The Pennsylvania State University

The Graduate School

Intercollege Graduate Degree Program in Genetics

PRENATAL NICOTINE EXPOSURE ALTERS GENE EXPRESSION IN A

SEXUALLY DIMORPHIC MANNER

A Thesis in

Genetics

by

Jennifer Foreman

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

May 2007 The thesis of Jennifer Foreman was reviewed and approved* by the following:

Guy F. Barbato Associate Professor of Poultry Science Chair of Committee

Gerald E. McClearn Evan Pugh Professor of Health and Human Development and Biobehavioral Heath Professor of Biobehavioral Health

Laura Cousino Klein Associate Professor of Biobehavioral Health

John Vanden Heuvel Professor of Veterinary Science

David J. Vandenbergh Associate Professor of Biobehavioral Health Research Associate, The Center for Development and Health Genetics Neuroscience Faculty, The Neuroscience Institute, The Huck Institutes for the Life Sciences Thesis Advisor

Richard Ordway Associate Professor of Biology Head of the Department of Genetics

*Signatures are on file in the Graduate School iii ABSTRACT

Nicotine has been demonstrated to regulate gene expression in brain reward pathways. In human adults these changes in expression are hypothesized to play a role in the maintenance of drug taking; however, how these changes affect the developing nervous system through second hand exposure from the mother during fetal development is just beginning to be understood. Previous research in prenatally exposed mouse pups, sacrificed on day of birth (PN0), demonstrated sexually dimorphic patterns of gene expression in response to nicotine. This dissertation work extends these findings by investigating how prenatal nicotine affects gene expression patterns in adolescents. The persistence of sexually dimorphic gene response to nicotine is of particular interest due to previously reported sex differences in the results of a behavior experiment conducted on the adolescent animals used in this study. Early nicotine exposure could permanently change the organization of the brain in a sexually dimorphic manner by altering gene expression. The use of Bioinformatic resources allows the interrogation of the genes exhibiting sexually dimorphic response to identify potential gene networks that nicotine may be acting upon. The use of these resources allows the merger of publicly available data with the microarray studies that gives a greater understanding of the biology underlying observed patterns. In this manner I have identified genes that are sexually differentially regulated by nicotine and suggest potential modes of action for the large number of sexually dimorphic responses seen after nicotine exposure. iv TABLE OF CONTENTS

LIST OF FIGURES...... vi

LIST OF TABLES ...... xi

ACKNOWLEDGEMENTS...... xii

Chapter 1 Introduction...... 1

Significance of Nicotine in Human Populations ...... 1 Addressing Problems of Nicotine Usage Through Animal Models ...... 7 Pharmacokinetics and Pharmacodynamics of Nicotine...... 13 Thesis Description and Hypotheses...... 17 References...... 19

Chapter 2 Gene Expression Patterns of Adolescent C57B/6J Mice Prenatally Exposed to Nicotine...... 25

Introduction ...... 25 Materials and Methods...... 31 Animal Sample ...... 31 RNA Isolation...... 32 Oligonucleotide Arrays ...... 32 Microarray Analyses...... 34 Results...... 36 Two Bottle Choice Test ...... 36 Analysis of Gene Expression Differences...... 37 Clustering with Non­Expression Markers...... 46 Conclusion...... 53 References...... 57

Chapter 3 Sexually Differential Regulation Networks Persist into Adolescence ...... 60

Introduction ...... 60 Materials and Methods...... 63 Microarray Experiments...... 63 Pearson Correlation Statistics...... 65 Edge Densities ...... 66 QTL Cluster Map...... 67 Over­represented Transcription Factor Binding Sites ...... 67 Results...... 67 Microarray Experiment with PN0 Mouse Experiment ...... 67 Correlation Networks...... 69 QTL Cluster Map...... 73 v Transcription Factor Identification ...... 75 Gene Network and Microarray Data from Adolescent Mouse Experiment ...... 78 Discussion ...... 81 References...... 86

Chapter 4 Importance of Kif1a and Pou3f1 and a Subset of Genes from a Gene Network that is Differentially Regulated by Nicotine in Males and Females ..... 87

Introduction ...... 87 Materials and Methods...... 89 Gene Expression ...... 89 Nicotine Regulation ...... 92 qrtPCR...... 93 Results...... 94 Gene Expression Patterns...... 94 Cell Lines ...... 101 Nicotine Regulation ...... 104 Conclusion...... 107 References...... 114

Chapter 5 General Discussion...... 116

References...... 124

Appendix A In Depth Outline of Microarray Experiments ...... 126

Experimental Layout...... 126 RNA Isolation...... 127 Validation and Normalization ...... 130 Analyses ...... 132 Microarray: Technical Validation...... 133 Microarray: Biological Validation...... 139 References...... 141

Appendix B Protocols...... 142

Cloning PCR Fragments ...... 142 Gel Shift Assay...... 143 vi LIST OF FIGURES

Figure 2­1: Bar chart of average nicotine preference for the males and females from the control and 50 µg/ml pre exposed group. Males pre­exposed to 50 µg/ml recorded a higher preference score then the other experimental groups. Reproduced with permission from Nicotine Tobacco Research...... 37

Figure 2­2: Hierarchical clustering of microarrays using correlation values...... 39

Figure 2­3: Two dimensional representation of the arrays clustered in the plane of the first two principle components ...... 41

Figure 2­4: Principle Component Analysis of Regulated Adolescent Genes ...... 42

Figure 2­5: Cluster of probe ids based on patterns of regulated gene expression...... 44

Figure 2­6: Bar chart indicating MAS 5.0 calls for the probe sets on the arrays separated by calls made on data from male and female mice. Panel A has the male gene actions across the bottom, the colors within the bars indicate what those probe sets calls are specified for date from female mice. Panel B is the same chart with data from female mice along the x­axis and the corresponding calls for probe data from male mice with in the bars...... 46

Figure 2­7: Array Clustering of non­expression Markers and Genes showing ≥ 2.0 fold nicotine regulation ...... 51

Figure 2­8: Cluster Branch for Log2 transformed Mean Cotinine...... 52

Figure 2­9: Cluster Branch for log2 Transformed Nicotine Consumption on day of Sacrifice...... 52

Figure 3­1: Cluster diagram generated using Eisen Cluster Tree program. The chips organize themselves first by sex and then by brain region. The notable exception is the male hypothalamus pre exposed to 50 µg/ml is clustering with the female hypothalamus rather than the male array expression profiles. The arrays were designated by the sex, brain region, a prenatal exposure of the animals whose cRNA was hybridized to the array. The abbreviations stand for male (M), female (F), nucleus accumbens (N), basal medial hypothalamus (H), preoptic area (P), and pre exposure of 50 µg/ml (05) or 200 µg/ml (20). The clusters were generated using ratios of experimental condition over control therefore no 0 µg/ml pre exposures are seen here. Also missing is the data for the female basal medial hypothalamus of mice pre exposed to 200 µg/ml because the array containing this data failed...... 68 vii Figure 3­2: Correlation network of the sexually differentially regulated genes. Connections are based on a Pearson correlation of .5 or greater between gene expression values of 44 lines of BxD RI mice...... 70

Figure 3­3: Plot of mean edge density vs. Pearson correlation threshold. Indicates difference between number of edges at the different correlation thresholds between the experimental gene list and the random gene lists. The mean and standard deviation of the random gene lists are represented...... 73

Figure 3­4: Clustering of the genes with sexually differential gene expression based on similarities of QTL analyses using gene expression levels as the phenotype. A strong red color indicates a significant QTL with DBA/2J as the increasing allele. A strong blue color indicates a significant QTL with C57BL/6J as the increasing allele. The cluster dendrogram indicates how close the QTL pattern of a gene is to another gene. Each chromosome is represented and the yellow triangles are where the gene is located on the genome...... 74

Figure 3­5: In situ hybridization results of Pou3f1. The light regions are those expressing Pou3f1. The area surrounded by the white box is the region of the brain where the nucleus accumbens is located. The images were copied with permission from the gene expression nervous system atlas, GENSTAT (www.ncbi.nlm.nih.gov/projects/gensat/)...... 76

Figure 3­6: Conservation of promoter regions of Nedd4 and Kif1a that contains the binding sites for Pou3f1. The blue peaks indicate regions of strong conservation. Copied with permission from the UCSC genome browser...... 77

Figure 3­7: Conservation of Pou3f1 binding sites in the Kif1a and Nedd4 promoter regions. The alignments showing the conservation of each Pou3f1 binding site is given in red text. Generated using the UCSC genome browser. . 78

Figure 3­8: Gene Network generated by WebQTL with arbitrary color representations of fold change differences from the adolescent female chips prenatally exposed to nicotine at 200 µg/ml (A) and 50 µg/ml (B). The red indicates genes that are increased by nicotine and the green indicates genes that are decreased by nicotine...... 80

Figure 3­9: : Gene Network generated by WebQTL with arbitrary color representations of fold change differences from the adolescent male chips prenatally exposed to nicotine at 200 µg/mg (A) and 50 µg/mg (B). The red indicates genes that are increased by nicotine and the green indicates genes that are decreased by nicotine...... 81

Figure 4­1: Expression profile of Pou3f1 (A), Nedd4 (B), Glul (C), and Myh9 (D) for the adrenal gland ♂ (1), liver ♂ (2), liver ♀ (3), kidney ♂ (4), kidney ♀ viii (5), testes ♂ (6), E9 head (7), E9 body (8), E12 head (9), E12 head (10), H12 body minus spinal cord (10), E15 head (11), E15 liver (12), E15 placenta (13), olfactory bulb ♂ (14), olfactory bulb ♀ (15), frontal cortex ♂ (16), frontal cortex ♀ (17), and caudate putamen ♂ (18)...... 95

Figure 4­2: Expression profile of Pou3f1 (A), Nedd4 (B), Glul (C), and Myh9 (D) for the caudate putamen ♀ (19), motor cortex ♂ (20), motor cortex ♀ (21), hypothalamus ♂ (22), hypothalamus ♀ (23), thalamus ♂ (24), thalamus ♀ (25), midbrain ♂ (26), midbrain ♀ (27), brain stem ♂ (28), brain stem ♀ (29), cerebellum ♂ (30), cerebellum ♀ (31), spinal cord ♂ (32), and spinal cord ♀ (33)...... 95

Figure 4­3: Expression profile of Kif1a for the adrenal gland ♂ (1), liver ♂ (2), liver ♀ (3), kidney ♂ (4), kidney ♀ (5), testes ♂ (6), E9 head (7), E9 body (8), E12 head (9), E12 head (10), H12 body minus spinal cord (10), E15 head (11), E15 liver (12), E15 placenta (13), olfactory bulb ♂ (14), olfactory bulb ♀ (15), frontal cortex ♂ (16), frontal cortex ♀ (17), and caudate putamen ♂ (18). Band D is of the expect sized based on primer sequences and published exon specifications. Band’s A­C are not predicted based on published sequence information for Kif1a...... 96

Figure 4­4: Expression profile of Kif1a for the caudate putamen ♀ (19), motor cortex ♂ (20), motor cortex ♀ (21), hypothalamus ♂ (22), hypothalamus ♀ (23), thalamus ♂ (24), thalamus ♀ (25), midbrain ♂ (26), midbrain ♀ (27), brain stem ♂ (28), brain stem ♀ (29), cerebellum ♂ (30), cerebellum ♀ (31), spinal cord ♂ (32), and spinal cord ♀ (33) ...... 97

Figure 4­5: Sequence for Kif1a splice variants in human and mouse...... 98

Figure 4­6: Schematic of where Kif1a splice variant fits into the Kif1a gene...... 99

Figure 4­7: This image was generated by focusing on the base pair view in the UCSC mouse genome browser of the Kif1a region containing exon 13a. The large blue peak indicates species conservation directly over the sequence for exon 13a. Image copied from http://genome.ucsc.edu...... 100

Figure 4­8: Gel images of Kif1a splice variants in human samples. Panel A: Lane 1­1 NAcc Kif1a crossing 13/14 junction. Lane 1­2 NAcc Kif1a from within Exon 13a. Lane 1­3 Caudate crossing 13/14 junction Lane 1­4 Caudate from within Exon 13a. Panel B: The fragments are from (­1) NAcc, (­2) CP, (­3) Placenta and (­4) Lymphoblast cDNA samples amplified with two different sets of primers. The 1­n contain fragments amplified with primers Kif1a 14’ and the 2­n contain fragments amplified with primers from Kif1a crossing 13/14 intron junction...... 101 ix Figure 4­9: 1­n shows Pou3f1 expression. 2­n shows the Kif1a splice variant without 13a. 3­n shows Kif1a splice variant with (A)13a and with (B) 13a & 14’. Sk­N­BE N­1. Basal, N­2.Glial Like, and N­3. Neuronal like...... 103

Figure 4­10: Expression of ActB (A), Pou3f1 (B), Nedd4 (C), Glul (D), Myh9 (E), and Kif1a (F) in a series of mouse cell lines. Panel B. The lanes contain PCR fragments amplified from cDNA of the following cell lines; L929 (1), Hepa1c17 (2), AtT20 (3), MEL (4), BMK­K1 (5), BMK­X2 (6), GT1­7 (7), Rat (8), PC12 (9), and a NAcc control (10). The NAcc control was not run for the ActB (A), or Pou3f1 (B) primer sets...... 104

Figure 4­11: Log2 transformed response to nicotine results from the qrtPCR experiments compared the microarray data for the four genes of interest. The log2 transformation puts ratios of increasing and decreasing fold changes on the same scale...... 105

Figure 4­12: Nicotine regulation of Pou3f1 in the adolescent nucleus Accumbens samples identified using qrtPCR...... 106

Figure 4­13: qrtPCR results of RNA isolated from Lymphoblast cells exposed to nicotine for 0, 1, 2 and 24 hours...... 107

Figure A­1: Depicts RNA run out on the Agilent Bioanalyzer. RNA quality and quantity can be determined. This is a subset of the female NA RNA samples. A. Simulates how the RNA would look run out on a gel. The 18S and 28S ribosomal RNA bands are visible. A bright tight band indicates high quality and good concentration. B. This panel shows the second set of images produced by the Bioanalyzer. The first spike is a spike in control and the second two spikes are the 18S and 28S ribosomal RNA. The top picture shows RNA of high quality and concentration. The bands are tight at the base and have a high spike. The second picture shows medium concentration and quality. The spikes are low and have broad bases. The last picture depicts a lane that was absent of RNA. The first sample is good for use on the Microarrays, the second two should be avoided...... 128

Figure A­2: Figures generated by the Agilent Bioanalyzer before and after fragmentation. A Gel image showing fragment sizes before fragmentation of a given sample and the adjacent lane shows the same sample after fragmentation. B. The Peaks on the images give an indication of the size of the RNA. The top panel is pre fragmentation and the bottom panel is post fragmentation...... 134

Figure A­3: RNA digestion plot generated by R’s affy package. The roughly parallel structure of the lines in the RNA digestion plots indicate that no gross difference in creation of the arrays existed between the two sets. Any x differences due to methodology that do exist are removed during normalization...... 136

Figure A­4: MvA of array data comparing gene expression values generated by the re­amplified male cRNA pool to the average gene expression values of all cRNA pools generated from female mice...... 137

Figure A­5: MvA of array data comparing gene expression values generated by the re­amplified male cRNA pool to the average gene expression values of all cRNA pools generated from male mice...... 138

Figure A­6: MvA of array data comparing gene expression values generated by a single pool of cRNA from male mice to the average gene expression values of all cRNA pools generated from male mice...... 138

Figure A­7: MvA of array data comparing gene expression values generated by a single pool of cRNA from female mice to the average gene expression values of all cRNA pools generated from female mice...... 139 xi LIST OF TABLES

Table 2­1: Principle Component Analysis...... 40

Table 2­2: Average Silhouette Widths ...... 43

Table 2­3: Gene Response by Sex...... 45

Table 2­4: Array Information...... 47

Table 2­5: Genes Clustering with Mean Cotinine...... 48

Table 2­6: Genes Clustering with Mean Cotinine Cont…...... 49

Table 2­7: Genes Clustering with Day of Sacrifice Nicotine Consumption...... 50

Table 3­1: Genes showing sexually differential gene expression in response to nicotine in both PN0 and Adolescents...... 65

Table 3­2: Data table containing edge values for the experimental correlation network and the eight correlation networks calculated with randomly permutated gene lists...... 72

Table 4­1: Primer Names and Sequences ...... 91

Table 4­2: Primer Pairs and Product Sizes ...... 92 xii ACKNOWLEDGEMENTS

Though mine is the only name to appear on this document, many individuals have been highly influential in helping me to achieve this goal. First, I would like to thank my advisor, David Vandenbergh, for his guidance throughout the process. He provided the perfect balance between support and independence, allowing me to develop as a scientist.

His depth of knowledge is truly remarkable and I could always count on him to provide the knowledge I was seeking or tell me where to find it myself. I could give him the briefest summary of a scientific finding and he could tell me who wrote the paper and where it could be located. There are many qualified scientists at Penn State but it was clear from the start that David’s lab was the right environment for me, and this remained true throughout my time at Penn State. David truly exemplifies all aspects of what it means to be an advisor.

Many other people at Penn State contributed their assistance and knowledge to my development. All members of the lab contributed to my education, especially Kate

Anthony. Kate answered my multitude of questions with a smile on her face, and, as anyone who knows me well can attest, that is a rare accomplishment considering the number of questions I ask. There were also several individuals outside of the lab whose assistance I greatly appreciated; Tim Beischlag, who taught me qrtPCR, and many others too numerous to list. Also, sincere thanks to my committee members, Laura Cousino

Klein, Guy Barbato, Jack Vanden Heuvel, and Jerry McClearn, and to the head of the genetics program Rick Ordway, for their advice throughout the process. Each of them was always available to me when needed. xiii I would like to thank my parents for their unfailing support in everything I’ve done, without which this would have been a much more difficult road to travel. I am grateful for both their environmental and genetic contribution to my success.

As well as support from my family, I have had support from a multitude of friends along the way. In particular I would like to thank Sara Anderson. She was with me when

I first started searching for a graduate school, and has remained by my side for the duration. I benefited from her organizational skills on multiple occasions. She was also there whenever I was stressed over school and lab work to make me laugh and remind me why I was doing this in the first place.

Last but not least I would like to thank Mary Miller. She has been instrumental not only in my graduate career but in my academic career since undergrad. Though my grammar still has much to be desired, it was even worse, if that can be imagined, before she got a hold of me. She has always pushed me to do my best and not just what was required. Her guidance has been invaluable and I can say without doubt that I have learned more from her then from any one source in my career. I knew I was truly achieving when I reached a point in my academic career when she no longer knew what I was talking about and had to go look it up. I thank her for remaining constant throughout; for taking the time and effort to go look it up, not to mention the hours she has spent editing all of my written work.

The adage “no man is an island” is particularly true of graduate studies. I would not have wanted to go through this process without the support of you all. Chapter 1

Introduction

Significance of Nicotine in Human Populations

Nicotine is the primary addictive agent in tobacco smoke. Smoking is the leading cause of preventable death in the United States today. Health care costs for treatment of illnesses directly related to smoking costs roughly $75 billion per year (16) (81). This estimate, however, does not include loss of productivity nor secondary health care expense such as burn treatments for smoking related fires, second hand smoke, or prenatal care for the infants of smoking mothers (81). Inclusion of these smoking related burdens brings the total economic cost to an estimated $150 billion per year (81).

Nearly a quarter of all Americans, 18 years of age and over are current smokers (17).

A greater understanding of nicotine and its influences on smoking is therefore of importance. Though cigarette smoking is the most popular form of nicotine intake there are several other available recreational methods such as chewing tobacco, smoking cigars, and tobacco snuff. There are also intake methods such as nicotine gum and a transdermal nicotine patch that are used as nicotine replacement therapy when a person is attempting to quit smoking (88). Smoking as the prevalent means of recreational nicotine intake has historical and economical, as well as, biological reasons.

The historical events that made smoking the most popular means of nicotine intake were the discovery of a drying method that made the smoke more palatable, mass 2 production of pre­rolled cigarettes, and the invention of portable matches (33, 41). In addition the intake of smoke into the lungs is an ideal drug delivery system. Nicotine is taken into the lungs and passes directly into the bloodstream where a significant portion of the newly oxygenated blood is pumped directly to the brain. The drug arrives at its receptor, the nicotinic acetylcholine receptor (nAChR), in the brain within seconds, in a highly concentrated form (82). The quick action from drug entry into the lungs to localization at the nicotinic receptor allows smokers to regulate their desired nicotine level in the blood stream, which is similar from day to day (76). This can be seen by changing the nicotine content in cigarettes. Smokers will increase their puffing on a low nicotine yield cigarette and decrease their puff rate on a high yield nicotine cigarette to maintain preferred nicotine levels (7, 31, 35). The ability to self regulate is important because large amounts of nicotine have adverse side effects, such as the dizziness and nausea experienced by a novice smoker (24, 59).

Nicotine has a half­life of approximately two hours, thus in heavy smokers the body does not have a chance to fully eliminate it before another cigarette is smoked (76,

82). As smokers continue their daily smoking consumption to avoid withdrawal effects, nicotine plasma levels build over the 6­8 hours to levels between 20­40ng/ml. These high levels of nicotine maintained consistently in the brain cause the desensitization of the nicotinic acetylcholine receptors (76, 82). This means that through out the day nicotine intake will no longer give the same pleasurable effects or that perhaps a level of desensitization among certain nAChRs populations may be reinforcing (9). Some researchers believe that the steady decline of nicotine levels in the brain while a smoker 3 sleeps and the offset of withdrawal is why many smokers categorize the first cigarette of the day as the most pleasurable.

Smoking prevalence is roughly equal between the men and women in the United

States today (79). Despite equal prevalence there are indications that the reinforcing effects of nicotine may differ between the sexes (44, 68), which is evidenced by sex differences in success rates for different quitting strategies (15, 67). It appears women are more greatly influenced by the reinforcing behaviors of smoking, i.e. the act of smoking, not just need for nicotine and it is believed this is why men are more successful with replacement therapies and have an easier time quitting overall than do women (4,

12, 67). This sex difference in quit rates could mean that smoking will pose a greater threat to women in the future. Recent statistics have shown that 30% of female high school seniors have smoked within the past 30 days, and given that a reported 22% of women 18 and older smoke this may indicate that smoking is on the rise for young women (79). This is of particular concern given that women are more prone to lung cancer then men and that many women continue to smoke throughout pregnancy (27, 79).

In recent years public health awareness has focused on indirect effects of smoking tobacco, such as second hand smoke and, pertinent to this work, the effect of exposure to offspring in utero. Prenatal exposure to nicotine is of particular interest because the prenatal environment is highly dependent on what the mother takes into her body. Many substances that are not harmful to an adult can be extremely harmful to a developing organism (5, 53, 87). It is important to note that nicotine is not responsible for the majority of negative health side effects seen in adult smokers (82). Nicotine in itself is not a carcinogen but it is the delivery system for nicotine, burning tobacco, which 4 produces carcinogenic materials that cause most of the health problems associated with smoking (47). However, in a prenatal environment where the negative effects of the intake method are not prevalent, nicotine has been shown to be toxic (34, 43, 75). The majority of its toxic effects have been identified in the brain, and nicotine has been labeled a neuroteratogen, because it increases the prevalence of neuronal cell damage in newborns (6, 74, 75). Brain development is particularly important because the brain function have been shown to influence behavior and shape certain aspects of personality.

Brain development is a highly plastic event that can be greatly influenced by external, non­genetic, factors. This plasticity makes the brain highly sensitive to the introduction of foreign chemicals, such as nicotine, during the developmental period. Therefore, early nicotine exposure could permanently change the organization of the brain and affect behavioral outcomes.

