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The effects of pre- and perinatal nicotine exposure and genetic background on histological and behavioural… Balsevich, Georgia Ann 2012

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The Effects of Pre- and Perinatal Nicotine Exposure and Genetic Background on Histological and Behavioural Phenotypes by Georgia Ann Balsevich  B.Sc., The University of Saskatchewan, 2008  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in The Faculty of Graduate Studies (Neuroscience)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) March 2012 © Georgia Ann Balsevich 2012  Abstract Approximately 11% of pregnant woman in Canada continue to use tobacco during pregnancy. Maternal tobacco use increases the risk of complications in pregnancy and also the risk of adverse foetal outcomes. Although tobacco smoke contains over 4000 compounds, studies have established nicotine as the principal component of tobacco smoke that leads to the majority of negative reproductive outcomes associated with maternal tobacco use. It appears the neuroteratogenicity of nicotine is mediated by complex gene-environment interactions. Genetic background contributes to individual differences in nicotine-related phenotypes. The aim of the current study was to investigate the interaction between pre- and perinatal nicotine exposure and genetic background on histological and behavioural measures using DBA/2J (D2) and C57BL/6J (B6) inbred mice. Alterations in neuronal cell populations, regional brain volumes, and behaviour - open field (OF) activity, novel object recognition (NOR), elevated plus maze (EPM), and passive avoidance (PA) - were evaluated on postnatal day (PN) 24 and PN75, following early exposure to nicotine solution (200 ♠g/ml)  starting from 30 days before pregnancy up to pups weaning. Data revealed no difference between treatment groups of dams in gestational weight gain or pup mortality. Histological data showed that early nicotine exposure resulted in decreased striatal volume among preadolescent females and reduced neuronal number within the striatum of preadolescent B6 mice. In the hippocampus the effects of early nicotine exposure appeared more subtle, in which only the granule cell layer of the dentate gyrus in D2 preadolescents was afflicted. Behavioural data showed that early nicotine exposure promoted hyperactivity in D2 female mice and disrupted NOR and PA memory. Specifically, NOR deficits were significant amongst adult male mice whereas PA deficits were unconditional. These data suggest that pre- and perinatal nicotine affects regional brain morphology and leads to neurobehavioural alterations. The observed treatment interactions suggest that genetic background, developmental stage, and sex interact with nicotine to influence nicotine-related phenotypes.  ii  Preface Ethics approval was given by the Animal Care Committee of the UBC Research Ethics Board (Certificate A10-0272 Prenatal and Postnatal Nicotine Exposure).  iii  Table of Contents Abstract  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ii  Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  iii  Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  iv  List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  viii  List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ix  List of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  x  Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  xii  1  Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1  1.1  Clinical Significance of Tobacco and Nicotine . . . . . . . . . . . . . . . . . . . .  1  1.2  Nicotine’s Developmental Mode of Action  . . . . . . . . . . . . . . . . . . . . .  2  1.2.1  Pharmacology of Nicotinic Acetylcholine Receptors . . . . . . . . . . . .  2  1.2.2  Prenatal Expression of Nicotinic Acetylcholine Receptors . . . . . . . . .  3  1.2.3  Developmental Roles of Nicotinic Acetylcholine Receptors . . . . . . . .  4  1.3  . . . . . . . . . . . .  5  1.3.1  nAChR Expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  5  1.3.2  Cell Proliferation and Cell Death  . . . . . . . . . . . . . . . . . . . . . .  6  1.3.3  Neurotransmitter Systems . . . . . . . . . . . . . . . . . . . . . . . . . .  6  Effects of Early Nicotine Exposure on Neurobehavioral Phenotypes . . . . . . . .  7  1.4.1  Addiction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  8  1.4.2  Cognition  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  8  1.4.3  Attention-Deficit Hyperactivity Disorder . . . . . . . . . . . . . . . . . .  9  1.5  Inbred Mouse Models to Study the Genetics of Early Nicotine Exposure . . . . . .  10  1.6  Factors Influencing Phenotypes Caused by Early Nicotine Exposure . . . . . . . .  11  1.6.1  Genotype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  11  1.6.2  Sex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  12  1.4  Effects of Early Nicotine Exposure on Neural Development  iv  Table of Contents 1.6.3  Temporal Vulnerability  . . . . . . . . . . . . . . . . . . . . . . . . . . .  13  1.6.4  Dose of Nicotine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  14  1.7  Methods of Nicotine Administration to the Developing Mouse . . . . . . . . . . .  15  1.8  Behavioural Approaches for Early Nicotine Exposure and Genetic Studies . . . . .  17  1.8.1  Open Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  18  1.8.2  Elevated Plus Maze  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  18  1.8.3  Novel Object Recognition . . . . . . . . . . . . . . . . . . . . . . . . . .  18  1.8.4  Passive Avoidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  19  1.8.5  Behavioural Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  19  1.8.6  Brain Areas Supporting Behavioural Function  20  1.9  2  Histological Approaches for Early Nicotine Exposure and Genetic Studies  . . . .  22  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  22  1.9.1  Volume Estimates  1.9.2  Cell Count Estimates  . . . . . . . . . . . . . . . . . . . . . . . . . . . .  22  1.10 Rationale and Thesis Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . .  24  General Methods  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  26  2.1  Animals and Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  26  2.2  Drugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  26  2.3  Testing Schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  26  2.4  Behavioural Testing Schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . .  27  2.5  Behavioural Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  27  2.5.1  Open Field Activity  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  27  2.5.2  Novel Object Recognition . . . . . . . . . . . . . . . . . . . . . . . . . .  28  2.5.3  Elevated Plus Maze  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  29  2.5.4  Passive Avoidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  29  2.6  Tissue Collection and Processing  . . . . . . . . . . . . . . . . . . . . . . . . . .  30  2.7  Immunohistochemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  30  2.8  Stereology  30  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  2.8.1  Striatum  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  2.8.2  Hippocampus  30  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  31  Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  32  Effects of Pre- and Perinatal Nicotine Exposure on Pregnancy Dynamics . . . . . .  35  3.1  Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  35  3.2  Methods  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  35  3.2.1  Animals and Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . .  35  3.2.2  Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  36  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  36  2.9 3  . . . . . . . . . . . . . . .  3.3  Results  v  Table of Contents  3.4 4  Maternal Fluid Intake  . . . . . . . . . . . . . . . . . . . . . . . . . . . .  36  3.3.2  Maternal Weight Gain . . . . . . . . . . . . . . . . . . . . . . . . . . . .  36  3.3.3  Birth Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  39  3.3.4  Offspring Growth Rates . . . . . . . . . . . . . . . . . . . . . . . . . . .  39  3.3.5  Perinatal Mortality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  40  3.3.6  Litter Size  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  40  3.3.7  Adult Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  40  Discussion  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  Effects of Pre- and Perinatal Nicotine Exposure on Behavioural Measures  40  . . . . .  44  4.1  Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  44  4.2  Methods  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  45  4.2.1  Animals and Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . .  45  4.2.2  Testing Schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  45  4.2.3  Behavioural Testing Procedure and Behavioural Tests  . . . . . . . . . . .  46  4.2.4  Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  46  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  46  4.3.1  Open Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  46  4.3.2  Elevated Plus Maze  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  50  4.3.3  Novel Object Recognition . . . . . . . . . . . . . . . . . . . . . . . . . .  53  4.3.4  Passive Avoidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  53  4.3  4.4  5  3.3.1  Results  Discussion  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  55  4.4.1  Locomotor Activity  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  56  4.4.2  Anxiety-Like Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . .  57  4.4.3  Cognition  59  4.4.4  Overall Behavioural Interpretation  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  62  Effects of Pre- and Perinatal Nicotine Exposure on Histological Measures . . . . . .  64  5.1  Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  64  5.2  Methods  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  65  5.2.1  Animals and Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . .  65  5.2.2  Tissue Collection and Processing . . . . . . . . . . . . . . . . . . . . . .  65  5.2.3  Stereology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  65  5.2.4  Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  66  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  67  5.3.1  Striatal Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  67  5.3.2  Striatal Neuronal Count . . . . . . . . . . . . . . . . . . . . . . . . . . .  67  5.3.3  Hippocampal Volume  69  5.3  Results  . . . . . . . . . . . . . . . . . . . . . . . . . . . .  vi  Table of Contents 5.3.4 5.4  Histological and Behavioural Correlations  Discussion  . . . . . . . . . . . . . . . . .  74  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  74  5.4.1  Striatum  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  5.4.2  Hippocampus  5.4.3  Overall Histological Interpretation  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  76 78  . . . . . . . . . . . . . . . . . . . . .  80  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  82  6.1  Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  82  6.2  Influencing Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  83  6.2.1  Genotype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  84  6.2.2  Sex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  84  6.2.3  Temporal Vulnerability  . . . . . . . . . . . . . . . . . . . . . . . . . . .  86  6.3  Significance to the Research Field . . . . . . . . . . . . . . . . . . . . . . . . . .  87  6.4  Limitations and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . .  89  Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  91  6  General Discussion  vii  List of Tables 1.1  Correlates of mouse and human behaviour . . . . . . . . . . . . . . . . . . . . . .  17  2.1  Data transformations for statistical analyses . . . . . . . . . . . . . . . . . . . . .  33  2.2  Summary of statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  34  4.1  Summary of subjects used for behavioural testing . . . . . . . . . . . . . . . . . .  45  4.2  Summary of subjects eliminated from NOR analysis. . . . . . . . . . . . . . . . .  46  5.1  Summary of subjects used for histological measurements . . . . . . . . . . . . . .  66  5.2  Parameters of linear regression analyses . . . . . . . . . . . . . . . . . . . . . . .  75  5.3  A cross-study comparison of hippocampal volume measurements . . . . . . . . . .  79  viii  List of Figures 1.1  Age correlates for human and mouse . . . . . . . . . . . . . . . . . . . . . . . . .  16  1.2  Representation of optical fractionator technique . . . . . . . . . . . . . . . . . . .  23  2.1  Behavioural test battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  28  2.2  Representative Nissl-stained striatal section . . . . . . . . . . . . . . . . . . . . .  31  3.1  Maternal fluid intake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  37  3.2  Maternal weight gain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  38  3.3  Average pup weight at birth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  39  3.4  Offspring growth rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  41  4.1  Total distance travelled in an open field . . . . . . . . . . . . . . . . . . . . . . .  48  4.2  Time spent in the open field center . . . . . . . . . . . . . . . . . . . . . . . . . .  49  4.3  Time spent in the open arms of an EPM . . . . . . . . . . . . . . . . . . . . . . .  51  4.4  Number of open arm entries in an EPM . . . . . . . . . . . . . . . . . . . . . . .  52  4.5  Novel object recognition as measured by the difference score and the preference ratio 54  4.6  Passive avoidance retention latency . . . . . . . . . . . . . . . . . . . . . . . . . .  55  5.1  Striatal volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  68  5.2  Total striatal neuronal count . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  70  5.3  Granule cell layer volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  71  5.4  Pyramidal cell layer volume of the CA1 subfield . . . . . . . . . . . . . . . . . . .  72  5.5  Pyramidal cell volume of the CA2/3 and CA1-CA3 subfields . . . . . . . . . . . .  73  ix  List of Abbreviations 5-HT1A  serotonin receptor 1A  5-HT2  serotonin receptor 2  ACh  acetylcholine  ACTH  adenocorticotropic hormone  ADHD  attention-deficit hyperactivity disorder  ANOVA  analysis of variance  B6  C57BL/6J  BDNF  brain-derived neurotrophic factor  CNS  central nervous system  CS  conditioned stimulus  D2  DBA/2J  DAT1  dopamine transporter 1  DRD4  D4 dopamine receptor  EPM  elevated plus maze  G  gestational day  GCL  granule cell layer  GFAP  glial fibrillary acidic protein  GG  Greenhouse-Geisser  HPA  hypothalamic-adrenal-pituitary  ITM  intermediate term memory x  List of Abbreviations LTM  long term memory  mEPSCs  excitatory postsynaptic responses  NAcc  nucleus accumbens  nAChR(s) nicotinic acetylcholine receptor(s) NOR  novel object recognition  NRT  nicotine replacement therapy  OF  open field  PA  passive avoidance  PCL  pyramidal cell layer  PN  postnatal day  PNS  peripheral nervous system  QTL  quantitative trait loci  RI  recombinant inbred  RM  repeated measures  s.c.  subcutaneous  S.E.M.  standard error of the mean  SERT  serotonin transporter  SIDS  sudden infant death syndrome  SN  subtantia nigra  SNP  single nucleotide polymorphism  STM  short term memory  SVZ  subventricular zone  US  unconditioned stimulus  VNTR  variable number of tandem repeats  VTA  ventral tegmental area  xi  Acknowledgements First I would like to thank my supervisor, Dr. Daniel Goldowitz, whose guidance and encouragement have helped me grow academically throughout my research and thesis writing. I would like to thank my supervisory committee members: Dr. Michael Kobor, Dr. Joanne Weinberg, and Dr. Catharine Winstanley for their valuable insights and feedback. A special thank you to Dr. Weinberg for her input on my statistical analyses. I would also like to thank all my fellow lab members for their great technical support throughout my research. I want to acknowledge Dr. Jennifer Wilking for her support, interest, and valuable ideas. Lastly I thank my parents and Philipp for their unconditional support throughout this journey.  xii  Chapter 1  Introduction 1.1  Clinical Significance of Tobacco and Nicotine  Tobacco and nicotine have been associated with mankind for more than 5,000 years (Musk & Klerk, 2003). Indigenous Native Americans used nicotine for its role in both religious and medicinal rituals. Apprehensions about the health effects of tobacco have also been recognized throughout history, dating back to as early as the 17th century (Perkins et al., 1996). To-date it is well recognized that both tobacco use and nicotine use pose an increased risk of adverse health outcomes to the consumer. Since the 1980’s passive smoking, including exposure in utero, has been implicated as a major factor leading to numerous diseases (Musk & Klerk, 2003). Despite increased public warnings about the harmful effects of smoking on developing foetuses, approximately 11 % of pregnant Canadian woman continue to use tobacco during pregnancy (Health Canada, 2007; AlSahab et al., 2010). Maternal tobacco use increases the risk of complications in pregnancy and also the risk of adverse foetal outcomes (Health Canada, 2009). According to the Centers for Disease Control, women tobacco smokers are at increased risk to experience premature rupture of membranes, placental abruption, and placenta previa during pregnancy (U.S. Department of Health and Human Services, 1989). Additionally they reported that babies born to mothers who smoked during pregnancy are at higher risk of being preterm and born with a low birth weight, which are associated with increased complications during delivery and result in increased perinatal morbidity and mortality. Postnatal effects of in utero tobacco exposure are also evident as a 2- to 3-fold increase in the chance of sudden infant death syndrome (SIDS) (Anderson & Cook, 1997; Duncan et al., 2008). The adverse effects of prenatal cigarette smoking extend far beyond complications during pregnancy or perinatal morbidity. In fact, two of the best documented outcomes from in utero smoking exposure are long-lasting alterations on brain functions affecting learning/memory and naturaland drug- reinforcement (reviewed by Pauly & Slotkin, 2008). Such long-lasting alterations manifest behaviourally as increased risk for attention-deficit hyperactivity disorder (ADHD), conduct disorders, depression, anti-social behaviour, cognitive deficits, and increased rates of adolescent smoking and substance abuse (Butler & Goldstein, 1973; Rantakallio, 1983; Naeye & Peters, 1984; Wakschlag et al., 1997; Fergusson et al., 1998; Milberger et al., 1998; Mick et al., 2002; Buka et al., 2003; Linnet et al., 2003). Although tobacco smoke contains over 4000 compounds (Hoffmann & Hoffmann, 1997), stud1  1.2. Nicotine’s Developmental Mode of Action ies using animal models have established nicotine as the principal component of tobacco smoke that leads to the majority of negative reproductive outcomes (reviewed by Slotkin, 2008). Yet nicotine replacement therapy (NRT) has been developed as a smoking cessation therapy for pregnant woman who have been unable to successfully quit smoking. To-date several studies have concluded, that while not ideal, NRT is a safer alternative relative to the potential adverse effects of continued maternal smoking during pregnancy (Ontario Medical Association, 2008; Glynn et al., 2009). Evidence demonstrating prenatal nicotine exposure leads to adverse effects on brain development suggests that NRT may not be a suitable smoking cessation strategy. Therefore, in addition to advocating abstinence, research into the exact neural mechanisms underlying the adverse outcomes from in utero nicotine exposure must be explored. A vast amount of effort has been poured into finding the mechanistic basis for the neurobehavioural problems resulting from prenatal nicotine exposure, but so far the situation has been very complicated and difficult to comprehend. It is known that nicotine is able to cross the placenta and is found at concentrations in foetal tissue that are equivalent to the plasma nicotine levels in the mother (Luck et al., 1985). Unfortunately, our understanding on how the molecular targets interact to produce deficits in learning and alter reward circuits is vague at best. The outcomes resulting from prenatal nicotine exposure depend on many factors, including maternal environment, foetal genetic background, and severity of exposure. To complicate the matter even further, nicotine can affect each brain region and cell type differently. For example, nicotine treatment enhances nicotinic receptor binding in a region-specific manner, indicating region-specific differences in the sensitivity to nicotine exposure. Research is required to understand how each process comes together to produce the learning- and drug reinforcement problems associated with maternal smoking.  1.2 1.2.1  Nicotine’s Developmental Mode of Action Pharmacology of Nicotinic Acetylcholine Receptors  Nicotine exposure mediates its actions primarily through nicotinic acetylcholine (ACh) receptors (nAChRs), whose endogenous ligand is acetylcholine (Dani & Bertrand, 2007). ACh is a neurotransmitter, well recognized for its role in sustaining or modulating different aspects of the central nervous system (CNS), as well as its important role during development (Abreu-Villaca et al., 2010). During development, ACh acts through either muscarinic or nicotinic AChRs to regulate progenitor cell proliferation and differentiation, neurogenesis, gliogenesis, neuronal maturation and plasticity, axonal pathfinding, regulation of gene expression, and cell survival (for review see Abreu-Villaca et al., 2010). Functional nAChRs are ligand-gated ion channels, expressed as transmembrane oligomers,  composed of 5 subunits, in which there are a total of 12 different neuronal subunits (from ❛2 to  ❛10 and from ❜2 – ❜4) (Dani & Bertrand, 2007).  Each subtype has a common basic structure, yet 2  1.2. Nicotine’s Developmental Mode of Action show distinct pharmacological, kinetic, and functional properties due to the wide range of different subunit combinations. The heteromeric ❛4❜2* subunit (where * indicates possibility of other  subunits) is the most prevalent subtype, accounting for more than 90% of all neuronal nAChRs; the homomeric ❛7* subunit is the second most abundant subtype. Whereas both ❛4❜2* and ❛7* nAChRs  are widely distributed throughout the CNS and both play a major role in modulating the effects of nicotine, other subunit combinations are found in more spatially limited patterns, consistent with more distinct functional roles (Lena & Changeux, 1997; Lukas et al., 1999). By nature, ligand-gated ion channels change conformation upon ligand binding. Nicotine alters the orientation of all 5 subunits, widening the water-filled pore to allow the passage of Na+, K+ ions and in some instances, Ca2+ ions (Barik & Wonnacott, 2009). Inevitably, this may lead to several downstream effects including the generation of action potentials or increased intracellular calcium concentrations, which may further induce a variety of effects through its role as a second messenger (Albuquerque et al., 1996; Leonard & Bertrand, 2001). Receptor desensitization (the loss of response after prolonged or repeated application of stimulus) is also known to occur after prolonged exposure to nicotine (Giniatullin et al., 2005; Dani & Bertrand, 2007). Furthermore, chronic exposure to nicotine can produce long-lasting changes in receptor sensitivity, from a lowaffinity state to a high-affinity, desensitized state - a process known as upregulation (i.e. increased agonist binding). Upregulation of nAChRs throughout the brain has been reported as a result of nicotine exposure during gestation, adolescence, and adulthood (Slotkin, 1998; Ernst et al., 2001; Smith et al., 2010).  1.2.2  Prenatal Expression of Nicotinic Acetylcholine Receptors  Temporal changes in nAChR expression are well recognized throughout brain development, and are critical in determining the effects of nicotine exposure on neurochemical and neurobehavioural phenotypes at various developmental stages. In the foetus, the expression of nAChR precedes the in-growth of acetylcholine-secreting neurons (Atluri et al., 2001). In humans, mRNA transcripts and binding sites for ❛4❜2 nAChRs are first detected in the spinal cord, pons, and medulla, followed  thereafter in the cortex, hippocampus, and basal ganglia (Agulhon et al., 1998; Hellstrom-Lindahl  et al., 1998). ❛7 mRNA transcripts and corresponding binding sites are first detected in the midbrain and pons, followed thereafter in the spinal cord, cortex, and subcortical regions (Hellstrom-Lindahl  et al., 1998; Hellstrom-Lindahl & Court, 2000). Similarly in rats, ❛4 and ❜2 mRNAs are first detected in the spinal cord by gestational day (G) 12 and extend to the cortex by G17-19 (Zoli et al.,  1995). Rat ❛7 mRNA transcripts are first detected in the cortex and thalamus by G13-G15 (Broide et al., 1995); by G16 they are also detected within the hippocampus and spinal cord (Tribollet et al., 2004). Therefore prenatal exposure to nicotine may have distinct effects on various brain regions.  3  1.2. Nicotine’s Developmental Mode of Action  1.2.3  Developmental Roles of Nicotinic Acetylcholine Receptors  nAChRs are critical for proper brain organization during prenatal brain development. Development of the CNS in the rat begins around G11, initially as a ventricle surrounded by proliferating cells (Bayer et al., 1993). These cells ultimately form the neural cell populations found in the mature CNS. The development of the cortex is organized with an ‘inside-out’ migration pattern of cortical neurons, whereby the earliest neurons born occupy the deepest layers of the cortex and subsequent cells migrate to the more superficial cortical layers (Angevine & Sidman, 1961). Following neuronal migration synaptic connections begin to form and subsequently chemical transmission occurs (reviewed by Jessell & Kandel, 1993). An exception to this time course are the monoaminergic neurons, which in fact express neurotransmitter before their target neurons are fully differentiated and serve as trophic factors to guide cell migration and synapse formation (Lauder & Krebs, 1978; Lankford et al., 1988; Todd, 1992; Tang et al., 2001). In the first 12 postnatal days (PN) of rat development (equivalent to the third trimester of human gestation) there is rapid brain growth characterized by dendritic arborisation, axonal growth, peak synaptogenesis, gliogenesis, and maturation of neurotransmission (Dobbing & Sands, 1979). nAChRs act, in various ways, throughout these key developmental events.  Early on, nAChRs play a regulatory role in axonal pathfinding whereby activation of ❛7 nAChRs  induces neurite retraction, while inhibition induces neurite extension (Lipton et al., 1988; Pugh &  Berg, 1994; Small et al., 1995). Furthermore, ❛7 nAChR function is vital for normal programmed cell death, whereby pharmacological inhibition of these receptors prevents apoptosis (Renshaw et al., 1993; Hory-Lee & Frank, 1995). Collectively nAChR signalling helps control cell number and the projection path of surviving neurons. The nAChRs are also involved in the regulation of dopamine neurons during prenatal develop-  ment. In fact dopamine cell bodies and axon terminals express functional ❛4❜2 nAChRs as early as G15, which corresponds closely to the appearance of these cells (Azam et al., 2007). Moreover  Azam et al. (2007) reported higher ❛4 nAChR mRNA expression in the subtantia nigra (SN) compared to the ventral tegmental area (VTA), suggesting that nAChR signalling may have a greater influence on SN neurons projecting to the striatum than the VTA neurons projecting to the limbic and cortical regions. nAChR signalling may likewise influence the targets of dopamine neurons given the influence it has on D1 and D2 dopamine receptors. In order to be functional, striatal D1 and D2 receptors require dopaminergic stimulation (Jung & Bennett, 1996). Activation of D2-like receptors mediates pro-mitogenic effects, whereas activation of D1 receptors mediates anti-mitogenic effects (Ohtani et al., 2003; Popolo et al., 2004). Therefore the modulation of dopamine release by presynaptic nAChRs may coordinate the survival of dopamine neurons, and in turn the development of the dopamine receptor system (Picciotto et al., 1998; Labarca et al., 2001; Parish et al., 2005).  