Design of versatile biochemical switches that respond to amplitude, duration, and spatial cues

Ultrasensitive dose-response for asbestos cancer risk implied by new inflammation-mutation model

Alterations in complex cellular phenotype each typically involve multistep activation of an ultrasensitive molecular switch (e.g., to adaptively initiate an apoptosis, inflammasome, Nrf2-ARE anti-oxidant, or heat-shock activation pathway) that triggers expression of a suite of target genes while efficiently limiting false-positive switching from a baseline state. Such switches exhibit nonlinear signal-activation relationships. In contrast, a linear no-threshold (LNT) dose-response relationship is expected for damage that accumulates in proportion to dose, as hypothesized for increased risk of cancer in relation to genotoxic dose according to the multistage somatic mutation/clonal-expansion theory of cancer, e.g., as represented in the Moolgavkar-Venzon-Knudsen (MVK) cancer model by a doubly stochastic nonhomogeneous Poisson process. Mesothelioma and lung cancer induced by exposure to carcinogenic (e.g., certain asbestos) fibers in humans and experimental animals are thought to involve modes of action driven by mutations, cytotoxicity-associated inflammation, or both, rendering ambiguous expectations concerning the nature of model-implied shape of the low-dose response for above-background increase in risk of incurring these endpoints. A recent Inflammation Somatic Mutation (ISM) theory of cancer posits instead that tissue-damage-associated inflammation that epigenetically recruits, activates and orchestrates stem cells to engage in tissue repair does not merely promote cancer, but rather is a requisite co-initiator (acting together with as few as two somatic mutations) of the most efficient pathway to any type of cancer in any reparable tissue (Dose-Response 2019; 17(2):1-12). This theory is reviewed, implications of this theory are discussed in relation to mesothelioma and lung cancer associated with chronic asbestos inhalation, one of the two types of ISM-required mutations is here hypothesized to block or impede inflammation resolution (e.g., by doing so for GPCR-mediated signal transduction by one or more endogenous autacoid specialized pro-resolving mediators or SPMs), and supporting evidence for this hypothesis is discussed.

Bayesian parameter estimation for dynamical models in systems biology

Dynamical systems modeling, particularly via systems of ordinary differential equations, has been used to effectively capture the temporal behavior of different biochemical components in signal transduction networks. Despite the recent advances in experimental measurements, including sensor development and '-omics' studies that have helped populate protein-protein interaction networks in great detail, modeling in systems biology lacks systematic methods to estimate kinetic parameters and quantify associated uncertainties. This is because of multiple reasons, including sparse and noisy experimental measurements, lack of detailed molecular mechanisms underlying the reactions, and missing biochemical interactions. Additionally, the inherent nonlinearities with respect to the states and parameters associated with the system of differential equations further compound the challenges of parameter estimation. In this study, we propose a comprehensive framework for Bayesian parameter estimation and complete quantification of the effects of uncertainties in the data and models. We apply these methods to a series of signaling models of increasing mathematical complexity. Systematic analysis of these dynamical systems showed that parameter estimation depends on data sparsity, noise level, and model structure, including the existence of multiple steady states. These results highlight how focused uncertainty quantification can enrich systems biology modeling and enable additional quantitative analyses for parameter estimation.

Two Complementary Signaling Pathways Depict Eukaryotic Chemotaxis: A Mechanochemical Coupling Model

Many eukaryotic cells, including neutrophils and Dictyostelium cells, are able to undergo correlated random migration in the absence of directional cues while reacting to shallow gradients of chemoattractants with exquisite precision. Although progress has been made with regard to molecular identities, it remains elusive how molecular mechanics are integrated with cell mechanics to initiate and manipulate cell motility. Here, we propose a two dimensional (2D) cell migration model wherein a multilayered dynamic seesaw mechanism is accompanied by a mechanical strain-based inhibition mechanism. In biology, these two mechanisms can be mapped onto the biochemical feedback between phosphoinositides (PIs) and Rho GTPase and the mechanical interplay between filamin A (FLNa) and FilGAP. Cell migration and the accompanying morphological changes are demonstrated in numerical simulations using a particle-spring model, and the diffusion in the cell membrane are simulations using a one dimensional (1D) finite differences method (FDM). The fine balance established between endogenous signaling and a mechanically governed inactivation scheme ensures the endogenous cycle of self-organizing pseudopods, accounting for the correlated random migration. Furthermore, this model cell manifests directional and adaptable responses to shallow graded signaling, depending on the overwhelming effect of the graded stimuli guidance on strain-based inhibition. Finally, the model cell becomes trapped within an obstacle-ridden spatial region, manifesting a shuttle run for local explorations and can chemotactically “escape”, illustrating again the balance required in the complementary signaling pathways.

