The RNA-dependent protein kinase (PKR), initially known as a virus infection response protein, is found to differentially regulate two major players in the insulin signaling pathway, IRS1 and IRS2. PKR up-regulates the inhibitory phosphorylation of IRS1 and the expression of IRS2 at the transcriptional level.
Initially identified to be activated upon virus infection, the double-stranded RNA–dependent protein kinase (PKR) is best known for triggering cell defense responses by phosphorylating eIF-2α, thus suppressing RNA translation. We as well as others showed that the phosphorylation of PKR is down-regulated by insulin. In the present study, we further uncovered a novel function of PKR in regulating the IRS proteins. We found that PKR up-regulates the inhibitory phosphorylation of IRS1 at Ser312, which suppresses the tyrosine phosphorylation of IRS1. This effect of PKR on the phosphorylation of IRS1 is mediated by two other protein kinases, JNK and IKK. In contrast, PKR regulates IRS2, another major IRS family protein in the liver, at the transcriptional rather than the posttranslational level, and this effect is mediated by the transcription factor, FoxO1, which has been previously shown to be regulated by insulin and plays a significant role in glucose homeostasis and energy metabolism. In summary, we found for the first time that initially known as a virus infection response gene, PKR regulates the upstream central transmitters of insulin signaling, IRS1 and IRS2, through different mechanisms.
As chronic inflammation is a hallmark of obesity, pathways that integrate nutrient and pathogen sensing pathways are of great interest in understanding the mechanisms of insulin resistance, type 2 diabetes, and other chronic metabolic pathologies. Here, we provide evidence that double-stranded RNA dependent protein kinase (PKR) can respond to nutrient signals as well as endoplasmic reticulum (ER) stress and coordinate the activity of other critical inflammatory kinases such as the c-Jun N-terminal kinase (JNK) to regulate insulin action and metabolism. PKR also directly targets and modifies insulin receptor substrate and hence integrates nutrients and insulin action with a defined pathogen response system. Dietary and genetic obesity features marked activation of PKR in adipose and liver tissues and absence of PKR alleviates metabolic deterioration due to nutrient or energy excess in mice. These findings demonstrate PKR as a critical component of an inflammatory complex that responds to nutrients and organelle dysfunction.
Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods.
Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity.
We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations.
Many cellular behaviors including growth, differentiation, and movement are influenced by external stimuli. Such external stimuli are obtained, processed, and carried to the nucleus by the signaling network—a dense network of cellular biochemical reactions. Beyond being interesting for their role in directing cellular behavior, deleterious changes in a cell's signaling network can alter a cell's responses to external stimuli, giving rise to devastating diseases such as cancer. As a result, building accurate mathematical and computational models of cellular signaling networks is a major endeavor in biology. The scale and complexity of these networks render them difficult to analyze by experimental techniques alone, which has led to the development of computational analysis methods. In this paper, we present a novel computational simulation technique that can provide qualitatively accurate predictions of the behavior of a cellular signaling network without requiring detailed knowledge of the signaling network's parameters. Our approach makes use of recent discoveries that network structure alone can determine many aspects of a network's dynamics. When compared against experimental results, our method correctly predicted 90% of the cases considered. In those where it did not agree, our approach provided valuable insights into discrepancies between known network structure and experimental observations.
The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology.
We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation.
The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available.
The cellular response to environmental signals is largely dependent upon the induction of responsive protein kinase signaling pathways. Within these pathways, distinct protein-protein interactions play a role in determining the specificity of the response through regulation of kinase function. The interferon-induced serine/threonine protein kinase, PKR, is activated in response to various environmental stimuli. Like many protein kinases, PKR is regulated through direct interactions with activator and inhibitory molecules, including P58IPK, a cellular PKR inhibitor. P58IPK functions to represses PKR-mediated phosphorylation of the eukaryotic initiation factor 2α subunit (eIF-2α) through a direct interaction, thereby relieving the PKR-imposed block on mRNA translation and cell growth. To further define the molecular mechanism underlying regulation of PKR, we have utilized an interaction cloning strategy to identify a novel cDNA encoding a P58IPK-interacting protein. This protein, designated P52rIPK, possesses limited homology to the charged domain of Hsp90 and is expressed in a wide range of cell lines. P52rIPK and P58IPK interacted in a yeast two-hybrid assay and were recovered as a complex from mammalian cell extracts. When coexpressed with PKR in yeast, P58IPK repressed PKR-mediated eIF-2α phosphorylation, inhibiting the normally toxic and growth-suppressive effects associated with PKR function. Conversely, introduction of P52rIPK into these strains resulted in restoration of both PKR activity and eIF-2α phosphorylation, concomitant with growth suppression due to inhibition of P58IPK function. Furthermore, P52rIPK inhibited P58IPK function in a reconstituted in vitro PKR-regulatory assay. Our results demonstrate that P58IPK is inhibited through a direct interaction with P52rIPK which, in turn, results in upregulation of PKR activity. Taken together, our data describe a novel protein kinase-regulatory system which encompasses an intersection of interferon-, stress-, and growth-regulatory pathways.
