Epigenetic Regulation in the Placenta and Its Role in Fetal Growth

Epigenetic regulation in the placenta and its role in fetal growth

Jose Carlos Pinto Barreto Ferreira

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Institute of Medical Science University of Toronto

Epigenetic regulation in the placenta and its role in fetal growth

Jose Carlos Pinto Barreto Ferreira

Doctor of Philosophy

Institute of Medical Science University of Toronto

2011 Abstract

Fetal growth potential reflects a complex regulatory system delivered by genetic and environmental factors acting directly on the fetus or through the placenta. Compromise of this potential, as seen in intrauterine growth restriction (IUGR), is associated with increased perinatal mortality and short and long term morbidity. The expression of several genes has been shown to be disturbed in placentas of fetuses with growth restriction. However, the primary causes for these changes have not yet been elucidated.

I proposed that epigenetic mechanisms, specifically DNA methylation, may be involved in placental development leading to modulation of the expression of specific genes, and that their altered regulation will impact fetal development and growth.

My primary objective was to identify DNA methylation variation in placenta, in association with variation of gene expression and with poor fetal growth.

I used a global genomic screening approach, with 24 selected placental samples, from newborns considered IUGR or normal controls, to identify candidate target genomic regions carrying epigenetic alterations. Candidate regions were followed up, by expression analysis of corresponding regulated genes, for associations with altered expression and by targeted

ii methylation analysis in an expanded cohort of 170 samples, for associations with birthweight percentile. I analyzed methylation variation at imprinting centers (IC), gene promoters and CpG islands.

In two genome-wide case control screening studies using distinct commercial microarray platforms I identified approximately 68 differentially methylated autosomal candidate genomic regions overlapping gene promoters. Hypomethylated CpGs mapping to gene promoters were found to be more abundant in placentas of growth restricted newborns than in controls. One of the most interesting candidates, WNT2, was analyzed in an extended sample cohort and showed an association of high promoter methylation to low expression as well as low birthweight percentile. This gene is involved in a pathway that diverts cells from programmed apoptosis. It is highly expressed in placenta, and in mice, targeted biallelic inactivation of Wnt2 has been shown to cause poor growth and perinatal death in 50% of the affected pups.

These findings support the hypothesis that dysregulation of epigenetic mechanisms are involved in abnormal placental development and can impact fetal growth.

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Acknowledgments

It is at this point in time, when I start thinking about the people that I have to acknowledge, that I realize how fortunate and how thankful I have to be for being able to engage so many people who have contributed, helped and/or supported all the work I have done during those past 4-5 years. So, you, the reader, prepare yourself for the big list that follows:

I first would like to thank the patients who participated in this study and their physicians. They are the subject and inspiration for all human medical research.

I thank my funding agency for the scholarship I was awarded - FCT (Science and Technology Foundation), Lisbon, Portugal - SFRH / BD / 28642 / 2006 - funded by POPH (Operational Program for Human Potential) co-participated by FSE (European Social Fund) and by national funds from MCTES (Ministry of Science, Technology and Higher Education) –the agency that funded the research – Canadian Institute for Health and Research (CIHR) - and the Center for Applied Genomics.

I thank the staff from the Research Centre for Women’s and Infants’ Health BioBank program of the CIHR Group in Development and Fetal Health (CIHR #MGC-13299), the Samuel Lunenfeld Research Institute and the Department of Obstetrics & Gynecology of Mount Sinai Hospital/University Health Network for the human specimens used in my studies. In particular, I would like to mention Dragica Curovic, Melanie Henriques, Ruchita Uxa, Ljiljana Petkovic, Richard Sullivan, Nora Tsao, Dr. Lee Adamson, for their insights and support. I truly and sincerely appreciated the relationships I had with all staff members of this wonderful resource made available to me and other researchers.

I thank all my friends, student colleagues, post-doctoral fellows and technicians from Dr. Rosanna Weksberg’s laboratory, past and present, for all their help and support, for passing on their knowledge, for the fruitful and challenging discussions (academic, politic, etc.). Listed in alphabetic order, here are their names – Adam Smith, Andrea Stachon, Chunhua Zhao, Darci Butcher, Daria Grafodatskaya, Jonathan Shapiro, Lin Guo, Richard McCurdy (my Espresso buddy), Sanaa Choufani, Yi-An Chen, Youliang Lou. I also want to thank the summer students Sarah Ickowicz for her collaboration, and Rageen Ragendram for his collaboration and for the nice academic and entertaining political and technical conversations. iv

I thank the administrative assistants of my supervisor, Kamalina Gupta and Khadine Wiltshire, for all their help and support.

I also want to thank Dr. David Chitayat, Cheryl Shuman and Micki Thomas for all the help in the organization of the collection of samples, on consent and ethics issues and their friendship and support.

I thank the friends from other labs for their friendship, in special, Elena Samiltchouk, Dalila Pinto, Pedro Castelo-Branco and their spouses. I also want to thank Schin-Itchi Horike for his collaboration.

I also thank my colleagues from the Obstetrics and Gynecology Department and from the Pathology Department of Mount Sinai Hospital, including Dr. John Kingdom, Dr. Sarah Keating, Dr. Greg Ryan, Dr. Alan Bocking, for all the help, support, teaching and friendship. I also want to mention Dr. Ariadna Grigoriu for the wonderful collaborative work and friendship.

I thank Theodore Chiang, Shuye Pu and Dr. Shoshana Wodak from the Center for Computational Biology, for being always available to help with my analytical and statistical questions.

I thank the members of my thesis supervisory committee: Dr. Johanna Rommens, Dr. Lucy Osborne and Dr. Lee Adamson for their guidance and support. Their ideas and suggestions were precious for the development of my research.

To my supervisor, Dr. Rosanna Weksberg, I thank for all the knowledge, the SUPPORT, help, friendship and effort she made to make me reach my goals. In spite of her many commitments in the last months she has been always available and I am sincerely thankful for that.

I also want to thank my teachers at the courses I took and the friends I made – Carlos Ruiz, Gwen Schwartz – while taking those courses.

Finally, I want to thank my family for the support in spite of my absence and, in special, my wife Maria Amélia Paiva, for her love, support and for having to stand one year of separation so that I could accomplish this work.

Contributors

Dr. Sanaa Choufani, a research associate in Dr. Rosanna Weksberg’s laboratory, contributed with clarification of the epigenetic and imprinting concepts, experimental design, pyrosequencing technical support and data analysis.

Dr. Daria Grafodatskaya, a current post-doctoral fellow in Dr. Rosanna Weksberg’s laboratory, contributed to the work described in Chapter 8 and to the development of the array technology in the laboratory and data analysis.

Dr. John Kingdom, a physician specialist in Obstetrics and Maternal-Fetal Medicine, contributed with clarification of the concepts of intrauterine growth restriction, small for gestational age and placenta insufficiency, experimental design and facilitation of sampling process.

Dr. Sarah Keating, a pathologist in the Pathology Department of Mount Sinai Hospital, contributed with clarification of placenta pathology concepts and experimental design, did the pathology assessments of the placentas and provided all the pathology images used in this thesis.

Dr. Shin-Itchi Horike and Dr. M. Meguro-Horike, former postdoctoral fellows in Dr. Steve Scherer’s Laboratory, contributed to the work described in Chapter 4

Sarah Ickowicz, a former summer student in Dr. Rosanna Weksberg’s laboratory, contributed to the work described in Chapter 5.

Yi-An Chen, a current MSc student in Dr. Rosanna Weksberg’s laboratory, contributed to the work described in Chapter 5.

Dr. Ariadna Grigoriu, a former Maternal-Fetal Medicine fellow in the Obstetrics and Gynecology Department of Mount Sinai Hospital, contributed to the work described in Chapter 6.

Rageen Ragendram, a summer student in Dr. Rosanna Weksberg’s laboratory, contributed to the work described in Chapter 8.

Theodore Chiang, a Bioinformatics Analyst in the Center for Computational Biology of the Hospital for Sick Children, contributed to the work described in Chapter 8. vi

Table of Contents

Acknowledgments ...... iv

Contributors ...... vi

Table of Contents ...... vii

List of Tables ...... xiv

List of Figures ...... xvi

List of Appendices ...... xix

Abbreviations ...... xx

Chapter 1: Introduction ...... 1

1.1 Intrauterine Growth Restriction ...... 3

1.2 The Role of the Placenta in Fetal Growth ...... 4

1.3 The Pathology of “Placental Insufficiency” ...... 4

1.4 Epigenetic Mechanisms ...... 6

1.4.1 DNA methylation ...... 7

1.4.1.1 Methods of study of DNA methylation ...... 10

1.4.2 Histone modifications ...... 16

1.4.3 MicroRNA ...... 16

1.4.4 Genomic imprinting ...... 17

1.5 Epigenetic Dysregulation ...... 19

1.6 Environmental Effects on Epigenetic Modifications ...... 20

1.7 IUGR and Epigenetics ...... 21

1.7.1 Evidence for epigenetic regulation in placenta ...... 21

1.7.2 Epigenetics dysregulation and IUGR ...... 23

1.7.3 MicroRNAs in disorders of placental / fetal growth and development ...... 25

1.8 Long Term Sequelae of IUGR – A Role for Epigenetic Programming? ...... 26

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1.9 Conclusions ...... 29

Chapter 2: Hypotheses, Research Aims and Thesis Outline ...... 31

2.1 Hypotheses ...... 32

2.2 Aims and thesis outline ...... 32

Chapter 3: Methods and Techniques ...... 36

3.1 Introduction and Study Design ...... 37

3.2 Sample Collection, Processing and Characterization ...... 39

3.3 Statistical Analysis ...... 44

3.4 DNA Extraction from Blood ...... 48

3.5 DNA and RNA Extraction from Placenta ...... 48

3.6 Nucleic Acid Samples Quantitation and Quality Assessment ...... 49

3.7 cDNA Preparation by Reverse Transcription ...... 50

3.8 Bisulfite Conversion of DNA ...... 50

3.9 Agilent® Methylation Arrays ...... 53

3.10 Illumina® Methylation Arrays...... 55

3.11 Illumina® Expression Arrays ...... 57

3.12 Southern Blot Analysis of DNA Methylation ...... 58

3.13 Pyrosequencing Analysis of DNA Methylation ...... 59

3.14 Quantitative Reverse Transcription PCR (qRT-PCR) ...... 61

3.15 Description of Samples Used in the Experiments ...... 62

Chapter 4: Postnatal Phenotype of Russell-Silver Syndrome Caused by Epimutation in Imprinting Center H19 DMR or UPD7...... 67

4.1 Summary ...... 68

4.2 Introduction ...... 68

4.3 Materials and Methods ...... 70

4.3.1 Clinical and biological samples ...... 70

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4.3.2 Methylation studies ...... 71

4.3.2.1 Southern blot ...... 71

4.3.2.2 Pyrosequencing ...... 71

4.4 Results ...... 73

4.4.1 Chromosome 11p15.5 methylation analyses ...... 73

4.4.2 Methylation analysis of other imprinted loci ...... 77

4.4.3 Phenotype analysis ...... 77

4.4.4 Complex mosaic patient ...... 87

4.5 Discussion ...... 87

Chapter 5: Comparative Analysis of DNA Methylation at Imprinting Centers in Placental DNA ...... 94

5.1 Summary ...... 95

5.2 Introduction ...... 96

5.3 Materials and Methods ...... 98

5.3.1 Sample selection ...... 98

5.3.2 DNA methylation analysis by pyrosequencing ...... 99

5.3.3 H19 allelic expression analysis ...... 99

5.3.4 DNA methylation analysis by microarray ...... 100

5.3.5 RNA expression analysis by microarray ...... 100

5.3.6 Data analysis ...... 101

5.3.6.1 H19 DMR methylation analysis ...... 101

5.3.6.2 Methylation array data analysis ...... 101

5.3.6.3 Comparison analysis of methylation and expression arrays ...... 103

5.4 Results ...... 104

5.4.1 H19 DMR methylation and fetal growth ...... 104

5.4.2 H19 DMR methylation in blood and H19 allelic expression in placenta of hypomethylated cases ...... 108 ix

5.4.3 Imprinting centers methylation and IUGR ...... 109

5.4.4 Effect of methylation aberration of ICs in expression of the respective imprinted genes ...... 119

5.5 Discussion ...... 123

Chapter 6: Cell Specific Patterns of DNA Methylation in the Human Placenta ...... 128

6.1 Summary ...... 129

6.2 Introduction ...... 129

6.3 Materials and Methods ...... 131

6.3.1 Sample selection and cell separation ...... 131

6.3.2 Sequential enzymatic separation ...... 131

6.3.3 Magnetic bead separation ...... 132

6.3.4 Immunocytochemistry protocol ...... 132

6.3.5 DNA/RNA extraction ...... 133

6.3.6 DNA methylation analysis by microarrays ...... 133

6.3.7 Methylation array data analysis ...... 133

6.3.7.1 Global comparisons between samples ...... 133

6.3.7.2 Differential methylation analysis and enrichment analysis ...... 133

6.3.8 Pyrosequencing validation of selected regions ...... 134

6.4 Results ...... 137

6.4.1 Placenta fractionation and cell population enrichment ...... 137

6.4.2 DNA methylation analysis ...... 137

6.4.3 Global methylation comparison among samples ...... 138

6.4.4 Cell specific differential methylation analysis ...... 145

6.4.5 Targeted validation of APC, TP73 and CGB5 ...... 151

6.5 Discussion ...... 158

Chapter 7: Global Genomic Comparative Analysis of Methylation of Placenta DNA Between Intrauterine Growth Restriction Cases and Controls Using MeDIP CpG Island Arrays and Follow-up Study of a Selected Candidate – WNT2 ...... 163

7.1 Summary ...... 164

7.2 Introduction ...... 164

7.3 Materials and Methods ...... 167

7.3.1 Sample selection, characterization and processing ...... 167

7.3.2 DNA methylation enrichment and microarray hybridization ...... 168

7.3.3 CGI microarray data analysis ...... 168

7.3.4 Pyrosequencing of WNT2 in the placenta and blood ...... 168

7.3.5 Quantitative real-time RT-PCR ...... 169

7.3.6 Statistical analyses of the results for the extended cohort ...... 169

7.4 Results ...... 173

7.4.1 Differential methylation screening between cases and controls ...... 173

7.4.2 Validation of WNT2 promoter methylation in microarrays by pyrosequencing . 180

7.4.3 WNT2 promoter methylation and WNT2 expression in the placenta ...... 187

7.4.4 WNT2 promoter methylation and expression in multiple samples from the same placenta ...... 192

7.4.5 WNT2 promoter methylation in cord blood ...... 197

7.4.6 Association of WNT2 promoter methylation with birthweight percentile ...... 197

7.5 Discussion ...... 202

Chapter 8: Assessment of Methylation Level Prediction Accuracy in Methyl-DNA Immunoprecipitation and Sodium Bisulfite Based Microarray Platforms...... 211

8.1 Summary ...... 212

8.2 Introduction ...... 212

8.3 Materials and Methods ...... 215

8.3.1 Data analysis ...... 218

8.4 Results ...... 220 xi

8.4.1 BC-arrays versus BC-pyro ...... 220

8.4.2 MeDIP-CGI-arrays versus BC-pyro ...... 221

8.4.3 MeDIP-CGI-arrays versus BC-arrays ...... 227

8.5 Discussion ...... 230

Chapter 9: Novel Global Genomic Comparative Analysis of Methylation of Placenta DNA Between Intrauterine Growth Restriction Cases and Controls Using Bisulfite Based CpG Arrays ...... 233

9.1 Summary ...... 234

9.2 Introduction ...... 234

9.3 Materials and Methods ...... 236

9.3.1 Sample selection ...... 237

9.3.2 DNA methylation analysis by microarray ...... 237

9.3.3 RNA expression analysis by microarray ...... 237

9.3.4 Data analysis ...... 238

9.3.4.1 Methylation array data analysis ...... 238

9.3.4.2 Methylation and expression array data analysis ...... 238

9.4 Results ...... 239

9.4.1 Differential methylation between IUGR and controls ...... 239

9.4.1.1 Cluster analysis of methylation in cases and controls ...... 240

9.4.1.2 Frequency of aberrant methylation frequency in cases and controls.... 240

9.4.1.3 Identification of CpGs with differences in methylation between cases and controls ...... 245

9.4.1.4 Cross validation of the Illumina® methylation data using WNT2 methylation data of Chapter 7 ...... 248

9.4.2 Gene expression analysis for the differentially methylated genes ...... 248

9.4.2.1 Correlation between methylation and expression for the differentially methylated genes ...... 251

9.4.2.2 Identification of differences in expression between cases and controls for the differentially methylated genes that correlated with expression 252 xii

9.4.2.3 Cross validation of the Illumina® expression data using WNT2 expression data of Chapter 7 ...... 258

9.5 Discussion ...... 259

Chapter 10: Summary and Conclusions, General Discussion and Future Directions ...... 266

10.1 Summary and Conclusions ...... 267

10.1.1 Candidate gene promoter regions with methylation changes in association with IUGR ...... 267

10.1.2 No consistent role for DNA methylation alterations of imprinting centers in IUGR ...... 268

10.1.3 Cell type specificity of DNA methylation in the placenta ...... 268

10.1.4 Evaluation of techniques used for DNA methylation assessment ...... 269

10.2 General Discussion ...... 269

10.3 Future Directions ...... 271

10.3.1 Follow-up studies to validate the most promising candidates identified in my studies ...... 271

10.3.2 Are the methylation alterations identified in the placenta of low birthweight percentile neonates relevant to the occurrence of adult onset disorders? ...... 274

10.3.3 Other issues that need to be addressed to clarify the role of epigenetic variation in placenta and placental disease ...... 276

10.3.4 Other more general epigenetics / DNA methylation unanswered questions ...... 279

References ...... 282

Appendices ...... 321

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List of Tables

Table 1.1: Techniques used in the study of DNA methylation ...... 11

Table 3.1: Inclusion and exclusion criteria for selection of placenta samples for methylation analysis ...... 40

Table 3.2: Placental lesions analyzed in the placental pathology exam ...... 45

Table 3.3: Details of samples run on the microarray– characteristics, DNA sources and arrays used ...... 64

Table 3.4: Characteristics of the samples analyzed with the Agilent® methylation arrays ...... 65

Table 3.5: Characteristics of the samples analyzed with the Illumina® methylation arrays ...... 65

Table 3.6: Characteristics of the samples used in the validation cohort ...... 66

Table 4.1: Oligonucleotide primers for southern blot probe and pyrosequencing PCR amplicons ...... 72

Table 4.2: PCR conditions for H19 DMR and promoter segments for pyrosequencing ...... 72

Table 4.3: Molecular findings of cases with domain 1 epimutations at H19 locus ...... 76

Table 4.4: Clinical phenotypes of cases found with chromosome 11 epimutations ...... 80

Table 4.5: Phenotypic features of published cases with chromosome 11 or 7 molecular alteration ...... 81

Table 4.6: Criteria developed based on the clinical findings of RSS cases with IC1 epimutation or mUPD7 in the cohort (excluding P11) and from reported cases ...... 86

Table 4.7: Comparison of molecular techniques used for H19 DMR assessment in different reports and their respective yields ...... 91

Table 5.1: DNA methylation and allelic expression for H19 DMR ...... 107

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Table 5.2: Difference between the frequency of outlier and far outlier probes in cases and controls for IC mapping probes and, for comparison, for all the probes ...... 118

Table 5.3: ICs and samples selected for comparison, between their methylation level categories, of the expression level of the corresponding imprinting regulated genes ...... 120

Table 6.1: Genes Selected from the Arrays as possible Tumor Suppressor Genes and Oncogenes ...... 135

Table 6.2: Oligonucleotide primers used for pyrosequencing PCR amplicons in genomic regions selected for validation ...... 136

Table 6.3: PCR conditions for pyrosequencing of genomic regions selected for validation ...... 136

Table 6.4: Probes more methylated in Cytotrophoblasts than Fibroblasts mapping to tumor suppressor genes ...... 152

Table 7.1: Oligonucleotide primers for WNT2 expression and methylation studies ...... 170

Table 7.2: PCR conditions for WNT2 promoter pyrosequencing ...... 170

Table 7.3: Autosomal genomic regions differentially methylated between IUGR cases and controls ...... 178

Table 8.1: Genomic regions covered in the comparisons between array and pyrosequencing data ...... 217

Table 9.1: Genes selected with differences in methylation between cases and controls ...... 246

Table 9.2: Details of differentially methylated candidate genes proposed for validation ...... 257

List of Figures

Figure 1.1: Proposed model for epigenetic fetal reprogramming underlying predisposition to late onset disease...... 28

Figure 3.1: Preparation of the samples prior to DNA and RNA extraction...... 42

Figure 3.2: Microscopic pathology images of the placenta demonstrating some of the lesions considered as evidence of placental dysfunction...... 46

Figure 3.3: Non methyl cytosine sodium bisulfite conversion reaction...... 51

Figure 4.1: Methylation analysis of chromosome 11p15.5 locus in individuals with RSS...... 74

Figure 4.2: Methylation analyses of the MEG3/GTL2, PEG1/MEST CpG islands and of the SNRPN DMR1...... 78

Figure 4.3: Blood DNA molecular tests of patient found to have epimutations in all tested sites...... 88

Figure 5.1: Boxplot of H19 DMR methylation values (Y-axis) of all samples (n=170)...... 105

Figure 5.2: Detailed results of the CpGs mapping to ICs...... 110

Figure 5.3: Comparison between the number of outliers and far outliers in IUGR cases and in controls...... 113

Figure 5.4: Comparison between the distributions of methylation of H19 DMR for the pyrosequencing and for the array data...... 116

Figure 5.5: Comparison, between methylation level categories of selected IC, of the expression level of the corresponding imprinting regulated genes...... 121

Figure 6.1: Immunocytochemistry of cells isolated from Placenta...... 139

Figure 6.2: Correlation between the biological replicates...... 141

Figure 6.3: Cluster analysis for Placenta, isolated Cytotrophoblasts and Fibroblasts (n = 17). . 143 xvi

Figure 6.4: Selection criteria of differentially methylated CpG dinucleotides...... 146

Figure 6.5: Methylation values of the CpG sites mapping to the CGB Genes in Cytotrophoblasts and Fibroblasts...... 149

Figure 6.6: Methylation values of CpG sites mapping to Tumor Suppressor Genes represented by multiple probes in Cytotrophoblasts and Fibroblasts...... 153

Figure 6.7: Pyrosequencing validation of the methylation values measured by the arrays...... 156

Figure 6.8: Pyrosequencing validation of the methylation differences identified by the array analysis...... 159

Figure 7.1: CGI microarray and pyrosequencing data for the WNT2 promoter of two case samples one with high and one with low promoter methylation...... 171

Figure 7.2: Correlations between placenta samples, between blood samples and between placenta and blood samples...... 174

Figure 7.3: Euclidean non-hierarchical cluster analysis of the arrays...... 176

Figure 7.4: UCSC bar plots of all cases and controls for each of the probes mapping to the CpG Island overlapping WNT2...... 181

Figure 7.5: Expression of WNT2 across tissues...... 183

Figure 7.6: Comparison of array and pyrosequencing data...... 185

Figure 7.7: Distribution of WNT2 promoter methylation analyzed by pyrosequencing...... 188

Figure 7.8: Correlation between methylation of WNT2 promoter methylation and expression. 190

Figure 7.9: Correlation between two sites from the same placenta for WNT2 methylation and gene expression...... 193

Figure 7.10: Comparison of term pathology samples from high and low WNT2 promoter methylation...... 195

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Figure 7.11: High WNT2 promoter methylation occurs in the placenta but not in cord blood. .. 198

Figure 7.12: Association between high WNT2 promoter methylation and reduced birthweight percentile...... 200

Figure 7.13: WNT2 expression and birthweight percentile...... 203

Figure 7.14: Frequency of high WNT2 promoter methylation in placenta DNA samples according to sex...... 205

Figure 8.1: Correlation between BC-pyro and the two array platforms...... 222

Figure 8.2: Correlation between BC-pyro and the transformed MeDIP-CGI-arrays output...... 225

Figure 8.3: Correlation between the two array platforms for the same sample...... 228

Figure 9.1: Euclidean non-hierarchical cluster analysis of the methylation arrays...... 241

Figure 9.2: Comparison between the number of outliers and far outliers in each of the cases and in each of the controls...... 243

Figure 9.3: Detailed categorical results of all the 60 CpGs selected as significantly different between cases and controls, excluding the X chr. mapping probes...... 249

Figure 9.4: Details of the genes selected for further follow-up: ...... 253

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List of Appendices

Appendix 1 – List of genes considered to have biologically meaningful differences in DNA methylation between cytotrophoblasts and fibroblasts (Chapter 6) ...... 322

Appendix 2 - List of publications ...... 340

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Abbreviations

AC Acute Chorioamnionitis ACADL Acyl-Coa Dehydrogenase, Long Chain ACOG American College of Obstetrics And Gynecology AIMS Amplification of Inter-Methylated Sites ANOVA Analysis of Variance APC Adenomatous Polyposis Coli ASE Allele-Specific Expression ASM Allele-Specific Methylation ATP Adenosine Tri-Phosphate AVM Advanced Villous Maturation BATMAN Bayesian Tool for Methylation Analysis BC Bisulfite Conversion BiMP Bisulfite Methylation Profiling β-ME Beta-Mercaptoethanol BSA Bovine Serum Albumin BSPP Bisulfite Padlock Probes BW% Birthweight Percentile BWS Beckwith-Wiedemann Syndrome cDNA Complementary DNA CGA Chorionic Gonadotropin, Alpha Polypeptide CGB Chorionic Gonadotrophin, Beta Polypeptide CGH Comparative Genomic Hybridization CGI CpG Island CHARM Comprehensive High-Throughput Arrays for Relative Methylation CHIP Chromatin Immunoprecipitation CNV Copy Number Variation COBRA Combined Bisulfite Restriction Analysis CTCF Ccctc-Binding Factor (Zinc Finger Protein) CYP11A1 Cytochrome P450, Family 11, Subfamily A, Polypeptide 1 CpG Cytosine-phosphate-Guanine dinucleotide DAB2 Disabled Homolog 2, Mitogen-Responsive Phosphoprotein DAB2IP Dab2 Interacting Protein DMEM/F12 Dulbecco’s Modified Eagle Medium Nutrient Mixture F12 DMH Differential Methylation Hybridization DMR Differentially Methylated Region DMSO Dimethyl sulfoxide DNA Deoxyribonucleic Acid DNMT DNA Methyltransferase DNP 2,4-dinitrophenol dNTP Dinucleotide Tri-Phosphate DTT Dithiothreitol DV Decidual Vasculopathy DVH Distal Villous Hypoplasia EDTA Ethylenediaminetetraacetic Acid EVT Extravillous Trophoblast FBS Fetal Bovine Serum FSH Follicle Stimulating Hormone FVT Fetal Vascular Thrombotic Lesions GA Gestational Age xx

GAPDH Glyceraldehyde-3-Phosphate Dehydrogenase GPX3 Glutathione Peroxidase 3 HBSS Hank’s Buffered Salt Solution hCG Human Chorionic Gonadotropin HDAC1 Histone Deacetylase 1 HELLP Hemolysis, Elevated Liver Enzymes, Low Platelets HELP Hpaii Tiny Fragment Enrichment by Ligation-Mediated PCR HPLC High-Performance Liquid Chromatography IC Imprinting Centre IGF2 Insulin-Like Growth Factor 2 INHA Inhibin, Alpha IQR Interquartile Range IUGR Intrauterine Growth Restriction IVT Intervillous Thrombosis LH Luteinizing Hormone LOM Loss of Methylation LUMA Luminometric Methylation Assay MALDI-TOF- MS Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry MCA Methylation CpG Island Amplification MCAM MCA With Microarray Hybridization MCA-RDA MCA With Representational Difference Analysis MeCP2 Methyl-CpG Binding Protein MeDIP Methyl DNA Immunoprecipitation MEST Mesoderm Specific Transcript Homolog MI Maternal Infarction Or Methylation Index MIRA Methylated CpG Island Recovery Assay miRNA Micro RNA MMASS Microarray-Based Methylation Assessment of Single Samples MORF4L1 Mortality Factor 4 Like 1 mRNA Messenger RNA MS-AP-PCR Methylation-Sensitive Arbitrarily Primed Pcr MSCC Methylation-Sensitive Cut Counting MSDK Methylation-Specific Digital Karyotyping MS-MA Methylation-Specific Melting Analysis MS-MLPA Methylation-Specific Multiplex Ligation-Dependent Probe Amplification MSO Methylation-Specific Oligonucleotide MS-PCR / MSP Methylation-Sensitive or Specific PCR MSRF Methylation Sensitive Restriction Fingerprinting MS-SNuPE Methylation-Sensitive Single Nucleotide Primer Extension MS-SSCP Methylation-Sensitive Single Strand Conformational Polymorphism MTHFR 5,10-Methylenetetrahydrofolate Reductase mUPD Maternal UPD NGS Next Generation Sequencing pUPD Paternal UPD PAPPA Pregnancy-Associated Plasma Protein A PAPPA2 Pregnancy-Associated Plasma Protein-A2 PCR Polymerase Chain Reaction PBS Phosphate Buffered Saline PEG3 Paternally Expressed 3 PFD Perivillous Fibrin Deposition PHLDA2 Pleckstrin Homology-Like Domain, Family A, Member 2 PLAGL1 Pleiomorphic Adenoma Gene-Like 1

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PRKCDBP Protein Kinase C, Delta Binding Protein qPCR Quantitative PCR RASSF1, F2, F5 Ras Association (Ralgds/Af-6) Domain Family Member 1, 2, 5 RISC RNA-Induced Silencing Complex RLGS Restriction Landmark Genomic Scanning RNA Ribonucleic Acid RRBS Reduced Representation Bisulfite Sequencing RSS Russell-Silver Syndrome RT-PCR Reverse Transcription PCR SAM S-Adenosyl Methionine SGA Small for Gestational Age SMRT Single-Molecule, Real-Time SNP Single Nucleotide Polymorphism sVEGRF Soluble VEGRF TNDM Transient Neonatal Diabetes Mellitus TP73 Tumor Protein P73 TSH Thyroid Stimulating Hormone TSS Transcription Start Site UCSC University of California, Santa Cruz UPD Uniparental Disomy VEGF Vascular Endothelial Growth Factor VEGFR Vascular Endothelial Growth Factor Receptor WGSBS Whole-Genome Shotgun Bisulfite Sequencing WNT2 Wingless-Type MMTV Integration Site Family Member 2 WNT2PrMe WNT2 Promoter Methylation

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Chapter 1: Introduction

The published version of this article has appeared in:

Ferreira, J.C., Choufani, S., Kingdom, J., Weksberg, R. (2010): Epigenetic Programming and Fetal Growth Restriction. Fetal and Maternal Medicine Review, 21 (3), 204-224.

The chapter that follows is an extended version.

Normal fetal growth and development depends on multiple molecular mechanisms that coordinate both placental and fetal development. Both genetic and environmental factors are likely to be involved. Efforts to better understand fetal/placental growth dysregulation are now being driven by several findings that highlight the long term impact of low birthweight on susceptibility to disease. The association of poor fetal growth with perinatal medical complications is well accepted but more recent data also show that low birthweight is linked to common serious adult health problems. Several large-scale human epidemiological studies from diverse countries have shown that conditions such as coronary heart disease, hypertension, stroke, type 2 diabetes mellitus, adiposity, insulin resistance and osteoporosis are more prevalent in individuals with a history of low birthweight (Antoniades et al., 2003; Barker et al., 1993; Barker et al., 1989; Dennison et al., 2005; Forsen et al., 2000; Godfrey and Barker, 2000; Hales et al., 1991; Leon et al., 1998; Lithell et al., 1996; Osmond et al., 1993; Roseboom et al., 2001; Yarbrough et al., 2000). More recently the association has been demonstrated to be with low birthweight percentile and not with prematurity (Hallan et al., 2008; Kaijser et al., 2008).

Fetal and placental growth are impacted by genetic and environmental factors. Of particular interest, both genetic and environmental factors can influence DNA transcription via stable somatic heritable epigenetic marks on both DNA and its associated chromatin proteins. That is, epigenetic marks modulate gene expression without changes in primary DNA sequence. In this way, genetically determined or environmentally induced effects may persist for long periods of time or across generations. This makes fetal epigenetic programming an attractive candidate mechanism by which to link early developmental events such as fetal growth restriction and later disease susceptibility.

In this review I will describe the body of experimental data that provide support for the hypotheses that epigenetic mechanisms have a role in placental development and function, that they influence fetal growth, and, finally, that they may underlie the association between fetal growth compromise and predisposition to adult onset disease.

1.1 Intrauterine Growth Restriction

A hallmark of poor fetal growth is low birthweight percentile. Newborns with birthweight percentiles less than the 10th percentile for gestational age are known as “small for gestational age” (SGA) (Chaddha et al., 2004). Some SGA infants are small but otherwise normal. Others have disorders directly affecting the fetus such as genetic or infectious diseases. Still others, mostly towards the lower end of the birthweight percentile spectrum, are growth restricted as a result of an underlying dysfunction of the placenta that prevents the fetus from achieving its normal growth potential. Placental dysfunction is considered a common cause of fetal growth compromise. Reduced fetal growth, when associated with placental dysfunction, or insufficiency, is referred to as Intrauterine Growth Restriction (IUGR). IUGR is a clinically relevant heterogeneous entity with increased risk for perinatal mortality and morbidity. Individuals with this condition are identified mainly among fetuses or newborns with lower birthweight percentile such as SGA plus other features. IUGR can present with pre- and postnatal clinical characteristics that reflect the compromise of oxygenation and nutritional support typically provided by a normally developed placenta (see Section 1.3 below). The frequency of IUGR among SGA cases is difficult to determine and varies according to the diagnostic criteria - prenatal clinical and ultrasound / Doppler studies or post natal / pathological and clinical assessments. For instance, in a study by Madazli and colleagues ~60% of fetuses found by ultrasound measurement to be SGA had abnormal Doppler velocimetry of umbilical or uterine arteries, whereas 77% had pathological anomalies (Madazli et al., 2003).

Even though familial and transgenerational effects have been described for IUGR (Selling et al., 2006), most cases seem to be sporadic. Several factors have been positively associated with fetal growth compromise (Maulik, 2006a). These include pre-conceptional (e.g. maternal cardiovascular disorders (Plouin et al., 1983; Sibai, 2002)) peri-conceptional (e.g. subfertility, assisted reproductive technologies (Helmerhorst et al., 2004; Jackson et al., 2004; McDonald et al., 2005)) and pregnancy-related diseases (e.g. primary placental insufficiency). Maternal hemodynamic, vascular or hypoxic conditions can impair, secondarily, the function of the placenta. However abnormal placental vascular development, demonstrable by pathologic characterization of this organ, can occur as a seemingly primary event with no identifiable predisposing maternal clinical condition (e.g. thrombophilia), although some common risk

factors, such as high blood pressure, maternal smoking, may be present. This idiopathic form appears to be the most common type of IUGR (Henriksen and Clausen, 2002).

Although many children with low birthweight percentile are normal / healthy, a significant number will require medical intervention during pregnancy, early postnatal period and / or childhood (Maulik, 2006b; Pallotto and Kilbride, 2006). The lack of observational prospective long term association studies between prenatal findings, placental lesions and postnatal early and late outcomes makes it difficult to use the former (prenatal findings, placental lesions) to predict the latter (postnatal early and late outcomes).

1.2 The Role of the Placenta in Fetal Growth

Maternal allocation of oxygen and nutrients to the developing embryo is mediated via the placenta and is crucial for the achievement of normal fetal growth. The task is achieved first by directing maternal blood flow to the implantation site via increased maternal cardiac output and enlargement of the uteroplacental arteries and second, by bringing maternal and fetal blood into intimate contact over a large surface area (via the development of specialized chorionic villi). The development of chorionic villi is critical for fetal survival since maternal blood is delivered to the intervillous space in excess of fetal oxygen requirements (Lang et al., 2003; Meschia, 1985; Wilkening and Meschia, 1983) in a newly created low resistance vascular system (Biswas et al., 2008; Meekins et al., 1994; Pijnenborg, 2000).

The placenta has other important functions that support fetal growth. For example, the placenta produces the Insulin-like Growth Factors (IGFs) which are the main growth promoting hormones during fetal life (Randhawa, 2008). It also has nutrient storage functions (Fisher and Laine, 1983).

1.3 The Pathology of “Placental Insufficiency”

IUGR is, by definition, attributed to a broad and vaguely-defined condition called “placental insufficiency” or “maternal vascular underperfusion” (Mayhew et al., 2007; Redline et al., 2004).

Placental insufficiency can present clinically not only as IUGR, but also in association with preeclampsia, fetal death, abruption placentae, and preterm birth (Chaddha et al., 2004).

To perinatologists, this term, placental insufficiency, acknowledges the placental basis of IUGR and implies several defects: reduced uteroplacental blood flow, reduced placental size, and damage to the placental structure (Chaddha et al., 2004; Toal et al., 2007). Commonly there is abnormal prenatal uterine and/or umbilical artery blood flow that reflects increased downstream vascular resistance (Baschat et al., 2000; Cnossen et al., 2008; Coleman et al., 2000; Ghosh et al., 2006; Kingdom et al., 2000; Todros et al., 1999). Further, several pathological placental lesions suggesting abnormal vascular development of the placenta are frequently found (Kadyrov et al., 2006; Mayhew et al., 2007; Redline, 2008; Redline et al., 2005). To a pathologist, the term encompasses a variety of features: small placental size, eccentric 2-vessel cord, pathological narrowing or thrombosis of the maternal feeding vessels (Decidual Vasculopathy (DV)), infarction of placental villi (Maternal Infarction (MI)), reduced numbers of villi (Distal Villous Hypoplasia (DVH)) and / or abnormal covering of trophoblast with excess “syncytial knot” formation characteristic of Advanced Villous Maturation (AVM). Primary chronic placental insufficiency is preceded by poor trophoblastic invasion of the spiral arteries of the placental bed (Chaddha et al., 2004; Figueroa and Maulik, 2006). It is associated with a chronic abnormal oxygenation state of the placenta and it likely has a variety of molecular and cellular causes.

It is also worth mentioning that the outcomes of early and late onset placental insufficiency are different. Also, newborns identified as having severe growth restriction (estimated birthweight less than 5th percentile) early in pregnancy (between the middle and the end of the second trimester) versus the ones identified with moderate growth restriction (estimated birthweight between 5th to 10th percentile) beyond the middle of the third trimester are likely to differ in etiology and pathogenesis. The former are more likely to present with all the classic clinical prenatal, postnatal and placenta pathology hallmarks of placental insufficiency.

The basic molecular mechanism(s) that cause poor trophoblastic invasion found in primary chronic placental dysfunction are still unknown. Research on these conditions has shed light onto some of the intermediary molecular mechanisms involved in placental development and growth (e.g. insulin-like growth factor pathway, reviewed elsewhere (Carter et al., 2004; Figueroa and Maulik, 2006; Mayhew and Desoye, 2004)). However, the primary molecular mechanisms

6 underlying abnormal placental development are yet to be identified. In this regard as pointed by Lisa A. Joss-Moore and colleagues “The persistent nature of altered mRNA levels of key genes in IUGR suggests epigenetic dysregulation of gene transcription.” (Joss-Moore and Lane, 2009).

1.4 Epigenetic Mechanisms

Epigenetic regulation refers to changes in gene expression that occur via chemical modification of the DNA and its associated proteins without a change in the DNA sequence itself. Epigenetic modifications include DNA methylation, covalent modifications of histone proteins, chromatin conformation and microRNA or non-coding RNA-mediated control of gene expression. The different epigenetic mechanisms are interdependent and determine chromatin packaging of DNA and nuclear organization. Although the work on this thesis focuses on DNA methylation, the other types of epigenetic modifications are layered onto the DNA along with DNA methylation.

Epigenetic modifications are characterized by somatic heritability, through mitosis (Feinberg, 2007), conferring stable modulation of gene expression. Epigenetic mechanisms in general and specifically DNA methylation, play an important role in a variety of processes that require programmed transcriptional regulation, e.g. cell and tissue differentiation (Kim et al., 2010; Reik et al., 2001), genomic imprinting (see below) (Edwards and Ferguson-Smith, 2007; Kacem and Feil, 2009; Recillas-Targa, 2002), X-inactivation (Valley and Willard, 2006) and silencing of transposons (portions of foreign DNA, such as viral DNA, incorporated into the human genome) (Emerman and Temin, 1984; Reiss et al., 2010; Whitelaw and Martin, 2001).

Some epigenetic regulatory systems are more complex than others. For example, epigenetic modifications can occur individually, e.g. at gene promoters, or multiple modifications can be layered hierarchically across large genomic domains as in genomic imprinting or X-inactivation.

Epigenetic programming occurs during normal embryonic and fetal development and differentiation. Some epigenetic marks are very stable as they are tightly regulated by the cell, e.g. genomic imprinting (see below). Others demonstrate developmental plasticity since environmental factors can alter the epigenetic patterns and thereby impact phenotype. That is, nutrition restriction or hypoxia may induce persistent adaptative changes via epigenetic

7 mechanisms that may influence an individual’s sensitivity to environmental factors later in life and thus the risk of adult-onset disease (Chawla et al., 1996; Heijmans et al., 2008; Mathers, 2007; Wellmann et al., 2008; Yang et al., 2009).

The following subsections will describe various types of epigenetic modifications, their role in imprinting and responses to the environment. A particular focus will be on normal epigenetic regulation in placenta, as well as the consequences of epigenetic dysregulation in this organ. Since the main focus of the work that I developed for this thesis was DNA methylation, this epigenetic mechanism will be more detailed, especially in the section devoted to the description of the molecular techniques used for its study.

1.4.1 DNA methylation

One of the most well-studied epigenetic mechanisms is DNA methylation. DNA methylation is a heritable yet reversible epigenetic mark that can be stably propagated following DNA replication and can influence gene expression. In eukaryotic species, DNA methylation involves the transfer of a methyl group to the carbon-5 of the cytosine ring, for the most part when cytosine is located in CpG dinucleotides (Bird, 2002). Distinct DNA methyltransferases (DNMT) catalyze de novo DNA methylation and maintenance of DNA methylation. DNA methyltransferases 3A, 3B and 3L (DNMT3A, 3B and 3L) are involved in the addition of de novo methyl groups, hence in the creation of de novo methylation patterns. DNMT1 is involved in the addition of methyl groups to the replicating newly formed DNA strand, wherever the template strand is already methylated, hence in maintenance of the methylation patterns. Demethylation may occur, passively, by inhibition of DNMT1. A DNA “demethylase” has also been postulated to exist in order to explain the occurrence of demethylation in non-dividing cells. However, at the time of this writing, such enzymatic activity has not been unequivocally demonstrated (De Carvalho et al., 2010).

The haploid human methylome consists of 28,233,094 CpGs, nearly 70% of which are methylated in normal cells (Brena and Costello, 2007). CpGs are non-randomly distributed throughout the human genome. In 99.32% of the genome they appear at a frequency of 1 per 50 dinucleotides; in 0.68% of the genome they are found at a frequency of 9 per 50 dinucleotides. These concentrations of CpG dinucleotides are called CpG islands (CGI) (Rollins et al., 2006). CGI are regions of the genome defined by several parameters including the ratio of observed to

8 expected CpG densities predicted based on nucleotide density (>0.5 or 0.65), G+C content (>0.5 or 0.55) and a minimum base pair (bp) length (200 or 500 bp) up to several kilobases (kb). Various cut-off values for each one of these parameters have been suggested (Aerts et al., 2004; Gardiner-Garden and Frommer, 1987; Takai and Jones, 2002). The concept of CGI arises from the observation that in most of the genome the observed CpG dinucleotide frequency is only around a quarter of what would be predicted by the density of Cs and Gs nucleotides. This is a consequence of the high frequency of C to T transition mutation rate (~18 times more frequent than the mean of other point mutations) at CpG methylated sites, due to deamination of 5- methyl-cytosine to thymine (Kondrashov, 2003). However CGI are protected from this deamination owing to being demethylated in the germ line (Laird, 2010). Thus, CGIs are evolutionary preserved and likely to have a biological role.

