Serotonin: The Mediator that Spans Evolution

Serotonin: The Mediator That Spans Evolution provides a comprehensive review of the widespread roles for serotonin in respiratory, cardiovascular and thermoregulatory control, and for growth and development in early life. This important resource highlights serotonin’s role in normal (unstressed) conditions, and in response to a variety of physiological stressors. It focuses on new animal models, comparing and contrasting data from mice and rats. In addition, the book compares and contrasts the physiological effects of brain and blood serotonin systems and includes new data suggesting that the influence of serotonin is in part through the regulation of gene expression.

Finally, it discusses the role of serotonin system dysfunction in a variety of pathophysiological conditions, including sleep apnea, obesity and hypertension, and presents compelling evidence that this dysfunction is involved in Sudden Infant Death Syndrome (SIDS).

• Includes the latest information on new animal models of serotonin system dysfunction
• Explores the wide scope of serotonin’s influence on multiple organ and physiological systems
• Highlights the autonomous functioning of the brain and body serotonin systems
• Provides compelling evidence of serotonin dysfunction in SIDS, a leading cause of death in infancy

• Language: English
• Publisher: Academic Press
• Release date: Sep 21, 2018
• ISBN: 9780128005842

Book preview

Serotonin

The Mediator that Spans Evolution

Editor

Paul M Pilowsky

Table of Contents

Cover image

Title page

Copyright

Contributors

Foreword

Part 1. Biophysics of Serotonin

Chapter 1. Biophysics of Serotonin and the Serotonin1A Receptor: Fluorescence and Dynamics

1. Serotonin: An Ancient Molecule With Diverse Properties

2. Serotonin: A Fluorescent Neurotransmitter

3. The Serotonin1A Receptor: A Representative Member of Serotonin Receptor Family

4. Receptor Dynamics: The Relevance of Time Scales

5. Membrane Lateral Dynamics of the Serotonin1A Receptor Utilizing Bleach Area–Dependent FRAP: Consequence of Membrane Domainization

6. Actin Cytoskeleton–Induced Confined Dynamics of the Serotonin1A Receptor Revealed by zFCS

7. Conclusion and the Road Ahead

Chapter 2. Serotonin in Plants: Origin, Functions, and Implications

Abbreviations

1. Introduction

2. Evolution and Occurrence in Plants

3. Functions in Plants

4. Serotonin Across Other Kingdoms and Applications Towards Phytoserotonin Research

5. Conclusion

Part 2. Anatomy

Chapter 3. Serotonergic Neurons in Vertebrate and Invertebrate Model Organisms (Rodents, Zebrafish, Drosophila melanogaster, Aplysia californica, Caenorhabditis elegans)

1. Introduction

2. Vertebrate Models

3. Invertebrates

Chapter 4. 5-Hydroxytryptamine in the Endocrine Pancreas

1. Serotonin (5-Hydroxytryptamine) in the Islets of Langerhans

2. 5-HT Synthesis, Vesicular Loading, Uptake, and Breakdown in Pancreatic β-Cells

3. Islet Hormone Secretion and 5-Hydroxytryptamine

4. Functional Impact of Species Differences

Chapter 5. Serotonin in Platelets

Abbreviations

1. Introduction: A Brief History of Platelets and Serotonin

2. The Biology of Platelets

3. Serotonin Within the Platelet

4. Clinical Manifestations of Altered Platelet Serotonin

5. Conclusion

Chapter 6. Anatomy of the Serotonin Transporter

1. Introduction

2. Anatomical Distribution of the 5-HTT in the Central Nervous System

3. Expression and Localization of the 5-HTT in Nonserotonin Neurons or Glia

4. Anatomical Distribution of the 5-HTT in the Peripheral Nervous System

5. Absence of Serotonin Transporter in a Subset of Serotonin Axons

6. Conclusion

Part 3. Physiology in the Periphery

Chapter 7. Cellular Regulation of Peripheral Serotonin

1. Introduction

2. Secretory Mechanisms of 5-Hydroxytryptamine Release From Enterochromaffin Cells

3. Neurochemical and Mechanical Stimulation of Enterochromaffin Cells

4. Enterochromaffin Cells Are Nutrient Sensors

5. Intestinal Bacteria Mediate Host Physiology Through Enterochromaffin Cell 5-Hydroxytryptamine

6. Gastrointestinal Hormonal Cross Talk With Enterochromaffin Cells

7. Concluding Remarks

Chapter 8. Role of 5-Hydroxytryptamine in the Control of Gut Motility

1. Introduction

2. Release of 5-Hydroxytryptamine From the Mucosa

3. Real-Time Recordings of 5-Hydroxytryptamine Release During Intestinal Motor Activities

4. Mucosal Compression and Activation of Peristalsis

5. Enterochromaffin Cells Release Many Substances

6. Serotonin—A Neurotransmitter in the Enteric Nervous System?

7. 5-HT Antagonists Can Block Peristalsis in Preparations Depleted of All 5-HT

8. Concluding Remarks

Chapter 9. Phenotype of Mice Lacking Tryptophan Hydroxylase 1

1. Introduction

2. Platelets, Immune System, and Inflammation

3. Erythropoietic System

4. Liver

5. Pancreas and Fat

6. Embryogenesis and Heart

7. Lung

8. Mammary Gland

9. Gut

10. Bone

11. Conclusions

Chapter 10. Serotonin and the Immune System

1. Introduction

2. Serotonin and the Innate Immune Response

3. Serotonin and Adaptive Immunity

4. Concluding Remarks

Chapter 11. Serotonin and Adipocyte Function

1. Adipose Tissue

2. Impact of Serotonin on Metabolic Regulation

3. Adipose Tissue-Specific Effects of 5-Hydroxytryptamine

4. Impact of 5-Hydroxytryptamine on Adipokine Secretion

5. 5-Hydroxytryptamine-Mediated Effects on Preadipocyte Differentiation

Chapter 12. Serotonin and Cardiovascular Diseases

1. Introduction

2. Serotonin in Cardiac Morphogenesis

3. Adult Origin of Serotonin

4. Role of 5-HT2B Receptors in Postnatal Maturation and Cardiovascular Development

5. Serotonin in Adult Heart

6. Serotonin in Carcinoid Heart

7. Serotonin in Cardiac Hypertrophy and Failure

8. Vascular Responses to Serotonin

Chapter 13. Involvement of 5-HT in Cardiovascular Afferent Modulation of Brainstem Circuits Involved in Blood Pressure Maintenance

