was induction of a refractory period during which leukocyte activation by specific stimulations was attenuated. PMID:19494279

SOCS proteins in regulation of receptor tyrosine kinase signaling

DEFF Research Database (Denmark)

Kazi, Julhash U.; Kabir, Nuzhat N.; Flores Morales, Amilcar

2014-01-01

Receptor tyrosine kinases (RTKs) are a family of cell surface receptors that play critical roles in signal transduction from extracellular stimuli. Many in this family of kinases are overexpressed or mutated in human malignancies and thus became an attractive drug target for cancer treatment....... The signaling mediated by RTKs must be tightly regulated by interacting proteins including protein-tyrosine phosphatases and ubiquitin ligases. The suppressors of cytokine signaling (SOCS) family proteins are well-known negative regulators of cytokine receptors signaling consisting of eight structurally similar...

Defective chemokine signal integration in leukocytes lacking activator of G protein signaling 3 (AGS3).

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Branham-O'Connor, Melissa; Robichaux, William G; Zhang, Xian-Kui; Cho, Hyeseon; Kehrl, John H; Lanier, Stephen M; Blumer, Joe B

2014-04-11

Activator of G-protein signaling 3 (AGS3, gene name G-protein signaling modulator-1, Gpsm1), an accessory protein for G-protein signaling, has functional roles in the kidney and CNS. Here we show that AGS3 is expressed in spleen, thymus, and bone marrow-derived dendritic cells, and is up-regulated upon leukocyte activation. We explored the role of AGS3 in immune cell function by characterizing chemokine receptor signaling in leukocytes from mice lacking AGS3. No obvious differences in lymphocyte subsets were observed. Interestingly, however, AGS3-null B and T lymphocytes and bone marrow-derived dendritic cells exhibited significant chemotactic defects as well as reductions in chemokine-stimulated calcium mobilization and altered ERK and Akt activation. These studies indicate a role for AGS3 in the regulation of G-protein signaling in the immune system, providing unexpected venues for the potential development of therapeutic agents that modulate immune function by targeting these regulatory mechanisms.

Improving N-terminal protein annotation of Plasmodium species based on signal peptide prediction of orthologous proteins

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Neto Armando

2012-11-01

Full Text Available Abstract Background Signal peptide is one of the most important motifs involved in protein trafficking and it ultimately influences protein function. Considering the expected functional conservation among orthologs it was hypothesized that divergence in signal peptides within orthologous groups is mainly due to N-terminal protein sequence misannotation. Thus, discrepancies in signal peptide prediction of orthologous proteins were used to identify misannotated proteins in five Plasmodium species. Methods Signal peptide (SignalP and orthology (OrthoMCL were combined in an innovative strategy to identify orthologous groups showing discrepancies in signal peptide prediction among their protein members (Mixed groups. In a comparative analysis, multiple alignments for each of these groups and gene models were visually inspected in search of misannotated proteins and, whenever possible, alternative gene models were proposed. Thresholds for signal peptide prediction parameters were also modified to reduce their impact as a possible source of discrepancy among orthologs. Validation of new gene models was based on RT-PCR (few examples or on experimental evidence already published (ApiLoc. Results The rate of misannotated proteins was significantly higher in Mixed groups than in Positive or Negative groups, corroborating the proposed hypothesis. A total of 478 proteins were reannotated and change of signal peptide prediction from negative to positive was the most common. Reannotations triggered the conversion of almost 50% of all Mixed groups, which were further reduced by optimization of signal peptide prediction parameters. Conclusions The methodological novelty proposed here combining orthology and signal peptide prediction proved to be an effective strategy for the identification of proteins showing wrongly N-terminal annotated sequences, and it might have an important impact in the available data for genome-wide searching of potential vaccine and drug

Protein enriched pasta: structure and digestibility of its protein network.

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Laleg, Karima; Barron, Cécile; Santé-Lhoutellier, Véronique; Walrand, Stéphane; Micard, Valérie

2016-02-01

Wheat (W) pasta was enriched in 6% gluten (G), 35% faba (F) or 5% egg (E) to increase its protein content (13% to 17%). The impact of the enrichment on the multiscale structure of the pasta and on in vitro protein digestibility was studied. Increasing the protein content (W- vs. G-pasta) strengthened pasta structure at molecular and macroscopic scales but reduced its protein digestibility by 3% by forming a higher covalently linked protein network. Greater changes in the macroscopic and molecular structure of the pasta were obtained by varying the nature of protein used for enrichment. Proteins in G- and E-pasta were highly covalently linked (28-32%) resulting in a strong pasta structure. Conversely, F-protein (98% SDS-soluble) altered the pasta structure by diluting gluten and formed a weak protein network (18% covalent link). As a result, protein digestibility in F-pasta was significantly higher (46%) than in E- (44%) and G-pasta (39%). The effect of low (55 °C, LT) vs. very high temperature (90 °C, VHT) drying on the protein network structure and digestibility was shown to cause greater molecular changes than pasta formulation. Whatever the pasta, a general strengthening of its structure, a 33% to 47% increase in covalently linked proteins and a higher β-sheet structure were observed. However, these structural differences were evened out after the pasta was cooked, resulting in identical protein digestibility in LT and VHT pasta. Even after VHT drying, F-pasta had the best amino acid profile with the highest protein digestibility, proof of its nutritional interest.

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

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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. Copyright © 2012 Wiley Periodicals, Inc.

Hepatitis C virus infection protein network.

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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.

Protein phosphorylation and its role in archaeal signal transduction

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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

Optimal neural networks for protein-structure prediction

International Nuclear Information System (INIS)

Head-Gordon, T.; Stillinger, F.H.

1993-01-01

The successful application of neural-network algorithms for prediction of protein structure is stymied by three problem areas: the sparsity of the database of known protein structures, poorly devised network architectures which make the input-output mapping opaque, and a global optimization problem in the multiple-minima space of the network variables. We present a simplified polypeptide model residing in two dimensions with only two amino-acid types, A and B, which allows the determination of the global energy structure for all possible sequences of pentamer, hexamer, and heptamer lengths. This model simplicity allows us to compile a complete structural database and to devise neural networks that reproduce the tertiary structure of all sequences with absolute accuracy and with the smallest number of network variables. These optimal networks reveal that the three problem areas are convoluted, but that thoughtful network designs can actually deconvolute these detrimental traits to provide network algorithms that genuinely impact on the ability of the network to generalize or learn the desired mappings. Furthermore, the two-dimensional polypeptide model shows sufficient chemical complexity so that transfer of neural-network technology to more realistic three-dimensional proteins is evident

Quality control methodology for high-throughput protein-protein interaction screening.

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Vazquez, Alexei; Rual, Jean-François; Venkatesan, Kavitha

2011-01-01

Protein-protein interactions are key to many aspects of the cell, including its cytoskeletal structure, the signaling processes in which it is involved, or its metabolism. Failure to form protein complexes or signaling cascades may sometimes translate into pathologic conditions such as cancer or neurodegenerative diseases. The set of all protein interactions between the proteins encoded by an organism constitutes its protein interaction network, representing a scaffold for biological function. Knowing the protein interaction network of an organism, combined with other sources of biological information, can unravel fundamental biological circuits and may help better understand the molecular basics of human diseases. The protein interaction network of an organism can be mapped by combining data obtained from both low-throughput screens, i.e., "one gene at a time" experiments and high-throughput screens, i.e., screens designed to interrogate large sets of proteins at once. In either case, quality controls are required to deal with the inherent imperfect nature of experimental assays. In this chapter, we discuss experimental and statistical methodologies to quantify error rates in high-throughput protein-protein interactions screens.

Identification of Two Protein-Signaling States Delineating Transcriptionally Heterogeneous Human Medulloblastoma

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Walderik W. Zomerman

2018-03-01

Full Text Available Summary: The brain cancer medulloblastoma consists of different transcriptional subgroups. To characterize medulloblastoma at the phosphoprotein-signaling level, we performed high-throughput peptide phosphorylation profiling on a large cohort of SHH (Sonic Hedgehog, group 3, and group 4 medulloblastomas. We identified two major protein-signaling profiles. One profile was associated with rapid death post-recurrence and resembled MYC-like signaling for which MYC lesions are sufficient but not necessary. The second profile showed enrichment for DNA damage, as well as apoptotic and neuronal signaling. Integrative analysis demonstrated that heterogeneous transcriptional input converges on these protein-signaling profiles: all SHH and a subset of group 3 patients exhibited the MYC-like protein-signaling profile; the majority of the other group 3 subset and group 4 patients displayed the DNA damage/apoptotic/neuronal signaling profile. Functional analysis of enriched pathways highlighted cell-cycle progression and protein synthesis as therapeutic targets for MYC-like medulloblastoma. : Using peptide phosphorylation profiling, Zomerman et al. identify two medulloblastoma phosphoprotein-signaling profiles that have prognostic value and are potentially targetable. They find that these profiles extend across transcriptome-based subgroup borders. This suggests that diverse genetic information converges on common protein-signaling pathways and highlights protein-signaling as a unique information layer. Keywords: medulloblastoma, protein-signaling, protein synthesis, MYC, TP53, proteome, phosphoproteome

Assuring SS7 dependability: A robustness characterization of signaling network elements

Science.gov (United States)

Karmarkar, Vikram V.

1994-04-01

Current and evolving telecommunication services will rely on signaling network performance and reliability properties to build competitive call and connection control mechanisms under increasing demands on flexibility without compromising on quality. The dimensions of signaling dependability most often evaluated are the Rate of Call Loss and End-to-End Route Unavailability. A third dimension of dependability that captures the concern about large or catastrophic failures can be termed Network Robustness. This paper is concerned with the dependability aspects of the evolving Signaling System No. 7 (SS7) networks and attempts to strike a balance between the probabilistic and deterministic measures that must be evaluated to accomplish a risk-trend assessment to drive architecture decisions. Starting with high-level network dependability objectives and field experience with SS7 in the U.S., potential areas of growing stringency in network element (NE) dependability are identified to improve against current measures of SS7 network quality, as per-call signaling interactions increase. A sensitivity analysis is presented to highlight the impact due to imperfect coverage of duplex network component or element failures (i.e., correlated failures), to assist in the setting of requirements on NE robustness. A benefit analysis, covering several dimensions of dependability, is used to generate the domain of solutions available to the network architect in terms of network and network element fault tolerance that may be specified to meet the desired signaling quality goals.

Fascin- and α-Actinin-Bundled Networks Contain Intrinsic Structural Features that Drive Protein Sorting.

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Winkelman, Jonathan D; Suarez, Cristian; Hocky, Glen M; Harker, Alyssa J; Morganthaler, Alisha N; Christensen, Jenna R; Voth, Gregory A; Bartles, James R; Kovar, David R

2016-10-24

Cells assemble and maintain functionally distinct actin cytoskeleton networks with various actin filament organizations and dynamics through the coordinated action of different sets of actin-binding proteins. The biochemical and functional properties of diverse actin-binding proteins, both alone and in combination, have been increasingly well studied. Conversely, how different sets of actin-binding proteins properly sort to distinct actin filament networks in the first place is not nearly as well understood. Actin-binding protein sorting is critical for the self-organization of diverse dynamic actin cytoskeleton networks within a common cytoplasm. Using in vitro reconstitution techniques including biomimetic assays and single-molecule multi-color total internal reflection fluorescence microscopy, we discovered that sorting of the prominent actin-bundling proteins fascin and α-actinin to distinct networks is an intrinsic behavior, free of complicated cellular signaling cascades. When mixed, fascin and α-actinin mutually exclude each other by promoting their own recruitment and inhibiting recruitment of the other, resulting in the formation of distinct fascin- or α-actinin-bundled domains. Subdiffraction-resolution light microscopy and negative-staining electron microscopy revealed that fascin domains are densely packed, whereas α-actinin domains consist of widely spaced parallel actin filaments. Importantly, other actin-binding proteins such as fimbrin and espin show high specificity between these two bundle types within the same reaction. Here we directly observe that fascin and α-actinin intrinsically segregate to discrete bundled domains that are specifically recognized by other actin-binding proteins. Copyright © 2016 Elsevier Ltd. All rights reserved.

Protein complex prediction based on k-connected subgraphs in protein interaction network

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Habibi Mahnaz

2010-09-01

Full Text Available Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy, containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods (CMC, MCL, PCP and RNSC in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision. Conclusions Many real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from http://www.bioinf.cs.ipm.ir/softwares/cfa/CFA.rar.

Weak signal transmission in complex networks and its application in detecting connectivity.

Science.gov (United States)

Liang, Xiaoming; Liu, Zonghua; Li, Baowen

2009-10-01

We present a network model of coupled oscillators to study how a weak signal is transmitted in complex networks. Through both theoretical analysis and numerical simulations, we find that the response of other nodes to the weak signal decays exponentially with their topological distance to the signal source and the coupling strength between two neighboring nodes can be figured out by the responses. This finding can be conveniently used to detect the topology of unknown network, such as the degree distribution, clustering coefficient and community structure, etc., by repeatedly choosing different nodes as the signal source. Through four typical networks, i.e., the regular one dimensional, small world, random, and scale-free networks, we show that the features of network can be approximately given by investigating many fewer nodes than the network size, thus our approach to detect the topology of unknown network may be efficient in practical situations with large network size.

Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities.

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Lapek, John D; Greninger, Patricia; Morris, Robert; Amzallag, Arnaud; Pruteanu-Malinici, Iulian; Benes, Cyril H; Haas, Wilhelm

2017-10-01

The formation of protein complexes and the co-regulation of the cellular concentrations of proteins are essential mechanisms for cellular signaling and for maintaining homeostasis. Here we use isobaric-labeling multiplexed proteomics to analyze protein co-regulation and show that this allows the identification of protein-protein associations with high accuracy. We apply this 'interactome mapping by high-throughput quantitative proteome analysis' (IMAHP) method to a panel of 41 breast cancer cell lines and show that deviations of the observed protein co-regulations in specific cell lines from the consensus network affects cellular fitness. Furthermore, these aberrant interactions serve as biomarkers that predict the drug sensitivity of cell lines in screens across 195 drugs. We expect that IMAHP can be broadly used to gain insight into how changing landscapes of protein-protein associations affect the phenotype of biological systems.

Subsurface event detection and classification using Wireless Signal Networks.

Science.gov (United States)

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

2012-11-05

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.

Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks

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Sandip Chakraborty

2016-01-01

Full Text Available Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes’ adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another.

Protein cysteine oxidation in redox signaling

DEFF Research Database (Denmark)

Forman, Henry Jay; Davies, Michael J; Krämer, Anna C

2017-01-01

Oxidation of critical signaling protein cysteines regulated by H2O2 has been considered to involve sulfenic acid (RSOH) formation. RSOH may subsequently form either a sulfenyl amide (RSNHR') with a neighboring amide, or a mixed disulfide (RSSR') with another protein cysteine or glutathione. Previ...

The late endosomal HOPS complex anchors active G-protein signaling essential for pathogenesis in magnaporthe oryzae.