Palmitic acid, the most common saturated free fatty acid, has been implicated in ER (endoplasmic reticulum) stress-mediated apoptosis. This lipoapotosis is dependent, in part, on the upregulation of the activating transcription factor-4 (ATF4). To better understand the mechanisms by which palmitate upregulates the expression level of ATF4, we integrated literature information on palmitate-induced ER stress signaling into a discrete dynamic model. The model provides an in silico framework that enables simulations and predictions. The model predictions were confirmed through further experiments in human hepatocellular carcinoma (HepG2) cells and the results were used to update the model and our current understanding of the signaling induced by palmitate.
The three key things from the in silico simulation and experimental results are: 1) palmitate induces different signaling pathways (PKR (double-stranded RNA-activated protein kinase), PERK (PKR-like ER kinase), PKA (cyclic AMP (cAMP)-dependent protein kinase A) in a time dependent-manner, 2) both ATF4 and CREB1 (cAMP-responsive element-binding protein 1) interact with the Atf4 promoter to contribute to a prolonged accumulation of ATF4, and 3) CREB1 is involved in ER-stress induced apoptosis upon palmitate treatment, by regulating ATF4 expression and possibly Ca2+ dependent-CaM (calmodulin) signaling pathway.
The in silico model helped to delineate the essential signaling pathways in palmitate-mediated apoptosis.
ATF4; Palmitate-induced ER stress; CREB1; Discrete dynamic model; Signal transduction
Cellular decisions are determined by complex molecular interaction networks. Large-scale signaling networks are currently being reconstructed, but the kinetic parameters and quantitative data that would allow for dynamic modeling are still scarce. Therefore, computational studies based upon the structure of these networks are of great interest. Here, a methodology relying on a logical formalism is applied to the functional analysis of the complex signaling network governing the activation of T cells via the T cell receptor, the CD4/CD8 co-receptors, and the accessory signaling receptor CD28. Our large-scale Boolean model, which comprises 94 nodes and 123 interactions and is based upon well-established qualitative knowledge from primary T cells, reveals important structural features (e.g., feedback loops and network-wide dependencies) and recapitulates the global behavior of this network for an array of published data on T cell activation in wild-type and knock-out conditions. More importantly, the model predicted unexpected signaling events after antibody-mediated perturbation of CD28 and after genetic knockout of the kinase Fyn that were subsequently experimentally validated. Finally, we show that the logical model reveals key elements and potential failure modes in network functioning and provides candidates for missing links. In summary, our large-scale logical model for T cell activation proved to be a promising in silico tool, and it inspires immunologists to ask new questions. We think that it holds valuable potential in foreseeing the effects of drugs and network modifications.
T-lymphocytes are central regulators of the adaptive immune response, and their inappropriate activation can cause autoimmune diseases or cancer. The understanding of the signaling mechanisms underlying T cell activation is a prerequisite to develop new strategies for pharmacological intervention and disease treatments. However, much of the existing literature on T cell signaling is related to T cell development or to activation processes in transformed T cell lines (e.g., Jurkat), whereas information on non-transformed primary T cells is limited. Here, immunologists and theoreticians have compiled data from the existing literature that stem from analysis of primary T cells. They used this information to establish a qualitative Boolean network that describes T cell activation mechanisms after engagement of the TCR, the CD4/CD8 co-receptors, and CD28. The network comprises 94 nodes and can be extended to facilitate interpretation of new data that emerge from experimental analysis of T cell activation. Newly developed tools and methods allow in silico analysis, and manipulation of the network and can uncover hidden/unforeseen signaling pathways. Indeed, by assessing signaling events controlled by CD28 and the protein tyrosine kinase Fyn, we show that computational analysis of even a qualitative network can provide new and non-obvious signaling pathways which can be validated experimentally.