Abbreviations

1. Introduction

2. Nucleus Tractus Solitarii

3. Overview

4. Parasympathetic Nuclei—Nucleus Ambiguus and Dorsal Vagal Nucleus

5. Sympathetic Premotor Neurones—Rostral Ventrolateral Medulla

6. Conclusion

Chapter 14. Regulation of Nociceptor Signaling by Serotonin

1. Terminology

2. The Pain Pathway

3. Serotonin Receptors

4. Ion Channels Involved in Nociception

5. Serotonergic Modulation of Nociception

Part 4. Physiology in the Brain

Chapter 15. Brain Serotonin and Energy Homeostasis

1. Introduction

2. Serotonin Receptors Regulate Feeding Behavior and Energy Expenditure

3. Neuroanatomy of Serotonin Circuits With Relevance to Energy Homeostasis

4. Serotonin Drugs and Obesity

5. Conclusions

Chapter 16. Serotonin in Central Cardiovascular Regulation: Ex Uno Plura (From One Comes Many)

1. Anatomy of Serotonin Cell Bodies and Fibers

2. Serotonin in the Brain and Reflexes

3. Sympathetic Preganglionic Neurons and Serotonin Inputs

4. Conclusion

Chapter 17. 5-HT3 Receptor–Mediated Neural Transmission of Cardiorespiratory Modulation by the Nucleus of the Tractus Solitarius

1. Introduction

2. Role of NTS 5-HT3 Receptors in the Cardiovascular Regulation

3. Role of NTS 5-HT3 Receptors in the Respiratory Regulation During Stress

4. Conclusion

Chapter 18. Serotonin Receptors as the Therapeutic Target for Central Nervous System Disorders

1. Introduction

2. Serotonergic System in the Brain

3. Therapeutic Role of 5-HT Receptors in CNS Disorders

4. Summary

Chapter 19. Serotonin and the Control of Eupneic Breathing

1. Breathing Generation and Neuromodulation

2. Control of Resting Breathing by Serotonin

3. Serotonin and Chemoreflex Control of Breathing

4. Serotonin and Plasticity of Respiratory Circuitries

5. Perspectives

Chapter 20. Life Without Brain Serotonin: Phenotypes of Animals Deficient in Central Serotonin Synthesis

1. Tryptophan Hydroxylase 2: Serotonin-Synthesizing Enzyme in the Brain

2. Genetically Modified Animal Models With Complete Loss of or Partial Reduction in TPH2 Activity

3. Impact of Central Serotonin Deficiency on Neurotransmitter Systems and Formation of Serotonergic Neurons and Neuronal Circuitry

4. Physiological Consequences of Central 5-Hydroxytryptamine Deficiency

5. Behavioral Consequences of Central Serotonin Deficiency

6. Conclusions

Index

Copyright

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Contributors

Natalia Alenina

Max-Delbrueck-Center for Molecular Medicine, Berlin-Buch, Germany

Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia

Michael Bader

Max-Delbrück Center for Molecular Medicine (MDC), Berlin Institute of Health (BIH), Charité University Medicine Berlin, German Center for Cardiovascular Research (DZHK) site Berlin, Berlin, Germany

Institute for Biology, University of Lübeck, Lübeck, Germany

Selena E. Bartlett,     Institute of Health and Biomedical Innovation (IHBI) at Translational Research Institute (TRI), Queensland University of Technology (QUT), Brisbane, QLD, Australia

Catherine Béchade,     Université Pierre et Marie Curie, Paris, France

Kate Beecher,     Institute of Health and Biomedical Innovation (IHBI) at Translational Research Institute (TRI), Queensland University of Technology (QUT), Brisbane, QLD, Australia

Arnauld Belmer,     Institute of Health and Biomedical Innovation (IHBI) at Translational Research Institute (TRI), Queensland University of Technology (QUT), Brisbane, QLD, Australia

Stefan Boehm,     Department of Neurophysiology and Neuropharmacology, Medical University of Vienna, Vienna, Austria

Amitabha Chattopadhyay

CSIR-Centre for Cellular and Molecular Biology, Hyderabad, India

Academy of Scientific and Innovative Research, Ghaziabad, India

Lauren A.E. Erland,     Department of Plant Agriculture, Gosling Research Institute for Plant Preservation, University of Guelph, Guelph, ON, Canada

Malin Fex,     Clinical Science Malmö, Lund University Diabetes Center, Lund University, Malmö, Sweden

Patricia Gaspar,     Inserm UMR-S 839, Institut du Fer à Moulin, Sorbonne Université, Paris, France

Yanlin He,     USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States

Patrick S. Hosford,     Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom

Claire F. Jessup

Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia

Department of Anatomy and Histology and Centre for Neuroscience, Flinders University of South Australia, Adelaide, SA, Australia

Damien J. Keating

Department of Human Physiology and Centre for Neuroscience, Flinders University of South Australia, Adelaide, SA, Australia

Nutrition and Metabolism, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia

G. Aditya Kumar,     CSIR-Centre for Cellular and Molecular Biology, Hyderabad, India

Lex Leong

Infection and Immunity, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia

SAHMRI Microbiome Research Laboratory, School of Medicine, Flinders University, Adelaide, SA, Australia

