Cellular Domains E-Book

Ivan R. Nabi

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1153

Veröffentlichungsjahr: 2011

Beschreibung

Cellular domains play vital roles in a wide range of cellular functions. Defining cellular domains and understanding the molecular basis of their formation is essential to the study of cell functionality. This authoritative reference provides the most comprehensive analysis available on cellular domains, with emphasis on the definition and molecular composition of the domain as well as the functional implications of domain organization.

Leseprobe

Table of Contents

Cover

Title page

Copyright page

DEDICATION

PREFACE

CONTRIBUTORS

PART I: MEMBRANE DOMAINS

CHAPTER 1 CYTOSKELETON-INDUCED MESOSCALE DOMAINS

DEFINITION

HISTORICAL PERSPECTIVE

ELECTRON TOMOGRAPHY OF THE THREE-DIMENSIONAL STRUCTURE OF THE CYTOPLASMIC SURFACE OF THE PLASMA MEMBRANE

ANSWERS TO THE TWO THREE-DECADE-OLD ENIGMAS

MEMBRANE-SKELETON-BASED MESODOMAINS ARE NOT DETECTED IN EVERY STUDY

MOLECULAR COMPOSITION OF THE MEMBRANE SKELETON

FUNCTIONS OF MEMBRANE-SKELETON-INDUCED MESODOMAINS

FUTURE PERSPECTIVES

ABBREVIATIONS

CHAPTER 2 CLATHRIN-COATED PITS

DEFINITION

HISTORICAL PERSPECTIVE

MOLECULAR COMPONENTS OF CLATHRIN-COATED PITS AND VESICLES

FUNCTIONAL IMPLICATIONS OF CLATHRIN-COATED PIT ORGANIZATION

ABBREVIATIONS

CHAPTER 3 CAVEOLAE

DEFINITION

HISTORICAL PERSPECTIVE

DISTRIBUTION AND MORPHOLOGY

MOLECULAR COMPONENTS

CAVEOLAR LIPIDS

CAVEOLAR NECKS: TRANSITION ZONE BETWEEN CAVEOLAR MEMBRANE AND PLASMALEMMA PROPER

CAVEOLAE BIOGENESIS

CELLULAR FUNCTIONS OF CAVEOLAE

INVOLVEMENT OF CAVEOLAE IN DISEASE

FUTURE PERSPECTIVES

ACKNOWLEDGMENTS

ABBREVIATIONS

CHAPTER 4 LIPID RAFTS

DEFINITION

HISTORICAL PERSPECTIVE

RAFT BIOPHYSICS: DO THEY REALLY EXIST?