Other neuronal subtypes are also regulated by nAChRs. For example, the ❛7 nAChRs expressed  on GABAA receptors are critically involved in the GABA switch, whereby GABA operates mainly 4  1.3. Effects of Early Nicotine Exposure on Neural Development as an excitatory transmitter on immature neurones (acting as a trophic factor to guide neuronal migration and neurite outgrowth), but after birth operates as an inhibitory transmitter (Ben-Ari et al., 1989; Miles, 1999; Rivera et al., 1999; Represa & Ben-Ari, 2005; Liu et al., 2006; Zago et al.,  2006). It was shown that ❛7 subunit knock-out mice maintain an excitatory GABA profile for an extended period compared to wildtype mice (Liu et al., 2006). Catecholaminergic neurons, namely norepinephrine and serotonin, also express functional nAChRs during prenatal development (Leslie et al., 2006; Galindo-Charles et al., 2008; O Leary et al., 2008). Further, studies have shown that nAChRs modulate serotonin and norepinephrine release in the developing brain (Gallardo & Leslie, 1998; Seth et al., 2002; O Leary & Leslie, 2006). During development norepinephrine and serotonin have been known to regulate cortical layer development and differentiation (Cases et al., 1998; Herlenius & Lagercrantz, 2001; Vitalis & Parnavelas, 2003). In summary, nAChRs regulate developmental events directly as well as indirectly through modulation of multiple neurotransmitter systems, which too play a role in normal brain development.  1.3 1.3.1  Effects of Early Nicotine Exposure on Neural Development nAChR Expression  By modulating nAChR activity and responsiveness, chronic nicotine exposure has the capacity to alter nAChR-related events. It is believed that nicotine from prenatal exposure, binds to the nAChRs during prenatal and/or early postnatal development, resulting in premature activation of the cholinergic system (Slikker et al., 2005). Considering nAChRs are widely expressed in both the CNS and peripheral nervous system (PNS), on dendrites, cell bodies, axons, as well as presynaptically and postsynaptically (Dani & Bertrand, 2007), premature activation of nAChR subtypes has the capacity to affect a broad array of brain functions. Indeed prenatal nicotine exposure is recognized for its ability to increase ❛4❜2* nAChR binding in foetal and neonatal brain, providing concrete evidence  that nicotine reaches foetal brain, which consequently is a necessary and important action leading to the adverse effects of prenatal nicotine exposure (Slotkin et al., 1987a; Navarro et al., 1989). Nevertheless this upregulation may be transient or confined to specific brain regions (reviewed by Smith et al., 2010). For example it appears that the upregulation observed in perinatal brains is reversed in select regions during adolescence, such that nAChR subunit mRNA expression is reduced in dopaminergic regions in adolescent rats (Chen et al., 2005). The premature activation of cholinergic signalling disrupts endogenous ACh signalling, by desensitizing nAChRs, and thus precluding nAChR activation (Slikker et al., 2005). Under physiological conditions, cholinergic terminals arrive at their targets to promote a switchover from cell replication to differentiation (Slotkin, 1992). Prenatal nicotine exposure mediates similar developmental events, but because this environmental exposure precedes the natural spike of cholinergic activity, the transition from replication to differentiation occurs prematurely and in a discoordinated 5  1.3. Effects of Early Nicotine Exposure on Neural Development fashion, indicated by the increased activity of ornithine decarboxylase, an enzymatic marker for cellular maturation of brain cells (Slotkin et al., 1987b).  1.3.2  Cell Proliferation and Cell Death  The ability of nicotine to mimic ACh through nAChR activation, likely explains the effects prenatal nicotine has on cell density, organization, and synaptic connectivity. Early reports first suggested that prenatal nicotine exposure may lead to cell death from the observed overall decrease in DNA content at birth in the brains of offspring exposed to nicotine throughout gestation (Slotkin et al., 1986). More recently, reports have shown that prenatal nicotine exposure is able to reduce neuronal number, decrease cell size, and induce apoptosis in a region-specific manner (Slotkin et al., 1987b; Roy & Sabherwal, 1994; Roy et al., 1998, 2002; Huang et al., 2007). Since the nAChRs display distinct spatial and temporal expression patterns, it is postulated that various brain regions are differentially sensitive to nicotine exposure during development. For example, in the hippocampus, nicotine increases heteromeric nAChR binding, decreases neuronal soma size, and increases packing density in both granule and pyramidal cell types (Roy & Sabherwal, 1998; Roy et al., 2002). Considering neuronal loss is the most severe result of an insult to the brain, it is so expected to have a considerable impact on brain functions.  1.3.3  Neurotransmitter Systems  In addition to nicotine’s effects on nAChR expression, studies have established that early nicotine exposure is also able to modulate various neurotransmitter systems. Indeed, prenatal nicotine exposure leads to alterations in the central catecholamine systems (Ribary & Lichtensteiger, 1989). It has been reported that prenatal nicotine exposure reduces tyrosine hydroxylase measures in the corpus striatum as well as reduces dopamine D2 receptor binding in the striatal terminal regions of hyperactive, prenatal nicotine-treated rats (Carr et al., 1985; Fung & Lau, 1989; Richardson & Tizabi, 1994). Furthermore it appears that prenatal nicotine exposure exerts an effect on dopamine content and/or turnover such that the direction of the effect is region-specific. For example, in the cortex, prenatal nicotine decreases dopamine turnover, resembling dopaminergic hypofunction (Navarro et al., 1988; Muneoka et al., 1997). By contrast prenatal nicotine results in increased dopamine synthesis, increased dopamine turnover, and decreased dopamine content in the striatum, resembling presynaptic hyperactivity of the dopamine pathway (Richardson & Tizabi, 1994; Slotkin & Seidler, 2010). In like manner, prenatal nicotine exposure was shown to attenuate nicotine-induced nucleus accumbens dopamine release in adolescent rats (Kane et al., 2004). Studies have reported similar effects of gestational nicotine on the serotonin system. Nevertheless the effects of nicotine on the serotonin system appear more variable dependent on the time of nicotine exposure, brain region, and sex. For example, in male rats, prenatal nicotine exposure  6  1.4. Effects of Early Nicotine Exposure on Neurobehavioral Phenotypes resulted in the persistent upregulation of serotonin receptors 1A (5-HT1A) and 5- HT2 as well as the upregulation of the serotonin transporter (SERT) (Slotkin & Seidler, 2010). By contrast females only showed a downregulation in the expression of the 5-HT1A receptor. Nevertheless both sexes demonstrated deficits in the concentration of serotonin and increased serotonin turnover, resembling presynaptic hyperactivity of the serotonin pathway. Other studies have similarly reported sex-dependent effects of prenatal nicotine on the serotonin system, in which males showed greater sensitivity to nicotine (Slotkin et al., 2007a). The norepinephrine neurotransmitter system is also sensitive to the effects of prenatal nicotine. This is evidenced by the increased norepinephrine content and turnover in the forebrain of rats exposed prenatally to nicotine (Ribary & Lichtensteiger, 1989). Furthermore, prenatal nicotine exposure increased nicotine-induced norepinephrine release in parietal cortical slices taken from pups after birth (Leslie et al., 2006), which suggests that early nicotine exposure may sensitize nAChRs on norepinephrine terminals. Finally early nicotine exposure has been associated with alterations in the electrophysiological properties of the hippocampus. Vaglenova et al. (2008) reported that prenatal nicotine exposure impairs AMPA receptor synaptic transmission (the major mediators of excitatory neurotransmission in the hippocampus) by affecting pre- and postsynaptic terminals. This was evidenced by both decreased frequency and amplitude of excitatory postsynaptic responses (mEPSCs) mediated by AMPA receptors. More recently, Damborsky et al. (2012) used field potential recordings from hippocampal slices to examine gross changes in hippocampal electrophysiological responses in adult rats exposed to nicotine during the first postnatal week. They reported increased excitation within the CA1 hippocampus in adulthood. While the ability of nicotine to alter electrophysiological properties of the hippocampus remains unclear, it has been shown that nicotine exposure during development stimulates the release of GABA and alters glutamatergic transmission (Maggi et al., 2001;  Le Magueresse et al., 2006). For example, in the developing hippocampus, ❛7-containing nAChRs have been reported to activate ‘silent’ synapses leading to high probability synapses, which in turn lay the foundation for adult hippocampal circuitry (Maggi et al., 2003). These studies provide evidence suggesting that early nicotine exposure may have the ability to alter adult hippocampal neurocircuitry.  1.4  Effects of Early Nicotine Exposure on Neurobehavioral Phenotypes  Prenatal nicotine exposure has numerous effects on the CNS, from alterations in regional morphology to changes in various neurotransmitter systems. Many of these changes conform to the numerous behavioural disorders associated with maternal smoking. Below is a summary of three common neurobehavioural phenotypes associated with early nicotine exposure. 7  1.4. Effects of Early Nicotine Exposure on Neurobehavioral Phenotypes  1.4.1  Addiction  The reinforcing effects of drugs of abuse, including nicotine, have traditionally been associated with dopamine release (Di Chiara & Imperato, 1988). In fact, all drugs of abuse increase the extracellular dopamine concentration in the nucleus accumbens (NAcc) of the striatum, a key component of the limbic system, influencing motivational, emotional, and affective behaviour (Di Chiara & Imperato, 1988; Pierce & Kumaresan, 2006). Exposure to nicotine during early development has an immense impact on normal development of the monoamine neurotransmitter systems, which in turn are central to both natural and drug reinforcement. Maternal smoking is associated with increased risk for substance misuse in adolescence (Kandel et al., 1994; Buka et al., 2003). In fact it has been reported that children exposed to maternal smoke during gestation are twice as likely to initiate smoking during adolescents and to develop dependence compared to non-exposed children (O Callaghan et al., 2009). Behavioural analyses following prenatal nicotine exposure have shown phenotypes that are associated with alterations in dopamine transmission, namely changes in locomotor activity and rewardbased learning and addiction. Specifically rodent models have shown increased spontaneous motor activity (Pauly et al., 2004; Vaglenova et al., 2004; Paz et al., 2007). Moreover, animal models of prenatal nicotine exposure show increased nicotine- and cocaine-mediated behavioural sensitization, defined as heightened locomotor sensitization upon re-administration of either nicotine or cocaine, which lasted into adulthood (Sobrian et al., 2008). Additionally, Paz et al. (2007) reported that cocaine-induced place preference is augmented in mice treated prenatally to nicotine, suggesting heightened motivation to the rewarding properties of cocaine. As was discussed above, prenatal nicotine exposure reduces nicotine-induced dopamine release and similarly reduces high-affinity nAChR binding in dopaminergic brain regions in adolescent rats, which may influence adolescent vulnerability to nicotine dependence. Collectively these data suggest that prenatal nicotine is able to alter the neural circuitry underlying addiction, likely explaining the increased risk for substance misuse during adolescence.  1.4.2  Cognition  Cognition refers to attention as well as learning and memory (Baddeley & Hitch, 1993). Many brain areas are integral to the multiple memory systems in the brain, each of which appear to handle a specialized set of problems that cannot be easily handled by another system. While the process of cognition is complex involving many brain structures and systems, it is clear that the hippocampus plays a critical role in the formation of long-term memories (Penfield & Milner, 1958) and is also critically involved in integrating and processing spatial and contextual information (Rudy & O Reilly, 1999; Burgess et al., 2002). In the hippocampus glutamatergic synaptic transmission acts as the major excitatory neurotransmission and plays an integral role in synaptic plasticity, which  8  1.4. Effects of Early Nicotine Exposure on Neurobehavioral Phenotypes appears to be fundamental to learning and memory (reviewed by Miyamoto, 2006). The expression profile of nAChRs in the hippocampus supports the likely influence of nicotine on hippocampal functions. Depending on whether the nAChRs are expressed at dendritic, somal, axonal, presynaptic or postsynaptic sites, nicotine can have variable effects. For example, postsynaptic nAChRs can influence synaptic plasticity by causing an increase in intracellular Ca2+ concentration, which helps alleviate the Mg2+ block of postsynaptic NMDA receptors (glutamate receptors). Moreover, presynaptic nAChR can mediate increases in glutamate release at hippocampal CA1 synapses, which is also known to contribute to the development of synaptic plasticity (Ge & Dani, 2005). Reports have shown that smoking during pregnancy affects the children’s mental development (Naeye & Peters, 1984; Fried et al., 1992; Batstra et al., 2003). Studies reporting altered hippocampal excitability following pre- and perinatal nicotine exposure lend support to the ability of early nicotine exposure to induce cognitive deficits (see Section 1.3.3). Animal studies examining aspects of cognition using various standardized behavioural tests further support the ability of early nicotine exposure to induce cognitive deficits. For example, Vaglenova et al. (2004) reported that prenatal nicotine exposure produced deficits in a two-way active avoidance paradigm in adolescent rats. A follow-up study revealed that the decreased ability to learn and remember a two-way active avoidance task following prenatal nicotine treatment persisted in both sexs until early adulthood (PN60), and was still present in males at the age of 6 months (Vaglenova et al., 2008). Similarly there have been reports that prenatal nicotine exposure reduces the performance in spatial memory tasks (Peters & Ngan, 1982; Sorenson et al., 1991; Levin et al., 1993, 1996; Eppolito & Smith, 2006). It should be noted however that the reports demonstrating deficits in spatial memory show subtle effects that often were dependent on sex or behavioural challenge. For example both Peters & Ngan (1982) and Eppolito & Smith (2006) reported that females, but not males, were affected by prenatal nicotine exposure on a spatial task (food maze task and Morris water maze, respectively). Likewise, Levin et al. (1996) reported a significant choice accuracy deficit in the radial arm maze task from prenatal nicotine exposure only when the testing location was changed from the training location. Collectively, behavioural analyses support the association between prenatal nicotine exposure and cognitive deficits, and similar to human studies, represent only subtle alterations.  1.4.3  Attention-Deficit Hyperactivity Disorder  ADHD is characterized by developmentally and impairing levels of inattentive, hyperactive, and impulsive behaviours (Kuntsi et al., 2006). Numerous studies have found reduced brain volume in ADHD patients, particularly prefrontal cortex, cerebellum, corpus callosum, and basal ganglia (Castellanos et al., 2002; Hill et al., 2003). Currently the aetiology underlying ADHD remains unknown, but is postulated that it is a complex disorder caused in part by the actions of multiple genes of relatively small effect. Moreover evidence supports that ADHD results from complex 9  1.5. Inbred Mouse Models to Study the Genetics of Early Nicotine Exposure interactions between genetic and environmental factors (Faraone & Doyle, 2001). Perhaps one of the strongest associations is between maternal tobacco smoking and ADHD. Studies reporting enhanced locomotor activity in animals prenatally exposed to nicotine support the association of ADHD with prenatal exposure to nicotine (Pauly et al., 2004; Vaglenova et al., 2004; Paz et al., 2007), Moreover the observation of cognitive deficits in animals prenatally exposed to nicotine lend additional weight to this association. Smoking during pregnancy fundamentally alters the environment in which the foetus develops, increasing the risk for diagnosis of ADHD, which appears to vary as a function of genetic factors. Neuman et al. (2007) reported that amongst children exposed to maternal smoking during pregnancy, those with both the D4 dopamine receptor (DRD4) 7-repeat allele and the 3´ variable number of tandem repeats (VNTR) 440 allele of the dopamine transporter 1 (DAT1) showed a higher risk for diagnosis of ADHD. Taken together, there is strong evidence that gene-environment interactions modulate the risk for ADHD. Genetic variation within genes involved in the regulation of catecholaminergic neurotransmission, particularly the dopamine system, seem to play a role in the development of ADHD. Perhaps the ability of early nicotine exposure to alter this pathway mediates this gene-environment interaction, thus modulating the risk for ADHD.  1.5  Inbred Mouse Models to Study the Genetics of Early Nicotine Exposure  To address the mechanisms that underlie the neurobehavioral phenotypes associated with maternal tobacco and/or nicotine exposure, preclinical animal models are required. When both environmental and genetic influences regulate the measured outcome, the mouse has proven to be a superb model in which to investigate the genetic basis for quantitative differences in complex behaviours (Wehner et al., 2001). The outcomes resulting from prenatal nicotine exposure indeed depend on many factors, including genotype, and therefore the mouse is an exceptional model for such studies. In particular, inbred mouse strains provide an exceptional research tool to study general principles pertaining to gene-by-environment relationships. By definition, inbred mouse strains represent a genetically identical population, homozygous at each gene locus (Beck et al., 2000). An inbred strain is produced by at least 20 consecutive generations of sister x brother or parent x offspring mating and can be traced to a single ancestral pair in the 20th or subsequent generations (PuglisiAllegra & Cabib, 1997).Therefore, each member of a given inbred mouse strain share a common genetic background. Differences in phenotypes between two inbred strains indicate that genetic background influences such traits. C57BL/6J (B6) and DBA/2J (D2) mice are two examples of such inbred mouse strains that have been inbred for over 200 generations and are among the most widely used mouse strains in genetic research (McNamara et al., 2009). B6 and D2 mouse strains differ at approximately 10  1.6. Factors Influencing Phenotypes Caused by Early Nicotine Exposure 1.8 million single-nucleotide polymorphisms (SNP), which corresponds to an average of 1 SNP every 1,500 base pairs (Chesler et al., 2004). Moreover, these two inbred mouse strains respond markedly different to nicotine exposure as adults (discussed below). Finally, the B6 and the D2 strains are the progenitor strains for the BXD recombinant inbred (RI) panel, which may be useful to identify quantitative trait loci (QTL). Behavioural, pharmacological, and physiological comparisons between these two inbred strains represent a preliminary stage for deeper genetic research, such as QTL analysis, to identify and map genes in mouse associated with nicotine-related behaviours.  1.6  Factors Influencing Phenotypes Caused by Early Nicotine Exposure  Early nicotine exposure is associated to physical, behavioural, and neurochemical abnormalities in offspring. While this is evident in both human populations and animal models of prenatal nicotine exposure, the measured outcomes demonstrate a large degree of heterogeneity. For example, not all infants born to mothers who smoked throughout pregnancy develop apparent deficits. Why some individuals are more vulnerable to the effects of a particular drug may arise from genetic variations or from environmental stimuli or a combination thereof. This suggests that modifying factors, potentially genotype, sex, periods of exposure, and dose are relevant to phenotype expression.  1.6.1  Genotype  It appears the neuroteratogenicity of nicotine is mediated by complex gene-environment interactions. Genetic background contributes to individual differences in nicotine-related phenotypes. Perhaps the most compelling evidence illustrating the complex interaction of genotype and prenatal nicotine is in reference to ADHD (see Section 1.4.3). Other studies have found an association between maternal genotype and the effects of smoking on birth weight and the prevalence of SIDS (Wang et al., 2002; Poetsch et al., 2010). These findings indicate a genetic interaction with the prenatal environment, such that some infants may be more vulnerable than others to nicotine’s actions. Indeed, early drug exposure is both driven by and interacts with genetic background (Goldman et al., 2005). Animal models support the influence of genotype on nicotine-related phenotypes (Marks et al., 1989b). Studies performed in adult B6 and D2 inbred mice have reported genetic control of sensitivity to nicotine for locomotor behaviour and regulation of body temperature (Marks et al., 1983). More extensive studies revealed that genetic factors influence variation in nicotine-induced changes in respiratory rate, heart rate, activity measures in the Y-maze, plus maze, acoustic startle, antinociception, and seizure susceptibility (Tepper et al., 1979; Marks et al., 1985; Collins et al., 1988; Marks et al., 1989a; Jackson et al., 2009). Data reveals that B6 and D2 mice differ in nicotine oral  11  1.6. Factors Influencing Phenotypes Caused by Early Nicotine Exposure self-selection, sensitivity to a first dose of nicotine, and in the development of tolerance (Marks et al., 1989b; Robinson et al., 1996; Grabus et al., 2006; Jackson et al., 2009). In general adult B6 mice display higher sensitivity to the acute effects of nicotine (established from Y-maze and body temperature measures), enhanced nicotine oral self-selection, and a greater tolerance to nicotine compared to adult D2 mice. Since B6 and D2 mice similarly metabolize nicotine, these differences in behavioural responses to nicotine do not arise from differences in brain levels of nicotine (Siu & Tyndale, 2007). Collectively, this data strongly suggests that genetic background has profound effects on nicotine-related phenotypes in adults. Precise genetic polymorphisms contributing to nicotine-related behaviours remain largely unknown. Nevertheless B6 and D2 mouse strains have shed light on the genetic bases underlying the adverse outcomes associated to prenatal nicotine exposure. Selective genetic differences between B6 and D2 inbred mouse strains have been found. For example, there is a T529A polymorphism in Chrna4 gene encoding the ❛4 nAChR subunit (Dobelis et al., 2002). B6 mice carry the Chrna4-  threonine allele whereas D2 mice carry the Chrna4-alanine allele. Although this point mutation does not alter the binding properties, SNPs in genes that encode nAChRs are likely to modulate the effects of nicotine considering the effects of nicotine are primarily mediated by its actions on nAChRs. Accordingly, Wilking et al. (2010) demonstrated that Chrna4 A529 knock-in mice exhibit heightened nicotine sensitivity. Data further indicates that nAChR expression and sensitivity contribute  to nicotine responses. For example, D2 mice have abnormally low numbers of ❛7 nAChRs in the hippocampus, relative to other inbred mouse strains, including B6 mice (Stevens et al., 1996). There are also multiple differences among mouse strains in the cellular protein expression of nAChR❛4  between subregions of the hippocampal field (Gahring & Rogers, 2008). This may be relevant to differences in nicotine-related cognitive phenotypes. Nevertheless there are clearly additional contributing genetic factors beyond differences in nAChR sensitivity and expression (Marks et al., 1983).  1.6.2  Sex  A number of sexually dimorphic behavioural and neurochemical responses to prenatal nicotine exposure have been reported. sex differences in measured outcomes of prenatal nicotine exposure have been reported for locomotor activity (Schlumpf et al., 1988; Shacka et al., 1997; Pauly et al., 2004; Vaglenova et al., 2004), preference for sweet drinking water (Lichtensteiger & Schlumpf, 1985), sexual behaviour (Peters & Tang, 1982; Lichtensteiger & Schlumpf, 1985), nicotine preference during periadolescence (Klein et al., 2003), cognition (Peters & Ngan, 1982; Eppolito & Smith, 2006; Vaglenova et al., 2008) as well as central cholinergic and monoaminergic systems (Fung & Lau, 1989; Ribary & Lichtensteiger, 1989; Slotkin et al., 2007a; Slotkin & Seidler, 2010). In general, these findings suggest that males are more sensitive to the effects of prenatal nicotine. However there are still other studies that report deficits from gestational nicotine exposure are generally more 12  1.6. Factors Influencing Phenotypes Caused by Early Nicotine Exposure severe in female offspring based on performance in a food maze task and Morris water maze (Peters & Ngan, 1982; Eppolito & Smith, 2006). The discrepancies between these studies may be due to variations in experimental design. For example nicotine dose, route of administration, timing of maternal nicotine exposure, differences in testing schedules, and the strain/species of the model system may interact with the offspring’s sex to predict the measured outcome from prenatal nicotine exposure. More importantly, the sex differences may be attributed to differences in the behavioural tasks used, or in the molecular targets measured. Regardless, developmental effects of nicotine exposure appear to differ significantly between sexes; causal mechanisms remain to be elucidated.  