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Handbook of Neurobehavioral Genetics and Phenotyping

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Handbook of Neurobehavioral Genetics and Phenotyping

Edited by Valter Tucci

Senior Group LeaderNeurobehavioural laboratoryIstituto Italiano di Tecnologia (IIT)Italy

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Copyright © 2017 by John Wiley & Sons, Inc. All rights reserved

Published by John Wiley & Sons, Inc., Hoboken, New Jersey

Published simultaneously in Canada

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permission.

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Library of Congress Cataloging-in-Publication Data:

Names: Tucci, Valter, editor.Title: Handbook of neurobehavioral genetics and phenotyping / [edited by]

Valter Tucci.Description: Hoboken, New Jersey : John Wiley & Sons, 2017. | Includes index.Identifiers: LCCN 2016036351 (print) | LCCN 2016052632 (ebook) | ISBN

9781118540718 (cloth) | ISBN 9781118540763 (pdf) | ISBN 9781118540794 (epub)

Subjects: LCSH: Neurogenetics. | Behavior genetics. | Phenotype.Classification: LCC QP356.22 .H36 2017 (print) | LCC QP356.22 (ebook) | DDC

612.8—dc23LC record available at https://lccn.loc.gov/2016036351

Cover image: Sebastian Kaulitzki/Gettyimages; Tose/Gettyimages; Adam Gault/Gettyimages

Set in 10/12pt Warnock by SPi Global, Chennai, India

10 9 8 7 6 5 4 3 2 1

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I dedicate this book to my kids Sophia and Thomas Oliver; being so different from each other they constantly provide me with a living example of how complex and exciting the study of the interplay between genetics and behaviors can be.

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vii

List of Contributors xixPreface xxv

1 Genetic Screens in Neurodegeneration 1Abraham Acevedo Arozena and Silvia CorrochanoIntroduction 1The Genetics of Neurodegenerative Disorders 2Neurodegeneration Disease Models 4Genetic Approaches to Discover New Genes Related to Neurodegeneration Using Disease Models 5

Saccharomyces cerevisiae 6Caenorhabditis elegans 8Drosophila melanogaster 9Danio rerio 10Mus musculus 11Human Cellular Models and Post-mortem Material 14

The Future 14Acknowledgments 15References 15

2 Computational Epigenomics 19Mattia PelizzolaBackground 19Profiling and Analyzing the Methylation of Genomic DNA 19

Experimental Methods 20Data Analysis 20

Array-based Methods 20Sequencing-based Methods 20

Profiling and Analyzing Histone Marks 26Experimental Methods 26Data Analysis 27

Issues of Array-based Methods 27Issues of NGS-based Methods 27

Integration with Other Omics Data 31Chromatin States 32

Contents

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Contentsviii

Unraveling the Cross-talk Between Epigenetic Layers 33References 33

3 Behavioral Phenotyping in Zebrafish: The First Models of Alcohol Induced Abnormalities 37Robert GerlaiIntroduction 37Alcohol Related Human Disorders: A Growing Unmet Medical Need 37Unraveling Alcohol Related Mechanisms: The Importance of Animal Models 38Face Validity: The First Step in Modeling a Human Disorder 39Acute Effects of Alcohol in Zebrafish: A Range of Behavioral Responses 39Chronic Alcohol Exposure Induced Behavioral Responses in Zebrafish 41Effects of Embryonic Alcohol Exposure 42Behavioral Phenotyping: Are We There Yet? 46Assembling the Behavioral Test Battery 49Concluding Remarks 50References 50

4 How does Stress Affect Energy Balance? 53Maria Razzoli, Cheryl Cero, and Alessandro BartolomucciIntroduction 53Stress 54Energy Balance and Metabolic Disorders 55

Pro-adipogenic Stress Mediators 57Pro-lipolytic Effect of Stress Mediators 57

How does Stress Affect Energy Balance? 57Animal Models of Chronic Stress and their Impact on Energy Balance 58

Physical and Psychological (non-social) Chronic Stress Models 58Mild Chronic Pain Models – Mild Tail Pinch, Foot Shock 58Thermal Models – Cold and Heat Stress 64Chronic Mild Stress Models: Chronic Mild Stress, Chronic Variable Stress, etc. 64Restraint or Immobilization 65

Chronic Social Stress Models 66Social Isolation, Individual Housing 66Unstable Social Settings 66Visible Burrow System 67Intermittent Social Defeat (Resident/Intruder Procedure) 67Chronic Psychosocial Stress, Sensory Contact, and Chronic Defeat stress 68

Stress, Recovery, and Maintenance: Insights on Adaptive and Maladaptive Effects of Stress 69

Molecular Mechanisms of Stress-Induced Negative and Positive Energy Balance 70

Serotonin (5-hydroxytryptamine, 5HT) 71Orexin 71Neuropeptide Y (NPY) 72Ghrelin and Growth Hormone Secretagogue Receptor (GHSR) 72Glucagon like Peptide 1 (GLP1) 73Leptin 73Amylin 74