Stability Analysis of a Signaling Circuit with Dual Species of GTPase Switches

GTPases are molecular switches that regulate a wide range of cellular processes, such as organelle biogenesis, position, shape, function, vesicular transport between organelles, and signal transduction. These hydrolase enzymes operate by toggling between an active ("ON") guanosine triphosphate (GTP)-bound state and an inactive ("OFF") guanosine diphosphate (GDP)-bound state; such a toggle is regulated by GEFs (guanine nucleotide exchange factors) and GAPs (GTPase activating proteins). Here we propose a model for a network motif between monomeric (m) and trimeric (t) GTPases assembled exclusively in eukaryotic cells of multicellular organisms. We develop a system of ordinary differential equations in which these two classes of GTPases are interlinked conditional to their ON/OFF states within a motif through coupling and feedback loops. We provide explicit formulae for the steady states of the system and perform classical local stability analysis to systematically investigate the role of the different connections between the GTPase switches. Interestingly, a coupling of the active mGTPase to the GEF of the tGTPase was sufficient to provide two locally stable states: one where both active/inactive forms of the mGTPase can be interpreted as having low concentrations and the other where both m- and tGTPase have high concentrations. Moreover, when a feedback loop from the GEF of the tGTPase to the GAP of the mGTPase was added to the coupled system, two other locally stable states emerged. In both states the tGTPase is inactivated and active tGTPase concentrations are low. Finally, the addition of a second feedback loop, from the active tGTPase to the GAP of the mGTPase, gives rise to a family of steady states that can be parametrized by a range of inactive tGTPase concentrations. Our findings reveal that the coupling of these two different GTPase motifs can dramatically change their steady-state behaviors and shed light on how such coupling may impact signaling mechanisms in eukaryotic cells.

Plexin-Bs enhance their GAP activity with a novel activation switch loop generating a cooperative enzyme

Plexins receive guidance cues from semaphorin ligands and transmit their signal through the plasma membrane. This family of proteins is unique amongst single-pass transmembrane receptors as their intracellular regions interact directly with several small GTPases, which regulate cytoskeletal dynamics and cell adhesion. Here, we characterize the GTPase Activating Protein (GAP) function of Plexin-B1 and find that a cooperative GAP activity towards the substrate GTPase, Rap1b, is associated with the N-terminal Juxtamembrane region of Plexin-B1. Importantly, we unveil an activation mechanism of Plexin-B1 by identifying a novel functional loop which partially blocks Rap1b entry into the plexin GAP domain. Consistent with the concept of allokairy developed for other systems, Plexin-B activity is increased by an apparent substrate-mediated cooperative effect. Simulations and mutagenesis suggest the repositioned JM conformation is stabilized by the new activation switch loop when the active site is occupied, giving rise to faster enzymatic turnover and cooperative behavior. The biological implications, essentially those of a threshold behavior for cell migration, are discussed.

Decanal Protects against UVB-Induced Photoaging in Human Dermal Fibroblasts via the cAMP Pathway

Solar ultraviolet (UV) radiation is the primary factor of cutaneous aging, resulting in coarse wrinkles and dryness. In this study, we aimed to test whether decanal, an aromatic compound found mainly in citrus fruits, inhibits UVB-mediated photoaging in human dermal fibroblasts and to explore whether its anti-photoaging effect occurs via cyclic adenosine monophosphate (cAMP) signaling. We found that decanal promotes collagen production dose-dependently. Meanwhile, it also increased the intracellular cAMP levels and decreased the number of molecules involved in the mitogen-activated protein kinase (MAPK)/activator protein 1 (AP-1) pathway, downregulating the collagen genes and upregulating the matrix metalloproteinase (MMP) genes in UVB-exposed dermal fibroblasts. Furthermore, it enhanced hyaluronic acid levels and hyaluronic acid synthase mRNA expression. Notably, the beneficial effects of decanal were lost in the presence of a cAMP inhibitor. Our results revealed the potential of decanal for preventing photoaging and suggested that its effects are cAMP-mediated in human dermal fibroblasts.