1.6.3  Temporal Vulnerability  Timing of Nicotine Exposure The timing of nicotine exposure is a key risk factor that influences the type or extent of deficit. In the rodent, the first critical period of development, from the 5th to 11th day of gestation (equivalent to human first trimester), is defined as the time when organogenesis occurs and the neural tube and crest are formed (Bayer et al., 1993; Quinn, 2005). The second critical period in the rodent (11th to 18th days of gestation) is a developmental stage when most of the areas of the nervous system are differentiating. Additionally, neuronal generation and migration occur in some areas of the brain (i.e., cerebral cortex, hippocampus). The last critical period of development, is commonly referred to as the “brain growth spurt”. It occurs postnatally in the rodent (from G18 to PN10) and is comparable to the third trimester of human gestation (Dobbing, 1971; Quinn, 2005). The brain growth spurt is defined by a major increase in the brain weight and is characterized by proliferation of astroglial and oligodendroglial cells, dendritic and axonal growth, synapse production, neuronal and synaptic pruning, and changes in neurotransmitter sensitivity (Dobbing, 1971; Dobbing & Sands, 1979). In rodents, neurogenesis, migration, and differentiation are still taking place in select brain regions during the brain growth spurt. For example, while the pyramidal cells in the hippocampus are generated prenatally, granule cells in the dentate gyrus are born during the first postnatal weeks (Bayer & Altman, 1995). Nicotine exposure through development likely interferes with nervous system development. Functional nAChRs are detected as early as G10 in foetal mouse cerebral cortex (Atluri et al., 2001), which coincides to the period when the cortex consists of dividing stem cells and progenitor cells. This time course supports the plausible mechanism that early nicotine exposure provides a premature signal to terminate cell division and initiate neuronal differentiation (Slotkin, 1998). Beyond the onset of nAChR expression, it is important to consider the different temporal expression profiles of nAChRs across various brain regions (see Section 1.2.2). The ability for nicotine to influence various brain functions depends on the timing of nicotine exposure, the maturation timescale of distinct brain regions, as well as the spatial and temporal expression of nAChRs. Thus, developmental  13  1.6. Factors Influencing Phenotypes Caused by Early Nicotine Exposure nicotine could have varying effects in different brain regions, depending on the developmental time course of nicotine exposure. Timing of Testing Schedule Mounting evidence indicates that nicotinic action during early development alters the cholinergic system, leading to permanent alterations in the cholinergic as well as catecholaminergic neurotransmitter systems (Shacka & Robinson, 1998; Slotkin, 2004; Slotkin et al., 2007c,b; Slotkin & Seidler, 2010). However until recently, most studies have failed to examine the persistent effects of prenatal nicotine, lasting beyond the time of exposure. Studies that have looked at both immediate and long-term time points following prenatal nicotine exposure indicate that the effects are indeed long-lasting, and at times, are present only as a long-term effect (Eppolito et al., 2010). It is thus postulated that discrepancies reported in the literature about the effects of prenatal nicotine on measured outcomes may, in part, be attributed to differences in the testing schedules. Besides immediate and persistent effects of prenatal nicotine exposure, age-dependent differences between adults and adolescents have been previously reported (Sobrian et al., 2003; Pauly et al., 2004; Eppolito et al., 2010). For example, developmental nicotine exposure was found to have an anxiogenic effect on adult rats (PN75), yet have no effect on anxiety-like behaviours in adolescent animals (PN30) (Eppolito et al., 2010). There are many neural and endocrine changes occurring during adolescence that may contribute to differences in responsiveness. For a thorough investigation of prenatal nicotine exposure, it is most desirable to evaluate the effects of prenatal nicotine exposure across different developmental stages.  1.6.4  Dose of Nicotine  In the human population, each smoker has an individual comfort level for nicotine and consequently unique peak nicotine plasma levels. Differences in the mother’s exposure may influence the effects of nicotine on the foetus. When animal models are utilized to examine the effects of nicotine exposure, the selection of nicotine dose is integral to the experimental design. Plasma nicotine levels in animals that are orders of magnitude greater than those achieved in humans reveal a poor experimental design. The dose used for any given experiment, is intended to mimic human tobacco exposure through cigarette smoking or nicotine replacement therapies. An average cigarette delivers approximately 10-30 ♠g/kg, typically resulting in 10-50 ng/ml peak plasma levels (Matta et al.,  2007). The majority of American women who report smoking during their pregnancies report smoking one pack or less per day, resulting in 0.3 mg/kg nicotine daily (Benowitz & Jacob, 1984; Martin et al., 2005). Nicotine levels in the breast milk of a smoker (or animal model) are ~ two- or threetimes that in plasma (~100ng/ml) due to partitioning of nicotine into the favourable high-lipid, more acidic milk medium (Luck & Nau, 1984). These values must be considered when designing a dose  14  1.7. Methods of Nicotine Administration to the Developing Mouse regimen for animal models of prenatal nicotine exposure. There are considerable differences in nicotine metabolism across species. The half-life of nicotine in the mouse is approximately 7–10 minutes (Petersen et al., 1984), compared to approximately 120 minutes in humans (Benowitz et al., 1982). Generally the mouse requires higher doses to elicit plasma nicotine levels that are comparable to human levels. Using a concentration of 200 ♠g/ml  nicotine in the dams drinking solution, studies have reported plasma cotinine levels similar to those reported in habitual human smokers, without being toxic to the developing foetus (Pauly et al., 2004; Matta et al., 2007).  1.7  Methods of Nicotine Administration to the Developing Mouse  Developing an animal model that closely parallels human cigarette use has been difficult. Three approaches for nicotine delivery have been widely used in rodent models. The first commonly used route for nicotine delivery is subcutaneous (s.c.) injections. When nicotine is administered subcutaneously, it is rapidly absorbed, producing peak plasma levels that exceed the threshold for foetal hypoxia-ischemia (McFarland et al., 1991; Slotkin, 1992). Moreover s.c. injections elevate glucocorticoid levels, introducing the possibility that any results of nicotine treatment may be confounded by the stress of the injection (Slotkin, 1998). The second approach used is a subcutaneously implanted osmotic mini-pump, which administers nicotine on a continuous basis, and thus avoids hypoxia-ischemia (Slotkin et al., 1987b). The advantage of this method is that steady-state plasma levels can be determined in order to adjust the dose rate to mimic human exposure levels. However this route of administration poses stress on the animal as a result of the surgery required for the implantation of the mini-pump. Additionally, the steady-state delivery of nicotine does not relate to human use nor does it account for the animals increase in weight over the course of the experiment. This is especially a concern when the experiment occurs throughout pregnancy. Thus the mean dose is calculated over the course of the experiment, resulting in a lower dose at the end of the study than at the beginning (Matta et al., 2007). To-date the best mechanism for delivering nicotine to pregnant dams is the oral administration paradigm whereby nicotine is delivered through drinking water (Pauly et al., 2004). Chronic oral administration has been used successfully to demonstrate dependence (withdrawal) and tolerance (Grabus et al., 2005), altered locomotor activity (Pauly et al., 2004) as well as alteration in signalling pathways (Brunzell et al., 2003). This method of nicotine delivery is most suitable for this study because it avoids any additional stress from repeated injections or surgery and also avoids the risk of hypoxic-ischemic episodes. Moreover this delivery paradigm most closely mimics typical human smoking patterns whereby plasma nicotine levels increase and decrease with each cigarette and thus takes into account the wake/sleep cycle and individual variability in tobacco consumption. For our 15  1.7. Methods of Nicotine Administration to the Developing Mouse purposes, this method has the additional advantage that the duration of nicotine exposure can be initiated prior to mating, in order to avoid any confounding effects of tolerance at the beginning of exposure. Nevertheless this method of administration has its own caveats. The precise control of dose is not feasible using this method. Additionally, nicotine delivered through the animal’s drinking water reduces fluid intake compared to the water controls (Pauly et al., 2004). However there is no reported decrease in mouse maternal weight (Klein et al., 2003; Pauly et al., 2004). In the majority of animal studies, nicotine treatment extends throughout gestation. However in the rodent, brain development during the first two postnatal weeks of life corresponds to the third trimester of human pregnancy (Figure 1.1). The postnatal period between PN0 and PN10 approximately coincides with the brain growth spurt, a time of rapid CNS growth and proliferation, which reflects a time of enhanced vulnerability to environmental exposures (Dobbing, 1971; Dobbing & Sands, 1979). As such, a complete rodent model of the gestational effects of nicotine must incorporate exposure to the drug during the first two postnatal weeks.  Figure 1.1: Age correlates for human and mouse from gestation to adulthood (Spear, 2000) The distinct timing of the brain growth spurt across different species must be acknowledged when any attempt is made to extrapolate results obtained in one species to any other. In other words, nicotine administration in the mouse must continue during the first two postnatal weeks of life, which includes the third trimester equivalent of human pregnancy. Studies have shown that nicotine is excreted into maternal milk, and as such, suckling offspring would be exposed to nicotine during the pre-weaning period, providing a methodology for perinatal exposure to the mouse pup (Luck & Nau, 1984). Rodent studies using this design have shown that the level of nicotine excreted through maternal milk was sufficient to induce upregulation of nAChRs, with the potential to alter long-term the functioning of synaptic activity and neuronal development (Narayanan et al., 2002; Chen et al., 2005). Additionally, Heath et al. (2010) performed a comprehensive evaluation on the effect of nicotine administration on maternal behaviour to show that there was no significant change 16  1.8. Behavioural Approaches for Early Nicotine Exposure and Genetic Studies in maternal care giving behaviour from nicotine exposure. One potential confound of studies using maternal nicotine exposure throughout the third trimester equivalent of human pregnancy is that nicotine withdrawal following treatment cessation could alter maternal behaviour. Such changes could lead to persistent behavioural alterations in the offspring, independent of the neuropharmacological effects of prenatal nicotine exposure. Extending nicotine treatment until the pups are weaned eliminates the potential confounding effects of nicotine withdrawal. Several previous animal studies used drinking solutions that were sweetened with 2% saccharin to increase the palatability of oral nicotine. While the addition of saccharin increases the total volume of fluid consumed across all mouse strains that have been tested to-date (Robinson et al., 1996), adding saccharin to drinking solutions may not be suitable for all studies examining the effects of nicotine exposure. For example, genetic studies comparing the response of inbred mouse strains to nicotine exposure should take caution when adding sweetener to the drinking solutions because it has been well documented that mouse strains differ in their avidity to consume sweet solutions (Pelz et al., 1973; Stockton & Whitney, 1974; Ramirez & Fuller, 1976; Lush, 1989). For our purposes, no sweetener was added to the drinking solutions.  1.8  Behavioural Approaches for Early Nicotine Exposure and Genetic Studies  Animal studies have identified low birth weight, alterations in locomotor activity, anxiety-like behaviour, and deficits in cognitive function that closely parallel the negative effects seen in humans (Levin et al., 1993, 1996; Pauly et al., 2004; Vaglenova et al., 2004, 2008; Eppolito et al., 2010). These studies have used behavioural paradigms developed and validated for the rodent, to evaluate various aspects of brain function that are sensitive to the effects of maternal smoking in humans. The behavioural tests used in this study are summarized in Table 1.1. Test  Measurements  Open Field  Total Distance, Time Spent in  Locomotor Activity,  the Center  Anxiety-Like Behaviours  Time Spent Exploring Familiar  Object Recognition Memory  Novel Object Recognition  Models Human Behaviour  and Novel Object Elevated Plus Maze  Time Spent in the Open Arms,  Anxiety-Like Behaviours  Number of Open Arm Entries Passive Avoidance  Retention Latency to Enter  Associative Fear Learning  Dark Chamber  Table 1.1: Correlates of mouse and human behaviour  17  1.8. Behavioural Approaches for Early Nicotine Exposure and Genetic Studies  1.8.1  Open Field  Analysis of rodent locomotor activity in an open field (OF) was first described by Hall as a test of emotionality (Hall, 1934). The procedure consists of subjecting an animal to an unknown environment from which escape is prevented by surrounding walls (Walsh & Cummins, 1976). This mildly stressful environment results in a conflicting desire for the animal to either explore the unfamiliar area or to avoid the brightly lit, anxiogenic open space. Less emotionality is characterized by an animal that explores the space more freely and spends more time in the center of the arena. More recently open field activity has been used to measure not only anxiety-like behaviours, but also sedation or activity. An increase in central locomotion or in time spent in the central part of the device can be interpreted as an anxiolytic-like effect, while increased total locomotion can be considered a stimulant effect (Prut & Belzung, 2003). In general, enhanced locomotor activity has been shown to be associated with in utero nicotine exposure (Shacka et al., 1997; Ajarem & Ahmad, 1998; Pauly et al., 2004).  1.8.2  Elevated Plus Maze  The elevated plus maze (EPM) is considered the ‘gold standard’ for examining anxiety-like behaviour in rodents. It is a behavioural paradigm designed to model aspects of anxiety in humans, and is therefore used to assess anxiety-like behaviour in small laboratory rodents (Pellow et al., 1985). Briefly, the maze is elevated off the ground and consists of two open arms and two enclosed arms. Rodents are placed at the junction of the four arms of the maze, and the exploratory pattern is examined. An increase in open arm activity (duration and/or entries) is interpreted as anxiolytic behaviour. The EPM and OF activity can both be used to assess emotionality and therefore can strengthen each other’s results. Gestational nicotine exposure has been shown to modulate anxietylike behaviours (Picciotto et al., 2002; Sobrian et al., 2003; Vaglenova et al., 2004; Eppolito et al., 2010).  1.8.3  Novel Object Recognition  The novel object recognition (NOR) task for rodents is a non-spatial, non-aversive memory test, examining recognition memory (Ennaceur & Delacour, 1988). It involves the ability to discriminate between familiar and novel stimuli as an index of learning and memory (Aggleton, 1993). Ennaceur & Delacour (1988) reported that rats display a preference to investigate novel rather than familiar objects. This recognition necessarily requires intact memory of the previously experienced (familiar) object. Memory is assessed as the relative time spent by a subject exploring two objects, one being familiar and the other being new. Simply by manipulating the retention interval one can test short- (~2 minute retention interval), intermediate- (~4 hour retention interval), and long-term  18  1.8. Behavioural Approaches for Early Nicotine Exposure and Genetic Studies memory (>24 hour retention interval) (Stough et al., 2006). Relatively little is known about the effects of chronic nicotine on object recognition, especially when exposure occurs prenatally.  1.8.4  Passive Avoidance  The passive avoidance (PA) test is often implemented to examine associative fear learning (for review see Oegren, 1985). Fundamental to the PA test is that a memory from a single experience is a rapid form of learning that provides recollection of an event or place that is adaptive to the subject. In general, recollection is assessed by the subject’s ability to avoid a test environment where it previously received a noxious stimulus (i.e. foot shock). Manipulations of muscarinic and nicotinic cholinergic neurotransmission have been demonstrated to affect every aspect of aversive conditioning, indicating a role of cholinergic transmission in PA learning and memory (Tinsley et al., 2004).  1.8.5  Behavioural Battery  Testing across multiple domains of brain function is preferable because it provides a greater understanding into the depth of functions affected by nicotine exposure. However realistically there are limited numbers of mice available for testing. Use of a behavioural battery allows the researcher to test across various brain functions using the same set of subjects. Besides reducing the total number of mice needed, using the same set of mice across multiple behavioural tests has the additional advantage of yielding more confidence in any observable phenotypic differences because we are assessing overlapping CNS circuitry (McIlwain et al., 2001). A disadvantage of studying several behavioural responses in the same mouse is that there may be an effect of training history or test interval that influence the responses from one test to another. Previous studies have been conducted to assess whether the behavioural responses of mice are influenced by: the use of a test battery, test order, and test interval (McIlwain et al., 2001; Paylor et al., 2006). It was concluded that certain tests, such as the open field are sensitive to test order, while others are resistant, such as conditioned fear. Therefore test order is an important factor to consider a priori. Behavioural batteries should begin with tests that are sensitive to test order in order to relieve potential confounding factors. Moreover these studies revealed that there are no major differences in performance between mice that are subjected to a test battery with a 1-week inter-test interval or a 1-2 day inter-test interval, suggesting that there is little effect of test interval on behavioural response (Paylor et al., 2006). Use of a behavioural battery provides an elegant design to assess various aspects of cognition, emotionality, and locomotor function.  19  1.8. Behavioural Approaches for Early Nicotine Exposure and Genetic Studies  1.8.6  Brain Areas Supporting Behavioural Function  Proper functioning of the various behaviours examined requires various brain areas. For example, motor function, emotion, and cognition generally implicate the striatum, amygdala, and hippocampus, respectively (Alexander & DeLong, 1986; LeDoux, 2000; Kandel, 2001). However the neural circuitry underlying each of these processes, is not so simple, and several components comprising many brain regions are involved. Furthermore it is important to remember that cognition encompasses many different domains of functioning. Different types of learning and memory have different mechanisms involving different cerebral areas. We decided a priori to examine two different aspects of learning and memory, namely NOR and PA memory. To our knowledge, no published studies have explored the long-term consequences of early nicotine exposure on either of these memory tasks. Here we highlight the role of the striatum and hippocampus in the various behavioural tasks examined. Motor function relates closely to the motor loop of the basal ganglia. Here output from several cortical areas send projections to a restricted portion of the striatum (putamen), which in turn sends converging projections to the globus pallidus and SN (reviewed by Alexander & DeLong, 1986). These regions subsequently project to regions of the thalamus, which finally projects back to the cortical areas. Dopaminergic input to the striatum modify transmission through these basal ganglia thalamocortical pathways. It is evident therefore that the striatum is integral to motor function. Furthermore the hippocampus has been implicated in motor activity (Day et al., 1991; Fanselow & Dong, 2010). For example, cholinergic activity in both the hippocampus and striatum correlate with locomotor activity (Day et al., 1991; Avale et al., 2008). The neural circuit underlying negative emotion (i.e. anxiety-like behaviours) encompass a set of limbic structures including the amygdala and insula, as well as interconnected structures (Etkin & Wager, 2007). Nevertheless the hippocampus has been shown to play a role in negative emotion (Kjelstrup et al., 2002; Bannerman et al., 2004). In fact, the ventral hippocampus may have a preferential role in brain processes associated with anxiety-related behaviours. Ventral hippocampal lesion studies reported behavioural effects resembling those induced by benzodiazepines on anxietylike behavioural tasks, suggesting a reduction of anxiety (Bannerman et al., 2000, 2002; Kjelstrup et al., 2002). In particular, rats with ventral hippocampal lesions spent an increased duration in the anxiogenic open arms of the EPM (Kjelstrup et al., 2002). Ventral striatal neurons have also been associated to anxiety-related behaviours (Wickens et al., 2007). According to the American Psychiatric Association (2000), avoidance behaviour is a cardinal symptom of anxiety disorders. Avoidance, defined as a decision to sacrifice potential rewards to avoid potential negative outcomes, has implicated the ventral striatum, indicating that this region is involved in orienting an organism towards rewards (Beaufour et al., 2001; Wickens et al., 2007). It is apparent therefore that the neural circuitry underlying emotional processing requires multiple brain regions. 20  1.8. Behavioural Approaches for Early Nicotine Exposure and Genetic Studies Despite the widespread use of NOR in rodents to assess recognition memory, a consensus has not developed about which brain structures are important for task performance. There is agreement that the perirhinal cortex is critically important for normal NOR performance, and furthermore lesion studies in rodents generally support an involvement of the hippocampus when the retention interval exceeds 5 minutes (Dere et al., 2007; Winters et al., 2008; Broadbent et al., 2010). Additionally, the expression level of NMDA receptors in both the striatum and hippocampus has been related to visual recognition memory in rats, suggesting that the hippocampus as well as the striatum (a brain region typically associated with procedural memory and diverse forms of implicit memory) play a role in this form of memory (Xu et al., 2005). PA learning and memory requires various brain structures and neurotransmitter systems for proper functioning. The PA test requires Pavlovian conditioned associations between the context (conditioned stimulus (CS)) and shock (unconditioned stimulus (US)) to influence subsequent behaviour (reviewed by Tinsley et al., 2004). However the PA test is complicated further because the foot-shock acts both to condition the dark compartment as an aversive contextual CS through Pavlovian conditioning processes as well as to penalize entry into the dark compartment through operant conditioning processes. Consequently, such an instrumental response may influence the neural substrates involved. The brain regions involved in Pavlovian fear conditioning have been extensively examined and has been demonstrated to include the amygdala, hippocampus, and frontal cortex (reviewed by Fendt & Fanselow, 1999). While PA learning and Pavlovian fear conditioning are similar in regard to pairing a previously neutral stimulus with shock, the greater inherent complexity of PA in regards to the operant conditioning processes changes the neural networks involved. Studies support a different involvement of the amygdala between these two tests whereby the amygdala plays a greater role in Pavlovian fear conditioning (For review, Tinsley et al., 2004). A substantial body of literature supports the role of the hippocampus, dorsal striatum, and various cortical regions (i.e. prefrontal cortex, perirhinal cortex, medial septal nuclei, and entorhinal cortex) in PA learning and memory. It is believed that the hippocampus is involved in the formation of context representations in PA learning but is not involved in the aversive association. By contrast, a number of studies have shown a critical role for the dorsal striatum in the consolidation of aversive conditioning in PA learning. Early nicotine exposure has the potential to alter aversive fear conditioning given the fact that cholinergic neurotransmission modulates the functions of the amygdala, cortex, and hippocampus, each of which plays different roles in various forms of aversive conditioning.  21  1.9. Histological Approaches for Early Nicotine Exposure and Genetic Studies  1.9  Histological Approaches for Early Nicotine Exposure and Genetic Studies  Structural changes in the brain are expected to have profound effects. Even small perturbations in regional brain volume or neuronal number may lead to phenotypic variability. To-date studies have shown that nicotine is able to alter regional morphology in select brain regions (Section 1.3.2). However our understanding on which brain regions are vulnerable to structural alterations induced by early nicotine exposure is vague at best. Indeed there is an incomplete picture in regard to these structural alterations, and therefore research is required to examine morphological changes in various brain regions individually. In order to evaluate such structural changes, a reliable method is required. Structural changes are best defined by stereological method, which utilizes a set of mathematical formulas to describe the interaction between geometric probes and structural features (West, 2002).  1.9.1  Volume Estimates  Estimates of regional brain volumes can be performed under Cavalieri’s principle (Cavalieri, 1635). This states that the volume of any object (Vobj ) may be estimated by sectioning it into parallel planes of a constant separation and summing up the cross-sectional area of the object in each of the planes (❙a), multiplying this figure by the distance between the planes (t): Vobj = ∑ a ∗ t This method requires random sampling from cut sections at consistent intervals throughout the region of interest. Provided all sections through the region have an equal chance of being sampled, Cavalieri’s principle yields an unbiased estimate, unaffected by the shape of the object and the orientation of sectioning of the volume (Gundersen et al., 1988). Simply stated, regional volume estimates are performed on the basis that the volume of an object approximates to its mean crosssectional area multiplied by its height.  1.9.2  Cell Count Estimates  Recently the development of design-based stereology has greatly advanced morphologic studies of the CNS (West, 1999; Schmitz & Hof, 2005). The term “design-based” defines the a priori design of the probes and sampling schemes used, in order to achieve a method that is independent of the size, shape, spatial orientation, and spatial distribution of the cells under investigation (West, 2002). This eliminates the need for information on the geometry of the object. Using a three-dimensional counting probe (disector), there are no assumptions about the size, shape, or orientation of the objects being counted. Namely this method involves direct counting of objects in a known volume 22  1.9. Histological Approaches for Early Nicotine Exposure and Genetic Studies of tissue. Design-based stereology utilizes the optical fractionator technique to obtain unbiased sub-samples of large sections, given it is often not practical to count every neuron that comprises a particular brain region of interest. Here, thick sections are used to estimate the total number of cells sampled with a systematic randomly sampled set of unbiased counting spaces (optical disector probes) covering the entire region of interest with uniform distance between the probes in directions X, Y, and Z (West et al., 1991). To do so, optical disector counting uses an areal counting frame, focusing through the thickness of the section, to count objects located within the defined threedimensional space (Figure 1.2). An object is considered to be in an optical disector if it comes into focus within the unbiased counting frame, as one focuses through the section.  