RAFTS IN HOST–PATHOGEN INTERACTIONS

FUTURE PERSPECTIVES

ABBREVIATIONS

CHAPTER 5 MODELING MEMBRANE DOMAINS

DEFINITION

HISTORICAL PERSPECTIVE

THEORETICAL BASICS

FRAP

FRET

SPT

FCS

FUTURE PERSPECTIVES

ACKNOWLEDGMENTS

ABBREVIATIONS

PART II: ORGANELLAR DOMAINS

CHAPTER 6 MITOCHONDRIA

DEFINITION

HISTORICAL PERSPECTIVE

THE BUILDING BLOCKS OF MITOCHONDRIA

RECENT AND PROSPECTIVE DEVELOPMENTS IN MITOCHONDRIAL RESEARCH

ACKNOWLEDGMENTS

ABBREVIATIONS

CHAPTER 7 THE ENDOPLASMIC RETICULUM

DEFINITION OF THE DOMAIN

INTRODUCTION AND HISTORICAL PERSPECTIVES

STRUCTURE AND DYNAMICS OF THE ER

ER PROTEOME

ER: A MULTIFUNCTIONAL ORGANELLE

CONCLUSION

ACKNOWLEDGMENTS

ABBREVIATIONS

CHAPTER 8 THE GOLGI APPARATUS

DEFINITION

HISTORICAL PERSPECTIVE

GOLGI GLYCOSYLATION

TRAFFICKING TO, FROM, AND WITHIN THE GOLGI

THE GOLGI AND CELLULAR SIGNALING AND POLARITY

CONCLUSION

ACKNOWLEDGMENT

ABBREVIATIONS

CHAPTER 9 ENDOSOMES

DEFINITION

AN OVERVIEW OF THE ENDOSOMAL NETWORK

MOLECULAR DOMAINS OF ENDOSOMAL MEMBRANES

ENDOSOMES AS ORGANIZERS OF CELL AND TISSUES

FUTURE PERSPECTIVES

ABBREVIATIONS

CHAPTER 10 LYSOSOMES AND PHAGOSOMES

DEFINITION

HISTORICAL PERSPECTIVE

THE PHAGOSOME PROTEOME

HOST–PATHOGEN INTERACTIONS

FUTURE PERSPECTIVES

ABBREVIATIONS

CHAPTER 11 ENDOPLASMIC RETICULUM JUNCTIONS

DEFINITION

HISTORICAL PERSPECTIVE

MOLECULAR COMPOSITION OF JUNCTIONS

UNRESOLVED ISSUES

CONCLUSIONS

ABBREVIATIONS

PART III: CYTOSKELETAL DOMAINS

CHAPTER 12 THE ACTIN CYTOSKELETON

DEFINITION

HISTORICAL PERSPECTIVES

MOLECULAR COMPOSITION OF THE DOMAIN

FUNCTIONAL IMPLICATIONS AND ROLES FOR DOMAIN ORGANIZATION

FUTURE PERSPECTIVES

ABBREVIATIONS

CHAPTER 13 MICROVILLI

DEFINITION

HISTORICAL PERSPECTIVE

MOLECULAR DETERMINANTS INVOLVED IN THE CONTROL OF THE MICROVILLI SHAPE

INTESTINAL MICROVILLI FORM A DOMAIN SPECIALIZED IN ABSORPTION

PERSPECTIVES ON THE BRUSH BORDER DYNAMICS

GENERAL CONCLUSIONS

ACKNOWLEDGMENTS

CHAPTER 14 MICROTUBULES

DEFINITION

HISTORICAL PERSPECTIVE

MOLECULAR COMPOSITION OF MICROTUBULES, STRUCTURAL PROPERTIES, AND ASSEMBLY OF MICROTUBULES

FUNCTIONAL IMPLICATIONS OF MICROTUBULE SPATIAL ORGANIZATION

ABBREVIATIONS

CHAPTER 15 CILIA

DEFINITION

HISTORICAL PERSPECTIVE

MOLECULAR COMPOSITION

FUNCTIONAL IMPLICATIONS OF DOMAIN ORGANIZATION

ABBREVIATIONS

CHAPTER 16 INTERMEDIATE FILAMENTS

DEFINITION

HISTORICAL PERSPECTIVE

KEY IF PROTEIN FEATURES

KERATIN INTERPLAY WITH SURFACE MEMBRANE DOMAINS

RELEVANCE TO HUMAN DISEASES

UNCERTAINTIES AND CONTROVERSIES

FUTURE PERSPECTIVES

ACKNOWLEDGMENTS

ABBREVIATIONS

PART IV: ADHESIVE AND COMMUNICATING DOMAINS

CHAPTER 17 FOCAL ADHESIONS

DEFINITION

HISTORICAL PERSPECTIVE

ASSEMBLY OF FCXS AND FAS

FA COMPLEXITY

MECHANISMS OF FA AND FCX DISASSEMBLY

IMAGING ADHESION DYNAMICS

FUTURE PERSPECTIVES

ABBREVIATIONS

CHAPTER 18 THE ADHERENS JUNCTION

DEFINITION

HISTORICAL PERSPECTIVE

PERIPHERAL MEMBRANE COMPONENTS OF THE AJ

METAZOAN DEVELOPMENT AND ORGANIZATION THROUGH THE AJ

THE AJ AND EPITHELIAL CELL POLARITY

PROTEIN TRAFFICKING AND POLARITY AT THE AJ

MAINTENANCE AND REMODELING THE AJ

AJ REGULATION OF THE CYTOSKELETON

FUTURE PERSPECTIVES

ACKNOWLEDGMENTS

ABBREVIATIONS

CHAPTER 19 SPECIALIZED INTERCELLULAR JUNCTIONS IN EPITHELIAL CELLS: THE TIGHT JUNCTION AND DESMOSOME

TIGHT JUNCTIONS

DESMOSOMES

FUTURE PERSPECTIVES

CHAPTER 20 GAP JUNCTIONS

DEFINITION

HISTORICAL PERSPECTIVE

MOLECULAR COMPOSITION OF THE GAP JUNCTION DOMAIN

GAP JUNCTION GROWTH, INTERNALIZATION, AND RENEWAL

SIGNIFICANCE OF THE GAP JUNCTION DOMAIN

FUTURE DIRECTIONS

ACKNOWLEDGMENTS

PART V: POLARIZED CELLULAR DOMAINS

CHAPTER 21 EPITHELIAL DOMAINS

DEFINITION

APICAL–BASOLATERAL POLARIZATION IN EPITHELIAL CELLS

MECHANISMS TO LOCALIZE PM PROTEINS APICALLY OR BASOLATERALLY

MECHANISMS INVOLVED IN THE SORTING OF NA,K-ATPASE

PROTON-COUPLED MONOCARBOXYLATE TRANSPORTERS

VARIATIONS IN THE SORTING MACHINERY AMONG EPITHELIA

FUTURE PERSPECTIVES

ABBREVIATIONS

CHAPTER 22 NEURONAL DOMAINS

DEFINITION

HISTORICAL PERSPECTIVE

NEURONAL DOMAINS

NEURONAL DOMAINS DURING DEVELOPMENT

NEURONAL DOMAINS AND MENTAL DISORDERS

FUTURE PERSPECTIVES

ACKNOWLEDGMENTS

ABBREVIATIONS

PART VI: DOMAINS REGULATING GENE EXPRESSION

CHAPTER 23 NUCLEAR DOMAINS

DEFINITION

CHROMATIN AND THE INTERCHROMATIN DOMAIN SPACE

NUCLEOLUS

PML NUCLEAR BODIES

CAJAL BODIES AND NUCLEAR GEMS

NUCLEAR SPLICING SPECKLES

POLYCOMB BODIES

PNC

SAM68/SAM68-LIKE MAMMALIAN (SLM) NUCLEAR BODIES

SUMMARY

ACKNOWLEDGMENTS

ABBREVIATIONS

CHAPTER 24 THE NUCLEAR PORE

DEFINITION

HISTORICAL PERSPECTIVE

MOLECULAR ORGANIZATION OF THE NPC

TRANSPORT THROUGH THE NPC

ESTABLISHING DIRECTIONALITY FOR NUCLEAR TRANSPORT

SUMMARY AND FUTURE PERSPECTIVES

ABBREVIATIONS

CHAPTER 25 CYTOPLASMIC RNA DOMAINS

DEFINITION

HISTORICAL BACKGROUND

RNPS AND POSTTRANSCRIPTIONAL GENE REGULATION

ABBREVIATIONS

Index

Copyright © 2011 by Wiley-Blackwell. All rights reserved

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Library of Congress Cataloging-in-Publication Data:

Cellular domains / edited by Ivan R. Nabi.

p. ; cm.

 Includes bibliographical references and index.

 ISBN 978-0-470-59544-2 (cloth)

 1. Cell membranes. I. Nabi, Ivan R.

 [DNLM: 1. Cell Membrane Structures. 2. Cell Physiological Phenomena. 3. Cytoplasmic Structures. QU 350]

 QH601.C435 2011

 571.6'4–dc22

oBook ISBN: 9781118015759

ePDF ISBN: 9781118015735

ePub ISBN: 9781118013742

This book is dedicated to my wife Hakima and children Nessim and Zachary as well as to my parents Ruth and Jim who have supported me throughout my career. It is first of all the work of the contributors, whom I thank enormously for their efforts. It is also the result of my own personal scientific journey that was shaped by my mentors, Avraham Raz and Enrique Rodriguez-Boulan, as well as by all the stimulating interactions I have enjoyed over the years with colleagues, collaborators, students and post-docs.

PREFACE

Cellular compartmentalization into and within organelles segregates biochemical reactions and increases local molecular concentrations, thereby promoting efficiency of cellular processes. Within membranes, subdomains generate lateral heterogeneity that organizes the spatial distribution of glycoprotein receptors and membrane proximal effectors. Morphologically identifiable plasma membrane domains include not only clathrin-coated pits and caveolae but also lipid rafts that form a class of membrane domains that are poorly defined morphologically. Cellular organelles, such as mitochondria, the endoplasmic reticulum, the Golgi apparatus, endosomes, and lysosomes, also define morphologically distinct domains whose functionality depends, in large part, on the establishment of “domains within domains.” Cellular organization is determined by cytoskeletal elements, including the actin and microtubule cytoskeletons, that generate cell surface microvilli and cilia, respectively, as well as intermediate filaments. Adhesive and communicating domains regulate interaction of the cell with the substrate through focal adhesions, as well as with other cells via adherens junctions, tight junctions, desmosomes, and gap junctions. The latter are particularly expressed in epithelial cells whose apical–basolateral polarization is critical to their transport function. Essentially, all cells are polarized, and the neuron represents a prime example of how cellular polarization results in the formation of functional domains. Nuclear domains control genetic regulation and transcription, and nuclear–cytoplasmic exchange and transport is mediated by the nuclear pore that delivers RNA to cytoplasmic domains that regulate RNA translation and degradation. Molecular determinants of cellular domains therefore include essentially all molecular components of the cell, including DNA, RNA, proteins, lipid, and glycans. Defining domains and understanding the molecular basis of their formation is central to understanding cellular function.

Ivan R. Nabi

CONTRIBUTORS

John D. Aitchison, PhD, Institute for Systems Biology, Seattle, WA 98103-8904

François Bordeleau, Centre de recherche en cancérologie de l’Université Laval and Centre de Recherche du Centre Hospitalier de Québec (CRCHUQ), Quebec City, Quebec, Canada

Keith Burridge, PhD, Department of Cell and Developmental Biology and Lineberger Cancer Center, University of North Carolina, Chapel Hill, NC 27599

Kendra L. Cann, Department of Pathology, Dalhousie University, Halifax, Nova Scotia, Canada

Marcelino Cereijido, MD, PhD, Center for Research and Advanced Studies, Department of Physiology, Biophysics and Neurosciences, Mexico City, Mexico

Jesse T. Chao, Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, British Columbia, Canada

Jared M. Churko, Department of Anatomy and Cell Biology, Dental Science Building, University of Western Ontario, London, Ontario, Canada

Daniel Coombs, PhD, Department of Mathematics and Institute of Applied Mathematics, University of British Columbia, Vancouver, British Columbia, Canada

Dale Corkery, Department of Pathology, Dalhousie University, Halifax, Nova Scotia, Canada

Raibatak Das, PhD, Department of Mathematics and Institute of Applied Mathematics, University of British Columbia, Vancouver, British Columbia, Canada

Graham Dellaire, PhD, Department of Pathology, Dalhousie University, Halifax, Nova Scotia, Canada

James W. Dennis, PhD, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada

Michel Desjardins, PhD, Département de pathologie et biologie cellulaire, Université de Montréal, Montreal, Quebec, Canada

Leonard J. Foster, PhD, Centre for High-Throughput Biology and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, British Columbia, Canada

Takahiro K. Fujiwara, PhD, Center for Meso-Bio Single-Molecule Imaging (CeMI) and Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto, Japan

Thierry Galvez, PhD, Max Planck Institute for Molecular Cell Biology and Genetics MPI-CBG, Dresden, Germany

Stéphane Gilbert, PhD, Centre de Recherche en Cancérologie de l’Université Laval and Centre de Recherche du Centre Hospitalier de Québec (CRCHUQ), Quebec City, Quebec, Canada

Jennifer S. Goldman, Center for Neuronal Survival, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada

Guillaume Goyette, PhD, Département de pathologie et biologie cellulaire, Université de Montréal, Montreal, Quebec, Canada