Christina Lillesaar,     Center of Mental Health, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Würzburg and Department of Physiological Chemistry, Biocenter, University of Würzburg, Würzburg, Germany

Jessica A. Maclean

Heart Research Institute, Newtown, NSW, Australia

Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia

Luc Maroteaux

INSERM UMR-S 839, Paris, France

Université Pierre et Marie Curie, Paris, France

Institut du Fer à Moulin, Paris, France

Alyce M. Martin,     Department of Human Physiology and Centre for Neuroscience, Flinders University of South Australia, Adelaide, SA, Australia

Laurent Monassier,     Laboratoire de Neurobiologie et Pharmacologie Cardiovasculaire (EA7296), Faculté de Médecine, Fédération de Médecine Translationnelle, Université et Centre Hospitalier de Strasbourg, Strasbourg, France

Valentina Mosienko

Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, Exeter, United Kingdom

School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, United Kingdom

Yukihiro Ohno,     Department of Pharmacology, Osaka University of Pharmaceutical Sciences, Osaka, Japan

Sreetama Pal

Academy of Scientific and Innovative Research, Ghaziabad, India

CSIR-Indian Institute of Chemical Technology, Hyderabad, India

Paul M. Pilowsky,     Department of Physiology, Heart Research Institute, University of Sydney, Newtown, New South Wales, Australia

Andrew G. Ramage,     Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom

Geraint B. Rogers

Infection and Immunity, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia

SAHMRI Microbiome Research Laboratory, School of Medicine, Flinders University, Adelaide, SA, Australia

Anne Roumier,     INSERM UMR-S 839, Paris, France

Isabella Salzer,     Department of Neurophysiology and Neuropharmacology, Medical University of Vienna, Vienna, Austria

Parijat Sarkar,     CSIR-Centre for Cellular and Molecular Biology, Hyderabad, India

Praveen K. Saxena,     Department of Plant Agriculture, Gosling Research Institute for Plant Preservation, University of Guelph, Guelph, ON, Canada

Simone M. Schoenwaelder

Heart Research Institute, Newtown, NSW, Australia

Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia

Caroline Sévoz-Couche,     Sorbonne Universités, UPMC Univ Paris 06, INSERM, Paris, France

Nick J. Spencer,     Department of Human Physiology and Centre for Neuroscience, Flinders University of South Australia, Adelaide, SA, Australia

Karin G. Stenkula,     Lund University, EMV, BCM, Lund, Sweden

Christina E. Turi,     Department of Plant Agriculture, Gosling Research Institute for Plant Preservation, University of Guelph, Guelph, ON, Canada

Pingwen Xu,     USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States

Yong Xu,     USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States

Richard L. Young

Nutrition and Metabolism, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia

Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia

Daniel B. Zoccal,     Department of Physiology and Pathology, São Paulo State University (UNESP), Araraquara, Brazil

Foreword

This book concerns the role that serotonin plays in many systems. Serotonin is an agent that has been present throughout evolution. It exerts its effects on receptors that have also developed over the time span of evolution.

The book is intended for undergraduates with a special interest in serotonin and other amines, Masters, and senior Graduate students with an interest in serotonin or psychiatry will hopefully find this book useful. Senior faculty may find some of the individual chapters of interest as well.

Readers will note that serotonin and its receptors have biophysical properties that are interesting in and of themselves (Chapter 1). Serotonin is not simply a mammalian (Chapter 3) and insect (Chapter 3) hormone, but it is also found in plants (Chapter 2).

The anatomy of the serotonin system is quite diverse, in the brain, and fibers are spread throughout, although cell bodies are only found in a localized part of the brainstem. The serotonin transporter is found throughout the brain and in the periphery and is essential for normal function of this hormone (Chapters 5 and 6). In the periphery, essentially all serotonin is produced in the gut and then released where it is taken up by platelets (Chapter 5). In the periphery, serotonin in platelets plays an important role in clotting, but abrogation of the function of serotonin in the periphery does not cause hemorrhage and death immediately.

The role of serotonin in endocrine function is discussed extensively where it is suggested that serotonin may play a role in glucose metabolism, and ultimately diabetes and weight regulation (Chapter 11).

In Chapter 7, the role of serotonin in gut into row chromaffin cells is assayed. Chapter 8 considers the role of serotonin in the control of gut motility.

Chapters 9 and 20 consider the phenotype of animals that lack serotonin entirely.

In Chapter 10 there is a consideration of serotonin, and the immune system, and in Chapter 11 a discussion of serotonin and adipocyte function is discussed. Chapter 12 considers the role of serotonin in cardiovascular disease in the periphery.

Chapter 13 considers the role of serotonin in afferent modulation of brainstem circuits that regulate cardiovascular homeostasis. A role for serotonin in nociceptive signaling is discussed in Chapter 14.

Brain serotonin and energy homeostasis is discussed in Chapter 15. Chapters 16 and 17 discuss the role for serotonin as a potential neurotransmitter in central cardiovascular control. Serotonin and central respiratory regulation is discussed in Chapter 18. The idea of survival without serotonin is considered is considered in Chapter 19. Finally, serotonin as a therapeutic target for central nervous system disorders is discussed in Chapter 17.