Only 7% of all CpGs are within CGIs whereas 45% of all CpGs in the genome are in repetitive elements, thus accounting for a large proportion of total 5-methyl-cytosine (Rollins et al., 2006). CGIs are mostly associated with gene promoters. In fact, approximately 50 to 75% of genes contain CGIs in their promoters (Bird, 1986; Ioshikhes and Zhang, 2000). Normally most CpGs in the genome are methylated, whereas those in CGIs are unmethylated with the exception of imprinted genes, X-inactivated genes, and retrotransposons (Costello and Plass, 2001).

DNA methylation is a potent mechanism for silencing gene expression and maintaining genome stability. In general, although with exceptions, current evidence suggests that methylation of cytosines at CpG dinucleotides mapping to promoter regions of genes (regions from up to 2 kilobases upstream to 500 base pairs downstream of the transcription start site (TSS)), either within or outside CpG islands, are associated with reduced expression of a gene. Current models assume that DNA methylation blocks the binding of transcription factors and binds methyl- binding proteins and, in concert with histone protein modifications (see 1.4.2 below), leads to chromatin compaction, thereby consolidating gene silencing (Klose and Bird, 2006). Conversely, methylation of CpG islands mapping to exonic or intronic regions of genes is associated with active transcription (Bogdanovic and Veenstra, 2009; Suzuki and Bird, 2008).

Methylation marks in the genome may be similar in both alleles, i.e., biallelic. However, there are several instances in which they occur only or predominantly in one of the two alleles (monoallelic or skewed). Monoallelic methylation can be parent of origin specific, as is the case

9 of the Eutherian mammal’s phenomenon called genomic imprinting (see 1.4.4 below) or it can be random, as is the case for X-chromosome inactivation in females (Jaenisch et al., 1998). Although randomly determined, X chromosome inactivation, once set is fixed, i.e., once one of the X-chromosomes is inactivated early in development, that chromosome is consistently inactivated in all cells derived from that original cell. However, this is not a general rule in mammals. In mouse placenta, the X-chromosome that gets methylated and inactivated is determined by the parent of origin; it is the paternal allele that is methylated; in this regard, X- inactivation in this setting can be said to be imprinted (Takagi, 2003). Furthermore, in humans, allelic X-inactivation is often skewed, especially in extra-embryonic tissues such as placenta (Zeng and Yankowitz, 2003). Finally, monoallelic methylation can also be determined by genetic factors, such as single nucleotide polymorphisms (SNP) (Kerkel et al., 2008; Schalkwyk et al., 2010; Shoemaker et al., 2010). SNP associated monoallelic methylation is very frequent. It can vary quantitatively or qualitatively by tissue, cell type, and among individuals. It can be determined not only by CpG site polymorphisms, but also and most often, by non CpG sites acting in cis. It has been shown that allele-specific methylation (ASM) is sometimes associated, with allele-specific expression (ASE) (Schalkwyk et al., 2010). Intermediate levels of methylation, as quantified by current methods (see 1.4.1.1 below), can result from variation in the number of cells that are biallelically methylated or the number of alleles that are methylated. The association between ASM and ASE may be one of the mechanisms that explain the association between noncoding SNPs and quantitative or even qualitative variation in gene expression. This has obvious implications for associations of such SNPs and phenotypic traits. ASE is, however, not exclusively determined by methylation; other cis and trans-acting genetic mechanisms or even allele-specific histone modifications may also play a role (Krueger and Morison, 2008). Further, methylation variation is not exclusively determined by genotype. There may be a continuum between allele-specific skewed methylation and totally genetically independent methylation variation. In line with this, there may be some alleles that are more susceptible to environmental modifiers of DNA methylation. Most genetically driven monoallelic expression described has been linked to SNPs. However, other genetic polymorphisms, such as Copy Number Variation (CNV) may also play a role and may impact disease. A report on a putative epigenetically dependent inherited familial colon cancer phenotype was later found to be linked to the presence of a deletion occurring upstream of the MSH2 gene, secondarily inducing methylation of its promoter (Chan et al., 2006b; Ligtenberg et

al., 2008). That is, it is possible that some deletions, duplications and even balanced translocations can induce epigenetic modifications, sometimes causing disease.

Importantly both, ASM, ASE and the molecular mechanisms underlying them are likely to play an important role in phenotypic variation, especially in quantitative traits. Furthermore they could also explain paradoxal inheritance patterns. Two carriers of the same disease-associated allele may express the disease differently depending on which allele is expressed. This can explain some phenotypic differences in monozygotic twins as well.

1.4.1.1 Methods of study of DNA methylation

There are several techniques to study DNA methylation. It is beyond the scope of this work to review all of them and comment on their strengths and weaknesses. This has been done by Ho and Tan and, more recently, by Laird (Ho and Tang, 2007; Laird, 2010). A list of those techniques with a short description of their characteristics is presented in Table 1.1.

The development of DNA methylation technology has been driven mainly by cancer research, in which dramatic large scale changes in methylation have been identified. This means that techniques that have been used successfully in cancer research may need to be modified to detect lower variation in methylation.

As in genetic approaches, DNA methylation techniques can be roughly grouped into targeted techniques and global genomic approaches. Usually targeted techniques are used when the aim is to study the methylation status of a given genomic region whereas with global genomic approaches the aim is the global profiling of the methylation status of many genomic regions at once, in an unbiased way. Frequently DNA methylation studies are initiated by genomic screening approaches, which will generate a list of candidate genomic regions that may have a specific biological role e.g., gene expression regulation. Follow-up studies are then required to prove the hypotheses generated by the genomic approaches. Targeted methylation study techniques can be used to confirm the methylation status of the candidate regions. In silico analysis can reinforce the likelihood of a biological role for the candidate region, e.g., methylation changes in CpG Islands overlapping transcription start sites of promoters are more likely to be biologically meaningful. Gene expression studies can be used to confirm an association between methylation and expression. In vitro studies using cell culture exposure to

Table 1.1: Techniques used in the study of DNA methylation A: Enzyme digestion based

Scope of DNA pre- Technique Comments Analytical step the References treatment analysis Methylation sensitive restriction (Huang et al., 1997) fingerprinting (MSRF) Methylation-sensitive arbitrarily primed (Liang et al., 2002) PCR (MS-AP-PCR) Amplification of inter-methylated sites Gel (Frigola et al., 2002) (AIMS) (Akama et al., 1997; Restriction landmark genomic scanning Hatada et al., 1991; 2D Gel (RLGS) Hayashizaki et al., 1993) Differential methylation hybridization (Huang et al., 1999) (DMH) using CGI arrays MethylScope Variant of DMH (Ordway et al., 2006) (Irizarry et al., 2008; Comprehensive high-throughput arrays Variant of DMH Ladd-Acosta et al., for relative methylation (CHARM) Array 2010) Microarray-based methylation assessment Genomic Variant of DMH (Ibrahim et al., 2006) of single samples (MMASS) HpaII tiny fragment enrichment by (Khulan et al., 2006) ligation-mediated PCR (HELP) Enzyme (Brunner et al., 2009) digestion Methyl–seq, MCA–seq, HELP–seq (Oda et al., 2009) NGS Methylation-sensitive cut counting (Ball et al., 2009) (MSCC) Modification of digital Methylation-specific digital karyotyping karyotyping, developed for Sequencing (Hu et al., 2005) (MSDK) DNA copy number analysis Methylation CpG island amplification Gel (MCA) or Sequencing (MCA) with representational difference Gel or Sequencing (Toyota et al., 1999) (MCA-RDA) or array analysis (MCA-RDA) or with microarray or array (Estecio et al., 2007) (MCAM) based hybridization (MCAM) Luminometric methylation assay (LUMA) global methylation Gel or (Karimi et al., 2006) using pyrosequencing measurement pyroSequencing Targeted (with target probe Targeted or Southern blot hybridization) or Genomic Gel (Cedar et al., 1979) Genomic (global methylation analysis) Methylation-specific multiplex ligation- dependent probe amplification (MS- Specific analysis (Procter et al., 2006) MLPA) Targeted Specific analysis: (Singer-Sam et al., HpaII-PCR standard PCR 1990)

Table 1.1: Techniques used in the study of DNA methylation B: Bisulfite conversion based

Scope of DNA pre- Technique Comments Analytical step the References treatment analysis Methylation-specific oligonucleotide Similar to Illumina (Gitan et al., 2002; Hou (MSO)/methylation target microarrays technology, customized et al., 2004) Bisulphite methylation profiling (BiMP) Small genomes (Reinders et al., 2008) Array Different by number of (Bibikova et al., 2006) GoldenGate and Infinium samples per slide and (Steemers and number of probes Gunderson, 2007) Bisulfite converted whole genome Sanger sequencing (Ranade et al., 2009) sequencing Reduced representation bisulfite (Meissner et al., 2005; Genomic sequencing (RRBS) Meissner et al., 2008) Bisulphite conversion followed by capture (Hodges et al., 2009) and sequencing (BC-seq) (Ball et al., 2009; Deng NGS Bisulphite padlock probes (BSPP) et al., 2009; Li et al., 2009) (Cokus et al., 2008; Whole-genome shotgun bisulfite Dunn et al., 2007; sequencing (WGSBS) Lister et al., 2009) Combined bisulfite restriction analysis (Xiong and Laird,

(COBRA) 1997) Methylation-sensitive or specific PCR Bisulfite (Herman et al., 1996) (MS-PCR or MSP) conversion Gel Methylation-sensitive single strand (Bian et al., 2001) conformational polymorphism (MS-SSCP), Methylation-sensitive single nucleotide (Gonzalgo and Jones,

primer extension (MS-SNuPE) 1997) Sequenom developed a platform specific for the Matrix-assisted laser desorption/ionization application of this Specific analysis: time-of-flight mass spectrometry (MALDI- (Schatz et al., 2004) technology to methylation MS TOF- MS) analysis – EpiTYPER – Targeted allowing high throughput Specific analysis: MethyLight analysis (Eads et al., 2000) qPCR Methylation-specific melting analysis (MS- Specific analysis: (Worm et al., 2001) MA) melting curves Pyrosequencing of bisulfite converted Specific analysis: (Tost et al., 2006) DNA Pyrosequencing Requires cloning of PCR product. Measures Bisulfite cloning and sequencing methylation on individual Sanger sequencing (Frommer et al., 1992) DNA strands. Gold standard for methylation quantitation

Table 1.1: Techniques used in the study of DNA methylation C: Affinity enrichment based and others

Scope of DNA pre- Technique Comments Analytical step the References treatment analysis Methylated DNA immunoprecipitation Methyl cytosine antibody (Weber et al., 2005b) (MeDIP, mDIP and mCIP) enrichment Array Methylated CpG island recovery assay (Rauch and Pfeifer, MBD2 enrichment (MIRA) Genomic 2005) Affinity (Down et al., 2008; enrichment MeDIP–seq and MIRA or MBD–seq NGS Ruike et al., 2010) (Li et al., 2010a) Specific analysis: MeDIP-PCR Targeted standard PCR Unique method, not classifiable as the others. It DNA High-performance liquid chromatography measures the content of 5 HPLC Genomic (Ramsahoye, 2002) hydrolysis (HPLC) methyl cytosine in the whole genome No Single-molecule, real-time (SMRT) Still in development NGS Genomic (Flusberg et al., 2010) preparation sequencing Adapted, modified and expanded from Laird, 2010. NGS – next generation sequencing

demethylating agents can prove a causal relationship between high methylation and transcription repression.

In spite of the diversity of techniques used to study methylation, in general they share some common basic characteristics. For instance, they all require a method to discriminate methylated from non-methylated cytosines because DNA methylation is erased by standard molecular techniques such as PCR. Furthermore, because the methyl group is in the major groove of DNA and not at the hydrogen bonds, it cannot be detected by hybridization (Laird, 2010). Thus, in order to distinguish between methylated and unmethylated cytosines many techniques often depend on “transformation” of the methylation marks by a methylation-dependent treatment. This can be accomplished by bisulfite conversion of DNA, by endonuclease digestion using methylation sensitive restriction enzymes, or by affinity enrichment using techniques that can separate methylated from non-methylated DNA. After this DNA “transformation”, several “identifier” analytical tools are used. These can include amplification by several types of PCR, frequently followed by gel analysis, hybridization (e.g. oligonucleotide arrays in genomic approaches) or sequencing, alone or in combination. Different combinations of these tools have been used to measure DNA methylation variation.

Bisulfite conversion of DNA refers to the use of sodium bisulfite to deaminate cytosine producing uracil. This, by PCR, is converted to thymine. The final result is the creation of an artificial single nucleotide polymorphism (C, if methylated, and T if not methylated) that can be analyzed using several common genetic techniques. Since with PCR amplification methylation is lost, if DNA is bisulfite converted prior to PCR, the methylation mark will be carried over and can be analyzed after PCR amplification.

Methylation sensitive restriction enzymes cut specific DNA sequences. Some of these restriction enzymes cut only at target sequences with unmethylated CpGs whereas other restriction enzymes cut at the target DNA sequence regardless of the methylation status of the CpGs. Comparisons of DNA using these enzymes in combination can provide a measure of the DNA methylation at these target sequences. Most commonly used are the isoschizomers (MspI and HpaII) or neoschizomer (XmaI and SmaI). This approach has utilized low frequency restriction site enzymes, restriction enzymes that target CGIs, or, reversely, that exclude CGI – in addition to a technique such as PCR, prior or after the restriction reaction - to positively or negatively select

CpGs depending on their methylation status. Several analytical techniques, such as gel electrophoresis and hybridization arrays, can be used to resolve the differences between study samples.

Bisulfite conversion of DNA and methylation sensitive restriction enzyme based techniques usually target and analyze specific CpG dinucleotides. Conversely, affinity enrichment techniques, (using either antibodies that bind methylcytosine – immunologic based Methyl DNA immunoprecipitation (MeDIP) - or methyl binding proteins with affinity for methyl cytosine (such as Methyl-CpG-binding protein 2 or MeCP2)) target methylated genomic regions.

These DNA transformations were initially incorporated into targeted techniques, i.e. techniques that could be used to analyze the methylation of a small genomic region. Later they were scaled up to whole genome approaches. Formerly, those whole genome approaches were based on two dimensional gel electrophoresis such as Restriction Landmark Genomic Scanning (RLGS). More recently, microarray technology has been adapted for DNA methylation analysis and currently microarrays represent the most common technique for whole genome screening methylation studies. Microarrays used for methylation can be either general application arrays, such as whole genome tiling arrays, or arrays targeting regions of interest such as CpG Island oligonucleotide arrays and promoter arrays. Several array manufacturers, such as Agilent®, NimbleGen®, Affymetrix® and Illumina® have been producing hybridization arrays specifically developed for methylation analysis. NimbleGen®, Affymetrix® and Agilent® produce oligonucleotide genome arrays, targeting specific regions of the genome. Illumina® produces oligonucleotide bead arrays targeting specific CpGs of the genome.

The development and increased availability of genome sequencing techniques opens the door for its application to bisulfite converted DNA (Cokus et al., 2008; Lister et al., 2008; Lister et al., 2009) or even to direct detection of DNA methylation using single-molecule real time sequencing (Flusberg et al., 2010) or nanopore sequencing (Clarke et al., 2009). Sequencing technologies can be targeted or whole genome based. Sequencing targets can be specific regions or multi-function based regions. When targeted, an enrichment step for the target sequences is required.

In spite of all the technological developments in the field of DNA methylation assessment, the data analytical tools have been lagging behind. Previously reported studies that have used these

technologies have used adaptations of analytical tools that were developed for other types of analyses, such as expression and copy number variation. Research in this area is currently under way and it is expected that soon more appropriate DNA methylation specific analytical tools will become available.

1.4.2 Histone modifications

DNA is wrapped around histone protein core complexes, which package the DNA and provide various signals that regulate gene transcription. A variety of modifications of histone H3 tails alter chromatin structure and signal either active transcription or silencing (Munshi et al., 2009). Acetylation of histone H3 (H3Ac) results in an open chromatin state facilitating transcription of genes (Esteller, 2007). Similarly, methylation of histone lysine residues recruits proteins to promote transcription, as is the case with trimethylation of lysine 9 (H3K9me3). Repression of transcription occurs in genomic regions associated with other histone tail modifications such as trimethylation of lysine 27 (H3K27me3) and lysine 4 (H3K4me3) (Kondo et al., 2008; Kondo et al., 2004). As for DNA methylation, there are several enzymes regulating histone modifications, e.g. histone acetyltransferases and deacetylases, histone methyltransferases and demethylases (for review see ref) (Munshi et al., 2009).

The techniques used to study histone modifications are mainly based in chromatin immunoprecipitation using antibodies that specifically bind each type of histone mark. After this fractional enrichment step, the DNA can be isolated from the chromatin and analyzed either using target assays such as quantitative PCR, or genomic approaches such as oligonucleotide arrays.

Causal effect relationships on expression can be analyzed, as for DNA methylation, through in vitro cell exposure to pharmacologic agents that can modify histone marks, such as inhibitors of histone deacetylase.

1.4.3 MicroRNA

MicroRNAs (miRNAs) comprise a novel class of endogenous, small (18-24 nt in length), single- stranded RNAs that control gene expression by targeting specific mRNAs for degradation and/or translational repression. MiRNAs together with a complex of associated proteins known as the RNA-induced silencing complex (RISC) bind to sites of complementarity in the 3′ untranslated

17 regions of mRNAs and inhibit their translation (Stefani and Slack, 2008). Further, miRNAs appear to control gene expression via epigenetic mechanisms that include chromatin conformation (Chuang and Jones, 2007). Bioinformatic studies estimate that miRNAs could regulate up to 30% of all human genes (Pavri et al., 2005). Data regarding miRNA function also demonstrates a responsiveness to environmental factors e.g. nutrition (Marsit et al., 2006). High- throughput sequencing has uncovered over 700 miRNA genes in the human genome, many of which have been highly conserved in vertebrates. All known miRNA are catalogued at the University of Manchester database http://www.mirbase.org/ (Griffiths-Jones et al., 2008). Many of these miRNAs have not yet been linked to specific pathways. Furthermore, miRNAs are often located within imprinted genes clusters (Royo and Cavaille, 2008) (see below).

1.4.4 Genomic imprinting

Epigenetic mechanisms form the basis for the control of genomic imprinting. Genes that undergo genomic imprinting have been shown to be important in developmental growth control and neurodevelopment. As such the expression of these genes is critical for normal fetal and placental development.

Imprinted genes demonstrate preferential or exclusive expression of only one of the two alleles, i.e. their expression is monoallelic. Furthermore for each imprinted gene only one is consistently expressed, either the maternal or the paternal allele i.e. it reflects parent of origin specific gametic programming. This is what distinguishes imprinted genes from other monoallelically expressed genes. Currently there are 64 imprinted genes known in humans, as per the Geneimprint curated database of imprinted genes (www.geneimprint.org) (Jirtle, 2008). Bio- informatically, 154 more genes are predicted to be imprinted based on their sequence context (Luedi et al., 2007).

With the exception of a few human single locus imprinted genes (e.g. NAP1L5, NNAT) most are clustered in genomic regions called domains and regulated epigenetically by imprinting centers (IC), also sometimes known as Imprinting Control Centers (ICC), Imprinting Control Regions (ICR) or Differentially Methylated Regions (DMR) (Edwards and Ferguson-Smith, 2007). These imprinting centers consist of regions within the imprinted domain that are methylated in a parent of origin specific manner and, as such, are known as differentially methylated regions (DMRs). These cis-acting DMRs, along with trans-acting factors, form the basis of the parent of origin

specific gene expression of imprinted genes. Thus, for imprinted genes, epigenetic control mechanisms provide a higher order of regulation. Genomic mutations of imprinting centers affect the establishment or maintenance of genomic imprints and hence the expression of all imprinted genes controlled by the associated imprinting center (Buiting et al., 1995; Horsthemke and Buiting, 2008). Differential DNA methylation at these imprinting centers is a useful proxy for many imprinting marks (Constancia et al., 1998).

At some clusters of imprinted genes, e.g. the IGF2/H19 locus at chromosome 11p15.5 (Jinno et al., 1995; Vu et al., 2000) and the DLK1-GTL2 locus at chromosome 14q32 (Kagami et al., 2010), there are multiple DMRs. When this happens there is a hierarchical relationship among them, allowing a classification into primary and secondary DMRs. The primary DMR is generally conserved across tissues whereas the secondary DMR demonstrates tissue variation.

Methylation marks at primary imprinting centers (IC) are maintained in somatic lineages. However, imprints need to be reset between generations to ensure that novel imprints can be properly reestablished according to the sex of the newly formed germline. As abnormal imprinting marks are associated with disease, it is important to understand the life cycle of imprinting, including the stages during which that process is most vulnerable to environmental influences. The mechanisms that establish germ line imprinting and early embryonic global epigenetic reprogramming have been studied extensively in mice. The imprinting life cycle comprises 3 stages: erasure of the incoming imprints, acquisition (establishment) of imprints according to the sex of the individual and appropriate maintenance of imprints in diverse tissues. Erasure of imprinting occurs in primordial germ cells, early in the differentiation of female and male germlines. Later, during a period of global genomic methylation in germline differentiation, imprinting is established through differential methylation of the imprinting centers. After their establishment in the germline, methylation imprints at primary DMRs are maintained from the zygote to the embryo, and throughout adulthood. Remarkably, the differential methylation at primary imprinting centers is maintained during preimplantation development, despite genome- wide DNA demethylation occurring during this period. In contrast, secondary DMRs are not maintained during this global demethylation event. For this reason the primary DMRs are considered to be germline-derived whereas the secondary DMRs are considered to be postfertilization-derived (Kagami et al., 2010). Following implantation, there is a wave of

genome wide de novo remethylation (reviewed by (Arnaud and Feil, 2005; Constancia et al., 1998)).

Imprinting is a biological phenomenon found in plants, in Eutherian (placental) mammals and, in a smaller scale, in Metatherian (marsupials) but not in Prototherian (egg-laying non-placental) mammals. It is thus considered a molecular phenomenon evolutionarily associated with the appearance of the placenta (Wagschal and Feil, 2006). In general, paternally expressed imprinted genes enhance fetal growth whereas maternally expressed genes suppress it (Eich et al., 1991; Reik et al., 2003; Tycko and Morison, 2002). These findings support the popular evolutionary parental conflict hypothesis (Moore and Haig, 1991) whereby maternally derived genes have to balance nutrient provision to the current fetus against that of future fetuses, whereas paternally derived genes drive nutrient acquisition from the mother for the current fetus to the potential detriment of future fetuses. An alternative theory for the evolutionary advantage of imprinting in mammals has been proposed by Varmuza and Mann (Varmuza and Mann, 1994). Accordingly, imprinting protects the ovary from the consequences of the development of parthenogenetically activated oocytes. Thus, imprinting would have an evolutionary selective advantage, prolonging the reproductive life of females.

1.5 Epigenetic Dysregulation

Genetic variation and mutations, including point mutations, deletions and duplications of genomic segments of variable size are currently accepted causes of dysregulation of expression of disease causing genes. Evidence is mounting in support of a role for epigenetic modifications as a disease causing mechanism via modulation of gene expression. Epigenetic alterations can occur as a result of an epigenetic or a genetic error. Epigenetic dysregulation has now been characterized in several growth related disorders such as Beckwith-Wiedemann syndrome (BWS) (overgrowth) (Squire et al., 2000; Weksberg et al., 1993; Weksberg et al., 2005), Russell- Silver syndrome (RSS) (growth restriction) (Abu-Amero et al., 2008; Gicquel et al., 2005; Horike et al., 2009) and transient neonatal diabetes (growth restriction) (Martin-Subero et al., 2008) (see further discussion below). In addition, alterations of epigenetic marks at the promoter regions of oncogenes and tumor suppressor genes have also been described in a wide range of

cancers, reviewed by Sharma and colleagues (Sharma et al., 2010). Thus, these data illustrate the importance of epigenetic regulation in normal and abnormal growth and development.

1.6 Environmental Effects on Epigenetic Modifications

Several drugs can interfere with the enzymes that modify DNA and histone proteins (Duvic and Vu, 2007; Szyf, 2009). Furthermore, nutritional factors interfering with the availability of methyl donors (e.g. folate), also have an impact on these processes (Park et al., 2005). The availability of methyl groups for epigenetic modification is mediated by an intermediate of one-carbon metabolism, S-adenosyl methionine (SAM). SAM is produced indirectly from dietary folate suggesting a link between compromised folate status and methylation errors. A functional SNP (C677T) in 5,10-methylenetetrahydrofolate reductase (MTHFR) is known to be associated with decreased red blood cell folate concentration. It has previously been associated with an increased risk for an imprinting disorder - Angelman syndrome – and also for autism, psychiatric disorders and several forms of cancer (Collin et al., 2009; Gilbody et al., 2007; Lissowska et al., 2007; Mohammad et al., 2009; Sharp and Little, 2004; Zogel et al., 2006). A large Canadian retrospective study showed that maternal exposure to folic acid antagonists appears to increase the risk of placenta-mediated adverse outcomes of pregnancy (i.e. IUGR, preeclampsia) (Wen et al., 2008).

Placentas associated with IUGR fetuses were shown to have an increase in transport of folic acid and other bio-active molecules, when compared with normal placentas (Keating et al., 2009). This increase in transport capacity was suggested to be due to generalized increase in expression of placental transporters. These results were interpreted as “an effect of compensation for the weakness of the IUGR placenta as an active mediator of the required communication between maternal and fetal environments” (Keating et al., 2009). Recent studies on rats suggest that the effect of methyl donor deficiency on fetal growth programming could be mediated through the remodeling of gastric cellular organization and the resultant decrease in ghrelin described in methyl donor deficient mice (Bossenmeyer-Pourie et al., 2010). Ghrelin is a hormone that impacts growth as a growth hormone releasing factor and as an appetite stimulating peptide (Kojima and Kangawa, 2005). The gene that encodes this peptide that influences fetal growth is

21 expressed in the stomach of fetuses and placenta (Torsello et al., 2003). These studies underscore the potential importance of environmental factors in epigenetic programming.

1.7 IUGR and Epigenetics

1.7.1 Evidence for epigenetic regulation in placenta

Epigenetic marks demonstrate normal variation among individuals, especially of different ethnicities, between sexes and across developmental stages, age, and tissues (Minard et al., 2009; Rakyan et al., 2008; Reiss et al.; Romanov and Vaniushin, 1980; Wilson et al., 1987). Therefore, in order to study epigenetic dysregulation related to human disease the specific epigenotype of the tissue and relevant cell types, age and sex of the individual must be considered.

DNA methylation has been studied in both mouse and human placenta. Epigenetic regulation in placenta has been the subject of a recently published review wherein the authors highlight the importance of epigenetic regulation for normal placental function (Maccani and Marsit, 2009).

The epigenotype of placenta demonstrates some interesting characteristics. For instance, human placental tissue has been shown to be relatively hypomethylated, being the organ with the lowest 5-methylcytosine content of all human tissues (Ehrlich et al., 1982). This content increases across gestational age (Fuke et al., 2004).

Furthermore, the biological behavior of placental cells and tumor cells are similar with respect to significant growth potential. In fact, several tumor suppressor genes demonstrate promoter hypermethylation in some cancers (e.g. APC, RASSF1A). These promoters also show increased methylation in placenta as compared to other tissues (Chiu et al., 2007; Novakovic et al., 2008; Wong et al., 2008). Interestingly, for another tumor suppressor gene – Maspin – what seems to regulate the variation in its expression throughout pregnancy (lower expression in the first trimester) are histone modifications (Dokras et al., 2006). Another example of epigenetic regulation of important placental functions is provided by the work of Novakovic and colleagues (Novakovic et al., 2009). Their work demonstrated epigenetic regulation of vitamin D bioavailability at the feto-maternal interface pointing to an important role of epigenetic regulation in normal placental functions.

Another characteristic of placenta relates to imprinted gene regulation. One of the most well studied imprinted regions in humans is located on chromosome 11p15.5, syntenic with mouse distal portion of chromosome 7. In this region there are two imprinted gene clusters. One of those clusters includes two genes important in growth and development – IGF2 (Insulin-like growth factor 2) and H19. The imprinting status of the two genes in this cluster is regulated by the imprinting center 1 (IC1) which is the primary imprinting center and is located in a region upstream of the promoter of the H19 gene. This is the differentially methylated H19 region or H19 DMR. In this region there are other secondary regulatory regions within the IGF2 gene that, in several cell types, are also differentially methylated (Monk et al., 2006b). There are 3 such regions in mice (DMR0, 1 and 2) and 2 in humans (DMR0 and 2). The regulatory role of these secondary DMRs in humans is still poorly understood. Secondary DMRs, in contrast to primary DMRs, do not resist demethylation during the global demethylation which occurs in the embryo after fertilization and prior to implantation. In placenta, whereas DNA methylation marks are maintained at the primary regional imprinting center, the spreading of this methylation in cis to

downstream promoters / regulatory elements (e.g. H19 promoter, IGF2 DMR2), commonly seen in other tissues, is absent (Guo et al., 2008; Jinno et al., 1995). Given that monoallelic expression of these imprinted genes is maintained, the imprinting center signal in placenta must be mediated by mechanisms other than promoter DNA methylation. Attractive alternatives include histone modification and microRNAs (miRNAs). Monk and colleagues suggested that maintenance of the imprinting of some genes imprinted only in the placenta are less dependent on promoter DNA methylation than imprinted genes in the embryo and involve repressive histone methylation (Monk et al., 2006a). Many genes with placenta specific imprinting are not conserved between mice and humans. In contrast, genes that are imprinted in both, embryonic and extra-embryonic tissues, are highly conserved and have their promoters regulated by DNA methylation (Wagschal and Feil, 2006).

MiRNA control of gene expression is also important in placenta. The miRNA regulation machinery is expressed in cultured trophoblast cells, as shown by Donker and colleagues (Donker et al., 2007). Screening of a small RNA library showed that most placenta-specific miRNAs are linked to a miRNA cluster on chromosome 19 containing the miR-506 and miR-515 gene families, as well as the miR-23 family of genes (Luo et al., 2009). This cluster of miRNA genes is located in close proximity to the genomic region that has been associated with recurrent

biparental hydatidiform moles (Deveault et al., 2009). Changes in the expression of chromosome 19-linked miRNAs mirror the physiological status of the human placenta. These miRNA molecules are also detected in maternal blood (Luo et al., 2009). Thus, these molecules could potentially represent biomarkers for placental dysregulation impacting fetal development e.g. PE and IUGR (Liang et al., 2007).

1.7.2 Epigenetics dysregulation and IUGR

Whereas only limited data are available regarding the involvement of epigenetic dysregulation in placental and fetal development in humans, studies in other mammals show that control of DNA methylation and imprinting are crucial for normal placental development and fetal growth (Ferguson-Smith and Surani, 2001; Reik et al., 2001; Sleutels et al., 2002; Tilghman, 1999). In mouse, targeted inactivating mutations of the enzyme Dnmt1 demonstrate that DNA methyltransferases are essential for normal embryonic development (Howell et al., 2001; Li et al., 1992). Homozygotes for inactivating mutations of Dnmt1, which maintains methylation, do not survive beyond embryonic development. They exhibit growth restriction and reduced global DNA methylation. Heterozygotes do not exhibit an abnormal phenotype (Li et al., 1992). The establishment of imprinting in the oocyte is dependent on a variant of Dnmt1, the maternal Dnmt1o (Howell et al., 2001). Most embryos of females homozygous for Dnmt1o mutations die in the second half of pregnancy or soon after birth with only a few reaching adulthood. The few survivors are normal morphologically but runted (small). Although global methylation is not affected, they fail to maintain the normal imprinting status of the H19 and Snrpn imprinted genes. This demonstrates that dysregulation of imprinting has extensive effects on development.

Also in the mouse, either traditional gene deletions or epigenetic alterations of several imprinted genes can affect the growth of mouse placenta and/or fetus, leading to either under- or overgrowth phenotypes (Caspary et al., 1999). Deletion of the paternally expressed allele for the genes Igf2, Peg1, or Peg3 results in a small placenta and SGA, whereas deletion or methylation of the maternally expressed allele of the H19 gene results in placental overgrowth. Deletions of the maternally expressed genes Cdkn1c, Phlda2, or Grb10 all result in placental overgrowth (Coan et al., 2005; Reik et al., 2003; Tycko and Morison, 2002). In humans, an imprinted region on chromosome 7 controls the allelic expression of MEG1/GRB10. Maternal duplications of this region have been found in a few individuals with growth restriction who were diagnosed with

Russell-Silver syndrome (RSS) (Joyce et al., 1999). A mouse model with maternal duplication of the syntenic region in mice, located on chromosome 11, showed prenatal and postnatal growth restriction (Shiura et al., 2009).

In humans with RSS, ~7 to 10% of cases have uniparental disomy for chromosome 7 (Eggermann et al., 1997; Kotzot et al., 2000; Nakabayashi et al., 2002) and up to a third of cases show H19 DMR hypomethylation of the paternal allele in white blood cells and fibroblasts (Bliek et al., 2006; Eggermann et al., 2006; Gicquel et al., 2005). This H19 DMR hypomethylation was shown to be associated with aberrant biallelic expression of IGF2 (Gicquel et al., 2005). It is noteworthy that our group has identified this same epimutation in the placenta of one of our SGA derived placental samples. This decrease in methylation of the H19 DMR was accompanied by H19 allelic “leakage”, i.e., although there was an allelic bias towards higher expression of one of the alleles, there was an abnormally low but still detectable second allele expression. Furthermore, in this sample, H19 and IGF2 expression levels were in the upper and lower ranges of expression, respectively, for the cohort tested (Guo et al., 2008). In this study the SGA group of newborns had a lower expression of IGF2 than the controls. The H19 epimutation in peripheral blood DNA, although reported in RSS, has not been reported in other individuals considered as isolated IUGR (Bartholdi et al., 2009; Schonherr et al., 2006). However, the associated phenotypic aspects are difficult to identify in the prenatal or even early postnatal period. Furthermore, most reports of H19 epimutation describe testing only in peripheral blood DNA. To date, only one report has looked at the methylation status of placentas of individuals found, post-natally, to have H19 hypomethylation (Yamazawa et al., 2008). Of 3 patients with phenotypic features of RSS, for whom DNA from both blood and placenta were available, all demonstrated loss of methylation at H19 in both tissues. Furthermore, the placentas had “hypoplastic chorionic villi”, a placental lesion found in association with IUGR. Although loss of methylation at H19 may be responsible for only a small proportion of growth restricted fetuses, its specific prognosis makes its identification important in cases of IUGR.

Anomalies at the IGF2/H19 locus have been sought in several series of human placental anomalies. H19 biallelic expression was reported in association with a cohort of patients with severe preeclampsia (Yu et al., 2009). Recently one study reported a small decrease in the methylation levels of the H19 DMR in association with isolated IUGR but not preeclampsia.

(Bourque et al., 2010). However, the biological significance of such small changes in methylation is unclear.

Recently, Haycock et al. (2009) suggested that the growth restriction effects on the placenta seen in pregnant mice exposed to ethanol may be mediated by loss of methylation at the H19 DMR in placenta but not in the embryo proper (Haycock and Ramsay, 2009).

Other imprinted genes have also been the target of studies in human placental disease. For instance, expression alterations (up or down regulation) of several imprinted genes (e.g. PHLDA2) were found to be associated with placental insufficiency and IUGR phenotypes in several placental expression association studies (Apostolidou et al., 2007; Diplas et al., 2009; McMinn et al., 2006).

1.7.3 MicroRNAs in disorders of placental / fetal growth and development

Given their profile of activity in mediating control of gene expression in vertebrates, miRNAs are excellent candidates for the molecular basis of epigenetic programming. Therefore investigation of defects in miRNA regulation is relevant to studies of IUGR. In fact, in mouse a loss-of-function mutation of the miR-17-92 cluster results in smaller mouse embryos that die postnatally. They have severely hypoplastic lungs and ventricular septal defects of the heart (Mendell, 2008).

In humans, no changes in miRNA associated with IUGR have been reported to date. However, recent studies have found altered patterns of miRNA expression in placentas of pregnancies complicated by preeclampsia (Pineles et al., 2007; Zhu et al., 2009). Pineles and colleagues assessed a selection of 157 miRNAs through quantitative reverse transcription-PCR and found an association between differential expression of miR-210 and miR-182 and preeclampsia and an association of the same miRNAs, among others, with preeclampsia and SGA. Of note, an association with SGA alone was not found (Pineles et al., 2007). Zhu and colleagues, using a miRNA microarray approach, reported differences in the expression of 34 miRNAs among pregnancies complicated by preeclampsia and controls (Zhu et al., 2009). Finally Donker and colleagues found, using in vitro assays of trophoblast cells, up-regulation of miR-93 and down-

regulation of miR-424 in a hypoxic environment (Donker et al., 2007). All these findings suggest a role for miRNA control of gene expression in the etiopathogeny of placental dysfunction.

1.8 Long Term Sequelae of IUGR – A Role for Epigenetic Programming?

Adults who had low birthweights, specifically low birthweight percentiles (SGA) demonstrate an increased incidence of hypertension, type 2 diabetes, stroke and coronary artery disease in adult life (Barker, 2004; Barker et al., 2005, 2007; Forsen et al., 1997). In fact, as term birthweight decreases from 9.9 lbs. (4.5 kg) to 5.5 lbs. (2.5 kg), the risk for coronary artery disease in men increases incrementally to 50%. These data have been extensively replicated among men and women in epidemiological studies worldwide, including Europe, North America and India (Barker, 2004).

Barker’s developmental origins hypothesis (Barker, 2004) attributes the increase in adult onset disease following low birthweight to developmental programming at critical periods of early development that have long term effects on a host of physiologic processes. This relationship highlights the existence of “developmental plasticity” (Barker, 2004), whereby disorders such as heart disease might result from an imbalance between the environment experienced by the embryo/fetus and the environment experienced in later life. It appears that rapid childhood weight gain, especially during ages 3 to 11 years, has been demonstrated to increase the risk of adult onset disease associated with low birth weight (Barker et al., 2002; Barker et al., 2007; Eriksson, 2000; Eriksson et al., 2001; Singhal et al., 2007). Possible mechanisms include effects on hormonal systems, such as insulin-like growth factor 1 (Singhal and Lucas, 2004).

Recent data suggests that it is IUGR, the primary placental insufficiency subtype of SGA, that is associated with the long-term sequelae of SGA (Barker, 2004; Brodszki et al., 2005). Preliminary evidence indicates that such individuals have smaller aortic dimensions and higher resting heart rates in adolescence (Brodszki et al., 2005). These findings likely play a role in adult onset cardiovascular disease.

The molecular mechanisms that underpin the developmental origins of adult disease are not understood. Epigenetic mechanisms, plastic and sensitive to environmental agents, and stably

mitotically transmissible, are an attractive and plausible bridge between the environmental influences and the long term effects (Figure 1.1).

Evidence for this possibility is provided by animal model studies that have reported alterations in the epigenetic status of several genes in liver, brain, pancreatic beta-cells and muscle of newborns with IUGR. A rat model for IUGR, in which both uterine arteries of the pregnant rat are ligated, has been studied. These rats present, in adult life, with fasting hypertriglyceridemia, hyperglycemia, and hyperinsulinemia (Lane et al., 2001; Simmons et al., 2001). In the liver of these rats, there is global hyperacetylation of Histone H3 in association with decreased nuclear protein levels of histone deacetylase 1 (HDAC1) and HDAC activity (MacLennan et al., 2004). In the same model system, Fu et al. (2004, 2006 and 2009) have found the same modifications in specific genes – Pgc-1, Cpti (Fu et al., 2004), Igf1 (Fu et al., 2009) (all previously identified as having lower expression in IUGR rats) and Dusp5 (Fu et al., 2006). Interestingly, these changes were found at birth and persisted through day 21, but only in males. A sex-specific effect was also described in the brain of male, but not female, IUGR rats. Specifically, decreased expression of the chromatin structure determinants, DNA methyltransferase (DNMT1), methyl-CpG binding protein (MeCP2) and histone deacetylase (HDAC1), in association with a global decrease in DNA methylation and histone 3 hyperacetylation, were identified in certain regions of the brain (hippocampus and white matter) of male IUGR rats. Later, in males there was a deacetylation, whereas in females this deacetylation effect was not present (Ke et al., 2006). Park et al. (2008) studied epigenetic marks of the Pdx1 gene, a pancreatic and duodenal homeobox 1 transcription factor critical for beta cell development and function, in the same rat model of IUGR. They found decreased expression of this gene associated with histone hyperacetylation of Pdx1 in fetal and early life but promoter methylation in adult life (Park et al., 2008). Also in the same model system, histone hyperacetylation was found, along with decreased expression of the gene Glut4 in skeletal muscle (Raychaudhuri et al., 2008). Interestingly, in this case, the persistence of the histone modifications to adulthood was identified only in females. Further, the same histone modifications were found in a nonhuman primate model of maternal obesity (Aagaard-Tillery et al., 2008). Taken together, these findings in animal model systems support the hypothesis that the long term effects of IUGR causing adult onset disease are mediated by epigenetic mechanisms. Such findings have not yet been reported in humans.

Figure 1.1: Proposed model for epigenetic fetal reprogramming underlying predisposition to late onset disease.

Despite the lack of direct evidence for epigenetic developmental programming in humans, support for such programming comes from epidemiological studies. For instance, through the Dutch Hunger Winter Families study it was verified that females born to mothers undernourished in early pregnancy gave birth to children with a lower than expected birthweight in the following generation (Lumey and Stein, 1997). Recently, a lower DNA methylation level of the imprinted

IGF2 gene secondary DMR (IGF2 DMR2) was identified in those same individuals (Heijmans et al., 2008). This supports the concept of transgenerational consequences of environmental effects. The biological characteristics of epigenetic mechanisms make them an attractive molecular candidate to explain such consequences.

1.9 Conclusions

Studies in animal model systems have demonstrated an important role for epigenetic mechanisms in placental / fetal growth and development. In the mouse, experimental data show an association between imprinted gene dysregulation and poor embryo and fetal outcomes. However, such evidence is only beginning to emerge in humans. Human studies show alterations in imprinted gene expression in association with fetal growth dysregulation. Human imprinting disorders, such as Beckwith-Wiedemann Syndrome, demonstrate an overgrowth disturbance phenotype. More recently, between a third and half of individuals diagnosed with Russell-Silver syndrome, a human growth restriction phenotype, were also shown to be associated with imprinting dysregulation. The apparently primary epimutation underlying this dysregulation - decrease in methylation levels of a genomic region known as Imprinting Center 1 (IC1) or H19 DMR – is the molecular counterpart of one of the causes of the overgrowth syndrome – increase in methylation of the same region.

In a study of the IC1 region in DNA collected from placentas of a small series of newborns identified as SGA, one of the studied samples was shown to be hypomethylated in that region (Guo et al., 2008). Furthermore the same study reported reduced levels of placental IGF2 mRNA in the group of SGA newborns in comparison with controls. This gene is imprinted in placenta and its allelic expression is regulated by DNA methylation of IC1 (Bell and Felsenfeld, 2000).

Imprinting is regulated through epigenetic mechanisms, including DNA methylation. Epigenetic mechanisms, including DNA methylation, 1) are stable and mitotically heritable, regulators of DNA transcription, 2) are likely to be genetically determined and important in cell differentiation and development and 3) are more prone to alterations (epimutations) induced by environmental factors than the actual primary DNA base sequence.

Research aimed at characterizing the epigenome of the human placenta / fetus in search of epigenetic factors important for growth regulation and IUGR susceptibility is required.