Operating regimes in a single enzymatic cascade at ensemble-level

Single enzymatic cascade, ubiquitously found in cellular signaling networks, is a phosphorylation-dephosphorylation reaction cycle causing a transition between inactive and active states of a protein catalysed by kinase and phosphatase, respectively. Steady-state information processing ability of such a cycle (e.g., MAPK cascade) has been classified into four qualitatively different operating regimes, viz., hyperbolic (H), signal-transducing (ST), threshold-hyperbolic (TH) and ultrasensitive (U). These four regimes represent qualitatively different dose-response curves, that is, relationship between concentrations of input kinase (e.g., pMEK) and response activated protein (e.g., pERK). Regimes were identified using a deterministic model accounting for population-averaged behavior only. Operating regimes can be strongly influenced by the inherently present cell-to-cell variability in an ensemble of cells which is captured in the form of pMEK and pERK distributions using reporter-based single-cell experimentation. In this study, we show that such experimentally acquired snapshot pMEK and pERK distribution data of a single MAPK cascade can be directly used to infer the underlying operating regime even in the absence of a dose-response curve. This deduction is possible primarily due to the presence of a monotonic relationship between experimental observables R_IQR, ratio of the inter-quartile range of the pERK and pMEK distribution pairs and R_M, ratio of the medians of the distribution pair. We demonstrate this relationship by systematic analysis of a quasi-steady state approximated model superimposed with an input gamma distribution constrained by the stimulus strength specific pMEK distribution measured on Jurkat-T cells stimulated with PMA. As a first, we show that introduction of cell-to-cell variability only in the upstream kinase achieved by superimposition of an appropriate input pMEK distribution on the dose-response curve can predict bimodal response pERK distribution in ST regime. Implementation of the proposed method on the input-response distribution pair obtained in stimulated Jurkat-T cells revealed that while low-dosage PMA stimulation preserves the H regime observed in resting cells, high-dosage causes H to ST regime transition.

Linear-No-Threshold Default Assumptions are Unwarranted for Cytotoxic Endpoints Independently Triggered by Ultrasensitive Molecular Switches

Crump's response in this issue to my critique of linear-no-threshold (LNT) default assumptions for noncancer and nongenotoxic cancer risks (Risk Analysis 2016; 36(3):589-604) is rebutted herein. Crump maintains that distinguishing between a low-dose linear dose response and a threshold dose response on the basis of dose-response data is impossible even for endpoints involving increased cytotoxicity. My rebuttal relies on descriptions and specific illustrations of two well-characterized ultrasensitive molecular switches that govern two key cytoprotective responses to cellular stress-heat shock response and antioxidant response element activation, respectively-each of which serve to suppress stress-induced apoptotic cell death unless overwhelmed. Because detailed dose-response data for each endpoint is shown to be J- or inverted-J-shaped with high confidence, and because independent pathways can explain background rates of apoptosis, LNT assumptions for this cytotoxic endpoint are unwarranted, at least in some cases and perhaps generally.

Extracting Diffusive States of Rho GTPase in Live Cells: Towards In Vivo Biochemistry

Resolving distinct biochemical interaction states when analyzing the trajectories of diffusing proteins in live cells on an individual basis remains challenging because of the limited statistics provided by the relatively short trajectories available experimentally. Here, we introduce a novel, machine-learning based classification methodology, which we call perturbation expectation-maximization (pEM), that simultaneously analyzes a population of protein trajectories to uncover the system of diffusive behaviors which collectively result from distinct biochemical interactions. We validate the performance of pEM in silico and demonstrate that pEM is capable of uncovering the proper number of underlying diffusive states with an accurate characterization of their diffusion properties. We then apply pEM to experimental protein trajectories of Rho GTPases, an integral regulator of cytoskeletal dynamics and cellular homeostasis, in vivo via single particle tracking photo-activated localization microcopy. Remarkably, pEM uncovers 6 distinct diffusive states conserved across various Rho GTPase family members. The variability across family members in the propensities for each diffusive state reveals non-redundant roles in the activation states of RhoA and RhoC. In a resting cell, our results support a model where RhoA is constantly cycling between activation states, with an imbalance of rates favoring an inactive state. RhoC, on the other hand, remains predominantly inactive.

