International Review Of Cell and Molecular Biology, Volume 283 (International Review of Cell & Molecular Biology)

V O LU M E T WO E I G H T Y T H R E E INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY INTERNATIONAL REVIEW OF...

Author: Kwang W. Jeon

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V O LU M E

T WO

E I G H T Y

T H R E E

INTERNATIONAL REVIEW OF

CELL AND MOLECULAR BIOLOGY

INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY Series Editors

GEOFFREY H. BOURNE JAMES F. DANIELLI KWANG W. JEON MARTIN FRIEDLANDER JONATHAN JARVIK

1949–1988 1949–1984 1967– 1984–1992 1993–1995

Editorial Advisory Board

ISAIAH ARKIN PETER L. BEECH ROBERT A. BLOODGOOD DEAN BOK KEITH BURRIDGE HIROO FUKUDA RAY H. GAVIN MAY GRIFFITH WILLIAM R. JEFFERY KEITH LATHAM

WALLACE F. MARSHALL BRUCE D. MCKEE MICHAEL MELKONIAN KEITH E. MOSTOV ANDREAS OKSCHE MANFRED SCHLIWA TERUO SHIMMEN ROBERT A. SMITH ALEXEY TOMILIN

V O LU M E

T WO

E I G H T Y

T H R E E

INTERNATIONAL REVIEW OF

CELL AND MOLECULAR BIOLOGY

EDITED BY

KWANG W. JEON Department of Biochemistry University of Tennessee Knoxville, Tennessee

AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier

Front Cover Photography: Cover figure by Maddy Parsons and Patricia Costa Academic Press is an imprint of Elsevier 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA 32 Jamestown Road, London NW1 7BY, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands First edition 2010 Copyright # 2010, Elsevier Inc. All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: permissions@elsevier. com. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material. Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress For information on all Academic Press publications visit our website at elsevierdirect.com

ISBN: 978-0-12-381254-4

PRINTED AND BOUND IN USA 10 11 12 10 9 8 7 6 5 4 3 2 1

CONTENTS

Contributors

1. Use of Virtual Cell in Studies of Cellular Dynamics

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Boris M. Slepchenko and Leslie M. Loew 1. Introduction 2. Modeling Capabilities of VCell 3. What One Can Accomplish with the VCell (Review of Published Studies) 4. Future Directions in Developing Tools for Modeling in Cell Biology Acknowledgments References

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48 49 49

2. New Insights into the Dynamics of Cell Adhesions

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Patricia Costa and Maddy Parsons 1. Introduction 2. Overview of Cell Adhesion 3. Overview of Cell-Adhesion Proteins 4. Regulation of Cell Adhesion Dynamics In Vitro 5. Regulation of Cell-Adhesion Dynamics: In Vivo Studies 6. Conclusions and Future Directions References

3. Axonal Ensheathment and Intercellular Barrier Formation in Drosophila

58 59 64 70 82 84 84

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Kevin Blauth, Swati Banerjee, and Manzoor A. Bhat 1. Introduction 2. Drosophila Axonal Ensheathment and Vertebrate Myelination 3. Axonal Ensheathment in the Drosophila PNS 4. Axonal Ensheathment in Drosophila CNS 5. BBB Formation in Drosophila 6. Concluding Remarks Acknowledgments References

94 96 100 106 113 120 121 121 v

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Contents

4. Making senses: Development of Vertebrate Cranial Placodes

129

Gerhard Schlosser 1. Introduction: A Diversity of Placodes 2. The Panplacodal Primordium: A Common Region of Origin for all Placodes 3. Induction and Specification of the Panplacodal Primordium 4. Regionalization of the Panplacodal Primordium and Development of Individual Placodes 5. Specification and Differentiation of Placodal Neurons and Sensory Cells 6. Morphogenesis of Placodes 7. Conclusions Acknowledgments References

5. Mechanisms of Protein Kinase A Anchoring

130 136 146 160 174 181 189 190 190

235

Philipp Skroblin, Solveig Grossmann, Gesa Scha¨fer, Walter Rosenthal, and Enno Klussmann 1. Introduction 2. Proteins Involved in Compartmentalized cAMP/PKA Signaling 3. AKAPs: Scaffolds for Local Signaling 4. Cellular Functions Regulated by AKAP-Anchored PKA 5. AKAP Dysfunction in Human Disease 6. Concluding Remarks Acknowledgments References Index

236 237 244 275 289 299 300 301 331

CONTRIBUTORS

Swati Banerjee Department of Cell and Molecular Physiology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA Manzoor A. Bhat Curriculum in Neurobiology; Department of Cell and Molecular Physiology; UNC-Neuroscience Center; and Neurodevelopmental Disorders Research Center, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA Kevin Blauth Curriculum in Neurobiology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA Patricia Costa Randall Division of Cell and Molecular Biophysics, King’s College London, New Hunts House, Guys Campus, London, United Kingdom Solveig Grossmann Leibniz-Institute for Molecular Pharmacology; and Department of Molecular Pharmacology and Cell Biology, Charite´—University Medicine Berlin, Berlin, Germany Enno Klussmann Leibniz-Institute for Molecular Pharmacology, Berlin, Germany Leslie M. Loew Richard D. Berlin Center for Cell Analysis and Modeling, Department of Cell Biology, University of Connecticut Health Center, Farmington, Connecticut, USA Maddy Parsons Randall Division of Cell and Molecular Biophysics, King’s College London, New Hunts House, Guys Campus, London, United Kingdom Walter Rosenthal Department of Molecular Pharmacology and Cell Biology, Charite´—University Medicine; and Max-Delbru¨ck-Center for Molecular Medicine, Berlin, Germany ¨fer Gesa Scha Leibniz-Institute for Molecular Pharmacology, Berlin, Germany vii

viii

Contributors

Gerhard Schlosser Zoology, School of Natural Sciences and Martin Ryan Institute, National University of Ireland, Galway University Road, Galway, Ireland Philipp Skroblin Leibniz-Institute for Molecular Pharmacology, Berlin, Germany Boris M. Slepchenko Richard D. Berlin Center for Cell Analysis and Modeling, Department of Cell Biology, University of Connecticut Health Center, Farmington, Connecticut, USA

C H A P T E R

O N E

Use of Virtual Cell in Studies of Cellular Dynamics Boris M. Slepchenko and Leslie M. Loew Contents 1. Introduction 2. Modeling Capabilities of VCell 2.1. Scope of applicability 2.2. BioModel workspace 2.3. Compartmental applications 2.4. Spatial applications 2.5. VCell solvers 2.6. Managing complexity 3. What One Can Accomplish with the VCell (Review of Published Studies) 3.1. Quantitative studies of calcium dynamics 3.2. Nucleocytoplasmic transport: Experiment and models 3.3. Compartmental and spatially resolved models of cell signaling 3.4. Analysis of fluorescence microscopy data 3.5. Modeling of cell electrophysiology 3.6. Intracellular transport: Interplay of binding, flow and ‘‘active’’ diffusion 3.7. Deterministic and stochastic modeling of gene regulatory networks 3.8. Problems in cell migration: Actin dynamics 4. Future Directions in Developing Tools for Modeling in Cell Biology Acknowledgments References

2 4 5 6 9 11 15 19 21 21 24 27 31 34 36 40 43 48 49 49

Abstract 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 Richard D. Berlin Center for Cell Analysis and Modeling, Department of Cell Biology, University of Connecticut Health Center, Farmington, Connecticut, USA International Review of Cell and Molecular Biology, Volume 283 ISSN 1937-6448, DOI: 10.1016/S1937-6448(10)83001-1

#

2010 Elsevier Inc. All rights reserved.

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Boris M. Slepchenko and Leslie M. Loew

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. Key Words: Virtual Cell, Cell biology, Mathematical models, Computer simulations, Experimental data. ß 2010 Elsevier Inc.

1. Introduction Why computational modeling in cell biology? A short answer is because it can help gain new insights and knowledge and make testable predictions (Mogilner et al., 2006). More specifically, given the complexity of cell processes, qualitative reasoning alone may not be sufficient for adequate interpretation of experimental data or for predicting the system behavior. Formulating assumptions mathematically allows an experimentalist to perform a rigorous logical test of a ‘‘theory’’ that he or she might have in mind. An adequate model almost always yields interesting predictions that, in turn, can be tested experimentally, but even a failure of the model to explain experimental observations often leads to a better understanding of the process under study. While cell biology, unlike physics, remains largely qualitative and mathematical modeling may not always be appropriate (May, 2004) or even possible, certain areas of cell science have benefited from combining experimental studies with physics-based modeling (Phillips et al., 2009). A model of action potential by Hodgkin and Huxley (1952), developed as part of the Nobel Prize-winning study of electric pulses in a giant squid axon, is perhaps one of the most successful examples of an application of modeling in cell-biological research. It is striking how a careful quantitative analysis of ion currents led to the prediction of gating mechanisms even before ion channels were discovered and characterized (Hille, 2001). Not only had the Hodgkin–Huxley model explained the dynamics of membrane potential in neurons, it also became a prototype for modeling the dynamics of other excitable cells, such as cardiac myocytes, pancreatic beta-cells, gonadotrophs, etc., and more broadly, laid a foundation for a new field: the theory of excitable systems (Keener and Sneyd, 1998). Theoretical methods have been successfully applied to other areas of cell biology as well, such as gene networks (Alon, 2007), cell metabolism (Covert et al., 2001; Curis et al., 2009), molecular motors (Mogilner et al., 2002; Wang and Oster, 1998), cell signaling (Kholodenko, 2006), chemotaxis (Yang and Iglesias, 2009), cell cycle (Tyson and Novak, 2008), calcium dynamics (Sneyd et al., 1995), cell motility (Mogilner, 2009), and others.

Virtual Cell and Cellular Dynamics

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It is hardly possible to specify with certainty the circumstances under which a mathematical model would be a useful tool in a cell-biological study: usually, you know it when you see it. Still, it is probably safe to say that a modeling effort is likely to succeed in situations where experimental studies have resulted in a substantial amount of quantitative information. The Hodgkin–Huxley model is an excellent example of the ‘‘data-driven’’ modeling. A typical goal of this type of analysis is to predict or explain the behavior of a complex system by building a model from multiple components that are carefully constrained by experimental data. A different modeling approach is utilized in investigating particular behaviors (oscillations, waves) or features (e.g., amplification of sensitivity). For these cases, building a model might not require precise knowledge of parameter values; rather, the analysis itself should yield conditions that need to be imposed on the model structure and/or parameters so that certain qualitative patterns could emerge. One such example is a seminal theoretical study of sensitivity amplification in activation–inactivation cycles and signaling cascades by Goldbeter and Koshland (1981).Their predictions were later used to explain enhanced sensitivity of the mitogen-activated protein kinase (MAPK) cascade in Xenopus oocyte extracts (Huang and Ferrell, 1996), and recent in-depth analyses of the Goldbeter–Koshland model have shown that the sensitivity amplification in signaling cascades is likely to be a fine-tuned property, as it requires a rare combination of unsaturated activation and saturated inhibition of enzymes (Bluthgen et al., 2006; Racz and Slepchenko, 2008). Finally, modeling proved to be helpful in analyzing raw experimental data. One can simulate an experiment in order to find a correct way of extracting valuable parameters, for example, diffusion or reaction rate constants (Moissoglu et al., 2006; Siggia et al., 2000), and in some cases, the model can even provide guidance for designing experiments (Elowitz and Leibler, 2000; Gardner et al., 2000; Kraikivski et al., 2008). Two developments have recently sparked renewed interest in quantitative approaches to cell-biological studies. First, new fluorescent biosensors have been discovered, especially the naturally fluorescent proteins (Giepmans et al., 2006; Lippincott-Schwartz and Patterson, 2003), that are used to quantify spatiotemporal dynamics of proteins in vivo (Wu et al., 2009). Second, development of new computational tools, accessible to cell biologists (Hucka and Schaff, 2009; Slepchenko et al., 2002), has made it possible to run simulations based on realistic models within reasonable computation time, owing to the exponential growth of computer power in the past two decades and development of new numerical techniques. As cell biology becomes more quantitative and a new generation of cell scientists with adequate mathematical training enters the field, their arsenal of research tools will most likely include computational modeling.

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This review is focused on the usage of the Virtual Cell (VCell) (Loew and Schaff, 2001; Moraru et al., 2002, 2008; Schaff and Loew, 1999; Schaff et al., 1997, 2000; Slepchenko et al., 2002, 2003), www.vcell.org, arguably the most versatile software tool for computational modeling in cell biology (Alves et al., 2006) designed for both experimental biologists and theoretical biophysicists. VCell is developed at the Richard D. Berlin Center for Cell Analysis and Modeling (CCAM) in the University of Connecticut Health Center. After discussing modeling capabilities of VCell in Section 2, we review recent publications in which various cell-biological processes have been simulated using VCell (Section 3). The chapter concludes with a discussion of directions in developing new tools for modeling in cell biology in Section 4.

2. Modeling Capabilities of VCell A computational project usually includes formulating a biological model, casting it in a mathematical form, solving the mathematical model, and comparing predictions from the model with experimental data. Implementation of these steps requires, in addition to expertise in cell biology, some knowledge in the areas of mathematical physics, applied mathematics, and computer programming, and therefore presents obvious technical challenges. The VCell was designed to help biologists overcome these barriers. Accordingly, VCell includes two workspaces, biological (BioModel) and mathematical (MathModel), of which the first, described in detail in Section 2.2, was developed to be used by experimentalists (theorists might find it attractive as well, given the ease of setting up a nontrivial model). It includes an intuitive graphical user interface that facilitates formulating biological models by allowing a user, in effect, to draw corresponding diagrams. While it is generally true that modeling is in essence the art of simplifying assumptions (May, 2004; Mogilner et al., 2006; Phillips et al., 2009), the very structure of user input in VCell (what are the compartments to be modeled?, what are the molecules that populate the compartments?, how are the molecules wired through their interactions?) may help the user formulate a model. Once the biological model is fully specified, VCell automatically translates it into a corresponding mathematical description. This is done by applying physics principles, such as local mass conservation and, in the context of membrane potential, conservation of electric charges (Slepchenko et al., 2003). The math description in the BioModel workspace is read-only in order to maintain one-to-one correspondence with the


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BioModel from which the math has been generated (since in general, it is not possible to unambiguously propagate the changes made in the math description back to the BioModel). This math description, however, can be moved to the MathModel workspace for further editing. In this case, it becomes a standalone mathematical model with no ties to any VCell BioModel, and the user takes on the responsibility to ensure its physical soundness (consistency with conservations laws, etc.). It is also possible, of course, to enter the mathematical description manually in the MathModel workspace from scratch. The VCell solves mathematical models numerically. This means that the solutions are obtained in the form of arrays of floating point numbers and therefore are not exact, although numerical error can be made very small. VCell solvers are described in Section 2.5 where we also discuss some pitfalls of the numerical procedures. The results, visualized as images and graphs, can also be exported in various formats, such as spreadsheets, images, and movies, so that the user can further analyze them with the aid of familiar software tools (see also Section 2.2).

2.1. Scope of applicability VCell accommodates modeling of a wide range of cellular phenomena, which include molecular interactions and transport in various subcellular compartments, as well as dynamics of membrane potential. These mechanisms might be interconnected and can be modeled as such. For example, VCell provides tools for coupling the membrane electrophysiology with reaction–diffusion processes in the volume (Slepchenko et al., 2003). These tools can be applied to modeling an interdependence of dynamics of membrane potential and concentrations of ion species, such as calcium (Li et al., 1997): the membrane potential regulates calcium fluxes through the plasma membrane voltage-sensitive channels and thus affects both spatial distribution of calcium and its overall content in the cytosol. The latter in turn affects the behavior of the membrane potential. Similar situations arise in the context of processes in mitochondria (Magnus and Keizer, 1997). What distinguishes VCell from most of the other software packages designed for biological applications is that it allows one to simulate cellular dynamics not only in time but also in space, using realistic experimental geometries taken from microscope images (Schaff et al., 2001). In Section 2.4.1, we explain how VCell handles geometry. Spatial applications in VCell can include both diffusive and directed molecular transport. The latter is exemplified by molecular motors moving along cytoskeletal tracks. In a continuous approximation that does not resolve individual molecules and filaments, directed transport is modeled as advective flow (Slepchenko et al., 2007).

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A newly developed capability to model diffusion on curved surfaces (Novak et al., 2007) permits coupling of a volumetric reaction–transport system to diffusion and reactions of molecules in the embedding membrane. This capability is necessary for modeling a mobile cytosolic protein that can bind to a mobile molecule in the plasma membrane, so that the complex can diffuse in the membrane, and then return to the cytosol upon unbinding (Moissoglu et al., 2006). Overall, the scope of applicability of VCell can be summarized as the dynamics of reaction–diffusion–advection (flow) systems on arbitrary geometries with arbitrary crossmembrane fluxes; these systems can be coupled to reaction–diffusion systems in the embedding membrane and, for nonneuronal cells, to the dynamics of membrane potential. Note that the geometry in the spatial model does not have to be necessarily geometry of a whole cell or of only one cell: one can model a part of the cell or a multicellular system. VCell resolves the dynamics of a system based on its initial state, or initial conditions. In mathematical terms, VCell solves an initial value problem. For most cases, this problem has a unique solution, or is well posed (Ames, 1977). In the situations where the model is designed to determine how the cell responds to stimuli, it is important to make sure that in the absence of the stimulus, the system remains at a stable steady state; in other words, the initial conditions of the problem should correspond to a steady state of the system; in other words, the initial conditions of the problem should correspond to a steady state of the system. Otherwise, the simulated behavior will include internal dynamics of the system that can be confused with a response to the stimulus. For a stationary steady state, which is often the case, the check is easy: one should simply run the simulation for some time with the stimulus turned off and make sure that molecular concentrations remain nearly constant. VCell solvers can in principle be used to find a steady state, again by running a sufficiently long simulation, so that the state variables (concentrations, membrane potential) have a chance to stabilize within an acceptable error. It is important to bear in mind, however, that the models in biological applications are often nonlinear (that is they include rates that are nonlinear functions of the state variables), which may have multiple stable steady states (multistability) (Slepchenko and Terasaki, 2004). In this case, the initial value solvers may result in different steady states, depending on the initial conditions. It is also important to realize that steady states are not always stationary: they may be oscillatory, wave-like, or even chaotic (Strogatz, 1994).

2.2. BioModel workspace Among the most useful and innovative design features of the VCell software is the hierarchical multilayered structure of a BioModel. The idea is similar to hypothesis-driven research, where a hypothesis can generate multiple experimental protocols (e.g., in vitro biochemistry, in vivo biosensor,

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gene knockouts, etc.) to probe the hypothesis and the experiments may be performed with multiple minor changes in conditions (e.g., titrations of ligand concentrations, varying current clamp steps, differing durations for following dynamic changes, etc.). Experimental results can then be interpreted in relation to the hypothesis. In a BioModel, the parent layer is called the Physiology, which can generate several Applications, which, in turn, can spawn multiple Simulations. This structure is illustrated in Fig. 1.1. The Physiology is a container for the identity of all the molecular species and variables within the model, where they are located within cellular structures and how they interact with each other through rate expressions and membrane

Physiology

Compartments, molecules, reactions

Applications

Compartmental, spatial, deterministic, stochastic, electrophysiology protocols

Simulations

Solvers, spatial resolution, parameter scans, sensitivities

Figure 1.1 Computational modeling with the VCell BioModel workspace. A set of mechanisms in Physiology generates multiple Applications, each of which in turn can spawn multiple simulations utilizing different solvers, spatial resolutions and parameter values.

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transport processes. Cellular structures are created by specifying the topological arrangement of membranes and membrane-bounded compartments. Biochemical reactions are defined within volumetric compartments of the cell as well as in membranes; molecular fluxes, and electrical currents are defined across membranes. The reaction rate or flux rate is determined as an explicit function of the local environment (e.g., concentrations, surface densities, membrane potential) and one or more kinetic parameters (e.g., kon and koff). Mass action and Michaelis–Menten rate laws are available automatically, but user-defined general kinetic expressions are also readily entered. Membrane transport kinetics can be specified with expressions for molecular flux or, for ions, the electric current. The transport kinetics can be described in terms of standard electrophysiological formulas (e.g., Goldman–Hodgkin– Katz permeability or Nernst conductance) or as user-defined molecular flux or current. Several Applications can probe the Physiology. These contain specification of the geometrical features of the model, boundary and initial conditions of the system and the kind of mathematical physics to apply to the model. For the latter, VCell offers choices of continuum reaction kinetics, discrete (stochastic) reaction kinetics, or continuum spatial reaction– diffusion models with geometries that can be derived from experimental images. These three categories of Applications will be described in more detail in Sections 2.3 and 2.4. For spatially explicit Applications, the structures defined in the Physiology are mapped to the corresponding regions within a geometry. As further detailed in Section 2.4, the geometry can be derived from either an analytical expression in Cartesian coordinates or from an experimentally derived image, two-dimensional (2D) or threedimensional (3D). An Application (together with the parent Physiology) is sufficient to completely specify a mathematical model. As noted above, VCell automatically generates a math description language from this input. The Application further branches to permit multiple Simulations. These might invoke different numerical solvers or discretization schemes (detailed in Section 2.5), or different durations. A common reason and a convenient way to generate multiple simulations is to do parameter scans, where the effect of differing initial concentrations of molecular species or different rate constants can be tested. Simulation results are visualized as graphical outputs in a number of convenient formats. For spatial simulations, the variables and rates are displayed as spatial fields mapped onto the model geometry; membrane variables in 3D geometries can be visualized as textured surface renderings. Results can also be exported to spreadsheets and, for spatial models, as images or movies. This hierarchical branching BioModel structure is extremely convenient for exploring the implications of a hypothesis describing a complex mechanism of a cellular process. The Physiology encompasses the major features of the hypothesis, while the Applications represent different scenarios for


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testing the hypothesis—akin to ‘‘virtual experiments.’’ Simulations drill down further in exploring the quantitative predictions of the model. Once the Physiology is defined, most of the work in formulating the model is done. Depending on the complexity of the model, it can take minutes to hours to months to develop a Physiology. But once a Physiology is formulated, Applications can be set up in minutes to hours and Simulations can be set up in seconds to minutes. Of course, the database structure of VCell also permits reuse of the components of a Physiology as components of new BioModels.

2.3. Compartmental applications Diffusion within a compartment can be fast compared to reactions and crossmembrane fluxes (this is generally true for relatively small compartments). For this case, spatial gradients of concentrations are small and concentration dynamics are similar at all locations of the compartment. There is no need for spatially resolving the compartment in these conditions; therefore the concentrations become functions of time only. The model reduction based on fast equilibration of variables in space is sometimes called a ‘‘well-mixed’’ or a whole-cell approximation. While the latter term refers to a single compartment, generally there might be multiple well-mixed compartments in the model. In VCell, the applications that are concerned only with time dependences of state variables are termed compartmental. In a deterministic mathematical model, functions of a single independent variable (time) are governed by ordinary differential equations (ODEs) as opposed to partial differential equations (PDEs) which describe functions of multiple independent variables (spatial coordinates and time). The deterministic compartmental model is therefore described by a set of ODEs. The VCell tool for modeling the membrane potential is designed for nonneuronal cells (as well as for space-clamped neurons) where equilibration of charges is fast. The membrane potential in VCell is therefore a variable that depends only on time and thus is described in compartmental applications by an ODE. When coupled to spatially resolved concentration dynamics, the membrane potential is governed by an integro-differential equation, since its rate of change in this case is obtained by integrating electric current densities over the membrane. Compartmental models might be a good starting point even when the ultimate goal is spatial modeling. The practical reason is that solving ODEs is obviously much faster than solving PDEs which involve multiple grid points in space. Thus the compartmental approximation provides a quick preview of ‘‘averaged’’ system dynamics governed by reactions and crossmembrane fluxes and in particular, gives the user an idea about time scales that should be expected for the full spatial solution. This information helps

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manage computationally expensive spatial simulations. By default, new applications in the BioModel workspace are generated as compartmental. When the number of molecules in a system is small, the deterministic approach to simulating a compartmental model may not be appropriate. In such cases changes in the state of the system should be described as discrete stochastic events. In the stochastic approach, the system is described by a probability distribution over a range of possible values of state variables. Stochastic processes are described by time-dependent probabilities and governed by local conservation. For cases, where the rate of change of the process is fully determined by the current state of the system, that is, the system has no memory (Markov processes), the conservation law is known as ‘‘master equation’’ (Gardiner, 2004). VCell solves the master equation numerically, using methods based on sampling individual trajectories (Monte Carlo simulations). VCell stochastic solvers, described in more detail in Section 2.5.4, utilize the Gibson–Bruck Next Reaction algorithm (Gibson and Bruck, 2000), an optimized version of one of the Gillespie algorithms (Gillespie, 1976), which interpret reactions as Poisson stochastic processes. The VCell stochastic tool allows a user to simulate individual trajectories as well as multiple trials with either default or user-specified seeds. In the case of multiple trials, VCell automatically calculates histograms of the number of copies for the species of interest. While the algorithm simulates events as they randomly occur, the user can specify regular time intervals at which the results should be displayed. One problem with deriving stochastic applications from a general VCell BioModel is that the reaction kinetics are introduced in the model as deterministic, and the default option for reaction kinetics in the BioModel workspace, ‘‘general kinetics,’’ allows for arbitrary rate expressions. However, an unambiguous interpretation of deterministic rates in terms of Poisson stochastic processes is possible only for mass-action kinetics, and even for this case, the reversible reactions should be decoupled into separate processes. (Indeed, it can be shown that, generally, different stochastic models can have the same deterministic limit.) A special analyzer tool in VCell automatically maps mass-action reaction rates to probabilistic propensities, and facilitates the mapping of other reactions. The tool identifies the kinetic type of a reaction or membrane flux, and in the case of general kinetics, automatically determines, by parsing the rate expression, whether the mechanism might be a combination of individual Poisson processes (e.g., if they are passive fluxes). It then informs the user: (i) which of the mechanisms with kinetics other than mass action can be directly translated into probabilistic propensities (and asks permission to do it automatically), and (ii) which of the mechanisms have to be recast manually by the user into a combination of one or more mass-action types.


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2.4. Spatial applications For situations, where spatial concentration gradients are of importance, VCell provides tools for running spatial simulations. In spatial applications, mechanisms defined in ‘‘Physiology’’ are mapped onto a geometry that must be created in a ‘‘Geometry’’ workspace (details of how to operate VCell can be found in VCell User Guide at http://vcell.org/userdocs/Rel/ user_guide.pdf). Geometry is either defined analytically or taken from experimental images. In analytical geometry, the shape of compartments is specified by inequalities in Cartesian coordinates; for instance, a unit sphere centered at the origin is defined as x2 þ y2 þ z2 < 1. A most recent version of VCell, VCell 4.7 Beta (http://www.vcell.org/vcell_software/ login.html), includes ‘‘an assistant’’ that automatically generates inequalities for a number of predefined primitives. Geometry can also be created using an experimental image. For this, VCell requires segmented images, in which all pixels belonging to a given compartment have the same pixel value and different compartments have different colors (a 3D image is entered as an ordered stack of 2D segmented slices in a .zip format). Standard image processing tools, such as thresholding can be utilized to segment the raw image. While currently this is done outside VCell, a set of relatively simple tools are being developed in VCell to help a user prepare a segmented image. A spatial application is mathematically described by a set of PDEs; it may also include a subset of ODEs if the model involves immobile molecules whose concentrations vary only due to reactions. Concentrations of immobile molecules may still vary in space because of their binding interactions with mobile components. Since numerical solution to a set of PDEs can be obtained only for a finite set of points in space, the spatial domain for which the solution is sought has to be discretized. The next section describes how this is achieved in VCell. 2.4.1. Handling geometry Discretization of space in VCell is performed using a relatively simple procedure which is automatic and fast. The geometry of interest, either analytical or experimental, is placed in a rectangular box (computational domain), to which uniform orthogonal meshing is applied (Fig. 1.2). The meshing of the computational domain generates a uniform grid of points with a default resolution and decomposes the computational domain into volume elements centered at grid points (except for ‘‘partial’’ subvolumes adjacent to the domain boundaries). The automatically generated default resolution can be modified by the user by specifying new numbers of mesh points for each Cartesian direction (in doing so, it is important to bear in mind that equilateral volume elements, cubes in 3D and squares in 2D, provide better accuracy, particularly in resolving surfaces, see below).

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A

Cell

Cell membrane

B Volume element Membrane grid point

Volume grid point

Membrane element

Figure 1.2 A simple 2D example of handling geometry in VCell. (A) Computation domain and a cell of an arbitrary shape. (B) Mesh lines and grid points.

The grid points define locations for which the solution is computed. For a given spatial resolution, approximate geometry is built by assigning a whole volume element to the compartment in which its center (the grid point) is located in the ‘‘exact’’ geometry defined analytically or by a fully resolved segmented image, as described in the preceding section. This automatically yields membranes as watertight pixilated surfaces separating different compartments. The membrane elements are rectangular facets between the volume elements belonging to different compartments; their centers form a surface grid for which densities of the crossmembrane fluxes and surface densities of the membrane-bound molecules are computed. Structured orthogonal gridding, adopted in VCell, facilitates automatic sampling of cell geometry, particularly when it is based on experimental microscope images where one can only tell whether a square pixel in the image lies inside or outside a compartment. It also allows one to move easily from 1D to 2D–3D simulations. The downside of this approach is that it results in a ‘‘staircase’’ approximation of cell membranes. While volumes and overall shapes of the compartments converge with an increasing spatial resolution to those prescribed by the user, neither a surface area of the ‘‘staircase,’’ nor


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distances between the points on the pixilated surface converge to those for a smooth surface when the mesh is refined. This creates difficulties in approximating crossmembrane fluxes and lateral diffusion in the membranes. In VCell, this issue is addressed by applying an optimized approximation of normal vectors to the exact surface in the presence of quantization noise (Novak et al., 2007; Schaff et al., 2001). A ‘‘flux correction’’ method and methods for modeling lateral diffusion, which are based on this approximation, produce converging solutions, as demonstrated by Novak et al. (2007). 2.4.2. Boundary conditions Solution to a spatial initial value problem depends on conditions at the boundaries of the computational domain. Imagine that a large number of molecules are injected at the center of the box and allowed to diffuse. It is clear that eventually their distribution will become uniform but the concentration level will depend on properties of the box walls. Indeed, for the impermeable walls (no flux boundary conditions), the final concentration of the molecules in the box will be given by the ratio of the total number of injected molecules and the box volume. However, if the walls are leaky and the outside concentration is clamped at zero or if the walls are ideally absorbing (zero-value boundary conditions), the final concentration of the molecules will be zero. Thus, in order to run spatial simulations, boundary conditions must be specified. In VCell, the term ‘‘boundary conditions’’ is reserved for the boundary planes, that is, the walls of the rectangular box enclosing the geometry. The VCell boundary conditions can be of two types, ‘‘flux’’ or ‘‘value.’’ For either type, the boundary condition can be specified as a constant or as an appropriate function of time and place on the boundary plane. They can also be functions of state variables. Conditions at membranes inside the computational domain, technically termed ‘‘jump conditions’’ because they determine a jump of concentration across the membrane, are generated in the BioModel workspace automatically in accordance with the membrane mechanisms, such as fluxes through channels and/or reactions of binding to the membrane-associated molecules and unbinding from the membrane. Thus, the jump conditions are generally functions of state variables. They can also depend on spatial coordinates, as is the case for polarized cells, and/or on time like in modeling of photobleaching (Section 3.4). The dependence on spatial coordinates, however, cannot be introduced directly in the reaction editor of the ‘‘Physiology’’ panel because by design, the mechanisms defined in ‘‘Physiology’’ can be combined with various geometries and experimental protocols specified in different Applications of the BioModel. Instead, one can introduce an auxiliary ‘‘regulator’’ reflecting the spatiotemporal attribute of the process. For example, if a certain membrane flux occurs only at the brush border of an epithelial cell, a ‘‘species’’ Brush_Border can be included

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in the BioModel as a ‘‘regulator’’ for the corresponding flux. In the Application with a particular geometry, an appropriate function of the Cartesian coordinates (and time) can then be specified as an initial condition for the ‘‘species’’ Brush_Border. At the same time, Brush_Border should be flagged as ‘‘clamped,’’ which will tell VCell that Brush_Border is not in fact a variable but rather a prescribed function of spatial coordinates. The MathModel workspace is less restrictive, and the dependence on spatial coordinates and time can be included in the jump conditions directly. Still, to ensure correct implementation of the jump conditions in the MathModel, one should carefully follow certain conventions used in VCell. In particular, the adopted notations for a volumetric concentration of molecule X near the membrane are X_INSIDE and X_OUTSIDE, and the ‘‘inside–outside’’ relation is encoded in the order in which the names of the adjacent compartments appear in the subtitle for a membrane subdomain. Consider a simple example of reversible binding of a cytosolic protein A to a nuclear pore complex (NPC). The format of the equation for the bound state A_NPC and the corresponding jump condition for the diffusing protein A is shown below: MembraneSubDomain nucleus cytosol { OdeEquation A_NPC { Rate (kon * A_OUTSIDE * NPC - koff * A_NPC); Initial 0.0; } JumpCondition A { InFlux 0.0; OutFlux (-KMOLE * (kon * A_OUTSIDE* NPC - koff * A_NPC)); } }

In the description above, ‘‘InFlux’’ denotes the flux (per unit area) coming from the membrane into an inside compartment. According to the convention, the name of the inside compartment, which in this case is the nucleus, comes first in the membrane subtitle. Because the binding of the cytosolic protein to the nuclear pore does not produce flux from the membrane into the nucleus, InFlux is zero. ‘‘OutFlux’’ stands for the flux coming from the membrane into the outside compartment, which in our example is the cytosol. Then OutFlux is minus the rate of binding, and the concentration of the protein A near the membrane on the cytosolic side is appended by ‘‘_OUTSIDE.’’ The conversion factor KMOLE is unity if the surface density of A_NPC is in molecules per mm2 and the concentration [A] is measured in molecules per mm3; if [A] is measured in mM and the flux


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density units are mm/s, as is the standard for VCell BioModels, then KMOLE ¼ 1/602. The ‘‘inside–outside’’ relationship will not be required in a new VCell release, VCell 5. This will result in a more intuitive notation for the membrane fluxes and the volumetric concentrations near the membrane: the ‘‘_INSIDE’’ and ‘‘_OUTSIDE’’ labels of will be replaced with the names of the corresponding compartments.

2.5. VCell solvers Realistic biological models do not generally lend themselves to an analytical treatment and therefore must be solved numerically. VCell provides a number of deterministic and stochastic solvers that can be found in the ‘‘Simulation ! Edit ! Advanced’’ panel. The numerical approach to deterministic models, which are formulated continuously in terms of differential equations, requires that the time interval and the spatial domain for which the solution is sought be sampled. The differential equations are then approximated by finite differences with respect to selected grid and time points. Essentially, they are replaced by a system of algebraic equations from which the concentrations can be found for a selected set of points in time and space. While the algebraic system is usually solved with high precision, it is important to realize that the solution is an approximation to that of the original system of differential equations. The magnitude of error depends on the time step and the mesh size (discretization parameters); as they decrease, the solution of the algebraic system should converge to the exact solution. However, small integration time steps and mesh sizes will result in a long computation time, hence a tradeoff between algorithm’s accuracy and efficiency. There are various discretization schemes—ways, in which differential equations can be approximated by a set of algebraic equations, and some of them can become numerically unstable if, for example, the integration time step is insufficiently small to resolve fast processes in the system. Numerical instability can manifest itself as qualitatively wrong behaviors, such as unphysical oscillations or negative concentrations, or an exponential growth of numerical error resulting in machine infinity. Thus, even when the goal of modeling is to get an idea about qualitative behavior of the system and numerical accuracy is not of major concern, the discretization parameters might still be constrained by conditions of numerical stability of the solver. On the other hand, when accuracy is the issue, it is important to remember that even unconditionally stable methods can produce inaccurate results if the time step is not small enough or tolerances are insufficiently strict. It is therefore a good practice to double-check results by running simulations with varying discretization parameters (tolerances) or with different solvers.