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Contents ix

Norepinephrine and β3-Adrenergic Receptor 74Conclusion 74References 75

5 Interactions of Experience-Dependent Plasticity and LTP in the Hippocampus During Associative Learning 91Agnès Gruart, Noelia Madroñal, María Teresa Jurado-Parras, and José María Delgado-GarcíaIntroduction: Study of Learning and Memory Processes in Alert Behaving Mammals 91Changes in Synaptic Strength During Learning and Memory 92

Classical Conditioning 92Instrumental Conditioning 95

Changes in Synaptic Strength Evoked by Actual Learning can be Modified by Experimentally Evoked Long-term Potentiation 96Other Experimental Constraints on the Study of the Physiological Basis of Learning Processes 100

Factors Modifying Synaptic Strength (Environment, Aging, and Brain Degenerative Diseases) 101Different Genetic and Pharmacological Manipulations Able to Modify Synaptic Strength 103

Functional Relationships Between Experimentally Evoked LTP and Associative Learning Tasks 106Future Perspectives 108

Context and Environmental Constraints 108Other Forms of Learning and Memory Processes 109Cortical Circuits and Functional States During Associative Learning 109

References 110

6 The Genetics of Cognition in Schizophrenia: Combining Mouse and Human Studies 115Diego Scheggia and Francesco PapaleoBackground 115Genetics of Schizophrenia 116Cognitive (dys)functions in Schizophrenia 117Translating Cognitive Symptoms in Animal Models 119Executive Control 120

Performance in Schizophrenia 122Animal Models 124

Working Memory 125Performance in Schizophrenia 126Animal Models 127

Control of Attention 128Performance in Schizophrenia 130Animal Models 130

Concluding Remarks 131References 132

7 The Biological Basis of Economic Choice 143David Freestone and Fuat BalciIntroduction 143

Translating from Animals to Humans 144

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Contentsx

Reinforcement Learning in the Brain 145Subjective Value 146

The Midbrain Dopamine System Updates Value 147From Stimulus Value to Action Value 150Model Based Learning 150The Prefrontal Cortex Encodes Value 152The Basal Ganglia Selects Actions 153

Optimal Decisions: Benchmarks for the Analysis of Choice Behavior 155The Drift Diffusion Model 157Temporal Risk Assessment 158

Timed-response Inhibition for Reward-rate Maximization 160Timed Response Switching 163Temporal Bisection 164

Numerical Risk Assessment 166Rodent Version of Balloon Analog Risk Task 167

Conclusion 167Acknowledgments 168References 168

8 Interval-timing Protocols and Their Relevancy to the Study of Temporal Cognition and Neurobehavioral Genetics 179Bin Yin, Nicholas A. Lusk, and Warren H. MeckIntroduction 179Application of a Timing, Immersive Memory, and Emotional Regulation (Timer) Test Battery 190Neural Basis of Interval Timing 191What Makes a Mutant Mouse “Tick”? 193Proposal of a TIMER Test Battery and Its Application in Reverse Genetics 199Behavioral Test Battery Applications in Forward Genetics 202

Order of Behavioral Tasks 205Location and Time of Behavioral Testing 205

Summary 205References 206Appendix I 226

Limitations of the Individual-trials Analysis for Data Obtained in the Peak-Interval (PI) Procedure 226

9 Toolkits for Cognition: From Core Knowledge to Genes 229Giorgio Vallortigara and Orsola Rosa SalvaIntroduction 229Core Knowledge: The Domestic Chick as a System Model 230

Numerical Competence 230Physical Properties 230Geometry of Space 232Animate Agents 232

A Comparative Perspective on the Genetic and Evolutionary Bases of Social Behavior 236

From Social Experience to Genes 239From Genes to Social Behavior 241

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Future Directions 243Conserved Mechanisms for Social Core Knowledge 243Interactions Between Experience and Genomic Information 243Neurogenetic Basis of Social Predispositions 243Epigenetics and the Development of the Social Brain 244Spatial Cognition, Another Promising Core-knowledge Domain 244

References 245

10 Quantitative Genetics of Behavioral Phenotypes 253Elzbieta Kostrzewa and Martien J.H. KasHuman Studies of Quantitative Traits 253Mouse Studies of Quantitative Traits 254

Classical Inbred Mice 254Quantitative Trait Loci (QTL) Analysis 254Knock-out (KO) Mouse Lines 256

Use of Mice as Animal Model for Complex Human Traits 257Comparative Genomic Approaches 257Evolutionarily Conserved Behavioral Phenotypes 257

Physical Activity – Definitions and Methods of Phenotypic Measurement 258Current Results of Quantitative Genetic Basis of PA in Humans 259Current Results of Quantitative Genetic Basis of PA in Mice 260

KO Studies 260QTL Studies 261

An Overlap of Genetic Findings Between the Species 261Conclusions 265References 265

11 Behavioral Phenotyping in Genetic Mouse Models of Autism Spectrum Disorders: A Translational Outlook 271Maria Luisa Scattoni, Caterina Michetti, Angela Caruso, and Laura RicceriIntroduction 271Measuring Social behavior in ASD Mouse Models 272