Paul M. Pilowsky

Part 1

Biophysics of Serotonin

Outline

Chapter 1. Biophysics of Serotonin and the Serotonin1A Receptor: Fluorescence and Dynamics

Chapter 2. Serotonin in Plants: Origin, Functions, and Implications

Chapter 1

Biophysics of Serotonin and the Serotonin1A Receptor

Fluorescence and Dynamics

Parijat Sarkar¹, G. Aditya Kumar¹, Sreetama Pal²,³, and Amitabha Chattopadhyay¹,²     ¹CSIR-Centre for Cellular and Molecular Biology, Hyderabad, India     ²Academy of Scientific and Innovative Research, Ghaziabad, India     ³CSIR-Indian Institute of Chemical Technology, Hyderabad, India

Abstract

Serotonin is an intrinsically fluorescent neurotransmitter found in organisms spanning a wide evolutionary range. It is implicated in the generation and modulation of cognitive and behavioral functions. In spite of vast literature on serotonin and its receptors, available information on intrinsic fluorescence of serotonin is rather limited. In this review, we highlight this aspect of serotonin with emphasis on photophysical and photochemical properties and applications in biology. We further provide examples of receptor dynamics, using the prototypical serotonin1A receptor in cellular membranes, covering a wide range of time scales. We discuss the use of fluorescence recovery after photobleaching and fluorescence correlation spectroscopy in detecting domain- and actin cytoskeleton-induced restricted lateral dynamics. In view of the fact that receptor dynamics is crucial for function, we envision that integration of results from measurements of receptor dynamics with available biochemical and pharmacological data would drive receptor research in future and provide novel leads toward improved therapeutics.

Keywords

FCS; FRAP; GPCR; Lateral dynamics; Serotonin; Serotonin1A receptor; Serotonin fluorescence; Serotonin imaging; Time scale

1. Serotonin: An Ancient Molecule With Diverse Properties

Serotonin (5-hydroxytryptamine, 5-HT) is a biogenic amine that acts as a neurotransmitter and has intrigued chemists and biologists since its discovery ∼70  years ago due to the multitude of functions facilitated by it. The serendipitous discovery of serotonin is a fascinating anecdote itself and has been discussed elsewhere [1,2]. Serotonin was isolated and characterized in the late 1940s by Page and coworkers [3] as a component of bovine serum. Subsequently, serotonin was identified as a crucial component in the central nervous system, considered a milestone discovery in neuroscience [1,4].

Serotonin is found in a wide variety of organisms ranging from worms to humans, and is distributed across a range of tissues, such as the intestinal mucosa, platelets, and most importantly the central nervous system [1,4,5]. The role of serotonin as a neurotransmitter and its involvement in neurological functions such as mood, stress, aggression, feeling, cognition, and sexual behavior has been extensively studied [6,7]. Interestingly, majority of serotonin is synthesized outside the central nervous system and almost all types of serotonin receptors are expressed in neural and nonneural tissues [8]. In recent years, it has become apparent that serotonin modulates a wide range of processes over and above its function in the central nervous system. For example, serotonin has now emerged as a major player in development [9], morphogenesis [10] and left–right asymmetry [10,11], liver regeneration [12], circadian rhythm [13], bone biology [14,15], erythropoiesis [16], and blood pressure [17]. As a consequence, drugs that target certain serotonin receptors are useful in the treatment of a variety of diseases. This functional plasticity of serotonin receptors acts as an opportunity, and challenge, in future drug development involving these receptors as drug targets [8].

2. Serotonin: A Fluorescent Neurotransmitter

In terms of chemical structure, serotonin (5-hydroxytryptamine) is a derivative of the naturally occurring intrinsically fluorescent amino acid tryptophan (see Fig. 1.1A) [19]. Serotonin is biosynthesized from tryptophan in a two-step enzymatic reaction: (1) the conversion of tryptophan into 5-hydroxytryptophan by tryptophan hydroxylase and (2) decarboxylation of 5-hydroxytryptophan to 5-hydroxytryptamine by aromatic L-amino acid decarboxylase [20,21]. Interestingly, the intrinsic fluorescence of serotonin was detected many years before its physiological role was clearly established [22–24]. However, a comprehensive understanding of serotonin fluorescence in terms of its photophysical and photochemical properties with an eye for its application in serotonin biology came much later [18,25–30].

The modulation of serotonin fluorescence by environment polarity and pH-induced ionization was comprehensively demonstrated by Chattopadhyay et al. [18]. Serotonin displays pH-dependent fluorescence, when monitored using multiple parameters such as intensity, emission maximum, and fluorescence lifetime (see Fig. 1.1B). This pH-dependent fluorescence could be attributed to changes in the ionization (protonation) of serotonin at high pH, with a characteristic pKa of ∼10.2 [18]. Both the hydroxyl and the amine group of serotonin could be responsible for such pH-sensitive fluorescence. The excited state dynamics leading to such pH dependence of fluorescence could be described by the existence of various conformations differing in terms of rotations along the Cα–Cβ bond of serotonin (termed rotamers; see Fig. 1.1C). Since the binding of serotonin to its receptors is associated with a change in polarity of its immediate environment due to the location of the binding pocket in the membrane interior [31], study of polarity-dependent fluorescence of serotonin assumes relevance. Serotonin fluorescence was found to be insensitive to solvent polarity (as opposed to tryptophan fluorescence), possibly due to the phenolic hydroxyl group [18,32]. An elegant approach for recording and quantitating serotonin fluorescence in live cells is the application of three-photon microscopy developed by Maiti and coworkers (see Fig. 1.2) [26–30]. The advantages of three-photon microscopy include avoiding photodamage and deeper penetration into the sample. This technique has been utilized to generate a serotonin map in brain sections of live rats [33].