Jody Groenendyk, PhD, Department of Biochemistry, School of Molecular and Systems Medicine, University of Alberta, Edmonton, Alberta, Canada

Laura K. Hilton, Department of Molecular Biology & Biochemistry, Simon Fraser University, Burnaby, British Columbia, Canada

Tom C. Hobman, PhD, Department of Cell Biology, University of Alberta, Edmonton, Alberta, Canada

Ziya Kalay, PhD, Center for Meso-Bio Single-Molecule Imaging (CeMI), Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto, Japan

Jonathan A. Kelber, PhD, UCSD School of Medicine, Department of Pathology and Moores Cancer Center, La Jolla, CA 92093-0612

Timothy E. Kennedy, PhD, Center for Neuronal Survival, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada

Richard L. Klemke, PhD, UCSD School of Medicine, Department of Pathology and Moores Cancer Center, La Jolla, CA 92093-0612

Keli Kolegraff, Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322

Akihiro Kusumi, PhD, Center for Meso-Bio Single-Molecule Imaging (CeMI), Institute for Integrated Cell-Material Sciences (iCeMS) and Research Center for Nano Medical Engineering, Institute for Frontier Medical Sciences, Kyoto University, Kyoto, Japan

Dale W. Laird, PhD, Department of Anatomy and Cell Biology, Dental Science Building, University of Western Ontario, London, Ontario, Canada

Bettina Lechner, PhD, Department of Botany, University of British Columbia, Vancouver, British Columbia, Canada

Christopher J.R. Loewen, PhD, Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, British Columbia, Canada

Anne Loranger, PhD, Centre de recherche en cancérologie de l’Université Laval and Centre de Recherche du Centre Hospitalier de Québec (CRCHUQ), Quebec City, Quebec, Canada

Daniel Louvard, PhD, CNRS, Institut Curie, Paris, France

Normand Marceau, PhD, Centre de recherche en cancérologie de l’Université Laval and Centre de Recherche du Centre Hospitalier de Québec (CRCHUQ), Quebec City, Quebec, Canada

Marek Michalak, PhD, Department of Biochemistry, School of Molecular and Systems Medicine, University of Alberta, Edmonton, Alberta, Canada

Jennifer S. Morrison, Department of Mathematics and Institute of Applied Mathematics, University of British Columbia, Vancouver, British Columbia, Canada

Ivan R. Nabi, PhD, Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, British Columbia, Canada

Porfirio Nava, Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322

W. James Nelson, PhD, Department of Biology, Stanford University, Stanford, CA 94305

Asma Nusrat, PhD, Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322

Henry Parker, PhD, Department of Cell Biology, University of Alberta, Edmonton, Alberta, Canada

Nancy Philp, PhD, Department of Pathology, Anatomy and Cell Biology, Jefferson Medical College, Thomas Jefferson University, Philadelphia, PA 19107

Christopher Ptak, PhD, Department of Cell Biology, University of Alberta, Edmonton, Alberta, Canada

Lynne M. Quarmby, PhD, Department of Molecular Biology & Biochemistry, Simon Fraser University, Burnaby, British Columbia, Canada

Andreas S. Reichert, PhD, CEF Makromolekulare Komplexe, Mitochondriale Biologie, Fachbereich Medizin, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany

Sylvie Robine, PhD, CNRS, Institut Curie, Paris, France

Enrique Rodriguez-Boulan, MD, Dyson Vision Research Institute, Departments of Ophthalmology and Cell Biology, Weill Medical College of Cornell University, New York, NY 10065

Liora Shoshani, PhD, Center for Research and Advanced Studies, Department of Physiology, Biophysics and Neurosciences, Mexico City, Mexico

Radu V. Stan, MD, Department of Pathology, Dartmouth Medical School, One Medical Center Drive, Lebanon, NH 03756

James R. Thieman, Department of Cell Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261

Caitlin Tolbert, Department of Cell and Developmental Biology, University of North Carolina, Chapel Hill, NC 27599

Christopher P. Toret, PhD, Department of Biology, Stanford University, Stanford, CA 94305

Linton M. Traub, PhD, Department of Cell Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261

Dan Tse, MD, Department of Pathology, Dartmouth Medical School, One Medical Center Drive, Lebanon, NH 03756

Florent Ubelmann, PhD, CNRS, Institut Curie, Paris, France

Geoffrey O. Wasteneys, PhD, Department of Botany, University of British Columbia, Vancouver, British Columbia, Canada

Richard W. Wozniak, PhD, Department of Cell Biology, University of Alberta, Edmonton, Alberta, Canada

Marino Zerial, PhD, Max Planck Institute for Molecular Cell Biology and Genetics MPI-CBG, Dresden, Germany

Michael Zick, Adolf-Butenandt-Institut für Physiologische Chemie, Ludwig-Maximilians-Universität München, München, Germany

PART I: MEMBRANE DOMAINS

MEMBRANE BILAYERS form hydrophobic dividers within the aqueous environment that exists both within and without the cell. The plasma membrane surrounds the cell, segregating the intracellular milieu from the outside environment. Domain organization of the plasma membrane is based not only on lipid- and protein-based interactions but also on the organization of the underlying actin cytoskeleton that impacts on molecular dynamics and function in the membrane (Chapter 1). Plasma membrane domains include not only clathrin-coated pits (Chapter 2), caveolae (Chapter 3), and less well-characterized lipid rafts (Chapters 4 and 5) but also cell-substrate adhesions (Chapter 17), cell–cell junctions (Chapters 18–20), and specialized polarized cellular domains (Chapters 13, 15, 21, and 22). Molecular transport across the plasma membrane and exchange with the extracellular milieu is the key to cellular functionality. It is mediated in large part by endocytosis via clathrin-coated pits (Chapter 2) and also, as described in later sections, by Golgi secretion (Chapter 8), exosomes (Chapter 9), transporters (Chapter 21), and gap junctions (Chapter 20). How lipids organize domains is a subject of intense investigation that has focused primarily on the role of lipid rafts (Chapters 1, 4, and 5). The underlying cytoskeleton controls and contributes to molecular dynamics in the plane of the membrane and to downstream signaling (Chapter 1; also Chapter 12), and the tools available to study and model membrane domain organization in living cells (Chapters 1 and 5) represent key elements of future research. Importantly, membrane domain organization is relevant not only to the plasma membrane but also to membranes of intracellular organelles (Chapters 6–11).

CHAPTER 1

CYTOSKELETON-INDUCED MESOSCALE DOMAINS

Ziya Kalay Takahiro K. Fujiwara Akihiro Kusumi

DEFINITION

The cell is much more than just a bag of protein juice. Indeed, many mechanisms exist in a living cell to keep its contents well organized. One of the most important apparatuses that the cell utilizes to organize the cytoplasm is the cytoskeleton. Therefore, the influence of the cytoskeleton on the two-dimensional fluid of the plasma membrane is an interesting subject. In this chapter, we will focus on this issue, and review the literature about the membrane domains delimited by the part of the actin-based cytoskeleton that is closely opposed to the cytoplasmic surface of the plasma membrane. This part of the cytoskeleton is referred to as the membrane skeleton. Due to its close association with the cytoplasmic surface of the plasma membrane, the membrane-skeleton meshwork directly influences the functions of the plasma membrane. As a consequence of the membrane-skeleton meshwork, the plasma membrane is effectively partitioned into mesoscale domains, or compartments, with sizes varying between 30 and 250 nm (with the exception of the larger 750-nm domain in the doubly nested compartments in normal rat kidney [NRK] cells; Fujiwara et al. 2002).

We emphasize the characteristic size of these compartments, one to several hundred nanometers, that falls in the mesoscale, where collective dynamics of molecules play critical roles. At this size scale, the number of molecules in the system is insufficient for thermodynamics to hold, but is still too big to be tractable for quantum mechanics. In the plasma membrane, there are three types of major mesoscale domains (mesodomains): (1) membrane compartments delineated by the actin-based membrane skeleton; (2) raft domains, where specific proteins, glycosphingolipids, and cholesterol are concentrated; and (3) the protein oligomer domains. In this chapter, we will concentrate on the membrane-skeleton-induced membrane compartments.