In fact, research over the past 20 years has shown that smoking while pregnant is harmful to the developing fetus and increases the probability of negative behavioral outcomes. Infants are more likely to be born prematurely and to have a decreased birth weight (5, 36, 53, 64). More tragically, these infants have an increased mortality rate and higher incidence of Sudden Infant Death Syndrome (SID), roughly five times that of infants born to a non­smoking mother (43, 84). In fact, it has been shown that newborns of women who smoked throughout pregnancy exhibit withdrawal symptoms and lower scores of neurological function than infants of women who did not smoke during pregnancy (34). Although children of smoking mothers do not show anatomical deficits or increased mortality once past infancy, there are still many associated risks (42). These risks include health issues such as an increased incidence of asthma (13), as well as 5 behavioral issues. Children of smoking mothers are more likely to be depressed, to have problematic hyperactivity, impaired learning and memory function, and to have an increased risk for substance abuse later in life (30, 48, 61, 65, 84, 85, 96). The increased smoking liability in adolescence is thought to be due, in part, to the high rates of depression seen in children of smokers, as well as to an increased sensitivity to withdrawal symptoms (50, 85).

Despite widespread knowledge about the negative effect smoking has on children,

12­22% of all pregnant women continue to smoke through pregnancy, and a third of those who stop while pregnant begin smoking again after pregnancy (79). Given that there are many active components of cigarette smoke, it is difficult to determine which components have detrimental effects. Most attention, however, has been focused on nicotine, which has been shown to have neurotoxic effects and is thought to be the primary rewarding agent of tobacco products (1, 96). Nicotine readily passes through the placenta and enters the fetal blood stream, and concentrations in the fetus have been measured to be 15% higher than concentrations found in the mother (34, 53). One study found an association between maternal genotype and effects of smoking on birth weight

(92). This finding indicates a genetic interaction with the prenatal environment; therefore some infants may be more vulnerable than others to nicotine’s actions. A similar association may exist between maternal genotype and the level of neurotoxicity observed, though this has not been documented. Infants continue to be exposed to nicotine postnatally through exposure to second hand smoke, as well as through their mother’s breast milk (21). 6 Adolescents exposed to prenatal smoking are more likely both to begin smoking, and to start smoking earlier, than their peers (96). This outcome further exacerbates potential dangers due to the fact that adolescence in itself is another vulnerable period for the brain (96). The onset of puberty initiates the adolescent period with its many physiological and hormonal changes. The brain is pruning synaptic connections and shows significant losses in grey matter during this time period (55). There is also a significant increase in risk taking behavior during adolescence that may be related to nucleus accumbens development, an area of the brain activated by nicotine (32). Most adult smokers begin in adolescence (63, 71), which may be an indication the general age when first exposure occurs, or may indicate that adolescents are particularly vulnerable to addiction. Smoking during adolescence has been shown to cause changes in dopaminergic and noradrenergic systems (89).

Studies of fetal development and adolescent human populations are particularly difficult because of both the inability to randomize the study population and the ethical issues involved. To expose a random group of pregnant women to nicotine during pregnancy without notice of its effects would not be ethical, therefore any studies done on human fetal development are done on a population of women that have chosen not to quit smoking despite known dangers. There is also the possibility that these women have made other negative choices during their pregnancy that may affect the fetus, such as lack of prenatal care, vitamins, or appropriate diet. 7 Addressing Problems of Nicotine Usage Through Animal Models

Experiments on humans in many cases are neither practical nor ethical, especially in cases of addictive drugs, such as nicotine, where a previously naive group would have to be exposed to substances that are known to be harmful. The ethical issues and convoluting factors can be circumvented by conducting experiments on animal models.

One of the earliest questions addressed by animal models was whether or not tobacco, and more specifically nicotine, was addictive. Addiction is generally indicated by inability to cease drug usage, pursuit of the drug despite negative consequences, and withdrawal symptoms (94). The current hypothesis of drug addiction is that drug initiation is due to rewarding properties, whereas drug maintenance occurs to alleviate withdrawal symptoms and neurological adaptations that lead to craving (91).

The rewarding aspect of nicotine has been demonstrated in animals by multiple behavior tests of reward such as self administration, conditioned place preference, and a willingness to work for nicotine (54, 82). An important concept to remember when working with animal models is that careful thought needs to go into the experimental design. For instance, though smoking is the vastly preferred administration method of choice in humans, smoke is not a good delivery system for nicotine in animals. Firstly the animals will not self administer smoke, and they change their breathing patterns in a smoky environment by taking low shallow breaths (11). With shallow breathing large quantities of smoke, and thus nicotine, do not enter the lungs. Secondly tobacco smoke contains many active chemicals and the effects of nicotine vs. other components of 8 cigarette smoke could not be determined by administrating tobacco smoke in animal models (54, 82).

An unwillingness to intake smoke in a manner replicating human smokers does not equate with a lack of addictive properties of the drug. Because animals are unwilling to self­administer nicotine through smoke, other methods are better suited for determining addictive properties. In nicotine studies, self administration can be achieved by adding nicotine to an animal’s drinking water or by intravenous (IV) administration.

The use of oral consumption of nicotine as a model for studying the effects of nicotine in human smokers is possible because nicotine survives the digestive process in its active form and levels in the blood stream can be made comparable to that of a human smoker, despite 1 st pass metabolism (52).

Administration of a two bottle choice test shows that mice will drink preferably from the bottle containing nicotine (2, 51). This test indicates whether an animal finds a substance rewarding and is done by giving a choice between two water bottles, one containing the drug, the other not. These bottles are rotated regularly so that any preference results are not due to placement preference. Place preference is a natural inclination by the animal to drink from one side or the other and would skew the results if an animal was drinking from the bottle because of its place in the cage rather than its contents. At specific intervals the amount of water consumed from each bottle is measured. In many cases saccharine is added to both bottles in an attempt to mask the taste of nicotine and to ensure that what is being measured is truly a drug preference and not a taste preference or aversion. 9 Conditioned place preference (CPP), another test of the rewarding properties of nicotine, takes advantage of the fact that mice have natural preference for certain environments. For example in a light/dark box, mice prefer to be in the dark section. In this test the animal is placed in the box several times and preference area is recorded.

After a period of time to familiarize the animal with the area and determine preference, the animal is placed in the non­preferred area, the preferred is closed off, and the animal is administered a drug. After several repetitions of this drug administration procedure the animal is then placed in the box with a choice between the non­preferred and preferred side. Mice that have been administered nicotine will spend more time in the previously non­preferred side (54). Those mice that had been injected with a placebo will revert to previous behavior and spend greater lengths of time in the preferred area (54). This test reflects the animal’s willingness to place itself in a situation that is naturally unappealing and avoided.

The third test of addiction mentioned reflects both an animal’s willingness to self­ administer a drug and how hard it is willing to work for the drug. In a fixed­ratio schedule the animal is taught to press a lever and at regular intervals a lever press is accompanied by a drug injection. The animal is then monitored to see how often it presses the lever. The animal’s willingness to work for the drug is tested by pairing the drug, in this case nicotine, with lever pressing. After acquisition of the behavior the drug is no longer delivered and willingness to work is measured by the length of time it takes for the behavior to extinguish (73). In nicotine studies, rats will lever press to obtain nicotine injections and continue the behavior for several days after the pressing ceases to 10 be paired with an injection. When the nicotine injection is reinstated the rats increase their lever pressing behavior (20).

As well as demonstrable rewarding properties, the question of nicotine’s effect on in utero development has also been addressed by animal models. Nicotine administered to pregnant mice has a similar effect on the pups as observed in human infants of smoking mothers. Mouse pups exposed to nicotine through their mother show decreased birth weight, as in humans, and are developmentally delayed. These delays include: later opening of eyes, delayed appearance of body hair fuzz, and slowed righting reflex (3).

As well as developmental differences, these pups show behavioral differences in locomotor activity, anxiety as expressed by time spent on the open arms of an elevated plus maze, and in sucrose intake (3, 69)}. These behaviors in mice have been used as indices of hyperactivity and depression, both of which seem to be increased in children of smoking mothers (3, 66, 69). Recent findings have demonstrated differences in substance abuse liabilities of mice exposed in­utero to nicotine when compared to their control counterparts. Pre­exposed male mice show a greater willingness to consume nicotine as adolescents, while the female littermates are unaffected (51). These findings in mice also parallel observations in humans, highlighting nicotine as a key agent for the outcomes seen in human studies.

Replication of the human condition in animal models implicates nicotine as the causative agent but does not indicate how it is exerting these effects. Recent studies have shown that nicotine has neurotoxic effects interfering with brain development and these changes may mediate some of the observed differences between control and nicotine treated animals (Slotkin 2005). The brainstem, forebrain, and cerebellum have all shown 11 a decrease in DNA synthesis during development in response to nicotine treatment

(McFarland 1991). These data coincide with evidence indicating a decrease in area, perimeter, and diameter of certain brain regions (74). These two pieces of evidence indicate that nicotine disrupts cell development. Further support of this hypothesis is evidenced by smaller cell sizes in the hippocampal dentate gyrus and an increase of cell death in the neuroepithelium of nicotine exposed animals (74). As well as decreasing neurons in certain brain regions, nicotine exposure has also been shown to cause a global increase of glial cells. This type of glial response is characteristic of a response to cell damage (Slotkin TA 2004).

New evidence from research on nicotine’s effects on neurons containing serotonin receptors suggests there may be a direct link between these neurotoxic effects and differences seen in nicotine treated animals (83, 85). The neurotoxic effects on the serotonin system may be what induces the increased incidence of depression in smokers and children whose mother’s smoked during pregnancy because decreased serotonin levels in the brain are found in patients with depression (85, 96).

In addition antidepressant drugs alleviate the symptoms of depression by increasing serotonin levels (78), though it is not entirely clear how antidepressants cause these increases, the effect is thought to be mediated through neurons containing serotonin transporters. Serotonin levels are increased by blocking reuptake into the cells and drugs targeting this process are known as selective serotonin reuptake inhibitors (SSRI’s).

Mice with the Serotonin 1A receptor gene knocked out do not show the behavioral effects of fluoxetine, an antidepressant drug. SSRI’s are not always successful and have begun to be paired with norepinephrine reuptake inhibitors to try to increase efficacy (60). New 12 literature also indicates antidepressants may exert their effects through neurogenesis in the hippocampus (78, 93). In contrast to neurogenesis, nicotine has been demonstrated to promote cell death in the brain and more specifically to damage the projection of serotonin neurons with both prenatal and adolescent exposures (96). This indicates a possible mode of action for the increased incidence of depression in children of smoking mothers, and for the performance of prenatally treated mice in behavior tests of depression (3, 85, 96).

Though nicotine appears to be harmful for the developing brain, it has shown some neuroprotective effects in adults (14). Increases in memory abilities and attentiveness have been attributed to nicotine intake (37, 56, 86) and there is evidence that nicotine relieves cognitive deficits that are associated with certain neurodegenerative diseases, such as Parkinson’s and Alzheimer’s disease, as well as relieving some symptoms of schizophrenia (26, 39, 77). The positive effects of nicotine in these disorders may contribute to the increased prevalence of smokers seen in these populations

(72, 77). Mice exposed to nicotine, and Alzheimer patients who smoke, show a reduction of β­amyloid levels (39). β­amyloid protein is the key component of Amyloid deposits, which are characteristic of Alzheimer’s disease (25). A decrease in these proteins may be the reason why Alzheimer patients who smoke show improved cognitive abilities, and may be directly related to nicotine’s influence on precursors of this protein (39). These positive effects may indicate a therapeutic role for nicotine. However, since the negatives of smoking as a nicotine delivery system far outweigh the positives, smoking cannot be condoned by these findings. Additionally the neurotoxic effects of prenatal nicotine suggest pregnant women should avoid exposure to nicotine in all forms, whether 13 they are therapeutic measures, such as the transdermal nicotine patch, or recreation usage, such as smoking.

Pharmacokinetics and Pharmacodynamics of Nicotine

Nicotine has a half life of approximately 2 hours and is broken down into its inactive form, cotinine, by the enzyme CYP2A6. Different allelic forms of CYP2A6 have been found that have different enzymatic activity and here is one obvious means for a genetic effect on susceptibility to nicotine addiction (8). The common intake method for nicotine is being suspended in smoke and inhaled into the lungs where it passes into the bloodstream. Nicotine can also be absorbed in its active form through the skin and through oral administration. In all cases nicotine enters the blood stream and is distributed throughout the body where it exerts its effect on the nAChR (82). Due to its short half­life the nicotine intake is often measure by cotinine levels in urine (82).

Nicotine exerts its reward by direct action on the mesolimbic dopamine system.

The mesolimbic dopamine system includes the ventral tegmental area (VTA) and the nucleus accumbens (NAcc). The VTA contains nicotinic acetylcholine receptors

(nAchR) on their cell bodies and projects into the NAcc where they release dopamine

(DA) when the nAchRs are activated. The pleasurable feeling induced by nicotine is mediated through the increase of dopamine in the NAcc. The NAcc has two sections, the core and the shell. The rewarding effects of drugs such as nicotine, and other rewarding behaviors, are mediated through the shell of the accumbens (10). 14 There are two types of Acetylcholine receptors, nicotinic receptors and muscarinic receptors. nAchRs are ligand gated ion channels. Acetylcholine receptors are activated by the endogenous neurotransmitter acetylcholine. The nicotinic receptors are found in the central and peripheral nervous system and nicotine molecules can activate the receptor by mimicking the endogenous neurotransmitter, acetylcholine (62, 82).

Acetylcholine is synthesized from two precursors, choline and acetate, in a one step reaction by the enzyme choline acetyltransferase (70). After being synthesized, the neurotransmitter is stored within synaptic vesicles until its release. Acetylcholine can bind both receptor types, which were named for their respective abilities to be activated by nicotine and muscarine (70). The muscarinic receptors are found in the central nervous system (45). Due to nicotine’s inability to activate muscarinic receptors, only nicotinic receptors will be discussed in detail here.

There are two types of nicotinic receptors, muscle receptors and neuronal receptors. Muscle receptors are found at neuromuscular junctions and activation signals contraction. Neuronal nicotinic receptors, located at the synapses between nerves and in the central nervous system, are thought to be involved in(40, 82) cognitive function, learning and memory, analgesia, motor control, and reward . In the peripheral nervous system, activation of these receptors mediates the release of epinephrine from the adrenal glands. This release causes increased blood pressure and heart rate, as well as increased tone, secretion and activity of the gastrointestinal tract (47). Activation of the gastrointestinal tract is probably what mediates nicotine’s ability to increase metabolism and decrease appetite; a side­effect many weight­conscious people, especially women, find desirable (47). 15 The complete nicotinic receptor is a pentameric structure and the two different types of nAChR have different subunits. The muscle type has two alpha subunits, one beta, one delta, and one epsilon (62). Neuronal nicotinic receptors can be comprised of

α2­7 and β2­4 subunits. Most of these structures are heterogenic and are comprised of two alpha and three beta subunits. Nicotine binds at the juncture between the alpha and beta subunits (62). The alpha 7 subunit can form a functional homopentamer but the 2:3 ratio, comprised of subunits other then alpha 7, is more common (62). Activation occurs when two acetylcholine, or nicotine, molecules bind to the receptor (62).

When the cholinergic receptors are bound by acetylcholine, or nicotine, they change conformation and open a pore in the middle of the receptor. This pore allows cations, primarily sodium, to flow through. The influx of sodium causes depolarization of the membrane leading to excitation of the neuron. At the same time, an influx of Ca+ acts through intra­cellular cascades, affecting the activation of certain genes and the release of neurotransmitters such as dopamine, acetylcholine, glutamine and GABA (22,

49, 57). One well documented example of nicotine induced gene expression involves regulation of Tyrosine Hydroxylase (TH) mRNA expression. This example is of particular interest because TH is responsible for the conversion of dopamine precursors.

Initial exposure to nicotine causes rapid activation and then desensitization of the receptor, and causes postsynaptic modulation of cytoplasmic second messenger systems such as activating transcription factor 2 (ATF­2) and cAMP responsive element binding protein (CREB) (23, 38). For example, in the case of tyrosine hydroxylase (TH), nicotine causes an increase in intracellular Ca +, the increase either activates cAMP which then activates protein kinase A (PKA), or the Ca + activates the PKA directly. The PKA then 16 phosphorylates and activates extracellular signal­regulated kinase (ERK) which causes an increase in TH gene expression (22, 80). GTP cyclohydrolase I (GTPCH), a rate limiting enzyme for an essential cofactor in TH activity, has also been shown to be regulated in vivo regulation by nicotine (28, 80). Many studies show nicotine regulation of gene expression which may have similar activation mechanism as TH (18, 38, 39).

nAChRs are expressed early in fetal development. Functional nAChRs have been identified by embryonic day18 in rats and may be functional even earlier (19). The early presence of these receptors indicates that nicotine may act on the receptors early in development, giving a long window of effect and a greater possibility to cause damage.

Many of the effects seen in infants of smoking mothers are probably due to direct action of nicotine on nAChRs. We cannot ignore the possibility, however, that some of these are indirect effects mediated by the physiological response caused by nicotine. Nicotine causes an increase in heart rate, blood pressure, and restriction of blood vesicles (53). In pregnant mothers these effects can cause a restriction of the blood supply to the fetus causing hypoxia, insufficient oxygen supply. The hypoxia is aggravated by the presence of carbon monoxide in cigarette smoke, further decreasing the levels of oxygen reaching the fetus (46, 53). Prolonged and severe hypoxia could cause some of the cell death phenomenon seen in prenatally exposed animals (90). In fact, an experiment with rats given too high a dose of nicotine caused spontaneous abortions due to oxygen starvation in the fetuses, and a dose response between maternal smoking rates and spontaneous abortions in humans (29). However, the physiological changes in the brain have been observed at concentrations that are too low to mediate differences in birth weight (Slotkin

TA). This indicates that though some of the effects may be due to lack of oxygen it is 17 still likely that many are directly caused by nicotine. The possibility remains, however, that these effects are not directly mediated by nicotine’s effect on its receptors.

There is evidence that nicotine activates the glucocorticoid system in a dose dependent manner to increase corticosteroid concentrations (95). It is possible that the activation of this, or some other system, is responsible for the observed changes in neuronal cell populations following prenatal nicotine exposure. Studies have indicated that increased levels of glucocorticoid in the brain can cause cell death, and are thought to play a role in Post Traumatic Stress Syndrome (78). Nicotine’s activation of hypothalamic­pituitary­adrenal responses would still be a specific effect, though once removed. Rather than acting directly on the HPA axis it is possible that nicotine causes stress to the body (potentially through toxic effects) and it is this general stress that activates the HPA­axis. This mode of action would indicate an indirect effect of nicotine and would mean that other stressors would mediate similar responses. The specific nature of these responses, and the ability to block the neurotoxic effects by blocking nicotine’s ability to bind its receptor, (42) seem to indicate a direct effect of nicotine’s actions and not an indirect effect of either oxygen deprivation or stress inducement (58).

Thesis Description and Hypotheses

The increased vulnerability of both prenatal and adolescent time periods and the consequences of prenatal exposure to nicotine on subsequent adolescent exposure make these groups of particular interest for study in animal models. The present literature contains many examples either of effects of prenatal nicotine exposure or nicotine 18 exposure in adolescence, but have only recently begun to investigate effects when exposure occurs at both critical time points. This research indicates prenatal exposure to nicotine causes cell loss in the midbrain and cerebral cortex as well as inducing elevations in membrane/total protein ratio (1). Nicotine given during Adolescence induces similar changes to a lesser degree. The changes caused by prenatal exposure persist into adolescence and individuals challenged with nicotine at both time points show increased damage roughly equivalent to an additive effect of exposure at both time periods. The alterations are hypothesized as a biological basis for the increased susceptibility of adolescent smokers to nicotine dependence, in particular those adolescents exposed in utero (1).

This thesis attempts to add to the body of literature investigating the effect of nicotine at both critical time points by examining microarrays generated with RNA from mice exposed to nicotine in utero. One set of microarrays will interrogate how early exposure affects gene expression by isolating the RNA from post natal day zero pups.

These arrays will be compared to a second set of arrays generated from a set of prenatally exposed mice that were allowed to grow to adolescence then exposed to nicotine through a two bottle choice test. This thesis hypothesizes that sexually dimorphic gene expression patterns will be apparent in adolescent animals prenatally exposed to nicotine.

In addition it is believed that in utero nicotine exposure alters sexually dimorphic gene expression networks in the brain in a manner that will persist into adolescence and may heighten sensitivity to nicotine exposure in this age group. 19

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Gene Expression Patterns of Adolescent C57B/6J Mice Prenatally Exposed to Nicotine

Introduction

Each cell in a given organism has identical DNA sequences. DNA is the cellular blueprint that contains the coding information for all genes. The term “gene” calls to mind varying degrees of definitional understanding, from a broad idea of a hereditary unit to a more narrow molecular definition of an open reading frame. Regardless of the level of understanding of what a gene is, it is where and to what degree the genes are expressed that is of interest.

Gene expression orchestrates cell differentiation. Each tissue has a unique pattern of expression composed of which genes are expressed and to what degree. For example the different genes will be expressed in a liver cell than a neuron and a compilation of these gene expressions creates a pattern that can differentiate a liver cell from a neuron.

By expressing or “turning on” one set of genes and silencing a different set, a cell determines its developmental fate (e.g. whether it will become a neuron or a hepatocyte).

The gene patterning which leads to cell differentiation is semi­permanent and once a cell fate has been decided rarely reverts back to an undifferentiated stem cell in vivo.

Although gene expression patterning that leads to cell differentiation is irreversible, gene expression in general is a fluid event that can be influenced by multiple factors (5, 23,

26). In fact, gene expression alters as organisms interact with their environment. For 26 example, the HPA axis responds to stressful situations by increasing the production of stress hormones. Learning and memory is another process that relies on the induction of a specific gene expression pathway (9, 12, 31).

Endogenous control of gene expression allows organisms to adapt appropriately to a given environment or situation, however these endogenous pathways can be influenced by foreign chemicals such as artificial steroids, replacement hormones, and drugs of abuse. Exposure to nicotine, a commonly abused drug, at any age alters gene expression levels in the brain and causes alterations in neurological pathways (2, 5). This effect is more profound when the exposure occurs during development, given that cells are in a state of differentiation and nicotine can potentially alter cell fates. Any alterations in gene expression are especially important in the brain. The brain expresses a greater number of genes present in an organism’s genome than any other tissue in the body and is the basis for many key processes, including behavior (18, 21). Alterations in cell fate and neuronal processing during development could have effects that persist throughout an organism’s life.

Microarray technology is a fairly new technique used to quantify mRNA expression on a large scale (16). Microarray chips have been designed that contain the entire known transcriptome, all transcribed regions, of an organism’s genome. They are beneficial in detecting gene expression changes between different tissue classes or treatment groups of the same tissue. The later experiment is the most common and can identify cellular response to nonnative stimuli or environmental differences. For example, microarray technology has been important in cancer research, allowing differentiation of previously indistinguishable tumors which allows more accurate 27 treatment(15, 29). This is only one area of investigation where microarrays have generated breakthroughs. They are being incorporated into virtually every field of the biological sciences.

Affymetrix is one of the leading commercial producers of microarray gene arrays.

Benefits of commercially­supplied arrays include technical support, optimized protocols, large gene sets of most model organisms, and well­tested probe sets. An Affymetrix gene array contains sections of probe sets that have between 16 and 20 probe pairs. The probes are 25 bp oligonucleotides complementary to mRNA of a specified organism.

One oligonucleotide of the pair is a perfect match (PM) for the mRNA that is able to hybridize cRNA fragments. The second oligonucleotide of the pair is a mismatch (MM), which has an incorrect base in the middle of the sequence. Alteration of the middle base pair significantly diminishes hybridization of the matched target and is intended to serve as a control for nonspecific hybridization. Normalization of microarray data has traditionally been done by subtracting the MM signal from the PM signal. The remaining signal represents the “true” mRNA content in the original sample. All the arrays in one experiment are centered around a target value, thus they have the same mean expression level. This method is currently how the Affymetrix MAS 5.0 program normalizes microarray data.

