International Review of Neurobiology, Volume 48

International REVIEW OF Neurobiology Volume 48 International REVIEW OF Neurobiology Volume 48 SERIES EDITORS RONALD...

Author: Ronald J. Bradley | R. Adron Harris | Peter Jenner

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International REVIEW OF

Neurobiology Volume 48

International REVIEW OF

Neurobiology Volume 48 SERIES EDITORS RONALD J. BRADLEY Department of Psychiatry Louisiana State University Medical Center Shreveport, Louisiana, USA

R. ADRON HARRIS Division of Neurobiology and Institute for Cellular and Molecular Biology University of Texas Austin, Texas, USA

PETER JENNER Pharmacology Group Biomedical Sciences Division King’s College London London, UK

EDITORIAL BOARD PHILIPPE ASCHER ROSS J. BALDESSARINI TAMAS BARTFAI COLIN BLAKEMORE FLOYD E. BLOOM DAVID A. BROWN MATTHEW J. DURING KJELL FUXE PAUL GREENGARD SUSAN D. IVERSEN

KINYA KURIYAMA BRUCE S. MCEWEN HERBERT Y. MELTZER NOBORU MIZUNO SALVADOR MONCADA TREVOR W. ROBBINS SOLOMON H. SNYDER STEPHEN G. WAXMAN CHIEN-PING WU RICHARD J. WYATT

International REVIEW OF

Neurobiology Volume 48 EDITED BY

RONALD J. BRADLEY Department of Psychiatry Louisiana State University Medical Center Shreveport, Louisiana

R. ADRON HARRIS Division of Neurobiology and Institute for Cellular and Molecular Biology University of Texas Austin, Texas, USA

PETER JENNER Pharmacology Group Biomedical Sciences Division King’s College London London, United Kingdom

San Diego San Francisco New York Boston London Sydney Tokyo

Au: Pl. supplied revised copyright page

CONTENTS

CONTRIBUTORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ix

Assembly and Intracellular Trafficking of GABAA Receptors EUGENE M. BARNES, JR . I. II. III. IV. V.

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assembly of GABAA Receptors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Surface Targeting and Intracellular Sorting of GABAA Receptors . . . . . . . . . . GABAA Receptor-Clustering Proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Endocytosis and Recycling of GABAA Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 3 8 11 14 23

Subcellular Localization and Regulation of GABAA Receptors and Associated Proteins ¨ BERNHARD LUSCHER AND JEAN-MARC FRITSCHY I. II. III. IV. V. VI. VII. VIII. IX. X.

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Functional Significance of GABAergic Inhibition in the Brain . . . . . . . . . . . . . . Structural Anatomy of GABAergic Synapses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structure of GABAA Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Subcellular Localization of GABAA Receptor Subtypes . . . . . . . . . . . . . . . . . . . . . . Factors Implicated in Exocytosis and Endocytosis of GABAA Receptors . . . . Synaptic Anchoring of GABAC Receptors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GABAA Receptor-Associated Signaling Proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Synaptic Plasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

v

32 33 33 34 35 46 48 49 50 54 54

vi

CONTENTS

D1 Dopamine Receptors XUEMEI HUANG, CINDY P. LAWLER, MECHELLE M. LEWIS, DAVID E. NICHOLS, AND RICHARD B. MAILMAN I. II. III. IV.

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Localization and Function of D1 -like Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Development of Drugs for D1 -like Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Therapeutic and Functional Actions of D1 Receptor Agonists and Antagonists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Conclusions and Future Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

66 68 89 100 116 117

Molecular Modeling of Ligand-Gated Ion Channels: Progress and Challenges ED BERTACCINI AND JAMES R. TRUDELL I. II. III. IV. V.

The Challenge of Modeling Transmembrane Ion Channels. . . . . . . . . . . . . . . . . Overview of Molecular Modeling Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Experimental Data: Techniques and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Molecular Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

141 143 148 155 161 162

Alzheimer’s Disease: Its Diagnosis and Pathogenesis JILLIAN J. KRIL AND GLENDA M. HALLIDAY I. II. III. IV. V. VI. VII.

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diagnostic Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pathogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Genetic Influences. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inflammation and Anti-inflammatory Drugs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estrogen Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vascular Pathology in AD. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

168 169 176 189 194 197 197 201

CONTENTS

vii

DNA Arrays and Functional Genomics in Neurobiology CHRISTELLE THIBAULT, LONG WANG, LI ZHANG, AND MICHAEL F. MILES I. II. III. IV. V.

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DNA Array Formats and Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Applications in Neurobiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Caveats and Future Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

219 221 232 244 248 248

INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CONTENTS OF RECENT VOLUMES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

263

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CONTRIBUTORS

Numbers in parentheses indicate the pages on which the authors’ contributions begin.

Eugene M. Barnes, Jr. (1), Marrs McLean Department of Biochemistry and Division of Neuroscience, Baylor College of Medicine, Houston, Texas 77030 Ed Bertaccini (141), Department of Anesthesia, Stanford University School of Medicine, Stanford, California 94305; and Department of Anesthesia, Palo Alto VA Health Care System, Palo Alto, California 94304 Jean-Marc Fritschy (31), Institute of Pharmacology and Toxicology, University of Zurich, 8057 Zurich, Switzerland Glenda M. Halliday (167), Prince of Wales Medical Research Institute, Sydney, New South Wales, Australia 2031 Xuemei Huang (65), Department of Neurology, University of North Carolina, Chapel Hill, North Carolina 27599 Jillian J. Kril (167), Centre for Education and Research on Ageing, Concord Hospital, Department of Medicine, The University of Sydney, Concord, New South Wales, Australia 2139; and the Department of Pathology, The University of Sydney, Sydney, New South Wales, Australia 2006 Cindy P. Lawler (65), National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709 Mechelle M. Lewis (65), Neuroscience Center, University of North Carolina School of Medicine, Chapel Hill, North Carolina 27599 Bernhard L¨uscher (31), Department of Biology and Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania 16802 Richard B. Mailman (65), Departments of Pharmacology, Psychiatry, and Medicinal Chemistry, Neuroscience Center, University of North Carolina School of Medicine, Chapel Hill, North Carolina 27599 Michael F. Miles (219), The Ernest Gallo Clinic and Research Center, Wheeler Center for the Neurobiology of Addiction and Department of Neurology, University of California, San Francisco, Emeryville, California 94608 David E. Nichols (65), Department of Medicinal Chemistry and Molecular Pharmacology, School of Pharmacy and Pharmacal Sciences, Purdue University, West Lafayette, Indiana 47907 ix

x

CONTRIBUTORS

Christelle Thibault (219), The Ernest Gallo Clinic and Research Center, Wheeler Center for the Neurobiology of Addiction and Department of Neurology, University of California, San Francisco, Emeryville, California 94608 James R. Trudell (141), Department of Anesthesia, Stanford University School of Medicine, Stanford, California 94305 Long Wang (219), The Ernest Gallo Clinic and Research Center, Wheeler Center for the Neurobiology of Addiction and Department of Neurology, University of California, San Francisco, Emeryville, California 94608 Li Zhang (219), The Ernest Gallo Clinic and Research Center, Wheeler Center for the Neurobiology of Addiction and Department of Neurology, University of California, San Francisco, Emeryville, California 94608

ASSEMBLY AND INTRACELLULAR TRAFFICKING OF GABAA RECEPTORS

Eugene M. Barnes, Jr. Marrs McLean Department of Biochemistry and Division of Neuroscience Baylor College of Medicine Houston, Texas 77030

I. Introduction II. Assembly of GABAA Receptors A. Receptor Subunit Composition and Stoichiometry B. Receptor Assembly in Heterologous Cells C. Receptor Assembly in Vivo III. Surface Targeting and Intracellular Sorting of GABAA Receptors A. Receptor Targeting in Vitro B. Receptor Targeting in Vivo IV. GABAA Receptor-Clustering Proteins A. Gephyrin B. GABARAP C. Rapsyn V. Endocytosis and Recycling of GABAA Receptors A. Receptor Endocytosis in Vivo B. Receptor Endocytosis and Recycling in Vitro C. Role of Insulin D. Purposes of GABAA Receptor Endocytosis References

I. Introduction

γ -Aminobutyric acid type A (GABAA) receptors provide channels for fast inhibitory postsynaptic currents in the central nervous system (CNS) of vertebrates. The psychotropic actions of widely used and abused drugs, including benzodiazepines, barbiturates, and ethanol, are mediated by positive modulation of GABAA receptors. The integral chloride channel of GABAA receptors is formed by a heteropentameric arrangement of polypeptide subunits from three families, α1– α6, β1–β4, and γ 1–γ 4 (Macdonald and Olsen, 1994; Rabow et al., 1995). Alternative splicing produces additional subunit variants within these families. Classes of GABAA receptor subunits, each having a single representative, δ, ε, π , and θ, have also been identiﬁed (Whiting et al., 1999). INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 48

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C 2001 by Academic Press. Copyright All rights of reproduction in any form reserved. 0074-7742/01 $35.00

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ASSEMBLY AND INTRACELLULAR TRAFFICKING OF GABAA RECEPTORS

FIG. 1. Model for the assembly and intracellular trafﬁcking of GABAA receptors. Receptor α, β, and γ subunits are translated on the ER membrane and then assemble into receptor complexes (step 1). Unassembled or misfolded receptor subunits are degraded (step 8), whereas assembled receptors are targeted via the Golgi apparatus to the plasma membrane (step 2). Surface receptors from this mobile pool cluster at synapses (step 3). Receptors can also dissociate from synaptic clusters (step 4) and reenter the mobile pool. Mobile receptors also collect in endocytic pits (step 5) that invaginate to form vesicles (i.e., endocytosis; step 6). Through endosomal compartments, the internalized receptors can recycle to the surface (step 7) or be degraded (step 9).

The arrangement of these 18 kinds of subunits into heteropentamers produces an astonishing diversity of GABAA receptor isoforms. This diversity is reﬂected in profound differences in tissue and subcellular distribution, ontogeny, pharmacology, and regulation of GABAA receptors. Although only a limited subset of receptor subunits is expressed within an individual neuron (Wisden et al., 1992; Fritschy and Mohler, 1995), their assembly must nevertheless follow a prescribed pattern to produce a conformationally mature heteropentamer. Progress in deﬁning some of the rules for the assembly of GABAA receptors is considered in this article. GABAA receptor assembly in vivo has also been treated in a previous minireview (Mohler et al., 1998).

EUGENE M. BARNES, JR.

3

Following assembly in the endoplasmic reticulum (ER), GABAA receptors are targeted to the plasmalemma, where they cluster in postsynaptic membranes and are activated by the presynaptic release of GABA. Because receptor clustering is a prerequisite for the establishment of fast inhibitory postsynaptic currents, there is considerable interest in this process. Alternatively, surface GABAA receptors may participate in an endocytic cycle. Ligand-induced GABAA receptor endocytosis has been implicated in the synaptic remodeling that may underlie physical dependence on benzodiazepines and related drugs (Poisbeau et al., 1997; Tehrani and Barnes, 1997). After endocytosis, GABAA receptors can recycle to the surface or be degraded. These processes, collectively referred to as intracellular trafﬁcking (Fig. 1), have lately come under increasing scrutiny. The relevant literature is reviewed here. An earlier minireview has also dealt with this subject (Barnes, 2000).

II. Assembly of GABAA Receptors

A. RECEPTOR SUBUNIT COMPOSITION AND STOICHIOMETRY It is generally accepted that the most common type of GABAA receptor in the mammalian brain contains α1, β2 or β3, and γ 2 subunits. This is supported by coimmunoprecipitation and immunoblotting studies of native receptors (De Blas, 1996) and by coexpression of these subunits in vitro (in transfected cells or Xenopus ooctyes). The in vitro studies show that the recombinant α1β2γ 2- or α1β3γ 2-subunit receptors have electrophysiological and pharmacological properties that are similar to those in many brain regions (Macdonald and Olsen, 1994). Accordingly, the examination of GABAA receptor assembly has focused primarily on α1, β2, β3, and γ 2 subunits. A heteropentameric arrangement of these subunits is suggested by images reconstructed from low-resolution electron-diffraction patterns of native GABAA receptors (Nayeem et al., 1994) and by sedimentation velocity analysis of recombinant receptors (Tretter et al., 1997). That the most probable stoichiometry of GABAA receptor subunits is (α1)2(β2/3)2(γ 2)1 rests on three independent lines of evidence obtained in vitro: (1) the relationship of individual subunits and their relative ratios to GABA-gated currents (Im et al., 1995; Chang et al., 1996), (2) ﬂuorescence energy transfer between epitope-tagged subunits (Farrar et al., 1999), and (3) quantitative immunoblotting of subunits from afﬁnity-puriﬁed pentamers (Tretter et al., 1997).

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ASSEMBLY AND INTRACELLULAR TRAFFICKING OF GABAA RECEPTORS

B. RECEPTOR ASSEMBLY IN HETEROLOGOUS CELLS 1. Assembly of Heteropentameric GABAA Receptors Heteropentameric receptors assemble in the endoplasmic reticulum by an ordered, multistep process. These steps have been well characterized for muscle-type nicotinic acetylcholine receptor (nAChR) heteropentamers (Green, 1999). The initial stage in the assembly of nAChR α 2βγ δ-subunit pentamers is the formation, perhaps cotranslationally, of αβγ -subunit trimers. Within this trimer, the α subunit folds to generate an α-bungarotoxin binding site. Then the δ subunit is recruited, forming αβγ δ tetramers containing an ACh binding site. The tetramers undergo further conformational rearrangements and a second α subunit is added, producing on the pentamer the second sites for toxin and ACh binding (Green and Wanamaker, 1998; Green, 1999). For GABAA receptors, the assembly pathway (Fig. 1, step 1) is much less well understood. The observation that, in a single neuron, only a subset of the available GABAA receptor subunits is oligomerized into receptors, suggests a highly selective process (Nusser et al., 1998). Presumably, complementary sites on the surfaces of neighboring subunits must enable their speciﬁc, high-afﬁnity association. As for the nAChR, the ordered arrangement of ﬁve subunits to form a central channel adds an additional layer of complexity to the assembly of GABAA receptors. Some of the subunit associations that seem to be involved in this process have been examined. a. Initial Steps in Receptor Assembly. In heterologous cells transfected with α1β2γ 2- or α1β3γ 2-subunit combinations, GABAA receptor pentamers are formed, but potential assembly intermediates have proved difﬁcult to reliably detect (Gorrie et al., 1997; Tretter et al., 1997; Knight et al., 1998; Elster et al., 2000). To interrupt pentameric assembly, Tretter et al. (1997) transfected human embryonic kidney (HEK) 293 cells with three binary combinations, α1γ 2, β3γ 2, and α1β3 subunits. In these three experiments, receptor multimers in cell extracts were resolved by sedimentation rate and analyzed by immunoblotting. This showed the formation, respectively, of α1γ 2 and β3γ 2 heterodimers or α1β3 heteropentamers. Although these could be potential intermediates of GABAA receptor assembly, it is not clear whether they represent true precursors or combinations resulting from missing subunits, as proved to be the case with nAChRs (Green, 1999). Further insight into the assembly process will probably require pulse-chase analysis to deﬁne precursor–product relationships and to identify misassembled multimers that are fated for degradation. b. Role of Subunit N-terminii. Nevertheless, the ordered subunit arrangement and stoichiometry of GABAA receptor pentamers point to the

EUGENE M. BARNES, JR.

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importance of α-β, α-γ , and β-γ subunit interfaces in directing pentameric receptor assembly (Im et al., 1995; Chang et al., 1996; Tretter et al., 1997). Klausberger et al. (2000) used mutated and chimeric γ 2 subunits of rat GABAA receptors to explore the α-γ and β-γ interfacial contacts established in HEK cells. It was determined that amino acid sequence γ 2-(91-104) is necessary for speciﬁc interactions with α1 subunits, whereas the γ 2-(83-90) sequence is involved in contacts with β3 subunits. Both of these N-terminal regions of γ 2 subunits are mostly conserved in γ 1, γ 3, and γ 4 subunits. For the α1-β2 subunit contact points that potentially lead to assembly of GABAA receptor pentamers in Sf 9 cells, Srinivasan et al. (1999) demonstrated the participation of two invariant amino acids in the rat α1 subunit, Trp-69 and Trp-94. These two residues are conserved within the ligandgated ion channel superfamily [including the nAChR, glycine, and serotonin (5HT3) receptors] and may represent “structural canonical residues” that constrain receptor folding, as suggested by Galzi and Changeux (1995). An additional region [α1-(58-67) and e.g., Gln-67] in the amino terminus of the murine α1 subunit appears to facilitate oligomerization with β3 subunits in HEK cells (Taylor et al., 2000). c. Conformational Maturation. It is generally believed that the assembly of ion channels is relatively slow and inefﬁcient (Green and Millar, 1995) in comparison to other multisubunit, transmembrane proteins such as the inﬂuenza virus hemagglutinin (HA). However, unlike HA and many other membrane proteins, ion channels are larger, more complex heterooligomers. So, it is perhaps not surprising that only 20–30% of nascent subunits are actually assembled into AChRs (Merlie and Lindstrom, 1983), voltage-dependent Na+ channels (Schmidt and Catterall, 1986), or cystic ﬁbrosis transmembrane regulators (Ward and Kopito, 1994). For these three kinds of ion channels, complete assembly requires 2 to 3 hr. A major contributing factor to inefﬁcient assembly, at least of nAChRs, is subunit misfolding. Misfolded monomers fail to oligomerize and are rapidly degraded (Green and Millar, 1995). In addition, oligomerization is a prerequisite for the subsequent folding steps involved in the conformational maturation of nAChRs. The assembly of GABAA receptors also appears to be similarly slow and inefﬁcient. Using pulse-chase techniques, Gorrie et al. (1997) showed that only 36% of the α1 and β2 subunits expressed in baby hamster kidney (BHK) formed α1β2 oligomers. The unassembled α1 and β2 subunits were both degraded with a half-life of 2 hr (Fig. 1, step 8). As with nAChRs, GABAA receptors seem to undergo conformational folding after initial assembly. In Spodoptera frugiperda insect cells (Sf 9 cells), coexpression of baculovirusencoded α1, β2, and γ 2 subunits produced binding sites for [3H]muscimol (a GABA agonist), [3H]ﬂunitrazepam (a benzodiazepine agonist), and

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ASSEMBLY AND INTRACELLULAR TRAFFICKING OF GABAA RECEPTORS

t-[35S]butylbicyclophosphorothionate (TBPS, a ligand for the Cl− channel). After baculovirus infection, the sites for [3H]muscimol binding were expressed 2 hr before those for [3H]ﬂunitrazepam and [35S]TBPS binding (Elster et al., 2000). Sedimentation velocity analysis indicated that both [3H]muscimol and [3H]ﬂunitrazepam binding sites were associated with GABAA receptor pentamers and not with smaller multimers. Thus, the α1β2γ 2-subunit pentamers that have acquired GABA binding sites undergo subsequent, relatively slow folding processes that produce mature Cl− channels and sites for benzodiazepine binding. It is not known whether the late stages of GABAA receptor folding/ maturation occur in the ER, Golgi, or plasma membrane. During part of this process, chaperone proteins presumably retain GABAA receptors in the ER. GABAA receptor α1, β2, and γ 2 subunits co-oligomerize in the ER of HEK cells in association with the molecular chaperones, calnexin, and the immunoglobulin heavy-chain binding protein (BiP) (Connolly et al., 1996a; Wisden and Moss, 1997). Calnexin and BiP probably bind to misfolded or misassembled GABAA receptors. The structural basis for their release from mature or maturing GABAA receptors is a mystery. Although GABAA receptors undergo N-linked glycosylation in the ER, this is apparently not required for the assembly of receptor subunits (Connolly et al., 1996a). 2. Assembly of Homo-oligomeric GABAA Receptors Unlike the nAChR α7 subunit whose homopentamers represent the α-bungarotoxin binding site in the brain (Drisdel and Green, 2000), GABAA receptor subunits are unlikely to form homopentamers in vivo. However, homo-oligomeric GABAA receptors produced in vitro have provided some additional information about receptor assembly. It is also worth noting that such homomers may be potentially useful for future diffraction analysis. In Sf 9 cells, unitary expression of human α1 or γ 2S subunits led to formation of homopentamers, whereas β2 subunits oligomerized into complexes with lower sedimentation rates (Elster et al., 2000). Binding sites for GABAA receptor ligands were absent in the Sf 9 cells expressing these unitary subunits. Under similar conditions, evidence for α1-subunit homo-oligomers was also obtained by Knight et al. (1998). These experiments in Sf 9 cells are inconsistent with those in BHK cells that failed to detect homo-oligomers of murine α1 or β2 subunits (Gorrie et al., 1997). There is agreement though that GABAA receptor channels are not found in heterologous cells expressing unitary α1, β2, or γ 2 subunits. The exceptions are GABAA receptor β3 and β4 subunits that produce unitary receptor channels, although there is no direct evidence for

EUGENE M. BARNES, JR.

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homopentamer formation. In HEK cells or Xenopus oocytes, murine β3subunit homo-oligomers formed spontaneously gated Cl− channels (Connolly et al., 1996b; Wooltorton et al., 1997). Sites for binding of [35S]TBPS, but not [3H]muscimol, were also found in HEK cells transfected with β3 subunits (Slany et al., 1995). These [35S]TBPS binding sites were insensitive to allosteric regulation by GABA, just like the currents produced from unitary β3-subunit channels. In contrast, robust GABA-gated currents were detected in oocytes expressing unitary β4 subunits (Liu et al., 1998). The GABAA receptor β4 subunit has been found only in chickens. Because the GABA afﬁnity was similar in unitary β4 and binary α1β4 and α1β2 receptor channels, it appears that the β4 subunit is capable alone of forming normal sites for GABA binding. The currents obtained from the β4-subunit hetero-oligomers were only one tenth of those obtained from α1β4-subunit channels, indicating that the former are not likely to be found in vivo. Taking advantage of the inability of β2 subunits to form heterooligomeric channels, Taylor et al. (1999) found additional sites on GABAA receptors that may facilitate subunit assembly. Using chimeras of β2-β3 subunits and mutagenesis of β2 subunits, four amino acids in the N-terminal region of the murine β3 subunit (Gly-171, Lys-173, Glu-179, Arg-180) were identiﬁed that confer on the β2 subunit the ability to form homo-oligomeric channels. Although these sites could constitute an “assembly signal” for unitary β3 receptor channels, they are not required for the assembly of binary α1β2 or α1β3 receptors. Furthermore, these sites are not conserved in chicken β4 subunits that form homo-oligomeric GABA-gated channels (Liu et al., 1998).

C. RECEPTOR ASSEMBLY in Vivo The diversity of GABAA receptor subtypes found in most brain regions places limitations on investigation of receptor assembly. Perhaps the most informative studies have been of adult cerebellar granule cells that abundantly express GABAA receptor α1, α6, β2, β3, γ 2, and δ subunits (Wisden et al., 1996; Nusser et al., 1998). By immunogold double-labeling experiments, Nusser et al. (1998) found evidence for 4 to 6 major receptor subtypes in granule cells, including α1β2/3γ 2, α6β2/3γ 2, and α6β2/3δ combinations. However, α1 subunits were never colocalized with δ subunits, suggesting that GABAA receptors with this combination seldom assemble. This conclusion is in agreement with experiments in which α6 subunits in the granule cell layer were eliminated by targeted disruption of the corresponding

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ASSEMBLY AND INTRACELLULAR TRAFFICKING OF GABAA RECEPTORS

gene ( Jones et al., 1997). In the granule cells of α6−/− mice, a coincident loss of α6- and δ-subunit immunoreactivity was found, whereas normal levels of δ subunits were found in the forebrain. Because the available α1, β2/3, and γ 2 subunits were unable to rescue the δ subunit in the α6-subunitdeﬁcient granule cells, it seems unlikely that α1β2/3δ oligomers assemble in vivo. However, such subunit combinations form readily in vitro. Coexpression of α1βxδ and α6βxγ 2 subunits in heterologous cells produces receptors displaying GABA-gated currents (Saxena and Macdonald, 1994; Wisden et al., 1996). This suggests that proteins unique to neurons may guide the selective assembly of GABAA receptors (Wisden and Moss, 1997). Similarly, only cells of neuronal origin are capable of correctly assembling some nAChR subtypes (Green, 1999). Embryonic neurons from the cerebral cortex of chick embryos prominently express GABAA receptor α1, β2, β4, and γ 2 subunits in culture (Baumgartner et al., 1994). These subunits assemble into GABAA receptors with a pharmacological proﬁle that is typical of the adult cortex (Barnes, 1996). By quantitative immunoblotting and ligand binding, Miranda and Barnes (1997) estimated that, of the available neuronal α1 subunits, only 20% were assembled into receptor complexes. Because α1 subunits are apparently produced in a large excess compared with the other GABAA receptor subunits in these cells, the accumulation of α1 subunits may reﬂect a relative deﬁciency of β and γ subunits, rather than an inefﬁciency of receptor assembly. In any event, these studies do not show, as some would assume, that GABAA receptor oligomerization is more efﬁcient in neurons than in heterologous cells. Indeed, for muscle-type nAChRs, a similar fraction (20–30%) of each subunit is assembled in muscle cells and mouse ﬁbroblasts (Green and Millar, 1995). Additional studies by pulse-chase labeling of cortical neurons showed that the unassembled GABAA receptor α1 subunits degraded with a half-life of 7.7 hr (Miranda and Barnes, 1997). As noted previously, the removal of monomeric α1 subunits in BHK cells is a more rapid process (Gorrie et al., 1997).

III. Surface Targeting and Intracellular Sorting of GABAA Receptors

A. RECEPTOR TARGETING in Vitro Following the oligomerization of subunits in the ER, GABAA receptors appear on the cell surface quite slowly (Fig. 1, steps 1 and 2, respectively).

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For example, the nascent α1β2-subunit combination arrived on the plasmalemma of BHK cells more than 6 hr after pentameric assembly (Gorrie et al., 1997). The reasons for this delay are not known. However, as noted previously, the maturation of GABAA receptor binding sites on heteropentamers is also a slow process (Elster et al., 2000). Because misassembled receptors are retained in the ER, a likely explanation is that nascent receptors remain on intracellular membranes until conformational maturity is achieved. One possible exception to this may be the γ 2S subunit. When epitopetagged murine γ 2S subunits were expressed alone in HEK cells, they were detected on the plasma membrane by indirect immunoﬂuorescence (Connolly et al., 1999b). But unitary α1, β2, or γ 2L subunits were retained in the ER and did not appear on the surface. Although these ﬁndings could suggest an involvement of the γ 2S subunit in surface targeting, their signiﬁcance is unclear. Coexpression of γ 2S subunits did not facilitate the surface targeting of α1β2-subunit receptors. Thus, in the absence of α1 and β2 subunits, it seems likely that γ 2S subunits could be misassembled and mistargeted. In the mammalian brain, GABAA receptors are found mainly on somatic and dendridic membranes, often in postsynaptic densities. Some of the determinants for selective intracellular sorting are believed to reside on speciﬁc GABAA receptor subunits. As an in vitro model, polarized Madin–Darby canine kidney (MDCK) cells have been employed to investigate GABAA receptor sorting to speciﬁc membranes. In MDCK cells expressing α1β2- and α1β3-subunit combinations, both had a basolateral localization, whereas α1β1-subunit complexes had a nonpolarized (apical and basolateral) distribution (Connolly et al., 1996b). For this model system, dendritic proteins are usually targeted to basolateral membranes, whereas apical sorting in MDCK cells may not have clear correlate in neuronal membranes ( Jareb and Banker, 1998). So, the experiments of Connolly et al. (1996b) are suggestive of a role of β subunits in the segregation of GABAA receptors to select membrane regions.

B. RECEPTOR TARGETING in Vivo Microanatomical studies of neurons have provided deﬁnitive information about the selective subcellular distribution of GABAA receptor α subunits. As one example of this, GABAA receptors containing α2 subunits occur on the axon initial segment of hippocampal pyramidal neurons, whereas α5 subunits are localized primarily on dendridic and somatic membranes

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ASSEMBLY AND INTRACELLULAR TRAFFICKING OF GABAA RECEPTORS

(Nusser et al., 1996; Fritschy et al., 1998). A second example is provided by alpha ganglion cells in the mammalian retina that show, on the somatodendridic membrane, clusters of GABAA receptors containing α1, α2, α3, and γ 2 subunits. However, the α1, α2, and α3 subunits did not colocalize in the same clusters, suggesting that different GABAA receptor isoforms segregate to speciﬁc postsynaptic sites (Koulen et al., 1996). To account for the speciﬁc sorting of neurotransmitter receptors into clusters at speciﬁc sites on the postsynaptic membrane, two possible mechanisms have been proposed (Craig et al., 1994). In the ﬁrst, nascent or recycling receptors are inserted all along the surface of the somatodendritic membrane and are then sorted into clusters (e.g., Fig. 1, step 3) by binding to speciﬁc proteins. The second mechanism involves receptor sorting into speciﬁc exocytotic vesicles that are targeted to selective postsynaptic sites. The ﬁnding that GABAA receptors are distributed diffusely at lower density on the somatodendritic membrane, as well as in distinct clusters (Nusser et al., 1995; Koulen et al., 1996), favors the ﬁrst model. As discussed here, this diffusable pool of GABAA receptors also plays an important role in receptor endocytosis. Another potential factor in postsynaptic receptor sorting is the presynaptic release of neurotransmitter. The clustering of nAChRs, glycine receptors (GlyRs), and glutamate (NMDA and AMPA) receptors requires receptor activation by agonist (Craig, 1998). However, this does not seem to be the case for GABAA receptors. Blockade of GABA binding had no effect on postsynaptic clustering of GABAA receptors in cultured hippocampal neurons (Craig et al., 1994). The most comprehensive description of synaptic sorting mechanisms for GABAA receptors has emerged from investigations of the γ 2 subunit. As noted in Section II.C, immunogold labeling experiments in cerebellar granule cells have identiﬁed the major GABAA receptor subtypes containing either γ 2 or δ subunits. Further investigations by Nusser et al. (1999) have shown that receptors containing a δ subunit were found in abundance on extrasynaptic dendritic and somatic membranes of cerebellar granule cells, but were not detectable in synaptic junctions. However, γ 2 and the other prominent GABAA receptor subunits were found concentrated in synapses with Golgi cells and also, at lower levels, in extrasynaptic membranes (Nusser et al., 1998). An important role for the γ 2 subunit in synaptic targeting has also been established by disruption of the corresponding gene. In embryonic γ 2−/− mice, Essrich et al. (1998) found a profound loss in the cerebral cortex of synaptic clusters of GABAA receptor α2 subunits and of α1 subunits in the molecular layer of the cerebellum. This defect was recapitulated in cortical

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neuron cultures derived from γ 2−/− mice. The mutant neurons showed a deﬁciency of α2-subunit clusters and GABAA receptor-mediated miniature postsynaptic potentials, but normal currents that were evoked by applied GABA. Thus, in the absence of γ 2 subunits, GABAA receptor channels are formed but not recruited into synapses.

IV. GABAA Receptor-Clustering Proteins

A. GEPHYRIN Gephyrin is a 93-kDa peripheral membrane protein that copuriﬁes with GlyRs and is necessary for their postsynaptic targeting (Pfeiffer et al., 1982; Kirsch et al., 1995). Because gephyrin also binds to components of microtubules and microﬁlaments, it is believed to act as a direct linker protein between GlyRs and the cytoskeleton (Kneussel and Betz, 2000; Sasso´e-Pognetto and Fritschy, 2000). In peripheral tissues, additional roles for gephyrin as a cofactor for molybdoenzymes (Feng et al., 1998) and in rapamycin-sensitive signaling (Sabatini et al., 1999) have been established. The ﬁrst suggestion that gephyrin might faciliate GABAA receptor clustering came from experiments showing their colocalization in regions of the retina that are devoid of GlyRs (Sasso´e-Pognetto et al., 1995). Complementary studies of hippocampal neurons indicated that gephyrin is clustered at GABAergic, but not at glutamatergic, synapses (Craig et al., 1996). It is therefore highly signiﬁcant that synaptic clusters of gephryin are lost in parallel with GABAA receptor clusters in γ 2−/− knockout mice (Essrich et al., 1998). In addition, depletion of gephyrin in hippocampal neurons from normal animals, produced by treatment with antisense oligonucleotides, also reduced the synaptic accumulation of GABAA receptor α2 and γ 2 subunits (Essrich et al., 1998). Finally, targeted disruption of the gephyrin gene also led to a reduction in GABAA receptor clusters (Kneussel et al., 1999). Because the assembly and surface targeting of GABAA receptors were not impaired in gephyrin −/− neurons, it has been proposed that gephryin stabilizes receptor clusters by providing a tether to the postsynaptic cytoskeleton. It is believed that gephyrin binds to the large intracellular loop of the GlyR β subunit, a polypeptide that is also required for GlyR clustering. However, parallel overlay assays failed to detect direct interactions of gephyrin with GABAA receptor subunits (Meyer et al., 1995). Gephyrin also does not seem to copurify with GABAA receptors (Kannenberg et al., 1997). Although

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ASSEMBLY AND INTRACELLULAR TRAFFICKING OF GABAA RECEPTORS

some data suggest a possible interaction of gephyrin with GABAA receptor β3 subunits (Kirsch et al., 1995), there is a scarcity of more recent studies that support this or similar direct interactions. Furthermore, in a signiﬁcant number of brain regions, gephyrin and GABAA receptors do not colocalize (Sasso´e-Pognetto and Fritschy, 2000). To account for this, the current assumption is that some other linker protein binds to GABAA receptors and provides additional sites for interaction with clustering complexes that contain gephyrin.

B. GABARAP One early candidate for such a linker molecule was the GABAA-receptor associated protein (GABARAP) identiﬁed in a yeast two-hybrid screen using the γ 2 subunit as bait (Wang et al., 1999). GABARAP is a 14-kDa polypeptide that binds to γ 2-subunit fusion proteins and, in brain extracts, is coimmunoprecipitated with GABAA receptor subunits. Like gephyrin, GABARAP is also found in many peripheral tissues and presumably performs additional functions unrelated to GABAA receptor binding. In fact, GABARAP was shown to interact in vitro with gephryin (Kneussel et al., 2000). In brain homogenates, GABARAP fusion proteins were capable of binding to gephyrin. Also, immunoﬂuorescence microscopy showed colocalization of GABARAP with gephryin when both were expressed in PC12 cells, a neuron-derived cell line. However, in spinal cord sections or in cultured cortical neurons derived from wild-type mice, GABARAP failed to colocalize with gephyrin. Furthermore, the intensity of puncta containing GABARAP was not diminished in gephyrin −/− knockout mice. Similarly, at inhibitory synapses in the retina, GABARAP was not clustered with gephyin or with GABAA receptor γ 2 subunits. Instead, the largest quantities of GABARAP were found intracellularly, associated with ER and/or Golgi compartments. These ﬁndings are consistent with a 57% sequence identity that GABARAP shares with p16, a late-acting intra-Golgi transport factor. Thus, instead of participating in synaptic clustering, GABARAP may function in the sorting of nascent GABAA receptors on intracellular membranes (Kneussel et al., 2000). The studies with GABARAP sound a cautionary note for reliance on transfected cell lines in the study of GABAA receptor trafﬁcking. In transfected PC12 cells, GABARAP immunoreactivity is mainly localized on the plasma membrane, and coexpression of gephyrin and GABARAP induces a shift of cytoplasmic gephyrin to membrane sites containing GABARAP. Similarly, coexpression of GABAA receptors and GABARAP in quail ﬁbroblast QT6 cells leads to receptor clustering (Kneussel et al., 2000). It is therefore

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troubling that GABARAP and gephyrin do not colocalize either in sections of spinal cord or retina or in cultured cortical neurons and that GABARAP is found in vivo mostly on intracellular membranes rather than on the somatodendritic membrane. The reasons for this disparity are not clearly understood, although overexpression of recombinant proteins could account for their misdirection or misassembly in vitro. Another possibility is that neuronal specializations are required to accurately guide the clustering of GABAA receptors.

C. RAPSYN Rapsyn was originally identiﬁed as a 43-kDa protein present in Torpedo membrane preparations that are enriched in nAChRs. Subsequently, it was demonstrated that rapsyn has an essential role in the clustering of muscletype nAChRs both in vitro and in vivo, for example, at the neuromuscular junction (Froehner et al., 1990; Colledge and Froehner, 1998). Despite vigorous investigation, the mechanism of the association of rapsyn with nAChRs and the nature of other rapsyn binding partners remain unresolved. Rapsyn has also been implicated in the surface aggregation of GABAA receptors in vitro. Two studies in transfected cells have shown that coexpression of rapysn and GABAA receptors produces receptor clustering at the plasma membrane. In QT6 cells, a GABAA receptor α1β1γ 2-subunit combination formed surface aggregates after cotransfection with rapsyn (Yang et al., 1997). A more detailed study in HEK cells (Ebert et al., 1999) revealed that GABAA receptor subunit combinations (α1β1, α1β2, α1β3, α1βxγ 2, β3γ 2, or β3 alone) had a diffuse distribution in the plasmalemma that became punctate when rapsyn was coexpressed. However, other subunits (α1, β1, β2, or γ 2 expressed individually or α1γ 2 together) that fail to reach the cell surface were not clustered when coexpressed with rapsyn. A β3-α1 chimeric subunit, expressed alone in HEK cells, reached the plasma membrane, but the chimera was also insensitive to aggregation by rapsyn. Because homooligomeric β3 subunits, as well as a β3-β2 chimera, were also clustered by rapsyn, it has been proposed that the intracellular domain of β subunits could provide regions involved in clustering (Ebert et al., 1999). The possible signiﬁcance of rapsyn to the synaptic aggregation of GABAA receptors is an open question. Rapsyn is found mainly in peripheral tissues rather than in the brain (Colledge and Froehner, 1998). Although some GABAA receptors are also located peripherally, it now seems necessary to elucidate their possible interactions with rapsyn in vivo.

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ASSEMBLY AND INTRACELLULAR TRAFFICKING OF GABAA RECEPTORS

V. Endocytosis and Recycling of GABAA Receptors

A. RECEPTOR ENDOCYTOSIS in Vivo 1. Ligand-Evoked Endocytosis in Neurons GABAA receptors, upon reaching the mobile surface pool via exocytosis, can also accumulate in endocytic pits and be taken back into the cell by the process of endocytosis (Fig. 1, steps 5 and 6). GABAA receptor endocytosis can occur constitutively in some cells or require binding of GABA or other speciﬁc agonists in others. The latter process seems to predominate in vivo and represents a key step in GABAA receptor downregulation (Barnes, 1996). In this article, the term downregulation refers to a loss of surface receptors by internalization, although it also involves the interplay of receptor recycling and/or degradation (Fig. 1, steps 7 and 9). Cultured neurons derived from the cerebral cortex of chick embryos have provided a model substrate for study of GABAA receptor downregulation. Application of GABA to these preparations initiates a sequence of regulatory processes that control the physiological function and intracellular distribution of GABAA receptors. After the opening of receptor channels, the ﬁrst of these ligand-dependent control mechanisms is rapid desensitization (tachyphylaxis). Desensitization of GABAA receptors, a process studied by many investigators (reviewed in Barnes, 1996), is characterized by a fading of receptor currents during continuous GABA application. The kinetics of desensitization are complex and show typical t1/2 values in the range of 10–1000 msec at room temperature, associated with a reduced frequency of channel openings (Hamill et al., 1983; Weiss, 1988). GABAA receptors desensitize rapidly in isolated membrane patches (Hamill et al., 1983) or in proteoliposomes reconstituted from puriﬁed receptors (Dunn and Thuynsma, 1994). Thus, the process of desensitization is easily distinguished from the endocytosis of GABAA receptors, although it precedes and may even be required for endocytosis. After GABAA receptor desensitization in cortical neuron cultures, the native receptors undergo ligand-dependent endocytosis. This process has been examined by two different biochemical techniques. In the ﬁrst, a membraneimpermeant benzodiazepine, SPTC-1012S, was employed as a displacing ligand to distinguish receptor sites on the neuronal surface from those that are intracellular (Tehrani and Barnes, 1991). By binding [3H]ﬂunitrazepam— a highly permeant benzodiazepine agonist—to intact neurons, it was estimated that 7% of the receptors were intracellular (i.e., not displaced by SPTC-1012S). Following exposure of the cells to GABA or clonazepam for

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1 to 4 hr at 37◦ C, the internal receptor fraction increased to 18–20% of the total, suggesting that receptor sequestration had occurred. Although the total number of receptor binding sites (intracellular plus extracellular) was not signiﬁcantly altered by acute treatment with agonist, a limitation of this method is an inability to show that the expanding pool of internal receptors is actually derived from the surface. Accordingly, a second approach involving labeling of extracellular GABAA receptors was used (Calkin and Barnes, 1994). An impermeant, SH-cleavable iodinating reagent, [125I]DPSgt, was used to tag surface receptors on intact cortical neurons at 0◦ C, a condition permitting little or no receptor endocytosis. Then the cells in fresh media containing GABA or other effectors were returned to 37◦ C, incubated for 2 hr to internalize receptors, and then washed with glutathione buffer to strip the neuronal surface of the remaining 125I. After immunoprecipitation of GABAA receptors from cell extracts, internalized receptor 125I-labeled subunits were detected on sodium dodecyl sulfate (SDS) gels. Consistent with the SPTC-1012S displacement assay (Tehrani and Barnes, 1991), approximately 16% of the receptor peptides on the surface were sequestered. The addition of GABA to the cultures was obligatory for endocytosis of native GABAA receptors. Little or no internalization of surface 125I-labeled receptors was detectable without exogenous GABA. Although the kinetics of receptor internalization were of interest, the relatively low yield of the iodination reaction precluded a more detailed analysis. Meyer et al. (2000) introduced an important new approach to the kinetic resolution of GABAA receptor endocytosis. Repetitive applications of 1 µM muscimol (3-sec pulses, 2 per min, for a total of 25 min) to hippocampal pyramidal neurons evoked a decline of GABAA receptor currents in wholecell patch-clamp recordings. This phenomenon, originally termed rundown (Gyenes et al., 1988, 1994), was unrelated to desensitization and could be mostly prevented by the presence of ATP in the patch pipet. Under these ATP-stabilized conditions, a rapid rundown of receptor currents (t1/2 of 4–5 min at room temperature) was produced by destruction of microﬁlaments with nocodazol or by depletion of actin ﬁlaments with either latrunculin B or the C3 toxin of C. botulinum. After these treatments, the amount of receptor rundown was increased from 20% to more than 70% during 25 min of muscimol pulses. Likewise, inactivation of Rac1, a GTPase required for actin polymerization, induced a similar decline in GABAA receptor currents. This provides good evidence that GABAA receptor function is regulated by the neuronal cytoskeleton. In parallel experiments, Meyer et al. (2000) examined surface clusters of GABAA receptors by ﬂuorescence immunostaining of α2 subunits. After

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ASSEMBLY AND INTRACELLULAR TRAFFICKING OF GABAA RECEPTORS

exposure of the pyramidal neurons to muscimol for 25 min, the number of receptor clusters was somewhat reduced. Treatments that destroyed the microtubular or actin cytoskeleton had little effect on receptor clusters before musimol application, but very few clusters in the treated neurons remained after agonist exposure. This massive ligand-induced downregulation is due most likely to GABAA receptor endocytosis. Such an interpretation can be justiﬁed by the scheme in Fig. 1. The distribution of GABAA receptors along the membrane is controlled by equilibrium between synaptic clustering (step 3) and ligand-induced endocytosis (downregulation, steps 5 and 6), competing processes that share the pool of mobile receptors (Barnes, 2000). As demonstrated by Meyer et al. (2000), stabilization of synaptic clusters occurs by anchoring of GABAA receptors to the cytoskeleton. Destruction of this ﬁlamentous network destabilizes receptor clusters, favoring receptor release to the mobile pool (step 4). But this alone is not sufﬁcient for substantial depletion of the clusters. GABAA receptor endocytosis must also be evoked by application of muscimol. This downregulation consumes the pool of diffusable receptors, driving the depletion of receptors from the destabilized synaptic clusters. What is the fate of the internalized GABAA receptors? In cortical neurons exposed to GABA for longer periods, the levels of [3H]ﬂunitrazepam and [35S]TBPS binding and GABAA receptor α1-subunit immunoreactivity undergo a coordinate decline. Because the rate of α1-subunit biosynthesis was not affected by GABA exposures of the same duration, it is believed that a portion of the sequestered receptors are degraded (Fig. 1, step 9) (Calkin and Barnes, 1994; Miranda and Barnes, 1997). It is currently not known whether the receptors remaining on neuronal endosomes can recycle to the surface. 2. Role of Clathrin-Coated Vesicles In the brain, the most active endocytic pathway involves the internalization of synaptic vesicle proteins following the release of neurotransmitter (De Camilli and Takei, 1996). These proteins collect in clathrin-coated pits that invaginate to form intracellular vesicles. A number of neurotransmitter receptors, including dopamine, muscarinic ACh, and β-adrenergic receptors, also undergo endocytosis via clathrin-coated vesicles (Chuang et al., 1986; Silva et al., 1986; Gonzalez-Calero et al., 1990). A series of studies have established that a fraction of GABAA receptors also copurify with clathrincoated vesicles from rat, mouse, and bovine brain (Tehrani and Barnes, 1993; Tehrani et al., 1997; Tehrani and Barnes, 1997). The probable occurrence of ligand-dependent endocytosis of GABAA receptors in the brain is suggested by ﬁndings of Tehrani and Barnes (1997). In this investigation, coated vesicles and synaptic membranes were isolated from mice that

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had been chronically treated with the benzodiazepine agonist lorazepam. Lorazepam exposure produced a small decline in [3H]ﬂunitrazepam binding sites and GABAA receptor α1-subunit immunoreactivity in synaptic membranes, whereas the level of binding sites and α1 subunits on coated vesicles increased by 60–80% in comparison to untreated controls. This ﬁnding is also consistent with a depletion of synaptic GABAA receptors by ligandevoked endocytosis. As part of the pathway for GABAA receptor downregulation (Barnes, 1996), it is well established that GABAA receptors undergo a process termed uncoupling in neurons exposed to GABA or benzodiazepine agonists (Friedman et al., 1996). This process is characterized by a loss of the normal allosteric coupling between ligand binding sites on GABAA receptors in synaptic membrane fragments (e.g., the enhancement of [3H]ﬂunitrazepam binding by coincubation with GABA). Conducting such assays with synaptic membranes and clathrin-coated vesicles isolated from bovine cortex, Tehrani et al. (1997) found that [3H]ﬂunitrazepam binding to coated vesicles was completely insensitive to GABA modulation, whereas binding to synaptic membranes was enhanced by 120%. Similarly, GABA noncompetitively displaced nearly all [35S]TBPS binding to synaptic membranes, but was completely ineffective on binding to coated vesicles. Because the binding of [3H]muscimol to coated vesicles was comparable to that of the other radioligands, most or all of the GABAA receptors on the clathrincoated vesicles lost the coupling between their binding sites for GABA, benzodiazepines, and TBPS (Cl− channels). Although the mechanism for GABAA receptor uncoupling is unknown, these experiments nevertheless provide further evidence that clathrin-coated vesicles play a role in receptor downregulation.

B. RECEPTOR ENDOCYTOSIS AND RECYCLING in Vitro GABAA receptors expressed in Xenopus oocytes or HEK cells also undergo endocytosis. An important advance was made by Chapell et al. (1998), who observed that application of phorbol myristate acetate (PMA) to oocytes coexpressing α1β2 or α1β2γ 2L subunits produced a >75% decline in GABA-gated currents. Most of this loss occurred within a 30-min period. The PMA-evoked decline in GABAA receptor currents was coincident with a decline in surface ﬂuorescence from α1 subunits tagged either by antibodies or by green ﬂuorescent protein (GFP). Thus, PMA seems to activate the endocytosis of GABAA receptors in oocytes. The effect of PMA, a well-known activator of protein kinase C (PKC), was prevented by coapplication of calphostin C, a PKC inhibitor. However, site-directed mutations

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ASSEMBLY AND INTRACELLULAR TRAFFICKING OF GABAA RECEPTORS

in the three potential PKC phosphorylation sites on the α1β2γ 2-subunit combination did not reduce the effect of PMA. This suggests that other proteins, perhaps those involved in regulation of the endocytic pathway, are the relevant substrates for PKC. These ﬁndings with oocytes have been conﬁrmed and extended by Filippova et al. (2000) through measurements of PMA-induced endocytosis of a GABAA receptor α1β2γ 2-subunit combination and a GABAC receptor ρ1-subunit homo-oligomer. Following addition of PMA, receptor currents from both receptor types declined with a time course similar to that reported by Chapell et al. (1998). A loss of surface ﬂuorescence from GFP-tagged GABAA receptors and in surface binding of [3H]GABA to GABAC receptors was coordinate with a loss of receptor currents (Filippova et al., 2000). As was the case with GABAA receptors, PKC phosphorylation sites on ρ1 subunits were not required for endocytosis. PMA had no effect on neuronal nAChR (α7 or α4β2 subunits) currents in oocytes, showing that the loss of GABA receptor currents was not due to generalized membrane retrieval. After the PMA-induced endocytosis of either GABAA or GABAC receptors, the receptor currents returned to nearly normal levels 24 hr after PMA washout. This suggests that the internalized receptors can recycle to the surface (cf. Fig. 1, step 7) (Filippova et al., 2000). Initial studies of GABAA receptor endocytosis in HEK cells were carried out by Connolly et al. (1999a). Using cells expressing α1β2 or α1β2γ 2 subunits that were epitope tagged on the N-terminus of the β2 or γ 2 subunits, respectively, antibodies were bound to the surface receptors at 0◦ C. After induction of internalization by incubation at 37◦ C, receptors were visualized by indirect immunoﬂuorescence. The results suggest that endocytosis of these receptors is constitutive. In HEK cells expressing α1β2γ 2 subunits, incubation with PMA at 37◦ C produced a decline in 125I-labeled antibody binding in subsequent assays at 0◦ C. Because wortmannin, an inhibitor of receptor recycling, produced a similar loss of 125I-labeled antibody binding, it was proposed that GABAA receptors recycle to the surface after constitutive endocytosis in HEK cells. A study of GABAA receptor internalization in HEK cells used some alternative approaches to this problem (Cinar and Barnes, 2000). In these experiments with α1β2- and α1β2γ 2-subunit combinations, only the β2 subunits were epitope tagged. Three kinds of assays were used. In the ﬁrst, the surface receptors were labeled with 125I-antibody at 0◦ C, internalization was induced at 37◦ C in the presence of effectors, and then 125I was stripped from the surface by a pH 1.5 wash at 0◦ C. The 125I that remained associated with the cells represented internalized receptors. In the second assay, surface receptors were labeled at 0◦ C using NHS-SS-biotin (a cleavable, impermeant reagent), internalized as before, and then biotin was stripped from the surface with

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mercaptoethanesulfonate at 0◦ C. Cell extracts were absorbed on streptavidin beads, and eluates from the beads were evaluated on Western blots probed with an α1-subunit antibody. This latter approach has the advantage that antibodies, which may themselves induce endocytosis, were never incubated with the cells. The third assay was based on immunoﬂuorescence, essentially as described by Connolly et al. (1999a), but with epitope-tagged β2 subunits only. In this study (Cinar and Barnes, 2000), the time course of GABAA receptor internalization, measured by the 125I assay, showed nearly identical rates (t1/2 = 4–5 min at 37◦ C) and extent of α1β2- and α1β2γ 2-subunit internalization (8–9% of the surface pool). The endocytosis of both subunit combinations was enhanced equally by PMA (90–100%), was insensitive to GABA, and could be blocked by hypertonic sucrose (75–85% inhibition). Comparable results were obtained with the biotinylation and immunoﬂuorescence assays. This indicates that GABAA receptor endocytosis in HEK cells is constitutive, enhanced by PMA, and does not require a γ 2 subunit. These properties are in accord with those found in Xenopus oocytes (Chapell et al., 1998; Filippova et al., 2000). Finally, it was shown that a dominant-negative mutant of dynamin (K44A) enhanced GABAA receptor endocytosis, but completely prevented the ligand-induced internalization of β 2-adrenergic receptors. This suggests that GABAA receptor endocytosis in HEK cells occurs by a clathrin-independent mechanism. The endocytosis of muscarinic m2 ACh and dopamine D2 receptors was previously shown to be independent of clathrin (Vogler et al., 1998; Roseberry and Hosey, 1999; Vickery and von Zastrow, 1999). The mechanism of PMA inactivation of neuronal GABAA receptor currents is controversial, but the phenomenon is consistently observed. For example, phorbol esters, including PMA, reduce GABAA receptor function in retinal bipolar cells (Gillette and Dacheux, 1996) and cervical ganglion neurons (Krishek et al., 1994). In the investigations noted previously, PMA also produced a loss of GABA-gated currents in Xenopus oocytes (Chapell et al., 1998; Filippova et al., 2000) that was attributed to receptor endocyosis. The physiological relevance of GABAA receptor regulation by PKC has not been established. However, it is intriguing that PKC is necessary for the downregulation of GABAA receptor α1 and β2/3 subunits in cerebellar granule cells exposed acutely to ﬂunitrazepam ( Johnston et al., 1998). Further examination of this seems to be warranted. A comparison of some other properties of GABAA receptor downregulation found in vivo and in vitro shows a disturbing discord. In hippocampal and cortical neurons (Calkin and Barnes, 1994; Meyer et al., 2000), receptor internalization required the application of GABA or related agonists. Constitutive endocytosis of GABAA receptor was not detectable.

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However, constitutive endocytosis seems to predominate in HEK cells (Connolly et al., 1999a; Cinar and Barnes, 2000). Although the receptor channels in HEK cells are GABA sensitive, GABA had no signiﬁcant effect on receptor endocytosis. Furthermore, clathrin-coated vesicles seem to play a role in ligand-dependent endocytosis of GABAA receptors in mouse brain (Tehrani and Barnes, 1997), whereas in HEK cells receptor endocytosis did not require dynamin (Cinar and Barnes, 2000), a hallmark of clathrin dependence. The reasons for this lack of agreement between the ﬁndings in vivo and in vitro are not readily apparent. One possibility is that differences in methodology (e.g., sensitivity of assays for endocytosis) could account for this disparity. Another is that inconsistent expression or overexpression of GABAA receptor subunits in heterologous cells could lead to misdirected trafﬁcking. A third possibility is that proteins unique to differentiated neurons are necessary to direct GABAA receptors into speciﬁc endocytic pathways. The latter two scenarios seem more likely because analogous difﬁculties were encountered in the study of GABAA receptor assembly (Section II.C) and synaptic clustering (Section IV.B). In the case of endocytosis, the internalization pathways in neurons and in heterologous cells are clearly distinguishable. These in vitro aberrations might therefore be turned to advantage in the search for proteins that contribute to neuronal specializations for endocytosis. Such molecules might include accessory or linker proteins that direct GABAA receptors from the neuronal surface to appropriate endocytic compartments.

C. ROLE OF INSULIN With the exception of GABA and benzodiazepine agonists, other small molecules that contribute to the regulation of GABAA receptor trafﬁcking have not been identiﬁed. That insulin could initiate signals for the synaptic recruitment of GABAA receptors was initially suggested by the ﬁndings of Wan et al. (1997). In HEK cells expressing GABAA receptor α1β2γ 2 subunits, a 1-hr exposure to insulin caused an increase in the number of surface receptors as detected by immunoﬂuorescence and immunogold labeling and by recording GABA-gated whole-cell currents. These effects of insulin were blocked by pretreatment of the cells with genistein, an inhibitor of the insulin receptor tyrosine kinase. Treatment with either insulin or genistein alone did not alter the total cellular immunostaining of the GABAA receptor β2 subunit, suggesting that insulin enhanced the recruitment of receptors to the surface from an intracellular pool, rather than through a generalized trophic effect on the HEK cells. Similar effects of insulin were

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observed in cultured hippocampal neurons. An 8-min bath application of insulin resulted in a 25–30% enhancement of spontaneous miniature inhibitory postsynaptic currents, as well as responses to applied GABA (Wan et al., 1997). Both insulin and insulin receptors are expressed at high levels in the brain, particularly in the cortex and hippocampus (Wozniak et al., 1993). However, glucose metabolism in the brain is generally believed to be unresponsive to insulin, so the neural function of insulin has remained a mystery. In highly responsive cells, such as adipocytes, tyrosine autophosphorylation of the insulin receptor kinase greatly stimulates the recruitment to the plasma membrane of intracellular vesicles containing glucose transporter (GLUT4). Much smaller, but signiﬁcant, effects of insulin are also observed for the membrane insertion of recycling proteins, such as GLUT1, transferrin receptors, and mannose 6-phosphate receptors (Pessin et al., 1999). Accordingly, the insulin-evoked recruitment of GABAA receptors to synaptic membranes (Wan et al., 1997) could occur by a similar stimulation of receptor recycling. Meyer et al. (2000) provided an alternative explanation for the mechanism of insulin action on GABAA receptors. In their study, exposure of hippocampal neurons to insulin prevented the use-dependent reduction in GABAA receptor currents (rundown; refer to Section IV.A.1). However, when insulin was added after rundown was established, rundown was not reversed. This suggests that insulin does not stimulate GABAA receptor recycling to the surface. Because insulin also was reported to be an activator of Rac1 (Nishiyama et al., 1994), the effect of the lethal toxin of C. sordellii, a potent inhibitor of Rac1 GTPase, was also tested. Lethal toxin completely blocked the effect of insulin on GABAA receptor rundown (Meyer et al., 2000). Thus, insulin, through activation of Rac1, may facilitate GABAA receptor recruitment to synaptic clusters by triggering rearrangements of the cytoskeleton.