Social Interaction Tests 272Male-female 277Female-female 278Male-male 278

Social-approach 279Sociability Test Phase 280Social Novelty 280

Social Recognition 280Repetitive Behavior 281

Motor Stereotypies 281Restricted Interests 281Behavioral Inflexibility 282

Behavioral Tests Targeting other ASD Symptoms 282Anxiety 282Epilepsy 283

Behavioral Phenotyping in ASD Mouse Pups 283

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Future Directions: ASD Mouse Models as a Resource for Gene-environment Interaction Studies 284Acknowledgments 285References 285

12 Genetics of Human Sleep and Sleep Disorders 295Birgitte Rahbek KornumThe Mystery of Human Sleep 295

Sleep is Essential for Mammalian Life 295The Function of Sleep 296

Extended Wakefulness Induces Sleep 296Homeostatic and Circadian Regulation of Sleep and Wake 297

Adenosine and Sleep Homeostasis 298Resistance to Sleep Loss is a Stable Phenotype 299

Genetic Markers of Response to Sleep Loss 299A Unique Activity Pattern Characterizes the Sleeping Brain 300

Sleep Stages and Sleep Cycles 300Genetics of the Human Sleep Electroencephalography 301Normal Sleep Architecture is Lost in Fatal Familial Insomnia 303

Circadian Regulation of Sleep and Associated Disorders 304Circadian Regulation of Sleep 304

Molecular Regulation of the Circadian Clock 305The Central Circadian Clock is Entrained By Light 306

Circadian Rhythm Sleep Disorders 307Advanced Sleep Phase Syndromes 307Delayed Sleep Phase Syndromes 308Short Sleep Times in Healthy Individuals 308

Destabilization of Sleep States and Narcolepsy 309Normal Regulation of Sleep Architecture 309

Wakefulness is Associated with Cortical Activation 309The Preoptic Area Contains Sleep-promoting Neurons 309Mutual Inhibition Regulates Transitions Between Wake and Sleep 310Regulation of REM Sleep 311

Narcolepsy, A Disorder of Wakefulness and REM Sleep 311Narcolepsy with Cataplexy is Caused By Hypocretin Deficiency 312Autoimmunity Toward Hypocretin Neurons 312Genetic Evidence Supports the Autoimmune Hypothesis of Narcolepsy 313

Restless Legs Syndrome, A Developmental Sleep Disorder 314Restless Legs Syndrome, A Mysterious Urge to Move 314

Restless Legs Syndrome and Dopamine Disturbances 315Iron Deficiency Exacerbates RLS Symptoms 315Genetic Studies Suggest Developmental Defects 316

Unresolved Issues and Future Perspectives 316What is the Molecular and Neuroanatomical Basis for the Ultradian Rhythm of NREM-REM Sleep? 317What is the Genetic Basis for Individual Variation in Complex Sleep Features such as Sleep Spindles and K-Complexes? 317What is the Basis for the Individual Differences in Resistance to Sleep Loss? 317

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Are Homeostatic and Circadian Mechanisms Genuinely Independent or Are They Intimately Linked? 318What Controls the Molecular and Anatomical Diversity of Sleep Regulatory Networks Across Species? 318

References 319

13 The Endocannabinoid System in the Control of Behavior 323Edgar Soria-Gomez, Mathilde Metna, Luigi Bellocchio, Arnau Busquets-Garcia, and Giovanni MarsicanoIntroduction 323

Cannabinoid Effects and Endocannabinoid Functions 324Role of the ECS in Memory Processes 325

Memory: General Background 325Role of the ECS in Synaptic Plasticity 325Memory Impairment Produced by Exogenous Cannabinoids 326Cannabinoid Regulation of Memory: Neurobiological Mechanisms 327Role of the ECS in Fear Processes 329Fear: General Background 329The ECS as an Endogenous Regulator of Fear Responses 331Cannabinoid Regulation of Fear: Neurobiological Mechanisms 332Implication of the ECS in Fear Coping Behaviors 333

Role of the ECS in Feeding Behavior 336Feeding Behavior: General Background 336The ECS as an Endogenous Regulator of Feeding Behavior 337The ECS and Food Reward Circuits 338The ECS in the Hypothalamic Appetite Network 338The ECS in the Caudal Brainstem and Gastrointestinal Tract 340Bimodal Control of Stimulated Food Intake by the ECS in the Brain 341

Paraventricular Hypothalamus Versus Ventral Striatum in Hypophagia induced by the ECS 342The Olfactory Bulb and the Hyperphagic Action of the ECS 342

Conclusions 343References 344

14 Epigenetics in Brain Development and Disease 357Elisabeth J. Radford, Anne C. Ferguson-Smith, and Sacri R. FerrónIntroduction 357Epigenetics and Neurodevelopment 358

Histone Modifications 358DNA Methylation 361Hydroxymethylation 364Genomic Imprinting 364Non-coding RNAs 365

Neurodevelopmental Disorders with an Epigenetic Basis 366Rett Syndrome 366Coffin–Lowry Syndrome 367Rubinstein–Taybi Syndrome 367Alpha-thalassemia Mental Retardation Syndrome 367