Another technique that has been developed for microarray analysis is “robust multi­array analysis” (RMA)(17). This technique was designed to address weaknesses of the MAS 5.0 analysis method. The creators of RMA believe it to be less variable and more accurate for genes with low expression values than the MAS 5.0 (17). A drawback of the RMA technique is that replicate arrays need to be used in the analysis, which, due 28 to the large expense of microarray technology makes this difficult at times. Since its inception, further improvements have been made to the RMA analysis and other forms of normalization have been proposed (8, 19, 25).

The most appropriate method for normalizing microarray results is still in question (28). Multiple labs have tested each of the most prevalent analysis methods

(including MAS 5.0 and RMA) and have reached conflicting conclusions. Most of these earlier comparisons based their conclusions on known quantities of mRNA that had been added to the sample pre­hybridization. In a recent comparison, data from an actual data set identified no clear method as better than the others. This conclusion was derived by checking the intensities reported by each analysis method using the gold­standard for mRNA quantification, rtPCR (27). One caveat of the study is that it checked only genes of medium or high signal and the previous conclusions identifying RMA or gcRMA

(which factors GC content of probes) as the best methods included genes of low signal intensity.

Due to the large amounts of data generated by microarrays and the broad range of normalization methods, a standardized presentation method is crucial for verification of results. Over the years an agreement has been reached on the minimum amount of information that must be included regarding the details of a microarray experiment to allow others to use published works. This format is known as the Minimum Information

About a Microarray Experiment (MIAME) and is essential for the standardization of information (4). The MIAME standards are adhered to in this manuscript. MIAME satisfactorily answers the question of how to present the material in an informative manner but there are still debates concerning the best method of normalization. 29 The analysis of microarrays is an ever­evolving process, often requiring advanced statistical applications. Even with in­depth knowledge of the different procedures it is often difficult to decide the appropriate method to apply to a data set, and many researchers working with microarrays have only basic statistics knowledge. Another drawback to these methods is that most are appropriate only for detecting overall patterns in the data, such as large scale differences between treatments or the number of large scale­factors driving expression. The statistical methods used to analyze microarrays are quite sophisticated, and can reveal valuable trends in the data, but are not designed to identify a single regulated gene or small sets of differentially­regulated genes. Often scientists wish to use microarrays as a screening tool for large quantities of genes and this is not the purpose for which these statistical analyses were designed.

Researchers are continuously searching for methods that will parse the microarray data to a more manageable set. One such method uses a known gene and identifies other genes in the microarray that behave in a similar manner. This method is most often used when a researcher has a well characterized gene known to be involved in certain processes (32). Using gene expression correlated to a known gene generates a relatively short candidate gene list but may not identify the genes having the largest effect. A second method used to identify genes of interest is by looking at fold­change in levels of expression between the experimental treatment and the control. There are two potential problems with this approach. First, from a statistical basis, when dealing with such a large data set, false positives can be incorporated in the select gene list (30). Second, small expression changes that have biological relevance will be overlooked. 30 Gene expression is important because it can give an idea of the global response of the organism. For example, if a treatment increases the cell death rate, a subsequent increase of gene expression of genes involved in apoptotic processes would indicate an alteration of cellular process rather than a toxic event. MessengerRNA (mRNA), however, does not precisely predict protein levels, and many post­translational modifications do occur, so this is neither a direct correlation nor a direct indication of function. The pattern of expression, however, is important, and the overall pattern of many genes is more stable than the expression of individual genes(22). A large range of fluctuation in expression signal on gene arrays, especially in those genes expressed at a lower level, is common in microarrays (11, 30). By examining patterns of expression and relating those genes by other means, such as gene ontology, there is a greater likelihood of identifying genes important in the response and not just random fluctuation.

Large scale alterations in gene expression patterns during development could have profound long lasting effects. These effects could manifest themselves in several manners, such as retarded growth, neural deficits, or more subtle behavioral alterations.

To determine if prenatal nicotine exposure alters behavior later in life, pregnant mice were given nicotine via their drinking water (0 µg/ml, 50 µg/ml, & 200 µg/ml) nine days after visual confirmation of a vaginal plug. The pups were left with their respective mothers until weaned then placed into group housing. On postnatal day 34, the mice were moved into separate cages with 2 bottles containing 2% saccharine. The following day (PN 35) 50 µg/ml of Nicotine was added to one of the water bottles and fluid consumption was measured until PN 42. A behavior difference was observed in males pre­exposed in­utero to 50 µg/ml of nicotine. The animals were sacrificed on the last day 31 of testing and the nucleus accumbens was dissected. The nucleus accumbens is an important part of the reward pathway. Expression changes in this region will be identified in adolescent animals after their second exposure to nicotine, their first exposure being in­utero. Analysis of these data may identify cellular processes in response to nicotine that have been altered by prenatal nicotine exposure.

Materials and Methods

Animal Sample

C57Bl/6J mice obtained from Jackson laboratories were used in this experiment.

A male was placed in a cage with three females and the females were checked daily for vaginal plugs. Once a plug was observed, females were separated into their own cage and monitored daily for weight change. Observation of a plug was counted as gestation day 0. At gestation day 9 the dam’s regular drinking water was replaced with a 2% saccharin solution that contained 0, 50, or 200 µg/ml of (­)­freebase nicotine.

At birth, pups were sexed, weighed, and their anogenital distances were measured. The pups were left with their mothers and the nicotine bottle until weaning was complete. After weaning, the mice were placed in group cages until PN35 at which point they were placed in separate cages and administered a preference test. The preference test was conducted by placing two drinking bottles in the cage on of which contains 50 µg/ml nicotine in 2% saccharine and the other is a 2% saccharine solution.

Preference testing was carried out from PN35 to PN42 with water consumption from the 32 bottles being measured daily. Bottles were rotated every other day to avoid side preference. The mice were sacrificed by cervical dislocation on the last day of testing and the NA was dissected.

RNA Isolation

Nucleus accumbens punches were taken on the day of sacrifice and stored in

RNAlater (Ambion Inc., Foster City, CA) until isolation. The brain tissue was homogenized in Trizol (Invitrogen, Carlsbad, CA) following standard procedure. RNA isolation was performed using RNeasy columns (Qiagen, Valencia, CA) according to the manufacturers’ instructions. The RNA was brought to a final concentration of 1 µg/µl.

Oligonucleotide Arrays

Affymetrix Mouse Genome 430a (M430A) microarray chips were purchased from Affymetrix (Santa Clara, CA) and contain 22,690 probe sets which allows for the analyses of expression values for approximately 14,000 well­characterized mouse genes.

Two sets of M430A microarray chips were generated and analyzed as a single experiment.

The first set of M430A microarray chips consisted of eight arrays hybridized with cRNA generated from male adolescent mice. These arrays were initially generated to investigate the difference in nicotine preference observed during the behavior experiment

(20). The microarrays were hybridized with pooled cRNA of three male mice from the 33 same prenatal exposure group that indicated similar nicotine preferences (20). Nicotine preference was measured by calculating the ratio of volume of water consumed from the bottle containing nicotine divided by volume of water consumed from both bottles. If an animal was randomly drinking from the water bottles it would consume roughly 50% of it’s total volume from each bottle, therefore it was determined an animal exhibited low preference (LP) if 40% or less of its’ total volume was consumed from the nicotine bottle and high preference if 60% or more of its’ total volume was consumed from the nicotine bottle. Four different groups of interest were generated by grouping the male mice by there prenatal exposure and preference designation. The group with no prenatal nicotine exposure and exhibiting LP in the behavior experiment were considered the control group. The three experimental groups were the prenatal exposure of 50 µg/ml of nicotine and designated as LP, prenatal exposure of 50 µg/ml and designated as HP, and prenatal exposure of 200 µg/ml and designated as LP.

The microarray hybridization and processing were of this set of microarrays was handled by Dr. David Drubin at the Hershey Medical Center (Hershey, PA). Biologically replicate microarrays were generated for the control group and the HP 50 µg/ml experimental group. In the case of the other two experimental groups there were not enough animals in these classifications to create biological replicates so technical replicate microarrays were generated for the LP 50 µg/ml, and LP 200 µg/ml groups.

The second set of M430A microarray chips consisted of microarrays hybridized with cRNA generated from the female adolescent mice and corresponding to one of the biological replicates from the male adolescent control group. An additional preference rating of no preference (NP) was generated the arrays hybridized with cRNA from the 34 female adolescents. This category indicated arrays where the mean preference for the animals pooled on the array fell between 40% and 60% of their total water volume being consumed from the nicotine bottle. There were five groups of interest when the female adolescent mice were categorized by prenatal exposure and preference rating. The control group was designated as those animals with no prenatal nicotine exposure and LP.

The four experimental groups consisted of no prenatal exposure and NP, prenatal exposure of 50 µg/ml and LP, prenatal exposure of 50 µg/ml and NP, and prenatal exposure of 200 µg/ml and LP.

The RNA for the second set of microarray chips was prepared using a Small

Sample Prep Kit () following the manufactures protocol. The biotin labeled cRNA was then sent to the Pennsylvania State University Microarray Facility. The complete process and protocols followed by the Microarray Facility can be found online at http://www.huck.psu.edu/stf/dnama/Protocols.html.

Microarray Analyses

The two sets of microarray chips were analyzed as a whole. Two separate analyses were conducted on the microarray chips. The first analysis normalized the arrays using MAS 5.0 software (Affymetrix, Santa Clara, CA) and a target value of 250.

The normalization procedure scales the arrays to have the same average intensity and range of expression. After normalization all probe sets given a call of absent by MAS 5.0 or indicated as not changing between any two arrays was removed from the data set. The remaining data probes were loaded into the R software package an open source program 35 for statistical computations produced initially by the Statistics Department of the

University of Auckland and a full list of contributors can be found at its website ( http://www.r­project.org/). The data set was then analyzed using the R affy software package (13).

In addition a ratio of each experimental condition divided by the corresponding control was generated and log2 transformed. The data was then filtered based on fold change. All probe sets that did not indicate a two fold increase or decrease of expression in comparison to the control for at least one of the experimental conditions was removed from the data set. The remaining data were combined with a behavioral marker (total nicotine consumed on final day) and a physiological marker (cotinine level on day of sacrifice) that were transformed in the same manner as the gene expression data. The combined data set was then clustered by a hierarchical clustering method using Eisen cluster software and Treeview graphical output (10).

The second analysis involved loading the CEL files of the two sets of arrays directly into the R environment. The CEL file contains probe intensity data and that have had no normalization steps applied. An automated microarray data analysis program,

AMDA, was applied to the data set (25). The program has a wrapper that inputs cell files, then preprocesses, normalizes and analyzes the data. The wrapper used gcRMA as the normalization method. The gcRMA is a robust means analysis similar to RMA but it includes information on the probe sequence, such as GC content, in its normalization process. The AMDA analyses include hierarchical clustering and principle component analysis, as well as looking for over represented functional groups in gene clusters by using the GO ontology database. 36

Results

Two Bottle Choice Test

The results for the two bottle choice test were analyzed and published by Klein et al (20). For ease of discussion in this manuscript the relevant results will be summarized.

Males pre­exposed to 50 µg/ml of nicotine in utero demonstrated a preference for nicotine in the preference test, with some individuals drinking as much as 89% of their water from the nicotine bottle. Control males, and both the 50 µg/ml and control females showed a low preference/avoidance of the nicotine bottle ( Fig 2­1 ). 37

Klein, et al., Nic Tob Res 2003. Figure 2­1: Bar chart of average nicotine preference for the males and females from the control and 50 µg/ml pre exposed group. Males pre­exposed to 50 µg/ml recorded a higher preference score then the other experimental groups. Reproduced with permission from Nicotine Tobacco Research.

Analysis of Gene Expression Differences 38 Overall patterns in the data are investigated by performing hierarchical clustering.

This method can be used to group genes with similar expression patterns across arrays, as well as to group arrays by the similarity between in expression patterns. Ideally, replicate arrays would group together, as would arrays from the same treatment group. Clustering of the arrays created sex­specific groupings with a few exceptions (Fig 2­2 ). The re­ hybridized array clustered on its own branch and the gene expression pattern of one of the high preference male groups was more similar to the expression patterns of the female mice than the pattern s of the other male mice. The gene expression patterns of male mice categorized as low preference and from two separate prenatal treatments, one from

0 µg/ml and one from 200 µg/ml pre­exposure, clustered on a branch with an array depicting female gene expression pattern of a low preference 50 µg/ml pre­exposure category. The replicate arrays, biological and technical, did not cluster together. Outside of the main sex groupings there were no obvious cluster groups. The fact that the treatment groups did not cluster does not mean gene expression changes specific to the treatment group did not occur but that the overall pattern of expression was not driven by the treatment. 39

Figure 2­2: Hierarchical clustering of microarrays using correlation values.

A second analysis was performed on the arrays by clustering arrays based on the differences in variance in the plane of the first two principle components ( Fig 2­3 ). This analysis was generated using AMDA and indicates that within the first two principle components the arrays also demonstrated sex­specific clustering. The arrays exhibiting male gene expression data grouped as one tight cluster and the arrays contained female gene expression data spread out into three clusters (Fig 2­3 ). Analysis of the genes designated differentially expressed using the MAS 5.0 normalization method, and pre­ filtering based on absence or lack of expression difference between arrays, indicated 11 principle components with the first two accounting for nearly 60% of the variance 40 (Table 2­1). A histogram of the % variance accounted for by the principal components depicts the large drop from the 2 nd principle component to the 3 rd component (Fig 2­4).

Table 2­1: Principle Component Analysis

Importance of components: PC1 PC2 PC3 PC4 PC5 PC6 PC7 Standard deviation 0.815 0.677 0.4295 0.417 0.3031 0.2898 0.2521 Proportion of Variance 0.35 0.242 0.0973 0.0917 0.0484 0.0443 0.0335 Cumulative Proportion 0.35 0.592 0.6892 0.7809 0.8293 0.8736 0.9071

PC8 PC9 PC10 PC11 Standard deviation 0.235 0.2159 0.2016 0.1837 Proportion of Variance 0.0291 0.0246 0.0214 0.0178 Cumulative Proportion 0.9362 0.9608 0.9822 1 41

Figure 2­3: Two dimensional representation of the arrays clustered in the plane of the first two principle components 42

Figure 2­4: Principle Component Analysis of Regulated Adolescent Genes

A cluster dendrogram of the genes showing differences of expression has one large cluster which branches off farther down and a small subset of genes that breaks from the large cluster of genes early in the branching ( Fig 2­5 ). The dendrogram does not give any indication of which arrays are driving the splitting of the branches. The number of clusters a data set can be split into can be controlled by cutting the tree at a specific point. Once these cuts are made, a silhouette plot can be generated that indicates the suitability of the clustering groups (data not shown). A silhouette plot was created for

2 to 18 clusters. A width of a silhouette close to 1 indicates a good cluster fit, near zero 43 indicates the gene could be placed in two clusters, and a negative number indicates a bad cluster. Averaging the silhouette widths determines how well the cluster fits the data.

The point at which there is a drastic drop in the average from one set of clustering to the next indicates the appropriate number of clusters that should be used for the data set.

Silhouette widths for the adolescent data set indicate it is appropriate to group the data into three clusters ( Table 2­2 ).

Table 2­2: Average Silhouette Widths

Clusters 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Avg. Width 0.52 0.52 0.27 0.27 0.1 0.11 0.11 0.1 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 44

Figure 2­5: Cluster of probe ids based on patterns of regulated gene expression.

As well as showing sexually specific clustering the data showed overall differences in the patterns of expression between the male and female arrays. MAS 5.0 determines whether a gene is absent on a given array based on level of the perfect match probe set in comparison to the mismatch probe set as well as the performance of the individual probes in the perfect match set. The program can also determine whether a gene is changing expression levels between two arrays. By comparing sex specific expression changes, it was determined that there were greater within sex changes in the males than the females (Table 2­3 , Fig 2­6 ). A chi­square analysis indicated there was a 45 significant difference between the gene response by sex (χ 2 =2403.281, DF = 2, P­value <

0.001). There were 5001 probe sets that were showing differential expression in the males but not the females. In contrast to the changing and not changing genes, there were a similar number of absent genes on the female and male arrays and for the most part the same probe sets were being called absent. The greater differences in gene expression between the males are not unexpected given the behavioral results which show a significant effect of prenatal treatment in the males but not the females.

Table 2­3: Gene Response by Sex Female Not Changing Changing Absent

Male Changing 5952 5001 569 11522

Not Changing 1324 415 0 3295

Absent 257 415 7201 7873 7533 7387 7770 22690 46

Figure 2­6: Bar chart indicating MAS 5.0 calls for the probe sets on the arrays separated by calls made on data from male and female mice. Panel A has the male gene actions across the bottom, the colors within the bars indicate what those probe sets calls are specified for date from female mice. Panel B is the same chart with data from female mice along the x­axis and the corresponding calls for probe data from male mice with in the bars.

Clustering with Non­Expression Markers 47 Clustering analysis correlates expression values on the different arrays and orders the arrays by correlated expression of the genes and clusters the genes by similar expression patterns on the separate arrays. By including non­expression markers it is possible to identify genes whose expression correlates with quantitative measures in the animals rather then discrete categories ( Table 2­4 ). Looking at only the genes with a 2.0 fold or greater expression change in response to nicotine creates a greater separation of clusters by sex. The arrays segregate into two distinct clusters based on sex (Fig 2­7).

The mean cotinine levels and nicotine consumed on day of sacrifice had a Pearson correlation of 0.75, but did not cluster in similar gene groupings. The transformed value for mean cotinine levels (Cotinine) clustered on a branch with bromodomain containing 4

(Brd4), myeloid/lymphoid or mixed lineage­leukemia translocation to 6 homolog (Mllt6), and atrophin 1 (Atn1) (Fig 2­8). GO categorization indicates each of these genes play a role in transcription and are localized in the nucleus (Table 2­6, Table 2­5 ). The transformed value for nicotine consumed on day of sacrifice (FD_Nic) clustered on a branch with acireductone dioxygenase 1 (Adi1) a gene. No clear protein function is discernable for the genes clustering with FD_Nic (Fig 2­9, Table 2­7).

Table 2­4: Array Information 48

Table 2­5: Genes Clustering with Mean Cotinine

Probe Set ID Gene Title Gene Symbol GO Biological GO Molecular GO Cellular Component Process Description Function Description Description 1421762_at potassium inwardly­ Kcnj5 transport /// ion ion channel activity integral to plasma rectifying channel, transport /// /// inward rectifier membrane /// membrane subfamily J, member 5 potassium ion potassium channel /// integral to membrane transport activity /// voltage­ gated ion channel activity /// potassium channel activity /// G­ protein activated inward rectifier potassium channel activity /// potassium ion binding

1424631_a_at Immunoglobulin Ighg ­­­ antigen binding membrane /// integral to heavy chain (gamma membrane polypeptide)

1424922_a_at bromodomain Brd4 protein amino acid nucleotide binding /// nucleus /// nucleus /// containing 4 phosphorylation /// DNA binding /// nucleolus /// nucleus positive regulation of protein binding /// DNA binding kinase activity /// transferase activity

1429897_a_at DNA segment, Chr D16Ertd472e ­­­ ­­­ ­­­ 16, ERATO Doi 472, expressed 1451685_at myeloid/lymphoid or Mllt6 /// regulation of DNA binding /// nucleus mixed lineage­ LOC639490 /// transcription, DNA­ protein binding /// leukemia LOC669933 dependent /// cellular zinc ion binding translocation to 6 physiological homolog (Drosophila) process /// regulation /// similar to of transcription, myeloid/lymphoid or DNA­dependent mixed lineage­ leukemia translocation to 6 homolog /// similar to myeloid/lymphoid or mixed lineage­ leukemia translocation to 6 1456247_x_at proteolipid protein 2 Plp2 /// chemotaxis /// chemokine binding membrane /// integral to /// similar to LOC620648 /// cytokine and membrane /// plasma Proteolipid protein 2 LOC669344 /// chemokine mediated membrane LOC672630 signaling pathway 1456319_at hypothetical protein LOC665081 ­­­ ­­­ ­­­ LOC665081 AFFX­LysX­5_at ­­­ ­­­ ­­­ ­­­ ­­­ AFFX­r2­Bs­lys­3_at ­­­ ­­­ ­­­ ­­­ ­­­ 49

Table 2­6: Genes Clustering with Mean Cotinine Cont…

Probe Set ID Gene Title Gene Symbol GO Biological GO Molecular GO Cellular Component Process Description Function Description Description 1416301_a_at early B­cell factor 1 Ebf1 transcription /// DNA binding /// nucleus /// nucleus regulation of transcription factor transcription, DNA­ activity /// protein dependent /// binding /// zinc ion development /// binding /// metal ion positive regulation of binding /// DNA transcription binding /// transcription regulator activity 1418897_at coagulation factor II F2 proteolysis /// acute­ thrombin activity /// extracellular region /// phase response /// serine­type extracellular space blood coagulation endopeptidase activity /// calcium ion binding /// peptidase activity /// hydrolase activity

1419368_a_at ring finger protein Rnf138 ubiquitin cycle zinc ion binding /// intracellular 138 metal ion binding /// nucleic acid binding /// protein binding 1420720_at neuronal pentraxin 2 Nptx2 ­­­ calcium ion binding extracellular space /// sugar binding /// metal ion binding 1421149_a_at atrophin 1 Atn1 negative regulation transcription nucleus /// nucleus /// of transcription from corepressor activity cytoplasm RNA polymerase II /// protein binding /// promoter /// toxin toxin receptor metabolism binding 50

Table 2­7: Genes Clustering with Day of Sacrifice Nicotine Consumption

Probe Set ID Gene Title Gene Symbol GO Biological Process GO Molecular Function GO Cellular Component Description Description Description 1417330_at solute carrier Slc23a2 transport /// ion transport transporter activity /// L­ membrane /// integral to family 23 /// sodium ion transport /// ascorbate:sodium symporter membrane /// integral to (nucleobase L­ascorbic acid activity /// L­ascorbate:sodium membrane /// integral to transporters), metabolism /// molecular symporter activity /// symporter membrane member 2 hydrogen transport /// L­ activity /// sodium ion binding /// ascorbic acid metabolism sodium­dependent multivitamin transporter activity

1424784_at RIKEN cDNA 1700029I01Rik regulation of transcription, zinc ion binding /// metal ion nucleus /// intracellular 1700029I01 gene /// LOC433791 DNA­dependent binding /// nucleic acid binding /// similar to zinc /// LOC666532 finger protein /// LOC671566 665 /// similar to zinc finger protein 665 /// similar to zinc finger protein 665 1424792_at ribonuclease P Rpp40 tRNA 5'­leader removal ribonuclease P activity /// nucleolar ribonuclease P 40 subunit ribonuclease P activity complex (human) 1425663_at interleukin 1 Il1rn lipid metabolism /// receptor activity /// interleukin­1 extracellular region /// receptor inflammatory response /// receptor binding /// interleukin­1 integral to plasma antagonist immune response /// cell receptor antagonist activity membrane surface receptor linked signal transduction /// insulin secretion

1427481_a_at ATPase, Na+/K+ Atp1a3 transport /// ion transport nucleotide binding /// magnesium nucleus /// cytoplasm /// transporting, /// cation transport /// ion binding /// catalytic activity plasma membrane /// alpha 3 potassium ion transport /// /// sodium:potassium­exchanging membrane /// integral to polypeptide sodium ion transport /// ATPase activity /// ATP binding membrane /// integral to metabolism /// monovalent /// monovalent inorganic cation plasma membrane /// inorganic cation transport transporter activity /// ATPase sodium:potassium­ /// potassium ion transport activity, coupled to exchanging ATPase /// sodium ion transport /// transmembrane movement of complex ATP hydrolysis coupled ions, phosphorylative mechanism proton transport /// sperm /// hydrolase activity /// hydrolase motility /// hydrogen ion activity, acting on acid homeostasis anhydrides, catalyzing transmembrane movement of substances /// potassium ion binding /// sodium ion binding /// metal ion binding /// sodium:potassium­exchanging ATPase activity

1427754_a_at dynamin 1 Dnm1 endocytosis nucleotide binding /// motor microtubule /// membrane activity /// GTPase activity /// coat /// cytoskeleton protein binding /// GTP binding /// hydrolase activity /// zinc ion binding /// metal ion binding

1433457_s_at G­rich RNA Grsf1 anterior/posterior pattern nucleotide binding /// nucleic ­­­ sequence binding formation /// acid binding /// RNA binding /// factor 1 morphogenesis of nucleic acid binding embryonic epithelium 1448276_at tetraspanin 4 Tspan4 ­­­ protein binding membrane /// integral to membrane 1448671_at ubiquitin­ Ube2e3 regulation of cell growth ubiquitin conjugating enzyme nucleus conjugating /// protein modification /// activity /// ubiquitin­protein enzyme E2E 3, ubiquitin­dependent ligase activity /// protein binding UBC4/5 protein catabolism /// /// ligase activity homolog (yeast) ubiquitin cycle 51

Figure 2­7: Array Clustering of non­expression Markers and Genes showing ≥ 2.0 fold nicotine regulation 52

Figure 2­8: Cluster Branch for Log2 transformed Mean Cotinine

Figure 2­9: Cluster Branch for log2 Transformed Nicotine Consumption on day of Sacrifice. 53 Conclusion

There are two periods of development where the brain undergoes dramatic changes. The first stage is neonatal development where the brain is forming. The second stage occurs during adolescence when the brain is pruning connections and large decreases in grey matter occur (24). Insult to neural development in either of these periods can influence brain development, the repercussions of which are not fully understood. Previous research has shown that nicotine exposure at one, or the other of these time points induces lasting changes in neuronal structures (1). In the data presented here, the animals have been exposed to nicotine at both vulnerable time periods. The most dramatic result of this research is that nicotine alters gene expression differently in males and females.