D. PURPOSES OF GABAA RECEPTOR ENDOCYTOSIS What possible relevance does GABAA receptor endocytosis have to the stability of inhibitory synapses in the brain? It has been suggested (Fig. 1) that endocytosis and synaptic clustering represent two processes that compete for a pool of mobile GABAA receptor receptors. Thus, the dynamic interplay between these two pathways could provide a mechanism for synaptic plasticity (Barnes, 2000). In hippocampal pyramidal neurons, evidence for this kind of competition was provided by Meyer et al. (2000). Ligand-evoked endocytosis can produce a downregulation of synaptic GABAA receptors. It

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is not known whether these synaptic modiﬁcations can be long lasting, but this is an attractive possibility. The t1/2 for the downregulation of hippocampal GABAA receptors was 4–5 min. During this period, muscimol was applied repetitively by pipet. However, under more physiological conditions, GABA released presynaptically would be rapidly taken up via the Na+-dependent GABA transporters in adjacent membrane processes. Thus, it must be questioned whether ligandevoked GABAA receptor endocytosis could ever occur in tissues. Sustained levels of GABA or related agonists may occur under a variety of circumstances. One of these is in the developing nervous system when GABA is released tonically by extrasynaptic mechanisms (Belhage et al., 1990; Valeyev et al., 1993). Under these circumstances, GABA acts as a trophic factor that promotes neuronal differentiation, including synapse formation (Belhage et al., 1998). A second occurrence of sustained GABA tone is in epilepsy, both in humans (Wilson et al., 1996) and in genetic absence epilepsy rats that show an impairment of the GABA reuptake mechanism (Sutch et al., 1999). A third, and perhaps most signiﬁcant, occurrence is associated with the chronic administration of benzodiazepines and related sedative drugs, producing tolerance and physical dependence. The establishment of habituation to these drugs may depend on contributions from GABAA receptor endocytosis (Tehrani and Barnes, 1997). In conclusion, it is worthwhile to consider the hypothesis that GABAA receptor endocytosis could represent an initial step in an intracellular signaling pathway that regulates receptor gene expression. Prolonged occupancy of surface GABAA receptors by GABA leads to repression of the synthesis of α1- and β2-subunit mRNA and α1-subunit polypeptides in cultured cortical neurons (Miranda and Barnes, 1997; Lyons et al., 2000). A related GABA-dependent silencing of transcription involves an upstream regulatory site on the β1-subunit gene (Russek et al., 2000). In developing cerebellar granule cells that are sensitive to the trophic actions of GABA, an induction of α1- and β2-subunit mRNA biogenesis is produced by application of agonists to GABAA receptors (Kim et al., 1993; Elster et al., 1995). Taken together, these investigations point to a pathway in which signals initiated by surface GABAA receptors are transmitted to the nucleus. So far, none of the intermediate steps in this hypothetical pathway have been characterized. However, it is tempting to speculate that some of these steps might be analogous to those for β 2-adrenergic receptor signaling. In groundbreaking work, Lefkowitz (1998) showed that agonist-dependent endocytosis of β 2-adrenergic receptors is a necessary step in the activation of MAP kinases and the mitogenic response. Other studies (Hyman et al., 2000) reveal that the protein epsin, a component of the clathrin coat, is imported into the nucleus and regulates a transcription factor. These ﬁndings lend

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some indirect support to the notion (Miranda and Barnes, 1997) that the agonist-dependent endocytosis of GABAA receptors may generate intracellular signals for transcriptional regulation. Although this is a tentative hypothesis, it may be entertained as an incentive to future investigations of GABAA receptor trafﬁcking.

Acknowledgments

The author thanks Dr. Hulusi Cinar for helpful comments on the manuscript and the National Institutes of Health for a grant (NS34253) in support of research in the author’s laboratory.

References

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Sutch, R. J., Davies, C. C., and Bowery, N. G. (1999). GABA release and uptake measured in crude synaptosomes from Genetic Absence Epilepsy Rats from Strasbourg (GAERS). Neurochem. Int. 34, 415–425. Taylor, P. M., Connolly, C. N., Kittler, J. T., Gorrie, G. H., Hosie, A., Smart, T. G., and Moss, S. J. (2000). Identiﬁcation of residues within GABAA receptor α subunits that mediate speciﬁc assembly with receptor β subunits. J. Neurosci. 20, 1297–1306. Taylor, P. M., Thomas, P., Gorrie, G. H., Connolly, C. N., Smart, T. G., and Moss, S. J. (1999). Identiﬁcation of amino acid residues within GABAA receptor β subunits that mediate both homomeric and heteromeric receptor expression. J. Neurosci. 19, 6360–6371. Tehrani, M. H., and Barnes, E. M., Jr. (1991). Agonist-dependent internalization of γ -aminobutyric acidA/benzodiazepine receptors in chick cortical neurons. J. Neurochem. 57, 1307–1312. Tehrani, M. H., and Barnes, E. M., Jr. (1993). Identiﬁcation of GABAA/benzodiazepine receptors on clathrin-coated vesicles from rat brain. J. Neurochem. 60, 1755–1761. Tehrani, M. H., and Barnes, E. M., Jr. (1997). Sequestration of γ -aminobutyric acidA receptors on clathrin-coated vesicles during chronic benzodiazepine administration in vivo. J. Pharmacol. Exp. Ther. 283, 384–390. Tehrani, M. H., Baumgartner, B. J., and Barnes, E. M., Jr. (1997). Clathrin-coated vesicles from bovine brain contain uncoupled GABAA receptors. Brain Res. 776, 195–203. Tretter, V., Ehya, N., Fuchs, K., and Sieghart, W. (1997). Stoichiometry and assembly of a recombinant GABAA receptor subtype. J. Neurosci. 17, 2728–2737. Valeyev, A. Y., Cruciani, R. A., Lange, G. D., Smallwood, V. S., and Barker, J. L. (1993). Clchannels are randomly activated by continuous GABA secretion in cultured embryonic rat hippocampal neurons. Neurosci. Lett. 155, 199–203. Vickery, R. G., and von Zastrow, M. (1999). Distinct dynamin-dependent and -independent mechanisms target structurally homologous dopamine receptors to different endocytic membranes. J. Cell Biol. 144, 31–43. Vogler, O., Bogatkewitsch, G. S., Wriske, C., Krummenerl, P., Jakobs, K. H., and van Koppen, C. J. (1998). Receptor subtype-speciﬁc regulation of muscarinic acetylcholine receptor sequestration by dynamin. Distinct sequestration of m2 receptors. J. Biol. Chem. 273, 12155– 12160. Wan, Q., Xiong, Z. G., Man, H. Y., Ackerley, C. A., Braunton, J., Lu, W. Y., Becker, L. E., MacDonald, J. F., Wang, Y. T. (1997). Recruitment of functional GABAA receptors to postsynaptic domains by insulin. Nature 388, 686–690. Wang, H., Bedford, F. K., Brandon, N. J., Moss, S. J., and Olsen, R. W. (1999). GABAA-receptorassociated protein links GABAA receptors and the cytoskeleton. Nature 397, 69–72. Ward, C. L., and Kopito, R. R. (1994). Intracellular turnover of cystic ﬁbrosis transmembrane conductance regulator. Inefﬁcient processing and rapid degradation of wild-type and mutant proteins. J. Biol. Chem. 269, 25710–25718. Weiss, D. S. (1988). Membrane potential modulates the activation of GABA-gated channels. J. Neurophysiol. 59, 514–527. Whiting, P. J., Bonnert, T. P., McKernan, R. M., Farrar, S., le Bourdelles, B., Heavens, R. P., Smith, D. W., Hewson, L., Rigby, M. R., Sirinathsinghji, D. J., Thompson, S. A., Wafford, K. A. (1999). Molecular and functional diversity of the expanding GABAA receptor gene family. Ann. N.Y. Acad. Sci. 868, 645–653. Wilson, C. L., Maidment, N. T., Shomer, M. H., Behnke, E. J., Ackerson, L., Fried, I., and Engel, J., Jr. (1996). Comparison of seizure related amino acid release in human epileptic hippocampus versus a chronic, kainate rat model of hippocampal epilepsy. Epilepsy Res. 26, 245–254.

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SUBCELLULAR LOCALIZATION AND REGULATION OF GABAA RECEPTORS AND ASSOCIATED PROTEINS

Bernhard L¨uscher1 Department of Biology and Department of Biochemistry and Molecular Biology Pennsylvania State University University Park, Pennsylvania 16802

Jean-Marc Fritschy Institute of Pharmacology and Toxicology University of Zurich 8057 Zurich, Switzerland

I. II. III. IV. V.

VI.

VII. VIII. IX.

X.

1

Introduction Functional Significance of GABAergic Inhibition in the Brain Structural Anatomy of GABAergic Synapses Structure of GABAA Receptors Subcellular Localization of GABAA Receptor Subtypes A. Extrasynaptic GABAA Receptors B. Postsynaptic GABAA Receptors C. Association of Postsynaptic GABAA Receptors with Gephyrin D. Association of GABAA Receptors with the Cytoskeleton E. Gephyrin-Interacting Proteins Factors Implicated in Exocytosis and Endocytosis of GABAA Receptors A. GABARAP B. GRUB1 Synaptic Anchoring of GABAC Receptors GABAA Receptor-Associated Signaling Proteins Synaptic Plasticity A. Development of Synapses: Role of Presynaptic Factors B. Development of Synapses: Role of Synaptic Activity C. Regulation of GABAergic Synapses by Neurotrophic Peptides Concluding Remarks References

Author to whom correspondence should be addressed.

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C 2001 by Academic Press. Copyright All rights of reproduction in any form reserved. 0074-7742/01 $35.00

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I. Introduction

Neuronal cells are uniquely specialized to communicate with each other by means of electrochemical signals. The transfer of the electrical impulse from the axon terminal of one neuron to the next involves chemical synapses, which consist of a presynaptic terminal and a postsynaptic membrane specialization in the target neuron. The action potential generated by a neuron travels along the axonal membrane to the presynaptic terminal, where it induces neurotransmitter release. In the postsynaptic membrane, the neurotransmitter then binds and activates neurotransmitter receptors. Activation of one class of these receptors, the ligand-gated ion channel receptors, leads to a rapid, local change in electrical potential across the membrane by opening an intrinsic ion channel. In neurons of the central nervous system (CNS), such local potentials can either promote or inhibit the firing probability of the postsynaptic neuron and are hence called excitatory or inhibitory, respectively. A cationic flux induces a change of the resting potential toward positive values (depolarization) and contributes toward the generation of an action potential in the postsynaptic neuron; it is therefore excitatory. An anionic membrane current counteracts the cationic flux triggered by the excitatory input and is therefore inhibitory. A neuronal cell continuously integrates a multitude of local excitatory and inhibitory potentials toward the generation of an action potential. Ligand-gated ion channel receptors for glutamate and γ -aminobutyric acid (GABA), by far the most common neurotransmitters in the CNS, are highly heterogeneous and subject to profound spatiotemporal regulation, both during development and in the adult nervous system. The particular receptor subtype, the functional state, and the local concentration of each type of receptor in the postsynaptic membrane are major determinants of the neurotransmitter response. Hence, activity-dependent structural and functional alterations at postsynaptic sites can have profound consequences with regard to the neurotransmitter response and thereby determine the firing pattern of the postsynaptic neuron. Lasting functional alterations that involve pre- and postsynaptic structures are collectively referred to as synaptic plasticity and are believed to provide a molecular basis of learning and memory. Consequently, the structure, function, and regulation of chemical synapses are pivotal to the most fundamental functions of the brain. Most studies of synaptic plasticity are concerned with structural and functional changes at glutamatergic synapses, which are excitatory. Synaptic inhibition can counteract synaptic excitation and exert powerful control over spontaneously firing nerve cells. Most important, synaptic inhibition can shape the firing pattern of neural cells, entrain coordinated rhythmic firing

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of large subsets of neurons, and paradoxically increase their excitatory efficacy. In the brain, most synaptic inhibition is mediated by the neurotransmitter GABA, whereas glycine is the primary inhibitory neurotransmitter in the brainstem and spinal cord. The aim of this article is to summarize progress in understanding the structure and function of inhibitory synapses in the brain, with special emphasis on GABAergic synapses and their postsynaptic organization. Knowledge of these structures and understanding of their functional modulation may help to elucidate the role of inhibitory neurotransmission in synaptic plasticity. II. Functional Significance of GABAergic Inhibition in the Brain

The functional significance of GABAergic neurotransmission is best illustrated by the diverse actions of the drugs that act as allosteric modulators of GABAA receptors. In particular, the ligands of the benzodiazepine (BZ) site of GABAA receptors can enhance or inhibit a variety of CNS states, including vigilance, anxiety, epileptic activity, and memory. Similar effects can be evoked by barbiturates, neuroactive steroids, and volatile anesthetics that act at distinct GABAA receptor binding sites. GABAA receptor dysfunctions are implicated in the pathogenesis of major mental and neurological disorders, including anxiety (Tiihonen et al., 1997; Abadle, 1999; Crestani et al., 1999), epilepsy (Buhl et al., 1996; Brooks-Kayal et al., 1998; Loup et al., 2000), and Angelman syndrome (DeLorey et al., 1998). These disorders can be modeled in mice by targeted mutagenesis of GABAA receptor subunit genes (DeLorey et al., 1998; Crestani et al., 1999; Huntsman et al., 1999). Thus, proper regulation of GABAA receptors is pivotal for maintaining normal brain function and mental health. III. Structural Anatomy of GABAergic Synapses

By morphological criteria, CNS synapses can be broadly divided into either asymmetric [also Type I (Gray, 1959)] or symmetric (Type II). Asymmetric synapses are characterized in electron micrographs by a prominent postsynaptic density (PSD). Current knowledge indicates that asymmetric synapses are all excitatory. Indeed, the PSD can be biochemically fractionated and contains the NMDA receptor, the NMDA receptor-associated protein complex (NRC), and most of the other glutamate-gated ion channels and associated proteins implicated in excitatory neurotransmission. An analysis of the purified NRC by electrospray ionization–mass spectrometry revealed 77 proteins organized into receptor, adapter, signaling, cytoskeletal,

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and novel proteins (Husi et al., 2000). In contrast, symmetric synapses lack a prominent PSD and are generally believed to be inhibitory, using either GABA or glycine as a neurotransmitter. Importantly, the postsynaptic specialization of inhibitory synapses includes a unique set of proteins that are not part of the PSD and the NRC (Husi et al., 2000). Proteins that are concentrated at inhibitory synapses include the GABAA or glycine receptors and, as far as known, the putative clustering and anchoring proteins. In contrast, however, at least some of the signaling proteins appear to be present at both excitatory and inhibitory synapses.

IV. Structure of GABAA Receptors

Structurally and functionally, GABAA receptors are heteropentameric chloride channels, and all the subunits are characterized by the typical gene structure and transmembrane topology common to members of the gene superfamily of ligand-gated ion channels. Differential spatiotemporal expression and assembly of GABAA receptor subunits encoded by at least 16 different genes (α1-6, β1-3, γ 1-3, δ, ε, π , and θ ) result in pronounced receptor heterogeneity (reviewed by Mohler et al., 1996; Hevers and Lu¨ ddens, 1998; Whiting et al., 1999). Coexpression of varied subsets of GABAA receptor subunits in different neuronal cell types would in principle allow the formation of a large number of receptor subtypes. However, receptor diversity is significantly reduced by the regionally limited overlap of the subunit expression patterns (Laurie et al., 1992; Persohn et al., 1992; Wisden et al., 1992; Fritschy and Mohler, 1995) and by the rules that govern assembly of subunits into functional receptors (reviewed by Wisden and Moss, 1997; Mohler et al., 1998; Sieghart et al., 1999). As shown by coexpression of recombinant subunits in nonneural cells, α and β subunits are sufficient to form functional GABA-gated chloride channels that can be modulated by barbiturates and steroids. However, addition of a γ subunit is required for expression of GABAA receptors containing BZ sites (reviewed by Macdonald and Olsen, 1994; Sieghart, 1995; Mohler et al., 1996; Sigel and Buhr, 1997). In vivo, most types of GABAA receptor are assembled from α- and β-subunit variants in combination with the γ 2 subunit. In contrast to γ 2, the γ 1 and γ 3 subunits are only part of minor receptor subtypes in restricted brain regions but can functionally substitute for the γ 2 subunit in the formation of recombinant BZ-sensitive GABAA receptors in vitro (Knoflach et al., 1991; Puia et al., 1991; Herb et al., 1992; T¨ogel et al., 1994; Benke et al., 1996). Similar to the γ subunits, the δ, ε, and θ subunits only form functional receptors when coexpressed with α and β subunits, and they represent comparatively minor receptor subtypes

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in vivo (reviewed by Whiting et al., 1999). In addition to the 16 GABAA receptor subunits, a class of homologous subunits, ρ1–3, has been identified. Members of the ρ subclass of subunits are atypical in that they do not seem to coassemble with members of the α- and β-subunit classes (Koulen et al., 1998). Instead, the different ρ subunits can form homomeric and heteromeric receptors by assembly with each other in vitro and probably in vivo (Enz and Cutting, 1999). Unlike typical GABAA receptors, which as a unifying feature are blocked by the competitive GABA antagonist bicuculline, the ρ-subunit-containing receptors are insensitive to bicuculline. Whereas the homology of the ρ subunits compared with the typical GABAA receptor subunits suggests that they are just another subclass of GABAA receptor subunits (Barnard et al., 1998), the lack of coassembly with other subunits and the unique pharmacology, channel properties, receptor-associated proteins, and distribution of ρ-subunit-containing receptors in the brain suggest that they are classified separately as GABAC receptors (Bormann, 2000; Chebib and Johnston, 2000). Therefore, for the purpose of this review, they are treated as a class of their own and referred to as GABAC receptors. The γ 2 subunit of GABAA receptors occurs as two alternatively spliced versions, γ 2S and γ 2L, that differ by an extra eight-amino-acid exon in the putative cytoplasmic loop region between the third and fourth transmembrane regions of γ 2L. Interestingly, the γ 2L-specific exon includes a putative phosphorylation site for protein kinase C (Moss et al., 1992) and calcium/calmodulin type 2-dependent protein kinase (McDonald and Moss, 1994). The two γ 2 isoforms are expressed throughout the brain with some remarkable temporal and spatial differences (Gutierrez et al., 1994; Miralles et al., 1994). The γ 2S subunit is expressed early during embryonic development, whereas γ 2L is delayed and increases steadily during the first postnatal weeks (Wang and Burt, 1991; Poulter et al., 1993), suggesting that γ 2L might be preferentially associated with more mature synapses. Despite several reports describing functional differences between the two γ 2-subunit isoforms in vitro, none of these differences have been confirmed in vivo (Zhai et al., 1998; Homanics et al., 1999; Baer et al., 2000; Wick et al., 2000). However, the γ 2 subunit has recently emerged as a major interface for interaction with other proteins (see below), and variations in the primary structure of this subunit are likely to have at least subtle effects on GABAergic transmission.

V. Subcellular Localization of GABAA Receptor Subtypes

Most neurotransmitter receptors do not diffuse freely in the neuronal membrane but are highly concentrated at specific cellular surfaces, such

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as postsynaptic sites, axon terminals, or nodes of Ranvier. The local receptor concentration in the cell membrane is likely influenced at many levels, including rate of synthesis, sorting and trafficking by means of transport vesicles, posttranslational modification, assembly of subunits, insertion into the surface membrane, lateral diffusion, anchoring to cytoskeletal structures, endocytosis, and rate of degradation. Cellular localization of ligand-gated ion channels is critical for several reasons. First, the efficacy of receptor activation varies with the cellular location of the receptors and is likely to be greatest at sites opposite neurotransmitter-releasing afferents. Second, the contribution of a local potential to the action potential varies with the cellular location of the respective synaptic input. Finally, receptor modulation by intracellular signaling cascades is likely to depend on the spatial proximity of the receptors and the signaling factors. Because these signal transduction components are not specific for different types of synapses, the activation of receptors in selected synapses may involve physical interaction of signaling components with the receptors and/or associated scaffold proteins. The synaptic inputs that contribute to the threshold potential and ultimately contribute to an action potential are found on dendritic and somatic membranes, including the axon initial segment (AIS), whereas axo-axonic inputs on axon terminals typically do not affect the action potential but modulate neurotransmitter release (MacDermott et al., 1999). Neurotransmitter receptors at different cellular surfaces tend to differ in their structure and function, and targeting of the receptor subtype to the proper type of synapse is critical for proper signal propagation. Differential subcellular targeting is evident for many types of neurotransmitter receptors, including GABAA receptors. For example, in hippocampal pyramidal neurons, GABAA receptors characterized by the presence of the α1 subunit appear equally distributed at all inhibitory synapses (on somata, proximal and distal dendrites, spines, and AIS), whereas receptors containing the α2 subunit are preferentially localized at synapses of the AIS, which is innervated by axo-axonic interneurons (Nusser et al., 1996; Fritschy et al., 1998). Inhibitory synaptic inputs to the AIS are highly effective, as illustrated by single axo-axonic cells that innervate and entrain large populations of several hundred pyramidal cells (Buhl et al., 1994).

A. EXTRASYNAPTIC GABAA RECEPTORS The concept of concentrating receptors at postsynaptic sites vis-`a-vis neurotransmitter release sites makes seemingly obvious sense because it provides a potential means for efficient modulation of the postsynaptic neurotransmitter response. However, GABAA receptors are also abundant on neuronal

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surfaces that lack synaptic specialization. A quantitative analysis of synaptic versus extrasynaptic receptors in cerebellar granule cells by immunogold electronmicroscopy revealed that GABAA receptors containing the α1 and β2/3 subunits were 230 and 180 times more concentrated at synapses than on the extrasynaptic somatic membrane (Nusser et al., 1995). Whereas these numbers confirm that GABAA receptors are highly concentrated at postsynaptic sites, they also allow a substantial pool of receptors in the extrasynaptic membrane, especially given the vastly greater surface area of the extrasynaptic than of the synaptic space on the neuronal surface. Immunofluorescent data addressing the subcellular distribution of GABAA receptors in cultured hippocampal or cortical neurons and in brain sections support this conclusion (Essrich et al., 1998; Sasso´e-Pognetto et al., 2000). For example, immunohistochemical staining with antisera directed against the α1, α2, α3, or γ 2 subunit of GABA receptors typically shows some continuous membrane staining, in addition to the punctate immunoreactivity that is indicative of synaptic receptors. Interestingly, staining for the α5 subunit reveals little punctate staining, suggesting that in hippocampus and cerebral cortex the α5 subunit is mainly present extrasynaptically. Evidence that continuous membrane staining represents extrasynaptic receptors is supported by observations made with mice that exhibit a selective reduction in synaptically clustered receptors due to the absence of the γ 2 subunit (Essrich et al., 1998). The unaltered diffuse GABAA receptor staining along the neuronal cell membrane and the remaining robust GABAinduced whole-cell currents in γ 2-deficient neurons suggest the presence of a prominent pool of extrasynaptic receptors that is largely unaffected by the loss of the γ 2 subunit. Similar conclusions can be drawn from mice lacking gephyrin (Kneussel et al., 1999) (see below). In cerebellar granule cells, the δ subunit is localized selectively at extrasynaptic sites, suggesting exclusively extrasynaptic function for δ-subunitcontaining GABAA receptors (Nusser et al., 1998b). This extrasynaptic localization, together with the slow channel kinetics of δ-subunit-containing recombinant receptors (Saxena and MacDonald, 1994), led to the conclusion that δ-subunit-containing GABAA receptors might preferentially mediate tonic inhibition, whereas phasic inhibition by synaptic GABA release is likely mediated by activation of receptors containing α1β2/3γ 2, α6β2/3γ 2, or α1α6β2/3γ 2 subunits (Brickley et al., 1996; Nusser et al., 1998b). However, immunofluorescent staining with an α6-subunit-selective antiserum indicated that receptors containing this subunit are largely absent at GABAergic synapses characterized by the presence of gephyrin (Sasso´e-Pognetto et al., 2000). Deletion of the α6 subunit in cerebellar granule cells results in a complete loss of δ-subunit-containing receptors from the cell surface ( Jones et al., 1997). Thus, the α6 subunit is often associated with the δ subunit and

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represents a major constituent of extrasynaptic receptors ( Jones et al., 1997; Nusser et al., 1998b; Sasso´e-Pognetto et al., 2000). Surprisingly, mice with a targeted deletion of the α6-subunit gene lack an overt behavioral phenotype associated with the loss of δ-subunit-containing receptors and have not provided evidence for an essential function of these extrasynaptic receptors so far ( Jones et al., 1997). Whereas the α6 subunit is selectively expressed in cerebellar granule cells, the δ subunit is also abundant in several thalamic nuclei and in dentate gyrus granule cells in conjunction with other α subunits (Fritschy and Mohler, 1995; Lu¨ scher et al., 1997). Analysis of miniature inhibitory postsynaptic currents (mIPSCs) in δ-subunit-deficient dentate gyrus granule cells revealed a significantly reduced current decay time but no change in the miniature current amplitude or frequency (Mihalek et al., 1999). These rather subtle functional alterations of synaptic currents might suggest a contribution of the δ subunit to postsynaptic receptors. Alternatively, they might represent subunit rearrangements of synaptic receptors in response to loss of the δ subunit in extrasynaptic receptors. Indeed, there is evidence that the δ and γ 2 subunit compete for assembly with the same α and β subunits in cerebellar granule cells (Tretter et al., 2001) (see below). Evidence for the coexistence of synaptic and extrasynaptic GABAA receptors was also found on neurons of the inferior nucleus (Devor et al., 2000). Whole-cell currents measured in response to GABA applied near dendrites revealed fast desensitizing currents. These were correlated with punctate immunoreactivity for the α2, β2/3, and γ 2 subunits and gephyrin that indicated the presence of postsynaptic receptors on dendrites. In contrast, application of GABA close to the soma revealed nondesensitizing long-lasting currents that correlated with the presence of diffuse immunoreactivity for the α3, β2/3, and γ 2 subunits. This diffuse staining was not colocalized with gephyrin and thus probably represents extrasynaptic receptors. Interestingly, immunoreactivity for the α3 subunit in other parts of the brain was perfectly colocalized with punctate gephyrin immunoreactivity (Baer et al., 1999; Sasso´e-Pognetto et al., 2000), which indicates that the α3, β2/3, and γ 2 subunits can be either preferentially postsynaptic or preferentially extrasynaptic, possibly depending on the other subunits present in the receptor or cell-type-specific GABAA receptor-associated proteins. B. POSTSYNAPTIC GABAA RECEPTORS The GABAA receptor subtypes so far identified at postsynaptic sites are characterized by the presence of the α1, α2, or α3 subunit (Sasso´e-Pognetto et al., 1995; Nusser et al., 1996; Sasso´e-Pognetto and W¨assle, 1997; Essrich et al., 1998; Nusser et al., 1998b; Baer et al., 1999; Simb¨urger et al., 2001;

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Sasso´e-Pognetto and Fritschy, 2000). In addition, the α6 subunit was in part detected at postsynaptic sites on cerebellar granule cells (Nusser et al., 1998b). All these receptors invariably contain the γ 2 subunit. Comparative analyses of recombinant receptors that differ in their subunit composition revealed that the γ 2 subunit is largely dispensable for surface expression of functional GABAA receptors containing α and β subunits (Pritchett et al., 1988; Malherbe et al., 1990; Sigel et al., 1990; Connolly et al., 1996; Gorrie et al., 1997). This notion also applies to neurons in vivo, as demonstrated by the analysis of γ 2-subunit-deficient mice. In γ 2-subunit-deficient brain, the number of GABAA receptors present was only marginally reduced. Moreover, the distribution of GABAA receptor β2/3 subunits in the membrane of dorsal root ganglion neurons, which lack synaptic specializations, was unaltered in the absence of the γ 2 subunit (Gunther ¨ et al., 1995). In cortical neurons, however, the γ 2 subunit was found to be required for postsynaptic clustering and function of GABAergic synapses, whereas whole-cell currents remained largely restored (Essrich et al., 1998). Consistent with a direct contribution of the γ 2 subunit to clustering and postsynaptic localization of GABAA receptors, this subunit is part of all naturally occurring synaptic GABAA receptors identified so far (with the exception of GABAC receptors). However, numerous γ 2-subunit-containing receptors are also found extrasynaptically throughout the brain, as discussed above for the inferior olive (Devor et al., 2000). Thus, no GABAA receptor subunit has been shown to be present only synaptically or extrasynaptically. Whereas the subunit composition is one factor contributing to the subcellular localization of a receptor subtype, other parameters such as the neuronal cell type appear to be equally important. Furthermore, subunits might compete with each other for forming either synaptic or extrasynaptic receptors, as shown in transgenic and knockout mice. For instance, the γ 2 subunit can substitute for the δ subunit in postsynaptic receptors of cerebellar granule cells (Nusser et al., 1998b) (see below). Indeed, mice with a targeted disruption of the δ-subunit gene exhibit an increase in BZ sites in the cerebellar granule cell layer and in other brain regions known to normally express this subunit (Mihalek et al., 1999; Tretter et al., 2001), indicating that the γ 2 and δ subunits compete for assembly with the same α and β subunits and thereby might modulate the pools of synaptic and extrasynaptic receptors. The relative contribution to synaptic and extrasynaptic receptors was also addressed for the γ 3 subunit, which is normally expressed at very low levels but is known to mimic most of the pharmacological and electrophysiological properties of the γ 2 subunit when analyzed in vitro. Transgenic overexpression of the γ 3 subunit in mice lacking the γ 2 subunit revealed that the γ 3 subunit can substitute for the γ 2 subunit and promote the clustering and postsynaptic deposition of GABAA receptors, similar to the γ 2 subunit in

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wild-type neurons (Baer et al., 1999). However, if both the γ 2 and the γ 3 subunit were present simultaneously, the γ 3 subunit failed to displace the γ 2 subunit from native receptors and the endogenous γ 3 subunit has not been detected at postsynaptic sites in wild-type brain. It is therefore possible that this minor GABAA receptor subtype is primarily localized extrasynaptically under natural conditions (see also below). Other candidate subunits that might contribute preferentially to synaptic or extrasynaptic receptors include the γ 1 (Ymer et al., 1990), ε(Davies et al., 1997; Whiting et al., 1997), and θ (Bonnert et al., 1999) subunits. Consistent with such a role, they are part of comparatively minor receptor subtypes in brain and known, much like γ 2, γ 3, and δ, to form functional receptors only when coexpressed and coassembled with α and β subunits. The ρ subunits contribute to postsynaptic GABAC receptors at synapses on axon terminals of rod and cone bipolar cells in the retina that are distinct from synapses containing GABAA receptors (Koulen et al., 1998). The unique proteins associated with GABAC receptors (Hanley et al., 1999) suggest that these sites have evolved independently of the postsynaptic sites containing GABAA receptors (see below).

C. ASSOCIATION OF POSTSYNAPTIC GABAA RECEPTORS WITH GEPHYRIN The mechanism by which GABAA receptors are clustered and targeted to the postsynaptic membrane is poorly understood. However, there is now compelling evidence that postsynaptic clustering of GABAA receptors depends critically on the clustering protein gephyrin. Gephyrin was first identified as a constituent of affinity-purified glycine receptor (Pfeiffer et al., 1984). The major gephyrin isoform in spinal cord represents a 93-kDa protein that binds to polymerized tubulin (Kirsch et al., 1991) and to an 18-amino-acid sequence in the cytoplasmic loop of the glycine receptor β subunit in vitro (Meyer et al., 1995). Moreover, recombinant glycine receptor and gephyrin colocalize in transfected cells and this effect is dependent on the same 18-amino-acid stretch on the receptor β subunit. Gephyrin thereby fulfills the molecular prerequisites of a protein that physically links glycine receptors to the cytoskeleton via the receptor β subunit. The essential role of gephyrin for postsynaptic clustering of glycine receptors has been documented convincingly in cultured spinal cord neurons by inhibition of gephyrin expression with antisense oligonucleotides (Kirsch et al., 1993b) and in mice with a targeted deletion of the gephyrin gene (Feng et al., 1998). In the absence of gephyrin, glycine receptors fail to become clustered at postsynaptic sites.

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Gephyrin is a ubiquitous protein with essential functions also in nonneural cells (Feng et al., 1998). Its broad expression in brain regions, where glycine receptors are essentially absent, led to the suspicion early on that gephyrin could function at sites other than glycinergic synapses (Malosio et al., 1991; Kirsch and Betz, 1993; Kirsch et al., 1993a). Specifically, in the mammalian retina, where both glycine and GABAA receptors are present, glycine receptor immunoreactivity was found to differ significantly from that of gephyrin (Grunert ¨ and W¨assle, 1993). In fact, gephyrin was found to be concentrated at GABAergic synapses in the retina (Sasso´e-Pognetto et al., 1995; Hering and Kroger, 1996; Sasso´e-Pognetto and W¨assle, 1997) and spinal cord (Bohlhalter et al., 1994; Cabot et al., 1995; Todd et al., 1995; Todd et al., 1996). However, whereas glycine and GABAA receptors are structurally and functionally closely related, GABAA receptors preparations, unlike purified glycine receptors, do not contain gephyrin (Kannenberg et al., 1997) and an interaction between gephyrin and GABAA receptor subunits could not be shown (Meyer et al., 1995). The identity of gephyrin immunoreactive sites in spinal cord (Triller et al., 1987; Bohlhalter et al., 1994; Cabot et al., 1995) and cerebellum (Chen and Hillman, 1993) was further complicated by the fact that in these regions glycine and GABA can be found in some of the same synaptic boutons (Ottersen et al., 1988; Todd and Sullivan, 1990; Jonas et al., 1998). In addition, similar to gephyrin, the glycine receptor β-subunit messenger RNA (mRNA) is known to be expressed widely in brain regions that lack glycine receptor α-subunit mRNAs and functional glycine receptors (Malosio et al., 1991). A functional role of gephyrin at GABAergic synapses has therefore remained elusive, even when gephyrin was found to be colocalized with GABAergic synapses in the olfactory bulb (Giusetto et al., 1998) and in cultured hippocampal neurons (Craig et al., 1996). Compelling evidence for gephyrin as a bona fide clustering molecule of major subtypes of GABAA receptors was obtained by analysis of neurons and mice that lacked the γ 2 subunit of GABAA receptors (Essrich et al., 1998). In wild-type mice, immunoreactive puncta for the GABAA receptor α1, α2, and γ 2 subunits were found to be extensively colocalized with gephyrin in cultured cortical and hippocampal neurons and in brain. In γ 2-subunit-deficient mice, loss of postsynaptic GABAA receptors was accompanied by a dramatic loss of synaptic function and loss of gephyrin from postsynaptic sites. Conversely, antisense inhibition of gephyrin expression in cultured hippocampal neurons revealed that gephyrin was essential for postsynaptic clustering of major GABAA receptor subtypes containing α1 or α2 subunits (Essrich et al., 1998). However, the robust GABA-induced wholecell currents that remained in the absence of the γ 2 subunit (Gunther ¨ et al., 1995; Baer et al., 1999) indicated that the γ 2 subunit and gephyrin were

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not required for translocation of GABAA receptors to the neuronal surface membrane. Subsequent analysis of gephyrin knockout mice confirmed that gephyrin was essential for clustering of GABAA receptors (Kneussel et al., 1999). Moreover, the only marginal reduction in GABA-induced currents in gephyrin-deficient neurons confirmed that translocation of GABAA receptors from the Golgi complex to the cell surface occurs independently of gephyrin. Gephyrin not only has been found at GABA synapses containing α1- or α2-subunit-containing GABAA receptors but also is present at some synapses characterized by α3-subunit-containing receptor subtypes (Baer et al., 1999; Sasso´e-Pognetto et al., 2000) (see also below). Importantly, gephyrin immunoreactivity analyzed by electron microscopy was invariably found concentrated at postsynaptic sites. Interestingly, α3-subunit-containing GABAA receptors in the developing thalamic reticular nucleus (RTN) remained clustered and colocalized with gephyrin in γ 2-subunit knockout mice. Whereas the endogenous γ 3 subunit is normally barely detectable in the RTN, this subunit is upregulated, clustered, and colocalized with the α3 subunit and gephyrin upon deletion of the γ 2 subunit (Baer et al., 1999). As mentioned earlier, the γ 3 subunit can also substitute for the γ 2 subunit with respect to the formation of postsynaptically clustered GABAA receptors, when overexpressed as a recombinant protein in γ 2-subunit-deficient mice. Under these circumstances, the γ 3 subunit allowed formation of postsynaptically clustered receptors even in the cerebellum, where the endogenous γ 3 subunit is normally entirely absent, suggesting that the factors involved in clustering of γ 2-subunit-containing receptors are also functional in the context of γ 3-subunit-containing receptors (Baer et al., 1999). Gephyrin appears to be absent at GABAergic synapses on retinal bipolar cell axons (Pourcho and Owczarzak, 1991; Grunert ¨ and W¨assle, 1993), in agreement with only partial colocalization of gephyrin and GABAA receptor clusters in the retina (Sasso´e-Pognetto et al., 1995). In particular, gephyrin has not been detected in conjunction with GABAC receptors at these sites (Sasso´e-Pognetto and W¨assle, 1997). This finding is consistent with some remaining GABAA receptor clusters in the retina from gephyrin knockout mice (Fischer et al., 2000). Other than in the retina, gephyrin is present at the large majority of GABAergic synapses in the mammalian brain and can be used as a reliable immunohistochemical marker for GABA synapses there (Sasso´e-Pognetto et al., 2000). The GABAA receptor subtypes at these synapses contain at least the α1, α2, or α3 subunit, together with a β subunit and the γ 2 subunit. However, there is no evidence that gephyrin interacts with GABAA receptors in vitro. Failure to detect such an interaction might indicate that it involves several proteins simultaneously, possibly including factors that are not yet known.

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D. ASSOCIATION OF GABAA RECEPTORS WITH THE CYTOSKELETON Functional alterations of GABAergic currents in response to microtubuledepolymerizing agents (reviewed by Whatley and Harris, 1996) and the presence of tubulin in purified GABAA receptor preparations (Kannenberg et al., 1997) suggest that GABAA receptors are associated with microtubules. Surprisingly, the subcellular distribution in hippocampal neurons of GABAA receptors and gephyrin, which has a tubulin-binding motif, can remain unaffected by depolymerization of microtubules or actin filaments or by detergent extraction of soluble proteins (Allison et al., 2000). These studies support the idea that gephyrin may act as a core scaffolding component at inhibitory synapses and that it is integrated into a postsynaptic structure that remains quite stable in the absence of the actin or tubulin cytoskeleton (Allison et al., 2000). Despite the observation that gephyrin binds to polymerized tubulin in vitro (Kirsch et al., 1991), this interaction appears nonessential for maintenance of receptors at mature synapses. However, disruption of microtubules or actin filaments in spinal cord neurons reduced the number of gephyrin and glycine receptor clusters at glycinergic synapses (Kirsch and Betz, 1995). Rather than indicating different roles of the cytoskeleton at different types of inhibitory synapse, these seemingly contradicting results might reflect differences in the maturity of the synapse at the time of drug treatment. GABAA receptors also appear to be associated with dystrophin (Knuesel et al., 1999). In wild-type brain, dystrophin is colocalized with GABAA receptors and gephyrin in a subset of clusters in cerebral cortex, hippocampus, and cerebellum. In brain of mice that lack dystrophin (mdx), a profound reduction in the size and number of GABAA receptor clusters was detected in regions that normally express dystrophin. Interestingly, gephyrin clusters were unaffected by the absence of dystrophin, suggesting that gephyrin and dystrophin act independently at GABAergic synapses, that gephyrin can cluster independently of GABAA receptors, and that interaction between GABAA receptors and gephyrin is likely to be indirect (Knuesel et al., 1999).

E. GEPHYRIN-INTERACTING PROTEINS In addition to the glycine receptor β subunit, gephyrin directly binds to a number of other proteins and these binding partners revealed many of the clues available today about the function of gephyrin, including at synapses. As mentioned above, high-affinity binding of gephyrin to polymerized tubulin qualifies gephyrin as a specialized microtubule-associated protein (MAP) (Betz et al., 1991) and provides a potential structural link

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between the subsynaptic specialization and the cytoskeleton. Results from yeast two-hybrid screens revealed that gephyrin functions as a molecular interface for interaction with a diverse set of other proteins. A particularly gratifying hint of how gephyrin might function at synapses was provided through the isolation of the gephyrin-binding protein collybistin (Kins et al., 2000). Collybistin is predominantly expressed in brain, and the primary amino acid sequence includes Dbl (diffuse B-cell lymphoma-like) and pleckstrin homology (PH) domains. The protein thus encodes a putative Dbl-family GDP/GTP exchange factor (GEF). Collybistin exists in two splice variants, one of which includes an N-terminal SH3 domain and a C-terminal coiledcoil region. Interestingly, cotransfection of collybistin II (the variant lacking these domains) with gephyrin and glycine receptors into nonneural cells, induces the formation of submembrane aggregates of gephyrin and glycine receptors (Kins et al., 2000). Sequence comparison of collybistin with other GEFs suggests that the Rho family kinases Rac and Cdc42 are the most likely targets for activation by collybistin. Interestingly, human collybistin homologue hPEM-2 specifically activates Cdc42 but not Rac or RhoA (Reid et al., 1999). However, Cdc42 is known to be a strong activator of Rac (Kozma et al., 1995; Nobes and Hall, 1995). Independent evidence for a role of Rac in regulating the function of GABAA receptors was provided by showing that disruption of actin fibers with latrunculin B, or inactivation of GTPases by clostridial toxins, was followed by run-down of muscimol-induced GABAA receptor currents in cultured hippocampal neurons (Meyer et al., 2000). Conversely, infusion into cells of recombinant Rac1, but not Cdc42, enhanced the current induced by the GABA agonist muscimol. Muscimol-induced rundown was paralleled by a loss of cell surface expression, suggesting that small GTPases regulate cell surface expression or recycling of postsynaptic GABAA receptors. Rho family GTPases are widely implicated in actin reorganization in the context of almost any type of change in cell shape (reviewed by Nobes and Hall, 1995; Joneson et al., 1996; Bishop and Hall, 2000). In addition, the data now suggest that Cdc42 and/or Rac GTPases are the collybistin targets that mediate deposition of gephyrin at inhibitory synapses. Gephyrin was independently identified as a binding partner using other proteins as a bait in yeast two-hybrid screens. A potentially interesting avenue was opened when Mammoto et al. (1998) found that profilin I binds gephyrin. Profilin occurs as two isoforms expressed by separate genes with complementary tissue specificity. Profilin I is present in all tissues except skeletal muscle, but is less abundant in heart and brain; profilin II is expressed primarily in brain, skeletal muscle, and kidney (Honore et al., 1993; Gieselmann et al., 1995; Witke et al., 1998). The two isoforms share many of the same features and both bind to membrane phospholipids, G-actin, and proline-rich domains in a large variety of target proteins (Gieselmann et al.,

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1995), and such a domain is also present in gephyrin (Prior et al., 1992). As a main function, profilins are believed to promote polymerization of G-actin into actin filaments (Sohn and Goldschmidt-Clermont, 1994; Gieselmann et al., 1995; Carlier and Pantaloni, 1997; Gutsche-Perelroizen et al., 1999). However, profilin-associated proteins appear to be somewhat species- and tissue-specific (Witke et al., 1998), and some of the profilin targets show remarkable differences in affinity for the two profilin isoforms (Lambrechts et al., 1997). Surprisingly, isolation of macromolecular complexes containing profilin I or II and associated proteins from mouse brain and mass spectrometric analysis of their peptides failed to detect gephyrin as a component of these complexes (Witke et al., 1998). In the context of a possible function at inhibitory synapses, it will therefore be critical to show that gephyrin interacts with profilin in vivo. Interestingly, profilins are also known to bind to Rho family GTPases (Reinhard et al., 1995; Gertler et al., 1996; for review, see Frazier and Field, 1997; Imamura et al., 1997). Gephyrin–profilin complexes therefore, might facilitate activation of Rho family members by collybistin. Moreover, profilin (Hartwig et al., 1989; Lu et al., 1996) and the PH domain of GEFs (Harlan et al., 1994; Lemmon et al., 1996) both associate with PtdInsP2 and PtdInsP3 membrane lipids. In addition, profilin binds to the p85 α subunit of phosphatidylinositol (PI) 3-kinase (Bhargavi et al., 1998). Thus, extracellular signal-induced formation of PtdInsP3 by PI3kinase might contribute to colocalization of collybistin and profilin at the cell surface membrane, and the two proteins might assist each other in recruiting gephyrin to the membrane. Profilins I and II interact with a large set of diverse proteins, including dynamin I, clathrin, synapsin, Rho-associated coiled-coil kinase, the Rac-associated protein NAP1, and a member of the NSF/sec18 family (Witke et al., 1998). Several of these proteins are implicated in presynaptic functions. However, the interaction between gephyrin and profilin extends the range of profilin functions to signaling pathways and cytoskeleton reorganization at inhibitory synapse. Conversely, through interaction with profilin and other proteins (see below), gephyrin might be involved in endocytic membrane flow. Gephyrin was also found to interact with RAFT1 (rapamycin and FKBP12 target 1; also called FRAP or mTor) (Sabatini et al., 1999), a member of a family of important regulators of mRNA translation that appear to be protein kinases. RAFT-1 immunoprecipitates phosphorylate p70 ribosomal S6 kinase and the eIF-4E binding protein, 4E-BP1. Interestingly, RAFT-1 mutants that fail to associate with gephyrin in vitro also fail to signal to downstream molecules such as the p70 ribosomal S6 kinase and 4E-BP1. The association of gephyrin with RAFT-1 suggests that gephyrin might be involved in regulating subsynaptic mRNA translation, possibly in a synaptic input-dependent manner. Indeed, the α1- and α2-subunit mRNAs of glycine receptors have

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been found to accumulate specifically at subsynaptic dendritic sites in spinal cord neurons (Racca et al., 1997). These synapses contain glycine receptors and protein components essential for postsynaptic protein synthesis (Gardiol et al., 1999). However, there is no evidence that such a mechanism might be involved in dendritic translation of GABAA receptor subunits. Preliminary evidence obtained in vitro indicates that gephyrin also binds to dynein through an interaction with a dynein light chain (Dlc) (Fuhrmann et al., 2000). The Dlc subunit is shared by myosin-V (an actin-based motor) and cytoplasmic dynein (a microtubule-based motor) and implicated in protein transport in many fundamental cellular processes (reviewed by King, 2000). In support of a function of Dlc at synapses, two different isoforms of Dlc (Dlc1 and Dlc2) were shown to interact with GKAP, a signaling molecule associated with PSD-95 at excitatory synapses (Naisbitt et al., 2000). Indeed, immunogold electron microscopy revealed a concentration of Dlc in the postsynaptic compartment of asymmetric synapses. However, it remains to be shown whether Dlc is also present at inhibitory synapses. Last but not least, gephyrin interacts with a GABAA receptor-associated protein GABARAP in vitro. However, the functional relevance of this interaction is currently unclear (Kneussel et al., 2000) (see below). Nevertheless, gephyrin interacts with a large number of functionally diverse proteins in vitro and many of these might contribute to the functional specialization of the submembrane compartment at inhibitory synapses.

VI. Factors Implicated in Exocytosis and Endocytosis of GABAA Receptors

A. GABARAP A yeast two-hybrid screen for factors that play a role at GABAergic synapses led to the identification of the GABAA receptor-associated protein GABARAP (Wang et al., 1999). This 13.9-kDa protein interacts with the γ 2 subunit of GABAA receptors in vitro and contains a tubulin-binding domain. GABARAP is related (31% identity) to the light chain 3 (LC3) (Mann and Hammarback, 1994) of MAP-1A and B, further qualifying GABARAP as a MAP. These features initially led to the suggestion that GABARAP might provide a linker between GABAA receptors and the cytoskeleton and that GABARAP might be involved in clustering and postsynaptic targeting of GABAA receptors (Wang et al., 1999). Indeed, lines of evidence support such a function: GABARAP binds to gephyrin in vitro, and heterologous coexpression of gephyrin and GABARAP in PC12 cells redirects intracellular gephyrin to GABARAP-enriched submembraneous domains (Kneussel et al.,

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2000). In addition, heterologous coexpression of GABARAP and GABAA receptors promotes the aggregation of recombinant GABAA receptors in QT6 cells (Chen et al., 2000). Overexpression of recombinant GABARAP and GABAA receptors in these cells resulted in reduced GABA affinity and altered channel kinetics, suggesting that in this heterologous expression system GABARAP interacts with GABAA receptors at the cell surface. More important, however, immunohistochemical analysis of native GABAA receptors and GABARAP revealed that the two proteins do not colocalize in vivo (Kneussel et al., 2000). GABARAP immunoreactivity was localized exclusively to intracellular compartments, but not to gephyrin-positive postsynaptic membrane specializations. Moreover, the cellular distribution of GABARAP immunoreactivity in gephyrin-deficient neurons, which exhibit a complete loss of postsynaptic GABAA receptor clusters, was not different from wildtype. Thus, the main function of GABARAP may not be at synapses. Instead, GABARAP might be involved in receptor assembly, subunit sorting, or membrane transport mechanisms. In support of this function for GABARAP, a homologue of GABARAP (57% identity), Golgi-associated ATPase Enhancer of 16 kDa (GATE-16), is a membrane transport modulator that interacts with N-ethylmaleimide-sensitive factor (NSF) and the Golgi v-SNARE GOS-28 (Sagiv et al., 2000). This study suggested that GATE-16 modulates intraGolgi transport through coupling of NSF activity and SNARE activation. Nevertheless, some of these interpretations need to be viewed with caution, as the antisera used to detect GABARAP and GATE-16 in vivo are likely to recognize several related proteins that might have different cellular expression patterns and functions. Indeed, we have identified yet an additional homologue of GABARAP, GATE-16, and LC3 that is most closely related to GABARAP (unpublished). Nevertheless, the results are consistent with a role of this family of proteins in transport between cellular compartments, rather than receptor clustering.

B. GRUB1 Bedford et al. (2000) have identified a GABAA receptor-associated ubiquitin-like protein (GRUB1) that interacts with GABAA receptor α and β subunits in vitro. GRUB1 immunoreactivity colocalizes with GABAA receptor immunoreactivity partly in cultured hippocampal neurons. Deletion analysis of the α1 subunit identified a 10-amino-acid binding domain that is partially conserved between α and β subunits and contains the GRUB1 binding domain. A synthetic peptide containing this domain blocked interaction of GABAA receptor α1 subunit and GRUB1 in vitro. The functional effect of this peptide was also examined by electrophysiological recordings of GABAA

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receptor-mediated chloride currents in transfected HEK293 cells, which express endogenous GRUB1. Perfusion of the competitor peptide into HEK293 cells transfected with GABAA receptors specifically resulted in a time-dependent reduction of GABA-induced whole-cell currents, suggesting a loss of receptors from the cell surface. However, loss of receptors in the presence of the competitor peptide was not due to increased internalization of GABAA receptors from the cell surface, suggesting that GRUB1 normally promotes the insertion of GABAA receptors into the cell surface membrane.

VII. Synaptic Anchoring of GABAC Receptors

None of the GABAA receptor-interacting proteins described here appears to directly interact with subsynaptic protein components, suggesting that the most important factors that physically link GABAA receptors to the postsynaptic cytoskeleton and gephyrin are yet to be identified. Similarly, none of the known GABAA receptor-associated proteins can account for the differential subcellular distributions of GABAA receptor isoforms. It is predicted that specific factors exist to allow the discrimination of GABAA receptors with different subunit compositions. To date, only one such protein that selectively interacts with a defined class of GABA receptor has been described. MAP-1B is believed to link retinal ρ1-subunit-containing GABA receptors (GABAC) to the cytoskeleton (Hanley et al., 1999). However, GABAC receptor-containing synapses in the retina lack gephyrin, and their subsynaptic specialization is therefore distinct from GABAA receptor-containing synapses (Sasso´e-Pognetto and W¨assle, 1997). Whereas ρ1 is abundant and probably part of most GABAC receptors in the retina, other brain regions likely contain ρ2 homo-oligomeric GABAC receptors that lack the ρ1 subunit (Enz and Cutting, 1999). The subcellular localization of these ρ2- and ρ3subunit-containing receptors has not been analyzed. If such receptors exist, they might not be concentrated at postsynaptic sites or they might depend on other proteins for linkage to the cytoskeleton that are yet to be identified. As a microtubule-interacting protein, MAP-1B fulfills the requirement for a physical link of GABAC receptors to the microtubule cytoskeleton. In addition, electron microscopic analysis revealed that MAP-1B is indeed concentrated at postsynaptic sites (Hanley et al., 1999). Nevertheless, the great abundance and broad distribution throughout the cell of MAP-1B suggest that other factors are likely to contribute to postsynaptic specificity of this interaction as well as to clustering and postsynaptic targeting of GABAC receptors.

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VIII. GABAA Receptor-Associated Signaling Proteins

GABAA receptors are subject to complex patterns of modulation by phosphorylation primarily of the large intracellular loops of β and γ 2 subunits (for review, seeMoss and Smart, 1996; Swope et al., 1999). For example, all the known β subunits contain consensus sites for phosphorylation by protein kinase C (PKC), whereas protein kinase A (PKA) will differentially phosphorylate the β subunits. Similarly, the γ 2S and L subunits include sites for PKC, calcium/calmodulin type 2-dependent protein kinase (CamKII), and vSrc, with an extra PKC/CamKII site present in γ 2L. Phosphorylation can have diverse effects on GABAA receptors ranging from enhancement to inhibition, depending on the sites phosphorylated and the cell type or receptor subtype expressed. Biochemical fractionation of GABAA receptor-associated brain protein showed that PKC-βII can directly and selectively bind to GABAA receptor β1 and β3 subunits in vitro (Brandon et al., 1999a). Moreover, the receptor for activated C-kinase (RACK-1) also bound to the β1-subunit intracellular loop; however, binding of PKC-βII was independent of RACK-1 (Brandon et al., 1999a). Thus, GABAA receptors associate with PKC by direct interaction with their β subunits, allowing receptor regulation by signaling pathways that alter PKC activity. An independently isolated β-subunit-specific serine kinase activity (GABAA receptor–tubulin complex-associated protein of mass 34 kDa, GTAP34) appears to be distinct from kinases previously shown to phosphorylate GABAA receptors. This activity was purified by its association with immunopurified GABAA receptors from calf brain and shown to be selective for the β3 subunit in native receptors (Kannenberg et al., 1999). The kinases known to phosphorylate GABAA receptors in vitro have all been detected as part of the NRC in the postsynaptic density of excitatory synapses (Husi et al., 2000). Nevertheless, GABAA receptor phosphorylation and dephosphorylation by these broad-specificity enzymes are likely to occur in a synapse-selective manner. RACK-1 may serve as the adapter for PKC, but PKC-βII can also bind to the β1 and β3 subunits directly. Similarly, PKA was found to adhere to GABAA receptors via a novel but so far ill-defined A kinase anchoring protein (AKAP) (Brandon et al., 1999b). Thus, compartmentalization of signaling proteins to ligand-gated ion channels might contribute to the synaptic selectivity of signal transduction mechanisms (for review, seeNewton, 1996; Sim and Scott, 1999). A novel type of signal transduction was uncovered by the finding that GABAA receptors engage in direct protein–protein interaction with the dopamine D5 receptor (Liu et al., 2000). Unlike GABAA receptors, dopamine receptors belong to the seven-transmembrane-domain receptor superfamily

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and as such exert their biological effects through G proteins and secondmessenger signaling cascades. The physical association between these two classes of receptors involves the C-terminal region of the D5 receptor and the intracellular loop of the γ 2 subunit of GABAA receptors and enables mutual inhibition of the two receptor systems. Protein–protein interactions across families of receptors greatly increase the number of possible ways neurotransmission can be modulated, although its physiological significance is unclear at present.

IX. Synaptic Plasticity

Whereas neuronal plasticity is primarily believed to be an activity-induced adaptation of excitatory synapses, accumulating evidence suggests that neuronal inhibition through GABAA receptors is also important and does more than dampen neuronal activity. In fact, it might play an active role in information processing and learning (Paulsen and Moser, 1998) and is subject to plastic changes in both the developing and the adult CNS. For example, analysis of mice that are deficient in the small isoform of glutamic acid decarboxylase (GAD65) revealed that GABAA receptor-mediated transmission is essential for neuronal plasticity during establishment of ocular dominance, an experience-dependent refinement of the visual cortex in early life (Hensch et al., 1998). Interestingly, LTP and LTD, which are considered the physiological correlates of synaptic plasticity underlying learning and memory formation, were normal and unable to predict the plasticity deficit in these mice. Generally, the potential for plasticity in the visual cortex (and possibly anywhere in the brain) appears to be retained throughout life until a GABAergic inhibitory threshold is attained (Fagiolini and Hensch, 2000). However, changes in GABAergic transmission are also important as a physiological and pathological regulatory mechanism in the adult organism. Changes in the ratio of α1:α2 mRNAs of GABAA receptor subunits are causal for a switch in fast synaptic inhibition of oxytocin neurons at parturition (Brussaard et al., 1997). In a steroid withdrawal model of the rat designed to simulate premenstrual syndrome, increased levels of the GABAA receptor α4 subunit result in altered receptor pharmacology and in a reduced GABAinduced current decay time that correlates with increased susceptibility to seizures (Smith et al., 1998a,b). In the kindling model of epilepsy, the lasting potentiation of postsynaptic currents at inhibitory synapses in dentate gyrus granule cells is associated with increased postsynaptic GABAA receptor density and an enlarged synaptic junctional area (Nusser et al., 1998a). In the mouse kainate model of epilepsy (Bouilleret et al., 2000), the increased postsynaptic GABAA receptor density is paralleled by an increase in gephyrin

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and in dystrophin in the molecular layer of the dentate gyrus (Knuesel et al., 2000), providing further evidence for structural plasticity of GABAergic synapses in adult brain. Most important, GABAergic deficits in human temporal lobe epilepsy are associated with a major loss of GABAergic neurons and a shift in the subunit composition in the remaining GABAA receptors (Loup et al., 2000). Thus, altered expression of individual GABAA receptor subunits, followed by alteration in the subunit composition of defined GABAA receptor subtypes, provides a molecular basis for neural plasticity (Brooks-Kayal et al., 1998).