Figure 1.2: Schematic representation of the optical fractionator technique to estimate cell numbers. Three-dimensional probes (optical disectors) are positioned in a systematic manner with uniform distance between optical disector probes in directions X, Y, and Z (shown in the inset) on sections selected for analysis. For accurate estimates of cell populations using the optical fractionator technique two potential sources of biases must be controlled. First, tissue sectioning leads to cell fragmentation, which if counted, would bias cell counts. It is important to count cells only when their characteristic point is found within the optical disector. Second, tissue sectioning leads to the loss of neurons at the upper and lower surfaces of sections. It is important to introduce guard zones, used to avoid sampling near the upper and lower section edges. Typically only 100 - 2 00 neurons must be counted in an animal to obtain high precision estimates of neuronal populations (West et al., 1991). In summary, design-based stereological methods derive an estimate of the total number of neurons within the region of interest from the number of neurons in randomly sampled counting spaces in a series of systematically and randomly sampled sections throughout this defined region (Schmitz & Hof, 2005). The importance and significance of using unbiased stereology has been progressively recognized, especially amongst neuropathologists. For our purposes, it represents the most effective  23  1.10. Rationale and Thesis Objectives way to examine structural changes induced from pre- and perinatal nicotine exposure. The sampling scheme is summarized below. N=  ∑Q SSF ∗ ASF ∗ TSF  N : Estimate of the total number of objects in the defined region of interest  ∑ Q : Number of cells counted SSF : Section sampling fraction ASF : Area sampling fraction (frame size / grid size) TSF : Disector thickness sampling fraction of section thickness  1.10  Rationale and Thesis Objectives  Human and animal studies suggest that early nicotine exposure has lasting effects on development and that these effects may persist throughout the lifespan. Still, studies of in utero nicotine exposure in both human populations and animal models have demonstrated a great degree of heterogeneity in various measured outcomes. Such heterogeneity may relate to numerous factors, including the degree of exposure, sex, time of behavioural testing, and biological predisposition (genotype). There are currently no published animal studies examining how genetic variation interacts with pre- and perinatal nicotine exposure. Moreover, the majority of research conducted on prenatal nicotine exposure has neglected to consider possible sex differences; meanwhile the few studies that have accounted for sex have shown marked differences in the response to prenatal nicotine exposure between males and females. The main objective of this thesis was to investigate the influence of genotype on the immediate and long-lasting effects of pre- and perinatal nicotine exposure in both males and females. To this end, we examined morphological features of the striatum and hippocampus as well as behaviours that measure locomotor activity, anxiety-like behaviours, and learning and memory in male and female B6 and D2 inbred mouse strains after pre- and perinatal nicotine exposure. Preliminary data from this thesis evaluated the validity of our animal model system. Our experimental questions were: (1) Does pre- and perinatal nicotine exposure result in altered striatal- or hippocampal morphology, and is the effect dependent on either genotype or sex? (2) Does pre- and perinatal nicotine exposure result in altered locomotor activity, anxiety-like behaviour, or cognitive function, and are these effects dependent on genotype or sex? This study explored these questions at two distinct timeframes: (i) Directly following pre- and perinatal nicotine exposure to account for the immediate effects, and (ii) During adulthood to account for the long-lasting effects of prenatal and early post24  1.10. Rationale and Thesis Objectives natal nicotine exposure. We hypothesized that: (1) pre- and perinatal nicotine exposure would lead to decreases in striatal neuronal number and volume as well as decreases in hippocampal regional volumes, in a manner depenent on genotype and sex. (2) These and other neuropathologies induced by pre- and perinatal nicotine exposure would manifest behaviourally as increased locomotor activity, increased anxiety-like behaviour, and deficits in cognitive capacities, in a manner dependent on genotype and sex. We hypothesized that that males and B6 mice would be more sensitive to the effects of early nicotine compared to females and D2 mice, respectively. These hypotheses were based on the findings that males are generally more sensitive to the effects of nicotine on a number of behavioural measures (Shacka et al., 1997; Klein et al., 2003; Pauly et al., 2004; Slotkin et al., 2007a; Slotkin & Seidler, 2010); likewise, as adults, B6 mice are more sensitive to the effects of nicotine compared to D2 mice (See section 1.6.1) (Marks et al., 1985; Collins et al., 1988).  25  Chapter 2  General Methods 2.1  Animals and Breeding  B6 and D2 mouse strains were used for all studies. Twenty B6 and twenty D2 dams, bred at the University of British Columbia, Center for Molecular Medicine and Therapeutics, were individually housed under standard conditions 30 days prior to mating. Mice were maintained on a 12:12 hr light/dark cycle (lights on at 06:00 hr), with controlled temperature (21 - 22 °C). Mice had ad libitum access to standard lab chow and tap water either with or without 200 ♠g/ml nicotine (see  below). Dams in the nicotine group only had access to the nicotine-containing solution. Thirty days after being singly-housed, the dams were housed together with a male stud until a vaginal plug was detected. At the onset of pregnancy, mice ranged from PN75 to PN150. The day pups were born was considered PN0. In the nicotine group, exposure to nicotine solution was continuous up until the pups were weaned. Pups were kept with their mother until weaning on PN 24, at which time half the pups were taken for testing, and the remaining half were individually housed and provided with access to water and pellet food (described previously). The University of British Columbia Animal Care Committee approved all animal use procedures and principles of laboratory animal care were followed (Animal Care ID: A10-0272).  2.2  Drugs  (-)-Nicotine (freebase) was purchased from Sigma Chemicals (St. Louis, MO, USA). Freebase nicotine was provided to dams and offspring at a concentration of 200 ♠g/ml dissolved in water.  The control group had access to water. The mice received fresh nicotine-solution or water every four days. All doses are expressed as the free base of the drug.  2.3  Testing Schedule  Pups were kept with their birth mother until weaning on PN24, at which point half the pups from each litter (sex-matched) were selected for evaluation of the immediate effects from pre- and perinatal nicotine exposure. At the time of weaning, nicotine treatment was terminated in the nicotine group, and the remaining pups from the nicotine-treated and water control groups were housed under 26  2.4. Behavioural Testing Schedule standard conditions, given continuous access to food and water until they reached PN75, at which time they were selected for testing, to examine persistent effects from pre- and perinatal chronic nicotine.  2.4  Behavioural Testing Schedule  Behavioural testing was performed between 7 am and 4 pm. All behavioural tests, excluding the PA test, were performed in the same testing room. On each day of testing, the mice were transferred, in their home cages, from the holding room to the testing room 1 h prior to testing. Overhead incandescent light bulbs provided room lighting and four floor lamps positioned at the outer edge of four OF arenas provided illumination inside the test chamber. White noise was present in the testing room at approximately 55 dB. Following the 1 h acclimation, mice were removed from their home cage and transferred directly into the behavioural testing unit. Following each test the mice were placed either back into their home cages (empty), or if littermates remained in the home cage, into a holding cage until all littermates had been tested. A behavioural battery consisting of four behavioural tests was carried out over two days (Figure 2.1). The OF test was conducted first, which additionally served as the habituation trial for the object recognition task. The training-phase for NOR was conducted immediately following the OF test. Four hours later, the mice were reintroduced into the OF arena for the NOR test. The EPM was always performed in the afternoon, following the NOR test. The training day for passive avoidance was the last test on day one. The PA chambers were located in an adjacent testing room. Before beginning the PA test, the mice were transferred, in their home cages, into an acoustically insulated chamber located in the same room as the PA chambers to acclimate for 30 minutes. The next day, the animals were tested between 8 am and 12 pm for passive avoidance following 1 h acclimation in the acoustically insulated chamber.  2.5  Behavioural Tests  2.5.1  Open Field Activity  Spontaneous locomotor activity was monitored in an open field arena. Mice were placed in the center of a 50 cm x 50 cm x 20 cm arena. Thirty-minute trials were video-recorded by an overhead camera. The arena was cleaned with 70% ethanol at the beginning of testing and in between animals. Analysis was done using Noldus Ethovision XT software (Noldus Information Technology). Total distance travelled and time spent in the OF center was measured. For analysis of time spent in the center of the arena, a center zone was defined as a square covering 16% of the total arena area (20 cm x 20 cm central square).  27  2.5. Behavioural Tests  Figure 2.1: Behavioural test battery  2.5.2  Novel Object Recognition  After completion of open field activity, mice were reintroduced into the open field arenas for the assessment of novel object recognition. Two identical objects, 3 cm x 3 cm x 3 cm dice were placed in opposite (NW and SE) corners of the 50 cm x 50 cm x 20 cm arena (as mentioned above), approximately 10 cm from each wall, and fixed in place. Mice were placed in the center of the arena and allowed to explore the two objects for 10 min, after which they were returned to their home cage. Approximately 4 hours after the training trial, mice were returned to the arena, in which one of the objects had been replaced with a novel one, a red 3 cm x 3 cm x 3.5 cm duplo block, and again allowed to explore the objects for 5 minutes. The arena was cleaned with 70% ethanol at the beginning of testing and in between animals. Both trials were captured on video from an overhead camera, and analysis was performed using Ethovision software. Total time spent exploring each object was measured. An area of 1 cm surrounding the objects was delineated using Ethovision software, and object exploration was defined as a mouse having its nose directed toward the object within approximately 1 cm (Bevins & Besheer, 2006). The object zones were defined such that the animal moving around the arena near the wall was not detected in the object zone. Exploratory behaviour during testing was transformed into difference scores and preference ratios for comparisons across groups.  28  2.5. Behavioural Tests The difference score indicates the difference in time spent exploring the novel over the familiar: Difference score = Exploration time of novel object − Exploration time of familiar object The preference ratio was calculated and defined as: Preference ratio =  2.5.3  Exploration time of novel object Exploration of novel & familiar objects  Elevated Plus Maze  Following completion of the object recognition task, mice were introduced into the elevated plus maze. The apparatus was elevated 50 cm above the floor and constructed in a “plus” shape, having two open arms and two enclosed arms. A central platform connected the four opposing arms. The mice were placed in the center of the maze, facing an open arm. Animals were allowed to explore for 5 minutes. The trials were captured on video from an overhead camera. Mice were hand scored for total time spent in each arm as well as the number of entries into each arm via observation on a computer monitor using hand-held stopwatches. Animals were considered to have entered an arm when all four paws crossed into that arm. All analyses were scored by one experimenter, blind to the treatment group, to eliminate variability in behavioural scoring. At the conclusion of each test, animals were returned to their home cages or holding cage (described in section 2.4). The plus maze was cleaned with a 70% ethanol solution at the beginning of testing and in between animals.  2.5.4  Passive Avoidance  Mice were tested in the passive avoidance test using the GEMINITM Avoidance System (San Diego Instruments, San Diego, CA). The test apparatus contained two chambers, each 21 x 25.5 x 16.5 (height) cm, separated by a sliding door. Subjects were introduced into the first chamber, and a thirty second acclimation period was given prior to the beginning of the trial. At the trial onset, the first chamber was illuminated, an auditory stimulus was sound, and the door (5 x 5 cm) separating the two chambers opened. The mice were allowed to enter into the second (dark, quiet) chamber. The time it took for the mouse to enter the second chamber after the door opens was recorded. The maximum time allowed to enter the second chamber was 180 seconds. Once all four paws had entered the second chamber, the door closed and the mouse received a mild electrical foot shock (0.5 mA, 2 s) (unconditioned stimulus (US)). The mouse was again tested 18-24 hours later and the latency to enter the second chamber was recorded. On the testing day (day 2), no shock was delivered if/when the mouse entered the second chamber. The passive avoidance chambers were cleaned with a 70% ethanol solution at the beginning of testing and in between animals.  29  2.6. Tissue Collection and Processing  2.6  Tissue Collection and Processing  After behavioural tests, mice were anesthetised with an intraperitoneal injection of avertin (1.25% (w/v), 2,2,2-tribromoethanol in tert-amyl alcohol) at a dose of approximately 0.3 ml solution/ 10 g body weight. Subsequently, a cut was made along the rib cage to expose the heart, the lower left ventricle was cannulated, and the right atrium was cut to allow outflow of the blood and perfusion solutions. Mice were transcardially perfused with 0.1M phosphate-buffered saline (PBS) followed by 4% (v/v) paraformaldehyde fixative. The brains were carefully removed from the skull and post-fixed in 4% (v/v) paraformaldehyde solution for 1-2 hours. After the postfix, the brains were transferred into 0.1M PBS for an overnight incubation at 4˚C, followed by an overnight incubation in a cryoprotectant solution (30% (w/v) sucrose in 0.1M PBS) at 4˚C. Fixed brains were embedded in OCT compound in cryomolds and stored at -80˚C until use. Coronal whole-brains sections were cryostat cut at 50 µm thickness and directly mounted onto a microscope slide. Serially cut sections  were arranged into 6 sequential series (sections spaced 300 ♠m apart). Slides were stored at -20˚C until stained.  2.7  Immunohistochemistry  A single series of sections was selected for cresyl violet staining. Mounted sections were rinsed in distilled water and immersed in 0.5% cresyl violet for 30 minutes and rinsed again in distilled water. Slides were then decolourized and dehydrated in a graded series of ethanol solutions (50, 70, 95, 100, 100% for 1 min each), cleared in three consecutive baths of pure xylene (3x 2 min each), and coverslipped with Permount.  2.8 2.8.1  Stereology Striatum  Stereo Investigator software (Stereo Investigator 9.0, MicroBrightField Inc.,Williston, VT, USA) attached to a Zeiss Axioscop II microscope equipped with a Zeiss Axiocam (Carl Zeiss, Munich Germany) was used to calculate striatal volume and striatal neuronal numbers. Anatomical boundaries of the striatum were delineated manually using an x2.5 objective lens. The anterior boundary of the striatum was defined at the level in which the corpus callosum first crosses the midline (~ Bregma 1.10 mm), and the first appearance of the hippocampus defined the posterior boundary (~ Bregma -0.82 mm). Within this anterior-posterior boundary, the following criteria was used to delineate the striatum: the superior boundary was defined by the corpus callosum, the lateral boundary by the external capsule, the medial boundary by the lateral ventricle and the corpus callosum, and the ventral boundary by the anterior commissure. Measurements were performed on every sixth 30  2.8. Stereology Nissl-stained section in both hemispheres of the brain. Striatal volume estimates were extrapolated using Cavalieri’s principle (Section 1.9.1) (Gunderson et al., 1988). Unbiased estimates of the number of striatal neurons, N, were obtained by counting with the optical fractionator technique. The  optical disectors were systematically positioned on the selected sections at a distance of 500 ♠m in  each dimension of the section plane, i.e. grid size was set as 500 x 500 ♠m. For each optical dissec-  tor probe, striatal neurons were counted within a counting frame of 25 x 25 ♠m. Guard zones of 1.5  ♠m from either the top or bottom of the section were used to avoid uneven surface problems. Using these parameters, more than 300 cells were counted per subject, and the coefficient of error (CE Schaeffer) was <1 for all probe runs. The counted neurons (Q-) across all sections that spanned the striatum were then extrapolated to estimate the total number of neurons in the striatum (see Section 1.9.2). Cell counts were performed using an x63 oil immersion lens. The following criteria were  used to distinguish neurons from glial cells: a large mean cellular diameter (> 5 ♠m), light staining of the nucleus, and the presence of Nissl bodies (Figure 2.2). In contrast particles smaller in size, with a lack of stained cytoplasm and darker nuclei are characteristic of glial cells.  Figure 2.2: Representative Nissl-stained 50 µm striatal section from DBA/2J mouse at PN75. Image was captured using a 63x objective. Each neuron (yellow arrowhead) was counted. Glial cells (black arrow), defined by their smaller size, lack of stained cytoplasm (clear background) and darker nuclei, were not counted. Crescent-shaped cells are endothelial cells lining capillaries. Scalebar, 10 µm.  2.8.2  Hippocampus  Stereo Investigator software (Stereo Investigator 9.0, MicroBrightField Inc.,Williston, VT, USA) attached to a Zeiss Axioscop II microscope equipped with a Zeiss Axiocam (Carl Zeiss, Munich Germany) was used to calculate hippocampal volume. The CA1, CA2/CA3 regions, and the dentate gyrus were defined. Within the dentate gyrus and the Ammon’s horn subfields, only the volumes of the granule cell layer (GCL) and pyramidal cell layer (PCL) were included. The CA2/CA3 subfields were analysed together because it is difficult to limit exact boundaries between these two hippocampal subfields. Anatomical boundaries of the hippocampus were delineated manually using an x2.5 objective lens. The anterior boundary of the dentate gyrus was chosen as the section in which the hippocampal formation first appeared (~ Bregma -0.94 mm), and the posterior boundary 31  2.9. Statistical Analysis was defined as the section where the granule cell layer of the dentate gyrus forms a complete circle (~ Bregma –3.52 mm). The anterior boundary of the CA1 and CA2/CA3 was chosen as the section in which the dentate gyrus first bends medially (~ Bregma -1.34 mm), and the posterior boundary was defined as the section where the CA3 reaches the ventral surface (~ Bregma -2.80 mm). Within this anterior-posterior boundary, we traced the inner and outer cyto-architectonic layers of the hippocampus subfields based on mouse brain stereotaxic coordinates (Paxinos & Franklin, 2004). In particular we defined the granular cell layer of the dentate gyrus and the pyramidal cell layer of the CA1 and CA2/CA3 subfields. The border between the CA1 and CA2/CA3 subfields is recognized by the differences in the organization of pyramidal cell bodies. Measurements were performed on every sixth Nissl-stained section in both hemispheres of the brain. Hippocampal volume estimates were extrapolated using Cavalieri’s principle (Section 1.9.1). (Gundersen et al., 1988).  2.9  Statistical Analysis  Following data collection, all variables were evaluated using IBM SPSS Statistics 19 software (IBM SPSS Statistics, IBM, Chicago, IL). All variables were evaluated with Exploratory Data Analysis to test multivariate assumptions. Data were evaluated as means and standard errors of the mean (S.E.M.), considering each animal as an experimental subject. Maternal weight gain, offspring growth rates, and pre- and perinatal fluid intake were analyzed by repeated measures (RM) analysis of variance (ANOVA) with strain and treatment group as the between subjects factors. Litter size, pup mortality, and birth weight were analyzed by ANOVA with strain and treatment group as the between subjects factors. Open field, novel object recognition, elevated plus-maze, and stereological data were analyzed by ANOVA with strain, age, treatment, and sex as the between subjects factors. Where the initial test yielded a significant interaction, lower-order ANOVAs were conducted to separate the effects according to relevant interactive variables. Statistical analyses were terminated at the level of main effects in cases where the main effects were present without any significant interactions. When there was no significant effect or interaction of a factor, data was collapsed across that factor. Where appropriate, the Greenhouse-Geisser (GG) correction was applied to data violating the assumption of sphericity. To account for heterogeneous variance, data were squareroot- or log-transformed on some measures (see Table 2.1). Passive avoidance retention latencies did not show a normal distribution, since a cut-off time was set, nor did data transformation yield normal distribution. Retention latencies were thus represented as median and interquartile range, and were analyzed using the non-parametric Mann-Whitney U test between strain-, age-, treatment-, and sex-groups. Significance was evaluated at the level of p < 0.05 for all main effects; however, for interactions at p < 0.1, we also examined lower order main effects. Data are presented as mean ± S.E.M. For a summary of all statistical tests, see Table 2.2.  32  2.9. Statistical Analysis  Test OF NOR EPM PA  Histology  Measurements Total Distance Center Duration Difference Score Preference Ratio Open Arm Duration Open Arm Entries Retention Latency Striatal Volume Striatal Neuronal Number GCL Volume CA1 PCL Volume CA2/3 PCL Volume CA1-CA3 Volume  Data Transformation N/A Square-Root-Transformed N/A N/A Square-Root-Transformed N/A Not Possible, Non-Parametric Test N/A N/A N/A N/A N/A N/A  Table 2.1: Data transformations used to generate data with normal distribution and homogeneous variance. Not applicable (N/A) was used to denote data that showed homogeneous variance without transformation.  33  2.9. Statistical Analysis  Measure  Test  Dependent Variable(s)  Maternal Weight Gain Offspring Growth Rates Pre- & Perinatal Fluid Intake Litter Size Pup Mortality Birth Weight (PN0) OF  2-Way RM ANOVA 2-Way RM ANOVA 2-Way RM ANOVA  - Weight - Weight - Volume of Fluid  2-Way ANOVA 2-Way ANOVA 2-Way ANOVA 4-Way ANOVA  NOR  4-Way ANOVA  EPM  4-Way ANOVA  PA  4-Way ANOVA  Striatal Stereology  4-Way ANOVA  Hippocampal Stereology  4-Way ANOVA  - Number - Number - Weight - Total Distance - Duration in Center - Preference Ratio - Difference Score - Duration in Open Arms - Number of Entries into Open Arms - Training Latency - Retention Latency - Striatal Volume - Striatal Neuronal Number - GCL Volume (Dentate Gyrus) - PCL Volume (CA1, CA2/CA3)  Independent Variable(s) Strain, Treatment Strain, Treatment Strain, Treatment Strain, Treatment Strain, Treatment Strain, Treatment Strain, Age, Treatment, Sex Strain, Age, Treatment, Sex Strain, Age, Treatment, Sex  Strain, Age, Treatment, Sex Strain, Age, Treatment, Sex Strain, Age, Treatment, Sex  Table 2.2: Summary of statistics  34  Chapter 3  Effects of Pre- and Perinatal Nicotine Exposure on Pregnancy Dynamics 3.1  Introduction  Prenatal nicotine exposure can result in a wide variety of adverse foetal outcomes, ranging from preterm delivery and low birth weight, to sudden infant death syndrome (Health Canada, 2009). However adverse foetal outcomes show a large degree of heterogeneity, and thus it is postulated that other factors may contribute to the pathogenesis of adverse foetal outcomes. These findings raise the possibility that genotype may be one such factor that significantly influences the measured outcomes mediated by prenatal nicotine exposure. In order to effectively examine the possible genotype x pre- and perinatal nicotine interaction, it is critical to establish an appropriate model system. We elected to use the oral administration paradigm to deliver nicotine (200 ♠g/ml) to  pregnant B6 and D2 dams through drinking water (Pauly et al., 2004). To ensure the validity of our animal model, we examined parameters that are indicative of the overt toxicological actions on the dam or her progeny. Therefore, to examine the effects of early nicotine exposure and genetic background on various pregnancy measurements, we compared the effects of pre- and perinatal nicotine exposure in B6 and D2 inbred mouse strains on: maternal fluid intake, maternal weight gain, birth weight, offspring growth rates, perinatal mortality, and litter size. We test the hypothesis that the oral delivery route is a valid model for subsequent investigation of the effects of pre- and perinatal nicotine exposure and genotype.  3.2 3.2.1  Methods Animals and Breeding  Twenty B6 and twenty D2 dams were used for breeding. The animal and breeding procedures were described in Chapter 2. From 30 days prior to mating, during mating, and thereafter until the pups were born, fluid consumption and maternal weight gain was monitored daily. Litter size was measured the days the pups were born (PN0). Offspring growth rates, averaged across litter were measured daily until PN24.  35  3.3. Results  3.2.2  Statistical Analysis  All statistical analyses were described in Chapter 2.  3.3  Results  All significant main effects and significant interactions were reported. If the effect of an independent variable was not mentioned this indicates it did not significantly exert an effect on the measured dependent variable.  3.3.1  Maternal Fluid Intake  RM ANOVA was performed with maternal fluid intake as the dependent variable, gestational day (G0 to G18) as the within-subjects factor, and strain and treatment as the between subjects factors (Figure 3.1). Maternal fluid intake over 19 days gestation (G0 to G18) differed significantly between treatment groups (F(1,28) = 9.028, p = 0.006). There was no main effect of strain on daily fluid consumption (F(1,28) = 1.031, p = 0.319). An average dose of 23.04 +/- 0.89 mg/kg/day in B6 mice and 23.89 +/- 0.88 mg/kg/day in D2 mice was observed during pregnancy. The treatment x strain interaction was not significant (F(1,28) = 0.878, p = 0.357). We performed a lower-order ANOVA subdivided by strain based on an a priori hypothesis that B6 and D2 mice show different levels of nicotine consumption (Robinson et al., 1996; Glatt et al., 2009). The lower-order ANOVA revealed that pre- and perinatal nicotine significantly reduced fluid intake in D2 dams (F(1,17) = 8.069, p = 0.011), but B6 dams were not significantly affected by early nicotine exposure (F(1,11) = 2.686, p = 0.129). A significant within-subjects effect of gestational day (F(9.9,279.7) = 5.075, p = 8.5E-7, GG correction), suggests a change in fluid intake through pregnancy. These results indicate that fluid intake within the D2 mouse strain is reduced by pre- and perinatal nicotine exposure, but fluid intake within the B6 mouse strain was insensitive to the effects of pre- and perinatal nicotine exposure.  3.3.2  Maternal Weight Gain  RM ANOVA was performed with maternal weight gain as the dependent variable, gestational day (G0 to G18) as the within-subjects factor, and strain and treatment as the between subjects factors (Figure 3.2). Maternal weight gain did not differ between treatment groups (F(1,31) = 1.419, p = 0.243). There was a main effect of strain (F(1,31) = 27.027, p = 1.21E-5) on maternal weight gain. The strain x treatment interaction was not significant (F(1,31) =0.014, p = 0.907). A significant within-subjects effect of gestational day (F(9.1,282.3) = 24.775, p = 2.1E-31, GG correction), suggests a change in maternal weight gain through pregnancy. These results indicate that B6 dams gained significantly more weight throughout pregnancy compared to D2 dams, but weight gain was not affected by pre- and perinatal nicotine treatment in either strain. 