Imprinted Neurodevelopmental Disorders 368

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Trinucleotide Repeat Disorders 368Fragile X Syndrome 370Friedreich’s Ataxia 370Myotonic Dystrophy 371Huntington’s Disease (HD) 371

Epigenetics of Neurodegenerative Disorders 372Parkinson´s Disease (PD) 372Alzheimer´s Disease (AD) 373

The Impact of the Environment on the Epigenome 374Epigenetic Therapy in Neurodevelopment 375

Untargeted Treatment 375Targeted Epigenetic Modulation 377

Concluding Remarks 377Acknowledgments 377References 378

15 Impact of Postnatal Manipulations on Offspring Development in Rodents 395Diego Oddi, Alessandra Luchetti, and Francesca Romana D’AmatoIntroduction 395

Early Postnatal Environment in Laboratory Altricial Rodents 396Rodents’ Responses to Postnatal Environment and Early Manipulations 397

Assessing Pups’ Responses to Postnatal Environment and Early Manipulation 397Neonatal Ultrasonic Calls: Isolation-induced Vocalizations and Maternal Potentiation 397Searching for Social Contact: Homing and Huddling Behaviors 398Early-life Environment and Stress-Response 398

Separation from the Mother 399Mother’s Stress 400The Cross-fostering Paradigm 401Repeated Cross-fostering as a Model of Early Maternal Environment Instability 403Environmental Enrichment 405

Conclusions 406References 407

16 Exploring the Roles of Genetics and the Epigenetic Mechanism DNA Methylation in Honey Bee (Apis Mellifera) Behavior 417Christina M. Burden and Jonathan E. BobekIntroduction 417Genetics of Adult Honey Bee Biology and Behavior 418

Nurse to Forager Transition 418Forager Preference 420

Techniques for Investigating the Genetic Bases of Behavior 420QTL Mapping 421RNA Techniques 421

Microarrays 421RNA Sequencing 422

Experimentally Modulating the Genes Correlated with Specific Behaviors to Test Causality 422

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DNA Methylation and Honey Bee Behavior 423Honey Bee DNA Methylation Machinery and Genome-Wide Patterns 423DNA Methylation and Task Specialization 424DNA Methylation and Memory Consolidation 425

Techniques for Detecting and Assaying DNA Methylation 426The Technological Bases for Most DNA Methylation Assays 426

Methylation-specific Restriction Endonucleases 426Protein-mediated Precipitation of Methylated DNA 428Bisulfite Conversion 428

Assaying Single CpGs, Short Sequences, and Target Regions 429Analyzing Genome-wide DNA Methylation Patterns: Microarray-based Methodologies 431Analyzing Genome-wide DNA Methylation Patterns: Sequencing-based Methodologies 432

Techniques for Manipulating DNA Methylation 434Pharmacological Manipulation of DNA Methylation 434RNA Interference as a DNMT Blockade 434

Concluding Remarks and Future Perspectives 435References 436

17 Genetics and Neuroepigenetics of Sleep 443Glenda Lassi and Federico TinarelliDefining Sleep 443Sleep is Genetically Determined 445

EEG and Heritable Traits 445Sleep Disorders and Genes 446Sleep and Gene Expression 447

Epigenetics 448DNA Methylation 450Posttranslational Modifications (PTMs) 450RNA interference 452

Neuroepigenetics 453Two Neurodevelopmental Disorders with Opposing Imprinting Profiles and Opposing Sleep Phenotypes 453

Neuroepigenetics of Sleep 454Fruit Fly 454Rodent Models 454Human Beings 456Sleep and Parent-of-origin Effects 458

Conclusions 460References 460

18 Behavioral Phenotyping Using Optogenetic Technology 469Stephen Glasgow, Carolina Gutierrez Herrera, and Antoine AdamantidisIntroduction 469Microbial Opsins 470

Fast Excitation Using Channelrhodopsin-2 and Its Variants 470Fast Optical Silencing 474Alternative strategies for cell-type specific modulation of neural activity 476

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Targeting systems 476Light Delivery in the Animal Brain 478Recording Light-evoked Neuronal Activity 479

Behavioral Phenotyping 479In-vivo Optogenetics: Defining Circuits 480Perspectives 484

Acknowledgments 484References 484

19 Phenotyping Sleep: Beyond EEG 489Sibah Hasan, Russell G. Foster, and Stuart N. PeirsonSleep Research 489Phenotyping Sleep in Humans 490

Introduction 490Actigraphy 490Cardiorespiratory Signals 491EEG 492

Phenotyping Sleep in Animal Models 494Introduction 494EEG 494

Introduction 494Tethered EEG 496Telemetered EEG 496NeuroLogger EEG 498

Beyond EEG 498Infrared Beam Break 499Movement Based on Implanted Magnets 499Piezo-electric Sensors 499Video Tracking 500