A novel approach to reveal intramolecular signal transduction network is proposed in this work. To this end, a new algorithm of network construction is developed, which is based on a new protein dynamics model of energy dissipation. A key feature of this approach is that direction information is specified after inferring protein residue-residue interaction network involved in the process of signal transduction. This enables fundamental analysis of the regulation hierarchy and identification of regulation hubs of the signaling network. A well-studied allosteric enzyme, E. coli aspartokinase III, is used as a model system to demonstrate the new method. Comparison with experimental results shows that the new approach is able to predict all the sites that have been experimentally proved to desensitize allosteric regulation of the enzyme. In addition, the signal transduction network shows a clear preference for specific structural regions, secondary structural types and residue conservation. Occurrence of super-hubs in the network indicates that allosteric regulation tends to gather residues with high connection ability to collectively facilitate the signaling process. Furthermore, a new parameter of propagation coefficient is defined to determine the propagation capability of residues within a signal transduction network. In conclusion, the new approach is useful for fundamental understanding of the process of intramolecular signal transduction and thus has significant impact on rational design of novel allosteric proteins.
Systems wide modeling and analysis of signaling networks is essential for understanding complex cellular behaviors, such as the biphasic responses to different combinations of cytokines and growth factors. For example, tumor necrosis factor (TNF) can act as a proapoptotic or prosurvival factor depending on its concentration, the current state of signaling network and the presence of other cytokines. To understand combinatorial regulation in such systems, new computational approaches are required that can take into account non-linear interactions in signaling networks and provide tools for clustering, visualization and predictive modeling.
Here we extended and applied an unsupervised non-linear dimensionality reduction approach, Isomap, to find clusters of similar treatment conditions in two cell signaling networks: (I) apoptosis signaling network in human epithelial cancer cells treated with different combinations of TNF, epidermal growth factor (EGF) and insulin and (II) combination of signal transduction pathways stimulated by 21 different ligands based on AfCS double ligand screen data. For the analysis of the apoptosis signaling network we used the Cytokine compendium dataset where activity and concentration of 19 intracellular signaling molecules were measured to characterise apoptotic response to TNF, EGF and insulin. By projecting the original 19-dimensional space of intracellular signals into a low-dimensional space, Isomap was able to reconstruct clusters corresponding to different cytokine treatments that were identified with graph-based clustering. In comparison, Principal Component Analysis (PCA) and Partial Least Squares – Discriminant analysis (PLS-DA) were unable to find biologically meaningful clusters. We also showed that by using Isomap components for supervised classification with k-nearest neighbor (k-NN) and quadratic discriminant analysis (QDA), apoptosis intensity can be predicted for different combinations of TNF, EGF and insulin. Prediction accuracy was highest when early activation time points in the apoptosis signaling network were used to predict apoptosis rates at later time points. Extended Isomap also outperformed PCA on the AfCS double ligand screen data. Isomap identified more functionally coherent clusters than PCA and captured more information in the first two-components. The Isomap projection performs slightly worse when more signaling networks are analyzed; suggesting that the mapping function between cues and responses becomes increasingly non-linear when large signaling pathways are considered.
We developed and applied extended Isomap approach for the analysis of cell signaling networks. Potential biological applications of this method include characterization, visualization and clustering of different treatment conditions (i.e. low and high doses of TNF) in terms of changes in intracellular signaling they induce.