A. DEVELOPMENT OF SYNAPSES: ROLE OF PRESYNAPTIC FACTORS Accumulating evidence suggests that presynaptic factors play a major role in synapse development. The most compelling evidence for a role of presynaptically released protein factors is available in the case of postsynaptic accumulation of nicotinic acetylcholine receptors (nAChRs) at the neuromuscular junction (reviewed by Sanes and Lichtman, 1999). In this case, the postsynaptic receptor concentration is increased by two separate mechanisms that are both independent of synaptic activity. One route involves focal induction of nAChR gene expression, which is mediated by neuregulin/ARIA. This motor-neuron–derived protein induces dimerization and thereby activation of ErbB receptor tyrosine kinase. The signal is transduced through the Ras/MAP kinase cascade and Ets transcription factors, which ultimately induce nAChR epsilon–subunit gene transcription (reviewed by Goldman and Sapru, 1998). A second route is essential for clustering of the newly synthesized nAChRs; it involves presynaptically released agrin, which induces the specific clustering of nAChR at the neuromuscular junction by indirect activation of MuSK receptors (reviewed by Colledge and Froehner, 1998; Ruegg and Bixby, 1998; Hoch, 1999). However, agrin is dispensable for synaptic aggregation of GABAA and glutamate receptors (Serpinskaya et al., 1999). At central excitatory synapses, postsynaptic neuroligin appears to represent a receptor for presynaptic neurexin (Irie et al., 1997; Song et al., 1999; Scheiffele et al., 2000) and clustering of AMPA-type glutamate receptors is induced by the extracellular immediate-early gene product Narp (O’Brien et al., 1999). Presynaptic factors are also important for formation of inhibitory synapses.Levi et al. (1999) showed that GABAA and glycine receptors accumulate specifically under boutons containing the corresponding neurotransmitter, whereas gephyrin accumulated under both types of synaptic terminals. In motoneurons cultured in the absence of GABAergic and glycinergic input, large gephyrin clusters accumulated at cholinergic synapses, whereas GABAA and glycine receptors remained exclusively

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extrasynaptic. Thus, gephyrin alone is not able to recruit glycine and GABAA receptors to synaptic sites, and it appears that glycinergic and GABAergic endings provide specific signals for accumulation of the corresponding receptor types (Levi et al., 1999). Thus, presynaptic factors are important for the formation of all the major types of synapses.

B. DEVELOPMENT OF SYNAPSES: ROLE OF SYNAPTIC ACTIVITY In the case of glycine receptors, drug-mediated activity-blocking experiments provided evidence that receptor activation is required for accumulation of glycine receptor at postsynaptic sites (Kirsch and Betz, 1998; Levi et al., 1998). However, the two reports differ in important parameters that await further clarification. First, Kirsch and Betz (1998) reported that receptor activation was essential for clustering of glycine receptor-associated gephyrin, whereas Levi et al. (1998) found that gephyrin was clustered independently of glycine receptors. Both findings are consistent with the earlier notion that gephyrin clusters appear at postsynaptic sites before glycine receptors (Kirsch et al., 1993b; Bechade et al., 1996). However, in one study (Kirsch and Betz, 1998), receptor activation was only important during early stages of cluster formation when activation of glycine receptors is believed to be excitatory but not after synaptogenesis. In the other report (Levi et al., 1998), receptor activation was also important for maintenance of clustered receptors at synapses. Finally, it is not clear whether evoked synaptic activity is required for stabilization of synaptic glycine receptor clusters (Kirsch and Betz, 1998) or whether spontaneous channel openings in the absence of presynaptic transmitter release are sufficient for clustering of glycine receptors (Levi et al., 1998). It is likely that subtle differences in culture conditions and/or drug applications account for at least some of these differences. Clearly, such activity-blocking experiments are not foolproof and are only valuable in the presence of other, independent evidence. Nevertheless, corresponding preliminary experiments with GABAergic synapses were interpreted to suggest that recruitment and clustering of GABAA receptors to GABAergic synapses were independent of receptor activation during neural maturation and synapse formation (Craig et al., 1994). Moreover, lesion of GABAergic afferents in the dentate gyrus results in a marked increase in postsynaptic gephyrin and GABAA receptor immunoreactivity in the denervated layers of the dentate gyrus (Simburger ¨ et al., 2001). These findings suggest that mechanisms of synapse formation are still operant in mature neurons when GABAergic inputs are clearly inhibitory. Unlike in the case of glycine receptors (Kirsch et al., 1993b; Bechade et al., 1996), we find that GABAA receptors are clustered before gephyrin is present in hippocampal neurons developing in culture (unpublished),

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which also suggests a mechanism for GABAergic synapse formation that is distinct from corresponding mechanisms at glycinergic synapses. The most direct evidence that GABAA receptor activation is dispensable for clustering was provided by analyses of microisland cultures in which glutamatergic hippocampal neurons were deprived of GABAergic input (Rao et al., 2000). Surprisingly GABAA receptors formed clusters on glutamatergic neurones in the absence of GABAergic input, and the majority of these clusters were localized opposite glutamatergic terminals rather than extrasynaptically. Moreover, inhibitory and excitatory postsynaptic components were segregated to separate clusters apposed to separate glutamatergic terminals. Thus, glutamate and GABA synapses may use the same or similar presynaptic signal(s) for postsynaptic differentiation.

C. REGULATION OF GABAERGIC SYNAPSES BY NEUROTROPHIC PEPTIDES Several lines of evidence indicate that the effective GABAA receptor concentration on the cell surface might be modulated by receptor endocytosis and exocytosis and that the phosphorylation state of the receptors may serve as signal for endocytosis. For example, GABAA receptor surface levels are reduced by endocytosis upon activation of PKC in a mechanism that requires the presence of the γ 2 subunit (Connolly et al., 1999). Similar endocytosis of GABAA receptors can be induced by ligands of the GABA or BZ site (Tehrani and Barnes, 1997; Tehrani et al., 1997; Brown et al., 1998; Connolly et al., 1999) and, at least in the case of BZ ligands, also appears to be mediated by PKC ( Johnston et al., 1998). Activation of tyrosine kinase receptors also regulates GABAA receptor cell surface expression. Insulin promotes the rapid recruitment of GABAA receptors to postsynaptic sites and this effect has been proposed to be mediated by the receptor β2 subunit (Wan et al., 1997). In contrast, brain-derived neurotrophic factor (BDNF) appears to mediate a rapid downregulation of the postsynaptic GABA response (Rutherford et al., 1997; Tanaka et al., 1997). In cultured hippocampal neurons, the amplitude, but not the frequency, of mIPSCs was reduced (Br¨unig et al., 2001), indicating that the effect was postsynaptic. Furthermore, the kinetics of the mIPSCs were unchanged after BDNF exposure, suggesting that the decrease in amplitude was due to a reduced number of postsynaptic GABAA receptors. The effect was not seen in all neurons, but all neurons that showed the effect were immunopositive for the BDNF receptor trkB (Br¨unig et al., 2001). It is not known whether the effect is due to endocytosis of postsynaptic receptors or to redistribution to extrasynaptic sites. However, preliminary evidence points to an involvement of the calcium/calmodulin-dependent phosphatase calcineurin in the signaling pathway activated by BDNF (Penschuck et al., 1999). Calcineurin, a major

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phosphatase, has also been implicated in regulation of the function of other ion channels ( Yakel, 1997). Thus, enhanced recruitment to the membrane and endocytosis of GABAA receptors may provide a potential mechanism for use-dependent adaptation and synaptic plasticity of GABA synapses.

X. Concluding Remarks

A remarkable variety of protein factors interact directly or indirectly with GABAA receptors or other proteins that are concentrated at inhibitory synapses. With the exception of gephyrin, little is known about the physiological importance of these individual factors in synaptogenesis or regulation of inhibitory neurotransmission. However, a common feature of most of these newly identified proteins is that they are abundant in many nonneural cell types. In addition to the association with GABAA receptors or GABAergic synapses, these proteins therefore must have related functions in nonneural cells. Conversely, regulation of GABAA receptors appears to rely on some of the same proteins factors that operate more generally in the membrane deposition and regulation of nonneural proteins. A notable exception is the gephyrin-binding protein collybistin, which is highly enriched in brain and implicated in membrane deposition of gephyrin. Collybistin thereby appears to unleash synapse-specific roles of gephyrin and its associated proteins. In general, the outcome of future experiments with GABAA receptor-associated proteins will critically depend on the cell types chosen for the analysis.

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D1 DOPAMINE RECEPTORS

Xuemei Huang Department of Neurology University of North Carolina School of Medicine Chapel Hill, North Carolina 27599

Cindy P. Lawler National Institute of Environmental Health Sciences Research Triangle Park, North Carolina 27709

Mechelle M. Lewis Neuroscience Center University of North Carolina School of Medicine Chapel Hill, North Carolina 27599

David E. Nichols Department of Medicinal Chemistry and Molecular Pharmacology School of Pharmacy and Pharmacal Sciences Purdue University West Lafayette, Indiana 47907

Richard B. Mailman1 Departments of Pharmacology, Psychiatry, Medicinal Chemistry, and Neuroscience Center University of North Carolina School of Medicine Chapel Hill, North Carolina 27599

I. Introduction A. Defining Dopamine Receptors: Origin of “D1 Receptor” B. Molecular Biology of Dopamine Receptors II. Localization and Function of D1 -like Receptors A. Methods for Mapping Dopamine Receptor Distribution B. Localization of D1 -like Receptors C. Localization of D2 -like Receptors D. Functional Chemoarchitecture of D1 -like Receptors E. Molecular and Biochemical Functions of D1 Receptors III. Development of Drugs for D1-like Receptors 1

Author to whom correspondence should be addressed.

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C 2001 by Academic Press. Copyright All rights of reproduction in any form reserved. 0074-7742/01 $35.00

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A. Early Progress in Design of D1 Receptor Ligands B. Development of Selective Dopamine Receptor Ligands C. Foundation for Pharmacophoric Models of Dopamine Receptor Ligands D. Current Issues in D1 -like Drug Design IV. Therapeutic and Functional Actions of D1 Receptor Agonists and Antagonists A. Models of Parkinson’s Disease B. D1 Receptors and Parkinson’s Disease C. Impact of D1 Receptors on Other CNS Disorders V. Conclusions and Future Goals References

I. Introduction

Although it has been more than half a century since drugs (e.g., early phenothiazines and reserpine) that affect dopamine function were ﬁrst used in clinical medicine, the underlying mechanisms are still an area of burgeoning investigation. In 2000, Arvid Carlsson shared the Nobel Prize in Medicine and Physiology for his work that was instrumental in evolving a role for dopamine as a neurotransmitter of special importance to psychiatry and neurology, as well as various functions of the peripheral nervous system and autocrine/exocrine tissues. The biochemical assays developed by Carlsson and others (Carlsson, 1959) and subsequent histological techniques (Hillarp et al., 1966), ultimately paved the way for an understanding of dopamine tracts in the brain. These studies showed there were three major pathways, including the nigrostriatal (from cells in the A9 region), the mesolimbic-cortical (from cells in the A10 or ventral tegmentum), and the tuberoinfundibular (hypothalamic) system (Ungerstedt, 1971b). This early awareness of the chemoarchitecture of dopamine systems opened the doors to understanding of the functional role of dopamine in complex phenomena mediated by the brain areas modulated by dopamine. Another critical early event was the discovery of chlorpromazine, and the subsequent demonstration that it decreased the incidence of acute agitation, hallucinations, prolonged immobility, and sudden aggression in psychotic patients to a degree not explained by its sedative effects. It soon became apparent that the beneﬁcial effects of this and similar drugs were frequently accompanied by disturbing and unwanted neurological side effects (e.g., druginduced parkinsonism, akathesia, acute dystonic reactions) now known as extrapyramidal side effects. The similarity between the symptoms of Parkinson’s disease and the early-onset drug-induced neurological side effects (Ehringer and Hornykiewicz,1960; Hornykiewicz,1971) suggested a mechanistic relationship (Carlsson and Lindqvist, 1963) that opened the horizon of dopamine receptor research. The development during the next 20 years

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of phenothiazine analogs of chlorpromazine, as well as antipsychotic drugs in other chemical classes (e.g., thioxanthines, butyrophenones), served as the foundation for the ﬁrst two generations of dopamine receptor research that has now exploded with the addition of molecular and genetic tools. This article provides an overview of the role of the D1-like dopamine receptors in nervous system function. Indeed, there are many excellent reviews that cover aspects of the material we have chosen to discuss (and we attempt to cite some of these). Our goal is to provide a broader, integrated overview that relates the structure and function of these interesting receptors to how the D1 receptors may interact with human neurological and psychiatric diseases.

A. DEFINING DOPAMINE RECEPTORS: ORIGIN OF “D1 RECEPTOR” Although dopamine receptors had been hypothesized since the early 1990s, the ﬁrst direct biochemical mechanism linked to them came from the laboratory of Paul Greengard, another of the 2000 Nobel Laureates. His laboratory demonstrated that dopamine could dose-dependently stimulate the synthesis of the second messenger cAMP (Kebabian et al., 1972) in a way that was antagonized by antipsychotic drugs (Clement-Cormier et al., 1974). The fact that both phenothiazine and thioxanthine antipsychotics competitively inhibited the dopamine-stimulated activity of adenylate cyclase in proportion to their clinical potency led to the notion that this was the major functional mechanism of dopamine in the central nervous system (CNS; Clement-Cormier et al., 1974; Iversen, 1975). With the introduction of new antipsychotics in even newer structural classes (e.g., butyrophenones, benzamides), marked discrepancies became apparent. For example, many of these new behaviorally potent antipsychotics had little potency in inhibiting dopamine-stimulated adenylate cyclase (Trabucchi et al., 1975). This discrepancy led to the idea that two types of dopamine receptors existed. One class was the original adenylate cyclase-linked receptor ﬁrst reported by Greengard’s group (Kebabian et al., 1972), which bound with high-afﬁnity thioxanthines and phenothiazine antipsychotics, but not drugs of the butyrophenone or benzamide classes (Garau et al., 1978). The other class of dopamine receptor was not linked to stimulation of adenylate cyclase, but bound all these drugs in proportion to their clinical potency (Burt et al., 1976; Creese et al., 1976; Seeman et al., 1976). This differentiation, coupled with other information about the localization and function of dopamine receptors, led to the speciﬁc hypothesis that there were two types of dopamine receptors (Garau et al., 1978). The ﬁeld has accepted the nomenclature proposed later by Kebabian and Calne (1979), in which the receptors linked to stimulation of

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cAMP synthesis are termed “D1,” and those that bind butyrophenones and benzamides with high afﬁnity are called “D2.” B. MOLECULAR BIOLOGY OF DOPAMINE RECEPTORS In the late 1980s and early 1990s, molecular cloning studies identiﬁed ﬁve distinct genes that produced the dopamine receptors, all of which have been members of the larger superfamily of G protein-coupled receptors (GPCRs). The D2-like family is coded by at least three different genes and consists of the D2, D3, and D4 receptors. The rat D2 subtype was the ﬁrst dopamine receptor cloned in 1988 (Bunzow et al., 1988), whereas the D3 subtype was cloned in 1990 (Sokoloff et al., 1990) and the D4 subtype in 1991 (van Tol et al., 1991). Each of the D2-like receptors is noted for several factors common to all of them: They are coded by complex genes with 5′ untranslated regions, their genes contain multiple introns, and alternative splicing of their genes may generate several different splice variants with the variations occurring in the third intracellular loop of the proteins. The D1-like family consists of two different receptors, named D1 and D5 in humans and D1A and D1B in rodents. The D1 (D1A) subtype was cloned in 1990 (Dearry et al., 1990; Monsma et al., 1990; Sunahara et al., 1990; Zhou et al., 1990) and the D5 subtype shortly thereafter (Sunahara et al., 1991; Tiberi et al., 1991). The D1 receptor consists of 446 amino acids in both the human and the rat, whereas the D5 receptor consists of 477 in human and 475 in rat. There is roughly 82% amino acid identity between the two receptors in the putative transmembrane-spanning regions, with substantially less similarity outside these regions ( Jarvie and Caron, 1993). The molecular biology of the dopamine receptors has been the subject of numerous articles and books that have covered this subject intensively (e.g., Jenner and Demirdemar, 1997; Sealfon and Olanow, 2000). However, this article focuses on the functional role of D1-like receptors; that is, their pharmacological, cellular, anatomical, and biochemical underpinnings.

II. Localization and Function of D1-like Receptors

A. METHODS FOR MAPPING DOPAMINE RECEPTOR DISTRIBUTION As with any protein, understanding of the functional roles of the D1 receptor is dependent on knowing the tissues, regions, and cell types in which the receptors are found. Although a general consensus has developed about

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many aspects, there are still inconsistencies and controversies, usually relating to which cell types express given receptors and whether a signal obtained reﬂects a functionally relevant receptor. In addition, complex functional interactions exist between the D1- and D2-like families, some of whose mechanisms are also not resolved. In the following sections, we attempt to provide a brief summary of the methods used in studies of the D1 receptors. Because of the importance of the D1- and D2-like interactions, we have also provided a summary of that family’s localization. 1. Receptor Binding Quantitative receptor autoradiography has been used extensively for localizing dopamine receptors in normal brain and for determining potential alterations in receptor distribution associated with various pathological states (Chabot et al., 1996). This method requires the availability of radiolabeled compounds that bind tightly to the ligand recognition site of the target receptor. Apposition of the sections to ﬁlm and subsequent analysis of ﬁlm images with densitometry create a spatial map of radioligand binding to thin brain sections. Although most ligand probes are labeled with radionuclides, a limited number of dopamine receptor ligands that incorporate ﬂuorescent moieties are available and have been used in conjunction with light microscopy for visualization and cellular resolution of receptorbinding sites (Ariano et al., 1989, 1991; Larson and Ariano, 1994, 1995). The primary advantage afforded by ligand-binding probes is an obvious one— such probes label only those receptor proteins with functional binding sites. The most signiﬁcant limitation is the requirement for compounds with high afﬁnity and selectivity for speciﬁc dopamine receptor isoforms. Selectivity becomes particularly important for distinguishing among receptor subtypes with disparate expression levels. Use of a “selective” ligand can result in the untoward labeling of many receptors that display only modest afﬁnity for the ligand if these are present in great excess relative to the higher afﬁnity receptor subtype of interest. 2. Immunological Methods Creation of antibodies directed at speciﬁc receptor domains provides a second method of receptor localization. Many publications have described the successful generation and use of dopamine receptor subtype-selective antibodies (Huang et al., 1992; Levey et al., 1993; Ariano et al., 1993; Ariano and Sibley, 1994; Bergson et al., 1995a, 1995b; Mrzljak et al., 1996; Defagot et al., 1997; Ariano et al., 1997a, 1997b; Khan et al., 1998a, 1998b; Ciliax et al., 2000; Khan et al., 2000). Typically, the antibodies are targeted at short peptide sequences corresponding to unique intracellular or extracellular domains of the receptor. Excellent molecular selectivity and spatial

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resolution can be achieved, allowing the use of light and electron microscopy to localize receptors to speciﬁc cellular and subcellular neuronal elements. Immunohistochemical techniques can be applied that produce signiﬁcant ampliﬁcation of the signal and enable detection of very small amounts of receptor protein. The most important disadvantage is the potential for crossreactivity with unknown homologous proteins. In addition, interpretation of immunoreactivity can be complicated by the potential for antibodies to recognize nonfunctional receptors that are in transport or in the process of synthesis or catabolism. 3. Localization of mRNA A third technique that has been used extensively for localization is in situ hybridization of receptor mRNA (Woodruff, 1998). In situ hybridization to detect mRNA expression is a tool made available by the cloning of the individual dopamine receptor isoforms; generation of usable subtype-selective ligands and antibodies is often a protracted process. Signiﬁcant speciﬁcity for a certain receptor isoform can be obtained with the use of complementary probes designed to hybridize to deﬁned nucleotide sequences that are contained within the receptor mRNA of interest, yet display low homology to closely related receptors. Localization of mRNA occurs by examining the spatial pattern of disintegration of the radiolabeled probe using ﬁlm emulsion or liquid emulsion applied directly to the slide-mounted brain slice. Probe generation and labeling are straightforward, making this technique accessible to a large number of research laboratories with standard molecular biological capabilities. Despite these attractive features, there are several important limitations. First, because mRNA synthesis occurs primarily in cell bodies, this method does not detect the location of the ﬁnal functional receptor in dendrites or axonal ﬁelds. As importantly, the level of mRNA may not reﬂect the abundance of the ﬁnal protein product. For example, mRNA present in low abundance may give rise to a very stable protein product, with the result that mRNA abundance vastly underestimates protein abundance. Multiple localization techniques are often used concurrently to capitalize on the unique advantages afforded by each technique. For instance, abundance and localization of dopamine receptor protein and receptor transcripts can be visualized simultaneously within the same section (Ariano et al., 1997a, 1997b). More commonly, comparisons are made with adjacent brain sections that have been processed separately for either dopamine receptor mRNA or ligand binding (Mansour et al., 1990; Fremeau et al., 1991; Mansour et al., 1992; Landwehrmeyer et al., 1993b). Receptor localization efforts may employ selective lesioning of speciﬁc brain nuclei or cell types, or make use of retrograde transport agents to identify afferent/efferent sources (Altar and Marien, 1987; Gerfen et al., 1990; Harrison et al., 1990;

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Ariano et al., 1992; Ince et al., 1997; Lu et al., 1998). Finally, use of null mutant mice deﬁcient in a speciﬁc receptor isoform can be used to advantage in instances where rare receptors are normally masked by the presence of an abundant closely related receptor type (Montague et al., 2001). This hindrance is eliminated by examining receptor distribution in homozygous mice with targeted deletion of the more abundant receptor isoform.

B. LOCALIZATION OF D1 -LIKE RECEPTORS The development of ligands with high afﬁnity and selectivity for D1-like receptors enabled the use of quantitative receptor autoradiography to map the distribution of dopamine D1 receptor-binding sites in brain (Boyson et al., 1986; Savasta et al., 1986; Altar and Marien, 1987; Wamsley et al., 1991). The prototypical D1 antagonist [3H]-SCH23390 has been employed most often, although localization has been achieved with a variety of structurally related analogs, including [3H]-SCH39166, [125I]-SCH23982, and [3H]-SKF83566. Savasta et al. (1986) reported a pattern of [3H]-SCH23390 binding that paralleled the known distribution of dopamine terminals. The highest levels of binding were observed in the forebrain areas, including caudate-putamen, nucleus accumbens, and olfactory tubercle. Labeling was also evident in structures comprising the basal ganglia outﬂow pathways, including the entopeduncular and subthalamic nuclei and the substantia nigra pars reticulata. D1 receptors were expressed in a number of limbic areas, notably the dentate gyrus of the hippocampus and several amygdaloid nuclei. Moderate to low densities of D1 binding sites were observed in several cortical divisions, with a preferential distribution in deeper layers. The highest cortical densities occurred within the anteromedial and suprarhinal prefrontal areas. The distribution of D1 receptor binding sites has been explored extensively in rodent brain, whereas fewer studies have been conducted with tissues from nonhuman primates or from neurologically intact humans (De Keyser et al., 1988; Lidow et al., 1991; Hall et al., 1993, 1994; Montague et al., 1999; Piggott et al., 1999). A direct comparison of D1 receptor ligandbinding sites in several species, including rodent and monkeys, revealed remarkable similarities in neuroanatomical distribution of sites within the basal ganglia (Richﬁeld et al., 1987), although differences were noted in patterns of cortical lamination (Richﬁeld et al., 1989). A detailed study of [3H]-SCH23390-labeled sites in rhesus monkey brain revealed a bilaminar pattern in most cytoarchitectonic cortical areas (Lidow et al., 1991). The highest concentrations of D1 receptors were observed in supragranular layers I, II, and IIIa and infragranular layer V and VI. The distribution of D1

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receptor-binding sites exhibited a marked gradient along the rostral–caudal neuraxis, with highest densities in the prefrontal cortical areas and lower concentrations in occipital cortex. 1. Distribution of D1A Receptors and mRNA Following the cloning of the D1 receptor (Dearry et al., 1990; Monsma et al., 1990; Sunahara et al., 1990; Zhou et al., 1990), a number of studies examined the distribution of the mRNA for this receptor (Mansour et al., 1990; Fremeau et al., 1991; Meador-Woodruff et al., 1991). Cloning of the D1A receptor also enabled the development of a subtype-selective ligand for regional, cellular, and subcellular resolution of the receptor protein (Levey et al., 1993; Ariano and Sibley, 1994; Yung et al., 1995). In most cases, D1 mRNA localization was consistent with the results obtained from D1 receptor-binding and antibody localization studies. This correspondence suggests that D1 receptors are typically expressed on cell soma and proximal dendrites rather than at distant sites. A few notable exceptions concerned the near absence of D1 mRNA in substantia nigra, enterpeduncular nucleus, and subthalamic nucleus—areas with clearly deﬁned D1 receptor-binding sites. This mismatch is consistent with the idea that D1 receptors are transported and expressed on axon terminals that project to these areas from other nuclei. Retrograde labeling and lesioning studies have conﬁrmed this hypothesis (Gerfen et al., 1990; Harrison et al., 1990). The results of immunocytochemical localization studies of the D1A receptor protein support a similar regional distribution in rodent and primate brain, especially in those areas that express the highest density of receptors (Levey et al., 1993). The cortical laminar distribution and overall level of expression may, however, vary between rat and primate (Levey et al., 1993; Smiley et al., 1994). An intriguing ﬁnding that has emerged from studies examining the subcellular distribution of D1A receptors with immunocytochemical methods is the location of a signiﬁcant proportion of D1A receptors at extrasynaptic sites (Smiley et al., 1994; Yung et al., 1995). These data imply that dopamine effects may depend on volume transmission. 2. Distribution of D1B/D5 Receptors and mRNA Cloning of a second D1-like receptor (Grandy et al., 1991; Sunahara et al., 1991; Tiberi et al., 1991; Weinshank et al., 1991) raised questions about the possible distinct role for this receptor, termed D5 (human) or, alternatively, D1B (murine). To date, no subtype-selective ligands are available for direct labeling of this receptor subtype (thus, localization has relied on the use of mRNA mapping and antireceptor antisera). Data provided by Tiberi et al. (1991) indicated a distribution of the D5 receptor that was limited relative to that of the D1A receptor. Likewise, Meador-Woodruff et al. (1992) reported that D5/D1B mRNA was restricted to hippocampus, lateral mamillary nuclei,

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and parafasicular nucleus. A report localized D5/D1B receptor-binding sites in D1A null mutant mice (Montague et al., 2001). The absence of D1A sites allowed the use of the nonselective ligand [3H]-SCH23390 to localize D5/D1B receptors in homozygous mice. Clearly identiﬁable binding sites were observed in only one area, hippocampus. The density of these sites was close to the limits of detection, however, leaving open the possibility that regions with lower receptor densities escaped detection. D5/D1B mRNA localization studies in primate brain have reported a more widespread distribution than would be predicted from the earlier rodent mRNA localization data (Huntley et al., 1992; Rappaport et al., 1993). Likewise, use of antibodies to label D5/D1B receptors in rodent and primate brain have indicated a broader distribution than originally envisioned (Ariano et al., 1997a; Ciliax et al., 2000; Khan et al., 2000). For example, using antipeptide antisera selective for the D1B receptor, Ariano et al. (1997a) reported intense staining in frontal and parietal cortices of rat brain. Significant staining was also observed in hippocampus and dentate gyrus, whereas a lower-intensity signal was observed in olfactory tubercule, dorsal aspects of the caudate putamen, and the cerebellar vermis. At present, the reasons for the discrepancies among studies are unclear. Development of radioligands selective for D1B/D5 (versus) D1A receptors would be of great value for resolving functional binding sites in brain. A comparison of the distribution of D1 versus D5 receptor immunoreactivity in primate brain (Bergson et al., 1995b) revealed intriguing differences in cellular and subcellular localization of these two receptors. Antibody staining for D1 and D5 receptors was widespread in prefrontal and premotor cortex, cingulate and entorhinal cortex, and hippocampus and dentate gyrus. Whereas both receptors were expressed in pyramidal cells in layers II, III, and V in many cortical regions, D5 receptors were expressed preferentially on apical dendritic shafts and D1 receptors were expressed more frequently on dendritic spines. Within the caudate nucleus, D1 and D5 receptors were expressed on medium spiny neurons, although D1 receptors were expressed at markedly higher levels. In contrast, the large, presumably cholinergic neurons expressed only D5 receptors. The distinct cellular and subcellular distributions of the two D1-like receptors imply their distinct roles in neurotransmission.

C. LOCALIZATION OF D2 -LIKE RECEPTORS Although the focus of this article is on the D1-like receptors, the function of these receptors often interacts with the D2-like receptors. Thus, understanding of the functional effects of the former class is frequently dependent on the latter. In the 1980s, quantitative receptor autoradiography studies of

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dopamine D2-like receptors began using a variety of radioligands, including the butyrophenone [3H]-spiperone, as well as several benzamides, including [3H]-sulpiride and its iodo analog [125I]-iodosulpiride (Gehlert and Wamsley, 1984; Jastrow et al., 1984; Boyson et al., 1986; Bouthenet et al., 1987; Joyce and Marshall, 1987; Richﬁeld et al., 1987; De Keyser et al., 1988). In another study, a ligand with pM afﬁnity and very low nonspeciﬁc binding, [125I]-epidepride, has been exploited to enable localization of low densities of D2-like receptor-binding sites (Kessler et al., 1993). A comprehensive survey of D2-like receptors in brain detected with [125I]iodosulpiride (Bouthenet et al., 1987) revealed the highest density of sites in the major dopamine terminal ﬁelds of forebrain, including caudate putamen, nucleus accumbens, and olfactory tubercule. The density of D2-like receptors in these areas was signiﬁcantly lower than that of D1-like receptors. Dopamine D2-like receptors were found in the substantia nigra, with dense labeling in both the pars compacta and ventral tegmental area, and lighter labeling in the pars reticulata. These binding sites represented receptors expressed by dopamine neurons, as they were dramatically reduced following lesion with the catecholaminergic cytotoxicant 6-OHDA. Mesocorticolimbic areas with known dopamine innervation were moderately labeled, including anterior olfactory nuclei and lateral septal nucleus. Other brain areas expressing moderate densities of D2-like receptors included the olfactory bulb, inferior and superior colliculi, and subthalamic nucleus. Brainstem, hippocampus, and anterior thalamic nucleus also expressed D2 binding sites. Occipital and frontal cortices were very lightly labeled. The localization of individual dopamine receptor subtypes in cortex has been of special interest in view of their potential impact on cognition. Studies in primate and rodent brain indicate a signiﬁcantly lower overall density and different laminar patterning of D2 versus D1 receptors in cortex (De Keyser et al., 1988; Richﬁeld et al., 1989; Lidow et al., 1991), suggesting a unique and preferential participation of D1 versus D2 receptors in cognitive functions. In contrast, D2 receptors have exclusivity for affecting neuroendocrine function via their localization in pituitary (Mansour et al., 1990; Ariano et al., 1991). D2 receptor ligand-binding sites are notable in the neurointermediate lobe of the pituitary, with moderate binding in the anterior lobe. 1. Distribution of D2 Receptors and mRNA Cloning of the D2 receptor allowed localization of its message and a comparison with the distribution of ligand binding. In many brain regions, the distribution of D2 mRNA matched the pattern observed with D2 receptor ligands (Meador-Woodruff et al., 1989; Weiner and Brann, 1989; MeadorWoodruff et al., 1991; Weiner et al., 1991; Landwehrmeyer et al., 1993a;

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Gurevich and Joyce, 1999). A good correspondence was obtained in caudate putamen, pituitary, nucleus accumbens, olfactory tubercule, globus pallidus, ventral tegmental area, and substantia nigra. Likewise, generation of D2 receptor-selective antibodies has produced results consistent with the prominent distribution of this receptor in the basal ganglia and associated elements (Levey et al., 1993). Despite the general concordance of D2 receptor and mRNA localization data, notable discrepancies have occurred. For example, discordance between D2 transcript and receptor-binding data has been observed in neocortex, hippocampus, and olfactory bulb (Mansour et al., 1990). Such discrepancies were suggested to reﬂect technical considerations and/or transport of mRNA to distant terminal ﬁelds. The extent and pattern of distribution of D2 receptors in cortical areas has been particularly troublesome, with different results obtained from application of the same localization methodology. For example, use of distinct D2-receptor antibodies has revealed results ranging from little or no labeling to extensive labeling throughout the cortical laminae (Ariano et al., 1993; Levey et al., 1993; Sesack et al., 1994). Such differences are likely to reﬂect the low abundance of the D2 receptor in these areas, coupled with differences in selectivity and/or sensitivity of the reagents employed. The discovery of an alternatively spliced variant of the D2 receptor, containing a 29 amino acid insert within the third cytoplasmic loop, prompted examination of the potential differential expression of the two isoforms. Most initial reports of mRNA regional abundance indicated that the long version (D2long or D2A) was the predominant receptor expressed in a variety of brain areas (Ariano et al., 1989; Giros et al., 1989; Neve et al., 1991; Snyder et al., 1991). A study using isoform-selective antibodies in primate brain revealed that the short version (D2short or D2B) is preferentially expressed by dopamine neurons in primate brain, whereas the long form is prominent in most target cells (Khan et al., 1998b). 2. Distribution of D3 Receptors and mRNA The initial description of the distribution of D3 receptor mRNA in rodent brain (Sokoloff et al., 1990; Bouthenet et al., 1991) indicated a much lower overall abundance of this transcript relative to that of the D2 receptor and a distribution restricted primarily to ventral striatum, nucleus accumbens, islands of Calleja, bed nucleus of the stria terminalis, dentate gyrus of the hippocampus, and mamillary hypothalamic nuclei. Low levels of mRNA transcripts were also observed in the ventral tegmental area, suggesting this receptor subtype is expressed by dopamine neurons and serves an autoreceptor role. Studies in primate brain have conﬁrmed a preferential localization in ventral striatum, but also have revealed the presence of D3 mRNA in

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a number of cortical areas and various components of the basal ganglia, including caudate nucleus and putamen (Meador-Woodruff et al., 1994, 1996; Murray et al., 1994; Suzuki et al., 1998; Gurevich and Joyce, 1999). Several studies have used ligand binding to visualize D3 receptors. [3H]quinpirole and [3H]-7-OH-DPAT were among the ﬁrst radioligands employed and were selected based on their apparent selectivity for D3 versus D2 receptors in clonal cell lines (Levesque et al., 1992; Levant et al., 1993). The interpretation of the data obtained with these compounds has been complicated by questions about their true degree of selectivity. The D3-selective agonist [3H]-PD 128907 has been developed and used for autoradiographic studies (Hall et al., 1996), although its selectivity for D3 versus D2 receptors has been estimated to be no greater than forty-fold. An indirect binding strategy using [3H]-epidepride has also been reported (Murray et al., 1994). This method involves displacement of the nonselective ligand [125I]-epidepride with D3 or D2-selective unlabeled compounds. Despite the inherent limitations of the available ligand-binding probes for D3 receptors, their use has generally conﬁrmed the preferential distribution of this receptor to limbic related structures. A number of questions remain, however, including the extent of D3 receptor expression in nonlimbic circuits and potential species differences in distribution and function of this receptor class (Larson and Ariano, 1995; Levant, 1998; Suzuki et al., 1998). Further development and use of antibodies and ligands with greater D3 receptor selectivity will be essential to resolving these issues. 3. Distribution of D4 Receptors and mRNA The cloning of dopamine D4 receptors attracted considerable interest because of the high afﬁnity of this receptor for the atypical antipsychotic clozapine (van Tol et al., 1991). D4 receptor mRNA localization in rodent and primate brain revealed a selective expression in limbic and cortical regions, with sparse expression in striatal areas (van Tol et al., 1991; O’Malley et al., 1992; Meador-Woodruff et al., 1994; Suzuki et al., 1995; Meador-Woodruff et al., 1996). Mapping of D4 receptors with antireceptor antisera in rodent and primate brain have corroborated the restricted distribution of this receptor type (Ariano et al., 1997b; Defagot et al., 1997; Khan et al., 1998a). Ariano et al. (1997b) observed antibody staining in several cortical areas, including frontal and parietal cortices, and in hippocampus in rodent brain. Lower levels of antibody staining were found in globus pallidus, thalamus, cerebellum, and substantia nigra. Little or no labeling was demonstrated in the caudate putamen. Results obtained by direct comparison of D4 receptor antibody staining in rodent and primate brain suggest that D4 receptors are expressed in greater abundance in rodent versus primate cortex (Khan et al., 1998a).

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Initial attempts to use ligand-binding methods to localize D4 receptors and to estimate their regional densities have been hindered by the lack of subtype-selective radioligands. Several studies have relied on an indirect method introduced by Seeman et al. (1993), whereby receptor densities obtained with [3H]-nemonapride (a ligand that binds to all three D2-like receptors) is subtracted from that obtained with [3H]-raclopride, a ligand that binds primarily to only the D2 and D3 receptors. This technique led to a report that there was a two- to sixfold elevation in the density of D4 receptors in striatal tissue from schizophrenic brains compared with those of control subjects (Seeman et al., 1993). This striking ﬁnding was quite controversial because of the indirect methodology on which it was based, criticism that has been supported by a variety of studies. Radioligands with signiﬁcant selectivity for D4 receptors have been developed (Primus et al., 1997; Bourrain et al., 1998; De La and Madras, 2000). High-afﬁnity binding of the D4-selective ligand NGD 94-1 has been reported in entorhinal and prefrontal cortex, hippocampus, and thalamus in postmortem human brain (Primus et al., 1997), yet with no high-afﬁnity binding sites detectable in striatum. Such D4 receptor ligands have allowed study of novelty-seeking behaviors (Oak et al., 2000), as well as a number of neuropsychiatric and neurological disorders, and have cast doubt on the hypothesis that D4 receptors play a critical role in the etiology of positive symptoms of schizophrenia.

D. FUNCTIONAL CHEMOARCHITECTURE OF D1 -LIKE RECEPTORS 1. Dopamine Receptor Localization: Implications and Clinical Relevance Elucidation of the precise regional, cellular, and subcellular localization of dopamine receptor subtypes in brain has been an essential component of attempts to understand the roles of these receptors and their associated signaling elements in normal and disordered brain function. As one example, the initial cloning of D3 and D4 receptor isoforms revealed a selective cortical and limbic expression; this ﬁnding has prompted intense speculation and drug development efforts directed toward the potential relevance of these receptors to neuropsychiatric disorders (Levant, 1997; Wilson et al., 1998; Tarazi and Baldessarini, 1999; Schwartz et al., 2000). Likewise, current models of basal ganglia function/dysfunction have incorporated neuroanatomical evidence of a signiﬁcant segregation of D1 and D2 receptors in the two principal striatal outﬂow pathways. These reﬁnements have directed research efforts to elucidate dopaminergic involvement in a variety of movement disorders and to develop more effective treatments (Robertson et al., 1992; Starr, 1995; Gerfen, 2000a, 2000b). Moreover, the experimental

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ﬁndings that administration of dopamine receptor ligands can induce regionally speciﬁc alterations in dopamine receptor densities have provided important clues for understanding the potential beneﬁts and liabilities of dopaminergic pharmacological therapies for various neurological and neuropsychiatric disorders, including Parkinson’s disease, schizophrenia, and depression (Bordet et al., 1997; Lidow et al., 1998; Joyce and Gurevich, 1999; Lammers et al., 2000). 2. A Neuroanatomical Model of Basal Ganglia Function A model of basal ganglia functional neuroanatomy emerged in the 1980s and early 1990s and has provided a useful framework for understanding movement disorders such as Parkinson’s disease (Albin et al., 1989; DeLong, 1990; Graybiel, 1990; Smith and Bolam, 1990; Alexander et al., 1990). This classic model posits that coordinated movement is regulated by two parallel and segregated pathways through the basal ganglia. These are formed by the GABAergic medium spiny projection neurons of the striatum and ultimately enable movement via disinhibition of thalamo-cortico circuits. a. Direct and Indirect Outflow Pathways. The medium-size spiny neurons constitute the primary circuit element within the striatum and are estimated to account for 90% of the neuronal population therein. Medium spiny neurons that express substance P and dynorphin form the direct pathway that projects from striatum to GABAergic neurons located in the major output nuclei of the basal ganglia, the substantia nigra pars reticulata (SNr), and the internal segment of the globus pallidus (GPi) or its rodent homolog, the enterpeduncular nucleus (Epn). The other major class of medium spiny GABAergic neurons contain enkephalin and form the ﬁrst of a multistage indirect pathway. GABA neurons projecting from the striatum to the external segment of the globus pallidus (GPe) form the ﬁrst stage. GABAergic neurons in the GPe then provide an inhibitory input to the subthalamic nucleus (STN). The third stage of the indirect pathway consists of an excitatory glutamatergic projection from the STN to the GABAergic neurons in the SNr. According to the original model, increased activity in the direct pathway has an effect on GABAergic neurons in the SNr and GPi that is opposite that achieved when the indirect pathway is activated. Stimulation of the direct pathway increases GABA release in the SNr and Epn/GPi, and reduces tonic inhibition provided by GABAergic neurons that project to the thalamus. This effect serves to disinhibit thalamic projection neurons that excite cortical motor areas. The multistage synaptic arrangement in the indirect pathway provides for a different outcome when stimulated (i.e., increased activity in this pathway ultimately increases the tonic inhibition of the thalamocortioco excitatory circuits). Coordinated movement requires a

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coordination of activity in the two pathways—increased activity in the direct pathway and reduced activity in the indirect pathway. A schematic of such a model is shown in Fig. 1. Since the early 1990s, this elegant model has framed our understanding of clinical disorders of the basal ganglia. It is clear, however, that elaboration and revision are needed to accommodate newly emerging functional and anatomical connectivities (Parent and Hazrati, 1995; Levy et al., 1997; Hauber, 1998; Blandini et al., 2000; Bolam et al., 2000; Filion, 2000; Graybiel et al., 2000). Of special interest are data that challenge classic views of the functional identities of elements that form the indirect pathway, particularly the GPe and STN (see Parent and Hazrati, 1995; Levy et al., 1997). The classic model views GPe as a simple relay station that regulates output from GPi and SNr indirectly through the STN. GPe is believed to provide the principal source of tonic inhibitory control of STN. This conceptual arrangment is at odds with anatomical data indicating that the termination sites of the GPe efferent ﬁbers that project to STN do not physically overlap with the STN neurons that project to GPi and SNr. This ﬁnding suggests that

FIG. 1. Schematic of key aspects of basal ganglia function as they relate to the location and actions of D1 and D5 receptors. (The density of the text indicates the relative level of the receptors.) Abbreviations: Glu, glutamate; ACh, acetylcholine; GABA, gamma aminobutyric acid; GP, globus pallidus; SN/zc, substantia nigra zona compacta; STN, subthalamic nucleus; EP(Gpi)/SNr, endopeduncular nucleus (internal segment of the globus pallidus in primate)/substantia nigra pars reticulata; “−”, inhibitory action; “+”, stimulatory action. See text for further details.

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the inﬂuence of GPe on STN output is more complex than originally envisioned. An additional complication arises from the ﬁnding that, contrary to the predictions of the original model, the hyperactivity in the subthalamic nucleus that is observed in PD is not accompanied reliably by hypoactivity in the GPe (Levy et al., 1997). To accommodate these and other discrepancies, new models of basal ganglia organization have been proposed (e.g., Parent and Hazrati, 1995; Levy et al., 1997) that recognize a variety of afferent and efferent sources for STN and GPe as potentially important modulators of information ﬂow. In addition to massive inputs from the cerebral cortex and the GPe, the STN is known to receive glutatmate-excitatory input from the parafasicular nucleus of the thalamus and a dopaminergic input from the substantia nigra pars compacta (SNc). Data reveal a broad projection of STN to a number of structures, including SNr, both pallidal segments, striatum, and cerebral cortex. Likewise, direct projections of GPe to the principal output structures of the basal ganglia, SNr, and Gpi, have been identiﬁed. Collectively, these data underscore the evolving nature of our understanding of basal ganglia functional anatomy and suggest alternative models for elucidating the pathophysiology of motor symptoms in PD and other movement disorders. b. Segregation of Dopamine Receptor Subtypes on Outflow Pathways. Dopamine is envisioned to be a primary modulator of basal ganglia outﬂow. Accordingly, the distribution of individual dopamine receptor subtypes within this circuit has been the subject of intense research interest and controversy (Surmeier et al., 1993). Results of in situ hybridization studies examining the distribution of mRNA in brain slices have generally supported segregation of D1 and D2 receptors, with D1 receptors expressed in substance P/dynorphin-containing neurons that form the direct pathway, and D2 receptors coexpressed with enkephalin in neurons that form the indirect pathway (Gerfen et al., 1990; Le Moine et al., 1991; Gerfen, 1992; Le Moine and Bloch, 1995), although some degree of D1 and D2 receptor mRNA colocalization has been reported as well (Meador-Woodruff et al., 1991; Lester et al., 1993). Results using antireceptor antibodies have been mixed, with evidence of either minimal, or extensive, segregation (Hersch et al., 1995; Larson and Ariano, 1995; Yung et al., 1995). When considered together, however, the weight of available evidence suggests a preferential distribution of D1 and D2 receptors in the direct and indirect pathway, respectively. The presumed segregation of D1 and D2 receptors on the two outﬂow pathways has been used to support the idea that dopamine facilitates movement by its opposing effects on neuronal activity mediated by D1 versus D2 receptors (Gerfen et al., 1990; Gerfen, 1992). According to this view, dopamine has stimulatory actions on D1 receptor-bearing neurons that form the direct pathway and inhibitory actions mediated by D2 receptors expressed on neurons that comprise the indirect pathway.

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Although this simple model has considerable appeal, it does not consider the possibility of functionally important colocalization of D1/D2 receptors in a minor proportion of striatal neurons. Functionally signiﬁcant colocalization has been supported by elegant experiments that combined electrophysiological recordings and single-cell PCR analysis of mRNA content in identiﬁed striatal neurons (Surmeier et al., 1992). Consideration of the role(s) of D3, D4, and D5 receptors is needed also in light of data indicating that the colocalization of D1- and D2-like receptors may occur by coexpression of a less abundant isoform (D3, D4, or D5; Surmeier et al., 1996). c. Localization of Dopamine Receptors on Striatal Cholinergic Neurons. In addition to the medium spiny projection neurons, a variety of other neuronal classes are present within the striatum and function as interneurons (see review by Kawaguchi et al., 1995). Although the small numbers of these interneurons and their varied morphological characteristics presented difﬁculties in initial categorization, use of immunohistochemical markers was instrumental in distinguishing subclasses and elucidating the functional properties of these interneurons. The most well-known striatal interneuron is the giant cholinergic neuron. These neurons can be identiﬁed by their large somata (20–50 µm) and by the expression of the acetylcholine biosynthetic enzyme choline acetyltransferase. Although these neurons constitute a minor proportion of striatal neurons (1–2%), their fundamental importance has been recognized historically. For example, disruption of the normal dopamine and acetylcholine balance was hypothesized to play a role in Parkinson’s disease and provided the rationale for the use of anticholinergic agents. The responsivity of these neurons to dopamine agonists led to the idea that they served to relay dopamine input to the striatal projection neurons. The discovery that the majority of dopamine ﬁbers synapse on projection neurons, rather than on cholinergic cells, made this simple hypothesis less attractive (Freund et al., 1984). Nonetheless, these neurons express both D2 and D5 dopamine receptors (Le Moine et al., 1990; Bergson et al., 1995b), receive some direct dopaminergic input, and exhibit systematic responses to dopaminergic stimulation. Cholinergic cells have been proposed to function as associative neurons on the basis of their widespread dendritic trees and primary synaptic contact with projection neurons. A hypothesis proposes that the primary role of ACh release in striatum is to facilitate corticostriatal NMDA receptor-mediated longterm potentiation in medium spiny projection neurons (Calabresi et al., 2000). 3. D1 Receptors and Prefrontal Cortical Working Memory Circuits An instrumental role in working memory processes has been ascribed to dopamine neurotransmission in primate prefrontal cortex (Goldman-Rakic et al., 2000). Seminal studies since the early 1990s have demonstrated that

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prefrontal cortical neurons possess transient memory ﬁelds that encode speciﬁc features of stimuli, such as object location and direction (Funahashi et al., 1989, 1993). The localization of dopamine receptor subtypes in prefrontal pyramidal and nonpyramidal neurons, together with the results of pharmacological studies with dopaminergic ligands, have supported an essential role of D1-like receptors in the elemental basis of these working memory processes (Sawaguchi and Goldman-Rakic, 1991, 1994; Williams and Goldman-Rakic, 1995; Murphy et al., 1996; Zahrt et al., 1997). These ﬁndings indicate that moderate occupancy of D1 receptors enhances the memory ﬁelds of individual prefrontal neurons, whereas such memory ﬁelds are reduced or abolished at either low or high D1 receptor occupancy. D1 receptors are the predominant dopamine receptor subtype expressed in prefrontal cortex (Goldman-Rakic et al., 1990; Lidow et al., 1991), where they are localized on the dendritic spines of pyramidal neurons (Fig. 2). A subset of GABAergic interneurons that provide inhibitory input to the perisomatic region of pyramidal neurons also express D1 receptors on dendritic shafts (Smiley et al., 1994; Bergson et al., 1995b; Muly et al., 1998). D5 receptors are expressed most often on the dendritic shafts that are a target of inhibitory input from GABAergic interneurons (Bergson et al., 1995b). D1 receptors in both pyramidal and nonpyramidal neurons are wellpositioned to interact with glutamatergic inputs via their proximity to asymmetric synapses that are presumed to arise from glutamatergic sources. A model of dopamine modulation of working memory posits a central role of D1 receptors in enhancing glutamatergic input to both pyramidal and nonpyramidal cells (Muly et al., 1998). The efﬁciency of this D1 receptormediated enhancement is proposed to differ in pyramidal and nonpyramidal neurons, and this difference is believed to produce the observed biphasic effects of dopamine on working memory performance. Accordingly, at moderate concentrations of dopamine, D1 receptors on pyramidal cells enhance glutamate inputs, with a corresponding increase in working memory performance. Higher concentrations of dopamine stimulate D1 receptors on both pyramidal and nonpyramidal cells. Under these conditions, the net effect of dopamine is dominated by its actions on nonpyramidal cells. D1 receptor activation on nonpyramidal cells leads to feed-forward inhibition of pyramidal cells, with a resulting decrement in working memory performance. D2-like receptors are also expressed in prefrontal cortex, albeit at much lower levels than D1-like receptors. D4 receptors have been localized to both pyramidal and nonpyramidal prefrontal neurons, whereas D2 and D3 receptors are expressed primarily on nonpyramidal GABAergic interneurons (Mrzljak et al., 1996; Khan et al., 1998a). The participation of D2-like receptors in working memory processes has not yet been elucidated, although the primary localization of D2-like receptors on GABAergic interneurons

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FIG. 2. This schematic of a pyramidal neuron and associated interneurons in cortical layer III illustrates the compartmentalization and interaction of D1 and D5 receptors. The D1 and D5 receptors are located primarily in distal portions (dendrites and spines) of the pyramidal neuron, but the D5 shows a preferential distribution on the proximal denstires. D3 and D4 receptors (not shown) are found on proximal aspects of the cell. VTA = ventral tegmental area, source of the innervating dopamine neurons. [Figure created from information in GoldmanRakic (1999).]

suggests that any D2 receptor-mediated effects on pyramidal neuron memory ﬁelds are achieved indirectly. 4. D1/D5 Receptors in the Hippocampus The hippocampus is known to be involved in the consolidation of memories. This structure receives cortical projections from the entorhinal cortex, whose axons synapse on cells in the dentate gyrus. These cells then send projections through the mossy ﬁbers to CA3. The CA3 region connects to CA1 through the Schaffer collaterals, completing the traditional hippocampal circuit. Dopaminergic cells from the substantia nigra and the ventral

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tegmental area (VTA) project to the dentate gyrus and CA1 region of the hippocampus. The exact role dopamine plays in the hippocampus in particular, and in memory more globally, has been the subject of numerous studies. Dopamine has been found to modulate the activation of NMDA receptors, known to be crucial for the induction of long-term potentiation (LTP), the mechanism believed to underlie initial memory consolidation. A speciﬁc role for dopamine receptors in the hippocampus was suggested by the ability of antagonists to decrease the late phase of LTP (Frey et al., 1990, 1991). D1- and D2-like receptors have been localized to the hippocampus (Yokoyama et al., 1994; Bergson et al., 1995b). Evidence suggests that both the D1 and D5 receptors are expressed in the hippocampus (Bergson et al., 1995b; Montague et al., 2001), where D1 receptors are found on dendritic spines and D5 receptors on dendritic shafts. This postsynaptic location expertly positions them to speciﬁcally affect synaptic plasticity. Dopamine modulation appears to rely on the activation of D1-like receptors, as Huang and Kandel (1995) found that D1-like agonists potentiate the late phase of LTP in the CA1 region. This phase of LTP is dependent on the synthesis of new proteins, and is thus believed to be important for the persistence of memories. Consistent with activation of D1-like receptors, the use of forskolin and cAMP analogs also enhances LTP, and mice lacking the D1 receptor do not express the late phase of LTP (Matthies et al., 1997). D1-like receptors have also been found to potentiate the early phase of LTP in the CA1 region (Otmakhova and Lisman, 1996), as well as inhibit depotentiation via a cAMP-dependent mechanism in this region (Otmakhova and Lisman, 1998). Bach et al. (1999) reported that memory defects are associated with defects in late phase LTP, and that spatial memory deﬁcits in older mice can be ameliorated with D1/D5 agonists. The effect of dopamine ligands appears to be region speciﬁc. SwansonPark et al. (1999) discovered that the D1-like antagonist SCH23390 attenuated the persistence of LTP in the CA1 region, but not in the dentate gyrus. In contrast, the β-adrenergic antagonist propranolol did not affect the persistence of LTP in the CA1 region, but did affect this measure in the dentate gyrus, along with altering the induction of LTP. Others have found that D1-like receptors may also affect the induction of LTP in the dentate gyrus (Kusuki et al., 1997). These data suggest that memories may be consolidated differently within different regions of the hippocampus and dependent upon different receptor systems. Taken together, these studies illustrate the important role D1-like receptors play in the hippocampus and suggest a potential use for D1-like ligands therapeutically in treating memory disorders.

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5. D1-Like Receptors in the Periphery Whereas D1-like receptors have been studied extensively in the CNS, their role in the periphery is only now becoming clear. D1-like receptors have been found in the cardiovascular system and the kidney (Amenta et al., 1995, 1999; Amenta, 1997), as well as on lymphocytes (Takahashi et al., 1992; Ricci et al., 1999), the superior cervical sympathetic ganglia (SCG) and dorsal root ganglia (DRG; Xie et al., 1998), the adrenal gland (Aherne et al., 1997), and the gastrointestinal tract (Vaughan et al., 2000). In the heart, dopamine increases myocardial contractility and cardiac output, while it causes vasodilatation in the vasculature. In the kidney, dopamine causes natriuresis and vasodilatation. The precise role dopamine plays in these other areas is not yet clear. D1-like receptors have been localized to the superior mesenteric artery and the renal artery and kidney using radioligand binding techniques. The pharmacological characterization of these receptors suggested they are D5, based on the submicromolar afﬁnity of dopamine to these receptors (Amenta et al., 1995). This result awaits further analysis to deﬁnitively make this conclusion. D5 receptors have been identiﬁed in peripheral lymphocytes by mRNA and Western blot analysis, but this study did not detect any D1 receptor mRNA or protein (Ricci et al., 1999), suggesting an essential role for D5 receptors in these cells. D1 and D5 receptors have been found in both the SCG and the DRG by PCR analysis, although their role in these regions is unclear (Xie et al., 1998). Within the gut, D1 receptors are located in the gastroesophageal junction, the stomach, small intestine, and colon (Vaughan et al., 2000). At present, their precise physiological role is unknown, but their cellular distribution suggests they are involved in modulation of motility, ﬂuid and electrolyte balance, and blood ﬂow. As mentioned, this area of D1-like receptor pharmacology and function is only now beginning to be explored. Although this area will undoubtedly be important in any therapeutic endpoint, the focus of this article is the neurobiology of D1-like receptors; several reviews about peripheral D1-like receptors can be consulted (Amenta, 1997; Amenta et al., 1999).

E. MOLECULAR AND BIOCHEMICAL FUNCTIONS OF D1 RECEPTORS 1. Molecular Mechanisms of D1 Receptors The D1 receptor has long been known to alter the function of adenylate cyclase (AC; Kebabian and Calne, 1979), stimulating the enzyme AC to increase the synthesis of cAMP. This measure is still the gold standard

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by which other D1-like receptors are characterized, although it has been shown that dopamine-induced synthesis of cAMP is not demonstrable in every major dopamine terminal ﬁeld, including those in the mesolimbic– cortical systems (Mailman et al., 1986a, 1986b; Kilts et al., 1988). Although it is generally assumed that the D1 receptor achieves this increase in cAMP by activation of the Gα s subunit of the G proteins, evidence suggests that the D1 receptor also (or predominantly) couples to Gα olf. This is based on the fact that in the striatum, nucleus accumbens, and olfactory tubercle, where the D1 receptor is abundantly expressed, there is little Gα s but high levels of Golfα (Herve et al., 1993). It is therefore suggested that the promiscuity of D1 receptors coupling to G proteins may be unappreciated to date. In fact, there is evidence that D1 receptors are able to couple to Gα i proteins in reconstituted phospholipid vesicles (Sidhu et al., 1991) and may interact with another inhibitory Gα i/Gα o protein (Kimura et al., 1995). In addition, D1 receptors have been shown to coimmunoprecipitate with both Gα o (Kimura et al., 1995) and Gα q (Wang et al., 1995). This ﬁnding suggests that, although D1 receptors are able to couple to the stimulation of AC, they may also couple to a variety of signaling systems, depending on the complement of G proteins and other cellular factors within a cell. There is evidence that D1 receptors may also modulate intracellular calcium levels by a number of different mechanisms. The prevailing thought is that this occurs through the stimulation of phosphatidylinositol (PI) hydrolysis by phospholipase C (PLC), which then increases the production of 1,4,5-trisphosphate and alters intracellular calcium stores. A number of studies have shown that stimulation of D1-like receptors does not stimulate PI hydrolysis in either COS-7 (Dearry et al., 1990; Tiberi et al., 1991; Demchyshyn et al., 1995) or CHO cells (Pedersen et al., 1994). However, Undie and Friedman (1990, 1994) have suggested that D1 receptors do increase PI hydrolysis. These data should be viewed with caution, however, as the concentrations at which these effects occurred were extremely high (100 µM ) and all D1-like ligands tested had the same EC50 despite having signiﬁcantly different afﬁnities. Liu et al. (1992) have demonstrated D1 agonist stimulation of PI hydrolysis in Ltk cells at DA concentrations of 1 µM, suggesting that this effect may be real. D1 receptors have also been shown to increase PI hydrolysis in the kidney via a cAMP-independent mechanism (Felder et al., 1989a, 1989b). Evidence suggests that D5 may also be linked to the hydrolysis of phosphatidyl inositol (Friedman et al., 1997). In addition, both human and goldﬁsh D1 receptors have been shown to increase intracellular calcium levels when expressed in HEK cells (derived from embryonic human kidney) via a cAMP-dependent mechanism (Lin et al., 1995). Pacheco and Jope (1997) demonstrated that D1 receptors appear to couple directly to PI hydrolysis in human brain membranes through Gα q/11.