Single particle tracking is a powerful tool that captures the diffusive dynamics of proteins as they undergo various interactions in living cells. Uncovering different biochemical interactions by analyzing the diffusive behaviors of individual protein trajectories, however, is challenging due to the limited statistics provided by short trajectories and experimental noise sources which are intimately coupled into each protein’s localization. Here, we introduce a novel, unsupervised, machine-learning based classification methodology, which we call perturbation expectation-maximization (pEM), that simultaneously analyzes a population of protein trajectories to uncover the system of diffusive behaviors which collectively result from distinct biochemical interactions. We validate the performance of pEM in silico and in vivo on the biological system of Rho GTPase, a signal transduction protein responsible for regulating cytoskeletal dynamics. We envision that the presented methodology will be applicable to a wide range of single protein tracking data where different biochemical interactions result in distinct diffusive behaviors. More generally, this study brings us an important step closer to the possibility of monitoring the endogenous biochemistry of diffusing proteins within live cells with single molecule resolution.

Signaling networks: information flow, computation, and decision making

Signaling pathways come together to form networks that connect receptors to many different cellular machines. Such networks not only receive and transmit signals but also process information. The complexity of these networks requires the use of computational models to understand how information is processed and how input-output relationships are determined. Two major computational approaches used to study signaling networks are graph theory and dynamical modeling. Both approaches are useful; network analysis (application of graph theory) helps us understand how the signaling network is organized and what its information-processing capabilities are, whereas dynamical modeling helps us determine how the system changes in time and space upon receiving stimuli. Computational models have helped us identify a number of emergent properties that signaling networks possess. Such properties include ultrasensitivity, bistability, robustness, and noise-filtering capabilities. These properties endow cell-signaling networks with the ability to ignore small or transient signals and/or amplify signals to drive cellular machines that spawn numerous physiological functions associated with different cell states.

Nonlinear signalling networks and cell-to-cell variability transform external signals into broadly distributed or bimodal responses

We show theoretically and experimentally a mechanism behind the emergence of wide or bimodal protein distributions in biochemical networks with nonlinear input-output characteristics (the dose-response curve) and variability in protein abundance. Large cell-to-cell variation in the nonlinear dose-response characteristics can be beneficial to facilitate two distinct groups of response levels as opposed to a graded response. Under the circumstances that we quantify mathematically, the two distinct responses can coexist within a cellular population, leading to the emergence of a bimodal protein distribution. Using flow cytometry, we demonstrate the appearance of wide distributions in the hypoxia-inducible factor-mediated response network in HCT116 cells. With help of our theoretical framework, we perform a novel calculation of the magnitude of cell-to-cell heterogeneity in the dose-response obtained experimentally.

Positive regulation of Rho GTPase activity by RhoGDIs as a result of their direct interaction with GAPs

Background

Rho GTPases function as molecular switches in many different signaling pathways and control a wide range of cellular processes. Rho GDP-dissociation inhibitors (RhoGDIs) regulate Rho GTPase signaling and can function as both negative and positive regulators. The role of RhoGDIs as negative regulators of Rho GTPase signaling has been extensively investigated; however, little is known about how RhoGDIs act as positive regulators. Furthermore, it is unclear how this opposing role of GDIs influences the Rho GTPase cycle. We constructed ordinary differential equation models of the Rho GTPase cycle in which RhoGDIs inhibit the regulatory activities of guanine nucleotide exchange factors (GEFs) and GTPase-activating proteins (GAPs) by interacting with them directly as well as by sequestering the Rho GTPases. Using this model, we analyzed the role of RhoGDIs in Rho GTPase signaling.

Results

The model constructed in this study showed that the functions of GEFs and GAPs are integrated into Rho GTPase signaling through the interactions of these regulators with GDIs, and that the negative role of GDIs is to suppress the overall Rho activity by inhibiting GEFs. Furthermore, the positive role of GDIs is to sustain Rho activation by inhibiting GAPs under certain conditions. The interconversion between transient and sustained Rho activation occurs mainly through changes in the affinities of GDIs to GAPs and the concentrations of GAPs.

Conclusions

RhoGDIs positively regulate Rho GTPase signaling primarily by interacting with GAPs and may participate in the switching between transient and sustained signals of the Rho GTPases. These findings enhance our understanding of the physiological roles of RhoGDIs and Rho GTPase signaling.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0143-5) contains supplementary material, which is available to authorized users.