The interferon-induced, double-strand RNA-dependent protein kinase (PKR) can play critical roles in inhibiting virus replication and inducing apoptosis. To develop new agents that may inhibit viral replication or induce apoptosis in cancer cells via the PKR signaling pathway, we screened a chemical library for compounds that have differential cytotoxic effects on wild-type (MEF/PKR+/+) and PKR-knockout (MEF/PKR−/−) mouse embryonic fibroblast cells. We identified a synthetic compound, 1H-benzimidazole-1-ethanol, 2,3-dihydro-2-imino-a-(phenoxymethyl)-3-(phenylmethyl)-, monohydrochloride (BEPP), that induces a cytotoxic effect more effectively in MEF/PKR+/+ cells than in MEF/PKR−/− cells. BEPP also relatively effectively inhibited the growth of a human lung cancer cell line overexpressing PKR, compared with other cancer cell lines. In sensitive cells, BEPP induced apoptosis with activation of caspase-3. Treatment with BEPP led to increased phosphorylation of PKR and eIF2α, increased expression of BAX, and decreased expression of Bcl-2. BEPP-induced apoptosis was PKR dependent and was blocked by adenovector expressing the dominant-negative PKR. Furthermore, pretreatment of HeLa cells at a noncytotoxic dose of BEPP effectively inhibited Vaccinia virus replication. Together, our results suggest that BEPP and its analogues may induce PKR-dependent apoptosis and inhibition of viral replication and that they can be a potential anticancer or antivirus agent.
Motivation: Primary purpose of modeling gene regulatory networks for developmental process is to reveal pathways governing the cellular differentiation to specific phenotypes. Knowledge of differentiation network will enable generation of desired cell fates by careful alteration of the governing network by adequate manipulation of cellular environment.
Results: We have developed a novel integer programming-based approach to reconstruct the underlying regulatory architecture of differentiating embryonic stem cells from discrete temporal gene expression data. The network reconstruction problem is formulated using inherent features of biological networks: (i) that of cascade architecture which enables treatment of the entire complex network as a set of interconnected modules and (ii) that of sparsity of interconnection between the transcription factors. The developed framework is applied to the system of embryonic stem cells differentiating towards pancreatic lineage. Experimentally determined expression profile dynamics of relevant transcription factors serve as the input to the network identification algorithm. The developed formulation accurately captures many of the known regulatory modes involved in pancreatic differentiation. The predictive capacity of the model is tested by simulating an in silico potential pathway of subsequent differentiation. The predicted pathway is experimentally verified by concurrent differentiation experiments. Experimental results agree well with model predictions, thereby illustrating the predictive accuracy of the proposed algorithm.
Supplementary information: Supplementary data are available at Bioinformatics online.
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.
signal transduction; phosphoproteomics; quantitative proteomics; computational modeling; systems biology
Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed. However, DBN approaches that use continuous variables, thus avoiding the information loss associated with discretization, have not yet been extensively assessed, and most of the proposed approaches have dealt with linear Gaussian models.
We propose a generalization of dynamic Gaussian networks to accommodate nonlinear dependencies between variables. As a benchmark dataset to test the new approach, we used data from a mathematical model of cell cycle control in budding yeast that realistically reproduces the complexity of a cellular system. We evaluated the ability of the networks to describe the dynamics of cellular systems and their precision in reconstructing the true underlying causal relationships between variables. We also tested the robustness of the results by analyzing the effect of noise on the data, and the impact of a different sampling time.
The results confirmed that DBNs with Gaussian models can be effectively exploited for a first level analysis of data from complex cellular systems. The inferred models are parsimonious and have a satisfying goodness of fit. Furthermore, the networks not only offer a phenomenological description of the dynamics of cellular systems, but are also able to suggest hypotheses concerning the causal interactions between variables. The proposed nonlinear generalization of Gaussian models yielded models characterized by a slightly lower goodness of fit than the linear model, but a better ability to recover the true underlying connections between variables.
Inflammation may be involved in the pathogenesis of Alzheimer's disease (AD). There has been little success with anti-inflammatory drugs in AD, while the promise of anti-inflammatory treatment is more evident in experimental models. A new anti-inflammatory strategy requires a better understanding of molecular mechanisms. Among the plethora of signaling pathways activated by β-amyloid (Aβ) peptides, the nuclear factor-kappa B (NF-κB) pathway could be an interesting target. In virus-infected cells, double-stranded RNA-dependent protein kinase (PKR) controls the NF-κB signaling pathway. It is well-known that PKR is activated in AD. This led us to study the effect of a specific inhibitor of PKR on the Aβ42-induced inflammatory response in primary mixed murine co-cultures, allowing interactions between neurons, astrocytes and microglia.