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A second theory suggests that D1 receptors may couple to PKC indirectly. D1-like agonists are able to cause neurite retraction in catﬁsh horizontal cells, an effect that also is observed with the use of phorbol esters or diacylglycerol (Rodrigues and Dowling, 1990). D1 receptors may also couple to calcium channels. D1 receptors have been shown to increase L-type calcium currents in both rat striatal neurons and GH4C1 cells expressing the D1 receptor (Liu et al., 1992; Surmeier et al., 1995), and these effects have been mimicked by both cAMP analogs and blocked by PKA inhibitors. D1 receptor activation has also been shown to reduce calcium currents through N- and P-type calcium channels in rat striatal neurons, with this effect again mimicked by cAMP analogs and blocked by PKA inhibitors (Surmeier et al., 1995). These authors proposed that phosphorylation/dephosphorylation of the channel is the mechanism by which D1 receptors modulate these activities. There is controversy as to whether D1 receptors affect either potassium channels or arachidonic acid (AA) release. In the case of potassium channels, D1-like agonists have been shown to increase potassium efﬂux from chick retinal cells though a cAMP-independent mechanism (Laitinen, 1993), but also to inhibit potassium channel current in rat striatal neurons (Kitai and Surmeier, 1993). In terms of AA release, while Piomelli et al. (1991) did not see an effect of D1 receptors alone on AA release in CHO cells, activation of coexpressed of D1 and D2 receptors in CHO cells led to a synergistic response. An inhibition of calcium-evoked AA release has been observed in striatal primary cultures with D1 agonists, an effect that is mimicked by forskolin. The role of D1 receptors in these areas requires additional study before ﬁrm conclusions can be drawn. D1 receptors are found not only in the CNS, but also in the periphery (Amenta et al., 1995). In the kidney, D1 receptors have been shown to cause an inhibition of the activity of the Na+/H+ exchanger by both cAMPdependent and cAMP-independent mechanisms (Felder et al., 1990, 1993). There is evidence to suggest that the effect of D1 agonists on the Na+/H+ exchanger may involve not only Gα, but also Gβ subunits of G proteins (Huang et al., 1997). In addition, D1 receptors have been linked to the inhibition of the Na+-K+-ATPase pump in the kidney (Bertorello and Aperia, 1990; Shahedi et al., 1995) and the chick retina (Laitinen, 1993). The evidence to date suggests that the D1 receptor can regulate the Na+-K+-ATPase, although the mechanism by which this occurs is dependent on the tissue and cellular location of the receptor. Evidence also suggests that D1 receptors in renal tissue couple to both the Gα s and the Gα q/11 G proteins (Hussain and Lokhandwala, 1997), suggesting a multitude of roles D1 receptors may play in this tissue. Reale et al. (1997) demonstrated that within the same Xenopus oocyte, a Drosophila D1-like receptor can couple to both a calcium-activated chloride

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channel and adenylate cyclase activity. The ﬁrst response is mediated by a pertussis toxin-insensitive G protein-coupled pathway, whereas the AC response is mediated by a pertussis toxin-sensitive pathway that appears to also require Gβ subunits. This study demonstrated that the pharmacological proﬁle of these two second messenger systems is different, suggesting agonistspeciﬁc coupling to these two second messenger systems. D1 receptors have also been linked to the protein calcyon that localizes to the dendritic spines of D1 receptor-expressing pyramidal cells in the prefrontal cortex (Lezcano et al., 2000). This study demonstrates that D1 receptors can couple to both Gsα and Gqα G proteins through its interaction with calcyon, thus raising interesting possibilities of the role of D1 receptors in receptor promiscuity and functional selectivity. 2. Interesting Molecular Differences between D1 and D5 Receptors D1 and D5 receptors couple to a number of different G proteins. It is well established that D1 and D5 couple to Gα s (Kimura et al., 1995; Uh et al., 1998), although there is now evidence that they also may couple to other G proteins, such as Gα z, Gα o, Gα i1, and Gα i2 (Sidhu and Niznik, 2000). The mechanism by which these interactions occurs is not well understood for GPCRs, in general, and for D1 and D5 receptors, in particular. The prevailing theory is that G protein coupling occurs with the receptor through interactions with the third intracellular loop (IC3) and the COOH tail. This hypothesis is based on the fact that while IC loops 1 and 2 of the D1 and D5 receptors are highly conserved, there is signiﬁcant divergence in IC3 and the COOH tails (Civelli et al., 1993; Gingrich and Caron, 1993; O’Dowd, 1993). Unfortunately, there are no known consensus sequences for G protein interaction, and much of this work is based on alteration of receptor structure or alterations caused by agonist stimulation or desensitization. It should also be noted that while the current theory suggests important roles for IC3 and the COOH tail, there is also evidence that other regions of the receptor may play a part in G protein interaction, speciﬁcally IC2, extracellular, and even transmembrane regions (Shenker et al., 1993; Arrigo et al., 1998). When the D5 receptor was originally cloned, one of the interesting ﬁndings was that this isoform appeared to have a tenfold higher afﬁnity for dopamine and 6,7-ADTN and slightly higher afﬁnity for other agonists. In contrast, D1 receptors showed a preference for antagonist ligands (Sunahara et al., 1991). Subsequently, D5 receptors have been shown to exhibit constitutive activity when expressed in HEK293 cells, demonstrated by increased basal activity, compared with D1. This activity is inhibited by (+)butaclamol and ﬂupentixol, but not by SCH23390 (Tiberi and Caron, 1994). Structurally, this constitutive activity appears to be conﬁned to the region of the

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receptor encompassing the third extracellular loop and the COOH tail. Iwasiow et al. (1999) made chimeras of the D1 and D5 receptors, replacing the terminal tail region beginning with TM6 of the D1 receptor with that of the D5 receptor. This chimera exhibited higher afﬁnity for dopamine and also showed constitutive activity indistinguishable from wt D5 receptors. The reverse also appears to apply, as replacing the carboxyy tail region of the D5 receptor with the D1 counterpart creates a receptor similar to the cognate wt D1 receptor. Demchyshyn et al. (2000) have further reﬁned these results, identifying amino acids 438–448 as necessary and sufﬁcient for the observed agonist high-afﬁnity and functional D5 characteristics. Whereas these results are interesting and compelling, it should be noted that these effects are all seen in cell lines, and the importance or signiﬁcance of D5 constitutive activity in vivo has yet to be demonstrated.

III. Development of Drugs for D1-like Receptors

From the perspective of a biologist, drugs are often nothing more than tools to be taken from the shelf for use in experimental or therapeutic applications. In fact, historically, and to a signiﬁcant extent in the modern molecular era, the availability of novel ligands (whether found serendipitously or empirically) has resulted in major contributions to understanding the function of neuroreceptors. In the case of the D1-like receptors, this is certainly true. Prior to the discovery of the ﬁrst selective D1-like agonist and antagonist, the importance of central dopamine D1 receptors was completely unknown and unappreciated. For the purpose of this article, the discussion focuses on agonists for D1-like receptors, a decision primarily based on the fact that D1-like agonists have immense therapeutic potential, whereas D1-like antagonists have not proven useful in a variety of conditions where they have been tested. A caveat, of course, is that there will be tremendous utility, at least in a research sense, for a selective D1 or D5 antagonist if one is developed.

A. EARLY PROGRESS IN DESIGN OF D1 RECEPTOR LIGANDS Much of the early understanding of structure–activity relationships of dopamine receptor ligands came from pioneering work by Cannon (1975, 1985), McDermed et al. (1978), and Seiler and Markstein (1982), among others. Yet at the time of most of their work, it was unclear that there were different classes of brain dopamine receptors, let alone a differentiation

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between D1 and D2 receptors, or isoforms within these subfamilies. Thus, early models were highly inclusive and blurred the important distinctions between D1- and D2-like receptors. Early work in identifying structural determinants for the D1 receptor probably had its clearest origin in studies of renovascular function. In the kidney (but also the mesenteric, coronary, and cerebral vascular beds), a dopamine receptor that mediated vasodilation had been studied extensively by Goldberg and colleagues (1977) at the University of Chicago. That receptor, which they named the DA1 receptor, had very different ligand requirements from the vast majority of dopaminergic ligands known at that time, because none of the classical dopamine agonists, which we now know are D2-like ligands, caused renal vasodilation. Indeed, only ligands that possessed a catechol function had signiﬁcant activity at the DA1 receptor, and then only a limited subset of those were active. Apomorphine (now known to be a nonselective D1/D2 ligand) was one of them, although it was rather weak in its action. In attempts to deﬁne the nature of the DA1 receptor, based on their experiments with hundreds of relatively inactive compounds, and ﬁnding that apomorphine was one of the more interesting ones, Goldberg et al. (1978a, 1978b) ﬁrst had proposed that a “hydrophobic site” might exist on the receptor to accommodate the nonhydroxylated phenyl ring of apomorphine. This possibility was later expanded and reﬁned by Erhardt (1983) to be consistent with the stereochemical arguments presented by McDermed et al. (1978). Although both groups discussed this site in the context of the renal dopamine receptor, neither Goldberg nor Erhardt recognized that this accessory hydrophobic region could be a distinguishing feature to be exploited for the development of DA1 subtype selective ligands. Following the discovery of SKF38393, however, the presence of a hydrophobic accessory site on the receptor seemed even more likely and served as the basis for the working hypothesis that a “β-phenyldopamine” moiety might serve as the pharmacophore for D1-selective ligands (Nichols, 1983; Riggs et al., 1987). Another distinguishing feature between D1 and D2 receptors was that the former lose afﬁnity for ligands when the amine nitrogen is alkylated ( Jacob et al., 1981). By contrast, D2 ligands retain or have enhanced afﬁnity with small N-alkyl groups. One hypothesis offered to explain this observation was that the dopamine D1 receptor required the nitrogen electron pair (or its protonated equivalent) to reside in a pseudoequatorial orientation, whereas for D2 agonists, the protonated pair would occupy a pseudoaxial orientation (Nichols, 1983). This hypothesis, based on empirical observations, received theoretical support some years later from computer-based conformational analysis calculations (Froimowitz and Bellott, 1995).

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The development of dopamine ligand structure–activity relationships through about 1985 has been thoroughly reviewed by Kaiser and Jain (1985), but we have tried here to give a general outline of key developments that ultimately led to the present state of the art. These early studies, and others, served as a solid foundation for more focused development of ligands for D1-like receptors. B. DEVELOPMENT OF SELECTIVE DOPAMINE RECEPTOR LIGANDS 1. SKF38393 The ﬁrst speciﬁc tool to study D1 dopamine receptors, SKF 38393, was initially described in 1978 to be an agonist at the renal DA1 receptor (Pendleton et al., 1978). Later that same year, it was also reported that SKF38393 “may activate only a certain subclass of dopamine receptors including the receptor in the renal vasculature and the adenylate cyclase coupled postsynaptic receptor in the caudate” (Setler et al., 1978). The similarity was therefore more clearly established between the structural requirements of the peripheral DA1 receptor and the central D1 receptor. The importance of exploiting the accessory-binding region to develop selective D1 ligands was also recognized, where the phenyl substituent in 1-phenylbenzazepines related to SKF 38393 might interact with a “chirally deﬁned accessory site” (Kaiser et al., 1982) Based on stereochemical arguments, Kaiser et al. (1983) proposed two alternate possible modes of receptor binding for the 1-phenylbenzazepines, one of which would be compatible with presently accepted ideas about the nature of the dopamine D1-binding site. 2. SCH23390 Reports in 1983 that the phenylbenzazepine compound SCH 23390 was a selective dopamine D1 receptor antagonist (Cross et al., 1983; Iorio et al., 1983) were followed the next year by the publication of 25 papers that employed this new antagonist to begin mapping the functions of the D1 receptor. Clearly, the availability of a new molecular tool is tremendously important to understanding the function of any biological target, and SCH 23390 still remains the primary antagonist for D1-like receptors, with more than 3000 papers having reported using it since its discovery. Both the antagonist SCH 23390, as well as the agonist SKF 38393, were apparently discovered as a result of serendipity, not through any systematic program of drug design. Nevertheless, these two compounds represented a breakthrough in the characterization of D1 receptor function. They were molecular structures that were exceedingly important in further developing a conceptual model of the dopamine D1 receptor.

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The most important structural feature of both of these 1-phenylbenzazepines was the appended phenyl ring, and it was this moiety that appeared to confer dopamine D1 receptor selectivity onto these molecules. Studies in 1989 with a related analog, SCH39166, clearly established that the pendant phenyl ring resides in a pseudoequatorial orientation, with the aromatic ring twisted essentially in a plane perpendicular to the substituted benzazepine phenyl ring (Berger et al., 1989), as had been predicted by Charifson et al. (1988, 1989). It is currently believed that the orientation of this pendant phenyl ring in SCH 23390, and a similar conformation in SKF 38393, is one of the factors responsible for the antagonist or partial agonist activity, respectively, of these two molecules.

C. FOUNDATION FOR PHARMACOPHORIC MODELS OF DOPAMINE RECEPTOR LIGANDS Ligand-based design of dopaminergic ligands has beneﬁted greatly from the fact that a large number of dopamine-like agents actually incorporate elements of the dopamine structure. This circumstance contrasts to many other classes of therapeutic agents where a synthetic agonist may bear little resemblance to the endogenous ligand. Although in some cases this analogy is not readily obvious—for example, with the dopaminergic ergolines— upon closer examination, the resemblance becomes more evident (Nichols, 1976). Whereas classes of D2-like dopaminergic drugs diverge most widely from the basic dopamine structure, agonists for the dopamine D1 receptor generally incorporate a very obvious dopamine fragment, even to the requirement (at least to date) that they possess a catechol moiety to be full agonists. The serious bioavailability problems created by earlier drugs containing a catechol moiety now have been solved in at least a few cases (e.g., Gulwadi et al., 2001). We have presented our view of the state of the ﬁeld (Mailman et al., 1997), but a few issues that are relevant to the current interest in D1 receptors as therapeutic targets merit discussion. 1. Biological Data Lead to Increased Interest in Drug Design for D1 Receptor The models discussed in the previous section served as a focus for many medicinal chemists, but by the mid-1980s, neuropharmacological advances made these matters of more than heuristic interest to a variety of neuroscientists. Following the demonstration of the critical role of the D1 receptor in brain function (Christensen et al., 1984; Mailman et al., 1984; Clark and White, 1987), a variety of converging lines of evidence made it clear that the pharmacological armamentarium available for characterizing the signiﬁcance of this receptor was woefully lacking, for both basic investigation

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of the function of this receptor and potential clinical uses. Although the selective antagonist SCH23390 had been reported (Cross et al., 1983; Iorio et al., 1983), the available agonists were also of the same structural class (i.e., 1-phenyltetrahydrobenzazepines), and most were either of partial intrinsic activity in rat striatum (Setler et al., 1978; Lovenberg et al., 1989) or had other pharmacological limitations (Truex et al., 1985; Andersen et al., 1987). It was for this reason that we began to focus on the atomic and molecular factors involved in ligand recognition by the D1 receptor, as well as the mechanisms governing whether a ligand was a full or partial agonist, or antagonist. Coincidentally, it was during this same period that the combination of improved software and affordable hardware allowed the ﬁrst widespread use of computer-assisted molecular drug design. Our group had, as a particular goal, the development of “new and improved” ligands that either had speciﬁc functional characteristics (e.g., being a full agonist) and/or were selective for molecular isoforms of the receptor (i.e., D1A versus D1B). The D1 receptor model proposes that several factors inﬂuence the afﬁnity of a ligand for the receptor and the ability of a ligand to act as an agonist. As discussed above, an intrinsic feature of this model is the so-called “hydrophobic accessory region.” One inherent hypothesis in this model is that a key determinant of whether a ligand is a partial agonist or antagonist, versus a full agonist, is the nature of the interaction of the ligand with this hydrophobic accessory region. In full intrinsic activity agonists, this ring must be near coplanarity with the catechol ring. The ethylamine fragment must also be in a trans, extended beta conformation. In antagonists, although structural determinants are less well understood, later ligand-based modeling of D1 antagonists and analogs also suggested that the size and shape of the hydrophobic group, as well as its spatial orientation, were of importance (Charifson et al., 1989). Based on this scheme, one of our foci has been on modeling the agonist pharmacophore, with special concern for functional groups that may be required for activation of the D1 receptor. These efforts resulted in the synthesis of dihydrexidine, the ﬁrst highpotency full D1 agonist. (The structures of several D1 agonists, including dihydrexidine and the partial agonist SKF38393, are shown in Fig. 3.) Not only did dihydrexidine have signiﬁcant afﬁnity for the D1 receptor (K0.5 = 10 nM), but unlike available D1 agonists it was equal to dopamine in activating the D1 receptor in striatal membrane preparations (Lovenberg et al., 1989; Brewster et al., 1990). Later studies have shown that dihydrexidine appears to have full intrinsic activity, in that it has efﬁcacy equal to dopamine in every preparation tested, including those with little or no receptor reserve (Lovenberg et al., 1991; Watts et al., 1993a; Gilmore et al., 1995; Watts et al., 1995a). Relative to its use as a drug in research, dihydrexidine was also shown to be bioavailable to brain after parenteral administration.

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FIG. 3. Structures of prototypical D1 agonists. Comparison of the structure of the active enantiomer of dihydrexidine (the ﬁrst high-potency ﬁll-intrinsic-activity D1 agonist) with that of SKF38393, the prototypical (albeit partial) D1 agonist. Also shown are the structures of four D1 full agonists that are discussed in this chapter. Note the structural similarity between 2-methyldihydrexidine and A86929.

These data provided the basis for using dihydrexidine as a tool to study brain function. It is particularly noteworthy that full agonists have, in several paradigms, been reported to cause functional effects in the intact organism not seen with agonists of less than full intrinsic activity (Arnsten et al., 1994; Schneider et al., 1994; Taylor et al., 1991). The mechanism(s) for such effects are unclear, but make these types of studies of some importance. 2. D1 Agonist Pharmacophore We have used computer-aided conformational analysis to reﬁne the agonist pharmacophore for D1 dopamine receptor recognition and activation that led to the synthesis of dihydrexidine (Mottola et al., 1996). These modeling efforts relied on dihydrexidine as a structural template for determining molecular geometry because it was not only a high-afﬁnity full agonist, but it had limited conformational ﬂexibility relative to other more ﬂexible, biologically active agonists. Using the active analogue approach (AAA) to pharmacophore building (Marshall et al., 1979), conformational analysis and molecular mechanics calculations were used to determine the lowest energy conformation of the active analogs (i.e., full agonists), as well as the conformations of each compound that displayed a common pharmacophoric geometry. We hypothesized that dihydrexidine and other full agonists may share a D1 pharmacophore made up of two hydroxy groups,

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the nitrogen atom (ca. 7 A˚ from the oxygen of the meta-hydroxyl) and the accessory ring system characterized by the angle between its plane and that of the catechol ring (except for dopamine and A77636). For all full agonists studied (dihydrexidine, SKF89626, SKF82958, A70108, A77636, and dopamine), the energy difference between the lowest energy conformer and those that displayed a common pharmacophore geometry was relatively small (<5 kcal/mol). The pharmacophoric conformations of the full agonists were also used to infer the shape of the receptor binding site. Based on the union of the van der Waals density maps of the active analogs, the excluded receptor volume was calculated. Various inactive analogs (partial agonists with D1 K0.5 > 300 nM) were subsequently used to deﬁne the receptor essential volume (i.e., sterically intolerable receptor regions). These volumes, together with the pharmacophore results, were integrated into a three-dimensional model estimating the D1 receptor active site topography (e.g., see Fig. 4). Another full intrinsic activity D1-selective agonist that we have named dinapsoline was designed using this receptor model. Dinapsoline only has very minor structural and spatial deviations from dihydrexidine, but does

FIG. 4. Excluded volume for the D1 agonist pharmacophore. The mesh volume shown by the black lines is a cross section of the excluded volume representing the receptor-binding pocket. Dihydrexidine (see text) is shown in the receptor pocket. The gray mesh represents the receptor essential volume of inactive analogs. For additional details, see Mottola et al. (1996).

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have somewhat increased afﬁnity for D2 receptors (Ghosh et al., 1996; Lewis et al., 1998; Gulwadi et al., 2001). Importantly, dinapsoline appears to have not only a behavioral proﬁle in rats similar to dihydrexidine, but also signiﬁcant oral availability (Gulwadi et al., 2001). In the future, it is likely to become an important research tool. The ability of this D1 receptor model to be predictive of activity gives validity to our approach and indicates that, as we reﬁne the model further by accommodating additional full agonists with different structures, it should gain further predictive utility. In conjunction with receptor modeling efforts, the features of this model may become very important in receptor docking studies, because the essential and excluded volumes of the model must be explainable within the context of the amino acid residues that comprise the receptor binding domain.

D. CURRENT ISSUES IN D1 -LIKE DRUG DESIGN Although for primary amines and secondary amines, the accessory phenyl ring of molecules such as SKF38393 and dihydrexidine seems to confer D1-like receptor selectivity, when an N-n-propyl group is placed on the basic nitrogen of dihydrexidine the molecule becomes a potent D2-like ligand. This ﬁnding suggests some basic reorientation of the ligand in the receptor must occur when the nitrogen atom is alkylated. If the conserved aspartate in helix 3 binds to the basic nitrogen atom, it seems quite possible that it could interact with the protonated amine either from an equatorial or axial direction, with respect to the plane of the ligand molecule (Nichols, 1983). These different binding approaches would clearly necessitate a reorientation of the ligand within the recognition domain of the receptor, and the accessory phenyl (or other hydrophobic group) might ﬁnd complementarity in the D2 receptor, whereas it was not tolerated within the binding cavity of the D1 receptor. It will be interesting to see how easily structure-based design can accommodate these apparently divergent binding orientations. Indeed, the ability to accommodate them will be a key test of the validity of any dopamine receptor models. In the ideal world, selective agonists and antagonists could be derived from an understanding of the structures of the relevant receptors. The threedimensional organization of the D1-like receptors, indeed of any of the mammalian GPCR neuroreceptors, is not known to sufﬁcient resolution at this time. Although it is theoretically possible to use computational algorithms and molecular graphics techniques to solve such problems, hindering such efforts is something termed the “protein folding problem.” It has been known for decades that, in vitro, denatured protein can often, under appropriate conditions, spontaneously refold into a correct and functioning

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three-dimensional structure. These data suggest that the three-dimensional structure of the protein somehow is encoded solely in the amino acid sequence. Clearly, a computational solution to the protein folding problem would instantly (at least given enough computer hardware) convert the huge genomic database into a gold mine of structural information that could be used for many purposes. Utility for these data would include not only rational drug design, but also the development of fundamental understanding of biomolecular mechanisms, as well as new approaches to gene therapy and protein engineering. The lack of a validated structure obviously complicates attempts at ligand docking that would theoretically allow design of new isoform-selective ligands. In practice, however, solely computational approaches to complex systems [e.g., the G protein-coupled receptors (GPCRs)] have failed, although semiempirical approaches combining computation and experiment have made many advances, albeit often painful. The prediction of threedimensional structure from sequence information has been approached using existing structural data, and also by attempting de novo folding via physical simulation and the exploration of a large conformational energy landscape. Methods using existing structural knowledge include secondary and tertiary structure prediction based on local sequence information— helix, sheet, turn propensity matrices, and homology model building. Homology modeling has probably been the most widely used and successful, at least in the area of drug design. This technique can often be employed when the three-dimensional structure of the target receptor is unknown, but a highly related protein structure is known. In the absence of any experimental information on the threedimensional structure of D1-like receptors, predictions from primary sequence remain the only source of generating the receptor structure. Earlier attempts to predict the structure of the GPCRs were limited to seven general transmembrane cylinder models based on the easily identiﬁable stretches of largely hydrophobic amino acid residues in the primary sequence of these receptors. Although there was no sequence homology between any of the GPCRs and bacteriorhodopsin, the alpha-helical backbone of bacteriorhodopsin was nevertheless used as a seven-helix scaffold on which to build tentative GPCR models. Determination of a high-resolution crystallographic structure for bovine rhodopsin (Palczewski et al., 2000), a sevenhelix transmembrane protein that possesses important sequence homology to GPCRs in the transmembrane regions, has assisted attempts to model the GPCRs at atomic resolution and illustrated the problems in the ﬁeld (i.e., by showing the limitations of homology modeling that had been based on bacteriorhodopsin). Early molecular modeling studies of GPCRs have been primarily concerned with ﬁnding reasonable orientations, register, and amino

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acid contacts between seven transmembrane helices. The high-resolution structure of bovine rhodopsin has resolved many of these uncertainties. The major feature of each particular receptor that distinguishes it from other receptors, however, is the ability to bind selectively ligands speciﬁc to that receptor. In early studies, a single ligand, most often a native neuromediator (e.g., dopamine), was docked into the postulated binding site, mainly to demonstrate that it could be reasonably well accommodated there. It has been recognized (Nordvall and Hacksell, 1993; Teeter et al., 1994), however, that accurate receptor modeling can only be realized if the homology model building is combined with the results of ligand-based pharmacophore receptor modeling. Thus, the active site in the proposed model must be able to accommodate all known active receptor ligands in their pharmacophoric conformation. Because the structures of the dopamine receptors were not solved at the time of the publication of this article, there have been numerous attempts to develop models of dopamine receptors and their ligand recognition sites (Livingstone et al., 1992; Trumpp-Kallmeyer et al., 1992; Malmberg et al., 1994; Teeter et al., 1994). Without substantial empirical data, however, such modeling can be misleading, resulting in models that are later shown to be inconsistent with physical data. Hibert et al. (1991) developed a simple D2 receptor model along with several models for other GPCRs and suggested that the active site is formed between helices 3, 4, 5, and 6 in the neighborhood of Asp 308 (near the top of transmembrane helix 3); the latter residue is believed to be responsible for interaction with the cationic portion of ligand molecules. The authors have shown that dopamine can be easily accommodated in the active site of their model such that the cationic head of the ligand interacts with Asp 308, and the rest of the ligand binds into a hydrophobic pocket formed by the adjacent aromatic residues in helices 4, 5, and 6. Livingstone et al. (1992) modeled D2, D3, and D4 receptors, and have shown that the proposed models may account for afﬁnity but not receptor speciﬁcity of dopaminergic ligands. The latter problem represents a challenge for molecular modelers; its resolution requires more sensitive construction of the active sites of different receptor subtypes, taking into account differences in their primary sequences. Detailed molecular models of D2 and D3 dopamine receptors were also developed by Malmberg et al. (1999). These authors showed that a number of 2-aminotetralins could be docked into the proposed active site of both receptor models so their protonated nitrogen interacts with Asp 308, the aromatic ring of the ligands makes face-to-edge interaction with a phenylalanine in TM 6, and the hydroxyl groups of the ligands interact with a serine residue in TM 5. Thus, the model was able to explain qualitatively the existing structure–activity

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relationships of known aminotetralins. This model agrees with other models of dopamine receptors in terms of active site residues involved in interaction with the ligands. These authors also concluded that agonists have a different mode of binding than antagonists. Site-directed mutagenesis data from other receptors (e.g., the 5-HT2A receptor; Choudhary et al., 1993), are consistent with this suggestion, and there is general awareness that in most cases agonists and antagonists do not engage identical residues, even though there may be some binding site overlap (e.g., the conserved ASP residue near the top of TM 3). In newer models under development based on bovine rhodopsin, helix 4 is placed on the outside of the helical bundle, “behind” helices 2 and 3, and does not appear to interact directly with agonist ligands. Rather, helices 3, 5, 6, and perhaps 7 appear to be the principal portions of the receptor involved in agonist binding. This reﬁnement from older bacteriorhodopsinbased homology models does not change many of the previous conclusions, however, because key residues identiﬁed as important for ligand recognition remain the same; most of those data were based on site-directed mutagenesis studies. Thus, the chief difference between older models and those that are now being developed around the high-resolution bovine rhodopsin structure lies in the overall helical packing arrangement, not with speciﬁc residues involved in ligand recognition. Thus, many of the ideas formulated in the older models probably still have general validity, at least with respect to the residues in the binding site. With regard to the adequacy of all model building, a major criterion should be the ability to dock structurally diverse ligands. Thus, for example, a major limitation of the models of Malmberg et al. (1994) is that it is focused only on one class of compounds, the aminotetralins. Prior to the submission of their work, our group had reported that one analog of dihydrexidine (N-n-propyl-4-methyldihydrexidine (4-MNPrDHX) had approximately the same D3/D2 selectivity as 7-OH-DPAT (Watts et al., 1993b). Because both 4-MNPrDHX and dihydrexidine are semirigid ligands having markedly different D2/D3 selectivity, it would seem that they provide an important test of any receptor model purporting to predict the D2 and D3 active sites. In the majority of papers referenced above, the active site residues are guessed from the alignment of the receptor and bacteriorhodopsin helices. An interesting and promising approach to more accurate mapping of the active site residues has been extensively employed by Javitch and coworkers (1998; Javitch, 1998a,b). This approach, called the substituted-cysteine accessibility method (SCAM), affords identiﬁcation of residues that are exposed in the binding site crevice of the receptor by systematic mutation of each residue to a cysteine, followed by chemical reaction with a methylthiosulfonate reagent that covalently labels only cysteine residues. Using

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this method, the authors ﬁrst showed that a cysteine in the middle of the third transmembrane segment of the D2 receptor is exposed in the active site ( Javitch et al., 1994). Later, by site-speciﬁc mutation of a number of residues within various transmembrane domains to cysteines, they were able to identify residues in the active site that were accessible to the external aqueous phase. When graphically superimposed on a homology-based receptor model built on the three-dimensional structure of bovine rhodopsin, these accessible residues show a high correlation with experiments that have identiﬁed putative binding domains in the receptor using other approaches. These experiments provide important constraints for the reﬁnement of the alignment and mutual orientation of the receptor helices. The modeling of GPCRs remains an exceptionally challenging task. The most advanced models obtained so far have been able to combine and explain various pieces of information about these receptors coming from homology model building, site-directed mutagenesis, and pharmacophore modeling. The real utility of these models for drug design, however, can only be assessed if they possess the power to predict structures of novel receptor ligands of high-afﬁnity and predictable functional characteristics. Thus far, no such studies have been reported in the literature. Despite this fact, the growing body of experimental information on receptors and their ligands, combined with rapid developments in the area of database searching and receptor-based drug design, promise that the quality of the models will ultimately be improved to afford such new leads.

IV. Therapeutic and Functional Actions of D1 Receptor Agonists and Antagonists

A. MODELS OF PARKINSON’S DISEASE Denervation models have been widely used in studying the nervous system and in developing models of neurological and psychiatric diseases, as well as for elucidating mechanisms of drug action. With regard to Parkinson’s disease, the most widely used model is that ﬁrst described by Ungerstedt (1971a). In this model, a unilateral lesion of the nigrostriatal dopaminergic pathway is produced by injection of 6-OHDA into one substantia nigra of the rat. This lesion results in a permanent depletion of dopamine in the ipsilateral striatum. After recovery, behavioral supersensitivity (i.e., contralateral circling) is seen when the rat is challenged with a direct-acting dopamine agonist. This easily measurable and robust pharmacological response has made this model of great interest to neuroscientists studying dopamine receptors and other aspects of dopamine systems. Moreover,

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because Parkinson’s disease is also a disorder largely of dopamine deﬁciency, this rat system has been frequently called a rat model of Parkinson’s disease. The mechanism underlying increased sensitivity to dopamine agonists on the lesioned side is of particular relevance because it is this response that is used to predict human clinical efﬁcacy. For many years, the focus was on receptor changes, in large measure because there was an increase in striatal dopamine D2 receptor density ipsilateral to the 6-OHDA lesion (Creese et al., 1977; Goldstein et al., 1980; Mishra et al., 1980; Heikkila et al., 1981). Such denervation also resulted in profoundly enhanced responsiveness to administration of agonists for the denervated system. Such “denervation supersensitivity” was once believed to be explicable by receptor up regulation (increases in the density of receptor sites), and concomitant functional changes in second-messenger systems (e.g., cAMP) linked to these receptors (Sporn et al., 1976). In fact, several reports have suggested that receptor up regulation may not be a principal or universal mechanism of response to such denervation. With the unilateral 6-OHDA model, Staunton et al. (1981) reported dissociating rotational behavior from dopamine receptor changes. Finally, it has been reported that intracisternal or bilateral injection of 6-OHDA can also cause profound behavioral supersensitivity to agonist challenge, yet change neither density nor afﬁnity of D1 or D2 receptors (Mileson et al., 1991). Although the “supersensitivity” in the unilateral 6-OHDA model may not be linked directly to changes in receptor properties, the model is still widely viewed to be a valid predictor of human antiparkinsonian responses. Interestingly, the model shows robust responses to a variety of pharmacological perturbations that are known to be involved in the rat basal ganglia circuitry. This includes not only D1 and D2 agonists, but also antagonists for cholinergic (Ondrusek et al., 1981; Olianas and Onali, 1996), glutamate (Loschmann et al., 1991; Morelli et al., 1992; Loschmann et al., 1997), adenosine (Vellucci et al., 1993), 5-HT2C (Fox and Brotchie, 1996; Fox et al., 1998), and opioid (Matsumoto et al., 1988) receptors. It is probably of use to note that while all these receptor classes have been suggested as novel targets for antiparkinsonian drugs based on this rat model, the results in primate have been less impressive. In general, drugs that cause robust responses in the rat unilateral 6-OHDA model have often given modest responses at best in human or nonhuman primates even after promising early data. Thus, drugs ranging from the new dopamine D2/D3 agonists to the NMDA antagonist remacemide have been found to have modest effects that do not approach that of the current gold standard levodopa (Pinter et al., 2000). Although the unilateral 6-OHDA model has clear heuristic value (e.g., for studying sensitization, priming, tolerance, etc.), in our view it seems to have failed the test of being a rat “model” of Parkinson’s disease (Parkinson Study Group, 2000, 2001).

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Although often unstated, it was many of these facts that led to the wide acceptance of the MPTP-nonhuman primate model, coupled with the fact that MPTP caused a phenotype in humans and monkeys that closely resembled idiopathic Parkinson’s disease (Davis et al., 1979; Langston et al., 1983; Bedard et al., 1992). This model has been used and reviewed widely, and the reader is directed to those sources for further detail. One point is worthy of note, however, and that relates to the unilateral versus bilateral (systemic) MPTP models. Some of the inconsistencies in the literature may be a result of the disparities between these models, a point to which the rat 6-OHDA models may offer guidance. For example, Smith et al. (1993) have reported that the hemiparkinsonian model has a limited subset of parkinsonian features, and it has been reported that the rotation behavior in hemiparkinsonian animals does not correlate well with parkinsonian behaviors (Wolters et al., 1988; Smith et al., 1993). Another model has been suggested to be of use, that induced by high-dose chronic administration of rotenone (Betarbet et al., 2000). Although this model has several interesting features, neither it or other models of environmental toxicant exposure (e.g., manganese, organophosphates, etc.) seem to have the face validity in terms of behavioral phenotypes that is found in the MPTP models. In our view, the bilateral MPTP model (at least to date) has best predicted human therapeutic responses. For this reason, we give it highest face validity in our evaluation of the role of D1 receptors as antiparkinsonian drugs.

B. D1 RECEPTORS AND PARKINSON’S DISEASE 1. Etiology and Treatment of Parkinson’s Disease Parkinson’s disease is a progressive degenerative disease affecting more than 1 million Americans. The disease is typically characterized by resting tremor, muscle rigidity, bradykinesia, and postural instability. It is caused principally by the degeneration of the dopaminergic cells in the substantia nigra pars compacta, with consequent loss of dopamine terminals in the striatum. The ideal therapy would be either to prevent the death of these dopamine neurons or to replace them with transplanted cells that could subserve critical functions (e.g., the synthesis and release of dopamine). Although there is promising research underway toward these ends (e.g., neuroprotective agents, fetal transplants, or gene therapy), these directions presently remain unproven. Two other approaches that have been used are neurosurgical ablation techniques (e.g., ventral pallidotomies) and deep brain stimulation (Mendis et al., 1999). Deep brain stimulation has gained popularity in the past 5 years, because, unlike pallidotomies, the procedure

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is theoretically reversible. Unfortunately, there are still inherent risks, including infection, bleeding, possibilities of breakage, or malfunction of the machinery, and the requirement for battery replacement every 5 years (e.g., Limousin et al., 1995; Olanow et al., 2000). In the absence of a “curative” intervention, the most widely used treatment for Parkinson’s disease is pharmacotherapy. “Dopamine replacement” using levodopa is dramatically effective for several years in early stage of the clinical disease, and efﬁcacy can be improved with better adjunct therapies (e.g., decarboxylase or COMT inhibitors; Kurth and Adler, 1998). Yet, the dopamine replacement approach as currently implemented begins to fail after a few years, with concomitant decreases in efﬁcacy (e.g., wearing off, freezing) and increases in side effects (e.g., dyskinesias, mental changes, “onoff” phenomena; Kopin, 1993a). The eventual loss of efﬁcacy of levodopa has been suggested to occur due to cumulative loss of dopamine cells as the disease progresses (Muenter et al., 1974; Marsden and Parkes, 1976; Spencer and Wooten, 1984; Nutt, 1987). With few remaining dopamine cells and terminals, less levodopa can be taken up by dopamine cells, converted to dopamine, stored, and/or released. The “on-off” state has been attributed most frequently to changes in receptor sensitivity and responsivity (Kopin, 1993b), although pharmacokinetics may be involved (Nutt, 1987). An obvious therapeutic direction would be to bypass the need for the metabolic conversion of levodopa to dopamine and use direct-acting dopamine agonists. There has been a great deal of scientiﬁc debate since the early 1980s about which dopamine receptor subtypes play the key role in treatment of PD. The parallel between parkinsonism induced by typical antipsychotic drugs (D2 dopamine antagonists) and some of the signs and symptoms of Parkinson’s disease, led to the widely held belief that the beneﬁcial actions of levodopa were due primarily to activation of D2-like receptors. In fact, a number of direct-acting D2 selective and nonselective dopamine agonists (Table I) have been developed and approved for clinical use (Calne, 1999; Hobson et al., 1999). The efﬁcacy of selective or nonselective D2 agonists has been largely disappointing, especially in late-stage, severely disabled PD patients. The use of these dopamine agonists is either limited to early stages of the disease to delay the need for the levodopa or as an adjunctive to levodopa therapy. Apomorphine is the only dopamine agonist in clinical use that has been shown to be efﬁcacious for severe PD patients by reducing daily off time by about 50% and dyskinesia in longer-term use (Poewe et al., 1988) when administered by subcutaneous infusion. Apomorphine is a mixed dopamine agonist (full efﬁcacy at D2-receptors and partial efﬁcacy at D1 receptors). Its clinical use as an oral formulation is limited by drug-related nephrotoxicity with azotemia, although the rapid onset of action with short duration has

TABLE I CURRENT CLINICALLY AVAILABLE DRUGS WITH D1 AGONIST PROPERTIES AND COMPARISON WITH CLINICALLY APPROVED DOPAMINE AGONISTS Generic name (U.S. trade name)

Comments

Receptor proﬁle

Pharmacodynamic and pharmacokinetics

Apomorphine

Oldest of the dopamine agonists

D1: modest efﬁcacy, partial agonist D2 agonist

Rapid onset, short duration; half-life: 0.5 h; only in use with SQ or experimental IV

Bromocriptine (Parlodel)

Longest in clinical use; ergoline derivative

Low efﬁcacy D1: agonist D2: agonist

Half-life: 7 h

Lisuride

Also a central serotonin agonist; ergoline derivative

D1: slightly partial antagonist D2: agonist

Pergolide (Permax)

Ergoline derivative

Cabergoline

New ergoline derivatives

D1: weak agonist D2: strong agonist D3: strong agonist D2 agonists

Half-life: 2–4 h Easily water soluble, allows its use as an oral, IV solution, and SQ or IV continuous infusion Half-life: 15–42 h But still requires bid to tid dosing Half-life: 65 h Can be given qd

Ropinirole (Requip)

Nonergoline derivatives

D2 agonist

Half-life: 6 h

Pramipexole (Mirapex)

Nonergoline derivatives

D2 agonist D3 agonist

Half-life: 13 h

Human experiences 1. Has been used as a challenge test in an attempt to establish the diagnosis and predict responsiveness to dopaminergic therapy 2. Subcutaneous administration, intermittently or by continuous infusion, has been shown to reduce daily off time by about 50% 1. Used extensively as an add-on therapy to levodopa 2. Monotherapy at the initiation of therapy for early PD 1. De novo monotherapy early and add on later to levodopa 2. Continuous SQ or IV infusion may beneﬁt severely ﬂuctuating patients Adjunct to levodopa treatment and de novo monotherapy at the initiation of therapy for early PD patient (ref) 1. Monotherapy in early PD 2. Adjunctive therapy for late PD with motor ﬂuctuations 1. Modest efﬁcacy as monotherapy in early PD 2. Adjunctive therapy (reduces levodopa dose by 20–30%) 1. Monotherapy in early PD 2. Adjunctive therapy to levodopa in patients with motor ﬂuctuations

Limitations 1. Oral formulation associated with nephrotoxicity with azotemia 2. Nausea, vomiting, and orthostatic hypotension require treatment with domperidone 3. Domperidone is not available in United States Pleuropulmonary and/or retroperitoneal ﬁbrosis, especially at high dose

As bromocriptine Recurrent dyskinesias, increase in “off” period, psychosis, and technical inconvenience As bromocriptine Nausea, vomiting, somnolence, and psychiatric disturbance As bromocriptine Nausea, vomiting, visual hallucinations, orthostatic hypotension, and dyskinesias Peripheral edma Nausea, vomiting, orthostatic hypotension

Nausea, vomiting, hallucinations, somnolence

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made the compound useful as a diagnostic tool for predicting responsiveness to levodopa treatment (Poewe et al., 1993; Colzi et al., 1998). Its use has been limited by side effects, including severe nausea, vomiting, orthostatic hypotension, and local skin irritation, plus the inconvenience of the pump required for drug administration (Kapoor et al., 1990; Van Laar et al., 1992; Hughes et al., 1993; Van Laar et al., 1996). Apomorphine use in the United States is further limited by the fact that domperidone, a peripheral D2 dopamine antagonist that is used in Europe to prevent these side effects, is not available in the United States. One hypothesis focused on the D3 dopamine receptor. Two drugs with D3/D2 selectivity received FDA approval, but neither pramipexole nor ropinirole offered marked advantages after controlled trials (Hubble et al., 1995; Shannon et al., 1997). In general, the idea widely extant in the late 1980s and early 1990s, that D2 receptors should be the primary target for therapy of Parkinson’s disease, was clearly found lacking. Thus, levodopa, in combination with carbidopa (a peripheral DOPA decarboxylase inhibitor), remains the most effective pharmacotherapeutic strategy. 2. Early D1 Agonists in PD and PD Models Although occupancy of D2 receptors by antagonists can cause druginduced parkinsonism, it has become clear that selective D2 agonists do not provide a dramatic attenuation of signs and symptoms of Parkinson’s disease, especially as the disease progresses. In the early 1980s, the involvement of D1 receptors in motor control and the interaction of D1 and D2 receptors was demonstrated (Mailman et al., 1984), suggesting a possible role for D1 agonists in the treatment of PD. Yet, studies with the selective D1 agonist SKF38393 in Parkinson’s disease failed to demonstrate antiparkinsonian actions in the bilateral MPTP primate model (Close et al., 1985; Falardeau et al., 1988; Nomoto et al., 1988; Bedard and Boucher, 1989; Boyce et al., 1990; Gomez-Mancilla and Bedard, 1991; Elliott et al., 1992; Blanchet et al., 1993), hemiparkinsonian monkeys (Domino and Sheng, 1993), or humans (Braun et al., 1987). Moreover, studies have shown that SKF38393 decreases the efﬁcacy of levodopa and the D2 agonist quinpirole (Nomoto et al., 1988; Bedard and Boucher, 1989; Elliott et al., 1992) when coadministered with these compounds in MPTP-treated monkeys. The only beneﬁcial effect of SKF38393 in MPTP-treated primates has been a reduction in the gradual loss of efﬁcacy that occurs when bromocriptine is administered alone (Rouillard et al., 1990; Bedard et al., 1993). Another D1 agonist that was tested extensively was CY208-243, an agent that failed to show dramatic antiparkinsonian effects in humans (Temlett et al., 1989; Tsui et al., 1989; Emre et al., 1992) despite some promising effects in monkeys (Temlett et al., 1988, 1991; Nomoto and Fukuda, 1993; Gomez-Mancilla et al., 1993). Although CY208-243 is most

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effective against tremor in human subjects (Tsui et al., 1989), it has been reported to have no effect on tremor in MPTP-treated African green monkeys (Gomez-Mancilla and Bedard, 1991). One interesting discrepancy is that CY208-243 had marked antiparkinsonian activity in the MPTP-treated monkey and modest effects in humans, whereas SKF38393 did not. Although several pharmacokinetic parameters have been suggested to account for these differences (Temlett et al., 1988), other factors are probably of greater importance. Some researchers have argued that CY208-243 has little selectivity for D1 versus D2 receptors (Andersen and Jansen, 1990), whereas SKF38393 is more D1 selective (Andersen and Jansen, 1990). Thus, the antiparkinsonian effects of CY208-243 may be dependent on its D2 properties, despite the fact that the drug is believed to be devoid of signiﬁcant D2 agonist activity (Markstein, 1988). Pharmacological studies have, however, not supported this idea. Temlett et al. (1988) found that CY208-243 caused effects that were similar, if perhaps lower in efﬁcacy, than levodopa and the D2 agonist PHNO, whereas SKF38393 was without effect. The actions of CY208-243 were blocked by pretreatment with the D1 antagonist SCH23390 and, to a lesser extent, by the D2 antagonist sulpiride, suggesting that CY208-243 is not dependent on coactivation of D2 receptors. The failure of the early D1 agonists to demonstrate dramatic effects in PD pharmacotherapy, coupled with the parkinsonism caused by D2 antagonists, led to the generally accepted view that “most investigators believe that the beneﬁcial effects of levodopa (and other dopaminergic agonists) in parkinsonism are mediated via D2 receptors” (Cederbaum and Schleifer, 1990). One of the important scientiﬁc issues that has been frequently ignored in attempting to resolve contradictory data are the speciﬁc characteristics of the drugs being used. Thus, although both SKF38393 and CY208-243 are partial agonists, their relative efﬁcacy is seldom considered. The fact that SKF38393 is a relatively low-efﬁcacy agonist at D1 receptors may well explain why it sometimes actually may have antagonist-like pharmacological effects (Elliott et al., 1992). Clearly, the availability of a full D1 agonist would be a powerful tool for exploring these issues. 3. Dihydrexidine Tests the Hypothesis of Antiparkinson Actions of Full D1 Agonists As described in Section III.C.2, more than a decade ago, the collaboration of the authors’ laboratories led to the design and synthesis of dihydrexidine (DHX), the ﬁrst high-afﬁnity full D1 dopamine receptor agonist (Lovenberg et al., 1989; Brewster et al., 1990; Mottola et al., 1992). This permitted testing of the hypothesis that unlike partial D1 agonists, full D1 agonists would have particular utility in the treatment of Parkinson’s disease. We tested DHX in ﬁve adult male monkeys (Cercopithecus aethiops sabaeus) who

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had been injected with MPTP (cumulative dose of 2.0 mg/kg, IM) 2 months before the study. Behavioral observations were made “blind” at 10-min intervals before and after im injections of dihydrexidine or saline. Doses of dihydrexidine ranged from 0.3 to 0.93 mg/kg (cumulative). Dihydrexidine or saline was administered intramuscularly in a balanced pseudorandom sequence over 2 days. The status of the monkeys was assessed using a Parkinsonian summary score that represented the sums of tremor, freezing, difﬁculty eating, food response, delayed initiation and poverty of movement, response to threat, appearance, and inability to stand (“facedown”). MPTP treatment resulted in unequivocal signs of parkinsonism—tremor, poverty of movement, difﬁculty in initiating movement, bradykinesia, motor freezing, and a decrease in eye blink rate. Dihydrexidine resulted in a dramatic reduction in parkinsonian signs (see Fig. 5), as well as an increase in blink rates, within minutes of injection. Conversely, signs of parkinsonism and decreased blink rates were unchanged following the saline injection. These data show that dihydrexidine dramatically attenuated parkinsonian

FIG. 5. Effects of dihydrexidine on parkinsonian signs in MPTP-treated African Green monkeys. [Modiﬁed from Taylor et al. (1991).]

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signs, including tremor, motor freezing, abnormal posture, rigidity, and bradykinesia, while increasing eye blink rates in MPTP-treated monkeys. Thus, contrary to then-accepted views, these results suggested a critical role for D1 receptor occupancy in attenuation of parkinsonian signs (Taylor et al., 1991). Although the D1 component of dihydrexidine was clearly essential for its action, dihydrexidine also has some D2 afﬁnity (i.e., it is only 10-fold D1:D2 selective), and it was of importance to determine the role of D2 activation in the acute antiparkinsonian effects (Mottola et al., 1992). Behavioral studies in rodents have shown that D1 effects predominate at low doses, whereas D2 effects may be seen as the doses increase (Darney et al., 1991). Pharmacological antagonism studies in MPTP-treated monkeys demonstrated that the D1 antagonist SCH23390 blocked the antiparkinson effects of dihydrexidine, whereas the D2 antagonist remoxipride did not. These data suggest that D1 activation alone is sufﬁcient for acute antiparkinsonian activity. It is unclear, however, whether the D2 properties of dihydrexidine or of drugs similar pharmacological properties such as ABT-431 (Michaelides et al., 1995; Shiosaki et al., 1996) are critical (e.g., in modulating adaptive changes that may occur in response to chronic D1 activation). Goulet and Madras (2000) demonstrated that D1 agonists are more promising for the treatment of advanced than of mild Parkinson’s disease. In this study, the authors were able to create mild and advanced parkinsonism in nonhuman primates with ﬁxed dosing of MPTP. In normal monkeys, SKF81297 and dihydrexidine did not promote increased motor activity. In advanced parkinsonian monkeys, D1 and D2 dopamine agonists effectively reversed the motor deﬁcits. In contrast, the therapeutic beneﬁts of the D1 agonists SKF81297 and dihydrexidine were relatively limited in monkeys with mild parkinsonism. The D2 agonists quinelorane and (+)-PHNO alleviated some symptoms in mild parkinsonism, but also reduced balance and induced more dyskinesias than did D1 agonists. One clinical study of DHX has been performed on four patients who had levodopa responsive, mild to moderate Parkinson’s disease [mean Hoehn and Yahr stage, 2.8 ± 0.1; mean Uniﬁed Parkinson’s Disease Rating Scale (UPDRS) motor score in the off state, 34.5 ± 1.4 points; Blanchet et al., 1998]. One patient was infused with DHX over 15 min, and a deﬁnite dosedependent antiparkinsonian response was observed. At the highest dose of 35 mg (0.5 mg/kg, rate of 31 µg/kg/min), the patient had a 71% decrease in UPDRS motor score. Infusion of lower doses of DHX (up to 25 mg, or 0.357 mg/kg, rate of 24 µg/kg/minute) was not effective. Most aspects of parkinsonism (including resting tremor, bradykinesia, and gait difﬁculties) were improved, but the subject also developed mild choreic dyskinesias equivalent to those observed with IV levodopa. The other three patients in their

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study did not tolerate DHX well due to side effects. At their maximal tolerated dose/rate (16–70 mg; 4 µg/kg/min; 14 µg/kg/min), DHX showed neither antiparkinsonian effects nor dyskinesias. The patient who responded to DHX reached the highest peak plasma level (117 ng/ml) of all the patients. The highest level correlated with maximal beneﬁt. None of the other patients reached plasma levels above 100 ng/ml, typically remaining below 30 ng/ml. All four patients experienced transient but dose-limiting side effects, including facial ﬂushing, perspiration, tachycardia, and decreases in mean arterial supine/standing blood pressures. Neither typical D2-mediated side effects (e.g., nausea, ; emesis, confusion, or hallucinations) nor seizure activity were observed. These data suggested the efﬁcacy of the D1 agonist in treatment of Parkinson’s disease in humans, but unfortunately the results were not entirely conclusive due to intolerable side effects and selection of only mild to moderately disabled patients. Given that the nonhuman primate data suggest that D1 agonists may be most effective in severe parkinsonism and less useful with mild symptoms, we suggest that future trials of D1 agonists should include severely disabled Parkinson’s disease patients. The dramatic antiparkinsonian effects of D1 agonists predicted by DHX in nonhuman primates (Taylor et al., 1991) were conﬁrmed in studies of later full D1 agonists (Kebabian et al., 1992; Shiosaki et al., 1996). Rascol et al. (1999) published the results of a double-blind, placebo-controlled study of ABT-431 on patients with mild to severe Parkinson’s disease. ABT-431 is a prodrug whose parent molecule is similar to DHX, both in structure and pharmacological properties. This study involved 14 subjects with mild to severe Parkinson’s disease (severity stage 3 or 4 on the Hoehn and Yahr scale, mean UPDRS motor subsection score of 28–31). ABT-431 was given as a single daily 1-hour intravenous infusion of 5, 10, 20, 30, or 40 mg. ABT431 induced a signiﬁcant dose-related progressive increase in the median of maximum percent improvement in the UPDRS motor scores. The maximum percentage improvement (at a 30-mg dose) was comparable to the maximum improvement from levodopa. Two of the 14 subjects who failed to respond to ABT-431 were taking the highest daily doses of levodopa (1600 and 1750 mg, respectively). Unfortunately, the dose of ABT-431 was not increased beyond 40 mg to determine whether a higher dose would be more effective. Moreover, these authors did not mention the severity of the Parkinson’s disease for the ABT-431 “unresponsive” patients (Rascol et al., 1999). A majority of the subjects experienced dose-dependent mild dyskinesia, but this effect was less severe than from levodopa. The most common side effects were infusion site reaction, headache, ﬂushing and nausea, and orthostatic hypotension. These adverse events were mild to moderate and transient. No patients were dropped from the study due to side effects, and no

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seizure activity was reported. Thus, ABT-431 showed clear efﬁcacy in relieving Parkinsonian patients motor symptoms comparable to levodopa, with reduced tendency to induce dyskinesia. The poor pharmacokinetic characteristics of this drug have, however, led the developer to abandon it. Rascol et al. (2001) reported the effects of ABT-431 in advanced Parkinson’s disease (average UPDRS motor score of 43 in one center, 54 in the other) patients who had ﬂuctuating responses to levodopa, complicated by dyskinesia. The authors demonstrated that ABT-431 can induce an antiparkinsonian response comparable to levodopa, but in contrast to their earlier study (Rascol et al., 1999), the authors noted that the induction of dyskinesia was similar to levodopa. It is also useful to note that while drugs of the 1-phenyl-tetrahydrobenzazepine family (e.g., SKF38393) were the ﬁrst high-afﬁnity selective D1 ligands, new members of this class have been useful research tools. Rupniak et al. (1992) studied the antiparkinson effects of SKF82958 in MPTP-treated primates (Saimiri sciureus); SKF82958 is purported to be a full D1 agonist, but has been shown to actually be a high-efﬁcacy partial agonist (Watts et al., 1995b). Rupniak et al. (1992) reported that SKF82958 alone had only weak effects on locomotor activity, whereas the D2 agonist (+)PHNO had marked effects. In contrast, it was reported that SKF82958 caused marked increases in locomotor activity that were similar to the D1 agonist CY208-243 and the D2 agonist quinpirole (Bedard et al., 1993; Nomoto and Fukuda, 1993). Blanchet et al. (1993) demonstrated that the increases in locomotor activity and improvement in parkinsonian score following administration of SKF82958 were similar to the full D1 agonist A77636, and Domino and Sheng (1993) found good efﬁcacy for stimulating contralateral turning in hemiparkinsonian monkeys. Other members of this chemical series (e.g., SKF81297) have been developed or are under development and may prove to be useful tools in a research or clinical setting. 4. Role of D1 Receptors in Dyskinesias Although it now appears clear that the efﬁcacy of levodopa is due principally to D1 receptor activation, it is unknown if D1 agonists will also have the same liability as levodopa for inducing dyskinesias, abnormal involuntary movements, and postures that are one of the major therapeutic problems with levodopa. These abnormal motor patterns range from dystonic postures to choreic or choreoathetotic movements or tics, and cannot be controlled by the patient even though they are affected by stress and by the level of activity of the patient (Vidailhet et al., 1994; Fahn, 2000). Levodopainduced dyskinesias have been attributed to denervation supersensitivity of dopamine receptors, but data suggest that the mechanisms are far more complex, with no single receptor as the sole culprit. Although some form

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of denervation supersensitivity may play a role, as noted in Section IV.A, it is not a response simply to changes in bulk expression of dopamine receptors and may well involve other neurotransmitter systems, such as GABA, excitatory amino acids, and peptides (Bedard et al., 1992). The role of D1 stimulation in levodopa-induced dyskinesia is among the most controversial subjects in Parkinson’s disease research. A role for the D1 receptor in the genesis of dyskinesia was hypothesized by Boyce et al. (1990), who showed that coadministration of the D1 antagonist SCH23390 prevented levodopa-induced chorea at the time of peak effect. Mehta et al. (2000) suggested that the subthalamic D1 receptor may play a role in the development of dyskinesia. This notion was based on the fact that D1, not D2, antagonists block the orofacial dyskinesia that is induced by local infusion of either the full D1 agonist A77636 or the nonselective partial agonist apomorphine into rat subthalamic regions after nigrostriatal lesions (Mehta et al., 2000). However, Boyce et al. (1990) also observed a rebound exaggeration of chorea after the SCH 233390 treatment, at the time when levodopa-induced chorea would normally be subsiding. A role for the D2 receptor has also been implied. Gomez-Mancilla and Bedard (1991) observed that all D2 agonists (full or partial) including quinpirole, (+)-4-propyl-9-hydroxynaphthoxazine, bromocriptine, terguride, and (−)-3-(3-hydroxyphenyl)-N-n-propylpiperone reproduced the same dyskinesia, the intensity and duration of which were dose dependent and paralleled the therapeutic effect. In their study, CY-208,243 induced antiparkinsonian effects at a low dose and a dyskinetic effect at a higher dose. The effect of either D1 and D2 agonist was totally suppressed by the dopamine depleting agent α-methyl-p-tyrosine, but the effect was restored by a small subthreshold dose of the other (complementary) agonist (Gomez-Mancilla and Bedard, 1992). The data suggested that dyskinesia cannot be ascribed solely to the D2 or the D1 receptor and that some cooperation between the two receptors appears necessary for their manifestation. Some studies have reported that D1 agonists have beneﬁcial effects on dyskinesia in levodopa-primed dyskinetic monkeys. Blanchet et al. (1993) studied the effects of the levodopa, as well as several D1 (SKF 38393, A77636, CY-208,243, SKF 82958) and D2 (quinpirole, BCT, PHNO) agonists in four monkeys that were already dyskinetic. The D1 class of compounds were as efﬁcacious as the D2 agents in alleviating parkinsonism in these animals, while somewhat less likely to produce dyskinesia. In addition, D1 agonists occasionally improved motor symptoms without concomitant dyskinesia, whereas D2 agonists or levodopa always produced some dyskinesia with improvement in motor function. These data do not support the hypothesis that selective activation of D1 receptors facilitates dyskinesia in primates. Pearce et al. (1995) demonstrated that quinpirole, bromocriptine,

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pergolide, apomorphine, and A77636 all produce dyskinesias in levodopaprimed marmosets that were identical in character to those caused by levodopa. The D1 agonist A77636 gradually abolished dyskinesias while leaving some antiparkinsonian intact. A later study from this group showed that two D1 agonists, A-86929 and A-77636 had greater antiparkinsonian action, and less propensity to produce dyskinesia, in levodopa-primed MPTP-lesioned subjects (Pearce et al., 1999). Although there are only a few published human studies with D1 agonists, Rascol et al. (1999) reported that ABT-431 showed clear efﬁcacy in relieving parkinsonian motor symptoms comparable with levodopa, while having reduced tendency to induce dyskinesia. Although these data were consistent with many of the nonhuman primate studies, this same group reported that the ABT-431 causes similar responses to levodopa both in terms of antiparkinsonian effects and induction of dyskinesias (Rascol et al., 2001). The one limited human trial of DHX also caused the same degree of dyskinesia as levodopa (Blanchet et al., 1998). Clearly, additional studies in humans will be needed to resolve these issues, yet several points should be noted. First, D1 agonists clearly have antiparkinson efﬁcacy at least equal to levodopa and greater than any other pharmacotherapeutic intervention that has been reported. Second, although D1 agonists may always induce dyskinesia in levodopa-primed patients, monotherapy with D1 agonists under carefully controlled conditions could possibly provide long-term symptomatic relief without induction of dyskinesia; the data are too preliminary and contradictory to know for sure. Finally, although D1 activation is clearly the most important effect of levodopa, the degree and type of D2 activation is unknown, especially for best long-term responses. These and other questions make for a fertile research ground for the next several years.

