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Maternal and fetal methionine metabolism and the implications on programming of the hypothalamic pituitary… O'Neill, Ryan Patrick 2010

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Maternal and Fetal Methionine Metabolism and the Implications on Programming of the Hypothalamic Pituitary Adrenal Axis by Prenatal Alcohol Exposure by Ryan Patrick O’Neill  B.A., The University of Montana, 2004  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in The Faculty of Graduate Studies (Anatomy and Cell Biology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) March 2010  © Ryan Patrick O’Neill, 2010  ABSTRACT Chronic ethanol exposure is known to disrupt the methionine cycle in liver of adult rats. Alterations in the methionine cycle are associated with changes in tissue methylation capacity and may affect DNA and histone methylation thereby altering epigenetic regulation of gene expression, causing long-term phenotypic changes. Data from our lab and others have shown that prenatal alcohol exposure (PAE) reprograms the hypothalamic-pituitary-adrenal (HPA) axis such that HPA tone is increased throughout life.  That is, PAE animals show increased HPA responsiveness and/or delayed or  deficient recovery to basal levels following exposure to stressors. These alterations persist into adulthood, can have negative consequences for health, and may be mediated by metabolic changes induced by prenatal ethanol exposure. To explore this hypothesis, mass-spectrometry and real time PCR were used to quantify several of the primary metabolites and enzymes involved in methionine metabolism using plasma and liver samples  from  pregnant  dams  and  fetuses  from  ethanol  (E), pair-fed (PF) and ad lib fed-control (C) groups on gestation day 21. Mass spectrometry analysis of plasma revealed increases (p<0.05) in homocysteine levels in E dams and increases (p<0.02) in methionine levels in both E dams and fetuses compared to their PF and C counterparts. Quantification of hepatic enzymes by real-time PCR showed significantly decreased mRNA levels (p≤0.05) of maternal methionine adenosyltransferase  (MAT)  1A,  MAT2A,  methionine  synthase  (MS),  and  phosphatidylethanolamine N-methyl transferase (PEMT) in addition to decreased fetal MAT1A mRNA. Together these data demonstrate marked alterations in methionine metabolism during prenatal exposure to ethanol.  ii  TABLE OF CONTENTS  ABSTRACT....................................................................................................................... ii TABLE OF CONTENTS ................................................................................................ iii LIST OF FIGURES .......................................................................................................... v LIST OF ABBREVIATIONS ......................................................................................... vi ACKNOWLEDGEMENTS ........................................................................................... vii 1 INTRODUCTION.................................................................................................... 1 1.1 The Fetal Alcohol Syndrome .................................................................................... 1 1.2 Mechanisms of Ethanol Teratogenesis ..................................................................... 3 1.3 Early Life Experience and Fetal Programming ........................................................ 6 1.4 The Hypothalamic-Pituitary-Adrenal Axis............................................................... 9 1.5 Prenatal Programming of the HPA Axis................................................................. 14 1.6 Prenatal Ethanol Exposure Programs the HPA axis ............................................... 15 1.7 Prenatal Under-Nutrition Programs the HPA Axis................................................. 17 1.8 Epigenetics.............................................................................................................. 18 1.9 DNA Methylation ................................................................................................... 19 1.10 Gene Silencing Through DNA Methylation and Histone Modifications ............. 24 1.11 Maternal Care Programs the HPA Axis................................................................ 28 1.12 Gene-Nutrient Interactions: Methionine and Homocysteine Metabolism ............ 29 1.13 The Complexity of the Methionine-Homocysteine Cycle .................................... 33 1.14 Ethanol and Methionine Metabolism.................................................................... 36 1.15 Ethanol Depletes SAM and Inhibits DNA Methylation ....................................... 37 1.16 Ethanol Alters the SAM:SAH Ratio ..................................................................... 38 1.17 Animal Models of FAS......................................................................................... 39 1.18 Thesis Objectives .................................................................................................. 42 2 METHODS ............................................................................................................. 44 2.1 Animals and Mating................................................................................................ 44 2.2 Administration of Ethanol Through Pregnancy and Calculation of Ethanol Intake....................................................................................................................... 45 2.3 Maternal and Fetal Sampling on G21 ..................................................................... 46 2.5 Isolating Total RNA, Measuring Purity.................................................................. 47 2.6 Relative Quantification of Gene Expression by Real-Time PCR........................... 47 2.7 Biochemical Analysis by LC-MS/MS .................................................................... 49 2.8 Statistical Analysis.................................................................................................. 49 3 RESULTS ............................................................................................................... 50 3.1 Ethanol Intake Through Gestation .......................................................................... 50 3.2 Maternal Body Weights During Gestation ............................................................. 50 3.3 Number of Live Young........................................................................................... 52 3.4 Plasma Metabolite Concentrations ......................................................................... 52 3.4.1 Methionine Concentrations.............................................................................. 53 3.4.2 Homocysteine Concentrations ......................................................................... 55 3.4.3 Dimethylglycine Concentrations ..................................................................... 57  iii  3.4.4 Betaine Concentrations .................................................................................... 59 3.4.5 Choline Concentrations.................................................................................... 61 3.5 Maternal and Fetal Liver Enzyme mRNA Levels by Real-time PCR .................... 63 3.5.1 MAT1A mRNA ............................................................................................... 63 3.5.2 MAT2A mRNA ............................................................................................... 65 3.5.3 Methionine Synthase mRNA ........................................................................... 67 3.5.4 BHMT mRNA ................................................................................................. 69 3.5.5 MTHFR mRNA ............................................................................................... 71 3.5.6 PEMT mRNA .................................................................................................. 73 4 DISCUSSION ......................................................................................................... 78 4.1 Effect of Ethanol Exposure on Pregnancy Outcome ............................................ 78 4.2 The Effect of Prenatal Ethanol Exposure on the Maternal and Fetal Methionine Cycle ........................................................................................................................ 79 4.2.1 Ethanol Dependent Increases in Methionine Levels as a Possible Result of the BHMT Salvage Pathway ....................................................................... 80 4.2.2 Ethanol Dependent Increases in Methionine Concentration as a Possible Result of Reduced MAT1A mRNA................................................................. 83 4.2.3 Increased Homocysteine in Ethanol Exposed Dams as a Possible Result of Reduced MS Transcripts.................................................................................. 86 4.3 Implications of Altered Methylation Capacity and Programming of the HPA Axis ......................................................................................................................... 91 4.4 Conclusions............................................................................................................. 95 4.5 Future Directions .................................................................................................... 95 4.5.1 Involvement of Alterations in Methionine Metabolism in Methylation Capacity at G21................................................................................................ 95 4.5.2 Impact of Prenatal Ethanol Exposure on Methylation Capacity Within the HPA Axis. .................................................................................................. 96 4.5.3 Impact of Prenatal Ethanol Exposure on Differential Methyaltion in Genes Involved in HPA Regulation................................................................. 97 4.5.4 Can Prenatal Supplementation of Choline/Betaine Attenuate the Effects of Prenatal Ethanol Exposure on the HPA Axis? ................................ 97 4.5.5 Will Methionine Restriction Attenuate Any of the Effects of Prenatal Ethanol Exposure? ........................................................................................... 98 WORKS CITED............................................................................................................ 100 APPENDIX 1 Composition of Lab Chow................................................................. 118 APPENDIX 2 Composition of Liquid Diets (Dyets Inc).......................................... 119 APPENDIX 3 UBC Animal Care Certificate ........................................................... 120  iv  LIST OF FIGURES Figure 1.1 Figure 1.2 Figure 1.3 Figure 1.4 Figure 3.1  The Hypothalamic-Pituitary Axis.................................................................. 12 The Methylation of DNA by the DNA Methyl Transferases ........................ 23 The Methylation of DNA and Chromatin Remodeling................................. 27 The Methionine-Homocysteine Cycle........................................................... 32 Maternal Body Weights (g) (Mean ± SEM) of E, PF and C Dams During Gestation ........................................................................................................ 51 Figure 3.2 Maternal and Fetal Plasma Methionine Levels (µM) (Mean ± SEM) .......... 54 Figure 3.3 Maternal and Fetal Plasma Homocysteine Levels (µM) (Mean ± SEM)...... 56 Figure 3.4 Maternal and Fetal Plasma DMG Levels (µM) (Mean ± SEM).................... 58 Figure 3.5 Maternal and Fetal Plasma Betaine Levels (µM) (Mean ± SEM)................. 60 Figure 3.6 Maternal and Fetal Plasma Choline Levels (µM) (Mean ± SEM) ................ 62 Figure 3.7 Maternal and Fetal Liver MAT1A mRNA (Mean ± SEM)........................... 64 Figure 3.8 Maternal and Fetal Liver MAT2A mRNA (Mean ± SEM)........................... 66 Figure 3.9 Maternal and Fetal Liver MS mRNA (Mean ± SEM)................................... 68 Figure 3.10 Maternal and Fetal Liver BHMT mRNA (Mean ± SEM) ........................... 70 Figure 3.11 Maternal and Fetal Liver MTHFR mRNA (Mean ± SEM)......................... 72 Figure 3.12 Maternal and Fetal Liver PEMT mRNA (Mean ± SEM)............................ 74 Figure 3.13 Summary of Observed Changes in Fetal and Maternal Methionine Metabolism at G21....................................................................................... 75 Figure 3.14 Summary of Observed Changes in E and PF Animals…..............................78 Figure 3.15 Summary of Observed Changes in PF Animals.. ......................................... 79  v  LIST OF ABBREVIATIONS 5-MTHF – 5-methylene Tetrahydrofolate 5,10-MTHF – 5,10-methylene tetrahydrofolate ACTH – Adrenocorticotrophic Hormone ADH – Alcohol Dehydrogenase ANOVA – Analysis of Variance ARND – Alcohol Related Neurodevelopmental Disorder AVP – Arginine Vasopressin BHMT – Betaine:Homocysteine Methyl Transferase CORT – Corticosterone CRH – Corticotrophin Releasing Hormone CNS – Central Nervous System D – Postnatal Day DMG – Dimethylglyceine DNMT – DNA Methyl Transferase EDTA – Ethylenediaminetetraacetic Acid FAS – Fetal Alcohol Syndrome FASD – Fetal Alcohol Spectrum Disorder G – Gestation Day GNMT – Glycine-N-methyltransferase GR – Glucocorticoid Receptor GSH - Glutathione HPA – Hypothalamic Pituitary Adrenal (Axis) IUGR – Intrauterine Growth Restriction LG-ABN – Licking, Grooming and Arched Back Nursing MAT1A – Methionine Adenosyl Transferase 1A MAT2A – Methionine Adenosyl Transferase 2A MR – Mineralocorticoid receptor MS – Methionine Synthase MTHFR – Methylenetetrahydrofolate Reductase NGFI-A – Transcription Factor: Nerve Growth Factor-induced Clone A PAE – Prenatal Alcohol Exposure PCNA – Proliferating Cell Nuclear Antigen POMC – Pro-opiomelanocortin SAH – S-adenosyl Homocysteine SAM – S-adenosyl Methionine SEM – Standard Error Measurement THF– Tetrahydrofolate  vi  ACKNOWLEDGEMENTS  Many, many thanks to:  Dr. Joanne Weinberg, my MSc supervisor, for welcoming me into her lab, for teaching, mentoring and supporting me, and for remaining patient throughout my time at UBC.  My committee, Dr. Sheila Innis and Dr. Liisa Galea with a special thanks to Dr. Innis for all of the LC-MS/MS work and analyses conducted in her laboratory at the BC Research Institute for Children & Women’s Health.  Dr. Angela Devlin for introducing me to the RT-PCR method and allowing me to conduct this part of my research in her laboratory.  All of the technicians who lent their helping hands to this project. Logistically these studies would not have been possible without their assistance. Thank you to Wayne Yu, Linda Ellis, Benny Chan, Rachel Wade and Ni Lan.  Jennifer Rurak, for your friendship, patience, emotional support and academic feedback.  vii  1  INTRODUCTION  1.1 The Fetal Alcohol Syndrome  It is generally understood that pre-natal exposure to ethanol through maternal consumption of alcoholic beverages is inextricably linked with life long post-natal deficits. The myriad of toxic effects that ethanol exerts on the developing fetus have been a topic of rigorous investigation for more than 30 years, yet many of the mechanisms through which ethanol produces the physical, behavioural and cognitive abnormalities characteristic of individuals diagnosed with either fetal alcohol syndrome (FAS) or related fetal alcohol spectrum disorders (FASD) are yet to be uncovered.  FAS can be diagnosed by three major criteria: 1. Growth deficits, including prenatal and postnatal growth deficiency; 2. alterations in the central nervous system (CNS) including microencephaly, delayed or altered intellectual development, impaired fine motor skills and behavioural deficits, such as hyperactivity, poor attention span, impaired habituation, impulsivity, lack of inhibition; and  3. a characteristic facial dysmorpholgy  including a narrow forehead, short palpebral fissures, small nose, small midface, a smooth philtrum and a thin upper lip (Jones & Smith, 1973); (Little & Streissguth, 1981; Mattson et al., 1999; Connor et al., 2006). However, the impact of ethanol exposure is not always as apparent as in the case of full blown FAS, which represents one extreme amongst a broad spectrum of disorders. The term FASD is an umbrella term used to encompass the wide range of outcomes secondary to prenatal ethanol exposure. There are five defined categories of FASD as follows: 1. FAS with confirmed maternal alcohol exposure. 2. FAS  1  without confirmed maternal alcohol exposure. 3. Partial FAS with confirmed maternal alcohol exposure. 4. Alcohol related birth defects (ARBD), in children displaying only physical abnormalities; and 5. Alcohol related neurodevelopmental disorder (ARND), in children who display behavioural or functional abnormalities. (Chudley et al., 2005; Stade et al., 2006). However, despite the graded slope and seemingly endless manifestations of FASD there remains at least one concrete, unifying characteristic: all individuals are prone to cognitive insufficiencies including learning disabilities, and short term memory deficits (Chudley et al., 2005; Stade et al., 2006). Of all the individuals in Canada who are born with FASD it is estimated 90% experience mental health problems, 60% are expelled from school or drop out, 60% have trouble with the law, 50% are incarcerated , and 30% have alcohol and drug use problems (Stade et al., 2004). Of those individuals 21 and older, 80% have problems with employment and require dependent living situations (Stade et al., 2004).  