Chapter 2: Hypotheses, Research Aims and Thesis Outline

2.1 Hypotheses

An association has been demonstrated between low birthweight percentile and late onset adult diseases. This association is likely to be explained by a combination of genetic and environmental factors that, acting during embryonic/fetal development, induce suboptimal fetal growth and abnormal metabolic responses to environmental exposures occurring later in childhood and adult life. As presented in Figure 1.1, a model can be proposed in which dysregulation of epigenetic mechanisms are responsible for both the short and long term effects. In the context of this model, epigenetic alterations or DNA methylation variants in placenta may be associated with poor fetal growth.

My hypothesis, for the purpose of this thesis, is that alterations of DNA methylation, one of the best studied epigenetic mechanisms, play a role in placenta function and when dysregulated can be associated with poor fetal growth.

2.2 Aims and thesis outline

To test my hypothesis, I undertook experiments to identify associations between placental DNA methylation variation and poor fetal growth, as measured by birthweight percentile. I performed a series of six investigations, each aimed at responding to specific questions, some technical in nature, that arose during the course of my research. The six studies will be presented in each of Chapters 4 to 9. Prior to this, in Chapter 3, I describe the methodologies and technologies that I used.

The following is a description of each study aim and a summary of the experiments that addressed them.

First, in Chapter 4, I characterize the phenotype of patients with Russell-Silver syndrome (RSS) who demonstrate a growth restriction phenotype caused by a loss of methylation at the H19 DMR on human chromosome 11p15.5. In Chapter 1, I referred to the discovery of this common molecular anomaly underlying RSS. I wanted to improve our understanding of the clinical phenotype of individuals with the H19 DMR epimutation and to determine if there were specific clinical features that would distinguish, prenatally or neonatally, RSS from the more common intrauterine growth restriction related to placental insufficiency. I describe the studies that led me to determine that the early clinical features of RSS would not be sufficiently useful in the early

33 identification of individuals that are diagnosed, only later, with this syndrome. Essentially, they would not be clinically distinguishable from other fetuses or newborns clinically identified as small for gestational age (SGA), secondary to placental insufficiency. Importantly, I also found that only partial loss of methylation in the DMR, specifically the most distal portion of the H19 DMR, including 3 of the 6 CTCF binding sites previously described in the region, was necessary and sufficient to be associated with the RSS phenotype. This was important information for the development of clinically relevant molecular tests to screen for the epimutation associated with RSS. This report, on which Shin-Ichi Horike and I are co-first authors, has been published in the American Journal of Medical Genetics (Horike et al., 2009).

The results from the RSS study above, together with the previously reported H19 DMR placental DNA epimutation in 1 of 20 SGA cases in a series (Guo et al., 2008), prompted expansion of this area of study. In my second study, reported in Chapter 5, H19 DMR methylation in placental DNA samples obtained from an extended and heterogeneous cohort was investigated. Two more samples with the H19 epimutation were identified, but no association was found with low birthweight percentile. I concluded that H19 DMR hypomethylation in placental DNA is not likely to be commonly associated with prenatal growth restriction. I then extended my analysis of DNA methylation variation to other imprinting centers using a genomic microarray approach in a set of placental DNA samples obtained from low birthweight percentile newborns (n=12) with placental lesions and controls (n=12). The results of this study are also part of Chapter 5. Although no specific differences were found in the samples selected, a tendency towards higher levels of methylation in the imprinting centers of the growth restricted cases was evident. Furthermore, the low birthweight cohort had a higher number of aberrantly methylated sites than the controls. The report of this work is planned to be submitted for publication after validation studies are completed.

The subsequent four studies, corresponding to chapters 6 to 9, describe the extensive studies I have done to address the variation in DNA methylation in placenta across the whole genome (beyond the imprinted regions) and its association to placental developmental disorders and poor fetal growth.

DNA methylation is known to vary among tissues and cell types and developmental stages. Therefore, methylation analysis in the placenta would give us information that is the composite

of the several cell types of the tissue specimen. I therefore undertook a study of differences in methylation between the main cell types in placenta – cytotrophoblasts and fibroblasts – using a microarray approach. This study is described in Chapter 6. Although the majority of the regions covered by the microarray did not demonstrate biologically relevant differences in DNA methylation between the two cell types analyzed, a relatively small number of CpG sites have shown significant and relatively important differences. Several of those CpG sites map to promoters of genes that are known to have, or likely to have, cell specific expression. Their cell specific expression is thus likely to be, at least partially, regulated by DNA methylation. When analyzing and interpreting DNA methylation data at the tissue level, it is important to be aware of those differences in methylation between cell types and to use methods that are capable of minimizing the effect of such differences in the results. Furthermore, there were more differences between placenta and fibroblasts than between placenta and cytotrophoblasts. This, together with the results of unsupervised non-hierarchical cluster analysis, suggested that CpG methylation in placental tissue is more representative of cytotrophoblast methylation than of fibroblast methylation. The report of this work, of which Ariadna Grigoriu and I are co-first authors, has been published in “Epigenetics” (Grigoriu et al., 2011).

In Chapter 7 I describe the first genome wide differential analysis of placental DNA methylation associated with neonates that attained different birthweight percentiles. For this I used CpG island oligonucleotide arrays and methyl DNA immunoprecipitation enrichment. After identification of possible candidate genomic regions, one of them – overlapping the WNT2 gene promoter - was selected for follow-up analysis. WNT2 stood out from the list of genes generated by the analysis of the array for a number of reasons. Much higher methylation across a relatively high number of consecutive array probes was observed for 3 of 8 IUGR cases and none of the controls. In addition, placenta normally demonstrates very high WNT2 expression. Animal data strongly support an important role for Wnt2 in placenta development, fetal growth and survival. WNT2 is a member of a family of genes whose protein product, once secreted, induces the WNT pathway. The effect on this pathway is to inhibit beta-cathenin ubiquitination and degradation, thus promoting cell division and inhibiting apoptosis. WNT2 was studied with targeted testing of methylation and expression in an extended and heterogeneous cohort of placenta samples. High methylation of the WNT2 promoter was found to be associated with low WNT2 expression and

with low birthweight percentile. The report of this work has been published in “Epigenetics” (Ferreira et al., 2011).

The complexity of the previous analysis, coupled with reports of biases present in the array technique, led me to do a technical appraisal of the methodologies used to analyze the microarray data generated in the previous study. This was done prior to the selection of other candidate genomic regions for follow-up. I analyzed several bias correction methods and assessed their relative strengths and limitations. I also compared the accuracy of the array platform used in the previous study with other newer platform. This work is reported in Chapter 8. I found that the data produced in the study described in Chapter 7 can be used to identify major differences, from very low or essentially no methylation to full methylation between samples. I also concluded that the newer type of array technology was more appropriate to identify more limited differences, for example, as may occur with deregulation of imprinting centers. However this second newer array platform is more limited in the genome wide coverage and density of coverage across the regions of interest. The report of this work, of which Rageen Rajendram and I are co-first authors, has been published in “Epigenetics” (Rajendram et al., 2011).

I decided to rescreen the placenta samples for methylation variation associated with placental development and fetal growth using the newer microarray technology given the results of the previously described study. This type of microarray is based on the quantitation of methylation at specific CpG sites in the genome, using hybridization and single nucleotide extension of bisulfite converted DNA. These new analyses are reported in Chapter 9. Here I identified several new candidate regions, overlapping promoters of genes whose expression and correlation with methylation was assessed using data generated from expression arrays of RNA extracted from the same placenta samples. Further validation steps to confirm the candidate array findings and follow-up studies in the extended cohort, as modeled by the WNT2 study, are required prior to submission of this data for publication. This work is currently underway under the direction of Dr. Sanaa Choufani, a Research Associate.

Chapter 3: Methods and Techniques

Pathology images in Figures 3.1 and 3.2 were provided by Dr. Sarah Keating, Department of Pathology, Mount Sinai Hospital, Toronto. Dr. Keating’s help with interpreting and establishing pathology criteria is also gratefully acknowledged

3.1 Introduction and Study Design

To avoid repetition and to increase readability, in each of the following sections I describe the shared sample collection protocols and technical methodologies employed for the generation of the results obtained and the conclusions drawn in the course of my work. The details specific to each study – technologies used, data generated and analysis performed - and the differences from what is described here, will be reported in each of the chapters addressing each of the studies performed.

Most studies used DNA and/or RNA extracted from placental samples and/or peripheral blood to determine DNA methylation and mRNA levels. Both, genomic (array based) and genomic region or gene targeted technologies and analyses, were performed.

For genomic/array based determination of DNA methylation, I used Agilent® 244K CpG island arrays (Agilent®, Santa Clara, CA, US) with methyl cytosine immunoprecipitation affinity enrichment and Illumina® Infinium HumanMethylation27 BeadChip arrays (Illumina®, San Diego, CA, US) with bisulfite converted DNA. For targeted determination of DNA methylation, I used Southern Blot of methylation-sensitive restriction enzyme digested DNA and pyrosequencing of bisulfite converted DNA. For genomic/array based determination of mRNA levels, I used Illumina® expression arrays. For targeted determination of gene expression, I used quantitative real time PCR of cDNA.

For the association studies reported here, between DNA methylation variation and fetal growth, as measured by birthweight percentile, (Chapters 5, 7 and 9), and, with the necessary adaptations, for the study of the methylation differences between placenta cell types, a common sequence of analytical steps was employed as summarized below:

1 – Screening step

For the studies in Chapters 5, 7 and 9, this step involved a genomic (array) approach in which the study design follows a case control association model. That is, samples used in those genomic studies are classified into cases and controls. Given the small sample size, and to decrease heterogeneity, this classification was based on birthweight percentile, here used as a two category variable (lower or higher than the 10th percentile) and the presence or absence of certain

38 placental lesions (see, below, section 3.2 for the description of the placental lesions). Case samples originated from placentas of newborns characterized by a birthweight percentile lower than the 10th and by the presence of placental lesions; those samples were referred to as intrauterine growth restricted (IUGR). Control samples originated from newborns with a birthweight percentile greater than the 10th and placentas characterized by the absence of lesions (see, below, section 3.15 for more detailed classification and characterization of the samples used). The analysis of the genomic/array generated data includes the simultaneous or sequential application of a set of selection criteria to select candidate regions for further study.

In the studies reported here, the array step was always envisioned as a hypothesis generator. The selection criteria used aimed mainly at extraction of a manageable number of biologically significant candidate regions for targeted association studies in an extended cohort. Then further prioritization of candidate regions for targeted evaluation in the extended cohort was based on several criteria detailed in each chapter. In general, associations with fetal growth, i.e., birthweight percentile were sought. Given the low power of the small sample sizes used in this step, I could not afford to aim at a comprehensive and more definitive analysis of the differences in methylation between the two defined groups. Thus, statistical test results were not always included in the selection criteria. When I did include results of such tests, the lack of stringency caused by not correcting for multiple testing was compensated for by relatively stringent cut off levels for DNA methylation differences between the groups. The choice of cut-off values was, in general, based on previous experimental data and/or biological assumptions on the effect of DNA methylation on gene expression.

2 – Follow-up step

This step was used in the studies reported in Chapters 5, 6, 7 and 9. I used targeted or alternative (expression) whole genomic approaches to address two objectives.

One objective was to validate array results using a different technique. For example, I measured DNA methylation of one or more regions targeted in the array, in the same set of samples, using a different DNA methylation measuring technique such as pyrosequencing (Chapters 6, 7 and 9).

The second objective was to test the hypotheses generated in the first step of a possible role of a specific DNA methylation variant in placental function and in fetal growth. This was done in

Chapters 7 and 9. A targeted measure of DNA methylation and gene expression was applied to an extended sample cohort (Chapter 7). Alternatively genomic expression studies were applied to the same set of samples (Chapter 9). For this objective, first associations were sought between variations in DNA methylation and variations in related gene expression. The assumption was that the effect of methylation on gene expression would still be measurable in post-delivery placentas, at least for some of the genes. Secondly, associations with relevant variables (see below), such as birthweight percentiles, were also tested in the extended cohort in the studies reported in Chapter 7.

Throughout this report, all referenced genomic coordinates refer to NCBI build 36/hg18 of the Human genome annotation.

3.2 Sample Collection, Processing and Characterization

The biological samples used for most of the studies here described were placental and blood samples, except for the study described in Chapter 4 (see chapter specific “Methods” section for details). The samples were obtained from the Research Centre for Women’s and Infants’ Health BioBank program of the CIHR Group in Development and Fetal Health, Mount Sinai Hospital, Toronto, Canada. Approval was obtained from the Research Ethics Boards of the Hospital for Sick Children and Mount Sinai Hospital.

Subjects were identified as potential candidates for these studies among patients admitted, prior to delivery, to the Obstetrics Department of Mount Sinai Hospital.

I established the selection and exclusion criteria for sample donor candidate selection. All patients delivering singletons without any of the exclusion criteria could be elected for sampling. Details of inclusion and exclusion criteria for sample donor selection are shown in Table 3.1. Although birthweight percentile was not part of the selection criteria for the study, special effort was put into the collection of samples from newborns predicted to have low birthweight percentile. That resulted in the skewing of the birthweight percentile variable.

Table 3.1: Inclusion and exclusion criteria for selection of placenta samples for methylation analysis INCLUSION CRITERIA Singleton pregnancies with prenatal recorded diagnosis of low or high birthweight percentile, with or without preeclampsia EXCLUSION CRITERIA Mothers diagnosed with any of the following i) preconceptional severe hypertension medicated since the beginning of the pregnancy ii) clinically significant acquired or inherited thrombophilia (with previous familial or personal documented history of deep venous thrombosis and/or previous recurrent fetal loss or stillbirth), iii) advanced renal, heart (class II or III) or liver failure, iv) type 1 diabetes mellitus with more than 10 years, or gestational diabetes treated with insulin v) anemia requiring one or more transfusions during pregnancy, vi) autoimmune disorders requiring therapy during pregnancy or involving kidney, heart or blood vessels. Fetuses with prenatal diagnosis of any of the following i) chromosomal abnormalities ii) CMV or toxoplasmosis infections iii) polymalformation syndromes iv) clinical amnionitis

Subsequently, specific sample selection criteria for each individual study were determined in accord with each specific objective. These criteria are summarized below (section 3.14) and detailed in the appropriate sections of each chapter.

After informed consent for participation was obtained from selected donors, sample collection was performed as previously described (Guo et al., 2008). Two random fresh placental tissue samples (~1 to 2 cm thick) were excised from the core of the placenta within 30 minutes after delivery. The core is the portion between the outer layers and is mainly composed of chorionic villi. This was confirmed by microscopic pathology evaluation of a few samples. After washing with PBS solution to eliminate residual blood, the samples are fragmented into smaller portions (~0.2 to 0.5 cm) and transferred, for RNA preservation, into RNAlater reagent (Ambion, Austin, TX) and stored at 4ºC for later processing. Meanwhile, whenever possible, ~5 ml of cord blood is also collected in BD (Franklin Lakes, NJ, US) Vacutainer® Blood Collection tubes containing ACD Solution B and maintained at room temperature for DNA isolation.

The ~0.2 to 0.5 cm placental fragments are heterogeneous in the relative amount of the trophoblast rich villi and of the fibroblast rich villous stems, septi and portions of decidua. In order to decrease inter sample variation attributable to cell type content, each placental fragment was macro dissected to select only distal villi for DNA and/or RNA extraction, as illustrated in Figure 3.1. Dissection was performed after a minimum 24h exposure to RNAlater. From the macrodissected distal villi rich material, ~50 mg was transferred to a 1.5 ml polystyrene tube and stored at -80ºC until DNA and/or RNA extraction. The remaining amount was stored in a different tube to serve as back-up material.

Each sample was characterized by birthweight percentile, gestational age, sex, ethnicity (when made available), presence or absence of labor (when made available), presence or absence of preeclampsia and presence or absence of placental lesions known to be associated with low birthweight percentile. Gestational age, newborn sex and birthweight was used for determination of birthweight percentile using Oken and colleagues tables (Oken et al., 2003) which provide an almost continuous classification of birthweight percentiles, with intervals of 1%. Preeclampsia is considered if the neonate mother had the ACOG criteria for this disorder (ACOG Practice Bulletin Nr. 33 ). The placenta was assessed, for its weight and for the presence or absence of standard clinically relevant macroscopic and microscopic lesions. All the placentas had been

Figure 3.1: Preparation of the samples prior to DNA and RNA extraction. Placenta fragments of ~5 mm are dissected prior to DNA and RNA extraction in order to increase cell content homogeneity between each sample. In photo A (top left corner) there is a fragment of placenta prior to dissection in the center showing the two main components: dv = distal villi, sv = stem villi. At the top corners of the photo there are 2 pieces previously dissected. Photo B shows the result of the dissection of the piece of placenta in the center of photo A. Photos C1 and C2 are the microscopic image of the distal villi portion, at 10x and 25x amplification. Photos D1 and D2 are the microscopic image of the stem villi portion. It can be seen that the relative amount of fibroblasts, in comparison with cytotrophoblasts is higher in the stem villi fragments than in the distal villi fragments. The distal villi portion was used for DNA and RNA extraction. (Microscopic pictures, courtesy of Dr. Sarah Keating).

Figure 3.1: Preparation of the samples prior to DNA and RNA extraction.

processed following a standard protocol based on the College of American Pathologist Guidelines (Langston et al., 1997). Hematoxilin eosin stained slides were posteriorly obtained and reviewed blindly by a single pathologist with expertise in placental pathology. From that assessment a report was issued listing the standard lesions identified in each sample (Redline, 2008). The placenta weight percentile was calculated using a table from Kraus and colleagues (Kraus FT, 2004), which allows to classify placenta weight percentile into intervals of 10. Each sample was then classified as having or not having any of the lesions known to be associated with low birthweight percentile as described in Table 3.2 and shown in Figure 3.2 (Redline, 2008; Redline et al., 2005).

3.3 Statistical Analysis

For most association studies (with exceptions referenced in the text), non-parametric statistics was used. Spearman correlation was used when both variables were quantitative, Mann-Whitney U or Kruskall-Wallis tests were used when one of the variables was quantitative and the other categorical (bi-categorical or more than 2 categories, respectively) and Chi-Square or Fisher exact test when both variables were categorical.

Each variable was classified into categorical or quantitative as follows:

The methylation variable was expressed either quantitatively, in percentage between 0 and 100, or categorically, as high or low methylation.

Gene expression was a quantitative variable and log transformed to correct the skewing, prior to the statistical analysis.

In the array studies birthweight percentile was treated as a two category outcome variable (below or above the 10th percentile) in association with the presence or absence of placental lesions, respectively; in the extended cohort analyses it was treated as a quantitative outcome variable and analyzed independently as a measure of fetal growth.

Gestational age was a quantitative variable.

Table 3.2: Placental lesions analyzed in the placental pathology exam maternal infarct in a preterm placenta or an infarct >1cm in lesions of maternal vascular a term placenta (MI) underperfusion (MVU) decidual vasculopathy (DV) advanced villous maturity for Lesion known to be associated with low gestational age (AVM) birthweight percentile distal villous hypoplasia, presenting as excessive non-branching angiogenesis (DVH) fetal vascular thrombotic lesions (FVT) placental weight less than 3rd percentile chorangiosis Other assessed lesions not associated perivillous fibrin deposition (PFD) with low birthweight percentile acute chorioamnionitis (AC)

Figure 3.2: Microscopic pathology images of the placenta demonstrating some of the lesions considered as evidence of placental dysfunction. Represented are 1 - Decidual vasculopathy, 2 - Placental infarct, 3 - Advanced villous maturity showing syncytial knots (sk) at 24 wks of gestation and a control image, on the right, to compare, 4 – Distal villous hypoplasia, also with control image, on the left to compare. (Microscopic pictures, courtesy of Dr. Sarah Keating).

Figure 3.2: Microscopic pathology images of the placenta demonstrating some of the lesions considered as evidence of placental dysfunction.

Presence of placental lesions, ethnicity, sex, presence of labor and preeclampsia were treated as categorical variables.

3.4 DNA Extraction from Blood

This technique was used in the studies described in Chapters 4, 7 and 8.

For Chapter 4, DNA was extracted from peripheral blood collected from venipuncture, as described in its specific “Methods” section. For Chapters 7 and 8, genomic DNA was extracted from neonatal umbilical cord blood obtained as described in section 3.2.

DNA was extracted from whole blood by a phenol–chloroform method. Briefly, the anticoagulated blood sample was mixed with equal volume of 2x Lysis Buffer (0.65 M Sucrose,

20 mM Tris-Cl pH 7.8, 10 mM MgCl2 and 2% Triton-X100) and incubated on ice for 5 min, after which it was pelleted by centrifugation at 1000 x g for 13 min at 4ºC. The pellets were suspended in 2 ml of 75 mM NaCl / 24 mM EDTA and 200 μl of 10% SDS. Fifty μl of 20 mg/ml Proteinase K was then added and the sample was left overnight for protein digestion at 37ºC. On the next day 4 ml of Phenol was added to a 15 ml Maxtract Low Density Tube (QIAGEN, GmbH, Germany), previously prepared by centrifugation, and then loaded with the deproteinized blood sample. After spinning at 1500 g for 5 min at 20ºC, 4 ml Phenol-Chloroform- Isoamyl alcohol (25:24:1) was added and the tube was spun again. The upper phase, containing the DNA was transferred into a clean 15 ml polypropylene centrifuge tube to which 100 μl of 2 M KCl and 100% alcohol was added. The DNA was spooled onto a pipet tip, dehydrated by washing twice with 70% alcohol, and finally diluted in 200-300 μl TE, after which it was stored at 4ºC until further use.

3.5 DNA and RNA Extraction from Placenta

Nucleic acids extracted from placenta were used in the studies described in Chapters 5 to 9.

DNA and RNA were extracted using filter columns based kits from QIAGEN®, GmbH, Germany. The “DNeasy Blood and Tissue kit” and the “RNeasy Mini kit” were used for DNA and RNA extraction, respectively. Alternatively, and for most of the studies comparing DNA methylation and RNA expression, the Allprep DNA/RNA mini kit was used for extraction of

DNA and RNA from the same tissue sample. The extraction procedures followed the manufacturer's instructions, and are described briefly below.

For RNA extraction, the placenta sample, once prepared as described above, was homogenized using a Pellet Pestle Cordless Motor (Kimble/Kontes, Vineland, NJ, USA) with a matching RNase-free disposable pestle in 600 μl buffer RLT (QIAGEN®, contained 1% β- Mercaptoethanol). After centrifugation of the lysate at 10,000 g for 3 min, the supernatant was transferred to the AllPrep DNA spin column (QIAGEN®) (when using the Allprep DNA/RNA kit). The flow-through of the Allprep DNA spin column, or the centrifuged lysate (when using the RNeasy Mini kit), was mixed with an equal volume of 70% ethanol. The mixture was then transferred to the RNeasy spin column (QIAGEN®) and centrifuged at 10,000 g for 15 s. The RNA was washed by Buffer RW1 and RPE (QIAGEN®) and eluted in 30 μl RNase-free water. Potentially contaminating DNA was removed from the RNA samples by DNaseI (from Invitrogen®, Carlasbad, CA, US) treatment for 15 min at room temperature. The RNA solution was stored at -80ºC until further use.

For the DNA extraction using the “DNeasy blood and tissue kit”, 180 μl Buffer ATL (QIAGEN®) and 20 μl proteinase K were added to the 1.5 ml polystyrene tube containing ~50 mg of placenta sample and incubated at 56°C until the tissue is completely lysed (~2h). 200 μl of Buffer AL (QIAGEN®) and 200 μl ethanol (96–100%) was then added to the sample. This mixture was pipetted into the DNeasy Mini spin column and centrifuged at 6000 x g for 1 min. The DNA in this column, or in the Allprep DNA spin column, was washed by Buffer AW1 and AW2 (QIAGEN®) and eluted in 100 μl of Buffer AE (QIAGEN®) and stored at 4ºC until use.

3.6 Nucleic Acid Samples Quantitation and Quality Assessment

Concentration of nucleic acids (DNA, RNA and cDNA) was measured in all studies using a NanoDrop ND-1000® spectrophotometer from Thermo Fisher Scientific, Wilmington, DE, US.

3.7 cDNA Preparation by Reverse Transcription

Reverse transcription was performed in the study described in Chapter 7 before quantitative real time PCR. Only RNA samples with a ratio of absorbance at 260 and 280 nm higher than 1.8 were used for this assessment. RNA was converted into cDNA using iScript™cDNA Synthesis Kit (Bio-Rad, Hercules, CA) according to the manufacturer's instructions. For the cDNA reverse transcription, 1.5 μg of total RNA was used. First-strand cDNA was synthesized by incubation with iScript Reverse Transcriptase and 5×iScript Reaction Mix (containing oligo (dT) and random hexamer primers) for 5 minutes at 25ºC, 30 minutes at 42ºC, 5 minutes at 85ºC and then brought down to 4ºC. For each sample, a no-RTase negative control was also prepared to rule out DNA contamination. The synthesized cDNA and no-RTase control were stored at −20 °C until ready for use.

3.8 Bisulfite Conversion of DNA

Bisulfite conversion of DNA was performed prior to all pyrosequencing reactions (see below) and prior to hybridization into Illumina® Methylation arrays (see below). The purpose of this technique is explained in section 1.4.1.1. This technique was used in the studies described in Chapters 4 to 9.

All bisulfite conversion reactions were performed using the Imprint DNA Modification Kit from Sigma, St. Louis, MO, US, or the EpiTect Bisulfite Kit from QIAGEN®, GmbH, Germany, according to the manufacturer’s instructions.

The chemistry of both kits are optimized modifications of the classic bisulfite conversion of DNA as described by Clark and colleagues (Clark et al., 1994). Bisulfite conversion is a chemical deamination of non-methylated cytosine converting it into uracil. It is performed in - denatured single strand DNA in a high pH Sodium Bisulfite solution (HSO3 ) at high temperature (Figure 3.3). Its basic steps are: 1 – DNA denaturation; 2 – Bisulfite conversion reaction (sulphonation at the position C6 of cytosine, followed by irreversible hydrolytic deamination at the position C4 generating 6-sulphonate-uracil); 3 – DNA desulfonation; 4 - DNA cleaning and purification. The main drawbacks in this reaction are DNA degradation by the harsh conditions to which it is submitted, incomplete conversion of the non-methylated cytosines, and excessive

Figure 3.3: Non methyl cytosine sodium bisulfite conversion reaction. - Cytosine is sulfonated in position C6 by exposure to sodium bisulfite (HSO3 ) (I) followed by hydrolytic deamination at position C4 (II). Desulfonation (III) produces uracil. Note that a methyl group at position C5 prevents sulphonation at C6.

NH 4

I II III

Figure 3.3: Non methyl cytosine bisulfite conversion reaction.

conversion leading to conversion also of methylated cytosines. Commercial proprietary protection buffers and modifications of the thermal sequence to which the DNA is submitted have been developed in order to optimize the full conversion only of the target cytosines and simultaneously protecting the DNA of degradation.

Briefly, using the QIAGEN® kit, as an example, 1.3 g of DNA is exposed to a 140 μl reaction mix containing sodium bisulfite and protection buffer and submitted to 3 five minutes cycles of denaturation at 95ºC alternating with 60ºC of incubation for 25, 85 and 175 minutes. The single stranded DNA is bound to the membrane of the EpiTect column, washed, desulfonated and washed again to remove the desulfonating agent. The converted DNA is then eluted in 40 μl of EB buffer (QIAGEN®).

3.9 Agilent® Methylation Arrays

Agilent® methylation arrays were used for genomic analysis of DNA methylation. This technique was used in the studies described in Chapters 7 and 8.

The Agilent® array platform I used for methylation studies was the Human 244K Agilent® CpG island microarray. Those arrays are produced for DNA methylation studies of CpG Islands. In the studies here described the microarrays were hybridized with affinity enriched DNA. This method allows the study of the methylation status of many regions (in this case CpG Islands) of several hundred base pairs each. It is thus a genomic, affinity enrichment based, array technique for the study of DNA methylation. The array covers 97.5% of UCSC annotated CGIs and it contains 237,220 data probes plus a series of control probes. The probes are 45–60 bp in length and are spaced at 100 bp from the middle of each probe to the next. In total, they cover 21 MB of the human genome.

I used the Methylated DNA Immunoprecipitation (MeDIP) affinity enrichment method to enrich for the methylated fraction of the genomic DNA. This technique involves a process of immunocapture of methylated fractionated genomic DNA using an antibody against α- 5’methylcytosine that binds to methylated regions of the genome, followed by fluorescent labeling and equimolar co-hybridization with the non-enriched alternatively labeled background

(input) DNA. MeDIP was performed as initially described by Weber and colleagues (Weber et al., 2005b) with slight modifications. For the immunocapture, the antibody has to be bound to magnetic beads. To this, 10 mg of 5 mC Antibody (Eurogentec, San Diego CA) were incubated with 50 ml of Dynabeads® M-28 Sheep anti-mouse IgG (Invitrogen, Burlington, ON) for 5 hours in IP buffer (10 mM Na phosphate pH7.0, 140 mM NaCl, 0.05% Triton X-100) at 4ºC. Four g of genomic DNA had to be sonicated to the sizes of 200-1000 bp. To this, I used Sonicator® Ultrasonic Liquid Processor, Model XL2000 (Misonix, Farmingdale, NY). Two g of the sonicated DNA were then added to the Antibody-beads complex and incubated overnight at 4ºC. The DNA-Antibody-Dynabeads complex is washed 3 times with IP buffer, and then, to release the DNA, it was incubated in TE pH 8.0, 1% SDS solution with 5ml of Proteinase K (10 mg/ml) for 2 hours at 55ºC. DNA was further purified using the Qiaquick PCR purification kit (QIAGEN®, GmbH, Germany).

Prior to hybridization, a quality control step was performed to assess the enrichment relative efficiency. Semi-quantitative PCR (low number of cycles of sequential dilutions), targeting an expected hemi-methylated region (H19 DMR) and an expected non methylated region (GAPDH promoter), was performed on the enriched fraction. The expectations were to have a detectable band up to a lower concentration of the H19 DMR than of the GAPDH promoter that, ideally, should not show a band in any concentration. However, in my experiments, I selected for hybridization any sample for which there were at least 2 dilution differences (100x) between the H19 DMR and the GAPDH detectable bands. To correct for differences in enrichment efficiency quantile normalization was performed in the 2 studies in which this technique was used.

Twenty one l of immunoprecipitated DNA solution (enriched fraction) and 250 ng of the sonicated but not enriched fraction of the same samples’ genomic DNA (input DNA) were labeled with Cyanine 3-dCTP and Cyanine 5-dCTP, respectively, from Perkin Elmer (Waltham, MA) using BioPrime® Array CGH Genomic Labeling kit from Invitrogen (Burlington, ON).

The two labeled fractions were purified, hybridized onto the array and later scanned. These three last steps were performed at the Microarray facility, The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada according to the Agilent® Microarray Analysis of Methylated DNA Immunoprecipitation Manual (Version 1.0, May 2008).

Data from scanned images (tiff.) were extracted using Feature Extraction software version 9.0 (Agilent®, Santa Clara, CA, US). For the study described in Chapter 7, Human Agilent® CpG 20070820 version grid file and CGH V4-91 protocol were used to extract the raw data from the glass slides. For the studies described in Chapter 8 a posteriorly released Agilent® issued grid file – CpG 20080424 – and a newer feature extraction protocol – CHIP 105 Dec 08 – also issued by Agilent®, were used. The most recent grid file excluded 37,600 probes that had been later considered, by the manufacturer, as poor quality data. Thus, the total number of data probes was reduced to 199,400.

The above referred protocols determined the parameters of the processing of the image data, including background correction. The final output is the processed amount of red (methylation enriched fraction) and green (input DNA) signals, whose log2 ratios were used for further downstream analysis. Downstream analyzes were specific to each study and are detailed in each of the relevant Chapters.

Technical quality control was based in parameters provided by the Agilent® Feature Extraction software, using specific proprietary quality control oligonucleotides. It was performed by the Microarray facility, The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada according to the manufacturer’s instructions.

3.10 Illumina® Methylation Arrays

Illumina® methylation arrays were used for genomic studies of DNA methylation and for targeted validation of results of analyzes using the Agilent® methylation arrays. This technique was used in the studies described in Chapters 5, 6, 8 and 9.

The Illumina® array platform I used for methylation studies is the Illumina® Infinium HumanMethylation27 BeadChip. Those arrays are based on Illumina® proprietary bead attached oligonucleotide technology. This specific platform accommodates, in each experiment glass slide, 12 samples which reduces inter array variation and makes it a powerful tool for multiple sample experiments. It targets 27,578 CpG sites mapping to promoter regions with an average coverage of 2 CpG sites per gene. For most genes each of the 2 CpG sites is located upstream

and downstream of the transcription start site (TSS). It interrogates the methylation status portions of the promoter of almost 14,475 genes or predicted genes. The gene selection is not unbiased. There is enrichment for cancer related genes and for imprinting centers, with more CpG sites for those regions than the average. There are also some genes represented by only one CpG site, normally located prior to the TSS.

The oligonucleotide sequences attached to the beads in the array are complementary to bisulfite converted DNA sequences. After hybridization a single base extension reaction is performed using 2,4-dinitrophenol (DNP) and biotin labeled dNTPs. There are differently labeled dNTPs for each of the alternative states of the cytosine at a particular CpG locus. The relative amount of fluorescence of each color represents, respectively, the relative amount of the two - methylation and unmethylation - states. The amount of the methylated state fluorescence is divided by the amount of the total fluorescence to give a methylation percentage for the target CpG site. It is thus a genome wide bisulfite converted DNA based array technique.

One g of genomic DNA was bisulfite transformed (see 3.8 above for details about this technique). The subsequent steps followed the manufacturer’s instructions and were performed at the Genetic Analysis facility, The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada. Briefly, the DNA was subjected to whole genome amplification and hybridization, followed by washing. Scanning, performed by Illumina® BeadScan software produced digital tiff images and proprietary software raw files that were imported and analyzed using the BeadStudio Methylation Module Software, V3.2.0, or Genome Studio Methylation Module Software V1.6.1, both from Illumina® (San Diego, CA, US). The methylation ratios (β value=C/(T+C)) were extracted after background correction was performed by the software default algorithm. The results provided, for each CpG, were a β value (methylation ratio) that ranges continuously from 0 (unmethylated) to 1 (fully methylated) and a detection p value that, when less than 0.05 means the signal is valid (above background). BeadStudio and GenomeStudio software also allow the exportation of raw data for downstream analysis using alternative software. Downstream analyzes were specific of each study and are detailed in each of the relevant Chapters.

Quality control was based mainly on a parameter provided by the software that measures the efficiency of the bisulfite conversion (“Bisulfite Conversion Green”) and on the number of valid

CpG sites or probes. The bisulfite conversion efficiency parameter is based on specific quality control non data probes targeting cytosines that are expected to be fully converted to T. Arrays were accepted for analysis if the “Bisulfite conversion green” parameter was above 1500 and if the number of valid data points was above 24,000, corresponding to 87%. These values were arbitrarily chosen, based on the averages of the results obtained from the different arrays, and within the limits proposed by the manufacturer. For the experiment described in Chapter 6, three samples were hybridized twice into different arrays for reproducibility assessment.

3.11 Illumina® Expression Arrays

Illumina® expression arrays were used for genomic analysis of gene expression and for targeted functional validation in the studies described in Chapters 5 and 9.

The Illumina® array platform I used for gene expression studies is the Illumina® single channel HumanHT-12 v3 Expression BeadChip (San Diego, CA, US). As the Illumina® methylation arrays, these arrays are also based on Illumina® proprietary bead attached average 50-mer oligonucleotides and it also accommodates 12 samples, in each experiment glass slide. With 48,791 probes it targets 37,840 transcripts corresponding to ~25,000 well-characterized genes, the remaining corresponding to gene candidates, and splice variants.

The hybridization procedure was a standard single color and follows the manufacturer’s instructions and it was entirely performed at the Genetic Analysis facility, The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada. The quality of the RNA sample was first assessed using the 2100 microcapillary Electrophoresis Bioanalyzer from Agilent® (San Jose CA, US). Only medium and high quality samples, as determined by an RNA Integrity Number (RIN) higher than 6.0 were used in the arrays (Imbeaud et al., 2005; Schroeder et al., 2006). Selected samples of RNA were then submitted to double strand reverse transcription to cDNA followed by in-vitro transcription amplification incorporating, in this step, biotin labeled nucleotides. The following steps were hybridization, washing, blocking and streptavidin Cy3 staining. The slides were than scanned producing proprietary software raw files that were imported and analyzed using the Genome Studio Gene Expression Module Software V1.6.0, from Illumina® (San Diego, CA, US). The expression readings were extracted after

background correction was performed by the software default algorithm, and were provided non log transformed. Normalization was performed by the percentile based cubic spline method given the non-linear relationship between samples from different arrays, as suggested by the manufacturer. A detection p value was also provided. When less than 0.05 it means the signal is valid (above background). This was used to exclude, from further analysis, readings that were not different from background. As for the methylation arrays, GenomeStudio software also allow the exportation of raw data for downstream analysis using alternative software. Downstream analyzes were specific of each study and are detailed in the relevant Chapter.

Quality control data is partially based in internal control features. The proprietary software summarizes the quality control metrics into a series of plots that will have to show a set of expected values in order to consider the data accepted for analysis. Arrays were accepted for analysis if the expected values were reached. For the experiments described in Chapter 5 and 9, two RNA samples were hybridized twice into different arrays for reproducibility assessment.

3.12 Southern Blot Analysis of DNA Methylation

Southern Blot analysis of DNA methylation was used for targeted methylation studies. This technique was used in the study described in Chapter 4.

This technique is used to semi quantitatively evaluate the methylation level of a single CpG dinucleotide. It is a targeted, enzyme restriction based gel technique for DNA methylation.

A reasonable amount of DNA (in this case 5 g) was digested with a combination of 2 restriction enzymes, one methylation sensitive and one non-methylation sensitive. The DNA was fractionated in 0.8% agarose gel and transferred overnight at 60 V to a positively charged nylon membrane (Hybond N+, Amersham Biosciences) in 0.4 N NaOH. The membrane was neutralized and pre hybridised in modified Church's buffer with salmon sperm DNA as a blocking agent prior to 65ºC overnight hybridization with 32P-dCTP radio-labeled probes specific for the target region. The membrane was then washed with serial washes of SSPE (2×, 1×, 0.5×, all containing 0.5% SDS). Details of the procedure used for imaging and analysis of the radio signal are described in the relevant chapter. If the target CpG is fully methylated or fully

unmethylated only one band of different sizes is visible. If two bands are visible, the top one corresponds to a larger fragment representing the methylated DNA molecules (undigested by methylation-sensitive enzyme) and the bottom one to a shorter fragment representing the unmethylated DNA molecules (digested by methylation-sensitive enzyme). A semi quantitative methylation index (%) in a sample can be calculated according to the following equation: Methylation index (MI)=[M/(M+U)]×100, where M is the optical density of the fragment of methylated allele and U is the optical density of the fragment of the unmethylated allele.

3.13 Pyrosequencing Analysis of DNA Methylation

Pyrosequencing analysis of bisulfite modified DNA (performed as explained above in 3.7) was used for targeted methylations studies. This technique was used in the studies described in Chapters 4, 6 and 8.

Pyrosequencing is a quantitative sequencing technique of up to 400 bp genomic stretches of known sequences. It is based on sequencing by synthesis, i.e. the sequence is determined based on the reading of a light signal emitted for each nucleotide incorporated (see http://www.pyrosequencing.com/ for details of the technique). It requires a PCR targeted amplification step prior to the sequencing reaction. Using a sequencing primer which anneals to one of the strands of the amplicon, a nucleotide at a time is added to the reaction mix according to the predicted sequence. The incorporation of the added nucleotide to the newly synthesized strand, by polymerase, releases pyrophosphate quantitatively dependent on the amount of incorporated nucleotide. The pyrophosphate is converted to ATP which is used to convert luciferin to oxyluciferin, releasing light in a measurable amount that is also proportional to the number of nucleotides incorporated. Prior to the addition of a new nucleotide, the non- incorporated nucleotide is degraded by apyrase, also present in the mix, together with all the other enzymes required for the other steps of the reaction. This allows the precise determination of the number of each given nucleotide, added sequentially to the mixture, that is incorporated in the sequence. It is mainly used for quantitating relative amounts of alternative nucleotides such as in SNPs. Adding the two alternative nucleotides in sequence and measuring their relative incorporation amount it is possible to know the frequency of each nucleotide in the mixture. Since the end result of bisulfite conversion of DNA followed by PCR is the creation of a C-to-T

SNP in Cytosines that are prone to methylation, i.e., cytosines followed by guanines, as it occurs in CpG dinucleotides, it can be also used to quantitate the methylation levels of all individual CpG sites in PCR amplicons. For this purpose, the DNA will have to be bisulfite modified, as described previously in 3.8, and then PCR amplified with primers designed for the modified DNA sequence prior to the pyrosequencing reaction. It is thus a targeted, bisulfite modification based, sequencing technique for DNA methylation analysis.

Details of this technique are provided elsewhere (Byun et al., 2007; Tost et al., 2006; Tost and Gut, 2007b). Briefly, bisulfite modified DNA was subject to PCR of a previously determined target region containing, usually 3 to 7 CpG sites, using Hot-Start Taq-polymerase (QIAGEN®, GmbH, Germany). PCR and sequencing primers were designed using Pyrosequencing assay design software Version 1.0.6 (QIAGEN®) or PSQ Design Software (Biotage, Uppsala, Sweden). The PCR designed primer opposite to the sequencing primer had a universal tag which annealed to the universal biotinylated primer also added to the PCR reaction as previously published by Royo and colleagues (Royo et al., 2007; Royo et al., 2006).

The PCR conditions varied slightly with each assay. The reactions were carried on a 25 μl volume. It includes 0.5 μl of 10 μM of each primer (forward, reverse and M13-biotin universal primer), 25 mM of MgCl2 when required (1.5 μl for WNT2 assay, 0.5 μl for APC assay and none for TP73, CGB5, H19 DMR or H19 promoter assays), 0.5 μl of 10 mM dNTP, 0.25 μl of hot start Taq polymerase, 1 μl of bisulfite converted DNA template at the concentration of ~10-20 ng/μl and, when needed, as in the case of the H19 promoter assay, 2.5 μl of PCR enhancer. This is a DMSO, betaine, DTT, BSA based buffer that enhances thermostability of Taq polymerase making it less sensitive to PCR-inhibiting contaminants. This buffer is useful for CpG rich regions. The PCR reaction was initiated by Taq activation at 95ºC for 15 min, followed by the amplification cycles (varied with the assay as described in each assay) and ended with 7 min at 72ºC.

Following PCR, biotinylated PCR products and the sequencing primer (15 pmol per reaction) were co-denaturated following the PyroMarTMQ24 system (QIAGEN®) preparation guide and the biotinylated strand was captured using the pyrosequencing vacuum prep tool (QIAGEN®). The single-strand PCR product acts as a template in the pyrosequencing reaction which was performed, after annealing to the sequencing primer, on a PyroMarTMQ24 system with the

Pyrogold reagent kit according to the manufacturer's instructions. Raw data were analyzed with the methylation quantitation algorithm with Pyro Q-CpG Software (QIAGEN®) provided with the instrument. Percentage methylation was quantified as a ratio of C (methylated C, not converted to U) to T (not-methylated C converted to U) plus C. Bisulfite modification efficiency can be analyzed by treating the non-CpG cytosines present in the target sequence also as possible SNPs corresponding the alternatives to the modified or the non-modified versions of the cytosine. Efficiencies of 100% should show 0% of the non-modified alternative – C or G.

3.14 Quantitative Reverse Transcription PCR (qRT-PCR)

Quantitative PCR (qPCR) of cDNA, obtained as explained above, was used for targeted gene expression / specific mRNA levels quantitation. This technique was used in the study reported in Chapter 7.