C. IMPACT OF D1 RECEPTORS ON OTHER CNS DISORDERS 1. Schizophrenia and Attentional Disorders Since the 1960s, dopamine receptor antagonists have been the most widely used and effective pharmacologic agents in the treatment of schizophrenia (Levinson, 1991; Trampus et al., 1991; Ellenbroek, 1993). This fact has led to the notion (however tenuous) that this disorder is the result of excessive dopaminergic tone. Whether dopamine hyperactivity is at the root of schizophrenia, dopamine receptors have remained the principal therapeutic targets. Whereas it was originally hypothesized that antipsychotic drugs ameliorated all the symptoms of schizophrenia, it became evident

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that these drugs more effectively abolished the positive (type I) symptoms of schizophrenia, namely, hallucinations, thought disorder, and delusions than the negative (type II) symptoms, such as poverty of speech, loss of drive, and ﬂattening of affect (Goldberg et al., 1967; Crow, 1980, 1981). The positive symptoms are associated more closely with acute schizophrenia, whereas the negative symptoms seem to occur more commonly in chronically schizophrenic patients, most of whom are institutionalized and who may have become intellectually impaired over the course of their illness (Crow, 1980, 1981). There are many interesting issues involved in the etiology of these syndromes, some associated with structural deterioration or changes in brain (Weinberger et al., 1979; Crow et al., 1980; Bilder et al., 1995). Historically, new “atypical” antipsychotic drugs have been targeted at controlling positive symptoms of schizophrenia, while minimizing the neurological and autonomic side effects caused by “typical” antipsychotics such as chlorpromazine and haloperidol. One trend has been the development of drugs selective for single dopamine receptor isoforms, although as exempliﬁed by trials of selective D4 antagonists (Merchant et al., 1996; Bristow et al., 1997; Trufﬁnet et al., 1999), this has been disappointing to date. The profound behavioral effects that could be demonstrated for SCH23390 (Mailman et al., 1984) led to the view that D1 antagonists might be novel and effective antipsychotic drugs, despite the data suggesting that such drugs would likely have adverse neurological side effects (Taylor et al., 1991). Clinical trials have been conducted with two different D1 antagonists, SCH39166 (an analog of SCH23390) and NNC 01-0687, and these drugs have no efﬁcacy for the treatment of schizophrenia (Den Boer et al., 1995; Karle et al., 1995; Labelle et al., 1998). Of particular importance to a discussion of D1like receptors, however, is the fact that none of the available antipsychotic drugs seems to have marked therapeutic beneﬁt for primary negative symptoms. We had proposed that D1 agonists may have utility for this condition, and the ﬁnding of D1 receptor hypoinervation in the prefrontal cortex of drug na¨ıve schizophrenics with primary negative symptoms supports such a direction (Okubo et al., 1997). 2. Utility of D1 Agonists in Memory and Cognition D1 receptors are present in high concentration (20 times the density of D2 receptors) in prefrontal cortex in nonhuman primates (Lidow et al., 1991). The optimal stimulation of this region is known to potentiate the signaling in neurons that is essential to the working memory process (Sawaguchi and Goldman-Rakic, 1991). The cognitive function of the prefrontal cortex is known to be modulated by ascending monoaminergic and cholinergic

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systems in rats and primates (Muir et al., 1994; Granon et al., 1995; Arnsten, 1997; Robbins et al., 1998). Lesions of the mesocortical dopamine projection impair working memory performance in monkeys (Brozoski et al., 1979) and rats (Simon, 1981). D1 receptor activation may provide cognitive beneﬁts based on ﬁndings that local injection of a D1 antagonist (but not a D2 antagonist) into the prefrontal cortex induced deﬁcits in working memory in rhesus monkeys (Sawaguchi and Goldman-Rakic, 1994). It has also been demonstrated that D1 agonists can improve cognitive function both in rodents (Hersi et al., 1995b; Steele et al., 1997) and nonhuman primates (Arnsten et al., 1994; Cai and Arnsten, 1997). This ﬁnding is consistent with the clinical experience that the D1/D2 agonist pergolide, but not the D2 selective agonist bromocriptine, has beneﬁcial effects on a spatial working memory paradigm (Muller et al., 1998). The partial agonist, SKF 38393, improves the memory performance of reserpine-depleted monkeys but does not improve young control animals. The full agonist dihydrexidine improved the memory performance in both young control monkeys, as well as in a subset of older monkeys (Arnsten et al., 1994). Schneider (1994) also demonstrated that dihydrexidine can cause performance improvements in MPTP-lesioned nonhuman primates. Finally, one of the most striking reports indicated that brief administration of ABT-431 can induce long-lasting reversal of antipsychotic-induced working memory deﬁcits in monkeys (Castner et al., 2000). Although there is overwhelming data supporting the beneﬁcial effects of D1 stimulation in cognition and memory, there is also a clear dose dependency for these effects and, paradoxically, higher doses have been demonstrated actually to impair memory performance in older monkeys (Castner et al., 2000). The mechanism by which D1 receptors affect memory and cognition is not fully understood. It has been shown that dihydrexidine increases extracellular acetylcholine levels in the rat striatum and frontal cortex (Steele et al., 1997), and SKF 38393 increases hippocampal acetylcholine release in memory impaired older rats (Hersi et al., 1995a; Mercuri et al., 1997). As noted earlier, D1 receptors in both pyramidal and nonpyramidal neurons may interact with glutamatergic inputs, and a model of dopamine modulation of working memory posits a central role of D1 receptors in enhancing glutamatergic input to such neurons (Muly et al., 1998). These and other mechanisms will undoubtedly be further explored, but it is clear that the D1 receptor has an important role in modulating cognitive performance under the control of the prefrontal cortex. It supports the potential use of D1 agonists in the treatment of cognitive deﬁcits and/or negative symptoms in a variety of conditions, including schizophrenia, Parkinson’s disease, and age-related memory decline.

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3. Substance Abuse Another area of great current interest is the role of dopaminergic neurotransmission in reinforcement and reward. It is widely accepted that the pleasurable and euphoric effects of psychostimulants, such as amphetamine and methamphetamine, are attributable to their ability to induce the release and/or block the reuptake of neuronal dopamine. In general, reward phenomena are mediated by dopamine pathways (Hiroi and White, 1991; Nakajima et al., 1993; Okubo et al., 1997; Koob, 1998). Cocaine is also a potent inhibitor of dopamine uptake, and it is a widely held hypothesis that the pleasurable and reinforcing effects of this substance are derived primarily from its dopaminergic actions (Fibiger, 1978; Wise and Bozarth, 1987; Ranaldi and Beninger, 1994). In fact, it has been hypothesized that most, if not all, drugs that are capable of producing dependence increase dopaminergic transmission in speciﬁc brain regions, particularly the nucleus accumbens. From this perspective, it has been hypothesized that appropriate pharmacological agents (e.g., partial agonists for speciﬁc dopamine receptor subtypes) might have utility in the treatment of abuse of both psychostimulants and other drugs whose rewarding effects are mediated via mesolimbic dopamine neurotransmission. Although dopamine agonists have long been of interest in the treatment of cocaine abuse, the available D2 agonists are self-administered (Davis and Smith, 1977; Woolverton et al., 1984; Caine and Koob, 1993), enhance cocaine’s reinforcing effects (Parsons et al., 1996; Caine et al., 1997), and reinstate nonreinforced responding for cocaine in an animal model of cocaineseeking behavior (Wise et al., 1990). Pergolide (a dopamine agonist that is 50–500 times more selective at D2 than D1 receptors) has been shown to decrease the ratings of cocaine dose, but increase the ratings of cocaine “craving” (Haney et al., 1998) and impair treatment compliance (Levin et al., 1999). D1 agonists are self-administered by rats (Self and Stein, 1992; Self et al., 1996b) and nonhuman primates (Weed et al., 1993; Weed and Woolverton, 1995; Weed et al., 1997), but D1 agonists increased the latency to initiate cocaine self-administration (Self et al., 1996a; Caine et al., 1997). Furthermore, D1 agonists do not reinstate nonreinforced responding on a cocaine-paired lever and, in fact, decrease the ability of cocaine nonreinforced responding in an animal model of cocaine seeking (Self et al., 1996a). This suggests that D1 agonists might decrease the likelihood that abstinent cocaine abusers in treatment would relapse. Haney et al. (1999) demonstrated that ABT-431 produced signiﬁcant decreases in the subjective effects of cocaine in a dosedependent manner and showed a trend for ABT-431 to decrease cocaine craving at the highest dose (4 mg) tested. Overall, ABT-431 was well tolerated

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in these trials with mild side effects, such as nausea, vomiting, headache, and fatigue, lightheadedness, and injection site reaction. Although the human data are still very limited with small sample sizes in both studies, the preliminary data do support the potential use of D1 agonists in the treatment of cocaine abuse.

V. Conclusions and Future Goals

In the early 1980s, the prevailing view in neuropharmacology was that the D1 receptor was “a receptor in search of a function” (Laduron, 1983). Even with the demonstration of profound roles of D1 receptors in numerous functions in the early 1980s, the importance of this predominant dopamine receptor, as well as its therapeutic potential, were not generally accepted. In this article, our goal was to provide a framework to understand why this receptor is now considered to be important, from both a functional and therapeutic point of view, in CNS function. It is noteworthy that the peripheral actions of D1-like receptors are also only beginning to be understood, and the actions of drugs on D1-like receptors in the vasculature, heart, kidney, lung, etc., are of both generic physiological interest, as well as of therapeutic importance for direct actions or as side effects of CNS targeted effects. Although many of the elegant studies described above have led to a greater understanding of the role of D1-like receptors in processes such as movement, memory, and cognition, the contribution of these receptors in other functional areas is less well understood. For example, whereas a role for D1-like receptors in seizures has been hypothesized, the data are quite muddy, and the underlying mechanisms are important if these drugs are to be used successfully as therapeutic agents. In this regard, despite the advances made since the 1990s in the pharmacology of this family, much remains to be done. Selective agonists and antagonists for the D1 and D5 receptors will be extremely important research tools and may also have speciﬁc therapeutic niches. In addition, nonselective dopamine agonists, as well as drugs with “functionally selective” properties may also be useful in exploiting the neurobiological database that increases every day. For example, it may well be that a drug that is a mixed agonist for both D1 and D4 or D4 and D5 receptors may have particular use in presently intractable conditions, such as negative symptoms of schizophrenia or autism. In addition to the extremely interesting neurobiological questions that have been reviewed in this article, there are also many scientiﬁc questions whose answers will be of pragmatic importance. As an example, some D1 agonists such as A77636 and A68390 produce rapid behavioral desensitization

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when administered either to animals or people (Britton et al., 1991; Kebabian et al., 1992; Asin and Wirtshafter, 1993; Blanchet et al., 1996). Other drugs such as either dinapsoline (Gulwadi et al., 2001) or A86929 and its prodrug ABT-431 (Shiosaki et al., 1996; Asin et al., 1997) retain effectiveness for weeks. Although some mechanisms have been suggested (Lin et al., 1996; Lewis et al., 1998; Gulwadi et al., 2001), a molecular and cellular explanation is yet to be achieved. The next decade should be one in which unambiguous answers to such questions will evolve, and from them a better understanding of D1 neurobiology and clues for improvement of therapeutic application. Another question with less immediate clinical but great long-term relevance is the identiﬁcation of the actual molecular mechanisms that transduce the activation of D1-receptors. Although most researchers focused on adenylate cyclase, this attention is primarily the result of the fact that this assay is the “street light” that facilitates experimental searches. In fact, there is no doubt that other second-messenger systems and events are of at least equal importance; elucidating all the molecular players and partners at the level of individual cells will be critical for a variety of reasons. It is clear that the roles of D1-like receptors in both normal nervous system function, as well as in a variety of neurological and psychiatric disorders, will yield to modern molecular, cellular, and imaging approaches. Concomitantly, advances in both the elucidation of the structural aspects of these receptors and advances in ligand- and structure-based drug design may open exciting new therapeutic possibilities resulting from the neurobiological advances.

Acknowledgments

This work was supported, in part, by Public Health Service research grants MH40537, MH42705, NS39036, and MH53356; center grants MH33127 and HD03310; and training grants DA07244 and HD07201.

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MOLECULAR MODELING OF LIGAND-GATED ION CHANNELS: PROGRESS AND CHALLENGES

Ed Bertaccini∗ ,† and James R. Trudell∗ ∗ Department of Anesthesia Stanford University School of Medicine Stanford, California 94305; and † Department of Anesthesia Palo Alto VA Health Care System Palo Alto, California 94304

I. The Challenge of Modeling Transmembrane Ion Channels II. Overview of Molecular Modeling Principles A. Homology Modeling B. Molecular Mechanics C. Molecular Dynamics D. Validation of Protein Structure III. Experimental Data: Techniques and Results A. Validity of Techniques B. Experimental Data Relevant to Modeling Tetrameric LGICs C. Experimental Data Relevant to Modeling Pentameric LGICs D. Binding Sites for Inhalational Anesthetics in Pentameric LGICs IV. Molecular Models A. Modeling Tetrameric LGICs B. Modeling Pentameric LGICs V. Summary References

I. The Challenge of Modeling Transmembrane Ion Channels

Most receptor proteins of biological interest are studied after their structure is determined via X-ray crystallography. These structures provide relatively exact atomic coordinates from which accurate three-dimensional representations may be developed. However, X-ray crystallography requires that an adequate amount of purified protein can be made and that reasonably sized crystals can be formed for examination. The difficulty of studying ligand-gated ion channels (LGICs) is that they are multisubunit transmembrane proteins (Fig. 1). Therefore, upon removal from their membrane environment, they often become denatured such that any further purification or crystallization steps become meaningless. Although there are now INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 48

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FIG. 1. The transmembrane domain of a pentameric ion channel. The diagram on the left depicts the cryoelectron micrograph of the torpedo AChR (adapted from Unwin et al., 1999). The drawing on the right is a LGIC formed from five subunits (adapted from Olsen and Tobin, 1990). Each subunit has four transmembrane segments and an extracellular ligand-binding domain. An ion-conducting pore is at the center. Our results suggest an anesthetic-binding site in the region between TM2 and TM3 (Mascia et al., 2000).

some examples of transmembrane receptors and ion channels that have high-resolution crystal structures (Chang et al., 1998; Doyle et al., 1998; Palczewski et al., 2000; Toyoshima et al., 2000), they are often truncated versions of bacterial proteins. The additional difficulty associated with crystallizing large multisubunit mammalian receptors renders this class of protein poorly amenable to study. In the case of pentameric LGICs, the current X-ray crystallographic methods of analysis are only able to render low-resolution structural images (Stowell et al., 1998). As a result, molecular modeling has been used to study such systems for the purposes of visualizing both protein structure and ligand binding (Sansom, 1998; Scott and Tanaka, 1998; Sutcliffe et al., 1998). Transmembrane ligand-gated ion channels have been shown to be very sensitive to the effects of volatile anesthetics (Mihic et al., 1997). In particular, data strongly suggest that alterations in the transmembrane component of these proteins are both necessary and sufficient to elicit the anesthetic effect (Krasowski et al., 1998; Miller et al., 1998; Wick et al., 1998; Flood et al., 1999; Mascia et al., 2000). We are studying mechanisms of general anesthetic action; hence, the thrust of this article focuses on the problem of modeling

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these elusive transmembrane ion channels. Our discussion is divided according to the number of subunits involved in the formation of the whole transmembrane domain. This allows the classification of the receptors into those that form tetramers of subunits [e.g., ionotropic glutamate receptors (iGluRs)] and those that form pentamers of subunits [e.g., superfamily that includes the acetylcholine receptor (AChR) the gamma aminobutyric acid receptor (GABAR), and the glycine receptor (glyR)].

II. Overview of Molecular Modeling Principles

A. HOMOLOGY MODELING Construction of a protein model begins with the acquisition of its primary structure or amino acid sequence (Fig. 2). This can be obtained from any of a variety of sources, most notably the Entrez database available through the National Library of Medicine (Marchler-Bauer et al., 1999). This database can be searched based on a variety of query types, including sequence

FIG. 2. Sequential processes of homology modeling and model refinement.

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similarity or functional motif. Each sequence is also cross-referenced to the scientific study responsible for its production. Once the amino acid sequence of a desired protein is obtained, the next step is to search for a protein of known three-dimensional structure that, based on a well-defined set of criteria, has some quantifiable similarity to the sequence in question. This protein of known three-dimensional structure will then serve as a template over which the amino acid sequence of the protein in question can be draped and modeled. This technique is referred to as homology modeling. One often begins with the search of a database of known three-dimensional structures for proteins with significant sequence similarity to the protein in question. This search can be performed using a variety of sequence similarity matrices (e.g., BLOSUM, PAM) that can be implemented into a variety of searching schemes (e.g., BLAST, PSI BLAST; Altschul et al., 1997). These sequence analysis techniques provide a means of scoring the similarity of one amino acid sequence relative to another, based on criteria for the likelihood of one amino acid being replaced by another over evolutionary time scales, physicochemical criteria, and the likelihood of gaps being introduced into the sequence. Unfortunately, in the case of LGICs, these schemes identify many similar sequence alignments between other LGICs. However, in most cases, those proteins identified do not also have a known three-dimensional structure on which to perform the aforementioned template draping. In this case, the next step is to attempt to add secondary structural information to the primary sequence information and repeat the search for templates with this added data. This is often referred to as analogy modeling, as opposed to homology modeling, because the resulting structures are often not evolutionarily or chemically similar to the sequence in question but may have a similar overall three-dimensional structure. Because this analysis now requires the secondary structural information of our protein in question, the initial data need to be predicted. There are several state-of-the-art bioinformatics techniques available to predict the topology of transmembrane proteins (Bertaccini and Trudell, 1999). HMMTOP is from the Hungarian Academy of Sciences and is based on Hidden–Markov modeling (Tusnady and Simon, 1998). TOPPRED2 is from Stockholm University and is based on hydrophobicity indices and the positive inside rule of transmembrane proteins (von Heijne, 1992). TMHMM is from the Technical University of Denmark and also uses a Hidden–Markov technique (Sonnhammer et al., 1998). TMPred is from EMBnet-CH and is based on a statistical analysis of the TMbase database (Hofmann, 1993). PHDhtm is from the European Molecular Biology Laboratory and incorporates protein phylogeny data, multiple sequence analysis, and a neural network for its predictions (Rost et al., 1995). Although each of these techniques has been studied for their own reliability, we have further

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tested each technique for its ability to specifically predict not only a transmembrane region, but also its ability to predict whether the identified transmembrane region was alpha helical in structure. This test was performed by obtaining the transmembrane topology prediction of each algorithm run against the sequence of a transmembrane porin with known beta barrel secondary structure. We found that only three techniques (HMMTOP, TMHMM, PHDhtm) accurately identified the transmembrane porin control as not having alpha helical secondary structure and are therefore useful in predicting the topology of transmembrane segments. The predicted secondary structural information can then be combined with the sequence information to perform a search of a three-dimensional structural database via a scoring scheme that combines both descriptive elements. SeqFold (MSI, San Diego, CA) is just such a software package and allows variability of the aforementioned sequence similarity parameters, as well as variable weighting of the sequence and secondary structural information (Olszewski et al., 1999). This process may at least identify an analogous protein to be used in subsequent homology modeling. Once identified, the coordinates of a protein that is somehow similar to the one in question can be used as a template onto which the sequence of unknown tertiary structure can be draped. This alignment of sequence to template is performed on the basis of the same scoring techniques as mentioned above for finding the template. If multiple templates are available, the CLUSTAL W algorithm can be used (Thompson et al., 1994). The results can be further refined based on other criteria that may include experimental data as well as optimization of potentials of mean force (Sippl, 1990). Once each amino acid is assigned a position along the alpha carbon backbone of the template, the amino acid side chains must be added and their conformations adjusted. This initial adjustment is based on extensive libraries of the various conformational degrees of freedom in each side chain that have been generally found to be energetically favorable (Dunbrack and Karplus, 1994). More refined adjustment is performed using the techniques of molecular mechanics described below.

B. MOLECULAR MECHANICS When an initial three-dimensional structure has been generated, there are often many components that are structurally and energetically unsound despite initial optimizations based on potentials of mean force and conformer libraries. Further refinement requires the implementation of more thorough analysis of system energetics and interactions. This is

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performed via molecular mechanics (Bowen and Allinger, 1990; Sankararamakrishnan and Sansom, 1995; Leach, 1996). Molecular mechanics energies are the sum of energy contributions from at least five separate components. There are energy contributions from bonded atoms that are due to variations in bond length, bond angle, and bond torsion, as well as cross-terms derived from combinations of these variations. These energy terms are typically described by springlike quadratic potentials. There are at least two energy contributions from nonbonded atoms that are due to electrostatic potential energy and van der Waals forces. Each of these energy terms has specific constants that have been parameterized to allow the molecular mechanics energy calculations to reproduce various measured properties for given types of systems (e.g., liquid density or enthalpy of vaporization). Together, the potential energy functions and their parameters form a molecular mechanics forcefield. There are a variety of forcefields currently in use (Leach, 1996). AMBER from the UCSF group is parameterized for protein calculations (Besler et al., 1990), as is CharMM from the Karplus group at Harvard (Brooks et al., 1983). MM3 and MMFF are designed for more general small molecule calculations. We reviewed this area and found that, for the purposes of modeling anesthetic interactions with LGICs, the CFF91 forcefield from MSI, Inc., is the most useful (Trudell and Bertaccini, 1998). This forcefield is not only well parameterized for the construction and refinement of protein models, but also has reasonably well-defined partial atomic charges for the polyhalogenated moieties. The latter charges are often not included in general forcefields, but they are particularly important for studies of anesthetic binding because halogens are common substituents of inhalational anesthetics. Electrostatic interactions make large contributions to ligand binding due to their relative strength over great distances. Once the total molecular mechanics energy can be calculated, the threedimensional geometry of the newly constructed protein must be optimized based on these energy criteria. Optimization algorithms take many forms and are often imposed sequentially (Leach, 1996; Sansom, 1998). The steepest descent method is often initially implemented to remove highly unfavorable energetic interactions. This is followed by an optimization based on the first derivative of the energy function, often a conjugate gradient method search for local minima in the multidimensional geometry versus energy surface. Finally, the optimization may be improved by application of a method that uses the second derivative of the energy function, usually a Newton–Raphson method. These methods are typically employed in this order because the steepest descent method is computationally quick but converges poorly, whereas the Newton–Raphson method is slow but typically converges nicely.

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C. MOLECULAR DYNAMICS Although molecular mechanics will allow the formation of a structure that has the relatively favorable energy of a local minimum on the potential energy surface, the construction of an even more refined protein structure involves the search for other more stable local minima than that produced by optimization alone. This requires the addition of kinetic energy to each atom so a thermal equilibration may be allowed to take place in an effort to reasonably and thoroughly sample the conformation space of the entire protein. This analysis is performed via molecular dynamics simulations (Leach, 1996; Sansom et al., 1998b; van Gunsteren and Berezesky, 1990; Zhang and Hermans, 1993). Molecular dynamics imparts a force to each atom in a molecular system by calculating the gradient of an atom’s individual molecular mechanics potential energy. This force can be divided by the mass of the atom to give the acceleration of the atom. A set of random velocities is initially assigned to each atom, based on their Boltzman distribution at a given temperature and the previously calculated atomic acceleration. The system can be set “in motion” by imparting to it kinetic energy that is in addition to its potential energy. A molecular system is typically heated to a certain temperature during an initial heating phase, it is equilibrated for a period of time during which there is validation of equilibration and actual system measurements made, followed by a period of cooling to allow the system to settle into a new local minimum. The temperature to which the system is heated depends on the perceived necessity of overcoming barriers between local minima. The time course for simulation depends on system stability during temperature changes and the adequacy of system equilibration. The simulation can be repeated multiple times in an effort to consistently reproduce a set of protein structures with little root–mean–square (RMS) deviation from one another. Because LGICs in vivo are suspended in a lipid bilayer, they would normally be subject to certain restraints on their lateral motion. In addition, water molecules would surround their extracellular and intracellular sides. Unfortunately, adding explicit water and lipid molecules to a model of a LGIC creates a long molecular dynamics calculation that would be intractable with current computing resources. Nevertheless, in an effort to at least simulate the limits of lateral motion imposed by the lipid bilayer, a series of restraints can be introduced into the protein for both static and dynamic calculations (Sankararamakrishnan et al., 1996; Sansom et al., 1998a). As discussed below, multiple pseudoatoms can be created within the alpha helices themselves as well as within the tetrameric subunits of the receptor. Sets of these pseudoatoms can be restrained to specific distances or angles throughout the calculation to simulate a membrane environment.

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D. VALIDATION OF PROTEIN STRUCTURE After all of this manipulation, the model protein structure must be validated based upon numerous standard criteria. An initial test of a model after threading is a “bump check” to look for van der Waals overlaps. One must also evaluate whether an amino acid sequence is compatible with the given three-dimensional structure. Compatibility criteria include ranges for typical bond lengths, angles, and torsions, especially the phi and psi amino acid torsions. The planarity of peptide bonds in the structure should also be checked. Amino acid packing should be reasonable based on the environment of the protein and the physicochemical properties of surface accessible amino acids. A manual check for unsatisfied hydrogen bonding and salt bridge formation should also be performed. Comparison of the final structure to those of previous MD simulations and optimizations should show little RMS deviation in the atomic coordinates of backbone carbon atoms. As is the case with any model, final validity is confirmed based on the consistent reproduction of experimentally derived relationships as well as the accuracy of predictions derived from the model.

III. Experimental Data: Techniques and Results

A. VALIDITY OF TECHNIQUES In Section II.D, we stated that molecular modeling techniques are often validated by comparison with experimental results. However, it should be kept in mind that the assumption that experimental results are correct is implicit in this validation. Unfortunately, experimental results, even from careful experiments, are not always accurate or applicable to protein structure in vivo. For example, circular dichroism (CD) accurately reports the percentage of alpha helical and beta sheet structure in a sample (Bazzi and Woody, 1985; Arkin et al., 1998). However, to obtain those values for the transmembrane domain of an ion channel, it is necessary to remove the cytoplasmic and intracellular domains of the protein (Corbin et al., 1998a). Otherwise, the latter domains would contribute much random coil content to the resulting average values. These domains are often removed by proteolysis, and the assumption is made that the secondary and tertiary structure of the transmembrane domains is undisturbed. The latter assumption is supported by studies in which interhelical loops within very stable transmembrane proteins, such as rhodopsin, were removed without changing helical content. In fact, there is insufficient evidence to confirm

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that the structure of the transmembrane domains of ion channels will be undisturbed by this treatment: Rhodopsin is a single bundle of seven transmembrane alpha helices (Palczewski et al., 2000) that undergoes a relatively small structural change upon activation (Farrens et al., 1996). In contrast, pentameric-transmembrane-ion channels are formed from five subunits that are held together by noncovalent bonds and that undergo large structural changes during activation (Vafa and Schofield, 1998; Corringer et al., 2000). A similar concern must be raised about experiments in which transmembrane ion channels are extracted from their native membrane before analytical techniques are applied. In a study, evidence that toxin-binding ability was retained during reconstitution was used to suggest that the receptor retained its native structure (Leite et al., 2000). In light of the aggressive detergents used in this reconstitution (e.g., Triton X-100), it is possible that individual or aggregated subunits were extracted. In general, it would be better to demonstrate that gating and inactivation of the receptor was retained before using the reconstituted proteins for labeling studies.

B. EXPERIMENTAL DATA RELEVANT TO MODELING TETRAMERIC LGICS It was initially believed that the superfamily of ligand-gated receptors would include the family of ionotropic glutamate receptors. However, cloning of a cDNA for a glutamate receptor subunit (Egebjerg et al., 1991) allowed a series of mutagenesis experiments that proved otherwise. The initial assumption that glutamate receptor subunits would have four transmembrane segments was replaced by models with either five segments (Roche et al., 1994; Taverna et al., 1994) or three transmembrane segments plus a reentrant loop (Bennett and Dingledine, 1995). A consensus of data from glycosolation and protease protection studies supports the latter structure (Bennett and Dingledine, 1995). In addition, attention has been called to the similarity of the reentrant loop with the corresponding segment that forms the P loop of the potassium channel (Wo and Oswald, 1995; Sutcliffe et al., 1996). There was similar controversy over the quaternary structure of the glutamate receptors: Was it a tetramer or pentamer? For example, an early modeling study considered four, five, and six subunits and preferred five (Sutcliffe et al., 1996). However, the amino acid sequence of the glutamate receptors has many similarities to the potassium channels (Wo and Oswald, 1995), and the latter channel has been shown to be a tetramer by X-ray crystallography (Doyle et al., 1998). Moreover, experiments that observed the unbinding of antagonist of the α-amino-3-hydroxy-5-methyl-4-isoxazol propionate (AMPA) receptor GluR3 showed that as agonists replace antagonists,

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the channel opens in three discrete steps (Rosenmund et al., 1998). These steps were seen as “substate” conductances smaller than that associated with the fully agonist-occupied channel. From a closed state (C ), the channel first opens to a small conductance (S ), then to a middle state (M ), and finally to the fully open large conductance. These experiments provide evidence that ionotropic glutamate receptors share a tetrameric structure with the voltage-gated potassium channels (Miller, 1998; Rosenmund et al., 1998). Although later sections focus on the effects of inhalational anesthetics on pentameric ion channels, we have also investigated anesthetic effects on glutamate receptors (Minami et al., 1998). This family of receptors has the important property that the AMPA subtype (GluR3) is inhibited by volatile anesthetics, whereas the kainite subtype (GluR6) exhibits enhanced function. A series of chimeric substitutions revealed that a specific amino acid residue, Gly 819, is important for enhancement of receptor function by halothane (Minami et al., 1998). Our molecular modeling placed Gly 819 near the extracellular end of TM4 in GluR6. An interesting point is that mutation of Gly 819 to any other amino acid residue, even alanine, had large effects on the enhancement of function by volatile agents. A glycine residue at this position would provide enhanced flexibility to the end of the alpha helix and would allow direct access of the anesthetic molecule to the carbonyl backbone region of the helix. Additional research will be required to decide between these possibilities.

C. EXPERIMENTAL DATA RELEVANT TO MODELING PENTAMERIC LGICS As mentioned above, current crystallographic techniques render inexact images of transmembrane proteins in general, and especially the LGICs (Unwin, 1993; Stowell et al., 1998). There are, however, other experimental means that shed some light on the physical structure of these proteins (Vafa and Schofield, 1998). These techniques include point mutations, construction of chimeras, lipophilic and hydrophilic labeling studies, and some forms of spectroscopic analysis, including circular dichroism and NMR (Table I). These data can be combined with molecular modeling in an iterative fashion to provide geometric restraints during modeling or to correct a model already developed (Ortells et al., 1997; Sansom et al., 1998b; Le Novere et al., 1999). 1. Controversy over the Predicted Secondary Structure Our initial hypothesis has been that there is common motif for the LGIC superfamily that includes AChR, GABA, and GlyR; a pentamer of subunits, with each subunit consisting of four antiparallel alpha helices (Methot and

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TABLE I SUMMARY OF VARIOUS TECHNIQUES THAT ATTEMPT TO PREDICT SECONDARY STRUCTURE OF TRANSMEMBRANE ELEMENTS OF PENTAMERIC LGICS (ALPHA = ALPHA HELIX, BETA = BETA SHEET) TM1 Cryoelectron microscopy Hydrophilic labeling Hydrophilic labeling NMR FTIR Topology prediction Proteolysis

beta beta alpha/beta alpha/beta beta

TM2

TM3

alpha alpha

beta

alpha

alpha

alpha/beta alpha

alpha/beta beta

TM4

alpha

alpha/beta alpha

Baenziger, 1998; Bertaccini and Trudell, 1999). The general motif of an ion channel composed of five subunits arranged around a central pore is strongly supported by a series of papers by Unwin (1993, 1995, 1997). It seems certain that five alpha-helical segments line the ion pore and that these amino acid residues are from transmembrane segment 2 (TM2). However, the suggestion that the other three transmembrane segments in each subunit are also alpha helices is much more controversial (GorneTschelnokow et al., 1994; Ortells and Lunt, 1994; Akabas and Karlin, 1995; Ortells et al., 1997; Unwin, 1997; Le Novere et al., 1999; Williams and Akabas, 1999; Corringer et al., 2000; Leite et al., 2000). a. Secondary Structure of TM2. Although it seems out of order, it is appropriate to consider TM2 first because it is the pore-lining segment and the most studied. Cryoelectron microscopy was used to demonstrate that electron density in the transmembrane region is consistent with five alpha helices lining the central pore of the nicotinic acetylcholine receptor (Akabas et al., 1994; Unwin, 1997). These alpha helices were identified as TM2 in the nicotinic acetylcholine receptor in a series of studies in which specific amino acid residues were mutated to cysteine and labeled with water-soluble reagents (Akabas et al., 1994; Akabas and Karlin, 1995; Williams and Akabas, 1999). A breakthrough for our proposed molecular modeling occurred recently, when the crystal structure of a bacterial mechanosensitive ion channel was published (1msl in the Protein Data Bank at the Research Collaboratory for Structural Bioinformatics: Chang et al., 1998). This ion channel contains five alpha helices arranged around a central pore with a supertwist and a funnel shape that is narrowest near the intracellular surface (Wilson and Karlin, 1998). This structure is entirely consistent with predictions based on electron density in the cryoelectron micrographs of acetylcholine receptors by Unwin (1993, 1995, 1997). Although early studies predicted

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a “kink” at the highly conserved leucine residue lining the ion channel (Unwin, 1993), this kink was not observed in a NMR study (Opella et al., 1999). Moreover, application of simulated annealing via restrained molecular dynamics (SA/MD) to a model of the AChR ion channel showed that the kink may be achieved by cumulative small distortions of the backbone from canonical alpha-helical geometry, rather than a marked loss of alphahelical geometry in the vicinity of the conserved leucine (Sansom et al., 1998b). Studies with chlorpromazine also identified these amino acids as pore-lining residues (Vafa and Schofield, 1998; Corringer et al., 2000). b. Secondary Structure of TM1. In general, transmembrane domains of proteins are either all alpha helical or all beta barrel. As described above, there is strong evidence that the pore-lining TM2 segments are alpha helices. As a result, the other three transmembrane segments identified by hydropathy algorithms have long been assumed to be alpha helices as well. However, some studies suggest that this may not be the case (Gorne-Tschelnokow et al., 1994; Ortells and Lunt, 1994; Corringer et al., 2000). For example, exposure of cysteine mutants of TM1 and TM2 of the acetylcholine receptor to a watersoluble probe revealed that TM2 was labeled in a manner consistent with an alpha helix (Akabas et al., 1994), whereas TM1 was labeled in an irregular manner and only near the extracellular surface, indicating incomplete exposure to the aqueous pore region (Akabas and Karlin, 1995). In addition, this exposure changed during channel gating, suggesting that the tertiary structure of the channel changes during gating. A similar result was obtained with the hydrophobic probe 3-trifluoromethyl-3-(m-[125I]-iodophenyl)diazirine in the Torpedo nicotinic acetylcholine receptor (Blanton and Cohen, 1994). The probe reacted nonspecifically with residues 222, 223, 227, and 228, a pattern of incorporation inconsistent with that expected from either a “face” of an alpha helix or a beta sheet. A model that we describe in Section IV.B provides an explanation for these labeling results. When a central pore is constructed in the shape of a funnel with the small opening at the cytoplasmic surface, the TM2 alpha helices form a tight cylinder at the intracellular surface but are splayed apart at the extracellular surface. This splay of the helices allows exposure of other TM segments, notably the extracellular third of TM1, to the hydrophilic reagents in the pore region. A similar conclusion was reached by Sansom and coworkers (1998b) with a model based on distance restraints. Additional uncertainty about the secondary structure of TM1 was provided by proteolytic digestion and mass spectrometry studies of the glycine receptor (Leite et al., 2000). This study found cleavage sites within the putative TM1 and TM3 transmembrane helices. The authors interpreted these cleavage sites as evidence for a mixture of alpha helices and beta

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sheets within this region (Leite et al., 2000). We suggest an alternative explanation—that the very aggressive detergents (Triton X-100) used in isolation of receptors before proteolytic digestion resulted in denaturation of the receptor and exposure of inappropriate cleavage sites. There is similar concern about the FTIR spectroscopy study that found only 50% alphahelical content after vigorous removal of the extracellular domain with proteinase K (Gorne-Tschelnokow et al., 1994). It is likely that proteolysis not only removed stabilizing interactions with the receptor domain, but also removed intersegment loops that orient the helices and prevent them from fraying. In contrast to the conclusion of the previous study, studies in the nicotinic acetylcholine receptor supported an all alpha-helical structure in the transmembrane domain (Methot et al., 1994; Methot and Baenziger, 1998). 2. Secondary Structure of TM3 This segment is the subject of much research and controversy. There is strong evidence that the loop that connects TM2 with TM3 is involved in transmission of a signal from the agonist-binding site to the ion channel pore (Ryan et al., 1994; Schofield et al., 1996; Lynch et al., 1997; Rajendra et al., 1997; Vafa and Schofield, 1998). If both TM2 and TM3 were alpha helices connected by a short loop, there would be analogy to the established mode of activation of transducin by rhodopsin (Farrens et al., 1996; Palczewski et al., 2000). In addition, it was shown that activation of GABA receptors by GABA increases the water accessibility of M3 membrane-spanning residues (Williams and Akabas, 1999). This result suggests that channel gating involves motion of the transmembrane segments relative to each other. It is of interest that the postulate on which interpretation of early scanning cysteine accessibility measurements (SCAM) was based (Akabas et al., 1994) is now being questioned. Williams and Akabas stated: “It is important to note that one of the original assumptions of SCAM was that only the water-accessible residues in membrane-spanning segments would be channel-lining residues; this appears to be incorrect” (1999). However, initial cryoelectron microscopy by Unwin (1993) did not observe clear evidence of alpha helices in the region of TM3. In the absence of this evidence, he proposed other, more random, structures for this segment. Support for Unwin’s suggestion was provided by a molecular modeling study in which it was found to be impossible to place five subunits of strictly antiparallel alpha helices around a central pore without severe steric overlap (Ortells and Lunt, 1994). In contrast, the model we describe in Section IV.B circumvents this problem by incorporating a left super twist in packing of the alpha helices in each subunit (Weber and Salemme, 1980). Our

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conclusion that TM3 is also an alpha helix is supported by an NMR study of a synthetic peptide corresponding to the putative TM3 from Torpedo californica. These authors concluded the TM3 segment has an alpha-helical structure (Lugovskoy et al., 1998). 3. Secondary Structure of TM4 The lipid–protein interface of the Torpedo nicotinic acetylcholine receptor was identified with the hydrophobic probe 3-trifluoromethyl-3(m-[125I]-iodophenyl)diazirine (Blanton and Cohen, 1994). The periodicity of the resulting labeling was consistent with both TM3 and TM4 being alpha helices. The cholesterol-binding domain in the nicotinic acetylcholine receptor has been located near TM4 with [125I]azido-cholesterol (Corbin et al., 1998b). Early studies demonstrated that the TM4 segment could be substituted with homologous sequences without loss of receptor function (Tobimatsu et al., 1987). As a result, this segment was believed to be less important than the other segments to channel function. However, more studies have shown that substitution of C418 in TM4 by tryptophan resulted in altered ion channel function (Tamamizu et al., 2000). The latter studies suggest that although TM4 is believed to be distant from the channel pore, it is important in gating of the ion channel. Moreover, the periodicity for the alteration of receptor assembly and ion channel function was consistent with an alpha-helical structure (Tamamizu et al., 2000).

D. BINDING SITES FOR INHALATIONAL ANESTHETICS IN PENTAMERIC LGICS There have been many studies on the sensitivity of LGICs to anesthetics (Mihic et al., 1997; Wick et al., 1998; Yamakura et al., 2000). Although these studies have been performed on many different LGICs, their association into a superfamily allows results from multiple receptors to be extrapolated to the relevant portions of other LGICs due to homologous amino acid sequences and techniques of multiple sequence alignment. This area has been reviewed (Yamakura et al., 2000), and only an overview will be presented here. In particular, the amino acid residue Ser 267 in the GlyR alpha 1 has been demonstrated as the prototype residue for modulation of anesthetic potency by site-directed mutation (Mihic et al., 1997; Wick et al., 1998). Although much evidence now supports the hypothesis that anesthetic action is directed at sites on receptor proteins (Franks and Lieb, 1984), there has remained a controversy as to whether site-directed mutations in these receptors alter a binding site for anesthetic molecules or affect the stability of

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the receptors by an allosteric mechanism such that anesthetic molecules can have an effect at lipid sites remote from the substitutions. In an attempt to settle this question, we mutated Ser 267 to a cysteine residue (S267C), and showed that covalent linkage of an anesthetic thiol (propanethiol) produces an anesthetic effect that is only partially reduced after washing to remove the agent from lipid and interfacial regions (Mascia et al., 2000). These studies suggest that anesthetic agents act directly at an intrasubunit binding site and that membrane and interfacial concentrations are probably irrelevant. These results also imply that mutation of Ser 267 in glyR alpha 1 affects the anesthetic binding site directly, as opposed to modulating sensitivity to anesthetics allosterically. There are amino acid residues on other TM segments that also affect potentiation by anesthetics and alcohols. We use the molecular models described below to demonstrate that these amino acid residues border a site that is both necessary and sufficient for the production of anesthesia (Yamakura et al., 2000). For example, in TM1 Ile 229 of the glycine receptor alpha 1 (GlyR alpha 1) (Greenblatt and Meng, 1999) has been shown to be important for anesthetic effects. In TM2, Ser 267 in GlyR alpha 1 (Mihic et al., 1997) and Met 270 in GABAaR beta 3 (Krasowski et al., 1998), and Leu 283 in AChR alpha 4 (Flood et al., 1999) are homologous amino acids and change anesthetic sensitivity when mutated (Lin et al., 1993; Moody et al., 1998). In TM3, Ala 288 in GlyR alpha 1 (Wick et al., 1998) and Ala 291 in GABAaR alpha 2 are also homologous amino acids that change anesthetic sensitivity when mutated.

IV. Molecular Models

A. MODELING TETRAMERIC LGICS Sutcliffe et al. (1996, 1998) used molecular modeling techniques to develop a model of the general class of ionotropic glutamate receptors. It is believed that these receptors are composed of a tetramer of trimers. Because there is no template for homology modeling that is readily identifiable by sequence matching alone, they began with predictions of transmembrane topology of several iGluRs via a combination of hydropathy plots and topology prediction algorithms. The three algorithms used were MEMSAT, TMAP, and PHD topology. The results from these procedures were combined with experimental data to arrive at a consensus set of sequences for the transmembrane regions and the order in which they were connected.

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Of particular significance was the data that suggested that the model of a tetrameric subunit was now really a trimeric subunit. The region formerly believed to traverse the membrane as the second alpha helix is now believed to form a nonmembrane-spanning loop that has similarity to the P segment of the potassium channel. Because many proteins with similar folds have markedly different primary structures, this secondary structure information could then be combined with the information on amino acid sequence in an effort to scan a database of proteins with known sequence and threedimensional structure. Noting the similarity of the transmembrane domain to that of the potassium channel, they searched the SCOP database for similar structures to no avail. Then, two databases (TOPITS and UCLA-DOE) containing proteins with known three-dimensional structure that are classified according to protein fold were searched. Although this search produced several appropriate templates for modeling the extra membranous portions of the receptor, it did not result in a viable template for the transmembrane region. A series of BLAST searches was then carried out in an effort to identify multiple sequences with homology to the transmembrane regions of the iGluR. Multiple-sequence alignment was carried out with both CLUSTAL W and Cameleon algorithms. These results were combined with results from the aforementioned transmembrane topology prediction algorithms to give a consensus of those regions that were conserved throughout evolutionary time scales, with regard to both sequence and fold homology. This consensus information was then used to identify the transmembrane region of the iGluR as being similar to that of the potassium channel. At the time, no published structure was available for the potassium channel, but one of the authors (Sutcliffe) had developed a preliminary structure of the channel. With this structure as a template, a final realignment of several iGluRs with the relevant potassium channel regions was performed. Only minimal insertions or deletions were allowed in the template structure during multiple sequence alignment. Amino acids known to be involved in surface glycosylation reactions, ligand binding, and disulfide bridge formation were aligned in such a way as to maintain these experimentally derived relationships. Final model construction was performed via three separate techniques. The first was with a fragment-based approach via the Composer software. The second was with a one-step approach via the Modeler algorithm. The third was via de novo modeling techniques incorporating various experimental data with methods from XPLOR, InsightII, and Sculpt. The final result was a model that agreed with a large body of experimental data. Even though some of this data were incorporated into initial model building, it is important to note that these relationships remained even after the many perturbations potentially introduced by the model-building and optimization process.

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B. MODELING PENTAMERIC LGICS Several molecular models of pentameric LGICs have been built, particularly for the AChR (Gready et al., 1997; Ortells et al., 1997; Sansom et al., 1998a; Corringer et al., 2000; Yamakura et al., 2000). The number of models is extensive, and the reader is encouraged to consult the original references because each model has its own strengths and weaknesses and each tries to incorporate different experimental and theoretical restraints. Here we present our own approach, which is directed at defining sites of anesthetic action. We obtained the sequences of nine related proteins in the superfamily of LGICs. These were AchR alpha 4, the Torpedo AchR, AchR alpha 7, GABAaR beta 1, GABAaR beta 3, GABAaR rho 1, GlyR alpha 1, GABAaR alpha 1, and GABAaR alpha 2. We predicted the topology of the transmembrane domains of these segments with the bioinformatics techniques PHDhtm and HMMTOP. We manually formed a consensus of the starting and ending residues in each transmembrane segment. The sequences were simultaneously aligned with the multiple-sequence alignments algorithm ClustalW. The averaged secondary structure predictions were added to the multiple-sequence alignment to give a clearer picture of regions of secondary structure and helical limits that were common to all nine sequences. This analysis clearly predicted that the four transmembrane segments were all alpha helices (TM1–4) with reasonably well-defined helical ends (Fig. 3). This secondary structure information was then used in conjunction with the SeqFold algorithm to search for a modeling template based on both sequence and fold homology/analogy. Although the SeqFold search of a modified version of the Protein Data Bank (PDB) produced several wellscored alignments, there was only one template that was of mammalian origin and showed an alignment over all four transmembrane domains. This template was the chain C domain 1 of bovine cytochrome oxidase (1 occ in the Protein Data Bank). In a qualitative search of both the SCOP and CATH fold databases, this same template was again the only one found that was a tetramer of alpha helices, mammalian in origin, and common to both databases (Fig. 4; see color insert). This template was then aligned with the sequence of GABAaR alpha 2. The alignment was initially scored based on sequence similarity, fold similarity, and the potential of mean force described by Sippl (1990). We used knowledge-based modeling to produce agreement with the hydrophilic and hydrophobic labeling studies (Fig. 5; see color insert), carried out in the homologous nicotinic acetylcholine receptors (Blanton and Cohen, 1994; Akabas and Karlin, 1995; Williams and Akabas, 1999), and with our proposed juxtaposition of Arg 273 and Asp 286 to form a salt bridge.

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FIG. 3. Multiple-sequence alignment and secondary structure prediction of LGICs. The primary sequences of nine LGICs within the nAChR superfamily were aligned with their superimposed secondary structure predictions. The shaded regions include transmembrane segments 1–4 (TM1–4). As described in the text, there is a small difference in the starting and ending residues predicted by different algorithms.

We also hypothesized that the reason the L232F mutation blocked the effect of halothane (Greenblatt and Meng, 1999) was that it was also in proximity to Ser 270 and Ala 291. Conformer libraries were used to provide reasonable initial orientations of the amino acid side chains. The loops between alpha helices were constructed to minimize fraying of helical ends. The TM1–2 and TM2–3 loops were constructed using the loop modeling features within the SwissPDB Viewer (Guex and Peitsch, 1997). These are based on loop fragment sequence similarities to loops of known three-dimensional structure. The large TM3–4 loop was removed and replaced by a series of six glycine residues. This substitution allowed adequate flexibility of the loop but maintained a reasonable distance between the end of TM3 and the beginning of TM4.

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The entire structure was subjected to sequentially restrained molecular mechanics energy optimization. We wanted to retain the interhelical packing of the helices in the template because previous studies have shown that a bundle of four antiparallel alpha helices provides stability (Weber and Salemme, 1980) and the possibility of internal binding sites for small molecules (Furois-Corbin and Pullman, 1986; Johansson et al., 1998). Because the sequence of GABAaR alpha 2 is quite different from the primary sequence of the template, we used a previously described technique to maintain the left-handed supertwist of the template of four alpha helices without imposing additional restraints on the backbone atoms (Sankararamakrishnan and Sansom, 1995). Each alpha helix was divided into an upper, middle, and lower third. Then a centroid of the backbone atoms of each third was defined (a pseudoatom). Distance restraints were applied to maintain an 11 A˚ separation between adjacent pseudoatoms of neighboring alpha helices. These restraints had the appearance of three squares, one each at the upper, middle, and lower levels of the tetramer. The structure was optimized using these restraints with Discover 98 (MSI, San Diego, CA) and the CFF91 forcefield. To allow the side chains of the tetramer to adjust to optimum packing before the helical backbone was distorted, the initial force constant of the centroid restraints was set to 100 kcal/A˚ 2, with subsequent reductions to 10 and 1 kcal/A˚ 2 in sequential optimizations. A template-forcing algorithm was then used to align the TM2 alpha helix of this tetramer onto the pore-lining alpha helix of the pentameric bacterial stretch receptor (1msl; Figs. 6 and 7; see color insert). This was repeated with five-fold symmetry to produce a pentamer of tetramers totaling 20 alpha helices that satisfied experimental data about proximity of residues (Fig. 8; see color insert). We found that simply superimposing the five subunits onto the template resulted in enormous repulsive energies due to unfavorable van der Waals contacts. A similar problem may have lead Ortells and Lunt (1994) to conclude that it was impossible to arrange five all alpha-helical bundles around a central pore. In our case, we solved the problem by making the initial superposition and then moving each subunit 5 A˚ in a radial direction out from the central pore. Then, with the template forcing algorithm described above limited to a movement of 0.2 A˚ per step, the assembly of five subunits was optimized with molecular mechanics in Discover 98. As the subunits moved toward their positions on the template, small distortions in the alpha helices of each subunit accommodated to the packing with nearby helices and prevented van der Waals overlaps. Simulated annealing with restrained molecular dynamics (SA/MD) produced our final structure (Fig. 9; see color insert). Two sets of restraints were used in these simulations. First, the distance restraints between alpha helices defined above were used for each subunit with a force constant of 10 kcal/A˚ 2.

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Second, the template forcing restraints that were used to construct the pentamer of TM2 segments onto the 1msl template were retained and assigned a force constant of 100 kcal/A˚ 2. This restrained structure was subjected to molecular dynamics with sequential steps (10,000 cycles of 1 femtosecond) of heating by 100K up to 500K, an equilibration step of 10,000 cycles at 500K, and then cooling steps of 100K. Finally, the structure was reoptimized with molecular mechanics and energies of both bonded and nonbonded interactions were recorded. This procedure produced a structure in which specific amino acid residues from all four transmembrane alpha helices were in direct proximity to one another. These residues are Leu 232 (TM1), Ser 270 (TM2), Ala 291 (TM3), and Val 407 (TM4). Although residues that were homologous to Ser 270 and Ala 291 were previously studied in the glycine receptor and found to be fundamental to anesthetic action, it is only more recently that the residue homologous to Leu 232 has also been shown to be important (Greenblatt and Meng, 1999). Last, this model predicts that Val 407 on TM4 should prove to be important for anesthetic binding in conjunction with the other three residues (Fig. 8; see color insert). 1. Kinetics and the State of a Receptor There is now reasonable evidence that the open state of the receptor is responsible for initially binding anesthetics (Raines and Zachariah, 2000). In the nicotinic acetylcholine receptor (nAChR), it is as though a site is available for anesthetic binding during channel opening and ion conduction such that the apparent binding affinity of ligand is enhanced due to anesthetic binding. This seems to occur via a change in the microscopic agonist-binding constant (Raines and Zachariah, 2000). Therefore, whether we are modeling the open or closed ion conductance state of the receptor is of paramount importance. Some of the amino acid labeling studies mentioned above have shown that sites of labeling are dependent on the open or closed state of the channel. The current model is based on the poreforming region of the bacterial stretch receptor, 1msl, that happens to be in its closed state (Fig. 6; see color insert). Current dimensions of the ion channel in our model, as well as that of the putative anesthetic binding site, would suggest a closed channel model. 2. Comparison of Current Theoretical Model with Experimental Results Our current model now accounts for a great deal of available experimental data on the pentameric LGICs. Most of the hydrophilic and hydrophobic labeling studies data, while initially incorporated into the model, remained valid even after multiple optimization steps and runs of molecular dynamics. The model agrees with results from NMR and cryoelectron microscopy with regards to the alpha helical nature of transmembrane region 2. Tryptophanscanning mutagenesis studies (Ueno et al., 2000) correlate well with the

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current model. Also, studies involving the binding of propanethiol to the glycine alpha 1 receptor (Mascia et al., 2000) are readily interpreted in terms of the current model. Two controversies surrounding our current model involve those of the secondary structure predictions of Le Novere et al. (1999) and the proteolytic data of Leite et al. (2000). Le Novere et al. (1999) used a combination of multiple algorithms to predict the secondary structure of the transmembrane portions of the pentameric LGICs. Their predictions state that the secondary structures of these regions is composed of a combination of alpha helices and beta sheets. The difficulty in their interpretation is that their prediction algorithms rely heavily on methods that are only appropriately applied to globular proteins and not those that span the membrane environment. In fact, they state: “This prediction should be taken with extreme caution, since, as noted above, the programs used were not designed to work on membrane proteins. Prediction methods based on analyses of globular proteins could incorrectly predict strands in helical transmembrane regions.” As we mentioned above, when we combined appropriate algorithms for the prediction of transmembrane topology with multiple-sequence alignment, the secondary structures of all transmembrane segments were predicted to be alpha helical. Leite et al. (2000) solubilized portions of the glyRα1 and subjected it to multiple proteolytic digestions. Although their data supports TMs 2 and 4 as being alpha helical, they found points of proteolytic digestion in the middle of the otherwise classically held limits of TMs 1 and 3. This led them to conclude that these latter two regions are not alpha helical in nature. However, their technique did submit the protein complex to very aggressive purification with stringent detergents and could easily have removed TM2. This possibility is supported by the fact that no trace of TM2 was found in any of their analysis. If TM2 were removed, then any proteolytic process would have ready access to the residues in question on TMs 1 and 3.

V. Summary

There has been rapid progress in molecular modeling of LGICs in recent years. The convergence of improved software for molecular mechanics/ dynamics, techniques of chimeric substitution and site-directed mutations, and the first X-ray structures of transmembrane ion channels will make it possible to build reasonable models of neuronal ion channels well in advance of publication of their crystal structures. These models will not only serve as guides for future site-directed mutagenesis, but they will also be a starting point for understanding the dynamics of ion channel gating.

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ALZHEIMER’S DISEASE: ITS DIAGNOSIS AND PATHOGENESIS

Jillian J. Kril1 Centre for Education and Research on Ageing, Concord Hospital Department of Medicine, The University of Sydney, Concord, New South Wales, Australia 2139, and Department of Pathology, The University of Sydney, Sydney, New South Wales, Australia 2006

Glenda M. Halliday Prince of Wales Medical Research Institute Randwick, New South Wales, Australia 2031

I. Introduction II. Diagnostic Issues A. Aβ Plaques and NFTs for Pathological Diagnosis B. Evaluation of Other Pathologies C. Clinical Correlates of AD Pathology D. Reproducibility of Current Clinical Diagnostic Protocols E. Summary III. Pathogenesis A. Brain Atrophy B. Neuronal Loss C. Aβ Deposition D. NFT Formation E. Mechanisms of Degeneration F. Summary IV. Genetic Inﬂuences A. Dominant Inheritance B. Genetic Risk Factors C. Summary V. Inﬂammation and Anti-inﬂammatory Drugs VI. Estrogen Therapy VII. Vascular Pathology in AD A. Vascular Risk Factors B. Summary References

1

Author to whom correspondence should be addressed.