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Ravikrishna Ramanujam

Full Text Available In Magnaporthe oryzae, the causal ascomycete of the devastating rice blast disease, the conidial germ tube tip must sense and respond to a wide array of requisite cues from the host in order to switch from polarized to isotropic growth, ultimately forming the dome-shaped infection cell known as the appressorium. Although the role for G-protein mediated Cyclic AMP signaling in appressorium formation was first identified almost two decades ago, little is known about the spatio-temporal dynamics of the cascade and how the signal is transmitted through the intracellular network during cell growth and morphogenesis. In this study, we demonstrate that the late endosomal compartments, comprising of a PI3P-rich (Phosphatidylinositol 3-phosphate highly dynamic tubulo-vesicular network, scaffold active MagA/GαS, Rgs1 (a GAP for MagA, Adenylate cyclase and Pth11 (a non-canonical GPCR in the likely absence of AKAP-like anchors during early pathogenic development in M. oryzae. Loss of HOPS component Vps39 and consequently the late endosomal function caused a disruption of adenylate cyclase localization, cAMP signaling and appressorium formation. Remarkably, exogenous cAMP rescued the appressorium formation defects associated with VPS39 deletion in M. oryzae. We propose that sequestration of key G-protein signaling components on dynamic late endosomes and/or endolysosomes, provides an effective molecular means to compartmentalize and control the spatio-temporal activation and rapid downregulation (likely via vacuolar degradation of cAMP signaling amidst changing cellular geometry during pathogenic development in M. oryzae.

Context-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literature

KAUST Repository

Chowdhary, Rajesh

2012-04-06

Background: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/. © 2012 Chowdhary et al.

Context-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literature

KAUST Repository

Chowdhary, Rajesh; Tan, Sin Lam; Zhang, Jinfeng; Karnik, Shreyas; Bajic, Vladimir B.; Liu, Jun S.

2012-01-01

Background: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/. © 2012 Chowdhary et al.

Signalling network construction for modelling plant defence response.

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Dragana Miljkovic

Full Text Available 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

Rap G protein signal in normal and disordered lymphohematopoiesis.

Science.gov (United States)

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. © 2013 Elsevier Inc. All rights reserved.

Phylogeny, Functional Annotation, and Protein Interaction Network Analyses of the Xenopus tropicalis Basic Helix-Loop-Helix Transcription Factors

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Wuyi Liu

2013-01-01

Full Text Available The previous survey identified 70 basic helix-loop-helix (bHLH proteins, but it was proved to be incomplete, and the functional information and regulatory networks of frog bHLH transcription factors were not fully known. Therefore, we conducted an updated genome-wide survey in the Xenopus tropicalis genome project databases and identified 105 bHLH sequences. Among the retrieved 105 sequences, phylogenetic analyses revealed that 103 bHLH proteins belonged to 43 families or subfamilies with 46, 26, 11, 3, 15, and 4 members in the corresponding supergroups. Next, gene ontology (GO enrichment analyses showed 65 significant GO annotations of biological processes and molecular functions and KEGG pathways counted in frequency. To explore the functional pathways, regulatory gene networks, and/or related gene groups coding for Xenopus tropicalis bHLH proteins, the identified bHLH genes were put into the databases KOBAS and STRING to get the signaling information of pathways and protein interaction networks according to available public databases and known protein interactions. From the genome annotation and pathway analysis using KOBAS, we identified 16 pathways in the Xenopus tropicalis genome. From the STRING interaction analysis, 68 hub proteins were identified, and many hub proteins created a tight network or a functional module within the protein families.

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

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Guang Hu

2017-01-01

Full Text Available 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.

Unveiling protein functions through the dynamics of the interaction network.

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Irene Sendiña-Nadal

Full Text Available Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes.

Signal peptides and protein localization prediction

DEFF Research Database (Denmark)

Nielsen, Henrik

2005-01-01

In 1999, the Nobel prize in Physiology or Medicine was awarded to Gunther Blobel “for the discovery that proteins have intrinsic signals that govern their transport and localization in the cell”. Since the subcellular localization of a protein is an important clue to its function, the characteriz...

Oncogenic Signaling by Leukemia-Associated Mutant Cbl Proteins

Science.gov (United States)

Nadeau, Scott; An, Wei; Palermo, Nick; Feng, Dan; Ahmad, Gulzar; Dong, Lin; Borgstahl, Gloria E. O.; Natarajan, Amarnath; Naramura, Mayumi; Band, Vimla; Band, Hamid

2013-01-01

Members of the Cbl protein family (Cbl, Cbl-b, and Cbl-c) are E3 ubiquitin ligases that have emerged as critical negative regulators of protein tyrosine kinase (PTK) signaling. This function reflects their ability to directly interact with activated PTKs and to target them as well as their associated signaling components for ubiquitination. Given the critical roles of PTK signaling in driving oncogenesis, recent studies in animal models and genetic analyses in human cancer have firmly established that Cbl proteins function as tumor suppressors. Missense mutations or small in-frame deletions within the regions of Cbl protein that are essential for its E3 activity have been identified in nearly 5% of leukemia patients with myelodysplastic/myeloproliferative disorders. Based on evidence from cell culture studies, in vivo models and clinical data, we discuss the potential signaling mechanisms of mutant Cbl-driven oncogenesis. Mechanistic insights into oncogenic Cbl mutants and associated animal models are likely to enhance our understanding of normal hematopoietic stem cell homeostasis and provide avenues for targeted therapy of mutant Cbl-driven cancers. PMID:23997989

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

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Cole Johnson

2014-03-01

Full Text Available 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.

Mechanism of inhibition of growth hormone receptor signaling by suppressor of cytokine signaling proteins

DEFF Research Database (Denmark)

Hansen, J A; Lindberg, K; Hilton, D J

1999-01-01

In this study we have investigated the role of suppressor of cytokine signaling (SOCS) proteins in GH receptor-mediated signaling. GH-induced transcription was inhibited by SOCS-1 and SOCS-3, while SOCS-2 and cytokine inducible SH2-containing protein (CIS) had no effect By using chimeric SOCS pro...

Detection of Locally Over-Represented GO Terms in Protein-Protein Interaction Networks

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LAVALLÉE-ADAM, MATHIEU; COULOMBE, BENOIT; BLANCHETTE, MATHIEU

2015-01-01

High-throughput methods for identifying protein-protein interactions produce increasingly complex and intricate interaction networks. These networks are extremely rich in information, but extracting biologically meaningful hypotheses from them and representing them in a human-readable manner is challenging. We propose a method to identify Gene Ontology terms that are locally over-represented in a subnetwork of a given biological network. Specifically, we propose several methods to evaluate the degree of clustering of proteins associated to a particular GO term in both weighted and unweighted PPI networks, and describe efficient methods to estimate the statistical significance of the observed clustering. We show, using Monte Carlo simulations, that our best approximation methods accurately estimate the true p-value, for random scale-free graphs as well as for actual yeast and human networks. When applied to these two biological networks, our approach recovers many known complexes and pathways, but also suggests potential functions for many subnetworks. Online Supplementary Material is available at www.liebertonline.com. PMID:20377456

Subsurface Event Detection and Classification Using Wireless Signal Networks

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Muhannad T. Suleiman

2012-11-01

Full Text Available 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.

The EH network

DEFF Research Database (Denmark)

Santolini, E; Salcini, A E; Kay, B K

1999-01-01

. Moreover, a number of cellular ligands of the domain have been identified and demonstrated to define a complex network of protein-protein interactions in the eukaryotic cell. Interestingly, many of the EH-containing and EH-binding proteins display characteristics of endocytic "accessory" proteins......, suggesting that the principal function of the EH network is to regulate various steps in endocytosis. In addition, recent evidence suggests that the EH network might work as an "integrator" of signals controlling cellular pathways as diverse as endocytosis, nucleocytosolic export, and ultimately cell...

A central integrator of transcription networks in plant stress and energy signalling.

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Baena-González, Elena; Rolland, Filip; Thevelein, Johan M; Sheen, Jen

2007-08-23

Photosynthetic plants are the principal solar energy converter sustaining life on Earth. Despite its fundamental importance, little is known about how plants sense and adapt to darkness in the daily light-dark cycle, or how they adapt to unpredictable environmental stresses that compromise photosynthesis and respiration and deplete energy supplies. Current models emphasize diverse stress perception and signalling mechanisms. Using a combination of cellular and systems screens, we show here that the evolutionarily conserved Arabidopsis thaliana protein kinases, KIN10 and KIN11 (also known as AKIN10/At3g01090 and AKIN11/At3g29160, respectively), control convergent reprogramming of transcription in response to seemingly unrelated darkness, sugar and stress conditions. Sensing and signalling deprivation of sugar and energy, KIN10 targets a remarkably broad array of genes that orchestrate transcription networks, promote catabolism and suppress anabolism. Specific bZIP transcription factors partially mediate primary KIN10 signalling. Transgenic KIN10 overexpression confers enhanced starvation tolerance and lifespan extension, and alters architecture and developmental transitions. Significantly, double kin10 kin11 deficiency abrogates the transcriptional switch in darkness and stress signalling, and impairs starch mobilization at night and growth. These studies uncover surprisingly pivotal roles of KIN10/11 in linking stress, sugar and developmental signals to globally regulate plant metabolism, energy balance, growth and survival. In contrast to the prevailing view that sucrose activates plant SnRK1s (Snf1-related protein kinases), our functional analyses of Arabidopsis KIN10/11 provide compelling evidence that SnRK1s are inactivated by sugars and share central roles with the orthologous yeast Snf1 and mammalian AMPK in energy signalling.

PDZ Protein Regulation of G Protein-Coupled Receptor Trafficking and Signaling Pathways.

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Dunn, Henry A; Ferguson, Stephen S G

2015-10-01

G protein-coupled receptors (GPCRs) contribute to the regulation of every aspect of human physiology and are therapeutic targets for the treatment of numerous diseases. As a consequence, understanding the myriad of mechanisms controlling GPCR signaling and trafficking is essential for the development of new pharmacological strategies for the treatment of human pathologies. Of the many GPCR-interacting proteins, postsynaptic density protein of 95 kilodaltons, disc large, zona occludens-1 (PDZ) domain-containing proteins appear most abundant and have similarly been implicated in disease mechanisms. PDZ proteins play an important role in regulating receptor and channel protein localization within synapses and tight junctions and function to scaffold intracellular signaling protein complexes. In the current study, we review the known functional interactions between PDZ domain-containing proteins and GPCRs and provide insight into the potential mechanisms of action. These PDZ domain-containing proteins include the membrane-associated guanylate-like kinases [postsynaptic density protein of 95 kilodaltons; synapse-associated protein of 97 kilodaltons; postsynaptic density protein of 93 kilodaltons; synapse-associated protein of 102 kilodaltons; discs, large homolog 5; caspase activation and recruitment domain and membrane-associated guanylate-like kinase domain-containing protein 3; membrane protein, palmitoylated 3; calcium/calmodulin-dependent serine protein kinase; membrane-associated guanylate kinase protein (MAGI)-1, MAGI-2, and MAGI-3], Na(+)/H(+) exchanger regulatory factor proteins (NHERFs) (NHERF1, NHERF2, PDZ domain-containing kidney protein 1, and PDZ domain-containing kidney protein 2), Golgi-associated PDZ proteins (Gα-binding protein interacting protein, C-terminus and CFTR-associated ligand), PDZ domain-containing guanine nucleotide exchange factors (GEFs) 1 and 2, regulator of G protein signaling (RGS)-homology-RhoGEFs (PDZ domain-containing RhoGEF and

Protein Annotation from Protein Interaction Networks and Gene Ontology

OpenAIRE

Nguyen, Cao D.; Gardiner, Katheleen J.; Cios, Krzysztof J.

2011-01-01

We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precis...

Hierarchical feedback modules and reaction hubs in cell signaling networks.

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Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

Hierarchical feedback modules and reaction hubs in cell signaling networks.

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Jianfeng Xu

Full Text Available Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

Science.gov (United States)

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

Evidence of probabilistic behaviour in protein interaction networks

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Reifman Jaques

2008-01-01

Full Text Available Abstract Background Data from high-throughput experiments of protein-protein interactions are commonly used to probe the nature of biological organization and extract functional relationships between sets of proteins. What has not been appreciated is that the underlying mechanisms involved in assembling these networks may exhibit considerable probabilistic behaviour. Results We find that the probability of an interaction between two proteins is generally proportional to the numerical product of their individual interacting partners, or degrees. The degree-weighted behaviour is manifested throughout the protein-protein interaction networks studied here, except for the high-degree, or hub, interaction areas. However, we find that the probabilities of interaction between the hubs are still high. Further evidence is provided by path length analyses, which show that these hubs are separated by very few links. Conclusion The results suggest that protein-protein interaction networks incorporate probabilistic elements that lead to scale-rich hierarchical architectures. These observations seem to be at odds with a biologically-guided organization. One interpretation of the findings is that we are witnessing the ability of proteins to indiscriminately bind rather than the protein-protein interactions that are actually utilized by the cell in biological processes. Therefore, the topological study of a degree-weighted network requires a more refined methodology to extract biological information about pathways, modules, or other inferred relationships among proteins.

Spatio-temporal remodeling of functional membrane microdomains organizes the signaling networks of a bacterium.

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Johannes Schneider

2015-04-01

Full Text Available 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.

Computational study of noise in a large signal transduction network

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Ruohonen Keijo

2011-06-01

Full Text Available Abstract Background Biochemical systems are inherently noisy due to the discrete reaction events that occur in a random manner. Although noise is often perceived as a disturbing factor, the system might actually benefit from it. In order to understand the role of noise better, its quality must be studied in a quantitative manner. Computational analysis and modeling play an essential role in this demanding endeavor. Results We implemented a large nonlinear signal transduction network combining protein kinase C, mitogen-activated protein kinase, phospholipase A2, and β isoform of phospholipase C networks. We simulated the network in 300 different cellular volumes using the exact Gillespie stochastic simulation algorithm and analyzed the results in both the time and frequency domain. In order to perform simulations in a reasonable time, we used modern parallel computing techniques. The analysis revealed that time and frequency domain characteristics depend on the system volume. The simulation results also indicated that there are several kinds of noise processes in the network, all of them representing different kinds of low-frequency fluctuations. In the simulations, the power of noise decreased on all frequencies when the system volume was increased. Conclusions We concluded that basic frequency domain techniques can be applied to the analysis of simulation results produced by the Gillespie stochastic simulation algorithm. This approach is suited not only to the study of fluctuations but also to the study of pure noise processes. Noise seems to have an important role in biochemical systems and its properties can be numerically studied by simulating the reacting system in different cellular volumes. Parallel computing techniques make it possible to run massive simulations in hundreds of volumes and, as a result, accurate statistics can be obtained from computational studies.

Load-induced modulation of signal transduction networks.

