Discovery of intramolecular signal transduction network based on a new protein dynamics model of energy dissipation

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.     

Signal propagation of the MAPK cascade in Xenopus oocytes: role of bistability and ultrasensitivity for a mixed problem

The MAPK signaling cascade is nowadays understood as a network module highly conserved across species. Its main function is to transfer a signal arriving at the plasma membrane to the cellular interior. Current understanding of ‘how’ this is achieved involves the notions of ultrasensitivity and bistability which relate to the nonlinear dynamics of the biochemical network, ignoring spatial aspects. Much less, indeed, is so far known about the propagation of the signal through the cytoplasm. In this work we formulate, starting from a Michaelis–Menten model for the MAPK cascade in Xenopus oocytes, a reaction-diffusion model of the cascade. We study this model in one space dimension. Basing ourselves on previous general results on reaction diffusion models, we particularly study for our model the conditions for signal propagation. We show that the existence of a propagating front depends sensitively on the initial and boundary conditions at the plasma membrane. Possible biological consequences of this finding are discussed.     

Detection of allosteric signal transmission by information-theoretic analysis of protein dynamics

Allostery offers a highly specific way to modulate protein function. Therefore, understanding this mechanism is of increasing interest for protein science and drug discovery. However, allosteric signal transmission is difficult to detect experimentally and to model because it is often mediated by local structural changes propagating along multiple pathways. To address this, we developed a method to identify communication pathways by an information-theoretical analysis of molecular dynamics simulations. Signal propagation was described as information exchange through a network of correlated local motions, modeled as transitions between canonical states of protein fragments. The method was used to describe allostery in two-component regulatory systems. In particular, the transmission from the allosteric site to the signaling surface of the receiver domain NtrC was shown to be mediated by a layer of hub residues. The location of hubs preferentially connected to the allosteric site was found in close agreement with key residues experimentally identified as involved in the signal transmission. The comparison with the networks of the homologues CheY and FixJ highlighted similarities in their dynamics. In particular, we showed that a preorganized network of fragment connections between the allosteric and functional sites exists already in the inactive state of all three proteins.     

Structural and functional protein network analyses predict novel signaling functions for rhodopsin

Orchestration of signaling, photoreceptor structural integrity, and maintenance needed for mammalian vision remain enigmatic. By integrating three proteomic data sets, literature mining, computational analyses, and structural information, we have generated a multiscale signal transduction network linked to the visual G protein‐coupled receptor (GPCR) rhodopsin, the major protein component of rod outer segments. This network was complemented by domain decomposition of protein–protein interactions and then qualified for mutually exclusive or mutually compatible interactions and ternary complex formation using structural data. The resulting information not only offers a comprehensive view of signal transduction induced by this GPCR but also suggests novel signaling routes to cytoskeleton dynamics and vesicular trafficking, predicting an important level of regulation through small GTPases. Further, it demonstrates a specific disease susceptibility of the core visual pathway due to the uniqueness of its components present mainly in the eye. As a comprehensive multiscale network, it can serve as a basis to elucidate the physiological principles of photoreceptor function, identify potential disease‐associated genes and proteins, and guide the development of therapies that target specific branches of the signaling pathway.     

Positional Information Generated by Spatially Distributed Signaling Cascades

The temporal and stationary behavior of protein modification cascades has been extensively studied, yet little is known about the spatial aspects of signal propagation. We have previously shown that the spatial separation of opposing enzymes, such as a kinase and a phosphatase, creates signaling activity gradients. Here we show under what conditions signals stall in the space or robustly propagate through spatially distributed signaling cascades. Robust signal propagation results in activity gradients with long plateaus, which abruptly decay at successive spatial locations. We derive an approximate analytical solution that relates the maximal amplitude and propagation length of each activation profile with the cascade level, protein diffusivity, and the ratio of the opposing enzyme activities. The control of the spatial signal propagation appears to be very different from the control of transient temporal responses for spatially homogenous cascades. For spatially distributed cascades where activating and deactivating enzymes operate far from saturation, the ratio of the opposing enzyme activities is shown to be a key parameter controlling signal propagation. The signaling gradients characteristic for robust signal propagation exemplify a pattern formation mechanism that generates precise spatial guidance for multiple cellular processes and conveys information about the cell size to the nucleus.     

Quantitative proteomics and phosphoproteomics reveal novel insights into complexity and dynamics of the EGFR signaling network

The epidermal growth factor receptor (EGFR/ErbB1/Her1) belongs to the ErbB family of receptor tyrosine kinases (RTKs) and is a key player in the regulation of cell proliferation, differentiation, survival, and migration. Overexpression and mutational changes of EGFR have been identified in a variety of human cancers and the regulation of EGFR signaling plays a critical role in tumor development and progression. Due to its biological significance the EGFR signaling network is a widely used model system for the development of analytical techniques. Novel quantitative proteomics and phosphoproteomics approaches play an important role in the characterization of signaling pathways in a time and stimulus dependent manner. Recent studies discussed in this review provide new insights into different aspects of EGFR signal transduction, such as regulation and dynamics of its phosphorylation sites, association with interaction partners and identification of regulated phosphoproteins. Correlation of data from functional proteomics studies with results from other fields of signal transduction research by systems biology will be necessary to integrate and translate these findings into successful clinical applications.     