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C 2001 by Academic Press. Copyright All rights of reproduction in any form reserved. 0074-7742/01 $35.00

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I. Introduction

In the century since the neuronal inclusions [neuroﬁbrillary tangles (NFTs); Fig. 1] and extracellular protein aggregates (Aβ plaques; Fig. 1) that form the pathological hallmarks of Alzheimer’s disease (AD) were described, our knowledge of all aspects of AD has grown markedly. AD is uniformly progressive and ultimately results in debilitating cognitive impairment. In the early stages, the impairment may only be apparent on

FIG. 1. The major pathologies resulting in dementia are neuroﬁbrillary tangles (NFTs; top left), Aβ plaques (top center), and Lewy bodies (top right, arrow). Diagnosis of AD was previously based on age-corrected densities of Aβ plaques; however, the ﬁnding that a signiﬁcant number of patients with dementia with Lewy bodies (DLB) also have Aβ plaques questions this practice. Similarly, NFTs can be found in other forms of dementia and thus are not speciﬁc for AD. Newer criteria proposed for the pathological diagnosis of AD use both Aβ plaques and NFTs. This change in the way in which AD is diagnosed pathologically will have a signiﬁcant impact on the clinical criteria for the identiﬁcation of AD. These clinical criteria have been validated using Aβ plaque-based pathology and now require re-evaluation in light of the advances in our understanding of the pathogenesis of the disease.

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neuropsychological testing; however, by end stage, few functions above the automatic remain unaffected (Forstl and Kurz, 1999). The pattern and sequence of functional deﬁcits and the relentlessness of the decline are relatively reproducible, with the course of the disease in one patient similar to that in others. AD remains the most prevalent form of late-life dementia and is the most signiﬁcant cause of morbidity in the elderly. Yet, we are still unable to, in most instances, accurately predict who will succumb to AD or to effectively treat it in those who do.

II. Diagnostic Issues

The deﬁnitive diagnosis of AD is made by pathological examination of the brain; however, the accurate and reproducible diagnosis of AD during life is of paramount importance. Not only is it essential to exclude possible treatable causes of dementia, but it is also necessary for the identiﬁcation of homogeneous groups of patients for evaluation and study, and for the recruitment of patients for drug and other therapeutic trials. Variability in the clinical diagnosis of AD is well recognized and major diagnostic issues continue to be addressed to develop accurate and reproducible criteria for its identiﬁcation. Yet, similar issues for the neuropathological diagnosis of AD are only beginning to be evaluated in a systematic fashion. In most instances, neuropathology is considered the “gold standard” for the diagnosis of AD, but considerable variation exists between diagnostic protocols. This has the potential to have a signiﬁcant impact on our understanding of the disease.

A. Aβ PLAQUES AND NFTS FOR PATHOLOGICAL DIAGNOSIS The majority of protocols for the pathological diagnosis of AD use only one of the major pathological lesions ﬁrst described by Alzheimer. The most commonly used lesion for the diagnosis of AD is the neuritic plaque as Aβ deposition is more distinctive for AD than other neurodegenerative diseases. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD; Mirra et al., 1991) developed the most widely used diagnostic protocol. It employs the earlier National Institutes of Health protocol based on age-corrected plaque densities (Khachaturian, 1985) in a semiquantitative fashion to arrive at diagnoses of varying certainties. The CERAD criteria (Mirra et al., 1991; Table I) has gained wide acceptance because of its accuracy and simplicity. In 142 cases with a clinical diagnosis of probable AD

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TABLE I CONSORTIUM TO ESTABLISH A REGISTRY FOR ALZHEIMER’S DISEASE (CERAD) CRITERIA a FOR NEUROPATHOLOGICAL DIAGNOSIS OF AD Plaque Densitiesb <50 Years

50–75 Years

Normal brain

N + No dementia

N + No dementia

Possible AD Probable AD

S, M, or F + No dementia Not deﬁned

Deﬁnite AD

S, M, or F + Dementia

S + No dementia S + Dementia, or M + no dementia S, M, or F + Dementia

>75 Years N + No dementia, or S + no dementia S + Dementia M + Dementia F + Dementia

a Regions examined—middle frontal, superior and middle temporal, inferior parietal cortices, hippocampus and entorhinal cortex, midbrain. b N = none, S = sparse, M = moderate, F = frequent plaques. From Mirra et al. (1991).

(the National Institute of Neurological and Communicative Disorders and Stroke (NINCDS)–Alzheimer’s Disease and Related Disorders Association (ADRDA) most certain clinical category, see Section II.C and Table II), 84% were found to have deﬁnite AD—7% probable and 2% possible (Mirra et al., 1991). In 7% of cases, the age-related plaque score was negligible, suggesting another cause for dementia in these cases. In an independent assessment of the accuracy of the CERAD criteria, subjects with a clinical diagnosis of probable AD had deﬁnite AD at autopsy in 27 out of 28 cases (96% Kosunen et al., 1996). Subsequent interlaboratory testing of the CERAD criteria revealed 75% agreement in the rank ordering of 10 cases between 24 neuropathologists at 18 centers (Mirra et al., 1994). The majority of variation was due to staining differences between laboratories, but the data suggest there is considerable variation between pathologists in the CERAD diagnosis of individual cases. In contrast to the plaque-based protocols, Braak and Braak (1991) proposed a staging scheme for the neuritic pathology of AD (NFTs and neuropil threads). This six-stage scale documents the temporal sequence and topographic spread of AD pathology. Although not proposed as a criteria for the neuropathological diagnosis of AD, dementia is reliably associated with stages V and VI and to a variable degree with stages III and IV (Braak and Braak, 1991; Harding et al., 2000). NFTs ﬁrst form in the pre-α layer of the transentorhinal cortex (stage 1, Table III), then the pre-α layer of the entorhinal cortex, the hippocampus, and ﬁnally the isocortex (Table III). Compared with the CERAD criteria, inter-rater reliability for neuritic staging is high (weighted kappa 0.85 to 0.97; Nagy et al., 1997b). However, the

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TABLE II NINCDS–ADRDA CRITERIA FOR CLINICAL DIAGNOSIS OF ADa Possible AD

Clinical diagnosis of • Dementia syndrome with variation in onset, presentation, or course but in absence of neurological, psychiatric, or systemic disorder sufﬁcient to cause dementia • Dementia in presence of second systemic or brain disorder sufﬁcient to produce dementia, but not considered to be cause of dementia • Single, severe, progressive deﬁcit in single cognitive domain

Probable AD Key features

Inclusion Criteria • Onset between 40 and 90 years • Dementia established on clinical examination and testing • Deﬁcits in two or more areas of cognition • Progressive worsening of functions Exclusion Criteria • No disturbance of consciousness • Absence of systemic or brain disease, which may account for progressive deﬁcits in memory and cognition

Probable AD Supportive features

• Progressive deterioration in speciﬁc cognitive function, such as language, motor skills, and perception • Impaired activities of daily living and altered behavior pattern • Family history of similar disorder • Normal CSF • Normal or nonspeciﬁc changes on EEG • Progressive cerebral atrophy on CT scan

Probable AD Suggestive features

After exclusion of other causes • Plateaus in course of progression • Associated symptoms of depression, insomnia, incontinence, delusions, illusions, hallucinations, catastrophic verbal, emotional or physical outbursts, sexual disorders, weight loss • Neurological abnormalities, especially in advanced patients—motor signs (increased muscle tone, myoclonus, or gait disorder), epilepsy • CT scan normal for age

Probable AD Features not consistent with AD

• Sudden apoplectic onset • Focal neurological signs—hemiparesis, sensory loss, visual ﬁeld deﬁcit, incoordination early in disease • Seizures or gait disturbance at onset or early in disease

Definite AD

• Clinical criteria for probable AD • Histopathologic evidence from biopsy or autopsy

a

Diagnostic certainty is ranked as possible, probable, or deﬁnite based on the features present. From McKhann et al. (1984).

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TABLE III BRAAK STAGING SCHEME OF NFT FORMATION DURING AGING AND AD Stage I II III

IV V

VI

Neuroﬁbrillary tanglesa NFTs in pre-α layer of the transentorhinal cortex Isolated NFTs in pre-α layer of entorhinal cortex proper Numerous NFTs in transentorhinal cortex Sparse NFTs in CA1 sector of hippocampal formation Severe involvement of transentorhinal cortex, including eNFTs Modest involvement of CA1 NFTs in subiculum Numerous NFTs in CA1, some in CA4 Mild involvement of isocortices, sparing of primary cortices All sectors of hippocampus involved Moderate involvement of subcortical nuclei Isocortex moderately involved Severe involvement of isocortices Severe involvement of subcortical nuclei Mild involvement of primary cortices

a

NFT = neuroﬁbrillary tangle, eNFT = extracellular or “ghost” neuroﬁbrillary tangle. From Braak and Braak (1991).

strict hierarchical order of NFT formation is not observed in all cases. In a study of 42 brains, Gertz and colleagues (1998) found that only six cases fully ﬁtted the expected pattern of NFT distribution. Most of these violations of staging order were in the early stages, suggesting they would not alter the effectiveness of the protocol for identifying AD. The NINCDS and ADRDA working group proposed a number of criteria for the neuropathological diagnosis of AD, which evaluated both Aβ plaques and NFT and excluded cerebrovascular disease (Tierney et al., 1988). However, the protocols have not been widely adopted, in part, because of their complexity and modest sensitivity. Comparison between NINCDS–ADRDA clinical and pathological criteria showed agreement in 8 of 9 nondemented controls, 18 of 38 cases with possible AD, and 18 of 19 cases with probable AD (Nagy et al., 1998). An evaluation of the Khachaturian, CERAD, NINCDS–ADRDA, and Braak methods for assessing AD in a group of 60 elderly subjects with known Mini-Mental State (MMS) score, revealed that all criteria accurately identiﬁed individuals with severe dementia (MMS 0–10; Jellinger et al., 1995). However, in moderately demented individuals (MMS 11–23) reliance on plaque-based criteria resulted in signiﬁcant underdiagnosis of AD. The majority of these subjects also had limbic NFTs (Braak stages III and IV). In addition, the use of plaque densities without assessing clinical state resulted in 5 of 9 nondemented subjects being classiﬁed as AD,

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indicating the presence of plaques is a poor indicator of the presence of dementia, at least in the very old. Because of these difﬁculties, the neuritic staging scheme of Braak has been combined with the CERAD protocol for assessment of plaques into the National Institute on Aging (NIA)–Reagan Institute criteria for the diagnosis of AD (Hyman and Trojanowski, 1997; National Institute on Aging and Reagan Institute Working Group, 1997; Newell et al., 1999). Topographical assessment of NFT type (intra- or extracellular) and Aβ plaque density is used to classify cases into high, intermediate, or low likelihood of AD. These criteria are the currently accepted “gold standard” for the diagnosis of AD, even though their reliability, reproducibility, and overall accuracy are yet to be determined.

B. EVALUATION OF OTHER PATHOLOGIES Further problems with assessing the accuracy of neuropathological diagnoses exist. In most studies, diagnostic validity is assessed by reporting cases that meet criteria for AD, regardless of whether other pathologies are present. In some instances, coexisting pathologies may contribute to the dementia process and thus be of importance in the assessment of the clinical criteria. In one study addressing this issue, diagnostic accuracy was 81% for AD (using CERAD criteria) including coexisting disease, but only 44% for pure cases (Bowler et al., 1998). The presence of infarction was the primary reason for the differences encountered. Furthermore, the identiﬁcation of a number of previously unrecognized dementing disorders, with clinical and/or pathological overlap with AD (e.g., dementia with Lewy bodies (DLB); Kosaka et al., 1984; McKeith et al., 1996, 1999; Fig. 1; and small vessel disease dementia (Pantoni et al., 1996)), calls into question the usefulness of many of the existing criteria for the diagnosis of AD. Because the majority of cases with DLB also have plaques (McKeith et al., 1996), the CERAD criteria cannot differentiate these disorders and the evaluation of intracellular pathology is required. In addition, the overlap between cerebrovascular disease and AD is well known (see Di Iorio et al., 1999; Breteler, 2000; de la Torre, 2000). However, the demonstration that small vessel disease alone can cause a clinical syndrome indistinguishable from AD (Pantoni et al., 1996) suggests that reevaluation of the role of this pathology is also necessary. Few studies have addressed these issues. The NIA–Reagan Institute criteria for the diagnosis of AD (Hyman and Trojanowski, 1997; National Institute on Aging and Reagan Institute Working Group, 1997) states that all pathologies should be evaluated, but does not suggest how overlapping diagnoses can be arrived at or how they

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should be incorporated into the diagnostic criteria. This represents a significant weakness in the current neuropathological criteria for the diagnosis of AD and must be addressed before the effectiveness of any criteria can be adequately evaluated.

C. CLINICAL CORRELATES OF AD PATHOLOGY Reevaluation of the clinical diagnosis of AD in light of the changing concepts of the pathology of the disease is necessary. It is no longer adequate that clinical criteria for AD alone are developed. Better diagnostic criteria for patients with similar core clinical features that differentiate the signs and symptoms of other pathologies are required. The most widely used clinical criteria are those established by the NINCDS–ADRDA (McKhann et al., 1984). Diagnoses of probable and possible AD (Table II) can be made using these criteria. Possible AD is considered when dementia is apparent in the presence of other systemic or brain disorders that, may themselves, result in dementia. A diagnosis of probable AD is considered when the patient is free from complicating diseases and when deﬁcits in two or more areas of cognition are present. The CERAD group (Morris et al., 1989) proposed a battery of clinical and neuropsychological tests to aid in the classiﬁcation of cases into the NINCDS–ADRDA possible and probable AD groups. A multicenter study of the NINCDS–ADRDA criteria, which evaluated 60 cases (40 with AD), showed an initial sensitivity of 0.81 and speciﬁcity of 0.73 (Blacker et al., 1994). These values were improved to 0.83 and 0.84, respectively, after consensus rating (Blacker et al., 1994). Validity studies that tested the NINCDS–ADRDA criteria against pathologically conﬁrmed cases show variable results depending on the cases included in the study. In “typical” cases, the agreement between probable and deﬁnite AD has been shown to be 100% (Martin et al., 1987; Morris et al., 1988). However, in unselected cases, accuracies of 68–76% and 88% for probable AD (Burns et al., 1990; Risse et al., 1990) and 78% for possible AD (Burns et al., 1990) were found. Thus, the NINCDS–ADRDA protocol for the diagnosis of AD has been well-validated within and across centers as correlating with the CERAD plaque-based pathological criteria. However, because these studies would have included cases with DLB and possibly other pathologies, reevaluation of the accuracy of these criteria is required. In addition to the NINCDS–ADRDA criteria for the clinical diagnosis of AD, a number of other diagnostic protocols for use in clinical and population settings have been developed. The Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV ) of the American Psychiatric Association (APA; World Health Organization, 1992) criteria require a

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deﬁcit in memory, as well as one other cognitive domain of gradual onset and progressive decline. These criteria result in similar classiﬁcation of patients to the NINCDS–ADRDA criteria (see above). In addition, the Clinical Dementia Rating (CDR; McCulla et al., 1989; Morris, 1993) and Mini-Mental State Examination (MMSE; Folstein et al., 1975) are used to determine the severity of dementia. These protocols are useful for screening for cognitive impairment as they are easy to administer and have been validated across a variety of social and ethnic populations. However, they lack speciﬁcity for AD. Pathological validation of these protocols has been performed, but as with other criteria, reevaluation is necessary in light of the changing nature of dementia diagnosis. It would be of interest for those centers with large clinical and pathological databases to reevaluate the clinical diagnosis of AD and similar dementia syndromes using the currently recommended neuropathological criteria for AD. This may help deﬁne better diagnostic tools that can then be evaluated longitudinally.

D. REPRODUCIBILITY OF CURRENT CLINICAL DIAGNOSTIC PROTOCOLS Several studies to test the reliability of the clinical criteria for AD have been performed. Both Lopez and colleagues (1990) and Kukall and colleagues (1990) used four raters to evaluate the NINCDS–ADRDA criteria. Using cases with dementia (AD and non-AD) and nondemented controls, percentage agreement ranged from 55% to 75% for pairs of raters (kappa coefﬁcients were 0.36–0.65) with the most experienced clinicians achieving the greatest agreement (Lopez et al., 1990). Interestingly, many of the disagreements in diagnosis were found between possible and probable AD categories. Although there is little disagreement on whether patients have dementia, the underlying causes of the dementia syndrome are less reliably agreed upon between clinicians. The inclusion of cases with multiple pathologies using the current criteria probably contributes to this variability.

E. SUMMARY Considerably more research is required on the diagnosis and deﬁnition of AD. The current recommended “gold standard” has yet to be widely validated, and protocols for overlapping pathologies need to be incorporated. This will, of course, have an impact on the clinical diagnosis of AD. At present, clinical criteria for AD cannot differentiate patients with different underlying disease mechanisms (e.g., DLB versus AD). In addition, it will be important to determine the clinical proﬁle of cases that have both

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NFTs and Aβ. Difﬁculties with the deﬁnition of the disease have important implications for any study of AD. From the point of view of researching the pathogenesis of AD, pure groups are desired to eliminate potential confounding causes. The continual modiﬁcation and improvement of criteria for the diagnosis of AD is therefore necessary until we understand, and either prevent or cure, this illness.

III. Pathogenesis

To understand the pathogenesis of AD, it is necessary to determine the sequence of events that occurs over the life of a patient. Because it is not possible to perform longitudinal cellular analyses in humans, most of our understanding of the pathogenesis of AD is inferred from patients sampled cross-sectionally at different time points in the disease process. Information concerning the initial events is the most patchy as it is extremely difﬁcult to determine, with accuracy, when the disease ﬁrst begins. The single greatest risk factor for the development of AD is age ( Jorm, 1990). However, we do not yet fully understand the normal aging process; thus, it is difﬁcult to distinguish the pathological process(es) underlying AD. We do know that dementia in old age is far from a universal phenomenon and that other factors must play a role in determining susceptibility to disease. These factors include genetic, environmental, and lifestyle factors, as well as coexisting disease. Although a decline in brain function with age is accepted as normal by many authors, increasing evidence suggests cognitive decline is not an inevitable consequence of aging (Rubin et al., 1998; Morris, 1999; Unger et al., 1999) but rather a manifestation of underlying disease processes. Longitudinal studies of community-dwelling elderly subjects do not ﬁnd a decrease in cognitive performance with advancing age (Rubin et al., 1998; Morris, 1999). Interestingly, those who do develop dementia may have a long preclinical period with stable deﬁcits (usually memory), which precedes a precipitous decrease in function (Rubin et al., 1998; Small et al., 2000). Such studies call into question the idea of an age-related decline in brain function and more likely represent cohort differences in health, education, and other factors. Nevertheless, many of these studies are performed on highly selected groups of elderly subjects who are free from neurological and systemic diseases, and although adequately addressing the question of age-associated cognitive decline, do not represent the majority of elderly subjects. Cognitive deﬁcits may be present in a proportion of elderly subjects, although these would be expected to have greater brain pathology. Numerous studies have shown an increased risk of AD in subjects with low education levels (primary school level or around 6 or less years of schooling)

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as compared with subjects with higher education levels. The level of increased risk varies between studies (Katzman, 1993; The Canadian Study of Health and Aging Study Center, 1994; Stern et al., 1994; Letenneur et al., 1999; Hall et al., 2000) but is generally found to be between 1.5 and 2 times that of the higher-educated reference groups. Nevertheless, the ﬁnding of an association between education and AD is by no means universal because a number of studies have found no relationship (Beard et al., 1992; Cobb et al., 1995). In particular, no association between autopsy-conﬁrmed AD and either education or occupation was found in a study of 115 patients with AD, although the authors suggest this may reﬂect different attitudes to consent to autopsy among groups with different education levels (Munoz et al., 2000). The mechanism that links low educational attainment and AD is unclear. Some authors suggested education provides increased functional capacity or “brain reserve,” which requires the brain to undergo a greater period of degeneration before the critical threshold for dementia is reached. Conversely, low education may reﬂect other factors such as lower socioeconomic status, increased likelihood of exposure to adverse events, or childhood deprivation (Hall et al., 2000). This latter hypothesis, referred to as “brain battering,” proposes that subjects with higher education have higher socioeconomic status and enjoy healthier lives with fewer coexisting brain diseases (Del Ser et al., 1999). This hypothesis is supported by the work of Del Ser and colleagues (1999), who, in an autopsy study, found patients with low education had more cerebrovascular disease than those with a high level of education. Gaining a better understanding of this association between low education and AD is of great importance because education is a modiﬁable factor and, unlike increasing age or genotype, amenable to intervention and possible correction.

A. BRAIN ATROPHY It is well established that the brains of older individuals are, on average, smaller than their younger counterparts (Dekaban, 1978). Although this may be interpreted as a loss of brain tissue with age, it may also represent cohort differences in body size as a result of improvements in nutrition and health standards (Miller and Corsellis, 1977). Cross-sectional in vivo studies have demonstrated atrophy of all brain compartments (Murphy et al., 1996; Yue et al., 1997), prefrontal grey matter (Raz et al., 1997), and hippocampus (Convit et al., 1995). In several in vivo studies, age-associated atrophy was found to be greater in men than in women (Matsumae et al., 1996; Murphy et al., 1996; Yue et al., 1997; Coffey et al., 1998). However, postmortem analysis of normal subjects ages 46 to 92 years ﬁnd no decrease in cortical volume,

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but signiﬁcant white matter atrophy (Double et al., 1996), suggesting that marked loss of cortical neurons is not a feature of normal aging. This is supported by studies in older primates (Peters et al., 1996) and by longitudinal MRI studies (Mueller et al., 1998; Fox et al., 2000). Because brain atrophy occurs in a number of conditions other than neurodegenerative disease (Kril and Halliday, 1999) and factors such as hypertension, smoking, and high alcohol consumption contribute to atrophy (Akiyama et al., 1997), rigorous exclusion criteria are necessary in cross-sectional samples investigating true age-related changes. A loss of cerebral white matter may underlie the slowing of mental processing identiﬁed in many elderly subjects (Howieson et al., 1993; Ylikoski et al., 1993). Overall, the data are consistent with the clinical ﬁnding that, at least in a proportion of the elderly, there is no substantial deﬁcit over time. In contrast to normal aging, cross-sectional studies show that there is marked cortical atrophy in AD (Fig. 2) and that the degree of atrophy correlates with the severity of dementia (Double et al., 1996; Mouton et al., 1998; Regeur, 2000). Atrophy in AD is most severe in the temporal lobe, particularly in the medial temporal lobe (Double et al., 1996; Convit et al., 1997; Detoledo-Morrell et al., 1997; Jack et al., 1998; Frisoni et al., 1999; Visser et al., 1999). More important, longitudinal analyses of brain volume conﬁrmed that marked temporal lobe atrophy distinguishes AD (Fox et al., 1996, 2000; Smith and Jobst, 1996; Kaye et al., 1997; Yamada et al., 1998) from the relatively constant brain volumes during healthy aging (Shear et al., 1995; Mueller et al., 1998). The greatly accelerated atrophy of the temporal neocortex, not the hippocampus, in AD patients is associated with the symptomatic onset of dementia (Fox et al., 1996, 2000; Smith and Jobst, 1996; Convit et al., 1997, 2000; Detoledo-Morrell et al., 1997; Kaye et al., 1997; Juottonen et al., 1998a, 1998b; Yamada et al., 1998), whereas atrophy of the hippocampus occurs 1 to 2 years before dementia onset (Fox et al., 1996; Convit et al., 1997). These data show that signiﬁcant cortical atrophy occurs in AD and distinguishes it from normal aging. The degeneration begins in the hippocampus and spreads to involve ﬁrst the temporal lobe and then other cortical association areas (Fig. 2).

B. NEURONAL LOSS Controversy exists over whether neuronal loss is a normal consequence of aging or is only related to disease processes. Many earlier studies using measures of neuronal density found widespread degeneration in older subjects (Brody, 1955; Henderson et al., 1980; Anderson et al., 1983; Terry et al., 1987), although this ﬁnding was not universal (Haug and Eggers, 1991).

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FIG. 2. At autopsy, AD is characterized macroscopically by generalized atrophy of the cerebral hemispheres (left panel), which results in widening of the sucli (upper), ventricular dilatation (V, lower), and atrophy of the hippocampal formation, causing dilatation of the temporal horn (TH, lower) of the lateral ventricles. Atrophy of the hippocampal formation can be detected in susceptible patients prior to the diagnosis of dementia (right upper). The atrophy progresses to involve the adjacent temporal lobe (right center) and then, ultimately, spreads to involve most regions of the brain (right lower).

The introduction of unbiased quantitative techniques has revolutionized quantitative neuropathology; however, in many instances, there is still uncertainty as to whether neuronal loss with aging occurs. Pakkenberg and Gundersen (1997) found a 10% decline in total estimated neuron number between 20 and 90 years of age. This study was performed on samples from the entire neocortex, regardless of anatomical or functional location, but has yet to be conﬁrmed by others. Interestingly, they also demonstrated a large (16%) difference in neuron number with gender, which is not as a result of differences in body height. Studies in which speciﬁc functional regions of the brain have been examined using unbiased techniques have reported variable results with regard

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to an age-associated loss of neurons. No loss of neurons was found in the superior temporal (Gomez-Isla et al., 1997) or entorhinal cortices (Gomez-Isla et al., 1996) of nondemented controls between the sixth and ninth decades, or from the locus coeruleus (Ohm et al., 1997). In the hippocampal formation, a loss of CA1 (West and Gundersen, 1990; Simic et al., 1997), CA4 (West et al., 1994), and subicular (West, 1993; West et al., 1994) neurons was reported. However, this is in contrast to the ﬁnding that the apparent reduction in CA1 neuron number with age can be accounted for by differences in cerebrum volume between younger and older adults (Harding et al., 1998). This relationship between premorbid brain size and hippocampal neuron number highlights some of the difﬁculties with cross-sectional cohort studies and suggests multiple factors need to be analyzed to determine potential cause and effect. The most consistent ﬁnding in AD is substantial neuronal loss from the entorhinal cortex and hippocampus (Fig. 3). This reﬂects the pattern of neuroﬁbrillary pathology, which is a cardinal feature of AD and appears to occur very early in the disease process (Braak and Braak, 1997). A 32% loss of neurons from the entorhinal cortex was found in AD patients

FIG. 3. Marked neuronal loss and NFT formation is seen in AD (right panels) compared with controls (left panels) in both the CA1 region of the hippocampus (upper panels) and cholinergic basal forebrain (Ch4, lower panels). Eventually, neuronal loss exceeds NFT formation in the hippocampus but is equivalent in the basal forebrain. Nickel peroxidase with cresyl violet counterstain.

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with a CDR score of 0.5, whereas a 48% loss was found in all AD patients (Gomez-Isla et al., 1996). When speciﬁc laminae were examined, the loss was more dramatic with a 60% loss of layer II neurons in mild AD and a 90% loss in severe AD (CDR = 3; Gomez-Isla et al., 1996). Marked neuronal loss from the hippocampus has also been described. Simic and colleagues (1997) found a 23% loss of neurons from the dentate gyrus and subiculum, whereas West and colleagues (1994) found a 25% loss from the CA4, 47% from the subiculum, and 68% from the CA1. The dramatic loss of neurons from the CA1 and subiculum has been conﬁrmed in other studies (Bobinski et al., 1995) and has been found to occur early in the disease process. Thus, the early atrophy noted clinically in medial temporal lobe structures (see above) is a result of marked neuronal loss in this region (Bobinski et al., 2000). Other consistently affected regions in AD are the cholinergic nucleus basalis (Vogels et al., 1990; Cullen et al., 1997; Fig. 3), the serotoninergic raphe nuclei (Aletrino et al., 1992; Halliday et al., 1992), and the noradrenergic locus coeruleus (Busch et al., 1997). These subcortical nuclei innervate cortical pyramidal neurons, capillaries, and arterioles, and play an important role in cortical synaptic neurotransmission and the neurogenic control of blood ﬂow through the capillary bed. The early loss of cortical cholinergic transmission is believed to lead to hyperactivity of acetylcholinesterase and a loss of cholinergic neurogenic control, thus signiﬁcantly contributing to the cognitive deterioration seen in AD (Bartus et al., 1982; Francis et al., 1999; Tong and Hamel, 1999). Hyperactivity of acetylcholinesterase underlies the currently recommended treatments for AD, which use cholinesterase inhibitors such as tacrine, donepezil, or rivastigmine (Francis et al., 1999; Ladner and Lee, 1998). Despite mixed success with such treatments, there is a great deal of evidence supporting the cholinergic hypothesis of AD. Choline acetyltransferase levels were found to correlate with cognitive impairment in AD (Baskin et al., 1999), whereas degeneration in cholinergic basal forebrain neurons correlates with MMSE score (Iraizoz et al., 1999), cortical atrophy (Cullen et al., 1997), the stage of cortical pathology (Cullen and Halliday, 1998; Iraizoz et al., 1999; Beach et al., 2000), and the earliest depositions of Aβ (Beach et al., 2000). Aβ potently inhibits various cholinergic neurotransmitter functions (Auld et al., 1998) by killing cortically projecting cholinergic neurons (Harkany et al., 2000). Furthermore, cortical cholinergic denervation elicits vascular Aβ deposition (Roher et al., 2000), suggesting a link between Aβ deposition, small vessel disease, and cholinergic cell loss in AD. In addition, it has been shown that the action of tacrine is through improving cerebral blood ﬂow rather than due its effects on neuronal cholinergic neurotransmission (Peruzzi et al., 2000). Although cortical atrophy is a consistent feature of AD (see above), whether this atrophy represents neuronal loss is not universally agreed upon.

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Earlier studies of neuron density found a widespread and marked loss of neurons in AD (Colon, 1973; Shefer, 1973; Ball, 1977). However, using unbiased techniques, Reguer and colleagues (1994) found no overall loss of cortical neurons in AD. This study, which was conducted on entire lobes of the brain, generated much debate (see commentaries in Neurobiology of Aging (1994) 15(3):353–380), the consensus of which was that regional and population differences do exist in AD and that they were masked by the quantitative technique used. Using unbiased techniques, total neuron number was found to decrease by 53% in the superior temporal gyrus (Gomez-Isla et al., 1997) and 30% in visual areas 17 and 18 (Leuba and Kraftsik, 1994). In addition, a study described the loss of microcolumnar ensemble organization in AD (Buldyrev et al., 2000), although the relationship between neuronal patterning and cell loss remains to be determined. A considerable amount of research is still required to evaluate the speciﬁcity of the disease process for cortical regions and neuron type, and to correlate these ﬁndings with atrophy, clinical indices, and the temporal sequence of events. As long as research remains concentrated on individual brain regions affected by AD, the entire disease process will not be fully understood.

C. Aβ DEPOSITION Aβ is a hydrophobic peptide, 39–43 residues long, which tends to form insoluble aggregates. There has been considerable debate about the toxicity of this peptide, with its neurotoxic activity believed to depend on its ability to form ﬁbrils (Haas, 1996; Neve and Robakis, 1998; Storey and Cappai, 1999; Wilson et al., 1999; Coughlan and Breen, 2000; Gandy and Petanceska, 2000). The peptide is derived by the proteolytic processing of its high molecular weight precursor, the amyloid precursor protein (APP). APP is a transmembrane protein with a small C-terminal cytoplasmic domain, one transmembrane domain, and a large N-terminal extracellular domain (Haas, 1996; Neve and Robakis, 1998; Storey and Cappai, 1999; Wilson et al., 1999; Coughlan and Breen, 2000; Gandy and Petanceska, 2000). The Aβ domain is partially embedded within the phospholipid bilayer. APP is cleaved via two proteolytic pathways, with only one pathway generating Aβ peptide (Haas, 1996; Neve and Robakis, 1998; Storey and Cappai, 1999; Wilson et al., 1999; Coughlan and Breen, 2000; Gandy and Petanceska, 2000). During transport to the cell surface, APP is cleaved at the membrane by an unknown protease called α-secretase into its soluble extracellular domain (sAPP) and a membrane-bound 10-kD C-terminal fragment. The membrane-bound fragment is further processed by, the as yet unidentiﬁed, γ -secretase at the C-terminal end of the Aβ domain into a small rapidly

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released peptide called p3. This pathway is the major processing pathway for APP and does not involve the production of Aβ. p3 is found in abundance in the plaques associated with aging (Dickson, 1997). Uncleaved APP that is reinternalized is processed in the endosome/lysosome system by two hypothetical enzymes called β- and γ -secretases. β-secretase cleaves APP at the N-terminus of the Aβ domain, creating a 12-kD intermediate peptide, which recycles back to the cell surface. γ -Secretase(s) cleave this intermediate peptide at the C-terminal end of the Aβ domain, releasing Aβ into the extracellular space. γ -Secretase cleavage occurs at one of two main sites producing mainly Aβ1–39/40 or sometimes Aβ1–42/43 (Haas, 1996; Neve and Robakis, 1998; Storey and Cappai, 1999; Wilson et al., 1999; Coughlan and Breen, 2000; Gandy and Petanceska, 2000). These peptides concentrate in the plaques found in AD (Iwatsubo et al., 1996; Dickson, 1997), although there is a general age-related increase in Aβ generation by neural cells (Turner et al., 1996), with the longer Aβ peptide being more amyloidogenic. The development of speciﬁc antisera for Aβ1–40 and Aβ1–42/43 has enabled the evolution and composition of plaques to be systematically studied (Iwatsubo et al., 1996; Dickson, 1997). The results suggest that Aβ1–42/43 initially forms the nucleus of a plaque, enabling the subsequent deposition of the more soluble Aβ1–40 and other protein fragments. This is consistent with the identiﬁcation of mainly Aβ1–42/43 in plaque cores of both demented and nondemented individuals (Fukumoto et al., 1996). Evidence suggests that protoﬁbrils of Aβ may also be toxic and that ﬁbril formation is concentration dependent (Hartley et al., 1999), with Aβ peptides changing from soluble forms in control brain to insoluble forms in AD brain (Wang et al., 1999). Although we know a lot about the production of Aβ, we know much less about its clearance from brain tissue. Evidence suggests that Aβ deposition is regulated by a speciﬁc protease that degrades extracellular Aβ (Iwata et al., 2000). Infusions of the protease inhibitor thiorphan into rat brain cause extracellular deposits of endogenous Aβ as diffuse plaques. The enzyme responsible for the clearance of Aβ peptides is neutral endoprotease or neprilysin (enkephalinase; Iwata et al., 2000). Cross-sectional analysis of cases at different stages of AD suggests the Aβ plaque deposition occurs only early in AD with resorption surpassing deposition at end-stage disease (Thal et al., 1998). This suggests that Aβ clearance mechanisms are largely intact throughout the disease process and that the disease starts with early excessive Aβ production and deposition. Cross-sectional studies suggest the progressive deposition of Aβ in the brain and microvasculature appears to precede the onset of dementia by many years. Examination of a large unselected autopsy series shows a small

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proportion of people in their 40s begin to deposit Aβ plaques in the basal cortex (Braak and Braak, 1997). Few people at these ages have dementia, and the low frequency of pathology is believed to represent very early “preclinical” disease. By the age of 74, 50% of the population will have Aβ plaque deposits (Duyckaerts and Hauw, 1997), although few people will have overt dementia at this age ( Jorm, 1990). At these and older ages, a subset of cognitively intact individuals have extensive neocortical Aβ plaque deposition (Price and Morris, 1999), reinforcing the concept of “preclinical” disease. Furthermore, an accumulation of AD-type pathology was shown to negatively correlate with the change in MMSE score in nondemented subjects, indicating that burden of pathology does reﬂect functional performance (Morris et al., 1996; Green et al., 2000) and thus may represent “preclinical” AD. However, the concept that normal aging is synonymous with preclinical AD, which then proceeds to clinical AD, requires close scrutiny prior to being universally accepted. Several sets of data are difﬁcult to reconcile with this model of a contiuum between aging and AD. NFTs are present in all autopsy samples from people ages 91–95 years, whereas approximately 20% of these subjects are free from plaques (Braak and Braak, 1997). This suggests that Aβ plaque accumulation may not be an inevitable component of aging. Alternatively, as discussed above, plaque-dominant AD has been proposed as a developmental stage of the disease only (Berg et al., 1998; Thal et al., 1998), with longitudinal data of cerebrospinal ﬂuid showing changes in Aβ levels are greatest within the ﬁrst 2 years of diagnosis (Tapiola et al., 2000). Although much research has concentrated on determining the cellular biology of Aβ production, there is only limited information on the relationship between Aβ deposition and measures of degeneration. Large cross-sectional studies incorporating volumetric, neuronal, Aβ deposition, and functional indices are necessary to determine the time sequence and relationship between these measures, particularly the role that Aβ may play in the neurodegeneration of AD.

D. NFT FORMATION NFTs were ﬁrst identiﬁed by Alzheimer in 1907. They consist of paired helical ﬁlaments of the microtubule-associated protein tau. In the normal brain, tau is bound to axonal microtubules where it stabilizes the microtubles, promotes their assembly, and allows fast axonal transport to occur (Goedert et al., 1991). In AD, tau becomes hyperphosphorylated and no longer binds to the microtubules, impairing their stability, and consequently impairing much of the normal function of the neuron. The hyperphosphorylated tau aggregates into paired helical ﬁlaments and ultimately NFTs. The

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gene for tau is on chromosome 17 and contains 15 exons. Alternative splicing of these leads to six isoforms of tau, ranging from 352 to 441 amino acids and with either three or four tandem repeats at the C-terminus end (Goedert et al., 1991; Tolnay and Probst, 1999). In normal brain, three and four repeat tau is expressed in approximately equal amounts, and these same isoforms are present, in a hyperphosphorylated form, in AD (Tolnay and Probst, 1999). NFTs progressively accumulate in the cell body and processes of neurons until the cell dies (Bancher et al., 1989; Braak et al., 1994). The earliest feature of NFT formation is the accumulation of hyperphosphorylated tau, which aggregates into insoluble granules (Bancher et al., 1989). This is called the “pretangle” stage and precedes the formation of the classical ﬁbrillar NFTs (“mature tangles”). Once the neuron dies, the largely insoluble NFT remains in the neuropil as a “ghost” or “tombstone” tangle (Bondareff et al., 1994). The time taken for an NFT to form and mature is unknown. Several estimates have been made based on extrapolation from relationships with disease duration. Bobinski and colleagues (1998) calculated it takes 3.4 years in the CA1 and 5.4 years in the subiculum for a mature NFT to become a ghost tangle. This, together with the ﬁnding of Morsch and colleagues (1999) that CA1 neurons with NFTs can survive for 15–25 years, suggests that NFTs are slow to develop and that the onset of pathology is many decades before the onset of clinical disease. This hypothesis is supported by the ﬁndings that the calculated time taken to progress from NFT stage I to IV is nearly 50 years (Ohm et al., 1995) and that lower scores on neuropsychological testing can be found as much as 10 years prior to onset of dementia (Elias et al., 2000; Small et al., 2000). NFTs and other abnormalities of tau are not unique to AD. Several other neurodegenerative diseases—such as Down syndrome, progressive supranuclear palsy, corticobasal degeneration, and parkinsonism–dementia complex of Guam and Pick disease—also have tau-positive inclusions (Tolnay and Probst, 1999). This has led to the collective name of tauopathies, and much effort has been expended to understand the commonality of these disorders. To date, a number of differences were found in the cellular populations affected and the tau isoforms expressed (Brion, 1998). However, similarities in types of tau deposited and clinical expression of the diseases were also described. Cross-sectional studies suggest that progressive NFT formation in the brain precedes the onset of dementia by many years. Examination of a large unselected autopsy series shows that a small proportion of people in their 20s begin to form NFT in the entorhinal cortex (Braak and Braak, 1997). Few people at these ages have dementia and the low frequency of pathology

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is not believed to affect cognitive function. By the age of 47, 50% of the population will have NFTs (Duyckaerts and Hauw, 1997), although few people will have overt dementia at this age ( Jorm, 1990). As mentioned above, all subjects ages 91–95 years have NFT formation (Braak and Braak, 1997), and although dementia is more prevalent at these ages, it is not inevitable ( Jorm, 1990). By 86 years of age, 50% of the population have sufﬁcient accumulation of NFTs to suspect a pathological diagnosis of AD, particularly in the presence of Aβ plaques (Duyckaerts and Hauw, 1997). At the age of 86 and older, approximately 20% of people meet NFT criteria for AD (Braak and Braak, 1997). This is consistent with the prevalence of clinical AD at these ages ( Jorm, 1990). In contrast to the Aβ deposits, NFTs accumulate in regions of neuron loss (Braak and Braak, 1997; Cullen and Halliday, 1998; Duyckaerts et al., 1998; Iraizoz et al., 1999), and their accumulation correlates with measures of functional decline (McKee et al., 1991; Arriagada et al., 1992; Bancher et al., 1993; Grober et al., 1999) and the degree of hippocampal atrophy (Bobinski et al., 1995; Nagy et al., 1996, 1999; Smith and Jobst, 1996). However, as described above, NFTs take many years to evolve and, therefore, the temporal relationship between the formation of NFTs and the rapid neuronal loss and brain atrophy in AD is difﬁcult to reconcile. In addition, as dementia is present only when NFTs occur in the neocortex and the extent of neocortical neuron loss is unclear in AD (see above), the association between this cortical degeneration and NFT and Aβ deposition needs to be further examined.

E. MECHANISMS OF DEGENERATION Studying the mechanism(s) of neuronal death in AD is difﬁcult because of the extended interval between the onset of symptoms and associated cell death, and investigation at autopsy. NFT formation is considered to be the major cause of neuron death in AD (Fig. 4), and cells dying as a result of NFT formation can be identiﬁed by the presence of ghost NFTs. However, reports show NFTs are not responsible for all the neuron loss seen in AD. Studies on the temporal (Gomez-Isla et al., 1997) and occipital (Leuba and Kraftsik, 1994) cortices, and hippocampus (Kril et al., 2000) have shown that neuronal loss exceeds the degree of NFT formation. This is in contrast to studies of the cholinergic basal forebrain in AD (Cullen and Halliday, 1998) and the parkinsonism–dementia complex of Guam (Schwab et al., 1998, 1999), where NFT formation does account for all the neuron loss. In the CA1 region of the hippocampus, NFTs were found to account for less than 20% of the neuron loss (Kril et al., 2000) suggesting that another

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FIG. 4. Many unresolved issues continue to plague our understanding of the pathogenesis of AD. Although it is known that neuronal death can occur due to NFT formation and cerebrovascular disease, such as cerebral amyloid angiopathy (CAA), altered perfusion, or microvascular pathology (pale arrows), the exact role of Aβ and inﬂammation are unknown (dark arrows). Aβ deposition and inﬂammation are both universal ﬁndings in the brain of AD, and the former is necessary for a pathological diagnosis of AD. However, whether either results in neuronal death, and if so by what mechanism, is yet to be determined.

major mechanism of neuronal degeneration occurs in AD. Unfortunately, the lack of a readily identiﬁable marker for this neuronal loss has made the identiﬁcation of its cause extremely difﬁcult. A study has shown that the prolyl isomerase, Pin1, is sequestered into the NFT and depleted in the brains of AD patients (Lu et al., 1999).

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Depletion of Pin1 may induce neuron death via mitotic arrest and apoptosis prior to the development of NFTs (Lu et al., 1996). The neuron-speciﬁc activator for cell proteins involved in the mitotic cycle is p35, which is proteolytically cleaved to produce p25, a fragment found to accumulate in the brains of patients with AD (Patrick et al., 1999). Application of Aβ1–42 induces the conversion of p35 to p25 (Lee et al., 2000). p25 links with cell cycle-dependent kinase 5 to hyperphosphorylate tau and promote apoptosis (Lee et al., 2000). Degenerating neurons in APP V717F Aβ-producing transgenic mice show chromatin segmentation and condensation, as well as increased TUNEL staining, suggestive of apoptosis (Nijhawan et al., 2000). This supports a link between Aβ deposition and apoptosis (Fig. 4). Increased TUNEL staining (Druganow et al., 1995; Lassmann et al., 1995; Smale et al., 1995; Bancher et al., 1997), as well as cleaved caspase 3 (Selznick et al., 1999; Stadelmann et al., 1999), an enzymatic marker of apoptosis, are found in vulnerable brain regions in AD. APP has been identiﬁed as a speciﬁc substrate for caspase 3 with the resultant peptides (including Aβ), inducing apoptosis (Gervais et al., 1999). Other apoptotic-speciﬁc caspases can also cleave APP (Pellegrini et al., 1999), and the resultant C-terminal fragment from such cleavage has been called C31 (Lu et al., 2000). C31 is also a potent inducer of apoptosis and was found in the brain of patients with AD (Lu et al., 2000), whereas caspase deﬁcient mice are resistant to this form of cell death (Nakagawa et al., 2000). Despite these studies that suggest apoptosis occurs in AD, apoptotic bodies and blebbing are not features of AD neuronal degeneration. In addition, the time sequence of such events remains to be determined. The chronic nature of the neurodegeneration in AD does not ﬁt well with the more rapid time course of apoptosis, which is believed to take only weeks or months at most (Stadelmann et al., 1999). Other mechanisms of neuronal death, such as necrosis, were also demonstrated in AD (Wolozin and Behl, 2000b). Indeed, the same triggers may cause either apoptosis or necrosis, including Aβ toxicity, oxidative stress, excitotoxicity, ischemia, and removal of trophic factors. The distinction between apoptotic and necrotic mechanisms, however, may be somewhat false given that neurons may begin with necrosis and then convert to apoptosis or alternatively begin with apoptosis and then undergo necrosis (Wolozin and Behl, 2000b). Although Aβ plaques are necessary for a diagnosis of AD, like NFTs, they are poorly related to the degree of neuronal loss. However, studies suggest the intracellular accumulation of Aβ may be neurotoxic (Fig. 4). There is an additional site of APP cleavage within the endoplasmic reticulum that gives rise to intracellular Aβ1–42/43, which over time reaches the concentration necessary for ﬁbril formation (Hartmann, 1999; Wilson et al., 1999). Cell rupture would release this intracellular Aβ into the surrounding extracellular milieu, which could stimulate further amyloid deposition. Although most

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cell types express APP, neurons produce the highest amount and preferentially use the intracellular pathways for Aβ production (Hartmann, 1999). It is difﬁcult to know how to prove or refute this model of AD neuronal vulnerability, although it is of interest that Aβ is not deposited within the vulnerable hippocampal formation or entorhinal cortex (Arnold et al., 1991) and no neuronal loss occurs in elderly APP-transgenic mice who show considerable Aβ deposits (Irizarry et al., 1997a, 1997b). Interestingly, a study identiﬁed nonpyramidal neurons containing Aβ1–42 around amyloid plaques in AD patients (Mochizuki et al., 2000), suggesting preserved neurons may concentrate these peptides intracellularly. In contrast, a number of studies suggest that soluble Aβ, and particularly Aβ1–40, is synaptotoxic without causing plaque formation or overt cell death (Mucke et al., 2000). Reductions in soluble Aβ1–40 concentrations correlate with synaptic loss in patients with AD (Lue et al., 1999). Interestingly, in the same patients, soluble Aβ1–40 levels correlate with cerebrovascular amyloid angiopathy and ApoEε4 allele frequency (Lue et al., 1999), suggesting a greater inﬂuence on vascular changes than neuronal degeneration.

F. SUMMARY Taken together, these studies suggest a multifactorial origin of neuronal loss in AD where a number of primary and secondary factors may cause neuronal death (Fig. 4). More work is needed to link all the potential cellular events that underlie the clinical symptoms of AD. At present, we do not have a good understanding of the association between Aβ deposition (required for a diagnosis of AD) and the degenerative process. The link between soluble Aβ and brain atrophy needs to be clariﬁed, and mechanisms of cell death other than NFT formation (and possibly apoptosis) need to be elucidated. It will be important to determine the time sequence of these events to target appropriate therapeutic measures.

IV. Genetic Influences

As many as 50% of patients with AD have at least one ﬁrst-degree relative with dementia (Writing Committee Lancet Conference 1996, 1996), and numerous studies have investigated family history as a risk factor for AD. Nine of the 14 case control studies reviewed by Jorm (1990) showed a signiﬁcantly increased risk of AD in subjects with a positive family history. The odds ratios ranged from 2.1 to 9.9 and reﬂect data obtained from prevalence and incidence studies.

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A. DOMINANT INHERITANCE It is estimated that between 5% and 10% of AD cases have a demonstrable pattern of inheritance. These cases, although rare, provide valuable insights into the pathogenesis of AD. To date, three genes have been identiﬁed. These are APP mutations on chromosome 21, presenilin-1 (PS-1) mutations on chromosome 14, and presenilin-2 (PS-2 ) mutations on chromosome 1. Each of these genes have an autosomal dominant pattern of inheritance, although PS-2 does not appear to have complete penetrance (St. George-Hyslop, 2000). These three identiﬁed genes do not fully account for all autosomal dominant cases of AD, suggesting other genes are yet to be identiﬁed. The APP gene encodes a transmembrane protein of 770 amino acids from which Aβ is derived (see section III.C above). The normal function of APP is not known, although it is highly conserved and expressed ubiquitously. In addition to AD, mutations in APP can also result in hereditary cerebral haemorrhage with amyloidosis–Dutch type (HCHWA-D). Mutations in the APP gene are mostly located in or around the amyloidogenic portion of the molecule, especially near the three secretase sites. Mutations in the PS-1 gene are the most common of the early-onset familial AD mutations, accounting for 30–50% of all autosomal dominant cases. PS-1 is a transmembrane protein that is also expressed ubiquitously and has six or eight transmembrane domains (Checler, 1999). There is an increasing body of evidence that suggests the presenilins function as the γ -secretase, or in close association with γ -secretase, in the production of Aβ (Checler, 1999; Ray et al., 1999; Wolfe et al., 1999) and thus increase the production Aβ1–42/43. More than 50 mutations in PS-1 have been identiﬁed. The majority of these are missense mutations and are scattered throughout the molecule. In addition, a number of splice acceptor mutations that cause the deletion of the sequence encoded by exon 9 were also described (Kwok et al., 1997; Crook et al., 1998; Smith et al., 2001). A proportion of PS-1 mutations with a deletion of exon 9 have AD with spastic paraparesis (SP; Crook et al., 1998; Verkkoniemi et al., 2000). In AD+SP, there is progressive weakness and wasting of the lower extremities and a later age of onset of dementia has been described in some of these families (Smith et al., 2001). The pathology of exon 9 mutations is also interesting in that very large, noncored, and faintly neuritic plaques are described (Crook et al., 1998; Smith et al., 2001). These have been termed “cotton-wool” plaques because of their size and uniform appearance (Fig. 5). The PS-2 gene encodes a transmembrane protein that is 67% homologous to PS-1 (Checler, 1999). Unlike APP and PS-1, PS-2 is expressed more strongly in peripheral tissues (pancreas, cardiac, and skeletal muscle)

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FIG. 5. Photomicrographs of the temporal neocortex of a patient with a presenilin-1 (PS-1) mutation. In the upper panel, both neuritic (arrows) and diffuse plaques can be seen. The diffuse plaques (inset) in these patients are unusual because they are large, only faintly neuritic, and lack cores. They have been termed “cotton wool” plaques and are found exclusively in patients with PS-1 mutations.

than in the brain (St. George-Hyslop, 2000). A small number of families with missense mutations in PS-2 have been identiﬁed, indicating they are much rarer than PS-1 mutations. The exact mechanism by which PS-2 mutations cause AD is unclear, although because of its sequence homology with PS-1, it is believe to have a similar function.

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The mechanism common to the known mutations is an increased production of Aβ1–42/43 and an increased rate of aggregation of Aβ plaques (see Wolozin and Behl, 2000a, for commentary). However, it appears that the PS mutations may also be involved in other aspects of the pathology of AD by participating in cell death due to apoptosis and in the phosphorylation of tau (see Checler, 1999; Czech et al., 2000). Our knowledge of AD has advanced substantially since the identiﬁcation of the mutations responsible for familial forms of AD and of the presenilins in particular. This rapidly moving ﬁeld of research provides valuable insights into the disease processes and has the potential for the development of strategies for therapeutic intervention. However, it is still unknown whether the knowledge gained from studying these cases is generally applicable to the majority of AD patients. In addition, the knowledge gained has still not elucidated the cause(s) of sporadic AD.

B. GENETIC RISK FACTORS Apart from dominant inheritance, the clustering of dementia within families must be viewed as evidence for the role of an individual’s genotype in determining their risk of AD. The apolipoprotein E (ApoE) gene, found on chromosome 19, encodes three isoforms of ε2, ε3, and ε4, and the presence of the ε4 allele has been found to increase the risk of AD (Katzman, 1994; Strittmatter and Roses, 1995). ApoE is involved in lipid transport and is present in the serum (Uterman, 1994). An association between ApoE ε4 and AD was ﬁrst described in 1993 in both sporadic (Saunders et al., 1993) and familial (Corder et al., 1993) AD. It has subsequently been conﬁrmed in many other studies of early- and late-onset AD and a variety of other neurological diseases, including other dementias (e.g., Roses, 1996; Stevens et al., 1997; Horsburgh et al., 2000). In addition, an allelic dose dependence has been shown where subjects who are homozygous for ε4 have a greater risk of AD at an earlier age than those who are heterozygous (Corder et al., 1993). In this study of families with late-onset AD, subjects with no ε4 had a mean age of onset of 84.3 years compared with 75.5 years in those with one ε4 allele and 68.4 years with two alleles. In addition to its effect on age of onset, ApoE genotype has also been shown to inﬂuence, albeit variably, the response to drug treatment. A poorer response to the cholinesterase inhibitor tacrine has been shown in patients with AD who possess the ApoE ε4 allele than those who do not (Poirier et al., 1995), although this effect has not been found in all studies (MacGowan et al., 1998). In addition, only patients with ε4 showed improvement in

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cognitive performance when treated with a drug that facilitiates noradrenergic and vasopressinergic activity in the brain (Richard et al., 1997). Some debate also exists over whether ApoE genotype modiﬁes the type or amount of AD pathology in individuals carrying the ε4 allele. Several studies(Schmechel et al., 1993; Nagy et al., 1995; Overmyer et al., 1999), but not all (Morris et al., 1995; Landen et al., 1996), have found an increase in the density of neuroﬁbrillary tangles and senile plaques in AD. Moreover, the correlation with brain pathology is further complicated by the ﬁnding that normal subjects in their forties and older who possess an ε4 allele have smaller right hippocampi than those without ε4 (Tohgi et al., 1997). It is unclear whether this ﬁnding represents a lifelong trait or is an indicator of “preclinical” AD. Longitudinal studies on such groups of subjects are necessary to clarify this issue. In patients with AD, greater brain atrophy (Lehtovirta et al., 1995; Juottonen et al., 1998b) and an increased rate of atrophy has been found in individuals with ε4 (Wahlund et al., 1999). However, this association has not been found in all studies (Barber et al., 1999). The mechanism of action of ApoE is not fully elucidated. ApoE is involved in the regulation of the transport of cholesterol and phospholipid and has an important role in the distribution of these molecules during periods of membrane remodeling, such as synaptic plasticity and membrane repair. In addition, ApoE–lipid complexes are believed to assist in the removal of Aβ via the low-density lipoprotein-related receptor (Wolozin and Behl, 2000a). Isoform differences in the behavior of ApoE have been identiﬁed (e.g., Strittmatter et al., 1993; Nathan et al., 1994), and these are believed to underlie the susceptibility to AD in individuals with the ε4 allele (Horsburgh et al., 2000; Wolozin and Behl, 2000a).

C. SUMMARY In addition to these genetic factors, other modifying inﬂuences have been identiﬁed (e.g., HLA, butyrylcholinesterase K, α 1 antichymotrypsin); however, the exact nature of the relationship between genotype and disease susceptibility remains obscure. Although there is strong evidence for an association between ApoE ε4 and AD, the presence of ε4 is not causative or is it necessary to develop AD. For these reasons, it is recommended that ApoE not be used for predictive testing (American College of Medical Genetics/American Society of Human Genetics Working Group on ApoE and Alzhemer’s Disease, 1995). Similar results are likely for other genetic risk factors. Nevertheless, such genotypes are important variables to be considered in research studies examining aspects of the pathogenesis

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and progression of AD, especially as reports of monozygotic twins that are discordant for AD (Creasey et al., 1989) suggest that inheritability is not solely responsible for one’s risk of AD. Few studies are integrating the multiple genotype analyses required to understand genetic versus environmental inﬂuences.