36  3.3. Results  Figure 3.1: Maternal fluid intake throughout gestation in pregnant B6 (top) and D2 (bottom) dams exposed to water or nicotine (200 ♠g/ml). Values are expressed as the mean +/- S.E.M. 37  3.3. Results  Figure 3.2: Maternal weight gain throughout gestation in pregnant B6 (top) and D2 (bottom) dams exposed to water or nicotine (200 ♠g/ml). Values are expressed as the mean +/- S.E.M.  38  3.3. Results  3.3.3  Birth Weight  Two-way ANOVA was performed with birth weight as the dependent factor and strain and treatment as independent variables (Figure 3.3). Birth weight, measured at PN0 and averaged across litter, was not affected by treatment (F(1,31) = 0.269, p = 0.608) or strain (F(1,31) = 2.079, p = 0.159). There was no significant treatment x strain interaction (F(1,31) = 2.171, p = 0.151) on birth weight. These results indicate that birth weight does not differ as a result of strain (genotype) or treatment.  Figure 3.3: Average pup weight at birth (PN0) of B6 and D2 offspring exposed prenatally to water or nicotine (200 ♠g/ml). Values are expressed as the mean +/- S.E.M.  3.3.4  Offspring Growth Rates  RM ANOVA was performed with pup weight gain as the dependent variable, postnatal day (PN0 to PN24) as the within-subjects factor, and strain and treatment as the between subjects factors (Figure 3.4). Analysis of offspring weight gain during the pre-weaning period revealed a significant main effect of treatment (F(1,31) = 6.142, p = 0.019) and strain (F(1,31) = 11.207, p = 0.002). There was no significant interaction between strain and treatment (F(1,31) = 0.191, p = 0.665). There was a significant within-subjects effect of postnatal day (F(9.9,279.7) = 5.075, p = 8.5E-7, GG correction), suggesting a change in offspring weight gain across time. These results indicate that pre- and perinatal nicotine exposure significantly reduces the growth rates in B6 and D2 pups 39  3.4. Discussion during the pre-weaning period. Additionally, the main effect of strain indicates that B6 pups show significantly higher growth rates from PN0 to PN24 compared to D2 mice.  3.3.5  Perinatal Mortality  Two-way ANOVA was performed with perinatal death as the dependent factor and strain and treatment as independent variables. Analysis on perinatal mortality revealed no effect of treatment (F(1,31) = 1.184, p = 0.285). A main effect of strain (F(1,31) = 8.417, p = 0.007) was observed for perinatal death. There was no significant strain x treatment interaction (F(1,31) = 0.015, p = 0.905). These results indicate that while pre- and perinatal nicotine exposure had no effect on perinatal death, there was a significant effect of strain, revealing a greater number of perinatal deaths in the B6 cohort compared to the D2 cohort.  3.3.6  Litter Size  Two-way ANOVA was performed with litter size at birth as the dependent factor and strain and treatment as independent variables. The number of pups per dam at birth varied between 3 and 10 pups. There was no significant main effect of treatment (F(1,31) = 0.963, p = 0.334) or strain (F(1,31) = 1.300, p = 0.263) on litter size. A strain x treatment interaction approached significance (F(1,31) = 3.546, p = 0.069). Lower-order ANOVA subdivided by strain revealed a significant effect of treatment on litter size in the B6 cohort (F(1,31) = 4.778, p = 0.046), but there was no effect of treatment on D2 mouse litter size (F(1,31) = 0.387, p = 0.542). These results indicate that prenatal nicotine exposure significantly increased the number of pups per dam at birth in B6 mice but had no effect on D2 litter size. Additionally there was no difference in litter size at birth between strains.  3.3.7  Adult Weight  Three-way ANOVA was performed with weight as the dependent factor and strain, treatment, and sex as independent variables. A significant main effect of sex (F(1,84) = 124.259, p = 3.1E-18) was revealed. There were no main effects of strain (F(1,84) = 2.099, p = 0.151) or treatment (F(1,84) = 0.107, p = 0.744) . No significant interactions were revealed. These results indicate that sex significantly affects body weight at PN75, whereby male mice weigh more than female mice. Preand perinatal nicotine exposure did not cause long-lasting effects on body weight.  3.4  Discussion  The oral administration paradigm was used to deliver nicotine (200 ♠g/ml) to pregnant dams through drinking water (Pauly et al., 2004). This method was chosen because it was most suitable for long-term nicotine delivery throughout pregnancy. Refer to specific experimental considerations 40  3.4. Discussion  Figure 3.4: Average postnatal pup weights were determined in B6 (top) and D2 (bottom) mice exposed pre- and perinatally to either water or nicotine (200 ♠g/ml). Values are expressed as the mean +/- S.E.M. 41  3.4. Discussion in Section 1.7. Precise control of dose is not feasible using this method. Our data revealed an average daily dose of 23.04 +/- 0.89 mg/kg/day in B6 mice and 23.89 +/- 0.88 mg/kg/day in D2 mice (the average daily intake was calculated over the duration of the pregnancy). This dose closely parallels studies that employ osmotic mini-pumps (24 mg/kg/day), a method that allows a priori determination of a dose to achieve steady-state plasma levels that mimic human exposure (Damaj et al., 2003). In mice a dose of 24 mg/kg/day for 14 days elicits both long-lasting somatic and  affective signs (Matta et al., 2007). This supports that oral nicotine delivery at 200 ♠g/ml achieves the proper dose of nicotine to parallel human exposure levels in both B6 and D2 mice. Plasma levels of the nicotine metabolite cotinine are often used to estimate the extent of nicotine exposure in mice because nicotine itself has a very short half-life in mice (t1/2 = 7-10 min), which makes it impractical to measure. For our purposes, we did not measure plasma cotinine levels. This decision was largely based on the observation that B6 and D2 mice metabolize cotinine at different rates (Siu & Tyndale, 2007) and therefore we would be unable to compare B6 cotinine levels (t1/2 = 24 min) to D2 cotinine levels (t1/2 = 51 min) as an estimate of the relative level of nicotine exposure. Previous studies have shown that pregnant B6 dams will consume nicotine in drinking water (Klein et al., 2003; Pauly et al., 2004). By contrast, no studies have used D2 mice in gestational nicotine studies. We report here for the first time that both B6 and D2 mice will consume nicotine through their drinking water at a dose that reflects human exposure levels. Our initial concern was that D2 mice would not consume nicotine through their drinking water. Nevertheless our data revealed a similar daily nicotine dose in D2 mice (23.89 +/- 0.88 mg/kg/day) and B6 mice (23.04 +/- 0.89 mg/kg/day). It is important to acknowledge further that water control D2 mice also showed an overall higher level of fluid consumption (4.03 +/- 0.20 ml/day) compared to water control B6 mice (3.60 +/- 0.20 ml/day), although this difference was not significant. The higher level of water consumption in D2 mice relative to B6 mice has been previously reported (Moore et al., 2011). Nevertheless we report here that any slight difference in fluid intake across strain was abolished on account of nicotine treatment, whereby D2 and B6 nicotine-treated mice displayed average fluid intakes of 3.16 +/- 0.16 ml/day and 3.14 +/- 0.09 ml/day, respectively. Despite similar levels of nicotine intake, the fluid consumption in D2 mice decreased significantly as a result of nicotine exposure (20.3 +/- 3.4% fluid reduction), whereas nicotine did not significantly lower fluid intake across B6 mice (11.4 +/- 4.3% fluid reduction). This is in agreement with previous studies that have reported that B6 mice display higher oral nicotine consumption compared to D2 mice (Robinson et al., 1996; Glatt et al., 2009). Therefore the heighted overall fluid consumption in D2 mice is responsible for the similar nicotine intake between B6 and D2 mice reported here. Furthermore, the reduced daily fluid intake resulting from nicotine exposure observed in D2 mice is consistent with those reported in previous studies using B6 mice (Klein et al., 2003; Pauly et al., 2004). To-date, no study has examined the potential effects of reduced fluid intake on offspring development. However given nicotine exposure did not cause any significant differ42  3.4. Discussion ences in average daily weight gain of the dams, the observed reduction in fluid volume might be attributed to the antidiuretic effects of nicotine. Nicotine is a powerful stimulant of antidiuretic hormone release and consequently reduces the output of urine (Burn et al., 1945). It is possible that the mice reduced their fluid intake to compensate for the reduced urine output. In agreement with this hypothesis, nicotine-exposed B6 dams also showed a reduction in fluid intake (by 11.4 +/- 4.3%), although not statistically significant. Daily measurements taken throughout pre- and perinatal period revealed no effect of nicotine on maternal weight gain, perinatal mortality, or birth weight. Nevertheless offspring growth rates were slowed in nicotine-exposed litters from birth to weaning, but the offspring‘s weight recovered during adulthood (measured at PN75). These data suggest that once nicotine exposure is terminated, there are no enduring effects on growth rates. The present study demonstrates that oral nicotine delivery to B6 and D2 pregnant dams is a viable method, and may be useful for future genetic studies on preand perinatal nicotine exposure.  43  Chapter 4  Effects of Pre- and Perinatal Nicotine Exposure on Behavioural Measures 4.1  Introduction  Numerous behavioural disorders have been associated with maternal tobacco smoking (Section 1.1). Animal studies have indicated that nicotine is largely responsible for the majority of negative outcomes associated with maternal smoking (Slotkin, 1998). Accordingly, we focus here on the effects of pre- and perinatal nicotine exposure, many of which are not unlike the negative effects seen in human smokers. Our purpose was to compare behavioural responses to pre- and perinatal nicotine exposure between B6 and D2 mouse strains in an attempt to enrich and expand our current understanding of mechanisms underlying behavioural deficits related to early nicotine exposure. The effects of pre- and perinatal nicotine exposure on behavioural outcomes have not been previously compared between inbred mouse strains to account for the genetic influences on nicotine responses. To examine the effects of genetic background and pre- and perinatal nicotine exposure on various brain functions we decided to utilize a multiple-test regimen, in which four behavioural tests were conducted over a two-day period. Our behavioural battery consisted of open field activity, novel object recognition, elevated plus maze, and passive avoidance (See section 1.8 for details). We were mindful of the order in which the behavioural tests were administered, beginning with the least invasive test before testing more invasive assays. As mentioned previously, past studies have examined the effects of testing history and interval time on behavioural responses and have concluded that interval time has no effect on responses, while testing history influences behaviours in a test-dependent manner. Although these studies used slightly different behavioural tests and inter-test intervals than the present study, we believe that our behavioural battery was designed to minimize any potential confounding variables of testing conditions.  44  4.2. Methods  4.2 4.2.1  Methods Animals and Breeding  Offspring from B6 and D2 dams that were exposed pre- and perinatally to nicotine or otherwise water controls were used. The number of subjects used for behavioural testing is summarised in Table 4.1. An average of 12 mice per group (subdivided by strain, age, treatment, and sex) were used for behavioural testing. The animal and breeding procedures were described in Chapter 2. Group B6 PN24 Female B6 PN24 Male D2 PN24 Female D2 PN24 Male B6 PN75 Female B6 PN75 Male D2 PN275 Female D2 PN75 Male Total  Treantment  Number of Litters (#)  Water Nicotine Water Nicotine Water Nicotine Water Nicotine Water Nicotine Water Nicotine Water Nicotine Water Nicotine  8 5 9 5 6 8 6 7 8 5 7 4 7 7 5 7  Number of Subjects Tested (#) 12 10 15 10 13 16 12 11 15 13 10 7 19 12 6 10 191  Table 4.1: Summary of subjects used for behavioural testing subdivided by strain, age, treatment, and sex.  4.2.2  Testing Schedule  Testing schedule was described in Chapter 2. Briefly, the days the pups were born was designated PN0. At PN24 half the pups from each litter (sex-matched) were taken for behavioural testing to examine the immediate effects of pre- and perinatal nicotine exposure. At PN75 the remaining animals were taken for behavioural testing to examine the persistent effects of pre- and perinatal nicotine exposure.  45  4.3. Results  4.2.3  Behavioural Testing Procedure and Behavioural Tests  Both the behavioural testing procedure and the behavioural tests themselves were thoroughly described in Chapter 2.  4.2.4  Statistical Analysis  All statistical analyses were described in Chapter 2. Set parameters were defined that resulted in the exclusion of subjects from behavioural analysis. In particular, mice that did not spend more than a total of 6 s exploring the objects on training (1% of total training session) or testing (2% of total test session) were excluded from NOR analysis (approximately 20% of all animals) (Table 4.2). Mice that fell off the EPM (n=7) were eliminated from the EPM analysis. Due to a malfunction in the PA chamber, 23 animals had to be removed from the PA analysis. Group B6 PN24 D2 PN24 B6 PN75 D2 PN75  Treatment Water Nicotine Water Nicotine Water Nicotine Water Nicotine  Total  Subjects Excluded (#) 0 2 4 6 2 5 12 8 39  Percentage Excluded (%) 0 10 16 22 8 25 48 36 21  Table 4.2: Summary of subjects eliminated from NOR analysis.  4.3  Results  All significant main effects and significant interactions were reported. If the effect of an independent variable was not mentioned this indicates that it did not significantly exert an effect on the measured dependent variable. Figures were graphed to illustrate the effect of each independent variable. Asterisks were added to the figures to denote individual values for which nicotine groups differed significantly (*p < 0.05) from the corresponding controls.  4.3.1  Open Field  Total Distance Travelled A four-way ANOVA was conducted that examined the effect of strain (genotype), age, treatment, and sex on the total distance travelled in an open field (Figure 4.1). There was a significant main 46  4.3. Results effect of strain (F(1,175) = 162.574, p = 9.3E-27) and age (F(1,175) = 68.295, p = 3.4E-14) on total distance travelled. There were significant strain x age (F(1,175) = 152.575, p = 1.3E-25), strain x treatment (F(1,175) = 9.097, p = 0.003), and strain x treatment x sex (F(1,175) = 4.273, p = 0.040) interactions. Data were subdivided accordingly to interpret significant interactions. Lower-order ANOVA subdivided by age revealed that as adults (PN75), B6 mice travelled significantly more compared to D2 mice (F(1,84) = 252.510, p = 4.8E-27), but there was no difference between strains at the preadolescent stage (PN24) (F(1,91) = 0.101, p = 0.752). Data subdivided by strain revealed that, as adults, B6 mice travelled significantly more than preadolescent B6 mice (F(1,84) = 231.942, p = 6.9E-26). Conversely in the D2 mouse strain, adults travelled significantly less compared to the preadolescent age group (F(1,91) = 7.745, p = 0.007). Lower-order ANOVA subdivided by strain and age revealed that as adults, female B6 mice display significantly greater activity than male B6 mice (F(1,41) = 4.895, p = 0.033). Lower-order ANOVA subdivided by age, strain, and sex revealed that pre- and perinatal nicotine exposure significantly increased total distance travelled in D2 females at PN24 (F(1,27) = 10.443, p = 0.003) and PN75 (F(1,30) = 9.621, p = 0.004). In addition pre- and perinatal nicotine exposure significantly decreased total distance travelled in B6 females at PN75 (F(1,26) = 7.552, p = 0.011). These results indicate that genotype, age, and sex interact with early nicotine exposure to predict the level of activity measured over 30 minutes in an open field arena. Time Spent in the OF Center A four-way ANOVA was conducted that examined the effect of strain (genotype), age, treatment, and sex on the time spent in the open field center (Figure 4.2). There was a significant main effect of strain (F(1,175) = 287.119, p = 9.6E-39), age (F(1,175) = 15.483, p = 1.2E-4), and sex (F(1,175) = 5.221, p = 0.024) on time spent in the open field center. A strain x treatment x sex interaction approached significance (F(1,175) = 3.046, p = 0.083). Data indicates (across all variables) that B6 mice spent significantly more time in the OF center than D2 mice and that preadolescent mice spent significantly more time in the OF center compared to adult mice. Analysis of the strain x treatment x sex interaction subdivided by age, strain, and treatment revealed that female B6 water-treated adults spent significantly more time in the OF center compared to male B6 water-treated adult counterparts (F(1,23) = 4.598, p = 0.043). Likewise, female D2 nicotine-treated preadolescent mice spent significantly more time in the OF center compared to male D2 nicotine-treated preadolescent counterparts ((F(1,25) = 4.424, p = 0.046). Univariate analysis on total time spent in the center revealed no significant difference between treatment groups, however the difference between nicotine- and water-D2 P24 females approached significance (F(1,27) = 3.779, p = 0.062).  47  4.3. Results  Figure 4.1: Total distance travelled in an open field at PN24 (top) and PN75 (bottom) determined in B6 and D2 mice exposed pre- and perinatally to either water or nicotine (200 ♠g/ml). Values are expressed as the mean +/- S.E.M. Asterisks (*) denote individual values for which nicotine groups differed significantly (p < 0.05) from the corresponding controls. 48  4.3. Results  Figure 4.2: Time spent in the open field center at PN24 (top) and PN75 (bottom) determined in B6 and D2 mice exposed pre- and perinatally to either water or nicotine (200 ♠g/ml). Values are expressed as the mean +/- S.E.M.  49  4.3. Results  4.3.2  Elevated Plus Maze  Time Spent in the Open Arms A four-way ANOVA was conducted that examined the effect of strain (genotype), age, treatment, and sex on the time spent in the open arms of an EPM (Figure 4.3). There was a significant main effect of age (F(1,167) = 5.492, p = 0.020) on the time spent in the open arms. There were significant strain x age (F(1,167) = 7.845, p = 0.006) and treatment x sex (F(1,167) = 5.485, p = 0.020) interactions. A strain x treatment interaction approached significance (F(1,167) = 2.965, p = 0.087). Examination of the strain x age interaction revealed that D2 preadolescent mice (PN24) spent significantly more time in the open arms compared to D2 adult mice (PN75) (F(1,84) = 17.263, p = 7.8E-5), but there was no difference across age in B6 mice (F(1,83) = 0.086, p = 0.770). Data subdivided by age and treatment revealed a significant effect of genotype (strain), in which as adults, B6 water-control mice spent significantly more time in the open arms compared to their D2 watercontrol counterparts (F(1,45) = 9.507, p = 0.003). Data subdivided by strain, age, and sex revealed that pre- and perinatal nicotine exposure significantly reduced the time spent in the open arms in B6 males as adults (F(1,15) = 4.555, p = 0.050). Open Arm Entries A four-way ANOVA was conducted that examined the effect of strain (genotype), age, treatment, and sex on the number of entries into the open arms of an EPM (Figure 4.4). There was a significant main effect of strain (F(1,167) = 10.207, p = 0.002) and age (F(1,167) = 7.485, p = 0.007) on time spent in the open arms. There were significant strain x age (F(1,167) = 7.929, p = 0.005) and strain x age x treatment (F(1,167) = 4.118, p = 0.044) interactions. Examination of the strain x age interaction revealed that D2 preadolescent mice (PN24) made significantly more entries into the open arms compared to D2 adult mice (PN75) (F(1,84) = 24.372, p = 4.0E-6), but there was no difference across age in B6 mice (F(1,83) = 0.002, p = 0.961). Data subdivided by age and treatment revealed a significant effect of strain in water-treated adults (PN75) (F(1,45) = 15.315, p = 3.1E-4) indicating that water-treated adult B6 mice made significantly more open arm entries compared to water-treated adult D2 mice. A significant effect of sex was observed when data were subdivided by strain, age, and treatment, revealing that female nicotine-treated D2 mice made significantly more open arm entries compare to male nicotine-treated D2 mice at PN75 (F(1,19) = 4.878, p = 0.040). Lower-order ANOVA subdivided by strain, age, and sex revealed that pre- and perinatal nicotine exposure significantly increased open arm entries in D2 females at PN75 (F(1,29) = 4.305, p = 0.047).  50  4.3. Results  Figure 4.3: Time spent in the open arms of an EPM at PN24 (top) and PN75 (bottom) determined in B6 and D2 mice exposed pre- and perinatally to either water or nicotine (200 ♠g/ml). Values are expressed as the mean +/- S.E.M. Asterisks (*) denote individual values for which nicotine groups differed significantly (p < 0.05) from the corresponding controls. 51  4.3. Results  Figure 4.4: Number of open arm entries in an EPM at PN24 (top) and PN75 (bottom) determined in B6 and D2 mice exposed pre- and perinatally to either water or nicotine (200 ♠g/ml). Values are expressed as the mean +/- S.E.M. Asterisks (*) denote individual values for which nicotine groups differed significantly (p < 0.05) from the corresponding controls. 52  4.3. Results  4.3.3  Novel Object Recognition  Difference Scores and Preference Ratios A four-way ANOVA was conducted that examined the effect of strain (genotype), age, treatment, and sex on object recognition memory (Figure 4.5). Object recognition memory was interpreted from difference scores and preference ratios. Significant main effects of age (difference score, F(1,136) = 4.370, p = 0.038; preference ratio, F(1,136) = 7.518, p = 0.007) and treatment (difference score, F(1,136) = 4.788, p = 0.030; preference ratio, F(1,136) = 6.615, p = 0.011) were indicated from both difference scores and preference ratios. There was a significant strain x age interaction for the difference score (F(1,136) = 4.484, p = 0.036) which approached significance for the preference ratio (F(1,136) = 2.813, p = 0.096). In addition, an age x sex interaction approached significance for the difference score (F(1,136) = 2.792, p = 0.097), while an age x treatment interaction approached significance for the preference ratio (F(1,136) = 2.891, p = 0.091). Data subdivided by strain revealed that D2 preadolescent mice showed reduced object recognition memory compared to D2 adults (difference score, F(1,61) = 6.119, p = 0.016; preference ratio, F(1,61) = 5.253, p = 0.025), but B6 mice showed no difference across age (difference score, F(1,75) = 0.001, p = 0.981; preference ratio, F(1,75) = 1.335, p = 0.252). Data subdivided by age and treatment revealed that, as adults, B6 water-treated mice showed significantly lower object recognition memory compared to D2 water-treated mice as indicated by both the difference score (F(1,32) = 9.503, p = 0.004) and preference ratio (F(1,32) = 10.592, p = 0.003). A significant effect of sex was observed when data were subdivided by age and strain revealing that preadolescent (PN24) B6 female mice showed significantly better object recognition compared to B6 male counterparts (difference score, F(1,41) = 5.819, p = 0.020; preference ratio, F(1,41) = 6.513, p = 0.015). Lower-order ANOVA subdivided by age and sex revealed that pre- and perinatal nicotine exposure significantly reduced object recognition memory in adult (PN75) males (difference score, F(1,20) = 5.001, p = 0.037; preference ratio, F(1,20) = 5.636, p = 0.028), but female adults were insensitive to the effects of pre- and perinatal nicotine exposure (difference score, F(1,41) = 0.173, p = 0.680; preference ratio, F(1,41) = 1.098, p = 0.301).  4.3.4  Passive Avoidance  Retention Step-Through Latency A Mann-Whitney U test was conducted that examined the effect of strain (genotype), age, treatment, and sex on the step-through latency 18-24 h following exposure to the US. Pre- and perinatal nicotine exposure significantly reduced the retention latency (U(170) = 2718.5, Z = -3.035, p = 0.002) (Figure 4.6). There was no significant effect of sex (U(170) = 3430.0, Z = -0.428, p = 0.669) on retention latency. The effect of strain (U(170) = 3046.5, Z = -1.929, p = 0.054) and age (U(170)  53  4.3. Results  Figure 4.5: NOR with a 4 h delay between training and testing as measured by both the difference score and the preference ratio. (A) Preference ratio and (B) difference score at PN24 determined in B6 and D2 mice exposed pre- and perinatally to either water or nicotine (200 ♠g/ml). (C) Preference ratio and (D) Difference score at PN75 determined in B6 and D2 mice exposed pre- and perinatally to either water or nicotine (200 ♠g/ml). Values are expressed as the mean +/- S.E.M. Asterisks (*) denote individual values for which nicotine groups differed significantly (p < 0.05) from the corresponding controls.  54  4.4. Discussion = 3093.5, Z = -1.643, p = 0.100) on retention latency approached significance whereby D2 mice showed higher retention latencies compared to B6 mice and likewise PN24 mice showed higher retention latencies compared to PN75 mice.  Figure 4.6: The effect of pre- and perinatal nicotine exposure (200 ♠g/ml) on PA performance across age and strain during the restention test 18-24 h after training. Boxplots show results analyzed by non-parametric statistics showing the median (bolded black line) as well as 25% and 75% quartiles (boxes). The end points of the two extending lines are determined by the data values of the more extreme cases, but do not extend more than the interquartile range. Asterisks denote values which are more than 3 box lengths from either end of the box, whereas the open circles denote values which are between 1.5 and 3 box lengths from either end of the box.  4.4  Discussion  This chapter describes the effects of pre- and perinatal nicotine exposure and genetic background in male and female mice at two temporal stages: preadolescence (immediately following nicotine exposure) and adulthood (well after pre- and perinatal nicotine exposure). The examination of these four variables has been a major methodological constraint in prenatal nicotine research. Many past studies have attempted to address one or two variables at a time, while no study has examined the effect of genetic background during the full equivalent of human gestation in an animal model at all. Given the complexity of statistical analysis, the manner in which the discussion is presented is intended to highlight the primary question of this study. That is, are there effects of pre- perinatal nicotine exposure on various brain functions, and does strain, age, or sex influence nicotine-related phenotypes?  