Future Perspectives 501Acknowledgements 502References 502

20 A Cognitive Neurogenetics Screening System with a Data-Analysis Toolbox 507C.R. Gallistel, Fuat Balci, David Freestone, Aaron Kheifets, and Adam KingIntroduction 507Mechanisms, Not Procedures 508Functional Specificity 508No Group Averages 509Physiologically Meaningful Measures 509Importance of Large-scale Screening and Minimal Handling 511Utilizable Archived Data with Intact Data Trails 511

The System 512The Toolbox 513

Core Commands 516Powerful Graphics Commands 517Results 518Summary 523

References 524

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21 Mapping the Connectional Architecture of the Rodent Brain with fMRI 527Adam J. Schwarz and Alessandro GozziIntroduction 527MRI Mapping of Functional Connectivity in the Rodent Brain 528

Networks of Functional Covariance 528Connectivity of Neurotransmitter Systems 529

The Dopaminergic System 529The Serotonergic System 531

Resting State BOLD fMRI 532Connectivity Networks of the Rodent Brain 533Do Rodent Brains have a Default Mode Network? 536Use of Anesthesia and Other Methodological Considerations 539

Transgenic Models: Genetic Manipulation of Functional Connectivity Patterns 541Future Perspectives 543References 545

22 Cutting Edge Approaches for the Identification and the Functional Investigation of miRNAs in Brain Science 553Emanuela de Luca, Federica Marinaro, Francesco Niola, and Davide De Pietri TonelliIntroduction 553

History 553Biology and Functions in the Brain 553

Identification of Novel MicroRNAs in the Brain 555miRNA Extraction and Purification 556miRNA Cloning 556Computational Identification of Novel miRNAs 557RNA Sequencing (RNA-Seq) 558

miRNA Expression Analysis in the Brain 559miRNA Profiling 559Analysis of miRNA Expression in Tissue 559

Target Identification 560Computational Identification of Targets 561Proteomics 561RISC-associated miRNA Targets 562RNomics 563

miRNA Manipulation/Target Validation 565miRNA Inhibition 565miRNA Over-expression 566Target Validation 567

New Frontiers in Small RNA-based Technologies to Cure Nervous System Deficits 567

Use of miRNAs in Gene Therapy 567Use of miRNAs in Gene Therapy in the Brain Requires Improved Delivery Strategies 571

Conclusion and Perspectives 572Are Circulating miRNAs Novel Biomarkers for Brain Diseases? 572Use of miRNAs in Cell Reprogramming Technology 573

Are miRNAs Just the “Tip of the Iceberg”? Emerging Classes of Noncoding RNAs and Novel Scenarios 574

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Acknowledgments 575Competing Financial Interests 575References 575

Index 585

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xix

List of Contributors

Antoine AdamantidisDepartment of PsychiatryMcGill University, Douglas Mental Health University InstituteMontreal, QuebecCanada

and

Department of Neurology University of BernInselspital University HospitalBernSwitzerland

Abraham Acevedo ArozenaMRC Mammalian Genetics UnitHarwellUK

Fuat BalciCollege of Social Science and HumanitiesKoç UniversityIstanbulTurkey

Alessandro BartolomucciDepartment of Integrative Biology and PhysiologyUniversity of MinnesotaMinneapolis, MinnesotaUSA

Luigi BellocchioGroup “Endocannabinoids and Neuroadaptation” NeuroCentre MagendieUniversity of BordeauxBordeauxFrance

Jonathan E. BobekSchool of Life SciencesArizona State UniversityTempe ArizonaUSA

Christina M. BurdenSchool of Life SciencesArizona State UniversityTempe ArizonaUSA

Arnau Busquets-GarciaGroup “Endocannabinoids and Neuroadaptation” NeuroCentre MagendieUniversity of BordeauxBordeauxFrance

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List of Contributorsxx

Angela CarusoNeurotoxicology and Neuroendocrinology SectionDepartment of Cell Biology and NeuroscienceIstituto Superiore di SanitàRomeItaly

Cheryl CeroDepartment of Integrative Biology and PhysiologyUniversity of MinnesotaMinneapolis, MinnesotaUSA

Silvia CorrochanoMRC Mammalian Genetics UnitHarwellUK

Francesca Romana D’AmatoInstitute of Cell Biology and Neurobiology (IBCN) National Research Council (CNR)/S. Lucia FoundationRomeItaly

José María Delgado-GarcíaNeuroscience DivisionUniversity Pablo de OlavideSevilleSpain

Emanuela de LucaNeuroscience and Brain Technologies – Istituto Italiano di TecnologiaGenoaItaly

Anne C. Ferguson-SmithDepartment of GeneticsUniversity of CambridgeCambridgeUK

Sacri R. FerrónDepartamento de Biología Celular- ERI BiotecmedUniversidad de ValenciaSpain

Russell G. FosterNuffield Department of Clinical Neurosciences (Nuffield Laboratory of Ophthalmology)University of OxfordOxfordUK

David FreestoneDepartment of PsychologyBucknell UniversityLewisburg, PennsylvaniaUSA

C.R. GallistelDepartment of PsychologyRutgers UniversityNew JerseyUSA

Robert GerlaiDepartment of PsychologyUniversity of TorontoMississauga, OntarioCanada