These are overwhelming statistics, particularly since an estimated 3 in 1000  Canadians are born with FASD and that the economic burden on society amounts to an estimated 344 million dollars annually, costs primarily related to specialized education and medical expenses (Stade et al., 2004).  The socio-economic weight of FASD is not restricted to Canada, however. It is a worldwide issue fingered as the leading cause of mental retardation in the western world where an estimated 0.97 births per thousand are born with some manifestation of the disorder (Abel, 1995) and in the U.S. alone that number increases to 1.95 births per thousand (Abel, 1995). If we combine the number of children born with FAS or ARND this rate increases to conservative estimates of at least 9.1 births per thousand (Sampson et  2  al., 1997). In other parts of the world, such as the western cape province of south Africa, rates of the full FAS have risen to a staggering 46.4 cases per thousand, thought to be caused primarily by the policy of distributing wine to workers as partial payment, as well as the high prevalence of illegal bars, in this area (May et al., 2000). It is important to keep in mind that although this is a global issue that places an immeasurable burden on individuals, families, societies and governments, FASD is not a disease. It is neither inherited, contractible nor acquirable.  However, despite the public awareness of this  disorder and the teratogenic effects of ethanol, it continues to affect individuals at alarming rates, and it is thus the onus of the scientific community to fully elucidate the etiology of this syndrome and its underlying mechanisms of action in order to better educate the public and to uncover potential clinical interventions.  1.2 Mechanisms of Ethanol Teratogenesis  Ethanol toxicity can be broadly divided into direct and indirect teratogenic effects on the fetus. Since ethanol freely crosses the placenta, fetal blood alcohol level typically mirrors that of the dam (Guerri & Sanchis, 1985) providing an avenue for a plethora of indirect effects. Ethanol can also diffuse across the plasma membrane and blood brain barrier accounting for the fetal susceptibility to its direct effects on cells and organ systems. The magnitude of the observed effects are generally dose-related for both humans and animals (Driscoll et al., 1990), yet it has been revealed that only two doses of ethanol administered during the gastrulation stage in pregnant mice, can produce the same craniofacial abnormalities characteristic of children born with FAS (Sulik et al., 1981).  3  Furthermore, in rats, a single day of alcohol exposure during the brain growth spurt (postnatal day 4) can induce brain weight restriction and purkinjie cell loss (Goodlett et al., 1990). Indeed the range of detrimental effects caused by ethanol directly relates to the timing, dose, pattern and duration of alcohol exposure, as well as genetic predisposition. (Cudd, 2005)  As a teratogen, ethanol exposure can lead to increased apoptosis, and altered cell proliferation and migration (Ozer et al., 2000). By suppressing neuronal activity and neurotransmitter systems in the developing brain (Taylor et al., 1984; Spuhler-Phillips et al., 1997; Thomas et al., 1997), ethanol dramatically increases cell apoptosis (Dunty et al., 2001) and results in microencephaly in both human and animal models of FAS (Xu et al., 2005; Xu et al., 2008). Ethanol also interferes with cell clustering and migration during development by inhibiting the human osteogenic protein-1 (Charness et al., 1994). Furthermore, ethanol directly interferes with numerous growth factors required for normal development, including nerve growth factor and insulin like growth factors, (Heaton et al., 2000). Moreover it delivers an immense metabolic strain by inducing oxidative stress, primarily by increasing hepatic peroxidation products and decreasing glutathione (GSH) levels, as well as reducing endogenous antioxidants (Devi et al., 1993; Henderson et al., 1995; Chen & Sulik, 1996). Further complicating the direct detrimental effects of prenatal ethanol exposure is the harm caused by acetaldehyde, the first biproduct of the catabolism of ethanol [reviewed by (Lieber, 2000)], which is a known potent teratogen (Campbell & Fantel, 1983; Lee et al., 2005). These direct factors all work in concert to ensure that organogenesis and proper development is impossible.  4  The indirect effects of prenatal ethanol exposure are also abundant. Ethanol alters placental blood flow thus impairing the transfer of nutrients from mother to fetus (Falconer, 1990; Burd et al., 2007). Additionally, by decreasing maternal intestinal absorption and by association, fetal nutrition (Lieber, 2000), ethanol delivers a powerful, nutritionallymediated blow to the developing fetus through deprivation of nourishment. Indeed, prenatal ethanol exposure has been linked with decreased birth weight as well as lower brain, heart, liver spleen, kidney and adrenal weights. (Potter et al., 1980; Gallo & Weinberg, 1986). Furthermore, ethanol also has the capacity to induce hormonal imbalances (maternal or fetal or both) and in this capacity it is known to disrupt fetal endocrine development. For example, prenatal ethanol exposure results in decreased circulating thyroid hormone concentrations in the mother and fetus as well as decreased fetal thyroid and thymus mass (Cudd et al., 2002).  Additionally, ethanol acts as a stressor, activating the maternal  hypothalamic-pituitary-adrenal  (HPA)  axis  resulting  in  increased  levels  of  adrenocorticotrophic hormone ACTH and circulating glucocorticoids (Rivier, 1996; Cudd et al., 2001; Cudd et al., 2002; Zhang et al., 2005). This is discussed further in section 1.6  In summary, the combination of direct and indirect teratogenic effects of ethanol make it difficult to delineate the primary pathways responsible for fetal alcohol syndrome, and the elucidation and identification of independent mechanisms and their combined synergy remains an important and fascinating area of research.  5  1.3 Early Life Experience and Fetal Programming  In the late 1900’s, Barker and Osmond popularized the “Fetal Origins of Adult Disease” hypothesis by stating that inadequate maternal nutrition can predispose offspring towards disease in later life (Barker & Osmond, 1986). In initial support of this was the striking correlation between low infant birth weight and increased risk of cardiovascular disease in later life (Barker et al., 1989). By reviewing birth records from both men and women born in the early 1900’s, Barker and colleagues reported that there was a link between low birth weight and subsequent death due to ischaemic heart disease, as well as high serum cholesterol concentrations in adult life (Barker et al., 1993). These infant follow-up studies provided scientists with an opportunity to link the fetal environment, which presumably lead to the low birth weights, to the onset of disease in adulthood. Similar corroborating studies used data collected at the end of World War II during the 1944-1945 food embargo imposed by Nazi Germany on west Holland. During this time, many expectant mothers endured months of inadequate nutrition and the study of their children’s birth records with subsequent investigation into their now adult lives revealed a surprising increased incidence of cardiovascular disease within this sample population (Barker & Martyn, 1992; Lumey, 1992). More recent research studying both humans and animal models has found that maternal malnutrition can also impact the propensity toward development of adult obesity (McMillen et al., 2005), type II diabetes (Rich-Edwards et al., 1999) and a hyperactive HPA axis (Lesage et al., 2006). As evidenced by the range of adult disorders secondary to maternal malnutrition, an adequate diet during gestation is crucial for proper development of the offspring. Furthermore, while early studies by Barker linked low birth weight to an increased risk of heart disease, this correlation does not 6  necessarily imply a direct causation. That is to say, it was not, in all likelihood, the low birth weight in itself that lead to adult disease but rather the fetal microenvironment that was the coincident cause of both the low birth weight and adulthood illness.  One factor that is common among these studies is that food deprivation may serve as a stressor for the pregnant woman which, in turn, activates the stress response and the maternal HPA axis [reviewed in (McMillen et al., 2004; Lesage et al., 2006; Vieau et al., 2007)]. This results in increased levels of stress hormones, specifically cortisol, which can lead to increased fetal exposure to maternal HPA hormones. Interestingly, an increase in fetal exposure to exogenous glucocorticoids alone has, itself, been linked to reduced birth weight (Seckl, 2004; Singh et al., 2007). Moreover, low birth weight babies have been shown to have high basal plasma cortisol levels throughout life suggesting that the fetal HPA axis can itself be prenatally programmed through alterations in maternal nutritional status, including malnutrition (Seckl, 2004; Lesage et al., 2006). In short, while low birth weight appears outwardly to be linked to the onset of disease in later life, it is merely one manifestation in a myriad of others mediated by fetal programming.  There exist other prenatal manipulations, aside from maternal malnutrition, that have been linked to cardiovascular disease in later life. For example, animal studies have shown that a high protein diet during pregnancy (Thone-Reineke et al., 2006) or exposure to cigarette smoke (Zhang, 2005; McDonald et al., 2006) share similar outcomes on cardiovascular health and programming in adult offspring, yet there are certainly mechanistic differences. While smoking during pregnancy invariably results in reduced  7  birth weight (Abell et al., 1991) and is linked with the onset of cardiovascular disease later in life (Zhang, 2005), a maternal high protein diet does not impact birth weight and programs male and female offspring differently such that blood pressure and glomerulosclerosis are elevated in male offspring only. Female offspring are characterized by an increased food efficiency, higher body weight, and increased fat pads (ThoneReineke et al., 2006).  Intrauterine programming of postnatal physiology has been  demonstrated experimentally in a number of species, using a range of techniques to compromise the gestational environment and alter fetal development [reviewed by (McMillen & Robinson, 2005)]. The phenomenon of fetal programming is now widely accepted and the detrimental outcomes caused by suboptimal intrauterine conditions are the focus of much research and review (Matthews, 2000; O'Regan et al., 2001; Lesage et al., 2002; Matthews, 2002; Seckl, 2004; de Kloet et al., 2005; McMillen et al., 2005; Szyf et al., 2005; Zhang, 2005; Fowden et al., 2006; Lesage et al., 2006). Suboptimal intrauterine conditions have been linked with a plethora of adult diseases (Matthews, 2000; O'Regan et al., 2001; Lesage et al., 2002; Matthews, 2002; Seckl, 2004; de Kloet et al., 2005; McMillen et al., 2005; Szyf et al., 2005; Zhang, 2005; Fowden et al., 2006; Lesage et al., 2006). Thus it is imperative that we focus on individual systems and manipulations in order to better understand the mechanisms underlying fetal programming.  One such system, which has been the focus of study in our lab and others, is the HPA axis. It is the programming of the HPA axis that is one focus of this thesis, and thus will be a topic of further discussion in sections 1.5-1.7. Of particular interest, the HPA axis has been implicated as a pathway through which the early prenatal environment may be  8  linked to adult-onset diseases.  For instance, in utero programming of the HPA axis  mediated by increased exposure to maternal glucocorticoids during development has been linked to the development of cardiovascular disease and type II diabetes in adulthood, a topic which is reviewed extensively in (Seckl et al., 2000; Seckl, 2001; Matthews, 2002). Specific examples providing evidence of this link are discussed in section 1.5.  The following sections examine prenatal manipulations that result in a hyperactive HPA axis in adulthood, an outcome which has been shown to be programmed early in development through various intrauterine conditions, but which will be discussed here in the context of prenatal ethanol exposure and a calorie restricted diet. A resonating theme throughout all of these sections will be the condition of prenatal stress which itself can program the fetal HPA axis (Bakker et al., 1997; Matthews, 2002; Seckl, 2004). In order to understand better the intricacies of HPA axis programming, however, a brief outline of the axis is needed.  1.4 The Hypothalamic-Pituitary-Adrenal Axis  Throughout life, every organism maintains a complex and dynamic physiological equilibrium known as homeostasis. When adverse conditions challenge this equilibrium, the organism’s ability to maintain homeostasis is threatened. Such threats are known as stressors which are broadly defined as any physical or psychological insult that disrupts the physiological balance (Johnson et al., 1992a; Tsigos & Chrousos, 2002; Jacobson, 2005). In humans and other animals, stress elicits a generalized response in both the sympathetic nervous system (SNS) and the HPA axis which work together to produce physiological 9  modifications including increased mobilization of energy substrates, the stimulation of cardiac and pulmonary functions, and the depression of digestion, growth and immunity. Behaviour is also modified by the stress response and activation leads to enhanced memory, increased arousal and alertness, focused attention, and altered sensory thresholds (Johnson et al., 1992b). These physiological and behavioural modifications allow an animal to cope with the stressor in what is commonly known as the fight or flight response. In short, the SNS and the HPA axis synergize to mediate the stress response; the SNS through the release of adrenaline and noradrenaline, and the HPA axis through the secretion of glucocorticoids (Johnson et al., 1992a; Tsigos & Chrousos, 2002; Jacobson, 2005).  In response to a stressor, afferent neurons from the hippocampus stimulate the paraventricular nucleus (PVN) of the hypothalamus and initiate a hormonal cascade along the HPA axis, eventually culminating in the secretion of glucocorticoids from the adrenal cortex (Fig 1.1). Stressor and circadian influences initiate the release of corticotrophinreleasing hormone (CRH) and arginine vasopressin (AVP) from the medial dorsal parvocellular region of the PVN. AVP and CRH then transit via the hypophyseal portal vessels to the anterior pituitary where they act to increase  the synthesis of pro-  opiomelanocortin (POMC) and the release of its peptide derivatives, namely ACTH and ßendorphin, into the systemic circulation (Jacobson, 2005). When ACTH circulates to the adrenal cortex, it binds a G-protein coupled receptor in the zona fasiculata and stimulates the secretion of species-specific glucocorticoids [cortisol in humans, Corticosterone (CORT) in rodents] into the general circulation (Jacobson, 2005). Increased circulating levels of CORT can then mediate the stress response by stimulating gluconeogenesis,  10  increasing heart rate and contractility and sensitizing blood vessels to noradrenaline (Jacobson, 2005). Importantly, while the secretion of CORT is imperative in initiating the cascade of events preparing the body to meet the demands of stress, the attenuation or termination of the stress response is equally important. Glucocorticoids ultimately regulate their own secretion through feedback inhibition of stress induced HPA activity (Jacobson, 2005).  11  STRESS Hippocampus  Prefrontal Cortex  PVN NEGATIVE FEEDBACK  CRH AVP  hypophyseal portal system  METABOLIC EFFECTS  ANTERIOR PITUITARY  ACTH  systemic circulation  ADRENAL CORTEX  CORT  stimulatory effect inhibitory effect  Figure 1.1 The Hypothalamic-Pituitary Axis The onset of stress activates the PVN of the hypothalamus which secretes CRH and AVP into the hypophyseal portal system. CRH and AVP act on the anterior pituitary and stimulate the synthesis and secretion of ACTH which enters systemic circulation and stimulates the secretion of CORT from the adrenal cortex. CORT then exerts numerous metabolic effects and through negative feedback, attenuates the stress response.  12  The physiological effects of CORT are mediated through the binding of CORT to two intracellular receptors; the glucocorticoid receptor (GR) and the mineralocorticoid receptor (MR).  Both types of receptor are present in the brain but differ in their  distribution and binding affinity to ligands (Reul & de Kloet, 1985). Generally, MRs bind with an approximately 10x higher affinity to CORT which suggests they are mainly involved in regulating basal levels, whereas GRs bind with less affinity and are predominantly occupied during high levels of circulating CORT , such as those achieved at the height of a stress response or at the circadian peak (Jacobson, 2005). Both receptor types are expressed in the brain and when bound to CORT, exert negative feedback on the stress response (Matthews, 2002; Jacobson, 2005) Interestingly, changes in glucocorticoid receptor expression directly affect the negative feedback response (Weaver et al., 2004). Since rapid development of GRs and MRs occurs in concert with rapid brain growth (Lingas et al., 1999) the differential expression of these receptors has become a focus in the field of fetal programming of the HPA axis (Lingas et al., 1999; Szyf et al., 2005; Weaver et al., 2006; Glavas et al., 2007) Numerous fetal manipulations and early life stressors can negatively impact the development of this system including prenatal ethanol exposure (Weinberg, 1989, 1993; Glavas et al., 2007), and undernutrition (Lesage et al., 2002; Lucas, 2005). Indeed increased prenatal exposure to stress and glucocorticoids alone can induce fetal programming of the HPA axis (Bakker et al., 1997; Matthews, 2002; Seckl, 2004).  