Science.gov (United States)

Jiang, Peng; Ventura, Alejandra C; Sontag, Eduardo D; Merajver, Sofia D; Ninfa, Alexander J; Del Vecchio, Domitilla

2011-10-11

Biological signal transduction networks are commonly viewed as circuits that pass along information--in the process amplifying signals, enhancing sensitivity, or performing other signal-processing tasks--to transcriptional and other components. Here, we report on a "reverse-causality" phenomenon, which we call load-induced modulation. Through a combination of analytical and experimental tools, we discovered that signaling was modulated, in a surprising way, by downstream targets that receive the signal and, in doing so, apply what in physics is called a load. Specifically, we found that non-intuitive changes in response dynamics occurred for a covalent modification cycle when load was present. Loading altered the response time of a system, depending on whether the activity of one of the enzymes was maximal and the other was operating at its minimal rate or whether both enzymes were operating at submaximal rates. These two conditions, which we call "limit regime" and "intermediate regime," were associated with increased or decreased response times, respectively. The bandwidth, the range of frequency in which the system can process information, decreased in the presence of load, suggesting that downstream targets participate in establishing a balance between noise-filtering capabilities and a circuit's ability to process high-frequency stimulation. Nodes in a signaling network are not independent relay devices, but rather are modulated by their downstream targets.

Gene Prioritization by Integrated Analysis of Protein Structural and Network Topological Properties for the Protein-Protein Interaction Network of Neurological Disorders

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Yashna Paul

2016-01-01

Full Text Available Neurological disorders are known to show similar phenotypic manifestations like anxiety, depression, and cognitive impairment. There is a need to identify shared genetic markers and molecular pathways in these diseases, which lead to such comorbid conditions. Our study aims to prioritize novel genetic markers that might increase the susceptibility of patients affected with one neurological disorder to other diseases with similar manifestations. Identification of pathways involving common candidate markers will help in the development of improved diagnosis and treatments strategies for patients affected with neurological disorders. This systems biology study for the first time integratively uses 3D-structural protein interface descriptors and network topological properties that characterize proteins in a neurological protein interaction network, to aid the identification of genes that are previously not known to be shared between these diseases. Results of protein prioritization by machine learning have identified known as well as new genetic markers which might have direct or indirect involvement in several neurological disorders. Important gene hubs have also been identified that provide an evidence for shared molecular pathways in the neurological disease network.

Topological properties of complex networks in protein structures

Science.gov (United States)

Kim, Kyungsik; Jung, Jae-Won; Min, Seungsik

2014-03-01

We study topological properties of networks in structural classification of proteins. We model the native-state protein structure as a network made of its constituent amino-acids and their interactions. We treat four structural classes of proteins composed predominantly of α helices and β sheets and consider several proteins from each of these classes whose sizes range from amino acids of the Protein Data Bank. Particularly, we simulate and analyze the network metrics such as the mean degree, the probability distribution of degree, the clustering coefficient, the characteristic path length, the local efficiency, and the cost. This work was supported by the KMAR and DP under Grant WISE project (153-3100-3133-302-350).

Engineering Synthetic Proteins to Generate Ca2+ Signals in Mammalian Cells.

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Qudrat, Anam; Truong, Kevin

2017-03-17

The versatility of Ca 2+ signals allows it to regulate diverse cellular processes such as migration, apoptosis, motility and exocytosis. In some receptors (e.g., VEGFR2), Ca 2+ signals are generated upon binding their ligand(s) (e.g., VEGF-A). Here, we employed a design strategy to engineer proteins that generate a Ca 2+ signal upon binding various extracellular stimuli by creating fusions of protein domains that oligomerize to the transmembrane domain and the cytoplasmic tail of the VEGFR2. To test the strategy, we created chimeric proteins that generate Ca 2+ signals upon stimulation with various extracellular stimuli (e.g., rapamycin, EDTA or extracellular free Ca 2+ ). By coupling these chimeric proteins that generate Ca 2+ signals with proteins that respond to Ca 2+ signals, we rewired, for example, dynamic cellular blebbing to increases in extracellular free Ca 2+ . Thus, using this design strategy, it is possible to engineer proteins to generate a Ca 2+ signal to rewire a wide range of extracellular stimuli to a wide range of Ca 2+ -activated processes.

Signaling mechanisms underlying the robustness and tunability of the plant immune network

Science.gov (United States)

Kim, Yungil; Tsuda, Kenichi; Igarashi, Daisuke; Hillmer, Rachel A.; Sakakibara, Hitoshi; Myers, Chad L.; Katagiri, Fumiaki

2014-01-01

Summary How does robust and tunable behavior emerge in a complex biological network? We sought to understand this for the signaling network controlling pattern-triggered immunity (PTI) in Arabidopsis. A dynamic network model containing four major signaling sectors, the jasmonate, ethylene, PAD4, and salicylate sectors, which together explain up to 80% of the PTI level, was built using data for dynamic sector activities and PTI levels under exhaustive combinatorial sector perturbations. Our regularized multiple regression model had a high level of predictive power and captured known and unexpected signal flows in the network. The sole inhibitory sector in the model, the ethylene sector, was central to the network robustness via its inhibition of the jasmonate sector. The model's multiple input sites linked specific signal input patterns varying in strength and timing to different network response patterns, indicating a mechanism enabling tunability. PMID:24439900

Protein intrinsic disorder in plants.

Science.gov (United States)

Pazos, Florencio; Pietrosemoli, Natalia; García-Martín, Juan A; Solano, Roberto

2013-09-12

To some extent contradicting the classical paradigm of the relationship between protein 3D structure and function, now it is clear that large portions of the proteomes, especially in higher organisms, lack a fixed structure and still perform very important functions. Proteins completely or partially unstructured in their native (functional) form are involved in key cellular processes underlain by complex networks of protein interactions. The intrinsic conformational flexibility of these disordered proteins allows them to bind multiple partners in transient interactions of high specificity and low affinity. In concordance, in plants this type of proteins has been found in processes requiring these complex and versatile interaction networks. These include transcription factor networks, where disordered proteins act as integrators of different signals or link different transcription factor subnetworks due to their ability to interact (in many cases simultaneously) with different partners. Similarly, they also serve as signal integrators in signaling cascades, such as those related to response to external stimuli. Disordered proteins have also been found in plants in many stress-response processes, acting as protein chaperones or protecting other cellular components and structures. In plants, it is especially important to have complex and versatile networks able to quickly and efficiently respond to changing environmental conditions since these organisms cannot escape and have no other choice than adapting to them. Consequently, protein disorder can play an especially important role in plants, providing them with a fast mechanism to obtain complex, interconnected and versatile molecular networks.

Electrocardiogram (ECG Signal Modeling and Noise Reduction Using Hopfield Neural Networks

Directory of Open Access Journals (Sweden)

F. Bagheri

2013-02-01

Full Text Available The Electrocardiogram (ECG signal is one of the diagnosing approaches to detect heart disease. In this study the Hopfield Neural Network (HNN is applied and proposed for ECG signal modeling and noise reduction. The Hopfield Neural Network (HNN is a recurrent neural network that stores the information in a dynamic stable pattern. This algorithm retrieves a pattern stored in memory in response to the presentation of an incomplete or noisy version of that pattern. Computer simulation results show that this method can successfully model the ECG signal and remove high-frequency noise.

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

Directory of Open Access Journals (Sweden)

Andre Terzic

2009-04-01

Full Text Available 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.

Discrete dynamic modeling of T cell survival signaling networks

Science.gov (United States)

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).

Protein kinase N2 regulates AMP kinase signaling and insulin responsiveness of glucose metabolism in skeletal muscle.

Science.gov (United States)

Ruby, Maxwell A; Riedl, Isabelle; Massart, Julie; Åhlin, Marcus; Zierath, Juleen R

2017-10-01

Insulin resistance is central to the development of type 2 diabetes and related metabolic disorders. Because skeletal muscle is responsible for the majority of whole body insulin-stimulated glucose uptake, regulation of glucose metabolism in this tissue is of particular importance. Although Rho GTPases and many of their affecters influence skeletal muscle metabolism, there is a paucity of information on the protein kinase N (PKN) family of serine/threonine protein kinases. We investigated the impact of PKN2 on insulin signaling and glucose metabolism in primary human skeletal muscle cells in vitro and mouse tibialis anterior muscle in vivo. PKN2 knockdown in vitro decreased insulin-stimulated glucose uptake, incorporation into glycogen, and oxidation. PKN2 siRNA increased 5'-adenosine monophosphate-activated protein kinase (AMPK) signaling while stimulating fatty acid oxidation and incorporation into triglycerides and decreasing protein synthesis. At the transcriptional level, PKN2 knockdown increased expression of PGC-1α and SREBP-1c and their target genes. In mature skeletal muscle, in vivo PKN2 knockdown decreased glucose uptake and increased AMPK phosphorylation. Thus, PKN2 alters key signaling pathways and transcriptional networks to regulate glucose and lipid metabolism. Identification of PKN2 as a novel regulator of insulin and AMPK signaling may provide an avenue for manipulation of skeletal muscle metabolism. Copyright © 2017 the American Physiological Society.

Kinome signaling through regulated protein-protein interactions in normal and cancer cells.

Science.gov (United States)

Pawson, Tony; Kofler, Michael

2009-04-01

The flow of molecular information through normal and oncogenic signaling pathways frequently depends on protein phosphorylation, mediated by specific kinases, and the selective binding of the resulting phosphorylation sites to interaction domains present on downstream targets. This physical and functional interplay of catalytic and interaction domains can be clearly seen in cytoplasmic tyrosine kinases such as Src, Abl, Fes, and ZAP-70. Although the kinase and SH2 domains of these proteins possess similar intrinsic properties of phosphorylating tyrosine residues or binding phosphotyrosine sites, they also undergo intramolecular interactions when linked together, in a fashion that varies from protein to protein. These cooperative interactions can have diverse effects on substrate recognition and kinase activity, and provide a variety of mechanisms to link the stimulation of catalytic activity to substrate recognition. Taken together, these data have suggested how protein kinases, and the signaling pathways in which they are embedded, can evolve complex properties through the stepwise linkage of domains within single polypeptides or multi-protein assemblies.

Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

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Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

2015-11-01

Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases.

Gene regulatory and signaling networks exhibit distinct topological distributions of motifs

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Ferreira, Gustavo Rodrigues; Nakaya, Helder Imoto; Costa, Luciano da Fontoura

2018-04-01

The biological processes of cellular decision making and differentiation involve a plethora of signaling pathways and gene regulatory circuits. These networks in turn exhibit a multitude of motifs playing crucial parts in regulating network activity. Here we compare the topological placement of motifs in gene regulatory and signaling networks and observe that it suggests different evolutionary strategies in motif distribution for distinct cellular subnetworks.

Topology-function conservation in protein-protein interaction networks.

Science.gov (United States)

Davis, Darren; Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Stojmirovic, Aleksandar; Pržulj, Nataša

2015-05-15

Proteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species. To improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms. © The Author 2015. Published by Oxford University Press.

The architectural design of networks of protein domain architectures.

Science.gov (United States)

Hsu, Chia-Hsin; Chen, Chien-Kuo; Hwang, Ming-Jing

2013-08-23

Protein domain architectures (PDAs), in which single domains are linked to form multiple-domain proteins, are a major molecular form used by evolution for the diversification of protein functions. However, the design principles of PDAs remain largely uninvestigated. In this study, we constructed networks to connect domain architectures that had grown out from the same single domain for every single domain in the Pfam-A database and found that there are three main distinctive types of these networks, which suggests that evolution can exploit PDAs in three different ways. Further analysis showed that these three different types of PDA networks are each adopted by different types of protein domains, although many networks exhibit the characteristics of more than one of the three types. Our results shed light on nature's blueprint for protein architecture and provide a framework for understanding architectural design from a network perspective.

Regulation of G protein-coupled receptor signalling: focus on the cardiovascular system and regulator of G protein signalling proteins

NARCIS (Netherlands)

Hendriks-Balk, Mariëlle C.; Peters, Stephan L. M.; Michel, Martin C.; Alewijnse, Astrid E.

2008-01-01

G protein-coupled receptors (GPCRs) are involved in many biological processes. Therefore, GPCR function is tightly controlled both at receptor level and at the level of signalling components. Well-known mechanisms by which GPCR function can be regulated comprise desensitization/resensitization

Optical Performance Monitoring and Signal Optimization in Optical Networks

DEFF Research Database (Denmark)

Petersen, Martin Nordal

2006-01-01

The thesis studies performance monitoring for the next generation optical networks. The focus is on all-optical networks with bit-rates of 10 Gb/s or above. Next generation all-optical networks offer large challenges as the optical transmitted distance increases and the occurrence of electrical-optical......-electrical regeneration points decreases. This thesis evaluates the impact of signal degrading effects that are becoming of increasing concern in all-optical high-speed networks due to all-optical switching and higher bit-rates. Especially group-velocity-dispersion (GVD) and a number of nonlinear effects will require...... enhanced attention to avoid signal degradations. The requirements for optical performance monitoring features are discussed, and the thesis evaluates the advantages and necessity of increasing the level of performance monitoring parameters in the physical layer. In particular, methods for optical...

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

Science.gov (United States)

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.

OPNET simulation Signaling System No.7 (SS7) network interfaces

OpenAIRE

Ow, Kong Chung.

2000-01-01

This thesis presents an OPNET model and simulation of the Signaling System No.7 (SS7) network, which is dubbed the world's largest data communications network. The main focus of the study is to model one of its levels, the Message Transfer Part Level 3, in accordance with the ITU.T recommendation Q.704. An overview of SS7 that includes the evolution and basics of SS7 architecture is provided to familarize the reader with the topic. This includes the protocol stack, signaling points, signaling...

Reverse engineering GTPase programming languages with reconstituted signaling networks.

Science.gov (United States)

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.

Integration of G protein α (Gα) signaling by the regulator of G protein signaling 14 (RGS14).

Science.gov (United States)

Brown, Nicole E; Goswami, Devrishi; Branch, Mary Rose; Ramineni, Suneela; Ortlund, Eric A; Griffin, Patrick R; Hepler, John R

2015-04-03

RGS14 contains distinct binding sites for both active (GTP-bound) and inactive (GDP-bound) forms of Gα subunits. The N-terminal regulator of G protein signaling (RGS) domain binds active Gαi/o-GTP, whereas the C-terminal G protein regulatory (GPR) motif binds inactive Gαi1/3-GDP. The molecular basis for how RGS14 binds different activation states of Gα proteins to integrate G protein signaling is unknown. Here we explored the intramolecular communication between the GPR motif and the RGS domain upon G protein binding and examined whether RGS14 can functionally interact with two distinct forms of Gα subunits simultaneously. Using complementary cellular and biochemical approaches, we demonstrate that RGS14 forms a stable complex with inactive Gαi1-GDP at the plasma membrane and that free cytosolic RGS14 is recruited to the plasma membrane by activated Gαo-AlF4(-). Bioluminescence resonance energy transfer studies showed that RGS14 adopts different conformations in live cells when bound to Gα in different activation states. Hydrogen/deuterium exchange mass spectrometry revealed that RGS14 is a very dynamic protein that undergoes allosteric conformational changes when inactive Gαi1-GDP binds the GPR motif. Pure RGS14 forms a ternary complex with Gαo-AlF4(-) and an AlF4(-)-insensitive mutant (G42R) of Gαi1-GDP, as observed by size exclusion chromatography and differential hydrogen/deuterium exchange. Finally, a preformed RGS14·Gαi1-GDP complex exhibits full capacity to stimulate the GTPase activity of Gαo-GTP, demonstrating that RGS14 can functionally engage two distinct forms of Gα subunits simultaneously. Based on these findings, we propose a working model for how RGS14 integrates multiple G protein signals in host CA2 hippocampal neurons to modulate synaptic plasticity. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

A homologous mapping method for three-dimensional reconstruction of protein networks reveals disease-associated mutations.