I used the Mx3005P QPCR System (Stratagene – An Agilent® Technologies Company, La Jolla, CA) with SYBR Green PCR mix. The amplification mix (20 μl) contained a cDNA template derived from ~5 to 7 ng of total RNA, diluted to an amount of ~100 ng of cDNA, 500 nM of each specific primer set, 10 μl iQ Fast SYBR Green Supermix with ROX (Bio-Rad, Hercules, CA), (containing dNTP, SYBR Green, hot-start fast Taq polymerase and buffer) and RNase-, DNase-free water. The PCR reaction was initiated with 3 min at 95 °C to activate hot-start Taq polymerase and for initial denaturation, followed by 40 cycles of denaturation at 95 °C for 3 sec, then annealing and elongation at 60 °C for 30 sec. Fluorescence detection was performed immediately at the end of each annealing step. The purity of each amplification product was confirmed by generating dissociation curves. For each placenta cDNA sample, qPCR was performed, in triplicate, for the gene of interest and for a normalizing gene. Also, for each placenta sample, a single negative control (obtained from a RNA fraction exposed to non- Reverse Transcriptase containing cDNA conversion reaction), was simultaneously submitted to the two qPCR reactions. When significant amounts of PCR product were observed in the negative control samples, either in the gene of interest or in the normalizing gene, the qPCR reaction of the specific sample was repeated. Relative amount of cDNA was determined using an adaptation of the standard curve method (Rutledge and Cote, 2003) and normalized by the expression of the IF2B housekeeping gene (Guo et al., 2008), which acted as a control for

62 unequal cDNA amount loading, and by the amount of ROX, which acted as a control for unequal amounts of SYBR Green added to each reaction tube. IF2B has been found previously as not varying with gestational age (Guo et al., 2008). The quantitation was performed as follows: standard curves were generated for the gene of interest and for the normalizing gene, by a duplicated series of four 10x serial dilutions of a pool of 10 randomly selected cDNA samples. The amount of RNA expected in each reaction at the highest concentration reaction was ~1000 ng. These curves have two functions: 1 - to control the efficiency of the reactions by the slope of the log transformed Ct Value versus expected relative concentration, 2 – to determine the relative amount of each study sample by comparing its Ct with the Ct of the four 10x serially diluted standards. The final expression value determined for the gene of interest was the result of the ratio between its relative calculated amount and the relative calculated amount of the normalizing gene. In order to compare samples from different plates, the relative position of the thresholds for each of the genes (gene of interest and normalizing gene) had to be kept the same in all the plates, since the determined quantity relative to the standard depends on the respective Ct and this depends on the threshold. MxPro qPCR software, v4.01, 2007 (Stratagene®) was used for the analysis.

3.15 Description of Samples Used in the Experiments

In the following tables I provide information about the samples that were used in the several studies described in chapters 5, and 7 to 9. Table 3.3 provides details on the samples hybridized into any of the arrays used – Agilent® methylation CpG Island arrays hybridized with MeDIP enriched and input control DNA, Illumina® methylation single CpG arrays hybridized with bisulfite converted DNA and Illumina® Expression arrays hybridized with cDNA. Table 3.4, 3.5 and 3.6, summarize the characteristics of the samples used, respectively, for the Agilent® methylation arrays (Chapter 7 and 8), for the Illumina® methylation arrays (Chapter 5, 8 and 9) and in the extended cohort studies (Chapter 5 and 7).

As briefly mentioned in section 3.1, above, for the array studies the samples were classified into cases (also referred as intrauterine growth restriction or IUGR) and controls respectively. Cases had a birthweight percentile ≤ 10th and placental lesions known to be associated with growth

restriction (Table 3.2). Controls had a birthweight percentile > 10th and none of those placental lesions. In all the array studies, preeclampsia was added to the exclusion criteria.

For the extended cohort studies these two main outcome variables (birthweight percentile and presence of placental lesions) were analyzed independently. This cohort was also assessed for effects on birthweight percentile caused by the following potential confounding variables – preeclampsia, ethnicity, gestational age, sex and presence of labor.

The samples used in the Agilent® methylation arrays were also used in the Illumina® methylation and expression arrays. In addition, there were 8 samples used for the Illumina® methylation arrays only. The use of the same samples in both arrays permitted the results of one type of array to cross validate those of the other type when results of the same target regions were available. Good quality RNA was not available for 5 of the samples used in the Illumina® methylation arrays. So expression array results for those samples are not available. For expression analyses, expression array data for 2 more samples was available. Methylation arrays for these 2 samples were not available.

The different arrays were not hybridized simultaneously. During the course of my project new array data was being generated as new hypotheses were raised, samples were made available, new technologies were made available and/or I acquired experience with the technologies. All these factors entered into the decision making process about which methods to use to address the new questions.

Table 3.3: Details of samples run on the microarray– characteristics, DNA sources and arrays used Sample Methyl. Methyl. Express. Sample GA Sex Category Placenta Pathology Sample Agilent Illumina Illumina ID (BW%) (Chapt)* (Chapt)* (Chapt)* 10G Placenta NA 6 Not used 10C 14 M (?) NAp NA Trophoblast NA 6 NA 10F Fibroblast NA 6 NA 12G Placenta NA 6 NA 12C 14 M (?) NAp NA Trophoblast NA 6 NA 12F Fibroblast NA 6 NA 8G Placenta NA 6 Not used 8C 14 F (?) NAp NA Trophoblast NA 6 NA 8F Fibroblast NA 6 NA 11G Placenta NA 6 Not used 11C 18 M (?) NAp NA Trophoblast NA 6 NA 11F Fibroblast NA 6 NA 14G Placenta NA 6 (x2) Not used 14C 18 M (?) NAp NA Trophoblast NA 6 (x2) NA 14F Fibroblast NA 6 (x2) NA 15G Placenta NA 6 Not used 15C 19 F (?) NAp NA Trophoblast NA 6 NA 15F Fibroblast NA Failed QC NA 8539 26 F Control (76) normal size, no lesions Placenta 7 and 8 5 and 9 5 and 9 3040 27 M Control (35) normal size, no lesions Placenta NA 5 and 9 NA 8331 28 F Control (28) normal size, no lesions Placenta 7 and 8 5 and 9 5 and 9 134 29 M SGA (8) small, MI, AVM Placenta 7 and 8 5 and 9 5 and 9 3010 29 M Control (55) normal size, no lesions Placenta 7 and 8 5 and 9 5 and 9 7285 29 F SGA (5) small, AVM Placenta NA 5 and 9 5 and 9 9157 30 M SGA (10) very small, AVM, DV Placenta 7 and 8 5 and 9 5 and 9 8678 31 M SGA (8) small, MI, FVT Placenta 7 and 8 5 and 9 5 and 9 7264 32 M SGA (1) small, AVM Placenta NA 5 and 9 5 and 9 620 33 M SGA (2) small, AVM Placenta 7 and 8 5 and 9 5 and 9 7633 33 M Control (51) normal size, no lesions Placenta NA 5 and 9 NA 8666 33 M SGA (5) small, FVT, AVM, DV Placenta 7 and 8 5 and 9 5 and 9 4843 33 F Control (57) Normal size, no lesions Placenta NA NA 9 4196 34 M Control (64) normal size, no lesions Placenta 7 and 8 5 and 9 5 and 9 8174 34 M SGA (6) small, MI Placenta 7 and 8 5 and 9 5 and 9 8298 34 F SGA (1) very small, DVH Placenta NA 5 and 9 NA 8811 36 M SGA (1) very small, MI Placenta 7 and 8 5 and 9 5 and 9 3086 37 M Control (42) normal size, no lesions Placenta 7 and 8 5 and 9 5 and 9 7740 37 F SGA (1) small, IVT, MI, DVH Placenta NA 5 and 9 NA 8177 38 M SGA (5) very small, MI, DVH, DV Placenta 7 and 8 5 and 9 5 and 9 8928 38 M SGA (1) very small, no lesions Placenta NA NA 9 1227 39 M Control (72) normal size, no lesions Placenta 7 and 8 5 and 9 5 and 9 1243 39 M Control (13) normal size, no lesions Placenta 7 and 8 5 and 9 5 and 9 1256 39 F Control (79) normal size, no lesions Placenta 7 and 8 5 and 9 5 and 9 9363 39 F Control (39) normal size, no lesions Placenta NA 5 and 9 5 and 9 9375 39 M Control (78) normal size, no lesions Placenta NA 5 and 9 NA * The numbers refer to the chapters that describe the study in which the array was used as data source. In bold, are the samples for which there are methylation and expression array data (19 samples) BW% - birthweight percentile; NA – not available; NAp – not applicable; AVM – advanced villous maturity; DV – decidual vasculopathy; DVH – distal villous hypoplasia; FVT – fetal vascular thrombotic lesions; IVT – Intervillous thrombosis; MI – maternal infarct; SGA – small for gestational age (≤10th percentile); Trophoblast – cytotrophoblast; no lesions – no significant lesions; very small – ≤3rd percentile; small – 3 to 10th percentile; normal size – >10th percentile; (x2) – same sample used in 2 different arrays in 2 different slides for reproducibility assessment control.

Table 3.4: Characteristics of the samples analyzed with the Agilent® methylation arrays (chapters 7 and 8). Cases or Controls** p IUGR* N 8 8 Sex (Fem/M) 0/8 3/8 0.2F GA in weeks 33, 29, 37 34, 26, 39 0.56MW (mean, min, max) Labor 1/0/7 2/3/3 0.16F Y/N/Unknown * Birthweight percentile ≤ 10th and presence of placental lesions; **Birthweight percentile > 10th and no placental lesions. IUGR – Intrauterine growth restriction; Fem – Female; M – Male; GA – Gestational age; Y – Labor present (vaginal delivery or C-section), N – Labor not present (elective C-section), Unknown – C-section (unknown about presence or absence of labor); F - Fisher exact test; MW - Mann-Whitney U test Note: preeclampsia and chorioamnionitis were an exclusion criterion for this cohort, placental lesions (Table 3.2) were an exclusion criterion for controls and an inclusion criterion for cases.

Table 3.5: Characteristics of the samples analyzed with the Illumina® methylation arrays (chapters 5, 8 and 9) (n=24) Cases or Controls** p IUGR* N 12 12 Sex (Fem/M) 3/9 4/8 1F GA in weeks 33, 29, 37 34, 26, 39 0.42MW (mean, min, max) Labor 2/2/8 3/5/4 0.33F Y/N/Unknown * Birthweight percentile ≤ 10th and presence of placental lesions; **Birthweight percentile > 10th and no placental lesions. IUGR – Intrauterine growth restriction; Fem – Female; M – Male; GA – Gestational age; Y – Labor present (vaginal delivery or C-section), N – Labor not present (elective C-section), Unknown – C-section (unknown about presence or absence of labor); F - Fisher exact test; MW - Mann-Whitney U test Note: preeclampsia and chorioamnionitis was an exclusion criterion for this cohort, placental lesions (Table 3.2) was an exclusion criterion for controls and an inclusion criterion for cases.

Table 3.6: Characteristics of the samples used in the validation cohort (chapters 5 and 7) (n=170) Association with Descriptive BW% (p value) Median – 21 BW% Mean – 29 (SD – 26.7) Not applicable (n=170) Range – 93 (1 to 94)* IQR – 6 to 51 Placental lesions associated with Without lesions – 68 (42.8%) low birthweight <0.0005MW With lesions – 91 (57.2%) (n=159)** Preeclampsia (PE) Without PE – 119 (70%) 0.148MW (n=170) With PE – 51 (30%) Caucasian – 68 (54.8%) Ethnicity Black – 23 (18.5%) 0.398KW (n=124) Asian – 33 (26.6%) Median – 33.5 GA (weeks) Mean – 33.3 (SD – 4.5) 0.217S (n=170) Range -17 (24 to 41) IQR – 30 to 38 Sex Female – 75 (44%) 0.755MW (n=170) Male – 95 – (56%) Y – 53 (31.2%) Labor Y/N/Unknown N – 69 (40.6%) 0.71KW Unknown – 48 (28.2%) * Percentile 1 includes newborns with BW less than 1st percentile ** See Table 3.2 for description of lesions associated with low birthweight percentile. BW% – Birthweight percentile; GA – Gestational age; Y – Labor present (vaginal delivery or C-section), N – Labor not present (elective C-section), Unknown – C-section (unknown about presence or absence of labor); MW - Mann- Whitney U test; KW - Kruskall-Wallis test; S – Spearman test.

Chapter 4: Postnatal Phenotype of Russell-Silver Syndrome Caused by Epimutation in Imprinting Center H19 DMR or UPD7.

The published version of this chapter appeared in:

Horike, S.*, Ferreira, J.C.*, Meguro-Horike, M., Choufani, S., Smith, A.C., Shuman, C., Meschino, W., Chitayat, D., Zackai, E., Scherer, S.W., Weksberg, R. (2009). Screening of DNA methylation at the H19 promoter or the distal region of its ICR1 ensures efficient detection of chromosome 11p15 epimutations in Russell-Silver syndrome. Am J Med Genet A 149A, 2415- 2423.

* I and Shin-Itchi Horike were co-first authors of this publication (Horike et al., 2009).

The chapter that follows is an adapted modified version.

For this portion of my work I had the contributions of two post-doctoral fellows from Dr. Steve Scherer’s laboratory, Drs. Shin-Itchi Horike and Makiko Meguro-Horike. Dr. Makiko Meguro- Horike contributed with some of the Southern blot experiments. Drs. Shin-Itchi Horike also contributed with some of the Southern blot experiments and with the Figures 4.1, 4.2 and 4.3. He also drafted the report of the Southern blot experiments in the first draft of this paper.

I did the validation of the Southern blots, analyzed the clinical data and did the genotype- phenotype correlations. I also reviewed all the literature and wrote the final draft of the paper.

4.1 Summary

Over a 10 year period blood samples were collected from 57 individuals with growth restriction, and Russell-Silver syndrome (RSS)-like features. The goal of this study was to identify epigenetic abnormalities in this cohort, including uniparental disomy of chromosome 7 (UPD7), methylation changes at chromosome11p15, as well as new epigenetic alterations and to analyze genotype/epigenotype - phenotype correlations.

The methylation status of seven imprinting centers on chromosomes 7, 11, 14 and 15 was evaluated. UPD 7 and chromosome 7 structural abnormalities had been previously identified in 5 patients. Epigenetic alterations on chromosome 11p15 were identified in 11 patients. I then analyzed the clinical features of those 11 and of the 5 UPD 7 patients and of all the patients reported in the literature carrying one of those 2 genetic/epigenetic anomalies.

Of interest, the minimum common clinical criteria for patients carrying one of those 2 molecular defects, in the prenatal or neonatal period is not distinguishable from newborns diagnosed with growth restriction secondary to placental insufficiency. Furthermore, in 2 of these 11 patients, the epigenetic alterations were limited to the H19 promoter and the distal region of its associated imprinting center, IC1 (also here referred to as H19 differentially methylated region or H19 DMR). In addition, in one patient, methylation changes consistent with maternal UPD at all tested imprinted regions were identified.

This patient series suggests that epimutations on chromosome 11p15 can be most efficiently detected in RSS patients by screening for DNA methylation defects at the H19 promoter or the distal region of IC1 and that, in the prenatal and neonatal period, their phenotype is not distinguishable from other more commonly diagnosed causes of intrauterine growth restriction.

4.2 Introduction

Russell-Silver syndrome (RSS) (OMIM 180860), is a clinically and genetically heterogeneous disorder usually diagnosed in children with prenatal and/or postnatal growth restriction or body mass index < 2SD, a relatively large head circumference, dysmorphic facial features and body asymmetry (Russell, 1954; Silver et al., 1953). The majority of RSS patients are sporadic but a

69 few familial cases have been described (Al-Fifi et al., 1996; Duncan et al., 1990; Robichaux et al., 1981; Teebi, 1992).

Two different epigenetic defects have been associated with RSS. These are maternal uniparental disomy (mUPD) for chromosome 7 and loss of paternal methylation at the differentially methylated region on chromosome 11p15.5 upstream of the H19 gene.

Maternal uniparental disomy of chromosome 7 (mUPD 7) was first described in a girl with short stature (Langlois et al., 1995) and later was reported in 7-10 % of RSS and RSS-like individuals (Eggermann et al., 1997; Nakabayashi et al., 2002; Preece et al., 1997).

A link between RSS and chromosome 11p15.5 was initially established by identifying RSS patients with structural chromosomal anomalies disrupting this region (Eggermann et al., 2005; Fisher et al., 2002; Schonherr et al., 2007b). Later, loss of methylation at the H19 differentially methylated region (H19 DMR), corresponding to the Imprinting Center 1 (IC1) of this imprinted region, was reported to occur in patients with RSS, including one of a pair of monozygotic twins with phenotypic discordance (Gicquel et al., 2005). Since then several reports have been published describing similar findings in a number of RSS, RSS-like and even isolated asymmetry patients (Bartholdi et al., 2009; Binder et al., 2008; Binder et al., 2006; Bliek et al., 2006; Eggermann et al., 2008a; Eggermann et al., 2008b; Eggermann et al., 2006; Netchine et al., 2007; Schonherr et al., 2006; Zeschnigk et al., 2008). The frequency of 11p15.5 epigenetic anomalies in RSS is estimated to be in the range of 35%.Thus, the two most commonly found molecular anomalies could explain approximately 50% of the RSS and RSS-like patients.

Human chromosome 11p15.5 harbors an imprinted gene cluster of 1 Mb with two imprinting centers (IC1 and IC2) (Figure 4.1a). The IC1 domain, besides the primary differentially methylated region, located between the IGF2 and H19 genes (H19 DMR), has other secondary differentially methylated regions – e.g. IGF2 DMR2 and IGF2 DMR0 – located within the IGF2 gene. In addition to its role in RSS, this imprinted domain is implicated in a wide variety of malignancies and the overgrowth disorder Beckwith-Wiedemann syndrome (BWS, OMIM 130650) (Weksberg et al., 2003). RSS and BWS display a number of opposite clinical features, and have, in fact, opposite epigenetic errors on chromosome 11p15.5. While epimutations and genetic structural anomalies have been found in both ICs associated with BWS, no primary IC2 epimutation has been reported to date in RSS patients. However, a single RSS patient with a

70 maternally inherited cryptic duplication involving IC2 has been reported (Schonherr et al., 2007b).

In spite of all the advances in delineating the causes of RSS, the etiology in a significant proportion of RSS (~50%) and RSS-like patients remains unclear. Given the known functional relationship between imprinted regions and growth, it is possible that individuals diagnosed with growth restriction phenotypes, including but not limited to RSS, may have a broader range of epigenetic abnormalities than is currently appreciated. A comprehensive analysis of such phenotypes could identify new epigenetic abnormalities beyond mUPD7 or 11p15.5 methylation anomalies. Therefore, we studied a cohort of patients with a broad clinical spectrum of RSS features for epigenetic abnormalities in several differentially methylated regions, including the

H19 promoter, IC1, IGF2 DMR2, and the IC2 on chromosome 11p15.5, PEG1/MEST on chromosome 7q32, MEG3/GTL2 on chromosome 14q32 and SNRPN on chromosome 15p11-13. Chromosome 7 anomalies, such as UPD 7 and structural chromosome 7 anomalies had been excluded in this cohort.

The comprehensive DNA methylation analysis of chromosome 11p15.5 identified epigenetic alterations in 11 RSS patients. Interestingly loss of methylation at the H19 promoter was found to be the most consistent epimutation in the 11 RSS patients. Furthermore, in one of the patients methylation changes consistent with mUPD across all 4 chromosomes tested was detected, prompting consideration of more complex diagnoses. Analysis of the phenotype and epigenotype data allowed the developement of a set of clinical criteria that will enable clinicians to better predict the likelihood of chromosome 11 epimutations or mUPD7 in patients with growth restriction phenotypes.

4.3 Materials and Methods

4.3.1 Clinical and biological samples

We used DNA obtained from peripheral blood of patients for whom RSS was in the differential diagnosis as per the referring physician. The most consistent clinical feature in the cohort was either fetal growth restriction (birthweight equal or less than 3rd percentile) or short stature at observation (height equal or less than 3rd percentile). Some patients had some RSS facial

dysmorphic features (triangular face, down-turned corners of the mouth, micrognathia/retrognathia, frontal bossing/high forehead, blue sclera, prominent nasal bridge, low set ears). All patients had a normal karyotype. Five other patients, that were originally part of this cohort, had been found to have mUPD7 or chromosome 7 structural anomalies (Nakabayashi et al., 2002) and were therefore excluded from the present analysis. The study was approved by the Research Ethics Board at the Hospital for Sick Children, Toronto, Canada.

4.3.2 Methylation studies

4.3.2.1 Southern blot

To analyze the methylation status of imprinted loci on chromosome 11p15.5, 7q32 and 14q32, 5 µg of DNA were digested with RsaI and methylation-sensitive SmaI restriction enzymes. For the imprinted locus in chromosome 15q11-13, the same amount of DNA was digested with XbaI and methylation-sensitive NotI restriction enzymes. The resulting fragments were fractionated on a 1.0% agarose gel and transferred by Southern blotting onto Amersham HybondTM-N+ (GE Healthcare UK, Buckinghamshire, England). The blots were hybridized with 32 P-labeled probes that were generated by PCR of genomic DNAs. Primer sequences used for the probes are presented in Table 4.1. X-ray films were exposed to the Southern Blots and they were analyzed qualitatively. The films of H19 promoter and IC1 were scanned using a HP digital scanner and the bands of the good quality images were quantitated using AlphaEaseFc Software, version 4.0.0, 2003 from Alpha Innotech, San Leandro, CA.

4.3.2.2 Pyrosequencing

For patients with abnormal DNA methylation results at IC1 or H19 promoter, if DNA was still available, the epimutations were validated using pyrosequencing of bisulfite converted DNA. The pyrosequencing assay targets a more distal region of IC1, relative to the H19 promoter region (chr11:1,977,711-1,977,722) than the position probed by the Southern blot probe (chr11:1,979,593-1,980,733) (Figure 4.1b). It measures the methylation of 3 consecutive CpG sites. This technique involves bisulfite conversion of the DNA and quantitative sequencing of a PCR fragment including several CG dinucleotides. The technique allows for quantitation of each SNP created by the bisulfite conversion of the DNA. Details of this technique are provided in section 3.11. DNA was bisulfite converted using Imprint DNA Modification Kit (Sigma, St. Louis, MO).

Table 4.1: Oligonucleotide primers for southern blot probe and pyrosequencing PCR amplicons

IC1 (H19 DMR) SB probe: Chr. 11: 1,979,593-1,980,714 (1121 bp) – CTCF Bs 1 Forward primer CCCGAGGGTTGTCAGAGATA Reverse primer GCATCTCAAACCTGCACTGA H19 prom SB probe: Chr. 11:1,975,140-1,976,279 (1140 bp) Forward primer GCGGTCTTCAGACAGGAAAG Reverse primer CGATCCCCTAAACCTCCTTC IGF2 DMR2 SB probe: Chr. 11:2,110,585-2,111,364 (780 bp) Forward primer AAGATGCTGCTGTGCTTCCT Reverse primer AAGTCCGAGAGGGACGTGT IC2 SB probe: Chr. 11:2,678,396-2,679,294 (899 bp) Forward primer CTCTCGGACAGGCAGATGAC Reverse primer GATGTTCTGAAGCCCCCACT PEG1/MEST CGI SB probe: Chr. 7:129,918,828-129,920,625 (1789 bp) Forward primer AAGGAAAGAGTTGGGGCACT Reverse primer CAAAGAGGCAACCTCTCAGG MEG3/GTL2 CGI SB probe: Chr. 14:100,359,248-100,360,258 (1011 bp) Forward primer GGTTTCAGCATGCACAGAGA Reverse primer AAGGGCATGAGTTGACGTTC SNRPN DMR1 SB probe: Chr. 15:22,751,647-22,754,371 (2725 bp) Forward primer ATCAGGGTGATTGCAGTTCC Reverse primer AACATTTCGGCAATGACACA IC1 (H19 DMR) pyrosequencing: Chr. 11:1,977,711-1,977,722 (11 bp) which includes 3 CpGs – CTCF Bs 6 Forward PCR primer TGAGTGTTTTATTTTTAGATGATTTT Reverse PCR primer (Biotinylated) ACAATACAAACTCACACATCACAAC Sequencing primer (Forward) GTGGTTTGGGTGATT H19 prom pyrosequencing: Chr. 11:1,975,658-1,975,667 (9 bp) which correspond to 2CpGs Forward PCR primer GGGAGGGTTTTGTTTTGATTG Reverse PCR primer (Biotinylated) ACTCTCCTCCAACACCCCATCT Sequencing primer (Forward) TATTTTAGTTAGAAAAAGTT Biotinylated sequence (attached to the 5’ end of the biotinylated CGCCAGGGTTTTCCCAGTCACGAC primers of all assays) IC – Imprinting Center; DMR – differentially methylated region; bp – base pairs; CTCF Bs – CTCF binding site; SB – Southern blot

Table 4.2: PCR conditions for H19 DMR and promoter segments for pyrosequencing Assay name Step down phase Fixed phase 95ºC 30 sec 95ºC 30 sec H19 DMR 60 to 55ºC in 0.5ºC steps 30 sec 10 cycles 55ºC 30 sec 38 cycles (150 bp) 72ºC 30 sec 72ºC 30 sec 95ºC 30 sec 95ºC 30 sec H19 promoter 55 to 45ºC in 0.5ºC steps 30 sec 20 cycles 45ºC 30 sec 20 cycles (219 bp) 72ºC 30 sec 72ºC 30 sec

The primers used in the PCR step are listed in Table 4.1. The specific conditions of the PCR, which did not require the use of MgCl2, are described in Table 4.2 (for other details see section 3.11). Sequencing was done on a PyroMarTMQ24 system from Biotage, Uppsala, Sweden. The relative location of the regions probed by the Southern Blot and by the Pyrosequencing analysis is depicted in Figure 4.1b.

4.4 Results

4.4.1 Chromosome 11p15.5 methylation analyses

In the present analysis, 11 out of 52 patients were found to have loss of methylation on chromosome 11. Details about the molecular findings at the imprinted sites tested in these 11 patients are listed in Table 4.3.

In normal controls, digestion of genomic DNA performed for the study of IC1 shows, in the Southern Blot, both methylated (1.5 kb) and unmethylated signals (1.0 and 0.5 kb), consistent with paternal-specific CpG methylation at IC1. The digestion performed for the study of IC1 in 8 of the patients (P1-P7 and P11) produced only 1.0 and 0.5kb fragments. These results indicate that paternal methylation at the IC1 locus was altered in these patients. As an example, the analysis of IC1 for 2 patients is presented (lane #3 and #7 in Figure 4.1bI). In the Southern Blot of the H19 promoter region, 10 patients in this series (P2-P11) showed loss of H19 promoter methylation with complete loss of the methylated signal (2.3 kb) (examples shown in lane #3 and #7 in Figure 4.1bII). The Southern Blot data of IC1 and H19 promoter was further validated using pyrosequencing of bisulfite converted DNA for five patients with available DNA. I was able to confirm the epimutation at the H19 promoter in P4, P5, P8 and P10. Further, I verified that P1 (inconclusive by Southern Blot) also had loss of methylation at the H19 promoter. Interestingly, for IC1, targeting of CTCF binding site 6 by pyrosequencing showed loss of methylation in all 5 patients tested, including P8 and P10 who showed normal methylation for

IC1 CTCF binding site 2 by Southern Blot. In contrast loss of methylation of IGF2 DMR2, demonstrated by the presence of only the smaller band in the Southern Blot (Figure 4.1c), was found only in 2 patients (P7 and P11) (data not shown). Thus, the most consistent epimutation identified in the RSS cohort was loss of methylation at the H19 promoter. The frequency of .

Figure 4.1: Methylation analysis of chromosome 11p15.5 locus in individuals with RSS. (a) Physical map of the imprinted gene cluster in the diagram of human chromosome 11p15.5. Previously identified genes or transcripts (boxes) are drawn approximately to scale. The transcriptional orientation is indicated by arrow. Blue, paternally expressed genes; Red, maternally expressed genes; Black, biallelically expressed genes. (b) Southern blot methylation analysis of IC1 (I) and H19 promoter region (II). The solid boxes in the top diagram indicate the exons of H19. The open box (h1~h7) indicates the CTCF binding sites. The probes used for Southern-blot analysis are shown below the restriction maps. R and S indicate the RsaI and SmaI restriction sites. (green boxes) indicates the pyrosequencing target regions. It is shown that Southern blot analysis of IC1 targets a dinucleotide in the area of CTCF binding site 2 whereas the pyrosequencing analysis targets a more distal region corresponding roughly to CTCF binding site 6. (c) Methylation analysis of IGF2 DMR2. The open boxes indicate the CpG islands. The transcriptional orientation is indicated by arrow. In the Southern blot examples shown, individual 3 and 7 showed loss of methylation of IC1 and

H19 promoter but normal methylation of IGF2 DMR2. In the figure, ICR1 and ICR2 correspond to IC1 and IC2 respectively.

Figure 4.1: Methylation analysis of chromosome 11p15.5 locus in individuals with RSS.

Table 4.3: Molecular findings of cases with domain 1 epimutations at H19 locus Hypomethylated IC1 SB quantitative IC1 pyrosequencing H19 promoter SB quantitative H19 promoter Patient #* MI % (CTCF-bs2) (CTCF-bs6) MI % pyrosequencing P1 38 29 Inconclusive 28 P2 27 DNA not available 12 DNA not available P3 25 DNA not available 14 DNA not available P4 22 19 25 17 P5 23 9 26 10 P6 32 DNA not available 9 DNA not available P7 14 DNA not available 6 DNA not available P8 65 26 14 22 P9 53 DNA not available 26 DNA not available P10 65 22 14 18 P11 18 DNA not available 7 DNA not available IC1 – Imprinting Center 1 SB – southern blot; CTCF-bs2 & bs6 – CTCF binding sites 2 and 6; MI – methylation index (control range 45- 65%) * There is no correspondence with the patient numbers in the Southern Blot images of Figure 4.1

epimutations of IC1 and/or the H19 promoter region in this study cohort is ~21% (11/52 – 95% CI: 12-35%).

4.4.2 Methylation analysis of other imprinted loci

The structural, spatial, and epigenetic characteristics of the MEG3/GTL2 and DLK1 domain reveal a striking similarity to the IGF2/H19 domain on chromosome 11p15.5. Thus, we also screened the same cohort of 52 patients for epimutation in the MEG3/GTL2 CpG island at 14q32. No change in methylation in any of the patients was found except for the patient with loss of methylation for multiple chromosomes (Figure 4.2a). Parallel methylation results were also obtained for the PEG1/MEST CpG island (Figure 4.2b) and for the SNRPN DMR1 (Figure 4. 2c).

4.4.3 Phenotype analysis

A detailed description of the clinical features of each patient with domain 1 epimutations is listed in Table 4.4. I performed a thorough analysis of these data and the available clinical information of all patients reported in the literature with mUPD7 or chromosome 11p15.5 epimutations (Table 4.5). I then developed a set of diagnostic criteria that would identify all patients with epigenetic alterations of chromosome 7 or 11, excluding P11 (Table 4.6). I applied these criteria to the patients who were negative for chromosome 11p15.5 epimutations and mUPD7. Only 19 of these 41 patients would meet such criteria. Further, of those 19 patients, 5 had features not previously described in the phenotypic spectrum currently accepted for RSS such as hypogammaglobulinemia, hypothyroidism, sensorineural hearing loss, cerebral cortical dysplasia, branchial arch cyst, coarctation of the aorta with bicuspid aortic valve. If I also exclude these patients, IC1 epimutations would then be found in 40% (10/25 – 95% CI: 22-61%) of patients selected by these stricter criteria. If I include the 5 patients with chromosome 7 anomalies previously identified in this cohort (Nakabayashi et al., 2002), IC1 epimutations are found in 10 out of 30 patients (33%, 95th CI 18 to 53%), and mUPD7 in 5 out of 30 patients (16%, 95th CI 5 to 35%). These data are consistent with the 50% rate of mUPD7 and chromosome 11 epimutations reported in the literature for patients with a clinical diagnosis of RSS. Furthermore, the minimum diagnostic criteria for selection for mUPD7 or IC1 epimutation described in Table 4.6 will include fetuses or newborns diagnosed as small for gestational age.

Figure 4.2: Methylation analyses of the MEG3/GTL2, PEG1/MEST CpG islands and of the SNRPN DMR1. (a) Methylation analysis of the MEG3/GTL2 CpG island, (b) Methylation analysis of the PEG1/MEST CpG island, (c) Methylation analysis of the SNRPN DMR1. The solid boxes indicate the exons. The open boxes indicate the CpG islands. The open box (h1~h4) indicates the CTCF binding sites. The probes used for Southern-blot analysis are shown below the restriction maps. R, S, X and N indicate the RsaI, SmaI, XbaI and NotI restriction sites respectively.

Figure 4.2: Methylation analyses of the MEG3/GTL2, PEG1/MEST CpG islands and of the SNRPN DMR1.

Table 4.4: Clinical phenotypes of cases found with chromosome 11 epimutations BW <10th Postnatal Patient HC>3rd % Body Mental Additional non-RSS Sex % or height <3rd % Facial RSS features Additional RSS features # or ~1.6SD Asymmetry development features ~1.3SD or ~1.6SD Clinodactyly, delayed bone age, Yes Yes Yes 1 feature: shoulder and elbow P1 F No Normal syndactyly of toes, delayed closure <3rd% <3 rd% ~25th% frontal bossing/high forehead dimples of anterior fontanel 4 features: triangular face, down- Yes Yes Yes Clinodactyly, syndactyly of toes, P2 M Yes Normal turned mouth, micrognathia, frontal <3rd% <3 rd% ~25th% thin skin bossing/high forehead 5 features: Yes Yes Yes Moderately to triangular face, down-turned P3 F Yes Clinodactyly, increased sweating <3rd% <3 rd% 50th% severely delayed mouth, micrognathia, frontal bossing/high forehead, blue sclera Yes 3 features: Yes Yes P4 M 2nd to Yes Normal triangular face, micrognathia, Clinodactyly, thin skin <3rd% <3 rd% 10th% frontal bossing/high forehead Yes Clinodactyly, delayed bone age, 3 features: Yes Yes 10th to delayed closure of anterior Increased levels of T3 P5 M No Normal triangular face, micrognathia, <3rd% <3 rd% 50th% fontanelle, thin skin, increased and T4 frontal bossing/high forehead (at birth?) sweating, genital anomalies, Selectively 2 features: Yes Yes Yes Clinodactyly, increased sweating, P6 M No moderately triangular face, frontal bossing/high 3rd to 10th% <3 rd% 25-75th% genital anomalies delayed forehead Yes Yes Yes Slightly more 1 feature: P7 F 3-10th% No Clinodactyly <3rd% <3 rd% delayed then twin frontal bossing/high forehead (at birth) NA 2 features: Yes Yes P8 M Observed as a Yes Normal triangular face, frontal bossing/high Clinodactyly Metopic ridge <3rd% 50th%? newborn forehead, 5 features: Yes Yes No triangular face, down-turned Clinodactyly, syndactyly of toes, P9 M Yes Normal <3rd% <3 rd% <2nd% mouth, micrognathia, frontal café au lait spots, hypoglycemia bossing/high forehead, blue sclera Yes Yes Yes 1 feature: P10 M Yes Normal 3rd to 10th% <3 rd% 50th% down-turned mouth, Clinodactyly, syndactyly of toes, Yes N Yes Moderately 1 feature: delayed closure of anterior Dizygotic twin with P11 M Yes <3rd% 25th% 50th% delayed down-turned mouth fontanelle, hypoglycemia, genital growth discrepancy anomalies BW – Birthweight; HC – Head Circumference; SD – Standard deviation; RSS – Russell-Silver syndrome; M – Male; F – Female; % - centile; the last patient, in bold, is the child with multiple methylation defects suggesting UPD for multiple chromosomes in blood DNA. Color coding - in grey are the characteristics that fit the criteria proposed in Table 4.6 (dark grey) or that fit incompletely or that can’t be precisely determined (light grey)

Table 4.5: Phenotypic features of published cases with chromosome 11 or 7 molecular alteration BW Postnatal Molecular HC>3rd <10th height Body Mental Aditional non-RSS Patient # Sex alteration % or Facial RSS features Aditional RSS features % or <3rd % Asymmetry development features proposed ~1.6SD ~1.3SD or ~1.6SD

(Nakabayashi et al., 2002) - Patients with anomalies of chromosome 7 5 features: Clinodactyly, Triangular facies, delayed bone age, Downturned mouth, delayed closure of F Dup(7p13-p14) Yes Yes Yes No Mild delay Micrognathia, anterior fontanelle, Frontal Bossing, hypoglycemia, labial Bluish sclera hypoplasia Clinodactyly, 2 features: delayed closure of M Inv(7p14-p21) Yes Yes Yes No Mild delay Downturned mouth, anterior fontanelle, Micrognathia increased sweating, genital anomalies Clinodactyly, 4 features: increased sweating, Triangular facies, delayed bone age, F UPD7 Yes Yes Yes Yes Normal Downturned mouth, syndactyly of toes, Micrognathia, delayed closure of Frontal Bossing anterior fontanelle, thin skin, 4 features triangular face, down-turned Yes Yes Yes F UPD7 No Mild delay mouth corners, Clinodactyly <3 <3 75 to 90 frontal bossing/high forehead, blue schlerae No 3 features: 25 to 50, Yes Yes triangular face, M UPD7 had No Normal Clinodactyly 3 50 to 75 frontal bossing/high forehead, growth blue schlerae hormone (Gicquel et al., 2005) - Chromosome 11 series NA, H19,IC1, 1 F Yes present at Yes Yes Normal Typical facial features Clinodactyly IGF2DMR2 birth NA H19, IC1, 3 M Yes present at Yes No Normal Typical facial features Clinodactyly IGF2DMR2 birth NA H19, IC1, 4 M Yes present at Yes Yes Normal Typical facial features Clinodactyly IGF2DMR2 birth

BW Postnatal Molecular HC>3rd <10th height Body Mental Aditional non-RSS Patient # Sex alteration % or Facial RSS features Aditional RSS features % or <3rd % Asymmetry development features proposed ~1.6SD ~1.3SD or ~1.6SD

F, NA H19, IC1, 6 monochorion Yes (present at Yes Yes Delayed speech Typical facial features Clinodactyly IGF2DMR2 ic twin birth) NA H19, IC1, 8 F Yes (present at Yes Yes Delayed speech Typical facial features Clinodactyly IGF2DMR2 birth) (Eggermann et al., 2006) 3 features out of the follwoing: asymmetry, relative macrocephaly, triangular face, downslanting corners of the mouth, irregular teeth, ear 16 cases out of 51 (sex ICR1 Yes Yes anomlies, clinodactyly, syndactyly, simian crease, café-au-lait spots, psychomotor retardation, muscular hypotrophy, squeaky voice, early not reported) puberty (Bliek et al., 2006) Delayed due to Dysmorphic features 1 F H19 promoter Yes Yes Yes Yes Clinodactyly encephalopathy reminiscent of RSS Café-au-lait spots, VSD, F, dizygotic Facial features “reminding” multiple joint 2 H19 promoter Yes Yes Yes Yes Normal twin of RSS contractures of hand and feet Genital anomalies, 3 F H19 promoter Yes Yes Yes Yes Normal Facial characteristics of RSS (absent ovariae and hypoplastic uterus) Dysmorphic features 4 M H19 promoter Yes NA Yes Yes NA Genital anomalies, ASD Severe liver cirrhosis reminiscent of RSS 5 F H19 promoter Yes NA Yes Yes Normal None None 6 F H19 promoter Yes Yes Yes Yes Norrnal None 3 small café-au-lait spots 7 F H19 promoter Yes No Yes Yes Normal None Clinodactyly Late onset insulin 8 M H19 promoter Yes No NA Yes Normal None None dependent diabetes 9 F H19 promoter Yes NA NA Yes Normal None None (Fisher et al., 2002), (Eggermann et al., 2005) – mat dup 11p15, (Schonherr et al., 2007b) Learning Slightly dysmorphic, not Fi 1 F Dup11p15 Yes Yes No No Clinodactyly Adult disabilities typical of RSS Short upslanting palpebral fissures with long eyelashes, a large nose with a Dup11p15, bulbous tip, a short 2 features: triangular face, Fi 2 F monosomy Yes Yes No No Delayed Clinodactyly philtrum, and thin small downturned mouth distal 20q upper lip, hypercalcemia, bilateral sensorineural deafness

Deep clefting between the first and second toes bilaterally, Hypoplastic external perimembranous, Trisomy 11p, Micrognathia, Fi 3 M Yes NA No No NA genitalia, ventricular bilobed lungs and a 15q septal defect partially malrotated small bowel, small malformed left kidney abnormally placed Egg 1 M Dup11p15 Yes Yes No No Delayed Micrognathia Clinodactyly Hypotonia Yes (recovered Frontal bossing, , narrow Slight epicanthus, Egg 2 F Dup11p15 Yes with Yes No Slightly delayed lower jaw, aspects Clinodactyly microphthalmia, growth reminiscent of SRS low set thumb hormone) Prominent forehead, a triangular face with a cryptic protruding Sch M Dup11p15 of Yes Yes Yes No Delayed philtrum, slightly downturned Clinodactyly IC2 corners of the mouth and a small chin, posteriorly rotated large ears (Spence et al., 1988) F matUPD7 NA Yes NA Yes Normal CF (Voss et al., 1989) M matUPD7 Yes Yes NA No Normal NA NA CF (Spotila et al., 1992) M matUPD7 Yes Yes NA No Normal Triangular face NA COL2A1 mutation (Eggerding et al., 1994) Pat UPD7p and F Yes Yes NA NA NA NA NA mat UPD7q (Kotzot et al., 2001) Triangular face, high Simian crease on the right Pat UPD7p and forehead and pointed chin, hand and lateral deviation M No Yes Yes No Normal mat UPD7q protruding lower lip, of the second toe on the and a high arched palate right foot. (Preece et al., 1997) F MatUPD7 Yes Yes Yes No NA Typical features F matUPD7 Yes Yes Yes No NA Typical features (Langlois et al., 1995)

F matUPD7 Yes Yes Yes No Normal (Eggermann et al., 1997) matUPD7 Yes Yes Yes Yes Normal Clinodactyly matUPD7 Yes Yes Yes No Mild Triangular face Clinodactyly matUPD7 Yes Yes Yes No Delayed Triangular face Clinodactyly (Hannula et al., 2001a), (Hannula et al., 2001b) - 2 reports M matUPD7 Yes Yes Yes Yes Speech delay Frontal bossing M matUPD7 Yes Yes Yes Yes Speech delay Frontal bossing F matUPD7 Yes Yes Yes Yes Speech delay Frontal bossing F matUPD7 Yes Yes Yes Yes Speech delay Frontal bossing slightly triangular face, a Partial bossed forehead, F Yes Yes Yes No Normal Clinodactyly matUPD7 slightly downturned mouth corners (Bernard et al., 1999) Yes (leg triangular face, frontal F matUPD7 Yes Yes Yes length Delayed bossing discrepancy) triangular face, M matUPD7 Yes Yes Yes No NA Clinodacyly prominent forehead (Monk et al., 2002) report 2 dup patient in this report, one of them was lately studied by Nakabayashi et al. 2002 and described above triangular face with a small chin, a Mat dup7p11.2- F Yes Yes NA No Mildly delayed relatively large down-turned Clinodactyly p13 mouth, frontal bossing, blue schlerae (Joyce et al., 1999) Yes (at Daughter Mat dup7p12.1- birth and blue sclera, small pointed Clinodactyly, delayed of patient F Yes Yes Mild facial Borderline p13 then chin bone age below dropped) Mother Pat dup7p12.1- of patient F Yes Yes No No Borderline relative micrognathia Clinodactyly p13 above (Kotzot, 1999) Triangular face, high M matUPD7 Yes Yes Yes No forehead Triangular face, high F matUPD7 Yes Yes Yes No forehead Triangular face, high F matUPD7 Yes No Yes Yes forehead, downturned corners of the mouth

F matUPD7 Yes Yes Yes No Triangular face high forehead F matUPD7 Yes Yes Yes Yes Triangular face Triangular face, high Mosaic trisomy M Yes Yes Yes Yes Delayed forehead, downturned corners 7 of the mouth Mosaic trisomy Triangular face, high F Yes Yes No No Delayed 7 forehead Triangular face, high M matUPD7 Yes Yes Yes No forehead, downturned corners of the mouth Triangular face, high M matUPD7 Yes Yes Yes Yes Delayed forehead, downturned corners of the mouth BW – Birthweight; HC – Head Circumference; SD – Standard deviation; RSS – Russell-Silver syndrome; M – Male; F – Female; % - centile; the last patient, in bold, is the child with multiple methylation defects suggesting UPD for multiple chromosomes in blood DNA; Color coding - in grey are the characteristics that fit the criteria proposed in Table 4.6 (dark grey) or that fit incompletely or that can’t be precisely determined (light grey

Table 4.6: Criteria developed based on the clinical findings of RSS cases with IC1 epimutation or mUPD7 in the cohort (excluding P11) and from reported cases ASYMMETRY and/or

POSTNATAL HEIGHT < 3%

HEAD CIRCUMFERENCE > 3%

triangular face, BIRTHWEIGHT < 10% and down-turned mouth, ONE OR micrognathia / retrognathia, MORE frontal bossing/high FACIAL forehead, FEATURES: blue sclera, low set ears Other anomalies previously described in RSS include: increased sweating, 5th finger clinodactyly, simian creases, joint contractures, delayed bone age, 2-3 syndactyly of toes, delayed closure of anterior fontanel, café-au-lait spots, neonatal hypoglycemia, renal anomalies, heart anomalies, thin skin, genital abnormality in males, uterine or ovarian anomalies in females, sacral abnormalities, vertebral abnormalities, muscular hypoplasia, early puberty, feeding difficulties, mental retardation, irregularities of teeth, ear anomalies, simian crease. The presence of features other than those listed above decreases the likelihood of finding H19 promoter/IC1 hypomethylation or mUPD7.