Primary mixed murine co-cultures were prepared in three steps: a primary culture of astrocytes and microglia for 14 days, then a primary culture of neurons and astrocytes which were cultured with microglia purified from the first culture. Before exposure to Aβ neurotoxicity (72 h), co-cultures were treated with compound C16, a specific inhibitor of PKR. Levels of tumor necrosis factor-α (TNFα), interleukin (IL)-1β, and IL-6 were assessed by ELISA. Levels of PT451-PKR and activation of IκB, NF-κB and caspase-3 were assessed by western blotting. Apoptosis was also followed using annexin V-FITC immunostaining kit. Subcellular distribution of PT451-PKR was assessed by confocal immunofluorescence and morphological structure of cells by scanning electron microscopy. Data were analysed using one-way ANOVA followed by a Newman-Keuls' post hoc test
In these co-cultures, PKR inhibition prevented Aβ42-induced activation of IκB and NF-κB, strongly decreased production and release of tumor necrosis factor (TNFα) and interleukin (IL)-1β, and limited apoptosis.
In spite of the complexity of the innate immune response, PKR inhibition could be an interesting anti-inflammatory strategy in AD.
Protein kinase RNA (PKR-regulated) is a double-stranded RNA activated protein kinase whose expression is induced by interferon. The role of PKR in cell growth regulation is controversial, with some studies supporting a tumour suppressor function and others suggesting a growth-promoting role. However, it is possible that the function of PKR varies with the type of cancer in question.
We report here a detailed study to evaluate the function of PKR in hepatitis C virus genotype 4 (HCV-4) infected patients. PKR gene was quantitated in HCV related malignant and non-malignant liver tissue by RT-PCR technique and the association of HCV core and PKR was assessed.
If PKR functions as a tumour suppressor in this system, its expression would be higher in chronic hepatitis tissues. On the contrary our study demonstrated the specific association of HCV-4 with PKR expressed in hepatocellular carcinoma (HCC) tissues, leading to an increased gene expression of the kinase in comparison to chronic hepatitis tissues. This calls into question its role as a tumour suppressor and suggests a positive regulatory role of PKR in growth control of liver cancer cells. One limitation of most of other studies is that they measure the levels rather than the quantitation of PKR gene.
The findings suggest that PKR exerts a positive role in cell growth control of HCV-4 related HCC, obtaining a cut-off value for PKR expression in liver tissue provides the first evidence for existence of a viral activator of PKR.
The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1267826959682402.
Genotype 4 HCV; Hepatocellular carcinoma; Liver; PKR
Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.
Many biological systems have been described as networks whose complex properties influence the behaviour of the system. Correlations of activity in such networks are of interest in a variety of fields, from gene-regulatory networks to neuroscience. Due to novel experimental techniques allowing the recording of the activity of many pairs of neurons and their importance with respect to the functional interpretation of spike data, spike train correlations in neural networks have recently attracted a considerable amount of attention. Although origin and function of these correlations is not known in detail, they are believed to have a fundamental influence on information processing and learning. We present a detailed explanation of how recurrent connectivity induces correlations in local neural networks and how structural features affect their size and distribution. We examine under which conditions network characteristics like distance dependent connectivity, hubs or patches markedly influence correlations and population signals.
Bistability in genetic networks allows cells to remember past events and make discrete decisions in response to graded signals. Bistable behavior can result from positive feedback, but feedback loops can have other roles in signal transduction as well.
We introduced positive feedback into the budding yeast pheromone response to convert it into a bistable system. In the presence of feedback, transient induction with high pheromone levels caused persistent pathway activation, while at lower levels a fraction of cells became persistently active while the rest inactivated completely. We also generated mutations that quantitatively tuned the basal and induced expression levels of the feedback promoter and showed that they qualitatively changed the behavior of the system. Finally, we developed a simple stochastic model of our positive feedback system and showed the agreement between our simulations and experimental results.
The positive feedback loop can display several different behaviors, including bistability, and can switch between them as a result of simple mutations.