Membrane lipids and proteins are both temporarily trapped in these membrane compartments with residency times between 1 ms and 1 second (Kusumi et al. 2005). Namely, the two key functional elements of the membrane are both influenced by the membrane skeleton, and a growing number of findings support the involvement of the membrane skeleton in many membrane processes (Gaidarov et al. 1999; Nakada et al. 2003; O’Connell et al. 2006; Lajoie et al. 2007; Chung et al. 2010; Treanor et al. 2010). In this respect, one of our main goals in this chapter is to provide an account of the various functions of these membrane-skeleton-based mesodomains.

HISTORICAL PERSPECTIVE

One of the most important characteristics of the plasma membrane is that it is a two-dimensional liquid. The fluid mosaic model proposed by Singer and Nicolson (1972) successfully accounted for many properties of the plasma membrane. In fact, this model is still widely believed to represent the basic structure of the plasma membrane (and intracellular membranes) of all living cells on earth. However, it fails to answer two basic questions, which have puzzled scientists for the past three decades:

1. Why do the membrane proteins and lipids diffuse faster in artificial membranes than in the cellular plasma membrane by a factor of ∼20 (ranging from 5 to 50)? (Murase et al. 2004)

2. How do molecular complexes become immobilized on the cell surface or diffuse at surprisingly lower rates, as compared with single molecules? (Kusumi et al. 2005)

Early Fluorescence Recovery After Photobleaching (FRAP) Observations

Early measurements of diffusion coefficients using FRAP revealed significantly slower diffusion in the plasma membrane, as compared with that in artificially reconstituted membranes, by a factor of ∼20 (ranging from 5 to 50) (summarized by Murase et al. 2004). Some of these findings suggested that the discrepancy might be due to interactions between the cytoskeleton and the plasma membrane. In 1980, Sheetz et al. found that the diffusion coefficient of band 3 proteins in mouse erythrocyte mutants lacking the spectrin network is an order of magnitude higher than that in normal cells. In these cells, the membrane skeleton is formed by the spectrin network, and therefore, the results obtained with these mutant cells suggested that the membrane skeleton directly interferes with the diffusion of membrane proteins. For some other early examples of FRAP measurements that detected significantly reduced diffusivity in the plasma membrane (but not rotational diffusion; Tsuji et al. 1988), see the following references: Axelrod et al. (1976), Golan and Veatch (1980), Chang et al. (1981), Tsuji and Ohnishi (1986), and Tsuji et al. (1988).

Early Single-Molecule Observations: Membrane-Skeleton Fence Model for Transmembrane Proteins

The advent of single-molecule imaging methods, such as single-particle tracking (SPT) and single fluorescent-molecule tracking (SFMT), allowed researchers to image and track single molecules in the plasma membrane of living cells. In 1994, by using SPT techniques, Sako and Kusumi observed that transferrin receptor and α2-macroglobulin receptor undergo a peculiar kind of motion, which is characterized by temporary confinement of the molecule in a bounded region of the membrane, with an average area of 0.25 µm2, interrupted by rare hops into adjacent, yet still temporarily confining, regions. This behavior was termed hop diffusion, as the membrane molecules seemed to be hopping between adjacent membrane compartments and diffusing freely within a compartment. Subsequent experiments showed that all of the transmembrane proteins in every cell type observed displayed hop diffusion as well.

As the single-molecule imaging technique became more popular, a wealth of data started to accumulate that needed to be properly analyzed. The single-particle trajectories that exhibit hop diffusion have been analyzed by fitting appropriate models to them (see also Chapter 5 for further discussion of modeling single-particle trajectories). An early theoretical treatment by Powles et al. (1992) considered the diffusion of a particle trapped between partially permeable barriers arranged periodically in space. Here, exact mathematical expressions for some transport quantities in the system were obtained. In more recent treatments by Kenkre et al. (2008) and Kalay et al. (2008), exact formulas for the time-dependent mean square displacement and diffusion coefficient for a particle moving in a similar compartmentalized space were acquired, and the effects of disordered fence strengths and compartment sizes were predicted. The results of these works have successfully been used to deduce compartment sizes from single-particle trajectories recorded in the plasma membrane.

Based on these findings, the membrane-skeleton fence model was proposed (Sako and Kusumi 1994; Kusumi and Sako 1996), in which the plasma membrane is effectively partitioned into mesoscale compartments. In this model, the membrane skeleton forms a meshwork near the cytoplasmic part of the plasma membrane with which the cytoplasmic domains of transmembrane proteins can interact. As a result of this interaction, transmembrane proteins are temporarily confined in membrane compartments induced by the membrane-skeleton meshwork. Transmembrane proteins can hop between adjacent compartments if the distance between the meshwork and the membrane becomes large enough, or if the meshwork temporarily and locally dissociates. See Figure 1.1 (top) for an illustration of these ideas.

Figure 1.1. Schematic illustrations of the membrane-skeleton fence and anchored-protein picket models, and the picket-induced slowdown effect. According to the membrane-skeleton fence model (top), transmembrane proteins with protrusions in the cytoplasm are temporarily confined in membrane-skeleton-based compartments formed by a dynamic meshwork of actin filaments. Consequently, these proteins undergo hop diffusion, as illustrated by the color-coded trajectory. Lipids in the upper leaflet of the plasma membrane can also undergo hop diffusion even though they lack cytoplasmic domains. In the anchored-protein picket model (middle), the presence of pickets, proteins that are anchored to the boundaries of membrane-skeleton-based compartments, restricts the motion of all membrane molecules including lipids. As explained in the text, the presence of an immobile protein may locally reduce the diffusivity of lipids. This so-called picket-induced slowdown effect is illustrated in the bottom figure. Here, a lipid molecule in the close proximity of a picket, which we refer to as the low diffusivity domain, diffuses at a slower rate as compared with those far from the picket, as indicated by the smaller step size in the particle’s trajectory.

This new, compartmentalized view of the plasma membrane was further supported by an atomic force microscopy study of its cytoplasmic surface (Takeuchi et al. 1998), which produced similar estimates for the spectrin membrane-skeleton mesh size to those obtained by SPT of band 3 proteins in human erythrocyte ghosts (Tomishige et al. 1998).

Super-Speed Single-Molecule Imaging: Hop Diffusion of Phospholipids as well as Transmembrane Proteins and Anchored-Protein Picket Model

Another striking discovery was made in 2002. Fujiwara et al. (2002) demonstrated that even phospholipids, which are the most basic molecular species for membrane formation, undergo hop diffusion between compartments with sizes similar to those detected by protein hop diffusion. In previous single-molecule observations, images were obtained at video (30 frames/s) or slower rates. However, the characteristic time for lipid residency within a compartment is generally much shorter than the time between two consecutive image frames employed in those observations. Therefore, the detection of membrane compartments for lipids had to wait for the development of ultrafast methods for performing SPT. Fujiwara et al. (2002) performed their measurements at a rate of 40,000 frames/s, the fastest single-molecule imaging ever made, and have increased the rate even further to 160,000 frames/s.

The discovery of lipid molecules undergoing membrane-skeleton-dependent hop diffusion in the outer leaflet of the plasma membrane was very surprising, since they only reach halfway through the membrane and lack the cytoplasmic domains of transmembrane proteins. To explain the hop diffusion of phospholipids in the outer leaflet, the anchored-protein picket model was proposed (Fujiwara et al. 2002; Kusumi et al. 2005), where lipids are envisaged to interact with the membrane skeleton indirectly through picket proteins that are attached to the membrane skeleton, as shown in Figure 1.1 (middle). Several different mechanisms for the interaction between lipids and protein pickets were proposed. First, the picket can block the passage of a lipid molecule due to volume exclusion, causing steric hindrance. Second, the lipid molecules can be packed more in the immediate vicinity of the picket than the bulk membrane, and thus the free area available for a lipid molecule to move into decreases, leading to diminishing mobility around the picket (Sperotto and Mouritsen 1991; Almeida et al. 1992). Third, lipid molecules can experience hydrodynamic slowing near an immobilized picket molecule, similar to a fluid particle that moves close to the boundary of a container (Bussell et al. 1994, 1995; Dodd et al. 1995). Therefore, lipid diffusivity can be reduced in the vicinity of a picket protein for different reasons, a phenomenon we call the picket-induced slowdown effect. A schematic illustration of the low diffusivity domain due to this effect is shown in Figure 1.1 (bottom). When many such immobilized picket proteins are aligned along the membrane-skeleton fence, the membrane molecules cannot easily pass through the compartment boundaries, and thus become temporarily confined within a compartment. Namely, in the anchored-protein picket model, the entire plasma membrane is partitioned into compartments by transmembrane protein pickets, lining the membrane-skeleton fence. By performing Monte Carlo simulations that account for the reduction in free area, the picket density along the compartment boundary necessary for reproducing the observed hop diffusion of lipids was estimated to be 20–30% (Fujiwara et al. 2002). Namely, the compartment boundaries do not have to be closed off by concentrating the transmembrane protein pickets there, but when only 1/5–1/3 of the boundary is occupied by picket proteins, it would be sufficient to induce temporary trapping of lipids within a compartment.