Systems biology approaches in cell signaling research

The use of methods for global and quantitative analysis of cells is providing new systems-level insights into signal transduction processes. Recent studies reveal important information about the rates of signal transmission and propagation, help establish some general regulatory characteristics of multi-tiered signaling cascades, and illuminate the combinatorial nature of signaling specificity in cell differentiation.     

Mitochondrial network determines intracellular ROS dynamics and sensitivity to oxidative stress through switching inter-mitochondrial messengers

Oxidative stresses caused by reactive oxygen species (ROS) can induce rapid depolarization of inner mitochondrial membrane potential and subsequent impairment of oxidative phosphorylation. Damaged mitochondria produce more ROS, especially the superoxide anion (O(2)(-)) and hydrogen peroxide (H(2)O(2)), which potentiate mitochondria-driven ROS propagation, so-called ROS-induced ROS release (RIRR), via activation of an inter-mitochondria signaling network. Therefore, loss of function in only a fraction of mitochondria might eventually affect cell viability through this positive feedback loop. Since ROS are very short-lived molecules in the biological milieu, mitochondrial network dynamics, such as density, number, and spatial distribution, can affect mitochondria-driven ROS propagation. To address this issue, we developed a mathematical model using an agent-based modeling approach, and tested the effect of mitochondrial network dynamics on RIRR for mitochondria under various conditions. Simulation results show that the intracellular ROS signaling pattern, such as ROS propagation speed and oxidative stress vulnerability, are critically affected by mitochondrial network dynamics. Mitochondrial network dynamics of mitochondrial distribution, density, activity, and size can mediate inter-mitochondrial signaling under certain conditions and determine the identity of the ROS signaling pattern. We further elucidated the potential mechanism of these actions, i.e., conversion of major messenger molecules involved in ROS signaling. If the average distance between neighboring mitochondria is large or mitochondrial distribution becomes randomized, messenger molecule of the ROS signaling network can be switched from O(2)(-) to H(2)O(2). In this case, mitochondria-driven ROS propagation is efficiently blocked by introduction of excess cytosolic glutathione peroxidase 1, while introduction of cytosolic superoxide dismutase has no effect. Together, these results suggest that mitochondrial network dynamics is a major determinant for cellular responses to RIRR through changing the key messenger molecules.     

Parallel tracking of cAMP and PKA signaling dynamics in living cells with FRET-based fluorescent biosensors

Proper regulation of cellular functions relies upon a network of intricately interwoven signaling cascades in which multiple components must be tightly coordinated both spatially and temporally. To better understand how this network operates within the cellular environment, it is important to define the parameters of various signaling activities and to reveal the characteristic activity structure of the signaling cascades. This task calls for molecular tools capable of parallelly tracking multiple activities in cellular time and space with high sensitivity and specificity. Here, we present new biosensors developed based on two conveniently co-imageable FRET pairs consisting of CFP-RFP and YFP-RFP, specifically Cerulean-mCherry and mVenus-mCherry, for parallel monitoring of PKA activity and cAMP dynamics in living cells. These biosensors provide orthogonal readouts in co-imaging experiments and display a comparable dynamic range to their cyan-yellow counterparts. Characterization of signaling responses induced by a panel of pathway activators using this co-imaging approach reveals distinct activity and kinetic patterns of cAMP and PKA dynamics arising from differential signal activation and processing. This technique is therefore useful for parallel monitoring of multiple signaling dynamics in single living cells and represents a promising approach towards a more precise characterization of the activity structure of the dynamic cellular signaling network.     

Signaling network triggers and membrane physical properties control the actin cytoskeleton-driven isotropic phase of cell spreading

Cell spreading is regulated by signaling from the integrin receptors that activate intracellular signaling pathways to control actin filament regulatory proteins. We developed a hybrid model of whole-cell spreading in which we modeled the integrin signaling network as ordinary differential equations in multiple compartments, and cell spreading as a three-dimensional stochastic model. The computed activity of the signaling network, represented as time-dependent activity levels of the actin filament regulatory proteins, is used to drive the filament dynamics. We analyzed the hybrid model to understand the role of signaling during the isotropic phase of fibroblasts spreading on fibronectin-coated surfaces. Simulations showed that the isotropic phase of spreading depends on integrin signaling to initiate spreading but not to maintain the spreading dynamics. Simulations predicted that signal flow in the absence of Cdc42 or WASP would reduce the spreading rate but would not affect the shape evolution of the spreading cell. These predictions were verified experimentally. Computational analyses showed that the rate of spreading and the evolution of cell shape are largely controlled by the membrane surface load and membrane bending rigidity, and changing information flow through the integrin signaling network has little effect. Overall, the plasma membrane acts as a damper such that only ∼5% of the actin dynamics capability is needed for isotropic spreading. Thus, the biophysical properties of the plasma membrane can condense varying levels of signaling network activities into a single cohesive macroscopic cellular behavior.     