13  1.5 Prenatal Programming of the HPA Axis  Exposure to stressors during pregnancy activates the maternal HPA axis and increases the secretion of glucocorticoids (GC). Normally, fetal exposure to maternal glucocorticoids is regulated by a placental enzyme known as 11-beta-hydroxysteroid dehydrogenase 2 (11β-HSD2) which catalyzes the oxidation of cortisol to cortisone thus neutralizing GCs (Jacobson, 2005). However, prolonged endogenous secretion of high levels of glucocorticoids or exogenous administration at high levels can negatively impact the fetus. Research into fetal exposure to GCs began in the 1970’s when it was discovered that GC treatment during development stimulated lung maturation in fetuses, and thus enhanced survival in fetuses born preterm (Obladen, 1978). While this treatment proved to be beneficial in promoting the survival of preterm infants, it became apparent with time that prenatal exposure to GCs, dexamethasone (a synthetic GC), or carbenoxolone (an 11βHSD2 inhibitor), results in permanent adulthood hypertension, hyperglycemia and increased HPA activity with behaviour reminiscent of anxiety (Seckl, 2004). Other studies have shown that a loss of function mutation in the gene encoding 11β-HSD2 manifests in low birth weight, adult hypertension and increased plasma CORT concentrations throughout life (Dave-Sharma et al., 1998; Seckl et al., 2000). Furthermore, in cases of growth restriction during pregnancy, 11β-HSD2 expression is decreased (McTernan et al., 2001). It has also been demonstrated that GC treatment of pregnant rats results in a persistent increase in the ratio of AVP to CRH in the mediobasal hypothalamus, indicating an altered pattern of neonatal peptide expression in CRH releasing neurons (Bakker et al., 1995). GC treatment also affects the developing thymus and spleen, both organs showing  14  decreased T cell numbers after birth (Bakker et al., 1997). It is important to note that the GC dosage used in the above mentioned studies represents normal plasma concentrations under stress.  These data suggest that fetal exposure to GCs has an impact on the developing fetal HPA axis which, in circumstances of prolonged or excessive exposure, can culminate in persistent, life long HPA axis abnormalities. Included in the immeasurable list of stressors that challenge the maternal and fetal HPA axes are maternal ethanol consumption and inadequate maternal nutrition, both of which have been reported to contribute to the programming of the fetal HPA axis through increased fetal exposure to GCs [reviewed by (McMillen et al., 2004; Zhang et al., 2005; Lesage et al., 2006)]. For the remainder of this manuscript, therefore, programming of the HPA axis will be discussed in the context of these two stressors.  1.6 Prenatal Ethanol Exposure Programs the HPA axis  Ethanol’s ability to freely cross the placenta and its known effects on endocrine function (Morgan, 1982; Adler, 1992) implicate endocrine imbalances in the etiology of FAS (Anderson, 1981). Indeed, data gathered in this field have shown that the HPA axis is affected by prenatal ethanol exposure at multiple levels and work focusing on animal models has demonstrated that there is an emergent pattern of programming evident from birth.  15  At birth, neonates that experienced prenatal alcohol exposure (PAE) exhibit elevated plasma and brain levels of CORT, but reduced levels of plasma β –endorphins, products of the cleavage of POMC, along with ACTH which are released during periods of excitement (Weinberg et al., 1996). Throughout the preweaning period, PAE animals exhibit blunted HPA and β–endorphin responses to an array of stressors including ether, novelty, saline injection, and cold stress (Taylor et al., 1986; Angelogianni & Gianoulakis, 1989; Weinberg, 1989). Interestingly, the blunted response of the HPA axis in the preweaning period is transient and weaned PAE animals are typically hyperresponsive to stressors and to drugs such as ethanol and morphine (Weinberg, 1993; Lee et al., 2000). To add to the complexities, differences in hyperactivity may vary between sexes depending on the nature of the stressor, the time course of the study and the hormonal endpoint being measured (Weinberg, 1988, 1992; Weinberg et al., 2008). Adult PAE animals of both sexes continue to exhibit increased CORT, ACTH, and β-endorphin responses to stressors such as repeated restraint, foot shock and immune challenges (Weinberg et al., 1996; Kim et al., 1999b). An increase in CRH mRNA expression immediately following stress has also been reported in PAE animals as have deficits in habituation to repeated restraint stress (Weinberg et al., 1996; Zhang et al., 2005).  The mechanisms mediating this prenatal ethanol induced HPA hyperresponsiveness are not fully known, however increased drive to the hypothalamus and deficits in feedback regulation of HPA activity have both been implicated in the robust response of a hyperresponsive HPA. (Zhang et al., 2005; Weinberg et al., 2008).  Indeed, definitive  changes in regulation and mRNA transcript levels have been documented in several areas  16  of the axis. In the hippocampus for example, it has been demonstrated that PAE females show a significantly greater MR response whereas males show a significantly greater GR response to adrenalectomy. (Weinberg et al., 2008) Furthermore, corticosterone replacement is less effective in normalizing MR mRNA levels in PAE compared to control males (Weinberg et al., 2008). These data support the hypothesis that PAE may alter both HPA drive and feedback regulation, yet there are likely multiple mechanisms that are simultaneously involved (Guerri, 2002) and our understanding of mechanisms underlying fetal programming of PAE animals is still nascent.  1.7 Prenatal Under-Nutrition Programs the HPA Axis  The stress of malnutrition placed on a pregnant female creates a systemic environment of high circulating GCs which are free to cross the placenta (Matthews, 2002) As discussed in section 1.5, overexposure to GCs alone are enough to impact programming of a developing fetal HPA axis and, in accordance, low birth weight offspring born to malnourished mothers also show compromised HPA axis function (McMillen et al., 2004; Lesage et al., 2006) in both humans and animal models. At 4 months of age, it has been shown that prenatally malnourished rats show increased MR gene expression in the CA1 region of the hippocampus and increased levels of plasma CORT after 30 minutes of restraint stress compared to controls (Lesage et al., 2002).  Another study of prenatal malnourishment and its effects on the HPA axis used a well developed animal model, exposing pregnant dams to a 50% reduction (FR50) of  17  daily intake during the last week of gestation and first week of lactation (Vieau et al., 2007) In FR50 fetuses, HPA axis function was reduced and associated with decreased placental 11β-HSD2 activity and a greater transplacental transfer of GCs from mother to fetus. Furthermore, at weaning, FR50 offspring had reduced HPA activity in response to ether inhalation stress, but in older animals (8 months) maternal under-nutrition was associated with chronic hyperactivity of the HPA axis (Vieau et al., 2007). Interestingly, this switch between hypo and hyperactivity, which is reminiscent of that observed in PAE (recall from section 1.6) may imply an increased buffering system in developing offspring designed to protect against the increased exposure to GCs (Zhang et al., 2005). In any event, the similarities between PM and PAE defined by a chronic hyperactivity of the axis in adulthood, as well as a switch of the axis from hypo to hyper active post weaning, illustrate that different prenatal stressors can result in similar outcomes. Uncovering the differential mechanisms underlying HPA axis programming due to PM and PAE is challenging, but a new area of research, in the field of epigenetics, may provide some answers.  1.8 Epigenetics  Epigenetics is the study of heritable, but potentially reversible changes in gene expression that are not a consequence of changes in the DNA sequence itself (Henikoff & Matzke, 1997). Research in this field studies differential gene silencing and activation, both of which occur as a normal part of the cascade of development since in any complex organism comprised of differentiated cells there are, by necessity, many different cell phenotypes stemming from identical genotypes derived  18  from the fertilized egg (Holliday, 2005). Sometimes, however, this process goes awry, and genes which should be activated become inactivated, and vice versa. As such, research into epigenetic mechanisms is rapidly becoming a primary focus in the areas of programming, cancer research, aging and inherited diseases (Agrelo et al., 2006; Esteller, 2007).  There are currently at least two known primary mechanisms of epigenetic regulation. These are DNA methylation and histone modifications (such as acetylation or methylation) both of which have been the focus of intensive research and, with respect to gene silencing, their roles are gradually becoming clearer (Robertson & Wolffe, 2000; Tamaru & Selker, 2001; Weaver et al., 2004; Holliday, 2005). Furthermore, while DNA methylation and histone acetylation are two distinct mechanisms, they are by no means mutually exclusive and one cannot be discussed without incorporating the other. The following sections serve as a review of the current understanding of these two mechanisms, their individual intricacies and their synergy in terms of transcriptional silencing.  1.9 DNA Methylation  DNA methylation is a process in which methyl groups are added to DNA bases where they can act as control switches, regulating the transcription of a specific gene (Robertson & Wolffe, 2000; Holliday, 2005) For example, the addition of methyl groups at strategic points can act to silence genes by preventing transcription factors  19  from binding to promoter regions thus circumventing the initiation of transcription altogether (Reik & Dean, 2001; Szyf et al., 2005).  With few exceptions, increased levels of methylation (hypermethylation) are commonly associated with gene silencing, transcriptional repression and a reduced risk of disease development throughout life (Maloney & Rees, 2005). In mammals, the most common area of methylation occurs on the 5’ position of cytosine bases paired with guanines (CpG dinucleotides), often within promoter regions of DNA (Robertson & Wolffe, 2000; Holliday, 2005). Interestingly, the distribution of these CpG dinucleotides within the genome is highly specific and non-uniform, occurring at their highest frequency in areas known as CpG Islands (Bird et al., 1985). Furthermore, there are approximately 45,000 of these CpG Islands in the human genome, and these CpG rich areas are generally found close to, or within, the first exon of a gene, a location designed to mediate transcription (Robertson & Wolffe, 2000). Indeed, increased methylation is associated with decreased transcription and gene expression, and decreased methylation has been associated with cancer and increases in oncogene expression (Davis & Uthus, 2004).  The methylation of DNA at CpG Islands is dependent upon a group of enzymes known as DNA methyltransferases (DNMTs).  There are at least 3 known DNMTs  responsible for the methylation of DNA: DNMT1, DNMT3a and DNMT3b (Bestor, 2000) (Fig. 1.2). These can be broadly subdivided into functionally distinct groups in which DNMT3a and DNMT3b are largely responsible for de novo DNA methylation, while  20  DNMT1 is mainly involved in the continued maintenance of methylation patterns during mitosis (Okano et al., 1999; Robertson et al., 1999; Rhee et al., 2000). This distinction is supple however, and evidence for functional overlap is abundant. For example, in cancer cell lines over expressing DNMT1 there is an increase in endogenous de novo methylation of CpG Islands suggesting that DNMT1 can also contribute to establishing new methyl domains (Vertino et al., 1996). Additionally, one recent study shows that cells devoid of DNMT1 can still maintain up to 80% of their normal methylation indicating that either the DNMT3 family of enzymes, or an as of yet unclassified set of DNMTs, are also capable of compensating for the loss of DNMT1 function (Rhee et al., 2000).  There remains  however, much convincing evidence that DNMT1 is largely responsible for maintaining, rather then establishing, methyl domains. DNMT1 is the most abundant DNMT in somatic cells (Robertson et al., 1999) and shows a 10-40 fold preference for newly synthesized, hemimethylated DNA over unmethylated DNA (Pradhan et al., 1997; Pradhan et al., 1999). Furthermore, at sites of DNA replication, DNMT1 localizes at the replication foci during the S-phase of the cell cycle (Leonhardt et al., 1992) and interacts with the proliferating cell nuclear antigen (PCNA), a DNA processing factor or “clamp” that encircles DNA during synthesis and repair, acting as a cofactor of DNA polymerase delta (Chuang et al., 1997) and ensuring the replication of methylation patterns throughout mitotic cycles.  While DNMT1 acts as a methylation maintainer, the DNMT3 enzymes primarily serve to catalyze new methyl additions. The DNMT3 methyltransferases comprise a set of highly conserved enzymes across species and are responsible for “waves” of de novo methylation (Okano et al., 1999; Holm et al.). Both DNMT3a and DNMT3b show a  21  preference for unmethylated DNA, transferring methyl groups in waves onto unmethylated sites. These waves of de novo methylation typically occur throughout the genome during embryonic implantation but have also been reported to take place following the integration of pathogenic retroviral sequences (Okano et al., 1999), as well as in the first days of life in the developing rat brain (Weaver et al., 2004). Whether methylation is occurring de novo or during DNA replication, it is important to underscore how critical these enzymes are in normal development; DNA methylation is an absolutely essential process and mutations in any of the three known DNA methyl transferase genes in mice are lethal, either during embryogenesis, or soon thereafter (Li et al., 1992; Okano et al., 1999). In short, methylation of DNA plays an important role in the silencing of genes.  22  1  2  Figure 1.2 The Methylation of DNA by the DNA Methyl Transferases 1 Unmethylated cytosines are recognized by DNMT3a and DNMT3b enzymes and de novo DNA methylation results in symmetrical methylation of cytosine residues resulting in differential methyl domains (DMD). 2 During DNA replication DNMT1 recognizes and methylates the hemimethylated DNA, maintaining DMDs.  23  1.10 Gene Silencing Through DNA Methylation and Histone Modifications  As discussed in the previous section, the methylation of CpG Islands is typically associated with the repression of transcription factors. The resultant silencing of genes is likely mediated by the interaction of methylated DNA with proteins that are characterized by a particularly high affinity for methylated CpG groups known as methyl CpG binding proteins (MeCP). Indeed several such proteins have been identified but their specific functions have yet to be elucidated. MeCP’s are commonly found complexed with histone deacetylases (HDAC) (Hendrich & Bird, 2000) which is of particular interest since it is known that histone acetylation is commonly associated with an active chromatin configuration (euchromatin) and gene expression and MeCP2 has been shown to interact with histone proteins to modify chromatin structure (Nan et al., 1997; Jones et al., 1998; Kaludov & Wolffe, 2000; Lorincz et al., 2001).  In 1992 (Lewis et al., 1992), purified and sequenced a novel choromosomal binding protein, methyl CpG binding protein 2 (MeCP2), which is now known to be an abundant mammalian protein that can act as a transcriptional repressor by selectively recognizing methylated DNA (Nan et al., 1997; Robertson & Wolffe, 2000). Upon binding to the chromosome, the transcription repressor domain (TRP) of MeCP2 can directly impede gene transcription by blocking mRNA transcription factor II, a basic prerequisite in the transcription of mRNA, from recognizing the promoter sequence (Kaludov & Wolffe, 2000). Interestingly, in vitro studies show that MeCP2 represses transcription from  24  methylated promoters but does not repress nonmethylated promoters and repression is dependent on the local density of methylation (Nan et al., 1997).  