3. The Serotonin1A Receptor: A Representative Member of Serotonin Receptor Family

The diverse physiological functions facilitated by serotonin are mediated via serotonin receptors that reside in the cell membrane. Serotonin receptors constitute the largest class of non-odorant G protein-coupled receptors (GPCRs) [34]. The origin of serotonin receptors has been traced back to 700–750 million years [35] (see Fig. 1.3A), which predates the emergence of other biogenic amine receptors such as muscarinic, dopaminergic, and adrenergic receptors. The existence of serotonin receptors was first proposed in a series of papers published by John Gaddum in the 1950s [36,37] on the basis of muscle contractile responses measured in extracts from guinea pig ileum and rabbit perfused ear. Initial steps toward classification of serotonin receptors were taken in 1957 by Gaddum and coworkers [38]. They classified these subtypes as M (receptors that could be blocked using morphine) and D (receptors that could be blocked by dibenzyline). Subsequently, extensive work on classification of serotonin receptors using radioligand binding assays led to the identification of 5-HT1 and 5-HT2 subtypes of the receptors by Peroutka and Snyder in 1979 [39]. The repertoire of serotonin receptors has expanded to encompass 16 receptor subtypes (see Fig. 1.3B) that mediate a wide variety of intracellular signaling events with implications in a multitude of pathophysiological conditions [34,40,41].

Figure 1.1  Chemical structure of serotonin and its pH-dependent spectroscopic properties. (A) Chemical structure of serotonin (5-hydroxytryptamine), with the hydroxyl and amine moieties responsible for its pH-sensitive fluorescence shown in maroon. The indole hydroxyl group (shaded in blue) till pH ∼9, beyond which a sharp decrease or increase is observed. The sharp change in fluorescence was attributed to the pH-dependent ionization and associated conformational changes of serotonin, corresponding to various rotamers. The change in fluorescence intensity could be utilized to derive an apparent pKa (∼10.2) for serotonin ionization. Data for panel (B) were from Ref. [18]. (C) Newman projection of serotonin rotamers along the Cα–Cβ bond of serotonin. Serotonin exists predominantly in conformation I or II at pH  <  pKa of serotonin. These two conformers are stabilized by favorable electrostatic interaction between the indole π electron cloud and the positively charged quaternary nitrogen. At pH  >  pKa, deprotonation of both the indole hydroxyl group and the quaternary nitrogen disrupt the favorable electrostatics associated with gauche conformers (I and II), and therefore rotamer III (in an anti conformation) dominates. The sharp changes in serotonin fluorescence (shown in (B)) at higher pH has its origin from predominance of the anti conformer (III). See text and Ref. [18] for more details. 

(C) Adapted and modified from Chattopadhyay A, Rukmini R, Mukherjee S. Photophysics of a neurotransmitter: ionization and spectroscopic properties of serotonin. Biophys J 1996;71:1952–60 with permission from Elsevier.

Figure 1.2  Serotonin imaging in live cells by multiphoton microscopy. Fluorescence from intracellular serotonin in a live neuronal cell upon three-photon excitation. Quantitative analysis of serotonin in neurons holds immense significance in understanding serotonergic signaling. Three-photon microscopy offers a useful tool to specifically image serotonin in the backdrop of cellular autofluorescence emerging from other cellular components, without any photodamage to the cell. 

Adapted with permission from Kaushalya SK, Maiti S. Quantitative imaging of serotonin autofluorescence with multiphoton microscopy. In: Chattopadhyay A, editor. Serotonin receptors in Neurobiology. Boca Raton (Florida): CRC Press/Taylor and Francis; 2007. pp. 1–15.

The serotonin1A (5-HT1A) receptor is one of the most comprehensively studied seven transmembrane domain GPCRs of the serotonin receptor family [2,41–43]. Analysis of molecular evolution of serotonin receptors based on amino acid sequences suggests that the serotonin1A receptor differentiated from serotonin1 receptors ∼650 million years ago, around the same time when vertebrates diverged from invertebrates [35]. The serotonin1A receptor, encoded by an intronless gene located on chromosome 5 in humans [44] and chromosome 13 on mice [45], was the first serotonin receptor to be cloned and sequenced [46]. The mRNA corresponding to the serotonin1A receptor gene is expressed predominantly in the brain, spleen, neonatal kidney, and gut [44]. In fact, the serotonin1A receptor was the first among all serotonin receptors for which polyclonal antibodies were obtained, thereby enabling their visualization in various regions of the brain [47].

Figure 1.3  Evolution and classification of serotonin receptors. (A) A time scale depicting the evolutionary history of serotonin receptors. Serotonin receptors constitute one of the oldest classes of G protein-coupled receptors, with an evolutionary history dating back to ∼725 million years. In the course of their evolution, serotonin receptors have made a mark in diverse life forms, ranging from planarians, nematodes, and flies to humans. (B) Classification of serotonin receptors. Over the course of their long evolutionary history, serotonin receptors have differentiated into several subtypes. With the exception of serotonin3 (5-HT3) receptors (which are ligand-gated ion channels), all subtypes of serotonin receptors belong to the GPCR superfamily. Diverse G-proteins that couple to each receptor subtype and the mechanism of action of the receptors are indicated. The serotonin1A (5-HT1A) receptor, one of the most extensively studied serotonin receptors and a major research interest in our laboratory, is highlighted. 5-HT, 5-hydroxytryptamine; GPCR, G protein-coupled receptor.

A variety of neurological functions are mediated via the serotonin1A receptor. The receptor has been shown to have a protective role for stressed neurons undergoing degradation and apoptosis and is involved in neuronal development [48–50]. As a consequence of its indispensable role in neurological functions, the serotonin1A receptor has emerged as a major drug target in the development of therapeutics against neuropsychiatric disorders such as anxiety [51], depression [52], schizophrenia, and Parkinson's disease [53,54]. The serotonin1A receptor is also implicated in non-neuronal physiology such as regulation of blood pressure, feeding behavior, and cancer. For this reason, the spectrum of therapeutic interventions targeting the serotonin1A receptor has recently broadened beyond its role in neurological function [41].

Work from a number of groups has demonstrated that the specific and general interactions of membrane lipids act as crucial regulators of GPCR function [55,56]. In this context, we have shown that membrane cholesterol and sphingolipids represent important modulators of the organization, dynamics, function, and oligomerization of the serotonin1A receptor [57–62]. The modulation of the serotonin1A receptor activity with membrane cholesterol could have implications in diseases of defective cholesterol biosynthesis such as Smith–Lemli–Opitz syndrome [63] and for chronic users of cholesterol-lowering drugs such as statins [64].