Network inference deals with the reconstruction of biological networks from experimental data. A variety of different reverse engineering techniques are available; they differ in the underlying assumptions and mathematical models used. One common problem for all approaches stems from the complexity of the task, due to the combinatorial explosion of different network topologies for increasing network size. To handle this problem, constraints are frequently used, for example on the node degree, number of edges, or constraints on regulation functions between network components. We propose to exploit topological considerations in the inference of gene regulatory networks. Such systems are often controlled by a small number of hub genes, while most other genes have only limited influence on the network's dynamic. We model gene regulation using a Bayesian network with discrete, Boolean nodes. A hierarchical prior is employed to identify hub genes. The first layer of the prior is used to regularize weights on edges emanating from one specific node. A second prior on hyperparameters controls the magnitude of the former regularization for different nodes. The net effect is that central nodes tend to form in reconstructed networks. Network reconstruction is then performed by maximization of or sampling from the posterior distribution. We evaluate our approach on simulated and real experimental data, indicating that we can reconstruct main regulatory interactions from the data. We furthermore compare our approach to other state-of-the art methods, showing superior performance in identifying hubs. Using a large publicly available dataset of over 800 cell cycle regulated genes, we are able to identify several main hub genes. Our method may thus provide a valuable tool to identify interesting candidate genes for further study. Furthermore, the approach presented may stimulate further developments in regularization methods for network reconstruction from data.
The double-stranded RNA-dependent protein kinase PKR plays multiple roles in cells, in response to different stress situations. As a member of the interferon (IFN)‑Stimulated Genes, PKR was initially recognized as an actor in the antiviral action of IFN, due to its ability to control translation, through phosphorylation, of the alpha subunit of eukaryotic initiation factor 2 (eIF2α). As such, PKR participates in the generation of stress granules, or autophagy and a number of viruses have designed strategies to inhibit its action. However, PKR deficient mice resist most viral infections, indicating that PKR may play other roles in the cell other than just acting as an antiviral agent. Indeed, PKR regulates several signaling pathways, either as an adapter protein and/or using its kinase activity. Here we review the role of PKR as an eIF2α kinase, its participation in the regulation of the NF-κB, p38MAPK and insulin pathways, and we focus on its role during infection with the hepatitis C virus (HCV). PKR binds the HCV IRES RNA, cooperates with some functions of the HCV core protein and may represent a target for NS5A or E2. Novel data points out for a role of PKR as a pro-HCV agent, both as an adapter protein and as an eIF2α-kinase, and in cooperation with the di-ubiquitin-like protein ISG15. Developing pharmaceutical inhibitors of PKR may help in resolving some viral infections as well as stress-related damages.
PKR; dsRNA; IFN; eIF2α; PRR; HCV; MAVS; ISG15; RIG-I
Epithelia are sheets of connected cells that are essential across the animal kingdom. Experimental observations suggest that the dynamical behavior of many single-layered epithelial tissues has strong analogies with that of specific mechanical systems, namely large networks consisting of point masses connected through spring-damper elements and undergoing the influence of active and dissipating forces. Based on this analogy, this work develops a modeling framework to enable the study of the mechanical properties and of the dynamic behavior of large epithelial cellular networks. The model is built first by creating a network topology that is extracted from the actual cellular geometry as obtained from experiments, then by associating a mechanical structure and dynamics to the network via spring-damper elements. This scalable approach enables running simulations of large network dynamics: the derived modeling framework in particular is predisposed to be tailored to study general dynamics (for example, morphogenesis) of various classes of single-layered epithelial cellular networks. In this contribution we test the model on a case study of the dorsal epithelium of the Drosophila melanogaster embryo during early dorsal closure (and, less conspicuously, germband retraction).
Epithelium; Cellular network; Nonlinear dynamical model; Spring-damper system; Discrete element method; Early dorsal closure; Morphogenesis
Reconstructing gene regulatory networks (GRNs) from expression data is one of the most important challenges in systems biology research. Many computational models and methods have been proposed to automate the process of network reconstruction. Inferring robust networks with desired behaviours remains challenging, however. This problem is related to network dynamics but has yet to be investigated using network modeling.
We propose an incremental evolution approach for inferring GRNs that takes network robustness into consideration and can deal with a large number of network parameters. Our approach includes a sensitivity analysis procedure to iteratively select the most influential network parameters, and it uses a swarm intelligence procedure to perform parameter optimization. We have conducted a series of experiments to evaluate the external behaviors and internal robustness of the networks inferred by the proposed approach. The results and analyses have verified the effectiveness of our approach.