4.4.4 Complex mosaic patient

In the screening of this series of RSS patients, a male patient (P11) was identified in whom we found changes in methylation at all sites surveyed (IC1 and H19 promoter region, IC2, MEG3/GTL2, SNRPN and the PEG1/MEST locus) (Figure 4.3a). These data were consistent with mUPD for all chromosomes tested. Although this patient is phenotypically male, no Y specific PCR product was detected in blood (Figure 4.3b). Follow-up clinical information included a chromosome analysis done on skin fibroblasts which showed a 46, XY karyotype (one cell out of one hundred with 69, XXY). Unfortunately biological material for further molecular characterization was not available. Therefore, I was unable to test the hypothesis that this individual is a mosaic for multiple cell lines: a 46,XY normal male cell line, a 69,XXY cell line and, in blood, a 46,XX cell line with mUPD for all chromosome pairs.

4.5 Discussion

We screened a series of patients with growth restriction syndromic phenotypes and tested for abnormalities at several imprinted regions. This comprehensive screen of chromosome 11p15.5 identified 11 patients with epimutations in the telomeric imprinted domain, which included both,

the primary imprinting center, IC1, and the imprinted H19 promoter as well as IGF2 DMR2. IGF2 is known to be involved in regulation of fetal growth and development (Guo et al., 2008). Loss of methylation of IC1 can result in reduced transcription of IGF2 (Bell and Felsenfeld, 2000; Hark et al., 2000).

Usually, the H19 genomic region, which includes both the imprinting center IC1 and the H19 promoter, is expected to reflect a consistent level of methylation. These data suggest a consistent concordance of H19 promoter methylation only in the distal region (CTCF binding site 6) of IC1. In contrast, the methylation in the proximal region (CTCF binding sites 1 to 3) is variably correlated with methylation at the H19 promoter. This type of subregional loss of methylation for the H19 region has not been reported before, although data from Eggermann and colleagues (Eggermann et al., 2008b) and from Zeschnigk and colleagues (Zeschnigk et al., 2008), showing that loss of methylation at IC1 may occur only at some CTCF binding sites, gives support to my findings. This is important when evaluating tests for epigenetic errors in this region, since these

Figure 4.3: Blood DNA molecular tests of patient found to have epimutations in all tested sites. (a) Methylation analysis of IC1, H19 promoter region, IC2, PEG1/MEST CpG island, MEG3/GTL2 CpG island showing that all loci (11p15.5, 7q32, 14q32) display maternal methylation pattern. (b) Molecular analysis of ZFX and ZFY; the PCR co-amplifies distinguishable products from X and Y chromosomes. PCR products are resolved in 1.5% agarose gels. Lane M: male control showing 2 bands, Lane F: female control showing only one band. The patient, third lane, failed to yield a Y-specific product, despite its male phenotype, contributing to the hypothesis of maternal UPD for all chromosomes in blood DNA. In the figure, ICR1 and ICR2 correspond to IC1 and IC2 respectively.

Figure 4.3: Blood DNA molecular tests of patient found to have epimutations in all tested sites.

data suggest that some subregions of the telomeric 11p15.5 differentially methylated regions are more likely than others to demonstrate loss of methylation in RSS patients. In retrospect, it is important to note that several of the first studies of this region in RSS likely underestimated the frequency of loss of methylation at the chromosome 11p15.5 telomeric imprinted region, as their assays were limited to the proximal IC1 subregion of the H19 genomic region (Table 4.7). Indeed, when some of the patients with normal methylation at IC1 were retested, they demonstrated epimutations in other regions of IC1 (Eggermann et al., 2008b; Zeschnigk et al., 2008).

Based on this comprehensive analysis of the chromosome 11p15.5 imprinted cluster, I propose that testing for epimutations at the H19 promoter or the distal region of IC1 will detect the majority of epimutations in the H19 imprinted domain in RSS patients. One additional important factor that must be considered when testing for chromosome 11p15 domain 1 epimutations is the tissue being tested. Of note, in the normal placenta, the H19 promoter is unmethylated on both parental alleles (Guo et al., 2008; Jinno et al., 1995). Therefore, in placenta, testing for DNA methylation at the H19 genomic region should focus on the CTCF binding site 6 region of IC1. Larger cohort studies will be needed to validate these recommendations.

Loss of methylation at the DMR2 region of the IGF2 gene has been reported previously by Gicquel and colleagues (Gicquel et al., 2005) in RSS patients. Indeed a study in mice showed

that loss of methylation of Igf2 DMR2 can disrupt Igf2 expression (Murrell et al., 2004). We found loss of CpG methylation of IGF2 DMR2 in only one of the patients in addition to the one with mUPD for several chromosomes.

Recently Kagami and colleagues reported a RSS patient with CpG dinucleotide changes in the PEG1/MEST DMR (Kagami et al., 2007). However, previous studies showed that CpG methylation patterns of the 5’ region of PEG1/MEST were normal in a total of 127 RSS or RSS- like patients (Kobayashi et al., 2001; Riesewijk et al., 1998; Schöherr et al., 2008). In my cohort, changes in methylation at the PEG1/MEST locus were detected only in the patient with multiple methylation defects at several imprinted loci. This contradiction between the previous reports is possibly explained by the relative rarity of this type of molecular anomaly or the different methylation assays used by various investigators to analyze methylation at one or more CpG dinucleotide sites.

Table 4.7: Comparison of molecular techniques used for H19 DMR assessment in different reports and their respective yields Detection Ref# Sites probed Techniques used Comments rate1 SB targeting CTCF- IC1 5/9 All RSS individuals carrying (Gicquel et al., bs3 epimutation were hypomethylated for 2005) H19 promoter SB 5/9 the 3 sites IGF2 DMR2 SB 5/9 MLPA targets CTCF-bs5 to 6 and 2/3 restricted IC1 and IGF2 All RSS individuals carrying (Bliek et al., portion distal to criteria or 2/9 DMR epimutation were hypomethylated for 2006) 0 CTCF-bs7 and unrestricted the 3 sites IGF2 DMR0 criteria H19 promoter SB (Binder et al., SB targeting CTCF- IC1 4/9 2006) bs3 (Eggermann et SB targeting CTCF- IC1 16/51 al., 2006) bs3 (Schonherr et SB targeting CTCF- IC1 5/25 al., 2006) bs3 SB targeting CTCF- It is not clear if all patients were (Netchine et al., IC1 bs3 37/55 concordant for the two sites 2007) H19 promoter SB investigated MLPA targets (Binder et al., IC1 and IGF2 CTCF-bs5 to 6 and 19/44 2008) DMR0 portion distal to CTCF-bs7 (Eggermann et SB targeting CTCF- The 63 cases had mUPD 7 excluded IC1 4/63 al., 2008a) bs3 (?) and the diagnostic criteria was loose MLPA targets Part of the cohort had been screened CTCF-bs5 to 6 and with SB before. All SB detected (Eggermann et IC1 and IGF2 portion distal to 28/63 cases were detected now plus 6 cases al., 2008b) DMR 0 CTCF-bs7 and previously found negative by SB. IGF2 DMR0 The 63 cases had mUPD 7 excluded. (Zeschnigk et QAMA targeting 5 patients were normal for CTCF-bs3 IC1 11/20 al., 2008) CTCF-bs3 and 6 but had LOM in CTCF-bs6 10 patients showed selective MLPA targets hypomethylation of IC1 and 2 CTCF-bs5 to 6 and patients selective hypomethylation of (Bartholdi et al., IC1 and IGF2 portion distal to 41/106 IGF2 DMR . The IGF2-specific 2009) DMR 0 0 CTCF-bs7 and probe showed a broader variation in IGF2 DMR0 controls as compared to the H19 probe IC1 – Imprinting Center 1; IGF2 DMR – Insulin-like growth factor 2 differentially methylated region; CTCF-bs - CTCF binding site; SB – Southern blot; RSS – Russell-Silver syndrome; UPD – Uniparental disomy; LOM – loss of methylation; QAMA - quantitative analysis of methylated alleles; MLPA – multiplex ligation-dependent probe amplification

1 Cases were selected using the most restrictive criteria selected by the authors. These criteria varied from paper to paper.

SNRPN and MEG3 DMRs were not previously assayed in RSS patients. This study did not identify any epimutations at these DMRs suggesting a low likelihood of their involvement in RSS.

Molecular heterogeneity in an unusual growth restriction phenotype was identified in one of the patients (P11). This patient had clinical features beyond those typically seen in RSS. In this patient the only RSS features present were prenatal growth restriction, relative macrocephaly and asymmetry. Molecular testing demonstrated multiple methylation defects that were best interpreted as maternal UPD for all chromosomes tested. Since mUPD for all chromosomes is expected to be lethal, it is likely that there is a more complex genetic constitution. This was supported by the finding of low level mosaicism with a normal male cell line in a follow-up karyotype (46,XY/69,XXY) of cultured fibroblasts. Although I did not have access to further biological specimens to confirm this finding, I hypothesize, based on my limited data, that this patient represents an example of mosaicism in which one diploid cell line has UPD for all chromosomes and another cell line has biparental contributions. This type of molecular anomaly in humans has been previously reported (Strain et al., 1995). This patient demonstrates that an epigenetic alteration at an IC in a patient with atypical phenotypic features should prompt consideration of other diagnoses. In this regard, testing of other imprinted loci may be informative in the detection of complex genetic alterations.

The consideration of more complex epigenetic alterations for RSS patients is further supported by the recent report of individuals with loss of methylation at multiple genomic sites in RSS (Azzi et al., 2009; Turner et al., 2010) and in other conditions as well (Mackay et al., 2006a; Mackay et al., 2006b). In contrast, my results did not reveal any epimutations outside the two RSS candidate regions on chromosomes 7 and 11 supporting what others had been previously reported (Schonherr et al., 2007a). This discrepancy may be explained either by the rarity of these epimutations in RSS patients and/or by the fact that we and others (Schonherr et al., 2007a) have tested only a subset of the known genomic imprinted regions.

In conclusion, these data and the data from others (Bliek et al., 2006; Kagami et al., 2007; Mackay et al., 2006a; Mackay et al., 2006b), lend support to the idea that there are likely to be growth restriction syndromic phenotypes caused by as yet unidentified imprinting defects involving other genomic regions e.g. GRB10 or PEG10 on chromosome 7. As high throughput

93 and more powerful methods of methylation analysis become available, it may be useful to revisit the patients in which no etiology has been established and to perform an analysis of multiple imprinted regions. Identification of the heterogeneous molecular causes of RSS and RSS-like phenotypes is important, not only from a mechanistic viewpoint, but also for prognostic considerations. In this regard, it is important to note that RSS patients with mUPD7 have a better response to growth hormone therapy than do patients with epigenetic anomalies on chromosome 11 (Binder et al., 2008).

Chapter 5: Comparative Analysis of DNA Methylation at Imprinting Centers in Placental DNA

The following chapter is planned to be submitted for publication after validation studies are completed.

For this portion of my work I had the collaboration of a summer student in our lab, Sarah Ickowicz, who did the targeted study of the H19 DMR in some of the samples. I also had the collaboration of another MSc student of our lab – Yi-An Chen – who did the selection of the array probes mapping to the imprinting centers.

The microarrays were run at the Center for Applied Genomics.

I did the targeted study of the H19 DMR in some of the samples, the analysis of the H19 DMR and of the array data.

5.1 Summary

“Genomic Imprinting”, defined as parent-of-origin dependent gene expression, is a phenomenon evolutionarily associated with the placenta. It is regulated by the establishment, during germline development, of allele specific epigenetic marks (including DNA methylation) in specific genomic regions called imprinting centers (IC). Growth restriction phenotypes have been associated with disruption of imprinting at several different loci either experimentally, in model organisms, or naturally occurring, in humans. Alterations in the placental expression of imprinted genes have also been reported in association with fetal growth restriction.

I hypothesized that aberrant DNA methylation of ICs in placental DNA, could alter the expression of imprinted genes and also impact fetal growth. Evidence of an association between such aberrant methylation in the placenta and growth restriction phenotypes in the fetus or newborn would lend support to this hypothesis.

Using a targeted assay I analyzed the DNA methylation pattern of a well-studied IC – H19 DMR – in 170 placenta DNA samples obtained from newborns that had in utero diagnosis of intrauterine growth restriction (IUGR), newborns whose mothers were diagnosed with preeclampsia, newborns that were just small for gestational age and from normally grown newborns. H19 DMR seems to be rarely affected by extreme variation – only 3 of the 170 samples assessed showed extreme variation at this site. No association was demonstrated with low birthweight percentile. It is thus unlikely that H19 DMR hypomethylation in the placenta is a frequent or important modifier of fetal growth.

A genome-wide approach was then used to analyze the methylation pattern of 14 ICs (including H19 DMR) in a subset of these samples, i.e. 12 IUGR (birthweight lower than the 10th percentile and with lesions known to be associated with low birthweight percentile (Table 3.2)) and 12 controls (birthweight above the 10th percentile and without those placental lesions), using Illumina® Infinium HumanMethylation27 BeadChip arrays. I analyzed, quantitatively and categorically, the differences in methylation at each CpG site mapping to ICs. Although no statistically significant differences were found between cases and controls for each individual CpG site, extreme variants, specifically in the higher methylation for CpGs mapping to ICs, defined in “Methods” as “positive far outliers”, were more frequent in the IUGR group than in the controls. The biological and clinical effect of the identified variation is not clear. These

results suggest that it is the addition of multiple small effects across several CpGs rather than a major effect of individual specific CpGs that may be contributing to poor fetal growth.

5.2 Introduction

Genomic imprinting is evolutionarily linked to the appearance of placenta in Eutherian mammals (Wagschal and Feil, 2006). Imprinted genes demonstrate complete or preferential parent of origin dependent gene expression. Currently 64 genes are known to be imprinted in humans (www.geneimprint.org) (Jirtle, 2008). Most imprinted genes cluster in genomic regions called domains. The imprinting status of each gene in each domain is usually, but not always, epigenetically regulated by imprinting centers (ICs) which have parent-of-origin defined epigenetic marks, i.e., one of the alleles is methylated whereas the other one is not. Thus, methylation measurements of these genomic regions, also known as Differentially Methylated Regions or DMRs, normally provide a value that is intermediate between the fully methylated and the unmethylated state. This characteristic allows identification of candidate imprinted regions, although proof of imprinting requires, at least, an association between alterations in methylation status and changes in allelic expression of the putative imprinted gene.

Fourteen primary ICs have been reported in humans (Arima et al., 2006; Arnaud et al., 2003; Astuti et al., 2005; Bastepe et al., 2001; Beatty et al., 2006; Bliek et al., 2009; Dasoula et al., 2007; Frevel et al., 1999; Kagami et al., 2008; Kainz et al., 2007; Kamiya et al., 2000; Li et al., 2002; Liu et al., 2000; Monk et al., 2008; Murphy et al., 2001; Riemenschneider et al., 2008; Riesewijk et al., 1997; Rosa et al., 2005; Valleley et al., 2010; Yuan et al., 2003; Zeschnigk et al., 1997). These ICs control the imprinting status of 55 of the 64 known imprinted genes. The remaining 11 genes are either not clustered in domains or an associated IC has not yet been identified. Only 3 of the known ICs are methylated on the paternal allele. These includes the 2 primary ICs, H19 DMR in the chromosome 11p15.5 imprinted region and DLK1/MEG3 DMR in the chromosome 14p32 imprinted region (Takada et al., 2002), and the secondary IC in the GNAS locus at 20q13.3 (Bastepe et al., 2001). For the remaining ICs the methylated allele is of maternal origin.

The imprinting status of genes is tightly regulated throughout embryogenesis, more so in the embryo proper than in the placenta (see section 1.7.2), where “imprinting leakage” (low expression level of the repressed allele) has been reported, especially when using highly sensitive techniques (Diplas et al., 2009; Guo et al., 2008; Lambertini et al., 2008; Pozharny et al., 2010). However only one of these reports has shown an association between the expression of the normally non-expressed allele and a decrease in methylation in the respective IC (Guo et al., 2008). In this report H19 DMR was reported hypomethylated and H19 was biallelically expressed in the placenta of a newborn with a birthweight lower than the 10th percentile. H19 DMR hypomethylation, as explained in the previous chapter, was shown to be associated with a growth restriction phenotype – Russell-Silver syndrome (RSS). I (Chapter 4 of this thesis) and others have screened, unsuccessfully, these patients for blood DNA methylation aberrations in other ICs, mostly using targeted limited approaches (Horike et al., 2009; Schonherr et al., 2007a). However similar studies performed in larger cohorts of patients with broadly defined growth restriction phenotypes and H19 DMR hypomethylation have shown loss of methylation at other ICs in 10 to 20% of the cases (Azzi et al., 2009; Turner et al., 2010).

Experimental animal data has shown that imprinting dysregulation via genetic alterations of imprinting centers or via inhibition of genes involved in imprinting establishment or maintenance induces growth restriction phenotypes (Li et al., 1992). Silencing mutations of several imprinted genes (e.g. Igf2, Mest, Phlda2) in mouse models are also associated with this phenotype (Constancia et al., 2002; Lefebvre et al., 1998; Salas et al., 2004).

Finally, imprinted genes frequently are reported in studies screening for gene expression changes in the placenta of small for gestational age (SGA) newborns (Apostolidou et al., 2007; Diplas et al., 2009; Guo et al., 2008; McMinn et al., 2006) (see section 1.7.2).

All the above data suggest the importance of adequate placenta imprint maintenance in fetal development and growth.

I hypothesized that alterations in the DNA methylation levels of ICs in placenta underlie dysregulation of imprinted genes expression (e.g, PHLDA2, MEST, H19, IGF2) contributing to placental dysfunction and poor fetal growth.

I thus decided 1) To determine how frequently H19 DMR hypomethylation can be found in placental DNA, to test the hypothesis of its association with growth restriction and to characterize the prenatal and placental phenotype of this epimutation in placenta; 2) To study the variation in methylation at imprinting centers between intrauterine growth restriction and controls.

For the first objective I measured the methylation levels of H19 DMR in placenta DNA samples from a cohort of 170 singletons with a range of phenotypes. For the second objective I compared, in placenta DNA samples of a group of growth restricted newborns and gestational age matched controls, the methylation levels of several CpG dinucleotides mapping to ICs using Illumina® Infinium HumanMethylation27 BeadChip arrays.

H19 DMR hypomethylation was identified in only 3 samples out of 170 and no association with any specific phenotype or with low birthweight percentile could be demonstrated.

Furthermore none of the individually analyzed CpG dinucleotides mapping to ICs has shown a significant difference in methylation levels or in the frequency of abnormal methylation, between the two groups. However, when the analysis targeted the number of abnormally methylated CpG sites per case versus controls or the number of all altered CpG sites in all cases versus controls, there were more hypermethylated CpGs in the growth restricted group than in the controls. Although the biological impact of the found associations is not clear, the results of this study suggest that imprinting dysregulation caused by methylation alterations in specific imprinting centers are an unlikely or rare contributor to fetal growth restriction. However additive small effects caused by mild dysregulation of multiple IC may contribute to the IUGR phenotype in some cases.

5.3 Materials and Methods

5.3.1 Sample selection

The placenta and blood samples were obtained and characterized as described in section 3.2. Exclusion criteria were the same used in the selection of patients for sampling as explained in Table 3.1. DNA was extracted from blood and placenta and RNA was extracted from placenta as

explained, respectively, in sections 3.3 and 3.4 and samples quantitated and quality assessed as explained in section 3.5.

One hundred seventy placenta DNA samples collected from singletons were selected for H19 DMR methylation levels assessment by pyrosequencing of bisulfite converted DNA. Samples selected for this study originated from placentas of neonates with all ranges of birthweight percentiles, both sexes, preterm and term, with or without placental lesions, with or without preeclampsia, with or without labor. The sample previously reported as having H19 DMR hypomethylation by Southern blot of methylation sensitive restriction digestion (Guo et al., 2008) was also tested. When available, blood DNA was selected among the samples with abnormal H19 DMR results in the pyrosequencing assay of the placenta DNA to assess for the same molecular defect using the same assay.

Twenty four placenta DNA samples obtained from 12 singletons with birthweight percentile less than 10th and with any of the placental lesions described in Table 3.2 (cases) and from 12 gestational age matched samples with birthweight percentile higher than 10th and without placental lesions (controls) were selected for hybridization to Illumina® methylation arrays. Preeclampsia was, in this study, an additional exclusion criterion. From this study group of placenta samples, good quality RNA was obtained from 9 controls and 10 cases and was used for hybridization into Illumina® expression arrays.

5.3.2 DNA methylation analysis by pyrosequencing

The pyrosequencing assay used in the H19 DMR study (for both placenta and blood DNA) is the same as the one used for the study described in Chapter 4 of this thesis. For this assay DNA was first bisulfite converted as explained in section 3.7. The pyrosequencing technique is described in section 3.11 and the assay is described in section 4.3.2.2. The methylation of the region was determined by the average of the methylation measured in each of the 3 CpG sites covered by the assay.

5.3.3 H19 allelic expression analysis

The allelic expression was assessed by testing one of two reported SNPs mapping to the coding region of the H19 gene in genomic DNA and, if heterozigosity was demonstrated, the same test was applied to cDNA, obtained from RNA samples as described in section 3.6. Cases showing

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heterozygosity in genomic DNA and monozygosity in cDNA were considered monoallelic, as expected from an imprinted gene. Cases for which the two alleles identified in genomic DNA were also identified in cDNA were considered as biallelic and, thus, as having loss of imprinting. For one of the polymorphisms (rs2075745) SNaPshot® Multiplex System (Applied Biosystems Inc., Foster City, CA) was used. This stands for single nucleotide primer extension assay. The other polymorphism (rs217727) is an RsaI restriction polymorphism and was assessed by a restriction digestion followed by PCR. Both methods are detailed in the report of Guo and colleagues (Guo et al., 2008).

5.3.4 DNA methylation analysis by microarray

Bisulfite converted DNA, obtained as explained in section 3.7, from the 24 placenta samples was randomly hybridized into 2 Illumina® Infinium HumanMethylation27, BeadChip silica slides, as detailed in section 3.9.

Arrays were selected for analysis if they passed quality control criteria, as defined in section 3.9. Background corrected methylation output values expressed in percentage were used for the analysis after filtering for reliable values (significantly higher than background, i.e., values with a detection p value <0.05). Each value corresponds to the methylation of one CpG site.

5.3.5 RNA expression analysis by microarray

Placenta RNA was assessed for quality, converted into cDNA, amplified and randomly hybridized into Illumina® single channel HumanHT-12 v3 Expression BeadChip arrays as detailed in section 3.10. Two samples were hybridized twice into different slides for quality control purposes.

Arrays were selected for analysis if they passed quality control criteria, as defined in section 3.10. Background corrected cubic splined normalized output was filtered for reliable values (i.e. detection p value <0.05). For genes represented by more than one probe in the array, if more than one value was valid, the results were averaged. One value for each gene was used in the downstream analyses.

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5.3.6 Data analysis

5.3.6.1 H19 DMR methylation analysis

The methylation of the H19 DMR was analyzed in a categorical fashion, as it is usually done in other studies of this region (Turner et al., 2010). Differently from other studies, instead of using standard deviations to define the normal and abnormal values, I used non-parametric statistics to define the category of each measured methylation. The results of the methylation were categorized according to the relative position of their value in the boxplot generated from all the samples, as follows (see boxplot in Figure 5.1 for an illustration of those categories):

1 – normal, if the methylation level was between the outer fences of the boxplots (between the 1st quartile minus 3 times the interquartile range (IQR) and the 3rd quartile plus 3 times the IQR).

2 – hypermethylated if above the 3rd quartile plus 3 times the IQR (above positive outer fence, in the range of the positive far outliers)

3 – hypomethylated if below the 1st quartile minus 3 times the IQR (below negative outer fence, in the range of the negative far outliers).

Positive far outliers correspond thus to increase in methylation, negative far outliers correspond to decrease in methylation.

The association of birthweight percentile with an abnormal result would be demonstrated by a significant difference in birthweight percentile between the abnormal and the normally methylated using the methylation category as a categorical independent variable and the birthweight percentile as a quantitative dependent variable and using non-parametric statistics – Mann-Whitney U test.

5.3.6.2 Methylation array data analysis

The characteristics of the Illumina® Infinium HumanMethylation27 BeadChip array are described in section 3.9. Each data point in the array corresponds to a CpG site.

From the 27,578 CpG sites targeted by these arrays I selected, for this study, 117 mapping to ICs. The selection of these CpG sites was based on the two main online curated databases of imprinted genes – Catalogue of Parent of Origin Effects (http://igc.otago.ac.nz) (Morison and

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Reeve, 1998) and “Geneimprint, The Genomic Imprinting Website” (http://www.geneimprint.org/) - and a review of the literature (see “Introduction” above). Based on those sources 64 CpG array sites map to ICs. A study from our laboratory aiming at identifying novel ICs regions (Choufani et al., 2011), proposes the enlargement of the number of these CpG sites to 117, including a new one in chromosome 16. This study compared the patterns of methylation between paternal uniparental disomy DNA from hydatidiform moles and maternal uniparental disomy DNA from teratomas using the same array platform. The annotated list of this CpG sites, is presented in Figure 5.2 (first 4 columns and last 3 columns, colored in gray; the columns colored in white correspond to results data).

The methylation data was analyzed in a categorical approach, similar to the H19 DMR, and the clinical definition of each sample as case or controls was also treated as a categorical variable, under a case control study model. Here an analysis strategy similar to, but slightly different, from the H19 DMR was used to categorize each methylation value. It is also based in a classification of the methylation value of each probe of each sample into normal (between the outer fences of the boxplots), hypermethylated (above the positive outer fence, in the positive far outlier range) or hypomethylated (below the negative outer fence, in the negative far outlier range). The main difference with the H19 DMR study is the use of only the control samples for the determination of the quartiles that are the base of the classification (see boxplot in Figure 5.1). Similar analyses, not reported here, were also done using all the samples together (cases and controls) to define the methylation categories (as was done for the H19 DMR), with similar results. Furthermore, a subcategory among the normal values – outliers, considered when the methylation values fell between the inner and outer fences – was also analyzed.

For each CpG site mapping to ICs, the number of case samples in each of the categories (positive or negative, outliers or far outliers) was compared with the number of controls also in any of those categories. If the difference was statistically significant, as determined by chi-square analysis, the probe would pass the first selection criterion. A second selection criterion was also defined by the difference between the probe methylation level and the 3rd quartile (in the case of the positive outliers) or 1st quartile (in the case of the negative outliers). If the difference was bigger than 0.10 (10% more or less methylated), the probe would pass the second selection criterion and would be selected as possible biologically abnormally methylated more often in cases than in controls.

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The 10% difference in methylation required as a criterion to consider a specific CpG as differentially methylated between the average of cases and controls is arbitrary although in agreement with the results of the H19 DMR (see below). It aims at decreasing the likelihood of selecting probes that may have a statistically significant difference in methylation between cases and controls but in which the difference would be too small and unlikely to have a measurable biologic effect. Furthermore that criterion would counterbalance the number of false positives, since I did not correct for multiple testing.

Further, the number of all CpGs mapping to ICs in the outlier or far outlier range was calculated for each sample and this quantitative variable was compared between cases and controls using Mann-Whitney U test. Then I analyzed the difference between the frequency of outlier and far outlier probes in all cases and all controls for probes mapping to ICs and, for comparison, for all the probes in the array, using Chi-square tests. Finally, quantitative analyses of the methylation data for each CpG mapping to ICs, between cases and controls, was also performed using Mann- Whitney U test of statistical significance and Levene’s and Brown-Forsythe’s tests of variance.

5.3.6.3 Comparison analysis of methylation and expression arrays

For assessment of the effects of the differences found in the methylation analysis, I compared the expression of imprinted genes between samples considered as hyper- or hypomethylated for the respective IC, and all the others. ICs would be considered hyper or hypo methylated if there were at least two probes mapping to the same IC in one sample in the far outlier category (positive or negative respectively) or even if there was only one probe and the region was represented by less than 5 probes.

Genes in the expression array would be selected for the analysis if they were imprinted, their imprinted status was regulated by an IC for which there was at least one abnormally methylated sample, and valid expression data was available. Expression data for a given gene was considered valid if, at least, 3/4 of the possible expression data points represented on the expression arrays had a reliable reading (i.e. detection p value <0.05).

For the imprinted genes for which there was valid expression data available, comparisons were done 1) between the samples abnormally methylated and the samples normally methylated and 2) between the cases and the controls. When appropriate, non-parametric testing – Mann-

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Whitney U test – would be used. The expression data were treated as a quantitative dependent variable and the methylation status of the corresponding IC or the clinical classification of the samples were treated as independent categorical variables.

5.4 Results

To address the hypothesis of association of IC methylation variation in placenta DNA with fetal growth variation I tested the H19 DMR in 170 placenta DNA samples obtained from singletons with several placental and clinical phenotypes. I further analyzed the probes mapping to ICs using data provided by 24 methylation arrays hybridized with placental DNA from 12 fetal growth restricted cases and 12 controls. The results of each one of those two studies is presented separately.

5.4.1 H19 DMR methylation and fetal growth

H19 DMR was tested by pyrosequencing of bisulfite converted DNA of a genomic region including 3 CpGs. The 170 samples tested were obtained from singleton placentas, with a range of gestational ages from 24 to 41 weeks, birthweight percentiles from less than 1st to 94th, both sexes, with and without placental pathology lesions, with or without preeclampsia. The clinical characteristics of the 170 samples tested are summarized in Table 3.6. It shows that the only clinical factor associated with the main outcome of interest – birthweight percentile – was the presence of placental lesions known to be associated with SGA (Table 3.2).

From the 170 singleton samples tested, there were 3 cases hypomethylated (negative far outliers). Five more samples were in the range of the negative outliers. Figure 5.1 shows a boxplot of the distribution of all samples showing the methylation values and the sample classification of each outlier. Table 5.1 provides detail on each of the 8 outliers or far outliers.

Among the 3 far outlier hypomethylated samples, 2 had birthweight less than 10th percentile (1st and 8th) and placental lesions and one was from a newborn with no placental lesions and a birthweight in the 45th percentile. Given the small number of abnormal cases no test statistic was performed to assess the association between H19 DMR hypomethylation in the placenta and any

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Figure 5.1: Boxplot of H19 DMR methylation values (Y-axis) of all samples (n=170).

The box corresponds to the interquartile range (IQR) (1st quartile = 50% methylation, 3rd quartile = 54% methylation). The bar in the boxplot corresponds to the median. The whiskers (vertical lines above or below the box) correspond to 1.5 times the IQR above or below, respectively, the 3rd or the 1st quartiles, if there are values at or above those. These limits are called inner-fence. Values above or below the upper or lower inner-fences are considered, respectively, as positive or negative outliers. Values above or below 3 times the IQR above the 3rd or below the 1st quartile, respectively (outer-fences), are considered positive or negative far outliers. In a normal distribution, the inner fence delimits 95% of the data points and the outer fence 99% of the data points. Outliers are shown as open circles, far outliers as stars. Among those, samples are labeled as LowBW (low birthweight percentile) if birthweight percentile was <10th and as HighBW (high birthweight percentile) if birthweight percentile was >10th. Patients from preeclampsia affected mothers are labeled as PE. Here methylation values between the outer fences were considered as within the normal range. When the methylation was outside the outer fences it was considered as hyper or hypomethylated. The plot presented here includes all the samples and it shows that there are 3 hypomethylated samples of which one is a HighBW and 2 are LowBW.

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Figure 5.1: Boxplot of H19 DMR methylation values (Y-axis) of all samples (n=170).

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Table 5.1: DNA methylation and allelic expression for H19 DMR Placenta Sample Blood H19DMR BW% Pathology H19DMR Placenta Allelic expression ID Methylation Methylation Far outliers 8928 1 Small placenta 35 NA Biallelic 8678 8 MI, FVT 22 NA Non-informative 9093 45 Normal 27 44 Biallelic Outliers 8666 5 FVT, AVM 43 Not done Monoallelic 4196 64 Big plac 43 NA Not done 3898 10 AVM 43 NA Not done 1243 13 Normal 41 Not done Non informative (Unknown for RsaI) 9157 10 AVM, DV 40 NA Monoallelic BW% - birthweight percentile. AVM – advanced villous maturity; DV – decidual vasculopathy; FVT – fetal vascular thrombotic lesion; MI – maternal infarction; NA – not available

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of the outcome variables of interest.

5.4.2 H19 DMR methylation in blood and H19 allelic expression in placenta of hypomethylated cases

Only for the hypomethylated sample with a methylation index (MI) of 27% from the newborn with the 45th birthweight percentile there was umbilical cord blood DNA available and this was also tested for methylation at the H19 DMR region using the same assay. It showed a methylation level in the normal range. Blood specimens of the other samples were not available. Thus I could not obtain support for the hypothesis that the low birthweight was present only when the embryo proper derived organs were also affected.

Two of the hypomethylated (negative) far outlier placenta samples, including the one obtained from the 45th birthweight percentile newborn, and two of the negative outlier range samples, from newborns with 10th and 5th birthweight percentile, were informative and assessed for allelic expression (the results of one of them had been already previously published (Guo et al., 2008)). The objective was to verify the level of methylation below which the imprinting status of the gene is lost. I hypothesized that it is the biallelic expression and not just the decrease in methylation that is biologically relevant. The 4 samples tested had, respectively, methylation index values of 43, 40, 35 and 27%. Only the last two showed biallelic expression – loss of imprinting. These data suggests that far outliers and/or differences in methylation of 10% below the 1st quartile are critical to cause loss of imprinting, at least for this IC. In spite of the limited data, I decided to use this 10% methylation difference as the minimum difference required to consider a difference in methylation as biologically meaningful in several of the methylation analysis described in this thesis.

All the above data suggest that there is a small proportion of placentas that have loss of imprinting of the H19 gene (3 out of 170, roughly 1.8% - 95% CI = 0.5 to 5%), likely caused by demethylation of H19 DMR. At least some of those are placenta specific. However, the small number of abnormal results does not allow any meaningful inferences about an association with any specific phenotype.

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5.4.3 Imprinting centers methylation and IUGR

For the study of variation in methylation in the ICs and its association to fetal growth I have analyzed the differences in the frequency of abnormal methylation values between the DNA placenta samples from a group of 12 SGA singleton newborns with placental pathology lesions suggestive of insufficiency and a group of 12 controls. Methylation was assessed using Illumina® Infinium HumanMethylation27 BeadChip array.

Details of each of the samples used in this study are presented in Table 3.3 (identifiable by “5” under the heading “Methyl Illumina”) and a descriptive summary of those samples are presented in Table 3.5. The data shown in this table shows that controls were matched with cases, for gestational age, sex and presence of labor.

All arrays passed quality control criteria (detailed in section 3.9). Reproducibility assessment of the methylation arrays is reported in Chapter 6 of this thesis (see below in section 6.4.2).

First I classified each methylation value of each selected CpG site mapping to ICs, as explained in Figure 5.1, into normal (between the inner fences), positive or negative outlier (between the inner and outer fences) or far-outlier. I then screened for differences in the frequency of each one of those categories between cases and controls for each individual selected CpG site.

None of the probes showed a statistically significant difference between cases and controls in the frequency of each of the categories (see table in Figure 5.2). However it was notable that cases had more CpGs in the abnormal methylation categories than controls. I thus decided to compare the number of hypermethylated and hypomethylated CpGs in cases versus controls. Cases have more positive probes (outlier and far outlier) than controls (Mann-Whitney U test p value = 0.009 and 0.025 respectively) (see last two lines of the columns under heading “Sample specific number of abnormal CpG sites” in Figure 5.2 and plots in Figure 5.3). Differences are observed only for increase in methylation. There is one case that stands out as having the highest number of positive outliers and far-outliers (Sample F620). But even excluding this case, the difference between cases and controls is still significant (p value = 0.015 and 0.043).

Interestingly, sample 8678, which had been analyzed for H19 DMR by pyrosequencing (see previous study in section 5.4.1) and shown to be hypomethylated, was also represented in the methylation arrays and the only 2 probes mapping to the H19 DMR also had a methylation value

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Figure 5.2: Detailed results of the CpGs mapping to ICs. The table in this figure details the results of the analysis of all CpGs present in the Illumina® methylation array that map to imprinting centers (IC). Each line of the table corresponds to one of the CpG sites. The 1st column corresponds to the chromosome location, the 2nd to the CpG Island (CGI) ID (arbitrarily chosen ordered number), the 3rd to the closest gene and the 4th to the genomic mapping location. In this last column, in light gray are the CpG sites that were identified through the study from our lab referred in the text. The dark gray correspond to the CpG sites that were identified through the literature. The columns grouped under the common heading “Sample specific number of abnormal CpG sites” correspond to the data analysis results for each of the CpG sites. The subheadings correspond to the Sample ID; it starts with a C for controls and F for cases. The last 3 columns correspond, respectively, to the allele that is methylated (M = maternal; P = paternal), to an IC arbitrary identifier and to the genes whose imprinting status is regulated by each of the IC. The columns with the data analysis results detail the sample categorization of each CpG site. The second row in this set of columns corresponds to the sample ID. The numbers correspond to the category of the probe for the sample: 0 is normal, ±1is positive or negative outlier and ±2 is positive or negative far outlier. For easier visualization, color coding was added as follows: 2 Positive far outliers with a high difference in methylation from the controls 2 Positive far outliers with a low difference in methylation from the controls 1 Positive outliers with a high difference in methylation from the controls -1 Negative outliers with a high difference in methylation from the controls -2 Negative far outliers with a low difference in methylation from the controls -2 Negative far outliers with a high difference in methylation from the controls High difference corresponds to more than 10% methylation above or below, respectively, the 3rd and 1st quartile of the controls. The last rows correspond to sums of the values above. It shows that the number of far outliers in each sample from cases is higher than in samples from controls (see Figure 5.5). Some probes in some cases are grouped with thicker margins. They correspond to the regions for which gene expression was assessed – Figure 5.3.

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Sample specific number of abnormal CpG sites Allele

CGI Closest Probe target IC Regulated genes Chr F134 F620

ID Related Gene coordinates F7264 F7285 F7740 F8174 F8177 F8298 F8666 F8678 F8811 F9157 ID C1227 C1243 C1256 C3010 C3040 C3086 C4196 C7633 C8331 C8539 C9363 C9375 Methyl 68285127 000000000000000000000000 M 1 68285238 000000000000000000110‐200 M 1 68285365 ‐100000000000000000001‐200 M 1 1 68285433 000000000000000000000‐200 M 1 68285559 000000000000000000000‐200 M 1 68285651 000000000000000000000‐100 M 1 68288376 000000000000000000000000 M TP73, DIRAS3

1 1 68288565 000000000000000000000000 M 1 DIRAS3 68288681 000000000000000000000000 M 1 68288860 000000000000000000000‐200 M 1 2 68289041 000000000010000000000‐200 M 1 68289051 000000000000000000000‐100 M 1 68289053 000000000‐110000‐100000‐200 M 1 68289215 000000000000000000000‐100 M 1 89837660 0000000000010‐20000010001 M 2 3 4 NAP1L5 NAP1L5 89838076 00000000‐1000120100011000 M 2 144370610 0001‐100000000200‐100200‐2 ‐2M 3 144370745 000000000000020000000000 M 3 144370865 000000000000000000000000 M 3 144371166 000000000000010000000000 M 3 6 4 PLAGL1, HYMAI 144371178 000000000000020000000000 M 3 PLAGL1 144371473 000000000011021000000000 M 3 144371522 000000000000000000000000 M 3 144371602 0000000‐1000000000‐1000000 M 3 50816662 000000000000000‐100000000 M 4 50816909 000000000000000‐20‐10‐10000 M 4

5 50817133 000000000000000000000000 M 4 DDC, GRB10

GRB10 50817425 000000000000010000000000 M 4 50818058 0000000000000‐20000000000 M 4 94123578 000000000000000000000‐100 M 5 94123896 000000000000010000000000 M

6 5 94124144 0000000000000‐20000000000 M 5 7 PEG10 TFPI2, SGCE, PEG10, 94124462 000000000‐102101000000‐100 M 5 PPP1R9A, DLX5 94124872 000000000000000000000000 M 5 7 94124889 000000000000000000000000 M 5 SGCE 94124891 000000000000000000000000 M 5 129917524 000000000000000000000000 M 6 129917832 00000000000001‐2000000000 M 6 8 CPA4, MEST, MESTIT1, KLF14

MEST 129918494 ‐20000000‐2022121112122100 M 6 129919741 0000000000000‐20000000000 M 6 9 1976144 000000000000000000000000 0 H19 7 H19, IGF2, IGF2AS, INS 10 1977136 000000000000000000000‐200 P 7 11 KvDMR 2676805 000000000000000000000000 M 8 KCNQ1OT1, KCNQ1, 12 KCNQ1DN 2847033 000000000000000000000000 M 8 KCNQ1DN, CDKN1C, 11 13 CDKN1C 2864250 000000000000000000000000 M 8 SLC22A18, PHLDA2, OSBPL5 14 32406576 000000000000000000000000 M 9 WT1 32408763 000000000000000000000000 M 9 WT1 15 32408946 000000010000001000000000 M 9 Figure 5.2 (part 1): Detailed results of all the CpGs mapping to ICs.