Science.gov (United States)

Huang, Sing-Han; Lo, Yu-Shu; Luo, Yong-Chun; Tseng, Yu-Yao; Yang, Jinn-Moon

2018-03-19

One of the crucial steps toward understanding the associations among molecular interactions, pathways, and diseases in a cell is to investigate detailed atomic protein-protein interactions (PPIs) in the structural interactome. Despite the availability of large-scale methods for analyzing PPI networks, these methods often focused on PPI networks using genome-scale data and/or known experimental PPIs. However, these methods are unable to provide structurally resolved interaction residues and their conservations in PPI networks. Here, we reconstructed a human three-dimensional (3D) structural PPI network (hDiSNet) with the detailed atomic binding models and disease-associated mutations by enhancing our PPI families and 3D-domain interologs from 60,618 structural complexes and complete genome database with 6,352,363 protein sequences across 2274 species. hDiSNet is a scale-free network (γ = 2.05), which consists of 5177 proteins and 19,239 PPIs with 5843 mutations. These 19,239 structurally resolved PPIs not only expanded the number of PPIs compared to present structural PPI network, but also achieved higher agreement with gene ontology similarities and higher co-expression correlation than the ones of 181,868 experimental PPIs recorded in public databases. Among 5843 mutations, 1653 and 790 mutations involved in interacting domains and contacting residues, respectively, are highly related to diseases. Our hDiSNet can provide detailed atomic interactions of human disease and their associated proteins with mutations. Our results show that the disease-related mutations are often located at the contacting residues forming the hydrogen bonds or conserved in the PPI family. In addition, hDiSNet provides the insights of the FGFR (EGFR)-MAPK pathway for interpreting the mechanisms of breast cancer and ErbB signaling pathway in brain cancer. Our results demonstrate that hDiSNet can explore structural-based interactions insights for understanding the mechanisms of disease

Completing sparse and disconnected protein-protein network by deep learning.

Science.gov (United States)

Huang, Lei; Liao, Li; Wu, Cathy H

2018-03-22

Protein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of cellular and intracellular processes. Recently, an increasing number of computational methods have shifted from pair-wise prediction to network level prediction. Many of the existing network level methods predict PPIs under the assumption that the training network should be connected. However, this assumption greatly affects the prediction power and limits the application area because the current golden standard PPI networks are usually very sparse and disconnected. Therefore, how to effectively predict PPIs based on a training network that is sparse and disconnected remains a challenge. In this work, we developed a novel PPI prediction method based on deep learning neural network and regularized Laplacian kernel. We use a neural network with an autoencoder-like architecture to implicitly simulate the evolutionary processes of a PPI network. Neurons of the output layer correspond to proteins and are labeled with values (1 for interaction and 0 for otherwise) from the adjacency matrix of a sparse disconnected training PPI network. Unlike autoencoder, neurons at the input layer are given all zero input, reflecting an assumption of no a priori knowledge about PPIs, and hidden layers of smaller sizes mimic ancient interactome at different times during evolution. After the training step, an evolved PPI network whose rows are outputs of the neural network can be obtained. We then predict PPIs by applying the regularized Laplacian kernel to the transition matrix that is built upon the evolved PPI network. The results from cross-validation experiments show that the PPI prediction accuracies for yeast data and human data measured as AUC are increased by up to 8.4 and 14.9% respectively, as compared to the baseline. Moreover, the evolved PPI network can also help us leverage complementary information from the disconnected training network

mTORC1 Is a Major Regulatory Node in the FGF21 Signaling Network in Adipocytes

Directory of Open Access Journals (Sweden)

Annabel Y. Minard

2016-09-01

Full Text Available FGF21 improves the metabolic profile of obese animals through its actions on adipocytes. To elucidate the signaling network responsible for mediating these effects, we quantified dynamic changes in the adipocyte phosphoproteome following acute exposure to FGF21. FGF21 regulated a network of 821 phosphosites on 542 proteins. A major FGF21-regulated signaling node was mTORC1/S6K. In contrast to insulin, FGF21 activated mTORC1 via MAPK rather than through the canonical PI3K/AKT pathway. Activation of mTORC1/S6K by FGF21 was surprising because this is thought to contribute to deleterious metabolic effects such as obesity and insulin resistance. Rather, mTORC1 mediated many of the beneficial actions of FGF21 in vitro, including UCP1 and FGF21 induction, increased adiponectin secretion, and enhanced glucose uptake without any adverse effects on insulin action. This study provides a global view of FGF21 signaling and suggests that mTORC1 may act to facilitate FGF21-mediated health benefits in vivo.

Kinome-wide Decoding of Network-Attacking Mutations Rewiring Cancer Signaling

DEFF Research Database (Denmark)

Creixell, Pau; Schoof, Erwin M; Simpson, Craig D.

2015-01-01

Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network...... and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase...

Predicting Protein Function via Semantic Integration of Multiple Networks.

Science.gov (United States)

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.

Evolution of an intricate J-protein network driving protein disaggregation in eukaryotes.

Science.gov (United States)

Nillegoda, Nadinath B; Stank, Antonia; Malinverni, Duccio; Alberts, Niels; Szlachcic, Anna; Barducci, Alessandro; De Los Rios, Paolo; Wade, Rebecca C; Bukau, Bernd

2017-05-15

Hsp70 participates in a broad spectrum of protein folding processes extending from nascent chain folding to protein disaggregation. This versatility in function is achieved through a diverse family of J-protein cochaperones that select substrates for Hsp70. Substrate selection is further tuned by transient complexation between different classes of J-proteins, which expands the range of protein aggregates targeted by metazoan Hsp70 for disaggregation. We assessed the prevalence and evolutionary conservation of J-protein complexation and cooperation in disaggregation. We find the emergence of a eukaryote-specific signature for interclass complexation of canonical J-proteins. Consistently, complexes exist in yeast and human cells, but not in bacteria, and correlate with cooperative action in disaggregation in vitro. Signature alterations exclude some J-proteins from networking, which ensures correct J-protein pairing, functional network integrity and J-protein specialization. This fundamental change in J-protein biology during the prokaryote-to-eukaryote transition allows for increased fine-tuning and broadening of Hsp70 function in eukaryotes.

Protein intrinsic disorder in plants

Directory of Open Access Journals (Sweden)

Florencio ePazos

2013-09-01

Full Text Available To some extent contradicting the classical paradigm of the relationship between protein 3D structure and function, now it is clear that large portions of the proteomes, especially in higher organisms, lack a fixed structure and still perform very important functions. Proteins completely or partially unstructured in their native (functional form are involved in key cellular processes underlain by complex networks of protein interactions. The intrinsic conformational flexibility of these disordered proteins allows them to bind multiple partners in transient interactions of high specificity and low affinity. In concordance, in plants this type of proteins has been found in processes requiring these complex and versatile interaction networks. These include transcription factor networks, where disordered proteins act as integrators of different signals or link different transcription factor subnetworks due to their ability to interact (in many cases simultaneously with different partners. Similarly, they also serve as signal integrators in signalling cascades, such as those related to response to external stimuli. Disordered proteins have also been found in plants in many stress-response processes, acting as protein chaperones or protecting other cellular components and structures. In plants, it is especially important to have complex and versatile networks able to quickly and efficiently respond to changing environmental conditions since these organisms can not escape and have no other choice than adapting to them. Consequently, protein disorder can play an especially important role in plants, providing them with a fast mechanism to obtain complex, interconnected and versatile molecular networks.

Regulation of heterotrimeric G-protein signaling by NDPK/NME proteins and caveolins: an update.

Science.gov (United States)

Abu-Taha, Issam H; Heijman, Jordi; Feng, Yuxi; Vettel, Christiane; Dobrev, Dobromir; Wieland, Thomas

2018-02-01

Heterotrimeric G proteins are pivotal mediators of cellular signal transduction in eukaryotic cells and abnormal G-protein signaling plays an important role in numerous diseases. During the last two decades it has become evident that the activation status of heterotrimeric G proteins is both highly localized and strongly regulated by a number of factors, including a receptor-independent activation pathway of heterotrimeric G proteins that does not involve the classical GDP/GTP exchange and relies on nucleoside diphosphate kinases (NDPKs). NDPKs are NTP/NDP transphosphorylases encoded by the nme/nm23 genes that are involved in a variety of cellular events such as proliferation, migration, and apoptosis. They therefore contribute, for example, to tumor metastasis, angiogenesis, retinopathy, and heart failure. Interestingly, NDPKs are translocated and/or upregulated in human heart failure. Here we describe recent advances in the current understanding of NDPK functions and how they have an impact on local regulation of G-protein signaling.

Signal Transduction Pathways of TNAP: Molecular Network Analyses.

Science.gov (United States)

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.

Specificity and evolvability in eukaryotic protein interaction networks.

Directory of Open Access Journals (Sweden)

Pedro Beltrao

2007-02-01

Full Text Available Progress in uncovering the protein interaction networks of several species has led to questions of what underlying principles might govern their organization. Few studies have tried to determine the impact of protein interaction network evolution on the observed physiological differences between species. Using comparative genomics and structural information, we show here that eukaryotic species have rewired their interactomes at a fast rate of approximately 10(-5 interactions changed per protein pair, per million years of divergence. For Homo sapiens this corresponds to 10(3 interactions changed per million years. Additionally we find that the specificity of binding strongly determines the interaction turnover and that different biological processes show significantly different link dynamics. In particular, human proteins involved in immune response, transport, and establishment of localization show signs of positive selection for change of interactions. Our analysis suggests that a small degree of molecular divergence can give rise to important changes at the network level. We propose that the power law distribution observed in protein interaction networks could be partly explained by the cell's requirement for different degrees of protein binding specificity.

Variability of signal-to-noise ratio and the network analysis of gravitational wave burst signals

International Nuclear Information System (INIS)

Mohanty, S D; Rakhmanov, M; Klimenko, S; Mitselmakher, G

2006-01-01

The detection and estimation of gravitational wave burst signals, with a priori unknown polarization waveforms, requires the use of data from a network of detectors. Maximizing the network likelihood functional over all waveforms and sky positions yields point estimates for them as well as a detection statistic. However, the transformation from the data to estimates can become ill-conditioned over parts of the sky, resulting in significant errors in estimation. We modify the likelihood procedure by introducing a penalty functional which suppresses candidate solutions that display large signal-to-noise ratio (SNR) variability as the source is displaced on the sky. Simulations show that the resulting network analysis method performs significantly better in estimating the sky position of a source. Further, this method can be applied to any network, irrespective of the number or mutual alignment of detectors

Hybrid digital signal processing and neural networks applications in PWRs

International Nuclear Information System (INIS)

Eryurek, E.; Upadhyaya, B.R.; Kavaklioglu, K.

1991-01-01

Signal validation and plant subsystem tracking in power and process industries require the prediction of one or more state variables. Both heteroassociative and auotassociative neural networks were applied for characterizing relationships among sets of signals. A multi-layer neural network paradigm was applied for sensor and process monitoring in a Pressurized Water Reactor (PWR). This nonlinear interpolation technique was found to be very effective for these applications

Linking proteins to signaling pathways for experiment design and evaluation.

Directory of Open Access Journals (Sweden)

Illés J Farkas

Full Text Available Biomedical experimental work often focuses on altering the functions of selected proteins. These changes can hit signaling pathways, and can therefore unexpectedly and non-specifically affect cellular processes. We propose PathwayLinker, an online tool that can provide a first estimate of the possible signaling effects of such changes, e.g., drug or microRNA treatments. PathwayLinker minimizes the users' efforts by integrating protein-protein interaction and signaling pathway data from several sources with statistical significance tests and clear visualization. We demonstrate through three case studies that the developed tool can point out unexpected signaling bias in normal laboratory experiments and identify likely novel signaling proteins among the interactors of known drug targets. In our first case study we show that knockdown of the Caenorhabditis elegans gene cdc-25.1 (meant to avoid progeny may globally affect the signaling system and unexpectedly bias experiments. In the second case study we evaluate the loss-of-function phenotypes of a less known C. elegans gene to predict its function. In the third case study we analyze GJA1, an anti-cancer drug target protein in human, and predict for this protein novel signaling pathway memberships, which may be sources of side effects. Compared to similar services, a major advantage of PathwayLinker is that it drastically reduces the necessary amount of manual literature searches and can be used without a computational background. PathwayLinker is available at http://PathwayLinker.org. Detailed documentation and source code are available at the website.

Applying Statistical and Complex Network Methods to Explore the Key Signaling Molecules of Acupuncture Regulating Neuroendocrine-Immune Network

Directory of Open Access Journals (Sweden)

Kuo Zhang

2018-01-01

Full Text Available The mechanisms of acupuncture are still unclear. In order to reveal the regulatory effect of manual acupuncture (MA on the neuroendocrine-immune (NEI network and identify the key signaling molecules during MA modulating NEI network, we used a rat complete Freund’s adjuvant (CFA model to observe the analgesic and anti-inflammatory effect of MA, and, what is more, we used statistical and complex network methods to analyze the data about the expression of 55 common signaling molecules of NEI network in ST36 (Zusanli acupoint, and serum and hind foot pad tissue. The results indicate that MA had significant analgesic, anti-inflammatory effects on CFA rats; the key signaling molecules may play a key role during MA regulating NEI network, but further research is needed.

Digital Signal Processing for a Sliceable Transceiver for Optical Access Networks

DEFF Research Database (Denmark)

Saldaña Cercos, Silvia; Wagner, Christoph; Vegas Olmos, Juan José

2015-01-01

Methods to upgrade the network infrastructure to cope with current traffic demands has attracted increasing research efforts. A promising alternative is signal slicing. Signal slicing aims at re-using low bandwidth equipment to satisfy high bandwidth traffic demands. This technique has been used...... also for implementing full signal path symmetry in real-time oscilloscopes to provide performance and signal fidelity (i.e. lower noise and jitter). In this paper the key digital signal processing (DSP) subsystems required to achieve signal slicing are surveyed. It also presents, for the first time...... penalty is reported for 10 Gbps. Power savings of the order of hundreds of Watts can be obtained when using signal slicing as an alternative to 10 Gbps implemented access networks....

Machine learning approaches for the prediction of signal peptides and otherprotein sorting signals

DEFF Research Database (Denmark)

Nielsen, Henrik; Brunak, Søren; von Heijne, Gunnar

1999-01-01

Prediction of protein sorting signals from the sequence of amino acids has great importance in the field of proteomics today. Recently,the growth of protein databases, combined with machine learning approaches, such as neural networks and hidden Markov models, havemade it possible to achieve...

Plasticity of the MAPK signaling network in response to mechanical stress.