4. Receptor Dynamics: The Relevance of Time Scales

A unique feature of biological membranes is its characteristic dynamics that spans a large range of spatiotemporal scale (see Fig. 1.4) [65–67]. In particular, the major motivation for exploring membrane receptor dynamics is the fact that understanding dynamics provides insight to receptor function [68]. Results from a number of laboratories have shown that membrane receptors such as GPCRs are characterized by flexible and dynamic structures and various ligands stabilize specific receptor conformations. The structural plasticity displayed by GPCRs, aided by a dynamic and fluid membrane milieu, provides the platform for diverse signaling pathways in response to specific ligands. The conformational dynamics of GPCRs in a dynamic membrane environment is beginning to be appreciated in relation to their function [69,70]. It is becoming increasingly clear that receptor dynamics holds the key to its function [71,72].

Figure 1.4  A schematic time scale map of dynamics and function of lipids and proteins in biological membranes. The wide range of time windows in which molecular motions take place in membranes could span more than 10 orders of magnitude. These range from side chain rotations occurring in ps to signaling by a GPCR which could take minutes. The corresponding fluorescence techniques that are sensitive to molecular motions in these time scales are indicated at the bottom. Considering the large range of time scales related to membrane-associated processes, it is not possible to address all these phenomena simultaneously using any single experimental technique. This implies that while some techniques could yield a static picture of membranes (since molecular motion could be slow relative to the time scale of measurement in the technique), other approaches would offer a time-averaged picture of a very rapidly moving molecule relative to the measurement time window. Careful choice of an experimental technique with corresponding time scale is therefore crucial for addressing problems in membrane biology. Judicious use of complimentary techniques would provide a comprehensive dynamic model of the membrane. FCS, fluorescence correlation spectroscopy; FRAP, fluorescence recovery after photobleaching; FRET, fluorescence resonance energy transfer.

At the ns time scale, molecular interactions play a role in the direct association of membrane lipids and cholesterol with membrane proteins [73,74]. Protein segmental (domain) motion occurs at the sub-μs time scale, whereas protein local dynamics takes place at ns to sub-μs time scale (see Fig. 1.4). Most functional processes of various classes of transmembrane proteins (e.g., transport of ions across channels, signaling by GPCRs) occur at longer time scales (ms–s or longer). Due to this reason, the study of membrane organization and dynamics necessitates diverse experimental methods spanning a wide range of time scales. A useful experimental strategy extensively used to explore membrane organization, dynamics, and lipid–protein interactions is based on fluorescence spectroscopy which offers the possibility of measuring membrane dynamics at various spatiotemporal resolutions [75]. This includes techniques such as fluorescence resonance energy transfer (FRET), fluorescence recovery after photobleaching (FRAP), fluorescence correlation spectroscopy (FCS), and monitoring solvent relaxation using fluorescence [75–77]. These measurements offer certain advantages due to their enhanced sensitivity, minimal perturbation, and diversity of measurable parameters, that allow the analysis of membrane molecular processes. A few representative examples from our laboratory on experimental approaches to analyze membrane receptor dynamics are discussed below, with emphasis on lateral dynamics of the serotonin1A receptor.

5. Membrane Lateral Dynamics of the Serotonin1A Receptor Utilizing Bleach Area–Dependent FRAP: Consequence of Membrane Domainization

An interesting source of lateral heterogeneity (domains) in plasma membranes is the confinement of diffusion of membrane components. Lateral diffusion of receptors represents a powerful property to understand their membrane dynamics. FRAP is a widely used approach to quantitatively measure lateral (translational) diffusion of lipids and proteins in membranes [77–81]. In FRAP, a concentration gradient of fluorescently labeled receptors is generated by irreversibly photobleaching (using a high power laser) a fraction of fluorophores in a region of interest (∼μm dimension) [82]. The time-dependent loss of this gradient due to lateral (transverse) diffusion of fluorescently tagged receptors into the bleached region from the unbleached regions of the membrane is a faithful indicator of the lateral mobility of the receptor in the membrane.

Since fluorescence recovery kinetics in FRAP measurements contains information on the area being monitored, it is possible to obtain a comprehensive understanding of the spatial organization of receptors in cell membranes by varying the area monitored in FRAP experiments [83]. Deviations in diffusion parameters of receptors obtained from FRAP experiments performed with bleach spots of different radii have been correlated to the presence of membrane domains with dimensions in the same range as the area monitored [77,84–88]. The interpretation from such an experiment is based on the theoretical model described below and has been previously independently validated by molecular dynamics simulations and FRAP experiments performed on physically domainized model membrane systems [89].

The recovery of fluorescence into the bleached area in FRAP experiments could be quantitatively estimated by two parameters, an apparent diffusion coefficient (D) and mobile fraction (Mf) [77]. D is estimated from the rate of fluorescence recovery of an ensemble of diffusing receptors, whereas Mf is extracted from the extent of fluorescence recovery in the time scale of FRAP measurements. For receptors undergoing lateral diffusion in a homogeneous membrane, D is independent of the dimensions (area) of the bleach spot. A small bleach spot (top panel in Fig. 1.5A, indicated by black open circle) results in faster recovery of fluorescence, whereas a large bleach spot (bottom panel in Fig. 1.5A) results in slower recovery of fluorescence; effectively yielding a constant value of D across various bleach spot sizes (Fig. 1.5B). In addition, if the dimension of the bleached area is significantly smaller than the total area of the cell membrane, the extent of fluorescence recovery would be the same resulting in a constant Mf (Fig. 1.5B). On the other hand, if receptor diffusion was confined to static closed domains (in FRAP time scale) of dimensions similar to the area of the bleach spot, D would no longer be a constant. A small bleach spot (top panel in Fig. 1.5C) would tend to monitor diffusion properties of receptors within domains, similar to that observed on a homogeneous membrane. However, a large bleach spot (overlapping with different domains to varying extents; see the bottom panel in Fig. 1.5C) would result in disproportionate bleaching of the domains because the bleached area would be partial for a few and complete for others. As a result, kinetics of fluorescence recovery in the entire region of observation would not be proportional to the actual size of the bleach spot. While kinetics of fluorescence recovery within domains would be proportional to the area bleached in these domains, the apparent D would show an increase (since D is calculated taking into account the actual bleach spot area). Importantly, a large bleach spot would reduce Mf because it could bleach an entire domain resulting in total loss of receptor fluorescence in such a domain (Fig. 1.5D).