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Sample specific number of abnormal CpG sites Allele

CGI Closest Probe target IC Regulated genes Chr F134 F620

ID Related Gene coordinates F7264 F7285 F7740 F8174 F8177 F8298 F8666 F8678 F8811 F9157 ID C1227 C1243 C1256 C3010 C3040 C3086 C4196 C7633 C8331 C8539 C9363 C9375 Methyl 21482544 0000000‐10000000010000000 M11 21482767 000000000000000000000000 M11 21483463 000000000000000000000000 M11 17

NDN 21483466 000000000000000000000000 M11 21483713 000001000000200000000100 M11 21483851 000000001000010000001000 M11 PWCR1, NDN, SNURF, 22644337 000000000000000000000000 M11 SNORD107, SNORD64, 22644459 000000000200000000000000 M11 SNORD108, SNORD109B, 18

15 22644549 000000000100000000000000 M11 MKRN3, MAGEL2, SNRPN, 22644614 000010000100000020000000 M11 SNORD109A, SNORD115, 22674380 000000000000000110000000 M11 SNORD115‐48, UBE3A, SNRPN 22674584 000000000000000000000000 M11 ATP10A 19 22674642 000000000000000000000000 M11 22674824 000000000000000000000000 M11 SNURF/SNR 22751346 0000000000000‐20000001000 M11

20 PN 22751499 000000000000000000000000 M11 (SNRPN) 22752317 000‐1000000000000‐20000‐100 M11 16 21 ZNF597 3433998 000000000000000000000000 M12 ZNF597 (?) 62041521 000000000000000000000000 M13 62041627 00‐1000000000000000010000 M13 62041816 ‐100000000000000000000000 M13 22

ZIM2 62041908 000000000000000000000000 M13 62042104 0000000000000‐20000000‐100 M13 62042315 0 ‐10000000000000000000000 M13

19 PEG3/ZIM2 62043025 000000000000000‐100000000 M13 ZIM2, PEG3, ZNF264 62043252 0000000000000‐20000000000 M13 62043454 0000000000000‐20000000000 M13 23 62043603 000000000000000000000000 M13 (ZIM2) 62043946 000000000000000000000000 M13 62044081 000000000000000000000000 M13 24 PEG3 62044469 0000000000000‐20000000000 M13 35583164 000000000000000000000000 M14

25 NNAT NNAT 35583475 000000000000000000000000 M14 ‐ 41575865 0000000‐200000000‐20000000 M15A 26 41575908 0000000‐200000000‐20000000 M

1 15A 41576494 00000000 0000‐11000000000 M15A 27 L3MBTL 41576510 000000000000010000000000 M15A 56847934 0000000000000‐10000000001 P15B 56848355 000000000000000000100001 P15B 56848772 000000000000000000000000 P15B

28 56849616 000000000100010000000001 P15B 56849901 000000000010001101200200 P15B 56850283 000000000000020001202200 P15B 56850628 0000000000000‐20000200200 P15B 56860144 000000000000000000000000 M15C 56860230 000000000000000000000000 M15C

29 56860330 000000000000000000000000 M15C 20 56860498 000000000000000000000000 M15C SANG, L3MBTL, GNASAS, 56860632 000000000000000000000000 M15C GNAS 56861037 000000000000000000000000 M15C

GNAS 56861133 000000000000000000000000 M15C

30 56861225 00000000000001000000‐1 ‐100 M15C 56861337 00000000000‐1000000000000 M15C 56861427 00 00 00 0‐1000 M15C 56862672 0000‐2000‐200102000‐2 ‐102‐200 M15C 56863253 000000000000010000000000 M15C 56863708 000000000000000000000000 M15C 31 56864058 00000000000010‐1010000000 M15C 56864311 000000000000000000000000 M15C 56864597 000000000000000000000000 M15C 56896922 000000000001001000000‐100 M15C

32 56898137 000000000001000000000000 M15C 56898834 000000000000000000000‐100 M15C 000000000112180011323300 Total positive far outliers 1000100220000101131000911 Total negative far outliers

Figure 5.2 (part 2): Detailed results of all the CpGs mapping to ICs.

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Figure 5.3: Comparison between the number of outliers and far outliers in IUGR cases and in controls. Barplots of the number of outliers and far outliers, positive (hypermethylation) and negative (hypomethylation) for each case and each control. Cases are color labeled in red and controls in green. There are more hypermethylated (positive far outliers) and more positive outlier probes in cases than in controls, but there are no statistically significant differences in the number of hypomethylated (negative far outliers) nor in negative outlier probes.

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Figure 5.3: Comparison between the number of outliers and far outliers in IUGR cases and in controls.

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in the negative far outlier (hypomethylation) range (Figure 5.4). The remaining samples that were not categorized as hypomethylated in the arrays were all in the normal or negative outlier range in the targeted pyrosequencing H19 DMR analysis. Although the numbers are small and refer only to one of IC probes, this can be viewed as a partial validation of the array data. Finally I also decided to analyze the data counting the number of hypermethylated CpGs and the number of hypomethylated CpGs in all the cases and all the controls. As shown in Table 5.2, there is a difference between cases and controls for the hypermethylated categories (positive outliers and far outliers) and for the extreme hypomethylated category (negative far outlier) (Chi-square p value = <0.001). The highest odds-ratio was for the probes in the positive far outlier category.

I did these same two comparisons for all the probes in the array to verify if this was a global trend or a specific feature of the probes probe mapping to ICs. For all the probes in the array, the cases also had more probes in all of the categories (Chi-square p value <0.00001) (Table 5.2). However, the highest odds-ratio, in all the probes, was for the negative far outliers. Furthermore, only for the positive far outlier category, there was a statistically significant difference between the group of probes mapping to the ICs and the combination of all the probes (95% CI of the odds ratios in this category do not overlap). Thus, what seems specific with respect to the probes mapping to ICs is a trend towards higher methylation in cases versus controls.

The biological and clinical meaning of the increase in the frequency of extreme variants in the IUGR groups is not clear. A possible explanation for the data could be that cases have a more relaxed control of the methylation levels of the imprinting centers. If that were the case I would expect to have a higher variance in cases than controls. In order to see if a quantitative analysis approach to methylation would capture this trend I analyzed the methylation level as an outcome quantitative variable using non-parametric statistics to identify statistically significantly differences in the methylation distribution and to assess equality of variance for each target CpG between cases and controls. I analyzed each CpG using Mann-Whitney U test and the Levene’s and Brown-Forsythe’s tests respectively. No difference was identified in the methylation distribution (p value >0.05 in all the CpGs) and only a few CpGs had statistically significant differences in the variance but none corresponded to the CpGs for which there were samples in the far outlier range. Thus my hypothesis of more relaxed control of methylation in cases was not supported by this analysis. Another possibility is the occurrence of random variation that may have an effect when reaching a critical combination and amount of abnormal methylation.

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Figure 5.4: Comparison between the distributions of methylation of H19 DMR for the pyrosequencing and for the array data. Boxplots of the H19 DMR methylation as assessed by pyrosequencing of all the 170 samples (left plot), of the same 24 samples used in the array (middle plot) and as assessed by the arrays. Sample ID8678 is the only outlier sample present in both series of samples and it was identified as far outlier by both techniques.

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Figure 5.4: Comparison between the distributions of methylation of H19 DMR for the pyrosequencing and for the array data.

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Table 5.2: Difference between the frequency of outlier and far outlier probes in cases and controls for IC mapping probes and, for comparison, for all the probes Comparison type: P value Odds ratio (95% CI) Difference in frequency of All probes IC probes All probes IC probes 1.4 3.02 positive outliers (category 1) <0.00001 <0.00001 (1.35-1.45) (1.72-5.29) 1.71 5.94 positive far outliers (category 2) <0.00001 0.0002 (1.60-1.83) (2.04-17.3) 2.57 1.9 negative outliers (category -1) <0.00001 0.0615 (NS) (2.43-2.72) (0.96-3.77) 4.5 4.87 negative far outliers (category -2) <0.00001 0.0001 (3.87-5.22) (2.01-11.85) In bold are the analysis that show a difference between the ICC and the global probe analyzes. Underlined are the results that have higher effect size (measured in odds ratios) on each one of the 2 analysis – higher in the negative far outliers in the global analysis, higher in the positive far outliers in the ICC analysis.

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5.4.4 Effect of methylation aberration of ICs in expression of the respective imprinted genes

I then wanted to know if the changes in methylation seen in some of the CpG sites would have an effect on gene transcription. To analyze this, I selected ICs for which there were at least two probes in one sample in the far outlier category or if there was only one probe but the region was represented by less than 5 probes. Each sample, for that region would be classified as normal, hypermethylated (positive far outlier) or hypomethylated (negative far outlier). Then I compared the methylation status of each sample with the corresponding expression of genes regulated by the respective IC, as determined by the Illumina® expression arrays. I was cognizant of the fact that the measured transcript levels correspond to the time of delivery, at which time, the regulatory effects of the changes in methylation at the IC may have been overcome by other regulatory mechanisms.

Table 3.3 provides information on each of the 19 samples hybridized into the Illumina® expression arrays that were used in this study (identifiable by “5” under the heading “Express Illumina”). All 19 expression arrays obtained passed quality control criteria (detailed in section 3.10). Reproducibility was assessed by the correlation between 2 pairs of technical replicates hybridized into 2 different slides (>0.99).

There were thus 19 methylation/expression array pairs available for analysis (see Table 5.3 for details of each paired analysis). Since the expression levels of some of the target genes are too low to be detected by the arrays (detection p value >0.05), some of them had to be excluded. In Figure 5.2, the regions that were selected for analysis are highlighted – thick lines. In Table 5.3 the selected regions and respective regulated genes are listed. An expression array of one more of the samples previously seen as having also H19 DMR hypomethylation (sample 8928), was also available. For genes IGF2 and H19 I also used this sample, considering it as hypomethylated.

As shown in Figure 5.5, among the 8 genes for which expression analysis was possible according to the category of the methylation status of their IC, only PLAGL1, IGF2 and ZNF264 had results that corresponded to what would be expected if indeed the methylation alterations identified in the CpGs mapping to their IC had an effect on the expression levels at the time of delivery. It is interesting to note that, although H19 expression does not behave as expected (as per IGF2, its opposing counterpart) it actually trends to the opposite direction. This type of

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Table 5.3: ICs and samples selected for comparison, between their methylation level categories, of the expression level of the corresponding imprinting regulated genes Hypermethylated Hypomethylated Genes with valid Selected Regions Expressed genes samples Samples data in the array Chr. 1: DIRAS3 DIRAS3 None F8678 None available region (IC ID:1) TP73 Chr. 6: PLAGL1 PLAGL1 F620 None PLAGL1 region (IC ID:3) HYMAI Chr. 7: MEST region C9363, C9375, F8174, C1227, C8331, MEST, CPA4, MEST, CPA4 (IC ID:6) F8298, F8666 F7264 MESTIT1, KLF14 Chr. 11: H19 region None F8678 H19, IGF2 H19, IGF2 (IC ID:7) Chr. 19: PEG3/ZIM2 PEG3, ZNF264, None F620 PEG3, ZNF264 region (IC ID:13) ZIM2 Chr. 20: GNAS GNAS, GNASAS, F8177, F8678 None GNAS region (IC ID:15B) SANG, L3MBTL IC ID – arbitrarily ID number of the Imprinting Center – correspond to the 2nd of the last group of columns in the table of Figure 5.2.

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Figure 5.5: Comparison, between methylation level categories of selected IC, of the expression level of the corresponding imprinting regulated genes. Scatter plots of the expression level of imprinted genes, as determined by expression arrays (Y- axis) according to the methylation category of the respective IC (X-axis), as determined by the categorization of the methylation level measured by the methylation arrays. The categorization is labeled, in each plot, as 0 (zero) for normal, as 2 for hypermethylation (positive far outlier) and as -2 for hypomethylation (negative far outlier). Groups of genes are delimited by boxes when their imprinting status is regulated by the same IC. In spite of the fact that the sample size is too small to make any definitive inference, with the possible exception of H19, IGF2 and ZNF264, no variation in expression level is observed according to methylation category of the IC controlling the imprinting status of each of the genes.

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Figure 5.5: Comparison, between methylation level categories of selected IC, of the expression level of the corresponding imprinting regulated genes.

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response to hypomethylation of the H19 DMR is similar to what had been described by Guo and colleagues, using RT-qPCR to measure H19 and IGF2 expression of the sample 8928. This sample also showed an IGF2 expression in the lower range and an H19 expression within the upper normal range (Guo et al., 2008).

Finally none of the imprinted genes showed statistically significant differences in expression between cases and controls in the expression cohort (n=11 cases and 10 controls) (Mann- Whitney U test p value was higher than 0.05).

5.5 Discussion

Here I have shown that significant alterations in the methylation status of specific ICs in placenta DNA, specifically H19 DMR hypomethylation variant, are unlikely to be a common molecular mechanism underlying fetal growth restriction. However it is possible that a sum of small effects from methylation aberrations at several ICs may be a contributing factor in a few cases.

The data presented in this and in the previous chapters, in association with the work reported by Guo and colleagues (Guo et al., 2008), still support the hypothesis that the H19 DMR hypomethylation variant in placenta could be a rare contributing factor to growth restriction through a reduction of IGF2 expression. Given its rarity and the fact that it is also found in normally grown fetuses, a large cohort will be required to demonstrate such an association.

Simultaneously it will be relevant to examine embryo-derived tissues for this epimutation when present in placenta. It is possible that the growth restriction phenotype will manifest only when also present in the embryo. Finally clinical outcome data will be needed to fully clarify the postnatal phenotypes associated with this epimutation in placenta DNA, whether alone or combined with equally abnormal embryonic tissues.

In this work I have only analyzed 3 CpGs mapping to the region whose hypomethylation had been found to be more consistently associated with Russell-Silver syndrome, as demonstrated in the work I reported in Chapter 4. That region corresponds to CTCF binding site 6. It is still possible that a more thourough and extended analysis of the region could reveal hypomethylation

124 in other upstream regions in a higher number of samples. Such alterations could also affect IGF2 expression as demonstrated in the first report of an association between H19 DMR hypomethylation and Russell-Silver syndrome by Gicquel and colleagues (Gicquel et al., 2003).

The H19 DMR data from this small series suggests that loss of imprinting (LOI) or, at least imprinting “leakage”, starts to occur at values below 40% to 35% methylation, corresponding to the far outlier range. A larger sample size will be required to precisely and accurately define this cut-off point. By determining the allelic expression across several values of methylation in the IC it will be possible to establish the level of H19 DMR hypomethylation below which biallelic expression, i.e. LOI occurs. This is important because most studies reporting H19 DMR epimutations associated with the Russell-Silver phenotype base their methylation cut-off value, measured in blood, on a statistical basis. Determining the cut-off based on demonstration of LOI would be a more biologically meaningful process to determine the methylation level below which H19 DMR methylation should be considered abnormal. Expressing that cut-off value as multiples of the median may increase its comparability across studies.

The analysis of the array data in this Chapter showed that, in placenta DNA, CpGs are more likely to be in the outlier range in IUGR newborns. In addition, a few of these newborns also carried several of these aberrantly methylated CpGs. Finally such aberrantly methylated CpGs, when analyzed together, are more likely to be hypermethylated if they map to ICs. Confirmation of the array data is still required. Pyrosequencing of bisulfite converted DNA will be necessary not only to confirm the abnormal methylation found in specific CpGs but also to confirm that it is also present in the surrounding region. If this aberrant methylation is confirmed in the samples for which it was found in the arrays, its association with low birthweight or the IUGR phenotype can be assessed in the extended cohort that has been collected. First targets for validation should be selected depending on previous association studies with growth restricted phenotypes and/or on the proportion of CpGs in the respective IC with abnormal methylation. I thus propose the PLAGL1, the PEG3 and the DIRAS3 related ICs (IC# 3, 13 and 1 in Figure 5.2) for priority validation. All those ICs show aberrations at several of the array CpGs mapping to their genomic location in one of the cases and none of the controls.

PLAGL1 (a.k.a. ZAC) codes for a protein with DNA binding properties and is a candidate tumor suppressor gene (Varrault et al., 1998). Hypomethylation of PLAGL1, a gene expressed only

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from the paternal allele, and its overexpression, has been associated with Transient Neonatal Diabetes Mellitus (TNDM) (Mackay et al., 2006b; Mackay et al., 2005) a rare metabolic neonatal syndrome also frequently accompanied by growth restriction. One of the case samples shows an increase in methylation in the IC of this gene, which is the opposite of what has been found in TNDM. However, lower expression of PLAGL1, as well as expression changes in other imprinted genes, such as PHLDA2 (increase in expression), MEST and IGF2 (decrease in expression), have been previously reported to be associated with IUGR, by targeted qRT-PCR based (Apostolidou et al., 2007; Diplas et al., 2009; Guo et al., 2008) and microarray based studies (McMinn et al., 2006). However, in this last study, methylation aberrations at the PHLDA2 and MEST respective ICs were not found. Other epigenetic mechanisms of regulation, such as histone modifications, could have been at play in those cases. Here and in other reported microarray expression studies (Lee et al., 2010; Lian et al., 2010; McCarthy et al., 2007; Okamoto et al., 2006; Roh et al., 2005; Sitras et al., 2009; Struwe et al., 2010; Toft et al., 2008) these expression changes in imprinted genes, in association with poor fetal growth, have not been reproduced, which may be the consequence of diverse case selection and/or analytical strategies. It can also be explained by the higher sensitivity of quantitative reverse transcription PCR as a measure of expression, in comparison with expression arrays. However, the possibility of changes in expression, of both imprinted and/or non-imprinted genes, at an earlier time in the pregnancy cannot be ruled out by strategies addressing expression at the time of delivery.

PEG3 is also paternally expressed in several tissues (Murphy et al., 2001; Van den Veyver et al., 2001) including placenta, where it is highly expressed (Hiby et al., 2001). It encodes a Kruppel- type ZNF protein (Kim et al., 1997) and it is a regulator of the Tumor Necrosis Factor (TNF) pathway (Relaix et al., 1998). Tumor suppressor activity was demonstrated in a glioma cell line (Kohda et al., 2001).

DIRAS3 is paternally expressed and it is also a putative tumor suppressor gene (Yu et al., 1999). Another gene that shares DIRAS3 IC, TP73, is a variable imprinted gene (Hu et al., 2000; Kaghad et al., 1997), related to p53 and also with debatable tumor suppressor characteristics (Ichimiya et al., 1999; Martinez-Delgado et al., 2002). It has been found to have variable imprinting status in placenta (Diplas et al., 2009).

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The data here presented, as mentioned above, suggest that rare cases may have a combination of methylation variants at several CpGs mapping to several ICs. If confirmed, the biological meaning of this association is not clear since, for most loci, it does not seem to have an impact on the expression level of the genes as measured by expression arrays after delivery. This may not be the biologically relevant timing or the appropriate measure of expression. It is possible that these small effect size variants have an impact on gene expression, not appreciable at the time of delivery but that could have occurred early in development. Under this hypothesis, the finding of methylation pattern alterations may be the residual sign that such effects may have occurred. Prospective case control studies involving the determination of methylation variants in stored samples from first trimester chorionic villous biopsies, after outcome assessment, could clarify this hypothesis.

It is thus possible that multiple additive small effects, not detectable by the techniques and sample size used, acting at specific developmental times, have a deleterious effect on placental development and fetal growth. The fact that the IUGR phenotype is associated with a much higher number of such small and dispersed “hits” supports this concept. Whether the number of “hits” constitute a continuous spectrum with a phenotypic expression towards the end of that spectrum, or if a predisposing factor increases the number of “hits” in certain individuals, (i.e. this is an epiphenomenon) remains to be clarified. Experimental methods that could prove this hypothesis are difficult if not impossible to design with the current available technology. This can only be resolved with epidemiological studies since modeling multiple small effects is currently not feasible.

Given all the body of work demonstrating the importance of imprinting in placenta development and fetal growth, it is surprising that an obvious association between poor fetal growth and aberrant DNA methylation at ICs could not be demonstrated. One other plausible explanation is the possibility that the effect of imprinting dysregulation by DNA methylation aberration in ICs is much more deleterious, even compromising the survival of the embryo. Under this hypothesis, such an abnormality would be more a cause of embryo wastage than of other later and less biologically severe clinical phenotypes, such as IUGR. The application of the methodology described here to the study of placental material from spontaneous miscarriages in comparison with the same material of medically induced terminations of pregnancy could clarify this hypothesis.

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In summary, it is unlikely that important imprinting dysregulation by aberrant DNA methylation of ICs is the underlying cause of the alterations in imprinted genes expression previously reported to be associated with IUGR. However it is not possible to rule out the hypothesis that imprinting is dysregulated through other molecular mechanisms (e.g, histone code aberrations).

It is likely however that, in rare instances, major aberrations in the methylation of a growth related imprinting regulator such as H19 DMR may be a significant contributor to this phenotype.

Alternatively it is possible that specific combinations of multiple frequently occurring variations in methylation across the genome could have a clinically relevant impact through an additive effect.

Finally, one reason I did not identify frequent or significant alterations in the methylation pattern of imprinting centers may be related to possible lethality of the most extreme of those alterations.

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Chapter 6: Cell Specific Patterns of DNA Methylation in the Human Placenta

The published version of this chapter appears in:

Grigoriu A*, Ferreira JC*, Choufani S, Baczyk D, Kingdom J and Weksberg R. Cell specific patterns of methylation in the human placenta. Epigenetics 2011; 6: 368-379.

* I and Ariadna Grigoriu were co-first authors of this publication (Grigoriu et al., 2011).

The chapter that follows is an adapted modified version.

For this portion of the work I had the collaboration of a clinical fellow of the Toronto Mount Sinai Hospital Maternal Fetal Medicine program, Dr. Ariadna Gregoriou, who did the placental cell collection, cell separation and DNA extraction components of the study. She also provided Figures 6.1 and 6.2.

The microarrays were run at the Center for Applied Genomics.

I carried out the data analysis and the experimental and analytical validations. I did Figures 6.3 to 6.8 and all the tables and the first draft of the manuscript.

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6.1 Summary

Epigenetic processes, such as DNA methylation, are known to regulate tissue specific gene expression. This concept was explored in the placenta to define whether DNA methylation is cell-type specific. Cytotrophoblasts and fibroblasts were isolated from normal midtrimester placentas. Using immunocytochemistry 95% purity was demonstrated for cytotrophoblasts and 60-70% for fibroblasts.

I compared DNA methylation profiles from cytotrophoblasts, fibroblasts and whole placental villi using bisulfite modified genomic DNA hybridized to the Illumina® Infinium HumanMethylation27 BeadChip array. Euclidean cluster analysis of the DNA methylation profiles showed 2 main clusters, one containing cytotrophoblasts and placenta, the other fibroblasts. Differential methylation analysis identified 442 autosomal CpG sites that differed between cytotrophoblasts and fibroblasts, 315 between placenta and fibroblasts and 61 between placenta and cytotrophoblasts. Three candidate methylation differences were validated by targeted pyrosequencing assays. Pyrosequencing assays were developed for CpG sites less methylated in cytotrophoblasts than fibroblasts mapping to the promoter region of the beta subunit of human chorionic gonadotropin 5 gene (CGB5), as well as 2 CpG sites mapping to each of 2 tumor suppressor genes that were more methylated in cytotrophoblasts than fibroblasts.

The data here generated suggest that epigenetic regulation of gene expression is likely to be a factor in the functional specificity of cytotrophoblasts. These data are proof of principle for cell- type specific epigenetic regulation in placenta and demonstrate that the methylation profile of placenta is mainly driven by cytotrophoblasts.

6.2 Introduction

Epigenetic mechanisms are stable, mitotically heritable chemical and conformational modifications of DNA or its associated histone proteins. These modifications regulate the access of transcriptional elements to the DNA sequence without altering the primary nucleotide sequence (Feinberg, 2007). Amongst other functions, epigenetic modifications are major regulators of cellular differentiation (Persson and Ekwall, 2010). Patterns of epigenetic

130 modifications differ between tissue types (Ghosh et al., 2010; Rakyan et al., 2008) and stages of development (Bogdanovic and Veenstra, 2009; Hawkins et al., 2010).

DNA methylation, one of the best studied post-transcriptional epigenetic modifications is characterized by the addition of a methyl group to the nucleotide cytosine by DNA methyltransferases. In eukaryotes this addition generally occurs on cytosines followed by guanine (CG) (Bird, 2002). When this chemical modification occurs in promoter regions of genes, transcription is usually altered (Bird, 2002; Bogdanovic and Veenstra, 2009).

DNA methylation, like other epigenetic processes, is known to differ among cell types within certain tissues (Bloushtain-Qimron et al., 2008; Rakyan et al., 2008; Sakamoto et al., 2007). This has been demonstrated in several breast tissue cell-types, (Bloushtain-Qimron et al., 2008) among different white blood cell types, (Rakyan et al., 2008) and between embryonic and adult stem cells (Bloushtain-Qimron et al., 2009). Thus, the degree of methylation measured in a tissue is an average of the methylation in all the existing cell types. Variations in the proportions of specific cell types among samples of the same tissue can make interpretation of methylation differences between such samples difficult.

The identification of differences in cell-specific methylation within the placenta provides the basis for understanding the epigenetic regulation of placenta cell type specific functions. Furthermore it will enhance the analysis and interpretation of placenta studies of epigenetic variation in association with disease.

My objective was to determine whether DNA methylation in the human placenta is cell type- specific. In this regard, an interesting aspect of trophoblastic cells behavior is its invasiveness, similar to cancer cells. In cancer cells epigenetic modifications have been found to alter their behavior (Feinberg, 2007; Lima et al., 2010) conferring the capacity for uncontrolled proliferation and invasion. In a similar but more organized fashion, the invasive extra-villous trophoblast (EVT) cells of the placenta invade the endometrium (Knofler, 2010). I hypothesized that this similarity in biological behavior between neoplasms and placenta would be translated into epigenetic marks that could be detected by DNA methylation profiling.

Therefore I undertook an analysis of genome wide methylation profiles in midgestation placenta and compared it to cell type specific fractions of cytotrophoblasts and fibroblasts. Although the

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separation of fibroblasts was incomplete, cell type specific differences in DNA methylation were identified.

This DNA methylation profiling study of placenta and two of its cell types, cytotrophoblasts and fibroblasts, demonstrates cell-type specific differences. I have validated the methylation differences for 3 of the genomic regions identified through the screening approach, thereby confirming epigenetic signatures that are specific for cell types in the placenta. Importantly, these data demonstrate that epigenetic studies of placental villi will reflect mainly the profile of cytotrophoblast cells.

6.3 Materials and Methods

6.3.1 Sample selection and cell separation

As for the previous and following studies, samples of human placenta used in this study were obtained by the staff of the Research Centre for Women’s and Infants’ Health BioBank program at Mount Sinai Hospital, Toronto, Canada with written informed consent and Research Ethics Board approval. Villous cytotrophoblasts and mesenchymal core fibroblasts were isolated from singleton, healthy 2nd trimester placentas at 14-15 weeks (n=3) and at 18-19 weeks (n=3) of gestation immediately following voluntary surgical termination of pregnancy. The tissues were placed in sterile phosphate buffered saline (PBS) and processed within 2 hours of collection. The placental villi were dissected and sequentially digested prior to magnetic bead separation to obtain specific cell fractions.

6.3.2 Sequential enzymatic separation

A preliminary wash (Mg2+ and Ca2+ free Hank’s buffered salt solution [HBSS]) and dissection eliminated debris and clots. A 40 mg representative sample of placental villous tissue was subjected to serial trypsin digestion (0.25% trypsin [Invitrogen, 27250-018] in HBSS). Three to four digestions were necessary, each one consisting of 60 minutes of incubation in 50 ml of trypsin solution at 37°C, on a gentle shaker. The liquid phase was collected, neutralized with 10% fetal bovine serum (FBS) and centrifuged (5 min, 362 g). The resulting cell pellet was further washed with a stabilizing PBS solution (PBS [Ca2+ and Mg2+] + 2% FBS).

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The remaining placental tissue was rinsed in HBSS (Mg2+ and Ca2+ free) and subjected to two sequential collagenase (2 mg/mL) (SIGMA, C6885-1G) digestions of 25-30 minutes duration, in 25 ml of collagenase, at 37°C, on the slow shaker. The cells were then centrifuged and stabilized as described above.

The cells obtained from the trypsin only and trypsin collagenase digestions were analyzed separately as outlined in the next section. The pellets obtained from the trypsin digestions were used for the isolation of cytotrophoblasts while the pellets from the collagenase digestions were enriched in fibroblasts.

6.3.3 Magnetic bead separation

The stabilized cells were centrifuged (5 min, 362 g) and each individual pellet was suspended in 2ml PBS and passed through a sterile metal mesh (Sigma, 200 m) to remove undigested groups of cells. The samples were placed on a 4 cc Ficoll 1.077 column (GE Healthcare, 17-1440-02) and centrifuged for 10 minutes (805 g) to facilitate the density gradient separation. A layer of cells was obtained, collected, washed with MACS Buffer (PBS + 0.25% BSA + 2 mM EDTA) and incubated for 30 minutes with magnetic-labeled, cell specific antibodies (20 L per 107 cells) (MACS MicroBeads, Miltenyi Biotec). The antibodies used were CD-45 (MACS, 130- 045-801) and anti-fibroblast (MACS, 130-050-601). Magnetic separation was performed according to the supplier’s positive selection/depletion protocol: the cytotrophoblasts were obtained by depletion and the fibroblasts by positive selection.

The resulting cell fractions — cytotrophoblasts and fibroblasts — were stored in RLT Buffer + 10% β-mercaptoethanol (β-ME) at 4°C for the upcoming steps. A small fraction of cells (40 µL containing 2 x 105 cells) was plated with growth media (Dulbecco’s modified eagle medium nutrient mixture F12 (DMEM/F12 [Gibco, 11039-021]) and Neomycin 0.5 ml/500 mL and 10% FBS) on a 6-well plate. Subsequently, the plates were incubated in atmospheric conditions

(O2/5% CO2 at 37 °C) overnight in preparation for immunocytochemistry (Baczyk et al., 2009).

6.3.4 Immunocytochemistry protocol

The plated cell fractions were fixed with 70% methanol for 30 minutes. The cells were then washed twice with PBS. The cells were blocked with Ready to Block reagent (DAKO Canada

Inc) for 30 minutes following the manufacturer’s protocol. Specific antibodies (1:150 dilution)

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targeting cytotrophoblasts (cytokeratin-7 [DAKO, IR619]) and fibroblasts (vimentin-9 [DAKO, IR630]) were added. A secondary (anti-mouse) antibody was added, followed by DAB staining (KPL, 54-10-00). The plates were allowed to incubate at room temperature and the positively stained cells were then counted under the microscope and expressed as a percentage of the total number of cells.

6.3.5 DNA/RNA extraction

Genomic DNA and RNA were extracted from the whole placenta samples and the purified cell fractions of cytotrophoblasts and fibroblasts as described in section 3.4. Sample quality assessment was performed as described in section 3.5. The RNA samples were further subjected to analysis using the Bio-Analyzer from Agilent®.

6.3.6 DNA methylation analysis by microarrays

DNA hybridization protocol to Illumina® Infinium HumanMethylation27, BeadChip silica slides and data output was described in section 5.3.4 with technical details provided in sections 3.7 and 3.9.

6.3.7 Methylation array data analysis

6.3.7.1 Global comparisons between samples

Global comparisons were performed between all the samples by non-hierarchical Euclidean cluster analysis, using Partek Genomic Suite version 6.5.

6.3.7.2 Differential methylation analysis and enrichment analysis

Probe specific differential methylation analysis was performed to identify differences between the cytotrophoblast and fibroblast fractions. Sex chromosome data was excluded. Given the non- normal distribution of the data, the unequal number of cytotrophoblast and fibroblast samples and the small sample size, we used non-parametric statistical analysis (Mann-Whitney U test). Probes with detection p values > 0.05 were excluded. I used an arbitrarily chosen 20% methylation difference cut-off level in addition to a p value less than 0.05, to identify probes that are most likely to have a biological significant difference between the 2 cell types and partially to overcome the challenge presented by incomplete cell separation (see “Results”).

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In order to define the methylation patterns of cytotrophoblasts and fibroblasts as they relate to tumor suppressor genes, a search was performed in the Genecards database , V3.02.128, 21 Mar 2010 (www.genecards.org/) using the key words “Tumor suppressor” in the fields “Summary” or “Function.” The resulting gene list was used for binomial enrichment analysis of our 2 lists of differentially methylated probes. For comparison, a similar strategy analysis was performed for oncogenes. The generated lists tumor suppressor genes and oncogenes are provided in Table 6.1.

6.3.8 Pyrosequencing validation of selected regions

Three differentially methylated CpG sites were selected for validation using universal biotinylated primer based pyrosequencing methylation analysis of bisulfite converted DNA, as previously described in section 3.12 and in previous reports (Grafodatskaya et al., 2010; Tost and Gut, 2007b). Assays were designed using Pyromark Assay Design software version 2.0.1.15 (Qiagen®), targeting a region flanking the Illumina® CpG site by up to 100 base pairs. Details of the primers used are given in Table 6.2 and of the PCR conditions to be performed prior to the Pyrosequencing reaction in Table 6.3. The same bisulfite converted DNA samples that were used for the Illumina® arrays hybridization were likewise used for pyrosequencing. PCR amplicons were analyzed with the PyroMarq Q24 also from Qiagen®, as specified by the manufacturer. The results, provided in % of methylation, were calculated using PyroMarq Q24 software version 1.0.10. Bisulfite efficiency was controlled by the absence of non-converted C at C sites not followed by a G, (i.e. expected to be non-methylated) thus all converted to T after PCR.

Validation analysis of the data included: 1) Pearson correlation between the methylation values of the specific array targeted CpG sites determined by the two methods (targeted pyrosequencing assay and array) (3 assays x 11 samples); 2) Pearson correlation between the differences in methylation for each CpG site for each pair of related samples (3 assays x 5 cytotropho- fibroblast pairs); 3) Differential analysis between methylation levels of cytotrophoblasts and fibroblasts, as determined by the pyrosequencing method, for each CpG site targeted by the pyrosequencing assay and for the site average, using the Mann-Whitney U test.

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Table 6.1: Genes Selected from the Arrays as possible Tumor Suppressor Genes and Oncogenes TUMOR SUPPRESSOR GENES ABTB1 ACTL6A ADAM11 ADAMTS18 AIM2 ANP32A ANP32C ANP32D APC APC2 ARL11 ATM ATR AXIN1 BAK1 BANP BAP1 BARD1 BAX BCL11B BEX2 BIN1 BLCAP BNIP3L BRCA1 BRCA2 BRD8 BRWD2 BTG1 C12orf5 C20orf20 CABC1 CAMTA2 CARS CAV1 CAV2 CBFA2T2 CBFA2T3 CCND1 CCND2 CCND3 CCNG1 CD81 CDC14A CDC14B CDC73 CDH20 CDK6 CDKN1A CDKN1C CDKN2A CDKN2B CHEK2 CHFR CLCA1 CLCA2 CNDP2 CORT CPNE7 CREBL2 CREG1 CTCF CTCFL CUL2 DAB2IP DAPK1 DCC DEC1 DHX34 DIDO1 DIRAS3 DKK3 DLC1 DLEC1 DLEU1 DLG1 DLG2 DLG3 DLG4 DLG5 DMAP1 DMBT1 DMTF1 DPH1 E2F1 E2F2 E2F3 E2F4 E2F5 E2F6 E4F1 EHF ENC1 ENO1 EPC1 EPHB2 EXT1 EXT2 FABP3 FANCG FAT2 FBLN1 FH FHIT FLCN FOXP1 FRK GAK GAS1 GAS8 GRLF1 GTSE1 H19 HDAC1 HDAC3 HIC1 HOXA5 HUWE1 ING1 ING3 ING4 INHBA INSM2 IRF1 JMJD5 KLF6 KLK10 LATS1 LATS2 LDOC1 LIMD1 LIN9 LLGL1 LRDD LRP12 LRP6 LZTS1 MAF MAFB MAPK14 MAPK9 MAPRE3 MCC MCTS1 MDM2 MEN1 MORF4 MORF4L1 MORF4L2 MPP2 MRVI1 MTAP MTUS1 MXD1 MXD4 MXI1 MYO18B NAP1L4 NAT6 NBL1 NBR2 NDUFA13 NEDD4 NIT1 NKX3-1 NPM1 NUDT2 NUP98 PARK2 PAWR PAX2 PCBP4 PCGF2 PDCD4 PDGFRL PEG3 PHB PHLDA2 PHLDA3 PINX1 PLAA PLAGL1 PML PNN POU6F2 PPARD PPM1A PPM1D PPP5C PRDM2 PRDM4 PRKCDBP PRR5 PRX PTCH2 PTEN PTGES PTPRG PYHIN1 RAD54L RARRES3 RASSF1 RASSF2 RASSF4 RASSF5 RASSF6 RB1 RB1CC1 RBAK RBBP6 RBBP7 RBBP8 RBL1 RBL2 RNF139 RNF40 RNF6 RPL10 RPS29 RRM1 RUNX3 RUVBL1 S100A2 SASH1 SATB1 SEMA3B SEPT9 SERPINB5 SEZ6L SHC1 SLC22A18 SLC5A8 SMAD2 SMAD4 SMARCB1 SPINT2 ST13 ST7 ST7L STARD13 STIM1 STK11 SYK TACC2 TAF1 TCEB1 TCEB2 TCEB3 TES TGFB1 TGFB2 TGFB3 TJP1 TJP2 TMEFF1 TNFRSF10B TNK1 TP53 TP53I3 TP73 TRIM35 TRIM8 TSC1 TSC2 TSPAN32 TSSC1 TSSC4 TUSC2 TUSC3 TUSC4 UBE2D1 UBE2D2 UBE2D3 UBE3A UBE4B VHL VILL WFDC1 WNT5A WT1 WTAP WWOX XRN1 YEATS4 ZGPAT ZMYND11

ONCOGENES ABL1 ABL2 ACTL6A AGR2 AKAP13 AKT1 AKT2 AKT3 ALK ARAF ATR BAG1 BARD1 BCAS1 BCL11A BCL3 BRAF BRD8 C20orf20 CASC4 CBL CBLB CCNL1 CDC25A CDC25B CDC42 CDCA4 CDCA7L CDKN2A CRKL DAXX DMAP1 DMTF1 DUSP1 ECT2 EFNA1 EHF ELK1 ELK4 EPC1 ERBB2 ERBB3 ERBB4 EREG ERG ETS1 ETS2 FER FES FGF23 FGF3 FGF4 FGF5 FGF6 FGFR3 FGR FOS FOSB FYN FZD6 G0S2 GFI1B GLI2 GOLGA5 GOLPH3 HGF HHCM HNRPDL HRAS HYAL2 IKBKE ILKAP ING3 ING4 JUN KIT KRAS LIN9 LMO4 LYN MAF MAFB MAFF MAFG MAFK MAP2K3 MAP3K8 MAS1 MCF2 MCTS1 MDM2 MET MFHAS1 MORF4 MORF4L1 MORF4L2 MOS MPL MRAS MUM1 MXI1 MYB MYBL1 MYBL2 MYC MYCL1 MYCN NELL1 NELL2 NMI NPDC1 NRAS NTRK1 NUP88 PAF1 PDGFB PES1 PFDN5 PIK3C2A PIK3C2B PIK3CA PIM1 PIM2 PIWIL2 PLAG1 PLCG2 PLEKHG2 PLK1 PML PRKAR1A PTK2 PTPN1 PTPN11 PTPN12 PTPN13 PTPN14 PTPN18 PTPN2 PTPN21 PTPN3 PTPN4 PTPN6 PTPN7 PTPN9 PTPRA PTPRB PTPRC PTPRD PTPRE PTPRF PTPRG PTPRH PTPRJ PTPRK PTPRM PTPRN PTPRN2 PTPRR PTPRS PTPRT PTPRU PVT1 RAB27A RAB30 RAB31 RAB33A RAB35 RAB36 RAB3A RAB3B RAB5A RAB5B RAB5C RAB7L1 RAB8A RAB9P1 RAF1 RALA RALB RANBP9 RAP1A RAP1B RAP2A RASA1 RASA2 RASAL1 RASSF3 RASSF6 RCHY1 RECK REL RET ROS1 RPL7A RPS6KA4 RPS6KA5 RRAS RUVBL1 SBF1 SH3BP1 SHH SIN3A SKI SP140 SPI1 SPIB SRC STK3 TGFB1 TGFB2 TGFB3 THPO TNFAIP2 TOB1 TOM1 TPM3 TPR TRAF3 TRIB2 TSKS UBE2V1 USP8 VAV1 VAV2 WT1 YAP1 YEATS4 YES1

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Table 6.2: Oligonucleotide primers used for pyrosequencing PCR amplicons in genomic regions selected for validation CGB5 probe – Chr. 19, position 54239014, Illumina probe ID – cg24908058, corresponding to methyl position 6 in the pyrosequencing target: chr19: 54,239,015-54,239,086 (71 bp) which includes 6 CpGs – PCR amplicons: 214 bp Forward PCR primer (Biotinylated) GGAGGGTTGAGGTTTTAATTTAG Reverse PCR primer AACCCTCTCCTCTCACTAAT Sequencing primer (Reverse) CTCCTCTCACTAATCC APC probe - Chr. 5, position 112101401, Illumina probe ID – cg20311501, corresponding to methyl position 1 in the pyrosequencing target: Chr5: 112,101,402-112,101,444 (42 bp) which includes 7 CpGs – PCR amplicons: 168 bp Forward PCR primer GGGAAGAGGAGAGAGAAGTAGTTGT Reverse PCR primer (Biotinylated) ACAACACCTCCATTCTATCT Sequencing primer (Forward) GGATTAGGGAGTTTTTTATT TP73 probe - Chr. 1, position 3557864, Illumina probe ID – cg04391111, corresponding to methyl position 3 in the pyrosequencing target: chr1: 3,557,847-3,557,935 (88 bp) which includes 9 CpGs – PCR amplicons: 147 bp Forward PCR primer GTGGGGGTTATTATGGGTAGA Reverse PCR primer (Biotinylated) ACCACCTACACCAAACCCTAACTA Sequencing primer (Forward) GGTTATTATGGGTAGAGG Biotinylated sequence (attached to the 5’ end of the biotinylated CGCCAGGGTTTTCCCAGTCACGAC primers of all assays) Note: there are 3 reported polymorphisms in the CGB5 target region (2 of them in CpG positions 3 and 6). Since we are measuring differences in samples of the same individual, this is not expected to affect the results.

Table 6.3: PCR conditions for pyrosequencing of genomic regions selected for validation Assay name Step down phase Fixed phase 95ºC 20 sec TP73 (147 bp) None 55ºC 20 sec 40 cycles CGB5 (214 bp) 72ºC 20 sec 95ºC 20 sec 95ºC 20 sec APC (168 bp) 60 to 55ºC in 0.5ºC steps 20 sec 10 cycles 55ºC 20 sec 35 cycles 72ºC 20 sec 72ºC 20 sec

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6.4 Results

6.4.1 Placenta fractionation and cell population enrichment

Sufficient amounts of cytotrophoblasts and fibroblasts were successfully separated by sequential enzymatic digestion and magnetic bead separation for the DNA methylation profiling experiments. However the cell purity was much greater for cytotrophoblasts than for fibroblasts. Trypsin is a protease that cleaves trophoblastic cells from placental villi and results in collection of good quantities of excellent quality cytotrophoblasts, as previously reported (Bloxam et al., 1997). In this experiment a lower concentration of trypsin (0.25%) was used for an increased length of time to ensure gentle digestion and to limit cell membrane disruption and release of intra-cytoplasmic RNA and DNA. The proteolytic enzyme collagenase was then used to disrupt the extracellular matrix and enhance the release of mesenchymal type cells. Its use allowed collecting significant amounts of fibroblasts, which were not obtained with trypsin digestion alone.

Placental cells can be identified by the specific antigens that they express on the cell surface, the hormones that they produce or their size. Cell fraction purity can be assessed using various modalities including immunocytochemistry or flow cytometry analysis; cytokeratin-7/vimentin-9 antibodies have been found to correlate well with trophoblast purity obtained by flow cytometry (Maldonado-Estrada et al., 2004). Purity of 95% in the cytotrophoblasts fractions and 60-70% in the fibroblasts was demonstrated employing immunocytochemistry (Figure 6.1). The fibroblast fraction was found to contain some debris; the contaminant cell population was cytotrophoblasts (immunostained by cytokeratin-7 antibody).