Directory of Open Access Journals (Sweden)

Andrea M Pereira

Full Text Available Cells display versatile responses to mechanical inputs and recent studies have identified the mitogen-activated protein kinase (MAPK cascades mediating the biological effects observed upon mechanical stimulation. Although, MAPK pathways can act insulated from each other, several mechanisms facilitate the crosstalk between the components of these cascades. Yet, the combinatorial complexity of potential molecular interactions between these elements have prevented the understanding of their concerted functions. To analyze the plasticity of the MAPK signaling network in response to mechanical stress we performed a non-saturating epistatic screen in resting and stretched conditions employing as readout a JNK responsive dJun-FRET biosensor. By knocking down MAPKs, and JNK pathway regulators, singly or in pairs in Drosophila S2R+ cells, we have uncovered unexpected regulatory links between JNK cascade kinases, Rho GTPases, MAPKs and the JNK phosphatase Puc. These relationships have been integrated in a system network model at equilibrium accounting for all experimentally validated interactions. This model allows predicting the global reaction of the network to its modulation in response to mechanical stress. It also highlights its context-dependent sensitivity.

Digital Signal Processing and Control for the Study of Gene Networks

Science.gov (United States)

Shin, Yong-Jun

2016-04-01

Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

PIN-G – A novel reporter for imaging and defining the effects of trafficking signals in membrane proteins

Directory of Open Access Journals (Sweden)

Hu Weiwen

2006-03-01

Full Text Available Abstract Background The identification of protein trafficking signals, and their interacting mechanisms, is a fundamental objective of modern biology. Unfortunately, the analysis of trafficking signals is complicated by their topography, hierarchical nature and regulation. Powerful strategies to test candidate motifs include their ability to direct simpler reporter proteins, to which they are fused, to the appropriate cellular compartment. However, present reporters are limited by their endogenous expression, paucity of cloning sites, and difficult detection in live cells. Results Consequently, we have engineered a mammalian expression vector encoding a novel trafficking reporter – pIN-G – consisting of a simple, type I integral protein bearing permissive intra/extracellular cloning sites, green fluorescent protein (GFP, cMyc and HA epitope tags. Fluorescence imaging, flow cytometry and biochemical assays of transfected HEK293 cells, confirm the size, topology and surface expression of PIN-G. Moreover, a pIN-G fusion construct, containing a Trans-Golgi Network (TGN targeting determinant, internalises rapidly from the cell surface and localises to the TGN. Additionally, another PIN-G fusion protein and its mutants reveal trafficking determinants in the cytoplasmic carboxy terminus of Kv1.4 voltage-gated potassium channels. Conclusion Together, these data indicate that pIN-G is a versatile, powerful, new reporter for analysing signals controlling membrane protein trafficking, surface expression and dynamics.

Protein annotation from protein interaction networks and Gene Ontology.

Science.gov (United States)

Nguyen, Cao D; Gardiner, Katheleen J; Cios, Krzysztof J

2011-10-01

We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precision and 60% recall versus 45% and 26% for Majority and 24% and 61% for χ²-statistics, respectively. Copyright © 2011 Elsevier Inc. All rights reserved.

Overload Control in a SIP Signaling Network

OpenAIRE

Masataka Ohta

2007-01-01

The Internet telephony employs a new type of Internet communication on which a mutual communication is realized by establishing sessions. Session Initiation Protocol (SIP) is used to establish sessions between end-users. For unreliable transmission (UDP), SIP message should be retransmitted when it is lost. The retransmissions increase a load of the SIP signaling network, and sometimes lead to performance degradation when a network is overloaded. The paper proposes an overload control for a S...

Combining neural networks for protein secondary structure prediction

DEFF Research Database (Denmark)

Riis, Søren Kamaric

1995-01-01

In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...

Sensor signal analysis by neural networks for surveillance in nuclear reactors

International Nuclear Information System (INIS)

Keyvan, S.; Rabelo, L.C.

1992-01-01

The application of neural networks as a tool for reactor diagnostics is examined here. Reactor pump signals utilized in a wear-out monitoring system developed for early detection of the degradation of a pump shaft are analyzed as a semi-benchmark test to study the feasibility of neural networks for monitoring and surveillance in nuclear reactors. The Adaptive Resonance Theory (ART 2 and ART 2-A) paradigm of neural networks is applied in this study. The signals are collected signals as well as generated signals simulating the wear progress. The wear-out monitoring system applies noise analysis techniques, and is capable of distinguishing these signals apart and providing a measure of the progress of the degradation. This paper presents the results of the analysis of these data, and provides an evaluation on the performance of ART 2-A and ART 2 for reactor signal analysis. The selection of ART 2 is due to its desired design principles such as unsupervised learning, stability-plasticity, search-direct access, and the match-reset tradeoffs

Efficient Mobility Management Signalling in Network Mobility Supported PMIPV6.

Science.gov (United States)

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.

Efficient Mobility Management Signalling in Network Mobility Supported PMIPV6

Directory of Open Access Journals (Sweden)

Ananthi Jebaseeli Samuelraj

2015-01-01

Full Text Available 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.

Applications of autoassociative neural networks for signal validation in accident management

International Nuclear Information System (INIS)

Fantoni, P.; Mazzola, A.

1994-01-01

The OECD Halden Reactor Project has been working for several years with computer based systems for determination on plant status including early fault detection and signal validation. The method here presented explores the possibility to use a neural network approach to validate important process signals during normal and abnormal plant conditions. In BWR plants, signal validation has two important applications: reliable thermal limits calculation and reliable inputs to other computerized systems that support the operator during accident scenarious. This work shows how a properly trained autoassociative neural network can promptly detect faulty process signal measurements and produce a best estimate of the actual process value. Noise has been artificially added to the input to evaluate the network ability to respond in a very low signal to noise ratio environment. Training and test datasets have been simulated by the real time transient simulator code APROS. Future development addresses the validation of the model through the use of real data from the plant. (author). 5 refs, 17 figs

Neural Networks for protein Structure Prediction

DEFF Research Database (Denmark)

Bohr, Henrik

1998-01-01

This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

Multiple-failure signal validation in nuclear power plants using artificial neural networks

International Nuclear Information System (INIS)

Fantoni, P.F.; Mazzola, A.

1996-01-01

The possibility of using a neural network to validate process signals during normal and abnormal plant conditions is explored. In boiling water reactor plants, signal validation is used to generate reliable thermal limits calculation and to supply reliable inputs to other computerized systems that support the operator during accident scenarios. The way that autoassociative neural networks can promptly detect faulty process signal measurements and produce a best estimate of the actual process values even in multifailure situations is shown. A method was developed to train the network for multiple sensor-failure detection, based on a random failure simulation algorithm. Noise was artificially added to the input to evaluate the network's ability to respond in a very low signal-to-noise ratio environment. Training and test data sets were simulated by the real-time transient simulator code APROS

Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks.

Science.gov (United States)

Lin, Wen-Hsien; Liu, Wei-Chung; Hwang, Ming-Jing

2009-03-11

Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure. Compared to a random selection of proteins, house-keeping gene-encoded proteins tended to have a greater number of directly interacting neighbors and occupy network positions in several shortest paths of interaction between protein pairs, whereas tissue-specific gene-encoded proteins did not. In addition, house-keeping gene-encoded proteins tended to connect with other house-keeping gene-encoded proteins in all tissue types, whereas tissue-specific gene-encoded proteins also tended to connect with other tissue-specific gene-encoded proteins, but only in approximately half of the tissue types examined. Our analysis showed that house-keeping gene-encoded proteins tend to occupy important network positions, while those encoded by tissue-specific genes do not. The biological implications of our findings were discussed and we proposed a hypothesis regarding how cells organize their protein tools in protein-protein interaction networks. Our results led us to speculate that house-keeping gene-encoded proteins might form a core in human protein-protein interaction networks, while clusters of tissue-specific gene

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

Science.gov (United States)

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.

Design principles for cancer therapy guided by changes in complexity of protein-protein interaction networks.

Science.gov (United States)

Benzekry, Sebastian; Tuszynski, Jack A; Rietman, Edward A; Lakka Klement, Giannoula

2015-05-28

The ever-increasing expanse of online bioinformatics data is enabling new ways to, not only explore the visualization of these data, but also to apply novel mathematical methods to extract meaningful information for clinically relevant analysis of pathways and treatment decisions. One of the methods used for computing topological characteristics of a space at different spatial resolutions is persistent homology. This concept can also be applied to network theory, and more specifically to protein-protein interaction networks, where the number of rings in an individual cancer network represents a measure of complexity. We observed a linear correlation of R = -0.55 between persistent homology and 5-year survival of patients with a variety of cancers. This relationship was used to predict the proteins within a protein-protein interaction network with the most impact on cancer progression. By re-computing the persistent homology after computationally removing an individual node (protein) from the protein-protein interaction network, we were able to evaluate whether such an inhibition would lead to improvement in patient survival. The power of this approach lied in its ability to identify the effects of inhibition of multiple proteins and in the ability to expose whether the effect of a single inhibition may be amplified by inhibition of other proteins. More importantly, we illustrate specific examples of persistent homology calculations, which correctly predict the survival benefit observed effects in clinical trials using inhibitors of the identified molecular target. We propose that computational approaches such as persistent homology may be used in the future for selection of molecular therapies in clinic. The technique uses a mathematical algorithm to evaluate the node (protein) whose inhibition has the highest potential to reduce network complexity. The greater the drop in persistent homology, the greater reduction in network complexity, and thus a larger

Cluster synchronization transmission of different external signals in discrete uncertain network

Science.gov (United States)

Li, Chengren; Lü, Ling; Chen, Liansong; Hong, Yixuan; Zhou, Shuang; Yang, Yiming

2018-07-01

We research cluster synchronization transmissions of different external signals in discrete uncertain network. Based on the Lyapunov theorem, the network controller and the identification law of uncertain adjustment parameter are designed, and they are efficiently used to achieve the cluster synchronization and the identification of uncertain adjustment parameter. In our technical scheme, the network nodes in each cluster and the transmitted external signal can be different, and they allow the presence of uncertain parameters in the network. Especially, we are free to choose the clustering topologies, the cluster number and the node number in each cluster.

The node-weighted Steiner tree approach to identify elements of cancer-related signaling pathways.

Science.gov (United States)

Sun, Yahui; Ma, Chenkai; Halgamuge, Saman

2017-12-28

Cancer constitutes a momentous health burden in our society. Critical information on cancer may be hidden in its signaling pathways. However, even though a large amount of money has been spent on cancer research, some critical information on cancer-related signaling pathways still remains elusive. Hence, new works towards a complete understanding of cancer-related signaling pathways will greatly benefit the prevention, diagnosis, and treatment of cancer. We propose the node-weighted Steiner tree approach to identify important elements of cancer-related signaling pathways at the level of proteins. This new approach has advantages over previous approaches since it is fast in processing large protein-protein interaction networks. We apply this new approach to identify important elements of two well-known cancer-related signaling pathways: PI3K/Akt and MAPK. First, we generate a node-weighted protein-protein interaction network using protein and signaling pathway data. Second, we modify and use two preprocessing techniques and a state-of-the-art Steiner tree algorithm to identify a subnetwork in the generated network. Third, we propose two new metrics to select important elements from this subnetwork. On a commonly used personal computer, this new approach takes less than 2 s to identify the important elements of PI3K/Akt and MAPK signaling pathways in a large node-weighted protein-protein interaction network with 16,843 vertices and 1,736,922 edges. We further analyze and demonstrate the significance of these identified elements to cancer signal transduction by exploring previously reported experimental evidences. Our node-weighted Steiner tree approach is shown to be both fast and effective to identify important elements of cancer-related signaling pathways. Furthermore, it may provide new perspectives into the identification of signaling pathways for other human diseases.

Detection of amide I signals of interfacial proteins in situ using SFG.

Science.gov (United States)

Wang, Jie; Even, Mark A; Chen, Xiaoyun; Schmaier, Alvin H; Waite, J Herbert; Chen, Zhan

2003-08-20

In this Communication, we demonstrate the novel observation that it is feasible to collect amide signals from polymer/protein solution interfaces in situ using sum frequency generation (SFG) vibrational spectroscopy. Such SFG amide signals allow for acquisition of more detailed molecular level information of entire interfacial protein structures. Proteins investigated include bovine serum albumin, mussel protein mefp-2, factor XIIa, and ubiquitin. Our studies indicate that different proteins generate different SFG amide signals at the polystyrene/protein solution interface, showing that they have different interfacial coverage, secondary structure, or orientation.

Predictive model identifies key network regulators of cardiomyocyte mechano-signaling.

Directory of Open Access Journals (Sweden)

Philip M Tan

2017-11-01

Full Text Available Mechanical strain is a potent stimulus for growth and remodeling in cells. Although many pathways have been implicated in stretch-induced remodeling, the control structures by which signals from distinct mechano-sensors are integrated to modulate hypertrophy and gene expression in cardiomyocytes remain unclear. Here, we constructed and validated a predictive computational model of the cardiac mechano-signaling network in order to elucidate the mechanisms underlying signal integration. The model identifies calcium, actin, Ras, Raf1, PI3K, and JAK as key regulators of cardiac mechano-signaling and characterizes crosstalk logic imparting differential control of transcription by AT1R, integrins, and calcium channels. We find that while these regulators maintain mostly independent control over distinct groups of transcription factors, synergy between multiple pathways is necessary to activate all the transcription factors necessary for gene transcription and hypertrophy. We also identify a PKG-dependent mechanism by which valsartan/sacubitril, a combination drug recently approved for treating heart failure, inhibits stretch-induced hypertrophy, and predict further efficacious pairs of drug targets in the network through a network-wide combinatorial search.

Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design.

Science.gov (United States)

Maguire, Jack B; Boyken, Scott E; Baker, David; Kuhlman, Brian

2018-05-08

Hydrogen bond networks play a critical role in determining the stability and specificity of biomolecular complexes, and the ability to design such networks is important for engineering novel structures, interactions, and enzymes. One key feature of hydrogen bond networks that makes them difficult to rationally engineer is that they are highly cooperative and are not energetically favorable until the hydrogen bonding potential has been satisfied for all buried polar groups in the network. Existing computational methods for protein design are ill-equipped for creating these highly cooperative networks because they rely on energy functions and sampling strategies that are focused on pairwise interactions. To enable the design of complex hydrogen bond networks, we have developed a new sampling protocol in the molecular modeling program Rosetta that explicitly searches for sets of amino acid mutations that can form self-contained hydrogen bond networks. For a given set of designable residues, the protocol often identifies many alternative sets of mutations/networks, and we show that it can readily be applied to large sets of residues at protein-protein interfaces or in the interior of proteins. The protocol builds on a recently developed method in Rosetta for designing hydrogen bond networks that has been experimentally validated for small symmetric systems but was not extensible to many larger protein structures and complexes. The sampling protocol we describe here not only recapitulates previously validated designs with performance improvements but also yields viable hydrogen bond networks for cases where the previous method fails, such as the design of large, asymmetric interfaces relevant to engineering protein-based therapeutics.

Stoichiometric balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis.