The structure of dynamic GPCR signaling networks

G-protein-coupled receptors (GPCRs) stimulate signaling networks that control a variety of critical physiological processes. Static information on the map of interacting signaling molecules at the basis of many cellular processes exists, but little is known about the dynamic operation of these networks. Here we focus on two questions. First, Is the network architecture underlying GPCR-activated cellular processes unique in comparison with others such as transcriptional networks? We discuss how spatially localized GPCR signaling requires uniquely organized networks to execute polarized cell responses. Second, What approaches overcome challenges in deciphering spatiotemporally dynamic networks that govern cell behavior? We focus on recently developed microfluidic and optical approaches that allow GPCR signaling pathways to be triggered and perturbed with spatially and temporally variant input while simultaneously visualizing molecular and cellular responses. When integrated with mathematical modeling, these approaches can help identify design principles that govern cell responses to extracellular signals. We outline why optical approaches that allow the behavior of a selected cell to be orchestrated continually are particularly well suited for probing network organization in single cells.

Cancer stem cells: a systems biology view of their role in prognosis and therapy

Evidence has accumulated that characterizes highly tumorigenic cancer cells residing in heterogeneous populations. The accepted term for such a subpopulation is cancer stem cells (CSCs). While many questions still remain about their precise role in the origin, progression, and drug resistance of tumors, it is clear they exist. In this review, a current understanding of the nature of CSC, their potential usefulness in prognosis, and the need to target them will be discussed. In particular, separate studies now suggest that the CSC is plastic in its phenotype, toggling between tumorigenic and nontumorigenic states depending on both intrinsic and extrinsic conditions. Because of this, a static view of gene and protein levels defined by correlations may not be sufficient to either predict disease progression or aid in the discovery and development of drugs to molecular targets leading to cures. Quantitative dynamic modeling, a bottom up systems biology approach whereby signal transduction pathways are described by differential equations, may offer a novel means to overcome the challenges of oncology today. In conclusion, the complexity of CSCs can be captured in mathematical models that may be useful for selecting molecular targets, defining drug action, and predicting sensitivity or resistance pathways for improved patient outcomes.

Covalent modification cycles through the spatial prism

Covalent modification cycles are basic units and building blocks of posttranslational modification and cellular signal transduction. We systematically explore different spatial aspects of signal transduction in covalent modification cycles by starting with a basic temporal cycle as a reference and focusing on steady-state signal transduction. We consider, in turn, the effect of diffusion on spatial signal transduction, spatial analogs of ultrasensitive behavior, and the interplay between enzyme localization and substrate diffusion. Our analysis reveals the need to explicitly account for kinetics and diffusional transport (and localization) of enzymes, substrates, and complexes. It demonstrates a complex and subtle interplay between spatial heterogeneity, diffusion, and localization. Overall, examining the spatial dimension of covalent modification reveals that 1), there are important differences between spatial and temporal signal transduction even in this cycle; and 2), spatial aspects may play a substantial role in affecting and distorting information transfer in modules/networks that are usually studied in purely temporal terms. This has important implications for the systematic understanding of signaling in covalent modification cycles, pathways, and networks in multiple cellular contexts.

Threshold sensing through a synthetic enzymatic reaction-diffusion network

A wet stamping method to precisely control concentrations of enzymes and inhibitors in place and time inside layered gels is reported. By combining enzymatic reactions such as autocatalysis and inhibition with spatial delivery of components through soft lithographic techniques, a biochemical reaction network capable of recognizing the spatial distribution of an enzyme was constructed. The experimental method can be used to assess fundamental principles of spatiotemporal order formation in chemical reaction networks.

A prospective overview of the essential requirements in molecular modeling for nanomedicine design

Nanotechnology has presented many new challenges and opportunities in the area of nanomedicine design. The issues related to nanoconjugation, nanosystem-mediated targeted drug delivery, transitional stability of nanovehicles, the integrity of drug transport, drug-delivery mechanisms and chemical structural design require a pre-estimated and determined course of assumptive actions with property and characteristic estimations for optimal nanomedicine design. Molecular modeling in nanomedicine encompasses these pre-estimations and predictions of pertinent design data via interactive computographic software. Recently, an increasing amount of research has been reported where specialized software is being developed and employed in an attempt to bridge the gap between drug discovery, materials science and biology. This review provides an assimilative and concise incursion into the current and future strategies of molecular-modeling applications in nanomedicine design and aims to describe the utilization of molecular models and theoretical-chemistry computographic techniques for expansive nanomedicine design and development.