For each of the 6 placenta samples, 3 DNA/RNA aliquots were successfully extracted from cytotrophoblasts, fibroblasts and whole placentas. The quality of the RNA extracted from fibroblasts was poor thus compromising the initial goal of correlating methylation differences between the cytotrophoblasts and the fibroblasts with expression changes.

6.4.2 DNA methylation analysis

Bisulfite modified DNA samples were hybridized to Illumina® Infinium Human Methylation27 BeadChip arrays. These arrays probe more than 27,000 CpG sites in the genome mostly mapping

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to promoter regions of ~14,000 genes. Generally, each gene is represented by 2 probes, located in the promoter of the genes, one on each side of the putative transcription start site (TSS). Less frequently there are single or multiple probes. Single probes also map mainly to promoter regions whereas multiple probes generally map to promoter and intragenic sequences. The Illumina® array was developed to include several cancer-related genes that are represented by 2 or more probes on the array. This manufacturer’s selection bias was considered when analyzing the data.

A total of 21 DNA samples were hybridized to 2 Illumina® array silica slides: 3 cell fractions for each one of the 6 placentas and a set of technical replicates (cytotrophoblast, fibroblast and placenta fractions of the same sample). Technical replicates were hybridized on different slides. One fibroblast sample was excluded due to suboptimal bisulfite conversion, as per the manufacturer’s quality control criteria. I identified a strong correlation between methylation profiles of samples and their respective technical replicates (R > 0.99, for placenta, fibroblast and cytotrophoblasts), indicating high reproducibility of these arrays (Figure 6.2).

6.4.3 Global methylation comparison among samples

I initially tested for obvious differences in global patterns of methylation between the two cell types. As expected, most probes in the array mapped to CpG islands in promoter regions and also, as expected, most had low methylation levels in all samples (interquartile range: ~0.02-0.4; median: 0.08 to 0.16; mean: 0.24 to 0.27) (Costello and Plass, 2001; Rakyan et al., 2008; Suzuki and Bird, 2008). Correlations among all the arrays were similar even between the different types of samples (R= 0.95 - 0.97 [mean = 0.96]). There was no significant variation with gestational age (R = 0.98) among the samples.

Non-hierarchical Euclidean cluster analysis was performed for 17 samples (Figure 6.3). Samples were labeled by sample number, gestational age group (early versus late second trimester) and by cell type. There was no obvious clustering by sample number or by gestational age group. However, the methylation profiles for cytotrophoblasts tended to cluster together in a group with whole placenta. These profiles were distinct from the data obtained for the fibroblast fraction. This observation suggests that methylation levels in non-fractionated placental samples are more likely to reflect the methylation levels of cytotrophoblasts than fibroblasts.

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Figure 6.1: Immunocytochemistry of cells isolated from Placenta. A) Cytotrophoblasts stained positively for cytokeratin-7 and B) did not stain for vimentin-9. The fibroblast fraction stained positively for both C) cytokeratin-7 and D) vimentin-9.

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Figure 6.1: Immunocytochemistry of cells isolated from Placenta.

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Figure 6.2: Correlation between the biological replicates. Scatter plots of the methylation values of 3 fractions from one sample (placenta, cytotrophoblasts and fibroblasts) and its biologic replicates.

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Figure 6.2: Correlation between the biological replicates.

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Figure 6.3: Cluster analysis for Placenta, isolated Cytotrophoblasts and Fibroblasts (n = 17). Non-hierarchical Euclidean cluster analysis was performed based on gestational age (Column A: 14-16 weeks (early); 17-18 weeks (late)), type of sample (Column B: fibroblast (Fibro), cytotrophoblast (Cyto) and whole placenta (Plac)) and sample case ID number (Column C). The degree of similarity between two samples is given by the sum of the length of the horizontal lines between the samples. I chose the fourth branching to define the clusters (marked by the dashed vertical gray line). There is no clustering by gestational age or sample case ID number. The sample type analysis reveals 2 main clusters (clusters 1 and 2): one composed of only cytotrophoblasts and placenta and the other contains mainly fibroblasts. The remaining three clusters (clusters 3, 4 and 5) contains only one sample each and may be due to the reduced number of significantly detected probes (24-25,000 vs >27,000) for these samples.

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Figure 6.3: Cluster analysis for Placenta, isolated Cytotrophoblasts and Fibroblasts (n = 17).

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6.4.4 Cell specific differential methylation analysis

Differential methylation analysis was carried out in order to identify representing CpGs in genomic regions with biologically meaningful differences in methylation between the two cell types. For this, I used the gene methylation selection criteria described in “Methods”.

A total of 442 autosomal probes showed a statistically significant methylation difference higher or equal to 20%, between fibroblasts and cytotrophoblasts (Mann-Whitney U test p value <0.05) (Figure 6.4). In comparison, 315 probes were different, by the same criteria, between placentas and fibroblasts and 61 probes show the same type of difference between placentas and cytotrophoblasts. This is compatible with the Euclidean cluster analysis which demonstrated that placentas and cytotrophoblasts tended to cluster together but not with fibroblasts.

The 442 probes differentially methylated in fibroblasts versus cytotrophoblasts map to 375 autosomal genes; among those, 310 probes (265 genes) were more methylated in fibroblasts and 132 probes (111genes) were more methylated in cytotrophoblasts. One of the genes – TP73 - had probes in both groups; therefore the sum of genes in both groups is more than the total of genes with differentially methylated probes. Appendix 1 provides an annotated list of the genes selected as differentially methylated between cytotrophoblasts and fibroblasts by my criteria (>20% difference in methylation).

Previous reports suggest that tissue-specific differentially methylated regions may be CpG content poor (Rakyan et al., 2008; Sakamoto et al., 2007). Therefore, I looked at the relative proportion of cell type-specific differentially methylated probes on my list, mapping to CpG rich (CpG Island) versus CpG poor promoter regions. Only the probes that were more methylated in fibroblasts than in cytotrophoblasts showed an enrichment for CpG probes located in CpG poor regions relative to the same proportion in the arrays. In this group of probes, 170 (55%) map to CpG poor regions and 141 (45%) map to CpG islands (CpG rich) whereas in the array, the frequency of CpG poor regions is 27% (binomial p value <0.001). In contrast, for probes more methylated in cytotrophoblasts there is no such enrichment, (32 (24%) map to CpG poor regions and 100 to CpG islands, p value = 0.5). The cell type- specific methylation data for placenta suggest that tissue- specific differentially methylated regions may not always occur preferentially in CpG poor regions in human tissues.

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Figure 6.4: Selection criteria of differentially methylated CpG dinucleotides. A) Histogram of DNA percent methylation differences between cytotrophoblasts and fibroblasts for each CpG site; it shows that large methylation differences (more than 20%) between the 2 cell types are rare. B) The scatter plot shows the distribution of differences in percent methylation as a differential score where a value of >13 or < -13 is equal to a p-value of <0.05. The probes with a significant difference in percent methylation (p-value < 0.05) and with methylation differences higher than 20% are shown in black filled dots. Positive differences (higher cytotrophoblast methylation) in the left upper quadrant and negative differences (higher fibroblast methylation) in the right lower quadrant.

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Figure 6.4: Selection criteria of differentially methylated CpG dinucleotides.

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In the list of genes showing a difference in methylation between cytotrophoblasts and fibroblasts I sought to identify genes that were known to be either expressed or repressed in cytotrophoblasts so that the transcriptional regulation of such genes could be assessed as part of the validation of the concept that cell specific epigenetic marks determines their cell specific expression. A lower methylation pattern was found in cytotrophoblasts, as compared to fibroblasts, in CpGs mapping to the promoter regions of several genes coding for the beta subunit of human chorionic gonadotrophin (beta-hCG) – CGB. Placental human chorionic gonadotrophin (hCG) is known to be produced by cytotrophoblasts (Pierce and Parsons, 1981). hCG is a glycoprotein with 2 subunits, alpha and beta. The alpha subunit is encoded by the CGA gene on chromosome 6q14-q21 and it is common to all the members of a family of peptide hormones (LH, FSH, TSH and hCG). The beta subunit is encoded by 4 genes – CGB7, 8, 5 and 3, all located in a 50 kb cluster on chromosome 19q13.3. Two other genes mapping to the same cluster, CGB1 and 2, encode two hypothetical, although not yet identified proteins (Henke and Gromoll, 2008). The Illumina® array has probes targeting CpG sites mapping to the promoter region of CGB1 (2 probes), CGB2 (2 probes), CGB3 (1 probe), CGB5 (2 probes) and CGB8 (1 probe). My selection criteria detected differential hypomethylation in cytotrophoblasts for one of the 2 probed CpGs present in the promoter region of CGB1. Also differentially hypomethylated in cytotrophoblasts were the 2 probed CpGs mapping to CGB5 and the single probes mapping to the CGB8 and the CGB3. In contrast, the 2 probed CpGs mapping to CGB2 were not differentially methylated between the cell types, nor was the probed CpG mapping to the alpha- subunit gene – CGA (Figure 6.5). Since lower promoter methylation is often associated with higher gene expression, these data suggest that CGBs trophoblast-specific expression is at least partially epigenetically regulated by promoter methylation.

I noted the presence of cancer-related genes in my gene list of differentially methylated CpG sites between cytotrophoblasts and fibroblasts. Several cancer-related genes in the array are represented by multiple probes, thus it was not surprising to find a disproportionate number of such probes meeting my selection criteria. In general, the CpGs mapping to the promoter regions of cancer genes encoding proteins with tumor suppressor function seem to be more methylated in the cytotrophoblast group.

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Figure 6.5: Methylation values of the CpG sites mapping to the CGB Genes in Cytotrophoblasts and Fibroblasts. A) CGB genes map to chromosome 19q (middle). At each CpG site there is lower methylation in cytotrophoblasts than in fibroblasts (top). The chromosome band containing the CGB genes is magnified (UCSC genome browser build 36.1 annotation) to illustrate the CpG islands and CpG probes as they relate to the genes (bottom). Six of eight probes show a reduction in methylation, 5 of these reach my stringent cut-off criteria: CGB3 (probe 1), CGB1, CGB5 and CGB8 (probes 5-8). B) Box-plot of the methylation values of CpG sites mapping to the CGB genes. Each of the 8 CpG probes mapping to CGB genes are represented. The methylation level is depicted on the Y-axis. The X-axis shows the CGB gene probes in the order in which they appear in the genome (proximal to distal) but is not to scale. Some genes have 2 probes and are separated by the interrupted vertical lines. The solid horizontal arrows represent the TSS and point in the direction of transcription. Two CpG probes show no difference in methylation (white box) between the cytotrophoblasts (left) and fibroblasts (right). Probes are less methylated in cytotrophoblasts (left) then in fibroblasts (right). Here each of the array probes mapping to the region are represented by a boxplot. The statistically significant different probes (p < 0.05 by Mann- Whitney U test) reaching or passing the 20% cut-off are represented by a dark gray bar, while the clear gray bar corresponds to the probe that has <20% difference in methylation but remains statistically significant.

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Figure 6.5: Methylation values of the CpG sites mapping to the CGB Genes in Cytotrophoblasts and Fibroblasts.

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To explore the possibility that tumor suppressor genes are more methylated in cytotrophoblasts than fibroblasts, I compiled a list of tumor suppressor genes and oncogenes for binomial enrichment analysis of two lists, i.e. higher or lower methylation of cytotrophoblasts versus fibroblasts. The only statistically significant enrichment was for probes mapping to tumor suppressor genes in the probes that are more methylated in cytotrophoblasts than fibroblasts. Of 26493 autosomal probes in the array, 831 probes map to the list of tumor suppressor gene regions (corresponding to 276 genes). Of the 131 probes in the group with higher methylation in cytotrophoblasts than fibroblasts, 16 probes map to 8 tumor suppressor genes (binomial test p value 4.52E-06). Table 6.4 presents an annotated list of the probes and Figure 6.6 shows their methylation levels in cytotrophoblasts and fibroblasts for the genes represented by multiple probes. Two genes, APC (Novakovic et al., 2008; Wong et al., 2008) and RASSF1 (Chiu et al., 2007), have been previously reported to exhibit unique methylation patterns in placentas and in cancer. Others not previously reported (TP73, RASSF5, DAB2IP, PRKCDBP, MORF4L1) are also shown by my data to have probes in the promoter region that are more methylated in cytotrophoblasts than in fibroblasts. In contrast, of the probes with higher methylation in fibroblasts than cytotrophoblasts, only 7 map to tumor suppressor genes. That is, there is no enrichment for these probes. Only one maps to a promoter region, the remainder map to introns. These findings highlight the overlapping biologic characteristics of placental tissue and tumors (Ferretti et al., 2007; Soundararajan and Rao, 2004) and suggest that such characteristics are, at least in part, epigenetically regulated.

6.4.5 Targeted validation of APC, TP73 and CGB5

I used pyrosequencing of bisulfite converted DNA to validate results of the array data for 3 probes selected as follows: The first, APC, maps to the promoter region of a tumor suppressor gene that previously was reported to be hypermethylated in placenta. The second, TP73, maps to the promoter of a putative tumor suppressor gene but has not previously been reported to be hypermethylated in placenta. For one of the CGB genes, CGB5, I chose to validate one of the 2 probes. The pyrosequencing assays measure the methylation of a variable number of CpG sites adjacent to the CpG site represented on the Illumina® arrays (See Table 6.2 and 6.3 for the details of each of the pyrosequencing assays).

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Table 6.4: Probes more methylated in Cytotrophoblasts than Fibroblasts mapping to tumor suppressor genes Difference Difference Probe F C in in Gene CGI Gene Distance position Probe ID Chr Methylation Methylation methylation methylation symbol ID Strand to TSS relative level level between C between Pl to TSS and F and F TP73 cg04391111 1 1 + 1125 Prior 0.53 0.78 0.25 0.20 cg25115460 1 1 + 37981 After 0.39 0.06 -0.33 -0.19 cg03846767 1 1 + 38163 After 0.33 0.07 -0.26 -0.16 cg26208930 1 1 + 38296 After 0.64 0.11 -0.53 -0.19 RASSF5 cg17558126 1 1 + 629 Prior 0.50 0.73 0.22 0.12 RASSF1 cg00777121 3 3 - 176 After 0.60 0.83 0.22 0.17 cg08047457 3 3 - 46 Prior 0.66 0.94 0.28 0.20 cg21554552 3 3 - 58 Prior 0.52 0.88 0.37 0.21 APC cg16970232 5 5 + 151 Prior 0.55 0.76 0.21 0.19 cg20311501 5 5 + 82 Prior 0.46 0.67 0.22 0.21 cg21634602 5 5 + 14 Prior 0.50 0.75 0.25 0.20 cg24332422 5 5 + 102 After 0.43 0.72 0.29 0.25 cg01240931 5 None + 459 After 0.59 0.86 0.27 0.17 DAB2IP cg13060154 9 9 + 349 After 0.55 0.91 0.35 0.20 cg24794433 9 9 + 523 After 0.73 0.96 0.22 0.19 cg08128768 9 9 + 533 After 0.68 0.96 0.28 0.21 PRKCDBP cg05628549 11 11 - 155 Prior 0.42 0.74 0.32 0.20 WT1 cg01693350 11 11 - 4900 After 0.67 0.91 0.24 0.16 MORF4L1 cg03589001 15 15 + 458 Prior 0.60 0.83 0.23 0.13 Genomic locations are based on NCBI genome build 36, Mar. 2006. CGI ID is based on UCSC genome browser. Chr – chromosome; CGI – CpG island; TSS – transcription start site; F – Fibroblasts; C – Cytotrophoblasts; Pl – Placenta.

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Figure 6.6: Methylation values of CpG sites mapping to Tumor Suppressor Genes represented by multiple probes in Cytotrophoblasts and Fibroblasts. For each of the box-plots the Y-axis corresponds to methylation levels and the X-axis are the probes; the solid horizontal arrows indicate the direction of transcription. All the CpG sites are between 2000 bp upstream and 500 bp downstream of the TSS (start point of the arrows). For each gene all the probes corresponding to a CpG Island that has at least one differentially methylated probe between cytotrophoblasts and fibroblasts are shown. Only dark grey boxes correspond to statistical significantly different probes (p value < 0.05 by Mann-Whitney U test). A) The solid vertical lines on the APC gene graph separate probes of two consecutive but distinct CpG islands. Of the 6 APC probes, one shows a non-statistically significant difference higher than 20% (light grey box). B) DAP2IP, C) PRKCDBP and D) RASSF5 have multiple probes that show no difference in methylation (white boxes). E) TP73 shows a non-statistically significant difference higher than 20% (light grey box). F) All 3 probes corresponding to RASSF1 are statistically different (dark grey box).

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Figure 6.6: Methylation values of CpG sites mapping to Tumor Suppressor Genes represented by multiple probes in Cytotrophoblasts and Fibroblasts.

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I assessed the correlation, this time expressed as R2, between the methylation levels measured by the Illumina® arrays and by pyrosequencing, for the same CpG sites. This assessment gives a measure of the accuracy of the array methylation measurement in predicting the methylation value of each CpG as measured by a different method. The R2 for the methylation measure provided by the array and pyrosequencing for the 3 selected CpGs analyzed were 0.84, with a slope of 0.72 for the TP73 CpG site, 0.84, with a slope of 1.05 for the APC CpG site and 0.95, with a slope of 0.5 for the CGB5 CpG site (Figure 6.7, plots column on the left, A).

I also assessed, using the same type of correlation analysis, the accuracy of differences obtained from the array for the methylation of each specific CpG sites in the cytotrophoblast fraction versus the same site in the fibroblast fraction of the same sample. The R2 for the methylation differences between the two cell fractions provided by the array and pyrosequencing for the 3 selected CpGs analyzed were 0.98, with a slope of 0.8 for the TP73 CpG site, 0.94, with a slope of 1.2 for the APC CpG site and 0.83, with a slope of 0.5 for the CGB5 CpG site (Figure 6.7, plots column on the right, B).

These data show a high correlation for the methylation values at 3 different CpG sites as determined by 2 independent methods. However, the fact that the slope is only 0.5 for the CGB5 CpG site, for both assessments – absolute methylation value and differences in methylation between the 2 cell types - evidences an overestimation of the methylation by the Illumina® arrays, in comparison with the pyrosequencing only for this particular site. These differences in the slopes of the curves of best fit suggest variation in the absolute value predictions by the Illumina® arrays, as assessed by pyrosequencing. Variable allelic biases in the PCR amplification efficiency in pyrosequencing or hybridization and extension allelic biases in the Illumina® arrays could explain the discrepancies.

Finally I analyzed the accuracy of the array in determining the difference in methylation level of only one CpG as a predictor for parallel differences in the surrounding regional CpG sites. I assessed the absolute differences in methylation level between cytotrophoblasts and fibroblasts at each CpG site tested by the pyrosequencing assay versus the average of all CpG sites in the region. With the exception of one CpG site in each of the TP73 and APC target regions, all the CpG sites and the averages had a statistically significant difference between the values in the

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Figure 6.7: Pyrosequencing validation of the methylation values measured by the arrays. Validation of the methylation values of 3 CpG sites mapping to the promoter region of the tumor suppressor genes APC and TP73 and of the CGB5 gene, one of the chorionic gonadotrophins beta subunit genes. Here we show how the correlation between the methylation values as measured by the arrays and the methylation values of the same CpG site as measured by the pyrosequencing method. The plots on the left (A) are scatter plot of the methylation values of the 3 probes in the 15 samples (X-axis) against the same values measured by pyrosequencing of bisulfite converted DNA (Y-axis). The R2, the regression slope and the regression formula are included in the graphic. The plots on the right (B) are the same type of graphic and data but it compares the differences in methylation between cytotrophoblasts and fibroblasts calculated from the array data and from the pyrosequencing data.

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Figure 6.7: Pyrosequencing validation of the methylation values measured by the arrays.

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cytotrophoblasts and fibroblasts (p value less than 0.05 by Mann-Whitney U test, n=6 for each group) (Figure 6.8). This shows that the differential methylation identified by the Illumina® array analysis extends beyond the CpG site represented in the array.

This validation of 3 sets of data obtained from the array analysis, by an independent molecular technology, supports the reliability of the microarray method used to screen the genome for significant differences in DNA methylation between cytotrophoblasts and fibroblast cell types in placenta.

6.5 Discussion

My experimental approach demonstrates for the first time, that although most genes present in the 2 main cell types of the placenta (cytotrophoblasts and fibroblasts) exhibit similar promoter methylation patterns, some specific genes show differential promoter methylation. Such differences may in part explain the underlying biologic basis of cell-specific differentiation within the developing human placenta.

Serial enzymatic digestion followed by Ficoll gradient centrifugation and magnetic bead separation was used to maximize the separation of the 2 main proliferating cell types within placental villi in the second trimester, namely villous cytotrophoblasts and mesenchymal fibroblasts. Most investigators use some, but not all, of these steps to obtain villous cytotrophoblast cells for trophoblast differentiation studies (Bloxam et al., 1997). To the best of my knowledge, there are no published reports on the separation of large quantities of fibroblasts from placenta. These cells are more difficult to separate from the extracellular matrix, but they exhibit significant growth potential in culture. Therefore only small amounts are necessary to generate large colonies. I did not culture fibroblasts because cell culture conditions and media could potentially affect epigenetic marks and become a source of bias (Grafodatskaya et al., 2010).

The immunocytochemical (cytokeratin-7/vimentin antibodies) assessment of the remaining samples showed effective purification of villous cytotrophoblasts, but only enrichment of the

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Figure 6.8: Pyrosequencing validation of the methylation differences identified by the array analysis. Three CpG sites mapping to the promoter region of A) CGB5 and the tumor suppressor genes B) TP73 and C) APC were analyzed. For each gene three box-plots are shown. From left to right: the first corresponds to the values for each CpG site as measured in the pyrosequencing assay, the second to the average of the values of all the CpG sites for each pyrosequencing assay and the third plot corresponds to the methylation value measured by the array. The white boxes correspond to non-statistically significantly different CpG sites and the grey boxes to statistically significantly different CpG sites between cytotrophoblast and fibroblast samples. The light grey boxes show the CpG of the pyrosequencing target that corresponds to the one that was targeted by the array.

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Figure 6.8: Pyrosequencing validation of the methylation differences identified by the array analysis.

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fibroblast fraction, despite the addition of a double immunomagnetic bead separation step. Despite the protocol limitations, the exploratory data do demonstrate epigenetic differences between these two cell types. Although the data do not permit precise quantification of the methylation differences between cytotrophoblasts and fibroblasts, they do support the hypothesis of cell-type specific gene regulation by differential DNA methylation.

Genes that demonstrate cell-type specific differences in methylation are likely to exhibit epigenetically regulated cell specific patterns of expression. Genes that demonstrate cell-type specific differences in methylation in our study included hCG genes and tumor suppressor genes. Since hCG is produced mainly in cytotrophoblasts, the lower promoter methylation levels of several genes coding for the hCG beta subunit is consistent with epigenetically regulated cell specific expression.

Moreover, several aspects of early placental development, especially in the extra-villous trophoblast cells, rely on rapid cell proliferation and invasion that is analogous to tumor behavior (Ferretti et al., 2007; Soundararajan and Rao, 2004). Promoter regions of tumor suppressor genes have been found to be methylated in association with reduced expression in tumors (Dokras, 2006; Richter et al., 2009). Some of these same promoter regions have also been reported to be methylated in placenta (Chiu et al., 2007; Lee et al., 2008; Novakovic et al., 2008; Wong et al., 2008). On the other hand, most normal tissues exhibit low methylation in these genomic regions, suggesting that some molecular methylation signatures of tumor suppressor genes are shared by tumors and placenta. If this epigenetic signature is driven by the placental cell type with invasive behavior, it would be expected that methylation of tumor suppressor genes in cytotrophoblasts should be higher than in fibroblasts. In the list of selected probes with higher methylation in cytotrophoblasts, there was enrichment for CpG sites mapping to tumor suppressor gene promoter regions. This data therefore suggest that repression, by promoter methylation, of tumor suppressor genes in cytotrophoblasts, facilitates extensive proliferation of this cell type within immature intermediate villi. Subsequent demethylation of these genes is one candidate pathway by which the third trimester placenta could reduce the proliferative potential of villous cytotrophoblasts near term.

There are several positive outcomes from this research. There was improvement on previous efforts to obtain pure populations of uncultured fibroblast cells. Further a list of genomic regions

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for which it appears there are cell type-specific DNA methylation differences was generated. This list is likely to represent only a subset of such differences. Other differentially methylated probes may have been masked by the suboptimal fibroblast purification. New microarrays with better probe coverage of the genome will certainly extend this list.

In summary, I have analyzed the DNA methylation patterns of cytotrophoblast and fibroblast enriched placental cell fractions for global and specific differences. Although the specific cell types do not demonstrate genome wide differentiation in DNA methylation, I have identified several genomic regions for which the methylation pattern is significantly different, likely representing true biological differences between cytotrophoblasts and fibroblasts. The identification of such differences underscores the importance of epigenetic mechanisms in normal human placental development and provides a useful resource for the interpretation of placental DNA methylation data. Importantly, these data support the use of placental villi for epigenetic studies targeting cytotrophoblast cells. Further, they provide a framework from which to propose hypotheses regarding critical epigenetic modifications that drive normal placental development as well as important epigenetic alterations that could cause placental related diseases such as preeclampsia and intrauterine growth restriction. In severe placental insufficiency syndromes, complicated by severe early-onset preeclampsia or intrauterine growth restriction, histologic evidence of defective differentiation of the extravillous trophoblast or of the chorionic villi (comprising stroma, endothelium and a covering of villous trophoblast) has been found (Kingdom et al., 2000). Defective epigenetic regulation affecting the differentiation of these different cell types may be a unifying mechanism that could illuminate currently unresolved questions about placental development (Aplin, 2010).

The normal function of these tissues likely depends not only on the correct epigenetic profiles but also the normal proportion of the different cell types carrying each profile. Variations in the cell-specific profiles or the proportions of specific cell types among samples of the same tissue could result in abnormal development and /or function of this tissue. I undertook a study of cell- specific methylation profiles in the placenta with the expectation that these data could provide a framework for understanding epigenetic regulation and cell-type specificity in the normal placentas. Such data is expected to generate hypothesis-driven research into the epigenetic basis of normal placental development across gestation as well as the role of epigenetic alteration in diseases of the placenta and the fetus.

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The published version of this chapter appears in:

Ferreira JC, Choufani S, Grafodatskaya D, Butcher DT, Zhao C, Chitayat D, Shuman C, Kingdom J, Keating S and Weksberg R. WNT2 promoter methylation in human placenta is associated with low birthweight percentile in the neonate. Epigenetics 2011; 6: 440-449

(Ferreira et al., 2011)..

The chapter that follows is a modified and expanded version.

Dr. Sarah Keating, pathologist at the Pathology Department of Mount Sinai Hospital, provided the pathology images here presented.

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7.1 Summary

I have proposed that fetal growth potential could be negatively impacted by the epigenetic dysregulation of specific genes in the placenta. Evidence from animal models and human disease had suggested that imprinted genes were likely candidates for epigenetic dysregulation at their imprinting control centers. I have screened for DNA methylation changes in imprinting control centers in association with a growth restriction phenotype secondary to placenta insufficiency and did not identify evidence supporting the hypothesis of major changes in discreet loci. Instead it seemed that multiple small effect variations in levels of methylation at several loci were associated with the phenotype assessed. I thus expanded the screening of epigenetic dysregulation to other regions of the genome. Using methyl DNA immunoprecipitation coupled with Agilent® CpG island microarrays, I analyzed the differences in DNA methylation between placentas of 8 intrauterine growth restricted newborns (birthweight lower than the 10th percentile and with lesions known to be associated with low birthweight percentile (Table 3.2)) and 8 controls (birthweight above the 10th percentile and without those placental lesions). I identified several candidate genomic regions with differential DNA methylation between the 2 groups. The DNA methylation differences identified in the promoter of the WNT2 gene were prioritized for further study in an extended cohort of 170 samples given the important function of this gene in mouse placental development and its high expression in human placenta. A variant demonstrating increased WNT2 promoter methylation (high WNT2PrMe) was found only in placental tissue and not in the cord blood of the fetus. It was significantly associated with reduced WNT2 expression in placenta and with low birthweight percentile in the neonate. These results show that WNT2 expression can be epigenetically downregulated in the placenta by DNA methylation of its promoter and that high WNT2PrMe is an epigenetic variant that is associated with reduced fetal growth potential.

7.2 Introduction

Low birthweight in general and low birthweight resulting from non-fetal factors interfering with the growth potential of the fetus in particular are known risk factors for perinatal and late onset poor outcomes, disease and mortality (Barker, 2004, 2006; Curhan et al., 1996a; Curhan et al., 1996b; Frankel et al., 1996; Geva et al., 2006; Hack et al., 2003; Jelliffe-Pawlowski and Hansen,

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2004; Lithell et al., 1996; Manning and Vehaskari, 2001; Pallotto and Kilbride, 2006; Rich- Edwards et al., 1999; Rich-Edwards et al., 1997; Strauss, 2000; Tamakoshi et al., 2006; Viggedal et al., 2004). These outcomes associated with low birth weight make it critically important to understand the molecular mechanisms involved in loss of fetal growth potential. Although low birth weight percentile is known to be etiologically heterogeneous, the molecular determinants have not been fully elucidated.

Studies in transgenic mice have identified a number of genes for which targeted disruption leads to placental dysfunction and also to poor fetal development and/or perinatal death. (Sapin et al., 2001).

In humans, a number of genome–wide analyses have identified gene expression changes in human genes associated with abnormal placental development and/or reduced fetal growth potential (Lee et al., 2010; Lian et al., 2010; McCarthy et al., 2007; McMinn et al., 2006; Okamoto et al., 2006; Roh et al., 2005; Sitras et al., 2009; Struwe et al., 2010; Toft et al., 2008). However, the causes for these expression differences in the placenta have not been investigated. Gene expression is controlled by a variety of mechanisms, including nucleotide sequence / functional single nucleotide polymorphisms (inherited or germinally/somatically acquired) (Pastinen, 2010; Wang et al., 2005), regulatory proteins and epigenetic mechanisms.

I propose that some of these expression differences result from genetic and/or environmental factors (Pastinen, 2010; Wang et al., 2005) that are modulated by epigenetic regulation to impact fetal growth potential. Specifically, I hypothesize that epigenetic dysregulation of specific genes in the placenta could be a common mechanism impacting fetal growth potential.

Epigenetic marks participate in normal developmental programming in a tissue- and developmental-time specific manner and, as referred in 1.6, they can be modulated by environmental factors such as nutrition and drugs. Studying the epigenetic variation in the development of the placenta may provide important insights into the molecular basis of fetal growth dysregulation.

Most of the evidence supporting this hypothesis, from animal models and human disease, relates to reported epigenetic changes affecting imprinting of genes and resulting in abnormal growth phenotypes (Bartholdi et al., 2009; Gicquel et al., 2005; Guo et al., 2008; Horike et al., 2009; Li

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et al., 1992; Shiura et al., 2009). However, my study of DNA methylation aberration in imprinting centers in association with growth restriction did not provide evidence supporting a high effect of this type of epigenetic anomaly on fetal growth. Therefore I decided to extend my screening for DNA methylation aberration to other portions of the genome.

In vertebrates, methylation commonly occurs in cytosines in CpG dinucleotides (Bird, 2002). In 98% of the genome CpG dinucleotides are rare and in the remaining 2% of the genome they are found in selectively conserved clusters called CpG islands (CGI). CGIs, range from 200 bp to several kb in length and often occur at the 5’ ends of genes (promoters) where they are involved in transcriptional regulation. The majority of CpG dinucleotides located in promoters, whether part of CGI or not, are unmethylated (Costello and Plass, 2001). Methylation of promoter CpG sites is most often associated with gene silencing, i.e. a decrease in the expression of the gene (Costello and Vertino, 2002).

In this study I followed the method generally described in 3.1. I first undertook a screening for DNA methylation alterations that negatively impact placental development/function and result in poor fetal growth. Using an antibody enrichment microarray assay, I screened for differences in DNA methylation in CGI between placentas of 8 intrauterine growth restricted newborns (IUGR) and 8 controls as defined in 3.14. Twenty one candidate differentially methylated regions were identified between the cases and controls. Based on the methylation data and a review of targeted disruption of genes in these genomic regions in mouse models, the candidate genomic region overlapping the promoter of WNT2 (wingless-type MMTV integration site family member 2) was prioritized for further study. In an expanded cohort of 170 neonates, WNT2 promoter methylation and expression were assayed by targeted pyrosequencing of bisulfite-modified DNA and by qRT-PCR of cDNA, respectively. A significant association was found between WNT2 promoter methylation (WNT2PrMe) and reduced WNT2 gene expression in the placenta. Moreover, a significant association was also demonstrated for WNT2PrMe and low birthweight percentile. In summary, my results show that WNT2 expression can be epigenetically downregulated in the placenta by DNA methylation of its promoter and, notably, high WNT2PrMe is a common epigenetic variant in placenta associated with an increased risk for low birthweight percentile in the neonate.

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7.3 Materials and Methods

7.3.1 Sample selection, characterization and processing

The placenta samples used in this study were obtained, prepared and characterized as described in section 3.2. Exclusion criteria were the same used in the selection of patients for sampling as explained in Table 3.1. DNA and RNA extraction was done using the Allprep DNA/RNA (Qiagen) allowing for the extraction of DNA and RNA from the same sample, as described in section 3.4. DNA and RNA sample assessment was done as described in section 3.5 and RNA samples were prepared for RT-PCR as described in section 3.6.

Cord blood DNA was also assessed for DNA methylation in a targeted assay. DNA was extracted from umbilical cord blood samples matched to placenta samples -7 with high WNT2 promoter methylation and 7 with low WNT2 promoter methylation - using a phenol–chloroform method as described in section 3.3.

For the array screening, a case control study design was used. The samples (a subset of the samples used in the array study described in Chapter 5) were characterized according to two of the ascertained variables: birthweight percentile (here treated as a 2 categories variable) and presence or absence of placental lesions. Eight samples collected from IUGR newborns (birthweight percentile less than or equal to the 10th percentile and with placental lesions known to be associated with poor fetal growth (Table 3.2)) were selected as cases, and eight samples collected from newborns with birthweight percentile above the 10th percentile and without placental lesions, were selected as controls. For the array study, preeclampsia was an additional exclusion criterion.

For the study of the extended cohort of 170 samples, I used the same samples used in the H19 DMR study described in Chapter 5. These samples originated from placentas of neonates with all ranges of birthweight percentiles, both sexes, preterm and term, with or without placental lesions, with or without preeclampsia, with or without labor. They included the samples used in the array study. These samples were not categorized as cases and controls. Associations were assessed between WNT2 promoter methylation and the variable of interest – birthweight percentile (here treated as a quantitative variable) – and the potential confounding factors that demonstrated an association with the main outcome variable.

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7.3.2 DNA methylation enrichment and microarray hybridization

I used a Methylated DNA Immunoprecipitation (MeDIP) method to enrich for the methylated fraction of the genomic DNA. The resulting enriched fraction and the untreated control are differentially fluorescent color labeled and hybridized to the Human 244K Agilent® CGI microarray (Agilent®, Santa Clara, CA). Details of this technique, of the arrays and its hybridization are provided in section 3.8. CpG island (CGI) methylation was evaluated in the 8 cases and 8 control placentas, defined as described above.

7.3.3 CGI microarray data analysis

Also as detailed in section 3.8, microarray images were quantified by Agilent® Feature Extraction software (v9.0) (Santa Clara, CA), using the Human Agilent® CpG version 20070820 grid file and CGH V4-91 protocol. The quantification files were imported into Partek® software, version 6.3 Copyright © 2008 Partek Inc., St. Louis, MO, USA. The raw background corrected

data (as per the Feature Extraction software protocol) was converted into the log2 of the ratios between the methylated enriched fraction and the non-enriched, input, DNA fraction and quantile normalized. The method used for the selection of differentially methylated regions between the cases and controls was based on fold change throughout a genomic window defined by a number of probes as previously described (Grafodatskaya et al., 2010). The Hidden Markov Model parameters used - set to analyze a 2.8 fold change of groups of 5 probes (approximately corresponding to 500 to 600 bp) - were defined based on the ability to identify a known

methylation difference in the H19 promoter and IGF2 DMR2 between blood and placenta (Guo et al., 2008). Sex chromosome data was excluded from the analysis to eliminate the effect of the differences in methylation of X chromosomes between males and females.

Statistical tests of the differences in methylation, although calculated for each probe, were not used for this selection. The sample size was too small to have power to detect small effect sizes as I was expecting.

7.3.4 Pyrosequencing of WNT2 in the placenta and blood

To measure the methylation of the prioritized candidate genomic region - WNT2 promoter - in placenta and blood DNA I developed a pyrosequencing assay of sodium bisulfite converted DNA (Byun et al., 2007). Details of the bisulfite conversion of DNA are provided in section 3.7 and of

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the assay design and pyrosequencing technique in section 3.12. For the assay design I targeted the region that showed differences in methylation in the array analysis. The final assay target corresponds to Chr. 7: 116,750,697 – 116,750,747, NCBI36/hg18, Mar. 2006). The PCR and sequencing primers are presented in Table 7.1. The conditions for the PCR prior to the pyrosequencing are provided in Table 7.2. The relative location of the region on the microarray and the pyrosequencing analysis are shown in Figure 7.1A. For each sample the methylation levels of 5 CpG sites were verified and the result given for each sample is the average of the 5 sites. Two representative pyrograms for two different samples, one considered as having a low methylation level and one considered as having a high methylation level are shown in Figure 7.1B.

7.3.5 Quantitative real-time RT-PCR

To verify the biological effect of the methylation changes found in WNT2 promoter, I analyzed the effect of WNT2 promoter methylation on expression by measuring WNT2 mRNA transcription levels in each sample by quantitative real time reverse transcription PCR. RNA was extracted, assessed and converted to cDNA as detailed in sections 3.4, 3.5 and 3.6 respectively. Details of quantitative real-time RT-PCR are provided in section 3.13. I used the primer sets detailed in Table 7.1. Expression of each gene was determined using the standard curve method (Rutledge and Cote, 2003) and normalized by the expression of the IF2B housekeeping gene (details of the quantitation are also provided in section 3.13).

7.3.6 Statistical analyses of the results for the extended cohort

With one exception pointed out in the text, WNT2 promoter methylation in placenta, having a bimodal distribution (see below in “Results”), was analyzed as a categorical variable, either as High (High WNT2ProMe) if the methylation level was >40%, determined by the lowest outlier value or as Low (Low WNT2ProMe). WNT2 expression was treated as a log transformed quantitative variable, for correction of the skewing. Gestational age was treated as a quantitative variable and sex, presence or absence of placental lesion, ethnicity and presence or absence of labor, were treated as categorical variables. Birthweight percentile here was treated as a quantitative variable. Given the sampling strategy its distribution was skewed and thus, non- parametric testing – Mann-Whitney test, Kruskall-Wallis test and Spearman correlation - were used according to the type of independent variables. Glass’s  and R correlation were used as

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Table 7.1: Oligonucleotide primers for WNT2 expression and methylation studies qRT-PCR of WNT2 Forward PCR primer TCGGTGGAATCTGGCTCTG Reverse PCR primer GGCACGCATCACATCTGG Primers used for the qRT-PCR of IF2B (normalizing gene) Forward PCR primer TGGAGTTGGGATGTGGAAGTG Reverse PCR primer CTGCCGGGCCTGCTTAG Bisulfite converted pyrosequencing of WNT2 promoter region – Chr.7:116,750,697- 116,750,747 (50 bp), which includes 5 CpG sites – PCR amplicon: 92 Forward PCR primer ATGTTTGGGGATGAGGTGA Reverse PCR primer (Biotinylated) CCCTAAAACTTCCCTAAAAATTCCT Sequencing primer (Forward) TTGGGGATGAGGTGA Biotinylated sequence (attached to the 5’ end of the biotinylated CGCCAGGGTTTTCCCAGTCACGAC primers of all assays)

Table 7.2: PCR conditions for WNT2 promoter pyrosequencing Assay name Step down phase Fixed phase 95ºC 30 sec 95ºC 30 sec WNT2 60 to 55ºC in 0.5ºC steps 30 sec 10 cycles 55ºC 30 sec 35 cycles 72ºC 30 sec 72ºC 30 sec

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Figure 7.1: CGI microarray and pyrosequencing data for the WNT2 promoter of two case samples one with high and one with low promoter methylation. A) The purpose of this graph is to illustrate the target of the pyrosequencing assay. It shows representative data for high methylation promoter (top) and low methylation (bottom) of the

WNT2 promoter. Data is shown as log2 of the ratio methylated DNA/total DNA of each of the array probes covering the region. Each vertical bar corresponds to a probe. The length of the bar corresponds to the log2 ratios, whose values are in the Y-axis. The data presented is a screenshot from UCSC genome browser. The horizontal bars at the bottom of the figure shows, from top to bottom the region targeted by the pyrosequencing assay, the location of the first exon and intron of the WNT2 gene as well as the CpG island targeted by this group of array probes. B) Pyrosequencing data of the samples shown in A. The shadowed area corresponds to a CG dinucleotide. The ratio between C and the C+T give the absolute methylation level for each cytosine in a CpG.

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Figure 7.1: CGI microarray and pyrosequencing data for the WNT2 promoter of two case samples one with high and one with low promoter methylation.

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measures of association. The analyses performed and the statistical tests used are described with the results. All the statistical analyses were performed with SPSS (version 16.0, 2007).

7.4 Results

7.4.1 Differential methylation screening between cases and controls

I first generated DNA methylation profiles in human placenta from 8 IUGR neonates and 8 controls. In this first step, I performed genome-wide microarray-based comparisons of CpG methylation from 5-Methyl DNA Immunoprecipitated (MeDIP) samples hybridized on the 244K human Agilent® CpG island array.

Details of each of the samples used in this study are provided in Table 3.3 (identifiable by “7” under the heading “Methyl Agilent”) and a descriptive summary is provided in Table 3.4. There are no statistical significant differences between the 2 groups for gestational age, sex and presence or absence of labor.

Overall levels of genome-wide methylation were not significantly different between cases and controls. There was a high degree of correlation among all the placental samples - 0.90 to 0.97 - suggesting that the overall methylation patterns are consistent across all placentas. In contrast, when correlating the placental array results with array results from other tissues (kindly provided by Dr. Grafodatskaya, a collaborator from the lab), there were much lower correlations between placenta and blood – 0.68 to 0.87 – demonstrating a tissue-specific methylation profile for placental tissue in comparison to other tissues (Figure 7.2). Euclidean cluster analysis also showed no obvious clustering by sample type (cases versus controls), even after batch correction (details provided in section 8.3.1) (Figure 7.3).

Differential methylation analysis between cases and controls identified 21 candidate differentially methylated regions. These candidate regions were distributed across the genome and some were associated with genes having diverse functions. Table 7.3 summarizes the features of these candidate differentially methylated regions including gene function, size of the

methylation difference (log2 ratio fold change), its position relative to the transcription start site

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Figure 7.2: Correlations between placenta samples, between blood samples and between placenta and blood samples. Correlations between each placenta array (cases and controls) between blood arrays, and between these two different tissues. It shows that within each tissue the correlations are high (>0.9) and that there is no higher correlation within cases and controls than between them.

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Figure 7.2: Correlations between placenta samples, between blood samples and between placenta and blood samples.

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Figure 7.3: Euclidean non-hierarchical cluster analysis of the arrays. Cluster analysis by batch and sample type, prior (A) and after (B) batch correction, using an ANOVA based method. It shows that there is some clustering by batch and that, after correction for this batch bias effect, there are 3 main clusters, with imperfect clustering by sample type.

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Figure 7.3: Euclidean non-hierarchical cluster analysis.