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2.5.1. ODE solvers A VCell suite of ODE solvers includes algorithms with fixed and variable time steps. The fixed time step solvers included in VCell sample the time interval uniformly with a step specified by the user and advance the solution from one time point to the next using rates evaluated at the ‘‘old’’ time point (explicit solvers). The explicit time discretization is not unconditionally stable but the methods with higher order of convergence are less susceptible to numerical instability (the order of convergence n determines the rate with which numerical error e decreases with decreasing time step Dt, n log(e) / log(Dt)). Solvers with adaptive (variable) time steps do not require a user to specify the integration time step. They automatically adjust the time step within given bounds so that the solution remains sufficiently accurate; for these algorithms, the solution error is controlled by setting up tolerances in the ‘‘Advanced’’ panel of the ‘‘Simulation ! Edit’’ window. Two of the solvers with variable time step, IDA and CVODE, are unconditionally stable (Ascher and Petzold, 2000). They propagate the solution in time on the basis of rates corresponding to the ‘‘new’’ time point. This is a so-called implicit discretization scheme resulting in a system of nonlinear algebraic equations that must be solved iteratively. These solvers are the best choice when dealing with ‘‘stiff’’ systems that involve disparate time scales (for more details about stiff systems, see Section 2.5.3). 2.5.2. PDE solvers VCell currently provides two solvers for running spatial simulations: semiimplicit and fully implicit. There are two versions of the semi-implicit solver, compiled and interpreted. The interpreted version has better error annotation, while the compiled version might be more efficient. The semiimplicit solver is a fixed time-step integrator that requires a user to specify the integration time step (note that simulation results do not have to be saved for each integration step; a time interval at which the data should be saved is specified separately). Numerical errors can be estimated by comparing results obtained with different time steps and mesh sizes. The algorithm is based on a discretization scheme that converts a system of m PDEs (m is the number of variables in the model) into m independent systems of linear algebraic equations. The systems of equations are solved using a robust iterative linear solver based on preconditioned conjugate gradients (a general minimal residual algorithm, GMRES), PCGPAK (Scientific Computing Associates, New Haven, CT) (Saad, 2003). Linearization of equations is achieved by treating reactions and fluxes explicitly, which makes the method prone to numerical instability if the time step is insufficiently small to resolve fast processes in the system. A recently developed fully implicit spatial solver, accessible through VCell 4.7 (and later versions), is unconditionally stable. The algorithm


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uses the method of lines (Schiesser, 1991) to convert the system of PDEs into mN coupled nonlinear ODEs (N is the number of grid points), which are then solved using robust solvers from SUNDIALS (Lawrence Livermore National Laboratory, CA) (Hindmarsh et al., 2005). These are adaptive time step solvers; therefore the user does not have to worry about the integration time step when using the fully implicit spatial solver. Accuracy of the solution is controlled by tolerances. The default relative and absolute tolerances are set in VCell at 1e-7 and 1e-9, respectively. 2.5.3. Handling disparate time scales Biological models often include processes with drastically different time scales; for example, some reactions can be several orders of magnitude faster than others. Mathematically, the systems with disparate time scales (‘‘stiff’’ systems) are particularly prone to numerical instabilities, as they require small time steps to resolve fast processes. As a consequence, stiffness impedes efficiency of computations, since in order to avoid numerical instabilities, very short time steps must be used even if only the long timescale behavior of the system is of interest. The adaptive solvers (IDA and CVODE, for ODEs, and the fully implicit spatial solver) are unconditionally stable. They are therefore well suited for treating stiff systems and sometimes called ‘‘stiff solvers,’’ but they can also be inefficient. In situations where some reactions continue to be much faster than others during the entire simulated time, as is the case, for example, with calcium buffering (Wagner and Keizer, 1994), a quasi-steady-state approximation (QSSA) provides an accurate and efficient alternative. In this approximation, fast reactions are considered to be near equilibrium, and the corresponding ODEs are replaced with algebraic equations corresponding to steady-state conditions (Murray, 1993) (the mathematically accurate formulation of this approach is sometimes called singular perturbation theory). VCell supports automatic implementation of QSSA with respect to any reactions labeled as ‘‘fast’’ (Slepchenko et al., 2000). In fact, this is all that a user has to do to invoke QSSA in VCell. The underlying algorithm automatically transforms the original model into a differential-algebraic system, where the subsystem of differential equations includes only slow reaction rates whereas the algebraic subsystem reflects instantaneous equilibration of fast reactions. For compartmental applications, where this transformation is exact, QSSA is performed in combination with Forward Euler or IDA ODE solvers (in VCell 4.7, also with the Combined Stiff Solver). For spatial simulations, QSSA is implemented in combination with the semi-implicit solver. In this case, the separation of the fast and slow subsystems is not exact and results in an additional ‘‘splitting’’ error (Yanenko, 1971) which is usually comparable to the error of solving diffusion. Overall, applying QSSA can improve simulation times by several orders of magnitude and make it practical to

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explicitly include the effects of rapid binding of buffers and fluorescent indicators on the behavior of molecules to which they bind. 2.5.4. Stochastic solvers Methods for simulating stochastic processes are based on generating (pseudo) random numbers. They are called Monte Carlo methods; the term was originally introduced by S. Ulam and J. von Neumann in the days of the Manhattan Project, as throwing a dice is actually a way of generating random numbers. The Monte Carlo algorithms rely on the quality of a random number generator, which produces pseudorandom sequences mimicking a random variable x uniformly distributed on the segment [0,1], and on a variety of methods that then transform x into a new variable Z with the probability distribution of interest (Press et al., 1999). Similarly to deterministic models, stochastic systems can be advanced in time using a fixed time step; this approach is utilized, for instance, in Brownian Dynamics simulations (Saxton, 2007). Generation of random numbers is used to determine whether a reaction will occur during the time step. The fixed time step methods are approximate, with the error determined by the size of the time step. In contrast, the exact, or eventdriven, algorithms, introduced in chemical dynamics by Gillespie (1976, 1977), are free of this error, as they simulate stochastically both the reaction that occurs next and the time between consecutive reaction events. The latter is achieved by taking into account that discrete reaction events are Poisson processes. In the so-called ‘‘first-reaction’’ method, for example, one uses current probability rates (propensities) to sample putative reaction times for all reactions, then the reaction with the least time is deemed to be occurring next and the system is updated accordingly. In VCell, stochastic simulations of compartmental models are based on the ‘‘next reaction’’ algorithm (Gibson and Bruck, 2000), which uses certain properties of Poisson processes and other important observations to drastically improve efficiency of the first-reaction method. It takes advantage of the fact that connectivity of reactants through reactions is usually sparse, which allows one to reuse reaction times that have not been affected by the current event, and uses a fast heap-like sorting algorithm, an ‘‘indexed priority queue,’’ in searching for the reaction with a minimal projected time. The exact stochastic simulator may be inefficient when applied to stiff systems with vastly different reaction rates. For this case, a number of approximate ‘‘hybrid’’ solvers have been proposed (Gillespie, 2001; Haseltine and Rawlings, 2002; Rao and Arkin, 2003). They advance the system in time with a reasonably large fixed time increment and thus allow for an appropriate compromise between accuracy and efficiency. The idea behind the hybrid methods is to treat fast reactions by superimposing the Gaussian noise over deterministic dynamics, while simulating slow reactions


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exactly. A challenge is to account for a possible feedback from the fast subsystem onto the slow one. VCell incorporates an open source library Hy3S (hysss.sourceforge.net/index.shtml) written by H. Salis, which implements hybrid methods optimized for accuracy (Salis and Kaznessis 2005; Salis et al., 2006). Still, it is important to remember that hybrid methods are approximate, as they use certain ‘‘reasonable’’ criteria for separating fast and slow reactions. The user may want to modify default parameters of these criteria, that are accessible in the ‘‘Simulation ! Edit ! Advanced’’ panel, to achieve a best-suited tradeoff between accuracy and efficiency of the simulation.

2.6. Managing complexity VCell provides a number of tools that facilitate the analysis and building of complicated models. 2.6.1. Parameter scanning and parameter estimation The option of parameter scanning allows a user to run a batch of simulations for a selected set of combinations of parameter values. For this, the user specifies ranges and the number of values within a range that will be selected, either uniformly or logarithmically, for scanning. This is done by checking boxes in the ‘‘Scan’’ column, under the ‘‘Edit ! Parameters’’ tab. VCell then automatically initiates simulations for all combinations of selected parameters. The results for individual parameter combinations can be viewed by selecting parameters from the table at the bottom of the ‘‘Results’’ window. The parameter estimation option includes two optimization solvers that automatically find a combination of parameters that minimizes differences between experimental time series and predictions form a compartmental model. Details of how to perform parameter estimation in VCell can be found in VCell User Guide at http://vcell.org/userdocs/Rel/user_guide. pdf. Caution should be exercised in using optimization techniques. First, optimization of a complex nonlinear model may yield different results for different initial guesses, that is, the optimization problem might have multiple solutions. Second, a minimum found by the solver might be shallow so that even large deviations from ‘‘optimal’’ parameters would result only in slight changes of the fit. This might indicate that the parameters of a model cannot be faithfully identified based on a given set of experimental data. Overall, it is important to bear in mind that fitting the data by parameter optimization may yield misleading results unless it is accompanied by a careful analysis of sufficiency of the available data for unique parameterization of the model.

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2.6.2. Analysis of sensitivities One way to estimate the ‘‘goodness-of-fit’’ is to analyze how the model responds to deviations of parameters from the optimal values. The model is usually expected to be not very sensitive to parameter changes unless there are reasons to believe that certain parameters have been fine-tuned by natural selection. The sensitivity analysis might also yield predictions that could be tested experimentally. These tests either validate the model or require its revision. Knowing local sensitivities of the model might also be helpful in finding the path of ‘‘steepest descent’’ when the fitting is done manually by trial and error. The local sensitivities (also known as control coefficients in the metabolic control analysis) are defined as the change in the model output, D[X], caused by perturbation of a particular parameter a, divided by the magnitude of the perturbation, Da. More precisely, the local sensitivity is the derivative @[X] / @a evaluated at a particular (optimal) point in the parameter space. Sometimes it is more informative to compute the ratios of relative changes of the output and the parameter; these ratios are called logarithmic sensitivities, @log[X] / @log a. VCell provides a tool for the local sensitivity analysis of the compartmental models. When invoked, it prompts VCell to solve the initial value problem for the local sensitivities (or logarithmic sensitivities) along with the solution of the model for a particular parameter set. 2.6.3. BioNetGen@VCell Building a large BioModel, involving binding interactions among multiple molecular species that generate numerous intermediate complexes, might be tedious. An intuitive and convenient way of handling combinatorial complexity that arise from molecules with multiple binding sites, known as rule-based modeling, has been implemented in the software called BioNetGen (Faeder et al., 2009). In this approach, each molecule in the system is characterized by a set of binding sites which may carry attributes (e.g., a tyrosine residue can be phosphorylated or unphosphorylated). Interactions between molecular species are governed by a set of reaction rules that specify reaction probability rates and conditions in which the reaction can occur. The rule also specifies modifications of attributes of interacting molecules as a result of their interaction. This method, which is particularly efficient for the case of noncooperative interactions, automatically keeps track of changes of attributes and connectivity. A VCell user can access this method through a BioNetGen@VCell service by clicking the ‘‘BioNetGen’’ button on the tool bar of the BioModel workspace. This will allow the user to write or upload a BioNetGen input file and perform reaction network generation and time courses simulation. A VCell BioModel can then be automatically generated and processed by the VCell.


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3. What One Can Accomplish with the VCell (Review of Published Studies) The goal of this section is to demonstrate how VCell can be applied to various cell-biological problems. For this, we review studies published by different research groups in which they used VCell to create and solve models. We discuss biological contexts of these applications and methods used to build models and run simulations. In some cases, we also discuss published models that were reproduced in VCell.

3.1. Quantitative studies of calcium dynamics Calcium plays a central role in relaying intracellular signals in both excitable and nonexcitable cells. Because it can readily be measured in cells with fluorescent indicators and quantitative microscopy and because calcium channels can be quantitatively studied with electrophysiological methods, a wealth of data is available for model input, constraint, and validation. In addition, calcium signals can display intriguing behaviors in cells such as oscillations and waves. For these reasons, intracellular calcium dynamics has become the subject of hundreds of mathematical models (Dupont et al., 2007; Holthoff and Tsay, 2002; Puglisi et al., 2004; Schuster et al., 2002; Sneyd and Falcke, 2005). It was natural, therefore, that calcium was the target of the first VCell models (Fink et al., 1999a,b, 2000; Schaff et al., 1997) and continues to be a subject that is especially appropriate for the VCell software (Brown et al., 2008; Coatesworth and Bolsover, 2008; Duman et al., 2008; Fridlyand et al., 2003, 2007, 2009; Hernjak et al., 2005; Horowitz et al., 2005; Johenning et al., 2002; Kowalewski et al., 2006; Lukas, 2004a,b; Pomorski et al., 2005). The two papers by Fink et al. (1999b, 2000) on calcium dynamics in a cultured neuronal cell line serve as good examples of how modeling can aid in the interpretation of experimental results, that is, ‘‘data-driven modeling.’’ The basic experimental result is shown on the left in Fig. 1.3. In these experiments, bradykinin, an agonist for a G-protein-coupled receptor, is added to a coverslip with adherent differentiated N1E-115 neuroblastoma cells. The experiment in Fig. 1.3 shows that calcium increases after a delay of about 2.5 s after addition of bradykinin. It first peaks in the thin neurite of the cell and shortly thereafter spreads to the soma. The calcium level reaches about 1.2 mM in all regions of the cell before decaying over the next 30 s. Time courses for the calcium changes in the neurite (green) and soma (yellow) are shown in the adjacent plots. These features of the spatiotemporal calcium response to bradykinin were stereotypical for cells with this general morphology. Simulations can then predict the bradykinin-induced

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Experiment

Simulations

1.4 s Best fit 1.1611 [Ca2+] mM Time after BK addition 0.05

2.4 s 1.2

2.6 s

[Ca2+] mM

0.8 s

0.05

1.4 s

2.8 s Plot of [Ca2+] vs. time: soma and neurite

8.0 s

No fura-2

1.323 2+ [Ca ] mM 0.05

2.078 2+ [Ca ] mM 0.05

2.4 s 2.6 s

1.0

% [Ca2+] cyt peak

3.2 s

4.3 s

Uniform ER

0.8

2.8 s

0.6

3.2 s

0.4

4.3 s Neurite Soma

0.2 0.0

0

2 4

6

8 10 12 14 16

Time after BK addition (s)

8.0 s 10.1 s 16.0 s

Neurite Soma

0 2 4 6 8 10 12 14 16 Time (s) after BK addition

Figure 1.3

Plot of [Ca2+] vs. time: soma and neurite [Ca2+]cyt (mM)

[Ca2+]cyt (mM)

16.0 s

2+ Plot of [Ca ] vs. time: soma and neurite

2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

Neurite Soma

0 2 4 6 8 10121416 Time (s) after BK addition

Plot of [Ca2+] vs. time: soma and neurite

[Ca2+]cyt (mM)

10.1 s

2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

Neurite Soma 0 2 4 6 8 10 12 14 16

Time (s) after BK addition

Calcium dynamics in a neuroblastoma cell: experiment and modeling.

changes in free Ca2þ within the experimental geometry derived from microscope images. Fluorescence microscopy of the calcium indicator Fura-2 provided the data (left of Fig. 1.3) for validation of a model. While the two papers (Fink et al., 1999b, 2000) used the MathModel workspace to create a series of VCell models, subsequent improvements in the BioModel workspace permitted us to implement them as multiple Applications within one BioModel (see the public model ‘‘Ca_Release_ Fink_etal_1999&2000’’ under username ‘‘CMC’’). The Physiology contained structures corresponding to the extracellular space, the cytosol, the endoplasmic reticulum (ER) and the nucleus. Activation of the bradykinin receptor leads to activation of phospholipase C, which produces inositol-1,4,5-trisphosphate (IP3) at the cytosolic side of the plasma membrane. IP3 then binds to the IP3 receptor in the ER membrane, activating it to release calcium. The IP3 receptor is also regulated by calcium in a complex and still somewhat controversial mechanism with evidence for both positive and negative feedback at different levels of calcium (Sneyd and Falcke, 2005). Other components of the mechanism that also must be


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included in the model are calcium leak and calcium pump fluxes across the ER membrane, binding of calcium by cytoplasmic buffers, extrusion of calcium form the cell via plasma membrane ATPase and degradation of IP3 via metabolic enzymes in the cytosol. All of the rate laws for these individual mechanisms as well as the concentration of the relevant molecular species could be derived from experiments. The Applications include a compartmental model, to establish the steady-state distributions of all the species before activation of the bradykinin receptor, and several 2D spatial models. For the latter, the extracellular space, cytosol, and nucleus were mapped to the corresponding regions within the experimental geometry; but the ER, because it is too fine and convoluted a structure to be resolved, was modeled as a continuously distributed compartment within the cytosol, using a volume fraction of 0.15. The simulation in Fig. 1.3 labeled ‘‘Best Fit’’ recapitulates the experimental results very well, but the Application that was implemented first in this study (labeled ‘‘Uniform ER’’) gave a very poor match to the experiment. This initial model produced a much higher calcium signal in the neurite than in the soma. This result was a consequence of the higher levels of IP3 that were produced in the neurite compared to the soma and this was a consequence, in turn, of the higher surface to volume ratio in the neurite: diffusion of IP3 was too slow to equalize concentrations throughout the cell before substantial degradation of the IP3 due to metabolic enzymes. However, this led to the realization that the higher transient levels of IP3 in the neurite could be compensated by a higher sensitivity to IP3 in the soma. One way to achieve this would be to have a higher density of ER in the soma compared to the neurite. This hypothesis was then tested by extensive immunofluorescence imaging of ER markers, including the IP3 receptor itself. The result was a finding that the ER density was, indeed, approximately twofold higher in the soma than in the neurite. When this nonuniform distribution of ER was mapped onto the geometry in a new spatial Application, the simulation labeled ‘‘Best Fit’’ in Fig. 1.3 was the result. Thus, this study showed how iterations between experiment and modeling can yield new insights into how morphology can be fine tuned to control cell signaling. To perform a calcium imaging experiment, an indicator is required. But the indicator binds to free calcium and must be present at concentration of 20–100 mM for sufficient fluorescence signal. The ‘‘Best Fit’’ model included the calcium indicator, Fura-2, at a level of 75mM as this was the intracellular concentration used in the experiment. But once this full model is constructed and validated, it is a simple operation to run a simulation in which the Fura-2 is set to zero. The results, shown in the rightmost column of Fig. 1.3, reveal that the Fura-2 has a profound effect on the measurement; the Ca2þ appears much earlier (0.8 s vs. 2.5 s) and has twice the amplitude

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compared to the model containing Fura-2. Thus the simulations can be used to back out the perturbing effect of the fluorescent indicator. In the original papers (Fink et al., 1999b, 2000), additional validations of the model against experiments were carried out, including experiments where bradykinin was applied focally to different regions of the cells and where IP3 was released simultaneously throughout the volume of the cell via photolysis of a caged IP3 that had been microinjected. In all cases, the simulations recapitulated the results of the experiments. This lends credibility to the basic accuracy of the ‘‘Best Fit’’ model.

3.2. Nucleocytoplasmic transport: Experiment and models Nucleocytoplasmic transport of proteins and nucleic acids is crucial for normal functioning of eukaryotic cells. It occurs by way of NPCs; small molecules passively diffuse through the nuclear pores, whereas passage of large molecules is facilitated by binding to transport receptors (carriers). In the nucleus, unloading of the imported cargo from the carrier requires binding to RanGTP, a GTP-bound form of a small GTPase Ran. After the transport receptor, free of cargo but bound to RanGTP, returns to the cytosol, it unbinds from Ran when RanGTP is hydrolyzed by the GTPaseactivating protein RanGAP, which completes the cycle. The process is thus controlled by activation and inactivation of Ran, which in turn cycles between the cytosol and the nucleus. Because at least one molecule of Ran is translocated in each direction for each complete cycle of carriercargo transport, the cargo flux can be estimated if the steady-state flux of Ran is known. Experimental and computational approaches were combined in a study of Ran transport (Smith et al., 2002), aimed at determining factors that contribute most toward the overall regulation of the nucleocytoplasmic flux at steady state. The computational component of the paper is another example of datadriven modeling. Given that measuring parameters of the overall transport in intact cells is hardly feasible, the behavior of the system is analyzed by building a model from well characterized components of Ran transport. An overall kinetic scheme of the Ran cycle that underlies the model is shown in Fig. 1.4. Directionality of fluxes of Ran in a strongly nonequilibrium steady state is maintained by a spatial separation of RanGAP localized exclusively to the cytosol from the Ran exchange factor RCC1 bound to chromatin. Hydrolysis of RanGTP in the cytosol, catalyzed by RanGAP, is irreversible and consumes metabolic energy. Nucleotide exchange in the nucleus facilitated by RCC1, while comprised of reversible reactions, is effectively irreversible as well, due to strong asymmetry in concentrations of GTP and GDP maintained at the expense of metabolic energy. All other mechanisms are reversible, including the passage of RanGTP and RanGDP

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NTF2: RanGDP RC

Ran GD

P

C1

RanGTP

NTF2 Cargo Cargo: Carrier Carrier

RanBP1

Cargo

Ra

nG

Carrier: RanGTP

AP RanBP1

Cytosol

Nucleus

Figure 1.4 Diagram of a minimal model of nucleocytoplasmic transport.

through NPC. The latter is facilitated by binding to the nuclear transport factor NTF2. The model was implemented and simulated with VCell. Results of spatial simulations performed on experimental 3D geometry were close to the compartmental approximation, indicating that the latter was sufficiently accurate. The reversible reactions of the type X þ A $ AX were described by the mass-action kinetics, v ¼ kon[X][A] þ koff[AX]; the irreversible enzyme-mediated reactions were approximated with the Michaelis–Menten rates, vX ¼ kcat[E][X](Km þ [X]) 1, where [E] is the enzyme concentration. The nuclear membrane flux densities were modeled as jX ¼ PX([X]cytosol [X]nucleus). Parameters of reversible binding interactions and enzyme-mediated reactions were constrained by reported data. To determine other parameters, in particular the permeability of NPC to NTF2:RanGDP and carrier:RanGTP, the fluorescently labeled recombinant Ran was injected into the cytosol of BHK-21 cells, and the nuclear accumulation of Ran was imaged at 0.5-s intervals until steady state was reached, usually within 12–30 s (at 23 C) (Smith et al., 2002). Additionally, microinjection of a fluorescently labeled mutant Ran(T24N) defective in binding NTF2 showed that passive diffusion of Ran through a pore constitute less than 4% of the facilitated transport. Both initial accumulation rates and the steady-state nucleus-to-cytosol ratio of total concentrations of fluorescent Ran were used to constrain the permeabilities and the activity of RCC1.

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Comparing experimental results with simulations was facilitated by explicit incorporation of fluorescent Ran in the model (note that in this case, the tagged molecules, X_tag, compete with the endogenous molecules X for the same enzyme and therefore the Michaelis–Menten rates for a tagged species should be vX_tag ¼ kcat[E][X_tag] / (Km þ [X] þ [X_tag]). Once the model was established, it was used to estimate the rate of the steady state nucleocytoplasmic transport and its sensitivity to various contributing factors. The total in vivo flux was estimated to be 260 molecules per NPC per second per direction, assuming 3000 pores per nucleus. The nucleocytoplasmic transport was found to be sensitive to the permeability of NPC to NTF2:RanGDP, the concentration of NTF2 and, most of all, to the activity of RCC1. Additional experiments with microinjection of NTF2 and RanBP1 were performed to validate results of the sensitivity analysis. Simulations also indicated a very steep gradient of Ran GTP across the nuclear envelope which was later confirmed by direct measurements (Kalab et al., 2002). The VCell MathModels used in Smith et al. (2002) (‘‘AliciaProblem1_5,’’ ‘‘AliciaProblem1_5Fast,’’ and ‘‘AliciaSpatial_NewFast’’) can be accessed under user ‘‘boris.’’ A public BioModel under user ‘‘les’’, ‘‘Smith et al. System Analysis of Ran Transport,’’ is a later version corrected for the competition between RanGDP and NTF2:RanGDP for the same site on RCC1. A conceptually similar model of nucleocytoplasmic transport was published by Gorlich et al. (2003). Constrained by experiments with HeLa cells, it predicted surprisingly different transport sensitivities: the authors arrived at the conclusion that the transport is much more sensitive to the NPC permeability for RanGDP than to the activity of RCC1. Comparative analysis of the two models is obscured by their apparent structural differences. Gorlich et al. (2003) provided a detailed description of the chain of reactions involving RCC1, whereas in the VCell model this was modeled by approximate Michaelis–Menten kinetics. On the other hand, Smith et al. (2002) explicitly introduced binding of Ran to ‘‘generic’’ karyopherins, whereas in Gorlich et al. (2003) this was modeled by an equivalent ‘‘load.’’ A careful comparison of the models showed that the effective Michaelis– Menten constants of the nucleotide exchange computed on the basis of detailed modeling of reactions involving RCC1, Vmax ¼ 9.6 mM/s and Km ¼ 1.7 mM, were indeed somewhat different from the values Vmax ¼ kcat[RCC1]total ¼ 3.4 mM/s and Km ¼ 1.1 mM used in the VCell model. But the major factor that led to the reversal in the prediction of a limiting factor was an almost 20-fold difference in the pore permeability to RanGDP (Mogilner et al., 2006). Indeed, Smith et al. estimated the permeability of the nuclear envelope to RanGDP (in effect, to NTF2:RanGDP) as 3.73 mm/s, which, when converted to the ‘‘bulk’’ permeability with respect to the nucleus using the surface-to-volume ratio of 0.6 mm 1, yields 2.24 s 1, compared to 0.12 s 1 used by Gorlich et al. (2003).


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How has this discrepancy come about, given that the initial accumulation rates upon injection of 1 mM of fluorescent Ran are similar: 0.4 mM/s in BHK-21 cells versus 0.48 mM/s in HeLa cells? The raw data indicate that both cell types have apparent ‘‘bulk’’ permeabilities in the 0.3–0.5 s 1 range. However, based on these data, the actual permeability for NTF2:RanGDP must be significantly higher because only a small fraction of the injected Ran gets to bind NTF2. Thus, the apparent value of the permeability, utilized in Gorlich et al. (2003) in order to avoid explicit incorporation of NTF2 in the model, is an underestimation of the actual permeability (the additional fourfold reduction was apparently an adjustment to the NTF2 level of 0.3 mM; in Smith et al. (2002), an estimate for the NTF2 average concentration was the equivalent of 1.5 mM of NTFbinding sites). While this approximation yields reasonable predictions for the overall Ran transport, it significantly overestimates the sensitivity of transport with respect to facilitated diffusivity of RanGDP through NPC. More detailed models of Ran transport (Kalab et al., 2006; Riddick and Macara, 2005), created on the basis of the minimal models discussed above, were simulated with the aid of VCell in Kalab et al. (2006).

3.3. Compartmental and spatially resolved models of cell signaling Many experimental groups have been using VCell to create models for, and numerically simulate, various aspects of cell signaling. These include mechanisms of G-protein-coupled receptor signaling (Falkenburger et al., 2010a; Suh et al., 2004), processes involving PIP2 (phosphatidylinositol 4,5-bisphosphate) (Brown et al., 2008; Falkenburger et al., 2010b; Hernandez et al., 2009; Horowitz et al., 2005; Xu et al., 2003), the JAK–STAT signaling pathway (Mayya and Loew, 2005), spatiotemporal dynamics of cAMP (Calebiro et al., 2009; Fridlyand et al., 2007; Neves et al., 2008; Saucerman et al., 2006; Zhong et al., 2009), pathways of small GTPases (Eungdamrong and Iyengar, 2007; Goryachev and Pokhilko, 2008; Lipshtat et al., 2010), signaling events in cell cycle (Li et al., 2007; Slepchenko and Terasaki, 2003), spatial gradient sensing in chemotaxis and signaling in cell migration (Haugh, 2007; Ma et al., 2004; Schneider and Haugh, 2005; Yang and Iglesias, 2009). Both compartmental and spatial applications have been implemented. To access them, simply click the ‘‘access’’ link at www.vcell.org/vcell_models/ published_models.html and follow the instructions. Below we discuss two representative applications of VCell to cell-signaling problems. 3.3.1. A compartmental model of G-protein receptor signaling In a recent paper (Falkenburger et al., 2010a), a comprehensive kinetic model of Gq-coupled M1 muscarinic (acetylcholine) receptor signaling has been formulated. The study is a characteristic example of data-driven

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modeling, where the authors seek to reproduce the time courses and concentration–response relationships that they measured using Fo¨ster resonance energy transfer (FRET). The model was implemented in VCell and the results of compartmental simulations were compared with whole-cell measurements. The public model ‘‘FalkenburgerJGP2010’’ can be found in the BioModel Shared folder under the username ‘‘hillelab.’’ We will consider the part of the model that is common for many different G-protein-coupled receptors (Fig. 1.5): binding of the agonist (ligand, L) to the receptor (R) increases the receptor’s affinity for G proteins whose Gaq subunit is in the inactive GDP-bound form; binding of G proteins to the bound receptor amplifies nucleotide exchange on Gaq subunit, which leads to dissociation of the GTP-bound form of the Gaq subunit from the G-protein abg trimer. The GTP-bound form of the Gaq subunit is active and acts as an effector for the downstream events (in particular, it activates PLC, which eventually turns off KCNQ2/3 channels). All the mechanisms in the model occur in the plasma membrane and all the species, except for the agonist, are membrane bound and are assumed to diffuse freely. The binding of the ligand to the receptor is modeled without accounting for ‘‘resting’’ forms of the receptor because the available FRET data are insufficient for distinguishing between the different ligand forms. On the same grounds, the model does not discriminate between the events of nucleotide exchange and G-protein dissociation. For demonstration purposes, we simplify the model further by ignoring the slow ‘‘parallel’’ processes, such as the binding of G protein to the receptor that is not bound to the ligand and the nucleotide exchange on G proteins that are not bound to the receptor or bound to the receptor that is not bound to the ligand. All reaction rates in the pathway, vreaction, are described by the massaction kinetics: L R binding : G RL binding :

½R½L kRL ½RL; vRL ¼ kRL f r

vGRL ¼ kGRL ½G½RL kGRL ½RLG; f r

Nucleotide exchange and trimer dissociation :

L R

GTP RL

RLG Gabg

vRLG ¼ kNX

GDP RLGbg GaqGTP

Figure 1.5 Diagram of G-protein receptor activation.

RLG ½RLG:


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One of the key steps in building a kinetic model is constraining rate constants and initial concentrations. In cases where direct measurements are not feasible, the parameters are estimated by fitting the model to dynamics of the system as a whole. In that case, it is important to analyze sufficiency of available experimental information for unique parameterization of the model. Falkenburger et al. (2010a) measured the concentrations directly, whereas the model kinetic constants were constrained by time courses recorded for the intermediate components of the system using FRET. The authors carefully annotated parameter values and provided their detailed discussion. For the reactions outlined above, the parameter values were as follows: kfR-L ¼ 2.8 mM 1 s 1, krR-L ¼ 5. 5 s 1, kfG-RL ¼ 2. 7 10 3 mm2 /s 1, krG-RL ¼ 0.68 s 1, kNX _ RLG ¼ 0.65 s 1. The endogenous total concentrations were [R] ¼ 1 mm 2 and [G] ¼ 40 mm 2. The results of Fig. 5 in (Falkenburger et al., 2010a) were obtained for the total concentration of the ligand [L] ¼ 10 mM. This information is sufficient for implementing the model in VCell and running simulations. We encourage readers to do this exercise and compare results with those obtained by Falkenburger et al. (2010a). 3.3.2. A spatial model of gradient sensing in chemotaxis We now consider an example of ‘‘conceptual’’ modeling which, unlike data-driven modeling, does not seek an accurate fit for a particular set of experimental data, but rather aims at elucidating a qualitative phenomenon, in this case, robustness of adaptation along with persistent gradient sensing in chemotaxis. Cells are able to detect chemical gradients in which the concentration of a chemoattractant varies by only a few percent across the cell size. Furthermore, gradient detection is robust over a wide range of average background concentrations. This excludes a simple threshold mechanism and requires a more sophisticated design of directional sensing. In a ‘‘local excitation global inhibition (LEGI)’’ model proposed by Levchenko and Iglesias (2002), an effector (signal S, Fig. 1.6A), activated by a G-protein receptor, regulates a downstream enzyme (a response element) R through activating intermediate activator and inhibitor enzymes (A and I, respectively). The model is based on three main ideas: (i) robust adaptation can be achieved if S activates both A and I with functionally similar kinetics but the activation of the inhibitor is slower than that of the activator; (ii) robust adaptation can coexist with signal amplification if A and/or I also regulate the supply of R; and (iii) polarization of the cell by spatial gradients of active R is achieved if A localizes to the membrane and does not diffuse, whereas cytosolic I diffuses rapidly throughout the cell. A generic version of the model was implemented and simulated in VCell (Yang and Iglesias, 2009). The BioModel ‘‘LEGI,’’ found in the shared BioModel folder under the username ‘‘LiuYang,’’ describes emergence of a

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A

B

S

15 mm

0

9.5 nM

R 0

I

A

R*

C 0

15 mm

15 mm

0.92 mm–2

0.64 mm–2

15 mm

Figure 1.6 LEGI model. (A) Mechanism of perfect adaptation (for notations, see text). (B) Simulated chemoattractant gradient (Application ‘‘Needle’’ of the public BioModel ‘‘LEGI,’’ username ‘‘LiuYang’’). (C) Simulated distribution of activated PI3K induced by the chemoattractant gradient in (B).

polar distribution of the membrane-bound molecules of activated PI3K in response to spatial gradients of the chemoattractant L (Fig. 1.6B, C). The spatial application ‘‘Needle’’ employs a simple 2D geometry with a point-like source of chemoattractant located at one of the corners of the computational domain. The cell shape is circular and all initial distributions are uniform. In the model, the source of chemoattractant is implemented by introducing an auxiliary ‘‘species’’ ‘‘L_source’’ mimicking a pipette in the extracellular (EC) compartment. At the level of Application, this species is flagged as ‘‘clamped’’ in the subpanel ‘‘Initial Conditions.’’ This tells VCell that ‘‘L_source’’ is not actually a variable and therefore does not have to be solved for. Instead, it is assigned a value specified in the column ‘‘Initial Conditions.’’ For a ‘‘clamped’’ species, this value can be a number or any expression in terms of Cartesian coordinates and time, and can therefore be used to define geometry, location, and duration of the source. In the application ‘‘Needle,’’ the source is permanent and defined as a small circle of radius 1 centered at a corner with coordinates [15, 15]. Correspondingly, the value is specified as inequality, (x 15)2 þ (y 15)2 < 1. This is a logical (Boolean) expression evaluated as either zero, for points whose coordinates do not satisfy the inequality, or one, otherwise. Note that ‘‘LEGI’’ is a minimal model which does not include signal amplification mechanisms (see assumption (ii) above).


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3.4. Analysis of fluorescence microscopy data In a number of studies, VCell was employed as a tool for analyzing microscopy data obtained in experiments that used the techniques of fluorescence redistribution after photobleaching (FRAP), photoactivation or photorelease (Gray et al., 2006; Holt et al., 2004; Moissoglu et al., 2006; Roy et al., 2001; Shen et al., 2008). Roy et al. (2001) explored light-directed perturbation techniques by activating a caged form of thymosin b4 (Tbeta4) in a defined local region of locomoting fish scale keratocytes. Computer simulations of a 2D VCell model of uncaging Tbeta4 (model ‘‘7_12_00_model1’’ under username ‘‘partharoy’’) predicted that rapid sequestration of actin monomers by uncaged Tbeta4 and the consequent reduction in the diffusional spread of the Tbeta4-actin complex could potentially affect locomotion of the keratocyte. Indeed, local uncaging of Tbeta4 at the wings of locomoting keratocytes resulted in a specific turning about the photorelease site thus providing insight into possible mechanisms of the turning behavior of keratocytes. A detailed model of a FRAP experiment was implemented in VCell (username ‘‘Lagnado Lab,’’ public model ‘‘TIR_FRAP’’) to analyze FRAP data in the study of vesicle dynamics at the ribbon synapse of retinal bipolar cells (Holt et al., 2004). The model includes excitation and emission processes, by which fluorescing and nonfluorescing states of a labeled particle alternate, and a photobleaching step. The distribution of light is specified through the initial conditions of an auxiliary ‘‘species’’ Light_spec, defined as ‘‘clamped’’ in the Application ‘‘FrapIt’’. The laser was turned on between 1 and 2 s, which was implemented by a multiplier ‘‘Light’’ in the rate expressions for excitation and bleaching: Light ¼ 1.0 þ 315.2 ((t 1.0) && (t < 2.0)). Simulations, run in an analytically defined 3D geometry, produced a reasonable fit to the experimental data with an effective diffusion coefficient of approximately 0.01 mm2s 1. Two-photon photoactivation of a postsynaptic scaffolding protein PSD95 tagged with photoactivatable GFP (paGFP) was used by Gray et al. (2006) to study dynamics of PSD-95 in the postsynaptic density (PSD), a protein-enriched postsynaptic region of a dendritic spine. Dendritic spines, on which most excitatory synapses terminate, vary in size and synaptic strength. PSD-95 is an abundant scaffolding protein that clusters glutamate receptors and is thought to determine the size and strength of synapses. Clusters of synaptic PSD-95 were photoactivated in layer 2/3 dendrites in the developing barrel cortex. While a subset of PSD-95 clusters was stable for days, a rapidly turning over dynamic pool of PSD-95 was observed, with retention times 1 h, which exchanged by diffusion with PSD-95 in neighboring spines. To simulate dynamics of PSD-95:paGFP, the authors created a 2D VCell model, using reasonable estimates of binding constants

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and diffusion rates. The analysis suggested that individual PSDs compete for PSD-95 and that the kinetic interactions between PSD molecules and PSDs are tuned to regulate the PSD size. Similar observations were made in a study of a different biological system, also thought to be very stable, the tight junctions of epithelial cells (Shen et al., 2008). Photoactivation and photobleaching experiments indicated that some of the tight-junction proteins, such as ZO-1 and occludin, were turning over on 1-h scale. A set of VCell BioModels (username ‘‘leshen’’) were created to simulate the experiments. As in (Holt et al., 2004), photomanupilation was implemented in the models by introducing a ‘‘clamped’’ species ‘‘Laser’’ that was used in Applications to define the location of the area affected by the laser and the duration of irradiation. By changing the duration of the beam, the same model can be used to simulate FRAP, where the laser is turned on for a short time, and FLIP (fluorescence loss in photobleaching), where bleaching is continuous. Similarly, by varying the size and location of the bleached area, one can simulate photobleaching of small and large parts of the tight junction or the bleaching of the cytosol or the plasma membrane in FLIP experiments. Simulations in (Shen et al., 2008) were run on an analytically defined 3D geometry. Analysis of the experimental data showed that the fluorescence recovery of ZO-1 was largely due to binding of the cytosolic ZO-1 to the tight junction. In contrast, the main mechanism behind the turnover of occludin was diffusion within the tight junction, with a possible contribution from binding of occludin from the surrounding membrane. FLIP was used in (Moissoglu et al., 2006) to quantify the in vivo interaction between the plasma membrane and a small GTPase Rac, a regulator of actin cytoskeleton. Rac cycles between the membrane and the cytosol as it is activated by nucleotide exchange factors (GEFs) and inactivated by GTPase-GAPs. Solubility in the cytosol is conferred by binding of Rac to guanine-nucleotide dissociation inhibitors (GDIs). To determine the dissociation rate constant (koff) of the membrane-bound Rac, NIH3T3 fibroblasts expressing GFP–Rac were bleached continuously by a laser beam everywhere except for a small masked region at cell periphery (Fig. 1.7A). Using a confocal microscope, fluorescence loss was simultaneously recorded in the unbleached region and in the proximal area in the bleached region (Fig. 1.7B). Decay of the signal in the unbleached region was caused by dissociation of Rac from the membrane followed by diffusion in the cytosol and, as it turned out, also by lateral diffusion of the membrane-bound Rac out of the unbleached region. A simple compartmental model built on the assumption of fast diffusion of Rac in the cytosol pointed to a two-step procedure of retrieving koff from the raw data: first, fit the fluorescence decay in the bleached region by a two-exponential function (the second component was likely due to

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B

Normalized fluorescence

A

Unbleached

Bleached


1 0.5

0 0 Areas where measurements were taken

10 Time (s)

20

From bleached area From unbleached area C

Figure 1.7 Modeling FLIP experiments. (A) Diagram of the experimental setup. (B) Fluorescence time courses measured in the bleached and unbleached areas. (C) 3D geometry used by Novak et al. (2007) to simulate FLIP experiments (reprinted from J. Comput. Phys., vol. 226. Novak et al., # 2007, with permission from Elsevier). The geometry was reconstructed in VCell from a z-stack of confocal slices. The cut shows the cell interior including the nucleus. The mask over the unbleached region, shown in black, mimics the experimental setup.

inhomogeneity of light in z-direction); second, fit the fluorescence decay in the unbleached region by a some of three exponentials, two of which are the same as in step 1; then the rate constant of the third exponential is koff þ kdiff, where kdiff, the rate constant due to the lateral diffusion of Rac, was estimated in a separate experiment. Using this procedure, the following values of koff were found: 0.048 s 1, for the wtRac, and approximately 10-fold less (0.004 s 1) for G12VRac, the mutant that remained constitutively in its GTP-bound conformation. Overexpression of the GEF Tiam1 unexpectedly decreased koff for wtRac, most likely by converting membranebound GDP–Rac back to GTP–Rac. Both overexpression and small hairpin RNA-mediated suppression of RhoGDI strongly affected the amount of membrane-bound Rac but surprisingly had only slight effects on koff. These results showed that RhoGDI controls Rac function mainly through influencing activation and/or membrane association.