Adenosine 2A receptor promotes collagen production by human fibroblasts via pathways involving cyclic AMP and AKT but independent of Smad2/3

Activation of adenosine A2A receptor (A2AR) promotes fibrosis and collagen synthesis. However, the underlying mechanism is still unclear, not least because cAMP, its principal effector, has been found to inhibit TGFβ1-induced collagen synthesis. Here, we show that in primary normal human dermal fibroblasts, A2AR stimulation with CGS21680 elicits a modest cAMP increase (150 ± 12% of control; EC50 54.8 nM), which stimulates collagen1 (Col1) and collagen3 (Col3), but maximal cAMP resulting from direct activation of adenylyl cyclase by forskolin (15,689 ± 7038% of control; EC50 360.7 nM) inhibits Col1 and increases Col3. Similar to Col1 expression, fibroblast proliferation increased following physiological cAMP increases by CGS21680 but was inhibited by cAMP increases beyond the physiological range by forskolin. The A2AR-mediated increase of Col1 and Col3 was mediated by AKT, while Col3, but not Col1, expression was dependent on p38 and repressed by ERK. TGFβ1 induced phosphorylation of Smad2/3 and increased Col3 expression, which was prevented by Smad3 depletion. In contrast, CGS21680 did not activate Smad2/3, and Smad2/3 knockdown did not prevent CGS21680-induced Col1 or Col3 increases. Our results indicate that cAMP is a concentration-dependent switch for collagen production via noncanonical, AKT-dependent, Smad2/3-independent signaling. These observations explain the paradoxical effects of cAMP on collagen expression.

Ultrasensitivity by molecular titration in spatially propagating enzymatic reactions

Delineating design principles of biological systems by reconstitution of purified components offers a platform to gauge the influence of critical physicochemical parameters on minimal biological systems of reduced complexity. Here we unravel the effect of strong reversible inhibitors on the spatiotemporal propagation of enzymatic reactions in a confined environment in vitro. We use micropatterned, enzyme-laden agarose gels which are stamped on polyacrylamide films containing immobilized substrates and reversible inhibitors. Quantitative fluorescence imaging combined with detailed numerical simulations of the reaction-diffusion process reveal that a shallow gradient of enzyme is converted into a steep product gradient by addition of strong inhibitors, consistent with a mathematical model of molecular titration. The results confirm that ultrasensitive and threshold effects at the molecular level can convert a graded input signal to a steep spatial response at macroscopic length scales.

BioFNet: biological functional network database for analysis and synthesis of biological systems

In synthetic biology and systems biology, a bottom-up approach can be used to construct a complex, modular, hierarchical structure of biological networks. To analyze or design such networks, it is critical to understand the relationship between network structure and function, the mechanism through which biological parts or biomolecules are assembled into building blocks or functional networks. A functional network is defined as a subnetwork of biomolecules that performs a particular function. Understanding the mechanism of building functional networks would help develop a methodology for analyzing the structure of large-scale networks and design a robust biological circuit to perform a target function. We propose a biological functional network database, named BioFNet, which can cover the whole cell at the level of molecular interactions. The BioFNet takes an advantage in implementing the simulation program for the mathematical models of the functional networks, visualizing the simulated results. It presents a sound basis for rational design of biochemical networks and for understanding how functional networks are assembled to create complex high-level functions, which would reveal design principles underlying molecular architectures.

Optical control demonstrates switch-like PIP3 dynamics underlying the initiation of immune cell migration