V. Inflammation and Anti-inflammatory Drugs

Numerous lines of evidence suggest a link between brain inﬂammation and AD (see Gahtan and Overmier, 1999; Halliday et al., 2000a). Initial evidence from clinical studies for a role of anti-inﬂammatory drugs in the prevention of AD came from case control studies that examined arthritis as a risk factor and found a reduced risk of dementia in patients who consumed anti-inﬂammatory drugs (Broe et al., 1990; Breitner, 1996). However, a number of similar studies were unable to identify a signiﬁcant reduction in risk (e.g., Heyman et al., 1984). This inconsistency may reﬂect the relatively small samples examined in each study individually because a meta-analysis of 17 studies showed a reduced risk of AD dementia in patients taking both steroidal and nonsteroidal anti-inﬂammatory drugs (NSAIDs; McGeer et al., 1996). It should be noted, however, that the majority of these studies were of cross-sectional design where signiﬁcant biases exist in selection of cases for study and the reporting of drug use (Stewart et al., 1997). Antigens of the major histocompatibility complex are intimately associated with inﬂammation and polymorphisms of the genes encoding these proteins have been associated with an increased risk of disease. In particular, CNS and peripheral diseases with an inﬂammatory basis occur more commonly in subjects who have a particular HLA genotype; notable among these is the association between rheumatoid arthritis and HLA-DR4 (Khan et al., 1983; Stastny et al., 1988). A number of different associations were described between AD and HLA alleles. In late-onset patients who do not have ApoE ε4 alleles, an increased risk of AD was found in patients with HLADR1, 2, or 3, and a reduced risk was found in patients with HLA-DR4 or 6 (Curran et al., 1997). However, these ﬁndings were not replicated by others (Middleton et al., 1999b), or only partly replicated (Neill et al., 1999), and the converse relationship (HLA-DR3 is protective) was found in a study of autopsy-conﬁrmed cases of AD (Culpin et al., 1999). In addition, an earlier age of onset by 3 years has been reported in subjects with HLA-A2 compared with other alleles (Payami et al., 1997; Combarros et al., 1998), and when the patient’s ApoE status was examined, the effect of HLA-A2 and

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ApoE ε4 appeared to be additive (Payami et al., 1997). A similar additive effect of HLA-A2 and ApoE ε4 has been found in early-onset familial AD (Ballerini et al., 1999). Other associations with HLA alleles were reported (Small et al., 1991; Middleton et al., 1999a), but these studies are yet to be replicated. It is therefore unclear whether the initial studies implicating anti-inﬂammatory medications as protective for AD are due to a direct effect on brain inﬂammation or are associated with genotype and disease susceptibility. To date, there have been only three longitudinal studies analyzing the question of drug protection in AD. Two of these studies (Stewart et al., 1997; Prince et al., 1998) found a beneﬁcial effect of NSAIDs. The Baltimore Longitudinal Study of Aging found a reduced risk of AD among users of NSAIDs and aspirin, which was increased the longer the drugs were used (Stewart et al., 1997). Prince and colleagues (1998) showed less decline in some tests of cognitive function in NSAID users, although the beneﬁt was reduced in older subjects. In contrast, a study of Australians ages 70 years or older (mean age of 80) found that NSAIDs or aspirin provided no protection against cognitive decline or incidence of dementia over a 3- to 4-year period (Henderson et al., 1997). Taken together, these studies suggest that some protection is conferred at ages when susceptibility is relatively low. It may be that sufﬁcient protection occurs only with long-term drug usage. It is therefore not surprising that clinical trials aimed at assessing the role of NSAIDs in preventing AD produced conﬂicting results. Rogers and colleagues (1993) performed a study of indomethacin in 28 patients and found a small but signiﬁcant slowing of cognitive decline in the treated patients. Conversely, Scharf and colleagues (1999) used an NSAID in combination with a gastroprotective agent and found no difference between groups in measures of cognitive performance. Drop-out rates in both studies were considerable (up to 50%) and follow-up times short (around 6 months), so neither study can be considered conclusive. Nevertheless, on cross-sectional analysis cognitive performance is improved in AD patients taking NSAIDs and aspirin (Broe et al., 2000) compared with their nontreated counterparts. Interestingly, this effect was present at low doses of aspirin, which are not considered to be anti-inﬂammatory suggesting the effect of these drug is not through reducing inﬂammation but through some other, possibly peripheral mechanism (Broe et al., 2000). Neuropathological studies demonstrated a close relationship between Aβ plaques and both reactive astrocytes and microglia (Rozemuller et al., 1992; McGeer and McGeer, 1995; Halliday et al., 2000b). Although a glial response might be expected to occur secondary to the degeneration in AD, evidence suggests the inﬂammatory response itself may contribute to the

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pathology of AD. Many of the proteins of the complement pathway, together with acute phase proteins, are found in Aβ plaques (see Walker, 1998) and are believed to be synthesized by microglia. In addition, activated microglia synthesize and excrete a number of inﬂammation-related substances that have been shown to be neurotoxic in rats (Weldon et al., 1998), and it has been suggested that microglia might facilitate Aβ deposition (see Gahtan and Overmier, 1999). Overall, the data show that patients with AD have an active immune response in the brain. An age-related increase in inﬂammatory microglia has also been found (Mattiace et al., 1990; Mackenzie and Munoz, 1998) which may reﬂect the brain’s reaction to the increased AD-type pathology in aging or, alternatively, indicate changes to the immune status of the elderly brain. Interestingly, this age-associated increase in activated microglia is ameliorated by NSAID use (Mackenzie and Munoz, 1998), unlike AD patients where NSAID use does not decrease inﬂammation (Halliday et al., 2000b). This suggests the disease process itself stimulates an immune response. Whether inﬂammation is a primary cause for the neurodegeneration in AD or a secondary event to aid in its clearance is still unclear because the sequence of these events is still poorly understood (Fig. 4). Although some epidemiological and clinical evidence suggests a beneﬁcial effect of treatment with NSAIDs, other research suggests any such beneﬁt is mediated through a noninﬂammatory mechanism (Broe et al., 2000; Halliday et al., 2000b). A clearer picture of the sequence of the early and subsequent cellular events in patients with AD would help clarify any direct role of inﬂammation in the disease process. The enhanced immune response in AD patients is now being used for a new type of treatment, Aβ peptide immunization (Schenk et al., 2000). Immunization trials are about to commence following the dramatic ﬁndings that transgenic mice that overproduce APP and deposit Aβ can recover following immunization (Schenk et al., 2000). Speciﬁcally, when the mice were immunized at a young age, they developed little if any Aβ depositions with advancing age. Moreover, the progression of both neuritic dystrophy and astrogliosis were signiﬁcantly reduced in the treated animals, suggesting the immunization had beneﬁts beyond simply reducing Aβ deposition. When immunization was begun at later ages when the mice exhibit Aβ deposition, further Aβ deposition was blocked and somewhat reversed, as was the neuritic dystrophy and astrogliosis. In addition, remaining Aβ deposits were often actively metabolized by microglia cells, questioning the premise that reduction of the activity of these cells by anti-inﬂammatory medications would be of beneﬁt in AD. These studies support the concept that the immune system may be harnessed into an appropriately targeted therapy for AD. If the trials of Aβ immunization are effective in AD, it will provide compelling evidence for its causative role in AD.

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VI. Estrogen Therapy

A number of studies examining gender as a risk factor for AD ﬁnd an increased risk in women, especially older women, even after controlling for education level and other factors, such as differential survival rates. This led to the hypothesis that hormonal factors may play a role in determining susceptibility to AD. This suggestion is supported by the ﬁnding that postmenopausal women receiving hormone replacement therapy have a reduced risk of AD (Paganini-Hill and Henderson, 1994, 1996). A reduced risk of AD was also identiﬁed in the Baltimore Longitudinal Study of Aging, a prospective study of the effect of estrogen replacement therapy on incident AD (Kawas et al., 1997). The mechanism by which estrogen protects from AD is unclear. It was suggested the mode of action is through estrogen-sensitive neurons in the hippocampus and cortex (Maki and Resnick, 2000), although evidence from transgenic mice showed that estrogen treatment increased the amount of the neuroprotective sAPP fragment, but did not reduce the production of Aβ (Vincent and Smith, 2000). Estrogen has also been shown to have antioxidant activity (Niki and Nakano, 1990) that may contribute to its protective role. Treatment of women with mild to moderate AD with estrogen for 1 year has not been found to improve cognitive function or slow the progression of the disease (Mulnard et al., 2000). Interestingly, however, nondemented subjects treated with estrogen have better cognitive performance and increased regional CBF than nontreated subjects (Maki and Resnick, 2000). This, together with the epidemiological evidence of reduced risk of AD in subjects treated with estrogen, suggests the beneﬁts of estrogen are lost after the onset of AD. This is not surprising as the regions found to be sensitive to estrogen (i.e., hippocampus, parahippocampus, and temporal cortex; Maki and Resnick, 2000) are the areas of the brain that are damaged earliest and to the greatest degree in AD (Braak and Braak, 1991; Gomez-Isla et al., 1996).

VII. Vascular Pathology in AD

There is mounting evidence for an etiological link between AD and vascular pathology. Although both AD and cerebrovascular disease are common in the elderly and their importance as independent causes of brain pathology is acknowledged (Kokmen et al., 1996; Snowdon et al., 1997;

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Shi et al., 2000), it is their possible synergy that provides exciting opportunities for the investigation of the pathogenesis of AD. Evidence exists to suggest that cerebrovascular disease may contribute to AD pathology by promoting non-NFT mediated neuronal loss (Grammas et al., 1999) and exacerbating Aβ plaque formation (Lin et al., 1999; Bennett et al., 2000). The relationship between AD and vascular disease is far from clear; however, we know that both infarction and microvascular pathology can be involved (Fig. 4). Abnormalities in cerebral white matter have been identiﬁed at autopsy in more than half the patients with AD (Englund, 1998). These include reduced vessel density, white matter pallor (rarefaction), and gliosis and thickening of the vessel wall. Such changes are believed to be the pathological correlate of the leukoaraiosis, which is frequently seen on neuroimaging in elderly subjects with and without dementia (Smith et al., 2000). In addition, numerous studies showed altered architecture of the cerebral microvasculature including atrophic vessels, glomerular loops, and tortuosities in AD (Ravens, 1978; Kalaria and Hedera, 1995; Buee et al., 1999). These were also shown to occur in normal aging, although they are more frequently encountered in AD. One of the main theories for how abnormal arterioles and capillaries can affect brain function is the disturbance of the normal laminar ﬂow that exists in blood vessels (de la Torre, 1997; Fig. 4). Brieﬂy, in situations of normal ﬂow, red blood cells travel in the center of a vessel where ﬂow is greatest. At the periphery, there is a cell-free zone with virtually no ﬂow, which allows for the transfer of nutrients and other molecules across the vessel wall. Alterations in vessel architecture result in turbulence with impaired ﬂow and, ultimately, impaired delivery of nutrients. Such turbulence would result in ischemic neuronal loss as a consequence of the failure of delivery of sustaining nutrients and may differentially affect those areas of the brain with higher metabolic demand, such as the hippocampus. Alternative mechanisms were also proposed for the link between AD and microvascular pathology. It has been demonstrated that microvessels isolated from patients with AD can result in neuronal death when cocultured with primary rat neurons (Grammas et al., 1999). The effect was also demonstrated when neurons were cultured with media conditioned by AD microvessels, suggesting a soluble substance is responsible for the neurodegeneration. The nature of the soluble toxin is not known at present, but a number of candidates such as nitric oxide, reactive oxygen species, and cytokines were suggested (Grammas et al., 1999). Several studies have reported an association between cognitive function in AD and the presence of brain infarction (Nagy et al., 1997a; Snowdon et al., 1997). In the Nun study, patients with AD and infarcts showed poorer cognitive performance than AD patients without infarction (Snowdon et al., 1997).

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Similarly, for an equivalent level of cognitive impairment, the density of plaques is less in AD patients with cerebrovascular disease than those without such disease (Nagy et al., 1997a). In addition, lacunar infarction with or without leukoencephalopathy was found in 20 of 25 cases with clinically probable AD and the majority of these had a lower Braak neuritic stage than demented patients without cerebrovascular disease (Goulding et al., 1999). Furthermore, it was demonstrated that impaired circulation can result in increased Aβ deposition. In experimental animals, chronic hypoperfusion can trigger the cleavage of APP and the formation of Aβ (Bennett et al., 2000), whereas in humans, soluble Aβ1–42/43 levels are similar in patients with multi-infarct dementia and AD (Kalaria, 2000). Overall, these studies show that AD pathology may be less severe when there is coexisting cerebrovascular disease and that cerebrovascular disease may contribute to AD-type pathology. The association between vascular disease and AD is further supported by the ﬁnding that subjects dying of cardiovascular disease show more AD-type pathology than those dying of noncardiac causes (Sparks et al., 1990; Sparks, 1997). In nondemented individuals dying of cardiac causes, the density of senile plaques is half that seen in AD (Sparks et al., 1990). The effect of ApoE ε4 in this population was not examined and a study by Irina and colleagues (1999), which was unable to conﬁrm the ﬁnding, suggested the association is due to ApoE ε4 and not cardiovascular disease per se. In addition to AD, an association between cardiovascular disease and ApoE genotype is well established (see Katzman, 1994), which further strengthens the link between vascular disease and AD.

A. VASCULAR RISK FACTORS Epidemiological evidence links cardio- and cerebrovascular factors with AD. In a longitudinal study, subjects who developed dementia had higher systolic blood pressure measured 15 years earlier than their nondemented counterparts (Skoog et al., 1996). Interestingly, at the time of diagnosis of AD, these same subjects had blood pressure similar to or lower than the nondemented subjects. This latter point may underlie the cross-sectional association described between higher blood pressure and better cognitive function in later life (Farmer et al., 1987). These studies suggest early and midlife events signiﬁcantly affect late-life neurodegenerative diseases. Diabetes mellitus, which is known to be associated with an increased risk of stroke, was also shown to be associated with an increased risk of AD (Kuusisto et al., 1997; Ott et al., 1999). A relative risk of 1.9 was found for both AD and dementia of any type (Ott et al., 1999). The risk is higher

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(RR 4.3, CI 1.7–10.5) in those treated with insulin, suggesting there is an increasing risk with increasing severity of diabetes. Patients with diabetes mellitus have a high incidence of vascular complications (West, 1978), and the reported association with AD may reﬂect increased cerebrovascular disease in these patients. A postmortem study comparing cerebrovascular pathology in diabetic and nondiabetic AD patients has not been performed. However, AD-type pathology was studied in diabetic subjects and is not increased (Heitner and Dickson, 1997). The associated risk is therefore likely to be due to non-AD pathology, most likely, vascular disease. Genetic predisposition may also play a role in the relationship between vascular disease and AD. In addition to the association with ApoE, polymorphisms of the angiotensin-converting enzyme (ACE) have been associated with an increased risk of AD (Hu et al., 1999; Kehoe et al., 1999). Despite associations between ACE and cardiovascular risk factors, the association is independent of ApoE (Hu et al., 1999). Thus, it appears there is a complex relationship between vascular disease and its risk factors and an increased risk of AD.

B. SUMMARY A hypothesis has been presented that links many of the identiﬁed and putative risk factors for AD and suggests a mechanism for their action. Crawford (1996, 1998) proposes an association between AD and cerebral blood ﬂow (CBF) by citing evidence that many of the factors that are linked with an increased risk of AD also decrease CBF (e.g., old age, depression, underactivity, head trauma). Similarly, it is suggested factors that increase CBF are associated with a decreased risk of AD (e.g., education, exercise, smoking, NSAIDs). Although the authors acknowledge that reduced CBF is not sufﬁcient to cause AD, the reported positive and negative associations provide tantalizing evidence for a common mode of action for many of the equivocal risk factors reported to date. This hypothesis is also consistent with other data that links microvascular damage and impaired blood ﬂow (de la Torre, 1997, 2000) and low education with increased cerebrovascular disease (Del Ser et al., 1999). Gaining a better understanding of the interaction between AD and vascular disease is of great importance. Not only will it provide insights into the pathogenesis of AD, but it may also provide us with a rare opportunity for the treatment and possible prevention of AD. A great many risk factors for vascular disease have been identiﬁed and intervention programs have successfully reduced the incidence of heart disease and stroke. The potential exists to provide the same level of success with AD.

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Acknowledgments

The authors are grateful to Heidi Cartwright for the preparation of the illustrations, Francoise Png and Smita Patel for bibliographic assistance, and Dr. Claire Shepherd for helpful discussions. Studies described in this article were conducted with ﬁnancial assistance from the Medical Foundation of The University of Sydney and the National Health and Medical Research Council (NHMRC) of Australia. J.J.K. is a Medical Foundation Fellow and G.M.H. is a Principal Research Fellow of the NHMRC.

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DNA ARRAYS AND FUNCTIONAL GENOMICS IN NEUROBIOLOGY

Christelle Thibault,1 Long Wang, Li Zhang, and Michael F. Miles2 The Ernest Gallo Clinic and Research Center, Wheeler Center for the Neurobiology of Addiction and Department of Neurology, University of California San Francisco, Emeryville, California 94608

I. Introduction II. DNA Array Formats and Technique A. DNA Array Formats B. Array-Based Expression Profiling C. Analysis of Array Results III. Applications in Neurobiology A. Gene Profiling in Neuronal Cells B. Gene Profiling in the Brain C. Applications in Neurogenetics IV. Caveats and Future Needs A. Sensitivity and Reproducibility B. Interpretation and Use of the Data C. Sharing Array Data D. Cost and Availibility of Arrays V. Conclusion References

I. Introduction

Initiatives such as the Human Genome Project have led to the characterization of an enormous amount of DNA sequence information. Already, the genome of 20 organisms, including those of S. cerevisiae, C. elegans, and D. melanogaster, have been fully sequenced. The identiﬁcation of every gene within genomes constitutes a tremendous task. However, characterization of gene function in isolation and within the context of an entire genome is clearly the challenge for the next generation of biological discovery. Such “functional genomics” efforts require a new type of science that combines high-throughput analytical methods together with extensive efforts in 1 Present address: Institut de G´en´etique et de Biologie Mol´eculaire et Cellulaire, B.P. 163, 67404 Illkirch Cedex, France. 2 Author to whom correspondence should be addressed.

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computional biology and bioinformatics. The development of DNA microarray technology is one such example of the emerging power of functional genomics. DNA microarrays theoretically allow the interrogation of expression levels for nearly all genes from even the largest of genomes. This technology has developed rapidly over the last 5 years, resulting in an exponentially increasing number of publications (Fig. 1). It has rapidly become apparent that expression proﬁling with DNA arrays is not simply a new technique for monitoring gene expression. Rather, this approach represents almost a new form of science that allows observation, hypothesis generation, and hypothesis testing in a nonbiased manner and at a genomic scale. In this sense, expression proﬁling is closely related to genetics in terms of evaluating the entire genome in a nonbiased manner. Furthermore, there are now abundant examples where expression patterns identiﬁed by DNA arrays have provided insight into the “phenotype” of a given experimental target. This “you are what you express” approach promises to contribute significantly to the functional classiﬁcation and treatment of cancer, the study of drug action and drug discovery, and the molecular understanding of disease. This review will provide a background discussion of the approaches and techniques underlying DNA arrays and the application of this form of study. DNA arrays can be used for purposes other than expression proﬁling, for example, for DNA sequencing (Pease et al., 1994) or single nucleotide polymorphism detection (Hacia et al., 1999). However, we focus this article on the use of these arrays in expression analysis. In particular, we highlight

FIG. 1. Growth of DNA array publications. The number of DNA array publications occurring in PubMed for the last 5 years are indicated.

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early work suggesting that DNA arrays may offer signiﬁcant advantages to the study of development and function of the central nervous system (CNS).

II. DNA Array Formats and Technique

A. DNA ARRAY FORMATS In essence, all DNA arrays are solid supports bearing series of DNA probes at discrete addresses available for hybridization to target DNA or RNA. Two main formats, cDNA and oligonucleotide arrays, can be distinguished by the size of the arrayed DNA fragments. The former usually contains cDNA inserts of more than 100 bases long, the latter oligonucleotides of 7 to 25 bases. Several variants of both formats differ by the method of arraying or support for DNA. Complementary DNA arrays can be made by robotically spotting or printing cDNA probes onto either glass microscope slides or nylon membranes. Although oligonucleotide arrays can also be made by robotic spotting, they are usually prepared by in situ synthesis of oligonucleotide probes directly onto a glass support or by chemical attachment of presynthesized oligonucleotides. Although oligonucleotide arrays are suited for both gene expression monitoring and DNA sequence analysis, cDNA arrays are mostly intended to measure mRNA levels. 1. Complementary DNA Arrays The glass cDNA microarray technology was largely developed in the laboratory of Pat Brown at Stanford University (Schena et al., 1995, 1996; DeRisi et al., 1996; Shalon et al., 1996). This technology, mainly used for mRNA levels quantiﬁcation, involves spotting of thousands of PCR-ampliﬁed cDNA inserts onto a glass surface and hybridization of these probes with cDNA or cRNA targets prepared from cell or tissue RNA samples (Fig. 2, Eisen and Brown, 1999). It has now been widely adopted by other academic investigators (Behr et al., 1999; Khan et al., 1999; Loftus et al., 1999; Luo et al., 1999a; Wang et al., 1999a; Whitney et al., 1999) and several biotech companies, such as NEN Life Science (http://www.nenlifesci.com/) and TeleChem International, Inc. (http://arrayit.com/). A series of reviews discusses many aspects of manufacturing and using cDNA arrays (Bowtell, 1999). In addition, the Brown Laboratory web site (http://cmgm.stanford. edu/pbrown/) contains complete manuals for constructing a cDNA arrayer and protocols for usage. A related web site also offers software for management and analysis of array data (http://cmgm.stanford.edu/pbrown/ mguide/software.html).

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FIG. 2. Diagram of cDNA array protocol. A typical scheme for preparation of spotted cDNA arrays is shown. The left-hand portion of the ﬁgure depicts preparation of the spotted cDNA arrays. The right-hand portion shows the generation of Cy3- or Cy5-labeled cDNA targets from two different starting RNA preparations. The Cy3/Cy5 ﬂuorescence ratio is generated from a single array and indicates the relative expression level for a given gene in the two starting RNA populations.

The source of spotted DNA may vary greatly. Clone sets carrying known genes and representative ESTs are commercially available at several companies, including American Tissue Culture Collection (http://www.atcc.org/), Genome System (http://www.genomesystems.com/), and Research Genetics (http://www.resgen.com/). The inserts in these plasmids may be ampliﬁed in 96 or 364 well plates using standard sets of PCR primers. Alternatively, cDNA libraries or collections of individual investigators may be used as a source for DNA probes. Thus, it is possible to construct an array biased toward a particular tissue, a given cell type, or a deﬁned set of functionally related genes. For example, Loftus et al. (1999) described microarrays enriched in neural crest-melanocyte cDNA. In this study, a database analysis approach was developed to identify a subset of ESTs preferentially expressed in neural crest-melanocyte tissues. Such a strategy could be useful for selecting appropriate cDNA to examine transcriptional proﬁles of developmental processes and diseases. This ﬂexibility has made cDNA array technology a particular attractive approach for many investigators.

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DNA spots are generated by deposition of a few nanoliters of puriﬁed PCR product, typically of 100 ∼ 500 µg/ml. Up to 5000 DNA spots per cm2 can be spotted on a glass surface. DNA spots range from 100 to 200 µm in diameter and are spaced by 200 to 500 µm in most cases. The support used is ordinary microscope slides. Different reagents including poly-L-lysine, amino silanes, or amino-reactive silanes are used for coating the slide surface to improve DNA coupling and limit the spread of spotted DNA droplets. DNA spotting on treated slides is performed robotically. The essential component most widely used is a quill-based spotting system (http://cmgm. stanford.edu/pbrown/). In this format, DNA is drawn into the quill by capillary action, and a small amount of DNA is then spotted by tapping the tip onto the glass slide (Schena et al., 1995). Before hybridization, spotted DNAs are cross-linked to the matrix by ultraviolet irradiation. The slides are further treated to reduce positive charges caused by residual amines and the spotted DNA is ﬁnally denatured by heat or alkali treatment. Different commercial manufacturers have subsequently developed arrayers displaying improved or different designs of spotting mechanisms. With an increase in the choice of arrayers and a concomitant decrease in price, it is now often preferable to buy a commercial arrayer. Additional information about commercial arrayers and many other issues regarding microarrays can be accessed through a variety of web sites (see http://industry.ebi.ac.uk/∼alan/MicroArray/). Although glass arrays have set the stage for cDNA array technology, other matrices may be used. Nylon membranes are the most common alternative support (Nguyen et al., 1995; Chen et al., 1998; Sehgal et al., 1998; Bertucci et al., 1999b; Bubendorf et al., 1999; Khodarev et al., 1999; Moch et al., 1999). Based on DNA spot diameters, spacing and densities, nylon membrane arrays are categorized into macroarrays and microarrays. Macroarrays refer to membranes with DNA spot diameter of typically 0.5 to 1 mm, spot spacing of 1 to 2 mm and a spot density of 10 to 100 per cm2. Microarrays typically contain DNA spots of 100 to 200 µm diameter spaced by less than 300 µm. Their spot density ranges between 500 and 5000 per cm2. In general, the same array spotting systems as described for glass arrays can be used for nylon membranes. However, certain types of pin such as the Pin-and-Ring system from Genetic Microsystems may perform better with membranes. Although some studies report the construction of custom membrane arrays (Bertucci et al., 1999a; Pi´etu et al., 1999; Song et al., 1999), a variety of commercial ﬁlters have been widely used (Chen et al., 1998; Ollila and Vihinen, 1998; Sehgal et al., 1998; Bubendorf et al., 1999; Khodarev et al., 1999; Moch et al., 1999; Rajeevan et al., 1999). These membranes contain anywhere from a few hundred to more than 20,000 cDNAs. For example, Clontech (http//www.clontech.com/atlas/) offers several cDNA arrays containing more than 500 human, mouse, or rat cDNAs with newer versions

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having 1176 cDNAs. Application-targeted arrays, spotted on membranes or glass slides, are also now being offered by a number of companies. 2. Oligonucleotide Arrays Unlike cDNA arrays, oligonucleotide chips are essentially commercial products. Affymetrix (Santa Clara, CA) has developed multiple high-density oligonucleotide arrays (GeneChip) for various applications, including largescale gene expression analysis, DNA resequencing, detection of single nucleotide polymorphisms (SNPs), and mutation screening (Lipshutz et al., 1995; Chee et al., 1996; Hacia et al., 1996; Lockhart et al., 1996). Hyseq, Inc., has developed universal sequencing chips containing an arrayed library of all possible oligonucleotides of a given length, usually 8 to 9-mer (Drmanac et al., 1998). Several companies also offer spotted oligonucleotide arrays, manufactured in a fashion similar to spotted cDNA arrays. These have generally been of low density due to the cost of synthesizing large numbers of oligonucleotides through traditional chemistry methods. The manufacturing of all GeneChip arrays involves a combination of solid-phase oligonucleotide synthesis chemistry and photolithography (Fodor et al., 1991, 1993; Pease et al., 1994). This approach allows the rapid production of very high-density arrays with a scalable manufacturing approach. The essential steps of the process are somewhat similar to photolithography techniques used by the semiconductor industry. The glass surface and nucleotides have reactive groups with a photo-labile protecting group. Oligonucleotides are synthesized in a massively parallel fashion simply by ﬂooding the glass surface with different nucleotides and using photolithography masks to dictate which sites have a nucleotide coupled when the array is exposed to light. Sequentially altering the nucleotide solutions and photolithography masks allows the synthesis of speciﬁc oligonucleotides (20 nucleotides in length) at known locations with very high density. The complete set of 4N polydeoxynucleotides of length N, can be synthesized in 4 × N cycles. Large-scale commercial manufacturing methods allow for approximately 300,000 polydeoxynucleotides to be synthesized on small 1.28- × 1.28-cm arrays. Oligonucleotide arrays carrying more than 1 million probes are also reportedly being developed (Lipshutz et al., 1999). The selection and ﬁnal arrangement of oligonucleotide probes on the arrays is application speciﬁc. For expression analysis, oligonucleotides are chosen based on sequence information from known genes and ESTs and served as probe for hybridization to RNA samples. Each gene is represented by a set of 20 different 20-mer oligonucleotide probe pairs synthesized side by side on the chips (Lockhart et al., 1996; Wodicka et al., 1997; Fig. 3). More

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FIG. 3. Oligonucleotide array protocol. The experimental protocol for preparation of target and hybridization of GeneChip oligonucleotide arrays is depicted. The arrays are prepared by photolithographic synthesis of oligonucleotides in situ, as described in the text. Biotinlabeled complementary RNA (cRNA) is synthesized from the starting RNA sample by reverse transcriptase (RT) and DNA polymerase (Pol) in the presence of an oligo(dT) primer containing a T7 RNA polymerase recognition site [Oligo(dT)-T7]. This then allows the synthesis of cRNA with T7 polymerase (T7 pol). Following hybridization, target cRNA is quantitated by staining with streptavidin/phycoerythrin and scanning confocal microscopy. Comparing the hybridization intensity for a given gene on two different arrays generates relative expression changes.

recent arrays disperse the 20 different oligonucleotide probes pairs randomly across the chip to avoid systematic errors from local background ﬂuctuations. Each pair corresponds to a perfect match (PM) oligonucleotide, perfectly complementary to the gene sequence of interest, and a mismatch (MM) oligonucleotide, identical to its PM counterpart except at its central position where a mismatch base is inserted. Gene expression levels are calculated based on the difference in PM and MM average intensity across the entire set of probes. This approach reportedly reduces the contribution of background and cross-hybridization, while increasing the quantitative accuracy and reproducibility of the measurements. Commercial oligonucleotide arrays currently are available for human (∼40,000 genes and ESTs), mouse (∼30,000 genes and ESTs), and rat (∼24,000 genes and ESTs) as well as a set containing all yeast open-reading frames. One drawback to this system is the cost. The oligonucleotide arrays themselves are expensive and require a dedicated scanner, ﬂuidics station, hybridization oven, computer workstation, and analysis software. The high price of this system has prevented its use by many academic centers. However, analysis on Affymetrix arrays

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can be done by a commercial source (Research Genetics), thus eliminating some of the startup equipment expenses. Furthermore, the cost of arrays has decreased signiﬁcantly during the last 2 years. 3. Summary Since the ﬁrst description of two-color hybridization to microarrayed DNA on a solid support (Schena et al., 1995), multiple DNA arrays technology have been developed. Currently, spotted cDNA or oligonucleotide arrays are the most widely used, due mainly to their relative affordability, ﬂexibility, and the signiﬁcant amount of web-based support for such systems (http://cmgm.stanford.edu/pbrown/). In the short time since the introduction of high-density DNA arrays, a large number of commercial vendors have developed products for this area. This offers the hope of rapid progress in both quality and sensitivity of the equipment and their widespread usage due to improved pricing. Furthermore, the equipment for making and analyzing spotted arrays is well-suited to core facilities due to the high throughput of the arrayers. Many universities have or are currently establishing such array core facilities. B. ARRAY-BASED EXPRESSION PROFILING All DNA array technology applications involve two main techniques: DNA sequencing and gene expression monitoring. Both are hybridizationbased and use the inherent property of nucleic acids to recognize and base pair with complementary sequence. Expression chips are designed to evaluate the absolute representation of thousands of RNA species in cell or tissues simultaneously, or to assay their relative abundance between two or more samples. They are commonly used to measure changes in gene expression as a function of cell or tissue type, physiological state, or pharmacological treatment. 1. Gene Expression Monitoring Array-based mRNA quantiﬁcation methods are a direct extension of Northern and dot blot analyses. In these traditional hybridization techniques, a complex mixture of RNA is spotted onto a solid support and interrogated for the abundance of a given target by incubation with a labeled complementary probe. When using DNA arrays, a large collection of probes is bound at discreet locations onto a solid substrate and a complex mixture of labeled target RNAs is monitored simultaneously. The number of probes present on the chip determines the number of target mRNAs screened in a single hybridization. The abundance of individual nucleic acids in the target

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sample is reﬂected by the intensity of hybridization to their corresponding probe on the chip. All experiments are conducted under conditions of a large excess of probe relative to labeled target to minimize interprobe competition. The nature and preparation of target samples as well as detection methods vary according to the type of array used. Nylon membrane arrays commonly use 32P- or 33P-labeled cDNA as targets and autoradiography detection methods (Bubendorf et al., 1999; Khodarev et al., 1999). However, analysis of gene expression using chemiluminescence and colorimetry methods has also been successfully conducted on high-density ﬁlter arrays (Chen et al., 1998; Rajeevan et al., 1999). Target samples are typically prepared by oligo (dT)-primed reverse transcription of an entire population of total or poly-A+ RNA, in the presence of labeled dCTP. Each sample is hybridized to a different membrane or sequentially to the same membrane after stripping off the previous target sample. Following hybridization and adequate washing, membranes are analyzed by high-resolution phosphor imaging. Commercial readers and arrayers usually provide adapted software for data analysis. Alternatively, custom software have also been developed (Bubendorf et al., 1999). The printed cDNA microarray technology uses ﬂuorescently labeled cDNA as targets and a two-color confocal detection method (see Fig. 2, Schena et al., 1996). Two RNA samples to be compared are reversetranscribed into cDNA in the presence of two spectrally distinct ﬂuorescent dyes. They are then mixed together and subsequently hybridized on the same chip. The relative representation of a given mRNA between two samples is determined by measuring the ratio of the ﬂuorescent intensities of the two dyes at the cognate probe. The most common combination of labeled nucleotides is Cy3- and Cy5-dUTP. Images produced using these two dyes can be acquired with a laser confocal microscope emitting light at 540 and 650 nm, respectively. Software for quantitation of Cy3/Cy5 hybridization intensities, identiﬁcation of spots, and data management are available from a number of commercial sources or as downloadable software (see http://industry.ebi.ac.uk/∼alan/MicroArray/ or http://cmgm.stanford. edu/pbrown/mguide/software.html). Quantiﬁcation of mRNA levels with Affymetrix GeneChips uses ﬂuorescently labeled cRNA as target and confocal scanning microscopy for detection (Fig. 3, Lockhart et al., 1996). The method of cRNA preparation is derived from the antisense RNA ampliﬁcation technique developed by Eberwine (Kacharmina et al., 1999). The entire population of total or poly-A+ RNA from cells or tissues is ﬁrst reverse-transcribed into doublestranded cDNA using an oligo (dT) primer containing the promoter sequence for the bacteriophage T7 RNA polymerase. This enzyme is then used to in vitro transcribe cDNAs into cRNAs in the presence of biotin-labeled

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dCTP and dUTP. This latter step produces a linear ampliﬁcation of the target. Before hybridization, biotinylated cRNAs are fragmented into small oligonucleotides to minimize secondary structure formation. Target cRNAs are then hybridized each to a different chip. Arrays are subsequently stained with phycoerythrin conjugated-streptavidin, washed, and scanned at a single wavelength. Following scanning, absolute and comparative analyses are completed using Affymetrix GeneChip software (Lockhart et al., 1996). Each of the methodologies described has speciﬁc advantages and limitations that may inﬂuence the choice of array used in a given experiment. For example, unlike oligonucleotide and cDNA glass arrays, which are singleuse only, membrane arrays can be stripped and rehybridized three to ﬁve times. The use of radioactive targets with these ﬁlters offers a high intrinsic sensitivity and a large dynamic range of detection. Expression levels can be measured using 1 to 10 µg of starting total RNA and relative mRNA abundance can be determined down to 1 in 10,000 or less (Granjeaud et al., 1999). Glass arrays, however, are unique by allowing a two-color hybridization approach valuable for direct comparison of two samples to the same target. The use of ﬂuorescence and confocal microscopy offers a high resolution of detection and quantiﬁcation of relative abundance of about 1 in 100,000 (Granjeaud et al., 1999). However, the use of ﬂuorescently labeled cDNA instead of 33P-labeled cDNA increases the amount of starting total RNA necessary to 100 or 200 µg (Bowtell, 1999). Ampliﬁcation of target samples using T7 RNA polymerase as described in Affymetrix protocols reduces this amount to 5 or 10 µg (Lockhart et al., 1996). Such an approach can, however, easily be adapted for hybridization to slide arrays (Luo et al., 1999a). Although the quality of printed cDNA arrays can vary greatly with the source of cDNA spotted, direct in situ synthesis of oligonucleotide probes onto glass generates highly reproducible chips. In addition, at present, no other method gives such high density of probes. The redundancy of oligonucleotides representing each gene on these arrays improves the quantiﬁcation, speciﬁcity, and reliability of measurement (Lockhart et al., 1996). The use of oligonucleotides is also advantageous by theoretically allowing one to avoid gene regions that are repetitive or homologous to other genes. However, because oligonucleotide probes are designed based on sequence information alone, unknown genes cannot be screened using these arrays. Furthermore, the design and synthesis of oligonucleotide arrays by photolithography also severely limits the ability to easily update or change the content of the arrays. 2. Summary DNA array technology offers tools for functional genomics through gene expression analysis and DNA sequencing. Array-based techniques all involve

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hybridization of a complex mixture of labeled nucleic acid targets to a deﬁned set of DNA probes on a chip. In expression studies, targets typically represent the entire mRNA population of a cell or tissue sample. They are prepared by reverse transcription followed or not by in vitro transcription and are radioactively or ﬂuorescently labeled. Multiple cDNA or oligonucleotides complementary to speciﬁc mRNA species are used as probes on the chip. Probes are typically oligonucleotides complementary to a given reference sequence and/or to its subsequences. Array studies mainly involve comparative analyses between a test and reference samples and use differences in pattern and intensity of hybridization signal to extract information on the nucleic acid target sequence or on its abundance. C. ANALYSIS OF ARRAY RESULTS 1. Data Management and Elemental Analysis In many regards, DNA array technology would not be possible without considerable advances in bioinformatics. The complexity of the hardware and the immense size of data involved in the microarray technology make it necessary to have computer assistance at every step from manufacturing the arrays, image scanning, data storage/retrieval, data mining and analysis, and ﬁnally, to publication (web based). The bioinformatics issues that involve these steps have been addressed in several reviews (Ermolaeva et al., 1998; Bassett et al., 1999; Claverie, 1999; Vingron and Hoheisel, 1999; Zhang, 1999). Here, we focus on data analysis issues. Simple sorting and ﬁltering of array data is often used to generate lists of genes showing the largest or “most signiﬁcant” changes due to a given biological perturbation. In many cases, the biologically interesting changes in gene expression do not exceed twofold, which is currently the common detection limit of the available methods (Audic and Claverie, 1997). Using traditional measures of statistical validity is problematic with array data given the large number of simultaneous observations. Correcting for the number of observations would require a prohibitively large number of experimental repetitions or high statistical thresholds (Claverie, 1999). Hilsenbeck et al. (1999) has proposed to use principal component analysis to identify signiﬁcant altered expression, but some controversies remain (Wittes and Friedman, 1999). However, in general, there appears to be agreement that multivariate approaches such as clustering (see below) allow detection of signiﬁcant correlation in expression proﬁles (Claverie, 1999). These correlated groups of genes or samples allow interrogation of expression changes that, for a single gene or sample, might not approach statistical signiﬁcance.

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Additional tools to aid DNA array data analysis have included multiple visualization techniques to look at the same data. Projecting the expression data onto known pathways and genetic circuits provides valuable clues to functional roles of the genes in context (DeRisi et al., 1997). There are commercially available programs that make this task easier (e.g., GeneSpring by Silicon Genetics, Inc., http://www.sigenetics.com/GeneSpring/ Overview.htm). 2. Multivariate Analysis DNA array technology generates large sets of multivariate data. It is thus natural to use multivariate statistical methods, such as clustering and multidimensional scaling, to organize and visualize these data. In a clustering algorithm, data are grouped into clusters so similar incidents are in the same clusters. For DNA array data, typically used clustering methods are hierarchical clustering (Eisen et al., 1998; Alon et al., 1999) and self-organizing maps (Tamayo et al., 1999; T¨or¨onen et al., 1999). Genes that display similar patterns of expression across a set of experimental conditions suggest there may be related biological functions among the genes. Ideally, functionally related genes are grouped into the same clusters. By examining the gene expression clusters, one may deduce new functions based on partial knowledge of the functions of the genes in the clusters. In addition, genes in a given cluster may share mechanisms of regulation such as common promoter motifs (DeRisi et al., 1997). Efforts have been made to reﬁne the performance of clustering algorithms for array data (Heyer et al., 1999). An additional use of applying clustering techniques to array data is to discern relationships between the “samples” rather than just the “genes.” Using two-dimensional hierarchical clustering approaches, several papers have produced a molecular classiﬁcation of cancer (Golub et al., 1999; Ross et al., 2000). The expression proﬁles of a given cancer biopsy or cell line contain predictive information about the biological behavior of the particular sample. Thus, such efforts may produce an improved understanding of cancer biology and the performance of various treatment modalities. Hierarchical clustering assumes that the association of genes in the cluster is based on an underlying relationship in terms of either function or mechanism of regulation. Thus, the importance of minimizing parsimonious inclusion of unrelated genes in a cluster and the elimination of clusters derived by trivial correlation due to an outlier (Heyer et al., 1999). In practice, however, there are multiple sources of potential error in correctly identifying meaningful clusters of genes. First, although the observed expression patterns are the results of a connected network of molecular interactions, there are many factors that obscure the causal relationship. These

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factors include (1) regulatory mechanisms that operate at the translation stage that are inaccessible from expression data; (2) secondary structure of mRNAs affecting hybridization interactions (Southern et al., 1999); (3) cross-hybridization of homologous sequences; (4) RNA alternative splicing; and (5) sample variation incurred in RNA extraction, labeling, hybridization, and detection. Thus, it is actually a technological wonder that expression data can reveal many coregulated gene groups by using a simple hierarchical clustering algorithm (Eisen et al., 1998). In addition, there is the challenge of sparse data. Here, the data are considered sparse in the sense that there are often not enough replicate experiments to assess errors in the data and to show reproducibility. A statistical analysis of cDNA array hybridizations strongly suggested that at least three replicates be used in designing such experiments (Lee et al., 2000). In contrast, many reported studies contain only duplicate hybridizations or time course studies with single determinations per time point (DeRisi et al., 1997). But perhaps even more important, there are often not enough biological “states” monitored by the designed experiments. This second kind of sparseness may have been less well appreciated. Obviously, measuring gene expression under the same biological condition over and over does not present more information (other than providing better assessment of data precision). Moreover, if the expression proﬁle (deﬁned as a vector of all gene expression levels measured) monitored at a new “state” is a linear combination of previously measured expression proﬁles, the new proﬁle does not provide any new information either. This point has been illustrated in Raychaudhuri et al.’s work using principal components analysis (Raychaudhuri et al., 2000). This showed that expression array data containing a 7-point time series could be roughly decomposed into two principal components (i.e., there are only two independent “states” being monitored). With only two states being monitored, clustering the gene expression data can only have limited resolution power; the boundaries of clusters of functional groups are nearly invisible in the space of expression proﬁles. Many statistical method alternatives to hierarchical clustering and SOMs are also being studied (Spanakis and Brouty-Boy´e, 1997; Golub et al., 1999; Vingron and Hoheisel, 1999). A simple linear model was developed to portray the temporal expression data from CNS development and CNS injury. In this model, the change of each gene’s expression is a linear combination of the expression levels of all genes on the chip. The method was successful in that it can reproduce the original data and it can reveal major functional gene interactions (D’Haeseleer et al., 1999). Another interesting observation made in this study is that a gene either plays the role of a positive or a negative regulatory gene but rarely a mixture, which seems to be consistent with what is commonly known in genetic networks.

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An analysis evaluated the use of support vector machines (SVMs) to characterize expression array data (Brown et al., 2000). SVMs are considered a supervised computer learning method. Unlike hierarchical clustering, SVM begins with a training set to specify in advance which data should cluster together. SVM would learn to discriminate between the members and the nonmembers of a given functional group. The method allows a researcher to start with a set of interesting genes and ask two questions: What other genes are coexpressed with my set? Does my set contain genes that do not belong? Brown et al. (2000) showed that SVMs worked well with some functional groups but did not work for others. Evidently, this is due to the fact that not all functional groups display a “theme” in expression proﬁles that can be captured by SVMs. Regardless of the difﬁculties entailed in efﬁcient grouping of genes by such methods as hierarchical clustering, a landmark study showed a proof of principle in terms of using array analysis to derive new biological information regarding the function of a gene or a given drug treatment (Hughes et al., 2000). Using a large matrix of yeast array data derived from analysis of 300 diverse mutations and chemical treatments, this study showed that uncharacterized genes could be correctly classiﬁed according to their position in the cluster diagram. Functionally relevant clustering of genes could be derived for even subtle changes in expression (<1.5-fold), such as those involving mitochondrial respiration genes. Furthermore, a potential novel mechanism of action was derived for dyclonine, a commonly used local anesthetic agent. Overall, this study strongly validates the power of collecting large DNA array datasets to generate novel functional information about uncharacterized genes or pharmacological agents. DNA arrays are still a young technology. Many problems faced by researchers are actually rooted in the high cost or low availability and reproducibility of the arrays. As the technology matures, the manufacturing costs of the arrays or equipment are expected to decrease and the quality of the arrays to improve. Moreover, as more and more data are accumulated and shared by more research laboratories in academia as well as in industry, “the array of hope” (Lander, 1999) is expected to ﬂourish in the postgenomic era.

III. Applications in Neurobiology

A. GENE PROFILING IN NEURONAL CELLS The phenotype of any given cell type is directly related to the set of genes it expresses. The level and timing of expression of these genes usually dictate

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development, differentiation, function, and physiology. Thus, determining the pattern of genes expressed in a cell should provide information on its state and function. Using traditional cellular and molecular techniques, enormous work is usually needed to identify which genes are expressed in a cell of interest and what their biological function is within this cell. DNA array-based expression analysis promises to greatly facilitate these studies. It offers, for the ﬁrst time, the possibility not only to access the identity and expression level of thousands of genes simultaneously, but also to investigate how these genes interact with each other. Over the past few years, genomewide transcriptional proﬁling has rapidly become an essential tool to study cell function and regulation. Below, we describe the use of this technology to ﬁngerprint neuronal cells and to characterize their molecular responses to drugs. 1. Analysis of Differential Gene Expression between Neuronal Subtypes The nervous system is characterized by a tremendous variety of cellular phenotypes. Each cell subtype, whether neuronal or glial, holds speciﬁc structural and functional properties. Neuron subpopulations are typically classiﬁed according to their location in the nervous system, their morphology, their electrophysiological properties, the neurotransmitter they synthesize, or the receptors they express. By studying differential expression between cell types or neuronal subtypes, DNA array technology should help identifying genes that are selectively expressed in a few given cell types. Identiﬁcation of these genes should provide information on the contribution of an individual cell type to a particular biological function or physiological state. In vitro, it should help characterize neuronal cell lines commonly used as cell models, whereas in vivo, it may provide a basis to deﬁne cellular diversity in the nervous system. In addition, the identiﬁcation of coordinate expression of a group of genes in a restrictive set of cells may indicate functional coupling between their encoded proteins and therefore help deﬁne their function. Proﬁling homogenous cultured cells such as neuroblastoma cell lines is relatively simple in a technical sense. However, to access cell-type speciﬁc gene expression in an organ such as the brain it is preferable to be able to analyze gene expression at the single cell level. This now appears possible with the advent of antisense RNA (aRNA) ampliﬁcation methods (Kacharmina et al., 1999). The sequential application of this technique allows ampliﬁcation of the entire mRNA population from a single cell to levels sufﬁcient for DNA array hybridization. It can be applied not only to single cells in culture, but also to individual cell in situ, in a ﬁxed tissue (Kacharmina et al., 1999). As described in the previous section, the mRNA population of a single cell is ﬁrst reverse transcribed into double-stranded cDNA in the presence of an oligo (dT) primer containing the promoter of T7 RNA polymerase.

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Complementary DNAs are futher in vitro transcribed into aRNA. A second and third round of ampliﬁcation can then be carried out using the aRNA produced as template for a new cDNA synthesis and in vitro transcription. Luo et al. (1999a) used this approach to analyze gene expression proﬁles not from single cell but from 1000 neurons simultaneously. The authors integrated laser capture microdissection, aRNA ampliﬁcation, and cDNA microarray technologies to examine differential gene expression between neighboring large- (>50 µm) and small- (<25 µm) size neurons in the dorsal root ganglia (DRG). These two populations of neurons are functionally distinct: While large neurons transmit mechanosensory information, small neurons dispatch nociceptive stimuli. Neurons of each population were microdissected from Nissl-stained rat DRG sections using laser capture microscopy (Luo et al., 1999a). Transcriptional proﬁles were subsequently compared using a printed cDNA array containing 477 cDNA clones. The authors identiﬁed 40 mRNA species preferentially expressed in either large or small neurons. Although there is a much greater diversity than just small and large neurons in DRG, this study constitutes a ﬁrst step in correlating gene expression proﬁles with speciﬁc neuronal function. DNA array technology is fairly new and we are still far from being able to extract cell-type speciﬁc gene expression patterns from the data generated to date. This will require proﬁling of hundreds of different cell types. However, it is not unreasonable to predict that such analyses will be possible in the future. In particular, current efforts seek to establish public repositories of expression data where consistent normalization schemes and relational database structures will allow deﬁning expression proﬁles correlated with given cell types or physiological states (http://www.ncbi.nlm.nih.gov/ geo/). 2. Characterization of Cellular Effects of Drugs or Ligands Cell function and survival is under constant regulation by autocrine, paracrine, or endocrine signals. Perhaps nowhere is the regulation of cellular phenotype by extracellular signals as complex as in the nervous system. Activation of membrane and nuclear receptors by extracellular stimuli is known to ultimately lead to changes in gene expression. Because of complex cross-talk between signaling pathways, it is often difﬁcult to identify target genes activated or repressed by these stimuli. Although useful, traditional candidate gene approaches, subtractive hybridization and differential display methods are greatly limited by the number of the genes monitored at a time. DNA array technology now provides tools to examine changes in gene expression on a genome-wide scale and offers, for the ﬁrst time, the opportunity to study transcriptional changes in the context of all complex networks operating in a cell.

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Although not conducted on neuronal cell models, several studies illustrate the power of DNA array technology to study signaling events. Der et al. (1998) generated gene expression proﬁles from interferon (IFN)-α, -β, and -γ treatment of the human ﬁbrosarcoma cell line HT1080. These different interferons are believed to bind their respective receptors and subsequently activate the JAK/STAT signaling cascade. This study successfully identiﬁed known IFN-stimulated genes but also revealed unsuspected IFNspeciﬁc inductions, as well as novel IFN-regulated genes, such as those coding for apoptosis regulators. Iyer et al. (1999) investigated the response of human ﬁbroblasts to serum, which contains most of the growth factors necessary for proliferation in culture. In this case, printed cDNA arrays were used to measure the temporal changes in gene expression of 8613 human genes after addition of serum-containing medium to primary quiescent ﬁbroblasts. As expected, addition of serum-induced changes in numerous genes known or likely to be involved in controlling and mediating the proliferative response. However, strikingly, serum also triggered changes in multiple genes with known roles in wound healing, suggesting an underestimated role of ﬁbroblasts in wound repair. In a third example of using DNA arrays to study ligand-induced signaling, Fambrough and colleagues (1999) studied growth factor receptor induction of immediate early genes. Surprisingly, these investigators showed that multiple signaling cascades regulated by the platelet-derived growth factor receptor produced overlapping sets of immediate early genes. Again, such results display the ability of DNA arrays to provide a nonbiased, and often surprising, analysis of expression patterns rather than single gene data. Applied to neuronal cell biology, DNA array technology could help gain a better understanding of the cellular effects of neurotransmitters, neuronal growth factors or CNS-acting drugs. Thus, in our laboratory, we have used oligonucleotide arrays to examine the cellular effects of ethanol in a cultured human neuronal cell line (Thibault et al., 2000). Identiﬁcation of ethanolresponsive genes has been difﬁcult, perhaps due to the pleotropic nature of this drug. Indeed, ethanol has been shown to modulate multiple neurotransmitter receptor functions and several signaling cascades (Diamond and Gordon, 1997). By screening 6000 genes simultaneously, we identiﬁed more than 40 genes reproducibly up or down regulated in SH-SY5Y neuroblastoma cells after 3 days of ethanol treatment. Among the genes up regulated by ethanol were multiple genes involved in norepinephrine biosynthesis, such as dopamine beta-hydroxylase. We veriﬁed that ethanol indeed increased releasable norepinephrine in these cultures. Numerous other studies have suggested a possible role for norepinephrine in ethanol-related behaviors. Our array studies also provided mechanistic information by showing that >30% of the ethanol-responsive genes were also regulated in a similar fashion by

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the cyclic AMP analog, dibutyryl cAMP. This result strongly suggested that cAMP signaling could be involved in a signiﬁcant portion of responses to ethanol. Thus, these studies demonstrated that, in a nonbiased fashion, arrays can contribute to understanding a “phenotype” by identifying multiple members of a biological pathway and generate mechanistic information by comparison to known modulators of speciﬁc signaling cascades. DNA array technology also provides an efﬁcient way to test the speciﬁcity of therapeutic drugs based on the transcriptional responses they trigger. It may be used to clarify their mechanism of action, predict their potential side effects, or deﬁne their efﬁcacy. In an elegant work, Gray et al. (1998) integrated combinatorial chemistry and DNA array technologies to investigate the speciﬁcity and afﬁnity of various cyclin-dependent kinase (cdk) inhibitors in yeast. This study showed that two drugs intended to inhibit the same cellular process and cellular proliferation, as well as produce distinct transcriptional proﬁles despite their similar in vitro activity (i.e., inhibition of cdk28). Such an approach would be undoubtedly useful to better characterize commonly used receptor agonists or antagonists. 3. Identification of Transcription Factor Targets Transcription factors are key players in the orchestration of cellular changes in gene expression. Following their activation through receptor stimulation, they recognize and bind speciﬁc DNA sequence in the promoter of target genes and directly activate or repress their transcription. One possible application of DNA array technology is the characterization of all genes whose expression changes when a transcription factor is mutated. The ﬁrst study of this type was reported by DeRisi et al. (1997) who identiﬁed all yeast genes whose expression is affected by the deletion of Tup1 transcription factor gene. In another study in neural cells, downstream gene targets of the Gsh-1 homeobox were screened for in murine cell lines derived from embryonic hypothalamus and hindbrain (Li et al., 1999). Gsh-1 is expressed in several discrete regions of the developing brain and is believed to play a role in dorsal-ventral patterning of the CNS. The authors used the promoter of Gsh-1 to drive the expression of the SV40 T antigen gene in Gsh-1 null mice. Introduction of this gene allowed the immortalization of cells normally expressing Gsh-1. By stably transfecting a tet-inducible DNA construct coding for Gsh-1 in clonal Gsh-1-/-cell lines, they were able to compare gene expression proﬁles between clonal cells expressing or not expressing Gsh-1. These transcriptional analyses suggested a role for Gsh-1 in the regulation of genes involved in cell growth, differentiation, and patterning. Many transcription factors are of particular interest because of their recognized or potential association with human diseases. Thus, mutation in the homeobox gene HESX1 has been linked among others with septo-optic

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dysplasia (Dattani et al., 1998). Similarly, several transcription factors, including p53, have been associated with the development of human neoplasia (Levine, 1997). DNA microarray technology now provides the opportunity to study the broad effects of these transcription factors on gene expression and potentially elucidate their role in pathogenesis. The main challenge of such studies remains the interpretation of the data. Indeed, all genes changing after mutation, deletion, or overexpression of a transcription factor may not be direct targets. Some of these changes may solely reﬂect adaptation of the cells to the absence or overexpression of the gene of interest. Alternatively, they may result from a secondary or tertiary effect of the mutated gene. The use of promoter sequence information from the GenBank database could facilitate the cataloging of the actual transcription factor targets through identiﬁcation of upstream regulatory sequences. Cho et al. (1998) characterized the expression proﬁles of all yeast genes during the cell cycle. These genes were subsequently clustered according to their expression pattern over time. Alignment of the promoter sequence of coregulated genes further lead to the identiﬁcation of previously undetected upstream regulatory elements. As more sequence and transcription data are available, such approaches promise to become increasingly efﬁcient. 4. Summary Quantiﬁcation of mRNA level has been used for a long time as a tool to study gene function and regulation. Knowing where and when a gene is expressed and under what conditions its expression is modulated generally provide strong clues about its function and its contribution to cellular processes. Most studies have focused on the characterization of one or few genes at a time. Recent work in neural or nonneural cells has shown that DNA arrays can provide information regarding changes in cellular phenotype, as well as mechanistic insight regarding operative signaling cascades or transcription factors.