Stephen GlasgowDepartment of PsychiatryMcGill University, Douglas Mental Health University InstituteMontreal, QuebecCanada

Alessandro GozziFunctional Neuroimaging LaboratoryIstituto Italiano di TecnologiaCenter for Neuroscience and Cognitive and Systems at UniTnRoveretoItaly

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List of Contributors xxi

Agnès GruartNeuroscience DivisionUniversity Pablo de OlavideSevilleSpain

Sibah HasanNuffield Department of Clinical Neurosciences (Nuffield Laboratory of Ophthalmology)University of OxfordOxfordUK

Carolina Gutierrez HerreraDepartment of PsychiatryMcGill UniversityDouglas Mental Health University InstituteMontreal, QuebecCanada

and

Department of NeurologyUniversity of BernInselspital University HospitalBernSwitzerland

María Teresa Jurado-ParrasNeuroscience DivisionUniversity Pablo de OlavideSevilleSpain

Martien J.H. KasDepartment of Translational Neuroscience Brain Center Rudolf MagnusUniversity Medical Center Utrechtthe Netherlands

and

Groningen Institute for Evolutionary Life SciencesUniversity of GroningenGroningenthe Netherlands

Aaron KheifetsDepartment of PsychologyRutgers UniversityNew JerseyUSA

Adam KingDepartment of Mathematics & Computer ScienceEvergreen State CollegeOregonUSA

Birgitte Rahbek KornumDepartment of Clinical Biochemistry and Danish Center for Sleep MedicineCopenhagen University Hospital RigshospitaletMolecular Sleep LaboratoryDenmark

Elzbieta KostrzewaDepartment of Translational Neuroscience Brain Center Rudolf MagnusUniversity Medical Center Utrechtthe Netherlands

Glenda LassiNeuroscience and Brain Technologies – Istituto Italiano di TecnologiaGenoaItaly

Alessandra LuchettiInstitute of Cell Biology and Neurobiology (IBCN)National Research Council (CNR)/S. Lucia FoundationRomeItaly

Nicholas A. LuskDepartment of Psychology and Neuroscience and Center for Behavioral Neuroscience and GenomicsDuke UniversityDurhamNorth CarolinaUSA

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List of Contributorsxxii

Noelia MadroñalFriedrich Miescher Institute for Biomedical ResearchBaselSwitzerland

Federica MarinaroNeuroscience and Brain Technologies – Istituto Italiano di TecnologiaGenoaItaly

Giovanni MarsicanoGroup “Endocannabinoids and Neuroadaptation” NeuroCentre MagendieUniversity of BordeauxBordeauxFrance

Warren H. MeckDepartment of Psychology and Neuroscience and Center for Behavioral Neuroscience and GenomicsDuke UniversityDurhamNorth CarolinaUSA

Mathilde MetnaGroup “Endocannabinoids and Neuroadaptation” NeuroCentre MagendieUniversity of BordeauxBordeauxFrance

Caterina MichettiNeurotoxicology and Neuroendocrinology Section Department of Cell Biology and NeuroscienceIstituto Superiore di SanitàRomeItaly

Francesco NiolaNeuroscience and Brain Technologies – Istituto Italiano di TecnologiaGenoaItaly

Diego OddiInstitute of Cell Biology and Neurobiology (IBCN)National Research Council (CNR)/S. Lucia FoundationRomeItaly

Francesco PapaleoDepartment of Neuroscience and Brain TechnologiesIstituto Italiano di Tecnologia MoregoGenovaItaly

Stuart N. PeirsonNuffield Department of Clinical Neurosciences (Nuffield Laboratory of Ophthalmology)University of OxfordOxfordUK

Mattia PelizzolaCenter for Genomic Science of IIT@SEMMFondazione Istituto Italiano di Tecnologia (IIT)MilanItaly

Elisabeth J. RadfordCambridge University Hospitals NHS Foundation TrustCambridgeUK

Maria RazzoliDepartment of Integrative Biology and PhysiologyUniversity of MinnesotaMinneapolis, MinnesotaUSA

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Laura RicceriNeurotoxicology and Neuroendocrinology Section Department of Cell Biology and NeuroscienceIstituto Superiore di SanitàRomeItaly

Orsola Rosa-SalvaCenter for Mind/Brain SciencesUniversity of TrentoItaly

Maria Luisa ScattoniNeurotoxicology and Neuroendocrinology Section, Department of Cell Biology and NeuroscienceIstituto Superiore di SanitàRomeItaly

Diego ScheggiaDepartment of Neuroscience and Brain TechnologiesIstituto Italiano di Tecnologia MoregoGenovaItaly

Adam J. SchwarzDepartment of Psychological and Brain SciencesIndiana UniversityBloomington, IndianaUSA

Edgar Soria-GomezGroup “Endocannabinoids and Neuroadaptation” NeuroCentre MagendieUniversity of BordeauxBordeauxFrance

Federico TinarelliNeuroscience and Brain Technologies – Istituto Italiano di TecnologiaGenoaItaly

Davide De Pietri TonelliNeuroscience and Brain Technologies – Istituto Italiano di TecnologiaGenoaItaly

Giorgio VallortigaraCenter for Mind/Brain SciencesUniversity of TrentoItaly

Bin YinDepartment of Psychology and Neuroscience and Center for Behavioral Neuroscience and GenomicsDuke UniversityDurham, North CarolinaUSA

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Preface

The confines of neurobehavioral genetics are fragile, resulting in a constant need to integrate dif-ferent approaches and technologies into the repertoire of neurobehavioral genetics laboratories.