Histone modifications such as methylation, and acetylation have also been identified as important epigenetic regulatory mechanisms. While acetylation of the H3 and H4 histone proteins is commonly associated with an open chromatin structure and gene expression, histone methylation is thought to promote a heterochromatic configuration and encourage gene repression (Wade et al., 1998; Tamaru & Selker, 2001). However, like other epigenetic mechanisms, there are exceptions to these rules and not only can histone modifications interact with each other in regulating chromatin configurations, they can also dynamically interact with DNA methyl residues further complicating our understanding of epigenetic regulation (Tamaru & Selker, 2001). In fact, histone modifications and DNA methylation are not just two interacting systems; they are dependent upon one another. For example, the methyl binding domain (MBD) of MeCP2 has numerous binding sites in genomic chromatin and upon binding can recruit HDACs thereby repressing transcription (Nan et al., 1997; Jones et al., 1998; Lorincz et al., 2001). A specific illustration of the collaboration between histone acetylation and methylation is seen when an acetyl group, bound to the 9th lysine of the H3 protein and encouraging gene expression, is removed by an HDAC. The same lysine can then be methylated by histone methylransferases (Tamaru & Selker, 2001). This methyl group then creates a binding site for heterochromatin protein 1 (HP1), which in turn can bind to a chromomethylase enzyme causing DNA methylation and stable gene silencing (Jackson et al., 2002).  25  Clearly there is no definable boundary when discussing DNA methylation and histone modifications. Furthermore deficiencies at any level of epigenetic regulation can directly and permanently influence gene expression. While not all of the mechanisms underlying the regulation of DNA methylation and other epigenetic modifications are fully understood, it is known that the process is sensitive to environmental factors throughout life. In terms of fetal programming, epigenetic regulation likely plays a role, and the next section discusses a series of studies that has implicated differential methylation patterns in the programming of the HPA axis.  26  1  2  Figure 1.3 The Methylation of DNA and Chromatin Remodeling DNA is targeted by DNA methyl transferase (DNMT) and methylates CpG Islands at the target region. 2 Following methylation, methyl binding proteins MBP (i.e. MeCP2) can selectively bind and attract histone deacetylases HDAC which will remove the acetyl group from the histone proteins and induce the compaction of chromatin. 1  27  1.11 Maternal Care Programs the HPA Axis  A series of elegant studies has recently been published that directly links the programming of the HPA axis with differential methylation patterns.  Weaver and  colleagues found that altered maternal behaviour in rats influences expression of GR. In their initial study, dams were selected for their maternal behaviour by the frequency of licking and grooming (LG), and arched back nursing (ABN) of pups. They found that increased LG-ABN by mothers altered the offspring epigenome at a GR gene promotor in the hippocampus (Weaver et al., 2004) whereby offspring of high LG-ABN mothers showed decreased levels of methylation in the promotor region of the GR sequence compared to offspring reared by low LG and ABN mothers (Weaver et al., 2004). This pattern emerged within the first week of life. The difference in methylation persisted through adulthood and in a subsequent study resulted in an increased stress response to open field behaviors in offspring raised by low LG-ABN dams versus those raised by high LG-ABN dams (Weaver et al., 2006).  Furthermore, these effects were found to be  reversed by cross fostering. That is, offspring born to low LG-ABN dams but raised by high LG-ABN dams did not show increased methylation at the GR promoter (Weaver et al., 2004). Furthermore, in adulthood, when rats raised by low LG-ABN dams were treated with Trichostatin A (TSA) (a histone deacetylase inhibitor) or methionine for 7 consecutive days starting at postnatal day (PND) 90, the effect of maternal care was reversed. That is, central infusion of TSA induced the hypomethylation of DNA in low LG-ABN offspring and eliminated the maternal effect on GR expression (Weaver et al., 2004). Further, following TSA treatment, the HPA response to stress was normalized; low LG-ABN  28  offspring no longer showed differences in open-field behaviour (Weaver 2006) nor increases in CORT following restraint stress (Weaver 2004).  In offspring born to high  LG-ABN mothers, methionine treatment had the opposite effect, with adult offspring exhibiting the same behaviour in the open field as offspring born to low LG-ABN mothers (Weaver et al., 2006)  Together these studies demonstrate that the epigenetic state of a gene can be established early in development, but is potentially reversible in adulthood. Importantly, if such epigenetic states are induced through fetal alcohol exposure (discussed in section 1.14), then there exists a possibility that interventions, applied during pregnancy, childhood, or adulthood, may be developed that could possibly attenuate some of the detrimental effects associated with FASD.  1.12 Gene-Nutrient Interactions: Methionine and Homocysteine Metabolism  In accordance with the findings of Weaver et al.’s 2006 study, evidence exists that during pregnancy, a maternal diet supplemented with methyl donors such as folic acid can act to suppress harmful genes leading to longer, healthier survival of offspring (Cooney et al., 2002). Since all methylation processes, including that of DNA, are dependent upon the supply of methyl groups (one carbon groups) which are ultimately supplied by essential nutrients, the impact of diet and nutrient metabolism on epigenetic modifications has emerged as a critical area of research [reviewed in (Davis & Uthus, 2004)]. Unraveling the details the interactions between the metabolites and enzymes associated with DNA synthesis, repair and methylation will be fundamental in understanding the causative 29  relationships between diet and genomic architecture. One-carbon metabolism is divided between two indiscreet pathways that interact with and regulate each other in a complicated cycle known as the methionine-homocysteine cycle. (Fig 1.4)  The first major function of the methionine-homocysteine pathway is to supply methyl groups involved in numerous biological reactions through the production of Sadenosylmethionine (SAM) (Fig 1.4). SAM is a universal methyl donor involved not only in the methylation of nucleic acids (DNA and RNA), but of proteins (including histones) and phospholipids. SAM is generated from methionine through an endothermic reaction involving ATP and is catalyzed by the methionine adenosyl transfer (MAT) enzymes. Methionine itself is either ingested directly (as an essential amino acid), or is produced, as discussed shortly, via the methylation of homocysteine. SAM then acts to donate its available methyl residue through a reaction that is catalyzed by an appropriate methyl transferase (MT) and SAM is reduced to S-adenosylhomocysteine (SAH). It is important to note that many different methyl transferases exist for specific purposes.  For instance,  the donation of a methyl group to DNA will be catalyzed by one of the DNMT enzymes (discussed in section 1.9) Interestingly, due to negative feedback (Chiang, 1998), SAH is a potent inhibitor of all methylation reactions (including the methylation of DNA) and so the intracellular ratio of SAM:SAH can be used as a reliable indicator of a cell’s methlylation capacity (Barak et al., 1987; Finkelstein, 1990; Gil et al., 1996; Holm et al.). Indeed, low SAM:SAH ratios have been implicated in the hypomethylation of DNA and a concurrent increase in gene expression.  Clearance of SAH is therefore important in order  to maintain methylation capacity, and after its production, SAH is immediately converted  30  into homocysteine through a reaction mediated by the enzyme S-Adenosylhomocysteine hydrolase (SAHH).  31  Figure 1.4 The Methionine-Homocysteine Cycle (1) Methioninie is remethylated from homocysteine through the enzymatic reactions involving either betaine:homocysteine methyltransferase (BHMT) or methionine synthase (MS). (2) Methionine is then converted to SAM by methionine adenosyl transferase (MAT) (3) SAM donates it available methyl groups to numerous substrates involving specific methyl transferases (MT), for example the methylation of DNA catalyzed by DNA methyltransferease (DNMT). (4) S-adenosylhomocysteine (SAH) is hydrolyzed to homocysteine by SAH hydrolase (SAHH). Other enzymes, substrates and cofactors represented include cobalamin (Cob); dihydrofolate (DHF); dimethylglycine (DMG); flavin adenine dinucleotide (FAD); methionine adenosyl transferase (MAT); 5methyltetrahydrofolate (5-MTHF); 5,10-methylenetetrahydrofolate (5,10-MTHF); methylene tetrahydrofolate reductase (MTHFR); serine hydroxymethyl transferase (SHMT); tetrahydrofolate (THF); zinc (Zn).  32  The second major function of the methionine-homocysteine pathway is to remethylate homocysteine back to methionine to complete the cycle in one of two fashions: 1. Homocysteine receives its methyl group from betaine, in a reaction catalyzed by betaine:homocysteine methyl transferase (BHMT); or 2. Homocysteine receives its methyl group from 5-methyltetrahydrofolate (5-MTHF) in a reaction catalyzed by methionine synthase (MS) (utilizing B12 as a cofactor) (Fig 1.4). In the first reaction, the methyl donor betaine is produced through the oxidation of choline, whereas in the second reactions, 5MTHF is reduced from 5,10-methylenetetrahydrofolate (5,10-MTHF) by the enzyme 5,10Methylenetetrahydrofolate reductase (MTHFR). Importantly, these two pathways work together to maintain methionine levels and deficiencies in the metabolites or enzymes in either the folate dependent or independent pathways can result in altered homocysteine and methionine concentrations thus impacting SAM dependent methylation reactions and methylation capacity.  1.13 The Complexity of the Methionine-Homocysteine Cycle  The summary of the methionine and homocysteine metabolism presented in the previous section is intended as a general overview of the cycle. It should, however, be carefully noted that the methionine cycle is incredibly complex and that both the folate dependent and independent pathways cooperate to maintain methionine status. There are also additional pathways important in one carbon metabolism and the system is by no means a simple as figure 1.4 somewhat misleadingly suggests. For example, choline is not only supplied through dietary intake, it can also be produced through the catabolism of  33  phosphatidylcholine (Walkey et al., 1997; Zhu et al., 2003).  Phosphatidylcholine  meanwhile is also generated from phosphatidylethanolamine in a reaction involving SAM and SAH, catalyzed by the enzyme phosphatidylethanolamine methyl transferase (PEMT). Additionally, the role of the trans-sulfuration pathway is critical in regulating intracellular homocysteine levels, thereby impacting the SAM:SAH ratio through its role in the clearance of homocysteine (Finkelstein, 1990) Furthermore, folate is involved not only in the production of SAM, but is also critical in the synthesis and repair of DNA (Finkelstein, 1990; Friso & Choi, 2005; Schalinske & Nieman, 2005) through the production of 5,10MTHF (Fig 1.5).  34  Fig 1.5  Folate Derivatives are involved in DNA synthesis and repair in addition to the remethylation of homocysteine  The synthesis and repair of DNA is also dependent upon the donation of methyl groups. 5,10-MTHF acts as the substrate for the enzyme thymidine synthase (TS) in a reaction that results in the donation of a methyl group to uracil, converting it to thymine.  35  Other reactions directly, or indirectly alter the SAM:SAH ratio, thereby altering methylation capacity. The enzyme glycine n-methyl transferase (GNMT) for example is a passive enzyme that produces a non-harmful byproduct of n-methyl glycine from glycine using SAM as a methyl donor (Avila et al., 2000; Villanueva & Halsted, 2004). These and other important pathways are critical in maintaining proper methylation capacity but are unfortunately beyond the scope of this thesis.  1.14 Ethanol and Methionine Metabolism  Ethanol has profound affects on the availability and metabolism of many hepatic nutrients (Lieber, 1995) to the extent that perturbations in methionine metabolism, in addition to oxidative stress (Ishii et al., 1997; Lieber, 1997; Albano, 2007) are associated with cellular injury and the onset of alcoholic liver disease (ALD) (Lieber, 2000; Halsted et al., 2002a). Interestingly, research has revealed that cellular injury is mediated partly through epigenetic alterations, particularly through histone modifications and the hypo and hypermethylation of DNA (Shukla & Aroor, 2006).  Furthermore, decreased DNA  methylation, accompanied with decreased DNMT activity after PAE has also been reported in fetal tissues (Garro et al., 1991). However, the underlying mechanisms of how ethanol can cause epigenetic alterations are not entirely known.  Distinguishing boundaries  between these mechanisms has therefore becoming a novel and exciting area of research and it has now been shown that ethanol’s ability to interfere with methionine metabolism is directly linked with impaired methylation capacity (Lu et al., 2000; Halsted et al., 2002a; Halsted et al., 2002b; Villanueva & Halsted, 2004). In terms of altered methylation, the dysregulation of methionine metabolism caused by ethanol manifests itself primarily 36  through 1. the decreased generation of SAM; and 2. an altered SAM:SAH ratio (Lu et al., 2000; Halsted et al., 2002a; Halsted et al., 2002b; Villanueva & Halsted, 2004). However, perturbations in methionine metabolism are varied, and increased levels of homocysteine and decreased levels of glutathione are also common (Schalinske & Nieman, 2005) Indeed, ethanol impacts methionine metabolism at various levels leading to altered methylation capacity and the following sections examine how ethanol exerts some of these effects.  1.15 Ethanol Depletes SAM and Inhibits DNA Methylation  Among the most important pathways for DNA methylation is the synthesis of methionine and SAM through the methylation of homocysteine by MS (discussed in section 1.12). Reduced levels of SAM are a clinical manifestation of alcoholic liver disease (Lu & Mato, 2005) and it is thought that a reduction in SAM can be caused in part through a change in expression of the MAT enzymes. In adult animals chronically fed ethanol, there is a shift in expression of the MAT enzymes such that MAT2A transcripts (typically only expressed in the fetal liver) are increased and there is a resulting increase in MATII activity. The downstream repercussions of this switch in expression leads to decreased levels of SAM, which in turn alter the SAM:SAH ratio and impair methylation capacity (Lu et al., 2000; Halsted et al., 2002a; Halsted et al., 2002b; Villanueva & Halsted, 2004). However, impaired MAT expression is not the only cause of SAM depletion. Further upstream from the MAT enzymes, methionine is remethylated from homocysteine via MS and it was shown in 1998 that acetaldehyde, a metabolite of ethanol, inhibits MS through the formation of an inhibiting covalent adduct (Kenyon et al., 1998). In conjunction with  37  decreased activity of the MAT enzymes, inhibition of MS by acetaldehyde further contributes to the decreased levels of SAM observed in alcoholic patients. Importantly it has also been shown that inhibition of MS is directly linked with altered DNA methylation (Mason & Choi, 2005) and even during pregnancy, acute ethanol administration can exert this teratogenic effect (Garro et al., 1991) .  In 1991 (Garro et al., 1991) found that acute ethanol administration (3 g/kg twice a day) to pregnant mice, from the 9th through the 11th day of gestation, resulted in the global hypomethylation of fetal DNA. They hypothesized that the observed decrease in DNA methylation was due to either 1. A decrease in SAM or 2. A decrease in DNMT activity. In order to test this hypothesis they measured the activity of MS in the presence of exogenously added SAM. Their results showed that fetal MS activity was significantly lower (p<0.001) in the fetuses from alcohol-fed animals compared with controls. Furthermore, they discovered that with a decrease in MS activity, there was a concomitant decrease in DNMT activity, and the combination of depleted SAM and impaired DNMT activity manifested itself in the global hypomethylation of DNA in fetal tissues (Garro et al., 1991).  1.16 Ethanol Alters the SAM:SAH Ratio  Various studies using animal models have demonstrated that deficiencies in one carbon metabolism can cause aberrant DNA methylation status as well as induce DNA strand breaks, and impair DNA repair (Choi et al., 1998; Friso & Choi, 2002). Importantly  38  the micronutrient folate is essential in methionine metabolism and in maintaining the SAM:SAH ratio. Recall from section 1.12, that 5-MTHF acts as a methyl donor in the reaction catalyzed by MS (discussed in section 1.12), and 5,10-MTHF provides the one carbon units required for the synthsis of nucleic acids (Selhub, 1999) (discussed in section 1.13). Importantly, studies have shown that chronic ethanol consumption causes reduced serum folate levels through reduced intestinal absorption (Halsted et al., 2002a) and increased urinary excretion (McMartin et al., 1989).  Furthermore, folate availability  directly affects the SAM:SAH ratio (Halsted et al., 2002b) and it has been shown that rats fed a folate supplemented diet for 20 weeks show significantly increased levels of hepatic DNA methylation, indicating a strong correlation between hepatic folate concentrations and genomic methylation in the liver (Choi et al., 2005).  