55  4.4. Discussion  4.4.1  Locomotor Activity  Locomotor activity was examined in the current study from total distance travelled in an OF. Previous research has assessed genotype (B6 and D2 inbred mouse strains) on levels of activity. Results from past studies have indicated that B6 mice generally display hyperactivity compared to D2 mice (Moore et al., 2011). Here we report results consistent with previous research. Our data revealed that, as adults, B6 mice display hyperactivity compared to D2 mice (indicated from total distance travelled). An age effect was also apparent in both B6 and D2 mice. In particular, B6 adults showed significantly higher locomotor activity than B6 preadolescents. This effect of age has been similarly reported by Moore et al. (2011) who noted that B6 adults (PN65-95) showed elevated activity relative to their adolescent (PN28-46) counterparts. Interestingly the contrary was observed in D2 mice whereby D2 adults showed significantly lower locomotor activity compared to D2 adolescents. This is consistent with reports that have repeatedly indicated that adolescents are often hyperactive in novel environments compared to adults (Spear et al., 1980; Darmani et al., 1996). In summary, the current work demonstrates that there are indeed differences in locomotor behaviour between adult and adolescent mice. Furthermore, this work shows that these age-related behavioural differences in mice are also dependent on genotype. The effects of genotype only become apparent in adults. Taken together, these results support the notion that age is an important factor in determining genotype-related differences in locomotor activity. The current study indicated that pre- and perinatal nicotine exposure had a hyperactive effect in female D2 mice at both the preadolescent- and adult stages. By contrast, pre- and perinatal nicotine exposure decreased locomotor activity in B6 females, an effect that was only evident in adults. Collectively these observations suggest that genotype indeed contributes to the measured outcome on locomotor activity mediated by early nicotine exposure. Previous studies have generally reported enhanced locomotor activity from early nicotine exposure. However there have been discrepancies between reports (Peters & Tang, 1982; Schlumpf et al., 1988; Shacka et al., 1997; Ajarem & Ahmad, 1998; Pauly et al., 2004; Vaglenova et al., 2004; Paz et al., 2007). To-date the heterogeneity in measured outcomes has been attributed largely to differences in experimental designs. This study suggests that genotype may also, in part, explain discrepancies in past studies. Furthermore, pre- and perinatal nicotine exposure selectively alters locomotor activity in females, with no effects seen in males. The current results showing greater sensitivity in female mice to the locomotor effects of chronic nicotine differ from previous results which report higher sensitivity in male offspring (Shacka et al., 1997; Pauly et al., 2004). Future studies will determine if the nature of these differences are due to experimental design, namely continuing nicotine exposure postnatally and cross-fostering. Regardless previous studies have indicated that nicotine affects males and females differently. Our data support that pre- and perinatal nicotine exposure exerts an effect on locomotor activity which is influenced by genotype, sex, and age.  56  4.4. Discussion  4.4.2  Anxiety-Like Behaviour  Anxiety-like behaviour was examined in the current study from OF (center duration) and EPM (open arm duration and open arm entries) measurements. Previous research assessing genotype (B6 and D2 inbred mouse strains) on anxiety-like behaviour have reported that D2 mice display significantly greater levels of anxiety-like behaviour compared to B6 mice (measured by time spent in the open arms of an EPM) (Yilmazer-Hanke et al., 2003; Moore et al., 2011). Here we report similarly that D2 mice generally showed elevated anxiety-like behaviours compared to B6 mice (Figures 4.2, 4.3, and 4.4). It is important to note that although the time spent in the OF center was affected unconditionally by genotype, EPM measurements (open arm duration and entries) were subject to interactive influences of age. In particular, as adults, B6 mice displayed increased open arm duration and open arm entries compared to D2 mice, but there was no strain difference in preadolescents, revealing that strain differences were much more robust in adults. It is well known that temporal changes in gene expression profiles exist across developmental stages (Berchtold et al., 2008; Colantuoni et al., 2011). Perhaps with ageing, the transcriptomes between strains become more differentiated, leading to heightened phenotypic variation in adults. Comparison between nicotine-treated and water-control mice on EPM measurements revealed that early nicotine treatment attenuated the natural strain differences observed in adults. For example, as adults, B6 water-control mice showed increased open arm duration compared to D2 watercontrol mice, but the genotype effect disappeared in mice exposed to pre- and perinatal nicotine (Figure 4.3). This effect was paralleled in the number of open arm entries (Figure 4.4). Our data indicate that through some unknown mechanism pre- and perinatal nicotine exposure normalizes the anxiety-like responses between B6 and D2 mice across EPM measurements. Provided this was not seen in OF measurements, this difference in response pattern may have resulted from testing under different experimental protocols, supporting the reports that different tests measure different forms of anxiety (Belzung & Le Pape, 1994; File, 1995; Turri et al., 2001; Holmes et al., 2003). In the current study pre- and perinatal nicotine exposure significantly decreased the time spent in the EPM open arms in adult male B6 animals, indicating an anxiogenic effect of nicotine. By contrast pre- and perinatal nicotine exposure had an anxiolytic effect in adult female D2 mice, revealed by the increased number of open arm entries. Taken together, these results indicate that either genetic background, sex, or both can have a major impact on the phenotypic consequences of early nicotine exposure. Not only are different mouse strains and/or sexes more or less sensitive to the effects of pre- and perinatal nicotine exposure on anxiety-like behaviours, but here nicotine exerted opposing effects, suggesting a modulatory effect rather than a definitive anxiogenic- or anxiolytic effect of nicotine. Previous studies support the modulatory effect of early nicotine exposure. Reports showing anxiogenic effects of pre- and perinatal nicotine exposure in adults have been noted (Vaglenova et al., 2008; Eppolito et al., 2010). By contrast other studies have reported anxiolytic effects from 57  4.4. Discussion prenatal nicotine exposure (Ajarem & Ahmad, 1998; Sobrian et al., 2003). The reported anxiogenic effects of pre- and perinatal nicotine were furthermore dependent on age, such that Vaglenova et al. (2008) found that although nicotine mediated anxiogenic effects in adult males and females, the anxiogenic effects were confined to males during adolescence. Likewise Eppolito et al. (2010) reported no effect at all during adolescence. Differences in the effects of early nicotine are likely the result of genotype (strain and/or specie), sex, dose, and/or temporal variability in experimental designs. The variability in anxiety-like behaviours observed here across genotype is not unlike that observed in human populations. Individuals differ markedly in anxiety behaviour as well as physiological and behavioural responses to stress. There is also high variability in the effects of nicotine on anxiety-like behaviours. sex appears to confer differential sensitivity to nicotine and anxiety-like behaviours (Grunberg et al., 1991; Faraday et al., 2005). For example in adults, women have less success quitting smoking and are more likely to relapse compared to men, which may arise, in part, from negative mood (Borland, 1990; Bjornson et al., 1995). Although these reports relate to adults, it appears there are biologically-based sex differences in nicotine-related anxiety-like responses which likely translate to prenatal exposure. The results of the current study indicate that pre- and perinatal nicotine exposure produces anxiolytic effects in adult D2 females whereas produces anxiogenic effects in adult B6 male. One possible explanation for the apparent differences across genotype and sex is the possible effect of pre- and perinatal nicotine on the hypothalamic-pituitary-adrenal (HPA) axis. The HPA axis is a central control regulatory system that connects the CNS with the hormonal system (Kudielka & Kirschbaum, 2005). It is one of the physiological systems involved in coping with environmental challenges. Under stress, the hypothalamus secretes corticotrophin-releasing hormone to induce the release of adrenocorticotropic hormone (ACTH) from the pituitary. ACTH triggers the secretion of glucocorticoids from the adrenal cortex. In the CNS, glucocorticoids regulate protein synthesis, neuronal excitability, and neurotransmitter metabolism and are also important in brain development (reviewed by McCormick & Mathews, 2007). Through the action of glucocorticoids, the HPA axis is involved in programming responses to future stressors, and is thus crucially related to anxiety-like behaviours (Goel & Bale, 2009; Oitzl et al., 2010). Although no study has evaluated the effects of pre- and/or perinatal nicotine exposure on HPA axis responsiveness, adult nicotine exposure has shown to be a potent stimulus for the secretion of ACTH (Matta et al., 1998). Moreover, maternal smoking has shown to increase umbilical ACTH, which may connect maternal smoking to HPA axis programming (McDonald et al., 2006). Therefore it is possible that early nicotine exposure leads to changes in the HPA axis. Provided that HPA functioning differs across B6 and D2 mouse strains (Belzung & Griebel, 2001; Jacobson & Cryan, 2007; Millstein & Holmes, 2007), males and females (Kitay, 1961; Hary et al., 1981, 1986; Kessler et al., 1994; Kudielka & Kirschbaum, 2005), and developmental stages (Goldman et al., 1973; Vazquez & Akil, 1993; Mc58  4.4. Discussion Cormick & Mathews, 2007), the different effects of pre- and perinatal nicotine exposure across genotype, sex, and age may, in part, result from baseline differences in HPA responsiveness. Future studies are required to assess the possible effects of early nicotine exposure on HPA axis responsiveness. In summary, our data suggests that early nicotine exposure does exert an effect on anxiety-like behaviour. Whether pre- and perinatal nicotine exposure induces changes in anxiety-like responses or rather attenuates natural baseline differences in anxiety measures between mouse strains, it is apparent that nicotine mediates changes in CNS function that manifest behaviourally. Despite the observed modulatory effect of nicotine on anxiety-like behaviours, precisely how these effects are mediated remains unknown. The effects of nicotine are complex and depend on several variables including genotype, age, and sex. Further studies will need to be conducted to determine the cause of the sex-specific and strain-specific effects described here.  4.4.3  Cognition  Different forms of learning and memory involve different brain systems (Thompson & Kim, 1996; Milner et al., 1998). It is thus postulated that differences in learning and memory processes observed across genotype or sex may be owing to differences in underlying neuroanatomical and neurochemical systems. Likewise it would be expected that early nicotine exposure influences various forms of learning and memory differently depending on the underlying neural circuitries involved. Moreover, nicotine may differentially affect learning and memory abilities between B6 and D2 mice, or males and females, based on basal differences. In this section we present our behavioural findings reporting the effects of nicotine, genotype, and sex on the two learning and memory tasks that we examined. The Effects of Nicotine Past studies evaluating the effects of pre- and/or perinatal nicotine exposure on spatial learning tasks, namely the Morris water maze and radial arm maze, have reported inconsistent findings (Martin & Becker, 1971; Peters & Ngan, 1982; Sorenson et al., 1991; Yanai et al., 1992; Levin et al., 1993). Therefore it may be interpreted that these tasks do not test the appropriate domains that are significantly affected by prenatal nicotine exposure. Based on these findings we decided to assess the effects of pre- and perinatal nicotine exposure and genetic background on two complex learning tasks that, to our knowledge, have not been previously examined. In the current study we evaluated NOR and PA, two sets of behaviours known to be affected by adolescent nicotine exposure, which also represents a critical developmental period (Trauth et al., 2000; Mateos et al., 2010). In addition these tests were selected on account that nicotinic cholinergic pathways are essential to both passive avoidance (Anglade et al., 1999; Mezey et al., 1999; Tinsley et al., 2004, 2011) and object recog-  59  4.4. Discussion nition behaviours (Sambeth et al., 2007; Roncarati et al., 2009; Tinsley et al., 2011). In the current study, we showed that nicotine does indeed produce deficits in both NOR and PA memory. An effect of pre- and perinatal nicotine exposure was noted on both NOR and PA memory interpreted from difference scores/preference ratios and retention latencies, respectively. Adult D2 water-control mice showed better performance in the one-trial object recognition task compared to adult B6 water-control mice, but the genotype effect disappeared in mice exposed to pre- and perinatal nicotine (Figure 4.5 C, D). Our data indicate that nicotine treatment attenuates the natural strain differences observed in adult object recognition memory, a result that parallels those seen on EPM measures (discussed above). Furthermore pre- and perinatal nicotine exposure lowers object recognition memory in adult males across both B6 and D2 mouse strains. On PA memory, pre- and perinatal nicotine exposure induced performance deficits (Figure 4.6). Lowered retention latencies were observed across strain, age, and sex suggesting that the effects of nicotine on PA memory are long-lasting, in which males and females as well as B6 and D2 mice are all vulnerable. The Effects of Genotype B6 and D2 mice differ in their hippocampal formation, amygdala and striatum (Rossi-Arnaud et al., 1991; Paylor et al., 1993; Ammassari-Teule et al., 1999, 2000; Peirce et al., 2003; Rosen et al., 2009) and in their cholinergic and dopaminergic systems (Albanese et al., 1985; Ciamei et al.,  2000). In particular, the ❛7 nAChR expression profile is lowered in D2 hippocampus compared to  B6 hippocampus (Stevens et al., 1996). It has been reported that ❛7 nAChRs mediate some of the  enhancing properties of nicotine associated with adult exposure (Levin et al., 2006b). Considering the variation within neurochemical and neuroanatomical systems involved in learning and memory between D2 and B6 mice, it would be expected that each mouse strain shows distinct basal cognitive function. Previous studies assessing genotype on memory tasks have validated the observation that inbred mouse strains show varying performance on cognitive tasks. In general D2 mice perform worse on hippocampal-dependent tasks compared to B6 mice. In particular, B6 mice generally perform better than D2 mice in spatial learning tasks (Ammassari-Teule et al., 1993; Paylor et al., 1996; Logue et al., 1997; Ammassari-Teule et al., 1998) and contextual fear conditioning (Paylor et al., 1994; Stiedl et al., 1999; Nguyen et al., 2000). PA also relies on hippocampal function (Ambrogi-Lorenzini et al., 1996; Tinsley et al., 2004), but nevertheless D2 mice perform similar if not better than B6 mice on emotional avoidance tasks (Weinberger et al., 1992; Heyser et al., 1999; Podhorna & Brown, 2002).The instrumental response involved in PA may influence the neural substrates involved, and may underlie the stronger performance observed in D2 mice compared to B6 mice. In regards to the current study, D2 mice generally showed similar if not better performance compared to B6 mice (retention latency approached significance (p = 0.054)). Our data therefore are in accordance with previous reports. To our knowledge, there have only been a few studies that have directly compared learning 60  4.4. Discussion performance across different inbred mouse strains on NOR (Podhorna & Brown, 2002; Brooks et al., 2005; Voikar et al., 2005). Brooks et al. (2005) performed NOR using a single training trial followed by 3 test trials after delays of 1-, 4-, and 24-h. When exploratory behaviour was expressed as a ratio of the total exploratory time, strain differences were found. Notably, D2 mice showed the highest performance amongst the 6 inbred strains tested (including the B6 strain). By contrast, D2 mice were impaired in one-trial object recognition compared to B6 mice at delays of 15-min (Podhorna & Brown, 2002) and 24-h (Voikar et al., 2005). Differences in the relative performance of B6 and D2 mice across reports may be attributed to different experimental designs. For example, by employing different time delays, NOR is able to assess short-term memory (STM), intermediateterm memory (ITM), and long-term memory (LTM). Each memory form is distinct by virtue of the unique molecular profiles (Kandel, 2001; Stough et al., 2006), and therefore may influence each memory form differently. For example, STM elapses over minutes and is independent of either protein synthesis or gene expression. ITM elapses over several hours, requires protein synthesis, but is independent of gene transcription. LTM requires gene-transcription and typically exhibits a retention period that is greater than, or equal to, 24h. In the current study, novel object recognition was performed with a 4-h delay between training and testing, thus examining ITM. We report here that as adults, D2 water-control mice showed better object recognition compared to B6 water-control mice. Although our results are in accordance with (Brooks et al., 2005), it came to us as a surprise that D2 mice showed stronger object recognition compared to B6 mice. Previous reports have suggested a role of the hippocampus in object recognition, and we thus believed that B6 mice would have shown better object recognition memory. Nevertheless the complexity of neural substrates involved likely attributes to the measured outcome observed. The Effects of Sex Differences in NOR performance were also evident between males and females. In particular it was noted that as preadolescents, B6 females showed higher object recognition compared to their B6 male counterparts (Figure 4.5). Sexual dimorphism in object recognition memory has been previously reported (Sutcliffe et al., 2007). They reported similarly that female rats performed significantly better than male rats on NOR with a 3-h inter-trial delay, concluding that gonadal hormones are able to influence components of object recognition memory. In addition to innate sexual dimorphic NOR behaviours, the current study also noted a sexual dimorphic effect of nicotine. A sex x treatment interaction revealed that only male mice were sensitive to the negative effects of pre- and perinatal nicotine exposure on NOR. Gonadal hormones have the ability to influence structural properties of brain regions involved in cognitive processes (Dohanich, 2003; Cahill, 2006). In particular, oestrogens and androgens have both been shown to alter hippocampal electrophysiology (Smith et al., 2002). Research has identi61  4.4. Discussion fied oestrogen receptors in several brain regions critical to learning and memory, including amygdala, cerebral cortex, cerebellum, hippocampus, and striatum (Kueppers & Beyer, 1999; Shughrue & Merchenthaler, 2000; Tsutsui et al., 2004). Such evidence supports the likely influence of sex on learning and memory. More importantly, the results from the current study, indicating that males are more vulnerable to the effects of nicotine on object recognition memory, suggest that mechanisms underlying nicotine’s mode of action are also sex-specific. One explanation for nicotine’s sex-specific effects could be the different levels of circulating sex hormones. For example, it has been previously reported that progesterone is capable of inhibiting nAChR activity (Valera et al., 1992). In addition, evidence also exists for sex differences in cholinergic neurotransmission within the hippocampus (Madeira & Lieberman, 1995). While the mechanisms underlying the sex-specific responses remain unclear, it appears that sex is an important experimental variable to consider. Moreover, not only are innate sexual dimorphism apparent, but the many effects of nicotine appear to also depend on sex, in a task-dependent manner.  4.4.4  Overall Behavioural Interpretation  Chapter 4 tested the hypotheses that: pre- and perinatal nicotine exposure would manifest behaviourally as increased locomotor activity, increased anxiety-like behaviour, and deficits in cognitive capacities. Additionally Chapter 4 tested whether differential effects of pre- and perinatal nicotine exposure on behavioural phenotypes were apparent between genotypes and sexes, with males and B6 mice being more sensitive to the effects of pre- and perinatal nicotine compared to females and D2 mice, respectively. Evaluation of locomotor activity revealed that the locomotor-activating effects of nicotine was dependent on genotype and sex, in which nicotine induced hyperactivity in D2 females. By contrast, we observed the unexpected result that pre- and perinatal nicotine exposure resulted in hypoactivity in B6 females as adults. Evaluation of anxiety-like behaviour yielded similar results in that preand perinatal nicotine produced modulatory effects rather than definitive anxiogenic- or anxiolytic effects that depended on genotype, age, and sex. In particular, pre- and perinatal nicotine exposure decreased EPM open arm duration in B6 males as adults, but increased EPM open arm entries in D2 females as adults. Given the effects of nicotine on anxiety-like behaviours were only evident in the EPM, and not the OF, it appears the effects were task-dependent suggesting different tests measure different forms of anxiety. Finally evaluation of the memory tasks revealed that pre- and perinatal nicotine exposure led to memory deficits in both NOR and PA. The effects of nicotine on object recognition memory were confined to male adults, whereas the effects of nicotine on PA memory were seen globally across genotype, age, and sex. Our hypotheses that B6 mice compared to D2 mice, and male mice compared to female mice, would show greater vulnerability to the effects of early nicotine exposure were not entirely validated. It appears that indeed the outcomes from pre- and perinatal nicotine exposure depend on 62  4.4. Discussion both sex and genotype. However the interactions between nicotine and either sex or genotype are complex and appear to be task-dependent. This is not surprising given the differing patterns of neural activity across tasks and the ability of nicotine to induce widespread effects on structural changes and neurotransmitter systems (for review see Slotkin, 1998; Smith et al., 2010). What is surprising is that pre- and perinatal nicotine exposure appears to attenuate the natural strain differences observed between B6 and D2 inbred mouse strains as adults, observed in both EPM and NOR measures. The effects of early nicotine exposure appear to be very long-lasting. The effects of pre- and perinatal nicotine were minimal immediately following treatment (PN24) but intensified in early adulthood (PN75), a conclusion drawn from observation across all behavioural tasks. This timeframe parallels the development of neuropsychiatric disorders that are associated with maternal smoking during pregnancy. The delayed effects of nicotine may be founded in ‘functional teratogenicity’ in which prenatal drug exposure may not cause any gross physical malformations at birth but may produce cellular and sub-cellular defects that form the basis for functional deficits which only become apparent gradually during maturation as subtle permanent behavioural deviations (Doerner, 1976; Doerner et al., 2001). The deficits reported here may rest on structural damage (cell loss, changes in cell size) evoked by pre- and perinatal nicotine exposure, or through unique effects on various neurotransmitter systems. In the next chapter we explored the impact of early nicotine exposure on the morphology of the striatum and hippocampus, two brain regions associated with the behavioural changes noted here. Lastly, we would like to acknowledge that mice were eliminated from NOR data analysis when they did not spend a total of 6 s exploring the objects on training or testing (~20% of all animals). This time limitation was implemented based on the test itself. The relative time spent exploring the novel and familiar object during the test trial is taken as an index of learning and memory. When animals do not explore the objects in either the training or test trials, it is impossible to evaluate NOR memory. Unfortunately the excluded mice were not evenly distributed across grouping variables (Table 4.2). There was a strong bias towards the exclusion of adult D2 mice, who also displayed the highest level of anxiety-like behaviours based on OF and EPM measurements. In future studies we will increase the test trial from 5 min to 10 min. We believe this will improve the time spent exploring the objects, and thus decrease the number of subjects eliminated from NOR analysis. In summary our data show that pre- and perinatal nicotine exposure exerts long-lasting effects on locomotor activity, anxiety-like behaviour, and memory processes in a manner dependent on genotype and sex. To our knowledge, no one has yet explored the genotype, age, treatment, and, sex simultaneously. Therefore past studies have been limited by methodological design to account for various factors contributing to the measured behavioural outcomes mediated by early nicotine exposure.  63  Chapter 5  Effects of Pre- and Perinatal Nicotine Exposure on Histological Measures 5.1  Introduction  In our previous chapter, we found that pre- and perinatal nicotine exposure exerts long-lasting effects on behavioural phenotypes in a manner dependent on genotype and sex. A number of underlying mechanisms may contribute to these alterations. For example, previous studies have shown altered cell-packing density, cell number, and cell size from early nicotine exposure (Roy & Sabherwal, 1994; Roy et al., 1998, 2002; Chen et al., 2005). Structural changes in the brain are expected to have profound effects. Even small perturbations in regional brain volume or neuronal number may lead to phenotypic variability. The present study was undertaken to determine whether the effects of pre- and perinatal nicotine exposure on morphological features extend to the striatum and hippocampus, and whether or not the effects depend on either genotype or sex. Stereological analysis of the striatum and hippocampus were selected as a result of their relationship to various brain functions vulnerable to the effects of pre- and perinatal nicotine exposure. The striatum of the basal ganglia is comprised of two nuclei, the caudate and putamen (Utter & Basso, 2008). While best known for its role in motor control, the striatum has also been implicated in cognitive processing and reward-related mechanisms (see Section 1.8.6). The striatum is the major input structure of the basal ganglia, receiving converging glutamatergic input from the cortex and thalamus as well as dopaminergic input from the midbrain. Integration of these extrinsic inputs is modulated by the intrinsic actions of ACh (Partridge et al., 2002). Indeed ACh is a prominent neurotransmitter within the striatum, acting in part through nAChRs, to regulate synaptic function. Differences in nAChR expression and activity have been related to altered learning and habit formation (Gattu et al., 1997; Partridge et al., 2002). Studies have shown long-lasting increased nicotinic receptor binding in the striatum of offspring prenatally exposed to nicotine, suggesting that nicotine exposure is able to alter striatal function (Van de Kamp & Collins, 1994). Furthermore the striatum shows both upregulation of growth factor expression that promote cell survival, as well as upregulation of death receptors and caspases following gestational nicotine exposure (Wei et al., 2011). The balance of these pathways may determine the final effect of nicotine on cell survival. To-date however, no studies have assessed the impact of early nicotine exposure on striatal morphology. It 64  5.2. Methods is possible that neuronal loss in the striatum may lead to altered motor control, cognitive processing, and reward-related mechanisms, behaviours all related to gestational nicotine exposure. The hippocampus is generally recognized to play a pivotal role in both cognition and emotion (i.e. anxiety) (reviewed by Fanselow & Dong, 2010). In the hippocampus nAChRs are expressed on GABAergic interneurons and on pyramidal cells (Jones & Yakel, 1997; Son & Winzer-Serhan, 2008). While early hippocampal development is driven by spontaneous oscillatory activity, which itself is regulated by excitatory GABA (Feller, 1999), it is indirectly controlled by ACh (Liu et al., 2006). Neuronal nAChRs are involved in the appropriate timing from excitatory to inhibitory GABAergic signalling. The interplay between nAChR signalling and GABA neurotransmission suggest an integral role for nAChR activity in hippocampal development and the potential for early nicotine to alter adult hippocampal circuitry via early discoordinated nAChR activation. There have been studies that have assessed the impact of early nicotine exposure on hippocampal regional volumes and cell counts (Abdel-Rahman et al., 2005; Chen et al., 2006). Yet these published reports have yielded inconsistent findings and have not considered either genotype or sex on the measured outcome. In order to address these inconsistent findings as well as account for possible genotype and sex effects, we chose to examine regional hippocampal volumes in male and female B6 and D2 mice. There is high correlation amongst neuronal number and hippocampal regional volumes (Abussad et al., 1999; Peirce et al., 2003), therefore hippocampal volume estimates provide a rapid first assessment to obtain first approximation of neuronal number.  