The microarrays generated from the adolescent animals show a clear sex specific clustering of gene expression patterns in both the hierarchical clustering and the principle component analysis (PCA) clustering (Fig 2­2, Fig 2­3). Although the segregation is not complete in the hierarchical clusters, there is a distinct separation in

PCA clusters. The PCA clusters graph the arrays based on their variation within the first two principle components, which account for nearly 60% of the variation on the arrays

(Table 2­1 ), therefore in the plane representing 60% of the variation in gene expression between the arrays, there is a complete segregation of expression patterns in the two sexes. One potential caveat of the PCA clustering is that the principle components do not have to represent biological variance but also can represent technical variation in sample handling. This is a potential problem with all data sets and this set in particular since the 54 microarrays were prepared in two sets on separate occasions. In addition the male data was processed almost exclusively with one set and the female data was processed exclusively in the second set. The potential that one of the first two principle components is due primarily to technical variation is decreased, however, because a replicate array generated with male cRNA was processed with the arrays generated with female cRNA and clusters with the other arrays generated with male cRNA. This clustering indicates that, at least, the first two principle components are due to biological variation.

The genes, as well as the arrays, can be clustered. An analysis of the k­ means clustering for the genes indicates that three clusters are most appropriate for this data set (Table 2­2 ). A dendrogram of the clustering of the genes indicates that the majority of genes fall into one large cluster with a small number of genes as members of the other two clusters (Fig 2­5 ). Investigation of the genes in the smaller clusters gives no indication of a biological interpretation of the clustering scheme.

In addition the genes were clustered along with a physiological and a behavioral measure, mean cotinine levels and nicotine consumption on day of sacrifice respectively.

These values can vary from mouse to mouse and by looking at actual physiological or behavioral indicators rather then qualitative groupings (such as high or low preference) it may be possible to identify genes important to nicotine regulation by viewing which genes cluster with these measures. Though no clear protein pathways emerged with the genes clustered around nicotine consumed on a day of sacrifice, the genes that clustered with mean cotinine levels shared common protein functions. These common functions,

DNA binding and ion channel activity, could be indicators of important pathways activated by nicotine in the central nervous system. 55 The whole scale sex differences in gene expression were also investigated.

All probe sets on the arrays were used along with the Affymetrix identification of absent, changing, or not changing. Primarily there is an almost universal overlap between the probe sets called absent on the male arrays and those called absent on the female arrays.

This result indicates nicotine was not activating genes that are not normally expressed in the brain, but it was changing the patterns of those genes which are already expressed and it was doing so in a sex specific manner (Table 2­3 ). Second, almost twice as many probe sets were identified as changing within the data from males than the data from females, 11, 522 as compared to 7,533. A X 2 analysis indicates nicotine is either activating or repressing expression in the males to a much greater degree than in the females. The greater effect of nicotine on male gene expression may explain why the effect of prenatal exposure was seen in the male animals and not the female animals (Fig

2).

The main observation of these analyses is the differential response of the sexes to both prenatal exposure to nicotine and subsequent adolescent exposure at both a molecular level (gene expression values) and a behavioral level (Klein et al). Nicotine interacts with the brain in a sexually dimorphic manner and may be altering the natural sexual dimorphism that occurs in the brain. These findings suggest a new avenue of investigation, not yet commonplace in the literature, which could lead to exciting results.

Sexual dimorphism of the brain could be represented by two separate models, or by a combination of both. The first model is based on the traditional view that sexual differentiation is solely mediated by sex hormones(14). The second model of influence, indicated in recent findings by Dewing et al, is that there is sexually dimorphic 56 gene expression in mouse brains prior to gonadal differentiation(7). This research seems to suggest that the sex chromosomes play a role in brain organization in a manner independent of sex hormones. For example, the sexual differentiation of tyrosine hydrozylase­immunoreactive neurons occurs without the presence of sex hormones(3).

Recent research is exploring the extent of sexual differences that are independent of sex steroids by creating a line of mice that have the Sry gene on an autosomal chromosome(6). The Sry gene is responsible for testis development, therefore the removal of it from the Y chromosome allows the generation of XX males and XY females(6). The implication is that there are modes of sexual differentiation in the brain that have yet to be full characterized. Brain/sex differentiation includes more than just the responsiveness to sex hormones.

The sexually dimorphic result presented here may be due to organizational or acute differences in response to nicotine between male and female brains. These changes may be induced through the same pathways as sex hormones, as in the first model, or by acting upon sex hormones directly to initiate immediate changes and perhaps long term organization changes similar to those that are well documented to be induced by the sex hormones themselves. The nicotine could also be acting through the more subtle changes indicated by Dewing et al. (2003) in the second model, and does not require sex hormones. The possibility also exists that nicotine acting in some combination of these two models is responsible for the sexually dimorphic response. Regardless of nicotine’s mode of action, the persistent sexual dimorphism in nicotine response highlights the importance of investigating nicotine dependence, or the effects of nicotine on neural development, in both male and female subjects. 57

References

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Voltage­gated channels block nicotinic regulation of CREB phosphorylation and gene expression in neurons. Neuron 32: 855­865, 2001. 6. De Vries GJ, Rissman EF, Simerly RB, Yang LY, Scordalakes EM, Auger CJ, Swain A, Lovell­Badge R, Burgoyne PS, and Arnold AP. A model system for study of sex chromosome effects on sexually dimorphic neural and behavioral traits. J Neurosci 22: 9005­9014, 2002. 7. Dewing P, Shi T, Horvath S, and Vilain E. Sexually dimorphic gene expression in mouse brain precedes gonadal differentiation. Brain Res Mol Brain Res 118: 82­90, 2003. 8. Diboun I, Wernisch L, Orengo CA, and Koltzenburg M. Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics 7: 252, 2006. 9. Dragunow M. A role for immediate­early transcription factors in learning and memory. Behav Genet 26: 293­299, 1996. 10. Eisen MB, Spellman PT, Brown PO, and Botstein D. Cluster analysis and display of genome­wide expression patterns. Proc Natl Acad Sci U S A 95: 14863­14868, 1998. 11. Etienne W, Meyer MH, Peppers J, and Meyer RA, Jr. Comparison of mRNA gene expression by RT­PCR and DNA microarray. Biotechniques 36: 618­620, 622, 624­ 616, 2004. 12. Finkbeiner S and Dalva MB. To fear or not to fear: what was the question? A potential role for Ras­GRF in memory. Bioessays 20: 691­695, 1998. 58 13. Gautier L, Cope L, Bolstad BM, and Irizarry RA. affy­­analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20: 307­315, 2004. 14. Goy RW, B. S. McEwen, el al. Sexual differentiation of the brain: based on a work session of the Neurosciences Research Program. 1980. 15. Hayashi Y. Gene expression profiling in childhood acute leukemia: progress and perspectives. Int J Hematol 78: 414­420, 2003. 16. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, and Speed TP. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 31: e15, 2003. 17. Irizarry RA, Hobbs B, Collin F, Beazer­Barclay YD, Antonellis KJ, Scherf U, and Speed TP. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4: 249­264, 2003. 18. Kandel E, Schwartz, JH, and Jessell, TM. Principles of Neural Science. New York: McGraw­Hill, 2000. 19. Katz S, Irizarry RA, Lin X, Tripputi M, and Porter MW. A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database. BMC Bioinformatics 7: 464, 2006. 20. Klein LC, Stine MM, Pfaff DW, and Vandenbergh DJ. Laternal nicotine exposure increases nicotine preference in periadolescent male but not female C57B1/6J mice. Nicotine Tob Res 5: 117­124, 2003. 21. Lewin B. Genes VII. Oxford: Oxford University Press, 2000. 22. Liang Y, Diehn M, Watson N, Bollen AW, Aldape KD, Nicholas MK, Lamborn KR, Berger MS, Botstein D, Brown PO, and Israel MA. Gene expression profiling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme. Proc Natl Acad Sci U S A 102: 5814­5819, 2005. 23. Liu Z, Neff RA, and Berg DK. Sequential interplay of nicotinic and GABAergic signaling guides neuronal development. Science 314: 1610­1613, 2006. 24. Paus T. Mapping brain maturation and cognitive development during adolescence. Trends Cogn Sci 9: 60­68, 2005. 25. Pelizzola M, Pavelka N, Foti M, and Ricciardi­Castagnoli P. AMDA: an R package for the automated microarray data analysis. BMC Bioinformatics 7: 335, 2006. 26. Pham TM, Winblad B, Granholm AC, and Mohammed AH. Environmental influences on brain neurotrophins in rats. Pharmacol Biochem Behav 73: 167­175, 2002. 27. Qin LX, Beyer RP, Hudson FN, Linford NJ, Morris DE, and Kerr KF. Evaluation of methods for oligonucleotide array data via quantitative real­time PCR. BMC Bioinformatics 7: 23, 2006. 28. Quackenbush J. Genomics. Microarrays­­guilt by association. Science 302: 240­ 241, 2003. 29. Talbot SG, Estilo C, Maghami E, Sarkaria IS, Pham DK, P OC, Socci ND, Ngai I, Carlson D, Ghossein R, Viale A, Park BJ, Rusch VW, and Singh B. Gene Expression Profiling Allows Distinction between Primary and Metastatic Squamous Cell Carcinomas in the Lung. Cancer Res 65: 3063­3071, 2005. 30. Wang Y, Barbacioru C, Hyland F, Xiao W, Hunkapiller KL, Blake J, Chan F, Gonzalez C, Zhang L, and Samaha RR. Large scale real­time PCR validation on gene expression measurements from two commercial long­oligonucleotide microarrays. BMC Genomics 7: 59, 2006. 59 31. West AE, Chen WG, Dalva MB, Dolmetsch RE, Kornhauser JM, Shaywitz AJ, Takasu MA, Tao X, and Greenberg ME. Calcium regulation of neuronal gene expression. Proc Natl Acad Sci U S A 98: 11024­11031, 2001. 32. Wu TD. Analysing gene expression data from DNA microarrays to identify candidate genes. J Pathol 195: 53­65, 2001. Chapter 3

Sexually Differential Regulation Networks Persist into Adolescence

Introduction

Microarrays generate a large amount of data. Some arrays contain entire genomes. It is challenging to sort through the mass of information and partition the data in an informative manner. Part of the challenge is to identify a gene or set of genes of interest. Previous studies have shown that the reliability of an individual gene’s expression is questionable because of background variation and the inevitable occurrence of false positives in large data sets, however, there is evidence that even if individual gene expression is not reliable the global patterns of gene expression are stable(1).

Researchers have begun to use published data that has been compiled into large date bases to interrogate large blocks of data rather than individual genes. Using the compiled knowledge allows one to make the best use of the large amounts of data generated by microarrays and make inferences about that data.

One commonly used tool is GO ontology. GO ontology identifies biological processes in which a gene is known to be involved. Several statistical programs have been generated to analyze lists of genes that show differential regulation in microarray studies. Within a set of genes with similar expression patterns in an experiment one can identify statistically over­represented biological processes based on published data for the genes in the list (7, 12). This analysis highlights biological pathways that may be 61 affected by an experimental condition. Though beneficial for indicating potential pathways, this analysis does not identify single genes. Other problems with this process are that some of the categories are rather broad and uninformative, such as “protein binding”, and that genes have multiple functions, possibly existing in several categories.

GO ontology’s main focus is genes with known functions and not novel genes, although it may be possible to infer function of a novel gene if it clusters with genes of a known function (4).

Another database, WebQTL, provides multiple tools that, when used in combination, provide valuable insight into candidate gene lists (11). WebQTL is an online database with built in statistical tools that take advantage of the isogenic nature of recombinant inbred (RI) animals. It combines a large database of complex traits collected using RI animals with built in software to perform QTL analysis and produce correlations of traits (11). WebQTL has genotypic information on five different RI lines including those derived from the C57/B6 and DBA inbred strains (BxDs) (11). The traits available include a large compilation of various drug phenotypes as well as gene expression data for many of the RI lines. With this resource, gene expression can be used as a phenotype in itself to detect sites that control whether genes are cis­ or trans­regulated. This website provides multiple tools to draw conclusions about genes and their relationships to each other.

As well as using WebQTL, this research also takes advantage of unpublished microarray data from this laboratory. The microarray experiment was designed to 62 investigate patterns of expression in the brain and to show how these patterns correspond across different brain sections in their response to nicotine. The array experiment investigating gene expression changes in adolescent mice discussed previously were a continuation of the unpublished array experiment. The animals used in the unpublished experiment were prenatally exposed to nicotine in the same manner as the adolescent mice and sacrificed at the day of birth, postnatal day 0 (PN0). Three brain regions were dissected from these animals, and microarray chips were hybridized with cRNA based on prenatal exposure, brain region, and sex. The original assumption for the experiment is that the gene expression patterns of the different brain regions would be similar to each other regardless of sex. This hypothesis was based on the idea that the majority of the genes response differences would be seen between brain regions because, although nicotine activates receptors similarly in male and female brains, the different brain regions have different concentrations of nicotinic receptors (5). When cluster analysis was performed on the data, the expression patterns did not segregate by brain region but by sex, indicating that the male and female brains were very different in their response to nicotine. Gene expression in the investigated brain regions for male mice responded to nicotine in a manner more similar to each other than to the corresponding brain region in the females. This result may seem counter intuitive at first, but indicates that sex differences in response to nicotine are greater than regional differences.

Utilizing data from the two previous experiments with the PN0 and Adolescent mice, the analysis in this chapter compares genes already identified from the PN0 arrays with those genes identified in the analyses of the arrays generated from the adolescent cRNA. A list of 74 genes that were more than 1.5 fold differentially regulated in the PN0 63 animals were compared to the adolescent chips. Of the 74 genes, 37 remained differentially regulated in the adolescent animals. By comparing genes identified as differentially­regulated in both the PN0 and adolescent mice, the possibility of having false positives is reduced and genes with fold changes not large enough to cause interest in a single experiment can be investigated. These genes may play an important role in the response differences seen in males and females, indicating a mechanism of action for nicotine’s effects on brain development and subsequent behavior. Using publicly available biological data, we were able to refine further the microarray data to infer biological relevance and develop testable hypotheses. By analyzing the two sets of microarray data with WebQTL, a gene network whose gene expression indicates persistently altered patterns from birth through adolescence have been identified. Such a network may aid understanding of the differential response of men and women to cigarette smoking so better modes of prevention and treatment in the two sexes can be identified.

Materials and Methods

Microarray Experiments

Unpublished microarray data from this laboratory, in addition to the microarray data from the experiment with the adolescent mice were used in this section. The mice used for both sets of microarray experiments had identical pre­natal treatment, both sets being exposed to three different concentrations of nicotine (0 µg/ml, 50 µg/ml, & 200 64 µg/ml). However, one set of pups were sacrificed within 4­8hrs of birth (PN0) and three parts of the brain were dissected: PreOptic Area (POA), Medial Basal Hypothalamus

(MBH), and Nucleus Accumbens (NA). The other set of pups, as discussed in Chapter 1, were reared to adolescence.

The arrays depicting PN0 expression were the first set created and the cRNA was hybridized to Affymetrix U74Av2 GeneChips. In this set of arrays a total of 18 chips were hybridized. For these chips mRNA from 4­5 animals was pooled together. Pools were generated for each treatment condition. Therefore each chip represented one brain region, one nicotine concentration, and one sex (i.e. male, PreOptic Area, 0 µg/ml nicotine).

The gene list for computational analysis was generated from microarray data exhibiting gene expression profiles of adolescent mice, the experiments were previously described in detail, and PN0 mice prenatally exposed to nicotine. The normalization procedure used to generate the individual gene lists from the array data was previously described for the PN0 mice and the standard MAS 5.0 normalization was used for the experiment with the adolescent mice. Genes differentially regulated by sex in response to nicotine were chosen in the PN0 based on a 1.5 fold or greater change in one sex in at least one condition, with no matching change in the opposite sex. Different Affymetrix chips were used in the Adolescent chip set and thus different probe sets. Any probe set representing a gene from the PN0 list was compiled. Any gene in the adolescent gene list that was identified by MAS 5.0 as changing in one sex but not the other, or that was shown to be changing in opposite directions, was retained ( Table 3­1 ). A less stringent fold­change requirement of 1.2 was placed on the adolescent chips. The lower stringency 65 for the adolescent genes was chosen to include genes that may have a large influence even with small expression changes. A lower stringency was also applied because it was apparent from the literature that brain development is a more plastic event prenatally than during adolescence and thus hypothesized that larger changes would be generated during the prenatal time period.

Table 3­1: Genes showing sexually differential gene expression in response to nicotine in both PN0 and Adolescents.

Adolescent Fold Changes PN0 Fold Changes s Gene Symbol M_05/00 M_20/00 M_HP05/00 M_LP05/00 F_05/00 F_20/00 NF05/00 NF20/00 NM05/00 NM20/00 Col1a1 1.791 1.916 1.377 2.561 1.195 1.005 1.626 1.705 2.682 2.131 Skd3 1.369 1.512 1.361 1.377 1.249 1.135 2.044 1.332 1.709 1.019 0610010K14Rik 1.301 1.425 1.296 1.306 1.153 1.113 2.016 3.148 2.267 1.527 Myh9 1.159 1.426 1.081 1.249 1.588 1.307 1.710 1.974 2.925 3.266 Actb 1.147 1.261 1.091 1.208 1.054 1.118 1.545 1.586 1.537 1.575 Actb 1.147 1.261 1.091 1.208 1.054 1.118 1.515 1.057 1.494 1.409 Nedd4 1.143 1.290 1.088 1.204 1.185 1.380 3.061 2.586 1.601 1.206 0610010K14Rik 1.141 1.095 1.099 1.185 1.031 1.585 2.016 3.148 2.267 1.527 Kif1a 1.132 1.166 1.200 1.071 1.256 1.439 1.475 1.052 1.988 3.247 Pim3 1.068 1.018 1.086 1.050 1.336 1.088 1.071 1.205 1.912 2.479 Actb 1.024 1.023 1.023 1.025 1.001 1.125 1.545 1.586 1.537 1.575 Actb 1.024 1.023 1.023 1.025 1.001 1.125 1.515 1.057 1.494 1.409 Kif1a 1.015 1.013 1.025 1.006 1.208 1.574 1.475 1.052 1.988 3.247 Rgs10 1.014 1.140 1.053 1.026 1.199 1.342 1.989 1.015 1.995 1.494 Psmb2 1.018 1.146 1.030 1.006 1.230 1.291 1.139 1.511 1.069 1.079 4930553M18Rik 1.021 1.019 1.058 1.015 1.087 1.376 1.048 1.539 1.567 1.606 Atp1a1 1.024 1.181 1.070 1.022 1.233 1.409 1.748 2.006 6.507 6.291 Atp1a1 1.040 1.038 1.101 1.021 1.319 1.197 1.748 2.006 6.507 6.291 Enpp2 1.068 1.064 1.032 1.104 1.141 1.382 3.428 2.441 2.352 2.313 Actb 1.069 1.033 1.068 1.070 1.462 1.326 1.545 1.586 1.537 1.575 Actb 1.069 1.033 1.068 1.070 1.462 1.326 1.515 1.057 1.494 1.409 D4Ertd196e 1.089 1.114 1.145 1.034 1.145 1.143 2.097 1.236 1.514 1.229 Glul 1.331 1.081 1.368 1.294 1.132 1.139 2.803 2.801 1.571 1.050

Pearson Correlation Statistics

WebQTL contains microarray data from whole brain extracts of several different

RI lines which are hybridized to Affymetrix m430a chips. The same chip type was used 66 in the adolescent experiments, therefore the Affymetrix identification information corresponding to the probes on the adolescent chips was retained in the gene list comparison between the PN0 and Adolescent data sets. These probe IDs were entered into the WebQTL site and a Pearson Correlation was generated for each pair based on expression levels across a panel of RI strains. The correlations were then diagramed by connecting genes with correlation levels above a designated threshold. The resulting diagrams show a network of correlated genes.

A QTL analysis was performed for the expression of each probe set. The results of these analyses were clustered based on their similarity. The QTL analysis indicates chromosomal regions that correspond with the regulation of a gene.

Edge Densities

A preliminary statistical analysis of eight random permutations of gene lists of an identical size to the sexually differential gene list were entered in WebQTL. Correlation networks were generated at Pearson Correlation thresholds of 0.3, 0.4, and 0.5. A correlation between two genes was represented by a connecting line, referred to as an

“edge”(10). The number of edges generated was counted, then edge density was calculated by dividing the number of observed edges by the number of potential edges.

The complete statistical analysis of edge densities is being conducted by Dr. Elissa

Chesler’s laboratory, one of the originators of WebQTL. 67 QTL Cluster Map

WebQTL, as it name indicates, can also be used for QTL analysis of the transcription of a gene. The program uses the transcription levels of a probe ID in the different RI strains as the trait then identifies regions of the genome that correspond to regulation differences. The results from the QTL analysis of multiple probe IDs can be clustered, based on the similarity of QTL maps.

Over­represented Transcription Factor Binding Sites

The promoter regions of the genes were analyzed for overrepresented transcription factor binding sites. The first 2000 bp upstream of the first exon was considered the promoter region. These sequences from genes with positive correlation scores in the WebQTL correlation network were entered into a website, Over­represented

Transcription Factor Binding Site Prediction Tool (OTFBS). OTFBS uses the

TRANSFAC matrix to search for over represented binding sites.

Results

Microarray Experiment with PN0 Mouse Experiment

The results of the analyses of the PN0 chips are being published in a different manuscript (8), however, it is relevant to introduce the main finding of those analyses here. The sexually differential response to nicotine indicated by the PN0 results (Fig 3­1 68 ), and by the outcome of the adolescent behavior experiment, demonstrates male and female mice respond differently to nicotine. The differences in the within­sex gene expression changes of the adolescent chips may indicate what pathways are responsible for the behavior differences observed.