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The method of measuring koff was derived on the assumption that diffusion of Rac in the cytosol is much faster than the processes contributing to the loss of fluorescence in the unbleached area. To assess the accuracy of this assumption, a 3D VCell MathModel was constructed to simulate the FLIP experiment on a realistic cell geometry (Fig. 1.7C) (Novak et al., 2007). The idea was to mimic the experimental protocol, collect the data in the way they were collected in real experiments, subject the simulated data to the fitting procedure and compare the obtained estimates of koff with the ‘‘exact’’ values used in the model. These numerical tests generally validated the fitting procedure but also pointed to a possible underestimation of koff (up to 30%) in cases where Rac is tightly bound to the membrane, because tight binding effectively slows down diffusion of Rac in the cytosol. The corresponding MathModel, ‘‘Schwartz_3D_FLIP,’’ can be accessed in the MathModel Database under username ‘‘boris.’’

3.5. Modeling of cell electrophysiology VCell can be used to model dynamics of membrane potential and ion currents (Falkenburger et al., 2010a,b; Fridlyand et al., 2003, 2005, 2007, 2009; Horowitz et al., 2005; Suh and Hille, 2006; Suh et al., 2004). In the BioModel workspace, this is done at the Application level by using the ‘‘Electrical Mapping’’ tool. This tool allows one to model voltage and current clamp protocols or stay in the default ‘‘no clamp’’ configuration. In modeling voltage/current clamps, the applied voltage/current can be specified either as a constant or as an arbitrary function of time. The latter can include inequalities, such as (t > t1)&&(t < t2), to model turning the clamp on and off at particular times t1 and t2 (>t1). In the ‘‘no clamp’’ configuration, one should check the box ‘‘Calculate V ?’’ and specify the initial conditions for the membrane potential V. The user can also override the default value of specific membrane capacitance set at 0.01 pF/mm2. The ion currents are defined in the ‘‘Physiology’’ window as membrane mechanisms, with an option to automatically account for the corresponding molecular fluxes. A default ‘‘General Current’’ kinetic type in the Reaction Editor window allows a user to enter an arbitrary equation for the ion current. Alternatively, one of the predefined approximations of the membrane ion currents can be selected. These tools were used by Falkenburger et al. (2010a,b), Horowitz et al. (2005), Suh and Hille (2006) and Suh et al. (2004) to create compartmental (nonspatial) electrophysiological models. The models can be accessed in the Shared folder of the BioModel Database under username ‘‘hillelab.’’ In the MathModel workspace, compartmental models includeP the membrane potential as an ODE variable with the rate of change ðI IX Þ=Cm where I is the applied current in the current clamp protocol (otherwise


35

I ¼ 0), the sum of ion currents is taken over all ion molecules X crossing the membrane, and Cm is the membrane capacitance. The membrane potential (voltage) is conventionally defined as V ¼ fin fout where fin and fout are the potentials inside and outside the cell, respectively. This convention assigns a positive direction to outward currents. The MathModel workspace was used by Fridlyand et al. (2003, 2005, 2007, 2009) to create compartmental models for simulating dynamics of membrane potential coupled to calcium dynamics in pancreatic beta-cells; these models can be accessed in the MathModels Database under username ‘‘Fridlyand.’’ VCell can also be used to reproduce previously published models. In 1997, Li et al. (1997) developed a model of an excitable endocrine cell gonadotroph. The model involves technically nontrivial coupling between spatially resolved dynamics of calcium and dynamics of membrane potential. VCell provides tools for the implementation of this type of model (see the BioModel ‘‘Gndph_wBuffer’’ under username ‘‘boris’’). The model is essentially a combination of two models, one for Ca2þ oscillations mediated by the ER through the inositol 1,4,5-trisphosphate (IP3)-receptor channels and another for Ca2þ oscillations driven by the plasma membrane potential. It is instructive to implement complex models by parts, each of which could be tested separately. The first two compartmental applications of the BioModel ‘‘Gndph_wBuffer’’ were designed for testing separately the voltage and calcium modules of the model. The modules were then combined to run spatial simulations on a simple analytical geometry (Application ‘‘spatial_analyt’’). The results from one such simulation are illustrated in Fig. 1.8. The spatial model of calcium dynamics coupled to dynamics of membrane potential can also be implemented in the MathModel workspace as a standalone mathematical model. The format of the VCell math description required for the implementation of this coupling can be gleaned from the description that is automatically generated in the BioModel workspace. For this, one should use the ‘‘View Math’’ tab in the Application window and then select ‘‘View Model Description Language.’’ Of note, the membrane potential in this description, as well as the corresponding equation type, is declared as ‘‘MembraneRegionVariable.’’ Li et al. demonstrated that their model agreed with existed experimental records of cytosolic and ER Ca2þ concentrations and electrical activity in gonadotrophs. On this basis, they argued that the sensing of ER Ca2þ content could occur without the Ca2þ release-activated Ca2þ current (Icrac), but rather through the coupling of IP3-induced Ca2þ oscillations to plasma membrane voltage spikes that gate Ca2þ entry. They concluded that in excitable cells that do not express Icrac, profiles of cytosolic Ca2þ provide a sensitive mechanism for regulating net calcium flux through the plasma membrane during both ER depletion and refilling.

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A

Ca (mM)

1.5

1

0.5

0 0

20

40

60 Time (s)

Cell periphery B

80

100

120

Cell interior

10

V (mV)

–10 –30 –50 –70 –90 0

20

40

60 Time (s)

80

100

120

Figure 1.8 Results of a simulation of the BioModel ‘‘Gdnph-wBuffer.’’ (A) Calcium dynamics at cell periphery (green) and cell interior (red). (B) Dynamics of membrane potential.

Mathematically, both of the excitable components of the gonadotroph model are essentially based on the Hodgkin–Huxley mechanism. The classic Hodgkin–Huxley model has also been implemented in VCell and can be accessed under username ‘‘Tutorial’’ in the Shared Model folder of the BioModel Database dialog.

3.6. Intracellular transport: Interplay of binding, flow and ‘‘active’’ diffusion VCell supports implementation of models that include diffusion and/or directed transport. The VCell capability of modeling directed flow has been recently used to describe possible effects of fluid flow inside moving keratocytes (Keren et al., 2009; Novak et al., 2008) and for modeling intracellular transport of organelles driven by molecular motors (Slepchenko et al., 2007). Similarly to diffusion, directed transport in VCell is described using a continuous approximation, that is, without


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resolving individual particles; this approximation is applicable when the number of particles participating in directed transport is sufficiently large. To invoke this capability, the user needs to define the direction and magnitude of the velocity of species undergoing directed flow, or in mathematical terms, to specify the ‘‘velocity vector field’’ everywhere in the computational domain. This is done by providing Cartesian components of the velocity, either as constant values or as functions of spatial coordinates (the velocities can also be functions of time and the state variables of the system). Below we discuss in more detail how this capability can be used for modeling intracellular organelle transport (Slepchenko et al., 2007). In cells, organelles and vesicles are transported along cytoskeletal tracks by molecular motors: the motors of kinesin and dynein families pull cargos along microtubules (MTs), whereas myosins move along actin filaments (AFs). MTs generally serve as tracks for long-range transport, whereas AFs support local movements. Because the organelle can bind simultaneously all types of motors, it can switch tracks along the way. The switching events in vivo cannot be detected reliably by light microscopy because of high density of MTs and AFs in the cytoplasm. Slepchenko et al. (2007) combined live cell imaging with computational modeling to estimate the rate constants for the switching of organelles between MT and AF transport systems in fish melanophores. These are pigment cells whose major function is redistribution of membrane-bounded pigment granules to ensure color changes of the fish in response to environmental cues: intracellular signals can induce aggregation of pigment granules at the cell center or their uniform dispersion throughout the cytoplasm, and during these movements the granules use both MT and AF tracks. Pigment aggregation occurs predominantly along MTs, while dispersion involves a combination of MT-based and AF-based transport; so the switching between the two types of cytoskeletal tracks must be tightly regulated by intracellular signals. Given that individual movements along MT and AF had been well characterized (this was done by tracking the granules in cells where one of the two transport systems was disabled), the idea was to use a computational model with the two well constrained modules coupled by the switching events. The switching rate constants could then be determined from fitting the model to an additional set of data—spatiotemporal dynamics of pigment measured at low magnification. The model was formulated in terms of pigment densities associated, for a given location and time, with MT and AF (the MT-associated density, in turn, was composed of densities of granules that were driven by plusor minus-end motors, or pausing). Given random directionality of AFs, the AF-bound transport was approximated by effective diffusion (the

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approximation is sometimes termed ‘‘active diffusion’’ because the movements of granules are not just thermal fluctuations but rather motor-driven). The movements of MT-associated pigment densities were described as directed flows. How are velocities defined for these flows? First, it is reasonable to approximate the geometry as 2D, because the lamellum where the pigment redistribution takes place is essentially flat. Second, given that MTs originate from a centrosome, one can assume, for modeling purposes, that they form an ideal radial array. Individual MTs are not resolved in the continuous approximation, but their spatial organization defines directions of velocities of the MT-associated flow of pigment. By placing the origin of the coordinate system at the position of the centrosome, the velocity projections of the MT-associated plus-end flow can be approximated as p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 2 vx ¼ vK x= x þ y þ e ; vy ¼ vK y= x2 þ y2 þ e2 ; where vK is the kinesin velocity and e is a technical parameter, which should be much smaller than the spatial resolution of geometry, included to avoid division by zero. The velocities of the minus-end flow have the opposite direction and should be scaled by the dynein velocity vD. Note that the circular symmetry reduces the spatial dimensionality of the problem, as all unknowns become dependent only on the distance to the center (Slepchenko et al., 2007). Special care is required in formulating jump and boundary conditions for variables described by directed flow. For example, modeling pigment dispersion can easily produce spikes of pigment density at the cell periphery that were not seen in the experimental data. In (Slepchenko et al., 2007), this problem was overcome by including in the model a realistic distribution of the MT plus ends; this distribution was a decreasing function approaching zero near the cell edge. Fitting the spatiotemporal dynamics of the pigment density by the model (Fig. 1.9) yielded the following switching rate constants: for dispersion, kMT ! AF ¼ 6.5 min 1 and kAF ! MT 0.0025 min 1, whereas for aggregation, kMT ! AF ¼ 4.5 min 1 and kAF ! MT ¼ 10.7 min 1. Note that kAF ! MT is spatially dependent because it is proportional to the local MT density which decreases toward the cell periphery; the values shown above correspond to the cell interior. Also, the aggregation value of kMT ! AF might be overestimated because the binding of the pigment granule to MT, occurring in the model with equal probability everywhere on the MT, can in fact be biased toward the MT plus end, as has been shown for frog melanophores (Lomakin et al., 2009); in these cells, the bias is caused by CLIP-170 bound to MT tips. Still, the results indicate that signals that induce transitions from dispersion to aggregation largely affect the rate constant for the transferring of granules from AFs to MTs, kAF ! MT, and to a lesser extent, kMT ! AF.

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B

A

12345

1 2 3 4 5

Normalized pigment density

Lamella

1

1 0.8

1 2 3 4 5

0.6 0.4

0.8

1 2 3 4 5

0.6 0.4 0.2

0.2

0

0 0

10

20

0

30

10

Normalized pigment density

1

1

1 2

0.8

2 3 4

0.6 4

0.4

5

0.2

30

1

0.8

0.6 0.4

20

Time (min)

Time (min)

5

0.2

0

0 0

2 4 Time (min)

6

0

2 4 Time (min)

6

Figure 1.9 Intracellular transport of pigment granules in fish melanophores, adapted from (Slepchenko et al., 2007) (# Slepchenko et al., 2007. Originally published in J. Cell Biol. doi:10.1083/jcb.200705146). (A) Experiment: a snapshot of pigment aggregation (top); time courses averaged over multiple scans in several cells, shown for equidistant locations along cell ‘‘radius’’ during dispersion (middle) and aggregation (bottom). (B) Model: idealized geometry, with the black circle mimicking the location of the pigment aggregate (top); simulated time courses of pigment density during dispersion (middle) and aggregation (bottom).

This correlates with sharp changes in the activity of the cytoplasmic dynein, indicating that the primary role in these transitions is played by regulation of dynein.

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3.7. Deterministic and stochastic modeling of gene regulatory networks VCell supports stochastic simulations of compartmental (nonspatial) models. As with deterministic simulations, there are two ways to set up a stochastic simulation in VCell: (i) by automatically generating a stochastic application in the BioModel workspace or (ii) by manually creating an editable stochastic math description in the MathModel workspace following a VCell template. As an example, below is shown a stochastic math description corresponding to the enzymatic reaction depicted in Fig. 1.10.

A

B

E

S

kon, Kd ES

Catalysis

E

Reversible binding

kcat

P

Number of ES molecules

40 30 20 10 0 0

Probabilities

C

1

2 Time (s)

3

4

0.1 0.08 0.06 0.04 0.02 0 0

6 10 16 22 28 34 40 47 Number of ES molecules

Figure 1.10 Stochastic simulations of a simple reaction network. (A) Diagram of an enzymatic reaction: substrate S reversibly binds enzyme E, after which intermediate compound ES irreversibly decays into E and product P (for parameter values, see the math description in the text). (B) Stochastic time course of the number of ES molecules. (C) Probability distribution of the ES copy number at time t ¼ 4.0 obtained from 10,000 trials.

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MathDescription { Constant Constant Constant Constant Constant Constant Constant Constant Constant Constant

kon 1000.0; Kd 0.01; kcat 0.1; koff (kon * Kd); E_initCount 301.0; S_initCount 602.0; ES_initCount 0.0; P_initCount 0.0; Size_Cell 1000.0; KMOLE 1/602;

StochasticVolumeVariable StochasticVolumeVariable StochasticVolumeVariable StochasticVolumeVariable Function Function Function Function

E ES P S

E_Conc (E * KMOLE / Size_Cell); P_reaction0 (kon * S * E_Conc); P_reaction0_reverse (koff * ES); P_reaction1 (kcat * ES);

CompartmentSubDomain Compartment { VariableInitialCondition VariableInitialCondition VariableInitialCondition VariableInitialCondition JumpProcess

E S ES P

E_initCount; S_initCount; ES_initCount; P_initCount;

reaction0 {

ProbabilityRate Effect S inc Effect E inc Effect ES inc

P_reaction0; -1; -1; 1;

} JumpProcess

reaction0_reverse {

ProbabilityRate Effect S inc Effect E inc Effect ES inc

P_reaction0_reverse; 1; 1; -1;

} JumpProcess

reaction1 {

ProbabilityRate Effect ES inc Effect P inc Effect E inc } } }

P_reaction1; -1; 1; 1;

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In the description above, the constant ‘‘Size_Cell’’ describes the cell volume in cubic microns. Copying this description into the MathModel workspace creates a valid VCell stochastic model. Figure 1.10 illustrates simulation results obtained with this model. Note that stochastic trajectories from different runs of the same model may differ unless the corresponding pseudorandom sequences are obtained with the same seed (by default, the seed is generated randomly but can be specified by the user in the ‘‘Advanced’’ panel). In contrast, histograms built on the basis of a large number of trials should be close for different runs of the same model. The stochastic modeling tool in VCell is designed to make it easy to treat the same model both deterministically and stochastically. In fact, the ‘‘Copy As’’ option in the Application dialog allows one to create a stochastic version of the model by a push of a button. This is how a stochastic version of a model was created within the VCell BioModel ‘‘CSH_09_circadian_ clock’’ (username ‘‘boris’’), that reproduces a generic model of circadian clocks proposed in Barkai and Leibler (2000), Vilar et al. (2002). The papers represent another example of ‘‘conceptual’’ modeling where the emphasis is placed on demonstrating the system design and mechanisms that are required to achieve particular properties, in this case for sustaining regular oscillations in the face of external and internal noise. Particular parameter values in this type of model are not an issue so long as they are in appropriate ranges. Circadian clocks are periodic biological processes by which organisms keep sense of daily time and regulate their behavior accordingly. Most of these clocks utilize intracellular genetic networks based on positive and negative regulatory elements. The minimal model described in (Vilar et al., 2002) includes essential components commonly found in experiments. It involves two genes, an activator and a repressor, which are transcribed into mRNA and subsequently translated into protein. The activator protein A binds to promoters of both genes which increases their transcription rate. It therefore acts as the positive element in transcription. The repressor protein sequesters the activator upon forming an inactive complex with it: the complex decays into a repressor molecule. Thus, the repressor acts as a negative regulator. Oscillatory behavior occurs when the dynamics of activators are significantly faster than those of the repressors and inactive complexes (for detailed analysis, see (Vilar et al., 2002)). When creating a VCell model with the intention to analyze it both deterministically and stochastically (the way the model was studied by (Vilar et al., 2002)), it is advisable to avoid using the ‘‘catalyst’’ tool but instead to implement the binding explicitly, as it is done in the BioModel ‘‘CSH_09_circadian_clock’’ (Fig. 1.11). The reason is that in VCell the mass-action kinetics, required for stochastic simulations in the BioModel workspace, cannot be applied to reactions implemented with the ‘‘catalyst’’ tool.

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B

A mRNA_A

mRNA_A

A

A

Translation

Translation

Figure 1.11 Implementation of translation events in the BioModel workspace. The mRNA_A can be included as a catalyst (A, dashed line) or through explicit binding (B, solid lines). Both variants are equivalent but in VCell, only variant (B) allows for the mass-action kinetic type required for stochastic simulations in the BioModel workspace.

The model by Vilar et al. (2002) demonstrates that with the positive and negative feedbacks at the gene level, circadian clocks need not rely on mRNA dynamics to oscillate, which makes them resistant to fluctuations. Oscillations persist even when the time average of the number of mRNA molecules becomes less than one. Moreover, simulations of the model indicate that stochasticity may even enhance robustness of oscillations: the stochastic version of the model by Vilar et al. (2002) retains oscillations in a wider parameter range than the corresponding deterministic model.

3.8. Problems in cell migration: Actin dynamics The VCell was applied to the analysis of various aspects of cell migration, including cell signaling (see BioModels under username ‘‘CMC’’) and gliding motility of keratocytes in which the cell shape does not change (e.g., MathModels under username ‘‘ignovak’’). In this section, applications of VCell to problems in cell migration are reviewed with the emphasis on actin dynamics. Actin, one of the most abundant molecules in the cell, is present as coexisting pools of monomer, called G-actin, and polymer, called F-actin. The F-actin polymer cytoskeleton provides structural support for the cell cortex. What is more, the fascinating dynamics of actin-containing structures are critical for such diverse morphological changes as muscle contraction, neurite pathfinding, endocytosis, cytokinesis, and cell motility. These varied and precisely controlled functions of the actin cytoskeleton require the interaction with many regulatory proteins and signaling molecules to control the dynamic organization of the actin polymer network. Indeed, scores of actin binding partners have been identified and the functions of many are known (Dominguez, 2009; dos Remedios et al., 2003; Pollard and Cooper, 1986, 2009; Stossel et al., 1985). In particular, the molecules controlling actin-driven protrusions at the leading edge of migrating cells

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have been intensely studied over the last 15 years. A key step in this process is activation of the Arp2/3 complex at the cytoplasmic face of the plasma membrane in the lamellipodium, which then binds to pre-existing actin filaments and nucleates new F-actin (Goley and Welch, 2006; Millard et al., 2004) to produce a dense, highly branched actin polymer network (Carlier and Pantaloni, 2007; Goley and Welch, 2006; Pollard, 2007; Pollard and Borisy, 2003). Electron microscopy of the lamellipodium reveals a thin, dense meshwork of filaments composed of relatively short segments compared to less branched, longer F-actin distributions to the rear of the cell (Svitkina and Borisy, 1999). The force produced by the rapidly polymerizing F-actin against the plasma membrane results in rearward movement of this network and also produces the protrusive force that drives the lamellipodium forward (Mogilner, 2006; Mogilner and Oster, 1996, 2003; Pollard and Borisy, 2003). As with modeling of calcium (Section 3.1), mathematical analysis of actin dynamics benefits from a wealth of quantitative data on the kinetics of polymerization in the presence of actin binding proteins, which provides excellent input data for models, as well as live cell imaging experiments against which models can be validated (Carlier and Pantaloni, 2007; Condeelis, 2001; Danuser and Waterman-Storer, 2006; Le Clainche and Carlier, 2008; Pollard, 2007; Pollard and Borisy, 2003; Suetsugu and Takenawa, 2003). However, modeling of polymerization, especially the complex mechanisms of regulated actin polymerization, presents some special challenges. This is because the rate of assembly and disassembly is different at the two ends of the polymer (the ‘‘barbed’’ and ‘‘pointed’’ ends); it also depends on the state of the subunits within the polymer and where those subunit states reside relative to either end. In particular, actin subunits can exist in three nucleotide bound states within the polymer and can be associated with various binding proteins. Furthermore, the rate of fragmentation or annealing of filaments as well as their diffusion rates depend on the length of the filaments. Detailed discrete stochastic models that follow individual monomers and filaments (Alberts and Odell, 2004; Michelot et al., 2007; Schaus and Borisy, 2008; Schaus et al., 2007) can solve this problem by keeping track of all the individual states, but this becomes computationally prohibitive for realistic numbers of actin molecules and their binding partners in a cell. There have also appeared continuum models that can recapitulate in vitro experiments on steady-state distributions of filament size and turnover, where the number of different species is relatively low (Beltzner and Pollard, 2008; Bindschadler et al., 2004; Dawes and Edelstein-Keshet, 2007; Mogilner and Edelstein-Keshet, 2002; Paul and Pollard, 2008, 2009) and several of these have employed VCell to help analyze the experiments (Beltzner and Pollard, 2008; Paul and Pollard, 2008, 2009; Roy et al., 2001). Another modeling approach is to avoid the details and develop phenomenological mathematical models that use physical principles to


45

reproduce a specific cellular mechanism—‘‘conceptual models.’’ This approach has indeed proven powerful in suggesting or explaining experiments. For example, recent papers have shown how models with a relatively small number of variables that abstract essential features of actin dynamics can explain the variable shape of motile keratocytes (Keren et al., 2008), show that G-actin diffusion is sufficient to deliver monomers to the actively polymerizing leading edge (Novak et al., 2008), and elucidate the relationship of protrusion velocity and the concentration of barbed ends (Mogilner and Edelstein-Keshet, 2002) or the relationship between severing and capping in controlling acting polymerization (Carlsson, 2006). Again, models of this type have used VCell (Keren et al., 2009; Novak et al., 2008). But detailed models that explicitly include as many of the known mechanisms as possible would also be extremely useful. First, they can permit simulations that mirror experimental manipulations and thus help to guide the design and interpretation of experimental results. For example, how would knockdown of a protein that caps filament ends change F-actin accumulation in the lamellipodium? At first glance, one might expect that if capping proteins inhibit addition of monomers to ends, such an experiment should increase F-actin. But that fails to account, for example, for the effect of capping on the available pool of G-actin, which is required for stimulated polymerization at the leading edge. Furthermore, this pool might depend not only on capping protein, but also on several other molecules that interact with both F-actin and G-actin. A model that explicitly includes all of these molecules and their interactions can be very useful in sorting out the key factors and making experimentally testable predictions. Second, computational models that integrate many mechanisms and molecules involved in a complex cellular process provide a powerful means for organizing our knowledge about the components of the system. Building the model requires evaluating differing, sometimes conflicting, data sources and deciding how to best formulate a biochemical or biophysical rate expression. These choices amount to an intense level of integration and curation of the available information. Of course, the modeling software has to be designed with layers to permit the expression of abstractions related to biochemical and biophysical mechanisms. As discussed in Section 2, this separate layer in VCell is the Physiology. The database structure of VCell allows each of the species and reactions comprising the Physiology to be treated as components that can be searched and cut, copied, or pasted from one model to another. Thus, in addition to producing simulations, the VCell model serves as a flexible container for biochemical and biophysical data that is readily interpretable and extensible and is much richer than a pathway database. A detailed model of this type was built in VCell for the process of actin dendritic nucleation, which governs cell protrusion (Ditlev et al., 2009). The model was formulated in a continuous approximation, that is, in terms

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of concentrations of molecules. The molecules included in the model were actin, a generic nucleation promoting factor associated with the membrane (called NWASP in the model), Arp2/3, capping protein, profilin (which catalyzes exchange of ATP for ADP on G-actin), ADF/cofilin (a filament severing protein) and thymosin-b4 (a molecule that act as a buffer for G-actin); also included were the association of the various actin species with ATP, ADP, and ADPPi (an intermediate in which ATP has been hydrolyzed, but phosphate has not yet fully dissociated to produce ADP). To describe all the mechanisms associated with this seemingly limited list of building blocks required 60 species and 155 reactions. It is important to appreciate that even this large number of reactions included many approximations where multiple individual transformations were lumped into single rate expressions. This was necessitated by the infinite number of states accessible to a polymerizing system. But, adding to the challenge of this ‘‘lumping’’ process, the rates of these individual transformations are often dependent on the length and nature of the polymer. An example will serve to illustrate how these approximations were made by Ditlev et al. (2009). For polymeric actin, a single fixed diffusion coefficient would not be appropriate, given the large spatial and temporal variation in the size and degree of branching of the filaments following localized stimulation of polymerization. Therefore, the F-actin diffusion was modeled according to the following equation: DFActin ¼

DGActin ð1 BranchFractionÞ FilamentLength

The inverse relationship of diffusion to length for a linear polymer is well established in polymer physical chemistry (Sept et al., 1999); the FilamentLength at each point in space is calculated from the state variables of the system as the sum of all F-actin species (i.e., the total F-actin) divided by the sum of all pointed end species. To account for the diminution of diffusion for a highly interconnected branched network, the equation reduces the diffusion coefficient by the fraction of filaments associated with branches; BranchFraction is determined from the state variables as the sum of all the species corresponding to an Arp2/3 capped pointed end bound to a mother F-actin filament (giving the concentration of branched filament segments), divided by the sum of all the pointed end species, whether Arp2/3 capped or not (giving the total concentration of filament segments). Full details on how other key modeling challenges were met (e.g., addition and dissociation of nucleotide-bound subunits to the pointed and barbed ends of filaments with varying nucleotide compositions) can be found in the paper (Ditlev et al., 2009) and in several public VCell models under username ‘‘les’’: BioModels ‘‘Actin Dendritic Nucleation’’ and ‘‘Actin

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Dendritic Nucleation_Detailed Mechanism’’; MathModels ‘‘Actin Advection and Diffusion’’ and ‘‘Actin Advection and Diffusion 2D Cylindrical Transform.’’ Parenthetically, at the time the work for this paper was carried out, it was not possible to express velocity fields and spatially variable diffusion within a BioModel, which is why MathModels were used to produce spatial simulations; since the paper was published, all of these features have been added to the BioModel interface. Simulations performed with this model were able to quantitatively reproduce many of the features of both in vivo and in vitro actin dynamics including: the accumulation of F-actin in the lamellipodium (Fig. 1.12B), A

B

Simulated geometry graded NWASP in the front of the lamellipodium X Y

Simulated total F-actin XY slice near cell bottom Z

920 mM

130 mM

C

Filament turnover from speckle microscopy

D 19 mM/s 0 mM/s

Blue Green

Simulated filament turnover; XY slice near cell bottom

Red

Depolymerization Polymerization

–1.3 mM/s

Figure 1.12 Selected features of the actin dendritic nucleation model. (A) Surface rendering of the outer membrane of the 3D geometry used for the VCell simulations. A graded band of active NWASP on the front of the lamelipodium membrane recruits and activates Arp2/3 to initiate nucleation. (B) Simulations run to steady state produce F-actin accumulation in the lamellipodium, as shown in this plane at the bottom of the 3D geometry. The scale shows how the colors are mapped to concentrations of actin subunits within filaments. (C) Map of net polymerization and depolymerization activity in the lamellipodium of an epithelial cell derived from speckle microscopy experiments (Ponti et al., 2005). Note the sharp transition between polymerization at the edge and depolymerization within 2 mm of the edge (white scale bar is 5 mm). (D) Simulation result for net actin polymerization rates at steady state. The white band shows a region of strong polymerization and is 2 mm wide before a sharp transition to depolymerization (negative rates). Behind these two bands of activity, the bulk of the cell displays near zero actin filament assembly rates. Figure 1.12C is reprinted from Biophysical Journal, vol. 89, Ponti et al., # 2005, with permission from Elsevier.

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the high concentration of barbed ends at the cell edge, the average lengths of filaments as a function of capping protein, the dependence of actin turnover on capping protein and profilin and the branching and filament length distributions within the lamellipodium. The model was also able to resolve conflicting reports on the affect of ADF/cofilin activity on the accumulation of F-actin in the lamelipodium: ADF/cofilin was shown in the model to be able to both inhibit and potentiate polymerization depending on the capping protein activity. Here, we will highlight one outcome of the simulations that illustrates how VCell simulations can provide valuable insights on not just the distribution of molecular species but also on the rates of reactions. Using a 3D analytical geometry that captures the generic structure of a cell migrating on a flat substrate, Fig. 1.12D shows how the model recapitulates the sharp boundary between polymerization and depolymerization discovered by speckle microscopy experiments (Fig. 1.12C) (Iwasa and Mullins, 2007; Ponti et al., 2004, 2005; Schaub et al., 2007). The model shows that this behavior does not require any special hypothetical depolymerization factor behind the leading edge, but rather emerges from the interplay of rearward flow of the actin network, barbed end capping, and dissociation of Arp2/3 branches to expose free pointed ends. In particular, the model predicts that stabilizing Arp2/3 branches should move the boundary toward the rear and decrease its sharpness.

4. Future Directions in Developing Tools for Modeling in Cell Biology VCell and other software packages designed for cell biologists provide a variety of tools for modeling a wide range of cell phenomena. Still, a number of limitations preclude realistic modeling of important processes that are of interest to biologists. For example, modeling diffusion in VCell with a constant diffusion rate amounts to describing the cytoplasm as uniform, a hardly realistic assumption. One can introduce the diffusion coefficient as a function of spatial coordinates but that might not be an accurate way of including effects of heterogeneity caused by internal membranes and cytoskeletal structures (Novak et al., 2009). Implementation of rigorous coarse-grain approaches that would account for processes occurring on multiple spatial scales might be necessary for fully understanding the properties of the intracellular environment (Bancaud et al., 2009). Multiscale modeling is also required for adequate description of reactions in crowded spaces. Methods that are currently employed in VCell do not resolve individual molecules, effectively assuming that they do not take any space. This ‘‘ideal gas’’ approximation hampers modeling of important cellular processes, such as molecular aggregation and polymerization.


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VCell provides tools for spatial simulations on experimental geometries, but the geometry must be fixed. This does not permit modeling of dynamic changes of cell shape which play an important role in cytokinesis, chemotaxis, and generally, in cell migration. Implementation of tools for simulating these processes within a general-purpose infrastructure is challenging, as it requires coupling of mechanics and chemical dynamics, fast automated front tracking, and solving diffusion–flow–reaction systems on domains with moving boundaries, which are all areas of active research in computational physics. But should all modeling tools be implemented within a single generalpurpose computational framework? One could argue that would mirror the way in which cells work. However, efficiency and manageability requirements place limits beyond which the software becomes slow and difficult to use and maintain. For some cases, a light-weight specialized package designed for a particular purpose might be more practical (Cowan et al., 2009). Resolving these and other issues will determine how VCell can be made even more useful and give direction for development of a new generation of tools for modeling in cell biology.

ACKNOWLEDGMENTS Development of the VCell has been a team effort of many colleagues over the last 15 years in the Richard D. Berlin Center for Cell Analysis and Modeling at the University of Connecticut Health Center. The authors are especially grateful to James Schaff and Ion Moraru who have provided tireless and creative leadership throughout. The Virtual Cell Project is supported by National Institutes of Health as a NCRR Biomedical Technology Research Resource through grant No. P41-RR13186.

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New Insights into the Dynamics of Cell Adhesions Patricia Costa and Maddy Parsons Contents 1. Introduction 2. Overview of Cell Adhesion 2.1. Types of cell-adhesion structures 2.2. Importance of dynamic cell adhesions in disease 3. Overview of Cell-Adhesion Proteins 3.1. Integrins 3.2. Actin-dependent signaling at adhesions 3.3. Proteins involved in cell–cell adhesion 3.4. Other adhesion proteins: Signaling and adaptor proteins 4. Regulation of Cell Adhesion Dynamics In Vitro 4.1. Regulation by integrin during migration 4.2. Regulation by signaling proteins 4.3. Regulation by the actin cytoskeleton 4.4. Regulation by microtubules 4.5. Techniques to study adhesion dynamics in vitro 5. Regulation of Cell-Adhesion Dynamics: In Vivo Studies 6. Conclusions and Future Directions References

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Abstract Adhesion to the extracellular matrix (ECM) and to adjacent cells is a fundamental requirement for survival, differentiation, and migration of numerous cell types during both embryonic development and adult homeostasis. Different types of adhesion structures have been classified within different cell types or tissue environments. Much is now known regarding the complexity of protein composition of these critical points of cell contact with the

Randall Division of Cell and Molecular Biophysics, King’s College London, New Hunts House, Guys Campus, London, United Kingdom International Review of Cell and Molecular Biology, Volume 283 ISSN 1937-6448, DOI: 10.1016/S1937-6448(10)83002-3

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2010 Elsevier Inc. All rights reserved.

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extracellular environment. It has become clear that adhesions are highly ordered, dynamic structures under tight spatial control at the subcellular level to enable localized responses to extracellular cues. However, it is only in the last decade that the relative dynamics of these adhesion proteins have been closely studied. Here, we provide an overview of the recent data arising from such studies of cell–matrix and cell–cell contact and an overview of the imaging strategies that have been developed and implemented to study the intricacies and hierarchy of protein turnover within adhesions. Key Words: Cell adhesion, Extracellular matrix, Integrin, Cadherin, Cytoskeleton, Signaling, Microscopy, Dynamics. ß 2010 Elsevier Inc.

1. Introduction Cell adhesion to other cells and/or to the extracellular matrix (ECM) is a fundamental requirement for normal embryonic development, adult homeostasis, and immune functions (Reddig and Juliano, 2005; Wozniak et al., 2004). The cell structures that mediate interactions with ECM can take a number of different forms depending upon both the cell type and the tissue environment. The protein composition, localization, and proteolytic capabilities of these so-called adhesion complexes all contribute to the classification and function of the structure. Cell–cell adhesion classically plays a role in the stability and integrity of both epithelial and endothelial cell layers. While the structure and components of cell–cell adhesive contacts are broadly different to cell–ECM adhesions, both share a large number of common signaling mediators that are responsible for regulating formation, maintenance, and dynamics. Cell adhesion is required for normal development in many different tissues, in the context of formation of specific tissue compartments, maintenance of barrier function, and cell migration. In many cases, these adhesive structures are not static but rather they undergo dynamic changes in composition and structure to enable the cells to respond to changing extracellular cues. The regulation of such dynamic changes is under tight spatial and temporal control by numerous signaling proteins that can dictate the type, location, and duration of adhesive contact formed. Recent progress in microscopic techniques has enabled closer observation and dissection of these fundamental events. Here, we will provide an overview of some of the strategies used to study these dynamic transient events in live cells, and review some of the recent findings in this field.