There is a dearth of approaches to experimentally direct cell migration by continuously varying signal input to a single cell, evoking all possible migratory responses and quantitatively monitoring the cellular and molecular response dynamics. Here we used a visual blue opsin to recruit the endogenous G-protein network that mediates immune cell migration. Specific optical inputs to this optical trigger of signaling helped steer migration in all possible directions with precision. Spectrally selective imaging was used to monitor cell-wide phosphatidylinositol (3,4,5)-triphosphate (PIP3), cytoskeletal, and cellular dynamics. A switch-like PIP3 increase at the cell front and a decrease at the back were identified, underlying the decisive migratory response. Migration was initiated at the rapidly increasing switch stage of PIP3 dynamics. This result explains how a migratory cell filters background fluctuations in the intensity of an extracellular signal but responds by initiating directionally sensitive migration to a persistent signal gradient across the cell. A two-compartment computational model incorporating a localized activator that is antagonistic to a diffusible inhibitor was able to simulate the switch-like PIP3 response. It was also able simulate the slow dissipation of PIP3 on signal termination. The ability to independently apply similar signaling inputs to single cells detected two cell populations with distinct thresholds for migration initiation. Overall the optical approach here can be applied to understand G-protein-coupled receptor network control of other cell behaviors.

Plexin structures are coming: opportunities for multilevel investigations of semaphorin guidance receptors, their cell signaling mechanisms, and functions

Plexin transmembrane receptors and their semaphorin ligands, as well as their co-receptors (Neuropilin, Integrin, VEGFR2, ErbB2, and Met kinase) are emerging as key regulatory proteins in a wide variety of developmental, regenerative, but also pathological processes. The diverse arenas of plexin function are surveyed, including roles in the nervous, cardiovascular, bone and skeletal, and immune systems. Such different settings require considerable specificity among the plexin and semaphorin family members which in turn are accompanied by a variety of cell signaling networks. Underlying the latter are the mechanistic details of the interactions and catalytic events at the molecular level. Very recently, dramatic progress has been made in solving the structures of plexins and of their complexes with associated proteins. This molecular level information is now suggesting detailed mechanisms for the function of both the extracellular as well as the intracellular plexin regions. Specifically, several groups have solved structures for extracellular domains for plexin-A2, -B1, and -C1, many in complex with semaphorin ligands. On the intracellular side, the role of small Rho GTPases has been of particular interest. These directly associate with plexin and stimulate a GTPase activating (GAP) function in the plexin catalytic domain to downregulate Ras GTPases. Structures for the Rho GTPase binding domains have been presented for several plexins, some with Rnd1 bound. The entire intracellular domain structure of plexin-A1, -A3, and -B1 have also been solved alone and in complex with Rac1. However, key aspects of the interplay between GTPases and plexins remain far from clear. The structural information is helping the plexin field to focus on key questions at the protein structural, cellular, as well as organism level that collaboratoria of investigations are likely to answer.

An investigation of spatial signal transduction in cellular networks

Background

Spatial signal transduction plays a vital role in many intracellular processes such as eukaryotic chemotaxis, polarity generation and cell division. Furthermore it is being increasingly realized that the spatial dimension to signalling may play an important role in other apparently purely temporal signal transduction processes. It is increasingly being recognized that a conceptual basis for studying spatial signal transduction in signalling networks is necessary.

Results

In this work we examine spatial signal transduction in a series of standard motifs/networks. These networks include coherent and incoherent feedforward, positive and negative feedback, cyclic motifs, monostable switches, bistable switches and negative feedback oscillators. In all these cases, the driving signal has spatial variation. For each network we consider two cases, one where all elements are essentially non-diffusible, and the other where one of the network elements may be highly diffusible. A careful analysis of steady state signal transduction provides many insights into the behaviour of all these modules. While in the non-diffusible case for the most part, spatial signalling reflects the temporal signalling behaviour, in the diffusible cases, we see significant differences between spatial and temporal signalling characteristics. Our results demonstrate that the presence of diffusible elements in the networks provides important constraints and capabilities for signalling.

Conclusions

Our results provide a systematic basis for understanding spatial signalling in networks and the role of diffusible elements therein. This provides many insights into the signal transduction capabilities and constraints in such networks and suggests ways in which cellular signalling and information processing is organized to conform to or bypass those constraints. It also provides a framework for starting to understand the organization and regulation of spatial signal transduction in individual processes.