Figure 3­1: Cluster diagram generated using Eisen Cluster Tree program. The chips organize themselves first by sex and then by brain region. The notable exception is the male hypothalamus pre exposed to 50 µg/ml is clustering with the female hypothalamus rather than the male array expression profiles. The arrays were designated by the sex, brain region, a prenatal exposure of the animals whose cRNA was hybridized to the array. The abbreviations stand for male (M), female (F), nucleus accumbens (N), basal medial hypothalamus (H), preoptic area (P), and pre exposure of 50 µg/ml (05) or 200 µg/ml (20). The clusters were generated using ratios of experimental condition over control therefore no 0 µg/ml pre exposures are seen here. Also missing is the data for the female basal medial hypothalamus of mice pre exposed to 200 µg/ml because the array containing this data failed. 69 Results that indicated sexually differential response in the mice were obtained in the PN0 microarray chips, the adolescent behavior experiment, and the analyses of the adolescent microarrays. Therefore genes differentially expressed between the sexes were the focus of the computational analysis. The list of sexually differential genes identified in the PN0 chips was compared to genes in the adolescent chips. A gene was retained if it was present on the PN0 list and fit one of the following three criteria in the adolescent chips: absent in one of the sexes and present in the other, changing in one sex but not the other, or was changing in different directions for the two sexes. The resulting list contains genes identified as differentially regulated by sex at both age groups (Table 3­1

).

Correlation Networks

Preliminary analyses have created a list of 17 genes that will be used to demonstrate this computational analysis. Genes on this list were identified as being differentially regulated by nicotine in a sexually dimorphic manner by a two­fold change or greater in PN0 pups, and remaining differentially regulated, though to a smaller degree, in the adolescent animals. Expression relationships among these genes were identified in untreated animals using WebQTL. Probe IDs for the genes on the differential expression list were retained for the MOE430a chips. WebQTL data from the

BXD INIA whole brain MAS 5.0 Oct 04 analyses were obtained for these probe IDs. A

Pearson correlation network was generated by connecting genes with a correlation score of 0.5 or greater ( Fig 3­2 ). For each gene, the Pearson Correlation is calculated using 70 expression levels from a panel of BXD RI animals. The 17 genes identified by their nicotine regulation showed a high degree of correlation (Fig 3­2). When the correlation threshold was decreased from .5 to .3, all of the genes in the sexually dimorphic expression list become highly interrelated (data not shown).

Figure 3­2: Correlation network of the sexually differentially regulated genes. Connections are based on a Pearson correlation of .5 or greater between gene expression values of 44 lines of BxD RI mice.

The eight randomly permutated networks were generated as part of a preliminary statistical analysis to determine if the network of nicotine regulated genes was statistically significant. The random permutations generated gene lists of the same length 71 as the sexually differential list. The edges were counted for networks generated Pearson

Correlation thresholds of 0.5, 0.4, and 0.3. In the final analysis 100 randomly permutated networks will be generated. If the experiment network is among the networks with the highest edge correlations (95 th to 100 th) it can be considered as being in the 95 th percentile which coincides to a p­value of less than 0.05. The preliminary data indicates the experimental correlation network has a higher edge density rating than all of the randomly permutated lists, which is an initial indication that the network will be statistically significant ( Table 3­2 , Fig 3­3 ). 72

Table 3­2: Data table containing edge values for the experimental correlation network and the eight correlation networks calculated with randomly permutated gene lists. Data Probe Potential Set Data Type Pearson ID's Edges Edges %Edges 1 Random 0.3 17 28 153 18.30 2 Random 0.3 17 31 153 20.26 3 Random 0.3 17 29 153 18.95 21 Experimental 0.3 17 42 153 27.45 4 Random 0.3 17 13 153 8.50 5 Random 0.3 17 22 153 14.38 6 Random 0.3 17 36 153 23.53 7 Random 0.3 17 27 153 17.65 8 Random 0.3 17 36 153 23.53 1 Random 0.4 17 7 153 4.58 2 Random 0.4 17 15 153 9.80 3 Random 0.4 17 12 153 7.84 21 Experimental 0.4 17 25 153 16.34 4 Random 0.4 17 3 153 1.96 5 Random 0.4 17 11 153 7.19 6 Random 0.4 17 12 153 7.84 7 Random 0.4 17 12 153 7.84 8 Random 0.4 17 16 153 10.46 1 Random 0.5 17 3 153 1.96 2 Random 0.5 17 4 153 2.61 3 Random 0.5 17 6 153 3.92 21 Experimental 0.5 17 16 153 10.46 4 Random 0.5 17 1 153 0.65 5 Random 0.5 17 4 153 2.61 6 Random 0.5 17 2 153 1.31 7 Random 0.5 17 3 153 1.96 8 Random 0.5 17 8 153 5.23 73

Figure 3­3: Plot of mean edge density vs. Pearson correlation threshold. Indicates difference between number of edges at the different correlation thresholds between the experimental gene list and the random gene lists. The mean and standard deviation of the random gene lists are represented

QTL Cluster Map

The WebQTL cluster map of the 17 probe sets showing sexually differential regulation by nicotine in both PN0 and adolescent chips indicates several distinct clusters

( Fig 3­4 ). The genes showing positive correlations in the Correlation Network tend to fall into similar clusters. The set of positively correlated genes, Myh9, Glul, Pim3, and

Rgs10, are all one branch. A second set of positively genes, Atp1a1, Enpp2,and Actb, which are connected to the first set by a positive correlation between Glul and Actb, also branch together. The two positively correlated sets of genes which are connected through

Glul and Actb are more closely related to each other than the other sets of genes (Fig 3­

4). 74

Figure 3­4: Clustering of the genes with sexually differential gene expression based on similarities of QTL analyses using gene expression levels as the phenotype. A strong red color indicates a significant QTL with DBA/2J as the increasing allele. A strong blue color indicates a significant QTL with C57BL/6J as the increasing allele. The cluster dendrogram indicates how close the QTL pattern of a gene is to another gene. Each chromosome is represented and the yellow triangles are where the gene is located on the genome. 75 Transcription Factor Identification

A high positive correlation (.84) of gene expression profiles between Nedd4 and

Kif1a indicated they might share a common transcription factor. Analysis of the promoter regions for these two sequences for over representation of transcription factor binding sites indicated one transcription factor, Pou3f1, whose binding sites showed statistically significant over representation (p­value < 0.001) (13). In situ hybridization done as part of the gene expression nervous system atlas (GENSAT) shows Pou3f1 expression in the region of the brain where the nucleus accumbens is located (Fig 3­5 )

(3). The promoter regions of these two genes, Kif1a and Nedd4, show high degrees of conservation across multiple species (Fig 3­6 ). Each gene contained four transcription factor binding sites for Pou3f1. The sequence containing these four binding sites is found in the regions of the promoter areas showing high degrees of conservation (Fig 3­7 ). 76

Figure 3­5: In situ hybridization results of Pou3f1. The light regions are those expressing Pou3f1. The area surrounded by the white box is the region of the brain where the nucleus accumbens is located. The images were copied with permission from the gene expression nervous system atlas, GENSTAT (www.ncbi.nlm.nih.gov/projects/gensat/). 77

Figure 3­6: Conservation of promoter regions of Nedd4 and Kif1a that contains the binding sites for Pou3f1. The blue peaks indicate regions of strong conservation. Copied with permission from the UCSC genome browser. 78

Kif1a Alignment block 1 of 5 in window, 93010155 ­ 93010249, 95 bps Mouse agggatttagactccaagtgttaacaggtaggtctcaaaaatctcatctggcttttgggccctatcacca Rat agggatctggactccaagtgttaacaggtgggtcttaaaaatgtcttctggcttttgggccagatcacca Human agggatgctggctccgggtg­taacaggtgcg­cggtgaaatcgcatcttgtgtcggggccggggcgccg Alignment block 3 of 5 in window, 93010378 ­ 93010690, 313 bps Mouse cacacggtcactccagcctaaa­tcctcaggacttcaacccacacgct­­­tccctggatcctcgatggc Rat cacactgtcactccagcctaaattccctggacctcaaccctggacgcc­­­tcccaggatcttcaatggc Human c­cccagcctgtccagtttaacttccttggacccccaagtcacacgcc­­­gctcgggggccaccccggc Cow caccctgtttctccagctcagc­tcgttgagac­­caagtcactggcg­­­gccctcggtccacagccgc Elephant caccctgtctctccaacctgactcccctggacc­­­­aaccaggcgccgaggctctgggtccacagtggc Alignment block 3 of 5 in window, 93010378 ­ 93010690, 313 bps Mouse gcagctgggtgaattacaggg­­­­ccaaccacag­cccggggctgagtaactcagctaaggcctatggt Rat gcagctcggtgaactacaggg­­­­ctgaccactg­cccagggctgaataaatcggataaagtctatggt Human gtatctctgtgcgcgccaccgtcctccctccaccgtcccagggctgggcaacttcgacaaagcctctggg Cow gcatctc­­tgcctgccaccggcctccatccacagccccaccgctgggcatttttgacaaagcctccggc Elephant gcatccc­­­­­­­­­­­­­­­­­­ctgccc­­­­­cccagggctgggcagccttgaaaaaccctagggc

Alignment block 5 of 5 in window, 93010767 ­ 93010920, 154 bps Mouse atatcacccgaataagaatac Rat atatcacccgaataaggatgc Human ctatttcctgga­­­­­­­ac

Nedd4 Alignment block 1 of 10 in window, 72693317 ­ 72693500, 184 bps Mouse ttggaattatcatta­­­­­­­­­­­­­­­­­­­­­­­­­tcaaatg­­­­­­­­­tttcggctcataat Rat ttggaattatcgtta­­­­­­­­­­­­­­­­­­­­­­­­­tcaaatg­­­­­­­­­cttcagcgcataat Human ttagaagtatcatcactaatac­­­­­­ctattggatacatcaaatggccaatttgctttactttataat Cow gggggattatcttcactactgctattagctatatga­­­­gctgatg­­­­­­­­­ctttactttataaa

Alignment block 2 of 10 in window, 72693501 ­ 72693647, 147 bps Mouse ccaggtgtggtagtgca­­gttttaatcctgactctcagggagcagtggcaggcagatctctatgagttt Rat ccaggtgtggtagaacatggttttaatcctggctctcagggagcagtagcaggcagatctctgtgagttt

Alignment block 9 of 10 in window, 72694243 ­ 72694314, 72 bps Mouse tcaaacggtgactagtttagagttcagaattagtttaattctcttggggaaaactaaaataagatctgta Rat ttacacggtgactagt­­­­aattcagaattagtttaattctcttgggaaaaattaaaa­­­­­­­­­­­ Human ttaaactgttaaaaat­­­­aaatcagagttattctgaaatcattgaggaaaataaaaattagattttga Cow ctgcacttgaacttgt­­­­­­­­­­­­­ttattctgatttccttaaggaaaatttaaattagattt­­­

Alignment block 10 of 10 in window, 72694315 ­ 72694499, 185 bps Mouse taat­­­­ctccttagctatctcgcggcccctgcaatcggattttgagagtctaatttctgc Rat ttatttacctccttagctatgtcgcggcccctgcaattagattttgagagtctaatttccgc

Figure 3­7: Conservation of Pou3f1 binding sites in the Kif1a and Nedd4 promoter regions. The alignments showing the conservation of each Pou3f1 binding site is given in red text. Generated using the UCSC genome browser.

Gene Network and Microarray Data from Adolescent Mouse Experiment 79 A color representation of fold change between nicotine­treated and control animals was imported onto the main Network generated by WebQTL. This visualization further highlights the relationship between the genes. Of greatest interest for long term follow up is the node containing Kif1a, Nedd4, Glul, and Myh9. Kif1a and Nedd4 show a correlation in untreated animals which remains in the nicotine treated animals (Fig 3­8,

Fig 3­9). The behavior of these two genes in response to nicotine is opposite in males and females. The two genes also maintain their negative correlation with Glul, however the correlation between Myh9 and the other three genes reverses in the presence of nicotine (Fig 3­8, Fig 3­9). 80

Figure 3­8: Gene Network generated by WebQTL with arbitrary color representations of fold change differences from the adolescent female chips prenatally exposed to nicotine at 200 µg/ml (A) and 50 µg/ml (B). The red indicates genes that are increased by nicotine and the green indicates genes that are decreased by nicotine. 81

Figure 3­9: : Gene Network generated by WebQTL with arbitrary color representations of fold change differences from the adolescent male chips prenatally exposed to nicotine at 200 µg/mg (A) and 50 µg/mg (B). The red indicates genes that are increased by nicotine and the green indicates genes that are decreased by nicotine.

Discussion

Making biological sense of the data generated from microarrays is often the most difficult task in a microarray experiment. Commercially available chips contain thousands of probe sets and recent advances in the technology have allowed the 82 placement of whole genomes on a single chip. The volume of data generated makes it difficult to parse both biological variation from random variance and real signal differences from false positives. Taking advantage of databases that compile published information makes this task more manageable.

One such database, WebQTL, is an online resource containing microarray data from multiple RI lines. The website contains expression data on whole brain mRNA extracted for several DBAxC57 derived RI lines. The same Affymetrix chip (MOE430a) was used to generate these data as the adolescent nicotine animals in this study. Using the website a correlation network was created for the Affymetrix probe sets shown to be differentially regulated by sex in the adolescent animals. These correlations are based on gene expression between RI strains not exposed to nicotine. High correlations across the panel of RIs may indicate involvement in the same pathways or regulation by the same transcription factors. The large number of edges generated by the 0.5 correlation (Fig 2) indicates a large portion of the genes identified as having sexually differential response to nicotine are part of a single network.

A color representation of fold changes in gene expression is a commonly used tool; green for down regulation and red for up regulation. In the color representation the brighter the color the greater the fold change. Representing a number with color makes it easier to recognize patterns and is often done when clustering long lists of genes.

Similarly, the color representation of the fold change between the control chips and the experimental chips has been imported onto the main correlation network of ten genes.

Genes that are positively correlated in network would be expected to be showing similar responses to nicotine. In some gene pairs this is the case, such as in the relationship 83 between Nedd4 and Kif1a and their relationship to Glul. Nedd4 and Kif1a are positively correlated in the correlation network and both genes are up regulated by nicotine in females and down regulated by nicotine in males (Fig 3­8, Fig 3­9). These genes are negatively correlated to Glul and Glul is regulated in an opposite pattern, being down regulated in females and up regulated in males. Not all genes maintain the expected relationship in response to nicotine. Myh9, for example, is positively correlated to Glul’s regulation and negatively correlated to Kif1 and Nedd4, yet it is regulated in response to nicotine more similarly to the latter genes rather than the former (Fig 3­8, Fig 3­9).

The deviation of the nicotine regulation from what one would expect based on the correlation network indicates nicotine is not affecting the genes in a unified manner. If all the genes are part of a network, and nicotine was changing the regulation of one of the primary genes in the network, one possible expectation is a cascade of alterations in the later genes and for the nicotine regulation to correspond with the correlation values. It is possible, however, that nicotine is affecting a gene later in the process and thus only the genes in the cascade after the regulated gene would be affected or nicotine is acting on a feedback loop and the genes are being regulated in opposition to each other. This form of nicotine regulation could cause a disruption of gene expression correlations seen in non­ exposed animals. The possibility also exists that nicotine affects multiple genes in the network, each in a different manner, or that the correlations are spurious or due to something other then involvement in the same pathway.

Of the ten genes connected in the correlation network, Nedd4 and Kif1a have the highest correlation between probe sets that do not represent the same gene (Fig 3­2 ).

The only higher correlation is between two probe sets representing the same gene, 84 Atp1a1. Focusing on the genes with the largest correlation increases the likelihood that these genes are regulated by the same transcription factors or are members of a biological cascade. An example of a biological cascade would be the activation of one gene in turn leads to the activation of the second gene.

In addition to having a high positive correlation, Kif1a and Nedd4 clustered on the same branch in a QTL cluster map (Fig 3­4 ). One option of WebQTL generates a

QTL analysis using the transcription levels of the gene in the separate RI lines as the phenotype. This analysis indicates chromosomal regions where the genotype in the region (B6 or D2) is significantly related to the transcription levels of a gene. The region most likely contains a transcription element that controls regulation of the gene or indicates a polymorphism within the gene itself that changes its transcription levels. The

QTL cluster map pairs genes which have similar chromosomal regions showing statistically significant control of their transcription. The high correlation of Nedd4 and

Kif1a, plus the pairing in the QTL cluster map, support the genes’ involvement in the same pathway and gives evidence that they share similar transcription factors rather than being part of a cascade event.

An analysis of the promoter region of the two genes, 2000 bp upstream of the first exon, indicated one transcription factor binding site, Pou3f1, that was statically over represented (p­value < 0.001). Pou3f1 is a class III POU domain transcription factor part of a family of transcription factors that bind to a POU domain in the DNA sequence.

This transcription factor is active during development and shows sexually differential regulation patterns (2, 6). Whether Pou3f1 is expressed in an adult animal is less clear. It is the age and location of expression that appears to be different between the sexes. 85 Published in situ hybridization data also indicates the transcription factor is active in the region of the nucleus accumbens at PN7 (Fig 3­5 ). The transcription factor binding sites for Pou3f1 in the promoter region of both genes, Kif1a and Nedd4, are in heavily conserved areas (Fig 3­7 ). Chromosomal regions conserved between species are usually maintained through positive selection and indicate a region of chromosome important in gene function (9).

Combining data from the PN0 and adolescent microarrays narrowed the focus of the analysis from thousands of probe sets to a list of 17 and decreased the chance of that list containing false positives. Further analysis of the 17 genes using publicly available data further narrowed the list of candidate genes from 17 to 2. In addition, the analysis drew attention to a transcription factor, that was not part of the original gene set, that may be mediating the differential regulation of the genes by nicotine. This type of analysis solves one of the difficulties of microarray experiments which focus primarily on genes whose expression is altered and cannot identify proteins whose functions may be altered.

The computation analysis also provides a reasonably sized gene list for future molecular experiments; such a list would have otherwise taken considerable time and money to identify. 86

References

1. Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, and Sherlock G. GO:TermFinder­­open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics 20: 3710­3715, 2004. 2. Frantz GD, Bohner AP, Akers RM, and McConnell SK. Regulation of the POU domain gene SCIP during cerebral cortical development. J Neurosci 14: 472­485, 1994. 3. Gong S, Zheng C, Doughty ML, Losos K, Didkovsky N, Schambra UB, Nowak NJ, Joyner A, Leblanc G, Hatten ME, and Heintz N. A gene expression atlas of the central nervous system based on bacterial artificial chromosomes. Nature 425: 917­925, 2003. 4. Hanai T, Hamada H, and Okamoto M. Application of bioinformatics for DNA microarray data to bioscience, bioengineering and medical fields. J Biosci Bioeng 101: 377­384, 2006. 5. Hogg RC, Raggenbass M, and Bertrand D. Nicotinic acetylcholine receptors: from structure to brain function. Rev Physiol Biochem Pharmacol 147: 1­46, 2003. 6. Ilia M, Sugiyama Y, and Price J. Gender and age related expression of Oct­6­­a POU III domain transcription factor, in the adult mouse brain. Neurosci Lett 344: 138­ 140, 2003. 7. Katz S, Irizarry RA, Lin X, Tripputi M, and Porter MW. A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database. BMC Bioinformatics 7: 464, 2006. 8. Vandenbergh D. Manuscript in Progress. 9. Vavouri T, Walter K, Gilks WR, Lehner B, and Elgar G. Parallel evolution of conserved noncoding elements that target a common set of developmental regulatory genes from worms to humans. Genome Biol 8: R15, 2007. 10. Voy BH, Scharff JA, Perkins AD, Saxton AM, Borate B, Chesler EJ, Branstetter LK, and Langston MA. Extracting gene networks for low­dose radiation using graph theoretical algorithms. PLoS Comput Biol 2: e89, 2006. 11. Wang J, Williams RW, and Manly KF. WebQTL: web­based complex trait analysis. Neuroinformatics 1: 299­308, 2003. 12. Whetzel PL, Parkinson H, and Stoeckert CJ, Jr. Using ontologies to annotate microarray experiments. Methods Enzymol 411: 325­339, 2006. 13. Zheng J, Wu J, and Sun Z. An approach to identify over­represented cis­ elements in related sequences. Nucleic Acids Res 31: 1995­2005, 2003. Chapter 4

Importance of Kif1a and Pou3f1 and a Subset of Genes from a Gene Network that is Differentially Regulated by Nicotine in Males and Females

Introduction

Quantitative real time Polymerase Chain Reaction (qrtPCR) is considered the gold standard for gene expression quantification. This method was employed to determine the accuracy of the previously described microarray experiments. During this process, two of the five genes under investigation (Pou3f1 and Kif1a) became of particular interest.

Both genes are important in normal neural development (5, 21).

Pou3f1 is also known as Oct6, Tst­1, and SKIP. It is a class III member of the POU domain transcription factors. All members of this family contain POU domains, which are DNA­binding sites in the protein that recognize the sequence motif

ATGCAAAT. The POU domain contains two sub­domains that are connected by a flexible linker. These subdomains (POU­specific and POU homeo­domain) can contact the DNA independently of each other and increase the flexibility of the binding site (18). This flexibility allows the protein to take on different conformations depending on the DNA motif and bind different cofactors (15). A unique aspect of Pou3f1 is its ability to affect transcription of its target gene even when the POU sequence motif has been removed from the promoter region; therefore the POU binding site is not required for Pou3f1 mediated transcription control. This ability is thought to be due to protein­protein 88 interactions with coactivators. Pou3f1 function in the peripheral nervous system is well characterized but its function in the central nervous system is less clear.

The second protein of interest, Kif1a, is a member of the kinesin superfamily.

Kinesins are motor proteins that transport cargo, such as presynaptic vesicles, along microtubules from the cell body to the synapse (8). Different kinesins transport different cargos such as synaptic vesicles, lysosomes, and mitochondria (8, 12, 14). In one type of

Kinesin, Kif1b, a splice­variant in the gene alters the cargo of the resulting protein from mitochondria to lysosomes (8, 12). Most kinesins contain a region just past their motor domain consisting of coiled­coil repeats that allow the proteins to form homodimers. The homodimer conformation, of two light chains and two heavy chains, allows the protein to move processively along microtubules using a hand­over­hand translocation. In this manner one foot of the protein is always in contact with the microtubule, and therefore the protein does not lose contact and diffuse away from the microtubule (16). Kif1a, however, has been identified as a monomeric protein based on the sequence of its neck region where it lacks the coiled­coil repeats that allow other kinesins to homodimerize

(8). How Kif1a moves along the microtubules is an area of intense research with new evidence indicating potential homodimerization of the protein (16).

The results described here demonstrate nicotine regulation of mRNA for both of these proteins as well as identify cell specific expression of transcripts. These results increase our knowledge of Pou3f1’s function in the central nervous system and characterize the genetics of an alternative splice variant for Kif1a. The Pou3f1 was observed to be glia specific which may indicate it performs a similar role in the central nervous system as it does in the peripheral nervous system. In addition the Kif1a splice 89 variant was found to be neuron specific which could have important implications for how its function differs from that of the previously identified variant that was present in both neuron and glia cells.