Science.gov (United States)

Holland, David O; Johnson, Margaret E

2018-03-01

Stoichiometric balance, or dosage balance, implies that proteins that are subunits of obligate complexes (e.g. the ribosome) should have copy numbers expressed to match their stoichiometry in that complex. Establishing balance (or imbalance) is an important tool for inferring subunit function and assembly bottlenecks. We show here that these correlations in protein copy numbers can extend beyond complex subunits to larger protein-protein interactions networks (PPIN) involving a range of reversible binding interactions. We develop a simple method for quantifying balance in any interface-resolved PPINs based on network structure and experimentally observed protein copy numbers. By analyzing such a network for the clathrin-mediated endocytosis (CME) system in yeast, we found that the real protein copy numbers were significantly more balanced in relation to their binding partners compared to randomly sampled sets of yeast copy numbers. The observed balance is not perfect, highlighting both under and overexpressed proteins. We evaluate the potential cost and benefits of imbalance using two criteria. First, a potential cost to imbalance is that 'leftover' proteins without remaining functional partners are free to misinteract. We systematically quantify how this misinteraction cost is most dangerous for strong-binding protein interactions and for network topologies observed in biological PPINs. Second, a more direct consequence of imbalance is that the formation of specific functional complexes depends on relative copy numbers. We therefore construct simple kinetic models of two sub-networks in the CME network to assess multi-protein assembly of the ARP2/3 complex and a minimal, nine-protein clathrin-coated vesicle forming module. We find that the observed, imperfectly balanced copy numbers are less effective than balanced copy numbers in producing fast and complete multi-protein assemblies. However, we speculate that strategic imbalance in the vesicle forming module

Seismic signal auto-detecing from different features by using Convolutional Neural Network

Science.gov (United States)

Huang, Y.; Zhou, Y.; Yue, H.; Zhou, S.

2017-12-01

We try Convolutional Neural Network to detect some features of seismic data and compare their efficience. The features include whether a signal is seismic signal or noise and the arrival time of P and S phase and each feature correspond to a Convolutional Neural Network. We first use traditional STA/LTA to recongnize some events and then use templete matching to find more events as training set for the Neural Network. To make the training set more various, we add some noise to the seismic data and make some synthetic seismic data and noise. The 3-component raw signal and time-frequancy ananlyze are used as the input data for our neural network. Our Training is performed on GPUs to achieve efficient convergence. Our method improved the precision in comparison with STA/LTA and template matching. We will move to recurrent neural network to see if this kind network is better in detect P and S phase.

Simultaneous multichannel signal transfers via chaos in a recurrent neural network.

Science.gov (United States)

Soma, Ken-ichiro; Mori, Ryota; Sato, Ryuichi; Furumai, Noriyuki; Nara, Shigetoshi

2015-05-01

We propose neural network model that demonstrates the phenomenon of signal transfer between separated neuron groups via other chaotic neurons that show no apparent correlations with the input signal. The model is a recurrent neural network in which it is supposed that synchronous behavior between small groups of input and output neurons has been learned as fragments of high-dimensional memory patterns, and depletion of neural connections results in chaotic wandering dynamics. Computer experiments show that when a strong oscillatory signal is applied to an input group in the chaotic regime, the signal is successfully transferred to the corresponding output group, although no correlation is observed between the input signal and the intermediary neurons. Signal transfer is also observed when multiple signals are applied simultaneously to separate input groups belonging to different memory attractors. In this sense simultaneous multichannel communications are realized, and the chaotic neural dynamics acts as a signal transfer medium in which the signal appears to be hidden.

Protein kinase C alpha controls erythropoietin receptor signaling.

NARCIS (Netherlands)

M.M. von Lindern (Marieke); M. Parren-Van Amelsvoort (Martine); T.B. van Dijk (Thamar); E. Deiner; B. Löwenberg (Bob); E. van den Akker (Emile); S. van Emst-de Vries (Sjenet); P.J. Willems (Patrick); H. Beug (Hartmut)

2000-01-01

textabstractProtein kinase C (PKC) is implied in the activation of multiple targets of erythropoietin (Epo) signaling, but its exact role in Epo receptor (EpoR) signal transduction and in the regulation of erythroid proliferation and differentiation remained elusive. We

Evidence for the additions of clustered interacting nodes during the evolution of protein interaction networks from network motifs

Directory of Open Access Journals (Sweden)

Guo Hao

2011-05-01

Full Text Available Abstract Background High-throughput screens have revealed large-scale protein interaction networks defining most cellular functions. How the proteins were added to the protein interaction network during its growth is a basic and important issue. Network motifs represent the simplest building blocks of cellular machines and are of biological significance. Results Here we study the evolution of protein interaction networks from the perspective of network motifs. We find that in current protein interaction networks, proteins of the same age class tend to form motifs and such co-origins of motif constituents are affected by their topologies and biological functions. Further, we find that the proteins within motifs whose constituents are of the same age class tend to be densely interconnected, co-evolve and share the same biological functions, and these motifs tend to be within protein complexes. Conclusions Our findings provide novel evidence for the hypothesis of the additions of clustered interacting nodes and point out network motifs, especially the motifs with the dense topology and specific function may play important roles during this process. Our results suggest functional constraints may be the underlying driving force for such additions of clustered interacting nodes.

All-optical signal processing for optical packet switching networks

NARCIS (Netherlands)

Liu, Y.; Hill, M.T.; Calabretta, N.; Tangdiongga, E.; Geldenhuys, R.; Zhang, S.; Li, Z.; Waardt, de H.; Khoe, G.D.; Dorren, H.J.S.; Iftekharuddin, K.M.; awwal, A.A.S.

2005-01-01

We discuss how all-optical signal processing might play a role in future all-optical packet switched networks. We introduce a concept of optical packet switches that employ entirely all-optical signal processing technology. The optical packet switch is made out of three functional blocks: the

Dynamic multiprotein assemblies shape the spatial structure of cell signaling.

Science.gov (United States)

Nussinov, Ruth; Jang, Hyunbum

2014-01-01

Cell signaling underlies critical cellular decisions. Coordination, efficiency as well as fail-safe mechanisms are key elements. How the cell ensures that these hallmarks are at play are important questions. Cell signaling is often viewed as taking place through discrete and cross-talking pathways; oftentimes these are modularized to emphasize distinct functions. While simple, convenient and clear, such models largely neglect the spatial structure of cell signaling; they also convey inter-modular (or inter-protein) spatial separation that may not exist. Here our thesis is that cell signaling is shaped by a network of multiprotein assemblies. While pre-organized, the assemblies and network are loose and dynamic. They contain transiently-associated multiprotein complexes which are often mediated by scaffolding proteins. They are also typically anchored in the membrane, and their continuum may span the cell. IQGAP1 scaffolding protein which binds proteins including Raf, calmodulin, Mek, Erk, actin, and tens more, with actin shaping B-cell (and likely other) membrane-anchored nanoclusters and allosterically polymerizing in dynamic cytoskeleton formation, and Raf anchoring in the membrane along with Ras, provides a striking example. The multivalent network of dynamic proteins and lipids, with specific interactions forming and breaking, can be viewed as endowing gel-like properties. Collectively, this reasons that efficient, productive and reliable cell signaling takes place primarily through transient, preorganized and cooperative protein-protein interactions spanning the cell rather than stochastic, diffusion-controlled processes. Copyright © 2014 Elsevier Ltd. All rights reserved.

CNNcon: improved protein contact maps prediction using cascaded neural networks.

Directory of Open Access Journals (Sweden)

Wang Ding

Full Text Available BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective

Predicting and validating protein interactions using network structure.

Directory of Open Access Journals (Sweden)

Pao-Yang Chen

2008-07-01

Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

Using the clustered circular layout as an informative method for visualizing protein-protein interaction networks.

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Fung, David C Y; Wilkins, Marc R; Hart, David; Hong, Seok-Hee

2010-07-01

The force-directed layout is commonly used in computer-generated visualizations of protein-protein interaction networks. While it is good for providing a visual outline of the protein complexes and their interactions, it has two limitations when used as a visual analysis method. The first is poor reproducibility. Repeated running of the algorithm does not necessarily generate the same layout, therefore, demanding cognitive readaptation on the investigator's part. The second limitation is that it does not explicitly display complementary biological information, e.g. Gene Ontology, other than the protein names or gene symbols. Here, we present an alternative layout called the clustered circular layout. Using the human DNA replication protein-protein interaction network as a case study, we compared the two network layouts for their merits and limitations in supporting visual analysis.

EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

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Kumari Sonal Choudhary

2016-06-01

Full Text Available 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.

EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

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

2016-06-01

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.

Computational modeling of allosteric regulation in the hsp90 chaperones: a statistical ensemble analysis of protein structure networks and allosteric communications.

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Kristin Blacklock

2014-06-01

Full Text Available A fundamental role of the Hsp90 chaperone in regulating functional activity of diverse protein clients is essential for the integrity of signaling networks. In this work we have combined biophysical simulations of the Hsp90 crystal structures with the protein structure network analysis to characterize the statistical ensemble of allosteric interaction networks and communication pathways in the Hsp90 chaperones. We have found that principal structurally stable communities could be preserved during dynamic changes in the conformational ensemble. The dominant contribution of the inter-domain rigidity to the interaction networks has emerged as a common factor responsible for the thermodynamic stability of the active chaperone form during the ATPase cycle. Structural stability analysis using force constant profiling of the inter-residue fluctuation distances has identified a network of conserved structurally rigid residues that could serve as global mediating sites of allosteric communication. Mapping of the conformational landscape with the network centrality parameters has demonstrated that stable communities and mediating residues may act concertedly with the shifts in the conformational equilibrium and could describe the majority of functionally significant chaperone residues. The network analysis has revealed a relationship between structural stability, global centrality and functional significance of hotspot residues involved in chaperone regulation. We have found that allosteric interactions in the Hsp90 chaperone may be mediated by modules of structurally stable residues that display high betweenness in the global interaction network. The results of this study have suggested that allosteric interactions in the Hsp90 chaperone may operate via a mechanism that combines rapid and efficient communication by a single optimal pathway of structurally rigid residues and more robust signal transmission using an ensemble of suboptimal multiple

SynGAP regulates protein synthesis and homeostatic synaptic plasticity in developing cortical networks.

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Chih-Chieh Wang

Full Text Available Disrupting the balance between excitatory and inhibitory neurotransmission in the developing brain has been causally linked with intellectual disability (ID and autism spectrum disorders (ASD. Excitatory synapse strength is regulated in the central nervous system by controlling the number of postsynaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs. De novo genetic mutations of the synaptic GTPase-activating protein (SynGAP are associated with ID and ASD. SynGAP is enriched at excitatory synapses and genetic suppression of SynGAP increases excitatory synaptic strength. However, exactly how SynGAP acts to maintain synaptic AMPAR content is unclear. We show here that SynGAP limits excitatory synaptic strength, in part, by suppressing protein synthesis in cortical neurons. The data presented here from in vitro, rat and mouse cortical networks, demonstrate that regulation of translation by SynGAP involves ERK, mTOR, and the small GTP-binding protein Rheb. Furthermore, these data show that GluN2B-containing NMDARs and the cognitive kinase CaMKII act upstream of SynGAP and that this signaling cascade is required for proper translation-dependent homeostatic synaptic plasticity of excitatory synapses in developing cortical networks.

Potato leafroll virus structural proteins manipulate overlapping, yet distinct protein interaction networks during infection.

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DeBlasio, Stacy L; Johnson, Richard; Sweeney, Michelle M; Karasev, Alexander; Gray, Stewart M; MacCoss, Michael J; Cilia, Michelle

2015-06-01

Potato leafroll virus (PLRV) produces a readthrough protein (RTP) via translational readthrough of the coat protein amber stop codon. The RTP functions as a structural component of the virion and as a nonincorporated protein in concert with numerous insect and plant proteins to regulate virus movement/transmission and tissue tropism. Affinity purification coupled to quantitative MS was used to generate protein interaction networks for a PLRV mutant that is unable to produce the read through domain (RTD) and compared to the known wild-type PLRV protein interaction network. By quantifying differences in the protein interaction networks, we identified four distinct classes of PLRV-plant interactions: those plant and nonstructural viral proteins interacting with assembled coat protein (category I); plant proteins in complex with both coat protein and RTD (category II); plant proteins in complex with the RTD (category III); and plant proteins that had higher affinity for virions lacking the RTD (category IV). Proteins identified as interacting with the RTD are potential candidates for regulating viral processes that are mediated by the RTP such as phloem retention and systemic movement and can potentially be useful targets for the development of strategies to prevent infection and/or viral transmission of Luteoviridae species that infect important crop species. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Urban Traffic Signal System Control Structural Optimization Based on Network Analysis

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Li Wang

2013-01-01

Full Text Available Advanced urban traffic signal control systems such as SCOOT and SCATS normally coordinate traffic network using multilevel hierarchical control mechanism. In this mechanism, several key intersections will be selected from traffic signal network and the network will be divided into different control subareas. Traditionally, key intersection selection and control subareas division are executed according to dynamic traffic counts and link length between intersections, which largely rely on traffic engineers’ experience. However, it omits important inherent characteristics of traffic network topology. In this paper, we will apply network analysis approach into these two aspects for traffic system control structure optimization. Firstly, the modified C-means clustering algorithm will be proposed to assess the importance of intersections in traffic network and furthermore determine the key intersections based on three indexes instead of merely on traffic counts in traditional methods. Secondly, the improved network community discovery method will be used to give more reasonable evidence in traffic control subarea division. Finally, to test the effectiveness of network analysis approach, a hardware-in-loop simulation environment composed of regional traffic control system, microsimulation software and signal controller hardware, will be built. Both traditional method and proposed approach will be implemented on simulation test bed to evaluate traffic operation performance indexes, for example, travel time, stop times, delay and average vehicle speed. Simulation results show that the proposed network analysis approach can improve the traffic control system operation performance effectively.

Mitogen-activated protein kinase signaling in plants

DEFF Research Database (Denmark)

Rodriguez, Maria Cristina Suarez; Petersen, Morten; Mundy, John

2010-01-01

crossinhibition, feedback control, and scaffolding. Plant MAPK cascades regulate numerous processes, including stress and hormonal responses, innate immunity, and developmental programs. Genetic analyses have uncovered several predominant MAPK components shared by several of these processes including...... of substrate proteins, whose altered activities mediate a wide array of responses, including changes in gene expression. Cascades may share kinase components, but their signaling specificity is maintained by spaciotemporal constraints and dynamic protein-protein interactions and by mechanisms that include...

Neural network committees for finger joint angle estimation from surface EMG signals

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Reddy Narender P

2009-01-01

Full Text Available Abstract Background In virtual reality (VR systems, the user's finger and hand positions are sensed and used to control the virtual environments. Direct biocontrol of VR environments using surface electromyography (SEMG signals may be more synergistic and unconstraining to the user. The purpose of the present investigation was to develop a technique to predict the finger joint angle from the surface EMG measurements of the extensor muscle using neural network models. Methodology SEMG together with the actual joint angle measurements were obtained while the subject was performing flexion-extension rotation of the index finger at three speeds. Several neural networks were trained to predict the joint angle from the parameters extracted from the SEMG signals. The best networks were selected to form six committees. The neural network committees were evaluated using data from new subjects. Results There was hysteresis in the measured SMEG signals during the flexion-extension cycle. However, neural network committees were able to predict the joint angle with reasonable accuracy. RMS errors ranged from 0.085 ± 0.036 for fast speed finger-extension to 0.147 ± 0.026 for slow speed finger extension, and from 0.098 ± 0.023 for the fast speed finger flexion to 0.163 ± 0.054 for slow speed finger flexion. Conclusion Although hysteresis was observed in the measured SEMG signals, the committees of neural networks were able to predict the finger joint angle from SEMG signals.