1.17 Animal Models of FAS  The mass of detrimental teratogenic effects of ethanol that have successfully been revealed to date has been achieved largely through the use of animal models. Numerous models have now been well established which can effectively demonstrate the behavioural and cognitive disabilities observed in children with FAS including the sheep, (Cudd et al., 2002), rat (Abel & Dintcheff, 1978; Weinberg, 1989), mouse (Randall et al., 1977) and monkey (Clarren et al., 1987). No one species however, can mimic all of the effects of prenatal ethanol exposure, thus the ultimate choice of animal depends the area of interest and practicality of use and care.  39  Rodents are an exceptional model which demonstrate behavioural and cognitive disabilities in that they show body and brain growth deficiencies (Abel & Dintcheff, 1978; Gallo & Weinberg, 1986), decreased brain myelination (Ozer et al., 2000), spatial learning and memory deficits (Berman & Hannigan, 2000), increased vulnerability to stress and anxiety-like disorders in adulthood (Hellemans et al., 2008), numerous physiological changes including hormonal hyperresponsiveness to stressors and immune challenge (Weinberg et al., 1996; Kim et al., 1999a) and neurotransmitter deficits (Rudeen & Weinberg, 1993; Tran & Kelly, 1999); Furthermore, mice and rats both show prenatal ethanol induced craniofacial anomalies (Ismail & Janjua, 2001). However, as with any animal model there exist limitations when using rats. Rodents do not readily consume ethanol, therefore establishing a method of delivery is a critical decision with severe implications on the outcome of the study.  There are numerous accepted methods of delivering ethanol to animals, including intragastric intubation, inhalation of vapor, administering alcohol in the drinking water and administering alcohol in a liquid diet (Lieber & DeCarli, 1989). Each of these methods has advantages and drawbacks as reviewed by (Riley & Meyer, 1984), and what may be suitable in one experiment could be confounding to another. For example, inhalation of alcohol vapour can result in high blood alcohol levels (Healey et al., 2008), but this is not representative of how humans consume ethanol.  Intraperitoneal injection and oral  intubation on the other hand deliver ethanol in its liquid form, but these modes of delivery can be stressful and invasive. Putting alcohol in the drinking water is non-invasive, but rodents are adverse to the taste and will decrease their fluid intake (Riley & Meyer, 1984).  40  Putting ethanol into a nutritionally complete liquid diet appears to be a relatively effective method of administering alcohol to rodents (Lieber & DeCarli, 1989). Ethanol administered via a nutritionally complete liquid diet provides adequate nutrition, and results in minimal stress to the animal. Blood alcohol levels are typically in the moderately high range, approximately 100-200 mg/dl (Weinberg, 1985; Lan et al., 2006), and are thus lower than can be obtained with intubation or vapor inhalation. This may be a drawback of this method if higher blood alcohol levels are needed for the investigation. Moreover, regardless of method of administration, the high caloric content of ethanol can displace other nutrients, and thus undernutrition is a typical side effect. For this reason, a nutritional control group is often included in experiments using this model (Riley & Meyer, 1984). Pair-fed (PF) dams receive a liquid diet with maltose dextrin substituted for ethanol in a caloric amount matched to an ethanol consuming female. The inclusion of the pair-fed control group serves, in effect, as a negative control and allows experimenters to identify and account for any baseline dietary effects secondary to alterations in nutrition that may not be a result of prenatal ethanol. In addition, a third control group is included consisting of dams fed standard laboratory rat chow or liquid control diet ad libitum throughout pregnancy in order to provide a comparison to a typical laboratory reared rat with no nutritional deficiencies.  For the present study we have elected to administer alcohol via the liquid diet method for the following reasons: 1) the emphasis of this thesis is on the HPA axis which is a key component of the stress system. As such, a non-stressful, non-invasive method of administration is critical to the validity of results and 2) relatively high blood alcohol levels  41  (BALs) can be achieved while still maintaining adequate nutritional status (Weinberg, 1985). Thus, offspring from ethanol-fed (E), pair-fed control (PF) and ad libitum-fed control (C) dams were used to collect the data reported throughout this thesis.  1.18 Thesis Objectives  The HPA axis is programmed through prenatal alcohol exposure (Weinberg, 1989, 1993; Zhang et al., 2005) and prenatal malnutrition (Lesage et al., 2002; Lucas, 2005) It has been proposed that programming of the HPA axis is mediated through permanent changes in gene expression that alter the ‘set point’ of the axis in the adult (O'Regan et al., 2001) and epigenetics is one mechanism through which the early environment directly alters gene expression.  In support of this, it has been shown that the HPA axis is  susceptible to epigenetic programming early in development (Weaver et al., 2004).  Studies have shown that the prenatal environment plays a critical role in influencing gene expression in the offspring, leading to genetic changes that are carried through to adulthood (Maloney & Rees, 2005). Furthermore, dietary intake (Wu et al., 2004) and ethanol consumption (Garro et al., 1991) can directly influence gene expression through altered methionine metabolism resulting in epigenetic modifications of DNA (Schalinske & Nieman, 2005). The objective of the present experiments is to ascertain if methionine metabolism is differentially altered in either the pregnant dam or the fetus by maternal ethanol consumption during pregnancy. In aid of this, key metabolites and mRNA levels associated with methionine metabolism in dams and fetuses of E, PF and C animals  42  have been quantified. Evidence of altered methionine metabolism in E fetuses could indicate that the HPA axis integrity is disrupted in these animals through epigenetic programming, thus providing one possible mechanism through which the axis is differentially affected in E compared to PF and C animals. Data from these studies could have significant implications in the accuracy of FASD diagnosis and the development of therapeutic strategies in FASD medicine.  43  2  METHODS  2.1 Animals and Mating  In all studies, Sprague-Dawley rats were obtained from the Animal Care Centre at The University of British Columbia. Upon arrival, male rats (weighing 300g-350g) and female rats (weighing 225g – 250g) were group housed according to sex for a 1 – 2 week period. This allowed for adaptation to the colony room, and time for the rats to recover from shipping. The colony room was kept at a controlled temperature of 21°C, under controlled lighting conditions (lights on at 6am on off at 6pm). All rats had ad libitum access to standard lab chow (Jamieson’s Pet Food Distributors Ltd., Delta, BC, Canada – Appendix 1) and water. After the recovery period, males were singly housed in stainless steel hanging cages (25cm x 18cm x 18cm), and a single female was placed with each male. Pregnancy was determined by the presence of vaginal plugs on wax paper sheets placed beneath the cages. The day the plug was discovered was considered day 1 of gestation (G1). At this time the female was removed from the hanging cage and randomly assigned to one of three treatment groups: (1) E: ad libitum access to a liquid ethanol diet (36% ethanol-derived calories; n=15); (2) PF liquid control diet (maltose-dextrin isocalorically substituted for ethanol) (Appendix 2), with each female fed the amount consumed by an E partner (g per kg body weight per day of gestation; n=16); and (3) C: ad libitum access to rat chow and water (n=15). Each male was housed singly for 2 days before being placed with another female. All animal use and care procedures were in accordance with the  44  National Institutes of Heatlh and Canadian Council on Animal Care guidelines and were approved by the University of British Columbia Animal Care Committee (Appendix 3).  2.2 Administration of Ethanol Through Pregnancy and Calculation of Ethanol Intake  The E diet is composed of 69mL of 95% ethanol in 1L of liquid diet (Appendix 2). On D1 of pregnancy when dams are first exposed to ethanol, they are given a ration of 100 mL liquid diet, consisting of 33.3 mL E diet and 66.7 mL PF liquid control diet. This is to accustom the animals to the taste of ethanol and ensure that an adequate amount of food is consumed. On D2 of pregnancy the E diet is increased to 66.7 mL by volume and the control diet decreased to 33.3 mL. From D3 – D20, E dams are fed only the E liquid diet. On D21, in order to ensure better parturition and initiation of lactation, dams are switched to an ad libitum diet of standard lab chow.  As an index of in utero alcohol exposure throughout development, ethanol intake by pregnant females was measured by weighing bottles containing the liquid ethanol diet before and after feeding.  Ethanol intake was calculated by using the weight of diet  consumed multiplied by the percent volume of ethanol (95% at a density of 0.8144g/ml). The amount of ethanol consumed was tabulated on a weekly basis and reported as an average daily intake as a function of gestation week.  45  2.3 Maternal and Fetal Sampling on G21  Pregnant females were weighed on G1, G7, G14, and G21. On G21, dams were anesthetized using 1.5% isofluorane and 1mL of blood was taken by cardiac puncture and collected in a 1.5mL eppendorf tube pretreated with 150uL EDTA.  One incision  approximately 2” long was made down the midline and two more perpendicular at each end of the incision opening up the abdominal cavity. The uterus was exposed and an incision was made down the midline exposing the fetuses. From each fetus, blood was collected from an axillary incision using capillary tubes. Three capillary tubes for each fetus were used, collecting approximately 100uL. Blood was taken while the fetus remained attached to the placenta and after collection the umbilical cord was severed and the fetus immediately euthanized by decapitation before the next sample was collected. Fetal blood from males and females was pooled in a 1.5mL eppendorf tube pretreated with 150uL EDTA and kept on ice. A total of approximately 1-1.2 mL of blood from 9-12 fetuses was collected and remaining fetuses were cut from the placenta and immediately euthanized by decapitation. Fetal and dam liver samples were taken during the course of blood sampling. From the first fetus removed, after being euthanized, an incision was made down the midline, the liver exposed and removed (approximately 20-30mg). A small section (approximately 30mg) of the dam liver was sectioned immediately after fetal blood sampling, at which time the dam was euthanized by decapitation. All liver samples were flash frozen on dry ice and stored at -80°C.  46  Immediately after collection fetal and dam blood was spun down for 10 minutes at 3500rpm on a centrifuge with a rotor diameter of 16.5 cm and then stored on dry ice until transferred to storage at -80°C.  2.5 Isolating Total RNA, Measuring Purity  Total RNA was extracted from fetal and dam liver samples (approximately 60mg) using the RNeasy Mini Kit (Qiagen, Mississauga, Ontario, CA) and treated with DNase I to avoid contamination by genomic DNA. Spectrophotometric measurements were taken from each sample to determine RNA concentration and purity (only samples with a 260:280 ratio between 1.9 and 2.1 were used). In addition, the integrity of the RNA samples was visualized by separating the total RNA by electrophoresis on a 1% agarose gel containing 1.3µM ethidium bromide and checking for the presence of 18s and 28s rRNA bands. All extracted RNA samples were stored at -80°C pending future use.  2.6 Relative Quantification of Gene Expression by Real-Time PCR  Total RNA was reverse transcribed using the High Capacity cCDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, U.S.A.) using 10X Reverse transtriptase buffer, 25x dNTP mix, 10x random primer mix, and 5U/µL Multiscribe™ Reverse Transcrpitase (5U/uL) along with 1ug of total RNA for a final reaction volume of 80µL. The samples were then incubated at 25 ºC for 10 minutes, then at 37 ºC for 2 hours and then stored at -20 ºC for later quantification of the transcripts.  47  The real-time PCR comparative Ct method (  Ct) (Livak & Schmittgen, 2001)  was used to measure relative quantification, using commercially available primers and TaqMan MBG probes (Fam fluorescent dye labled from Applied Biosystems, Foster City, CA.) specific for the genes encoding MS (MTR gene), MTHFR, BHMT, MAT1A, MAT2A and PEMT from fetal and dam liver samples. In all cases expression of ß-actin was measured as an endogenous control and each sample was run in duplicate. 2uL samples (2µl of cDNA reaction) were mixed with 1X Taqman Universal PCR mix and 1X TaqMan primer/probe mix (20µL final reaction volume) and incubated in a 7500 Real Time PCR System (Applied Biosystems, Foster City, CA, U.S.A) at: 50ºC for 2min, and 95ºC for 10 min followed by 50 cycles at 95ºC for 15 sec, and 60ºC for 1 min. The 7500 System Sequence Detection Software version 1.2.3 (Applied Biosystems) was used for all data analysis.  48  2.7 Biochemical Analysis by LC-MS/MS  Plasma choline, betaine, dimethylglycine, methionine, and homocysteine were analyzed by HPLC-MS/MS from plasma samples taken at G21 (n=6 for PF and C, n=5 for E). All LC-MS/MS analyses were done in the laboratory of Dr. Sheila Innis at the BC Research Institute for Children & Women’s Health. The method is described in detail in (Friesen et al., 2007).  2.8 Statistical Analysis  Metabolic data, including all metabolites and mRNA transcript levels were analyzed using one-way analysis of variance (ANOVA) for the factors of prenatal treatment group (E, PF and C) with the Newman-Keuls post hoc analysis performed on significant main effects. Statistical significance of group x day interactions for weight gain through development was determined by two way ANOVA. All data were analyzed using the PC Software Statistica v6.0 (StatSoft Inc., Tulsa, OK, USA). Values are expressed as Mean ± Standard Error Measurement (SEM) of the Mean.  49  3  RESULTS  3.1 Ethanol Intake Through Gestation  Ethanol intake was found to be consistently high throughout gestation, with average daily amounts of 9.25 ± 0.30, 13.20 ± 0.35 and 12.80 ± 0.36 g/kg body weight for weeks 1, 2 and 3 of gestation, respectively. In previous breedings, similar ethanol intake has resulted in peak blood alcohol levels (BALs) of approximately 150-190 mg/dl (Lan et al., 2006)  3.2 Maternal Body Weights During Gestation  Repeated measures ANOVA on maternal weight gain during gestation revealed significant effects of group [F(2,38)=25.57, p<0.001] and day [F(3,114)=191.53, p<0.001], and a group x day interaction [F(6,114)=6.15, p<0.001] (Fig 1).  Post-hoc analysis  indicated that E and PF dams did not differ from C dams on G1 but weighed significantly less than C dams (*p<0.05) on G7, G14 and G21. C dams showed significant weight gains on G7, G14 and G21 (p<0.001), whereas E and PF dams did not increase in weight on G7, but showed a gain in weight on G14 and G21 (p<0.05) (Fig 1).  50  Maternal body weights (g) during gestation 500  *  450  Weight (g)  E 400  PF  *  C 350  *  300  21 D G  G  D  14  7 G D  G D  1  250  Figure 3.1 Maternal Body Weights (g) (Mean ± SEM) of E, PF and C Dams During Gestation Body weights of E and PF dams were lower than those of C damns (* p<0.05) on G7, G14 and G21. C dams showed a significant increase over the G1 weight each time point, whereas E and PF dams showed no increase from G1 to G7 but significant increases on G14 and G21.  51  3.3 Number of Live Young  Ethanol exposure during pregnancy can affect litter size via fetal resorption (Ghimire et al., 2008), and infant mortality (Fernandez et al., 1983), thus data from each litter were recorded. Data collected showed the average number of live-born pups to be 16.0 ± 0.62 in the E group, 15.2 ± 0.61 in the PF group and 15.1 ± 0.99 in the C group. Number of still born pups averaged 0.22 ± 0.15 in the E group, 0.2 ± 0.2 in the PF group and 0 in the C group. One way ANOVA revealed that there were no significant differences among groups for number of live or stillborn pups,.  3.4 Plasma Metabolite Concentrations  To quantify the changes affecting both the maternal and fetal methionine cycle, we utilized mass spectrometry to measure levels of methionine, homocysteine, betaine, dimethylglycine and choline in plasma samples taken at G 21 from E, PF and C dams and fetuses (E, PF and C dams and fetuses had n’s of 5,6,6, respectively). Data were analyzed by one way ANOVAs to reveal significant differences among groups. Averages of all fetal and maternal measurements are summarized in table 2. Figures 3.13-3.15 summarize the significant changes observed in fetal and maternal methionine metabolism.  