It is important to note that the effect of anchored-protein pickets is not limited to that of mere protein crowding. Several studies have indicated that if the obstacles are mobile, then they do not lead to a significant decrease in the diffusion coefficient of the rest of the mobile particles. Monte Carlo studies by Saxton (1987, 1990), which did not consider hydrodynamic effects, demonstrated that mobile obstacles do not reduce the diffusivity as much as their immobile counterparts. Later on, Bussell et al. (1994, 1995) and Dodd et al. (1995) showed that the inclusion of hydrodynamic effects did not change this conclusion, provided that the lipids can be assumed to form a continuum. This assumption would not hold in cases where the protein diameter is comparable with the diameter of lipids (we will address this point later in this chapter), and one might consider the possibility that the details of collective protein–lipid dynamics on the mesoscale may determine the properties of the system. However, since the diffusion coefficients of proteins and lipids within a compartment are similar to those measured in liposomes, reconstituted membranes, and membrane blebs (see figs. 1 and 2 in Fujiwara et al. 2002 and figure 8 in Murase et al. 2004), the immobilization of obstacles (pickets) seems to be a key factor in the picket-induced slowdown effect. In addition, the proteins do need not be continually anchored in order to function as effective pickets. If the proteins are immobilized for more than ∼10 µs at a time, then this may suffice to produce the observed hindrance of lipid diffusion.

ELECTRON TOMOGRAPHY OF THE THREE-DIMENSIONAL STRUCTURE OF THE CYTOPLASMIC SURFACE OF THE PLASMA MEMBRANE

Perhaps the most direct evidence for the membrane-skeleton-based partitioning of the plasma membrane was obtained in 2006 by Morone et al., who imaged the three-dimensional structure of the cytoplasmic surface of the plasma membrane by electron tomography. Here, electron tomography was first applied to platinum-replicated samples: the three-dimensional structure of the cytoplasmic surface of the plasma membrane was reconstituted from the platinum-coated membrane specimen, prepared with minimal intrusion, by using the freeze-etching technique. These images clearly demonstrated that the membrane skeleton entirely covers the cytoplasmic surface of the plasma membrane, except for certain membrane domains, such as clathrin-coated pits (CCPs) (Chapter 2), caveolae (Chapter 3), and focal adhesions (Chapter 17), that the membrane-skeleton meshwork is primarily composed of actin filaments since almost every filament exhibited a distinct striped pattern with a 5.5-nm periodicity (see Fig. 1.2 for typical electron micrographs, although they are normal two-dimensional images), and that some of the meshwork is located as close as within a nanometer of the plasma membrane.

Figure 1.2. Electron microscopic images of the membrane skeleton of NRK (upper left) and fetal rat skin keratinocytes (FRSKs) (lower left) cells, where the scale bars in the main figures correspond to 100 nm. The inset in the lower left image highlights the 5.5-nm periodic striped pattern characteristic of actin filaments, showing that the membrane skeleton is primarily composed of actin filaments (scale bar 50 nm). Note that both of these images also contain clathrin-coated pits, which are distinguished by their lattice structure

(from Morone et al. 2006).

In the lower right corner, the trajectory of a gold-tagged phospholipid (DOPE) in an NRK cell, obtained by SPT at 40,000 frames/s, is displayed. The trajectory was color coded after performing a quantitative analysis that detects jumps between adjacent domains. The histograms in the upper right corner show that the size distribution of the membrane-skeleton meshes directly contacting the cytoplasmic surface of the plasma membrane, as determined by electron tomography, is very close to that of the compartments determined from the DOPE diffusion data in either NRK or FRSK cells, whereas the distributions for these two cell types are entirely different

(in part from Fujiwara et al. 2002).

Based on these images, the size distribution of the actin skeleton mesh on the cytoplasmic surface of the plasma membrane was obtained and found to agree well with the compartment sizes found by analyzing phospholipid hop diffusion as revealed by SPT. This result strongly supports the partitioning of the plasma membrane by membrane-skeleton fences and transmembrane protein pickets lining the fence.

ANSWERS TO THE TWO THREE-DECADE-OLD ENIGMAS

The fence–picket model gives straightforward answers to the two three-decade-old questions raised in the beginning of this section.

1. Why do membrane proteins and lipids diffuse faster in artificial membranes than in the cellular plasma membrane by a factor of ∼20 (ranging from 5 to 50)?

As stated in the previous paragraph, the diffusion coefficient within a compartment is not small, as compared with that in artificial membranes. However, if the diffusion coefficient is measured on much greater scales, for example, a FRAP spot size of ∼500 nm, or at slower observation rates, for example, single-molecule tracking observed at a rate of 30 frames/s or slower, then the diffusion appeared to be slow because what was observed is the apparent diffusion coefficient, which is affected by the presence of compartment boundaries. It takes time to hop from one compartment to an adjacent one, which makes the macroscopic diffusion of lipids and proteins in the plasma membrane very slow.

2. How do molecular complexes become immobilized on the cell surface or diffuse at surprisingly lower rates, as compared with single molecules?

This can be explained by the “oligomerization-induced trapping” effect of the fences and pickets. Monomers of membrane molecules may hop across the intercompartment boundaries with relative ease, but upon forming oligomers or molecular complexes, the entire complex, rather than single molecules, has to hop across the picket–fence all at once, and therefore, these complexes are expected to hop across the boundaries at much slower rates. In addition, due to the avidity effect, molecular complexes are more likely to be bound to the membrane skeleton, perhaps temporarily, which also induces (temporary) immobilization or trapping of oligomers and molecular complexes. Such enhanced confinement and binding effects induced by oligomerization or molecular complex formation are collectively termed oligomerization-induced trapping (Kusumi and Sako 1996; Iino et al. 2001; Kusumi et al. 2005).

One might argue that even in the absence of membrane-skeleton fences and anchored pickets lining the fences, the oligomerization of membrane proteins could greatly reduce the diffusion coefficient. However, experimental and theoretical studies clearly showed that this does not occur with transmembrane proteins that contain approximately three or more membrane-spanning α-helices (Saffman and Delbrück 1975; Peters and Cherry 1982; Vaz et al. 1982; Liu et al. 1997; Gambin et al. 2006). Based on these studies, we predict that tetramer formation from monomers (a twofold increase in radius) will only decrease the diffusion coefficient by a factor of 1.1, and even 100mers (a 10-fold increase in radius) will have a diffusion rate reduced by only a factor of less than 2 from that of monomers. Therefore, the large reductions of the diffusion coefficient, upon oligomerization or molecular complex formation, clearly indicate that the plasma membrane cannot be considered as a two-dimensional fluid continuum, and that the fence–picket model is consistent with the experimental observations and theoretical predictions.

In artificial membranes without the membrane skeleton, Liu et al. (1997) and Gambin et al. (2006) reported decreases of the diffusion coefficient by a factor of ∼2 when the probe hydrodynamic diameter (in the cross section of the transmembrane domain parallel to the membrane surface) was increased by a factor of ∼2 from the original diameter of ∼0.5 nm. In this spatial scale, the probe (solute) size is comparable with the solvent molecular size, and the continuum fluid model is no longer correct. Therefore, the translational diffusion coefficient of the test particle strongly depends on its diameter on this particular spatial scale.