Biased and g protein-independent signaling of chemokine receptors

DEFF Research Database (Denmark)

Steen, Anne; Larsen, Olav; Thiele, Stefanie

2014-01-01

), different receptors (with the same ligand), or different tissues or cells (for the same ligand-receptor pair). Most often biased signaling is differentiated into G protein-dependent and β-arrestin-dependent signaling. Yet, it may also cover signaling differences within these groups. Moreover, it may...

Enhancing the Functional Content of Eukaryotic Protein Interaction Networks

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Pandey, Gaurav; Arora, Sonali; Manocha, Sahil; Whalen, Sean

2014-01-01

Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS) to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks. PMID:25275489

Protein-Protein Interaction Network and Gene Ontology

Science.gov (United States)

Choi, Yunkyu; Kim, Seok; Yi, Gwan-Su; Park, Jinah

Evolution of computer technologies makes it possible to access a large amount and various kinds of biological data via internet such as DNA sequences, proteomics data and information discovered about them. It is expected that the combination of various data could help researchers find further knowledge about them. Roles of a visualization system are to invoke human abilities to integrate information and to recognize certain patterns in the data. Thus, when the various kinds of data are examined and analyzed manually, an effective visualization system is an essential part. One instance of these integrated visualizations can be combination of protein-protein interaction (PPI) data and Gene Ontology (GO) which could help enhance the analysis of PPI network. We introduce a simple but comprehensive visualization system that integrates GO and PPI data where GO and PPI graphs are visualized side-by-side and supports quick reference functions between them. Furthermore, the proposed system provides several interactive visualization methods for efficiently analyzing the PPI network and GO directedacyclic- graph such as context-based browsing and common ancestors finding.

Activated protein synthesis and suppressed protein breakdown signaling in skeletal muscle of critically ill patients.

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Jakob G Jespersen

Full Text Available BACKGROUND: Skeletal muscle mass is controlled by myostatin and Akt-dependent signaling on mammalian target of rapamycin (mTOR, glycogen synthase kinase 3β (GSK3β and forkhead box O (FoxO pathways, but it is unknown how these pathways are regulated in critically ill human muscle. To describe factors involved in muscle mass regulation, we investigated the phosphorylation and expression of key factors in these protein synthesis and breakdown signaling pathways in thigh skeletal muscle of critically ill intensive care unit (ICU patients compared with healthy controls. METHODOLOGY/PRINCIPAL FINDINGS: ICU patients were systemically inflamed, moderately hyperglycemic, received insulin therapy, and showed a tendency to lower plasma branched chain amino acids compared with controls. Using Western blotting we measured Akt, GSK3β, mTOR, ribosomal protein S6 kinase (S6k, eukaryotic translation initiation factor 4E binding protein 1 (4E-BP1, and muscle ring finger protein 1 (MuRF1; and by RT-PCR we determined mRNA expression of, among others, insulin-like growth factor 1 (IGF-1, FoxO 1, 3 and 4, atrogin1, MuRF1, interleukin-6 (IL-6, tumor necrosis factor α (TNF-α and myostatin. Unexpectedly, in critically ill ICU patients Akt-mTOR-S6k signaling was substantially higher compared with controls. FoxO1 mRNA was higher in patients, whereas FoxO3, atrogin1 and myostatin mRNAs and MuRF1 protein were lower compared with controls. A moderate correlation (r2=0.36, p<0.05 between insulin infusion dose and phosphorylated Akt was demonstrated. CONCLUSIONS/SIGNIFICANCE: We present for the first time muscle protein turnover signaling in critically ill ICU patients, and we show signaling pathway activity towards a stimulation of muscle protein synthesis and a somewhat inhibited proteolysis.

Protein complex prediction based on k-connected subgraphs in protein interaction network

OpenAIRE

Habibi, Mahnaz; Eslahchi, Changiz; Wong, Limsoon

2010-01-01

Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on ...

Protein kinase C alpha controls erythropoietin receptor signaling

NARCIS (Netherlands)

von Lindern, M.; Parren-van Amelsvoort, M.; van Dijk, T.; Deiner, E.; van den Akker, E.; van Emst-de Vries, S.; Willems, P.; Beug, H.; Löwenberg, B.

2000-01-01

Protein kinase C (PKC) is implied in the activation of multiple targets of erythropoietin (Epo) signaling, but its exact role in Epo receptor (EpoR) signal transduction and in the regulation of erythroid proliferation and differentiation remained elusive. We analyzed the effect of PKC inhibitors

G Protein-Coupled Receptor Signaling in Stem Cells and Cancer.

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Lynch, Jennifer R; Wang, Jenny Yingzi

2016-05-11

G protein-coupled receptors (GPCRs) are a large superfamily of cell-surface signaling proteins that bind extracellular ligands and transduce signals into cells via heterotrimeric G proteins. GPCRs are highly tractable drug targets. Aberrant expression of GPCRs and G proteins has been observed in various cancers and their importance in cancer stem cells has begun to be appreciated. We have recently reported essential roles for G protein-coupled receptor 84 (GPR84) and G protein subunit Gαq in the maintenance of cancer stem cells in acute myeloid leukemia. This review will discuss how GPCRs and G proteins regulate stem cells with a focus on cancer stem cells, as well as their implications for the development of novel targeted cancer therapies.

G Protein-Coupled Receptor Signaling in Stem Cells and Cancer

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Jennifer R. Lynch

2016-05-01

Full Text Available G protein-coupled receptors (GPCRs are a large superfamily of cell-surface signaling proteins that bind extracellular ligands and transduce signals into cells via heterotrimeric G proteins. GPCRs are highly tractable drug targets. Aberrant expression of GPCRs and G proteins has been observed in various cancers and their importance in cancer stem cells has begun to be appreciated. We have recently reported essential roles for G protein-coupled receptor 84 (GPR84 and G protein subunit Gαq in the maintenance of cancer stem cells in acute myeloid leukemia. This review will discuss how GPCRs and G proteins regulate stem cells with a focus on cancer stem cells, as well as their implications for the development of novel targeted cancer therapies.

Phospholipase D and phosphatidic acid in plant defence response: from protein-protein and lipid-protein interactions to hormone signalling.

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Zhao, Jian

2015-04-01

Phospholipase Ds (PLDs) and PLD-derived phosphatidic acids (PAs) play vital roles in plant hormonal and environmental responses and various cellular dynamics. Recent studies have further expanded the functions of PLDs and PAs into plant-microbe interaction. The molecular diversities and redundant functions make PLD-PA an important signalling complex regulating lipid metabolism, cytoskeleton dynamics, vesicle trafficking, and hormonal signalling in plant defence through protein-protein and protein-lipid interactions or hormone signalling. Different PLD-PA signalling complexes and their targets have emerged as fast-growing research topics for understanding their numerous but not yet established roles in modifying pathogen perception, signal transduction, and downstream defence responses. Meanwhile, advanced lipidomics tools have allowed researchers to reveal further the mechanisms of PLD-PA signalling complexes in regulating lipid metabolism and signalling, and their impacts on jasmonic acid/oxylipins, salicylic acid, and other hormone signalling pathways that essentially mediate plant defence responses. This review attempts to summarize the progress made in spatial and temporal PLD/PA signalling as well as PLD/PA-mediated modification of plant defence. It presents an in-depth discussion on the functions and potential mechanisms of PLD-PA complexes in regulating actin filament/microtubule cytoskeleton, vesicle trafficking, and hormonal signalling, and in influencing lipid metabolism-derived metabolites as critical signalling components in plant defence responses. The discussion puts PLD-PA in a broader context in order to guide future research. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

WRKY proteins: signaling and regulation of expression during abiotic stress responses.

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Banerjee, Aditya; Roychoudhury, Aryadeep

2015-01-01

WRKY proteins are emerging players in plant signaling and have been thoroughly reported to play important roles in plants under biotic stress like pathogen attack. However, recent advances in this field do reveal the enormous significance of these proteins in eliciting responses induced by abiotic stresses. WRKY proteins act as major transcription factors, either as positive or negative regulators. Specific WRKY factors which help in the expression of a cluster of stress-responsive genes are being targeted and genetically modified to induce improved abiotic stress tolerance in plants. The knowledge regarding the signaling cascade leading to the activation of the WRKY proteins, their interaction with other proteins of the signaling pathway, and the downstream genes activated by them are altogether vital for justified targeting of the WRKY genes. WRKY proteins have also been considered to generate tolerance against multiple abiotic stresses with possible roles in mediating a cross talk between abiotic and biotic stress responses. In this review, we have reckoned the diverse signaling pattern and biological functions of WRKY proteins throughout the plant kingdom along with the growing prospects in this field of research.

Optimal Signal Design for Mixed Equilibrium Networks with Autonomous and Regular Vehicles

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Nan Jiang

2017-01-01

Full Text Available A signal design problem is studied for efficiently managing autonomous vehicles (AVs and regular vehicles (RVs simultaneously in transportation networks. AVs and RVs move on separate lanes and two types of vehicles share the green times at the same intersections. The signal design problem is formulated as a bilevel program. The lower-level model describes a mixed equilibrium where autonomous vehicles follow the Cournot-Nash (CN principle and RVs follow the user equilibrium (UE principle. In the upper-level model, signal timings are optimized at signalized intersections to allocate appropriate green times to both autonomous and RVs to minimize system travel cost. The sensitivity analysis based method is used to solve the bilevel optimization model. Various signal control strategies are evaluated through numerical examples and some insightful findings are obtained. It was found that the number of phases at intersections should be reduced for the optimal control of the AVs and RVs in the mixed networks. More importantly, incorporating AVs into the transportation network would improve the system performance due to the value of AV technologies in reducing random delays at intersections. Meanwhile, travelers prefer to choose AVs when the networks turn to be congested.

Non Linear Programming (NLP) formulation for quantitative modeling of protein signal transduction pathways.

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Mitsos, Alexander; Melas, Ioannis N; Morris, Melody K; Saez-Rodriguez, Julio; Lauffenburger, Douglas A; Alexopoulos, Leonidas G

2012-01-01

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.

Non Linear Programming (NLP formulation for quantitative modeling of protein signal transduction pathways.

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Alexander Mitsos

Full Text Available Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i excessive CPU time requirements and ii loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.

Plant morphogenesis, auxin, and the signal-trafficking network incompleteness theorem

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Karl J. Niklas

2012-03-01

Full Text Available Plant morphogenesis (the development of form and function requires signal-trafficking and cross-talking among all levels of organization to coordinate the operation of metabolic and genomic networked systems. Many if not all of these biological features can be rendered as logic circuits supervising the operation of one or more signal-activated metabolic or genome networks. This approach simplifies complex morphogenetic phenomena and allows for their aggregation into diagrams of larger, more "global" networked systems. This conceptualization is illustrated for morphogenesis in model plants such as maize (Zea mays and Thale cress (Arabidopsis thaliana from an evolutionary perspective. The phytohormone indole-acetic acid (IAA is used as an example for a well-known signaling chemical and discussed in terms of the logic circuits and signal-activated sub-systems for hormone-mediated wall loosening and cell expansion as well as polar/lateral intercellular IAA transport. For each of these phenomena, a circuit/sub-system diagram highlights missing components, either in the logic circuit or in the sub-system it supervises, that must be identified experimentally if each of these basic phenomena is to be fully understood within a phylogen

Signal Processing Device (SPD) for networked radiation monitoring system

International Nuclear Information System (INIS)

Dharmapurikar, A.; Bhattacharya, S.; Mukhopadhyay, P.K.; Sawhney, A.; Patil, R.K.

2010-01-01

A networked radiation and parameter monitoring system with three tier architecture is being developed. Signal Processing Device (SPD) is a second level sub-system node in the network. SPD is an embedded system which has multiple input channels and output communication interfaces. It acquires and processes data from first level parametric sensor devices, and sends to third level devices in response to request commands received from host. It also performs scheduled diagnostic operations and passes on the information to host. It supports inputs in the form of differential digital signals and analog voltage signals. SPD communicates with higher level devices over RS232/RS422/USB channels. The system has been designed with main requirements of minimal power consumption and harsh environment in radioactive plants. This paper discusses the hardware and software design details of SPD. (author)

Research on the Wire Network Signal Prediction Based on the Improved NNARX Model

Science.gov (United States)

Zhang, Zipeng; Fan, Tao; Wang, Shuqing

It is difficult to obtain accurately the wire net signal of power system's high voltage power transmission lines in the process of monitoring and repairing. In order to solve this problem, the signal measured in remote substation or laboratory is employed to make multipoint prediction to gain the needed data. But, the obtained power grid frequency signal is delay. In order to solve the problem, an improved NNARX network which can predict frequency signal based on multi-point data collected by remote substation PMU is describes in this paper. As the error curved surface of the NNARX network is more complicated, this paper uses L-M algorithm to train the network. The result of the simulation shows that the NNARX network has preferable predication performance which provides accurate real time data for field testing and maintenance.

eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks.

Science.gov (United States)

Clarke, Daniel J B; Kuleshov, Maxim V; Schilder, Brian M; Torre, Denis; Duffy, Mary E; Keenan, Alexandra B; Lachmann, Alexander; Feldmann, Axel S; Gundersen, Gregory W; Silverstein, Moshe C; Wang, Zichen; Ma'ayan, Avi

2018-05-25

While gene expression data at the mRNA level can be globally and accurately measured, profiling the activity of cell signaling pathways is currently much more difficult. eXpression2Kinases (X2K) computationally predicts involvement of upstream cell signaling pathways, given a signature of differentially expressed genes. X2K first computes enrichment for transcription factors likely to regulate the expression of the differentially expressed genes. The next step of X2K connects these enriched transcription factors through known protein-protein interactions (PPIs) to construct a subnetwork. The final step performs kinase enrichment analysis on the members of the subnetwork. X2K Web is a new implementation of the original eXpression2Kinases algorithm with important enhancements. X2K Web includes many new transcription factor and kinase libraries, and PPI networks. For demonstration, thousands of gene expression signatures induced by kinase inhibitors, applied to six breast cancer cell lines, are provided for fetching directly into X2K Web. The results are displayed as interactive downloadable vector graphic network images and bar graphs. Benchmarking various settings via random permutations enabled the identification of an optimal set of parameters to be used as the default settings in X2K Web. X2K Web is freely available from http://X2K.cloud.