Sensitivity analysis is crucial to identifying the most sensitive parameters that govern the network dynamics. It can further be used to derive constraints for network parameters in the network reconstruction process. The experimental results show that the proposed approach can successfully infer robust GRNs with desired system behaviors.
RNA-dependent protein kinase is an interferon-induced, double-stranded (ds), RNA-activated serine/threonine protein kinase involved in the eukaryotic response to viral infection. While PKR also functions in cellular differentiation, growth control and apoptosis, its role in human cancer remains poorly understood. To explore a role for PKR in human cancer, we evaluated PKR expression and function in a series of cancer cell lines from different tumor types. We observed that PKR protein expression is high in various cancer cells and low in normal cells. Knockdown of PKR protein expression by PKR siRNA induced cell death, indicating a PKR-dependent survival pathway under normal growth conditions. Inhibition of PKR signaling using a dominant negative adenoviral PKR mutant (Ad-Δ6PKR) also induced cancer cell apoptosis via a mechanism that blocks activation of AKT-mediated survival while simultaneously inducing ER stress. ER stress-mediated apoptosis was evidenced by unregulated expression of phosphorylated JNK (p-JNK), phosphorylated cJun (p-cJun), and caspase-4 and was significantly reduced in cancer cells treated with JNK and caspase-4 inhibitors. We further demonstrated that inhibition of PKR signaling via either siRNA or Ad-Δ6PKR sensitizes cancer cells to etoposide or cisplatin-mediated cell death. Our results suggest a rationale to develop therapeutic strategies that target PKR signaling in human cancer cells.
PKR; gene therapy; adenovirus
Polarity in mammalian cells emerges from the assembly of signaling molecules into extensive biochemical interaction networks. Despite their complexity, bacterial pathogens have evolved parsimonious mechanisms to hijack these systems. Here, we develop a tractable experimental and theoretical model to uncover fundamental operating principles both in mammalian cell polarity and bacterial pathogenesis. Using synthetic derivatives of the enteropathogenic E. coli guanine-nucleotide exchange factor (GEF) Map, we discover that Cdc42 GTPase signal transduction is controlled by the interaction between Map and F-actin. Mathematical modeling reveals how actin dynamics coupled to a Map-dependent positive feedback loop spontaneously polarizes Cdc42 on the plasma membrane. By rewiring the pathogenic signaling circuit to operate through β-integrin stimulation, we further show how Cdc42 is polarized in response to an extracellular spatial cue. Thus, a molecular pathway of polarity is proposed, centered on the interaction between GEFs and F-actin, which is likely to function in diverse biological systems.
The partitioning of biological networks into coupled functional modules is being increasingly applied for developing predictive models of biological systems. This approach has the advantage that predicting a system level response does not require a mechanistic description of the internal dynamics of each module. Identification of the input-output characteristics of the network modules and the connectivity between the modules provide the necessary quantitative representation of system dynamics. However, determination of the input-output relationships of the modules is not trivial; it requires the controlled perturbation of module inputs and systematic analysis of experimental data. In this report, we apply a system theoretical analysis approach to derive the time-dependent input-output relationships of the functional module for the human epidermal growth factor receptor (HER) mediated Erk and Akt signaling pathways. Using a library of cell lines expressing endogenous levels of EGFR and varying levels of HER2, we show that a transfer function-based representation can be successfully applied to quantitatively characterize information transfer in this system.
In an effort to understand the dynamic organization of the protein interaction network and its role in the regulation of cell behavior, positioning of proteins into specific network localities was studied with respect to their expression dynamics. First, we find that constitutively expressed and dynamically co-regulated proteins cluster in distinct functionally specialized network neighborhoods to form static and dynamic functional modules, respectively. Then, we show that whereas dynamic modules are mainly responsible for condition-dependent regulation of cell behavior, static modules provide robustness to the cell against genetic perturbations or protein expression noise, and therefore may act as buffers of evolutionary as well as population variations in cell behavior. Observations in this study refine the previously proposed model of dynamic modularity in the protein interaction network, and propose a link between the evolution of gene expression regulation and biological robustness.
dynamic modularity; network robustness; protein interaction networks