However, this should not be confused with the reduction in the diffusion coefficient when single-pass transmembrane proteins, such as many receptor molecules, form oligomers in the plasma membrane. The size-dependent decrease of the diffusion coefficient observed in artificial membranes (Liu et al. 1997; Gambin et al. 2006) occurs only for test particles of ∼0.5 nm in diameter undergoing virtually simple diffusion, which exhibits a monomer diffusion coefficient of ∼10 µm2/s. In the plasma membrane, the effective diffusion coefficients of single-pass transmembrane protein monomers are generally ∼0.2 µm2/s, which are already slower by a factor of 50 than those found in artificial membranes. Oligomerization of such a single-pass transmembrane protein tends to decrease the diffusion coefficient by a factor of ∼2, but the weak resemblance of these reduction factors is merely incidental. The oligomerization-induced reduction of the diffusion coefficient in the plasma membrane was not due to changes in protein interaction with membrane lipids, as was found in artificial membranes, but rather to the compartment boundaries. Evidence for this statement comes from high-speed single-molecule tracking data. Single-pass transmembrane proteins generally exhibit hop diffusion between compartments, with a microscopic diffusion coefficient within a compartment of 5–10 µm2/s and a residency time of 20–100 ms, and oligomerization lengthened the residency time without affecting the compartment size and the microscopic diffusion coefficient within a compartment (Murase et al. 2004; Kusumi et al. 2005). This clearly indicates the necessity for being careful in interpreting long-range, slow-speed diffusion measurements, for carrying out high-speed single-molecule tracking, and for careful attention to loosely applying the results of Liu et al. (1997) and Gambin et al. (2006) to the observations made in the plasma membrane.

In addition to the corralling effect of the membrane skeleton and associated transmembrane protein pickets, three major factors are frequently discussed in the literature to account for the slower diffusion of membrane molecules in the cellular plasma membrane, observed by methods with low spatiotemporal resolutions: the crowding effect of transmembrane proteins, the trapping or exclusion effect of raft domains, and the ordering effect of cholesterol. All three of these factors can lead to a decrease in molecular mobility in the plasma membrane, but even if all of these three factors are combined, a decrease in the diffusion coefficient by a factor of 20 cannot be explained (perhaps, a factor of 2 could be explained). Some of the phenomena including the immobilization of receptor complexes can only be explained by considering the effects of the membrane skeleton.

MEMBRANE-SKELETON-BASED MESODOMAINS ARE NOT DETECTED IN EVERY STUDY

A number of studies failed to detect the effect of the membrane skeleton on the diffusivity of membrane molecules. Two FRAP studies (Schmidt and Nichols 2004; Frick et al. 2007) found that disruption of the cortical actin did not lead to significant changes in the diffusivity of some membrane proteins and a lipid analog. However, experiments involving drug-induced actin (de)polymerization revealed various effects, depending on the drug concentration, the treatment duration, and the cell type, and thus comparisons of the results are difficult (Vrljic et al. 2005; Umemura et al. 2008). For the intricate cell reactions to such treatments, see Suzuki et al. (2005). Furthermore, Fujiwara et al. (2002) and Murase et al. (2004) found that without directly observing the compartment size and the residency time within a compartment, detecting drug-induced changes will be difficult.

In studies using fluorescence correlation spectroscopy (FCS) (Wawrezinieck et al. 2005; Lenne et al. 2006) and stimulated emission depletion–FCS (STED-FCS) (Eggeling et al. 2009), phospholipid analogs were observed to diffuse freely. To detect hop diffusion in FCS experiments, the focal area of the laser should be less than the characteristic compartment size. In STED-FCS (Eggeling et al. 2009), the diameter of the laser beam can be as small as 30 nm, which is comparable with the smallest compartment sizes reported in the literature (Murase et al. 2004). Therefore, a further decrease in the diameter of the laser beam seems to be necessary for determining the true nature of molecular diffusion by FCS techniques.

In addition to these results obtained by measuring the signal from many molecules simultaneously, several single-molecule/particle studies also did not find hop diffusion. For instance, Wieser et al. (2007, 2008) found simple Brownian diffusion of lipids and a glycosylphosphatidylinositol (GPI)-anchored protein CD59, and Crane and Verkman (2008) reported the observation of freely diffusing aquaporin-1 water channels. In these studies, the rate at which the images were acquired was 2000 Hz or less, resulting in determination of the positions of single molecules at relatively sparse time points such that it would simply be impossible to detect fast hop diffusion among mesoscale compartments.

In fact, careful attention must always be paid to the camera frame rates relative to the molecular hop frequency in each cell type when interpreting the single-molecule tracking results, since this is a key factor in the ability to detect confinement. The residency time of a molecule in a compartment may vary between one to hundreds of milliseconds, and will be shorter for lipids (Fujiwara et al. 2002; Murase et al. 2004) and longer for proteins (Tomishige et al. 1998; Tomishige and Kusumi 1999). Especially for lipids, to obtain statistically significant results, single-molecule/particle tracking must be performed at very high speeds. At a camera speed of 40,000 Hz, which was used by Fujiwara et al. to detect the hop diffusion of lipids, the time between two consecutive frames is 25 µs, thus allowing one to obtain 40 data points even for short residency times such as 1 ms (Fujiwara et al. 2002; Murase et al. 2004). Readers are referred to Murase et al. (2004) for the residency times of an unsaturated phospholipid, 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE), in a number of different cells. The residency time divided by 40 essentially provides a good estimate of the inverse frame rate that is necessary to detect hop diffusion.

For example, Sahl et al. (2010) reported that they failed to detect the hop diffusion of a fluorescent phospholipid analog, but instead found alternating temporary entrapment within a domain (30% of the observed duration) and simple Brownian diffusion (70%) in the plasma membrane of PtK2 cell, using an SFMT method with a time resolution of 0.5 ms. Using the same cell line, the average residency time of a phospholipid within a compartment was found to be ∼1 ms (Fujiwara and Kusumi, unpublished observations). This result indicates that, to detect the hop diffusion of phospholipids, the time resolution of the instrument must be better than 0.025 ms, suggesting that the results obtained by Sahl et al. (2010) are due to the lack of time resolution (on average, they made only two coordinate determinations during the average residency time of 1 ms): when a molecule stays in a compartment much longer than average, even the 0.5-ms observation rate would be sufficient to detect confinement, but when the molecule stays in compartments for shorter periods, a 0.5-ms resolution would be insufficient to detect confinement, thus reporting simple Brownian diffusion.

MOLECULAR COMPOSITION OF THE MEMBRANE SKELETON

As shown previously (Heuser and Kirschner 1980; Hirokawa and Heuser 1981; Hirokawa et al. 1982; Morone et al. 2006; Hanson et al. 2008), the membrane skeleton mainly consists of actin filaments (see also Chapter 12). Actin filaments grow at their fast growing ends, which interact with the membrane (Small et al. 1978; Wang 1985). Therefore, the membrane compartments should depend on the proteins involved in actin polymerization, including the Arp2/3 complex, Wiskott–Aldrich syndrome protein (WASP), suppressor of cyclic AMP receptor (SCAR)/WASP family verprolin-homologous protein (WAVE), cortactin, and the formin family of proteins (Pollard 2007; Campellone and Welch 2010), as well as those involved in actin depolymerization such as the cofilin family of proteins (Bernstein and Bamburg 2010). Actin filaments and the plasma membrane are also linked by proteins that can laterally bind to actin, including ponticulin; the ezrin/radixin/moesin family of proteins; the villin–gelsolin superfamily proteins; the epithelial protein lost in neoplasm, filamin, dystrophin and utrophin, and tropomyosin; and the myosin family of proteins (Morone et al. 2008). Furthermore, actin filaments bind to various other membrane-associated proteins and lipids with low affinities, to facilitate dynamic associations with the membrane. This easy binding to and dissociation from the membrane would be needed for the dynamic regulation of the diffusivities of various molecules in the plasma membrane. Despite such low affinities, the binding sites would be numerous, making the dynamic binding very effective, although rendering their binding undetectable in general pull-down assays. As indicated in a previous section, transmembrane proteins transiently bound to the membrane skeleton would serve well as a diffusion barrier, if the anchored durations are greater than ∼10 µs.