Materials and Methods

Gene Expression

RNA was isolated from a panel of somatic tissues and male testes, as well as from several cell lines using a standard Qiagen RNA isolation kit. Total RNA from Human brain caudate nucleus (#636566) and nucleus accumbens (#636569) was obtained from

Clontech. cDNA was then generated using the High Capacity cDNA Archive Kit

(Applied Biosystems, Foster City). The cDNA was amplified using either the Hot Start method or RockStart Buffer (1). The Hot Start Method created a top and bottom mix of

PCR components. The top mix has a final volume of 5 µl and contained 0.5 µl 10x

Buffer (Sigma­Aldrich, St. Louis), and 1 µl Taq. The bottom mix had a final volume of

20 µl and contained 2 µl 10x Buffer (Sigma­Aldrich, St. Louis), 0.5 µl 10mM dNTPs, 2.5

µl 25 µM MgCl2, 2.5 µl 5 µM Left Primer, 2.5 µl 5 µM Right Primer (1.25 µM ) and 2 µl cDNA. The bottom mix is brought to 72ºC, at which point the top mix is added to create a 25 µl final volume, and the PCR reaction is allowed to continue as normal. The

RockStart (RS) method has a final volume of 20 µl and contains 2 µl RS Buffer, 2 µl 35

µM MgCl2, 0.4 µl 10mM dNTPs, 1 µl Taq, 1.2 µl 5 µM Forward Primer, 1.2 µl 5 µM

Reverse Primer, and 2 µl cDNA. Primer pairs are outlined in Table 4­1 . The same 90 thermocycler program was followed for both amplification procedures. After an initial denaturation step at 94°C for 5 minutes, 30 cycles were carried out at 94°C for 30 seconds (sec), 62°C for 60 sec, and 72°C for 90 sec, followed by a final extension step of

72° C for 7 minutes. The PCR products were then separated by size by gel electrophoresis on 5% polyacrylamide borate gels at 150 volts for approximately 1 hour.

Primer sequences and expected band sizes can be seen in Table 4­1 and Table 4­2 . 91

Table 4­1: Primer Names and Sequences Primer Name Orientation Primer Sequence 5'­3' mGlulF Fwd GATGGCCCTACTGTGAAGGA mGlulR Rev CATCCTCACCCCTACCTCAA mNeddF Fwd CCAGGTCATCCACTGGTTCT mNedd4R Rev TCGTCAAAGGATTCGTAGGG mKif1aF Fwd GACCCCAACAACAAGCTGAT mKif1aR Rev AAGATGCGCTCATGGAGACT mMyh9F Fwd TTCTCCAAGGTGGAGGACAT mMyh9R Rev TCTGTGATGGCGTAGATGTG mrhPou3f1F Fwd AGTTCGCCAAGCAGTTCAAG mrhPou3f1R Rev GTCTCCTCCAGCCACTTGTT GlulFn Fwd GTTGGCATTTCTTGGTCCTC GlulRn Rev GCACCCAGTGAATAGGATGG Nedd4Fn Fwd GGCTGTTTGGATGATGGATT Nedd4Rn Rev AGGATTGTGGTCCATTCGAG Kif1aFn Fwd AGACCTGCTGTATGCCCAAG Kif1aRn Rev GGACAGGGCTGAGAGTGAAG Myh9Fn Fwd GCCTGTTCTGTGTGGTCATC Myh9Rn Rev GGGTTGATGACCACACAGAA Pou3f1Fn Fwd GTGTTCTCGCAGACCACCAT Pou3f1Rn Rev TGGTCTGCGAGAACACGTTA GlulFn Fwd GCGAAGACTTTGGGGTGATA GlulRn Rev CAGTTTGTCAATGGCCTCCT hNedd4F Fwd CAGCCCATCTGAGAGGAAAG hNedd4R Rev CGAGGATCTTCCCATTGTGT hKif1aF Fwd CTCAGCCGATGAAGTCAACA hKif1aR Rev GTCACACGGAAGGTGAAGGT hMyh9F Fwd GATCATCCTGGAGGACCAGA hMyh9R Rev TTGCCTCATGCTTGTTCTTG hGlulF Fwd TGAGGGTTCCAACAGTGACA hGlulR Rev GGTGTGCCTCAAATTGGTCT mKif1a(1286)F Fwd GCGATATCACTGACATGACCA mKif1a(10a)F Fwd AACACTGTGCCCGGAGGA mKif1a(1484)R Rev AGCTTCTCCTCCCAGGTCTC hKif1a(1312)F Fwd GCGACATCACTGACATGACCA hKif1a(10a)F Fwd AACACTGTGCCTGGAGGA hKif1a13a Fwd CTGGAGGACCCAAATTGACC hKif1a13ab Fwd CCTGGAGGACCCAAATACG 92

Table 4­2: Primer Pairs and Product Sizes Primers Product Size Intron Spanning MGlulF/mGlulR 235bp no mNeddF/mNeddR 237bp no mKif1aF/mKif1aR 188bp, 215bp no mMyh9F/mMyh9R 227bp no mrhPou3f1F/mrhPou3f1R 190bp no GlulFn/GlulRn 144bp yes GlulFn/mGlulR 207bp yes mGlulF/GlulRn 172bp yes Nedd4Fn/Nedd4Rn 119bp yes mNedd4F/Nedd4Rn 213bp yes Nedd4Fn/mNeddR 143bp yes Kif1aFn/mKif1aR 135bp, 162bp yes mKif1aF/Kif1aRn 147bp, 174bp yes Myh9Fn/mMyh9R 121bp yes mMyh9F/Myh9Rn 131bp yes Pou3f1Fn/mPou3f1R 102bp yes mPou3f1F/Pou3f1Rn 105bp yes hNedd4F/hNedd4R 145bp yes hKif1aF/hKif1aR 150bp yes hMyh9F/hMyh9R 143bp yes hGlulF/hGlulR 139bp yes mKif1a(1286)F/mKif1a(1484)R 199bp yes mKif1a(10a)F/mKif1a(1484)R 212bp yes hKif1a(1312)F/mKif1a(1484)R 199bp yes hKif1a(10a)F/mKif1a(1484)R 209bp, 284bp yes hKif1a13a/mKif1a(1484)R 199bp yes hKif1a13ab/mKif1a(1484)R 275bp yes

Nicotine Regulation

Gt1­7 and Lymphoblast cell lines were nicotine­treated for four different time exposures: 0 hrs, 1 hrs, 2 hrs, and 24 hrs. Four sets of nearly confluent cells were obtained from existing cell cultures and the RNA was isolated from each condition at the same time. Nicotine (200 mM) was added to the plates 24, 2, and 1 hour(s) before 93 extraction time. The RNA was isolated by draining off the medium and adding 1 ml of

Qiazol directly to the plate. The plate was then scraped, and the Qiazol was pipetted up and down with a 1000 µl pipetteman then placed in tubes on ice. A standard Qiazol extraction protocol was followed for the remainder of the procedure.

qrtPCR

Quantitative Real Time Polymerase Chain Reaction (qrtPCR) was performed on the cDNA from the nicotine­treated cell lines, and on the original cDNA from the adolescent mice that was used on 430A Affymetrix Microarrays. Pools of cDNA identical to those used on the male adolescent chips were tested, but, due to limited materials for the female samples, pools identical to the female chips could not be replicated. Animals from the appropriate experimental group were used but each animal was not represented equally as in the microarray samples. IQ SYBR Green supermix

(Bio­Rad) was used for the reactions. The expression of Pou3f1, Nedd4, Kif1a, Glul and

Myh9 was tested for each experimental condition except for the nicotine­treated lymphoblast cells which were not tested for Pou3f1 expression. The experiments were performed in triplicate. The housekeeping gene Glyceraldehyde­3­phosphate dehydrogenase (Gapdh) was used as a control with each experiment. The expression levels of the genes of interest were normalized to the Gapdh standard. 94 Results

Gene Expression Patterns

Gene expression of Pou3f1, Nedd4, Kif1a, Glul, and Myh9 was determined in a panel of somatic tissues, including neuronal and embryonic samples. The panel was amplified once for each primer set. Negative results were verified by a replication of the amplification. Pou3f1 was differentially­expressed across the brain regions, present in embryonic tissues, but not present in body tissues (Fig 4­1 and Fig 4­2). The strongest expression occurred in brain tissues isolated from the frontal cortex, caudate­putamen, and motor cortex. The nucleus accumbens signal was equal to that of the caudate putamen but is not pictured here. The cerebellum has the lowest signal strength of all the brain tissue samples taken. The embryonic tissue exhibit strong Pou3f1 expression as well but most of these have significant populations of neurons. The E12 body that does not include the spinal cord has a low intensity band and the E15 liver indicates no expression. The only non­neuronal tissues that indicate expression are the male testes, and E15 placenta. Nedd4, Glul, and Myh 9 were ubiquitously expressed in all tissues

(Fig 4­1 and Fig 4­2). The one lane of Glul that exhibits no expression is due to failed

PCR reaction rather than a lack of Glul expression in male motor cortex, as demonstrated by a repeat of the PCR experiment (data not shown). Kif1a was ubiquitous in the brain tissues and showed regional expression in the body tissues (Fig 4­3 and Fig 4­4). The primer set for Kif1a generated four bands rather than the expected one band. Band D was the expected fragment size. Bands A, B, and C were unexpected and larger than the 147 bp predicted sequence (Fig 4­3). The four band pattern appeared only in the neural 95 tissues. The PCR reaction for the E9 body contained only one fragment but this fragment was larger than the expected size predicted from published sequence.

Figure 4­1: Expression profile of Pou3f1 (A), Nedd4 (B), Glul (C), and Myh9 (D) for the adrenal gland ♂ (1), liver ♂ (2), liver ♀ (3), kidney ♂ (4), kidney ♀ (5), testes ♂ (6), E9 head (7), E9 body (8), E12 head (9), E12 head (10), H12 body minus spinal cord (10), E15 head (11), E15 liver (12), E15 placenta (13), olfactory bulb ♂ (14), olfactory bulb ♀ (15), frontal cortex ♂ (16), frontal cortex ♀ (17), and caudate putamen ♂ (18).

Figure 4­2: Expression profile of Pou3f1 (A), Nedd4 (B), Glul (C), and Myh9 (D) for the caudate putamen ♀ (19), motor cortex ♂ (20), motor cortex ♀ (21), hypothalamus ♂ (22), hypothalamus ♀ (23), thalamus ♂ (24), thalamus ♀ (25), midbrain ♂ (26), midbrain ♀ (27), brain stem ♂ (28), brain stem ♀ (29), cerebellum ♂ (30), cerebellum ♀ (31), spinal cord ♂ (32), and spinal cord ♀ (33).. 96

Figure 4­3: Expression profile of Kif1a for the adrenal gland ♂ (1), liver ♂ (2), liver ♀ (3), kidney ♂ (4), kidney ♀ (5), testes ♂ (6), E9 head (7), E9 body (8), E12 head (9), E12 head (10), H12 body minus spinal cord (10), E15 head (11), E15 liver (12), E15 placenta (13), olfactory bulb ♂ (14), olfactory bulb ♀ (15), frontal cortex ♂ (16), frontal cortex ♀ (17), and caudate putamen ♂ (18). Band D is of the expect sized based on primer sequences and published exon specifications. Band’s A­C are not predicted based on published sequence information for Kif1a. 97

Figure 4­4: Expression profile of Kif1a for the caudate putamen ♀ (19), motor cortex ♂ (20), motor cortex ♀ (21), hypothalamus ♂ (22), hypothalamus ♀ (23), thalamus ♂ (24), thalamus ♀ (25), midbrain ♂ (26), midbrain ♀ (27), brain stem ♂ (28), brain stem ♀ (29), cerebellum ♂ (30), cerebellum ♀ (31), spinal cord ♂ (32), and spinal cord ♀ (33)

Of the four products generated by the Kif1a primers that were visible as bands by gel electrophoresis, the two larger fragments were not found in any plasmid isolates when cloning was attempted. The inability to clone these fragments indicated they were not simple DNA fragments. Given that larger fragment sizes as well as non­linear fragments would migrate at a slower pace through the gel it was hypothesized that the larger bands were due to non­linear formations caused when the DNA re­annealed. This hypothesis was supported by bacterial colonies that contained fragments of both sizes. The colony composition would only be able to occur if two colonies grew in close proximity and were sampled as one or if a double stranded DNA picked up by a bacterial plasmid 98 contained a fragment of each size. In a second scenario when the bacteria replicates its

DNA the plasmid would split and create two new plasmids, each containing one of the

DNA fragments of different sizes. A melting and renaturation experiment containing bands C and D (Fig 4­3) indicated a reaction containing only these two DNA fragments could regenerate the original four band patter (data not shown). Based on these experiments it was concluded that the upper bands were artifacts caused by the two different size fragments with similar sequence, binding imperfectly and creating loops, causing them to run through the gel at a slower pace.

The sequence for DNA extracted from the two smaller (C & D, Fig 4­3) Kif1a fragments was determined ( Fig 4­5 ). DNA from the lower bands were shown to be sequence­concurrent with Kif1a. Band D, which was the expected fragment length, contained DNA that matched sequence from Kif1a exons 13 and 14. Band C also contained DNA that matched sequence from Kif1a exons 13 and 14 but also included a small portion of sequence matching the intron between the exons ( Fig 4­6 ).

A Mouse Sequence (13a)CCAACACTGTGCCCGGAGGACCCAAAT

B. Human Sequence (13a)CCAACACTGTGCCTGGAGGACCCAAAT/(14’)ACGTGTCCGACCTTGAGAA CAATAACCTTAACCGTGGCGGGACGGTGAATGAAGCCCCTGACCCTCTCTCC ACAG/(14)TGACCAATGCCCTGGTGGGTATGAGCCCCTCATCCTCGCTCTCAGC CCTGTCCAGCCGCGCGGCCTCCGTGTCCAGCCTCCACGAGCGCATCTTGTTTG CCCCGGGCAGCGAGGAGGCCATTGAAAGACTGAAGGAAACAGAGAAGATCA TAGCTGAGCTCAATGAGACCTGGGAGGAGAAGCT Figure 4­5: Sequence for Kif1a splice variants in human and mouse 99

Figure 4­6: Schematic of where Kif1a splice variant fits into the Kif1a gene.

The additional sequence was 27 base pairs. This new sequence was labeled exon

13a; it creates an in­frame insertion of 9 amino acids. Species conservation of the alternatively spliced exon was demonstrated with the UCSC genome browser (Fig 4­7 ).

Because of the large degree of conservation, it was hypothesized that a similar variant might exist in humans and primers from the region containing exon 13a were created from human sequence and used to amplify human cDNA. The 13a splice variant is present in the human nucleus accumbens and caudate putamen (Fig 4­8 ). Further investigation of this region of the human KIF1a gene indicated the existence of an additional splice event in humans that is not present in the mice. The second alternative splice found in humans contains Exon 13a in addition to an extended Exon 14 (14’). 100

Figure 4­7: This image was generated by focusing on the base pair view in the UCSC mouse genome browser of the Kif1a region containing exon 13a. The large blue peak indicates species conservation directly over the sequence for exon 13a. Image copied from http://genome.ucsc.edu. 101

Figure 4­8: Gel images of Kif1a splice variants in human samples. Panel A: Lane 1­1 NAcc Kif1a crossing 13/14 junction. Lane 1­2 NAcc Kif1a from within Exon 13a. Lane 1­3 Caudate crossing 13/14 junction Lane 1­4 Caudate from within Exon 13a. Panel B: The fragments are from (­1) NAcc, (­2) CP, (­3) Placenta and (­4) Lymphoblast cDNA samples amplified with two different sets of primers. The 1­n contain fragments amplified with primers Kif1a 14’ and the 2­n contain fragments amplified with primers from Kif1a crossing 13/14 intron junction.

Cell Lines

By taking advantage of pre­existing cell lines, direct in vitro experiments on nicotine regulation and expression properties of the genes could be conducted. To begin this process cell lines expressing the genes of interest had to be identified. Literature searches indicated Gt1­7 cells expressed Pou3f1. Gt1­7 cells were obtained, along with several other cell lines, to confirm the literature findings and test for the presence of

Pou3f1. RNA was extracted from nine rodent cell lines (L929, Hepa1c17, AtT20, MEL, 102 BMK­K1, BMK­x2, Gt1­7, Rat, and PC12) and two human cell lines (Lymphoblast and

SK­N­Be). cDNA was then created from the RNA and the samples were PCR amplified to check the expression of the five genes of interest (Pou3f1, Nedd4, Kif1a, Glul, and

Myh9). The entire panel was amplified once for each primer set. Amplification for the

GT1­7 and lymphoblast cell lines was replicated for each primer set. SK­N­Be amplification was replicated for the Pou3f1 and Kif1a primer sets.

SK­N­BE cells can be induced to differentiate towards a neuronal phenotype or a glial phenotype by adding either Retinoic Acid (RA) or 5­bromodeoxyuridine (BrdU), respectively (3, 7, 13). Pou3f1 has been characterized in the peripheral nervous system as being essential in the myelination process of Schwann cells but its expression pattern in the nervous system is less clear (6). Evidence indicates Pou3f1 is expressed in immature neurons migrating from the ventricular zone (4) and in regions of the adult mouse brain but whether the adult expression is glial or neuronal in nature has not be defined (9). In addition it is not known whether the newly identified Kif1a splice variants have a similar expression pattern to the known Kif1a variant or they are expressed in a different manner. To address these questions Pou3f1 and Kif1a expression was identified in SK­N­Be cell and the expression of the Kif1a splice variant without 13a, with 13a, and with 13a & 14’ were examined.

Pou3f1 expression was increased in cell populations that had been differentiated towards a glial phenotype and decreased in cell populations differentiated towards a neuronal phenotype ( Fig 4­9 ). This result coincides with the vast majority of the literature indicating the Pou3f1 protein is active in glial cells. The Kif1a variant without exon 13a was equally expressed in basal, neuron like, and glia like SK­N­Be cells. The 103 alternative splice containing Exon 13a maintained expression or was increased in cell populations differentiated to have a neural phenotype (Fig 4­9, Band A). A more sensitive quantification method, such as qrtPCR, would be needed to differentiate between the expression levels of this variant in non differentiated cells and neuronally differentiated cells. The 13a variant, however, was clearly down regulated in the glia like

SK­N­Be cells (Fig 4­9, Band A). The band representing the splice variant containing

13a and the extended exon 14 (14’) indicated a similar pattern to the exon 13a variant, but expression levels were too low to assess accurately in the end point assay used (Fig 4­

9, Band B).

Figure 4­9: 1­n shows Pou3f1 expression. 2­n shows the Kif1a splice variant without 13a. 3­n shows Kif1a splice variant with (A)13a and with (B) 13a & 14’. Sk­N­BE N­1. Basal, N­2.Glial Like, and N­3. Neuronal like. 104 In the other ten cell lines Pou3f1 and Kif1a expression was cell line specific

(Fig 4­10). Nedd4, Glul, and Myh9 were ubiquitously expressed in all cell lines (Fig 4­

10 ). The Gt1­7 cells and the lymphoblast cells indicated expression of all five genes, and were chosen for nicotine regulation experiments because of evidence of nicotinic receptor expression. This evidence was gathered from data in the literature (11) and unpublished data from Weirman et al., University of Colorado Health Science Center, indicating expression of the nicotinic receptor subtypes in the Gt1­7 cells.

Figure 4­10: Expression of ActB (A), Pou3f1 (B), Nedd4 (C), Glul (D), Myh9 (E), and Kif1a (F) in a series of mouse cell lines. Panel B. The lanes contain PCR fragments amplified from cDNA of the following cell lines; L929 (1), Hepa1c17 (2), AtT20 (3), MEL (4), BMK­K1 (5), BMK­X2 (6), GT1­7 (7), Rat (8), PC12 (9), and a NAcc control (10). The NAcc control was not run for the ActB (A), or Pou3f1 (B) primer sets.

Nicotine Regulation

Partial verification of the array data from the adolescent mice for the five genes was obtained ( Fig 4­11 ). The samples obtained from the male mice gave the closest replication of the expression patterns seen on the microarrays, however, with lower fold changes detected. The one exception was Nedd4, where the qrtPCR experiment did not 105 detect any nicotine regulation. The female samples that qrtPCR was conducted on did not match the previous microarray results except for the Glul expression in the 200 µg/ml group and the Nedd4 expression in the 50 µg/ml group. In addition, though Pou3f1 did not demonstrate nicotine regulation in the adolescent microarrays, qrtPCR analysis indicates this gene is sexually differentially regulated by nicotine. The females prenatally exposed to 50 µg/ml of nicotine indicated a 3.7 fold increase in expression. In the prenatally exposed males the gene’s expression was decreased in both exposure groups (

Fig 4­12 ).

Figure 4­11: Log2 transformed response to nicotine results from the qrtPCR experiments compared the microarray data for the four genes of interest. The log2 transformation puts ratios of increasing and decreasing fold changes on the same scale. 106

Figure 4­12: Nicotine regulation of Pou3f1 in the adolescent nucleus Accumbens samples identified using qrtPCR.

In an attempt to determine if nicotine directly regulated gene expression or if expression changes were due to an indirect effect of nicotine’s actions, regulation was tested in two cell lines, Gt1­7 and Lymphoblasts. Of the five genes of interest, none were nicotine­regulated in the Gt1­7 cells and only Kif1a indicated regulation in the lymphoblast cell line ( Fig 4­13 ). Though GT1­7 expresses mRNA for nicotinic receptors, expression of functional receptors has not been demonstrated; therefore it is possible the lack of regulation was due to the absence of functional receptors, rather than no direct action of nicotine. In the lymphoblast cells the Kif1a signal increased after 1 and 2 hours of nicotine exposure, and then returned to basal levels after 24 hours of 107 exposure (Fig 4­13). This result indicates that nicotine can have a direct action on the regulation of Kif1a expression.

Figure 4­13: qrtPCR results of RNA isolated from Lymphoblast cells exposed to nicotine for 0, 1, 2 and 24 hours.

Conclusion

Molecular analyses of the five genes of interest identified by their sexually differential response to nicotine, and by computational investigations, were not as straightforward as expected. First, the qrtPCR results for the genes identified as sexually differentially regulated by nicotine on the microarrays did not replicate array findings

(Fig 4­12 ). The male expression indicated the same general patterns but did not exhibit the same degree of expression alterations that were seen in the microarray results. The female expression was variable in regards to its similarity to the microarray data, at times showing opposite trends. The cDNA used for the male qrtPCR was the same cDNA that 108 was used to generate the male adolescent chips, whereas the cDNA used for the female qrtPCR was new cDNA generated from stored RNA. Due to the limited nature of the

RNA, pools of RNA identical to those used for the female adolescent chips could not be created. RNA from animals given the same prenatal exposure and preference designation was used, but, within those animal sets, there were variations in cotinine levels and nicotine consumed on the day of sacrifice (Table 4­1 and Table 4­2). The greater precision of the male qrtPCR results is probably due to the replicate samples used. The deviation of the female data could be due to technical variability introduced during cDNA generation, or to biological variability resulting from the different RNA ratios in the pools.

In addition, Pou3f1, which did not demonstrate any regulation by nicotine on the adolescent microarray chips, indicated sexually differential regulation by nicotine in the qrtPCR results (Fig 4­13). Gene expression was increased 2.7 fold in the females prenatally exposed to 50 µg/ml, and down regulated 1.4 fold and 1.2 fold in the males prenatally exposed to 200 µg/ml and 50 µg/ml of nicotine respectively. The female up­ regulation is even more dramatic when the 200 µg/ml prenatal group is further separated into preference groups; the low preference females having a 3.7 fold increase and the no­ preference females having a 1.9 fold increase. This result was unexpected due to the lack of nicotine regulation demonstrated by the original Adolescent Microarrays treated with cDNA from the same tissue samples.

The lack of consistency between these two experiments may be explained by errors in the two Probe sets used to identify the Pou3f1 gene on the Affymetrix 430A chips. A blast comparison of the sequence used to generate one of the probe sets indicated that this 109 sequence aligned with a POU­like gene and not Pou3f1 itself (data not shown). The sequence used to generate the second probe set was from the appropriate genomic region but investigation of the performance of each probe in that probe set showed large variations between probes and often greater expression levels of the mismatched set (data not shown). This evidence indicates that data for Pou3f1 on the adolescent microarray chips were not reliable.