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2. Overview of Cell Adhesion 2.1. Types of cell-adhesion structures 2.1.1. Cell–matrix adhesions The adhesion structures that form between a cell and ECM can take a number of different forms, ranging from classical focal adhesions (FAs) to podosomes and invadopodia. The classification of these structures is based upon several factors, including localization, proteolytic capability, and protein composition. At least 150 different proteins have been found to be involved with the formation, maintenance, and disassembly of these various adhesion structures (Zaidel-Bar et al., 2007) and for recent reviews see Dubash et al. (2009), Harburger and Calderwood (2009), and VicenteManzanares et al. (2009). The integrin family of heterodimeric transmembrane receptors are important in the initiation and stability of all types of adhesive structure. With both extracellular and intracellular domains, these receptors have the unique ability to bind the ECM and recruit proteins to their cytoplasmic face, thereby linking the cell exterior to interior. Actinbinding proteins such as talin, filamin, and a-actinin are recruited to the cytoplasmic tail of the integrin receptor upon ECM binding and are able to form direct links to the actin cytoskeleton to initiate stress fibers and thus providing a mechanical scaffold (Albiges-Rizo et al., 2009). Each protein recruited to the integrin tail is also in turn able to recruit other specific binding partners to the point of adhesion. Adaptor proteins and kinases such as Src and focal adhesion kinase (FAK) are among the proteins that are recruited, each playing their part in the adhesion signaling cascade and assisting in determining the lifespan of the structure. Integrins are not the only family of receptors involved with adhesion signaling. Syndecans are a family of heparan sulfate proteoglycans that can bind directly to ECM and soluble extracellular growth factors. Syndecans can also act in synergy with integrins, assisting in the recruitment of proteins to adhesion sites (Morgan et al., 2007). Recent evidence has shown that syndecan-4 controls recruitment of protein kinase C (PKC) and subsequent downstream control of the GTPase Rac in cooperation with a5b1 integrin (Bass et al., 2007). Early studies revealed that different types of adhesive structures can exist in a single cell at any one time (Izzard and Lochner, 1980). Three classical structures initially described in these studies were focal complexes (FCs), FAs, and fibrillar adhesions (FBs), each having their own specific characteristics (Puklin-Faucher and Sheetz, 2009; Zaidel-Bar et al., 2004, 2007). FCs are small, transient structures, typically located behind the leading edge of a spreading or migrating cell (Figs. 2.1A and 2.2B). These are short-lived structures, assembling and disassembling in the order of minutes and are said to ‘‘sample’’ the local ECM before disassembling or moving on to form more

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A

Talin Paxillin Vinculin PY proteins

Talin Paxillin Vinculin a-actinin PY proteins

Talin Paxillin N Vinculin a-actinin PY proteins

Focal adhesions

Focal complexes

Fibronectin fibres

Fibrillar adhesions

N

a5b1 tensin

a5b1 tensin

Fibrillar adhesions

B N

Cortactin

Actin filaments

PY ECM

MT1-MMP integrin

Degraded ECM MMPs

MMPs Invadopodia

Invadopodia

N Actin filaments Vinculin ECM

Integrin

Podosomes

Podosomes

C

Claudins occludin Actin filaments

Tight junctions ZO-1/2/3 Adherens junctions p120ctn b-catenin a-catenin

Cadherins N

N

Gap junctions Connexin Actin filaments Talin Paxillin FAK Vinculin a-actinin

Intermediate filaments

Integrins Focal adhesions

Desmocollin

Desmoglein a6b4 integrin Hemi-desmosomes

Desmosomes Plakoglobin Plakophilins Desmoplakin

Basal membrane

Figure 2.1 Adhesion types and localization. (A) General composition and localization of focal complexes and focal adhesions (top image) and matrix-associated fibrillar adhesions (bottom cell). (B) Localization of invadopodia (top cell) and podosomes (bottom cell). (C) General composition and localization of tight junctions, adherens junction, desmosomes, hemidesmosomes, and focal adhesions found in cells in contact (such as epithelial or endothelial cells).

A

N

N

N

N

Cadherin homodimers

Actin arcs

N

N

Actin arcs

Rufles and lamellipodia formation

Cadherin puncta formation

Adhesion belt formation

B N

Resting cell/ stable focal adhesions

N

Cell spreading/ focal adhesion formation

N

Cell retraction/ focal adhesion turnover Focal adhesion disassembly

Stable focal adhesions

Nascent focal adhesions

Figure 2.2 Adhesion dynamics. (A) Formation of junctions between opposing epithelial cells. Contacts are initiated through formation of actin-based lamellipodia (top) followed by homodimerization between opposing E-cadherin molecules (middle) and subsequent acto-myosin-dependent adhesion belt strengthening (bottom). (B) Focal adhesion dynamics. Focal adhesions are formed between cell and ECM and stabilized by actin stress fibers (top panels). Upon cells membrane extension and protrusion, nascent adhesion complexes are formed beneath the leading edge (middle) and this is coupled to disassembly of adhesions at the rear (bottom) to enable acto-myosindependent rear edge retraction and cell movement.

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stable structures. FAs are both larger and more stable structures than FCs and in some cases are formed by maturation of a preexisting FC. These adhesions contain multiple proteins, ensuring stability of the adhesion and that traction forces are transmitted from the ECM and vice versa and as such have lifetimes in the order of tens of minutes. FBs are long, stable structures that run parallel to bundles of fibronectin (FN) in vivo and are highly enriched in tensin and a5b1 integrin (Fig. 2.1A; Green and Yamada, 2007). These adhesive structures are also sites of localized matrix deposition and FN fibrillogenesis. Although the molecular composition of these adhesions share similarities, studies have shown there are also subtle differences among them, for example, zyxin is not found in FCs and b3 integrin is not found in FBs (Cukierman et al., 2001; Zaidel-Bar et al., 2003). Our knowledge of these structures is by no means complete and as such further studies will be required to explain how these differences in protein composition are regulated. Two other classes of cell–ECM adhesion structures, podosomes and invadopodia differ further in their ability to act as local ECM degradation sites by recruiting matrix metalloproteinases (MMPs; Fig. 2.1B). Podosomes typically appear in cells of monocytic origin, such as macrophages, or osteoclasts, whereas invadapodia appear in malignant cells (Linder, 2007). Podosomes are composed of cores of F-actin and actin-binding proteins such as cortactin within a ring of integrins. Typically, b1 integrins are localized to the core of these structures when b2, b3, and vinculin localize to the ring (Fig. 2.1B). Although the protein composition of these podosomes and invadopodia are similar, one difference between appears to be that invadopodia are more punctate, finger-like projections into the ECM possibly conferring differences in mechanical stability (Albiges-Rizo et al., 2009). Finally, hemidesmosomes represent a further class of cell–matrix adhesion structure and are found in specialized epithelial cells such as keratinocytes. These are small, integrinbased adhesions forming two plaques structures that maintain structural links between the keratin-intermediate filaments and the underlying basement membrane zone (Fig. 2.1C, Margadant et al., 2008). Typically, these structures comprise of plectin proteins that associate directly with the intermediate filaments and form a bridge between b4 integrin and the cytoskeleton to permit firm anchorage to the underlying ECM. 2.1.2. Cell–cell adhesions In addition to forming adhesions with the surrounding ECM, many cell types also form specialized adhesions with neighboring cells. Cell–cell adhesion is key to the formation of intact epithelial and endothelial cell layers in vivo, and convey critical mechanical stability and polarity for assembly of cells within complex 3D tissue architecture. Epithelia in glandular structures contain an apical membrane that faces the lumen and a basolateral surface that interacts with the neighboring cells and the basement membrane. This asymmetric organization is referred to as apical–basal cell


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polarity and is a characteristic trait of all epithelial cells (Caplan et al., 2008). Cell–cell adhesions are mediated by different types of junctional complexes, including tight junctions (TJs), adherens junctions (AJs), gap junctions, and desmosomes (Giepmans and van Ijzendoorn, 2009) (Figure 2.2C). These junctions comprise transmembrane proteins with extracellular domains that mediate interactions between neighboring cells and intracellular surfaces that facilitate interaction with signaling molecules and cytoskeletal proteins. In polarized epithelial cells, the junctional complexes are asymmetrically localized. For example, TJs are located at the apical–basal border and act to separate the apical and basolateral membrane domains, hold adjacent cells together and create an impermeant fluid barrier between cells. AJs are located basal to the TJ and are considered as primary determinants of cell–cell adhesion. The mechanisms by which cells develop cell–cell junctions and localize proteins to create the intracellular asymmetry are currently poorly understood. Most of our understanding of the molecular mechanisms by which cell polarity is established and maintained stems from genetic studies in model organisms and biochemical studies in cultured cells. However, the application of new live imaging approaches to study these events in vivo is beginning to provide novel insight into regulation of cell adhesion within complex tissues and whole organisms (Prasad and Montell, 2007; Serrels et al., 2009).

2.2. Importance of dynamic cell adhesions in disease Dysregulation of adhesion formation or disassembly is a common hallmark in many different disease contexts. Cancer is one of the best-studied examples of such diseases. Regulation of cell adhesion is important in order for a tumor cell to successfully detach, invade, extravasate, and metastasize to distant organs. These steps include cell interaction with the local microenvironment, migration, invasion, resistance to apoptosis, and the ability to induce angiogenesis. All of these functions are regulated by adhesion and ECM proteolysis, which together provide the most fundamental molecular effector mechanisms upon which a metastatic cell relies (Bacac and Stamenkovic, 2008). Adhesion and proteolysis also determine tumor cell interaction with other cells and with the ECM, help create a path for migration, promote angiogenesis, and both directly and indirectly trigger survival signals. Large-scale genetic studies have revealed a number of adhesion proteins to be aberrantly regulated during tumor progression, including members of the integrin and cadherin receptor families. Altered cell–cell and cell–ECM adhesion is also fundamental to the onset and development of many different skin diseases. In stratified epithelia, such as in skin as well as in other complex epithelia, multiprotein complexes called hemidesmosomes are involved in promoting the adhesion of epithelial cells to the underlying basement membrane. Epidermolysis bullosa (EB) is a family of severe skin adhesion defects caused by disruption of the

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epidermal–dermal junction. EB is classified into simplex (EBS), junctional (JEB), or dystrophic (DEB) forms, depending on the level at which the junction is compromised, that is, above, within, or below the basement membrane (Epstein, 1996). A subclass of JEB is due to autosomal recessive mutations in one of the three chains of laminin 332, a key ECM component of the epidermal–dermal junction linking the keratinocyte-specific a6b4 integrin to the type-VII collagen dermal fibrils. Other more rare conditions such as Kindler Syndrome are caused by loss of function mutations in specific proteins in this case the integrin-associated adhesion protein Kindlin 1 leading to defects in keratinocyte adhesion and barrier function (Lai-Cheong et al., 2009). At sites of inflammation, infection, or vascular injury, local proinflammatory or pathogen-derived stimuli render the luminal vascular endothelial surface attractive for leukocytes. This innate immunity response consists of a well-defined and regulated multistep cascade involving consecutive steps of adhesive interactions between the leukocytes and the endothelium (Langer and Chavakis, 2009). During the initial contact with the activated endothelium, leukocytes roll along the endothelium via a loose adhesive bond. Subsequently, leukocytes are activated by chemokines presented on the luminal endothelial surface, which results in the activation of integrins and firm arrest of the cell on the endothelium (Shulman et al., 2009). After their firm adhesion, leukocytes use additional adhesive molecules to facilitate either transcellular or paracellular migratory routes to pass through the endothelial layer. In addition, further circulating cells, such as platelets arrive early at sites of inflammation contributing to both coagulation and to the immune response in parts by facilitating leukocyte–endothelial interactions. Dysregulation of the adhesion machinery at any of these stages can trigger either loss or hyperactivation of the adhesion and transmigration cascade, resulting in different pathological conditions. More than 30 years of research has yielded a wealth of information regarding the various proteins and lipids that contribute to the formation of functional FA complexes. However, until recently, little was known about the dynamics controlling the hierarchy of protein recruitment or removal within these specialized structures. Advances in both fluorescence tagging and imaging technology have now made it possible to address complex questions about the dynamic behavior of proteins within adhesions both in vitro and in vivo.

3. Overview of Cell-Adhesion Proteins 3.1. Integrins 3.1.1. Integrin structure and specificity Integrins are a family of heterodimeric transmembrane receptors. Each receptor consists of noncovalently linked a- and b-subunits. Members of this family have been found at all levels during evolution, from chicken and


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zebrafish down to lower eukaryotes, including sponges, the nematode Caenorhabditis elegans and the fruit fly Drosophila melanogaster (Takada et al., 2007). In mammals, 18 a- and 8 b-subunits have been identified which can associate to form 24 different heterodimers. Structurally, each subunit can be divided into three distinct domains: an extracellular globular ligandbinding head domain, a single hydrophobic transmembrane domain, and a short cytoplasmic tail (Askari et al., 2009). Electron microscopy studies have demonstrated that the integrin extracellular domain stands on two long and extended C-terminal legs or stalks, which connect to the cytoplasmic domains of each subunit (Nermut et al., 1988). Moreover, X-ray crystal structures of the integrin avb3 provided the finding that the legs were severely bent (Xiong et al., 2000, 2001). It was later established that integrins exist in low, intermediate, and high-affinity states and that the bent conformation represents the physiological low-affinity state, whereas ligand binding is associated with a conformation rearrangement in which the integrin extends with a switchblade-like motion resulting in an active conformation (Askari et al., 2009; Takagi et al., 2001). Integrins bind to the ECM through their extracellular domain and the differential association of a- and b-subunits defines ligand-binding specificity. Integrin–ligand combinations can be divided into four main classes: the RGD-binding integrins, integrins that recognize ligands containing an RGD tripeptide active site, in molecules such as FN and vitronectin (VN); the LDV-binding integrins, integrins that bind to an acidic motif, termed LDV, that is functionally related with RGD; the A-domain b1 integrins that form a laminin/collagen-binding subfamily of integrins; and the non-aA-domain-containing laminin-binding integrins which mediate adhesion to basement membrane laminins (Humphries et al., 2006). 3.1.2. Integrin inside-out and outside-in signaling Integrin affinity for their extracellular ligands is a complex and tightly regulated event. Many integrins are not constitutively active, they are often expressed on the cell surface in an inactive state, in which they do not bind ligands and do not signal (Hynes, 2002). Integrin signaling is unique as these receptors can both transmit signals to the cell in response to the extracellular environment (‘‘outside-in’’), and also respond to intracellular cues and alter the way in which they interact with the ECM (‘‘inside-out’’). This process regulates integrin activity status by modulating the affinity and avidity of integrins for their ECM ligands, which can in turn regulate cell invasion and migration (Brown, 2002). Many studies have now shown that binding of the actin-associated protein talin to the b cytoplasmic tail can regulate integrin affinity by triggering a conformational change in the integrin extracellular domain (Critchley and Gingras, 2008; Tadokoro et al., 2003). More recently, the kindlin proteins have also been shown to be able to modulate integrin affinity for the ECM, possibly in cooperation with

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talin (Lai-Cheong et al., 2009). Recruitment of these ‘‘activation’’ proteins to the cytoplasmic face of the integrin then triggers recruitment of numerous other proteins to the adhesion site such as FAK, c-Src, paxillin, vinculin, and adapters such as Cas/Crk (Takada et al., 2007).

3.2. Actin-dependent signaling at adhesions Dynamic actin and microtubules are essential for cell migration, morphological changes, and cell polarization. The Rho-family of small GTPases have been described for being upstream of these events by transmitting signals from cell surface receptors as integrins and growth factor receptors. The activity of these small GTPases is controlled by guanine-nucleotide exchange factors (GEFs), which favor the active GTP-bound form in opposition with the GTPases-activating proteins (GAPs) that favor the inactive GDP-bound form (Symons and Segall, 2009). Inactive GDPbound Rho proteins can be sequestered in the cytoplasm by guaninenucleotide dissociation inhibitors (GDIs; DerMardirossian and Bokoch, 2005). The Rho-family of small GTPases is composed of 21 members in humans, that can be subdivided into different subgroups including the RhoA, Rac1, and Cdc42 subgroups and those that lack GTPase activity subgroup (Wherlock and Mellor, 2002). RhoA is important for actin stress fibers and FA formation (Ridley and Hall, 1992) through activation of its effector proteins Rho kinase (ROCK1 and 2) and mammalian diaphanous (mDia1 and 2; Narumiya et al., 2009). On the other hand, Rac1 is involved in the focal contact formation and lamellipodia extension by promoting actin polymerization and membrane ruffling at the leading edge of the cell and also stress fiber and FA disassembly at the rear of a moving cell (Ridley et al., 1992). During cell migration Rac and Rho activity need to be very well balanced and regulated. It has been suggested that Rho and Rac may have antagonist and complementary functions during cell migration with Rac active at the leading edge of a moving cell promoting protrusion formation and lamellipodia extension and suppressing the activity of Rho. In parallel, Rho is activated at both the front and the rear of the cell to suppressing Rac activity and promote cell contractility necessary for movement, possibly through activation of myosin light chain (Machacek et al., 2009). RhoA signaling can also promote actin stress fibers assembly and stability by activation of mDia. mDia activation facilitates actin nucleation and polymerization by activation of the actin-binding protein profilin (Li and Higgs, 2003). mDia activation can also regulate the formation and orientation of stable microtubules (Palazzo et al., 2001). Activation of ROCK1 and ROCK2, both effectors of RhoA, induce activation and phosphorylation of LIM kinase which in turn phosphorylates and inhibits the actin severing protein cofilin. Cofilin stimulates actin-filament disassembly by accelerating


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the off-rate of actin monomers from the pointed ends of actin filaments (depolymerization) and by severing actin filaments resulting in enhanced cell protrusion (Kiuchi et al., 2007). Cofilin can be activated by dephosphorylation on serine 3 by the phosphatase slingshot. Inhibition of cofilin results in less free actin barbed ends as well as inhibition of depolymerization of old actin filaments (DesMarais et al., 2005). Increasing actin polymerization leads to stabilization of actin stress fibers. Moreover, ROCK1 and ROCK2 can directly phosphorylate the myosin light chain and inhibit the MLC phosphatase. Increasing p-MLC increases contractibility. By promoting actin stress fibers assembly and stability, activation of ROCK1 and 2 results in integrin clustering, FA assembly and cell adhesion (Totsukawa et al., 2004). Activation of Cdc42 is known to be important in the formation of actin-rich finger-like structures called filopodia (Nobes and Hall, 1995). By extending filopodia the cell can then sense the extracellular environment and control cell polarity and directionality of movement during directed cell migration.

3.3. Proteins involved in cell–cell adhesion Cell junctions are specialized cell–cell or cell–ECM contacts. In epithelial cells, cell–cell junctions are typically formed by AJ, and apical TJ. Desmosomes and GAP junctions are also found in certain groups of specialized epithelial cells. At the contacts between the cell and the ECM there is also formation of FAs and hemidesmosomes. Both TJs and desmosomes are formed by transmembrane adhesion proteins of the cadherin family whereas FAs and hemidesmosomes are formed predominantly by transmembrane proteins of the integrin family. Although endothelial cells show more flexible organized junctions they are able to form AJs and TJs as in epithelial cells (Dejana, 2004). Tight junctions appear in epithelial cells at the most apical part of the cell. These junctions are composed of the transmembrane proteins claudin and occludin, and are associated with the ZO family of proteins, which can in turn bind to actin to form a mechanical scaffold (Fig. 2.1C). Epithelial AJs are composed of cadherin adhesion molecules. These epithelial cadherins are Ca2þ-dependent transmembrane adhesion proteins that form homodimers at the plasma membrane between adjacent cells similar to a zipper-like structure (Cavey and Lecuit, 2009; Fukata and Kaibuchi, 2001). At their cytoplasmic domain cadherins are known to bind actin through association with a-, b-, and p-120 catenins. Interaction of cadherins with the cortical actin cytoskeleton is a dynamic and tightly regulated process. This was traditionally thought to be due to direct physical interactions between E-cadherin and b-catenin, and indirectly to a-catenin thus connecting to actin filaments. However, recent biochemical and dynamics analysis have shown that this link may not exist and that instead, a constant shuttling of a-catenin between cadherin/b-catenin complexes and actin

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may be key to explain the dynamic aspect of cell–cell adhesion (Drees et al., 2005; Yamada et al., 2005). After formation of cell–cell contact that is dependent upon actin polymerization, formation of membrane protrusions and ruffles, cadherin homodimers start to cluster at contacting sites (Kametani and Takeichi, 2007). Clustering of cadherins leads to generation of acto-myosin tension at contact sites, which in turn generates a ‘‘pulling’’ force that facilitates formation of thick actin bundles resulting in contact expansion (Cavey and Lecuit, 2009) (Figure 2.2A). Desmosomes are specialized cell–cell junction sites that can bind intermediate filaments (e.g., keratin, desmin) at their cytoplasmic domain (Figure 1.1C). Desmosomes are composed of three major gene families: cadherins (such as desmogleins and desmocollins), armadillo proteins (such as plakoglobin, plakophilins), and plakins (such as desmoplakin) the latter of which link to the intermediate filaments. Lateral interactions among proteins in the junctional plaque reinforce its stability (Green and Simpson, 2007). Following contact, desmosomal proteins are recruited to sites of cell– cell apposition in two phases; one fast from local pools of protein, and one slower recruitment phase from translocating particles in the cortical region of the cell (Godsel et al., 2005). The membrane-associated components of the adhesion are associated with the microtubule network, whereas desmoplakin associates with intermediate filaments. These relative associations play a role in strengthening the adhesive plaque over time. Once formed, desmosomes in intact epithelial layers are relatively immobile. However, studies have shown that these structures become considerably more dynamic upon insult, such as wounding of the skin (Green and Simpson, 2007). This is often coupled to increased levels of active PKC that may lead to phosphorylation of one or more of the components of desmosomal adhesive plaques thus modulating dynamic changes (Wallis et al., 2000). GAP junctions are specialized cell–cell junctions in which the plasma membrane of the adjacent cells is penetrated by protein assemblies called connexons (Kojima et al., 2007) (Figure 2.1C). The apposed lipid bilayers are penetrated by connexons, each of which is formed by six connexin subunits. Two connexons join across the intercellular gap to form a continuous aqueous channel connecting the two cells. Gap junctions are composed of clusters of connexons that allow molecules smaller than about 1000 daltons to pass directly from the inside of one cell to the inside of the next. Cells connected by gap junctions share many of their inorganic ions and other small molecules and are therefore chemically and electrically coupled (Kjenseth et al., 2010). Gap junctions are important in coordinating the activities of electrically active cells, and they have a coordinating role in other groups of cells as well. While the dynamics of molecule transport through GAP junctions have been well described, dynamic changes in the proteins within the junctions themselves remain poorly understood.


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3.4. Other adhesion proteins: Signaling and adaptor proteins FA structures are formed by the dynamic recruitment of many cytoplasmic proteins. One of the first to be recruited is talin, which binds directly to the b integrin subunit, followed by paxillin and vinculin that function as adaptors between integrin and the actin cytoskeleton, and tyrosine kinases such as FAK and Src that initiate several signaling cascades. Following integrin engagement, FAK is recruited to the FAs and becomes autophosphorylated at tyrosine 397. Src can then be recruited through its SH2 domain, and both kinases are able to phosphorylate various target proteins at the adhesion site, in turn creating docking sites for SH2-containing proteins. Further signaling proteins such as paxillin and vinculin are subsequently also recruited to the nascent adhesions at the leading edge of the cell. Both FAK and Src can then phosphorylate paxillin on Y31 and Y118 (Brown et al., 2005; Webb et al., 2004). Paxillin phosphorylation then facilitates exposure of additional docking sites for other downstream signaling molecules. Previous studies have shown that paxillin appears to be phosphorylated only in FCs and FAs and not in FBs suggesting a role for paxillin in promoting the assembly of adhesions. On other hand, vinculin is recruited to sites of talin–integrin adhesion under tension and its activity can regulate both paxillin recruitment and integrin turnover. Vinculin can also be found at cadherin-mediated cell–cell junctions. Previous studies shown that vinculin overexpression increases cell migration, studies with vinculin-null cells have shown that they are less adherent, show reduced spreading, are more motile and have fewer and smaller FAs than wild-type cells. Additionally phosphorylation of FAK on Tyr397 and paxillin on Tyr118 is increased in vinculin-null cells. Interestingly, FAK-null and Src-null cells, in which tyrosine phosphorylation of FA-associated proteins is reduced, display larger and more stable FAs than normal cells (Brown et al., 2005). b-Catenin stability and therefore b-catenin-dependent gene expression is regulated in part through the Wnt canonical pathway (Cadigan and Peifer, 2009). When Wnt is not present, b-catenin appears in a complex composed by Axin, APC (adenomatous polyposis coli gene product), CK1 (casein kinase 1), and GSK3 (glycogen synthase kinase 3). After phosphorylation by CK1 and GSK3, cytoplasmic b-catenin can then be recognized by the E3 ubiquitin ligase b-Trcp and subsequently be degraded by the proteosome. Keeping the low levels of b-catenin in the cytoplasm prevents it from reaching the nucleus. When Wnt is present it can bind to the membrane receptors Frizzled (Fz) and the low-density-lipoprotein receptor related protein 5 and 6 (LRP5/6) at the cell surface (Lee et al., 2006). The recruitment of Dvl (Disheveled) by Fz leads to phoshorylation of LRP5/6 and Axin recruitment. By preventing the formation of the Axin-b-catenin complex, b-catenin can then accumulate in the nucleus of the cell where it forms a complex with T cell transcription

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factor and promotes the expression of the Wnt-responsive genes (Cadigan and Peifer, 2009). Wnt/b-catenin signaling has an important role in regulating cell-to-cell interactions during embryogenesis and also in human diseases including cancer. In addition to Wnt signaling, b-catenin can also regulate adhesion through its binding to cadherins at the cell membrane. Cadherin– catenin complex is mainly regulated by phosphorylation of b-catenin. Moreover cadherins can also regulate b-catenin/Wnt signaling by sequestring b-catenin from the nucleus thereby repressing Wnt target genes. Rho GTPases might also regulate cadherin-mediated cell–cell adhesion by controlling cadherin transport (Habas and He, 2006).

4. Regulation of Cell Adhesion Dynamics In Vitro 4.1. Regulation by integrin during migration For efficient cell migration to occur, cells must coordinate the formation and disassembly of adhesions at the protruding front and retracting rear. Observations showing that compromised endocytosis and/or exocytosis can disrupt cell polarity during cell migration led to early proposals that integrins could be recycled from disassembling adhesion at the rear of the cell to facilitate formation of new adhesions the leading edge (Bretscher, 1989). However, more recent studies have demonstrated that integrins are able to undergo local recycling within newly forming protrusions thus facilitating rapid responses to changes in extracellular environments (Caswell et al., 2007). Emerging evidence also suggests that the small GTPases such as Arf6 and members of the Rab family are involved in this local recycling and are required for redistributing integrins at the leading edge in a migrating cell (Caswell et al., 2009). Integrin endoexocytic traffic is now widely accepted to contribute to cell migration by supporting adhesion site dynamics and the localized targeting of the adhesion receptors. Integrin endocytosis has been described via several entry routes including clathrin-dependent and -independent pathways. Small GTPases have been shown to regulate integrin traffic in part based on their distinct expression patterns. Rab21 small GTPase regulates integrin endo/ exocytosis by interacting with the a-subunit of most integrin heterodimers. More recently, a number of studies have demonstrated that integrins orchestrate the intracellular trafficking of growth factor receptors (Caswell et al., 2008; Reynolds et al., 2009) and as such integrins may also contribute to their own recycling and dynamics through a growth factor-dependent positive feedback loop. The observation that some integrins are in a continuous state of endoexocytic flux has led to the proposal that receptor recycle pathways form an integral part of the cellular machinery that elicits polarization during migration. Manipulations that result in compromised endocytosis profoundly


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disrupt the ability of cells to polarize their cytoskeleton and that disruption of endosomal transport and/or of the exocytic fusion of recycle vesicles can compromise polarity during cell migration (Caswell et al., 2009). Integrins play additional roles in regulating cell–cell adhesion, in part through association with coreceptors. One example of this is the complex between the a3b1 laminin-binding integrin and the tetraspanin CD151. This association was characterized a number of years ago (Yauch et al., 2000) but has only recently been shown to be important in regulating the stability of cell–cell junctions (Johnson et al., 2009). This study demonstrated that loss of the CD151-a3b1 complex led to hyperactivation of the small GTPase RhoA and this resulted in increased remodeling of epithelial cell junctions with neighboring cells (Johnson et al., 2009).

4.2. Regulation by signaling proteins During formation of cell–ECM contacts, a common series of events takes place upon integrin engagement with the matrix proteins. Upon initial engagement, the integrin undergoes a conformational change to form an active state with complete activation being achieved upon talin binding to the b chain cyto tail (Banno and Ginsberg, 2008). Once activated, integrins cluster to provide a platform for the recruitment and assembly of other associated adaptor and signaling proteins. Recent studies in live cells have begun to reveal the complex hierarchy of protein recruitment and release within adhesions (Brown et al., 2006; Digman et al., 2009). The assembly of adhesions is dependent on the conformation, binding motifs, and signaling domains contained within each protein recruited. Not all of these proteins are able to bind the integrin directly and so a hierarchical chain is established where proteins such as talin, paxillin, and filamin, act as linkers that bind the integrin and recruit other proteins. Adhesion disassembly can occur at different rates dependent on the type, composition, and where it is located within the cell. FCs both assemble and disassemble quickly in order to allow continuous protrusion of the leading edge. FAs are more stable structures and can be localized to the leading edge, under the cell body or at the trailing edge and thus will disassemble at different rates depending on the local environmental cues (Figure 2.2B). Several factors have been shown to influence FA disassembly. One recently reported example is the action of FAK on p190RhoGAP activation in migrating fibroblasts. This activation results in decreased levels of active Rho and increased Rac activity, thereby decreasing myosin-dependent tension at the leading edge and promoting Rac-dependent adhesion instability (Tomar et al., 2009). FAK has also been shown to be important for lysophosphatidic acid (LPA) induction of adhesion dynamics in fibroblasts. FAK signaling to PDZ-Rho GEF and ROCK was required for FAKinduction of adhesion turnover suggesting an important role for this kinase

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in parallel pathways (Iwanicki et al., 2008) Additionally, the calciumdependent protease calpain is known to cleave talin at sites of adhesion, leading to increased rates of adhesion disassembly (Franco et al., 2004). Calpain can also regulate formation of invadopodia in cancer cells through regulation of Src signaling (Cortesio et al., 2008). Recently, calpain has also been shown to cleave FAK, and this is required for efficient assembly and disassembly of FAs highlighting the multifunctional role of this protease in regulating dynamics (Chan et al., 2010). Recent studies have also demonstrated indirect roles for actin-binding proteins in the regulation of FA dynamics. The actin-binding protein coronin 2A localizes to stress fibers and a subset of more stable FAs. Depletion of coronin 2A in tumor cells led to enhanced phosphorylation and inactivation of cofilin resulting in a reduction in both FA disassembly and cell migration speed (Marshall et al., 2009). This appears to be due to coronin 2A directly interacting with the cofilin phosphatase Slingshot1 and suggests that regulation of actin free-barbed end formation at adhesion sites by cofilin can promote dynamic turnover of these structures. Another actinbundling protein, fascin, has also recently been shown to regulate the disassembly of adhesions in human colon carcinoma cells (Hashimoto et al., 2007). Fascin has been shown to be highly upregulated during tumor progression and is known to play a role in stability of actin-based protrusions during cell motility and invasion in a PKC and Rac-dependent manner (Anilkumar et al., 2003; Parsons and Adams, 2008). Depletion of fascin from carcinoma cells results in highly stable adhesions, despite the fact that fascin is not localized to FAs in these cells (Hashimoto et al., 2007). This suggests that the actin-bundling properties of fascin at peripheral actin-rich sites may contribute toward a positive feedback loop that permits rapid reorganization of FAs during cell invasion. Key signaling proteins involved in regulating cell–matrix adhesions are also now being identified as key players in the regulation of cell–cell contacts. One example of this is the classical FA protein vinculin that was recently identified as a binding partner for b-catenin at cell–cell junctions (Peng et al., 2010). This study demonstrated that loss of vinculin in epithelial cells leads to decreased E-cadherin at AJs without a change in total E-cadherin. The authors further demonstrate that a mutant form of vinculin that no longer binds to b-catenin could not rescue E-cadherin cell surface levels or support assembly of intact epithelial junctions thus placing vinculin as a potential multiregulator of both cell–cell and cell–matrix adhesion dynamics.

4.3. Regulation by the actin cytoskeleton The actin cytoskeleton is known to be vital in the regulation of cell adhesion assembly and disassembly in many contexts. Many types of cell motility are driven by the polymerization of an actin network that pushes the membrane


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forward resulting in the formation of a number of dynamic structures such as lamellipodia, filopodia, and membrane ruffles (Pollard and Borisy, 2003). Studies have shown that the lamellipodium contains a dense network of branching actin filaments that undergo fast retrograde flow and form a cohesive, separable layer of actin in front of a less dynamic actin network called the lamella. At the rear of the lamellipodium, the motor protein myosin II pulls actin filaments and condenses them into lamellar actin bundles, causing periodic edge retraction as a result of mechanical breakage of the link between FAs and stress fibers. This leads to initiation of new adhesion sites and force generation that is accompanied by assembly of actin into stress fibers. Live-cell imaging has shown that during the forward movement of the lamellipodium, small cell–matrix adhesions form and grow into mature FAs that eventually localize to the interface with the lamellae (Giannone et al., 2007; Hu and Chien, 2007). Even though the lamellipodial actin structure has been well described, the precise organization of the actin filaments that emerge from FAs is poorly understood, mostly because of the absence of ultrastructural studies that define both adhesion sites and the cytoskeleton in the same image. Live-cell imaging has revealed that actin within integrin-dependent podosomes undergo cycles of rapid polymerization and depolymerization, and have a life-span of 2–4 min (Destaing et al., 2003; Ochoa et al., 2000). Invadopodia are thought to have a much longer life-span of 30 min but, in Src-transformed cells, inhibition of protein tyrosine phosphatases speeds up invadopodial dynamics to give half-lives similar to those of podosomes (Badowski et al., 2008). Invadopodia formation is initiated by nucleation of F-actin filaments that are oriented perpendicularly to the substrate (Artym et al., 2006) in contrast to FAs that are initiated by occupancy of integrins by ECM components and integrin clustering, followed by formation of stress fibers (Vicente-Manzanares et al., 2009). In FAs, the relationship between integrins and the actin-nucleation machinery is not well understood. Vinculin can associate transiently with the Arp2/3 complex upon cell adhesion to FN (DeMali et al., 2002), but neither Arp2/3 nor N-WASP has been identified in mature FAs. FAK can interact with the Arp2/3 complex and N-WASP, providing a link between integrin engagement, formation of new adhesions and actin polymerization (Serrels et al., 2007). Arp2/3 forms a complex with FAK at nascent adhesions during spreading and is then released from maturing adhesion structures. Autophosphorylation of FAK at Tyr397 destabilizes the Arp2/3-WASP-FAK complex, inhibits Arp2/3-dependent lamellipodium extension and prevents or delays stress-fiber assembly. In addition to nucleation factors actinelongation factors also seem to have a role in adhesion formation and dynamics. Both Ena/VASP-family proteins and formins act as elongation factors to promote actin polymerization at the barbed ends of actin filaments. Ena/VASP proteins localize at FAs, the edge of lamellipodia

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and the tips of filopodia where they promote actin-filament assembly by competing with capping proteins (Bear and Gertler, 2009). Mechanical forces are required for the assembly and maturation of FAs. A recently identified subclass of ‘‘nascent’’ adhesions form at the base of the lamellipodium in a myosin-II-independent manner, and their assembly rate is proportional to the rate of lamellipodial protrusion. At the rear of the lamellipodium, nascent adhesions either disassemble or mature through a sequential mechanism that is coupled to myosin-II-induced tension (Choi et al., 2008). At FAs, there is active polymerization of actin filaments that can be cross-linked by a-actinin. Myosin II incorporates into the a-actinincross-linked actin-filament bundles and displaces a-actinin allowing dynamic contraction of actin and tension generation (Cai and Sheetz, 2009). Maturation of FAs can take tens of minutes and corresponds to an order of magnitude increase in force at these sites (Gallant et al., 2005). Stress fibers are anchored to FAs, which grow in response to contractile force. Traction forces generated by stress fibers are in the order of several hundred nanonewtons (nN). Analyses of actin dynamics in stress fibers indicate that preformed actin filaments are added to the adhesion sites and enable the rapid turnover of actin in stress fibers (Hotulainen and Lappalainen, 2006). Maturation and growth of FAs involve force reinforcement that is dependent on talin (Zhang et al., 2008). Maturation into FAs is mediated by ROCK and mDia1, both of which are effectors of the small GTPase RhoA (Narumiya et al., 2009). ROCK stimulates myosin-IIdependent contractility by inactivating myosin-light-chain phosphatase whereas mDia1 is involved in actin nucleation and in the elongation of parallel arrays of actin filaments.