In normal erythrocytes, the organization of the membrane skeleton has been studied extensively and is known to be different from those of other cell types. The erythrocyte membrane-skeleton mesh is composed of a network of spectrin tetramers, acting as fences, which are cross-linked by protein complexes formed by short actin filaments, adducin, band 4.1, and the transmembrane protein glycophorin C, anchoring the entire network to the plasma membrane (Goodman et al. 1988; Bennett 1990; Anong et al. 2009). Spectrin tetramers form by the tail-to-tail linkage of spectrin dimers, and the dimers and the tetramers exist in a very dynamic equilibrium. For the passage of transmembrane proteins across the spectrin fences, a spectrin tetramer dissociation gate model (SPEQ gate model) was proposed. In the model, the dissociation of a spectrin tetramer into dimers entails transient gate opening, allowing transmembrane molecules to pass through the compartment boundaries (Tomishige et al. 1998). Malfunctions of the human erythrocyte membrane skeleton often lead to serious types of anemia, indicating the important roles played by the membrane skeleton in the functions, stability, and flexibility of the plasma membrane (Bennett and Healy 2008; Kodippili et al. 2009).

FUNCTIONS OF MEMBRANE-SKELETON-INDUCED MESODOMAINS

Effects of the Membrane Skeleton on Signaling

B-Cell Receptor (BCR) Signal Transduction: The Membrane Skeleton May Be Involved in the Formation of Receptor Oligomers and/or Engaged-Receptor-Based Lipid Rafts

Several different mechanisms for the relationship between the actin cytoskeleton and BCR signaling have been proposed. In the oligomeric BCR complex model (Reth et al. 2000; Schamel and Reth 2000), disruption of BCR oligomers was considered to be required for signaling, and the role of the membrane skeleton in regulating the formation and maintenance of oligomers was discussed.

In another approach, the effect of lipid rafts was emphasized (see also Chapters 4 and 5). In this model, BCR is considered to enter rafts only upon antigen binding, which is accompanied by the coalescence of smaller rafts. This in turn enables BCR to react with raft-associated kinases to start signaling (Tolar et al. 2005; Gupta et al. 2006; Gupta and DeFranco 2007; Sohn et al. 2008). The membrane skeleton can participate in the regulation of signaling as it restricts the growth and coalescence of lipid rafts. Recent observations by Treanor et al. (2010) showed that the treatment of cells with actin-modulating chemicals resulted in up to a 10-fold change in the diffusivity of BCR, suggesting that the membrane skeleton can control the mobility of these molecules. Furthermore, the expression level of ezrin, which is among the proteins that can form a link between the cytoskeleton and membrane proteins, also caused a change in BCR diffusivity by a factor greater than 3. This finding is also consistent with the anchored-protein picket model, since ezrin could act as a link between the protein pickets and the membrane skeleton. Moreover, the cross-linking of BCR and the dynamics of the membrane skeleton were correlated, and BCR signaling upon antigen binding leads to significant cytoskeletal reorganization (Fleire et al. 2006; Arana et al. 2008; Lin et al. 2008). Interestingly, alteration of the actin cytoskeleton also led to B-cell signaling comparable with that elicited by BCR cross-linking. Therefore, a clear link exists between the membrane skeleton and BCR signaling, implying that membrane mesodomains can play a critical role in this process, by controlling the distribution of membrane molecules.

Epidermal Growth Factor (EGF) Receptor (EGFR) and IgE-Fc (FC portion of Immunoglobulin E) Receptor Signal Transduction: The Membrane Skeleton May Increase the Receptor Oligomerization Rate and Promote Receptor Activity Together with the Galectin Lattice

Lajoie et al. (2007) raised the question of how the three mechanisms of regulating the dynamics, clustering, distribution, and function of the EGFR are coordinated: (1) Galectin-3 is located on the extracellular surface of the plasma membrane and cross-links β1,6GlcNAc-branched N-glycans on cell-surface glycoproteins, including EGFR, to form a heterogeneous lattice; (2) caveolin-1 (Cav1), a major constituent of caveolae, acts as a negative regulator of growth factor signaling (Parton and Simons 2007), and in addition, Cav1 forms noncaveolar microdomains, which are likely to contain at least 15 Cav1 molecules (Parton et al. 2006); (3) membrane-skeleton fences and pickets suppress the diffusion of EGFR, and particularly that of the signal-capable receptor oligomers. Lajoie et al. (2007) showed that the extracellular galectin-3 lattice interacts with the N-glycans on EGFR and impedes its diffusion, which predominantly protects EGFR from loss to caveolae and Cav1 microdomains where EGFR signaling is suppressed. Disruption of the actin membrane skeleton with latrunculin A increased the mobile fraction of EGFR measured by FRAP, suggesting that the galectin-bound EGFR is further stabilized by the actin membrane skeleton. It is likely that galectin-3 cross-links EGFR to other actin-associated membrane glycoproteins or pickets, thus generating actin-stabilized signaling domains.

Using quantum-dot-based SPT techniques, Chung et al. (2010) measured the diffusion coefficient of EGFR as a function of time. Assuming a relationship between diffusivity and molecular size, the authors inferred that EGFRs form transient oligomers, even in the absence of ligand, with lifetimes ranging from a few to a few tens of seconds. Interestingly, the probability of finding receptor dimers was reportedly higher at the periphery of EGFR-overexpressing A431 and BT20 cells, in an actin-dependent manner. The abundance of dimers along the cell periphery might be a consequence of the increased collision frequency of receptors confined in the mesodomains delimited by the membrane skeleton. Previous observations provided evidence for the increased density of actin filaments near the leading edge of migrating cells (Borisy and Svitkina 2000; Pollard et al. 2000). In this respect, another function of the membrane-skeleton-induced compartments could be promoting the formation of receptor complexes, and enabling the polarized response after ligand binding that is necessary in certain cellular processes such as chemotaxis.

The involvement of raft domains induced by receptor engagement in signaling, by enhancing the recruitment of Lyn kinase and reducing the collisions with phosphatases, was shown in the case of the IgE-Fc receptor, FcεRI (Field et al., 1997; Wu et al. 2004; Young et al. 2005). In addition, the involvement of membrane-skeleton-delimited compartments was found by simultaneous observations of the quantum-dot-labeled FcεRI and green fluorescent protein (GFP)-tagged actin (Andrews et al. 2008). The diffusion rate of cross-linked receptor in cells with a disrupted actin cytoskeleton was significantly higher than that in intact cells, in accordance with oligomerization-induced trapping. Immobilization of cross-linked receptors at the onset of signaling (Menon et al. 1986) also enables the cell to remember the position of the stimulus for short periods (on the order of 10 seconds) and to perform localized responses. This is essential in certain processes, such as chemotaxis. Therefore, the actin meshwork is also involved in reliably responding to local changes in the environment (Kusumi and Sako 1996; Kusumi et al. 2005).

In fact, receptor redistribution and clustering are key steps in many signal transduction pathways (Petrini et al. 2004; Minguet et al. 2007; Briegel et al. 2009; Nikolaev et al. 2010). Several reports have indicated the active roles played by the cytoskeleton in inhibiting (Wang et al. 2001; Boggs and Wang 2004) or enabling (Gomez-Mouton et al. 2001; Rodgers and Zavzavadjian 2001; Baumgartner et al. 2003) the redistribution/clustering of membrane molecules. In oligodendrocytes, stimulation of the cells induced actin depolymerization that was followed by coclustering of membrane molecules, including myelin basic protein and galactosylceramide (Boggs and Wang 2004). However, when the actin filaments were artificially stabilized, coclustering was not observed. This observation is consistent with the presence of membrane-skeleton-based mesodomains, since lateral diffusion, which is necessary for coclustering, is hindered by the presence of actin-based membrane-skeleton fences and pickets.