Our analysis of fluorescence recovery kinetics of serotonin1A receptors tagged with EYFP in control cells with varying size bleach spots yielded an invariant D and Mf with respect to bleach spot radius ([90], see blue lines in Fig. 1.5E and F). The relatively constant values of D and Mf over a range of bleach spot size in control cells suggest that serotonin1A receptors experience a homogeneous membrane environment, in the spatiotemporal resolution of FRAP measurements. Interestingly, FRAP experiments performed on cholesterol-depleted cells with an identical range of bleach spot size showed a marked dependence of D and Mf of serotonin1A receptors on the size of the bleach spot (see red lines in Fig. 1.5E and F). As described above, this type of dependence of D and Mf in cholesterol-depleted membranes is consistent with a model describing confined diffusion in a domainized membrane [84–89]. These observations suggest that cholesterol depletion from cells results in dynamic confinement of the serotonin1A receptors into membrane domains. These domains restrict receptors within their boundaries, resulting in dependence of diffusion coefficient and mobile fraction of the receptor on the bleach spot size. The functional implications of such domain restriction of the receptor would be manifested in signaling carried out by the receptor [90].

Figure 1.5  Fluorescence recovery outcomes of fluorescence recovery after photobleaching (FRAP) performed on a homogeneous and domainized membrane with varying bleach spot size. A schematic model for FRAP performed on homogeneous (A and B) and domainized membranes (C and D) is shown. The homogeneous membrane is characterized by free molecular diffusion throughout the total area of the membrane in the experimental time scale (see panel A). The diffusion coefficient and mobile fraction in homogeneous membranes (panel B) would be independent of the size of the bleach spot (provided the bleach area, indicated by black open circles, is significantly smaller than the total area of the membrane). In contrast, molecular diffusion in the domainized membrane (see panel C) is confined to closed areas of comparable dimension (indicated by white meshwork, termed as domains) as that of the size of the bleach spot (shown as black open circles) in the time scale of FRAP experiments. In this case, fluorescence recovery kinetics for a small or large bleach spot would be dependent on the area bleached within the domains. FRAP measurements on such a domainized membrane therefore shows an apparent diffusion coefficient that varies with the size of the bleach spot. This results in an increase in diffusion coefficient and decrease in mobile fraction with increasing bleach spot size (see panel D). The diffusion coefficients (E) and mobile fractions (F) of serotonin1A-EYFP receptors obtained from FRAP measurements with bleach spots of different sizes are shown for control (blue line) and cholesterol-depleted (red line) cells. The lack of invariance of diffusion coefficient and mobile fraction of the serotonin1A receptor with bleach spot radius indicates dynamic confinement of the receptor in cholesterol-induced domains. See text and Ref. [90] for more details. 

Adapted and modified from Pucadyil TJ, Chattopadhyay A. Cholesterol depletion induces dynamic confinement of the G-protein coupled serotonin1A receptor in the plasma membrane of living cells. Biochim Biophys Acta 2007;1768:655–68 with permission from Elsevier.

6. Actin Cytoskeleton–Induced Confined Dynamics of the Serotonin1A Receptor Revealed by zFCS

FCS represents another powerful and sensitive technique for measuring molecular diffusion with improved spatiotemporal resolution [91,92]. In this approach, the spontaneous fluctuations in fluorescence intensity as fluorophores diffuse in and out of an open confocal volume (typically  ∼  fl) are monitored. Unlike FRAP, FCS measurements do not provide any information of the immobile molecules since they are not detected. The resultant autocorrelation function of the fluorescence fluctuations contains information on diffusion coefficient and number of particles. However, single point FCS measurements sometimes result in erroneous estimation of diffusion parameters in case of membrane-bound receptors. This is because the typical axial length of the FCS observation volume (∼1  μm) is approximately three orders of magnitude larger than the dimension of the membrane bilayer (∼5  nm thick) [93]. This problem could be avoided in a variation of the FCS measurement, termed z-scanning FCS (zFCS). In case of zFCS, the uncertainty in the positioning of the focused laser beam is avoided by the z-scan in which the diffusion times are determined in steps as the z-axis is scanned in small increments [94–96]. A characteristic plot of diffusion time (τD) versus transverse area of the confocal volume generates the FCS diffusion laws that provide information on the organization at submicron level (such as confinement and/or free diffusion, see Fig. 1.6A) of the diffusing species ([97,98]; see Fig. 1.6B). According to FCS diffusion laws, information on the nature of confinement experienced by the diffusing molecule could be obtained from the linear fit of data when extrapolated to zero spot width. In case of receptors undergoing free (random) diffusion, the intercept of the linear fit is close to zero, whereas if the receptor experiences confinement, a negative intercept is obtained (see Fig. 1.6B). In our previous work, we combined the principle of zFCS and FCS diffusion laws to address the organization and confined dynamics of the serotonin1A receptor in live cell membranes [96]. Our results showed that the serotonin1A receptor displayed confined diffusion, as apparent from the negative intercept (∼−21  ms) of the plot of τD versus Δz² for control cells (Fig. 1.6C). Interestingly, the intercept value of −21  ms is similar to the previously reported values for membrane proteins confined by underlying actin cytoskeleton. The possible confinement of the serotonin1A receptor by actin cytoskeleton (suggested by the negative intercept) is consistent with our earlier observations using FRAP [71], where we showed that the mobile fraction of receptors was modulated by the status of the actin cytoskeleton. Interestingly, the estimated diffusion coefficient was found to be ∼4  μm²/s, an order of magnitude higher than that obtained in the same cell type probed by FRAP (∼0.14  μm²/s) [71,90]. The apparent variation in diffusion coefficient obtained by FCS and FRAP is due to higher spatiotemporal resolution associated with FCS, thereby implying that the size of the observation area are different in FCS and FRAP measurements (see Fig. 1.4). Interestingly, organization and dynamics of the serotonin1A receptor did not exhibit any change in presence of its natural ligand serotonin (Fig. 1.6D). The intercept of the plot of diffusion time versus Δz² was found to be ∼−21  ms, similar to the intercept obtained for control cells (see Fig. 1.6C). In addition, the diffusion coefficient of the receptor remained similar (3.8  μm²/s) under this condition.