4.4. Regulation by microtubules Microtubules have also been proposed to mediate adhesion disassembly through a number of mechanisms. Many studies have demonstrated that microtubule tips can repeatedly target a subset of adhesions at both the leading edge and rear of a migrating cell, although the reasons for this remain unclear (Broussard et al., 2007). Contact with FAs appears to affect microtubule dynamic instability. Microtubule plus ends found in proximity to FAs can undergo phases of extended pausing and become resistant to depolymerization by nocodazole (Kaverina et al., 1998). However, growing microtubules that do not become stabilized at the FAs have a significantly increased chance to undergo a catastrophe (shrinking) at these sites indicating that microtubule–FA interactions can have multiple outcomes (Efimov et al., 2008). Targeting can also drive adhesion disassembly through a combination of local Arg-mediated inhibition of Rho activity coupled with FAK and dynamin-dependent endocytosis (Ezratty et al., 2005; Miller et al., 2004). Conversely, a recent study shows that the role of


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microtubules in regulating FA dynamics may be restricted to specific subsets of adhesive sites. The application of cyclic stretch results in realignment of cells in an axis that is perpendicular to the direction of load. This is coupled to force-induced ‘‘sliding’’ of FAs which occurs in a Rho dependent but microtubule-independent manner (Goldyn et al., 2009). Indeed, in this study the authors found that depolymerization of microtubules using nocodazole had no effect on RhoA activation. Therefore the role of microtubules in regulating GTPases and thus adhesion dynamics remains controversial. A similar phenomenon of microtubule contact can also occur at cadherin-based cell–cell contacts. In contrast to the apparently negative impact of microtubule targeting on integrin-based FAs, it rather appears to have a positive impact on cadherin-based cell–cell contacts. In epithelia, microtubules are often found running parallel to the lateral surfaces with their plus ends oriented basally (Bacallao et al., 1989; Bartolini and Gundersen, 2006). In addition, populations of microtubules have been identified that extend radially outward to reach into cadherin-based cell–cell contacts (Broussard et al., 2007; Ligon and Holzbaur, 2007; Stehbens et al., 2006). Labeling with þTIP markers confirmed that these have their plus ends directed toward the adhesive contacts (Stehbens et al., 2006). Interestingly, microtubules also reorganize to extend into these sites of cell adhesion to beads coated with functional cadherin ligands, suggesting that cadherin ligation can potentially induce redistribution of microtubules. Depolymerizing microtubules disrupt the integrity of cadherin-based cell–cell junctions (Meng et al., 2008). Moreover, junctional integrity is also perturbed by agents that disrupt plus-end dynamics but do not disassemble microtubules, such as low doses of nocodazole and overexpression of a microtubulestabilizing factor (Stehbens et al., 2006). Thus, the dynamic behavior of the plus ends appears to exert a specific influence on cadherin-based contacts in a fashion that is distinct from the overall integrity of the microtubule network. Much of what is currently known about adhesion localization and turnover has been gleaned through visualizing protein dynamics using a range of different microscopy techniques. We will now outline a number of these techniques used to study different aspects of adhesion behavior in living cells.

4.5. Techniques to study adhesion dynamics in vitro 4.5.1. Widefield fluorescence Multiple methods can be used in order to image adhesion structures, each offering their own advantages and providing different kinds of data for the structure or protein being studied. The majority of these techniques will involve a protein of interest being fluorescently tagged. A number of different labeling strategies are available but the most common is the use

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of genetically encoded fluorescent protein tags such as green fluorescent protein (GFP) or one of its more recently developed spectral variants such as mRFP (red), YFP (yellow), or CFP (cyan), to name but a few (Shaner et al., 2007). Imaging of these fluorescent tags require excitation at a specific wavelength, which then leads to emission of light at a higher wavelength. For example, GFP is optimally excited at a wavelength of 488 nm and emits at 514 nm. Tagged constructs are typically transfected into cells of interest and viewed using the appropriate microscope depending upon the nature of the experiment. The most widely used method to imaging protein dynamics is widefield microscopy. Also known as epifluorescent microscopy, this technique that can be performed with a basic fluorescent microscope equipped with a charge-coupled device (CCD) camera and the appropriate excitation and emission filter sets in order to distinguish the relevant wavelengths. These systems generally allow the user to view cells through both fluorescent and phase contrast channels thus allowing the user to view protein localization within the cell. Images can be acquired over short or long time periods, the length of time being dependent on a number of factors. The more sensitive the camera, the lower the illumination level required to visualize the tagged protein resulting in a lower risk of cell toxicity and allowing for a longer viewing time. Additionally, in the case of transfected cells the expression levels of the protein will also determine the exposure times required to visualize the protein of interest. Acquiring such time-lapse movies allows the user to follow a protein or adhesion marker of interest and by using appropriate software can then use this information to calculate adhesion numbers, rates of adhesion assembly, disassembly, and intensity profile changes over time. A recent example of this technique was in a study using GFP-cortactin as a marker of invadopodia formation and turnover in MTLn3 cells. Data from these movies revealed that the cells treated with siRNA to knockdown FAK had greater numbers of invadopodia, and that these adhesions showed higher rates of assembly and disassembly compared to control cells (Chan et al., 2009). A different study used a similar approach to dissect the function of cortactin phosphorylation and subsequent regulation of cofilin and Arp2/3 to control invadopodia maturation (Oser et al., 2009). By imaging size and dynamics of invadopodia over time the authors demonstrated that dephosphorylation of cortactin led to loss of actin severing by cofilin and subsequent maturation and stabilization of these adhesive structures. Rates of assembly and disassembly of adhesions are calculated through measuring the incorporation or loss of fluorescent signal of the protein being studied. Increase of signal will be the result of adhesion assembly and growth and disassembly will result in loss of fluorescent signal. Plotting these intensity values over time on semilogarithmic graphs will provide a profile of intensity ratios over time. These ratios are calculated using the formula


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In(I/I0) for assembly and In(I0/I) for disassembly (where I0 is the initial fluorescence intensity value and I is the intensity value for the relevant time point). Rates can then be calculated from the gradient of the line of best fit. Spinning disk microscopy can also be used to calculate the rates of adhesion assembly and disassembly and provides a more sensitive approach compared to widefield microscopy. Here, samples are illuminated by a standard white light source (Hg/xenon) passing through a radial array of pinholes. This allows very thin optical sections of a sample to be imaged at high speeds. Laser light sources can also be used and with the addition of an acousto-optical tunable filter (AOTF) this allows switching of excitation wavelengths in the order of microseconds. This modification also allows fast imaging of multiple fluorophores within a sample. There are many examples of use of this technique, including an early study by Franco et al., who used it to show the effect of calpain proteolysis on talin within FAs. Expressing calpain2 siRNA in fibroblasts reduced disassembly from 0.09 min 1 in control cells to 0.04 min 1, thus suggesting that calpain proteolysis of talin increases adhesion disassembly (Franco et al, 2004). A more recent study has used a similar approach to subsequently show that calpain induced cleavage of talin leads to Cdk5 and Smurf1-mediated ubiquitylation of the talin head. This results in degradation of the talin head and ultimately enhanced FA stability (Huang et al., 2009). The analysis of FA dynamics using widefield or spinning disk imaging approaches is relatively fast and as such may represent a good method in future for small scale screening of siRNA libraries to identify new regulators of adhesion turnover. Proof of principle of using such an approach was clearly demonstrated in a recent study where hits identified from an siRNA screen for regulators for adhesion size and morphology in fixed cells were verified using live imaging of paxillin in HeLa cells (Winograd-Katz et al., 2009). 4.5.2. Confocal microscopy In cases where increased sensitivity and spatial resolution is required, confocal microscopy provides an alternative method (Joshi et al., 2008). Here, samples are excited through lasers of specific wavelengths passing through a pinhole, with excitations being received through another pinhole an equivalent distance away. Due to the light arriving at the detector from a narrow focal plane, this means that the z-resolution is considerably improved from other methods such as widefield microscopy. Speed of imaging is dependent on the system being used, with most standard systems having the ability to image up to 4 frames/s at a resolution of 512 512 pixels. The introduction of new resonant scan heads can increase this rate up by anything up to 30 frames/s, thus allowing highly dynamic processes to be imaged and even allowing for movies of multiple planes to be made. The drawback of using confocal microscopy for live imaging is increased photobleaching due to

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intense laser excitation. A balance therefore has to be reached between laser power and expression levels of the protein being imaged in order for confocal microscopy to be used for longer-term live imaging. 4.5.3. Total internal reflection fluorescence microscopy Total internal reflection fluorescence microscopy (TIRF-M) is an ideal alternative method to image cell–matrix interactions. It is dependent on the production of an evanescent wave that comes about when light passes from a solid to a liquid phase. As the wave can only penetrate a very short distance, this allows only a limited region to be imaged, a depth of approximately 100 nm. This enables enhanced signal to noise ratio due to loss of background interference signals, as well as high-sensitivity excitation at a single focal plane, thus making TIRF an ideal technique for study of adhesion structures and protein recruitment to them. Marshall et al. (2009) recently used TIRF to show that a cofilin–TagRFP construct localizes specifically to the proximal end of FAs. 4.5.4. Photobleaching and photoactivation There are a number of different methods used to calculate kinetics of protein movement within cells. The use of photoactivatable (PA) GFP fluorescent tags is proving to be a very useful tool in the study of adhesion biology. These tags are transfected and expressed in the same way as their normal GFP counterparts. However, they can only be imaged following a preactivation step. For example activation of PA GFP requires a burst of 405 nm light prior to being imaged using a 488 nm laser. Therefore using these tags allows the user to select for a certain population of tagged protein at a particular point in a cell and follow this protein after the activation step. These tags have been used to investigate the kinetics of actin dynamics (Osborn et al., 2006) and adhesion protein behavior (Betzig et al., 2006). A more recent study used a PA-GFP-a5 integrin construct to investigate the effect of Rab25 binding to a5b1 integrin on tumor cell invasion. Exposing Rab25 positive vesicles to a photoactivation pulse led to appearance of PAa5 and within 60 s there was a fourfold reduction in fluorescence intensity with PA-a5 appearing at the plasma membrane therefore suggesting that Rab25 is involved in a5 recycling to the membrane (Caswell et al., 2007). Fluorescence recovery after photobleaching (FRAP) is another commonly used technique to quantify protein kinetics in living cells. Cells are transfected with a fluorescently tagged protein, imaged live and then subjected to a bleach step where a specific point or region of interest (ROI) is exposed to high intensity burst(s) of laser emission. Cells are then imaged over a period of time and the recovery of the tagged protein into the bleached ROI is calculated. Dynamics of protein movement can be calculated through the recovery rates in the bleached ROI. Recovery can be expressed as half-life, which is the time required for signal intensity to return


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to half of its full recovery. Very fast recovery would be shown as a short half-life and would hint at diffusion-based movement of protein. Slower recovery, or a longer half-life, suggests a more regulated mode of transport. A recent example of the use of FRAP in relation to adhesions was performed by Himmel et al. who investigated kinetics of a construct encompassing the IBS-2 and ABS-3 domains of talin. The group employed the use of a double scan-headed confocal, allowing for simultaneous bleaching and image acquisition, thus preventing the time lost when a single confocal scan head switches from bleach mode to acquisition (particularly useful when imaging a highly dynamic protein). Transfecting a GFP-tagged TalinC construct into C2C12 myoblast cells revealed that the protein has a shorter half-life than full-length talin, 45.3 2.2 s compared to 33.5 3.6 s (Himmel et al., 2009). An additional method using a similar premise to FRAP is fluorescence loss in photobleaching (FLIP). This method sees a ROI repeatedly bleached, thereby restricting any recovery that would have taken place in that region. Over time, regions other than the bleached ROI will lose fluorescent signal, due to the proteins within the bleached ROI moving to different areas of the cell. Monitoring the spread of bleached protein provides data on protein transport in a cell and can also provide data on where proteins recruited to adhesions originate. 4.5.5. Fluorescence speckle and correlation microscopy Due to developments in CCD technology, the study of kinetics of movement can now be taken down to single molecules using fluorescence speckle microscopy (FSM). This involves expressing fluorescently tagged proteins at very low levels in live cells. These proteins appear as speckles and can be detected with a combination of a widefield microscope and a CCD. Visualization of these speckles allows the user to monitor movement of turnover of the tagged protein within structures. Initially used to study actin dynamics within newly formed lamellipodia in migrating cells (WatermanStorer et al., 1998), the technique has been used for investigating adhesion proteins such as in a recent study by Hu et al. This study revealed that integrins move slowly in adhesions (0.1 mm/min) when compared to other FA proteins such as FAK (0.15 mm/min), talin (0.25 mm/min), and a-actinin (0.27 mm/min). Kinetics of adhesion-associated actin were then correlated with this data and the efficiency of motion of subsets of proteins relative to their actin-binding capabilities determined. This hierarchical control of motion within these groups of proteins relative to actin has shed considerable light on the functional relevance of the numerous F-actin-binding and -associated proteins within FAs (Hu et al., 2007). With over 150 different proteins being recruited to adhesions, there is an increasing interest in being able to investigate and quantify protein–protein interaction at these sites. One method in use to study this is fluorescence

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correlation spectroscopy (FCS), which allows the user to measure the concentration of fluorescently tagged proteins at forming adhesions. It requires laser excitation of a small region of a cell, over many time intervals. Postacquisition software is then used to determine rates of diffusion, aggregation, and flow velocities. If two different fluorophores are used in the experiment, then crosscorrelation analysis also allows calculation of relative rates of transport and therefore kinetics of protein association or formation of adhesion complexes (Brown et al., 2006). 4.5.6. Fluorescence resonance energy transfer Fluorescence resonance energy transfer (FRET) is an alternative technique that allows the user to measure interactions between two spectrally overlapping fluorescent molecules and thus direct protein–protein interactions. When the donor protein (tagged with shorter wavelength protein) comes into close proximity with the acceptor protein (typically 300 kDa) that consist of mainly unstructured parts AKAP18d: RBD

RIIa

P E D A E L V R L S K RL V E N A V L KA V Q Q Y

H4R⬘24 Q6

T⬘19 Q⬘16E⬘13

Q15

Q16

R24

Figure 5.3 A schematic representation of potential H-bonding interactions (light gray) and salt bridges (dark gray) between the RIIBD of AKAP18d and the D/D domain of RIIa. The residues of the two protomers of the D/D domain dimer are distinguishable by the presence and absence of apostrophes. The scheme is based on molecular modeling studies. Adopted from Hundsrucker et al. (2006b).

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Philipp Skroblin et al.

displaying no secondary or tertiary structures as predicted by in silico analyses (Malbon et al., 2004). This complicates the expression of AKAPs since large proteins cannot be expressed in common expression systems as full-length proteins. In addition, AKAPs often form insoluble aggregates. Moreover, AKAPs are often associated with cellular structures such as membranes, the cytoskeleton, or are part of multiprotein complexes. Any of these components may play an important role for the stabilization of AKAP structures and without them structural analysis of the protein may not be possible (Malbon et al., 2004). Consequently, full-length AKAPs are difficult to purify. Therefore, often only truncations can be used. Partial structures are available for some AKAPs but none of the fragments resolved by NMR or X-ray crystallography contain their RIIBD. Although they lack information about the RIIBDs’ environments and the PKA binding mechanism, these partial structures point to other AKAP functions. For instance, the structure of the AKAP18d core domain (AKAP18dCD), comprising amino acids 76–292, contains a His-X-Thr motif which is a common feature of the 2H-phosphoesterase family (PDB ID: 2VFL; Gold et al., 2008). Cocrystallization with AMP and CMP revealed binding of these nucleotides to the His-X-Thr motif (Gold et al., 2008). However, the function of this motif and the nucleotide binding in AKAP18d are not known. Further examples are an NMR structure of the Dbl homology/pleckstrin homology (DH/PH) domain of AKAP-Lbc (Sugawara et al., 2009) and crystal structures of the protein 4.1, ezrin, radixin, moesin (FERM) domains of Ezrin (PDB ID: 1NI2; Smith et al., 2003) and Moesin (PDB ID: 1SGH; Finnerty et al., 2004). To date, the only full-length AKAP whose structure has been resolved is GSKIP (PDB ID: 1SGO, NMR analysis by the Northeast Structural Genomics Consortium). GSKIP was identified as an AKAP in an in silico screening of the Swissprot data bank using an extended model for a consensus sequence of RIIBDs (see above; Hundsrucker et al., 2010). GSKIP (15.6 kDa) is one of the smallest AKAPs. Its small size and excellent solubility permitted analysis of its structure by NMR. GSKIP can structurally be divided into four parts: a highly flexible unstructured N-terminus of unknown function (amino acids 1–32), an adjacent a-helix (amino acids 33–48), a central antiparallel b-sheet region (amino acids 49–115), and a second C-terminal a-helix (amino acids 116–139; Fig. 5.4A). The RIIBD of GSKIP is located between amino acid 28 and 52 and thus mostly a-helical with the conserved hydrophobic residues located in this helix in positions 37, 40, 41, 44, 45, and 48 (Fig. 5.4B). As expected for an amphipathic helix, the polar face is on the surface of the protein while the hydrophobic face is covered by the b-sheet region (Fig. 5.4). Because solvent-exposed hydrophobic residues are energetically unfavorable, it is likely that the hydrophobic part of RIIBDs of other AKAPs are also buried in the absence of PKA. As a consequence, the interaction of

Mechanisms of PKA Anchoring

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A

B

Figure 5.4 The NMR structure of GSKIP (PDB ID: 1SGO). (A) GSKIP consists of an unstructured N-terminus (gray, amino acids 1–32) followed by an a-helix (red, amino acids 33–48), a central b-sheet region (blue, amino acids 49–115) and a C-terminal a-helix (green, amino acids 116–139). Adopted from Hundsrucker et al. (2010). (B) The surface of the protein is illustrated in gray, the RIIBD is shown in red and conserved hydrophobic residues therein are labeled yellow.

AKAPs and the D/D domain of PKA regulatory subunits would have to be accompanied by conformational changes of the AKAP. Consistently, NMR experiments with GSKIP and the RIIa D/D domain show that the hydrophobic face of GSKIP’s RIIBD is involved in the interaction with the D/D domain dimer. In addition, structural alterations in other regions of GSKIP, namely the b-sheet that functions as a lid for the hydrophobic face of the RIIBD in free GSKIP, and amino acids in the C-terminal helix adjacent to the RIIBD helix, were observed (Hundsrucker et al., 2010). These changes in the b-sheet structure are most likely conformational rearrangements necessary to compensate for the loss of hydrophobic interactions between the bottom of the b-sheet and the hydrophobic face of the RIIBD helix. Conformational changes induced by R subunit binding are likely to occur in other AKAPs such as AKAP18d. Peptides comprising the RIIBD of AKAP18d (amino acids 292–321) as well as an N-terminally truncated AKAP18d (amino acids 124–353) bind with higher affinity to RIIa than the full-length protein (Henn et al., 2004; Hundsrucker et al., 2006b), suggesting that PKA binding causes a conformational change involving N-terminal domains of AKAP18d. Such changes may make the

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hydrophobic RIIBD accessible for binding. The understanding of conformational changes in AKAPs upon PKA binding will contribute to explain differences in binding affinities of various AKAPs to PKA that are independent from the residues in the RIIBD. PKA binding could also alter the conformation of other protein–protein interaction surfaces on AKAPs, thereby facilitating or inhibiting the binding of further proteins. On the other hand, proteins other than PKA interacting with AKAPs might modulate PKA binding. 3.2.3. PKA type I versus type II anchoring There are differences in the specificity of AKAPs with regard to the binding of RI and RII isoforms: RII subunits generally bind with higher affinity to AKAPs and have a lower off-rate than RI subunits. For example, the dualspecific D-AKAP1 binds RIIa (KD ¼ 0.5 nM) with a higher affinity than RIa (KD ¼ 185 nM; Herberg et al., 2000). RI binds to fewer AKAPs and, in general, more mutations in R-binding peptides abolish RI binding (Burns-Hamuro et al., 2005). Several studies have revealed the molecular basis for this isoform specificity of RIIBDs: Comparison of the R isoforms shows differences in surface charge distribution. While RIIa has a mostly hydrophobic interaction surface, RIa contains additional acidic and basic residues at the proposed interaction site (Banky et al., 2003). NMR (amino acids 12–61), and more recently, crystallographic structure analyses (amino acids 1–61) of the RIa D/D domain show—similar to RIIa—an X-type four-helix bundle with antiparallel orientation of the protomers (Banky et al., 2003; Sarma et al., 2010). RIa contains an extended, yet ordered, N-terminus forming the additional helix N1 (Banky et al., 2000). H/D exchange experiments suggested that this N1 helix is involved in AKAP binding to RI subunits (Burns-Hamuro et al., 2005). A peptide derived from the RIIBD of D-AKAP2 binds diagonally onto the AKAP-binding site of RIa, similarly as to the D/D domain of RIIa (Sarma et al., 2010). This X-ray structure confirms the predicted participation of the N1 helices of RIa in AKAP binding and explains the role of the cysteine residues C16, C37, C160 , and C370 : RIa is oxidized in complex with the peptide and disulfide bridges are found between C16–C370 and C160 –C37, respectively, thereby cross-linking the N1 helices of one protomer to the I helix of the other protomer (Fig. 5.5). Thus the cysteines apparently confer an ordered structure to the N-terminus of RIa. The cysteine residues are also a part of the binding pockets interacting with D-AKAP2 (see below; Sarma et al., 2010). A comparison of the RIa with the RIIa structure in complex with the D-AKAP2 peptide discloses general molecular determinants for the R subunit specificity of AKAPs (Sarma et al., 2010). Four hydrophobic binding pockets were identified in RIa which interact with pairs of hydrophobic amino acids from four turns of the D-AKAP2 helix (Leu634, Ala635;

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A

B N

C

C

Ile3 Ile5

ix I

Heli

C37 x II

I’ lixI⬘ Hleix He

Hel

C37’ C37⬘

Heli

HHeeli lix x IIII

HHeelili xxII⬘I’

I eliixx I HHel

N

x III’⬘

N1 N1 C16

I’ lixI⬘ eleix HH

N C16’ N1⬘ C16⬘ N1’

Ile5⬘ Ile3⬘

C

C

N

Figure 5.5 A schematic view on top of the AKAP-binding site of the D/D domain dimer of RIIa (A) and RIa (B). The two protomers depicted in light and dark gray are arranged in an antiparallel manner thereby forming the characteristic X-type four-helix bundle. The residues of the two protomers of the D/D domain dimer are distinguishable by the presence and absence of apostrophes. (A) Ile3, Ile30 , Ile5, and Il50 , located in the unordered N-terminal tails of the protomers, are involved in recognition and binding of RIIBD-derived peptides. (B) The N-terminal helices N1 and N10 as well as the disulfide bridges between C16–C370 and C160 –C37, respectively, are indicated. Adopted from Kinderman et al. (2006). See text for details.

Ile638, Ala639; Ile642, Val643; and Val646, Met647), while there are only two hydrophobic binding pockets in RIIa interacting with pairs of hydrophobic amino acids from two turns of the D-AKAP2 peptide (Ile638, Ala639 and Ile642, Val643). This observation corresponds to earlier results which predicted that a longer sequence within RIIBDs interacts with the RIa D/D domain compared to RIIa. This is probably one reason why RIa subunits interact with only a few AKAPs. An important factor limiting sequence recognition by RIa is the already mentioned presence of disulfide bridges. They reduce the flexibility of the dimer and restrict access to the AKAP-binding site in RIa compared to RIIa. Based on the available structures of the D-AKAP2 peptide with the RI and RII D/D domains, Sarma et al. (2010) developed an approach to determine specificity for other AKAP sequences by using a combination of sequence alignment and projection of the sequence onto the available structures. This was, for example, used to analyze the determinants for isoform specificity of the peptides RI-anchoring disruptor (RIAD) (Carlson et al., 2006) and AKAP-IS (Gold et al., 2006). RIAD and AKAP-IS were developed as isoform-selective R-binding peptides (see Section 5.2). The apparent RI specificity of RIAD is based on amino acids that probably sterically interfere with RII binding while the core

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binding region seems to fit to both RI and RII D/D domains. AKAP-IS on the other hand is RII-preferring due to one aliphatic residue that does not fit into the first core binding pocket of RIa (Sarma et al., 2010). It was also recently shown that several dual-specific AKAPs have an additional PKA binding determinant termed RI specifier region (RISR) located outside the common amphipathic helix motif (Jarnaess et al., 2008). Within this region, basic residues are essential for RI binding. RISRcontaining peptides bind specifically to RI subunits but not to RII subunits (Jarnaess et al., 2008).

3.3. Diversity within the AKAP family According to the wide array of local PKA functions, AKAPs are present in virtually any tissue and cell type and they can occupy most organelles like the plasma membrane, the cytoskeleton, mitochondria, the Golgi apparatus, the nucleus, or vesicular structures (Wong and Scott, 2004). Table 5.3 summarizes the canonical AKAPs (i.e., proteins interacting with the D/D domain of PKA R subunits dimers via an amphipathic helix), their alternative names, expression patterns, localizations, protein–protein interactions, and selected functions. Not all AKAPs bind to the D/D domain via an amphipathic helix. These ‘‘noncanonical AKAPs’’ include proteins such as pericentrin, whose PKAbinding domain is a longer (100 amino acids) nonhelical, leucine-rich region that interacts with RII (Diviani et al., 2000). Table 5.4 summarizes noncanonical AKAPs. Only a few noncanonical AKAPs are known, probably because they are often missed in screens for new AKAPs, in particular, when these are conducted with the D/D domain and not full-length R subunits as bait (Goehring et al., 2007). Or they may be classified as false positive hits when their binding to PKA is not diminished by PKA-anchoring disruptor peptides (Hundsrucker et al., 2010). Thus it is likely that a number of unidentified noncanonical AKAPs add to the regulation of PKA. Table 5.4 contains two proteins termed ‘‘putative AKAPs’’ as their mode of binding to R subunits has not yet been determined.

3.4. The evolution of AKAPs The majority of identified AKAPs are from human or rodent origin (see Tables 5.3 and 5.4). Orthologs of several mammalian AKAPs were also found in lower vertebrates. In Xenopus, AKAP12 (X-gravin like; Isoldi et al., 2010; Klingbeil et al., 2001), Rab32 (Park et al., 2007), and Moesin (Semenova et al., 2009) have been described. Danio rerio homologs of DAKAP2, MTG8, MyRIP (Goehring et al., 2007), and Myospryn (Reynolds et al., 2007) have been identified as PKA-binding proteins. Additional database entries indicate the existence of further lower vertebrate AKAPs

Table 5.3

AKAPs

Canonical AKAP (HGNC name)

D-AKAP1 AKAP140 AKAP149 AKAP121 AKAP84 S-AKAP84 (AKAP1)

Consensus RIIBDa/R specificity

Yes/dual

Tissue expression pattern

Cellular localization

Ubiquitous, high expression in testis, thyroid, oocytes

Outer mitochondrial membrane, inner mitochondrial compartment, ER, nuclear envelope, sperm midpiece

Interactions

AMY-1 AAT-1 PP1 CaN/PP2B PTPD1 Lamin B PDE4A HIV-1 RT RNA

Functions and properties (references)

Multiple splice variants; contains RNA-binding KH-Tudor domain; regulates nuclear envelope integrity via lamin phosphorylation status; binds HIV-1 reverse transcriptase and is involved in reverse transcription (Abrenica et al., 2009; Asirvatham et al., 2004; Chen et al., 1997; Furusawa et al., 2001, 2002; Ginsberg et al., 2003; Huang et al., 1997a, 1999; Lemay et al., 2008; Lin et al., 1995; Newhall et al., 2006; Rogne et al., 2009; Sardanelli et al., 2006; Steen and Collas, 2001; Steen et al., 2000, 2003; Trendelenburg et al., 1996; Yukitake et al., 2002) (continued)

Table 5.3 (continued) Canonical AKAP (HGNC name)




Interactions

AKAP-KL (AKAP2)

Yes/dual

Kidney, lung, thymus, cerebellum, heart

Actin cytoskeleton/ apical membrane of epithelial cells

AKAP110 Fibrousheathin-1 FSP95 (AKAP3) AKAP82 FSC1 (AKAP4)

No/dual

Testis

Acrosome, fibrous sheath of sperm tail

Ga13 PDE4A AKAP4

No/dual

Testis

Fibrous sheath of sperm tail

AKAP3 FSIP1 FSIP2

Yes/RII

Ubiquitous, high expression in brain

Plasma membrane, postsynaptic densities

CaN/PP2B Epac-1 IQGAP1 PKB/Akt

AKAP79 (human) AKAP75 (bovine) AKAP150 (murine) (AKAP5)


Multiple splice variants; disruption of the AKAP2 gene might play a role in Kallmann syndrome (Dong et al., 1998; Panza et al., 2007; Scholten et al., 2006) Structural sperm protein (Bajpai et al., 2006; Mandal et al., 1999; Niu et al., 2001; Vijayaraghavan et al., 1999) Multiple splice variants; most abundant protein of fibrous sheath; marker for multiple myeloma (Brown et al., 2003a; Carrera et al., 1994; Chiriva-Internati et al., 2008; Miki and Eddy, 1998; Miki et al., 2002) Targeted to plasma membrane via polybasic sequence; regulates multiple ion channels and receptors;

PKC AC5/AC6 AC8 NMDA receptor AMPA receptor mGluR1/5 b1-AR, b2-AR TRPV1/4 channel TREK-1 channel KCNQ2 channel L-type Ca2þ channel ASIC1/2a PSD-95 SAP97

mediates feedback inhibition of AC5/6 by PKA; required for recycling of b1-AR (Bauman et al., 2006; Brandon et al., 2003; Bregman et al., 1989; Carr et al., 1992a; Chai et al., 2007; Coghlan et al., 1995; Colledge et al., 2000; Dart and Leyland, 2001; Dell’Acqua et al., 1998, 2002; Fan et al., 2009; Fraser et al., 2000; Gao et al., 1997a; Gardner et al., 2006; Glantz et al., 1992; Gomez et al., 2002; Hall et al., 2007; Higashida et al., 2005; Hirsch et al., 1992; Hoshi et al., 2003, 2010; Jeske et al., 2008; Kashishian et al., 1998; Klauck et al., 1996; Lu et al., 2008; Lynch et al., 2005; Nauert et al., 2003; Nijholt et al., 2008; Oliveria et al., 2007; Sandoz et al., 2006; Tunquist et al., 2008; Willoughby et al., 2010; Zhang et al., 2008) (continued)

Table 5.3

(continued)


mAKAP AKAP 100 (AKAP6)


Yes/RII



Heart, brain, skeletal muscle

Nuclear envelope, sarcoplasmic reticulum

Interactions

RyR2 PDE4A PDE4D3 nesprin-1a Epac1 (via PDE4D3) ERK5 (via PDE4D3) PP2A PP3 AC2, AC5 NCX HIF-1a VHL PDK-1 Siah2


Spectrin repeat domains target mAKAP to nesprin in the nuclear envelope; potentiates PKA phosphorylation/ activation of RyR; mediates feedback inhibition of AC5 by PKA; conveys ERK5induced cardiac hypertrophy (Carlisle Michel et al., 2005; Dodge et al., 2001; DodgeKafka et al., 2005; Kapiloff et al., 1999, 2001, 2009; Marx et al., 2000; McCartney et al., 1995; Pare et al., 2005a; Schulze et al., 2003; Wong et al., 2008; Zakhary et al., 2000) reviewed in DodgeKafka and Kapiloff (2006)

AKAP18/AKAP15 (AKAP7) Isoform a

Yes

Heart, brain, lung, pancreas, kidney

Plasma membrane

Brain sodium channel (a-subunit) ENaC PKCa L-type Ca2þ channel

Isoform b

Yes

Kidney, brain

Plasma membrane

Isoform g

Yes/dual

Heart, brain, placenta, lung, pancreas

Cytosol, nucleus

Isoform d

Yes

Heart, kidney inner medulla

Cytosol, sarcoplasmic PDE4D3 reticulum, Secretory Phospholamban vesicles

Lipid anchored to plasma membrane; inhibits ENaC sodium channel by recruiting PKCa; interacts with L-type Ca2þ channel via leucine zipper and mediates its PKA phosphorylation; enhances glucose-stimulated insulin release (Bengrine et al., 2007; Fraser et al., 1998; Hulme et al., 2003; Josefsen et al., 2010; Tibbs et al., 1998; Trotter et al., 1999) Lipid-anchored to plasma membrane; function unknown (Trotter et al., 1999) Inhibits glucose-stimulated insulin release (Brown et al., 2003b; Josefsen et al., 2010; Trotter et al., 1999) High-affinity AKAP; involved in AVP-induced AQP2 shuttle in the renal inner medulla; regulates cardiac Ca2þ uptake into SR by mediating PKA (continued)





Interactions

AKAP95 (AKAP8)

Yes/RII

Ubiquitous

Nuclear matrix

PDE4A hCAP-D2/Eg7 fidgetin DDX-5 AMY-1 MCM2 Cyclin D3 Caspase 3

AKAP350 AKAP450 AKAP120 CG-NAP Hyperion (AKAP9)

Yes/RII

Ubiquitous

Centrosomes (most cell types), golgi (epithelial cells),

FBP17 CIP4 PKN PKCe PP1 PP2A CLIC GCP2/3


phosphorylation of phospholamban; AKAP18d peptides used to disrupt PKA anchoring (Henn et al., 2004; Hundsrucker et al., 2006a,b; Lygren et al., 2007; Stefan et al., 2007) Involved in chromatin condensation; (Akileswaran et al., 2001; Arsenijevic et al., 2004; Asirvatham et al., 2004; Coghlan et al., 1994; Collas et al., 1999; Eide et al., 1998, 2003; Furusawa et al., 2002; Kamada et al., 2005; Yang et al., 2006; Yun et al., 1998) Regulates microtubule dynamics; targeted to centrosomes via PACT domain (Dransfield et al., 1997a; Eide et al., 1998; Gillingham and Munro, 2000; Kim et al., 2007; Larocca et al., 2004, 2006;

Isoform Yotiao (AKAP9)

Yes

Brain, heart, placenta, skeletal muscle, pancreas, testis

Plasma membrane (postsynaptic density, neuromuscular junction)

NMDA receptor PP1 IP3R1 AC1/2/3/9 PDE4D3 IKS (subunit KCNQ1)

Schmidt et al., 1999; Shanks et al., 2002; Sillibourne et al., 2002; Takahashi et al., 1999, 2000; Witczak et al., 1999) Interacts with membrane proteins via leucine-zipper motif; regulates cardiac IKS Kþ channel currents; Yotiao mutation S1570L in KCNQ1-binding site causes long QT syndrome; Modulates NMDA receptor currents by recruiting PKA and PP1; facilitates bradykinin-induced PKA phosphorylation of IP3R1; mediates phosphorylation of AC by PKA (Chen et al., 2005, 2007; Feliciello et al., 1999; Hur et al., 2005; Lin et al., 1998; Piggott et al., 2008; Terrenoire et al., 2009; Tu et al., 2004; Westphal et al., 1999) (continued)

Table 5.3 (continued) Consensus RIIBDa/R specificity



D-AKAP2 (AKAP10)

Yes/dual

Ubiquitous

Mitochondria, cytosol, endosomes

AKAP220 (AKAP11)

Yes/dual

Testis, brain, kidney

Vesicles, peroxisomes, centrosome


Interactions


Contains two RGS domains and a PDZ-binding motif; I646V SNP increases RIbinding, causes arrhythmia and increases the risk of sudden cardiac death, colorectal cancer and familial breast cancer formation; regulates transferrrin receptor recycling; (Eggers et al., 2009; Gisler et al., 2003; Huang et al., 1997b; Kammerer et al., 2003; Neumann et al., 2009; Tingley et al., 2007; Wang et al., 2001, 2009; Wirtenberger et al., 2007) PP1 Mediates GABAc-dependent GSK3b PKA activation; GABAc receptor overexpressed in oral squamous cell carcinomas; AQP2 colocalizes with AQP2, PDZK1 Rab4 Rab11

Gravin (human) AKAP250 SSeCKS (Srcsuppressed C kinase substrate, rodent) (AKAP12)

Yes

Ubiquitous except for liver

Cytosol/Actin cytoskeleton

PKC b2-AR Calmodulin CaN/PP2B cyclin D

might be involved in PKA phosphorylation of AQP2; AKAP220 facilitates GSK3b phosphorylation by inhibiting PP1 and recruiting PKA (Garnis et al., 2005; Lester et al., 1996; Okutsu et al., 2008; Reinton et al., 2000; Schillace and Scott, 1999; Schillace et al., 2001; Tanji et al., 2002; Yang et al., 2008) Autoantigen in myasthenia gravis; involved in cell cycle regulation; regulates cytoskeletal architecture and migratory processes; tumor suppressor protein, expression reduced in many tumors (Akakura et al., 2008; Fan et al., 2001; Gelman et al., 1998, 2000; Gordon et al., 1992; Grove et al., 1994; Lin and Gelman, 2002; Lin et al., 1996; Liu et al., 2006b; Nauert et al., 1997; Streb et al., 2004) reviewed in (Gelman 2002) (continued)

Table 5.3 (continued) Consensus RIIBDa/R specificity



AKAP-Lbc Ht31 Rt31 Brx-1 (AKAP13)

Yes

Ubiquitous

Cytosol/Actin cytoskeleton

AKAP28 (human) TAKAP80 (rat) (AKAP14)

Yes

Lung (AKAP28), testis (TAKAP80)