B. GENE PROFILING IN THE BRAIN One of the greatest tasks of neuroscience is to determine how the brain mediates sensory and motor functions, as well as more elaborate processes such as emotions or learning. Traditionally, neuroanatomy and (electro)physiology studies have been the methods of choice to locate brain activity in relation to behavior. Imaging techniques such as positron emission tomography or functional magnetic resonance imaging have permitted the generation of new overall maps of brain activity in a noninvasive manner (Volkow et al., 1997). These technologies have yielded valuable insights into the

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biological interrelation of sensory, motor, and cognitive functions, as well as brain disease. However, they give little information on the molecular mechanisms involved in the generation of these maps. Advances in molecular biology, however, gave rise to a more reductionist approach aimed at correlating single genes with a given behavior. However, few if any behaviors are regulated by a single gene, and it is essential to understand the contribution of all genes at the system level. DNA array technology now offers a new and complementary paradigm for studying brain organization and function. By proﬁling the expression of thousands of genes simultaneously in brain subregions during development or in response to experience/treatments, array studies should help integrate molecular and functional neurobiology. A major caveat to the use of DNA arrays for studying brain gene expression concerns the sensitivity of the method. Existing literature on cDNA or oligonucleotide arrays generally quotes a sensitivity of 1 : 100,000 for detecting a twofold change in expression (Bertucci et al., 1999a). This corresponds to a level of ∼3 molecules/cell, assuming that a typical cell has about 3 × 105 mRNA molecules. Considering the heterogeneity of cell types (neurons and nonneuronal) existing in the brain, this implies that a rare transcript expressed in a small subset of neurons will likely go undetected by existing array technology. Methods to address this issue are discussed below (Section IV.A). 1. Regional and Temporal Gene Expression Mapping The nervous system is anatomically highly organized. Furthermore, its structural organization closely reﬂects its functional organization. There is compelling evidence that different regions of the brain are specialized for different functions. One classic example of this functional regionalization concerns the neocortex, which can be divided into primary sensory and motor cortices, higher order sensory and motor cortices, and cortical association areas that all specialize in different functions. However, it also appears that even the simplest behavior involves multiple parallel neural systems and pathways—sensory, motor, and motivational—in the brain. A clear understanding of the anatomical organization of the brain is essential to the understanding of behavior in both normal and disease states. It is predicted that neurons of different brain regions having unique functions will have different patterns of gene expression. Comparison of gene expression proﬁles between brain regions using DNA array technology may thus be used as a tool to deﬁne the molecular neuroanatomy of the brain. Such an approach was reported using oligonucleotide arrays to study expression of 11,000 genes/ESTs in murine brain (Sandberg et al., 2000). These investigators compared expression patterns across six different brain regions in two different inbred mouse lines, 129SvEv and C57BL/6. They found that approximately 1% of expressed genes were differentially

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expressed between the two mouse lines in at least one brain region. Most important, these studies identiﬁed candidate genes that could contribute to phenotypic differences between the two mouse lines, namely, their different susceptibility to seizures. This work not only showed the feasibility of doing array studies on microdissected brain tissue, but also displayed the ability of arrays to characterize expression changes dictated by different genetic backgrounds. This could have important implications for the study of gene-targeted mouse lines. The different anatomical and functional areas of the brain are patterned according to a precise developmental plan involving neural induction, neuronal cell migration, and segregation. The study of neuronal development has proven a valuable approach to understand brain organization. DNA arrays allow a detailed analysis of changes in gene expression associated with neuronal development. Wen et al. (1998) generated a temporal map of gene expression during the development of rat cervical spinal cord from embryonic day 11 to postnatal day 12. This study was conducted using reverse transcription and PCR to study expression levels for 112 genes. Wen et al. (1998) showed that functionally related genes display remarkably similar patterns of expression during this period of spinal cord development. Although most of the genes under this study had known functions, one can imagine that similar studies using arrays with many thousands of genes will produce novel information regarding the function of “unknown” genes by correlation with expression proﬁles of other known genes. 2. Molecular Characterization of Neuronal Plasticity One of the key properties of the brain is its ability to reorganize structurally and functionally in response to experience. This phenomenon, known as neuronal plasticity, may involve changes in synaptic activity, either through alterations in intrinsic properties of existing synapses/neurons or by structural modiﬁcation of synapses. This synaptic reorganization ultimately produces behavioral changes. Among the most studied factors that can trigger neuronal plasticity are brain injury and aging, learning and memory, and drug addiction. It is now clear that long-term neuronal plasticity involves complex transcriptional reprogramming (Nguyen et al., 1994; Nestler and Aghajanian, 1997). However, attempts to functionally correlate behavior-altered synaptic function changes in the expression of single or even limited numbers of genes have generally been inconclusive. DNA arrays, through detection of changes in the pattern of expression for large numbers of genes, may provide new insight into molecular events, including neuronal plasticity. In our laboratory, we are currently using oligonucleotide arrays to characterize transcriptional changes accompanying the development of sensitization to drugs of abuse, such as ethanol and cocaine in mice. Sensitization

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is deﬁned as an increase in behavioral responses to repeated intermittent administration of a given dose of a drug (Robinson and Berridge, 1993). Increases in locomotor activity are commonly used as a measure of sensitization. Importantly, sensitization appears to increase the rewarding effect of abused drugs (Horger et al., 1990, 1992). Sensitization persists long after cessation of drug intake and is believed to involve long-term changes in gene expression in different regions of the brain, including the dopaminergic mesolimbic system (Nestler and Aghajanian, 1997). To characterize these changes, we have compared transcriptional proﬁles in the nucleus accumbens and ventral tegmental area of sensitized and control mice. Initial studies show a striking reorganization of the molecular responses to cocaine in sensitized versus na¨ıve animals (Wang et al., 1999b). It is hoped that the identiﬁcation of gene pathways participating in the initiation or expression of sensitization will provide novel targets for intervention in some aspects of drug addiction. Learning and memory constitute another form of plasticity that has been shown to require gene expression reprogramming in the hippocampus and amygdala. Two reports used DNA arrays to characterize the molecular basis of memory formation and decline, respectively (Dubnau et al., 1999; Luo et al., 1999b). Luo et al. (1999b) compared transcriptional proﬁles of the hippocampus of young and old rats subjected to a T-maze learning protocol to characterize the decline of memory function with age. Dubnau et al. (1999) used oligonucleotide arrays to study memory formation in Drosophila melanogaster by using a combination of behavioral training protocols and various mutant ﬂies with disrupted memory function. These few studies illustrate how DNA array technology can be used to gain a better understanding of the molecular basis of neuronal plasticity. Future studies should involve careful cellular mapping and testing of the contribution of particular candidate genes to the development or maintenance of neuronal plasticity. This can be achieved through their manipulation in knockout or transgenic animals. Alternatively, speciﬁc pharmacological agents may be used to block expression or the function of the encoded protein. Identiﬁcation of a speciﬁc pattern of gene expression causally linked to a form of neuronal plasticity could provide much more functional information regarding the mechanism of plasticity than does identifying a single gene. 3. Gene Profiling in Animal Models of Neuronal Abnormalities A great deal of what we know about brain function has been obtained through studies on animal models. Because of the large number of inbred strains available (more than 450), the mouse has long been a model of choice. Many of these strains were originally bred for speciﬁc phenotypes,

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and many have been used to study the genetics of simple or complex traits. The crossing of phenotypically different inbred strains is used for mapping quantitative and qualitative trait loci (Crabbe et al., 1994). However, the identiﬁcation of causal polymorphisms remains a major challenge to the genetics of complex traits. Expression proﬁling with DNA arrays may offer a major advance for such studies by allowing the identiﬁcation of “patterns” of gene expression changes associated with a given mouse phenotypic model. This pattern may impart functional information not easily discerned by studying a single gene. In some cases, this might largely obviate the actual need to isolate the polymorphism(s) associated with a single gene contributing to a given animal model. 4. Gene Profiling of Human Neurological Diseases DNA array technology is well adapted for studying the complex molecular changes occurring during the development and progression of chronic neurological diseases. It provides simple tools to identify novel markers for disease detection and targets for therapeutic approaches. The suitability of this technology for proﬁling diseases is now well documented in the literature. The most extensive studies focused on the transcriptional changes associated with the development of various cancers, including renal, colon, prostate, and ovarian cancers (Alon et al., 1999; Bubendorf et al., 1999; Moch et al., 1999; Wang et al., 1999a). One study also examined differential gene expression between normal brain tissues and glioblastoma multiform tumor tissue in humans using Clontech macroarrays (Sehgal et al., 1998). Among the 143 genes detected in these tissues, more than 100 were differentially expressed. The majority of genes overexpressed in normal brain were oncogenes and tumor-suppressor genes, such as the retinoblastoma gene, as well as genes coding for DNA-binding proteins. Conversely, genes coding for cell surface proteins or proteins involved in signal transduction were preferentially overexpressed in tumor compared with normal tissues. Because of the phenotypic and genetic heterogeneity of tumors, it often appears necessary to survey changes in gene expression in a large number of tumor specimens. Few studies report the combined use of cDNA and tissue microarray technologies to facilitate the identiﬁcation of clinically relevant genes (Bubendorf et al., 1999; Moch et al., 1999). Candidate genes were ﬁrst isolated through DNA array screening of one tumor specimen. Changes in their expression level were further conﬁrmed using immunohistochemical methods on tissue microarray containing a large number of different ﬁxed tumor samples. By determining changes that occur at higher frequency among tumors, one can distinguish genes with the most promising clinical applications. Others have used clustering of transcriptional proﬁles from multiple tumor specimens to catalog genes potentially coregulated or having

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similar cellular function. Clustering and display methods have also proved useful for the molecular classiﬁcation of cancers based on their gene expression pattern (Ross et al., 2000). Such an approach has obvious applications for diagnostic testing. DNA array technology has been applied to the study of several neurological diseases. Whitney et al. (1999) compared transcriptional proﬁles of normal white matter and acute multiple sclerosis lesions from the brain of a single patient. In a more extensive study of frontal cortex tissue from control and alcoholic subjects, Lewohl et al. (1999) documented the coordinate down-regulation of multiple myelin-related genes in the tissue from alcoholics. This study employed two independent groups of controls and alcoholics, each consisting of multiple subjects. Decreased expression of myelin-related genes in alcoholics may have important pathophysiological implications for cognitive dysfunction and demyelinating disorders suffered by alcoholics (Miles and Diamond, 1998). Although array-based transcriptional analyses potentially provide long lists of candidate genes, the interpretation of the changes observed remains difﬁcult. Pathological conditions are often the cumulative result of genetic susceptibility factors and multiple damage and compensatory responses in many different cell types. Tremendous work will be needed to evaluate the contribution of each candidate gene isolated to the initiation or progression of the disease of interest. Comparison with results from appropriate animal models of disease may greatly aid interpretation of array results from human disease tissue. 5. Summary DNA array studies on brain tissue have only begun to be reported. Initial results are promising in terms of arrays providing novel insight into brain function and dysfunction in disease. However, considerable caution on interpretation of results seems warranted, given the enormous complexity of the brain and important issues regarding array sensitivity (see Section IV.A).

C. APPLICATIONS IN NEUROGENETICS Within the last few months, the Human Genome Project has released the ﬁrst complete reference sequence of the human chromosomes. Already, many groups are concentrating on identifying sequence variations among individuals and between species. Genotypic variations are believed to underlie most of the phenotypic differences in normal and disease states. Numerous diseases directly affecting the nervous system have a genetic basis. Some of these disorders have been shown to involve mutation in a single gene;

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yet, many arise from mutations at multiple loci. Thus, Huntington’s disease results from a characteristic expansion of CAG repeat in the Huntington gene. However, mutations in four genes, including presenilin 1 and 2, have been implicated in the development of Alzheimer’s disease. Although some mutations are monomorphic, many disease genes can be affected by a large spectrum of distinct mutations. DNA array technology has been proposed as a new high-throughput tool to carry out exhaustive screening of mutations in these complex disease genes. Hacia et al. (1998) used high-density oligonucleotide arrays to complete a mutational analysis of the ATM gene linked to the autosomal recessive disorder ataxia–telangiectasia (AT). AT is a pleiotropic disease characterized by immunodeﬁciencies, radiation sensitivity, genetic instability, and gradual loss of Purkinje cells in the cerebellum leading to progressive neuromotor deterioration. The responsible gene encodes the ATM protein, a kinase of 350 kDa, with sequence homology to phosphatidylinositol 3-kinases (PI3K). This protein is believed to play a crucial role in signaling pathways that respond to DNA strand breaks, such as in meiosis, genetic recombination, or apoptosis. The ATM gene is characterized by a complex genomic structure, spanning 146 kb of genomic DNA and containing 62 exons. More than 100 distinct ATM mutations widely distributed throughout the gene have been documented in AT patients. Because of the large size of this gene and the diversity and broad distribution of mutations that can affect it, diagnostic mutation screening has been difﬁcult. Hacia et al. (1998) have therefore designed a pair of DNA arrays containing more than 95,000 oligonucleotides and aimed at detecting all possible heterozygous germ-line sequence variations in the ATM coding sequence. Each position in the gene was interrogated with 10 separate 25-mer oligonucleotides, 5 for each sense and antisense strands (2 wild type and 3 containing a base substitution in their central position). In a blinded study, 17 of 18 known distinct heterozygous and 8 of 8 different homozygous sequence variants were detected. In addition, 5 new mutations were identiﬁed and previous genotyping assignments were corrected. Similar strategies were used to screen for sequence variations in the BRCA1, a gene important in breast cancer (Hacia et al., 1996). Apart from sequence variations, abnormalities in DNA copy number also contribute to numerous genetic disorders. Thus, various developmental abnormalities such as Down, Prader Willi, or Angelman syndromes result from gain or loss of one copy of a chromosome or chromosomal region. Mapping of the genes involved in these abnormalities and determination of their copy number is essential for understanding disease phenotypes. Comparative genomic hybridization (GHC) to microarrays constitutes a new method to investigate these DNA copy number aberrations. Geschwind et al. (1998) have used this approach to test gene dosage in patients with Klinefelter

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syndrome (KS), a sex chromosomal abnormality. Affected males carry an additional X chromosome (XXY), which results in hypogonadism, androgen deﬁciency, and impaired spermatogenesis. A number of neurological abnormalities have also been recognized in these patients. The data presented in Geschwind’s study, together with previous reports, suggest KS patients have speciﬁc verbal learning disabilities, an increase in left-handedness when measured by skill, and an abnormal functional laterality for phonologic processing (Geschwind et al., 1998). Such phenotypes suggest an atypical pattern of cerebral laterality or anomalous dominance. The authors hypothesized that alterations in gene dosage in the pseudoautosomal region (PAR) of the sex chromosomes were responsible for anomalous cerebral laterality in these patients. They developed an ordered DNA microarray of inter-Alu fragments covering the X chromosome PAR at high resolution. The ratio of gene dosage in the XPAR was compared in four separate XY : XY and XXY : XX hybridizations. As expected, the ratio was 1 : 1 in XY : XY hybridizations, whereas it was close to 1.4 : 1 in XXY : XX hybridizations. The authors proposed that such an approach might eventually lead to the identiﬁcation of autosomal loci contributing to structural or functional cerebral dominance.

IV. Caveats and Future Needs

A. SENSITIVITY AND REPRODUCIBILITY Because of its novelty, the limitations of DNA array technology are still unclear and a number of basic questions remained to be answered. Although numerous studies have now demonstrated the power of this technology in model organisms such as yeast, performance for gene expression monitoring in complex tissues such as the brain is still uncertain. Perhaps the major question regarding use of arrays for expression studies on the brain concerns sensitivity. The limit of detection for cDNA or oligonucleotide arrays is generally stated as 1–3 mRNA copies per mammalian cell, assuming each cell contains an average of 300,000 individual mRNA molecules. However, 85% of mammalian genes are known to be expressed at very low abundance. Furthermore, many mRNAs of interest in the brain, such as those coding neurotrophins or neurotransmitters, are often only detected in restrictive population of cells. Because the brain is a highly heterogeneous tissue, the ability to reliably quantitate a rare mRNA expressed in a small subset of neurons among the many other types of neuron and glia remains an unproven ability for array studies. Methods to improve the sensitivity and dynamic range of arrays are clearly needed.

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Microdissection techniques such as laser capture microscopy appear feasible approaches for improving analysis of genes with restricted expression patterns. Such techniques also serve to decrease the complexity of the RNA populations under analysis. However, the quantitative robustness of such methods has not been established. Another strategy would be to isolate only mRNA molecules that are localized in certain cellular compartments (e.g., polyribosome bound, synaptosomal) or in cell types that can be easily sorted (e.g., by ﬂuorescent-activated cell sorting). A current report documents the use of such approaches to identify secreted and membraneassociated gene products (Diehn et al., 2000). Most array technologies are adapted to detect changes in expression in the order of twofold or more. It is likely, however, that smaller changes in expression are biologically relevant. Such changes are technically much harder to distinguish, particularly for genes with low expression levels. In traditional Northern blot or RT-PCR studies where one or a few genes are monitored simultaneously, repeat experiments and statistical analyses are a standard for estimating the conﬁdence in the changes observed. Variability between replicates is often quite large, and multiple data points are generally needed to distinguish between true differences in expression from changes due to experimental variability. Mainly due to costs, many array experiments have been performed at most in duplicate. Therefore, the accuracy of measurement in these experiments may be questionable. Conﬁrmation of the most salient changes observed by more traditional methods may be required. New statistical approaches are greatly needed for analysis. Furthermore, establishing suitable methods for comparing array data between different laboratories and array platforms is crucial.

B. INTERPRETATION AND USE OF THE DATA As discussed above (Section II.C), one of the greatest challenges in largescale expression analyses is to organize the deluge of data generated and to extract functional information. Most of the studies conducted on mammalian systems concern pairwise comparisons between two conditions, normal versus disease, or control versus drug treated. A typical approach to select candidate genes in such settings is based on the ratio of expression between the two samples. For example, all genes whose expressions deviate from that of the control by more than twofold or any other arbitrary percentage can be selected. The end product of these selections often represents a limited but still complex list of genes. A natural ﬁrst step in using this information is to focus on the extremes. Although useful, this approach does not exploit the full potential of DNA array studies.

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The ultimate goal in large-scale analysis of gene expression is to be able to group genes according to similar expression patterns and to extract functional and casual relationships between genes. Indeed, it is generally assumed that genes which follow similar patterns of expression across a range of conditions or time are likely to share common molecular regulatory processes or are likely to participate in similar or complementary functions. It is expected that computational methods, such as cluster analysis combined with graphical representation, will help establish such relationships. However, methods for optimizing the performance of such multivariate studies on array data are still being developed (Heyer et al., 1999). Nevertheless, a number of comprehensible patterns of gene expression have been obtained in yeast. This organism represents the ultimate model for gene expression analysis because all its genes have been identiﬁed and sequenced, and can be interrogated in a single hybridization. Furthermore, a function has already been attributed to a large number of these genes. The interpretation of DNA array studies on mammalian system represents a much greater challenge. The majority of the probes used on DNA arrays encode uncharacterized ESTs. The incomplete monitoring of the genome and the lack of gene function information complicate the interpretation of the data. In addition, although establishing causality between genes in homogeneous samples such as yeast or tumor cell lines is more straightforward, studies on heterogeneous tissues may produce inextricable patterns. Another level of complexity inherent to the nervous system arises from the intricate neuronal networks essential for brain function. It is unclear how such complexity can be addressed by DNA array technology. Thus, primary responses in one neuron will be intermixed with compensatory responses from other neurons “downstream” in a given network. Deciphering such complexity will require a continuation of genetic and pharmacological approaches combined with array studies.

C. SHARING ARRAY DATA It is already abundantly clear that DNA array studies will require a new era of cooperation among scientists of various disciplines. The data is simply too massive and complex for any one laboratory to fully extract even a portion of signiﬁcant patterns within a reasonable time. The Brown laboratory has again set the pace for how the ﬁeld should proceed. Their posting of articles, ﬁgures, software, statistical analyses, and raw data have become the standard for how array data is shared (http://cmgm.stanford.edu/pbrown/). The National Center for Biotechnology Information (NCBI) has established a web site for depositing array data (http://www.ncbi.nlm.nih.gov/geo/). This “Gene Expression Omnibus” (Geo) seeks to compile data from a variety

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of array platforms. The mechanics of this are obviously complicated, insofar as being able to compare expression patterns of a given gene across various platforms. Furthermore, the issue of data integrity is likely to become very complex as the quality of array studies varies greatly. To date, only 10 samples are contained in the Geo database. The posting of array data has numerous beneﬁts. Constructing a “compendium” of array results, as has been done for yeast (Hughes et al., 2000), should allow much more rigorous multivariate analyses with resultant identiﬁcation of important functional or regulatory patterns. It is impractical for individual laboratories to construct such a large resource. Thus, submission of array data to a common site or widespread exchange of data may allow efforts to generate such a compendium of array data for brain samples. In addition, the widespread availability of array datasets will speed the development of new analysis tools. Several studies of new approaches to array analysis take advantage of data posted on public web sites (Heyer et al., 1999)

D. COST AND AVAILIBILITY OF ARRAYS Regardless of the type of platform, there have been problems with access to DNA arrays for several reasons. In the case of commercial oligonucleotide arrays, the major factor has been cost. The ﬁrst generation of such chips for human or mouse studies, consisting of ∼6500 genes/ESTs, had a list price of about $3000 per array. This was compounded by large startup costs for equipment. These factors have greatly improved during the early 2000s. Sites are now available at numerous academic centers for the hybridization/analysis of oligonucleotide arrays. Commercial resources are also available. Furthermore, the price of arrays as of May 2000 decreased to approximately $1500 per array for arrays containing ∼11,000 human, mouse, or rat genes/ESTs. This price is even lower for universities having academic licensing agreements or other high-use contracts. Spotted cDNA arrays have dramatically lower prices on a “per-array” basis. Some centers have quoted labor/material costs of about $25 per array for arrays containing 6000–10,000 genes/ESTs. However, there is substantial overhead in labor/equipment for amplifying cDNA inserts, arraying PCR fragments, scanning arrays, and performing sophisticated analysis. Furthermore, maintaining reproducible high-quality array production has not proven to be a trial task. Thus, there is a substantial infrastructure required for producing quality spotted cDNA arrays. Again, some of this has improved since the late 1990s, with a great increase in the number of options available for arrayers, scanners, and other equipment. Furthermore, several large collections of cDNA clones have become available through a variety of sources, including commercial vendors (e.g., http://www.incyte.com/).

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V. Conclusion

The explosion of studies being performed with DNA arrays is no accident. Despite considerable challenges in the setup, performance, and analysis of such studies, a large number of commercial and academic investigators have begun array studies. This seems largely driven by the power of the approach to achieve nonbiased, novel insights into phenotypic or regulatory relationships among all genes in a given genome. Hence, the term “functional genomics” is not merely a catchphrase for a new technique, but rather, describes an entirely new scientiﬁc discipline—one that promises to greatly accelerate our understanding of disease, physiology, and drug action. Although application of array studies to the nervous system poses several new challenges in regard to sensitivity and complexity of the analysis, early results again suggest great power for expression proﬁling. The rapid development of new methodologies for RNA isolation, array preparation, and data analysis/retrieval is highly likely given the exponential growth in array studies. Given some of the special issues regarding expression proﬁling in neurobiology, it seems likely that a subdiscipline of “functional neurogenomics” may eventually develop. Finally, perhaps the most exciting aspect of DNA array studies is the tremendous integrative and collaborative nature of such work. This approach is highly suited to neuroscience, a discipline long versed in the value of integrative science.

Acknowledgments

This work was supported by a grant provided by the State of California for medical research on alcohol and substance abuse through the University of California, San Francisco. The authors want to thank other members of Miles’ laboratory for their assistance preparing this manuscript.

References

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INDEX

A A77636, 111–112 AA, see Arachidonic acid AAA, see Active analogue approach ABT-431 and dyskinesias, 112 in PD tests, 109–110 ACE, see Angiotensin-converting enzyme Active analogue approach, 94 AD, see Alzheimer’s disease ADRDA, see Alzheimer’s Disease and Related Disorders Association AKAP, see A kinase anchoring protein A kinase anchoring protein, 49 Algorithms, CLUSTAL W, 145, 156 Alzheimer’s disease Aβ deposition, 182–184 anti-inﬂammatory drugs, 194 –196 brain atrophy, 177–178 brain inﬂammation, 194 –196 clinical diagnostic protocols, 175 degeneration mechanisms, 186 –189 dominant inheritance, 190 –192 estrogen therapy, 197 genetic risk factors, 192–193 neuronal loss, 178–182 NFT formation, 184 –186 pathogenesis, 176–177 pathology Aβ plaques and NFTs, 169–173 clinical correlates, 174 –175 related types, evaluation, 173–174 vascular pathology, 197–200 Alzheimer’s Disease and Related Disorders Association, 170 –175 AMBER, 146 γ -Amino butyric acid type A receptors assembly, 3 heteropentameric receptors, 4 –6

homo-oligomeric receptors, 6 –7 in vivo, 7–8 association with cytoskeleton, 43 basic function, 1–3 endocytosis ligand-evoked, in neurons, 14 –16 purpose, 21–23 and recycling in vitro, 17–20 role of clathrin-coated vesicles, 16 –17 extrasynaptic receptors, 36–38 GABARAP, 46 – 47 gephyrin-interacting proteins, 43– 46 GRUB1, 47– 48 postsynaptic receptors, 38– 42 structure, 34 –35 subtypes, subcellular localization, 35–36 synaptic anchoring, 48 synaptic plasticity, 50 –51 GABAergic synapse regulation, 53 role of presynaptic factors, 51–52 role of synaptic activity, 52–53 targeting in vitro, 8–9 targeting in vivo, 9–11 α-Amino-3-hydroxy-5-methyl-4-isoxazol propionate receptor, 149–150 Amino terminus, in GABAA receptor assembly, 4 –5 AMPA receptor, see α-Amino-3-hydroxy5-methyl-4-isoxazol propionate receptor Amyloid precursor protein in Aβ deposition, 182–183 mutations in AD, 190–192 Anesthetics, inhalation, 154 –155 Angiotensin-converting enzyme, 200 Animal models, neuronal abnormalities, 240 –241 Anti-inﬂammatory drugs, 194 –196 Aβ plaques in AD pathological diagnosis, 169–173 deposition, 182–184

255

256

INDEX

ApoE, 192–193 APP, see Amyloid precursor protein Arachidonic acid, 87 Array-based expression proﬁling, 226–228 Attentional disorders, 112–113 B Baby hamster kidney cells, GABAA receptors, 5–6, 8 Basal ganglia, neuroanatomical model, 78–81 BHK cells, see Baby hamster kidney cells Binding sites, in pentameric LGICs, 154 –155 Biological data, for D1 receptor drug design, 92–96 Brain atrophy in AD, 177–178 GABAergic inhibition, 33 GABAA receptor subunit composition, 3 gene proﬁling, 237–242 inﬂammation in AD, 194 –196 C Cell survival, drug and ligand effects, 234 –236 Central nervous system associated disorders, D1 receptor role, 112–116 basic GABAA receptor function, 1–3 CERAD, see Consortium to Establish a Registry for Alzheimer’s Disease Cerebellar granule cells, 37–38 CharMM, 146 Chemoarchitecture, D1 dopamine-like receptors basal ganglia model, 78–81 in hippocampus, 83–84 and memory circuits, 81–83 in periphery, 85 receptor localization, 77–78 Chick embryo neurons, GABAA receptors, 8 Chromosomes, PS mutations, 190 –192 Clathrin-coated vesicles, 16–17 Clinical diagnosis, AD, 175 CLUSTAL W algorithm, 145, 156 CNS, see Central nervous system Cognition, D1 receptor role, 113–114 Computer programs

AMBER, 146 CharMM, 146 CLUSTAL W algorithm, 145 MM3, 146 MMFF, 146 SeqFold, 145, 157 Conformational maturation, in GABAA receptor assembly, 5–6 Consortium to Establish a Registry for Alzheimer’s Disease, 169–173 Cyclic AMP, in D1 receptor function, 85– 87 Cytoskeleton, association with GABAA receptors, 43 D Data biological, for D1 receptor drug design, 92–96 from DNA arrays interpretation, 245–246 management, 229–230 sharing, 246 –247 experimental, see Experimental data D1/D5 dopamine receptors, 83–84 D1B/D5 dopamine receptors, 72–73 D1 dopamine-like receptors binding, 69 chemoarchitecture basal ganglia model, 78–81 in hippocampus, 83–84 and memory circuits, 81–83 in periphery, 85 receptor localization, 77–78 immunological methods, 69–70 localization, 71–73 mRNA localization, 70 –71 D1 dopamine receptors agonists, 106–110 agonists and antagonists, 100–102 in attentional disorders, 112–113 in cognition, 113–114 D5 receptor comparison, 88–89 in dyskinesias, 110 –112 ligands drug design issues, 96–100 early design, 89–91 pharmacophoric models, 92–96 selective ligand development, 91–92 in memory, 113–114

INDEX

molecular mechanisms, 85–88 origin, 67–68 and PD early agonists, 105–106 etiology and treatment, 102–105 in schizophrenia, 112–113 in substance abuse, 115–116 D1A dopamine receptors, and mRNA distribution, 72 D2 dopamine receptors, 73–77 D3 dopamine receptors, 75–76 D4 dopamine receptors, 76–77 D5 dopamine receptors, 88–89 Degeneration, mechanisms in AD, 186–189 DHX, see Dihydrexidine Diabetes mellitus, and AD, 199–200 Diagnosis, protocols for AD, 175 Differential gene expression, 233–234 Dihydrexidine, 99, 106–110 Dlc, see Dynein light chain DNA array technology availability, 247 cDNA arrays, 221–224 cost, 247 data interpretation, 246–247 data management, 229–230 data usage, 245–246 elemental analysis, 229–230 expression proﬁling, 226–228 gene proﬁling in brain, 237–242 gene proﬁling in neuronal cells, 232–237 multivariate analysis, 230–232 in neurogenetics, 242–244 oligonucleotide arrays, 224 –226 reproducibility, 244 –245 sensitivity, 244 –245 Dominant inheritance, in AD, 190–192 Dopamine receptors, molecular biology, 68 Drugs cellular effects, 234 –236 for D1-like receptors design, 96–100 early design, 89–91 SCH23390, 91–92 SKF38393, 91 for D1 receptor ligands, 92–96 NSAIDs, for AD brain, 194 –196 Dynein light chain, 46 Dyskinesias, 110–112

257

E Elemental analysis, DNA arrays, 229–230 Endocytosis, GABAA receptors GABARAP, 46 – 47 GRUB1, 47– 48 insulin role, 20–21 purpose, 21–23 in vitro, 17–20 in vivo, 14–17 Endoplasmic reticulum, GABAA receptor targeting, 8–9 Enterpeduncular nucleus, 78–80 Epn, see Enterpeduncular nucleus ER, see Endoplasmic reticulum Estrogen therapy, and AD, 197 N -Ethylmaleimide-sensitive factor, 47 Exocytosis, GABAA receptors GABARAP, 46– 47 GRUB1, 47– 48 Experimental data and LGIC modeling, 149–150 and pentameric LGIC modeling, 150–154 pentameric LGICs, 160–161 technique validity, 148–149

G GABAergic synapses inhibition in brain, 33 regulation by neurotrophic peptides, 53 structural anatomy, 33–34 GABARAP, see GABAA receptor-associated protein GABAA receptor-associated protein, 12–13 GABAA receptor-associated signaling proteins, 49–50 GABAA receptor-associated ubiquitin-like protein, 47–48 GABAA receptor-clustering proteins GABARAP, 12–13 gephyrin, 11–12 rapsyn, 13 GABAA receptors, see γ -Amino butyric acid type A receptors GDP–GTP exchange factor, 44 – 45 GEF, see GDP–GTP exchange factor Gene expression mapping, and DNA arrays, 238–239 monitoring, and cDNA arrays, 226–228

258

INDEX

Gene proﬁling in brain, 237–242 in neuronal cells, 232–237 Genetic risk factors, for AD, 192–193 Gephyrin in GABAA receptor clustering, 11–12 and postsynaptic GABAA receptors, 40– 42 Gephyrin-interacting proteins, 43– 46 GFP, see Green ﬂuorescent protein Glass cDNA microarrays, 221–224 Globus pallidus, 78–80 Glycine receptors, 11–12 GlyRs, see Glycine receptors GPe, see Globus pallidus GPi, see Globus pallidus Green ﬂuorescent protein, 17–18 GRUB1, see GABAA receptor-associated ubiquitin-like protein H HEK cells, see Human embryonic kidney cells Heterologous cells, GABAA receptor assembly heteropentameric receptors, 4–6 homo-oligomeric receptors, 6–7 Heteropentameric GABAA receptors, assembly, 4–6 Hippocampal pyramidal neurons, 9–11 Hippocampus, D1/D5 receptors, 83–84 HMMTOP, 144 –145 Homology modeling, 143–145 Homo-oligomeric GABAA receptors, 6–7 Human embryonic kidney cells GABAA receptors, 4 –7, 9, 17–20 rapsyn, 13 I Immunoﬂuorescence, extrasynaptic GABAA receptors, 37 Inﬂammation, in AD brain, 194 –196 Inhalation anesthetics, LGIC binding sites for, 154 –155 Insulin, in GABAA receptor trafﬁcking, 20–21 Intracellular sorting, GABAA receptors, 8–11 Ion channels, transmembrane, modeling, 141–143

K Kinetics, pentameric LGICs, 160 L LGIC, see Ligand-gated ion channels Ligand-gated ion channels modeling, 141–143 pentameric, 150–155, 157–161 tetrameric, 149–150, 155–156 Ligands, cellular effects, 234 –236 Long-term potentiation, D1/D5 receptors, 84 LTP, see Long-term potentiation M Madin–Darby kidney cells, GABAA receptor targeting, 9 MAP, see Microtubule-associated protein Mapping D1 dopamine-like receptors, 69–71 gene expression, and DNA arrays, 238–239 Maturation, conformational, in GABAA receptor assembly, 5–6 MDCK cells, see Madin–Darby kidney cells Memory D1 receptor role, 113–114 prefrontal cortex circuits, 81–83 Messenger RNA D1B/D5 dopamine receptors, 72–73 D1 dopamine-like receptors, 70 –71 D1A dopamine receptors, 72 D2 dopamine receptors, 74 –75 D3 dopamine receptors, 75–76 D4 dopamine receptors, 76–77 Microtubule-associated protein, 43–44 Miniature postsynaptic inhibitory currents, 53 Mini-Mental State Examination, 175 Mini-Mental State score, 172 mIPSCs, see Miniature postsynaptic inhibitory currents MM3, 146 MMFF, 146

INDEX

MMSE, see Mini-Mental State Examination MMS score, see Mini-Mental State score Models animal, neuronal abnormalities, 240–241 homology, principles, 143–145 molecular, see Molecular modeling neuroanatomical, basal ganglia, 78–81 for PD, 100–102, 105–106 pharmacophoric, dopamine receptor ligands, 92–96 transmembrane ion channels, 141–143 Molecular biology, dopamine receptors, 68 Molecular dynamics, principles, 147 Molecular mechanics, principles, 145–146 Molecular modeling homology modeling, 143–145 molecular dynamics, 147 molecular mechanics, 145–146 pentameric LGICs, 150–154, 157–161 protein structure validation, 148 tetrameric LGICs, 149–150, 155–156 MPTP in DHX tests of D1 agonists, 107–108 model, 102 Multivariate analysis, in DNA arrays, 230–232 Mutations, in AD, 190–192 N National Institute of Aging–Reagan Institute criteria, 173–174 National Institute of Neurological and Communicative Disorders and Stroke, 170–175 Neurobiology, gene proﬁling in brain, 237–242 in neuronal cells, 232–237 Neuroﬁbrillary tangles for AD pathological diagnosis, 169–173 degeneration mechanisms, 186–189 formation, 184 –186 Neurogenetics, DNA array applications, 242–244 Neurological diseases, gene proﬁling, 241–242 Neuronal plasticity, 239–240 Neurons

259

abnormalities, animal models, 240–241 GABAA receptors, 8, 14 –16 gene proﬁling in cells, 232–237 hippocampal pyramidal, 9–11 loss in AD, 178–182 striatal cholinergic, 81 subtypes, differential gene expression, 233–234 Neuroreceptors, GPCR, 96–100 Neurotrophic peptides, 53 NFTs, see Neuroﬁbrillary tangles NIA–Reagan Institute criteria, see National Institute of Aging–Reagan Institute criteria NINCDS, see National Institute of Neurological and Communicative Disorders and Stroke NMDA receptor, 33–34 NMDA receptor-associated protein complex, 33–34 Nonsteroidal anti-inﬂammatory drugs, 194–196 NRC, see NMDA receptor-associated protein complex NSAIDs, see Nonsteroidal anti-inﬂammatory drugs NSF, see N-Ethylmaleimide-sensitive factor O 6-OHDA, model, 100–102 Oligonucleotide arrays, 224 –226 Outﬂow pathways, and basal ganglia function, 78–81 P Parkinson’s disease D1 agonist tests, 106–110 D1 role in dyskinesias, 110–112 early D1 agonists, 105–106 etiology and treatment, 102–105 models, 100–102 Pathogenesis, AD, 176–177 Pathology, in AD, 173–175, 197–200 PD, see Parkinson’s disease Periphery, D1-like receptors, 85 Pharmacophoric models, dopamine receptor ligands, 92–96 PHDhtm, 144 –145

260

INDEX

PH domains, see Pleckstrin homology domains Phorbol myristate acetate, and GABAA receptors, 17–19 Pin1, in AD degeneration, 187–188 PKC, see Protein kinase C Plasticity neuronal, 239–240 synaptic plasticity, 50–53 Pleckstrin homology domains, and GABAA receptors, 44 – 45 PMA, see Phorbol myristate acetate Postsynaptic density, and GABAergic synapses, 33–34 Prefrontal cortex, memory circuits, 81–83 Presenilin-1, mutations in AD, 190–192 Presenilin-2, mutations in AD, 190–192 Presynaptic factors, in synapse development, 51–52 Proﬁling array-based expression, 226–228 gene in brain, 237–242 in neuronal cells, 232–237 Prolyl isomerase, in AD degeneration, 187–188 N -n-Propyl-4-methyldihydrexidine, 99 Protein kinase C, and GABAA receptors, 17–19, 49 Proteins AKAP, 49 APP, 182–183, 190–192 GABAA receptor-associated, 11–13, 47–50 gephyrin-interacting, 43– 46 green ﬂuorescent, 17–18 microtubule-associated, 43– 44 structure validation, 148 PS-1, see Presenilin-1 PS-2, see Presenilin-2 PSD, see Postsynaptic density

Risk factors, for AD genetic factors, 192–193 vascular factors, 199–200 S SCH23390 development, 91–92 and dyskinesias, 111 Schizophrenia, D1 dopamine role, 112–113 Secondary structure, pentameric LGICs, 150–153 SeqFold, 145, 157 Sequence similarity matrices, 144 SKF38393 development, 91–92 in memory and cognition, 114 SKF81297, in PD tests, 108 SKF82958, in PD tests, 110 Spotted DNA, in cDNA arrays, 222–224 STN, see Subthalamic nucleus Striatal cholinergic neurons, 81 Subcellular localization, GABAA receptor subtypes, 35–36 Substance abuse, D1 receptor role, 115–116 Substantia nigra, D1/D5 receptors, 83–84 Substantia nigra pars reticulata, 78–80 Subthalamic nucleus, 78–80 Surface targeting, GABAA receptors, 8–11 Synaptic anchoring, GABAA receptors, 48 Synaptic plasticity, GABAA receptors GABAergic synapse regulation, 53 overview, 50–51 role of presynaptic factors, 51–52 role of synaptic activity, 52–53 T

R RACK-1, see Receptor for activated C-kinase RAFT1, 45– 46 Rapsyn, 13 Receptor for activated C-kinase, 49 Recycling, GABAA receptor, 17–21 Regional gene expression mapping, 238–239

Temporal gene expression mapping, 238–239 TM1, see Transmembrane segment 1 TM2, see Transmembrane segment 2 TM3, see Transmembrane segment 3 TM4, see Transmembrane segment 4 TMHMM, 144 –145 Transcription factors, 236–237

INDEX

Transmembranes, ion channel, modeling, 141–143 Transmembrane segment 1, 152–153 Transmembrane segment 2, 151–152 Transmembrane segment 3, 153–154 Transmembrane segment 4, 154 U Uniﬁed Parkinson’s Disease Rating Scale, 108–110

261

UPDRS, see Uniﬁed Parkinson’s Disease Rating Scale V Vascular pathology, in AD, 197–200 Vascular risk factors, for AD, 199–200 Ventral tegmental area, D1/D5 receptors, 83–84 Vesicles, clathrin-coated, 16–17

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CONTENTS OF RECENT VOLUMES

Volume 33

Acetylcholine at Motor Nerves: Storage, Release, and Presynaptic Modulation by Autoreceptors and Adrenoceptors Ignaz Wessler

Olfaction S. G. Shirley Neuropharmacologic and Behavioral Actions of Clonidine: Interactions with Central Neurotransmitters Jerry J. Buccafusco

INDEX

Volume 35

Development of the Leech Nervous System Gunther S. Stent, William B. Kristan, Jr., Steven A. Torrence, Kathleen A. French, and David A. Weisblat GABAA Receptors Control the Excitability of Neuronal Populations Armin Stelzer Cellular and Molecular Physiology of Alcohol Actions in the Nervous System Forrest F. Weight INDEX

Biochemical Correlates of Long-Term Potentiation in Hippocampal Synapses Satoru Otani and Yehezkel Ben-Ari Molecular Aspects of Photoreceptor Adaptation in Vertebrate Retina Satoru Kawamura The Neurobiology and Genetics of Infantile Autism Linda J. Lotspeich and Roland D. Ciaranello Humoral Regulation of Sleep Levente Kap´as, Ferenc Ob´al, Jr., and James M. Krueger

Volume 34 Neurotransmitters as Neurotrophic Factors: A New Set of Functions Joan P. Schwartz Heterogeneity and Regulation of Nicotinic Acetylcholine Receptors Ronald J. Lukas and Merouane Bencherif Activity-Dependent Development of the Vertebrate Nervous System R. Douglas Fields and Phillip G. Nelson A Role for Glial Cells in Activity-Dependent Central Nervous Plasticity? Review and Hypothesis Christian M. M¨uller

Striatal Dopamine in Reward and Attention: A System for Understanding the Symptomatology of Acute Schizophrenia and Mania Robert Miller Acetylcholine Transport, Storage, and Release Stanley M. Parsons, Chris Prior, and Ian G. Marshall Molecular Neurobiology of Dopaminergic Receptors David R. Sibley, Frederick J. Monsma, Jr., and Yong Shen INDEX

263

264

CONTENTS OF RECENT VOLUMES

Volume 36 Ca2+ , N -Methyl-D-aspartate Receptors, and AIDS-Related Neuronal Injury Stuart A. Lipton Processing of Alzheimer Aβ-Amyloid Precursor Protein: Cell Biology, Regulation, and Role in Alzheimer Disease Sam Gandy and Paul Greengard Molecular Neurobiology of the GABAA Receptor Susan M. J. Dunn, Alan N. Bateson, and Ian L. Martin The Pharmacology and Function of Central GABAB Receptors David D. Mott and Darrell V. Lewis The Role of the Amygdala in Emotional Learning Michael Davis Excitotoxicity and Neurological Disorders: Involvement of Membrane Phospholipids Akhlaq A. Farooqui and Lloyd A. Horrocks Injury-Related Behavior and Neuronal Plasticity: An Evolutionary Perspective on Sensitization, Hyperalgesia, and Analgesia Edgar T. Walters

Exploration and Selection in the Early Acquisition of Skill Esther Thelen and Daniela Corbetta Population Activity in the Control of Movement Apostolos P. Georgopoulos Section III: Functional Segregation and Integration in the Brain Reentry and the Problem of Cortical Integration Giulio Tononi Coherence as an Organizing Principle of Cortical Functions Wolf Singer Temporal Mechanisms in Perception Ernst P¨oppel Section IV: Memory and Models Selection versus Instruction: Use of Computer Models to Compare Brain Theories George N. Reeke, Jr. Memory and Forgetting: Long-Term and Gradual Changes in Memory Storage Larry R. Squire Implicit Knowledge: New Perspectives on Unconscious Processes Daniel L. Schacter

INDEX

Section V: Psychophysics, Psychoanalysis, and Neuropsychology

Volume 37

Phantom Limbs, Neglect Syndromes, Repressed Memories, and Freudian Psychology V. S. Ramachandran

Section I: Selectionist Ideas and Neurobiology Selectionist and Instructionist Ideas in Neuroscience Olaf Sporns Population Thinking and Neuronal Selection: Metaphors or Concepts? Ernst Mayr Selection and the Origin of Information Manfred Eigen Section II: Development and Neuronal Populations Morphoregulatory Molecules and Selectional Dynamics during Development Kathryn L. Crossin

Neural Darwinism and a Conceptual Crisis in Psychoanalysis Arnold H. Modell A New Vision of the Mind Oliver Sacks INDEX

Volume 38 Regulation of GABAA Receptor Function and Gene Expression in the Central Nervous System A. Leslie Morrow

CONTENTS OF RECENT VOLUMES

Genetics and the Organization of the Basal Ganglia Robert Hitzemann, Yeang Olan, Stephen Kanes, Katherine Dains, and Barbara Hitzemann Structure and Pharmocology of Vertebrate GABAA Receptor Subtypes Paul J. Whiting, Ruth M. McKernan, and Keith A. Wafford Neurotransmitter Transporters: Molecular Biology, Function, and Regulation Beth Borowsky and Beth J. Hoffman

265

Genetic Models in the Study of Anesthetic Drug Action Victoria J. Simpson and Thomas E. Johnson Neurochemical Bases of Locomotion and Ethanol Stimulant Effects Tamara J. Phillips and Elaine H. Shen Effects of Ethanol on Ion Channels Fulton T. Crews, A. Leslie Morrow, Hugh Criswell, and George Breese INDEX

Presynaptic Excitability Meyer B. Jackson Monoamine Neurotransmitters in Invertebrates and Vertebrates: An Examination of the Diverse Enzymatic Pathways Utilized to Synthesize and Inactivate Biogenic Amines B. D. Sloley and A. V. Juorio Neurotransmitter Systems in Schizophrenia Gavin P. Reynolds Physiology of Bergmann Glial Cells Thomas M¨uller and Helmut Kettenmann INDEX

Volume 39

Modulation of Amino Acid-Gated Ion Channels by Protein Phosphorylation Stephen J. Moss and Trevor G. Smart Use-Dependent Regulation of GABAA Receptors Eugene M. Barnes, Jr. Synaptic Transmission and Modulation in the Neostriatum David M. Lovinger and Elizabeth Tyler The Cytoskeleton and Neurotransmitter Receptors Valerie J. Whatley and R. Adron Harris

Volume 40 Mechanisms of Nerve Cell Death: Apoptosis or Necrosis after Cerebral Ischemia R. M. E. Chalmers-Redman, A. D. Fraser, W. Y. H. Ju, J. Wadia, N. A. Tatton, and W. G. Tatton Changes in Ionic Fluxes during Cerebral Ischemia Tibor Kristian and Bo K. Siesjo Techniques for Examining Neuroprotective Drugs in Vivo A. Richard Green and Alan J. Cross Techniques for Examining Neuroprotective Drugs in Vitro Mark P. Goldberg, Uta Strasser, and Laura L. Dugan Calcium Antagonists: Their Role in Neuroprotection A. Jacqueline Hunter Sodium and Potassium Channel Modulators: Their Role in Neuroprotection Tihomir P. Obrenovich NMDA Antagonists: Their Role in Neuroprotection Danial L. Small

Endogenous Opioid Regulation of Hippocampal Function Michele L. Simmons and Charles Chavkin

Development of the NMDA Ion-Channel Blocker, Aptiganel Hydrochloride, as a Neuroprotective Agent for Acute CNS Injury Robert N. McBurney

Molecular Neurobiology of the Cannabinoid Receptor Mary E. Abood and Billy R. Martin

The Pharmacology of AMPA Antagonists and Their Role in Neuroprotection Rammy Gill and David Lodge

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CONTENTS OF RECENT VOLUMES

GABA and Neuroprotection Patrick D. Lyden Adenosine and Neuroprotection Bertil B. Fredholm Interleukins and Cerebral Ischemia Nancy J. Rothwell, Sarah A. Loddick, and Paul Stroemer Nitrone-Based Free Radical Traps as Neuroprotective Agents in Cerebral Ischemia and Other Pathologies Kenneth Hensley, John M. Carney, Charles A. Stewart, Tahera Tabatabaie, Quentin Pye, and Robert A. Floyd Neurotoxic and Neuroprotective Roles of Nitric Oxide in Cerebral Ischemia Turgay Dalkara and Michael A. Moskowitz A Review of Earlier Clinical Studies on Neuroprotective Agents and Current Approaches Nils-Gunnar Wahlgren INDEX

Volume 41 Section I: Historical Overview Rediscovery of an Early Concept Jeremy D. Schmahmann Section II: Anatomic Substrates The Cerebrocerebellar System Jeremy D. Schmahmann and Deepak N. Pandya Cerebellar Output Channels Frank A. Middleton and Peter L. Strick Cerebellar-Hypothalamic Axis: Basic Circuits and Clinical Observations Duane E. Haines, Espen Dietrichs, Gregory A. Mihailoff, and E. Frank McDonald Section III. Physiological Observations Amelioration of Aggression: Response to Selective Cerebellar Lesions in the Rhesus Monkey Aaron J. Berman Autonomic and Vasomotor Regulation Donald J. Reis and Eugene V. Golanov

Associative Learning Richard F. Thompson, Shaowen Bao, Lu Chen, Benjamin D. Cipriano, Jeffrey S. Grethe, Jeansok J. Kim, Judith K. Thompson, Jo Anne Tracy, Martha S. Weninger, and David J. Krupa Visuospatial Abilities Robert Lalonde Spatial Event Processing Marco Molinari, Laura Petrosini, and Liliana G. Grammaldo Section IV: Functional Neuroimaging Studies Linguistic Processing Julie A. Fiez and Marcus E. Raichle Sensory and Cognitive Functions Lawrence M. Parsons and Peter T. Fox Skill Learning Julien Doyon Section V: Clinical and Neuropsychological Observations Executive Function and Motor Skill Learning Mark Hallett and Jordon Grafman Verbal Fluency and Agrammatism Marco Molinari, Maria G. Leggio, and Maria C. Silveri Classical Conditioning Diana S. Woodruff-Pak Early Infantile Autism Margaret L. Bauman, Pauline A. Filipek, and Thomas L. Kemper Olivopontocerebellar Atrophy and Friedreich’s Ataxia: Neuropsychological Consequences of Bilateral versus Unilateral Cerebellar Lesions Th´er`ese Botez-Marquard and Mihai I. Botez Posterior Fossa Syndrome Ian F. Pollack Cerebellar Cognitive Affective Syndrome Jeremy D. Schmahmann and Janet C. Sherman Inherited Cerebellar Diseases Claus W. Wallesch and Claudius Bartels Neuropsychological Abnormalities in Cerebellar Syndromes—Fact or Fiction? Irene Daum and Hermann Ackermann

CONTENTS OF RECENT VOLUMES

Section VI: Theoretical Considerations Cerebellar Microcomplexes Masao Ito Control of Sensory Data Acquisition James M. Bower Neural Representations of Moving Systems Michael Paulin How Fibers Subserve Computing Capabilities: Similarities between Brains and Machines Henrietta C. Leiner and Alan L. Leiner

267

Posttranslational Regulation of Ionotropic Glutamate Receptors and Synaptic Plasticity Xiaoning Bi, Steve Standley, and Michel Baudry Heritable Mutations in the Glycine, GABAA , and Nicotinic Acetylcholine Receptors Provide New Insights into the Ligand-Gated Ion Channel Receptor Superfamily Behnaz Vafa and Peter R. Schofield INDEX

Cerebellar Timing Systems Richard Ivry

Volume 43

Attention Coordination and Anticipatory Control Natacha A. Akshoomoff, Eric Courchesne, and Jeanne Townsend

Early Development of the Drosophila Neuromuscular Junction: A Model for Studying Neuronal Networks in Development Akira Chiba

Context-Response Linkage W. Thomas Thach

Development of Larval Body Wall Muscles Michael Bate, Matthias Landgraf, and Mar Ruiz G´omez Bate

Duality of Cerebellar Motor and Cognitive Functions James R. Bloedel and Vlastislav Bracha Section VII: Future Directions Therapeutic and Research Implications Jeremy D. Schmahmann

Volume 42 Alzheimer Disease Mark A. Smith Neurobiology of Stroke W. Dalton Dietrich Free Radicals, Calcium, and the Synaptic Plasticity–Cell Death Continuum: Emerging Roles of the Trascription Factor NFκB Mark P. Mattson AP-I Transcription Factors: Short- and LongTerm Modulators of Gene Expression in the Brain Keith Pennypacker Ion Channels in Epilepsy Istvan Mody

Development of Electrical Properties and Synaptic Transmission at the Embryonic Neuromuscular Junction Kendal S. Broadie Ultrastructural Correlates of Neuromuscular Junction Development Mary B. Rheuben, Motojiro Yoshihara, and Yoshiaki Kidokoro Assembly and Maturation of the Drosophila Larval Neuromuscular Junction L. Sian Gramates and Vivian Budnik Second Messenger Systems Underlying Plasticity at the Neuromuscular Junction Frances Hannan and Yi Zhong Mechanisms of Neurotransmitter Release J. Troy Littleton, Leo Pallanck, and Barry Ganetzky Vesicle Recycling at the Drosophila Neuromuscular Junction Daniel T. Stimson and Mani Ramaswami Ionic Currents in Larval Muscles of Drosophila Satpal Singh and Chun-Fang Wu Development of the Adult Neuromuscular System Joyce J. Fernandes and Haig Keshishian

268

CONTENTS OF RECENT VOLUMES

Controlling the Motor Neuron James R. Trimarchi, Ping Jin, and Rodney K. Murphey

What Neurological Patients Tell Us about the Use of Optic Flow L. M. Vaina and S. K. Rushton INDEX

Volume 44 Human Ego-Motion Perception A. V. van den Berg

Volume 45

Optic Flow and Eye Movements M. Lappe and K.-P. Hoffman

Mechanisms of Brain Plasticity: From Normal Brain Function to Pathology Philip. A. Schwartzkroin

The Role of MST Neurons during Ocular Tracking in 3D Space K. Kawano, U. Inoue, A. Takemura, Y. Kodaka, and F. A. Miles

Brain Development and Generation of Brain Pathologies Gregory L. Holmes and Bridget McCabe

Visual Navigation in Flying Insects M. V. Srinivasan and S.-W. Zhang Neuronal Matched Filters for Optic Flow Processing in Flying Insects H. G. Krapp A Common Frame of Reference for the Analysis of Optic Flow and Vestibular Information B. J. Frost and D. R. W. Wylie Optic Flow and the Visual Guidance of Locomotion in the Cat H. Sherk and G. A. Fowler Stages of Self-Motion Processing in Primate Posterior Parietal Cortex F. Bremmer, J.-R. Duhamel, S. B. Hamed, and W. Graf Optic Flow Analysis for Self-Movement Perception C. J. Duffy Neural Mechanisms for Self-Motion Perception in Area MST R. A. Andersen, K. V. Shenoy, J. A. Crowell, and D. C. Bradley Computational Mechanisms for Optic Flow Analysis in Primate Cortex M. Lappe Human Cortical Areas Underlying the Perception of Optic Flow: Brain Imaging Studies M. W. Greenlee

Maturation of Channels and Receptors: Consequences for Excitability David F. Owens and Arnold R. Kriegstein Neuronal Activity and the Establishment of Normal and Epileptic Circuits during Brain Development John W. Swann, Karen L. Smith, and Chong L. Lee The Effects of Seizures of the Hippocampus of the Immature Brain Ellen F. Sperber and Solomon L. Moshe Abnormal Development and Catastrophic Epilepsies: The Clinical Picture and Relation to Neuroimaging Harry T. Chugani and Diane C. Chugani Cortical Reorganization and Seizure Generation in Dysplastic Cortex G. Avanzini, R. Preafico, S. Franceschetti, G. Sancini, G. Battaglia, and V. Scaioli Rasmussen’s Syndrome with Particular Reference to Cerebral Plasticity: A Tribute to Frank Morrell Fredrick Andermann and Yvonne Hart Structural Reorganization of Hippocampal Networks Caused by Seizure Activity Daniel H. Lowenstein Epilepsy-Associated Plasticity in gammaAmniobutyric Acid Receptor Expression, Function and Inhibitory Synaptic Properties Douglas A. Coulter

CONTENTS OF RECENT VOLUMES

269

Synaptic Plasticity and Secondary Epileptogenesis Timothy J. Teyler, Steven L. Morgan, Rebecca N. Russell, and Brian L. Woodside

Local Pathways of Seizure Propagation in Neocortex Barry W. Connors, David J. Pinto, and Albert E. Telefeian

Synaptic Plasticity in Epileptogenesis: Cellular Mechanisms Underlying Long-Lasting Synaptic Modifications that Require New Gene Expression Oswald Steward, Christopher S. Wallace, and Paul F. Worley

Multiple Subpial Transection: A Clinical Assessment C. E. Polkey

Cellular Correlates of Behavior Emma R. Wood, Paul A. Dudchenko, and Howard Eichenbaum Mechanisms of Neuronal Conditioning David A. T. King, David J. Krupa, Michael R. Foy, and Richard F. Thompson Plasticity in the Aging Central Nervous System C. A. Barnes Secondary Epileptogenesis, Kindling, and Intractable Epilepsy: A Reappraisal from the Perspective of Neuronal Plasticity Thomas P. Sutula Kindling and the Mirror Focus Dan C. McIntyre and Michael O. Poulter Partial Kindling and Behavioral Pathologies Robert E. Adamec The Mirror Focus and Secondary Epileptogenesis B. J. Wilder Hippocampal Lesions in Epilepsy: A Historical Review Robert Naquet Clinical Evidence for Secondary Epileptogensis Hans O. Luders Epilepsy as a Progressive (or Nonprogressive “Benign”) Disorder John A. Wada Pathophysiological Aspects of LandauKleffner Syndrome: From the Active Epileptic Phase to Recovery Marie-Noelle Metz-Lutz, Pierre Maquet, Annd De Saint Martin, Gabrielle Rudolf, Norma Wioland, Edouard Hirsch and Chriatian Marescaux

The Legacy of Frank Morrell Jerome Engel, Jr.

Volume 46 Neurosteroids: (Beginning of ) The Story E. E. Baulieu, P. Robel, and M. Schumacher Biosynthesis of Neurosteroids and Regulation of their Synthesis S. Mellon and H. Vaudry Neurosteroid 7-hydroxylation Products in Brain R. Morfin and L. Starka Neurosteroid Analysis Ahmed. A. Alomary, Robert L. Fitzgerald, and Robert H. Purdy Role of Peripheral-Type Benzodiazepine Receptors in Adrenal and Brain Steroidogenesis R. C. Brown and V. Papadopoulos Formation and Effects of Neuroactive Steroids in the Central and Peripheral Nervous System R. C. Melcangi, V. Magnaghi, M. Galbiati, and L. Martini Neurosteroids and GABAA Receptor Function Jeremy J. Lambert, Sarah C. Harney, Delia Belelli, and John A. Peters GABAA Receptor Plasticity During LongTerm Exposure to and Withdrawal From Progesterone G. Biggio, P. Follesa, E. Sanna, R. H. Purdy, and A. Concas Stress and Neuroactive Steroids M. L. Barbaccia, M. Serra, R. H. Purdy, and G. Biggio

270

CONTENTS OF RECENT VOLUMES

Neurosteroids in Learning and Memory Processes M. Vallee, W. Mayo, G. F. Koob, and M. LeMoal Neurosteroids and Behaviour S. R. Engel and K. A. Grant Ethanol and Neurosteroid Interactions in Brain A. L. Morrow, M. J. VanDoren, R. Fleming, and S. Penland Preclinical Development of Neurosteroids as Neuroprotective Agents for the Treatment of Neurodegenerative Diseases P. A. Lapchak and D. M. Araujo Clinical Implications of Circulating Neurosteroids A. R. Genazzani, P. Monteleone, M. Stomati, F. Bernardi, L. Cobellis, E. Casarosa, M. Luisi, and F. Petraglia Neuroactive Steroids and CNS Disorders M. Wang, T. Backstrom, I. Sundstrom, G. Wahlstrom, T. Olsson, D. Zhu, I-M. Johansson, I. Bjorn, and M. Bixo Neuroactive Steroids in Neuropsychopharmacology R. Rupprecht and F. Holsboer

Neurosteroids and Human Disease Lisa D. Griffin, Susan C. Conrad, and Synthia H. Mellon

Volume 48 Assembly and Intracellular Trafficking of Gaba A Receptors Eugene Barnes Subcellular Localization and Regulation of Gaba A Receptors and Associated Proteins Bernhard L¨uscher and Jean-Marc Fritschy D-1 Dopamine Receptors Richard Mailman Molecular Modeling of Ligand-Gated Ion Channels, Progress and Challenges Ed Bertaccini and James R. Trudel Alzheimer’s Disease: Its Diagnosis and Pathogenesis Jillian J. Kril and Glenda M. Halliday DNA Arrays and FUnctional Genomics in Neurobiology Christelle Thibault, Long Wang, Li Zhang, and Michael F. Miles