5.2 5.2.1  Methods Animals and Breeding  Offspring from B6 and D2 dams that were exposed pre- and perinatally to nicotine or otherwise water controls were used. The number of subjects used for histological measurements is summarised in Table 5.1. An average of 4.5 mice per group (subdivided by strain, age, treatment, and sex) were used for histology, with a minimum of 4 per group. The animal and breeding procedures were described in Chapter 2.  5.2.2  Tissue Collection and Processing  Tissue collection and immunohistochemical techniques were described in Chapter 2.  5.2.3  Stereology  Stereological estimates were described in Chapter 2. Briefly, a pilot study was performed initially to determine the sampling and counting schedule. The first section in a series to be analyzed was  65  5.2. Methods Group B6 PN24 Female B6 PN24 Male D2 PN24 Female D2 PN24 Male B6 PN75 Female B6 PN75 Male D2 PN75 Female D2 PN75 Male Total  Treatment Water Nicotine Water Nicotine Water Nicotine Water Nicotine Water Nicotine Water Nicotine Water Nicotine Water Nicotine  Number of Litters 4 6 4 4 4 4 4 4 4 4 5 4 5 5 4 6  Number of Subjects 4 7 4 4 4 4 4 4 4 4 5 4 5 5 4 6 72  Table 5.1: Summary of subjects used for histological measurements subdivided by strain, age, treatment, and sex. chosen at random and thereafter every successive sixth section was collected from the series, repre-  senting a one-sixth sampling fraction (SSF). The counting frame used was 25 ♠m2 in a 500 ♠m2 grid size, yielding an area sampling fraction (ASF) of 25 ♠m2 /500 ♠m2 . The thickness sampling fraction  (TSF) was 28 ♠m/50 ♠m. Estimates of total neuron number were calculated from the number of  counted neurons (❙Q ) using the optical fractionator technique (Section 1.9.2). Volume estimates  were calculated according to Cavalieri’s principle (Section 1.9.1). Volumetric and cell count data were expressed as group means based on 4 to 7 cases per strain, age, treatment, and sex.  5.2.4  Statistical Analysis  Statistical analyses were described in Chapter 2. Using IBM SPSS Statistics 19 software (IBM SPSS Statistics, IBM, Chicago, IL), linear regression analysis was performed to examine the relationship between striatal- and hippocampal volumes and behavioural measurements (OF total distance, EPM open arm duration and entries, NOR preference ratio and difference scores, and PA retention latency).  66  5.3. Results  5.3 5.3.1  Results Striatal Volume  A four-way ANOVA was conducted that examined the effect of strain (genotype), age, treatment, and sex on striatal volume (Figure 5.1). There was a significant main effect of strain (F(1,55) = 107.510, p = 1.5E-14), age (F(1,55) = 144.557, p = 5.1E-17) and treatment (F(1,55) = 5.531, p = 0.022) on striatal volume. There was a significant age x treatment x sex interaction (F(1,55) = 4.228, p = 0.045). Data revealed that striatal volume was significantly larger in B6 mice (9.5 +/- 0.3 mm3 ) compared to D2 mice (7.3 +/- 0.3 mm3 ), representing a 23% difference. Moreover striatal volume was significantly larger in adults (9.7 +/- 0.2 mm3 ) compared to preadolescent mice (7.1 +/- 0.28 mm3 ), representing a 27% difference. Analysis of the age x treatment x sex interaction revealed that pre- and perinatal nicotine exposure significantly reduced the striatal volume of preadolescent B6 and D2 female mice (7.7 +/- 0.8 mm3 ) compared to preadolescent female water counterparts (6.7 +/- 0.5 mm3 ), representing a significant 13% difference (F(1,15) = 5.414, p = 0.034).  5.3.2  Striatal Neuronal Count  A four-way ANOVA was conducted that examined the effect of strain (genotype), age, treatment, and sex on neuronal counts within the striatum (Figure 5.2). There was a significant main effect of strain (F(1,55) = 22.794, p = 1.4E-5), age (F(1,55) = 11.181, p = 0.001) and sex (F(1,55) = 9.452, p = 0.003) on neuronal number. An effect of treatment approached significance (F(1,55) = 3.029, p = 0.087). An age x treatment x sex interaction (F(1,55) = 3.577, p = 0.064) and a strain x age x treatment x sex interaction (F(1,55) = 3.621, p = 0.062) approached significance. The Data revealed that striatal neuronal number was significantly larger in B6 mice (2.58 +/- 0.06 x 106 ) compared to D2 mice (2.26 +/- 0.05 x 106 ), representing a 12% difference. Additionally striatal neuronal number was larger in adults (2.52 +/- 0.06 x 106 ) compared to preadolescent mice (2.31 +/- 0.06 x 106 ), representing an 8% difference. Data subdivided by treatment and strain revealed that the increased neuronal number in adults is attributed to a significant increase in the B6 nicotine-treated cohort (F(1,15) = 12.313, p = 0.003). For further analysis of the effects of pre- and perinatal nicotine exposure, data were subdivided by strain and age to reveal a significant effect of treatment in B6 mice as preadolescence (F(1,15) = 4.919, p = 0.042), indicating that pre- and perinatal nicotine exposure significantly reduced striatal neuronal number in this group. To interpret the effects of nicotine on each group, data was subdivided by strain, age, and sex. Results revealed that preand perinatal nicotine exposure significantly decreased neuronal number in preadolescent B6 males (F(1,6) = 6.851, p = 0.040) and adult D2 males (F(1,8) = 6.836, p = 0.031). By contrast early nicotine exposure increased neuronal number in preadolescent D2 male mice (F(1,6) = 7.562, p = 0.033). A sex effect was also noted in nicotine-treated preadolescent D2 mice, indicating that these females had significantly fewer neurons compared to their male counterparts (F(1,6) = 24.032, p = 67  5.3. Results  Figure 5.1: Striatal volume (mm3 ) at PN24 (top) and PN75 (bottom) determined in B6 and D2 mice exposed pre- and perinatally to either water or nicotine (200 ♠g/ml). Values are expressed as the mean +/- S.E.M. Asterisks (*) denote values for which nicotine groups differed significantly (p < 0.05) from the corresponding controls. 68  5.3. Results 0.003).  5.3.3  Hippocampal Volume  Granule Cell Layer of the Dentate Gyrus Only the GCL of the dentate gyrus was considered. A four-way ANOVA was conducted that examined the effect of strain (genotype), age, treatment, and sex on the GCL volume. A main effect of treatment approached significance (F(1,55) = 3.124, p = 0.083). There was no significant effect or interactions of strain, age, or sex. We performed a lower-order ANOVA subdivided by strain based on an a priori hypothesis that B6 and D2 mouse hippocampus will show different sensitivity to nicotine based on the distinct hippocampal nAChR expression profile between them (Stevens et al., 1996). There was no significant effect or interaction of sex or age and therefore data was collapsed across these variables. Data subdivided by strain revealed that pre- and perinatal nicotine exposure significantly reduced GCL volume in D2 mice (F(1,34) = 5.465, p = 0.025), but B6 mice were unaffected by nicotine (Figure 5.3). Specifically, early nicotine exposure led to a significant 14% reduction in GCL volume between D2 nicotine-treated (0.31 +/- 0.01 mm3 ) and water-control mice (0.36 +/- 0.02 mm3 ). Pyramidal Cell Layer of the CA1 Subfield Only the PCL of the CA1 subfield was considered. A four-way ANOVA was conducted that examined the effect of strain (genotype), age, treatment, and sex on CA1 volume. There was no significant effect or interaction of sex and therefore data was collapsed across sex. There was a significant main effect of strain (F(1,61) = 4.356, p = 0.046) (Figure 5.4). A strain x age (F(1,61) = 3.154, p = 0.081) interaction approached significance. Analysis of the strain x age interaction revealed that CA1 volume was significantly larger in D2 mice (0.21 +/- 0.01 mm3 ) compared to B6 mice (0.15 +/- 0.01 mm3 ) as adults, representing a 28% difference, but there was no difference as preadolescents. Pyramidal Cell Layer Volume of the CA2/3 Subfield Only the PCL of the CA2 and CA3 subfields was considered. A four-way ANOVA was conducted that examined the effect of strain (genotype), age, treatment, and sex on CA2/CA3 volume. There was no significant effect or interaction of age or sex and therefore data was collapsed across these variables. There was a significant main effect of strain (F(1,65) = 9.072, p = 0.004) (Figure 5.5), indicating that CA2/CA3 volume was significantly larger in D2 mice (0.35 +/- 0.02 mm3 ) compared to B6 mice (0.27 +/- 0.02 mm3 ), representing a 23% difference. No effect or significant interactions with treatment were detected.  69  5.3. Results  Figure 5.2: Total striatal neuronal count at PN24 (top) and PN75 (bottom) determined in B6 and D2 mice exposed pre- and perinatally to either water or nicotine (200 ♠g/ml). Values are expressed as the mean +/- S.E.M. Asterisks (*) denote values for which nicotine groups differed significantly (p < 0.05) from the corresponding controls. Brackets group values for which nicotine groups differed significantly from the corresponding controls. 70  5.3. Results  Figure 5.3: GCL volume (mm3 ) determined in B6 and D2 mice exposed pre- and perinatally to either water or nicotine (200 ♠g/ml). Values are expressed as the mean +/- S.E.M. Asterisks (*) denote values for which nicotine groups differed significantly (p < 0.05) from the corresponding control.  71  5.3. Results  Figure 5.4: CA1 PCL volume (mm3 ) at PN24 and PN75 determined in B6 and D2 mice. Values are expressed as the mean +/- S.E.M. Asterisks (*) denote values for which there is a significant (p < 0.05) effect of strain.  72  5.3. Results  Figure 5.5: CA2/3 and CA1-CA3 PCL volumes (mm3 ) determined in B6 and D2 mice. Values are expressed as the mean +/- S.E.M. Asterisks (*) denote values for which there is a significant (p < 0.05) effect of strain.  73  5.4. Discussion Pyramidal Cell Layer Volume of the CA1-CA3 Subfield The volume of the PCL (CA1-CA3) was obtained by adding the volumes of CA1 and CA2/CA3 subfields. A four-way ANOVA was conducted that examined the effect of strain (genotype), age, treatment, and sex on CA1-CA3 volume. There was no significant effect or interaction of age or sex and therefore data was collapsed across these variables. There was a significant main effect of strain (F(1,65) = 7.370, p = 0.008) (Figure 5.5), indicating that CA1-CA3 volume was significantly larger in D2 mice (0.55 +/- 0.03 mm3 ) compared to B6 mice (0.45 +/- 0.03 mm3 ), representing an 18% difference. No effect or significant interactions with treatment were detected.  5.3.4  Histological and Behavioural Correlations  A simple regression test was performed for analysis of the linear relationship between two parameters. Specifically the relationships between behavioural performance and striatal, GCL, and CA1CA3 PCL volumes were examined (Table 5.2). Regression analysis revealed there was a significant relationship between striatal volume and hyperactivity, anxiogenic-like behaviour, and poor performance in the PA test. Similarly, regression analysis revealed that GCL volume positively related to anxiolytic-like behaviour interpreted from the time spent in the OF center. The regression typically described between 8- and 15% of the total variance in the behavioural phenotypes. A significant fraction of the variation in the behavioural phenotypes cannot be predicted on the basis of striatal or hippocampal subfield volume.  5.4  Discussion  Natural variation in either the absolute or relative size of different brain regions is substantial amongst human populations (Blinkov & Glezer, 1968). Differences in the size and structure of various brain regions are known to be associated with differences in brain development, behaviour, and disease susceptibility (Nowakowski & Rakic, 1981; Nowakowski, 1986; Williams & Herrup, 1988). As in humans, hippocampal volume (Wimer et al., 1976, 1978; Lu et al., 2001; Peirce et al., 2003) and striatal volume (Harris et al., 1999; Rosas et al., 2001; Rosen et al., 2009) are highly variable amongst inbred mouse strains. Baseline structural variation amongst either human populations or inbred mouse strains may influence their response to early nicotine exposure. Similarly, nicotine exposure may differentially alter the size and structure of various brain regions among genetically diverse populations. For example, developmental nicotine could have varying effects across different brain regions as well as different strains depending on nAChR subtype expression. In the present study we tested the hypothesis that pre- and perinatal nicotine exposure exerts an effect on the structural integrity of the striatum and hippocampus, two brain regions involved in behavioural phenotypes associated with developmental nicotine exposure. We hypothesized that baseline differ-  74  5.4. Discussion  Total Distance  Center Duration  Open Arm Duration  Open Arm Entries NOR PA retention Latency  Striatal Volume  GCL Volume  CA1-CA3 PCL Volume  DisTotal = 540VS + 7100 R2 adj = 8% F(1, 41) = 4.821, p = 0.034  /  /  /  DurCenter = 720VGCL 70.8 R2 adj = 15% F(1, 39) = 8.151, p = 0.007  /  /  /  /  /  /  /  /  /  DurOpen = -5.1VS + 93 R2 adj = 10% F(1, 38) = 5.396, p = 0.026 EntriesOpen = -0.49VS + 8.3 R2 adj = 9% F(1, 38) = 4.773, p = 0.035 / Tret = -11.1VS + 230 R2 adj = 13% F(1, 37) = 6.886, p = 0.01  Table 5.2: Parameters of linear regression analyses performed between regional volumes and behavioural measures. A slash (/) denotes no significant correlation. Abbreviations: DisTotal, total distance; DurCenter, center duration; DurOpen, open arm duration; EntriesOpen, open arm entries; Tret, rentention latency; VS, striatal volume; VGCL, granule cell layer volume; R2 adj, adjusted R-square  75  5.4. Discussion ences between strains would be evident, and that nicotine exposure would differentially affect B6 and D2 mice based on natural structural variation and distinct nAChR expression profiles between them.  5.4.1  Striatum  In the current study structural variation within the striatum was assessed from striatal volume and neuronal count measurements. We report here that B6 mice had significantly larger striatal volumes and greater striatal neuronal number compared to D2 mice, suggesting that these traits are heritable. Previous studies support these findings, having reported that striatal volume varies across inbred mouse strains (Rosen et al., 2009). Our data also indicated an effect of age on both striatal volume and neuronal number, whereby adults showed a greater striatal volume as well as greater neuronal number relative to preadolescent counterparts. Striatal neuronal number would not be expected to increase with aging. However the increase in neuronal number observed in adult mice resulted from a significant increase amongst the B6 nicotine-treated cohort. Since pre- and perinatal nicotine exposure significantly reduced striatal neuronal number in B6 mice as preadolescents, the increase likely represents a compensatory mechanism to attenuate the effects of early nicotine exposure. Several mechanisms may underlie the increased neuronal number observed across age in the B6 cohort. For example, previous studies have revealed that, in the adult brain, new neurons generated from the subventricular zone (SVZ) can replace those lost following ischemic stroke in the striatum (Arvidsson et al., 2002; Parent et al., 2002; Thored et al., 2006; Yamashita et al., 2006; Hou et al., 2008). These studies further indicated that neuroblasts, that migrated from the SVZ into the injured striatum, appear to differentiate into a region-appropriate phenotype, suggesting that the adult brain has the capacity for self-repair after injury. Hypothetically, cell insult caused from early nicotine exposure may similarly activate cell proliferation in the SVZ and neuroblast migration and differentiation into the striatum. Alternatively, the observed increase in neuronal number may reflect an increase in astroglial cells. Certainly past studies have indicated that prenatal nicotine increases glial fibrillary acidic protein (GFAP) expression in the cerebellum and hippocampus (Abdel-Rahman et al., 2003, 2005). Considering astroglial cells are a larger type of glial cell that show variable irregular shapes as well as colour and size (Nauta & Feirtag, 1986), it is possible that they were inappropriately counted as a neuron. Lastly, we cannot exclude the possibility that the increased neuronal number reflects an artifact of the quantitative technique used. Nevertheless we find this possibility unlikely since the increase in neuronal number was confined to the B6 nicotine-treated cohort, and further since the quantification of neurons was performed using the optical fractionator technique, which in theory, is independent of structural volume and orientation of objects (West, 2002). The current study revealed an effect of pre- and perinatal nicotine exposure on striatal volume and neuronal number. In particular early exposure to nicotine led to a significant decrease in striatal 76  5.4. Discussion volume among the B6 and D2 females as preadolescents. No effect of treatment was observed on striatal volume in adults suggesting that the effects of nicotine exposure on striatal volume may not be long-lasting or that compensatory mechanisms exist. Similarly the effects of pre- and perinatal nicotine on striatal neuronal number were most evident at the preadolescent stage. In particular pre- and perinatal nicotine exposure significantly reduced neuronal number within the striatum of B6 mice as preadolescents, but not as adults. Previous studies have shown that early exposure to nicotine mediates immediate deficits in the number of neural cells in specific brain regions, but later there is recovery of neural loss (Slotkin et al., 1986; Roy & Sabherwal, 1994; Roy et al., 2002; Slotkin et al., 2007a). In keeping with this sequence, in the present study, we observed only inconsistent changes in neuronal loss from early nicotine exposure in adults, whereby only adult D2 males showed neuronal deficits on account of nicotine. Interestingly, nicotine also exhibited unique, unexpected effects on D2 male mice as preadolescents, revealed by an increase (rather than decrease) in neuronal number within the striatum on account of pre- and perinatal nicotine exposure. The unexpected findings demonstrating an opposite effect of nicotine in the D2 male cohort at both preadolescence and adulthood relative to all other grouping cohorts (Figure5.2) suggest that the effects of nicotine may depend on genotype and sex. The influence of genotype and sex is also reflected in the fact that pre- and perinatal nicotine exposure selectively impacted B6 striatal neuronal number and female striatal volume during preadolescence (Figures 5.2 and 5.1). The varying effects of nicotine on striatal morphology may result from differences in nAChR expression across genotype or sex. For example, reports show sexual dimorphism in nicotine-induced upregulation of CNS nAChRs (Koylu et al., 1997; Mochizuki et al., 1998). Similarly, hyperactivity induced by prenatal nicotine has been associated with elevated nAChR expression in the cortex and striatum of male, but not female rats (Tizabi et al., 1997). Nicotine similarly exhibited distinct effects in D2 males across age, reflected by increased striatal neuronal number in preadolescent D2 male mice in contrast to a decrease in adult D2 male mice. This distinction may be due to changes in hormonal levels through puberty. Preadolescence represents a pre-pubertal stage of development whereas adulthood represents a post-pubertal stage. Changes in levels of gonadal hormones may influence the outcomes resulting from pre- and perinatal nicotine exposure and may furthermore interact with genotype (i.e. nAChR expression). Indeed gonadal hormones have been shown to interact with nAChRs to modulate their activity (Damaj, 2001). Finally, given the close anatomic association of nAChRs and the dopamine transmitter systems, differences in nicotine-induced striatal phenotypes across genotype may, in part, arise from differences in striatal dopamine neurotransmission across B6 and D2 mice. For example, B6 mice exhibit lower numbers of D2 dopamine receptors within the VTA and a higher number of D1- and D2 dopamine postsynaptic receptors within the striatum compared to D2 mice (Ng et al., 1994; PuglisiAllegra & Cabib, 1997). During development the dopamine system has been shown to regulate 77  5.4. Discussion both neurite outgrowth an cell cycle progression (Reinoso et al., 1996; Parish et al., 2001; Ohtani et al., 2003; Zhang et al., 2004). In particular Zhang et al. (2004) showed that D1 receptor activation suppresses the proliferative activity in primary cultures of cerebral cortical precursor cells. Together, these data suggest that alterations in dopamine signalling in the foetus on account of early nicotine exposure (Section 1.3.3) may have functional effects on structural phenotypes. Basal differences in the dopamine system between B6 and D2 mice may confer different susceptibilities to nicotine-induced alterations on the dopamine system, which is discussed further in Section 6.2.1.  5.4.2  Hippocampus  In the current study structural variation within the hippocampus was assessed from volume measurements within the GCL of the dentate gyrus as well as the PCL within the CA1- and CA2/3 subfields. The PCL from CA1 through CA3 was obtained by adding the volumes of CA1 and CA2/3 measurements. We report here that there was no difference between strains in GCL volume. However, D2 mice had significantly larger PCL volumes compared to B6 mice reflected across CA1, CA2/3, and CA1-CA3 measurements (although this strain difference was only significant in the adult cohort for the CA1 subfield). Previous reports have been published indicating that hippocampal volumes vary across inbred mouse strains (Abussad et al., 1999; Lu et al., 2001; Peirce et al., 2003). However there are inconsistencies amongst reported absolute and relative volumes between research groups (Table 5.3). For example, Abussad et al. (1999) reported that D2 mice showed significantly larger GCL and PCL volumes compared to B6 mice. By contrast, Peirce et al. (2003) reported that B6 mice showed significantly larger GCL and PCL volumes compared to D2 mice. Our data is more similar to that reported by Abussad et al. (1999) since our data also indicates that D2 mice show greater regional hippocampal volumes compared to B6 mice. However, unlike our colleagues, we did not find significant differences in GCL volumes between B6 and D2 mice. Further research is necessary to clarify this point. One possible explanation for these discrepancies could be the procedure used for the volumetric measures, which were evident across studies. For example differences in age, embedding techniques, section thickness, and/or tracing (defining) the region of interest may all yield variation in reported results. Despite these rather inconsistent findings, the structural differences in the current study and previous studies suggest that genotype influences regional hippocampal volumes. The current study revealed a minimal effect of pre- and perinatal nicotine exposure on regional hippocampal volume. Our results indicated that pre- and perinatal nicotine exposure reduced the GCL volume in D2 mice, but B6 mice were not affected by the effects of nicotine exposure. The effects of nicotine on D2 mouse GCL volume appeared more robust at PN24, even though age was not statistically significant. Moreover, there were no other significant effects of nicotine exposure on hippocampal morphology as assessed from volumetric analysis of the PCL of the CA1 and CA2/3 subfields. 78  5.4. Discussion  Age  Current Study  PN24 & PN75  Strain  B6 D2  Abussad et al. 1999 Peirce et al. 2003  PN63  B6 D2  ~PN88  B6 D2  CA1CA3 PCL Volume (mm3 ) 0.45 +/0.03 0.55 +/0.03 0.55 +/0.01 1.32 +/0.02 1.41 +/0.03 1.18 +/0.03  Relative (B6:D2) CA1CA3 Volume (%) 82  42  119  GCL Volume (mm3 ) 0.35 +/0.01 0.33 +/0.01 0.15 +/0.02 0.26 +/0.03 1.09 +/0.05 0.75 +/0.02  Relative (B6:D2) GCL Volume (%) 106  57  145  Table 5.3: A cross-study comparison of hippocampal volume measurements determined in B6 and D2 mice across studies. Values are expressed as the mean +/- S.E.M. Although the current findings do not altogether support the hypothesis that early nicotine exposure leads to a significant and long-lasting decrease in regional hippocampal volumes, it should not be interpreted that pre- and perinatal nicotine exposure is generally harmless to the developing PCL of the hippocampus. In fact published studies have reported that nicotine exposure during brain development alters the physiology of cells and the size of individual cells within various subfields of the hippocampus. For example, it has been shown that prenatal nicotine exposure resulted in a decrease in cell size, an increase in free ribosomes, and dilation of the rough endoplasmic reticulum and Golgi apparatus in both the granule and pyramidal cells of the hippocampus (Roy & Sabherwal, 1998; Roy et al., 2002). Similar to the current findings, a previous study reported that pre- and perinatal nicotine exposure had no effect on the estimated pyramidal cell number or volume in the adult CA1 and CA3 fields. Furthermore however, this study found no effect from nicotine treatment in the granule cell number or volume in the adult dentate gyrus (Chen et al., 2006). Unlike Chen et al. (2006) we observed differences in GCL volumes between nicotine-treated and control D2 mice. One possible explanation for this discrepancy could be the rodent model used, such that our colleagues used rats. As our data suggests, genotype exerts a strong influence on measured outcomes, which appear to be region-specific, possibly influenced by the spatial expression profile of various nicotinic receptor subtypes as well as the developmental timeframe of each region. In summary, the present findings do not show any effect of early nicotine exposure on the volume 79  5.4. Discussion of hippocampal PCL. A reduction in GCL was shown in D2 mice from early nicotine exposure. It is critically important to recognize that the lack of effect of nicotine on various measured outcomes in this study does not suggest that nicotine is not damaging to that particular region of the developing brain. Cell loss or regional volume changes represent only one of the many forms of injury that may occur to a developing neuron and/or brain region.  5.4.3  Overall Histological Interpretation  There is a wealth of information suggesting that nicotine alters replications and differentiation of neural cells (Levin & Slotkin, 1998; Slotkin, 1998). However a number of important questions concerning the mechanisms and specificity of nicotine’s effects have not been addressed. To this end, Chapter 5 tested the hypotheses that: pre- and perinatal nicotine exposure would lead to decreases in striatal neuronal number and volume as well as decreases in hippocampal regional volumes. Additionally Chapter 5 tested whether differential effects of pre- and perinatal nicotine exposure on morphological phenotypes were apparent between genotypes and sexes, with males and B6 mice being more sensitive to the effects of nicotine compared to females and D2 mice, respectively. Evaluation of striatal neuronal number and volume indicated that the ability of nicotine to alter striatal structure was largely limited to the preadolescent cohort, suggesting the effects were not long-lasting. Furthermore the effects of nicotine were dependent on genotype and sex, in which early exposure to nicotine led to a significant decrease in striatal volume among the B6 and D2 females as preadolescents and significantly reduced neuronal number within the striatum of B6 mice as preadolescents. Here it appears that female mice and B6 mice are more sensitive to the effects of nicotine on striatal structure, which does not fully satisfy our hypothesis that males and B6 mice would in fact be more sensitive. This reinforces our behavioural data indicating that the interactions between nicotine and either sex or genotype are complex and appear to be task-dependent and region-specific. Evaluation of hippocampal regional volumes supports the region-specific effects of nicotine. While pre- and perinatal nicotine exposure reduced the GCL volume in D2 mice, there were no effects noted on PCL volume. This contradicted our hypothesis that early nicotine exposure would lead to volume reductions in both the dentate gyrus as well as the CA1 and CA2/3 subfields. In summary our data show that pre- and perinatal nicotine exposure exerts immediate effects on striatal structure and GCL volume in a manner dependent on both genotype and sex. The observed changes in neuronal number and regional volumes that result from early nicotine exposure are likely due to several mechanisms. For example, gestational nicotine exposure is thought to pre-empt the natural roles of acetylcholine neurotransmission resulting in the premature onset of cell differentiation at the expense of cell replication (Slikker et al., 2005). This may lead to structural alterations in regional brain areas. The decrease in cell number may also result from nicotine’s ability to decrease DNA synthesis, inhibiting mitosis, as well as its ability to induce cell damage postnatally (Slotkin et al., 1987a,b; McFarland et al., 1991). Indeed studies revealing 80  5.4. Discussion that gestational nicotine exposure alters gene expression profiles in the striatum across cell deathand survival-related pathways provides a possible mechanism to explain our findings (Wei et al., 2011). Regardless of the underlying mechanism, the current findings provided data on explicit sites of morphological changes induced by pre- and perinatal nicotine exposure. Future studies will be able to build on this data to investigate the underlying biochemical and molecular basis for these morphological changes.  