Interactions


RhoA Ht31 peptide derived from PKD RIIBD used to disrupt PKA PKC anchoring; mediates Ga12 catecholamine-induced cardiac hypertrophy; Rho14-3-3 GEF activated by Ga12; CEstrogen receptor terminal truncation increases alpha Rho-GEF activity in LC3 oncogenic Lbc; induces stress a-catulin fiber formation in a Rhodependent manner (AppertCollin et al., 2007; Baisamy et al., 2005, 2009; Carnegie et al., 2004, 2008 Carr et al., 1992b; Diviani et al., 2001; Diviani et al., 2004; Klussmann et al., 2001; Rubino et al., 1998; Sterpetti et al., 1999) reviewed in Diviani et al. (2006) Probably involved in the Cilia of airway epithelia regulation of ciliar/flagellar (AKAP28), fibrous beat (Kultgen et al., 2002; sheath of sperm tail Mei et al., 1997) (TAKAP80)

GSKIP C14ORF129 HSPC210

Yes/RIIa/b Ubiquitous

Cytosol

GSK3b

MyRIP SlaC2-c

Yes

Ubiquitous

Secretory vesicles, actin Rab27a cytoskeleton, retinal actin myosin Va melanosomes myosinVIIa

Rab32

Yes

Ubiquitous

Mitochondria, melanosomes

Varp

SKIP SPHKAP

Yes

Heart, brain, ovary, spleen

Cytosol

SPHK-1

NMR structure solved (PDB: 1SGO), mediates inhibitory phosphorylation of GSK3b Ser-9 by PKA (Chou et al., 2006; Hundsrucker et al., 2010) Involved in retinal melanosome and insulin secretion; required for exocytosis of pathogenic E. coli (ElAmraoui et al., 2002; Fukuda and Kuroda, 2002; Goehring et al., 2007; Lopes et al., 2007; Song et al., 2009) Involved in mitochondrial fission (Alto et al., 2002; Bao et al., 2002; Park et al., 2007; Tamura et al., 2009; Wasmeier et al., 2006) Antiproliferative due to inhibition of sphingosine kinase-1 (SPHK-1) activity (Lacana et al., 2002; Scholten et al., 2006) (continued)

Table 5.3

(continued)


BIG2 (brefeldin Ainhibited guanine nucleotideexchange protein 2) ARFGEF2 ARFGEP2 Ezrin AKAP78 Villin-2




Interactions


Yes/dual, 3 Placenta, lung, heart, brain, kidney, PKAbinding sites pancreas

Golgi, cytosol

FKBP13 GEF for ADP-ribosylation GABAA receptor factors; involved in GABAA receptor trafficking (Charych PDE3A et al., 2004; Kuroda et al., PP1g 2007; Li et al., 2003; Puxeddu et al., 2009)

No/dual

Actin cytoskeleton

EBP50/NHERF Interacts with CFTR via NHERF2, complex NHERF2 enhances PKA activation of CD43 CFTR; mediates inhibition CD44 of T cell immune functions ICAM-1 by PKA (Bonilha et al., 2006; ICAM-2 Dransfield et al., 1997b; Actin Gronholm et al., 1999; FAK Heiska et al., 1998; Merlin Koltzscher et al., 2003; S100P Poullet et al., 2001; Reczek and Bretscher, 1998; Ruppelt et al., 2007; Saotome et al., 2004; Stokka et al., 2010; Sun et al., 2000a,b; Takahashi et al., 1997; Tamura et al., 2005; Yao et al., 1996; Yonemura et al., 1998; Yun et al., 1998)

Blood cells, placenta, secretory epithelia, brain

WAVE-1 Scar

No

Brain, platelets, liver

Actin cytoskeleton, mitochondria

MAP2 Isoforms a, b, c, d

No

Brain, ovaries

Microtubules

Regulates actin cytoskeleton Actin dynamics; regulates apoptosis WRP and glycolysis (Danial et al., Arp2/3 2003; Eden et al., 2002; Kang Rac et al., 2010; Machesky and Abl Insall, 1998; Miki et al., 1998; Abi-1/2 Oda et al., 2005; Rawe et al., Bad 2004a,b; Soderling et al., Glucokinase 2002, 2003, 2007; Westphal PP1 et al., 2000) Bcl-2 Tubulin Involved in neuritogenesis, Actin synapse formation and Grb-2 dendrite remodeling; PKA Src phosphorylation of MAP2 Fyn decreases tubulin and Myosin VIIa increases actin binding; (MAP2b) MAP2D mediates NEFL phosphorylation of GSK3b GSK3b (MAP2D) by PKA (Davare et al., 1999; PP2A Flynn et al., 2008; Frappier L-type Ca2þ et al., 1991; Hall et al., 2007; Harada et al., 2002; Khuchua channel et al., 2003; Kim et al., 1979; Lim and Halpain, 2000; Obar et al., 1989; Ozer and Halpain, 2000; Roger et al., 2004; Salvador et al., 2004; Teng et al., 2001; Todorov (continued)

Table 5.3

(continued)





Neurobeachin

No/RIIa, RIIb

Ubiquitous, high in brain

Golgi, postsynaptic plasma membrane

MTG8 RUNX1T1

No

Brain, lymphocytes

Golgi

Myosin VIIA

No/RIa

Ubiquitous

Actin cytoskeleton

Interactions

Actin MyRIP Calmodulin MAP2b


et al., 2001; Zamora-Leon et al., 2001) Contains WD40 and BEACH domains; essential for evoked transmission at neuromuscular junctions; gene disruption can cause autism (Castermans et al., 2003; Medrihan et al., 2009; Su et al., 2004; Wang et al., 2000) A reciprocal chromosomal translocation t(8;21)(q22; q22), resulting in an MTG8/ AML-1 fusion gene causes acute myeloid leukemia (AML-M2; Fukuyama et al., 2001; Miyoshi et al., 1993) (Kussel-Andermann et al., 2000; Todorov et al., 2001)

MTG16b CBFA2T3 ZMYND4

No/dual

Ubiquitous

Golgi

Synemin desmuslin

No

Muscle (skeletal, heart, Intermediate filaments, a-dystrobrevin smooth) Z-discs Desmin vimentin

PAP7

No/dual

Ubiquitous

Myospryn

No/3 PKA- Muscle (skeletal, heart) Z-discs binding sites RIIa

Mitochondria

PDE4A PDE7A Plexin

PBR

a-actinin Dysbindin Desmin Dystrophin

Chromosomal translocation t (16;21)(q24;q22) results in an MTG16b/AML-1 fusion gene, causing acute myeloid leukemia (AML-M1 or -M2; Asirvatham et al., 2004; Fiedler et al., 2010; Gamou et al., 1998; Schillace et al., 2002) Overexpressed in failing hearts; possibly involved in cytoskeletal remodeling during cardiac hypertrophy and failure (Granger and Lazarides, 1980; Mizuno et al., 2001; Russell et al., 2006) Involved in regulation of cholesterol transport and steroid synthesis (Li et al., 2001a; Liu et al., 2003, 2006a) Member of tripartite motif (TRIM) superfamily; dysregulated PKA signaling due to reduced myospryn expression contributes to the pathogenesis of Duchenne (continued)





Interactions

SFRS17A XE7

No/dual

Ubiquitous

Nucleus (splicing factor ASF/SF2 compartments) ZNF265

Merlin schwannomin Neurofibromin 2

No/RI

CNS

Actin cytoskeleton, adherens junctions

Spectrin b chain, brain1 Ezrin

Moesin

No

Blood, epithelia, Xenopus melanophores

Cytoplasm, plasma membranecytoskeleton interface, Pigment granules

ICAM1 ICAM3 CD43 VCAM-1


muscular dystrophy; K2906N polymorphism associated with cardiac left ventricular hypertrophy (Durham et al., 2006; Kouloumenta et al., 2007; Nakagami et al., 2007; Reynolds et al., 2007, 2008) reviewed in Sarparanta (2008) Involved in pre-mRNA splicing ( Jarnaess et al., 2009; Mangs et al., 2006) Tumor suppressor, mutation causes neurofibromatosis type 2 (Golovnina et al., 2005; Gronholm et al., 1999, 2003) Facilitates actin-dependent transport of pigment granules; marker protein for basal carcinomas (CharafeJauffret et al., 2007; Dransfield et al., 1997b; Semenova et al., 2009; Serrador et al., 1997; Shcherbina et al., 1999)

AKAP85 Not cloned Radial spoke protein No/RII 3 homolog (RSPH3)

Lymphocytes Epithelial cells

Golgi Motile cilia

Drosophila melanogaster AKAP550 DAKAP550 rugose neurobeachin D. melanogaster AKAP200 DAKAP200

Ubiquitous Yes/ 2 canonical RIIBDs

Plasma membrane/ cytosol

No

Oocytes, olfactory neurons

Plasma membrane

D. melanogaster AKAP Yu

No

Mushroom bodies in brain

ERK1/2

F-actin Ca2þ/ calmodulin

Rios et al. (1992) Interacts with ERK1/2, ERK phosphorylation of RSPH3 reduces RII binding (Jivan et al., 2009) Required for retinal pattern formation; contains WD40 and BEACH domains (Han et al., 1997; Shamloula et al., 2002) MARCKS-like protein; PKC substrate; regulates actin structures during oogenesis; targeted to the plasma membrane by myristoylation; involved in olfactory map formation ( Jackson and Berg, 2002; Li et al., 1999; Rossi et al., 1999; Zhang et al., 2006) Required for olfactory longterm memory formation; homolog of AKAP1; molecular determinants of PKA binding not characterized (Lu et al., 2007a) (continued)


D. melanogaster Nervy



No

Neurons


Plexin A

Caenorhabditis elegans No/RI AKAPCE

No Chlamydomonas reinhardtii Radial spoke protein 3 (RSP3) AKAP97 a

Unicellular organism

Interactions

Flagellar axonemes


Involved in axon guidance, required for PKA regulation of Semaphorin-1a-mediated axon repulsion; homolog of MTG8/16 (Terman and Kolodkin, 2004) Contains an FYVE-finger and a TGFb receptor-binding domain (Angelo and Rubin, 1998, 2000; Herrgard et al., 2000) Located at the base of the radial spoke stalk, important for flagellar movement (Gaillard et al., 2001, 2006)

Consensus RII-binding domain sequence [AVLISE]-X-X-[AVLIF]-[AVLI]-X-X-[AVLI]-[AVLIF]-X-X-[AVLISE] (X ¼ any amino acid) according to Hundsrucker et al. (2010).

Table 5.4 Noncanonical AKAPs that do not interact with R subunits via an amphipathic helix and putative AKAPs with uncharacterized PKA binding mechanism

Noncanonical AKAP

Putative AKAP

Tissue expression R specificity pattern

Ubiquitous, Centrosomes high in skeletal muscle

Pericentrin kendrin

RSK1 (inactive) p90 ribosomal S6 kinase-1 (p90RSK-1) MAPKAP1A

Localization

a/b-tubulin

Actin

RI

Ubiquitous Cytosol Nucleus

RI

Ubiquitous Cytosol

Interactions Functions and properties (references)

g-tubulin PKCbII dynein PCM1 AKAP350

Ubiquitous Actin Myosin cytoskeleton

Unique RII-binding site, a 100-amino acid leucinerich region interacts with RII; targeted to centrosomes via PACT domain; regulates centrosome function, cell cycle checkpoints, spindle formation, cytokinesis, pericentrin mutations cause primordial dwarfism (Chen et al., 2004; Delaval and Doxsey, 2010; Diviani et al., 2000; Doxsey et al., 1994; Eide et al., 1998; Gillingham and Munro, 2000; Li et al., 2001b; Rauch et al., 2008; Takahashi et al., 2002) reviewed in Delaval and Doxsey (2010) Inactive RSK1 binds RI and facilitates C subunit release, active RSK1 binds C subunits and promotes PKA holoenzyme formation; RSK1/RI interaction insensitive to AKAP–PKA disruptor Ht31 (Chaturvedi et al., 2006; Frodin and Gammeltoft, 1999; Gao and Patel, 2009; Gao et al., 2010) Molecular determinants of PKA binding not characterized; SDS-stable tubulin/RI complex (Kurosu et al., 2009) Putative noncanonical AKAP; disruption of canonical AKAP–PKA interactions does not abolish actin/ RII-colocalization; direct interaction with PKA not shown (Rivard et al., 2009)

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whose PKA binding has not yet been experimentally validated. In this context it is noteworthy that all vertebrate orthologs of the human AKAP GSKIP bind RII, suggesting a conservation of its AKAP function within vertebrates. Apparently, none of the invertebrate or fungal GSKIP orthologs bind RII, indicating that the ability of GSKIP to anchor PKA was gained with vertebrate evolution (Hundsrucker et al., 2010). Invertebrate AKAPs were identified in the nematode Caenorhabditis elegans and the fruit fly D. melanogaster. The only known AKAP in C. elegans is AKAPCE (Angelo and Rubin, 1998, 2000). AKAPCE binds human RI but not RII subunits and is thus prototypical for RI-specific AKAPs (Angelo and Rubin, 2000). C. elegans only expresses one regulatory isoform of PKA, RCE, which is homologous to human RIa (Angelo and Rubin, 1998). The known Drosophila AKAPs are AKAP550, AKAP200, AKAP Yu, and Nervy (Table 5.3). AKAP550, a protein with two consensus RIIBDs both able to bind Drosophila and human RII subunits (Han et al., 1997; Hundsrucker et al., 2010), is a homolog of the human AKAP neurobeachin (Han et al., 1997; Wang et al., 2000). Surprisingly, the respective RIIBDs are not located in homologous regions of the proteins and are possibly of different evolutionary origin. Drosophila Nervy is homologous to the human AKAPs MTG8 and MTG16b (Terman and Kolodkin, 2004). The Nervy mutation Val423Pro disrupts RII binding, as does the corresponding mutation Val408Pro in human MTG16b, indicating that Nervy and MTGs bind PKA with the same motif (Schillace et al., 2002; Terman and Kolodkin, 2004). In addition, both Nervy and MTG16b interact with and control Plexin, a protein involved in axon guidance and immunological synapse formation (Fiedler et al., 2010; Terman and Kolodkin, 2004). Another neuronal process in Drosophila involves AKAP Yu. It is required for olfactory long-term memory formation (Lu et al., 2007a). Apparently other AKAPs fulfill similar functions in other organisms: A yet unidentified AKAP is necessary for synaptic plasticity in the sea hare (Aplysia californica; Liu et al., 2004a) and many mammalian AKAPs are involved in synaptic function, most importantly AKAP79/150 (Bauman et al., 2004). In Chlamydomonas reinhardtii, a unicellular green alga with two motile flagella, radial spoke protein 3 (RSP3) is the only established nonanimal AKAP (Gaillard et al., 2001). PKA anchoring to flagella by RSP3 is required for normal flagellar motility (Gaillard et al., 2006). The human ortholog of RSP3, RSPH3 was also shown to bind RII and, in addition, ERK1/ 2 ( Jivan et al., 2009). Its function is not clear but its presence in motile cilia of epithelial cells also implies a regulatory role in ciliar motility. In summary, the knowledge of invertebrate AKAPs is still scarce. It is likely that vertebrates express a higher number of AKAPs than invertebrates, contributing to the coordination of the signaling processes which are essential for their specialized cell functions and complex communication systems between cells in a multicellular organism. The examples given here


275

illustrate that the mechanism of PKA anchoring is similar in AKAPs from evolutionarily early species to vertebrates and that there are common processes in which AKAPs seem to be indispensible. These are, for example, flagellar/ciliar movement or synaptic processes.

4. Cellular Functions Regulated by AKAP-Anchored PKA AKAPs regulate important functions in every human cell. Prime examples are synaptic plasticity (Dell’Acqua et al., 2006), sperm motility (Carr and Newell, 2007), T-cell immune responses (Torgersen et al., 2008) and several exocytic processes (Szaszak et al., 2008). As an example for the ability of AKAPs to integrate cellular signaling, we will outline the link between PKA and GSK3b established by several AKAPs and other scaffolding proteins in the following section. In following sections, we will focus on the role of various AKAPs in the control of cardiac myocyte contractility (Section 4.2) and renal vasopressin-mediated reabsorption (Section 4.3).

4.1. AKAPs, PKA, and GSK3b Glycogen synthase kinase 3 (GSK3) is a serine/threonine protein kinase involved in many cellular processes including glycogen metabolism, proliferation, and differentiation. Evolutionary highly conserved orthologs of GSK3 with sequence identities >50% are found in all eukaryotes. In mammals, there are two ubiquitously expressed isoforms of GSK3 encoded by distinct genes: GSK3a (51 kDa) and GSK3b (47 kDa), which share 83% sequence identity. Their functions are partially redundant but GSK3b has been much better characterized (Ali et al., 2001; Doble and Woodgett, 2003; Forde and Dale, 2007). Two features determine a close functional connection of GSK3b to other protein kinases: (1) GSK3b is constitutively active and the main mechanism of its regulation is an inhibitory phosphorylation of its N-terminus by other kinases. (2) Many GSK3b substrates need to be primed, that is, prephosphorylated by another kinase. Here, we outline on the involvement of PKA and AKAPs in GSK3b function, first in the context of phosphorylation of the GSK3b N-terminus, then with regard to primed GSK3b substrates. 4.1.1. AKAPs control the inhibition of GSK3b Several kinases catalyze the inhibitory phosphorylation of GSK3b at Ser9: PKA, PKB, p70 ribosomal S6 kinase (p70RSK), RSK1, PKG (Zhao et al., 2005), Serum/glucocorticoid-regulated kinase 1 (SGK1; Sakoda et al., 2003), integrin-linked kinase (ILK; Persad et al., 2001), and different PKC isoforms (Fang et al., 2002). The fact that all of the abovementioned

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kinases phosphorylate the same residue, the lack of specific kinase inhibitors and the cross talk between the different kinases complicate the unequivocal identification of GSK3b-phosphorylating kinases in a specific cellular context. Various physiological agonists have been described to mediate a PKAdependent phosphorylation of GSK3b, such as adrenaline ( Jensen et al., 2007), prostaglandin E2 (PGE2; Fujino et al., 2002), endothelin 1 (Taurin et al., 2007), Wnt5a (Torii et al., 2008), parathyroid hormone (Suzuki et al., 2008), glucagon-like peptide-1 ( Juhaszova et al., 2004) and -2 (Yusta et al., 2002), human chorionic gonadotropin (hCG; Flynn et al., 2008), corticotropin-releasing hormone (Bayatti et al., 2003), and basic fibroblast growth factor (O’Driscoll et al., 2007). The PKA-dependent GSK3b phosphorylation triggered by these agents is in several instances facilitated by AKAPs. The involvement of an unidentified AKAP has been demonstrated for PGE2-induced and PKA-dependent GSK3b phosphorylation. It is abolished by the AKAP–PKA-anchoring disruptor peptide Ht31 (Kleiveland et al., 2008). For hCG-induced GSK3b phosphorylation, the relevant AKAP has been identified as MAP2D. In resting preovulatory granulosa cells, MAP2D interacts with type II PKA, GSK3b, and PP2A. In this complex, the constitutively active GSK3b phosphorylates MAP2D on Thr256 and Thr259 (Flynn et al., 2008). Stimulation of the luteinizing hormone receptor by hCG activates the Gas/AC system and cAMP levels rise. cAMP activates PKA, which catalyzes an inhibitory phosphorylation of GSK3b on Ser9 and an activating phosphorylation of PP2A in the MAP2D complex (Fig. 5.6). This simultaneous inactivation of GSK3b and activation of PP2A leads to a dephosphorylation of MAP2D on Thr256 and Thr259 which increases microtubule binding of MAP2D and may affect microtubule dynamics (Flynn et al., 2008). In addition to MAP2D, two further AKAPs interact with GSK3b and promote its PKA-dependent phosphorylation: AKAP220 and GSKIP (Hundsrucker et al., 2010; Tanji et al., 2002). AKAP220 interacts with RIIa subunits of PKA, GSK3b, and PP1 in rat PC12 cells, which has a dual effect on GSK3b (Fig. 5.6). AKAP220 facilitates PKA phosphorylation of GSK3b Ser9 and suppresses dephosphorylation of the same residue by inhibiting PP1 (Tanji et al., 2002). The inhibition of PP1 is enhanced by the presence of RII in the complex (Schillace et al., 2001). GSKIP has originally been described as an interaction partner of GSK3b (Chou et al., 2006). Recently, it was revealed that GSKIP is an AKAP and that PKA anchoring by GSKIP facilitates PKA phosphorylation of GSK3b at Ser9 (Fig. 5.6; Hundsrucker et al., 2010). In addition, the direct binding of GSKIP inhibits GSK3b (Chou et al., 2006). GSKIP enhances PKA phosphorylation of GSK3b on Ser9 independently of the AKAP function, probably by altering the conformation of GSK3b (Hundsrucker et al., 2010). The classical view is that AKAPs merely provide docking sites for proteins and thereby control their localization. The induction of

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A AKAP complexes inhibit GSK3b PKA inactive C C

hCG C

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B Priming of GSK3b substrates by PKA 1. C PKA

b-catenin P P P

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PKA PS1

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R

Figure 5.6 PKA–GSK3b complexes formed by AKAPs (A) and other scaffold proteins (B). (A) AKAPs facilitate inhibition of GSK3b. (B) Other scaffolding proteins mediate the priming of GSK3b substrates by PKA (see text for details).

conformational changes by binding of proteins to AKAPs (or scaffolding proteins in general) is poorly understood. AKAP binding could change the properties of interacting proteins. AKAP220, for example, inhibits PP1 (see above) and it was recently demonstrated that the interaction with AKAP79 protects PKC from inhibition by certain ATP-competitive inhibitors (Hoshi et al., 2010). Thus modulation of proteins through interaction with AKAPs or other scaffolding proteins could contribute to the regulation of signal transduction processes. 4.1.2. PKA as a priming kinase for GSK3b GSK3b can phosphorylate serine or threonine residues in two types of substrates. Primed substrates need to be phosphorylated 4 amino acids C-terminal of the residue phosphorylated by GSK3b within the consensus

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site S/T-X-X-X-SP/TP (Fiol et al., 1987). In unprimed substrates, negative residues mimic a phosphorylation of the priming site. PKA can function as a priming kinase for GSK3b, as was demonstrated for the substrate proteins b-catenin (Kang et al., 2002), Gli/cubitus interruptus (Ci; Jia et al., 2002), tau (Liu et al., 2004b), and CREB (Fiol et al., 1994). In the canonical Wnt signaling pathway, casein kinase I (CK1) acts as the priming kinase for GSK3b by phosphorylating Ser45 on b-catenin. This enables GSK3b to phosphorylate Thr41 and subsequently Ser37/33 of b-catenin. These consecutive phosphorylations induce proteasomal degradation of b-catenin (Liu et al., 2002). An analogous pathway for b-catenin degradation, which is not regulated by Wnt signaling, involves presenilin 1 (PS1). PS1 forms a complex containing PKA, GSK3b, and b-catenin, in which PS1 facilitates PKA priming, GSK3b phosphorylation, and proteasomal targeting of b-catenin (Fig. 5.6; Kang et al., 2002). PS1 interacts directly with b-catenin (Murayama et al., 1998) and GSK3b (Gantier et al., 2000) but it is unclear whether PS1 interacts with PKA directly. PS1 contains a canonical AKAP consensus sequence (DTETVGQRALHSILNAAIMISVIVV) but the cognate peptide does not bind PKA RII subunits (Hundsrucker et al., 2010). Thus it is conceivable that PS1 is either a noncanonical AKAP or that complex formation with PKA is mediated by another protein. The transcription factor Ci is a central mediator of the hedgehog signaling pathway in Drosophila. In the absence of a hedgehog signal, Ci is phosphorylated by the protein kinases CK1, PKA, and GSK3b, which induces its proteolytic conversion into a transcriptional repressor (Smelkinson et al., 2007). GSK3b phosphorylation of Ci requires priming by PKA (Jia et al., 2002). The scaffolding protein costal-2 (Cos2) binds Ci and its upstream kinases directly, that is, CK1, PKA catalytic subunits and GSK3b (Fig. 5.6; Zhang et al., 2005). Ci phosphorylation depends on the presence of Cos2, demonstrating the importance of this scaffold for the association of substrate and kinases (Aikin et al., 2008). One of the best described PKA substrates is CREB. CREB is phosphorylated by PKA but also by other kinases on S133. This phosphorylation enhances the interaction of CREB with the transcriptional coactivator CREB-binding protein (CBP; Sands and Palmer, 2008). CREB phosphorylated on S133 is also a primed substrate for GSK3b, which then phosphorylates S129 (Fiol et al., 1994). This phosphorylation decreases binding of CREB to CBP (Martin et al., 2005). Thus, GSK3b negatively regulates PKA-induced CREB-dependent transcription (Tullai et al., 2007). In a similar scenario PKA phosphorylates and activates the transcription factor heterogeneous nuclear ribonucleoprotein D (hnRNP D) at Ser87, thereby allowing phosphorylation of Ser83 by GSK3b, which inhibits transcriptional activity (Tolnay et al., 2002). Thus CREB and hnRNP D integrate PKA and GSK3b signaling. Phosphorylation of CREB and hnRNP D only leads to transcriptional activation if phosphorylation by GSK3b is prevented.


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4.2. AKAP-dependent protein–protein interactions in the control of cardiac myocyte contractility Stimulation of b-adrenoceptors enhances the chronotropic (heart rate), inotropic (contraction), and lusitropic (relaxation) response of the heart by triggering cAMP production and activation of PKA subpopulations which regulate Ca2þ-cycling via the L-type Ca2þ channel, the ryanodine receptor 2 (RyR2) and the sarco/endoplasmic reticulum Ca2þ-ATPase 2 (SERCA2; Diviani, 2008; Jurevicius and Fischmeister, 1996; Zaccolo and Pozzan, 2002). For this AKAP–PKA interactions are required (Mauban et al., 2009; Figs. 5.7 and 5.8): AKAP18a anchors PKA to L-type Ca2þ channels (Cav1.2) in the sarcolemma and mAKAP anchors PKA to RyR2 in the membrane of the sarcoplasmic reticulum (SR), facilitating b-adrenoceptortriggered, PKA-catalyzed phosphorylation of the respective channels (Dodge-Kafka et al., 2006; Hulme et al., 2006). Such phosphorylation events increase the channel open probability, resulting in cytosolic Ca2þ increases. AKAP18d binds directly to phospholamban (PLN) at the SR and

Figure 5.7 AKAP complexes in cardiomyocytes. See text for details. ACII, adenylyl cyclase II; ACV, adenylyl cyclase V; b-AR, b-adrenoceptor; C, catalytic subunit; CaNab, calcineurin Ab; Epac, exchange protein activated by cAMP; ERK5, extracellular signal-regulated kinase 5; KCNQ1, IKS Kþ channel; LTCC, L-type Ca2þ channel; MEK5, mitogen signal-regulated kinase kinase 5; NCX1, Naþ–Ca2þ exchanger 1; PDE4D3, phosphodiesterase 4D3; PKA, protein kinase A; PKC, protein kinase C; PKD, protein kinase D; PLN, phospholamban; PP1, protein phosphatase 1; PP2A, protein phosphatase 2A; PP2B, protein phosphatase 2B; R, regulatory subunit; RhoA, Ras homolog gene family member A; RyR, ryanodine receptor; SERCA, sarco/endoplasmic reticulum Ca2þ-ATPase.

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Figure 5.8 Calcium signaling in cardiac myocytes. See text for details. b-AR, b-adrenoceptor; C, catalytic subunit; KCNQ1, IKS Kþ channel; LTCC, L-type Ca2þ channel; NCX1, Naþ–Ca2þ exchanger 1; PLN, phospholamban; R, regulatory subunit; RyR, ryanodine receptor; SERCA, sarco/endoplasmic reticulum Ca2þ-ATPase.

facilitates its b-adrenoceptor-induced PKA phosphorylation. PhosphoPLN dissociates from SERCA2, which is thereby activated. The result is an increased Ca2þ reuptake into the SR (Lygren et al., 2007). The AKAP Yotiao anchors PKA to sarcolemmal Kþ-channels (IKs) and facilitates their b-adrenoceptor/PKA-dependent phosphorylation. Phosphorylation of these channels enhances repolarization of the myocytes (Chen et al., 2005). In addition, PKA phosphorylates several proteins of the contractile apparatus upon b-adrenoceptor activation (e.g., cardiac troponin I (cTnI) and myosin-binding protein C (MyBP-C); Colson et al., 2008; Dong et al., 2007). Collectively, these phosphorylations contribute to an increase of cardiac myocyte contractility upon b-adrenoceptor stimulation (Figs. 5.7 and 5.8). In the failing heart, b-adrenergic signaling is altered and phosphorylation of PKA substrates such as PLN, MyBP-C, and cTnI is reduced (Bodor et al., 1997; El-Armouche et al., 2006; Hasenfuss, 1998; Sipido and Eisner, 2005; Waggoner and Kranias, 2005; Zakhary et al., 2000). Interfering with AKAP–PKA interactions in cardiac myocytes or in hearts in vivo using PKA-anchoring disruptor peptides such as Ht31, abolished the


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phosphorylation of cTnI, RyR2, and MyBP-C and still increased the rate and amplitude of cell shortening and relaxation compared to control cells upon stimulation of b-adrenoceptors (Fink et al., 2001; McConnell et al., 2009). This shows that it is anchored PKA that specifically regulates phosphorylation events and underlines the relevance of compartmentalized PKA signaling in the heart. Here, we point out the roles of AKAP18a and d, AKAP-Lbc, mAKAPb, and Yotiao in cardiac myocyte control (Fig. 5.7; Diviani et al., 2001; Fraser et al., 1998; Gray et al., 1997; Kurokawa et al., 2004; Lygren et al., 2007; Potet et al., 2001; Reynolds et al., 2007; Ruehr et al., 2004). Several further AKAPs were identified in the heart (Table 5.3) but their functions are not clear. For further reading we recommend recent reviews (Diviani, 2008; Dodge-Kafka et al., 2006; Mauban et al., 2009; Ruehr et al., 2004; Scott and Santana, 2010). 4.2.1. AKAP18a The AKAP18 family consists of four isoforms (a, b, g and d) with apparent molecular weights ranging from 15 to 53 kDa (Fraser et al., 1998; Gray et al., 1997, 1998; Henn et al., 2004; McSorley et al., 2006; Trotter et al., 1999). Except for AKAP18b, all isoforms are expressed in cardiac tissue (Henn et al., 2004; Lygren et al., 2007; Trotter et al., 1999) with AKAP18 a and d being of importance in the regulation of cardiac contractility (Fig. 5.8). AKAP18a, the shortest AKAP18 isoform, contains 81 amino acids and is located at the plasma membrane due to palmitoylation and myristoylation of its N-terminus (Fraser et al., 1998; Gray et al., 1997). There it forms a complex with the L-type Ca2þ channel, the major voltage-gated Ca2þ channel in the heart (Burton et al., 1997; Fraser et al., 1998; Gray et al., 1997, 1998; Fig. 5.7). The binding between the two proteins occurs directly via a leucine-zipper motif between amino acids 1774 and 1841 in the C-terminus of the a1 subunit of the Ca2þ channel and amino acids 25–54 of AKAP18a (Hulme et al., 2002). It appears that AKAP18a facilitates the PKA-dependent phosphorylation of the L-type Ca2þ channel since expression of an AKAP18a mutant with defective plasma membrane targeting in HEK293 cells transiently expressing the Ca2þ channel abolishes the increase in Ca2þ currents upon PKA activation (De Jongh et al., 1996; Fraser et al., 1998; Gao et al., 1997a; Gray et al., 1997, 1998; Hulme et al., 2006; Kamp and Hell, 2000). In addition, disruption of the interaction between AKAP18a and the L-type Ca2þ channel using synthetic peptides derived from AKAP18a, or an AKAP18a mutant that cannot bind the L-type Ca2þ channel inhibits the voltage-dependent potentiation in MM14 skeletal myotubes and blocks b-adrenergic regulation of the channel in ventricular myocytes (Fraser et al., 1998; Gao et al., 1997b; Hulme et al., 2002, 2003). b-adrenergic stimulation also fails to induce L-type Ca2þ currents in the

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presence of AKAP18d-derived peptides that displace PKA from the complex (Hulme et al., 2003; Hundsrucker et al., 2006b). Therefore, anchoring of PKA to the L-type Ca2þ channel via AKAP18a allows for a rapid and specific response to changes in cAMP levels upon b-adrenoceptor stimulation (Dodge-Kafka et al., 2006). The exact phosphorylation site responsible for the increased Ca2þ flux through the channel is under controversy. While one study describes serine 1928 of the a1C subunit as the critical phosphorylation site for channel modulation (Gao et al., 1997a), another study revealed that a substitution of this site by an alanine does not abolish the b-adrenergic response (Ganesan et al., 2006). AKAP18a may also interact with PP2B (Sacchetto et al., 2001) and could tether PP2B to L-type Ca2þ channels. PP2B was suggested to play a role in the regulation of the associated L-type Ca2þ channel currents (Sacchetto et al., 2001). Additionally, a direct interaction of AKAP18a with the b2-adrenoceptor and the L-type Ca2þ channel has been identified in the brain (Davare et al., 2001). This complex may also exist in cardiac myocytes and thereby safeguard a tight regulation of b-adrenergic responses. 4.2.2. AKAP18d AKAP18d is the longest isoform of the AKAP18 family (353 amino acids, apparent molecular weight 53 kDa) and plays a major role in the b-adrenoceptor-induced Ca2þ reuptake into the SR of cardiac myocytes (Henn et al., 2004; Lygren et al., 2007; Figs. 5.7 and 5.8). AKAP18d, PKA, SERCA2, and PLN form a complex at the SR in cardiac myocytes (Fig. 5.7). AKAP18d promotes the PKA-dependent phosphorylation of PLN on serine 16 in its cytoplasmic N-terminus, which in turn induces the dissociation of PLN from SERCA2 and SERCA2 activation. The interaction between AKAP18d and PLN is direct and can be disrupted using synthetic peptides derived from PLN’s AKAP18d-binding site. This disruption results in a decreased phosphorylation of PLN, reduced Ca2þ reuptake into the SR and thus reduced lusitropic effects upon b-adrenergic stimulation. Silencing of AKAP18d in cardiac myocytes has a similar effect (Lygren et al., 2007). Based on these data, AKAP18d has been suggested as a potential drug target for the treatment of heart failure (Diviani, 2008; Lygren and Taskeń, 2008). 4.2.3. AKAP-Lbc AKAP-Lbc, 320 kDa large, not only associates with PKA but also with the small GTP-binding protein RhoA, PKC, and PKD in cardiac myocytes (Fig. 5.7; Appert-Collin et al., 2007; Baisamy et al., 2005, 2009; Carnegie et al., 2004, 2008; Diviani et al., 2001, 2006; Klussmann et al., 2001). AKAP-Lbc functions as a guanine nucleotide exchange factor (GEF) for


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RhoA but not Rac or Cdc42 (Diviani et al., 2001). Activation of RhoA is mediated by tandem DH and PH domains present in the C-terminus of AKAP-Lbc that induce the exchange of GDP for GTP (Diviani et al., 2001, 2006; Klussmann et al., 2001). The Rho-GEF activity of AKAP-Lbc is activated upon a1-adrenoceptor stimulation via the G protein Ga12 (Appert-Collin et al., 2007; Diviani et al., 2001). Inhibition of the GEF activity is mediated by b-adrenoceptor-induced PKA-dependent phosphorylation of Ser1565 of AKAP-Lbc which facilitates the binding of an adaptor protein of the 14-3-3 family (Diviani et al., 2004). This inhibitory effect only occurs when AKAP-Lbc has homooligomerized (Baisamy et al., 2005). This homooligomerization is mediated by two leucine-zipper motifs in the C-terminal region of AKAP-Lbc (amino acids 2616–2679; Baisamy et al., 2005). AKAP-Lbc mediates hypertrophic responses of the heart. Stimulation of a1-adrenoceptors with phenylephrine (chronic infusion) increased the cardiac weight index (ventricular weight/body weight) of mice and the levels of AKAP-Lbc mRNA expression in ventricular myocytes. This effect as well as the activation of RhoA was inhibited in rat neonatal cardiac myocytes by silencing AKAP-Lbc expression (Appert-Collin et al., 2007; Diviani et al., 2001). The Rho effectors Rho kinase, PKN, and stress-activated protein kinase (SAPK) have been suggested to mediate the hypertrophic effects of AKAP-Lbc as they control the transcription of prohypertrophic genes downstream of Rho (Maruyama et al., 2002; Morissette et al., 2000; Yanazume et al., 2002). AKAP-Lbc-bound PKD also seems to mediate a hypertrophic response upon a1-adrenergic stimulation by activating the fetal gene expression program as overexpression of AKAP-Lbc in neonatal cardiac myocytes increases PKD activity and activates the PKD/HDAC5 (histone deacetylase 5)/MEF2 (myocyte enhancer factor-2) pathway (Carnegie et al., 2004, 2008). Taken together, the interactions of AKAPLbc–RhoA as well as AKAP-Lbc–PKD play key roles in the development of cardiac hypertrophy and thus could be novel drug targets for the treatment of cardiac diseases such as hypertrophy and consequent heart failure. 4.2.4. Muscle-selective AKAP 4.2.4.1. mAKAP complexes Two alternatively spliced variants of mAKAP have been identified: mAKAPa and mAKAPb. mAKAPa is mainly expressed in the brain whereas the shorter mAKAPb (lacking the Nterminal 224 amino acids compared to mAKAPa) is preferentially expressed in cardiac myocytes (Carlisle Michel et al., 2005) but it is also present in skeletal muscle (Ruehr et al., 2003). In the perinuclear region and SR of cardiac myocytes, the 250 kDa mAKAPb forms complexes with PKA, PDE4D3, RyR2, PP1 and PP2A, nesprin-1a, calcineurin Ab (CaNab), Epac, 3-PDK-1, ERK5, and the cardiac Naþ–Ca2þ exchanger (NCX1; Bers, 2002; Blaustein and Lederer, 1999; Carlisle Michel et al., 2005; Dodge

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et al., 2001; Dodge-Kafka et al., 2005; Kapiloff et al., 1999, 2001; Marx et al., 2000; Mauban et al., 2009; McCartney et al., 1995; Pare et al., 2005a; Ruehr et al., 2003; Schulze et al., 2003; Yang et al., 1998; Fig. 5.7). Thus, mAKAPb integrates cAMP signaling with that of Ca2þ and MAP kinases. The interaction between mAKAPb, PKA, and RyR2 is important for RyR2-dependent Ca2þ entry into the cytosol (Fig. 5.8). mAKAPb associates with RyR2 via a leucine-zipper motif (Marx et al., 2000, 2001). badrenoeptor-induced and PKA-dependent phosphorylation of cardiac RyR2 at serine 2809 increases the channel open probability thereby increasing cytosolic Ca2þ and consequently the contraction of cardiac myocytes (Bers, 2002; Kapiloff et al., 1999; Marx et al., 2000, 2001; Ruehr et al., 2003). Studies in which an RII phosphomimetic (RIIS96D) was overexpressed resulted in an increased PKA phosphorylation of the RyR2 (serine 2809) in neonatal rat cardiac myocytes (Manni et al., 2008). The impact of RyR2 phosphorylation is still controversially discussed: it has been proposed that the effects of phosphorylation are only minor and mainly lead to increased Ca2þ release and Ca2þ cycling (Benkusky et al., 2007; Ginsburg and Bers, 2004; MacDonnell et al., 2008) whereas others report that PKA-mediated hyperphosphorylation of RyR2 results in channel leakage and plays a role in the development of heart failure (Doi et al., 2002; Marx et al., 2000; Reiken et al., 2003). mAKAPb is targeted to the nuclear envelope by three spectrin-repeat domains within amino acids 772–1187 of mAKAPb of which the third (mAKAP TF) interacts with the membrane-spanning nesprin-1a. Nesprin1a itself is inserted into the nuclear envelope by a C-terminal Klarsichtrelated transmembrane domain (Kapiloff et al., 1999; Pare et al., 2005a; Zhang et al., 2001). Displacement of mAKAPb from the nuclear envelope can be achieved by overexpressing mAKAP TF or a truncated form of nesprin-1a that lacks the transmembrane domain (nesprinDTM; Kapiloff et al., 1999; Pare et al., 2005a; Zhang et al., 2001). Nuclear mAKAPb also interacts with the plasma membrane-resident AC5 in cardiac myocytes thus constituting a link between the nuclear envelope and the plasma membrane. The region binding AC5 is located within amino acids 245–340 in mAKAPb and does not overlap with binding sites for other known mAKAPb interaction partners (Dessauer, 2009; Kapiloff et al., 2009). Another binding partner of the mAKAPb complex is PDE4D3 (Dodge et al., 2001), which is regulated by PKA and the phosphatase PP2A (DodgeKafka et al., 2010). PDE4D3 contains two PKA phosphorylation sites: Ser54 and Ser13 (Sette and Conti, 1996). Phosphorylation at Ser54 increases the catalytic activity of PDE4D3 two- to threefold and the subsequent decrease in cAMP inhibits PKA activity (Dodge-Kafka and Kapiloff, 2006; Sette and Conti, 1996). Activation of PP2A by phosphorylation of its B56d subunit by PKA (Ahn et al., 2007) leads to increased dephosphorylation of mAKAPb-bound PDE4D3 at Ser54 (Dodge-Kafka et al., 2010).