Functional characterization of the guanine nucleotide exchange factor (GEF) motif of GIV protein reveals a threshold effect in signaling

Heterotrimeric G proteins are critical signal-transducing molecules controlled by a complex network of regulators. GIV (a.k.a. Girdin) is a unique component of this network and a nonreceptor guanine nucleotide exchange factor (GEF) that functions via a signature motif. GIV's GEF motif is involved in the regulation of critical biological processes such as phosphoinositide 3 kinase (PI3K)-Akt signaling, actin cytoskeleton remodeling, cell migration, and cancer metastasis. Here we investigated how the GEF function of GIV affects the wiring of its signaling pathway to shape different biological responses. Using a structure-guided approach, we designed a battery of GIV mutants with different Gαi-binding and -activating properties and used it to dissect the specific impact of changes in GIV's GEF activity on several cellular responses. In vivo signaling assays revealed a threshold effect of GEF activity for the activation of Akt by GIV in different cell lines and by different stimuli. Akt signaling is minimal at low GEF activity and is sharply increased to reach a maximum above a threshold of GEF activity, suggesting that GIV is a critical signal amplifier and that activation of Akt is ultrasensitive to changes in GIV's GEF activity. A similar threshold dependence was observed for other biological functions promoted by GIV such as remodeling of the actin cytoskeleton and cell migration. This functional characterization of GIV's GEF motif provides insights into the molecular interactions between nonreceptor GEFs and G proteins and the mechanisms that govern this signal transduction pathway.

The pursuit of oncotargets through understanding defective cell regulation

More effective anticancer agents are essential, as has too often been demonstrated by the paucity of therapeutics which preserve life. Their discovery is very difficult. Many approaches are being applied, from testing folk medicines to automated high throughput screening of large chemical libraries. Mutations in cancer cells create dysfunctional regulatory systems. This Perspective summarizes an approach to applying defective molecular control mechanisms as oncotargets on which drug discoveries against cancer can be based.

The pursuit of oncotargets through understanding defective cell regulation

More effective anticancer agents are essential, as has too often been demonstrated by the paucity of therapeutics which preserve life. Their discovery is very difficult. Many approaches are being applied, from testing folk medicines to automated high throughput screening of large chemical libraries. Mutations in cancer cells create dysfunctional regulatory systems. This Perspective summarizes an approach to applying defective molecular control mechanisms as oncotargets on which drug discoveries against cancer can be based.

Analysis of the CYP1A1 mRNA dose-response in human keratinocytes indicates that relative potencies of dioxins, furans, and PCBs are species and congener specific

Reports indicate that toxic equivalency factors (TEFs) based primarily on rodent data do not accurately predict in vitro human responsiveness to certain dioxin-like chemicals (DLCs). To investigate this in cells responsive to dioxins and relevant to chloracne, normal human epidermal keratinocytes were treated with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and several DLCs, each with a TEF value of 0.1, representing three classes of congeners. We estimated half maximal effective concentration (EC50)-based donor-specific relative potency (REP) values for cytochrome P450 1A1 (CYP1A1) messenger RNA (mRNA) induction for TCDD, 1,2,3,6,7,8-hexachlorodibenzo-p-dioxin (HxCDD), 2,3,7,8-tetrachlorodibenzofuran (TCDF), 1,2,3,6,7,8-hexachlorodibenzofuran (HxCDF), and 3,3',4,4',5-pentachlorobiphenyl (PCB 126). We also determined EC50-based population-level REP values (n = 4) for CYP1A1 mRNA induction for TCDD, HxCDF, and PCB 126. Furthermore, an alternative factor, the relative threshold factor (RTF) based on the low end (threshold) of the dose-response curve, was calculated. Our results demonstrated that HxCDF had a population-based REP value of 0.98, 9.8-fold higher than its assigned TEF value of 0.1. Conversely, PCB 126 had an REP value of 0.0027 and an RTF of 0.0022, 37-fold and 45-fold less than its assigned TEF of 0.1, respectively. The REP values for HxCDD and TCDF were 0.24 and 0.10, respectively, similar to their assigned value of 0.1. Therefore, although the DLCs tested in the current study all possessed the same assigned TEF value of 0.1, congener-specific differences in REPs and RTFs were observed for human keratinocytes. These congener-specific discrepancies are likely because of differences in interspecies factors that have yet to be defined.

Use of virtual cell in studies of cellular dynamics

The Virtual Cell (VCell) is a unique computational environment for modeling and simulation of cell biology. It has been specifically designed to be a tool for a wide range of scientists, from experimental cell biologists to theoretical biophysicists. The models created with VCell can range from the simple, to evaluate hypotheses or to interpret experimental data, to complex multilayered models used to probe the predicted behavior of spatially resolved, highly nonlinear systems. In this chapter, we discuss modeling capabilities of VCell and demonstrate representative examples of the models published by the VCell users.