81  Chapter 6  General Discussion 6.1  Summary  The main objective of this thesis was to investigate the influence of genotype and sex on the effects of pre- and perinatal nicotine exposure at two temporal timeframes. In particular we investigated the possibility that early nicotine-induced changes on behavioural tasks and/or selective histological features are dependent on genotype and/or sex. These data are the first to comprehensively evaluate genotype-dependent behavioural and neurochemical consequences following nicotine exposure during the full equivalent of human gestation in an animal model. Our findings indicate that alterations in nicotine-induced changes are indeed dependent on both genotype and sex in a task-dependent and region-specific manner. In the first study we evaluated the efficacy of the model system that we employed to determine the effects of pre- and perinatal nicotine exposure and genetic background on behavioural and histological phenotypes. Nicotine (200 ♠g/ml) was administered orally to pregnant B6 and D2 dams  starting 30 days before mating and continuing until the pups were weaned. We report here for the first time that both B6 and D2 mice consumed nicotine through their drinking water at a dose that reflects human exposure levels. Thus, this oral administration paradigm to administer nicotine to pregnant dams is a viable method for genetic studies comparing nicotine-related responses between B6 and D2 mouse strains. In our second study, we tested the hypotheses that pre- and perinatal nicotine exposure would manifest behaviourally as increased locomotor activity, increased anxiety-like behaviour, and deficits in cognitive capacities, and furthermore that genotype and sex would influence the outcomes induced by pre- and perinatal nicotine exposure. Our findings support that genotype and sex indeed influence nicotine-related phenotypes in a task-dependent manner. It appears that nicotine has complex effects that can lead to opposing effects that vary depending on genotype and sex. For example, our findings demonstrated that nicotine produced anxiogenic-like effects in B6 males as adults, whereas produced anxiolytic-like effects in D2 females as adults. Likewise, where nicotine induced hyperactivity in D2 female mice, it induced hypoactivity in B6 adult females. Finally, we report that, in general, males were more sensitive to the cognitive effects of nicotine such that only adult males showed deficits in object recognition; nevertheless nicotine led to deficits in PA memory across all variables. 82  6.2. Influencing Factors In our third study, in an attempt to relate our behavioural data to possible morphological changes, we tested the hypothesis that pre- and perinatal nicotine exposure would lead to decreases in striatal neuronal number and volume as well as decreases in hippocampal regional volumes at two distinct timeframes. Furthermore we hypothesized that genotype and sex would influence the outcomes induced by early nicotine exposure. To our surprise, the effects of early nicotine exposure on striatal and hippocampal structure were evident only at the immediate timeframe, and disappeared at the later testing phase indicating that the effects of nicotine on regional morphology were not long-lasting. This is in sharp contrast to our behavioural data, indicating the effects of nicotine became much more robust during the later testing phase (in the adult cohort). Nevertheless, our histological findings support that genotype and sex indeed influence nicotine-related phenotypes in a regional-specific manner. In the striatum, early nicotine exposure resulted in decreased striatal volume among preadolescent females and reduced neuronal number within the striatum of preadolescent B6 mice. In the hippocampus the effects of early nicotine exposure appeared more subtle, in which only the GCL of the dentate gyrus in D2 preadolescents was afflicted. Our findings do not reveal a clear association between behavioural and morphological phenotypes induced by early nicotine exposure. In fact our morphological data indicated more robust effects of pre- and perinatal nicotine exposure immediately at the preadolescent stage, whereas our behavioural data indicated the contrary, whereby the effects of nicotine were more evident at the adult stage. Although there is a discrepancy between the time course of observed phenotypes across behavioural and histological data, one cannot conclude that the immediate effects of early nicotine exposure on regional morphology do not contribute to the long-lasting effects on behavioural phenotypes. For example, although it appears that the structural changes induced by nicotine exposure later recover, it remains unknown whether synaptic function is forever changed. Furthermore, it is important to remember that cell loss or regional volume reduction are only two possible outcomes of early nicotine exposure, and that the normal function of a neuron can still be compromised without apparent structural alteration.  6.2  Influencing Factors  Certainly previous studies have highlighted the complexity of nicotine-related responses, concluding that the direction of response varies depending on the behavioural model, nicotine dose, and route or time course of administration (reviewed in Ernst et al., 2001; Picciotto et al., 2002). However our study was the first to characterize the effects of early nicotine exposure and genetic background in male and female mice at two temporal stages: preadolescence (immediately following nicotine exposure) and adulthood (well after pre- and perinatal nicotine exposure). Simultaneous examination of these four variables has been a major methodological constraint in prenatal nicotine research. These findings suggest a new avenue of research, not yet widespread in literature. 83  6.2. Influencing Factors Future studies will be able to build on this data to investigate the genetic basis as well as possible mechanisms that underlie the sexual dimorphic responses to nicotine. In the following section, the possible mechanisms that may contribute to the differential effects of nicotine across genotype, sex, and age are reviewed.  6.2.1  Genotype  Basal differences between B6 and D2 mouse strains may contribute to their differential responses to nicotine exposure. The most obvious explanation for the strain-dependent effects of early nicotine exposure is differences in nicotinic receptor transmission. These differences have already been addressed in Section 1.6.1. However, there are clearly additional contributing genetic factors beyond differences in nAChR sensitivity and expression. Given the close anatomic association between nAChRs and the dopamine system, divergence in gene expression patterns across the dopaminergic system may be a candidate mechanism underlying individual susceptibility to the effects of nicotine. Notably, B6 and D2 mice show distinct mesolimbic dopamine neurotransmission. Differences in dopamine metabolism, release, receptor density, receptor distribution, dopamine transporter (DAT) density, and morphological localization have all been reported (Ng et al., 1994; Puglisi-Allegra & Cabib, 1997; Janowsky et al., 2001; Cabib et al., 2002). Studies have linked differences in dopamine neurotransmission to observed behavioural differences across B6 and D2 mice. Perhaps not unlike the interaction between prenatal smoking and variations in the DAT1 and DRD4 loci associated with specific subtypes of ADHD (Section 1.4.3), early nicotine exposure interacts with genes related to dopamine transmission in B6 and D2 mice to influence the measured outcome. Compelling evidence illustrating the effects of prenatal nicotine exposure on the dopamine system, support the suggestion that nicotine-related phenotypes may, in part, arise from altered dopamine neurotransmission (Section 1.3.3). Our results indicate that B6 and D2 mice exhibit different genetic predispositions to the locomotor effects of nicotine. In general, D2 mice were more susceptible to nicotine-mediated hyperactivity compared to B6 mice (Figure 4.1). Indeed the dopamine system is well known for its role in motor control (for review Lima et al., 2009). It is possible that genotype-dependent changes of dopamine-mediated responses induced by early nicotine exposure account for these measured outcomes. Nevertheless, nicotinic receptor transmission is modulatory and is involved in the regulation of multiple neurotransmitter systems (Sher et al., 2004). Other strain differences, apart from the dopamine system, likely play a role in the resulting nicotine-induced phenotypic differences across strains.  6.2.2  Sex  Our data suggest that early nicotine exposure interacts with the brain in a sexual dimorphic manner. Indeed sex differences have been reported from nicotine exposure. In general results obtained from  84  6.2. Influencing Factors rodents suggest that males are more sensitive to the effects of pre- and/or perinatal nicotine exposure. This hypothesis is based on the observations that impairments to the cholinergic and serotonergic systems are more pronounced in male offspring, and early nicotine exposure leads to increased periadolescent nicotine preference and adult hyperactivity exclusively in males (Shacka et al., 1997; Klein et al., 2003; Pauly et al., 2004; Slotkin, 2004; Slotkin et al., 2007a; Slotkin & Seidler, 2010). Sex differences in the present study do not reflect a general heightened sensitivity of a particular sex, but instead reveal different sensitivities dependent upon the measured phenotype. There are several potential mechanisms that may explain sex differences in response to pre- and perinatal nicotine exposure. First, animal models and human studies both found that males and females metabolize nicotine differently, whereby females eliminate nicotine faster compared to their male counterparts (Hatchell & Collins, 1980; Benowitz et al., 2006). Second, chronic nicotine exposure has been shown to differentially regulate nAChRs across males and females, whereby males show increased nAChR upregulation compared to females (Koylu et al., 1997; Mochizuki et al., 1998). Third, steroid hormones modulate responses to nicotine. For instance, it has previously been shown that progesterone can inhibit nAChR activity (Valera et al., 1992), and both progesterone and 17❜-estradiol, but not testosterone, were shown to block nicotine-induced analgesia (Damaj, 2001).  Likewise, oestrogen was shown to enhance dopamine release in females, but decrease dopamine release in males (Dluzen & Anderson, 1997). These findings were based in adults. Although several studies have reported different behavioural outcomes between males and females from early nicotine exposure, fewer studies have interpreted differences in the underlying biological mechanisms. One study reported that prenatal nicotine treatment lowers foetal plasma testosterone exclusively in males (Lichtensteiger & Schlumpf, 1985) and leads to decreased perinatal male sexual behaviour (Segarra & Strand, 1989). von Ziegler et al. (1991) reported that aromatase activity, responsible for androgen to oestrogen conversion, is lowered in male offspring from prenatal nicotine exposure, but female aromatase activity is not affected. Indeed aromatase activity has been reported to interact with various neurotransmitter systems (Stewart & Rajabi, 1994; Cornil et al., 2005; Balthazart et al., 2006). Therefore considering these previously reported sex specificities of early nicotine exposure, it is possible that innate sexual dimorphism as well as sexual dimorphic effects of early nicotine exposure provide plausible mechanisms underlying the sex-dependent effects reported here. A discrepancy between studies was noted in regards to nicotine-mediated hyperactivity. Studies have generally indicated that female mice are less sensitive to the locomotor activating effects of nicotine (Shacka et al., 1997; Pauly et al., 2004; Vaglenova et al., 2004). Yet other studies have reported both female and male mice are vulnerable to nicotine-mediated locomotor changes, whereby females showed a stronger hyperactive response compared to males (Paz et al., 2007).We report heightened sensitivity in females. Differences in experimental design likely contribute to such discrepancies. For example, although Pauly et al. (2004) used a similar experimental design to the 85  6.2. Influencing Factors current study, two major differences existed. First, in the previous study nicotine exposure was terminated at birth, and second pups were cross-fostered to nicotine naive lactating dams. These two differences may potentially have major impacts. The experimental design of Pauly et al. (2004) did not take into account that, in the rodent, brain development during the first two postnatal weeks of life corresponds to the third trimester of human pregnancy. The brain growth spurt occurs postnatally and is recognized as a time that is especially vulnerable to environmental exposures (Dobbing & Sands, 1979). Therefore a complete rodent model, examining the effects of gestational nicotine, must incorporate exposure during the first two postnatal weeks. Additionally it is possible that cross-fostering alters behavioural outcomes. Pauly et al. (2004) chose to cross foster pups to control for possible differences in maternal behaviours on account of nicotine. However, a more recent paper from Heath et al. (2010) critically assessed maternal caregiving behaviours between mothers exposed to nicotine through their drinking water and controls to conclude that there was no effect of nicotine on maternal caregiving. Thus we chose not to cross-foster our pups, and believe that the differences between our results and previous reports are more likely on account of differences in temporal exposures rather than cross-fostering protocols. We cannot eliminate the possibility that the lack of effect from nicotine exposure in male adults is not by virtue of too small a group size and therefore not enough statistical power (See Table 4.1). A simple power analysis with the statistical significance level set at 0.05 suggests that there was not enough statistical power (observed power = 0.1) to test this question thoroughly amongst adult male treatment groups (Cohen, 1988).  6.2.3  Temporal Vulnerability  The immediate effects of pre- and perinatal nicotine exposure were assessed in rodents post-weaning, corresponding to preadolescence (PN20 – PN28), whereas the persistent effects of pre- and perinatal nicotine were assessed during adulthood (PN75) (Spear, 2000). The effects of early nicotine exposure appeared to be very long-lasting. It is interesting to note that many of the neurobehavioural deficits induced by nicotine exposure in the present study were either exclusively evident in adults or otherwise more prominent in adults. For example, the effects of nicotine on both anxiety-like behaviours and object recognition memory were confined to adult animals. Similar late-emerging effects of pre- and perinatal nicotine exposure on anxiety-like behaviours have been reported (Eppolito et al., 2010). Undoubtedly the many neural and endocrine changes occurring during preadolescence may mask the neurobehavioural deficits in emotional and memory responses. For example, age-related differences in the response of the HPA axis to a stressful experience, i.e. placement in a novel environment, were noted between periadolescent (~PN33) and adult (> PN60) mice, whereby periadolescent mice showed a unique profile of integrated behavioural and physiological hyporesponsivity to a stressful experience (Adriani & Laviola, 2000). These and other basal behavioural 86  6.3. Significance to the Research Field and/or physiological age-related differences may very well disguise the effects of nicotine. An alternate hypothesis underlying the late-emerging behavioural deficits induced by early nicotine exposure is the basis of “functional teratogenicity” which was described in Section 4.4.4. Clearly the timeframe of our phenotypic changes parallel the development of neuropsychiatric disorders that are associated with maternal smoking during pregnancy. In contrast to our behavioural data, the structural changes observed in selective brain regions were generally confined to the preadolescent testing period, which reflects the immediate effects of pre- and perinatal nicotine exposure. Taken together, these data from the present study suggest that early nicotine exposure produces immediate structural alterations in select brain regions and longterm changes in locomotor activity, anxiety-like behaviour, object recognition memory, and passive avoidance memory that manifest in adulthood. Although the structural and neurobehavioural effects are evident at different timeframes, it is not to say that they are mutually exclusive. For example, Slotkin et al. (2007a) reported that although nicotine-induced structural alterations recovered in adults, there were still abnormal cholinergic and serotonergic mechanisms, which may be attributed to early neural loss. Furthermore, these results are not to be taken to mean that there are no detrimental neurobehavioural effects for the preadolescent animal following pre- and perinatal nicotine exposure, as several studies have shown both mechanistic and behavioural changes in developing animals (i.e. adolescents) following gestational nicotine exposure (Roy et al., 2002; Kane et al., 2004; Chen et al., 2005; Levin et al., 2006a)  6.3  Significance to the Research Field  Nicotine is clearly a neuroteratogen, yet there are still gaps in our current knowledge in terms of the role early nicotine exposure has as a contributing factor to subtle long-term effects which manifest behaviourally as ADHD, conduct disorders, depression, anti-social behaviour, cognitive deficits, and so forth. It is difficult to study the effects of gestational nicotine exposure in human populations due to ethical issues as well as uncontrollable confounding factors. Moreover the connections between nicotine and these complex phenotypes are not straightforward. Therefore strong animal models are essential to gain insight into the effects of early nicotine exposure. Data from this thesis show that B6 and D2 mice represent an animal model to test genotype-dependent effects of early nicotine exposure. Few clinical studies have focused on possible genotype- or sex-dependent effects resulting from maternal smoking. This model system, in conjunction with RI BXD lines, may present a useful preclinical tool to identify novel genes as well as specify genetic targets of nicotine that may underlie individual vulnerabilities to the effects of nicotine. Clinical studies provide strong associations between an increased incidence of ADHD and prenatal smoking exposure (for further details see Section 1.4.3). Furthermore gene-environment interactions seem to be relevant for the development of ADHD. The enhanced mouse locomotor activity 87  6.3. Significance to the Research Field and cognitive deficits seen in this study could potentially be useful as an animal model of ADHD. The genotype-dependent sensitivity to nicotine exposure reported here lends further support to this animal model system. Our findings indicate that only D2 mice are vulnerable to the locomotor enhancing effects of pre- and perinatal nicotine exposure. In addition, although both B6 and D2 mice show cognitive deficits mediated by nicotine exposure, the effects are more robust in D2 mice. Our data suggest that D2 inbred mice display increased ADHD-like traits compared to B6 mice. In this regard, several groups have tested for differences between D2 and B6 mouse strains using an array of behavioural tests postulated to measure ADHD-like phenotypes. Such studies have reported that D2 mice were less accurate and made more impulsive responses in the 5-choice serial reaction time task compared to B6 mice (Patel et al., 2006). Moreover D2 mice possess a SNP in the tryptophan-hydroxylase-2 gene that significantly lowers frontal and striatal serotonin levels (Zhang et al., 2004), which in turn is associated with impulsive behaviour (Soubrié, 1986; Oades, 2002)). Lastly as discussed above (Section 1.4.3), VNTR 440 allele of DAT1 is a known risk allele for the development of ADHD. Although the mechanism behind this association is unknown, culminating evidence implicates variation in gene expression, in which the risk allele increases DAT1 expression (VanNess et al., 2005). Interestingly, Janowsky et al. (2001) reported a 50% higher DAT density in D2 mice compared to B6 mice in the neostriatum. These studies have confirmed differences in ADHD-like traits between B6 and D2 mice, which are now used for fine-scale QTL mapping alongside the RI BXD strains (for review Mill, 2007). Collectively our data corroborates previous data indicating that D2 inbred mice show increased ADHD-like traits compared to B6 mice. More importantly, we demonstrate for the first time the gene-environment susceptibility, which seems to be relevant for the development of ADHD. In conclusion this research provides data on explicit sites of morphological changes as well as detailed behavioural phenotypes induced by pre- and perinatal nicotine exposure. This information helps bridge several gaps in the current knowledge, which is ultimately critical for understanding the associations between in utero nicotine exposure and adverse foetal outcomes. The changes induced by nicotine, reported here, reinforce the current data indicating the potential for adverse outcomes from smoking during pregnancy and furthermore strengthen the case for discouraging NRT as a safe alternative. Our findings illustrating that selective behavioural and morphological effects induced by early nicotine exposure are dependent on genotype and sex, open up a new avenue of research which has not been examined thoroughly in either animal or human studies. Identification of the exact genetic- and sexual dimorphic mechanisms underlying individual vulnerabilities to nicotine exposure will provide insights into the role that nicotine plays in neurological disorders of the developing brain which could be used to later devise more effective treatments of these associated disorders.  88  6.4. Limitations and Future Directions  6.4  Limitations and Future Directions  Pre- and perinatal nicotine research using B6 and D2 mice can aid in uncovering genetic differences involved in nicotine neuroteratology, but there are some limitations. Using two inbred mouse strains helps identify whether genetic background indeed contributes to the effects of early nicotine exposure, however cannot provide the depth required to identify specific genetic loci mediating these effects. Nevertheless B6 and D2 mice are the progenitor strains for the BXD RI mouse lines in which there are a total of 81 existing strains for which genotype and phenotype data are publicly available (Peirce et al., 2004). Each of the BXD strains is a ‘mosaic’ of chromosomal segments from either the B6 or D2 mouse strains (Taylor et al., 1999). Use of a large reference panel increases the QTL mapping precision, enabling identification of QTLs and potentially identification of candidate genes. Our findings show that although the structural changes are evident immediately, there is a gradual development of behavioural abnormalities as a result of early nicotine exposure, paralleling the time course of clinical deficits associated with maternal smoking. While the timeframe of our phenotypic changes correlate well with human studies, the current study was limited because it did not account for the underlying molecular mechanisms that mediate these delayed effects. Indeed past studies suggest that prenatal nicotine exposure mediate their effects by altering developmental trajectories, consequently leading to differences in the adult brain, which account for the observed time course (Slikker et al., 2005). A limited number of studies have further identified long-lasting nicotine-induced changes in gene expression across various brain regions (Chen et al., 2005; Slotkin et al., 2007a; Eppolito et al., 2010; Cao et al., 2011; Schneider et al., 2011). Nevertheless there is a void in research addressing how pre- and perinatal nicotine exposure causes alterations in gene expression that are maintained into adulthood, long after the exposure period. An intriguing mechanism to explain the link between environment and gene function is through modulation of epigenetic mechanisms. ‘Epigenetic’ refers to mechanisms that modulate gene expression without altering the genetic code (Bird, 2002). In contrast to genetic information, the epigenome is dynamic and is thus, in principle, influenced by the environment. Despite the vulnerability of the developing brain to nicotine, to our knowledge, no animal study has examined possible modulation of epigenetic mechanisms from early nicotine exposure. Studies examining prenatal exposure to maternal cigarette smoking indicate that the long-term effects from early exposure may be mediated by epigenetic changes in the genome, suggested by an overall higher rate of DNA methylation, as well as selective increased DNA methylation in the brain-derived neurotrophic factor (BDNF)-6 exon in exposed offspring (Terry et al., 2008; Toledo-Rodriguez et al., 2010). Although these studies suggest that the observed epigenetic changes in the brain are likely mediated through the actions of nicotine, the main psychoactive component in cigarettes, there has been no direct examination. Future studies are required to thoroughly examine whether early nicotine exposure is  89  6.4. Limitations and Future Directions able to modulate epigenetic mechanisms. Our findings further suggest that the effects of pre- and perinatal nicotine exposure are varied whereby some individuals are much more resilient than others. It appears that genotype and sex confer differences in sensitivity to the effects of early nicotine exposure in a task-dependent and region-selective manner. Subtle genetic differences (between strains or sexs) may confer vulnerability or resilience to the effects of early nicotine exposure by means of variable epigenetic regulation. For example, certain genotypes may be associated with varying DNA methylation states, which has been observed for BDNFVal66Met (Mill et al., 2008). If genetic background in fact confers distinct sensitivity to epigenetic changes, this may underlie genotype- or sex-dependent vulnerabilities to nicotine-induced deficits. Future studies should furthermore determine the role that genetic mechanisms play in epigenetic-mediated changes in gene expression. Together these data suggest that pre- and perinatal nicotine exposure exerts immediate structural changes as well as a gradual development of behavioural abnormalities that are influenced by genotype and sex in a task-dependent and region-specific manner. We provide preliminary insight into a new avenue of research, not yet widespread in literature. 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