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Phosphorylation of Ser13 does not affect cAMP hydrolysis but increases the binding affinity of PDE4D3 for mAKAPb (Carlisle Michel et al., 2004). This tight local regulation of PKA, in turn, allows for close control of PKAdependent RyR2 phosphorylation. PDE4D3 bound to mAKAPb also serves as an adaptor for ERK5 and Epac at the mAKAPb complex (Dodge-Kafka et al., 2005). ERK phosphorylation of PDE4D3 on Ser579 decreases its activity resulting in increased cAMP levels and subsequent PKA and Epac activation (Hoffmann et al., 1999; Li et al., 2010; Pidoux and Tasken, 2010). Epac, in turn, can activate the small GTP-binding protein Rap1, which inhibits the ERK5 upstream activator MEKK and thereby results in an inhibition of ERK5 and PDE4D3 (Dodge-Kafka et al., 2005). This suggests the association of a number of parallel negative and positive feedback loops in the mAKAPb complex regulating myocyte cAMP signaling (Dodge-Kafka et al., 2010). Defects in this cAMP signaling can lead to various cardiac diseases including hypertrophy (Dodge-Kafka et al., 2008). At the plasma membrane of cardiac myocytes, mAKAPb interacts with NCX1 (Bers, 2002; Blaustein and Lederer, 1999; Mauban et al., 2009; Schulze et al., 2003) thereby regulating the extrusion of Ca2þ from cardiac myocytes. Naþ entering the cell is extruded by a Naþ/Kþ pump (NKA), which is regulated by phospholemman (PLM), a small sarcolemmal protein (72 amino acids). PLM interacts with NKA in a similar manner as PLN interacts with SERCA2. Phosphorylation of PLM by PKA is associated with a decrease in the interaction and induces activation of NKA (Bers and Despa, 2009). This limits the rise in intracellular Naþ and, as a consequence, the Ca2þ transient amplitude during b-adrenergic stimulation (Bers and Despa, 2009). Recent evidence suggests that NCX1 is also controlled by PLM as b-adrenoceptor-induced phosphorylation of PLM by PKA inhibits NCX1 activity (Cheung et al., 2007). This appears to contribute to a rise in intracellular Ca2þ and an increase in contractility upon b-adrenoceptor stimulation (Cheung et al., 2007). An association of PKA RI subunits with the mAKAPb–NCX1 complex has been suggested (Schulze et al., 2003). However, in surface plasmon resonance (SPR) studies the canonical RIIBD of mAKAPb did not bind RI (Zakhary et al., 2000). 4.2.4.2. mAKAP and cardiac hypertrophy Knockdown (RNA interference) of mAKAPb inhibits isoproterenol-, phenylephrine-, and leukemia inhibitory factor (LIF)-induced cardiac hypertrophy (Dodge-Kafka et al., 2005; Pare et al., 2005b). In addition, b-adrenoceptor-mediated phosphorylation of the mAKAPb-bound RyR2 results in an increase in perinuclear Ca2þ that activates CaNab. CaNab then dephosphorylates and thereby activates the prohypertrophic transcription factor nuclear factor of activated T cells (NFATc). Active NFATc translocates into the nucleus and promotes cell growth and differentiation (Dodge-Kafka and Kapiloff, 2006; Kapiloff et al., 2001; Pare et al., 2005b). A similar mechanism has been found to be

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controlled by the mitochondrial AKAP121, which, like mAKAPb, is a negative regulator of cardiac myocyte hypertrophy via CaNa and NFATc (Abrenica et al., 2009). The interaction of ERK5 with mAKAPb was also suggested to play a role in the development of cardiac hypertrophy (Dodge-Kafka et al., 2006). ERK5 is an activator of the prohypertrophic transcription factor MEF2c downstream of a1-adrenoceptors and glycoprotein 130/LIF receptors (Nicol et al., 2001; Zhao et al., 2009). The hypertrophic effect of LIF which requires mAKAP expression (see above) is blocked by active Epac. Epac appears to be the cAMP effector responsible for cAMP-mediated inhibition of mAKAP-bound ERK5 (Dodge-Kafka et al., 2005). Therefore, it was suggested that ERK5 contributes to the regulation of hypertrophic genes and that disruption of the mAKAPb/ERK5 interaction provides a novel concept for the treatment of cardiac hypertrophy (Diviani, 2008; Nicol et al., 2001). A dysregulation in cAMP production (AC5) and degradation (PDE4D3) at the nuclear mAKAP complex apparently causes various diseases. For example, deletion of the PDE4D3 gene can lead to cardiomyopathy, heart failure, and arrhythmia in mice (Lehnart et al., 2005). Overexpression of the AC5-binding domain of mAKAPb results in increases in basal and isoproterenol-stimulated cAMP levels and consequently to cardiac hypertrophy (Dessauer, 2009; Piggott et al., 2008). In contrast, the deletion of AC5 protects from cardiac stress and hypertrophy (Dessauer, 2009; Okumura et al., 2003; Yan et al., 2007). 4.2.5. Yotiao Yotiao is the smallest splice variant of AKAP9 (210 kDa). In cardiac myocytes, it directly interacts with the a subunit of the IKS Kþ channel (KCNQ1) and thereby tethers other interactions partners, PKA, PDE4D3, PP1, and AC to this Kþ channel (Fig. 5.7; Dessauer, 2009; Marx et al., 2002; Piggott et al., 2008; Terrenoire et al., 2009). Upon b-adrenoceptor stimulation, Yotiao-bound PKA phosphorylates KCNQ1 at Ser27 and thereby enhances the current and thus cardiac myocyte relaxation (Fig. 5.8; Chen and Kass, 2006; Chen et al., 2007; Marx et al., 2002; Potet et al., 2001). Substitution of this serine by an asparte or glutamate and coexpression with Yotiao increases channel currents and decreases channel deactivation in CHO cells (Kurokawa et al., 2003, 2004). The direct interaction of Yotiao and KCNQ1 is mediated by two domains of Yotiao: an N-terminal 17 amino acid long binding site and a C-terminal leucine-zipper motif. This leucine zipper interacts with a leucine-zipper motif of KCNQ1 (Chen et al., 2007; Marx et al., 2002). Interfering with this interaction by disruption with a leucine-zipper peptide derived from KCNQ1 (KCNQ1-LZm) or by amino acid substitutions G589D and S1570L in KCNQ1 decreases PKA-dependent


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phosphorylation of KCNQ1 (Chen et al., 2005, 2007; Marx et al., 2002; Westphal et al., 1999). The amino acid substitutions G589D and S1570L have also been identified in patients suffering from long QT syndrome (LQTS), a disease characterized by a prolonged repolarization of the cardiac action potential. LQTS is associated with cardiac arrhythmias and may lead to sudden cardiac death (Chen and Kass, 2006; Chen et al., 2007; Marx et al., 2002; Saucerman et al., 2004; Westphal et al., 1999). In addition to PKA, Yotiao also recruits PDE4D3 to the IKS channel where PDE4D3 regulates channel activity locally through cAMP hydrolysis (Terrenoire et al., 2009). Moreover, Yotiao targets PKA to AC. In cardiac myocytes and in the brain Yotiao seems to interact with the AC isoforms 2 and 9 (Dessauer, 2009; Piggott et al., 2008). However, only for AC2 a direct binding has been demonstrated. AC9 seems to be associated with Yotiao through other proteins (Dessauer, 2009; Piggott et al., 2008; Terrenoire et al., 2009). Upon activation of AC and the generation of cAMP, Yotiao-bound PKA is activated and phosphorylates AC. This, in turn, inhibits the cyclase and thus terminates cAMP signaling.

4.3. AKAP–PKA interactions and water reabsorption in the kidney The arginine vasopressin (AVP)-induced water reabsorption is another process that depends on compartmentalized cAMP signaling (Fig. 5.9). In renal collecting duct principal cells, vasopressin V2 receptors (V2R) are activated on the basolateral surface by AVP resulting in AC activation and cAMP elevation. cAMP activates PKA which phosphorylates aquaporin2 (AQP2) at Ser256 in its C-terminal cytoplasmic domain (Christensen et al., 2000). This PKA-dependent phosphorylation leads to a redistribution of AQP2 from intracellular vesicles into the apical plasma membrane thereby increasing the osmotic water permeability of the cells facilitating water reabsorption from the collecting duct. It is the translocation of AQP2 into the plasma membrane that constitutes the molecular basis of AVPregulated water reabsorption. When water homeostasis is restored, AQP2 is being internalized and the principal cells are watertight again (Boone and Deen, 2009; King et al., 2004; Klussmann et al., 2000; Nedvetsky et al., 2009; Takata et al., 2008; Valenti et al., 2005). In contrast to many other exocytic events such as neurotransmitter release, the translocation of AQP2 is a rather slow process taking about 20 s for the vesicles to fuse with the plasma membrane (Lorenz et al., 2003). The redistribution of AQP2 into the plasma membrane is prevented when AKAP–PKA interactions are disrupted with peptides mimicking the RIIBD of AKAP-Lbc and AKAP18d (Hundsrucker and Klussmann, 2008; Klussmann et al., 1999; Nedvetsky et al., 2009; Szaszak et al., 2008). Therefore, the interactions of AKAPs with PKA are essential for

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Apical

C

R

AK

AP 22 AK

AQP2

PKA

AKAP18d

C

P P P P

AP 22

0

0

R

R

R

C

R R

C

PDE4D3

P

C AKAP18d

C

d 3 18 D AP E4 AK PD

PKA

C

H2O

R R

PDE4D3 P P P P

P

C

cAMP

AC VI

Gas

Basolateral AVP V2R

Figure 5.9 AVP-induced water reabsorption. See text for details. ACVI, adenylyl cyclase VI; AQP2, aquaporin 2; AVP, arginine vasopressin; C, catalytic subunit; PDE4D3, phosphodiesterase 4D3; PKA, protein kinase A; R, regulatory subunit; V2R, vasopressin V2 receptor.

AVP-induced water reabsorption. In the AVP-activated signaling cascade, AKAP18d and AKAP220, which colocalize with AQP2, appear to play a role as they sequester PKA in close proximity of the water channel (Henn et al., 2004, 2005; Klussmann and Rosenthal, 2001; Klussmann et al., 1999; Okutsu et al., 2008). AKAP220 was suggested to form a complex with AQP2 and to facilitate the AVP-induced phosphorylation of the water channel by PKA (Okutsu et al., 2008). AKAP18d not only binds PKA but also PDE4D (Stefan et al., 2007). PDE4D controls local cAMPs levels and thus PKA activity and thereby presumably PKA phosphorylation of AQP2 and its localization (Hundsrucker and Klussmann, 2008; Stefan et al., 2007). Defects in the AVP-induced AQP2 redistribution cause nephrogenic diabetes insipidus (NDI), a disease characterized by a massive loss of hypotonic urine (polyuria) and polydipsia (King et al., 2004; Klussmann et al., 2000; Nedvetsky et al., 2009; Robben et al., 2006; Valenti et al., 2005). On the other hand, heart failure is associated with elevated AVP levels and a consequent upregulation of AQP2 expression and predominant localization of AQP2 in the plasma membrane of renal principal cells (Chen and Schrier, 2006; King et al., 2004; Kwon et al., 2009; Schrier and Cadnapaphornchai, 2003).


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This is prevented by V1a/V2 receptor antagonists (vaptans) which improve clinical symptoms of chronic heart failure and result, for example, in increased urine output and reduced body weight without affecting blood pressure or heart rate (Gheorghiade et al., 2003; Lemmens-Gruber and Kamyar, 2006; Udelson et al., 2001). An alternative strategy may be the interference with AKAP–PKA interactions which also prevents the AVP-induced AQP2 redistribution (see above; Klussmann et al., 1999; Stefan et al., 2007). In conclusion, the AVP-mediated redistribution of AQP2 from intracellular vesicles into the plasma membrane of renal collecting duct principal cells depends on compartmentalized cAMP signaling maintaining close local control of PKA activity in the vicinity of AQP2.

5. AKAP Dysfunction in Human Disease 5.1. Lessons from AKAP KO mouse models KO and mutant mouse models are invaluable tools to study in vivo protein function in a mammalian system. Several genes encoding AKAPs were disrupted or mutated, resulting in diverse phenotypes. These are summarized in Table 5.5. Other reviews analyzed the KO models for AKAP149/AKAP1, AKAP4, AKAP150/AKAP5, mAKAPa/AKAP6, WAVE-1, MAP2, and Ezrin (Carnegie et al., 2009; Hundsrucker and Klussmann, 2008; Kirschner et al., 2009; Mauban et al., 2009; Welch et al., 2010). Here, we focus on recently developed AKAP KO and mutant mouse models.

5.1.1. SSeCKS/AKAP12 SSeCKS (Src-suppressed C kinase substrate)/AKAP12, the rodent ortholog of gravin, is a tumor suppressor protein involved in the regulation of cell cycle and cell migration (Gelman, 2002). SSeCKS KO causes prostate hyperplasia, hyperactivation of Akt/PKB and the loss of basal epithelial cells and E-cadherin cell–cell contacts, which are hallmark features of a hyperplasia/early neoplasia transition (Akakura et al., 2008). This is in line with the observation that SSeCKs/gravin expression is reduced in prostate cancer and the chromosomal location of the gravin gene 6q24–25.2 is a deletion hotspot in advanced prostate, breast, and ovarian cancers (Wan et al., 1999; Xia et al., 2001). A recent study also revealed that AKAP12 expression is lost in radiation-induced osteosarcomas (Daino et al., 2009).

Table 5.5 Mouse models to study AKAP functions AKAP

Genotype

Lethality

Phenotype (references)

AKAP140 AKAP149 (AKAP1) AKAP4

Knockout

No

Knockout

No

Knockout

No

Female KO mice subfertile due to defects in oocyte maturation; mitochondrial localization of RIIa during oocyte maturation is lost (Newhall et al., 2006) Male KO mice infertile due to impaired sperm motility; defects in sperm fibrous sheath formation; shortened sperm flagella; redistribution of RIIa from particulate to soluble fraction in sperm (Huang et al., 2005; Miki et al., 2002) Loss of b-AR mediated L-type Ca2þ channel phosphorylation by PKA; various neuronal processes abnormal: deficits in spatial memory, reduced anxiety, defective motor coordination, resistant to pilocarpineinduced seizures; hypotension and protection from angiotensin II-induced hypertension (Hall et al., 2007; Navedo et al., 2008; Tunquist et al., 2008) RIIa and RIIb protein levels reduced in postsynaptic densities; hippocampal long-term potentiation impaired in 8- but not in 4-week-old KO mice; PGE2-mediated modulation of TRPV1 channels by PKA impaired, leading to diminished PGE2-induced thermal hyperalgesia (Lu et al., 2007b, 2008; Schnizler et al., 2008) Reduced body weight and size (Carlisle Michel et al., 2005)

AKAP150 (AKAP5)

No Truncation mutant; C-terminal 36 aa of AKAP150 deleted (loss of PKA anchoring)

mAKAPa (AKAP6)

Knockout

Yes (reduced number of KO mice compared to wiltype and heterozygous littermates)

AKAP95 (AKAP8) D-AKAP2 (AKAP10)

SSeCKS (AKAP12)

Knockout

No

Yes (50% of homozygous Truncation mutant; mutant mice die within C-terminal 51 aa of first year of life) D-AKAP2 deleted (loss of PKA anchoring) Knockout No

AKAP-Lbc (AKAP13)

Knockout

Yes (embryonic day 11)

WAVE-1

Knockout

Yes (1/3 of KO mice die 24–48 h after birth)

MAP2

Knockout

No

No Truncation mutant; Nterminal 158 aa of MAP2 deleted (loss of PKA anchoring)

Without phenotype; AKAP95/Fidgetin double KO mice die postnatally due to cleft palate (Yang et al., 2006) Increased cardiac cholinergic response; arrhythmia (Tingley et al., 2007)

Prostate hyperplasia, hyperactivation of Akt/PKB and the loss of basal epithelial cells and E-cadherin cell–cell contacts (Akakura et al., 2008) Defective cardiac development; thinned myocardium; KO mice die from cardiac arrest; decreased cardiac MEF2c and SRF-dependent transcription (Mayers et al., 2010) Reduced body weight and size; sensorimotor retardation; reduced anxiety; deficits in hippocampal learning and memory; reduced spine density and altered synaptic plasticity of hippocampal neurons (Soderling et al., 2003, 2007) Reduced body weight and size; decreased microtubule density and levels of PKA RII and C subunits in dendrites; reduced dendrite length; MAP2/MAP1b double KO mice die postnatally due to defects in microtubule bundling and neurite elongation (Harada et al., 2002; Teng et al., 2001) Decreased MAP2 phosphorylation by PKA; aberrant hippocampal CA1 neuron architecture; altered contextual memory (Khuchua et al., 2003) (continued)

Table 5.5 (continued) AKAP

Genotype

Lethality

Phenotype (references)

Ezrin

Knockout

Yes (die within 25 days after birth)

Yes (93% die within 25 days after birth)

Pericentrin

Knockdown (insertion of neomycin cassette between exons 2 and 3 of the ezrin gene) Knockout

Abnormal intestinal villus morphology; retardation in photoreceptor development; reduction of apical microvilli in retinal pigment epithelium and Mu¨ller cells (Bonilha et al., 2006; Saotome et al., 2004) Achlorhydria due to defects in the formation/expansion of apical canaliculi in gastric parietal cells (Tamura et al., 2005)

Transgenic expression of Pericentrin

No

No

Neurobeachin Knockout

Yes (immediately after birth)

Ht31 (300 aa Transgenic expression of Ht31, forebrainfragment of specific AKAPLbc, contains RIIBD, PKAanchoring disruptor)

No

Primordial dwarfism with microcephaly; loss of astral microtubules; misoriented mitotic spindles (Delaval and Doxsey, 2010) Aneuploidy; increased number of centrosomes; multipolar spindles; mice develop a syndrome resmbling human myelodysplasia, carcinoma, and sarcoma (Delaval and Doxsey, 2010) Absence of evoked neuromuscular transmission; mice die from inability to breathe; abnormalities in fetal synapse formation and function (Medrihan et al., 2009; Su et al., 2004) Defects in hippocampal long-term potentiation and spatial memory (Nie et al., 2007)


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5.1.2. Pericentrin The noncanonical AKAP pericentrin (Table 5.4; Diviani et al., 2000) is localized at the centrosomes where it regulates centrosome function, cell cycle checkpoints, spindle formation, and cytokinesis. Mutations in the pericentrin gene cause primordial dwarfism with microcephaly. These symptoms are most likely due to defective chromosomal segregation during mitosis (Rauch et al., 2008). Similarly, pericentrin KO mice show primordial dwarfism, loss of astral microtubules, and misoriented mitotic spindles, that is, they are not perpendicular to the longitudinal dimension of the cells (Delaval and Doxsey, 2010). Pericentrin is upregulated in solid tumors where it leads to the formation of multiple mitotic spindle poles and consequently chromosomal aberrations and aneuploidy in daughter cells. This was also observed in a mutant mouse model with increased pericentrin levels. These mice have a syndrome similar to human myelodysplasia, carcinoma, and sarcoma (Delaval and Doxsey, 2010). In summary, patient data and KO/mutant mouse experiments underline the importance of balanced pericentrin expression and its oncogenic potential. The exact role of PKA anchoring for processes involving pericentrin remains unclear. In addition to binding PKA directly, pericentrin interacts with AKAP350/AKAP9 in centrosomes, which could add to recruiting PKA to pericentrin complexes. PKA is known to phosphorylate dynein and could thus contribute to centrosomal microtubule dynamics (Inaba et al., 1998). This might be dependent on anchoring by pericentrin and/or AKAP350. 5.1.3. Neurobeachin Neurobeachin is a WD40- and beige and Chediak–Higashi syndrome (BEACH)-domain containing protein which appears to be involved in membrane trafficking (Wang et al., 2002). Both mammalian neurobeachin and its Drosophila ortholog AKAP550/rugose are AKAPs (Han et al., 1997; Wang et al., 2000). The neurobeachin gene lies within a candidate region for autism on chromosome 13 identified by linkage analysis (Barrett et al., 1999). A disruption of the neurobeachin gene by a chromosomal translocation (5;13)(q12.1;q13.2) has been found in a patient with idiopathic autism (Castermans et al., 2003). Moreover, a deletion of the region on chromosome 13 containing the neurobeachin gene was identified in another autism patient (Smith et al., 2002). Su et al. (2004) described the absence of evoked neuromuscular transmission due to a failure in neurotransmitter release in neurobeachin KO mice. Newborn neurobeachin KO mice lacked spontaneous movement, or reflexive movement in response to tail pinch and died immediately after birth from the inability to breathe. A recent study from Medrihan et al. (2009) confirmed the previous observations. Because of the perinatal lethality of the neurobeachin KO mice, further experiments were

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performed at embryonic day 18. These studies identified several additional abnormalities in the formation and function of central synapses: altered postsynaptic currents, probably due to reduced transmitter release, a reduced number of asymmetric contacts in the fetal brainstem, and reduced expression levels of several synaptic marker proteins. Taken together, the disruption of neurobeachin causes defects in neurotransmitter release and thereby an excitatory–inhibitory imbalance. The involvement of PKA anchoring in neurobeachin-mediated membrane trafficking processes is unknown. PKA has, however, been shown to increase neurotransmitter release and regulate synapse formation (Abel and Nguyen, 2008; Byrne and Kandel, 1996). Thus it is conceivable that impaired PKA anchoring by neurobeachin contributes to synaptic abnormalities and is involved in the development of autism in patients with inactivation of one neurobeachin allele. 5.1.4. AKAP-Lbc AKAP-Lbc (see Section 4.2.3) KO mice were recently generated (Mayers et al., 2010). The homozygous ablation of AKAP-Lbc expression is lethal, embryos show a cardiac arrest at embryonic day 9 and die around embryonic day 11. The myocardium of AKAP-Lbc KO embryos is thinned due to reduced proliferation of myocytes. Apparently, embryonic expression of AKAP-Lbc is required for cardiac development. AKAP-Lbc induces serum response factor expression via RhoA activation (Mayers et al., 2010) and MEF2c expression via PKC/PKD/HDAC5 (Carnegie et al., 2008). An activation of this pathway in adult hearts, for example, through a1-adrenoceptors (Appert-Collin et al., 2007), increases MEF2c, causing a ‘‘fetal gene response’’ that leads to hypertrophy (Carnegie et al., 2008). 5.1.5. Transgenic expression of PKA-anchoring disruptors The existing KO mouse models underline the relevance of AKAPs for the control of various neuronal, cardiac, and reproductive functions. In several cases the observed phenotypes reflect the situation in human patients in whom genetic alterations affect the respective ortholog. However, while complete ablation of an AKAP by KO yields information about its overall functions, it remains unclear to what extent a particular protein–protein interaction such as the one with PKA is important. Transgenic expression of PKA-anchoring disruptors could answer this question. The only established mouse model based on this approach features a forebrain-specific expression of Ht31, a 300-amino acid fragment of AKAP-Lbc (see Section 5.2), containing the high-affinity RIIBD (Nie et al., 2007) but not other functional domains identified in AKAP-Lbc (Diviani et al., 2006). This forebrain-specific disruption of PKA anchoring causes an impairment of hippocampal long-term potentiation, emphasizing


295

the relevance of AKAPs for synaptic function (Nie et al., 2007). Additional studies based on this approach may elucidate the role of PKA anchoring in organs other than the brain, and the use of transgenic mice expressing RI- or RII-specific PKA-anchoring disruptors such as RIAD (Carlson et al., 2006) or SuperAKAP-IS (Gold et al., 2006) would further narrow down the roles of anchored PKA type I and II, respectively. 5.1.6. Targeted deletion of RIIBDs Alternatively, to clarify the role of PKA anchoring by distinct AKAPs, additional mutant mouse models expressing AKAPs that lack PKAbinding ability but retain their other functions need to be generated. One example for this approach is a mouse model with a homozygous mutation in the D-AKAP2/AKAP10 gene. In the mutant protein, the 51 C-terminal amino acid residues containing the PKA-binding region are deleted. Mutant mice display an increased cardiac cholinergic response and arrhythmia. Fifty percent of the homozygous mutant mice die within their first year of life (Tingley et al., 2007). The cause of death is unknown but likely to be related to the observed cardiac abnormalities. The human SNP I646V, which is located in the PKA-binding region of the D-AKAP2/AKAP10 gene, leads to an increased anchoring of RI subunits of PKA by D-AKAP2/AKAP10 and is also associated with a cardiac phenotype (Kammerer et al., 2003). Moreover, the I646V SNP increases the risk of colorectal cancer (Wang et al., 2009) and familial breast cancer formation (Wirtenberger et al., 2007). Taken together, data from D-AKAP2/AKAP10 mutant mice and human genetic analyses demonstrate the requirement of balanced PKA anchoring by D-AKAP2/AKAP10 for proper cardiac function and tumor suppression, thus providing starting points for a detailed analysis of the cellular and molecular function of AKAP10. Another example for a PKA-binding defective system is a mouse model for a truncated version of AKAP150 (Lu et al., 2007b, 2008). These mice express a variant of AKAP150 lacking the 36 C-terminal amino acids which contain the RIIBD. RIIa and RIIb protein levels are decreased in postsynaptic densities of the animals and hippocampal long-term potentiation is impaired in 8- but not in 4-week-old mice (Lu et al., 2007b, 2008). Another study showed that PGE2-mediated modulation of TRPV1 channels by PKA, which normally causes enhanced thermal sensitivity, is impaired in these mice (Schnizler et al., 2008). Deletion of the 158 N-terminal amino acids of the neuronal AKAP MAP2, which harbor the RIIBD, resulted in decreased MAP2 phosphorylation by PKA, aberrant hippocampal CA1 neuron architecture and altered contextual memory formation in mice (Khuchua et al., 2003). This demonstrates that PKA anchoring is necessary for MAP2 phosphorylation by PKA and correct hippocampal neuron architecture and function. In summary,

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the disruption of PKA binding was achieved by truncating the C-termini of D-AKAP2/AKAP10 and AKAP150 and the N-terminus of MAP2, revealing the importance of anchored PKA for the control of cardiac contraction and various neuronal functions.

5.2. Understanding AKAP functions through pharmacological interference with their protein interactions: Implications for novel therapeutic concepts The depletion of AKAPs by knockdown (reviewed in Hundsrucker and Klussmann, 2008), or KO experiments (see above) leads to a specific phenotype and yields evidence for the relevance of the respective AKAP in certain signaling pathways and defined physiological processes. AKAP functions are mainly defined by their protein–protein interactions. Therefore, the pharmacological disruption of each of these interactions may be used for the elucidation of their function. This could also lead to new therapeutic strategies because many AKAPs are involved in pathological processes (see above). 5.2.1. Peptides targeting AKAP-dependent protein–protein interactions To address the question whether PKA anchoring by AKAPs is involved in a process, AKAP–PKA interactions can be disrupted with peptides. As described in Section 3, AKAPs are a structurally diverse family of proteins that only display similarity within the RIIBDs. Peptides comprising the RIIBD of AKAPs can bind to R subunit dimers and effectively disrupt their interaction with AKAPs (for a more detailed overview see Hundsrucker and Klussmann, 2008). This was first demonstrated for the peptide Ht31 which represents the RIIBD of AKAP-Lbc/AKAP13 (Carr et al., 1992b). Ht31 can disrupt both RI– and RII–AKAP complexes (Herberg et al., 2000) in cells and in animal experiments (McConnell et al., 2009). Ht31 peptides bind RIIa with low nanomolar affinity (KD ¼ 2.2 0.03 nM; Newlon et al., 2001). Further peptides with higher affinities were established: AKAP18d is a high-affinity AKAP and, accordingly, a peptide encompassing its RIIBD (or modified versions thereof such as AKAP18d-L314E) bind RII subunits with high affinity (KD ¼ 0.4 0.3 nM; Hundsrucker et al., 2006a,b). The R subunit selectivity of the AKAP18d peptides is not defined, but they are presumably nonselective. AKAP-IS was derived from an alignment of RIIBDs of 10 AKAPs and optimized for RII binding. It binds RII subunits with 500-fold higher affinity than RI (RII: KD ¼ 0.45 0.07 nM, RI: KD ¼ 227 55 nM; Alto et al., 2003). Based on the AKAP-IS sequence, R subunit selectivity was refined, resulting in the RI-preferring peptide RIAD (RI: KD¼1.0 0.2


297

nM, RII: KD¼1760 290 nM; Carlson et al., 2006) and the RII-preferring SuperAKAP-IS (Gold et al., 2006). An alternative approach resulted in RIand RII-preferring peptides derived from the dual-specific D-AKAP2, named AKB (A-Kinase Binding)-RI (RI: KD¼5.2 0.5 nM, RII: KD¼456 33 nM) and AKB-RII (RI: KD¼2493 409 nM, RII: KD¼2.7 0.1 nM; Burns-Hamuro et al., 2003). In addition to PKA anchoring, other protein–protein interactions of AKAPs can be targeted with peptides. AKAP18a/AKAP15 interacts with the L-type Ca2þ-channel via a leucine-zipper motif (see Table 5.3 and Section 4.2.1). The peptide AKAP15LZ, comprising this motif, prevents PKA phosphorylation of the channel at serine 1928 by disrupting its interaction with the AKAP–PKA complex (Hulme et al., 2002). The splice variant AKAP18d interacts with PLN in the SR of cardiac myocytes (see Table 5.3, Section 4.2.1 and Fig. 5.7). A peptide containing 8 amino acids of the PLN cytosolic domain can bind to AKAP18d and disrupts the PLN/ AKAP18d interaction, thus preventing PKA phosphorylation of PLN and reducing b-adrenoceptor induced reuptake of Ca2þ into the SR (Lygren et al., 2007). These protein–protein interaction disruptor peptides can be modified for immobilization, visualization, or cell permeation by conjugation with affinity tags, fluorescent dyes, or cell penetrating tags (e.g., stearate, poly-arginine, penetratin, MAP- or Tat-peptide; Faruque et al., 2009; Hundsrucker and Klussmann, 2008; Smith et al., 2007; Vives, 2005; Zorko and Langel, 2005). However, the application of PKA-anchoring disruptor peptides in cells or in vivo has limitations: (1) The global disruption of AKAP–PKA complexes elevates free PKA in the cytosol (Wojtal et al., 2006). This leads to an ablation of compartmentalized PKA signaling and an aberrant phosphorylation of PKA substrates; (2) Depending on the modification, peptides could be enriched in certain compartments and thus not be distributed evenly within cells; (3) Peptide stability in cell culture or in vivo experiments is limited. The oral applicability is also limited due to enzymatic degradation in the digestive tract; (4) Chemical peptide synthesis is expensive and may technically be highly demanding. In cell or animal models, some of these drawbacks can be overcome by encoding peptides genetically or by utilizing peptidomimetics or small molecules (Hundsrucker and Klussmann, 2008; Klussmann and Rosenthal, 2008). For example, adenoviral expression of Ht31 in the rat heart has been achieved. It leads to global disruption of AKAP–PKA interactions, has been shown to increase contractility (McConnell et al., 2009). Peptidomimetics comprise peptides containing unnatural amino acids or molecules mimicking a peptide structure ( Jochim and Arora, 2009). In general, peptidomimetics are more stable than peptides and more resistant to enzymatic degradation. Recently, peptidomimetics were derived from the RIAD peptide (Torheim et al., 2009). These peptidomimetics retain the

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ability to selectively bind to RI but not to RII subunits. Their in vitro stability in human serum is drastically increased compared to the peptide RIAD which suggests usability in in vivo studies. 5.2.2. AKAPs as potential drug targets The major current drug targets are GPCRs, nuclear receptors, ion channels and pumps, and enzymes (Overington et al., 2006). Targeting protein– protein interactions mediated by scaffold proteins has several advantages compared to interfering with the conventional drug targets (Yin and Hamilton, 2005). Influencing protein–protein interactions, such as AKAP-dependent ones, would specifically alter a defined cellular event (like substrate phosphorylations), thus increasing drug specificity and lowering side effects (Klussmann and Rosenthal, 2008). The plethora of intracellular protein–protein interactions presumably accommodates numerous drug targets which would increase drug diversity and open up new therapeutic options. Most oral drugs are small molecules that comply with Lipinski’s rule-offive, which predicts water solubility and cell permeability (Lipinski et al., 2001). They usually bind to ligand-binding pockets of receptors or substratebinding pockets of enzymes which have a rather small surface. In addition, the knowledge of the natural ligands simplifies the identification of potential synthetic ligands. In contrast, protein–protein interactions are often achieved by multiple molecular interactions distributed over large surfaces. Nevertheless, it is possible for small molecules to inhibit protein–protein interactions as they may target specific regions on the interaction surfaces, so called hotspots, which contribute most of the binding energy (Wells and McClendon, 2007). Because of the similarity between different AKAP–PKA interactions, small molecules binding to the interaction surface would most likely disrupt all AKAP–PKA interactions or in the best case display RI/RII specificity. AKAP-specific disruption of PKA anchoring could be achieved by small molecules binding allosterically to AKAPs. Allosteric small molecules targeting intracellular protein–protein interactions have only recently been developed (Arkin and Wells, 2004; Arkin and Whitty, 2009; Gorczynski et al., 2007; McMillan et al., 2000). Allosteric AKAP-binders would allow for selective modification of compartmentalized cAMP signaling events. Such molecules could be identified by screening approaches or rational drug design (Erlanson, 2006; Villoutreix et al., 2009). The latter approach is limited by the lack of structural information on AKAPs and their interactions. AKAPdependent protein–protein interactions are considered potential drug targets in cancer, cardiac, and neurological disorders (see above). In the following, AKAPs as potential targets in human immunodeficiency virus (HIV) infection and contraception are briefly highlighted.