The Interactomic Analysis Reveals Pathogenic Protein Networks in Phomopsis longicolla Underlying Seed Decay of Soybean

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Shuxian Li

2018-04-01

Full Text Available Phomopsis longicolla T. W. Hobbs (syn. Diaporthe longicolla is the primary cause of Phomopsis seed decay (PSD in soybean, Glycine max (L. Merrill. This disease results in poor seed quality and is one of the most economically important seed diseases in soybean. The objectives of this study were to infer protein–protein interactions (PPI and to identify conserved global networks and pathogenicity subnetworks in P. longicolla including orthologous pathways for cell signaling and pathogenesis. The interlog method used in the study identified 215,255 unique PPIs among 3,868 proteins. There were 1,414 pathogenicity related genes in P. longicolla identified using the pathogen host interaction (PHI database. Additionally, 149 plant cell wall degrading enzymes (PCWDE were detected. The network captured five different classes of carbohydrate degrading enzymes, including the auxiliary activities, carbohydrate esterases, glycoside hydrolases, glycosyl transferases, and carbohydrate binding molecules. From the PPI analysis, novel interacting partners were determined for each of the PCWDE classes. The most predominant class of PCWDE was a group of 60 glycoside hydrolases proteins. The glycoside hydrolase subnetwork was found to be interacting with 1,442 proteins within the network and was among the largest clusters. The orthologous proteins FUS3, HOG, CYP1, SGE1, and the g5566t.1 gene identified in this study could play an important role in pathogenicity. Therefore, the P. longicolla protein interactome (PiPhom generated in this study can lead to a better understanding of PPIs in soybean pathogens. Furthermore, the PPI may aid in targeting of genes and proteins for further studies of the pathogenicity mechanisms.

Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.

Directory of Open Access Journals (Sweden)

Dániel Bánky

Full Text Available Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks that compensates for the low degree (non-hub vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well, but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms (Mycobacterium tuberculosis, Plasmodium falciparum and MRSA Staphylococcus aureus, and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures

Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.

Science.gov (United States)

Bánky, Dániel; Iván, Gábor; Grolmusz, Vince

2013-01-01

Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks) that compensates for the low degree (non-hub) vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges) of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well), but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms (Mycobacterium tuberculosis, Plasmodium falciparum and MRSA Staphylococcus aureus), and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures importance in the

Data management of protein interaction networks

CERN Document Server

Cannataro, Mario

2012-01-01

Interactomics: a complete survey from data generation to knowledge extraction With the increasing use of high-throughput experimental assays, more and more protein interaction databases are becoming available. As a result, computational analysis of protein-to-protein interaction (PPI) data and networks, now known as interactomics, has become an essential tool to determine functionally associated proteins. From wet lab technologies to data management to knowledge extraction, this timely book guides readers through the new science of interactomics, giving them the tools needed to: Generate

Traffic signal synchronization in the saturated high-density grid road network.

Science.gov (United States)

Hu, Xiaojian; Lu, Jian; Wang, Wei; Zhirui, Ye

2015-01-01

Most existing traffic signal synchronization strategies do not perform well in the saturated high-density grid road network (HGRN). Traffic congestion often occurs in the saturated HGRN, and the mobility of the network is difficult to restore. In order to alleviate traffic congestion and to improve traffic efficiency in the network, the study proposes a regional traffic signal synchronization strategy, named the long green and long red (LGLR) traffic signal synchronization strategy. The essence of the strategy is to control the formation and dissipation of queues and to maximize the efficiency of traffic flows at signalized intersections in the saturated HGRN. With this strategy, the same signal control timing plan is used at all signalized intersections in the HGRN, and the straight phase of the control timing plan has a long green time and a long red time. Therefore, continuous traffic flows can be maintained when vehicles travel, and traffic congestion can be alleviated when vehicles stop. Using the strategy, the LGLR traffic signal synchronization model is developed, with the objective of minimizing the number of stops. Finally, the simulation is executed to analyze the performance of the model by comparing it to other models, and the superiority of the LGLR model is evident in terms of delay, number of stops, queue length, and overall performance in the saturated HGRN.

Induction of the unfolded protein response by constitutive G-protein signaling in rod photoreceptor cells.

Science.gov (United States)

Wang, Tian; Chen, Jeannie

2014-10-17

Phototransduction is a G-protein signal transduction cascade that converts photon absorption to a change in current at the plasma membrane. Certain genetic mutations affecting the proteins in the phototransduction cascade cause blinding disorders in humans. Some of these mutations serve as a genetic source of "equivalent light" that activates the cascade, whereas other mutations lead to amplification of the light response. How constitutive phototransduction causes photoreceptor cell death is poorly understood. We showed that persistent G-protein signaling, which occurs in rod arrestin and rhodopsin kinase knock-out mice, caused a rapid and specific induction of the PERK pathway of the unfolded protein response. These changes were not observed in the cGMP-gated channel knock-out rods, an equivalent light condition that mimics light-stimulated channel closure. Thus transducin signaling, but not channel closure, triggers rapid cell death in light damage caused by constitutive phototransduction. Additionally, we show that in the albino light damage model cell death was not associated with increase in global protein ubiquitination or unfolded protein response induction. Taken together, these observations provide novel mechanistic insights into the cell death pathway caused by constitutive phototransduction and identify the unfolded protein response as a potential target for therapeutic intervention. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

Network evolution: rewiring and signatures of conservation in signaling.

Directory of Open Access Journals (Sweden)

Mark G F Sun

Full Text Available The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate evolutionary mechanisms in Src Homology 3 (SH3 domain and kinase interaction networks using high-resolution specificity profiles. We constructed and examined networks for 23 fungal species ranging from Saccharomyces cerevisiae to Schizosaccharomyces pombe. We quantify rates of different rewiring mechanisms and show that interaction change through binding site evolution is faster than through gene gain or loss. We found that SH3 interactions evolve swiftly, at rates similar to those found in phosphoregulation evolution. Importantly, we show that interaction changes are sufficiently rapid to exhibit saturation phenomena at the observed timescales. Finally, focusing on the SH3 interaction network, we observe extensive clustering of binding sites on target proteins by SH3 domains and a strong correlation between the number of domains that bind a target protein (target in-degree and interaction conservation. The relationship between in-degree and interaction conservation is driven by two different effects, namely the number of clusters that correspond to interaction interfaces and the number of domains that bind to each cluster leads to sequence specific conservation, which in turn results in interaction conservation. In summary, we uncover several network evolution mechanisms likely to generalize across peptide recognition modules.

Discovering functional interdependence relationship in PPI networks for protein complex identification.

Science.gov (United States)

Lam, Winnie W M; Chan, Keith C C

2012-04-01

Protein molecules interact with each other in protein complexes to perform many vital functions, and different computational techniques have been developed to identify protein complexes in protein-protein interaction (PPI) networks. These techniques are developed to search for subgraphs of high connectivity in PPI networks under the assumption that the proteins in a protein complex are highly interconnected. While these techniques have been shown to be quite effective, it is also possible that the matching rate between the protein complexes they discover and those that are previously determined experimentally be relatively low and the "false-alarm" rate can be relatively high. This is especially the case when the assumption of proteins in protein complexes being more highly interconnected be relatively invalid. To increase the matching rate and reduce the false-alarm rate, we have developed a technique that can work effectively without having to make this assumption. The name of the technique called protein complex identification by discovering functional interdependence (PCIFI) searches for protein complexes in PPI networks by taking into consideration both the functional interdependence relationship between protein molecules and the network topology of the network. The PCIFI works in several steps. The first step is to construct a multiple-function protein network graph by labeling each vertex with one or more of the molecular functions it performs. The second step is to filter out protein interactions between protein pairs that are not functionally interdependent of each other in the statistical sense. The third step is to make use of an information-theoretic measure to determine the strength of the functional interdependence between all remaining interacting protein pairs. Finally, the last step is to try to form protein complexes based on the measure of the strength of functional interdependence and the connectivity between proteins. For performance evaluation

NT2 derived neuronal and astrocytic network signalling.

Directory of Open Access Journals (Sweden)

Eric J Hill

Full Text Available A major focus of stem cell research is the generation of neurons that may then be implanted to treat neurodegenerative diseases. However, a picture is emerging where astrocytes are partners to neurons in sustaining and modulating brain function. We therefore investigated the functional properties of NT2 derived astrocytes and neurons using electrophysiological and calcium imaging approaches. NT2 neurons (NT2Ns expressed sodium dependent action potentials, as well as responses to depolarisation and the neurotransmitter glutamate. NT2Ns exhibited spontaneous and coordinated calcium elevations in clusters and in extended processes, indicating local and long distance signalling. Tetrodotoxin sensitive network activity could also be evoked by electrical stimulation. Similarly, NT2 astrocytes (NT2As exhibited morphology and functional properties consistent with this glial cell type. NT2As responded to neuronal activity and to exogenously applied neurotransmitters with calcium elevations, and in contrast to neurons, also exhibited spontaneous rhythmic calcium oscillations. NT2As also generated propagating calcium waves that were gap junction and purinergic signalling dependent. Our results show that NT2 derived astrocytes exhibit appropriate functionality and that NT2N networks interact with NT2A networks in co-culture. These findings underline the utility of such cultures to investigate human brain cell type signalling under controlled conditions. Furthermore, since stem cell derived neuron function and survival is of great importance therapeutically, our findings suggest that the presence of complementary astrocytes may be valuable in supporting stem cell derived neuronal networks. Indeed, this also supports the intriguing possibility of selective therapeutic replacement of astrocytes in diseases where these cells are either lost or lose functionality.

Network based approaches reveal clustering in protein point patterns

Science.gov (United States)

Parker, Joshua; Barr, Valarie; Aldridge, Joshua; Samelson, Lawrence E.; Losert, Wolfgang

2014-03-01

Recent advances in super-resolution imaging have allowed for the sub-diffraction measurement of the spatial location of proteins on the surfaces of T-cells. The challenge is to connect these complex point patterns to the internal processes and interactions, both protein-protein and protein-membrane. We begin analyzing these patterns by forming a geometric network amongst the proteins and looking at network measures, such the degree distribution. This allows us to compare experimentally observed patterns to models. Specifically, we find that the experimental patterns differ from heterogeneous Poisson processes, highlighting an internal clustering structure. Further work will be to compare our results to simulated protein-protein interactions to determine clustering mechanisms.

[A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

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Zhang, Y; Liu, A; Yu, K

1999-06-01

A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

Determination of outdoor signal propagation via visibility analysis in outdoor wireless networks

Directory of Open Access Journals (Sweden)

Mustafa Coşar

2017-02-01

Full Text Available Wireless networks on university campuses has gained importance in recent years. These networks in major areas such as university campuses, are faced with many problems during the planning, design and establishment. These problems are among the first that comes to mind, the physical properties of the campus and is selected according to the characteristics of network equipment. There is no doubt at all points of a wireless network set up in order to provide uninterrupted service and quality of the signal is expected to be good. However, it should be understood literally cannot meet these expectations. Therefore, to solve many problems to campus planning and design can be made to have acceptable signal distribution will have the appropriate use of and satisfaction with increasing effect. In this study, due to the start of construction on the North Campus of Hitit University, wireless signal spread using the current spread has been determined with the help of geographic information systems visibility analysis. An area of 56 hectares, with the total of 9 AP the acceptable signal distribution was obtained.

Multiple-Localization and Hub Proteins

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Ota, Motonori; Gonja, Hideki; Koike, Ryotaro; Fukuchi, Satoshi

2016-01-01

Protein-protein interactions are fundamental for all biological phenomena, and protein-protein interaction networks provide a global view of the interactions. The hub proteins, with many interaction partners, play vital roles in the networks. We investigated the subcellular localizations of proteins in the human network, and found that the ones localized in multiple subcellular compartments, especially the nucleus/cytoplasm proteins (NCP), the cytoplasm/cell membrane proteins (CMP), and the nucleus/cytoplasm/cell membrane proteins (NCMP), tend to be hubs. Examinations of keywords suggested that among NCP, those related to post-translational modifications and transcription functions are the major contributors to the large number of interactions. These types of proteins are characterized by a multi-domain architecture and intrinsic disorder. A survey of the typical hub proteins with prominent numbers of interaction partners in the type revealed that most are either transcription factors or co-regulators involved in signaling pathways. They translocate from the cytoplasm to the nucleus, triggered by the phosphorylation and/or ubiquitination of intrinsically disordered regions. Among CMP and NCMP, the contributors to the numerous interactions are related to either kinase or ubiquitin ligase activity. Many of them reside on the cytoplasmic side of the cell membrane, and act as the upstream regulators of signaling pathways. Overall, these hub proteins function to transfer external signals to the nucleus, through the cell membrane and the cytoplasm. Our analysis suggests that multiple-localization is a crucial concept to characterize groups of hub proteins and their biological functions in cellular information processing. PMID:27285823

Modeling of axonal endoplasmic reticulum network by spastic paraplegia proteins.

Science.gov (United States)

Yalçın, Belgin; Zhao, Lu; Stofanko, Martin; O'Sullivan, Niamh C; Kang, Zi Han; Roost, Annika; Thomas, Matthew R; Zaessinger, Sophie; Blard, Olivier; Patto, Alex L; Sohail, Anood; Baena, Valentina; Terasaki, Mark; O'Kane, Cahir J

2017-07-25

Axons contain a smooth tubular endoplasmic reticulum (ER) network that is thought to be continuous with ER throughout the neuron; the mechanisms that form this axonal network are unknown. Mutations affecting reticulon or REEP proteins, with intramembrane hairpin domains that model ER membranes, cause an axon degenerative disease, hereditary spastic paraplegia (HSP). We show that Drosophila axons have a dynamic axonal ER network, which these proteins help to model. Loss of HSP hairpin proteins causes ER sheet expansion, partial loss of ER from distal motor axons, and occasional discontinuities in axonal ER. Ultrastructural analysis reveals an extensive ER network in axons, which shows larger and fewer tubules in larvae that lack reticulon and REEP proteins, consistent with loss of membrane curvature. Therefore HSP hairpin-containing proteins are required for shaping and continuity of axonal ER, thus suggesting roles for ER modeling in axon maintenance and function.

Pythoscape: A framework for generation of large protein similarity networks

OpenAIRE

Babbitt, Patricia; Barber, AE; Babbitt, PC

2012-01-01

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 pr

Protein sorting by lipid phase-like domains supports emergent signaling function in B lymphocyte plasma membranes.

Science.gov (United States)

Stone, Matthew B; Shelby, Sarah A; Núñez, Marcos F; Wisser, Kathleen; Veatch, Sarah L

2017-02-01

Diverse cellular signaling events, including B cell receptor (BCR) activation, are hypothesized to be facilitated by domains enriched in specific plasma membrane lipids and proteins that resemble liquid-ordered phase-separated domains in model membranes. This concept remains controversial and lacks direct experimental support in intact cells. Here, we visualize ordered and disordered domains in mouse B lymphoma cell membranes using super-resolution fluorescence localization microscopy, demonstrate that clustered BCR resides within ordered phase-like domains capable of sorting key regulators of BCR activation, and present a minimal, predictive model where clustering receptors leads to their collective activation by stabilizing an extended ordered domain. These results provide evidence for the role of membrane domains in BCR signaling and a plausible mechanism of BCR activation via receptor clustering that could be generalized to other signaling pathways. Overall, these studies demonstrate that lipid mediated forces can bias biochemical networks in ways that broadly impact signal transduction.

Signal peptide of eosinophil cationic protein is toxic to cells lacking signal peptide peptidase