Both behavioral and biological processes are intrinsically characterized by substantial varia-tion and it is sometimes controversial whether, as researchers, we should attempt to reduce or decode such variation when we investigate particular behaviors, complex brain circuitries, and/or genetic and epigenetic regulatory mechanisms.

I have had the fortune during my career of witnessing different discussions on the topic of genes and behavior, across different disciplines. As result of this exposure to different fields of investi-gation, I have often seen the solutions to the queries of one discussion table residing on a differ-ent table, the real obstacle being the communication between subgroups and the appreciation of different points of view. In particular, I have participated in lengthy debates within groups of scientists that had diverse points of view regarding behavioral phenotyping in mice. For example, mouse geneticists dealing with functional genomics are mainly concerned with the robustness of phenotypic measures, whether the time for a mouse to escape from a water pool, the immobility response to a mild electrical shock, or the periodicities of mouse activity on a wheel over 24 hours. The interest in this field is to determine which behavioral output can be associated to different genes or different variations of the same gene. In functional genomics is mandatory to have good control of the phenotypic variation. The goal can be either to identify outliers imposing a good signal-to-noise rapport (e.g., in mutagenesis programs that are phenotype driven) or to take advantage of different phenotypic expressions across different populations of individuals (e.g., in quantitative genetics). The obvious solution in behavioral functional genomics has been to mini-mize variation, for example by standardizing animal conditions, protocols, and procedures. Such a task is difficult: in some cases it was possible while in other cases the behavioral measures identified were too difficult to standardize. An opposite approach has been to maximize varia-tion in order to account for all the biological variations that characterize a behavioral phenotype. This latter approach, although in some instances appearing to provide better reproducibility of behavioral phenotypes, is not suitable for the process of discovering new functional genes in large-scale enterprises.

Whilst functional genomics is less concerned with the intrinsic meaning of a specific behav-ior, provided it is informative and predictive of disease conditions, pure behaviorists (e.g., ethologists, psychologists, and neuroscientists) concern themselves with the trait and the mechanism under investigation. This latter category of investigators has access to an enormous background literature into the investigation of each trait and can provide a true understanding of what an animal response is telling us about the behavioral repertoire and cognitive pro-cesses: how fundamental neuronal properties, either during development or in adulthood, may determine brain circuitries and therefore influence behavioral outcomes.

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The rationale in assembling this book has been to provide a description of different approaches to the investigation of links between genetic variables and behavioral phenotypic expression, hoping to favor new understanding and insights across neurobehavioral genetic approaches that barely know each other. Indeed, although multidisciplinary approaches are always encouraged and claimed in many scientific works, understanding across disciplines has been often elusive.

In this book we present examples of genetic screens (Chapter 1) as well as methodological aspects in quantitative genetics (Chapter 10) and computational considerations in genomic and epigenomic regulatory mechanisms (Chapter 2). The book alternates various approaches by addressing some of the most relevant topics in neurobehavioral genetics nowadays. Specific neurobehavioral processes are presented by discussing the biology of different species (Chapters 3, 9, and 16), including humans (Chapter 12). Moreover, a detailed analysis involving fundamental developmental behaviors (Chapters 11 and 15) and developmental epigenetic neuronal mechanisms (Chapters 14 and 16) allows us to address the backbone of the interplay between genetic and epigenetic mechanisms in setting adult behavioral traits. Chapter 4 exam-ines metabolic and physiological aspects related to stress and looks into the genetic and epige-netic mechanisms. Specific translational aspects are discussed for cognitive (Chapter 6) and neurodegenerative (Chapter 1) diseases and the fundamental mechanisms of sleep and the circadian clock (Chapters 12, 17, 18, and 19). In this book we have addressed the link between neuronal properties and specific behavioral traits by exploring the learning and memory sys-tem (Chapter 5) and the endocannabinoid system (Chapter 13), two of the most studied phe-nomena in brain sciences. This book will represent perhaps the first attempt to combine genomic and neurobiological approaches with some deep behavioral understanding. To that end, I have asked internationally appreciated psychologists to participate in this venture. In particular, cognitive processes are fully discussed across species (Chapters 7, 8, 9, and 20), which would constitute an enormous potential if combined with genomic biology. Last but not least we conclude by presenting new technological advances in optogenetics (Chapter 18), high-throughput phenotyping (Chapter 19), and brain imaging (Chapter 21), and by exploring the role of non-coding genomic elements (Chapter 22).

This book is intended to reach students at different levels of their curricula in neurobehavio-ral genetics. However, it provides a forum for discussion of various scientific and technical aspects, making different parts of the book of interest for young and experienced scientists actively involved in disciplines embracing neuroscience and genetics.