52  3.4.1 Methionine Concentrations  Maternal  Analysis by one way ANOVA revealed a significant effect of group for methionine concentrations, [F(2,14)=19.13, p<0.0001]. The Newman-Keuls (NK) post hoc analysis revealed that average methionine concentrations in E animals were significantly greater than those in PF and C animals (E>PF=C, p<0.001). Interestingly, average maternal methionine concentrations in E females were nearly double the levels observed in PF and C females. (Fig 3.2)  Fetal  Similar to maternal measurements, analysis of fetal methionine concentrations also showed a prenatal treatment effect, as revealed by one way ANOVA, [F(2,14)=5.638, p=0.015]. Fetal methionine concentrations were significantly higher in E compared to C fetuses (E>C, p<0.05), and the difference between E and PF fetuses approached significance (E>PF, p<0.06).) (Fig 3.2)  53  Maternal (G21) Plasma Methionine (µM) 300  200  µM  * 100  C  PF  E  0  Fetal (G21) Plasma Methionine (µM) 300  * #  µM  200  100  C  PF  E  0  Figure 3.2 Maternal and Fetal Plasma Methionine Levels (µM) (Mean ± SEM) Maternal and fetal plasma methionine levels on G21 (n=5-6 litters/group). One way ANOVA revealed significantly higher methionine levels in both maternal (*E>PF=C), p <0.001) and fetal (*E>C, p<0.02, #E>PF, p<0.06) samples  54  3.4.2 Homocysteine Concentrations  Maternal Analysis by one way ANOVA of maternal showed a significant effect of group for plasma concentrations of homocysteine [F(2,14)=3.79, p<0.05]. As expected, average concentrations in E animals were significantly higher than those of PF and C (E>PF=C, p<0.05) (Fig 3.3).  Fetal Unlike maternal measurements, no significant effect of group was revealed by one way ANOVA in fetal homocysteine plasma concentrations. [F(2,14)=1.17, p=0.34].  55  Maternal (G21) Plasma Homocysteine (µM) 8  *  µM  6  4  2  C  PF  E  0  Fetal (G21) Plasma Homocysteine (µM) 8  µM  6  4  2  C  PF  E  0  Figure 3.3 Maternal and Fetal Plasma Homocysteine Levels (µM) (Mean ± SEM) Maternal and fetal plasma homocysteine levels on G21 (n=5-6 litters/group). One way ANOVA revealed significantly higher levels in maternal (*E>PF=C, p <0.05), but not fetal samples.  56  3.4.3 Dimethylglycine Concentrations  Maternal Analysis by one way ANOVA revealed a significant effect of group for DMG concentrations, [F(2,14)=14.56, p<0.001]. Average plasma concentrations in E animals were significantly lower than C (E<C, p<0.001), and the difference between E and PF animals approached significance (E<PF, p<0.07) (Fig 3.4)  Fetal Analysis by one way ANOVA of fetal plasma DMG concentrations also revealed a significant effect of prenatal group [F(2,14)=12.35, p<0.001].  DMG concentrations in E  and PF fetuses were significantly lower than those in C fetuses (E=PF<C, p<0.01) (Fig 3.4).  57  Maternal (G21) Plasma Dimethylclycine (µM) 25  20  * 15  µM  # 10  5  C  PF  E  0  Fetal (G21) Plasma Dimethylglycine (DMG) (µM) 25  *  20  µM  15  10  5  C  PF  E  0  Figure 3.4 Maternal and Fetal Plasma DMG Levels (µM) (Mean ± SEM) Maternal and fetal plasma DMG levels on G21 (n=5-6 litters/group). One way ANOVA revealed significantly lower levels in maternal samples in E compared to C animals (*E<C, p<0.001) and the same trend was observed in E compared to PF (#E<PF, p<0.07). Fetal measurements also revealed a significantly lower levels in E and PF DMG levels compared to C (*E=PF<C,p<0.01)  58  3.4.4 Betaine Concentrations  Maternal Analysis by one way ANOVA revealed a group effect that approached significance for betaine concentrations [F(2,14)=3.62, p=0.054]. Post-hoc tests indicated a trend for lower maternal betaine concentrations in E and PF compared to C animals (E=PF<C, p<0.08) (Fig 3.5).  Fetal One-way ANOVA analysis showed no significant effect of prenatal treatment on fetal plasma betaine concentrations [F(2,14)=2.13, p=0.155](Fig 3.5).  59  . Maternal (G21) Plasma Betaine (µM) 150  100  µM  * # 50  C  PF  E  0  Fetal (G21) Plasma Betaine (µM) 150  µM  100  50  C  PF  E  0  Figure 3.5 Maternal and Fetal Plasma Betaine Levels (µM) (Mean ± SEM) Maternal and fetal plasma betaine levels on G21 (n=5-6 litters/group). One way ANOVA revealed significantly lower levels in maternal samples in PF compared to C (*PF<C, p<0.05) and the same trend was observed in E compared to C (#E<C, p<0.08). No significant differences were revealed in fetal measurements  60  3.4.5 Choline Concentrations  Maternal One way ANOVA on maternal plasma choline concentrations revealed no significant effect of group [F(2,14)=0.43, p=0.66] (Fig 3.6).  Fetal As with maternal samples, no significant group differences were found for fetal choline concentrations [F(2,14)=1.24, p=0.32)] (Fig 3.6).  61  Maternal (G21) Plasma Choline (µM) 40  µM  30  20  10  C  PF  E  0  Fetal (G21) Plasma Choline (µM) 40  µM  30  20  10  C  PF  E  0  Figure 3.6 Maternal and Fetal Plasma Choline Levels (µM) (Mean ± SEM) Maternal and fetal plasma choline levels on G21 (n=5-6 litters/group). One way ANOVA did not reveal significant differences in maternal or fetal samples.  62  3.5 Maternal and Fetal Liver Enzyme mRNA Levels by Real-time PCR  In order to determine the effects of ethanol on the maternal and fetal methionine cycle, mRNA levels of key enzyme were quantified using liver samples from E, PF and C dams and fetuses (n’s of 6 in each group). In each case a small portion of the liver was used to determine mRNA levels of MATIA, MAT2A, BHMT, MTHFR, MS and MTHFR by real-time PCR.  Analysis by one way ANOVA revealed significant differences in  expression of several of these hepatic enzymes.  3.5.1 MAT1A mRNA Maternal Analysis by one-way ANOVA revealed a significant effect of group for MAT1A mRNA levels [F(2,14)=2.14, p=7.045] . Maternal mRNA levels were lower in E and PF animals (*E<PF, p<0.01) and the same trend was observed when comparing E and C (E<C, p<0.10) (Fig 3.7)  Fetal Analysis of fetal MAT1A mRNA levels also revealed a significant effect of group [F(2,14)=2.15, p=6.35].  Fetal MAT1A mRNA levels were significantly lower when  comparing E and PF, and E and C fetuses (*E<PF=C, P<0.05) (Fig 3.7)  63  Maternal MAT1A mRNA 2.0  #  * Relative Quantity  1.5  1.0  0.5  C  PF  E  0.0  Fetal MAT1A mRNA 2.0  Relative Quantity  1.5  1.0  * 0.5  C  PF  E  0.0  Figure 3.7 Maternal and Fetal Liver MAT1A mRNA (Mean ± SEM) Maternal and fetal liver Mat1A mRNA on G21 (n=6 litters/group). One way ANOVA revealed significantly less Mat1A mRNA E compared to PF (*E<PF, p<0.01) and the same trend was observed in E compared to C (#E<C, p<0.1). Fetal measurements also showed significantly less Mat1a mRNA in E compared to PF and C (*E<PF=C, P<0.05)  64  3.5.2 MAT2A mRNA  Maternal Analysis by one way ANOVA revealed a significant effect of group for MAT2A mRNA levels [F(2,14)=12.86, p<0.001]. MAT2A mRNA levels were significantly lower in E compared to both PF and C animals (E<PF=C, p<0.01) (Fig 3.8).  Fetal Fetal MAT2A mRNA levels analyzed by one way ANOVA also showed a group effect that approached significance [F(2,15)=3.15, p=0.0721]. Post-hoc analysis revealed a trend for lower mRNA levels in PF compared to E and C fetuses (PF<E, p<0.09; PF<C, p<0.07) (Fig 3.8).  65  Maternal Liver MAT2A mRNA  Relative Quantity  1.5  1.0  * 0.5  C  PF  E  0.0  Fetal Liver MAT2A mRNA  Relative Quantity  1.5  1.0  0.5  C  PF  E  0.0  Figure 3.8 Maternal and Fetal Liver MAT2A mRNA (Mean ± SEM) Maternal and fetal liver MAT2a mRNA on G21 (n=6 litters/group). One way ANOVA revealed significantly less maternal MAT2a mRNA in E compared to PF and C animals (*E<PF=C, p<0.01) Fetal measurements did not reveal significant differences.  66  3.5.3 Methionine Synthase mRNA  Maternal Maternal MS mRNA levels showed a significant effect of group when analyzed by one way ANOVA [F(2,14)=13.08, p<0.001]. There were significantly lower mRNA levels in E compared to PF and C animals (E<PF=C, p<0.01) (Fig 3.9).  Fetal Fetal measurements did not reveal any significant effect of prenatal treatment on methionine synthase mRNA levels. (E=PF=C) (Fig 3.9).  67  Maternal Liver MS mRNA 2.0  Relative Quantity  1.5  1.0  * 0.5  C  PF  E  0.0  Fetal Liver MS mRNA 2.0  Relative Quantity  1.5  1.0  0.5  C  PF  E  0.0  Figure 3.9 Maternal and Fetal Liver MS mRNA (Mean ± SEM) Maternal and fetal liver MS mRNA on G21 (n=6 litters/group). One way ANOVA revealed significantly less maternal MS mRNA in E compared to PF and C (*E<PF=C, p<0.01) Fetal measurements did not reveal significant differences.  68  3.5.4 BHMT mRNA  Maternal An analysis by one way ANOVA revealed a significant effect of group for BHMT mRNA levels [F(2,14)=3.91, p<0.05].  BHMT mRNA levels in C animals were  significantly lower than those of PF and (C<PF, p<0.05) and there was a trend for lower BHMT mRNA levels in E compared to PF animals (E<PF, p<0.1) (Fig 3.10).  Fetal Analysis by one way ANOVA of fetal BHMT mRNA levels also revealed an effect of group [F(2,15)=5.05, p=0.021], with significant decreases in BHMT mRNA in E and PF compared to C fetuses (E=PF<C, p<0.05) (Fig 3.10).  69  Maternal Liver BHMT mRNA 2.5  #  *  Relative Quantity  2.0  1.5  1.0  0.5  C  PF  E  0.0  Fetal Liver BHMT mRNA 2.5  Relative Quantity  2.0  * 1.5  1.0  0.5  C  PF  E  0.0  Figure 3.10 Maternal and Fetal Liver BHMT mRNA (Mean ± SEM) Maternal and fetal liver BHMT mRNA on G21 (n=6 litters/group). One way ANOVA revealed significantly higher levels of BHMT mRNA in PF animals compared to C (*PF>C, p<0.05), and the same trend was observed in PF animals compared to E (#PF>E, p<0.1). Fetal measurements revealed no difference in E and PF animals, but mRNA levels from both groups were significantly less compared to C (E=PF<*C, p<0.05).  70  3.5.5 MTHFR mRNA  Maternal Analysis by one way ANOVA revealed a significant effect of group for maternal MTHFR mRNA levels [F(2,14)=9.88, p=0.021].  PF MTHFR mRNA levels were  significantly greater than those in C and E (p’s < 0.05 and 0.01, respectively) and the difference in levels between E and C animals approached significance (p<0.08). (Fig 3.11)  Fetal A one way ANOVA on fetal measurements revealed an effect of group that approached significance [F(2,15)=3.4, p=0.061 Post-hoc tests revealed a trend, with levels in E and PF fetuses marginally lower than those of C (E=PF <C, p<0.08) (Fig 3.11).  71  Maternal Liver MTHFR mRNA 2.5  #  *  * Relative Quantity  2.0  1.5  1.0  0.5  C  PF  E  0.0  Fetal Liver MTHFR mRNA 2.5  Relative Quantity  2.0 # 1.5  1.0  0.5  C  PF  E  0.0  Figure 3.11 Maternal and Fetal Liver MTHFR mRNA (Mean ± SEM) Maternal and fetal liver MTHFR mRNA on G21 (n=6 litters/group). One way ANOVA revealed significantly less maternal MTHFR mRNA E and C compared to PF animals (C<PF, p<0.05, E<PF, p<0.01) and a similar trend was observed in E compared to C (#E<C, p<0.08). Fetal measurements also revealed a trend in E and PF animals which were less than C (E=PF <#C, p<0.08).  72  3.5.6 PEMT mRNA  Maternal Analysis by one way ANOVA of PEMT mRNA levels revealed a significant effect of prenatal treatment [F(2,14)=7.602,p =0.006]. Maternal PEMT mRNA levels were lower in E than in PF and C animals (E<PF=C, p<0.05) (Fig 3.12).  Fetal Analysis of fetal liver mRNA levels similarly revealed an effect of group [F(2,15)=4.82, p<0.05]. PEMT mRNA levels were lower in E and PF than in C fetuses (E=PF <C, p<0.05) (Fig 3.12).  73  Maternal Liver PEMT mRNA 2.0  Relative Quantity  1.5  1.0  * 0.5  C  PF  E  0.0  Fetal Liver PEMT mRNA 2.0  *  Relative Quantity  1.5  1.0  0.5  C  PF  E  0.0  Figure 3.12 Maternal and Fetal Liver PEMT mRNA (Mean ± SEM) Maternal and fetal liver PEMT mRNA on G21 (n=6 litters/group). One way ANOVA revealed significantly less maternal PEMT mRNA in E compared to PF and C animals (*E<PF=C, p<0.01). Fetal measurements also revealed less mRNA in E and PF animals compared to C (E=PF <*C, p<0.05).  74  Figure 3.13. Summary of Observed Changes in E Animals Figure 3.13 summarizes the significant changes observed in E animals compared to PF and C. The enzymes and metabolites measured are printed in bold. Solid arrows indicate significant changes in the DAM and outlined arrows indicate significant fetal changes. *E < C, p<0.10 #E<PF, p<0.10.  75  Figure 3.14 Summary of Observed Changes in E and PF Animals Figure 3.14 summarizes the significant changes observed in E and PF animals compared to C (i.e., changes due primarily to the nutritional effects of the diet or of reduced food intake). The enzymes and metabolites measured are printed in bold. Solid arrows indicate significant changes in the DAM and outlined arrows indicate significant fetal changes. *E=PF<C, p<0.10.  76  Figure 3.15 Summary of Observed Changes in PF Animals Figure 3.15 summarizes the significant changes observed in PF compared to E and C animals (i.e., effects of the pair-feeding treatment per se). The enzymes and metabolites measured are printed in bold. Solid arrows indicate significant changes in the DAM.  77  4  DISCUSSION  Ethanol disrupts hepatic methionine metabolism in the adult. Numerous animal models, including the ethanol-fed rat, pig and baboon, have demonstrated that the most significant alterations of methionine metabolism due to chronic ethanol exposure include impaired methylation capacity (altered SAM:SAH ratio), decreased liver SAM levels, increased SAH and homocysteine levels and decreased MAT and MS activity (Finkelstein, 1990; Lieber et al., 1990; Trimble et al., 1993; Barak et al., 2002; Halsted et al., 2002a; Halsted et al., 2002b; Barak et al., 2003; Mason & Choi, 2005). The results of the present study demonstrate, for the first time, that prenatal ethanol exposure alters methionine metabolism in both the dam and the fetus at the end of gestation. Importantly, both E dams and fetuses exhibit increased plasma levels of methionine and reduced MAT1A mRNA expression.  4.1 Effect of Ethanol Exposure on Pregnancy Outcome  Ethanol intake was consistently high throughout gestation, averaging 11.95 ± 0.39, 17.06 ± 0.46 and 16.54 ± 0.4725 g/kg body weight/day of gestation for weeks 1, 2 and 3, respectively. Consistent with previous studies in our lab (Weinberg, 1988, 1992; Gabriel  et al., 2001), E and PF dams failed to gain weight during the first week of gestation. Furthermore, while body weights increased in all groups on G14 and G21, E and PF animals weighed significantly less than C throughout gestation.  Additionally, in the  present study, neither gestation length nor the number of live born pups was affected by  78  prenatal treatment. Previous studies suggest that gestation length is sometimes increased by PAE (Weinberg, 1988, 1992), however it is not uncommon for length of gestation to remain unchanged (Glavas et al., 2007). Similarly, a decrease in the number of live pups may be observed (Weinberg, 1989), but the present study is consistent with those reporting no change (Weinberg, 1988, 1992; Lan et al., 2006). While there were no significant weight differences between E and PF dams during gestation, it should be noted that ethanol disrupts digestion, absorption and utilization of essential nutrients (Weinberg, 1984; Lieber, 2000) in addition to altering placental blood flow (Falconer, 1990; Burd et al., 2007) and may therefore cause dietary deficiencies that cannot be controlled for with the inclusion of a pair-fed group. Furthermore, while the focus of this study was to elucidate specific perturbations of methionine metabolism in E compared to control fetuses at G21, the use of a chow control diet, which is compositionally different from the liquid diets, (Appendix 1 and 2) prevents a comparison of the specific nutritional differences between animals on the liquid diet versus those on chow. The C animals can therefore be considered a baseline or standard control conditions, and the specific effects of ethanol can only be conclusively determined when E differs from both PF and C groups.  4.2 The Effect of Prenatal Ethanol Exposure on the Maternal and Fetal Methionine Cycle  In the present study there are a number of novel findings on the effects of maternal ethanol consumption on methionine metabolism in both the dam and fetus at G21. Importantly, both E dams and fetuses exhibit increased plasma levels of methionine and decreased liver MAT1A mRNA levels compared to PF and C animals. Other specific  79  effects of ethanol are evident only in maternal samples which reveal a significant increase in homocysteine and significant decreases in MAT2A, MS, and PEMT mRNA levels. The metabolism of choline and its downstream products betaine and DMG do not appear to be affected by ethanol exposure in either the dam or the fetus.  4.2.1 Ethanol Dependent Increases in Methionine Levels as a Possible Result of the BHMT Salvage Pathway  The present study reports significantly increased methionine levels in the ethanol exposed dam compared to PF and C animals. Fetal samples, however, are significantly increased in E only relative to C animals yet still show an increasing trend when compared to PF. Contrary to this finding, other studies report methionine levels to be reduced in response to alcohol exposure (Halsted et al., 2002b) while many others report no data on methionine at all, focusing instead on perturbations of MS activity or SAM compromise (Lu et al., 2000; Watkins et al., 2002; Villanueva et al., 2006). It is well accepted that alcohol exposure disrupts MS activity through allosteric inhibition of the enzyme by ethanol’s degradation byproduct acetaldehyde causing an understandable reduction in methionine levels secondary to reduced remethylation of homocysteine. With this information, the increased levels of methionine reported here in both maternal and fetal samples is intriguing and warrants further discussion.  One challenge in interpreting these data is that most of the previous reports of ethanol induced changes of methionine focus on an animal model of alcoholic liver disease (ALD) (Halsted et al., 2002b; Lu & Mato, 2005; Kharbanda, 2007); a model mandating  80  longer periods of ethanol exposure, up to 14 weeks, than the 20 days of exposure used in the present study (Halsted et al., 2002b; Lu & Mato, 2005; Villanueva et al., 2006; Kharbanda, 2007).  