Figure 1.6  Simulated and experimental z -scanning fluorescence correlation spectroscopy (FCS) diffusion laws for membrane models with varying degrees of confinement: validated by cytochalasin-induced actin destabilization. (A) In the free diffusion model, receptors (depicted as blue dots) show pure Brownian (random) motion and fluoresce under the laser excitation spot (maroon dashed circle). In the meshwork model, multiple adjacent domains are separated by cellular barriers such as the actin cytoskeleton network (regular lattice shown in maroon) preventing the diffusion of molecules. (B) Dependence of the diffusion time on the area of observation for two different models (free and meshwork). In a conventional microscope, diffusion time cannot be estimated experimentally below the optical diffraction limit (shown as a dotted line). In such a case, the extrapolated intercept to the limit of zero spot size provides a measure of the nature of diffusion experienced by the receptor. (C) Representative data plot of lateral diffusion time versus Δz² (FCS diffusion laws) for serotonin1A-EYFP receptor with restricted (confined) diffusion in the plasma membrane. Note that the serotonin1A receptor displays confined diffusion, evident from the negative intercept of the plot (as shown in panel B). (D) Receptor activation by 10  μM serotonin shows negative intercept of the plot, similar to control cells. Panels (E) and (F) show the role of the actin cytoskeleton on dependence of lateral diffusion time for serotonin1A-EYFP receptors on the z position of the focus. The actin cytoskeleton is destabilized utilizing 5 (panel E) and 10 (panel F) μM cytochalasin D. It is indeed satisfying to observe the progressive release of receptor confinement upon increasing actin destabilization, as apparent from the dose-dependent increase in the intercept of the plot (shown as arrows). The 95% confidence interval (green dashed line) and 95% prediction band (blue dotted line) for the fitted data are also shown. 

Adapted and modified from Ganguly S, Chattopadhyay A. Cholesterol depletion mimics the effect of cytoskeletal destabilization on membrane dynamics of the serotonin1A receptor: a zFCS study. Biophys J 2010;99:1397–407 with permission from Elsevier.

The typical dependence of diffusion time versus Δz² for the serotonin1A receptor under conditions of increasing actin destabilization is shown in Fig. 1.6E and F. As mentioned above, based on our earlier observations that receptor mobile fraction increases upon actin destabilization [71], the apparent confinement experienced by the serotonin1A receptor in zFCS measurements could be due to the underlying cytoskeleton network. This was confirmed when the receptor confinement was found to be progressively reduced (evident from a reduction in negative intercept, see Fig. 1.6E and F) on treatment with increasing concentrations of cytochalasin D, which acts as a potent inhibitor of actin polymerization. The intercept of the plot reduced from ∼−21  ms (control) to ∼−5.9 ms in presence of 5  μM cytochalasin D (a reduction of ∼72%) (see Fig. 1.6E), clearly indicating the involvement of actin cytoskeleton in receptor confinement. We envisage that combined application of zFCS and the FCS diffusion laws could be a powerful tool to explore membrane heterogeneity and receptor confinement.

7. Conclusion and the Road Ahead

As highlighted in this article, serotonin exerts its multitude of cellular functions via its many receptors. The serotonin receptors are ancient in terms of biological evolution, yet new roles for them are still being discovered with continued progress in receptor research. This assumes clinical significance since these receptors are major drug targets with an ever expanding spectrum of diseases for which these drugs are prescribed (e.g., see Ref. [41]). While these are fascinating observations and serotonin research is going through an exciting phase, a comprehensive molecular insight into the mechanisms underlying these processes require understanding receptor dynamics at various time scales, since the function of membrane proteins is intimately related to their dynamics. Although there has been some recent success in the area of membrane dynamics (e.g., using diffraction-limited dynamic measurements [99]), extrapolating these approaches to receptor biology poses considerable challenge. Nonetheless, future integration of such spatiotemporally resolved dynamic insight with available biochemical and pharmacological information would help to provide a comprehensive understanding of receptor function in health and disease, thereby offering novel therapeutic avenues.

Acknowledgments

P.S. and G.A.K. thank the Council of Scientific and Industrial Research (Govt. of India) for the award of Shyama Prasad Mukherjee Fellowship and Senior Research Fellowship, respectively. S.P. thanks the University Grants Commission for the award of a Senior Research Fellowship. A.C. gratefully acknowledges J.C. Bose Fellowship (Department of Science and Technology, Govt. of India). A.C. is an Adjunct Professor of Tata Institute of Fundamental Research (Mumbai), RMIT University (Melbourne, Australia), Indian