The completion of the book has been a long process but rewarding in the end. I must thank the editorial team at Wiley Blackwell for their incredible assistance throughout different phases of the preparation of the book. Moreover, I am grateful to all authors that contributed to this project, sharing critical aspects and insights of their laboratory work. As I went through differ-ent chapters I had the chance to establish new collaborations with some of the authors, already proving that this book has a tremendous potential in fostering new scientific opportunities around the frontiers of neurobehavioral genetics.

Genoa, Italy Valter Tucci

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Handbook of Neurobehavioral Genetics and Phenotyping, First Edition. Edited by Valter Tucci. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

Introduction

One of the major challenges of modern biology is to further understand the molecular etiology of human diseases with the ultimate aim of providing cures or disease‐ameliorating therapies.

Neurodegenerative disorders are a heterogeneous group of diseases leading to the death of specific neuronal populations. Generally, neurodegenerative diseases are fatal disorders for which there are currently no effective therapies. They share common features such as the pro-gressive nature of the disease and the association with increased age. Thus, with a growing aging population, the prevalence of neurodegenerative diseases is steadily increasing. Another common end‐point is the development of unique proteinaceous inclusions and the death and/or dysfunction of particular neuronal populations that are signatures of each disorder. The appearance of these inclusions is common to the major neurodegenerative disorders, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD) and amyo-trophic lateral sclerosis (ALS). Inclusions are formed through protein misfolding and therefore these disorders can be classified as protein conformation diseases or proteinopathies.

From the genetic point of view, neurodegenerative diseases fall into two major categories: (i) familial or single gene disorders in which a single causative gene is inherited in a dominant or recessive manner, and (ii) sporadic or idiopathic cases, in which no previous family history exists and no mutations in previously identified causative genes can be generally found. For the major multigenic neurodegenerative disorders such as AD, PD, or ALS, the majority of cases are sporadic, making the identification of novel therapeutic targets more challenging.

Thus, advancing our understanding of the pathogenesis of neurodegenerative diseases is critical, aiming to find not only new causative genes but also to identify new genes and path-ways that affect disease onset or progression rate. Lessons from other diseases suggest that a deeper understanding of disease pathogenesis can ultimately lead to novel drug targets and therapies. Indeed, research into neurodegeneration pathogenesis is currently at a critical turn-ing point, starting to translate pathomechanistic findings into disease‐ameliorating treat-ments (Fig. 1.1).

The identification of the causative mutations in familial cases represents the main entry point for investigations into neurodegeneration molecular pathogenesis. In some rare cases, the identification of causative mutations has provided immediate insight into the biology of the disease. However, moving from a causative gene to understanding disease pathogenesis, typi-cally using a number of disease models, has proven to be an extremely difficult task for a wide

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Genetic Screens in NeurodegenerationAbraham Acevedo Arozena and Silvia Corrochano

MRC Mammalian Genetics Unit, Harwell, UK

Handbook of Neurobehavioral Genetics and Phenotyping 2

variety of disorders. This perhaps reflects the inherent limitations of studying very complex disease etiologies together with the necessary reductionism linked to functional work, includ-ing the use of disease models to inform us about human disease. Even for Mendelian disorders such as HD, with all cases explained by mutations in a single gene, despite decades of work using human material and disease models, we still do not understand how expansions in the polyglutamine tract of huntingtin lead to the death of particular neuronal pools. Thus, despite an intense research focus, more than 25 years after the identification of the causative gene no disease‐ameliorating treatments have yet been approved for HD [1]. However, despite its pit-falls, this cycle of gene identification followed by functional analysis using disease models is the basis for the current theories of pathogenesis for many neurodegenerative diseases.

Genetic approaches have been developed using a wide variety of disease models aimed at understanding pathogenesis while elucidating genetic and biochemical pathways leading to different forms of neurodegeneration. These include high‐throughput genetic screens in cel-lular models from yeast to patient‐derived induced pluripotent stem (iPS) cells, as well as invertebrate animal models such as worms or flies together with vertebrates such as zebrafish and mammals like the mouse. Here, we aim to give an overview of current strategies, findings, and limitations of functional genetic screens on neurodegeneration using model organisms from yeast to mice.

The Genetics of Neurodegenerative Disorders

The genetics of neurodegenerative disorders varies from single‐gene disorders such as HD and other polyglutamine disorders, to very complex multigenic disorders such as AD, PD, or ALS.

Rarely, single‐gene mutations can also cause cases of AD, PD, and ALS that in general is pathologically indistinguishable from sporadic cases, although they tend to cause earlier dis-ease symptoms [2]. These cases are termed familial and have a previous family history of the disease. Some sporadic cases can have a concealed family history or may carry de‐novo

PatientsGeneticbiochemistry

Neurodegeneration genes

Understanding pathogenesis

Novel genes/pathways

Disease models

Genetic screensCell biologyBiochemistrySystem biology

Potential noveldrug targetsTreatments

Figure 1.1 Understanding disease pathogenesis to generate novel treatments.