Shorter periods of ethanol exposure have revealed that methionine  levels can be transiently preserved via the BHMT salvage pathway, leading to the question of whether such a process could account for the increase methionine reported here (Barak  et al., 1987; Barak et al., 1996).  Briefly, the BHMT salvage pathway describes the increased activity of BHMT in response to reduced MS function (Finkelstein et al., 1982; Barak et al., 1987; Barak et al., 1996). For periods of up to 2 months, this increase in BHMT activity can be sustained and methionine levels maintained even when MS activity is decreased up to 50% (Barak et al., 1987; Barak et al., 1996).  This salvage process can continue as long as betaine levels do  not become depleted and DMG levels are not increased. As discussed in section 4.2.3, the present study reports no significant change in betaine or DMG levels between the groups. These data suggest that at G21, BHMT is still working to remethylate homocysteine to methionine and can reasonably be assumed to be compensating for reduced MS activity as reported in previous studies (Barak, Beckenhauer et al. 1987; Barak, Beckenhauer et al. 1996). However, while betaine supplementation is capable of prolonging the duration of the salvage pathway, no evidence exists that increased BHMT activity, even with supplemented betaine, can cause increased levels of methionine concentrations. The increased methionine concentrations reported in this study can therefore likely not be attributed to an increase in BHMT activity.  Other factors for increased methionine  concentrations must therefore be considered.  81  Intriguingly, a recently published study using a prenatal intrauterine growth restriction rat model (IUGR) reports findings very similar to those of the present study including increased methionine in fetuses born to IUGR dams (MacLennan et al., 2004). In this model uterine arteries are ligated in view of creating a hypoxic fetal environment which emulates uteroplacental insufficiency in which placental transfer is impaired. As with PAE, a high degree of oxidative stress (Amini et al., 1996; Henderson et al., 1999) is placed on the IUGR fetus and offpsring born in IUGR models are typically underweight (Shahkhalili et al., 2009).  Importantly, (MacLennan et al., 2004) reports marked  disruptions within the methionine cycle including compromised methylation capacity (as indicated by a decreased SAM:SAH ratio), global hypomethylation of fetal DNA and increased methionine concentrations on post natal day 1 (P1). Disappointingly, (MacLennan et al., 2004) make no mention of the increased methionine levels in their discussion, nor do they speculate on its etiology. However, being a gestational model with restricted placental transfer and resultant oxidative stress, the similarities between the IUGR model and the PAE model used here make for valuable comparisons when considering the data in this study and may offer clues into the increased methionine reported in both studies. For instance, the present study reveals the fetal PF animals have methionine levels midway between the ethanol and control group, showing a 12% increase over control. This is comparable to the 16% increase reported in the IUGR offspring compared to controls in (MacLennan et al., 2004). While our value is not statistically significant, it is tempting to speculate that the significant 32% increase shown in E fetuses  82  over C results from the summation of the impaired nutrient transfer and increased oxidative stress which are both imparted by ethanol exposure. Importantly the present results, as well as those reported by (MacLennan et al., 2004), report decreased levels of MAT1A and MAT2A mRNA levels. As discussed in section (1.1.2) the MAT enzymes are responsible for converting methionine to SAM and deficiencies in these enzymes are known to increase methionine (Lu et al., 2001) suggesting that impairment of these enzymes could be responsible for the increase in methionine levels reported here.  4.2.2 Ethanol Dependent Increases in Methionine Concentration as a Possible Result of Reduced MAT1A mRNA  As discussed in the previous section, both the IUGR model and the FASD model induce oxidative stress within the fetal environment (Amini et al., 1996) and both show possible compromised MAT function through reduced transcript number (MacLennan et al., 2004). It has been well defined using rat models that both long and short term alcohol exposure lead to a hypoxic state and the resultant ethanol-induced oxidative stress is strongly associated with liver injury (Tsukamoto & Xi, 1989; Arteel et al., 1996). Interestingly, cirrhosis of the liver in humans, the end-stage manifestation of chronic alcoholic liver disease [reviewed by (Ishak et al., 1991)], is largely attributed to the detriments of oxidative stress and is associated with reduced mRNA abundance of several enzymes involved in methionine metabolism, including MAT1A (Avila et al., 2000). Moreover, a reduction in MAT enzyme activity is known to result in increased levels of methionine by virtue of disrupted conversion of methionine to SAM and a resultant pile up  83  of methionine (Lu et al., 2001). However, in spite of cirrhosis being a result of chronic ethanol exposure, these findings are difficult to compare directly to the present study since numerous years of alcohol abuse are required before development this condition [reviewed by (Ishak et al., 1991)]. More relevant data are none the less available in a study which directly measures MAT1A and MAT2A mRNA abundance as well as MAT activity in rat hepatocytes in response to acute hypoxia, a condition strongly associated with oxidative stress (Avila et al., 1998).  As reported in (Avila et al., 1998) only 6 hours of induced hypoxic conditions cause a significant decrease in MAT activity in rat hepatocytes and acute ethanol exposure during gestation are reported in the literature to be associated with notable oxidative stress (Amini  et al., 1996). Furthermore, this decrease in activity appears to be mediated in part by a decreased expression of the MAT enzymes, evidenced through decreased MAT1A and MAT2A mRNA levels in the hypoxic cells (Avila et al., 1998). In the present study, both MAT1A and MAT2A mRNA levels are significantly decreased in the E dam, and MAT1A mRNA levels are significantly reduced in the E fetus. In view of the data from cirrhotic patients, in which MAT1A levels are reduced secondary to alcohol exposure, it is possible that MAT1A transcripts are reduced secondary to ethanol induced oxidative stress. Similarly, in conjunction with (MacLennan et al., 2004)’s report of increased methionine, compromised MAT function is also reported as indicated by reduced mRNA levels of both MAT1 and MAT2A in the IUGR fetus, implying a causative relationship involving oxidative stress. With the findings of (Avila et al., 1998) in mind, it is reasonable to assume that MAT activity is also compromised in the ethanol exposed dam and fetus since the  84  reduced MAT transcript levels reported here imply reduced enzyme expression and hence function. Furthermore, plasma methionine levels increase by an astonishing 776% after 3 months in a MATIA knockout mouse model lending strength to the present hypothesis that the increased methionine levels are a result, at least in part, of compromised MAT mediated conversation of methionine to SAM (Lu et al., 2001). Assuming that oxidative stress is implicated in the disruption of MAT expression and hence activity, the etiology of this oxidative stress remains to be determined. (MacLennan et al., 2004) report reduced fetal MAT1A mRNA levels in their IUGR model that are comparable, when expressed as a percentage of control, to that found in the present study. This is of interest since compromised placental transfer, a characteristic of the IUGR model (MacLennan et al., 2004), is itself a result of prenatal ethanol consumption (Sanchis & Guerri, 1986; Singh et al., 1989). As such, it is unknown whether the reduced MAT transcript expression is directly a result of ethanol induced oxidative stress or secondary to the oxidative stress related to compromised placental transfer. In any event, prenatal ethanol consumption is associated in this study with a significant decrease in MAT mRNA which may account for the increased methionine levels reported here in E animals and so PAE appears to be capable of disrupting methionine metabolism regardless of whether the effect is direct or not.  85  4.2.3 Increased Homocysteine in Ethanol Exposed Dams as a Possible Result of Reduced MS Transcripts  This study reports homocysteine concentrations that are moderately, yet significantly increased in the ethanol exposed dam and unchanged in the fetus at G21. These data are consistent with previous findings which show increased homocysteine levels secondary to ethanol exposure [reviewed by ](Schalinske & Nieman, 2005)]. Recall from section 1.12 that homocysteine is remethylated from methionine through the enzymatic reactions catalyzed by either MS or BHMT. This study’s findings describe significantly decreased MS mRNA levels in E dams. Moreover, MS enzyme activity is well documented to be inhibited by acetaldehyde (Kenyon et al., 1998) and the pregnant dams fed the same Lieber-Decarli Ethanol diet used in the present report circulating levels of acetaldehyde consistent with MS inhibition (Guerri & Sanchis, 1985; Sanchis & Guerri, 1986). In view of these data it is likely that the folate-dependent remethylation pathway of the ethanol exposed dams is compromised and that this could be a contributor to the observed increase in homocysteine in this group.  In further support of this hypothesis, a decreasing trend in MTHFR mRNA is also reported in this study in E dams (Fig 3.13). Recall from Figure 1.4 that MTHFR irreversibly reduces 5,10-MTHF to 5-MTHF, the substrate used by MS. Importantly, a decrease in both transcript level and activity of MTHFR have been reported following ethanol exposure in the micropig (Villanueva & Halsted, 2004) and a deficiency in activity of this enzyme is linked to hyperhomocysteinemia in both mice and humans (Devlin et al., 2004; Devlin et al., 2006) This suggests that a reduction of this enzyme function can also  86  be implicated in the moderately high homocysteine concentrations reported here since loss of MTHFR activity will restrict MS function by limiting the supply of 5-MTHF available for conversion to THF.  In contrast to the findings reported here in the E dams, PF dams show significantly higher MTHFR transcript levels compared to both E and C animals while PF homocysteine levels remain at levels comparable to C (Fig 3.15). Interestingly, MTHFR activity increases in rats fed a high-fat, high sucrose diet and changes in activity are accompanied by similar changes in enzyme mRNA concentrations (Fonseca et al., 2000). How this gene is regulated by excessive fats or sugars is unknown. One possibility, is that changes in expression may be due to elevated blood insulin (Fonseca et al., 2000). This hypothesis is supported by increases in MTHFR activity in human hepatocyte cell lines when administered insulin (Dicker-Brown et al., 2001). Regardless of the mechanism, this finding allows for speculation that the increase in MTHFR mRNA transcripts in PF dams may be secondary to increased levels of insulin due to relatively higher levels of maltosedextrin in the liquid control compared to the liquid ethanol diet. However, in this study PF fetuses do not show the same increase in MTHFR mRNA. Indeed, transcript levels in both E and PF fetuses are somewhat lower than those in C fetuses. In order to assess the validity of this hypothesis therefore, further studies investigating MTHFR activity in the PF group are needed.  Intriguingly, in this study, homocysteine levels are only elevated in the maternal samples and not in the fetal samples. This finding may be explained by the variable MS  87  transcript number found between maternal and fetal samples. While MS mRNA is reported here to be significantly reduced in the E dams, no change was found in MS transcript number in the fetal samples. This finding suggests that MS activity may not be inhibited to the same degree in fetal samples and that its continued ability to properly convert homocysteine to methionine accounts for the unchanged homocysteine levels. What’s more, these findings suggest that the PAE fetus may have systems in place that protect against perturbations of this component of methionine metabolism during pregnancy. A seminal study conducted by (Guerri & Sanchis, 1985) outlines a possible mechanism through which the fetus may be buffered from the detrimental effects of ethanol exposure.  Guerri’s paper reports that, throughout gestation, acetaldehyde concentrations are significantly lower in fetal rat blood than in maternal. Recall from section 1.15 that acetaldehyde is a byproduct of ethanol catabolism and is incriminated as a primary agent of ethanol’s teratogenecity (Campbell and Fantel 1983; Lee, An et al. 2005) and as an allosteric inhibitor of the MS enzyme (Kenyon et al., 1998). The mechanism underlying this differential abundance of acetaldehyde in maternal and fetal samples is not entirely clear, however alcohol dehydrogenase (ADH), the enzyme which catalyzes the oxidation of alcohol to acetaldehyde, is not expressed in the fetus until G18 (Raiha et al., 1967) and even at birth, the activity of the fetal enzyme is only 25% that of the dam (Raiha et al., 1967). As with (Guerri & Sanchis, 1985), it is possible that in this study, differences in acetaldehyde concentrations between the fetus and dam may play a role in protecting fetal tissues from the full effects of this teratogen during pregnancy. Further, (Guerri & Sanchis,  88  1985)’s findings may explain why increased homocysteine is reported in the dam but not the fetus on G21.  Notably, in their IUGR model, (MacLennan et al., 2004) report increased homocysteine in the IUGR fetus. While this discrepancy is difficult to reconcile with the results of the present study, their study does not report the activity of MS, BHMT or any of the enzymes directly involved in homocysteine metabolism, making it difficult to speculate as to the cause of the increase. Differences in model design or homocysteine sampling sources (i.e. MacLennan measures hepatic levels whereas the present study reports circulating levels) may be accountable for the differences reported in homocysteine levels between these two studies, however without further experiments it is not possible to know for certain.  While both the BHMT and MS pathways work to convert homocysteine to methionine, it appears that the increased levels of homocysteine in the E dam are due to impairments in folate dependent MS remethylation rather than impairments in the BHMT pathway. Although plasma DMG concentrations are lower in E and PF animals (Fig. 3.14), which could be interpreted to suggest that BHMT activity is compromised, this decrease is not accompanied by an increase in betaine, and BHMT transcript levels are unchanged in E dams compared to C. In fact, BHMT transcripts even show a moderate increase in PF dams suggesting that BHMT is working appropriately, if not excessively, in these animals. Additionally, E and PF dams both show decreased levels of betaine suggesting that decreased DMG levels could be linked to decreased levels of this nutrient, possibly as a  89  result of differences in betaine in the PF and E liquid diets compared to control. In support of this hypothesis, choline concentrations are unchanged in E and PF animals compared to C, suggesting that reduced betaine levels can not be attributed to a concomitant decrease in E or PF choline. In 2000, (Chern et al., 2000) showed that although betaine levels were reduced in an ethanol-fed rat model similar to the one used in the present study, choline levels as well as oxidation remained unaffected. Further, (Chern et al., 2000) speculate that one possible cause of the betaine level reduction was the activation of the BHMT salvage pathway (discussed in section 4.2.1). However, in the present study, reduced betaine levels are not restricted to the E group, strongly implicating dietary factors, or generalized nutrient restriction, in the etiology of this change. Future studies should therefore ensure that all 3 diets are compositionally identical in order to eliminate any possible discrepancies.  Furthermore, although there is a significant decrease in PEMT mRNA in E dams and in E and PF fetuses, there is no corresponding decrease in choline concentrations, a finding consistent with other studies reporting no change in choline concentrations even after 4 weeks of ethanol exposure (Chern et al., 2000). Deficiencies in PEMT are not typically linked to altered choline concentrations and the reduced PEMT transcripts levels reported here do not imply compromise of the BHMT pathway. Together these findings implicate deficiencies in MS expression and/or activity in the increased homocysteine levels reported in ethanol exposed dams.  90  4.3 Implications of Altered Methylation Capacity and Programming of the HPA Axis Intrauterine programming of postnatal physiology has been demonstrated experimentally in a number of species, using a range of techniques to compromise the gestational environment and alter fetal development, and is the subject of much review (Matthews, 2000; Lesage et al., 2002; de Kloet et al., 2005; McMillen et al., 2005; McMillen & Robinson, 2005; Zhang et al., 2005). Suboptimal intrauterine conditions can cause permanent detrimental structural/functional changes that persist throughout adulthood (Seckl, 2004; Wu et al., 2004; Zhang et al., 2005; Lesage et al., 2006; Remacle  et al., 2007). Ethanol freely crosses the placenta, and has both direct and indirect teratogenic effects on the developing fetus, one manifestation of which is the disruption of the HPA axis such that PAE offspring are hyperresponsive throughout adulthood (Weinberg, 1989; Weinberg et al., 1996; Zhang et al., 2005). Although the underlying mechanisms of how this programming occurs are not yet known, the data reported in the present study provide a possible mechanism through which ethanol, by way of altered methionine metabolism, induces a change in methylation capacity, thus altering the epigenome of the axis and permanently changing gene expression throughout life.  