The function of Pou3f1 (Oct6, Tst­1, SKIP) is best identified by its role in differentiating Schwann cells and Schwann cell myelination (10). Its role in the CNS is not well documented and much of the research in the CNS was generated in a laboratory that later withdrew portions of its data (2). The gene is known to be developmentally expressed, which can be seen in our E9, E12, and E15 cDNA samples. In our samples, the gene was also expressed in all the adolescent (Fig 4­1 and Fig 4­2) brain regions tested, with the highest expression levels in the Frontal Cortex, Caudate Putamen, and

Motor Cortex (Fig 4­1 and Fig 4­2). The strength of signal corresponds with earlier literature where post­natal expression has been shown in sub­populations of neurons in the Cerebral Cortex (layers V and II/III) (9). In addition, expression in the Nucleus

Accumbens was identified in the adolescent cDNA samples (data not shown).

Expression of Pou3f1 in the Accumbens and other regions of the limbic system (the CA1 field of the hippocampus (9), hypothalamus) may indicate this gene is in part responsible for the proper development of this brain region. Given the importance of the limbic system in the rewarding properties of drugs of abuse, alterations in this brain region could affect response to drug stimulus. Therefore prenatal regulation of Pou3f1 by nicotine may be important in behavioral changes seen later in life. 110 Investigation of both male and female tissue did not demonstrate any expression differences obvious in a PCR end point assay (Fig 4­1 and Fig 4­2). Determination of regulation, however, is difficult in end point assays. Given the previous reports of gender different expression, (9) differences may have been observed if a more sensitive method had been employed (i.e. qrtPCR). There is evidence, however, that Pou3f1 is regulated by an estrogen sensitive enhancer in glial cells (17). Though regulation of Kif1a and

Nedd4 was not demonstrated, these genes contain binding sites for Pou3f1 which are conserved across species. Sequence conservation indicates selective pressure against mutation in the region. Conservation of this type in the sequence just upstream of the start site for transcription is often due to sequence important for transcription factor binding. Estrogen regulation of Pou3f1 may mediate the sexually differential expression of Nedd4 and Kif1a. This is a potential line of inquiry for future research, which could be done using the Gt1­7 cells which are estrogen responsive and express the three genes

(19).

Given that the NAcc homogenate contained both neural and glial cells, the cDNA pool was generated from both cell types, yet these have very different functions in the

CNS. A human neuroblastoma cell line, Sk­N­Be, can be induced to differentiate into either a glia­like state or a neuron­like state by treating the cell culture with either BrdU or Retinoic acid, respectively. Pou3f1 was expressed in untreated Sk­N­Be cells.

Treatment of the cells with BrdU to become glia­like increased the expression and treatment with retinoic acid decreased the expression (Fig 4­9). This finding supports

Pou3f1 regulation occurring primarily in the glial cells. The fact that a glia specific gene 111 is nicotine regulated indicates that these cells may play an important role in how drugs of abuse affect the brain and is an important area of investigation.

Investigation into expression patterns of Kif1a identified an alternative splice variant. Two cDNAs containing the alternatively spliced exon were present in the UCSC genome browser expression profile but the spliced exon was not identified in any of the databases containing known information on the gene (UniProt, RefSeq, and GenBank).

The variant has also been identified in the literature from a mouse clone used for protein interaction experiments (16). However, the presence of the alternative splice and its conservation has not been documented in any of the literature on this gene.

The alternative splice, exon 13a, is conserved in humans (Fig 4­8 ) and shows sequence conservation across multiple other species (Fig 4­7 ). Humans contain an additional splice variation in this region which includes Exon 13a and elongation of Exon

14 (designated as 14’). This variation is not present in mouse cDNA pools but is present in both the human Nucleus Accumbens and Caudate Putamen.

Though gender specific regulation of Kif1a was not replicated in the adolescent cDNA pools, lymphoblast cells treated with nicotine increased Kif1a. The gene’s expression increased 2­fold after 1 hour of nicotine treatment and 3 fold after 2 hours

(Fig 4­9). Expression returned to normal after 24 hours of exposure, indicating an early action of Kif1a in the cells response to nicotine. Unfortunately, the lymphoblast cells do not express either of the human splice variants; therefore their nicotine regulation could not be determined in this cell line.

One potential reason for the deviation of the qrtPCR results from the microarray results is that the probe sets used on the Affymetrix array would detect both splice 112 variants, whereas the primer sets used in the qrtPCR experiment were specific for either the transcript without 13a or the transcript containing 13a. However, because the Kif1a trend was in the opposite direction in the qrtPCR rather than simply weaker, this is not a clear explanation. The splice variant did exhibit a greater degree of down­regulation by nicotine in the female 200 µg/ml group, indicating a greater degree of nicotine sensitivity by some conditions.

Further experimentation is needed to determine nicotine’s effect on the Kif1a splice variants. A greater sensitivity of the 13a splice variant to nicotine would be of particular interest given results in the Sk­N­Be cells. The Kif1a variant that does not include 13a does not demonstrate large expression differences between the untreated,

BrdU treated, or the Retinoic Acid treated cells (Fig 4­9). The variant containing 13a, however, is increased in the Retinoic Acid­treated cells and down regulated in the BrdU­ treated cells. This result implies the exon 13a variant is neuron specific and becomes more prolific as the cells differentiate into neurons.

Kif1a is a motor protein that is known to be responsible for anterograde transport of synaptic vesicles. The vesicles have been shown to contain synaptic vesicle precursors such as synaptophysin, synaptotagmin, and Rab3A (20). Knock­out mice that do not contain the Kif1a gene die shortly after birth, and inspection of cells from these knock­ out mice indicate a buildup of vesicle the cell bodies. Immature neurons can survive in the absence of Kif1a but it is essential for the maintenance and survival of mature neurons (21). Given the importance of Kif1a in mature neurons, and the up­regulation of the exon 13a variant in cultures of maturing neurons, this splice variant may be an essential part of that survival. 113 Despite inconsistencies between the qrtPCR and microarray results, molecular investigation of the genes implicated by computational analysis yielded valuable information on the nicotine regulation of these genes, as well as revealing new gene variations for Kif1a. Identification of the Pou3f1 regulation and the Kif1a splice variant are valuable additions to the literature. Further experiments will be needed to clarify the importance of these genes and how their response to prenatal nicotine modifies the reward pathways of the brain. 114

References

1. Barnes WM and Rowlyk KR. Magnesium precipitate hot start method for PCR. Mol Cell Probes 16: 167­171, 2002. 2. Beasley C, Meijer, D, Kerwin R, Cotter D, et al. Author Retraction. The American Journal of Psychiatry 163: 1454, 2006. 3. Ciani E, Severi S, Contestabile A, Bartesaghi R, and Contestabile A. Nitric oxide negatively regulates proliferation and promotes neuronal differentiation through N­ Myc downregulation. J Cell Sci 117: 4727­4737, 2004. 4. Collarini EJ, Kuhn R, Marshall CJ, Monuki ES, Lemke G, and Richardson WD. Down­regulation of the POU transcription factor SCIP is an early event in oligodendrocyte differentiation in vitro. Development 116: 193­200, 1992. 5. Frantz GD, Bohner AP, Akers RM, and McConnell SK. Regulation of the POU domain gene SCIP during cerebral cortical development. J Neurosci 14: 472­485, 1994. 6. Friedrich RP, Schlierf B, Tamm ER, Bosl MR, and Wegner M. The class III POU domain protein Brn­1 can fully replace the related Oct­6 during schwann cell development and myelination. Mol Cell Biol 25: 1821­1829, 2005. 7. Helson L and Helson C. Human neuroblastoma cells and 13­cis­retinoic acid. J Neurooncol 3: 39­41, 1985. 8. Hirokawa N and Takemura R. Kinesin superfamily proteins and their various functions and dynamics. Exp Cell Res 301: 50­59, 2004. 9. Ilia M, Sugiyama Y, and Price J. Gender and age related expression of Oct­6­­a POU III domain transcription factor, in the adult mouse brain. Neurosci Lett 344: 138­ 140, 2003. 10. Jaegle M, Ghazvini M, Mandemakers W, Piirsoo M, Driegen S, Levavasseur F, Raghoenath S, Grosveld F, and Meijer D. The POU proteins Brn­2 and Oct­6 share important functions in Schwann cell development. Genes Dev 17: 1380­1391, 2003. 11. MacManus JP, Boynton AL, Whitfield JF, Gillan DJ, and Isaacs RJ. Acetylcholine­induced initiation of thymic lymphoblast DNA synthesis and proliferation. J Cell Physiol 85: 321­329, 1975. 12. Matsushita M, Tanaka S, Nakamura N, Inoue H, and Kanazawa H. A novel kinesin­like protein, KIF1Bbeta3 is involved in the movement of lysosomes to the cell periphery in non­neuronal cells. Traffic 5: 140­151, 2004. 13. Melino G, Piacentini M, Patel K, Annicchiarico­Petruzzelli M, Piredda L, and Kemshead JT. Retinoic acid and alpha­difluoromethylornithine induce different expression of neural­specific cell adhesion molecules in differentiating neuroblastoma cells. Prog Clin Biol Res 366: 283­291, 1991. 115 14. Nangaku M, Sato­Yoshitake R, Okada Y, Noda Y, Takemura R, Yamazaki H, and Hirokawa N. KIF1B, a novel microtubule plus end­directed monomeric motor protein for transport of mitochondria. Cell 79: 1209­1220, 1994. 15. Phillips K and Luisi B. The virtuoso of versatility: POU proteins that flex to fit. J Mol Biol 302: 1023­1039, 2000. 16. Rashid DJ, Bononi J, Tripet BP, Hodges RS, and Pierce DW. Monomeric and dimeric states exhibited by the kinesin­related motor protein KIF1A. J Pept Res 65: 538­ 549, 2005. 17. Renner K, Sock E, Bermingham JR, Jr., and Wegner M. Expression of the gene for the POU domain transcription factor Tst­1/Oct6 is regulated by an estrogen­ dependent enhancer. Nucleic Acids Res 24: 4552­4557, 1996. 18. Schonemann MD, Ryan AK, Erkman L, McEvilly RJ, Bermingham J, and Rosenfeld MG. POU domain factors in neural development. Adv Exp Med Biol 449: 39­ 53, 1998. 19. Shen ES, Meade EH, Perez MC, Deecher DC, Negro­Vilar A, and Lopez FJ. Expression of functional estrogen receptors and galanin messenger ribonucleic acid in immortalized luteinizing hormone­releasing hormone neurons: estrogenic control of galanin gene expression. Endocrinology 139: 939­948, 1998. 20. Shin H, Wyszynski M, Huh KH, Valtschanoff JG, Lee JR, Ko J, Streuli M, Weinberg RJ, Sheng M, and Kim E. Association of the kinesin motor KIF1A with the multimodular protein liprin­alpha. J Biol Chem 278: 11393­11401, 2003. 21. Yonekawa Y, Harada A, Okada Y, Funakoshi T, Kanai Y, Takei Y, Terada S, Noda T, and Hirokawa N. Defect in synaptic vesicle precursor transport and neuronal cell death in KIF1A motor protein­deficient mice. J Cell Biol 141: 431­441, 1998. Chapter 5

General Discussion

Complex traits are challenging to study because of gene­gene and gene­environment interactions. These challenges are exacerbated in human studies because of genetic heterogeneity and low statistical power in many instances. Addressing the study of complex traits in light of the results of an animal model is a crucial step in the investigation of human behavior.

Drug response is a quantitative trait with a range of drug sensitivities in individuals. These native differences create differential responses to the rewarding aspects of drugs, which may alter an individual’s progression from drug taking to drug dependence; a key step in the acquisition of an addictive behavior. Identification of gene networks that are altered by prenatal nicotine exposure and remain altered into adolescence may give insight into differences in naturally occurring systems that contribute to differences in abuse liabilities seen in individuals. This work is the beginning of an attempt to answer the question of genetic susceptibility to drugs of abuse and how the system interacts with the environment to increase or decrease the degree of susceptibility.

As observed in the microarray studies nicotine exposure both prenatally and during adolescent causes wide scale changes in gene expression. This result demonstrates that the investigation of a single gene will not tell the whole story and other approaches that con incorporate information from multiple genes needs to be considered. 117 Microarrays generated large amounts of expression information and correlation of genes expression often present in genes sharing common functions. Utilizing the correlation information and the principle of common function can identify genes which of a common response to nicotine. By comparing correlations between multiple genes expression networks can be visualized. The hypothesis of this work is that altered patterns of gene expression in the nucleus accumbens observed after prenatal nicotine exposure will persist in adolescence and indicate pathways that are modified by drug exposure.

A handful of genes whose expression patterns are permanently altered in Nucleus

Accumbens (NAcc) by prenatal nicotine have been identified by investigating drug response in mice. One specific gene set, which included Pou3f1, Glul, and Kif1a, was responsive in a sexually dependent manner. These sexually influenced genes maintained differential regulation in adolescents, indicating a neural network that was permanently changed in its response to nicotine. Characterization of the genes in identified as part of this network can be used to extrapolate information to the system as a whole and the function of the network in addictive behaviors. In addition any given network generally has a hub that generates a large influence on the network where a large degree of information flows into and out of. Identification of a gene hub for a gene network whose regulation in altered by nicotine would enable the investigator to manipulate the system at one point and see large scale effects. The genes identified here play an important role in the network of genes whose response is sexually differentially regulated by nicotine.

Characterization of these genes implicates a greater role of glial/synapse relationship in drug response. 118 The differential gene expression that persists into adolescence also indicates the reward system had been altered, at least at the expression level, in a permanent manner.

This change also appears to have occurred in a sexually dimorphic manner. Possible consequences for this change are differential responses to a drug challenge. These differential changes could lead to an increases susceptibility to the addictive aspects of the drug as seen in the results of an analysis of nicotine­preference employing a two­ bottle choice test (15).

Sexually differential response to drugs, both therapeutic and recreation, is being recognized to a greater degree (5, 7, 15, 16, 18), and only in recent years has the importance of research on sex differences in response been recognized (11). Recognizing these differences is important as treatment programs for clinical disease and drug prevention have mostly been developed with research on white males and these options may not be applicable across race and sex as was originally determined. The identification of genes involved in sexually differential response to nicotine can indicate pathways which are also differentially regulated between the sexes. These pathways may give a better idea for which intervention and cessation programs are optimal in men and women.

For example animal studies seem to indicate that males may be more susceptible to the pharmacological aspects of nicotine (15, 19, 20). This seems to be reflected in the increased efficacy of nicotine replacement therapies in human males but not females (4).

In addition this could help explain differences in results from animal research (15), where prenatal nicotine increased susceptibility in males, and human research (14) where prenatal nicotine increased susceptibility in daughters prenatally exposed to nicotine. 119 The research indicates women are more influenced by the behavioral aspects of smoking

(4) and thus daughters may be more influenced by the social aspects such as role modeling, and peer pressure then their male counter parts. This difference would explain the deviation in the human and animal findings where such social interactions are not measured.

Classification of the genes in the sexually differential network will help indicate why males are more susceptible to the physiological effect of nicotine and whether this can be exploited for prevention methods. Further investigation will be needed to determine if induction of the genes identified by sexually differential regulation by nicotine are a by­product of morphological changes initiated by nicotine exposure, or if they play a causative role in these changes. A promising target for further investigations is the transcription factor Pou3f1. Previous research has demonstrated that Pou3f1 can activate transcription of the alpha three nAchR subunit in PC12 cells (12). Data presented in the literature using qrtPCR experiments recorded an increase in expression of the alpha 3 subunit of the nAchR by 75.8% in the NAcc core of animals prenatally exposed to nicotine(6). Nicotine regulation of the Pou3f1 transcription factor has been demonstrated in this thesis, and may be the causal agent for the nicotine induced increased in transcription of the alpha 3 subunit mRNA previously recorded in the NAcc

(6). These results support a direct action of nicotine on Pou3f1 regulation and its subsequent gene targets.

Though this thesis work did not identify regulation of any nAchR, this result could be explained by the fact that the experimental procedure did not differentiate between the NAcc core and shell. Each section of the NAcc has a distinct role in 120 response to reward stimuli and therefore it is logical to assume they could have differential gene responses. The NAcc core has been hypothesized to be responsible for acquisition of Pavlovian responding to drugs of abuse and to play a role in the rewarding properties of secondary cues and drug maintenance (3). In addition, it is thought that nicotine’s main rewarding properties come from its ability to increase the firing of the

Mesolimbic Dopamine system by activating nAchR in the Ventral Tegmental Area

(VTA). The VTA neurons project to the NAcc and it is in the NAcc where the increase in dopamine (DA) is seen. The increase of DA in the NAcc is associated with the rewarding properties of drugs of abuse. Whether or not a direct action of nicotine on the

NAcc has an affect on its addictive properties has not been documented. Blocking receptor function acutely in the VTA, however, is enough to abolish the DA increases associated with nicotine exposure (17), therefore it is not clear what is the exact role of nAchR in the NAcc.

To determine possible roles for nicotine in the NAcc future experiments could attempt to identify whether Pou3f1 expression changes are selective to either the core or the shell. In addition, it would need to be determined if Pou3f1 regulation by nicotine is exclusive to either glial or neural cells. These experiments could be conducted using the Sk­N­Be cell cultures by inducing either glial or neural development then bathing the cells in nicotine. However, functional nAchR would need to be demonstrated in these cells before such experiments could be conducted.

The work described in Chapter 4 suggests that transcription of Pou3f1 is increased by nicotine. Demonstrating a causative link between nicotine and Pou3f1 expression is important because it is a possibility that increases in the transcription factor are a by­ 121 product of nicotine’s other actions rather than being directly induced by nicotine. For example, prenatal exposure to nicotine causes cell loss in the midbrain and cerebral cortex, as well as inducing elevations in membrane­bound/total protein ratio. The increase in membrane/total protein ratio can be a sign of an increase of glial cell number

(1). Given that Pou3f1 is important in glial maturation, it could be that the increase in number of this cell type results causes a greater proportion of the mRNA sample to have originated from glial, rather than neural cells, and account for the increase transcript number rather than a direct action of nicotine on Pou3f1 transcription.

Whether nicotine is acting directly on Pou3f1, or whether the transcriptional alterations are a result of increased glial numbers, an intriguing aspect of this research is the implication that glial cells potentially play an important role in the changes induced by nicotine exposure. Traditionally glia were considered mainly as structural and metabolic cells. New research is identifying previously unknown aspects of glial function that indicate they play a greater role in brain function than was originally thought (21, 23). Glial cells can alter neural excitability and synaptic activity. The discovery of glial involvement in synaptic activity has led to the creation of a new phrase, tripartite synapse (13). They have also been shown to be involved in certain neurodegenerative diseases, and play an important role in the blood brain barrier (9, 13).

Of the genes identified in this body of literature, both Pou3f1 and Glutamine

Synthetase (Glul) are important for glial cell development and maintenance. Glul is also important for the communication between glial cells and neurons (21). The protein product of this gene has functions which support neuron maturation and excitability of the synapse (21). In contrast Kif1a, also nicotine regulated, is important for neural 122 maturation and anterograde transport of synaptic vesicles to the synapse (24). The regulation of these genes may be important in how neurons and glial cells communicate to alter synapse function in addiction. These functions fit well with the developing theories of how drugs of abuse exert their effect on the brain.

The current theory is that drugs of abuse alter synaptic plasticity by using mechanisms already present in the brain, such as those used for learning and memory storage (8, 10). It is well documented that nicotine alters synaptic properties in adults even to an extent that years after quitting, when withdrawal has ceased, smokers can experience intense cravings. It is naive to think that a drug, which can have such lasting effects on an adult brain, does not alter the developing brain in an important manner.

New research is focusing on these alterations and showing that the effects are more profound in the developing brain (1, 2, 22). Many of the initial observations of prenatal effects of nicotine were correlations between prenatal exposure and later behavioral alterations. Later experiments demonstrated alterations in cell number and synaptic function. This thesis adds to the body of literature by presenting a network of genes whose expression is altered by prenatal exposure and may participate in pathways that lead to the observed cellular and behavioral alterations of prenatal exposure.

In addition, the identification of the Pou3f1 transcription factor using computational methods, and the subsequent discovery of its sex specific nicotine regulation, supports computational analysis as a viable means for identifying biological significant information from large data sets generated by microarray experiments, as well as identifying genes potentially over looked due to faulty probe sets. Most biologists are familiar with the available computation tools such as BLAST, USC Genome Browser, 123 and NCBI. The programs they may be less familiar with, such as WebQTL, have easily negotiable interfaces and can be used successfully without in­depth training. This ease is in direct contrast to the complex statistical modeling that is currently used for microarray analyses that are not geared towards the questions that biologists wish to address.

Microarrays also generate large quantities of potentially informative data. Investigating these data without a clear starting point is difficult and subsequent molecular analysis can be time consuming and expensive. This thesis demonstrates that, by interrogating data in a logical manner using publicly available tools already familiar to many biologists, a manageable gene set can be identified for further investigation. In addition several candidate genes have been identified that may be crucial to nicotine’s effect on the brain reward response. Further research will help elucidate networks of gene interactions and the importance of these networks and may be able to be extrapolated to the process of addiction in general. 124

References

1. Abreu­Villaca Y, Seidler FJ, and Slotkin TA. Does prenatal nicotine exposure sensitize the brain to nicotine­induced neurotoxicity in adolescence? Neuropsychopharmacology 29: 1440­1450, 2004. 2. Azam L, Chen Y, and Leslie FM. Developmental regulation of nicotinic acetylcholine receptors within midbrain dopamine neurons. Neuroscience, 2006. 3. Balfour DJ. Neuroplasticity within the mesoaccumbens dopamine system and its role in tobacco dependence. Curr Drug Targets CNS Neurol Disord 1: 413­421, 2002. 4. Bohadana A, Nilsson F, Rasmussen T, and Martinet Y. Gender differences in quit rates following smoking cessation with combination nicotine therapy: influence of baseline smoking behavior. Nicotine Tob Res 5: 111­116, 2003. 5. Carroll ME, Lynch WJ, Roth ME, Morgan AD, and Cosgrove KP. Sex and estrogen influence drug abuse. Trends Pharmacol Sci 25: 273­279, 2004. 6. Chen H, Parker SL, Matta SG, and Sharp BM. Gestational nicotine exposure reduces nicotinic cholinergic receptor (nAChR) expression in dopaminergic brain regions of adolescent rats. Eur J Neurosci 22: 380­388, 2005. 7. Dale KM, Coleman CI, Shah SA, Patel AA, Kluger J, and White CM. Impact of gender on statin efficacy. Curr Med Res Opin 23: 565­574, 2007. 8. Dani JA, Ji D, and Zhou FM. Synaptic plasticity and nicotine addiction. Neuron 31: 349­352, 2001. 9. Fellin T, Pascual O, and Haydon PG. Astrocytes coordinate synaptic networks: balanced excitation and inhibition. Physiology (Bethesda) 21: 208­215, 2006. 10. Ferrario CR, Gorny G, Crombag HS, Li Y, Kolb B, and Robinson TE. Neural and behavioral plasticity associated with the transition from controlled to escalated cocaine use. Biol Psychiatry 58: 751­759, 2005. 11. Franconi F, Brunelleschi S, Steardo L, and Cuomo V. Gender differences in drug responses. Pharmacol Res 55: 81­95, 2007. 12. Fyodorov D and Deneris E. The POU domain of SCIP/Tst­1/Oct­6 is sufficient for activation of an acetylcholine receptor promoter. Mol Cell Biol 16: 5004­5014, 1996. 13. Halassa MM, Fellin T, and Haydon PG. The tripartite synapse: roles for gliotransmission in health and disease. Trends Mol Med 13: 54­63, 2007. 14. Kandel DB, Wu P, and Davies M. Maternal smoking during pregnancy and smoking by adolescent daughters. Am J Public Health 84: 1407­1413, 1994. 15. Klein LC, Stine MM, Pfaff DW, and Vandenbergh DJ. Laternal nicotine exposure increases nicotine preference in periadolescent male but not female C57B1/6J mice. Nicotine Tob Res 5: 117­124, 2003. 125 16. Klein LC, Stine MM, Vandenbergh DJ, Whetzel CA, and Kamens HM. Sex differences in voluntary oral nicotine consumption by adolescent mice: a dose­response experiment. Pharmacol Biochem Behav 78: 13­25, 2004. 17. Laviolette SR and van der Kooy D. The motivational valence of nicotine in the rat ventral tegmental area is switched from rewarding to aversive following blockade of the alpha7­subunit­containing nicotinic acetylcholine receptor. Psychopharmacology (Berl) 166: 306­313, 2003. 18. McCully JD, Rousou AJ, Parker RA, and Levitsky S. Age­ and gender­related differences in mitochondrial oxygen consumption and calcium with cardioplegia and diazoxide. Ann Thorac Surg 83: 1102­1109, 2007. 19. Tizabi Y, Russell LT, Nespor SM, Perry DC, and Grunberg NE. Prenatal nicotine exposure: effects on locomotor activity and central [125I]alpha­BT binding in rats. Pharmacol Biochem Behav 66: 495­500, 2000. 20. Trauth JA, Seidler FJ, McCook EC, and Slotkin TA. Persistent c­fos induction by nicotine in developing rat brain regions: interaction with hypoxia. Pediatr Res 45: 38­ 45, 1999. 21. Tsacopoulos M. Metabolic signaling between neurons and glial cells: a short review. J Physiol Paris 96: 283­288, 2002. 22. Vaglenova J, Birru S, Pandiella NM, and Breese CR. An assessment of the long­term developmental and behavioral teratogenicity of prenatal nicotine exposure. Behav Brain Res 150: 159­170, 2004. 23. Verkhratsky A and Toescu EC. Neuronal­glial networks as substrate for CNS integration. J Cell Mol Med 10: 826­836, 2006. 24. Yonekawa Y, Harada A, Okada Y, Funakoshi T, Kanai Y, Takei Y, Terada S, Noda T, and Hirokawa N. Defect in synaptic vesicle precursor transport and neuronal cell death in KIF1A motor protein­deficient mice. J Cell Biol 141: 431­441, 1998. Appendix A

In Depth Outline of Microarray Experiments

Experimental Layout

Generation of microarray chips were done in two stages. Because of the difference in behavior of the male mice pre­exposed to 50 µg/ml of nicotine, the male mice were selected for micro array analysis. The chips were generated to analyze expression differences between the high preference males and the low preference males.