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5.2.3. AKAP149 (AKAP1) in HIV infection AKAP149 interacts with the reverse transcriptase (RT) of the HIV type 1 and is necessary for viral replication in HeLa cells (Lemay et al., 2008). AKAP149 expression is upregulated by the HIV transactivator of transcription (Tat) protein (Liang et al., 2005), which might promote efficient virus replication. Although PKA has been shown to be involved in HIV infection (Skalhegg et al., 2005), it is not known whether its anchoring to AKAP149 is required. In any case, AKAP149 could be a drug target in HIV therapy because blocking the AKAP149–RT interaction could inhibit early viral replication. 5.2.4. AKAPs and novel contraceptives AKAPs may be suitable targets in the development of novel contraceptive agents. AKAPs are highly abundant in sperm and are essential for sperm function (Carr and Newell, 2007). The disruption of PKA anchoring with the Ht31 peptide (see above) is known to inhibit sperm motility (Vijayaraghavan et al., 1997). A recent study has shown that quinones can alkylate AKAP3 and AKAP4 in human spermatozoa, thereby abolishing PKA anchoring (Hughes et al., 2009). As a consequence, sperm motility and PKA-dependent activation of Src kinase are inhibited (Hughes et al., 2009). These spermostatic compounds are far less cytotoxic than the approved and clinically used spermicide nonoxynol-9 (N9), which damages the vaginal epithelium (Hughes et al., 2007). In addition, a microbicidal effect has been observed for the spermatostatic quinones. They alkylate the major outer membrane protein of Chlamydia, the most common bacterial pathogen causing sexually transmitted disease (STD), thereby reducing infectivity (Hughes et al., 2009). Hence, quinones targeting AKAPs could lead to better topical contraceptives that also decrease the risk of infection with STD.

6. Concluding Remarks Apart from their similarities in PKA binding, AKAPs are a highly diverse family of proteins, anchoring PKA to most organelles. By binding components of the cAMP signaling machinery such as GPCRs, ACs, and PDEs, AKAPs are crucial for the spatial and temporal control of cAMP/PKA signaling. This facilitates specific responses to multiple cAMP-elevating stimuli. While the vast majority of PKA-interacting proteins are canonical AKAPs that bind to the PKA regulatory subunit dimer via an amphipathic helix, an increasing number of noncanonical AKAPs and proteins that bind catalytic subunits of PKA are emerging. AKAPs achieve the integration of

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cAMP and other signaling pathways by interacting with further signaling proteins including protein kinases, PPs, small GTP-binding proteins, ion channels, and cytoskeletal proteins. AKAPs have been found in all metazoans and the green alga Chlamydomonas. The PKA binding mechanism is conserved and essentially the same in all AKAPs. The number and functional diversity of AKAPs has increased throughout evolution, thus contributing to compartmentalization and cross talk of signaling pathways. AKAPs may regulate ubiquitously required processes such as ciliar/flagellar motility from Chlamydomonas to mammals, and have apparently acquired novel functions with the development of specialized cell types, for example, in the regulation of synaptic processes (invertebrates and vertebrates) or the control of cardiac development and contractility (vertebrates). Most structural information on AKAP–PKA interactions has been obtained with AKAP-derived peptides bound to D/D domain dimers of R subunits. This has, in combination with amino acid substitution analyses, revealed the amino acids important for R subunit binding and for discrimination between RI and RII. The NMR structure of GSKIP, to date the only available full-length structure of an AKAP, revealed that the hydrophobic residues essential for binding PKA are buried in the core of the protein in the absence of PKA. This implies that PKA binding requires structural rearrangements, which may have consequences on other protein– protein interactions an AKAP is engaged in. It will be a major challenge to solve three-dimensional structures of protein complexes composed of fulllength AKAPs and their interaction partners to gain a more dynamic view of AKAPs. Our understanding of the physiological and pathophysiological functions of AKAPs has greatly increased through the development of KO and mutant mouse models. These results are often in line with clinical data, revealing the involvement of AKAPs in human diseases such as cancer, heart failure, or arrhythmia. AKAP-mediated protein–protein interactions are intriguing targets for the development of novel drugs, for example, for the treatment of cardiovascular diseases or as contraceptive agents. In summary, new findings from structural studies, animal models, and clinical data will help to shape a more refined understanding of AKAP functions, opening new opportunities for a therapeutic exploitation of this class of scaffolding proteins.

ACKNOWLEDGMENTS This work was supported by grants from the Deutsche Forschungsgemeinschaft (Kl1415/3-2 and 4-2) and the GoBio program of the Bundesministerium fu¨r Bildung und Forschung (FKZ 0315516).


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Index

A Acousto-optical tunable filter (AOTF), 77 ACs, see Adenylyl cyclases Actin cytoskeleton cell motility types, 72–73 Ena/VASP proteins, 73–74 live-cell imaging, 73 stress fibers, 74 Actin dendritic nucleation model F-actin accumulation, lamellipodium, 47–48 features, 46–47 Actin filaments (AFs), 37 Adenohypophyseal placode anterior neural ridge, 161, 163 definition, 132–133 transcription factors, 161 Adenylyl cyclases (ACs) cAMP, 236 isoforms, 237–238 AFs, see Actin filaments A-kinase anchoring proteins (AKAPs) cardiac myocyte contractility AKAP18a, 281–282 AKAP18d, 282 AKAP-Lbc, 282–283 calcium signaling, 279–280 cAMP production and activation, 279 development, 286 mAKAP complexes, 283–285 prohypertrophic transcription factor, 285–286 protein phosphorylation, 280 Yotiao, 286–287 Chlamydomonas reinhardtii, 274 diversity, 254 evolution, 254–272 GSK3b inhibition, 275–277 orthologs, 275 PKA, priming kinase, 277–278 human diseases dysfunction knockout mouse models, 289–296 novel therapeutic concepts, 296–299 interactions, PKA D/D domain, 248 GSKIP structure, 249–252 helical wheel analysis, 246 RIIBDs, 248–249

RII-binding domains, 246–247 x-ray structures, 247 invertebrate, 274 nomenclature, 246 noncanonical, 273 RIIa D/D domain, 245 RIIBD consensus sequence, 249 RII subunits, 244–245 type I vs. type II, PKA, 252–254 water reabsorption, PKA interaction AVP-activated signaling, 288 cAMP signaling, 287–288 V1a /V2 receptor antagonists, 289 AOTF, see Acousto-optical tunable filter Arginine vasopressin (AVP) phosphorylation, 288 water reabsorption, 287–288 Ash1 gene, 180–181 AVP, see Arginine vasopressin Axonal ensheathment, Drosophila brain cortex glia, 112–113 neuropile glia partition axons, 111–112 secondary neurons, 111 embryonic PNS critical events, 101 Crn, 103 peripheral glia, 101–102 proteins, 104 sensory neuronal precursors, 102 larval and pupal PNS fray mutants exhibition, 104–105 morphogenesis and adult, 105 olfactory system, 105–106 VNC axonal commissures, 106–107, 109 LG ensheath longitudinal axon tracts, 107 MG ensheath ACs and PCs, 107–111 B BBB, see Blood–brain barrier BEB, see Blood–eye barrier Binary competence model neural crest, 146–147 nonneural vs. neural competence, 152 preplacodal induction, 159 transcription factor expression, 148

331

332

Index

BioModel workspace access, “les” user, 26 applications, 8–9 Gndph_wBuffer, 35–36 LEGI, 29–30 mass action and Michaelis–Menten rate laws, 8 multilayered structure, 6–7 multiple simulations, 8–9 physiology, 7–8 BioNetGen@VCell, 20 Blood–brain barrier (BBB) formation, Drosophila BEB, ommatidia and BRB, vertebrates, 117–118 SJs, 118–120 BNB COs, 115 inner and perineurial glia, 114–115 CNS glial cell layers, 115–116 non-SJ-related, 117 SJs, 116–117 proprioreceptors, 113 Blood–eye barrier (BEB), Drosophila ommatidia and BRB, vertebrates, 117–118 SJs, 118–120 Blood–nerve barrier (BNB), Drosophila PNS COs, 115 inner and perineurial glia, 114–115 Bone morphogenetic protein (BMP) signaling neural-specific transcription factors, 149 panplacodal primordium, 147 C Caenorhabditis elegans, 65, 139, 274 Calcium dynamics “Best Fit” model, 22–24 bradykinin, 21–22 components, 22–23 ER density, 22, 23 IP3 receptor, 22, 24 in neuroblastoma cell, 21–22 cAMP, see Cyclic adenosine monophosphate Cardiac myocyte contractility AKAP18a amino acids and isoforms, 281 PKA anchoring, 282 AKAP18d, 282 calcium signaling, 280 cAMP production and activation, 279 mAKAP complexes, 283–285 hypertrophy, 285–286 Yotiao, 286–287 PKA–AKAP interactions cAMP signaling, 287 V1a /V2 receptor antagonist, 287–288 water reabsorption, 287–288

Cell adhesions actin-dependent signaling Cdc42 activation, 66 GTPases activity, 65 RhoA, 65–66 cell–cell epithelia, glandular structures, 62 junctional complexes, 63 cell–matrix FAs, FBs and FCs, 59, 62 podosomes, 62 syndecans, 59 description, 58 importance dysregulation, 63 EB, 63–64 leukocytes, 64 integrin inside-out and outside-in signaling, 65–66 migration, in vitro regulation, 70–71 structure and specificity, 64–65 proteins involved, cell–cell constant shuttling, a-catenin, 67–68 desmosomes, 68 GAP junctions, 68 tight junctions, 67 regulation in vitro, 70–75 in vivo, 82–83 signaling and adaptor proteins b-catenin stability, 69–70 FAK and Src, 69 techniques, in vitro confocal microscopy, 77–78 FRET, 80–81 FSM, 79–80 photobleaching and photoactivation, 78–79 siRNAs, 81–82 TIRF-M, 78 widefield fluorescence, 75–77 Central nervous system (CNS) BBB formation, Drosophila glial cell layers, 115–116 non-SJ-related, 117 SJs, 116–117 VNC axonal commissures, 106–107, 109 LG ensheath longitudinal axon tracts, 107 MG ensheath ACs and PCs, 107–111 Chlamydomonas reinhardtii, 274 CNS, see Central nervous system Confocal microscopy technique drawback, 77–78 imaging speed, 77 Crooked Neck (Crn), 103 Cyclic adenosine monophosphate (cAMP) ACs, 237–238 interaction, proteins

333

Index

caveolin-1, 244 PKA C subunits, 242–243 PDEs, 238–239 PKA activation, 239–240 isoforms, 240–241 structural aspects, 241 production and activation, 279 protein phosphatases, 239 R2D2 proteins, 242 signaling level, 237 D Dach gene, 140 Drosophila AKAPs, 274 axonal ensheathment axoglial junction formation, 98–99 CNS, 106–113 domains, 96–97 hemolymph, 97–98 PNS, 100–106 and septate junctions, 95, 97 SJs formation, 98–100 and vertebrate myelination, 96–100 BBB formation BEB, ommatidia, 117–120 BNB, 114–115 CNS, 115–117 Dach and Pax gene, 140 D. melanogaster, 65 Eya proteins, 139 hedgehog signaling pathway, 278 sine oculis and eyes absent, 143–144 vertebrate glia, 94–95 E EB, see Epidermolysis bullosa Epibranchial placodes definition, 135 Eya1 and Six1 mutants, 142 FGF signals, 166 gene encoding transcription factors, 165 pharyngeal pouches, 170 sensory organ development, 168 visceromotor neurons, 183 Epidermolysis bullosa (EB), 63–64 Eya1 gene dosage dependent, 175–176 mutations, 141 neural crest cells, 169 pituitary defects, 142 placodal size reduction, 143 roles, cell survival, 142–143 transcription factors, 148

F Fluorescence correlation spectroscopy (FCS), 79–80 Fluorescence lifetime imaging microscopy (FLIM), 81 Fluorescence loss in photobleaching (FLIP) modeling, 32–33, 79 Fluorescence microscopy data analysis FLIP experiment modeling, 32–33 GEF Tiam1, overexpression, 33 PSD-95:paGFP model, 31–32 Tbeta4 model, 31 ZO-1 and occludin, 32 Fluorescence recovery after photobleaching (FRAP), 78–79 Fluorescence resonance energy transfer (FRET), 80–81 Fluorescence speckle microscopy (FSM), 79–80 Fox gene epidermis and, 148 transcription factors, 168 G GEF, see Guanine nucleotide exchange factor Glycogen synthase kinase 3b (GSK3b) AKAPs control inhibitory phosphorylation, 275 physiological agonists, 276 PKA, 276–277 cellular process, 275 G-protein-coupled receptors (GPCRs) activation, 237–238 bradykinin, 21 cAMP signaling pathway, 238 potential drug targets, 298 G-protein receptor signaling activation structure, 28 characteristics, 27–28 reaction rates, 28 steps, 29 GSK3b, see Glycogen synthase kinase 3b Guanine nucleotide exchange factor (GEF), 282–283 H Helical wheel analysis, 246 I Integrins, cell adhesions inside-out and outside-in signaling, 65–66 migration, in vitro regulation endoexocytic traffic, 70 role, 71 structure and specificity, 64–65

334

Index

Intracellular transport, VCell MTs and AFs, 37–38 pigment granules, fish melanophores, 38–39 velocities, defined, 38 velocity vector field, 37 Irx gene ectodermal domain, 165 Wnt dependence, 169 K Knockout mouse model AKAP-Lbc, 294 neurobeachin, 293–294 pericentrin, 293 PKA-anchoring disruptors, 294–295 RIIBDs targeted deletion, 295–296 SSeCKS/AKAP12, 289–292 L Lateral line placodes axons, 184 FGF signals, 165–166 nonanterior vs. anterior, 167 organ development, sensory, 168 sensory cells and neurons, 174–175 signaling centers, 166 transcription factor encoding, 165 Lens placodes anterior neural folds, 161 definition, 134 FGF signals, 170 formation, 171 invagination, 187 signaling molecules, 145 Leukemia inhibitory factor (LIF) receptors, 286 Local excitation global inhibition (LEGI) model characteristics, 29 description, 29–30 “L_source” and “Needle” applications, 30 M MathModel access, “boris” user, 26 description, 4–5 Schwartz_3D_FLIP, 34 Microtubules (MTs) characteristics, 37 junctional integrity, 75 targeting, adhesion disassembly, 74–75 Modeling capabilities, VCell applicability initial value problem, solvers, 6 reaction–diffusion processes, tool, 5–6 BioModel workspace, 6–9 BioNetGen@VCell, 20 compartmental applications

averaged system dynamics, 9–10 diffusion, 9 mass-action reaction rates, 10 master equation, 10 MathModel, description, 4–5 parameter scanning and parameter estimation, 19 sensitivity analysis, 20 solvers, 15–19 spatial applications, 11–15 Morphogenesis placodes cell delamination and migration actin-binding proteins, 185 neural crest cells, 183 Neurogenins, 182–183 olfactory and vomeronasal nerves, 184 SDF1 receptor, 184–185 signaling pathways, 182 Snail and Sox9 factors, 181–182 invagination and complex shape formation apical constriction, cell, 188 cell shape, 186–187 Ena/Vasp loss, 188–189 intrinsic and extrinsic forces, 187 lens and otic vesicles, 189 neural tube, 185 signaling pathways and transcription factors, 186 MTs, see Microtubules N Neurogenins, 143–144, 175, 181–183 Neurons and sensory cells adenohypophyseal and lens placode, 174 mechanism genes regulation, 175 lineage decisions, 176–177 profundal/trigemina placode, 175–176 proneural gene definition, 179 placodal cell types, 180 SoxB1 genes, dual functions CNS, 178 conflicting functions, 178–179 HMG, 177 pluripotent stem cells, 177–178 Novel therapeutic concepts, AKAPs HIV infection, 299 novel contraceptives, 299 potential drug targets, 298 protein–protein interactions PKA anchoring, 297 RIIBD, 296 Nucleocytoplasmic transport, VCell characteristics, 24 mechanisms, 24–25 NTF2:RanGDP, 26–27

335

Index

Ran cycle, 24–25 Ran GTP steep gradient, 26 reversible binding reactions, 25 total in vivo flux, 26 O ODEs, see Ordinary differential equations Olfactory placodes description, 133–134 developmental defects, 141 ectoderm, 161 Ordinary differential equations (ODEs), 16 Otic placodes definition, 134 description, 165–166 Eya1/Six1 mutants, 143–144 formation, 145 hair cells, 141 Irx gene, 168 lens invagination, 187 sensory neurons, 174–175 Sox9 gene, 182 stages, 168 P Panplacodal primordium description, 136 induction and specification BMP signaling, 150 ectodermal expression domains, 148 fold stages, neural, 146 neural crest/lateral neural plate, 150–155 neural plate border state, 146–147 preplacodal ectoderm, 150–152 Xenopus, 148–149 multistep induction adenohypophyseal, olfactory and lens, 161–164 models, 169–170 otic, lateral line and epibranchial, 165–166 profundal and trigeminal, 166–167 preplacodal ectoderm amphibians and amniote embryos, 136 developmental process, 144–145 dosage dependent effects, 144 Drosophila, 141 Eya gene and proteins, 139 Eya1/Six1 mutants, 143 genes encoding, 138 neural crest genes, 145 origins, 136–137 otic transcription factors, 141–142 placodal properties, 141 Six genes, 138–139 zebrafish, mutations, 142 signals BMP, 155–157

FGF, 157–158 Wnt, 158–160 Partial differential equations (PDEs) accuracy, 17 algorithm, 16–17 cAMP level, 236 as signaling machinery, 299 superfamily, 238 Pax gene, 138, 140, 145 PDEs, see Partial differential equations; Phosphodiesterases Peripheral nervous system (PNS) embryonic critical events, 101 Crn, 103 peripheral glia, 101–102 proteins, 104 sensory neuronal precursors, 102 larval and pupal fray mutants exhibition, 104–105 morphogenesis and adult, 105 olfactory system, 105–106 Phosphodiesterases (PDEs) cyclic nucleotide, 238 isoforms, 239 Photobleaching and photoactivation technique FLIP, 79 FRAP, 78–79 PAGFP, use, 78 ROI, 78–79 Pitx gene, 138 PKA, see Protein kinase A Placodes individualization embryonic ectoderm, 172 fate mapping double labeling, 172, 174 overlapping, 172–173 mechanisms, 171 panplacodal primordium, 170–171 PNS, see Peripheral nervous system Profundal and trigeminal placodes definition, 135–136 induction stages, 169 Irx gene, 167–168 neural tube, 170 Pax3 gene, 167 sensory neurons, 175 Protein kinase A (PKA) AKAPs cellular functions, 275–289 dysfunction in human diseases, 289–299 evolution, 254–275 family diversity, 254 interactions, structural aspects, 246–257 nomenclature, 246 cAMP signaling adenylyl cyclases, 237–238

336

Index

Protein kinase A (PKA) (cont.) catalytic and regulatory subunit isoforms, 240–241 PDEs, 238–239 PPs, 239 proteins interaction, C subunits, 242–244 R2D2 proteins, 242 structural aspects, 241 Q Quasi-steady-state approximation (QSSA), 17–18 R Region of interest (ROI), 78–79 RI-anchoring disruptor (RIAD) amino acids, 253–254 PKA-anchoring disruptors, 295 R-binding peptides, 253 RII-binding domain (RIIBD) AKAPs, 245 consensus sequence, 249 Drosophila, 274 environment, 250 helical wheel analysis, 246 helix, 251 NMR structures, 246 targeted deletion, 295–296 S Sarcoplasmic reticulum (SR) AKAP18d, 297 cardiac myocytes, 282 Schwartz_3D_FLIP, 34 Septate junctions (SJs) BEB, 118–120 CNS, 116–117 molecular components, 100 vs. paranodal axoglial, 98 proteins, 98–99 Sexually transmitted disease (STD), 299 Signaling proteins, in vitro regulation adhesion disassembly, 71 FAK signaling, 71–72 fascin, 72 Six genes Six1/2, 138, 140–141 Six4/5, 138, 141 SJs, see Septate junctions Solvers, VCell discretization schemes, 15 ODEs, 16 PDEs, 16–17 stochastic hybrid, 18–19 Hy3S, open source library, 19 “indexed priority queue,” 18

“next reaction” algorithm, 18 time scales handling QSSA, 17–18 “stiff solvers,” 17 SoxB1 genes dual functions neuronal differentiation, 178 retina and otic placode, 179 roles, 178–179 transcription factor, 177 sensory and neuronal lineages, 143 target genes, 180 Sox gene, 138 Spatial applications, VCell boundary conditions A_NPC equation format, 14 Brush_Border, as regulator, 13–14 InFlux and OutFlux, 14 “inside–outside” relationship, 15 KMOLE conversion factor, 14–15 types, 13 geometry handling 2D structure, 11–12 grid points, 12 space discretization, 11 staircase approximation, 12–13 mechanisms, defined, 11 SR, see Sarcoplasmic reticulum STD, see Sexually transmitted disease T Tbx gene, 138, 165, 176–177 Total internal reflection fluorescence microscopy (TIRF-M), 78 V VCell, see Virtual Cell Ventral nerve cord (VNC) LG ensheath longitudinal axon tracts, 107 MG ensheath ACs and PCs stages, 108 wrapper and nrx IV, 109–111 Vertebrate cranial placodes adenohypophyseal, 132–133 amniotes, profundal and trigeminal, 132 developmental process, 131 epibranchial, 135–136 morphogenesis cell delamination and migration, 181–185 invagination and shape formation, 185–189 neural crest, 130–131 neurons and sensory cell mechanisms, 174–177 proneural genes, 179–181 SoxB1 gene functions, 177–179 olfactory, 133–134 otic and lens, 134

337

Index

panplacodal primordium individualization, 170–174 multistep induction, 160–170 signals, 155–160 transcription factor role, 148–155 preplacodal ectoderm origins, 136–137 transcription factors, 138–145 profundal and trigeminal, 135 Virtual Cell (VCell) actin dynamics Arp2/3 complex activation, 44 assembly and disassembly rates, 44 conceptual models, 44–45 database structure, 45 dendritic nucleation model, features, 46–48 F-actin polymer cytoskeleton, 43 Filament-Length and BranchFraction, 46 lumping process, 46 mechanisms, 45 applicability, 5–6 BioModel workspace, 6–9 BioNetGen@VCell, 20 cell electrophysiology model developing tools, 48–49 “Electrical Mapping” tool, 34 Gndph_wBuffer, 35–36 MathModel workspace, 34–35 compartmental applications, 9–10 developments, 3 fluorescence microscopy data analysis, 31–34 gene regulatory network model

circadian clocks, 42 math description, 41–42 steps, 40 translation events implementation, 42–43 Goldbeter–Koshland model, 3 G-protein receptor signaling, 27–29 Hodgkin–Huxley model, 2–3 intracellular transport, 36–39 LEGI model, 29–30 MathModel, description, 4–5 multiscale modeling, 48 nucleocytoplasmic transport, 24–27 parameter scanning and parameter estimation, 19 quantitative studies, calcium dynamics, 21–24 sensitivity analysis, 20 solvers, 15–19 spatial applications, 11–15 VNC, see Ventral nerve cord W Widefield fluorescence technique assembly and disassembly rates, 76–77 epifluorescent microscopy, 76 labeling strategies, 75–76 spinning disk microscopy, 77 Y Yotiao, 286–287

A

B

Simulated geometry graded NWASP in the front of the lamellipodium X Y

Simulated total F-actin XY slice near cell bottom Z

920 mM

130 mM

C

Filament turnover from speckle microscopy

D 19 mM/s 0 mM/s

Blue Green

Simulated filament turnover; XY slice near cell bottom

Red

Depolymerization Polymerization

–1.3 mM/s

Boris M. Slepchenko and Leslie M. Loew, Figure 1.12 Selected features of the actin dendritic nucleation model. (A) Surface rendering of the outer membrane of the 3D geometry used for the VCell simulations. A graded band of active NWASP on the front of the lamelipodium membrane recruits and activates Arp2/3 to initiate nucleation. (B) Simulations run to steady state produce F-actin accumulation in the lamellipodium, as shown in this plane at the bottom of the 3D geometry. The scale shows how the colors are mapped to concentrations of actin subunits within filaments. (C) Map of net polymerization and depolymerization activity in the lamellipodium of an epithelial cell derived from speckle microscopy experiments (Ponti et al., 2005). Note the sharp transition between polymerization at the edge and depolymerization within 2 mm of the edge (white scale bar is 5 mm). (D) Simulation result for net actin polymerization rates at steady state. The white band shows a region of strong polymerization and is 2 mm wide before a sharp transition to depolymerization (negative rates). Behind these two bands of activity, the bulk of the cell displays near zero actin filament assembly rates. Figure 1.12C is reprinted from Biophysical Journal, vol. 89, Ponti et al., # 2005, with permission from Elsevier.

A

repo-GAL4; UAS-tau-GFP

GFP REPO FAS II

B

GFP

REPO

a

b

c

d

FAS II

Kevin Blauth et al., Figure 3.1 Axonal ensheathment in the Drosophila embryonic PNS. (A, B) Whole mount stage 16 repo-Gal4; UAS-tauGFP (A) and higher magnification (Ba–d) of a portion of the embryo shown in (A) is stained with anti-GFP (green; A, Ba, Bd), anti-Repo (red; A, Bb, Bd), and anti-Fas II (blue; A, Bc, Bd). The GFP staining reveals the glial processes that surround the Fas II labeled motor axons. The glial nuclei expressing Repo show the arrangement of glial cells along the length of the axon trajectories.

A nrx IV::GFP

B

nrx IV::GFP

GFP REPO

C

nrx IV::GFP

GFPREPO

VNC

GFP REPO

D +/+

a a a m a a

Kevin Blauth et al., Figure 3.2 Septate junctions and axonal ensheathment in the larval peripheral nerve fibers. (A–C) A portion of the nrx IV::GFP third instar larval ventral nerve cord (VNC) with peripheral nerves stained with anti-GFP (green) and anti-Repo (red). nrx IV::GFP expresses GFP in endogenous Nrx IV pattern. The peripheral nerves (A, B) reveal glial membrane expression and SJ localization of Nrx IV (arrowheads, A, B) along the length of the axon, while VNC shows localization of Nrx IV in surface glia (arrows, A, C), which are known to have SJs. Under the surface glial layer, there are Repo-positive glial cells (C, red). A wild-type third instar larval peripheral nerve (D) in cross section shows the presence of SJs (arrowheads) between outer and inner glial membranes. A large number of axons (a) are tightly fasciculated and ensheathed by glial processes (m).

A

GFP

WRAP

a

b

BP102

B

c

d

GFP

WRAP

a

b

BP102 AC PC

c

d

Kevin Blauth et al., Figure 3.3 Ensheathment of commissural axons in the Drosophila embryonic CNS. (A, B) sim-Gal4, UAS-tau-GFP embryo at a lower (Aa–d) and higher (Ba–d) magnifications show staining with anti-GFP (Aa, Ba, green), anti-Wrapper (Ab, Bb, red), and BP102 (Ac, Bc. blue). The GFP staining highlights the Sim-positive midline glia and neurons (Aa, Ba) while Wrapper expression is in the midline glia (Ab, Bb) and BP102 (Ac, Bc) labels the anterior commissure(AC) and posterior (PC) commissure. Note the midline glial processes (arrow, Aa, Ba) that ensheath the AC and PC (see merged panels, Ad, Bd). The midline glia express Wrapper (arrowheads, Bb).

A CC

NRX IV CRB

B

C L

PC CC

PSC

PC

CC PR

PC PR

Kevin Blauth et al., Figure 3.4 Photoreceptor ensheathment and septate junctions in adult Drosophila eye. (A, B) A light microscopy image (A) and ultrastructural view in longitudinal section (B) of a single Drosophila ommatidium of the adult compound eye. Accessory cells (A), namely the cone cells (CC) and pigment cells (PC) express Nrx IV (green) while photoreceptors (PR) express the apical protein Crumbs (Crb). The ultrastructure at a lower magnification (B) reveals the anatomy of the ommatidium. On top of the pseudocone (PSC) is the lens (L) and at the bottom are the CC, PC, and PR. A higher magnification (C) reveals presence of extensive SJs (arrows) basal to the adherens junctions (arrowhead) that are formed between CC and PC. These SJs serve as protective barriers and seals the PR for proper phototransduction.

A Neural plate border state model

Binary competence model

B

Epidermal–placodal competence Neural–neural crest competence Placode inducing signals Neural crest inducing signals

Epidermis Panplacodal primordium Neural crest Neural plate

aWnt

BMP BMP FGF aBMP? aWnt?

BMP Wnt

BMP Wnt FGF? Wnt BMP

aBMP aWnt, FGF?

Wnt

BMP

BMP Wnt

BMP Wnt FGF Wnt

FGF Wnt

Gerhard Schlosser, Figure 4.3 Induction of the panplacodal primordium at the neural plate border (modified from Schlosser, 2006). (A) The ‘‘neural plate border state’’ model proposes that neural crest and panplacodal primordium are induced from a common neural plate border region (purple) by signals from adjacent tissues (blue and red arrows). In contrast, the ‘‘binary competence’’ model proposes that neural crest and panplacodal primordium are induced at the border of a neural (light green) and nonneural (light yellow) competence territory, respectively. (B) Summary of signals involved in induction of neural crest (blue, left side) and panplacodal primordium (red, right side) according to the binary competence model. Neural crest is induced in neurally competent ectoderm (faint green) by BMP, Wnt, and FGF signals from endomesoderm and nonneural ectoderm, while Wnt inhibitors prevent neural crest induction rostrally. The panplacodal primordium is induced in nonneurally competent ectoderm (faint yellow) by FGFs, BMP inhibitors, and Wnt inhibitors from endomesoderm and the neural plate, while Wnts in the absence of Wnt inhibitors prevent its induction in the trunk.

Neural plate

A

Neural crest

Panplacodal Epidermis primordium

B

Dlx3, Dlx5 GATA2, GATA3 FoxI1a, FoxI1b, FoxI1c Vent2 AP2 Msx1, Msx2 Zic1, Zic2, Zic3, Zic4, Zic5 c-Myc Id3 Hairy2a, Hairy2b Sox2, Sox3 Snail1, Snail2 (slug) FoxD3 Six1, Six4 Eya1

Sox2, Sox3 Slug, FoxD3 Six1, Six4, Eya1 Dlx3, Dlx5, GATA2, GATA3, FoxI1 Vent2, AP2

Gerhard Schlosser, Figure 4.4 Expression domains of transcription factors at the neural plate border of Xenopus. (A) Summary of expression domains. White hatching indicates that transcription factors are expressed only in subregions of the panplacodal primordium. For details see text. References for expression patterns are listed elsewhere (Schlosser, 2006, 2008) except for Vent2 (Ladher et al., 1996; Onichtchouk et al., 1996; Papalopulu and Kintner, 1996; Schmidt et al., 1996). ERNI is not shown, because it has not been identified in Xenopus. (B) Expression domains in relation to neural plate (green), neural crest (blue), and panplacodal primordium (red) in a dorsal view. Dlx3/5, GATA2/3, and FoxI1 are expressed in ectoderm peripheral to hatched orange line, while Vent2 and AP2 are expressed in ectoderm peripheral to hatched purple line. This suggests that Dlx3/5, GATA2/3, and FoxI1 may be involved in setting the border between neural (green or blue) and nonneural (white or red) ectoderm, Vent2 and AP2 may be involved in setting the border between neural plate (green) and neural crest (blue).

A

B

C Ot

LL/Ot/EB

Pr

Sox2, Sox3 Six1, Six4, Eya1 FoxD3, Slug Pax6 (Ad/Ol, L, V) Pax3 (Pr) Pax8, Pax2 (LL/Ot/EB)

Pr

V

AD M

Ol Ol

V L

IX/M

P X2/3

L

X1 IX

VII

V VII/AV cg

AV

D Six1, Six2, Six4, Eya1, Grg4, Grg5, Dlx3, Dlx5, Dlx6, GATA1, GATA2, GATA3, NZFB, FoxI1, Id3, Hes6?, ESR6e Ngnr1, Id4, Hairy2a?, Hairy2b? Irx1, Irx2, Irx3, Msx1, Msx2, Gbx2 Pax6

Tbx2 Sox2, Sox3, Sox11, Pax2, Pax8, Lmx1b

Six3, Six6, Dmrt4, Otx2, Otx1?, Otx4?, Otx5, Nkx5.1, Pitx1, Pitx2c, FoxE4

Sox9, Sox10, *Nkx5.1, *ATH1

ANF1, ANF2, Sox2, Sox3, Sox11, FoxG1

Pitx3 Lhx3

Ad

*Sox9 *Emx2 *Eomes.

Ol

ATH5 *HRT1

*Sox2, *Sox3 *MafB,*L-Maf *Pitx3 *Msx1 *Tbx2

L

*Tbx2

V

*Emx2 *FoxK1 *Pax5 Ot *Tbx1

Pax3

Pr

LL

*Tbx3

*FoxI2, *Phox2a,*Phox2b *Ngnr1

EB

Gerhard Schlosser, Figure 4.5 Specification of placodes by a nested hierarchy of transcription factors. (A–C) Development of placodes in Xenopus laevis (modified from Schlosser and Ahrens, 2004). Lateral views of neural plate stage (A), early neural tube stage (B), and early tailbud stage (C) embryos. Colored outlines in (A) depict transcription factor expression domains in relation to neural plate (grey), neural crest

(blue), and panplacodal primordium (red). At subsequent stages, green colored placodes (olfactory, lens, trigeminal, as well as the adenohypophyseal placode, which is not shown) continue to express Pax6, the yellow colored profundal placode expresses Pax3, and the brown (lateral line placodes), pink (otic placode), and orange (epibranchial placodes) colored components of the posterior placodal area express Pax2 and Pax8. Neural crest streams are indicated by broken blue lines in (B). (D) Schematic summary of transcription factor expression domains in the placodal ectoderm of neural plate stage Xenopus embryos (from Schlosser, 2006). The position of prospective placodes within the panplacodal primordium (faint red) is indicated by colored rectangles. Colored lines enclose expression domains of the transcription factors listed. Transcription factors preceded by asterisks refer to expression domains established at later stages. Question marks indicate that precise domain boundaries are not known. For details and references see Schlosser (2006). Transcription factor expression domains form a nested hierarchy centered on two foci, the anterior placodal area (with prospective adenohypophyseal and olfactory placodes), and posterior placodal area (with prospective lateral line, otic, and epibranchial placodes). Abbreviations: Ad, Adenohypophyseal placode; Ad/Ol, Anterior placodal area; AV, Anteroventral lateral line placode; cg, Cement gland; EB, Epibranchial placodes; L, Lens placode; LL, lateral line; LL/Ot/EB, Posterior placodal area; M, Middle lateral line placode; Ol, Olfactory placode; Ot, Otic placode; P, Posterior lateral line placode; Pr, Profundal placode; V, Trigeminal placode; VII, Facial epibranchial placode; IX, Glossopharyngeal epibranchial placode; X1, First vagal epibranchial placode; X2/3, Second and third vagal epibranchial placodes (fused).

PP EB LL Ot LL

Pr/V

EF

R4

B MH

OI

ANR

L

Ad

FGF

GF PD FGF, GF wnt FG F

BM

FGF

P, F

FGF

Wnt

Not

Shh

MP

Anti-Wnt

FG

F, B

Wnt

Gerhard Schlosser, Figure 4.6 Multistep induction of placodes. Summary of signals (arrows) and signaling centers involved in the induction of placodes (colored ovals within red panplacodal primordium) or multiplacodal areas (differently shaded areas or colored outlines within red panplacodal primordium). The anterior placodal area (green outline) comprises the prospective adenohypophyseal and olfactory placodes. Together with the prospective lens placode these form the extended anterior placodal area. The posterior placodal area (brown outline) comprises the prospective otic, lateral line, and epibranchial placodes. Together with the prospective profundal and trigeminal placodes, these form the extended posterior placodal area. To provide a general overview, signaling events at various stages of development are depicted here in a neural plate stage embryo (neural plate: grey; neural crest: blue; panplacodal primordium: red). Solid arrows indicate signaling events at neural plate stages while broken arrows indicate signaling events at later stages of development. See text for further explanation. Abbreviations: Ad, Adenohypophyseal placode; ANR, Anterior neural ridge; EB, Epibranchial placodes; EF, Eye field; L, Lens placode; LL, Lateral line placodes; MHB, Midbrain-hindbrain boundary; Not, Notochord; Ol, Olfactory placode; Ot, Otic placode; PP, Pharyngeal pouches; Pr/V, Profundal/trigeminal placodes; R4, Rhombomere 4.

A

B

Philipp Skroblin et al., Figure 5.2 The crystal structure of the RIIa D/D domain dimer in complex with a D-AKAP2 peptide (PDB ID: 2HWN). The two RIIa protomers are shown in green and yellow, respectively. Coloring of the D-AKAP2 peptide represents amino acid polarity (blue: hydrophobic, red: polar). (A) View on top of the AKAP interaction site. (B) View from the side (visual axis ¼ peptide helix axis) showing the interaction of the unpolar (Blue) side of the peptide with the hydrophobic groove of the AKAP interaction site on the top left side. Adopted from Kinderman et al. (2006).

A

B

Philipp Skroblin et al., Figure 5.4 The NMR structure of GSKIP (PDB ID: 1SGO). (A) GSKIP consists of an unstructured N-terminus (gray, amino acids 1–32) followed by an a-helix (red, amino acids 33–48), a central b-sheet region (blue, amino acids 49– 115) and a C-terminal a-helix (green, amino acids 116–139). Adopted from Hundsrucker et al. (2010). (B) The surface of the protein is illustrated in gray, the RIIBD is shown in red and conserved hydrophobic residues therein are labeled yellow.

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