Information routing driven by background chatter in a signaling network

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

Emerging Methods and Applications to Decrypt Allostery in Proteins and Nucleic Acids

Many large protein-nucleic acid complexes exhibit allosteric regulation. In these systems, the propagation of the allosteric signaling is strongly coupled to conformational dynamics and catalytic function, challenging state-of-the-art analytical methods. Here, we review established and innovative approaches used to elucidate allosteric mechanisms in these complexes. Specifically, we report network models derived from graph theory and centrality analyses in combination with molecular dynamics (MD) simulations, introducing novel schemes that implement the synergistic use of graph theory with enhanced simulations methods and ab-initio MD. Accelerated MD simulations are used to construct “enhanced network models”, describing the allosteric response over long timescales and capturing the relation between allostery and conformational changes. “Ab-initio network models” combine graph theory with ab-initio MD and quantum mechanics/molecular mechanics (QM/MM) simulations to describe the allosteric regulation of catalysis by following the step-by-step dynamics of biochemical reactions. This approach characterizes how the allosteric regulation changes from reactants to products and how it affects the transition state, revealing a tense-to-relaxed allosteric regulation along the chemical step. Allosteric models and applications are showcased for three paradigmatic examples of allostery in protein-nucleic acid complexes: (i) the nucleosome core particle, (ii) the CRISPR-Cas9 genome editing system and (iii) the spliceosome. These methods and applications create innovative protocols to determine allosteric mechanisms in protein-nucleic acid complexes that show tremendous promise for medicine and bioengineering.     

The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks

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.     

Light signaling induces anthocyanin biosynthesis via AN3 mediated COP1 expression

Light signaling plays a pivotal role in controlling plant morphogenesis, metabolism, growth and development. The central process of light signaling pathway is to build the link between light signals and the expression of genes involved. Although studies focused on light signaling toward metabolism have been documented well in the past several decades, most regulation networks of light signaling in a specific metabolic production largely remained unknown. Anthocyanin accumulation in plant tissues depends on the availability of light signals, but only little is known about the potential regulation network underlying light signal controls anthocyanin biosynthesis. Here, we briefly review the recent progress on the light-triggered anthocyanin biosynthesis via ANGUSTIFOLIA3 (AN3) and CONSTITUTIVE PHOTOMORPHOGENIC1 (COP1) network in Arabidopsis.     

Dynamic Redox Regulation of IL-4 Signaling

Quantifying the magnitude and dynamics of protein oxidation during cell signaling is technically challenging. Computational modeling provides tractable, quantitative methods to test hypotheses of redox mechanisms that may be simultaneously operative during signal transduction. The interleukin-4 (IL-4) pathway, which has previously been reported to induce reactive oxygen species and oxidation of PTP1B, may be controlled by several other putative mechanisms of redox regulation; widespread proteomic thiol oxidation observed via 2D redox differential gel electrophoresis upon IL-4 treatment suggests more than one redox-sensitive protein implicated in this pathway. Through computational modeling and a model selection strategy that relied on characteristic STAT6 phosphorylation dynamics of IL-4 signaling, we identified reversible protein tyrosine phosphatase (PTP) oxidation as the primary redox regulatory mechanism in the pathway. A systems-level model of IL-4 signaling was developed that integrates synchronous pan-PTP oxidation with ROS-independent mechanisms. The model quantitatively predicts the dynamics of IL-4 signaling over a broad range of new redox conditions, offers novel hypotheses about regulation of JAK/STAT signaling, and provides a framework for interrogating putative mechanisms involving receptor-initiated oxidation.     

Method of determination of the reticulocyte age distribution from flow cytometry count by a structured-population model.

BACKGROUND: A flow cytometry count of reticulocytes (RET) provides information about distribution of signal emitted by reticulocyte RNA. A new method of determination of the RNA degradation rate in RET is provided. This technique allows one to determinate the age distribution of RET. METHODS: The method is based on a series of flow cytometry counts of cultured RET. From those counts, the changes of signal distribution of RET over time are obtained. The RNA degradation rate of individual RET is then resolved based on changes in the signal distribution of the whole population of cells. The obtained relation between signal and age allows obtaining a RET age density that can be used to characterize RET age distribution and age dependence of processes that control the population dynamics. RESULTS: The total maturation time of RET in rats is 3 days. The median time that a homeostatic RET spends in blood or before it becomes a mature RBC is about 0.6 days, whereas a stress RET needs 0.8 days. CONCLUSIONS: The proposed method provides means for studies in vivo RET dynamics using age-structured models.