The female adolescent chips were created at a later date to investigate differential gene expression between the sexes. All of the RNA from the adolescent animals was isolated in the same lab (Vandenbergh lab). The male samples were sent to the Hershey Medical center for processing and hybridization whereas the female samples were processed in the

Vandenbergh lab and hybridized at the Pennsylvania State University Microarray Center.

Affymetrix M430a chips were used for all of the adolescent samples. Duplicates of each condition were created for the males but only one chip was created for each of the female conditions. RNA was pooled from 2­3 mice for each of the adolescent chips.

Pools of RNA were based on sex, prenatal nicotine concentration, and preference demonstrated in the two bottle choice test. Only one brain region, the Nucleus

Accumbens, was isolated from the adolescent animals. The three preference designations 127 were High prefers (HP), no preference (NP), and low preference (LP) where animals consumed 60%, 40­60%, or less than 40% of their daily drinking water from the nicotine bottle. No preference was designated in the 40­60% range because it was determined that if an animal was drinking the same amount of water from each bottle (i.e. 50% from the nicotine) this indicated that the animal did not prefer one over the other and was visiting the bottles randomly. The determination was made that a visitation between 40 and 60% of the time could not be differentiated from random bottle choice. Because the chips were generated with pooled RNA for the purpose of these analyses, the adolescent chips were designated as HP, NP, or LP based on the average preference of the animals pooled on the chip. The isolation and processing methods for the female RNA will be outlined here as they were prepared specifically for this thesis. Details for the other samples and

Arrays were prepared in a similar manner.

RNA Isolation

Nucleus accumbens punches were taken on the day of sacrifice and stored in

RNAlater until isolation. The brain tissue was homogenized in Trizol and then a standard

RNA isolation purification protocol using RNeasy columns was followed (Qiagen). The isolated RNA was then electrophoresed on an Agilent Bioanalyzer to test quality and concentration. A subset of these samples can be seen (Fig A­1 ). All samples were then diluted to 1ug/ul concentration. In some cases the samples were lyophilized to dryness and then brought back up to a 1ug/ul concentration. RNA showing good quality based on the Agilent results were chosen to be carried forward for Microarray preparation. A low 128 number of representatives from each condition with quality RNA allowed only two animals to be pooled for the chips. In the case of one chip (LP 50 µg/ml exposure) only one animal of that designation had quality RNA.

Figure A­1: Depicts RNA run out on the Agilent Bioanalyzer. RNA quality and quantity can be determined. This is a subset of the female NA RNA samples. A. Simulates how the RNA would look run out on a gel. The 18S and 28S ribosomal RNA bands are visible. A bright tight band indicates high quality and good concentration. B. This panel shows the second set of images produced by the Bioanalyzer. The first spike is a spike in control and the second two spikes are the 18S and 28S ribosomal RNA. The top picture shows RNA of high quality and concentration. The bands are tight at the base and have a high spike. The second picture shows medium concentration and quality. The spikes are low and have broad bases. The last picture depicts a lane that was absent of RNA. The first sample is good for use on the Microarrays, the second two should be avoided. 129

In order to enhance biological variance of a given condition and decrease individual animal variance, the RNA was pooled with equal amounts of RNA from each animal. The RNA was labeled using the Small Sample Labeling Protocol (Affymetrix).

This protocol uses two rounds of amplification to increase RNA yield and undergoes two rounds of cDNA synthesis and purification. The cDNA is then used to create cRNA which is labeled by incorporating Biotin labeled ribonucleotides. After labeling, the cRNA was sent to the Pennsylvania State University Microarray facility.

The microarray facility caries out a fragmentation step and then hybridizes the fragmented samples to the m430a chips. The fragmentation step is necessary because successfully amplified cRNA contains sample sizes from 50­3000 base pairs in length.

Long transcripts cause two problems. Firstly, long fragments do not bind well to 25mers

(the probe length) and increase the risk of cross hybridization to non­specific chip probes.

Secondly, the larger transcripts often contain secondary structures which prevent correct binding because the sequence is already bound to itself. Fragmentation shortens the cRNA to 50­200 bp long fragments, making them less complex and better targets for the specific probe sequences on the chip which raises the average feature signal intensity on the microarray. The success of fragmentation is determined by running a fraction of the sample out on a gel, both pre­ and post­fragmentation processing, and checking for bands of the appropriate length.

The labeled and fragmented cDNA were lyophilized to dryness and resuspended in hybridization buffer (50% formamide, 5X SSC, 0.1% SDS, and 3 ug Cot1 DNA) as part of a pre­processing step. The labeled samples were denatured in a 100 ºC water bath 130 to separate any double stranded fragments and to prepare them for hybridization to the probe sequence. The denatured labeled sample was then pipetted under a lifter slip, which spreads the sample over the chip surface and provides an optimal reaction area for contact between the probes and labeled fragments. The chip was hybridized at 42 ºC for

20­24 hours to allow maximal hybridization and then put through a series of washes to remove unbound fragments to reduce background signal. The chips are scanned immediately for fluorescent signal following the wash process, using an Affymetrix

Genechip Instrumentation System.

Validation and Normalization

Several validation steps were carried out to determine if the chips were valid for use. These steps ensure that synthesis and hybridization occur optimally. For example,

DNA and RNA are synthesized in the 3’ to 5’ direction. During artificial synthesis steps, such as that which occurs during the RNA amplification processes, it is not unusual for the polymerase to fall off the template before the sequence is complete. Because of the direction of synthesis there is a larger abundance of 3’ fragments than of 5’ fragments.

To determine if the synthesis step is performed optimally, the amount of 3’ signal is divided by the 5’ signal to give the 3’ to 5’ ratio. Ideally the ratio would be 1 (equal signal from both ends of the mRNA sequence) but ratios of 3 are commonly seen.

Acceptable ratios are can be even larger in small sample preparations like the one used here since the samples undergo two rounds of amplification. 131 Two measures are checked to determine optimal hybridization; the signal intensities of spike in controls and of housekeeping genes. Spike in controls are known quantities of labeled cRNA that are added to the sample prior to the hybridization stage.

Spike in controls are generally of a bacterial or yeast origin and are thus discernable from the experimental sample. The signal intensities can then be verified by comparing the known quantity of cRNA with the reported quantity. Housekeeping genes are genes that are ubiquitously expressed in all cells and are usually involved in general cell processes.

These genes are usually present at high levels and are normally not altered by experimental conditions. The expression of these genes can be used as an internal control to determine if equal amounts of sample were added to each chip. These are technical validation steps to ensure the all the chips were processed similarly. Following the technical validation, biologically validity was determined by expression values of genes known to be turned on or off in a specific setting (i.e. female or male specific genes).

The reproducibility of replicates with the experiment were checked using M versus A plots (MvA). The plot shows the variability of one chip (M) compared to the log 2 of the average of all chips (A). The log 2 of the average is taken to scale the data around zero. The closer to 0 the plot falls the less variance there is between the individual chip and the average.

The R affy software package was used to do several quality control checks of overall chip quality and probe set quality. An R package called AMDA was also used to run a set of pre­programmed analysis steps on the adolescent chips set. This included gcRMA normalization and clustering of the chips. Further analyses of the chips could not be done by this program because of a lack of replicates in the female data set. 132 The data set used to determine gene expression differences was normalized using the MAS 5.0 method, with a target intensity value of 150. MAS 5.0 subtracts the mismatch values from the perfect match values to obtain a background correction. The normalization procedure also scales the chips to have the same average intensity and range of expression, allowing comparison across chips. The adolescent female and male chips were normalized as a batch to allow direct comparison. After normalization, any probe set called absent across all chips, or indicating no expression change between any chip, was removed. The remaining gene set was used for analyses.

Analyses

The ratio of the signal from the nicotine treated animals, divided by the signal from the control animals, was used in analysis. This ratio was log two transformed so that regulation up and down was on the same scale. The set of genes present and indicating a change in expression on at least one of the chips was uploaded into the R program and analyzed using the R stat and affy packages.

The data from the Cell files was also analyzed as a whole using an automated microarray data analysis R package abbreviated as AMDA (4). A Cell file is the fluorescent image digitized by the scanner and gives the raw signal data. The program has a wrapper that inputs cell files, then preprocess, normalizes and analyzes the data.

The wrapper used gcRMA as the normalization method. The gcRMA is a robust means analysis similar to RMA but it includes information on the probe sequence, such as GC content, in its normalization process. The AMDA analyses include hierarchical 133 clustering and principle component analysis, as well as looking for over represented functional groups in gene clusters by using the GO ontology database.

The data were also clustered using 2 physiological markers, mean cotinine level and nicotine consumed on day of sacrifice. Only genes that had an expression level 2 fold or larger then the respective control were used in this cluster. The ratio of the experimental values over control gene expression values were log2 transformed before clustering. The physiological data were log transformed in the same manner as the gene expression data to give comparable values. The data were clustered using Eisen cluster software and visualized using TreeView (1).

Microarray: Technical Validation

Array performance can be directly related to the degree of cRNA fragmentation.

Large post cRNA synthesis fragments indicate the whole gene sequence has been copied.

The success of fragmentation is determined by running a fraction of the sample out on a gel pre and post fragmentation and checking for bands of the appropriate length (Fig A­

2). Quality checks are important in a microarray experiment to ensure the gene expression values generated by the array accurately represented gene expression of the tissue of interest. 134

Figure A­2: Figures generated by the Agilent Bioanalyzer before and after fragmentation. A Gel image showing fragment sizes before fragmentation of a given sample and the adjacent lane shows the same sample after fragmentation. B. The Peaks on the images give an indication of the size of the RNA. The top panel is pre fragmentation and the bottom panel is post fragmentation.

The mRNA from the male and female animals were hybridized to Affymetrix

430a mouse expression arrays. The RNA for these array sets, however, was prepared at two separate time points, though using the same methods. To determine if RNA was treated similarly during the two preparations, a plot of the intensity of the probes based on placement along the gene was generated (Fig A­3 ). RNA processing is not 100% efficient, however there should be no obvious trends within a set of sample preparations.

An obvious pattern would indicate one sample set was treated differently then the second 135 sample set, i.e. degradation or inefficient synthesis. The RNA degradation plot is a good quality control as it can identify both poor quality in the initial RNA and poor cRNA elongation which would indicate any large trends in the different sample preps. The lines in the plot have similar slopes and are parallel to each other with no obvious pattern in one set of preparations, which indicates similar RNA quality and preparation treatment.

This result indicates the male and female arrays can be analyzed as a unit. 136

Figure A­3: RNA digestion plot generated by R’s affy package. The roughly parallel structure of the lines in the RNA digestion plots indicate that no gross difference in creation of the arrays existed between the two sets. Any differences due to methodology that do exist are removed during normalization

The RNA amplification and hybridization for the two arrays sets were also done at different times. This time difference necessitated checking that the variation seen was due to biological sex differences in gene expression and not artifacts of amplification differences. An RNA pool isolated from male control mice was re­amplified and 137 hybridized at the same time as the female RNA. MvA plots of re­amplified cRNA from male mice compared to the average of the arrays hybridized with cRNA from female mice(Fig A­4) and the average of the first set of arrays hybridized with male cRNA

(Fig A­5) demonstrate variation of the re­hybridized array to the two array groups. These are compared to MvA plots generated using array data of the gene expression of a single male cRNA pool compared to the average gene expression of all male cRNA pools

(Fig A­6 ) and the gene expression of one female cRNA pool compared to the average gene expression of all female cRNA pools (Fig A­7). This set of graphs shows there is a similar amount of variation due to hybridization time as there is to sex, therefore some of the effects seen may be due to hybridization variation but a large fraction of observed expression changes are due to biological differences between the sexes. The graphs also show there is less overall variation and tighter grouping with genes that are expressed at a higher level, indicating a greater reliability in reported expression values for highly expressed genes. Overall the graphs indicate the data are valid for comparison.

Figure A­4: MvA of array data comparing gene expression values generated by the re­ amplified male cRNA pool to the average gene expression values of all cRNA pools generated from female mice. 138

Figure A­5: MvA of array data comparing gene expression values generated by the re­ amplified male cRNA pool to the average gene expression values of all cRNA pools generated from male mice.

Figure A­6: MvA of array data comparing gene expression values generated by a single pool of cRNA from male mice to the average gene expression values of all cRNA pools generated from male mice. 139

Figure A­7: MvA of array data comparing gene expression values generated by a single pool of cRNA from female mice to the average gene expression values of all cRNA pools generated from female mice.

Microarray: Biological Validation

Once technical validation was completed the arrays were checked for biological relevance. This validation can be achieved by taking advantage of the published literature, which is a vast repository of known gene function. The biological validity of the arrays can be quickly and efficiently determined by using the literature. Genes whose expression patterns in the adolescent mouse brain are known from published data were examined in the present microarray results for concordance with expectation. Two classes of genes were examined to confirm appropriate age expression. First, genes that are expressed early in development but turned off by adolescence, and second, genes expected to be expressed during adolescence. Telomerase associate genes are active in the developing brain but are not expressed in juvenile brains (5). Consistent with expectations, probe sets for the telomerase genes Tert and Tep1 were called absent in the 140 adolescent arrays (data not shown). The same was true of Gdnf and GFra3 which are expressed in Juveniles (3). More importantly, because of the risk of false positives and negatives genes previously demonstrated to be positively expressed in juveniles, such as the Dopamine Receptor D3, Kena1, Homer1, Rs4, 9 and 11, were called present by the analysis (2, 6, 8).

Certain genes are known to have sex specific gene expression. Therefore, sex appropriate expression of genes on the array was also checked. One distinct difference between males and females is the sex chromosomes. Males have an XY genotype and females have an XX genotype. The X is a much larger chromosome, 165.5 x 10 6 bps vs.

16.0 x 10 6 bps, with approximately 30 times more genes than the Y chromosome. To compensate for the larger number of genes, and to ensure females do not have over expression of genes residing on the X chromosome, one of the X chromosomes is inactivated (7). The X inactivation occurs early in development and is a random process.

Only a small portion of the inactivated chromosome retains transcriptional ability. This section transcribes the Xist gene, whose transcript coats the inactive portion of the chromosome and is found in all tissue from female origins. In the adolescent arrays, the

Xist gene was expressed on all female arrays and not expressed on any of the male arrays

(data not shown). 141

References

1. Eisen MB, Spellman PT, Brown PO, and Botstein D. Cluster analysis and display of genome­wide expression patterns. Proc Natl Acad Sci U S A 95: 14863­14868, 1998. 2. Hallows JL and Tempel BL. Expression of Kv1.1, a Shaker­like potassium channel, is temporally regulated in embryonic neurons and glia. J Neurosci 18: 5682­ 5691, 1998. 3. Naveilhan P, Baudet C, Mikaels A, Shen L, Westphal H, and Ernfors P. Expression and regulation of GFRalpha3, a glial cell line­derived neurotrophic factor family receptor. Proc Natl Acad Sci U S A 95: 1295­1300, 1998. 4. Pelizzola M, Pavelka N, Foti M, and Ricciardi­Castagnoli P. AMDA: an R package for the automated microarray data analysis. BMC Bioinformatics 7: 335, 2006. 5. Prowse AB, McQuade LR, Bryant KJ, Van Dyk DD, Tuch BE, and Gray PP. A proteome analysis of conditioned media from human neonatal fibroblasts used in the maintenance of human embryonic stem cells. Proteomics 5: 978­989, 2005. 6. Stanwood GD, McElligot S, Lu L, and McGonigle P. Ontogeny of dopamine D3 receptors in the nucleus accumbens of the rat. Neurosci Lett 223: 13­16, 1997. 7. Straub T and Becker PB. Dosage compensation: the beginning and end of generalization. Nat Rev Genet 8: 47­57, 2007. 8. Thomas EA, Danielson PE, and Sutcliffe JG. RGS9: a regulator of G­protein signalling with specific expression in rat and mouse striatum. J Neurosci Res 52: 118­ 124, 1998. Appendix B

Protocols

Cloning PCR Fragments

PCR fragments for cloning were gel­isolated using a standard protocol and inserted into a Qiagen pDrive Cloning Vector. Qiagen’s ligation and transformation protocols were followed. To test for successful incorporation the fragment was digested using EcoRI restriction enzyme, then visualized on a standard acrylamide gel. Colonies identified as containing the fragments of interest were sent to Pennsylvania State

University Nucleic Acid Facility for sequencing. Determination of PCR product identity when multiple products were generated with one pair of primers included a melting and renaturation experiment. Plasmids containing the two generated products were combined into a single tube, cut using EcoRI, then brought to 95º C for 5 minutes followed by 72º

C for 10 minutes. Finally an aliquot was run on a standard acrylamide gel to determine if

DNA from only two fragment sizes could recreate the original band pattern. 143

Gel Shift Assay

A gel shift assay was attempted for this dissertation but the results were inconclusive and thus not included. The protocol from Flint’s lab was followed, http://well.ox.ac.uk/flint/EMSA.htm. The following was used as probes.

Gel Shift probes

Control sequence from Wierman paper PMID: 9032292:

Mol Cell Biol. 1997 Mar;17(3):1652­65.

Repression of gonadotropin­releasing hormone promoter activity by the POU homeodomain transcription factor SCIP/Oct­6/Tst­1: a regulatory mechanism of phenotype expression?

Wierman ME, Xiong X, Kepa JK, Spaulding AJ, Jacobsen BM, Fang Z, Nilaver G, Ojeda SR.

Department of Medicine, University of Colorado Health Science Center, Denver 80220, USA. [email protected]

Wt­343/­314 ...... tcgATTATCTGATTTAAATGTTTCCTTTTACAG AATAGACTAAATTTACAAAGGAAAATGTCagct

Kif1a (Gene is on the ­ strand)

Pou site is on ­ strand Site1 tcgaAGGGATTTAGACTCC TCCCTAAATCTGACCagct

Pou site is on + strand Site 2 tcgaCCAGCCTAAATCCTC GGTCGGATTTAGGAGagct

Nedd4 (Gene is on the + strand)

Pou site is on + strand Site1 tcgaTTGGAATTATCATTA AACCTTAATAGTAATagct 144 Pou site is on ­ strand Site2 tcgaAGAGAATTAAACTAA TCTCTTAATTTGATTagct

To acquire these sites the plus strand and the reverse compliment of the minus strand were sent to Deb grove at the DNA facility.

GnRH.C+ TCGATTATCTGATTTAAATGTTTCCTTTTACAG GnRH.C­ TCGACTGTAAAAGGAAACATTTAAATCAGATAA

Kif1a.1+ TCGAAGGGATTTAGACTCC Kif1a.1­ TCGAGGAGTCTAAATCCCT

Kif1a.2+ TCGACCAGCCTAAATCCTC Kif1a.2­ TCGAGAGGATTTAGGCTGG

Nedd4.1+ TCGATTGGAATTATCATTA Nedd4.1­ TCGATAATGATAATTCCAA

Nedd4.2+ TCGATTAGTTTAATTCTCT Nedd4.2­ TCGAAGAGAATTAAACTAA VITA

Jennifer E. Foreman

OFFICE ADDRESS HOME ADDRESS Genetics Intercollege Graduate Degree Program 1814 Bayfield Court Center for Development and Health Genetics State College, PA 16801 The Pennsylvania State University 225 Research East University Park, PA 16802 Tel:(814) 863­1325 Fax:(814) 863­8429 [email protected]

EDUCATION B.S. (Biology w/ Honors) 2000 North Carolina Wesleyan College Rocky Mount, NC Ph.D. (Genetics) May 2007 expected The Pennsylvania State University University Park, PA Thesis: Prenatal nicotine exposure alters gene expression in a sexually dimorphic manner EMPLOYMENT Graduate Student, with Dr. David J. Vandenbergh, The Pennsylvania State University. 2002­current.

High School Teacher, Lee County School Board, Cypress Lake High School, Fort Myers Fl Biology and Earth Space Science 2000­2001 AP Calculus and Algebra I 2001­2002

Undergraduate Research, with Dr. Erica Kosal, North Carolina Wesleyan College. Honors Thesis: The Effects of Environment Stress on Medaka Fish. 1999 ­ 2000

FUNDING GRADUATE RESEARCH ASSISTANT, DAVID J. VANDENBERGH (2005­2007)

INSTITUTIONAL, NIA PREDOCOTORAL (T32) GRANT (AG00276; G. VOGLER) (2003­2005)

UNIVERSITY GRADUATE FELLOWSHIP, PENNSYLVANIA STATE UNIVERSITY (2002­2003

PUBLICATIONS

Foreman, J.E., Blizard, D.A., Lionikas, A., Gerhard, G., Vogler, G.P., Stout, J.T., Griffith, J.W., Lokoski, J.M., Hofer, S.M., McClearn, G.E., and Vandenbergh D.J. Genetic Architecture underlying activity measures across substantial time intervals in young, middle­aged and old mice. (In Preparation)

Foreman, J.E., Blizard, D.A., Gerhard, G., Mack, H.A., Lang, T.C., Van Nimwegen, K.L., Vogler, G.P., Stout, J.T., Shihabi, Z.K., Griffith, J.W., Lokoski, J.M., McClearn, G.E., and Vandenbergh D.J. Quantitative Trait Loci for Alkaline Phosphatase Activity in Mouse Serum include a Region Containing the Alkaline Phosphatase 2 (Akp2) Gene. Physiological Genomics. 2005 Nov 17;23(3):295­303.

Foreman, J.E. non­peer reviewed publication. Contributed to the Sex and Gene Expression Meeting report by summarizing two Presenters, Dr. Mary Jeanne Kreek and Dr. Moshe Szyf, for Conference Proceeding (SAGE VI). 2005. Washington DC