Kv2.1 Potassium Channels: The Membrane Skeleton Is Involved in Cluster Maintenance and Distribution

FRAP and quantum-dot-based imaging revealed that Kv2.1 potassium channels form dynamic clusters with sizes and spatial distributions that are influenced by the actin-based membrane skeleton (O’Connell et al. 2006). Interestingly, the individual channels diffused freely within a cluster, suggesting that the clusters might actually be channels corralled by the membrane skeleton. This proposal was further supported by the results obtained upon disruption of the membrane skeleton by latrunculin A treatment, which resulted in a 10-fold increase in the average cluster area and a decrease in the number of clusters, indicating that the smaller clusters merged during the treatment.

Furthermore, the spatial distribution of Kv2.1 channels in hippocampal neurons was also affected by the disruption of the membrane skeleton (O’Connell et al. 2006). In cells with an intact cytoskeleton, the channels were restricted to the cell body. However, after the latrunculin A treatment, the channels were found in the neurites as well as in the cell body. These observations suggest that the Kv2.1 clusters are maintained in membrane-skeleton-based mesodomains and that an actin-based mechanism is involved in determining the spatial distribution of Kv2.1 channels.

Micron-to-Cell-Sized Diffusion Barriers Generated by the Membrane-Skeleton Fences and Associated Pickets

Numerous experiments have indicated that diffusion barriers, with sizes between a micron to several tens of microns, thus producing macroscopic plasma membrane domains, might be generated by the actin-based membrane-skeleton fences and the transmembrane picket proteins lining the fence. In the following, we will discuss a few such cases.

A study on fish keratocytes revealed the presence of an F-actin-dependent lipid diffusion barrier, located at the leading edge of migrating cells (Weisswange et al. 2005). In boar sperm cells, observations indicated the presence of a diffusion barrier between the equatorial segment and the postacrosome, which prevents the passage of large molecular complexes. Based on the diffusivity measurements and the topographical properties of the cell surface revealed by atomic force microscopy, the authors suggested that a high concentration of anchored transmembrane proteins in the region could be involved in forming the barriers (James et al. 2004). The plasma membrane of the neuron consists of two distinct domains: the somatodendritic and axonal domains (Kobayashi et al. 1992; Winckler et al. 1999). Single-molecule imaging of a phospholipid revealed the developmental formation, by day 10 after birth in cultured hippocampal neurons (see also Chapter 22 on neuronal domains), of a diffusion barrier that blocks the diffusion of even phospholipids in the plasma membrane, in the boundary region between the two domains, called the initial segment (Nakada et al. 2003). Moreover, the barrier was formed by highly concentrating various transmembrane proteins and membrane skeletal proteins, including actin filaments and ankyrin, which bound to each other to create very dense rows of anchored pickets. This would effectively block diffusion, even that of phospholipids, in accordance with the anchored-protein picket model. More recently, Renner et al. (2009) made a surprising discovery concerning lipid diffusion, not necessarily involving diffusion barriers, in the synaptic membranes of mature hippocampal neurons. The authors showed that the lipids ganglioside GM1 and DOPE, and the artificial fluorescent lipid probe DiIC18, undergo confined diffusion in submicron-sized regions whose extent is proportional to molecular size, being the smallest for DOPE (∼110 nm). For a review on how single-molecule techniques have played an important role in the advancement of synaptic biology, the readers are referred to Triller and Choquet (2008).

The Role of the Membrane Skeleton in CCP Formation during Endocytosis

CCP-mediated endocytosis constitutes the major route for the uptake of nutrients and signaling ligands, as well as for the entry of viruses and toxins (see also Chapter 2 on CCPs). CCPs are very closely associated with or bound by the actin filaments in the membrane skeleton, as seen in the electron microscopic images in Figure 1.2. Consistent with this observation, the mobility of CCPs visualized by GFP-tagged clathrin light chain was found to be highly restricted to submicron regions (0.5–0.8 µm in diameter) in the plasma membrane, and the disruption of actin filaments by latrunculin B led to the increased mobility of CCPs (Gaidarov et al. 1999). In a different study (Ehrlich et al. 2004), the analysis of the experimental data suggested that the CCPs are preferentially formed in the so-called active domains of ∼400 nm in diameter, which are similar in size to the membrane-skeleton-based domains. Total internal reflection fluorescence microscopic studies (Merrifield et al. 2002; Yarar et al. 2005) have revealed that actin polymerization is required when CCPs are undergoing invagination and scission. These results suggest the involvement of the dynamic actin meshwork in CCP formation. However, others have shown that this is not always true, depending on the cell type (Fujimoto et al. 2000) and the choice of locations on the cell surface (Saffarian et al. 2009; upper surface vs. lower surface in contact with the coverslip). Thus, CCP researchers are still vigorously discussing the possible roles of both actin polymerization (to generate force for membrane deformation) and depolymerization (to remove the dense actin meshwork that would otherwise form a steric barrier) in the regulation of CCP formation.

Regulation of Lipid Rafts by the Membrane-Skeleton-Based Compartments

Lipid domains/rafts are important, and difficult to detect, constituents of the plasma membrane (see also Chapters 4 and 5 on lipid rafts). They are considered to participate in many membrane processes, including the formation of platforms for signaling molecules. In resting cells, rafts could be highly dynamic entities with a wide range of diameters. In the literature, the estimated diameters of raft-associated molecule clusters have been reported to be 5–15, 20, 50–80, 120, and 700 nm. Many different structures and processes have been proposed to describe the generation and maintenance of rafts (Kusumi et al. 2004, 2010). Among these, membrane-skeleton-induced compartmentalization can be a major regulator of raft dynamics. Most transmembrane proteins are known to exclude cholesterol from their boundary regions. Therefore, an array of anchored-protein pickets would prevent the motion and growth of rafts in their vicinity. As a result, one would expect the raft size to be limited by the typical membrane-skeleton-delimited compartment size of 30–250 nm, which is quite consistent with experimental findings.

Reaction Kinetics: Increased Collision Rate due to the Presence of Membrane-Skeleton-Based Compartments

When reactants are confined to a region with a characteristic size that is comparable with their mean free path, the frequency of collisions between them will increase (see, for instance, Saxton 2002 for a discussion). However, the overall efficiency of the reaction can increase or decrease depending on the details of the compartmentalization and the properties of the reactants (Melo et al. 1992). The collision frequency of molecules in the plasma membrane has not been directly measured. Nevertheless, as mentioned earlier in this section, at least one experiment in the live-cell plasma membrane suggested a modification in the oligomerization rate of EGFR by the increased density of the actin-based membrane skeleton (Chung et al. 2010).

The presence of membrane-skeleton-based compartments has two competing effects on reaction kinetics. One of them is to increase the time required for the reactants to collide for the first time (if initially they are not in the same compartment), since the long-term diffusivity is reduced by compartmentalization. The other is a substantial decrease in the first recollision time, along with a significant increase in the collision frequency, when the reactants are within the same compartment. Together, these effects can selectively control the kinetics of reactions in the membrane. For instance, at low concentrations, chemically limited reactions that require many collisions would be promoted, whereas diffusion-limited reactions would be suppressed. A detailed analysis of the modifications of reaction kinetics due to membrane-skeleton-based compartments is in progress in our laboratories.

Artificial Regulation of Membrane Molecular Dynamics Using Fabricated Mesostructures

Recently, membrane-skeleton-induced mesodomains or compartments have inspired new lines of research. Using nanofabricated materials, structures similar to membrane mesodomains have been conducted. Several recent examples of such studies can be found in Mossman et al. (2005), Tsai et al. (2008), Kam (2009), Takimoto et al. (2009), and Salaita et al. (2010).

Tsai et al. (2008) constructed supported lipid bilayers on a glass substrate patterned with 50-nm-wide chromium or titanium stripes, separated by 125–250 nm. The stripes were tall enough to act as perfect barriers to lipid diffusion, but they contained periodically placed gaps that allowed the lipids to pass. Using FRAP, the authors demonstrated that the characteristics of lipid diffusion on large and small spatial/temporal scales are very different, with the short-range/time diffusivity being much higher. Although this artificial system is quite different from the cellular plasma membrane, it is a good example of a controllable nano–meso structure that can mimic biological systems.