This study demonstrates that ethanol exposure during pregnancy alters metabolite concentrations and mRNA levels of key enzymes involved in methionine metabolism in both the pregnant dam and fetus at G21.  Moreover, as evidenced in other studies,  perturbations reported within this cycle have been associated with altered methylation capacity via an altered SAM:SAH ratio (Lu et al., 2000; Lu et al., 2001; Halsted et al., 2002b; Devlin et al., 2004; Villanueva & Halsted, 2004), suggesting that, through  91  epigenetic mechanisms, these changes may partially account for the abnormal HPA function reported in PAE animals (Weinberg et al., 1996; Kim et al., 1999a; Zhang et al., 2005).  Increased methionine, as evidenced in both E dams and fetuses, has been shown in other studies to be associated with an increase in methylation capacity. For instance, (Devlin et al., 2004) and (Cooney et al., 2002) have demonstrated that mouse diet supplemented with methionine increases DNA methylation capacity and can lead to hypermethylation (Cooney et al., 2002). However, in the present study, reduced MAT transcript levels imply, as reported elsewhere (Lu et al., 2001) that the increased methionine indicates a lower SAM:SAH ratio due to decreased conversation of methionine to SAM. Furthermore, homocysteine levels are used by some as a proxy indicator for methyl group supply (Ingrosso et al., 2003; Fryer et al., 2009) in as much as homocysteine levels increase in response to impairment of the folate dependent MS pathway (Watkins et  al., 2002). In support of this hypothesis, a recent study published by (Fryer et al., 2009) reports a highly inverse correlation between plasma homocysteine and DNA methylation at the time of delivery in humans such that higher homocysteine levels, such as those reported here in the E dam, are accompanied by a decrease in DNA methylation  Of further interest, phosphatidylcholine (PC) synthesis via PEMT is a large consumer of methyl groups when compared to DNMTs and other enzymes responsible for the conversion of SAM to SAH, using 3 molecules of SAM for every molecule of PE that is converted to PC (Brosnan et al., 2004). As such, deficiencies in PEMT, as evidenced in  92  PEMT knockout mice, exhibit increased DNA methylation in the mouse hippocampus at G17 by virtue of increased availability of SAM (Zhu et al., 2004).  If the reduced  transcript expression of PEMT reported in this study is indicative of impaired enzyme activity, then it is possible that, through this mechanism, some of the other perturbations in methionine metabolism reported here may be attenuated and thus help offset any reduction in the SAM:SAH ratio. However, a study by (Furtado et al., 2002) demonstrated that chronic ethanol exposure is associated with an increase in PEMT activity, a condition that would further exacerbate a decrease in methylation capacity. In support of this, (Garro et  al., 1991) report hypomethylation of DNA in PAE mice.  It is well recognized that epigenetic mechanisms can permanently alter gene expression via DNA methylation or histone modifications (Robertson & Wolffe, 2000; Tamaru & Selker, 2001; Weaver et al., 2004; Holliday, 2005). Important for the purposes of the present study, epigenetic processes are described for genes specific to the HPA axis. For example, the promoter region of POMC contains a well defined CpG island that is specifically methylated in non POMC-expressing tissues, but is unmethylated in cancer cells and tissues normally expressing POMC (Newell-Price, 2003). Other genes specific to the HPA axis are also mediated by epigenetic modifications. A study by (AlikhaniKoopaei et al., 2004) demonstrates that CpG islands covering the promoter region of the 11β-HSD2 gene are densely methylated in tissues and cell lines with low expression of the 11β-HSD2 enzyme but not those with high expression. Furthermore, NR3C1, the gene encoding GR, appears particularly sensitive to early life programming as evidenced through a series of studies conducted by (Weaver et al., 2004; Szyf et al., 2005; Weaver et  93  al., 2006) in which altered maternal care leads to differential methylation patterns within this gene. More recently a study by (Oberlander et al., 2008) provides further evidence of the sensitivity of NR3C1 to epigenetic alterations, showing that babies born to depressed mothers have increased methylation at the promoter region of the NR3C1 gene, implying silencing. In essence, the HPA axis is reported in a number of studies to be disrupted through altered DNA methylation patterns and the results of the present study suggest that similar processes may govern, at least in part, the HPA alterations characteristic of PAE.  Indeed, PAE is reported to affect global DNA methylation patterns in mice (Garro  et al., 1991), a finding that may be mediated through an altered SAM:SAH ratio. Time and logistical restrictions prevented measurement of SAM and SAH levels in the present study and so we are unable to conclusively determine whether the alterations in methionine metabolism reported here translate into an altered methylation capacity. It is possible that the DNA hypomethylation reported secondary to alcohol exposure is related to other pathways responsible for methylation. For instance, (Garro et al., 1991) show that certain DNMTs are inhibited by acetaldehyde and implicate disruptions in these processes in their findings of reduced DNA methylation. Without information about the SAM:SAH ratio it is impossible to definitively conclude whether methylation capacity is altered in the present study and if so, through which mechanism this is occurring. As such, further studies into the mechanisms of PAE induced HPA disruption are warranted.  94  4.4 Conclusions  The primary objective of this study was to determine if methionine metabolism in the ethanol exposed dam and fetus is altered compared to PF and C animals. Significant increases in homocysteine levels in E dams and signficant increases in methionine levels in both E dams and fetuses are reported. Additionally, significant decreases in maternal MAT1A, MAT2A, MS, and PEMT mRNA levels and a significant decrease fetal MAT1A mRNA are reported in ethanol exposed animals. These data suggest that methionine metabolism detriments may be a consequence of prenatal ethanol exposure and may be associated with hypomethylation of DNA in this model. Further, these studies provide a basis for further investigation into the potential epigenetic etiology of HPA compromise secondary to prenatal ethanol exposure.  4.5 Future Directions  4.5.1 Involvement of Alterations in Methionine Metabolism in Methylation Capacity at G21 In view of the results of the present study, further investigation of potential differences in methylation capacity among E, PF and C fetuses is needed. The primary aim of that study would be to quantify SAM and SAH in the fetal liver at G21, such as by high-performance liquid chromatography using UV detection as described by (Bottiglieri, 1990), and to attempt to correlate these values with disruptions in the methioninehomocysteine cycle. To add further insight to the findings of this study, enzymatic activity  95  of MS, BHMT, PEMT, MTHFR, MAT1A and MAT2A in the fetus and dam on G21 could be measured and compared to transcript levels in order to investigate the relationship between the two and to provide a broader perspective on the impact of perturbations within the cycle on methylation capacity.  4.5.2 Impact of Prenatal Ethanol Exposure on Methylation Capacity Within the HPA Axis.  Marked differences in DNA methylation in the NR3C1 gene specifically localized within the hippocampus have been reported previously (Weaver et al., 2004). As the gene encoding GR, methylation patterns within the promoter region of this gene have direct implications for the functioning of the HPA axis and because of its central role in HPA control, ethanol induced changes in methylation capacity within the hippocampus itself could be important to understanding PAE alterations in HPA activity. Through altered methylation mediated disruption of GR (and other HPA receptor and protein) expression, drive and/or feedback mechanisms within the HPA could be altered thus perturbing its function. Similar to the proposal outlined above, the method described by (Bottiglieri 1990) could be used to quantify SAM and SAH in hippocampal tissue.  96  4.5.3 Impact of Prenatal Ethanol Exposure on Differential Methyaltion in Genes Involved in HPA Regulation  Altered DNA methylation patterns in the NR3C1 gene are reported by (Weaver et al., 2004) and by (Oberlander et al., 2008) using the bisulfite and pyrosequencing methods respectively. As both methods can reliably detect changes in DNA methylation (Vieau et  al., 2007; Reed et al., 2009). Either could be used to quantify methylation patterns in genes specific to the HPA axis in E animals. Carrying out these studies during the first week of life, during pre-weaning (D10) and in adulthood (D60) could offer insights as to how methylation patterns are established during the development of the HPA axis of PAE animals.  This in turn may lead to further knowledge about the etiology of HPA  dysfunction in PAE exposure and could provide clues into clinically relevant interventions.  4.5.4 Can Prenatal Supplementation of Choline/Betaine Attenuate the Effects of Prenatal Ethanol Exposure on the HPA Axis? A recent study by (Thomas et al., 2009) demonstrates that choline supplementation during pregnancy significantly attenuates ethanol's detrimental effects on birth and brain weight as well as most behavioral measures. Given that choline is oxidized to betaine and that betaine has been shown to lower elevated SAH levels in hepatocytes from ethanol-fed rats (Barak et al., 2003), supplementation of these metabolites may attenuate the effects of PAE on the HPA axis by acting as methyl donors in the remethylation of methionine from homocysteine via BHMT. If HPA function was restored in these animals (i.e. if CORT, ACTH, and β-endorphin responses to stressors are normalized in E adult animals after repeated restraint, foot shock and/or immune challenges),  then further studies could  97  examine whether this effect is mediated by altered methylation patterns in genes specific to the HPA axis (as outlined in the previous study).  4.5.5 Will Methionine Restriction Attenuate Any of the Effects of Prenatal Ethanol Exposure? Methionine is a sulfur containing amino acid that, when consumed in excess, is toxic to tissues, primarily by the induction of oxidative stress (Mori & Hirayama, 2000; Park et al., 2008) “Methionine toxicity”, which can occur when methionine intake is as low as 2% of the diet, leads to growth retardation and histopathologic changes in the liver, kidney and spleen. [reviewed by (Harper et al., 1970)]. Since the liver metabolizes a large proportion of total methionine (Finkelstein, 1990), it is not surprising that this organ is particularly prone to the toxic effects of excess methionine, which manifest in severe liver damage, primarily through the generation of reactive oxygen species (Gomez et al., 2009). However, methionine toxicity is not isolated to the liver. Excessive methionine is also associated with an increase in oxidative stress, reduced energy metabolism, and the inhibition of Na+,K+-ATPase activity in the rat hippocampus (Stefanello et al., 2005). Moreover, it has been shown that restriction of methionine results in decreased mitochondrial reactive oxygen species generation in the rat brain (Sanz et al., 2005). In view of the known toxicity of this amino acid and the finding that both dams and fetuses exposed to ethanol show increased plasma methionine concentrations, further studies could examine whether restricting this amino acid in the E diet has any benefit to the development of E offspring, perhaps by the attenuation of any of the growth or behavioural detriments associated with prenatal ethanol exposure.  98  In summary, while the results of this study suggest that alterations in methionine metabolism, possibly resulting in epigenetic effects of ethanol on HPA function, may be one possible mechanisms mediating fetal programming of the HPA axis by ethanol, they have given rise to many additional questions that require further investigation. The experiments described above have been proposed to offer guidance and recommendations into future directions that could be investigated to further elucidate our knowledge of the role that prenatal ethanol exposure plays in the alteration of methionine metabolism and epigenetic programming pathways. One avenue of investigation would be to focus on unraveling the intricacies of the different subpathways within the methionine-homocysteine cycle which together work to govern methylation patterns and hence appropriate physiological processes. It is my hope that future research will continue to strive to gain understanding of the role that these early-life events, whether driven through prenatal ethanol exposure, poor maternal nutrition or other adverse fetal environments, play in health and well-being over the life course.  99  WORKS CITED Abel EL. (1995). An update on incidence of FAS: FAS is not an equal opportunity birth defect. Neurotoxicology and teratology 17, 437-443. 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The Biochemical journal 370, 987-993.  117  APPENDIX 1 Composition of Lab Chow  Rat Diet 5012 (Jamieson’s Pet Food Distributors) Nutrients Protein Arginine Cystine Glycine Histidine Isoleucine Leucine Lysine Methionine Phenylalanine Tyrosine Threonine Tryptophan Valine Serine Aspartic Acid Glutamic Acid Alanine Proline Taurine  % % % % % % % % % % % % % % % % % % % %  22.5 1.42 0.35 1.12 0.58 1.22 1.85 1.36 0.43 1.07 0.66 0.89 0.027 1.17 1.2 2.68 4.64 1.4 1.59 0.02  Fat (ether extract) Fat (acid hydrolysis) Cholesterol Linoleic Acid Linolenic Acid Arachadonic Acid Omega-3 Fatty Acids Total Saturated Fatty Acids Totoal Monosaturated Fatty Acids Fiber (Crude) Neutral Detergent Fiber Acid Detergent Fiber Nitrogen-Free Extract (by difference)  % % ppm % % % % % % % % % %  4 5 180 1.81 0.12 <0.01 0.31 0.74 1.03 4.6 14.3 6.4 52.9  Starch Glucose Fructose Sucrose Lactose Total Digestible Nutrients Gross Energy Physiological Fuel Value Metabolizable Energy  % % % % % % % kcal/gm kcal/gm  38.6 0.31 0.36 3.31 0 77 4.05 3.38 3.11  Minerals Ash Calcium Phosphorus Phosphorus (non-phytate) Potassium Magnesium Sulfur Sodium Chlorine Fluorine Iron Zinc Manganese Copper Cobalt Iodine Chromium Selenjum  % % % % % % % % % ppm ppm ppm ppm ppm ppm ppm ppm ppm  6 1.01 0.74 0.47 1.08 0.21 0.25 0.28 0.43 18 250 71 69 12 1.5 1.2 2.9 0.23  Vitamins Carotene Vitamin K (as menadione) Thimin Hydrochloride Riboflavin Niacin Pantothenic Acid Choline Chloride Folic Acid Pyridoxine Biotin B12 Vitamin A  ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm IU/g  4.3 0.5 13 4.5 92 13 1900 1.7 6.5 0.2 20 12 3.3  Vitamin D (added) Vitamin E Ascorbic Acid  IU/g IU/g IU/g  32 --  Calories provided by: Protein Fat (ether extract) Carboyhdrates  % % %  26.658 10.664 62.678  Nutrients expressed as percent of ration except where otherwise indicated. Moisture content is assumed to be 10.0% for the purpose of calculations Physiological Fuel Value (kcal/gm) = sum of decimal fractions of protein, fat and carbohydrate (use Nitrogen Free Extract) x 4,9,4 kcal/gm respectively  118  APPENDIX 2 Composition of Liquid Diets (Dyets Inc)  g/L of Diet  Ingredient  Casein, High Nitrogen (80 Mesh) L-Cystine  Experimental  Control  57.6 0.65  57.6 0.65  DL-Methionine  0.4  0.4  Maltose Dextrin  53.94  143.6  Soybean Oil (stab. w/tBHQ)  18.6  18.6  Cellulose Salt Mix #210032  10  10  8.75  8.75  Vitamin Mix #310011  2.5  2.5  Choline Bitartrate  0.66  0.66  3  3  Xanthan Gum  Vitamin Mix: Lieber-DeCarli  (use at 9.28 g/L)  Mineral Mix: AIN-93G  (use at 2.5 g/L)  Ingredient  g/Kg  Ingredient  g/Kg  Thiamin HCl  0.60  Calcium Carbonate  357.00  Riboflavin Pyridoxine HCl Niacin  0.60 0.70 3.00  Potassium Phosphate, monobasic Potassium Citrate H2O  196.00 70.78  Sodium Chloride  74.00  Calcium Pantothenate Folic Acid Biotin Vitamin B12 (0.1%) Vitamin A Acetate (500,000 IU/g) Vitamin D3 (400,000 IU/g) Vitamin E Acetate (500 IU/g) Menadione Sodium Bisulfite P-Amino Benzoic Acid Inositol Dextrose  1.60 0.20 0.02 10.00 4.80  Potassium Sulfate Magnesium Oxide Ferrous Sulfate 7H2O Zinc Carbonate  46.60 24.00 5.21 1.65  Manganous Carbonate  0.63  0.40 24.0 0.08 5.00 10.00 939.00  Cupric Carbonate Potassium Iodate Sodium Selenate Ammonium Paramolybdate 4H2O Sodium Metasilicate 9H2O Chromium Potassium Sulfate 12H2O Lithium Chloride Boric Acid Sodium Fluoride Nickel Carbonate Ammonium Vanadate Sucrose, finely powdered  0.30 0.01 0.01025 0.00795 1.45 0.275 0.0174 0.0815 0.0635 0.0318 0.0066 221.876  119  APPENDIX 3 UBC Animal Care Certificate  120  

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