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Table 7.3: Autosomal genomic regions differentially methylated between IUGR cases and controls

Fold change # Direction Overlapping Placenta Highest Chr. Start Length Location Function region probes of change Gene expression expression average overlaps with 5'UTR, 1st Could be involved in cardiac chr1 1.5 5 79244815 387 + ELTD1 Median Smooth muscle exon and 1st intron development Mediates physiological effects chr11 1.6 5 100505449 287 + PGR contained within 5' UTR Median Uterus of progesterone, Target activity of protein contained within the 17th NBEA Median Brain kinase A to specific intron subcellular sites chr13 2.3 9 34947548 666 + Eye and cerebellum Contained within the only MAB2L1 Median Brain development, role in exon psychiatric disorders contained within 5'UTR, 1st Development of nervous chr13 1.6 7 105984465 564 + EFNB2 < Median Brain exon and 1st intron system and erythropoiesis nd OTX2 isoform contained within 2 intron Member of the bicoid sub- of one of the isoforms, and family of homeodomain- chr14 2.0 8 56344294 712 + Median in the promoter of the other containing transcription OTX2 isoform isoform factors Anti-apoptotic agent; chr15 1.6 8 97212914 596 + IGF1R contained within 2nd intron > Median Prostate enhances cell survival located ~35kb upstream - chr15 1.6 8 96908827 585 + FLJ39743 hypothetical protein Unknown function

LOC283777 2 CpG islands; 1st overlaps CCDC11 Median Testicle Unknown function with 5'UTC and 1st exon chr18 -1.6 10 46046680 2115 - Repress transcription from MBD1 2nd located inside last intron > Median lymphocytes methylated gene promoters overlaps with promoter and chr19 1.5 5 56766094 334 + ZNF175 Median Leukemia cells Zinc finger protein first exon overlaps with promoter, Lymphocytes and chr2 1.9 7 19965047 507 + TTC32 5'UTR, and 1st exon of Median Unknown function endothelial cells TTC32

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Fold change # Direction Overlapping Placenta Highest Chr. Start Length Location Function region probes of change Gene expression expression average overlaps with promoter, chr2 1.5 10 210797606 821 + ACADL 5'UTR, 1st exon and 1st Median Lipid metabolism

intron Oligodendrocyte transcription chr21 1.6 9 33365786 620 + OLIG1 contained within 5'UTR Median Brain factor located in the promoter Lymphoblasts and chr5 1.5 9 32745609 698 + NPR3 Median Natriuretic peptide C region kidney contained within the Unknown function, have chr6 2.6 10 30289444 810 + TRIM26 Median promoter DNA binding motif overlapping promoter, 1st chr6 1.8 8 28475150 536 + ZSCAN12 Median Zinc finger protein exon and 1st intron contained within 1st exon Transcriptional activator chr6 1.6 15 133604502 1182 + EYA4 Median and 1st intron important for organ of Corti overlaps junction of 1st chr6 1.4 6 26609682 418 + BTN1A1 > Median Cell surface receptor function intron and 1st exon contained within 5'UTR, 1st Role in promoting chr6 -1.6 20 5949076 3338 - NRN1 > Median Brain exon and 1st intron neuritogenesis Regulates cell fate and overlaps promoter, 5'UTR, chr7 1.9 12 116749999 965 + WNT2 > Median Placenta patterning during 1st exon and 1st intron embryogenesis chr8 1.5 7 21923631 557 + XPO7 located ~5kb upstream < Median Nuclear transport receptor Involved in transcriptional chr8 1.4 8 71109358 535 + PRDM14 located ~17 kb upstream Median regulation Start and length indicates start and length of region with change in methylation. Direction of change shows gain (+) or loss (-) of methylation in cases compared to controls. # probes is showing the number of consecutive probes with the change of methylation. Over lapping genes column shows the genes present within 1000 bp of the detected region.

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of the closest gene, the number of probes (genomic length) involved in the difference and the expression level of the gene in human placenta. I prioritized the candidate genomic region associated with the WNT2 gene for further validation based on an assessment of the candidates by a series of selection criteria. The WNT2 associated region had a high average fold change (1.9 X) across a large number of probes (12 probes), and the genomic position of the probes demonstrating methylation variation was near the transcription start site (promoter region) of the gene (Chr. 7: 116,749,973 – 116,751,336, NCBI36/hg18, Mar. 2006) (Figure 7.1A).

Visualization of the log2 ratios in the UCSC genome browser showed higher methylation in a subset of 3 of the cases and in none of the controls (see Figure 7.4 for the UCSC genome browser plots of this region across all samples). Further, as annotated in expression databases, the highest level of WNT2 expression in humans is found in the placenta (http://biogps.gnf.org/#goto=welcome) (Su et al., 2004; Wu et al., 2009) (Figure 7.5).

Finally, and very importantly, Wnt2 is known to be critical for vascular patterning of the mammalian placenta (Monkley et al., 1996). Mice carrying an engineered mutation of the Wnt2 gene present with alterations in the size and structure of the placenta and perinatal death occurs in 50% of the null homozygotes (Monkley et al., 1996). Since none of the other 20 candidate regions considered fulfilled my selection criteria to the same extent as WNT2, I deemed this gene to be the most attractive candidate for further validation.

7.4.2 Validation of WNT2 promoter methylation in microarrays by pyrosequencing

In order to validate the differential methylation of the WNT2 promoter in controls versus cases I developed a pyrosequencing assay. This assay was designed to measure DNA methylation levels of the promoter region of WNT2 in a genomic region overlapping the candidate CpG island identified by microarray. Bisulfite converted genomic DNA obtained from placental biopsies of the same case and control samples were tested for methylation changes at 5 CpG sites partially overlapping one of the array probes (Chr. 7: 116,750,697 – 116,750,747, NCBI36/hg18, Mar. 2006) found to be differentially methylated between cases and controls. The data from the pyrosequencing assay (exemplified in Figure 7.1B) validated the methylation values from the arrays with a Pearson correlation (r) value of 0.95 and essentially discriminated the same set of case samples with increased methylation at the promoter of WNT2 as the arrays (Figure 7.6).

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Figure 7.4: UCSC bar plots of all cases and controls for each of the probes mapping to the CpG Island overlapping WNT2. The data presented is, as in Figure 7.1, a screenshot from UCSC genome browser. It shows the

log2 of the ratio methylated DNA/total DNA of each of the array probes covering the CpG Island overlapping WNT2 for each of the samples used in the array. Each plot corresponds to a sample. On the left, in green are the plots of the controls and, on the right, in red, each plot corresponds to the cases. Each vertical bar corresponds to a probe. The length of the bar corresponds to the log2 ratios, whose values are in the Y-axis. The 3 case samples with high WNT2 promoter methylation are signed by arrows.

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Figure 7.4: UCSC bar plots of all cases and controls for each of the probes mapping to the CpG Island overlapping WNT2.

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Figure 7.5: Expression of WNT2 across tissues. Plot obtained from the BioGPS corresponding to a gene expression atlas based on HG_U133A/GNF1H and GNF1M Gene Atlas Data sets. (see text for reference). It shows that placenta has the highest expression for WNT2.

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Figure 7.5: Expression of WNT2 across tissues.

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Figure 7.6: Comparison of array and pyrosequencing data. The left plot corresponds to the composite box and dot plot of one of the array probes mapping to the WNT2 pyrosequencing assay target. The right plot corresponds to the same type of plot for the pyrosequencing assay results. Note that each plot was done with the assistance of different software (Partek® for the array data, SPSS® and SYSTAT®, for the pyrosequencing data).

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Figure 7.6: Comparison of array and pyrosequencing data.

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7.4.3 WNT2 promoter methylation and WNT2 expression in the placenta

I predicted that methylation of the promoter of the WNT2 gene would be associated with reduced WNT2 expression and possibly with reduced fetal growth. Thus, to better understand the biological role of WNT2PrMe in placenta, I explored several aspects of this finding including its frequency, effect on expression, placental distribution and organ specificity, as well as its clinical significance that is, association with birthweight percentile. I tested 170 samples for WNT2PrMe and WNT2 expression. Table 3.6 provides details of this sample cohort. From the listed potential confounding variables assessed in this cohort for an association with birthweight percentile (sex, gestational age, ethnicity presence of labor, preeclampsia and presence or absence of placental lesions), only the last one showed an association with birthweight percentile.

To assess the frequency of WNT2PrMe in the larger cohort of placental samples, I performed pyrosequencing on the bisulfite converted DNA obtained from these 170 placental samples and observed a bimodal distribution of DNA methylation (Figure 7.7). Thirty five samples (21%) were found to have WNT2PrMe values in a distinct, outlier, range of the distribution of values across all samples (i.e. methylation index (MI) 40-58%). Samples with this epigenetic variant were classified as High WNT2PrMe; the remaining 135, with MI=3-24% were classified as Low WNT2PrMe.

I then tested whether high WNT2PrMe was associated with reduced expression of WNT2. I performed quantitative real time PCR on cDNA from 164 of the samples. A statistically significant difference was found between the levels of WNT2 expression (here presented in a log transformed format, as explained in Methods and demonstrated in Figure 7.8C) in the high (mean=-0.83, n=35) versus low (mean=-0.51, n=135) WNT2PrMe samples (two sided t-test p value of log transformed expression data, p<0.0005), with an effect size, measured by Glass’s , of 0.78 (Figure 7.8A). Non-parametric test analysis of the non-logged transformed data using Mann-Whitney U test gave the same result (p<0.0005). Notably, when WNT2PrMe is low, there is a wide range of expression whereas with high WNT2PrMe the range of expression is much lower (Figure 7.8B).

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Figure 7.7: Distribution of WNT2 promoter methylation analyzed by pyrosequencing. One hundred seventy placenta samples were analyzed showing a bimodal distribution (the two modes are separated by the thick vertical lines). Methylation levels of more than 40% (high WNT2PrMe) were found in 21% of the samples. Methylation levels in the remaining 79% of the samples were less than 24% (low WNT2PrMe). None of the samples had methylation levels between 24 and 39%.

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Figure 7.7: Distribution of WNT2 promoter methylation analyzed by pyrosequencing.

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Figure 7.8: Correlation between methylation of WNT2 promoter methylation and expression. A) Bar graph demonstrating the correlation between DNA methylation (X-axis) and log transformed expression values of WNT2 (Y-axis) determined by RT-PCR demonstrating a significant difference (p<0.001) in expression between high and low WNT2 promoter methylation samples. Error bars show 95% confidence intervals (CI) of the means. Chorionamnionitis samples were excluded from the expression studies. B) Scatter plot of the non-log expression qRT-PCR readings demonstrating a tighter distribution of the lower expression values when WNT2Pr is methylated. C) Histogram of the expression showing the results of the log transformation of the expression values on the distribution.

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Figure 7.8: Correlation between methylation of WNT2 promoter methylation and expression.

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7.4.4 WNT2 promoter methylation and expression in multiple samples from the same placenta

I also considered whether the variation in WNT2PrMe could be caused by differences in cell type content of each placental sample. This explanation is unlikely for several reasons:

First, in the preparation of the samples prior to DNA and RNA extraction, I enriched for a specific portion of the placenta – specifically the most distal villi – as detailed in 3.2, making them as homogeneous as possible and thus limiting the possibility of sample heterogeneity with respect to content. That is, I removed the large proximal fibroblast-rich stem villi (Figure 3.2).

Another possible explanation of the epigenetic variation of WNT2PrMe in placenta that we considered was variation in cell composition in the distal villi among patients. That is, there could be a difference in cell composition of the distal villi and not a difference in WNT2PrMe associated with low birthweight percentile. However the absence of an obvious difference in cell content, in a qualitative microscopic blind evaluation of 3 control samples with high and 3 with low WNT2PrMe, all of the same gestational age – 38 weeks - makes this explanation for the findings less likely (Figure 7.9).

Still, to further address this issue I determined whether the changes in WNT2PrMe were focal or generalized in the placenta. I selected 29 samples to be tested for methylation and expression at two independently sampled placental sites. The correlation between the two sampled sites (measured by Pearson R) was high for methylation, here treated as a quantitative variable (0.92), and lower for expression (0.53) (Figure 7.10). Since multiple sampling across a single placenta usually produced uniform results with respect to WNT2PrMe the data suggest that this epigenetic variation in placenta is usually an early event in placental development resulting in a generalized, rather than a focal, distribution of WNT2PrMe.

Furthermore, the higher correlation in methylation than expression between the samples from different sites of the same placenta suggests that WNT2PrMe is a more tightly regulated and/or more stable biological phenomenon than expression. Expression is more likely to have other locally acting and more dynamic regulating factors.

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Figure 7.9: Correlation between two sites from the same placenta for WNT2 methylation and gene expression. A) Methylation levels in the WNT2 promoter are similar between two sites in the same placenta. B) The expression of WNT2 is more variable between the two sites of the placenta, similar to the variation in expression seen in all of the samples. Both graphs show least squares regression line and 95% confidence intervals for the individual predicted Y values from the regression line. R square values are the square of the Pearson correlations referred to in the text. It shows a much higher correlation for the methylation than for the expression values.

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Figure 7.9: Correlation between two sites from the same placenta for WNT2 methylation and gene expression.

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Figure 7.10: Comparison of term pathology samples from high and low WNT2 promoter methylation. Pathology images of 6 samples all from 38 weeks gestational age placenta samples. For each sample there are 2 images, 10x and 25x amplification. The images from the top row correspond to 3 samples with low WNT2PrMe and the ones at the bottom row correspond to 3 samples with high WNT2PrMe. By visual inspection, according to the pathologist, there are no obvious differences in cell content proportion among any of the samples.

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Figure 7.10: Comparison of term pathology samples from high and low WNT2 promoter methylation.

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7.4.5 WNT2 promoter methylation in cord blood

In order to define the range of tissues affected by the epigenetic variant identified in the WNT2 promoter of a subset of placental samples, I tested the methylation of the WNT2 promoter for 14 neonates in their respective cord blood samples (7 high WNT2PrMe and 7 low WNT2PrMe in placenta) using bisulfite modified DNA pyrosequencing. I compared the values in the blood with the respective placenta values and with all placenta values. All blood samples showed low methylation of the WNT2 promoter. The methylation values were also, on average, lower in blood than in placenta (median MI was 4%, with a range of 3 to 10 in blood and 11%, with a range of 3 to 24 in the low WNT2 promoter methylation samples in placenta). There was no correlation between WNT2PrMe in the placenta and the blood (Pearson R=-0.26 with p=0.36) (Figure 7.11). These data suggest that WNT2PrMe is most likely a placenta-specific epigenetic variant. In spite of the lower promoter methylation in blood, expression of WNT2 in blood is lower than in placenta (http://biogps.gnf.org) (Su et al., 2004; Wu et al., 2009) suggesting that specific cellular factors required for WNT2 gene expression can be accessed in placenta but not in blood.

7.4.6 Association of WNT2 promoter methylation with birthweight percentile

I demonstrated that high WNT2PrMe in the placenta is associated with reduced expression of WNT2. Furthermore, a targeted disruption of Wnt2 in a murine model demonstrates a phenotype suggestive of placental compromise and loss of fetal growth potential (Monkley et al., 1996).

Therefore I tested the hypothesis that high WNT2PrMe could be a risk factor for low birthweight percentile by testing for an association between high WNT2PrMe and low birthweight percentile. Here it is reported for the first time that high WNT2PrMe is significantly associated with low birthweight percentile (median birthweight percentile in high WNT2PrMe is 10, n=35, versus 24 in low WNT2PrMe, n=135, Mann-Whitney test p value = 0.03) (Figure 7.12). This association showed a small effect size (Rs=-0.17), as expected for a complex trait. I excluded the association of high WNT2PrMe and the presence of placental lesions, a potential confounding factor given its association with birthweight percentile (Table 3.6) – Chi-square p value 0.8.

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Figure 7.11: High WNT2 promoter methylation occurs in the placenta but not in cord blood. A) Scatter plot comparing WNT2PrMe from 7 placenta DNA samples with their matching blood DNA samples. The blood DNA samples corresponding to the high WNT2PrMe in placenta DNA are not different from the ones corresponding to low placenta DNA WNT2PrMe. B) Box plot comparing WNT2PrMe (Y-axis) of 14 cord blood DNA samples with the 170 placenta DNA samples (X-axis). The blood DNA samples have a lower and less variable WNT2PrMe than the placenta DNA.

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Figure 7.11: High WNT2 promoter methylation occurs in the placenta but not in cord blood.

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Figure 7.12: Association between high WNT2 promoter methylation and reduced birthweight percentile. A) Box plot comparing low and high WNT2 promoter methylation (X-axis) to birthweight percentile (Y-axis). B) Histogram of birthweight percentiles showing skewing.

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Figure 7.12: Association between High WNT2 promoter methylation and reduced birthweight percentile.

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In spite of the association of high WNT2PrMe with low birthweight percentile and with low expression, an association between low WNT2 expression and low birthweight percentile was not demonstrated (Spearman correlation p value = 0.094) (Figure 7.13).

I also tested for an association between WNT2PrMe and other potential confounding factors such as gestational age, sex, presence of labor and ethnicity. Unexpectedly, I also found a significant association between WNT2PrMe and newborn sex (high WNT2PrMe frequency in females is 28%, n=76, versus 15% in males, n=94, Chi-square test p value = 0.037) (Figure 7.14). However, the absence of an association between newborn sex and birthweight percentile (Table 3.6) excluded sex as a confounding factor driving the association between high WNT2PrMe and low birthweight percentile. This association also showed a small effect size (OR, 2.18, 95% CI - 1.02, 4.66). No other association was statistically significant.

7.5 Discussion

Here it is reported the first demonstration of an early, placenta- specific DNA methylation mark in the promoter of the WNT2 gene in association with reduced growth potential of the fetus. I identified a statistically significant association between high WNT2 promoter methylation and low birthweight percentile. These data support my hypothesis that epigenetic alterations of placental genes can impact fetal growth.

The WNT2 gene belongs to a large family of several secreted glycoproteins that bind to the Frizzled (FZD) family of receptors. The binding of WNT family proteins to their receptors activate several signaling pathways, including the canonical pathway which signals through ß- catenin. This pathway induces the activation of several target anti-apoptotic genes (Li et al., 2006). During development, the WNT pathway is involved in cell fate, proliferation, migration, polarity, and cell death (Li et al., 2006). Each of these mechanisms is required for normal development of the placenta to enable fetal growth.

Whereas WNT2 is known to be highly expressed in human placenta at all stages of development (Sonderegger et al., 2007) studies in mice have shown that this gene is most highly expressed in early placental development (Monkley et al., 1996). Highly supportive of an important role of

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Figure 7.13: WNT2 expression and birthweight percentile. A) Scatter plot of birthweight percentile versus WNT2 expression, B) Error bar plot of expression according to birthweight percentile. There is no association between those two variables.

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Figure 7.13: WNT2 expression and birthweight percentile.

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Figure 7.14: Frequency of high WNT2 promoter methylation in placenta DNA samples according to sex. Stacked bar plot showing higher frequency of high WNT2PrMe in females.

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Figure 7.14: Frequency of high WNT2 promoter methylation in placenta DNA samples according to sex.

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the WNT2 protein in placenta is the placental and fetal phenotype of mice with a homozygous targeted disruption of Wnt2 (Monkley et al., 1996) or of its receptor partner Fzd5 (Ishikawa et al., 2001). The offspring of such mice demonstrate poor fetal growth and perinatal death occurs in 50% of the pups.

The timing of WNT2Pr gain of methylation is important for understanding the impact of this epigenetic variant on placental and fetal development. High WNT2PrMe can be demonstrated fairly consistently in multiple samples from a single placenta and it has not been observed in our study of matched cord bloods from the same pregnancy, as previously reported by Yuen and colleagues who also identified this methylation variant to be placenta-specific as it was not found in other fetal organs (Yuen et al., 2009). These findings make it more likely that the polymorphic acquisition of methylation at the promoter of WNT2 originates in the trophectoderm after its differentiation from the blastocyst. In fact, there may be tissue-specific or developmental factors in the placenta that are permissive for this event. This is supported by the observation of confined placental mosaicism wherein the placenta can be perceived as more forgiving of genomic variation than is the embryo. My data suggest that high WNT2PrMe is an early, stable biological event that is evident in post-delivery placenta.

The profile of WNT2PrMe observed in my studies was consistent with a monoallelic gain of methylation. The maximum methylation observed was always below 60%, most cases being between 40 and 55% which is consistent with the monoallelic expression of this gene when the WNT2 promoter is methylated (Yuen et al., 2009). I propose that it is possible that a more severe biological phenotype such as fetal loss could be one consequence of biallelic WNT2PrMe and/or a higher methylation level, which was not observed in my cohort. If demonstrated, this finding would be analogous to the high fetal loss rate (~50%) in mice carrying a targeted homozygous null mutation of Wnt2 (Monkley et al., 1996). Prospective studies using, for instance, DNA extracted from chorionic villous biopsies or from products of miscarriage, could help clarify the range of phenotypes associated with high WNT2PrMe.

The cause of WNT2PrMe variation in placenta is unknown. It could be genetically determined and driven by genetic polymorphisms within or near the WNT2 genomic sequence. WNT2PrMe variation could also be modulated by parent of origin-specific factors. Alternatively it could be controlled by other maternal/fetal factors such as genetic or environmental determinants of

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folate/vitamin B metabolism e.g. maternal nutrient intake and maternal exposures to drugs or infectious agents.

My data suggest that WNT2PrMe is a stable epigenetic mark established early in pregnancy. Notably, samples with high WNT2PrMe showed a “narrow” distribution of low expression values when tested in term placenta. However, WNT2 gene expression levels demonstrate a highly variable profile when examined in post-delivery placenta, in samples with low WNT2PrMe. This broader distribution of expression, with more samples showing high expression values (Figure 7.12B) in the absence of WNT2PrMe suggests that the effect of this epigenetic variant on gene expression is more an inhibition of expression upregulation by as yet unidentified factors rather than an actual lowering of the basal expression levels. These data suggest that the methylation level of the promoter acts as an upstream repressor. Several other factors may also act locally to regulate WNT2 expression.

In spite of the association between WNT2PrMe and birthweight percentile there was no association between birthweight percentile and WNT2 expression. It may be that the effects of WNT2PrMe are not biologically relevant at the time of the pregnancy when the samples are collected. WNT2 function is likely to be more important early in gestation when the placenta is first developing, and when WNT2 expression is the highest according to the mouse studies (Monkley et al., 1996). I propose that low WNT2 expression in early pregnancy is associated with reduced birthweight percentile but that other factors may secondarily affect WNT2 expression in the developing placenta, partly overcoming the effect of WNT2PrMe. To address this issue, a large cohort of first trimester placental samples should be assessed for WNT2 expression and WNT2PrMe in early pregnancy.

The small effect size of the association between WNT2PrMe and birthweight percentile can be explained by several factors. First, as noted above, the WNT pathway is redundant; there are several WNT proteins sharing several of the receptors (Ishikawa et al., 2001; Monkley et al., 1996). It is thus possible that partial compromise of WNT2 expression is compensated by expression of other WNT genes. Second, an alteration in WNT2PrMe is certainly only one of several risk factors involved in the regulation of fetal growth. In multifactorial models of pathogenesis, each contributing factor is expected to have a weak association with the phenotype in heterogeneous samples, such as the ones I used. These findings suggest that, as for other traits,

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there are rare variants with major effects on phenotype, versus other common ones, with more modest effects. The study of common copy number variations (CNV) in humans is relevant to this discussion. Major effects on growth are caused by genomic alterations such as chromosome 15q25 deletion or epigenetic effects on chromosomes 11 or 7, resulting in Russell-Silver syndrome. Variation in birthweight in the normal population is likely to be regulated by multiple genetic and epigenetic molecular factors with small effect sizes. The data here presented show that epigenetic downregulation of WNT2 represents one such effect.

In the study by Yuen and colleagues (Yuen et al., 2009), a lack of association of the high WNT2PrMe variant was reported with birthweight. The apparent discrepancy with my data stems from the fact that the previous report assessed the association of WNT2PrMe with birthweight whereas in my study the association tested was of WNT2PrMe with birthweight percentile which takes into account the gestational age of the neonate. Furthermore, I approached birthweight percentile as a quantitative variable. This not only increases the power of my analysis but it also avoids the problem of including, as controls, individuals that actually had a higher growth potential than the actual birthweight they ended up with. It is well known that some newborns with birthweight percentile higher than 10th are also growth restricted.

In spite of the association between WNT2PrMe and birthweight percentile I did not find an association between high WNT2PrMe and the presence of placental lesions (which was part of the characterization of the samples in the array study). This makes it likely that WNT2 acts on fetal growth in a way that does not affect the morphology of the placenta.

The longterm clinical outcomes of newborns with high WNT2PrMe in placenta are not yet known. In this regard, it is interesting that mice with homozygous targeted disruption of Wnt2 that survive beyond the neonatal period show catch-up growth and are apparently otherwise normal (Monkley et al., 1996), consistent with observations of children born with low birthweight (Polo Perucchin et al., 2011; Toumba et al., 2005). Therefore, it will be important to follow up SGA children that are known to have placental high WNT2PrMe to determine whether they exhibit catch up growth in childhood, as do mice with targeted mutations of Wnt2, and whether they exhibit an increased incidence of adult onset disorders.

My observation of a significant sex difference in WNT2PrMe is difficult to explain. The increased frequency of high WNT2PrMe in female fetuses is consistent with a previous

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observation that severe placental dysfunction is more common in male vs. female fetuses (Edwards et al., 2000). Since sex was not associated with birthweight percentile in my cohort, a possible explanation could be that female fetuses are more tolerant to potential earlier lethal effects of high methylation of WNT2 promoter in male fetuses.

In conclusion, WNT2 expression can be significantly downregulated in placenta by promoter methylation and methylation of this promoter is associated with a reduction in birthweight percentile. These data are a proof of principle and it is encouraging for my hypothesis that epigenetic alterations of placental genes can impact fetal growth. Notwithstanding, it is surprising that I ended up selecting only one candidate gene from the CGI MeDIP methylation array experiment. This gene was in fact the only one that passed all my strict criteria i.e. that had all the characteristics I would be expecting. As will be explained in next chapter, this technology seems to be very insensitive to small differences in DNA methylation, precisely the ones that are more likely to play a role in the multifactorial model of poor fetal growth. Thus I decided to test my hypothesis using a different array platform, although keeping the same experimental framework. This study will be reported in Chapter 9.

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Chapter 8: Assessment of Methylation Level Prediction Accuracy in Methyl-DNA Immunoprecipitation and Sodium Bisulfite Based Microarray Platforms.

The published version of this chapter appears in:

Rajendram R*, Ferreira JC*, Grafodatskaya D, Choufani D, Chiang T, Pu S, Wodak S, Butcher DT and Weksberg R. Assessment of methylation level prediction accuracy in Methyl-DNA immunoprecipitation and sodium bisulfite based microarray platforms. Epigenetics 2011; 6:410- 415.

* I and Rageen Rajendram were co-first authors of this publication (Rajendram et al., 2011)

The chapter that follows is a modified version.

For this portion of the work I had the collaboration of a summer student, Rageen Rajendram, who, under my guidance, produced the data that we both analyzed and provided the figures and the table here presented. For these analyses, we used data previously generated by my experiments outlined in Chapter 7 and by parallel studies performed in our lab using the same technologies by a Post-Doctoral fellow from our lab, Dr. Daria Grafodatskaya. Theodore Chiang, a Bioinformatics Analyst at the Center for Computational Biology of the Hospital for Sick Children, provided technical support in one of the correcting algorithms here described.

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8.1 Summary

Despite advancement in microarray technology, precise quantitative assessment of DNA methylation levels using microarray analysis continues to have some technical issues. In this study, I verified the accuracy of 2 array methods - methylated DNA immunoprecipitation coupled with CpG island microarrays (MeDIP-CGI-array) and sodium bisulfite conversion based microarray platform (BC-array) - in predicting regional methylation level as measured by pyrosequencing of bisulfite converted DNA (BC-pyrosequencing). To test the accuracy of MeDIP-CGI-arrays and BC-arrays I used the Agilent® Human CpG island microarray and the Illumina® Human Methylation27 respectively, and compared microarray outputs to the data from targeted BC-pyrosequencing assays from several genomic regions of corresponding samples. I observed relatively high correlation with BC-pyrosequencing data for both array platforms, R=0.87 for BC-Array and R=0.79 for MeDIP-CGI array. However, MeDIP-CGI array were less reliable in predicting intermediate levels of DNA methylation. Several bioinformatics strategies, beyond quantile normalization, to ameliorate the performance of the MeDIP-CGI- Arrays - batch correction, Bayesian tool for methylation analysis (Batman; adjustment for CpG density) and combined Z scores (adjustment for differences in melting temperatures between the probes) - did not improve the correlation with BC-pyrosequencing data. While MeDIP-CGI- arrays may offer good results if the objective is to identify big differences between samples in large genomic regions, bisulfite treated DNA based array methods are more reliable in predicting intermediate methylation measured by sequencing methods. The high scalability, low cost and simpler analysis of BC-arrays, together with the recent extended coverage may make them a more versatile methylation analysis tool.

8.2 Introduction

Methylation of DNA is the best studied epigenetic mark in eukaryotic genomes. DNA methylation plays an important role in cellular processes, such as tissue specific gene regulation, transposable element silencing, X-inactivation and genomic imprinting (Edwards and Ferguson- Smith, 2007; Emerman and Temin, 1984; Kacem and Feil, 2009; Kim et al., 2010; Recillas- Targa, 2002; Reik et al., 2001; Reiss et al., 2010; Valley and Willard, 2006; Whitelaw and Martin, 2001).

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Studying the distribution, function, and regulation of DNA methylation is of great interest for understanding its role in normal development, cell differentiation and transcription regulation, as well as disease susceptibility.

Changes in DNA methylation have been associated with various disorders, such as cancer and imprinting disorders (Horsthemke and Buiting, 2008; Iacobuzio-Donahue, 2009; Li, 2002).

Several recent genome-wide studies employing high throughput technologies, mostly array based, have been providing insights into characteristics of DNA methylation patterns at genomic scale. For instance, DNA methylation patterns seems to depend on its genomic location (inter/intra-genic), location within the gene (enhancer, promoter, transcription start site, exon, and introns) and CpG density, and frequently correlate with gene transcription status (Edwards et al., 2010; Lister and Ecker, 2009; Weber and Schubeler, 2007).

As pointed in section 1.4.1.1 of this thesis, most current methods for DNA methylation analysis can be divided into two major categories: genome-wide and locus-specific methylation analysis. Also as said in section 1.4.1.1, for all DNA methylation assessment methods, a “transformation” step of DNA is required to make the methylation mark resistant to the required DNA processing methods. Among the locus-specific category of methylation analysis methods, “transformation” treatment of genomic DNA with sodium bisulfite followed by cloning and Sanger sequencing is presently considered the gold standard method of locus–specific DNA methylation analysis (Laird, 2010). PCR-amplified sodium bisulfite converted DNA can also be analyzed using higher throughput and less labor intensive technologies, such as mass-spectrophotometry based Sequenom/EpiTyper and pyrosequencing (Hayatsu et al., 2008; Laird, 2010). Sodium bisulfite treated DNA can also be used for next generation sequencing (NGS), to decipher the methylome on a whole genome level, and has successfully been applied to several eukaryotic species, including human, mouse, zebra fish, Arabidopsis thaliana and silkworm (Cokus et al., 2008; Feng et al., 2010; Laurent et al., 2010; Li et al., 2010b; Lister et al., 2009; Xiang et al., 2010). Significant obstacles to its wide-spread use are the prohibitive costs to deep sequence whole genome mammalian DNA methylation and the challenges of bioinformatic analysis due to the reduced complexity of sodium bisulfite treated DNA. Oligonucleotide microarrays targeting specific areas of the genome such as promoters or CpG islands provides economically more accessible genomic approaches for DNA methylation studies. Two of the most widely used

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genome-wide methylation array analysis methods are based on methylated DNA affinity enrichment and DNA bisulfite conversion. The first method relies on hybridization of two differentially labeled DNA fractions, one enriched for DNA methylation and the other comprised of comparative input DNA. Enrichment for methylated regions can be accomplished using immunoprecipitation of sheared genomic DNA with an antibody specific for methylated cytosine (MeDIP) or methyl-binding proteins with affinity for methylated genomic DNA (Cross et al., 1994; Weber et al., 2005a). Input DNA and enriched DNA are then differentially labeled with fluorescent tags and co-hybridized to an oligonucleotide microarray, usually covering promoters,

CpG islands (MeDIP-CGI-arrays) or both. The methylation levels are extracted as log2 ratios of the intensity of the enriched fraction divided by the intensity of the input. Agilent®, Nimblegen® and Affymetrix® all have developed CGI and/or promoter arrays that can be used with this type of DNA enrichment method. Another, more recent, of the currently used microarray techniques uses sodium bisulfite conversion (BC) of DNA followed by microarray hybridization to methylation specific DNA oligomers and single base pair extension (BC-arrays). Illumina® has developed their patented bead technology based array for the study of methylation which is based on hybridization of sodium bisulfite converted, fragmented and amplified DNA sample to methylation state specific (C vs. T) oligomers followed by single nucleotide base extension. Two probes are present for each analyzed locus, a methylated (C) and an unmethylated (T). The level of methylation is determined at each locus by the intensity of the two possible fluorescent signals. The methylation level is calculated as the ratio of methylated probe signal to the total signal intensity.

The advantages of BC-arrays are high accuracy in the prediction of DNA methylation level as well as single CpG resolution, whereas the advantage of enrichment based oligonucleotide microarrays is broader genome coverage. The output of one probe of this oligonucleotide microarrays represents the overall DNA methylation of the sheared fragments of DNA prior to enrichment (usually ~500 bp), as opposed to the absolute methylation level at a single CpG site for the BC-Array.

In previous Chapters of this thesis I have reported studies using these two types of arrays:

1) BC-arrays for the study of methylation differences in imprinting centers of placenta DNA between growth restricted newborns and controls and for the study of differences in methylation

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between placenta cell types DNA (Chapters 5 and 6, respectively); 2) MeDIP-CGI-arrays for the study of methylation differences in CGI of placenta DNA between growth restricted newborns and controls (Chapter 7).

This last type of technology was selected for this study because, at the time a decision was taken, 1) the MeDIP enrichment was considered a more comprehensive method of DNA methylation mark selection than the alternative, at the time, restriction enzyme based and 2) the analysis of CGI would provide the opportunity to identify potential regulatory regions not confined to promoter regions.

Analytical methodology difficulties arose at the time of analysis and biases related to the MeDIP technique begun being reported in the literature. Meanwhile experience with the use of the newer and analytically less demanding Illumina® BC-array platform and with BC-pyrosequencing started building up, both in our lab and in the literature. Data on those 3 technologies was being produced in our lab.

My objective was thus to analyze the accuracy of these two widely used microarray technologies for genome-wide DNA methylation assessment in predicting the regional DNA methylation level assessed by pyrosequencing of sodium bisulfite converted DNA (BC-pyro). For this I used the output of BC-Pyro assays for targeted comparison to the corresponding MeDIP-CGI-arrays and BC-arrays data.

8.3 Materials and Methods

In this study I first compared the ability of the BC-arrays and the MeDIP-CGI-arrays processed outputs in predicting differences in methylation of genomic regions between different samples. I identified distinct samples that have been tested by, respectively, Agilent® Human CpG Island 244,000k microarray and/or Infinium HumanMethylation27 BeadChip arrays (Agilent, 2010; Illumina, 2010) and for which there was pyrosequencing generated data of at least one of the genomic regions represented in the array. Bisulfite converted DNA followed by pyrosequencing was used as the “gold standard” in this analysis. Afterwards I compared the MeDIP-CGI arrays

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processed output from one sample with the BC-arrays of the same sample in order to verify the ascertainment of those two platforms of differences in methylation in one genome.

The data was generated in the course of several studies that have been performed in our laboratory. Samples used are DNA samples obtained from placenta and blood as described in sections 3.3 and 3.4. For this study the samples were anonymized. The methylation assessment techniques used in those different studies followed the same methodology, which allows combining, for the purpose of this study, the data generated by them. Descriptions of the arrays and pyrosequencing used in this study are provided in sections 3.8, 3.9 and 3.12. In section 3.7 is described the bisulfite conversion of DNA.

Methylation readings from the 3 distinct assessed technologies were obtained. The readings were obtained from samples for which there was pyrosequencing and corresponding array data available.

For the first assessment, I obtained all the methylation readings of probes from BC and MeDIP arrays that mapped to regions that were close to or within the target of available data from pyrosequencing assays developed in the lab. The pyrosequencing assays had been designed to specifically assess the same region for the purpose of cross validation of the regions putatively identified as differentially methylated across samples. Each pyrosequencing assay used in this study targeted 11 to 95 bp including 2 to 8 CpG sites, and the average of all CpG sites within the assay was taken as the BC-pyro methylation level of the region. If the BC-pyro assay was designed between two equidistant BC-array probes or overlapping two MeDIP array probes, the data for both probes was used. For the second assessment I used Pearson correlation between the methylation of the CpG site targeted by one BC-array and the output corresponding to the sample matched MeDIP array probe that included the same CpG. Each data point thus correlated the results of both types of arrays for one given region from one given sample. The data for this study were collected from several different experiments, so not all samples were used in each pyrosequencing assay.

All arrays used have passed the Quality Control criteria suggested by the manufacturer. Details of the regions probed in this study are presented in Table 8.1.

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Table 8.1: Genomic regions covered in the comparisons between array and pyrosequencing data A: Agilent Assays

Gene (chromosome) Agilent Probe(s)-(Mapping coordinates) Pyro Coverage A_17_P10849372- 18q23 (chr18) 3 CpGs: 76006290 – 76006312 (76006279-76006324) A_17_P05722032 (116750642-116750688) WNT2 (chr7) 5 CpGs: 116750697-116750746 A_17_P05722033 (116750710-116750755) A_17_P16260704 (124029468-124029512) LHX6 (chr9) 8 CpGs: 124029485-124029549 A_17_P06946488 (124029663-124029718) A_17_P15776118- DUSP22 (chr6) 3 CpGs: 237486-237518 (237477-237521) A_17_P00497445- (145016741-145016788) 1q21.1 (chr1) 4 CpGs: 145016830-145016852 A_17_P00497446- (145016883-145016927) A_17_P16156108- (6705931-6705975) 9p24 (chr9) 5 CpGs: 6705975-6706010 A_17_P06567332- (6706000-6706044) A_17_P16932356- (57075-57119) 17p13 (chr17) 5 CpGs: 57086-57130 A_17_P10185435- (57158-57202) A_17_P17298673- PLXNB2 (chr22) 4 CpGs: 49080763-49080785 (49080755-49080799) A_17_P16512639- ESAM (chr11) 4 CpGs: 124134356-124134367 (124134340-124134385) A_17_P10846095 (75258809-75258861) NFATC1 (chr18) 6 CpGs: 75258875-75258899 A_17_P10846096 (75258925-75258969) A_17_P11241797 (56861038-56861082) GNAS CpG27 (chr20) 8 CpGs: 56861086-56861138 A_17_P17204216 (56861099-56861143) A_17_P07603965- (2678138-2678182) KvDMR (chr 11) 8 CpGs: 2678168-2678263 A_17_P16411984- (2678219-2678263) B: Illumina Assays

Gene (chromosome) Illumina Probe(s)-(Mapping position) Pyro Coverage AXL (chr 19) cg14892768-(46417172) 4 CpGs: 46417172-46417206 CDH5 (chr 16) cg22319147-(64958100) 2 CpGs: 64958096-64958119 CDKN1C (chr 11) cg05559445-(2864250) 4 CpGs: 2864232-2864255 MGC15523 (chr17) cg00466249-(76884479) 3 CpGs: 76884474-76884494 MGC15523 (chr 17) cg06850526-(76884481) 3 CpGs: 76884474-76884494

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8.3.1 Data analysis

Correlations were calculated for all the comparisons – MeDIP array data with BC-pyro, BC- array data with BC-Pyro, and MeDIP array data with BC-array data.

The BC-arrays output data – methylation ratios - obtained as explained in section 3.9, was used directly in the correlation analysis with the BC-pyro data.

The MeDIP-arrays data consisted of the basic transformed data and the data from 3 further transformations aimed at reducing the non-biological experimental biases. The results of each of the 4 processing algorithms were compared with the BC-Pyro data in a similar manner.

To obtain the basic transformed MeDIP arrays output, the files produced by the array signal reading software – Feature Extraction software, version 9.0, as explained in section 3.8 – was imported into Partek® software, version 6.5 Copyright © 2008 Partek Inc., St. Louis, MO, USA. The background corrected green (input DNA) and red (enriched fraction) signals were converted into log2 ratios and quantile normalized to reduce the effects of differences in enrichment, labeling and hybridization efficiency between samples (Sadikovic et al., 2008). The results of this processing steps were used for the basic correlation analysis of the MeDIP arrays output and the BC-pyro data.

The following were the 3 different data transformations that were analyzed in comparison:

1) Batch correction was performed in order to reduce batch-specific biases caused by the grouping of different microarray experiments (Johnson et al., 2007; Leek et al., 2010; Sharma et al., 2005). To accomplish this, I used the same Partek® software. First I did cluster analysis to verify that there was clustering of the arrays according to the day in which they were hybridized (Figure 7.3A). An ANOVA model was created in which the type of sample (case or control) and the batch to which the array belonged were used as the factors or independent variables. The batch variable was coded as random variable and the sample type as fixed variable. The ANOVA model was applied to each probe and each data point was modified in order to eliminate the variation attributable to the batch variable. After confirmation of the elimination of the batch effect by analyzing the contribution of each variable to the variation between the probes and by

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new cluster analysis (Figure 7.3B), the transformed data was used to compare with the pyrosequencing data.

2) A new Bayesian deconvolution strategy - Bayesian Tool for Methylation Analysis (Batman) – was used on the Agilent® dataset to generate quantitative methylation values based on the microarray signal intensity. The underlying principle is to model the effects of varying density of CpG dinucleotides, and the effect this has on MeDIP enrichment of DNA fragments. It employs a Gaussian probability density function in order to generate quantitative methylation values based on microarray signal intensity, MeDIP fragment length, and surrounding CpG content. The model was developed using the software freely provided by Down and colleagues (Down et al., 2008). It was applied to the array data, after Lowess normalization (instead of quantile normalization) as suggested by the authors, to reduce intensity-dependent variation in dye bias (Down et al., 2008; Saidi, 2003). Most regions of low CpG density are constitutively methylated while most regions of high CpG density (CpG islands) are constitutively unmethylated, and Batman utilizes this observation to fit a model of CpG influence vs. log ratio; it focuses on the low CpG density region, which features a linear relationship because the output from the array (after some transformation) is expected to scale linearly with the CpG influence (in fully methylated DNA). The default parameters of the Batman algorithm were used with the exception of the last step where the most likely methylation state were summarized in 52 bp windows instead of the default 100 bp to accommodate for my average probe size. The absolute methylation value for each probe was then extrapolated and mapped from these estimates of likely methylation state. The first observation that indicated a possible source of failure in using Batman on the Agilent® methylation arrays was the lack of data in the low CpG density region of the model calibration plot which would then affect all downstream estimates. As referred in the Discussion, this may be the main reason for the lack of improvement of the array output correlation with the comparative method of methylation assessment.

3) Transformation of the log2 ratios into “combined Z scores” using Agilent® Genomic Workbench 5 software® was done to correct for probe melting temperature (Tm) bias (Agilent, 2009). Probes have different melting temperatures, which correlate with the CG content. The efficiency of the hybridization varies accordingly. Thus, readings from probes of different melting temperatures may correspond to different methylation values (Agilent, 2009). Agilent® developed an analytical software tool – Agilent® Genomic Workbench, version 5.0 – that

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applies a transformation to the Agilent® Feature Extraction software output data (log2 ratios) aiming at the correction of this bias. First the probes are binned according to their Tm. Then a

histogram of the log2 ratios for each Tm bin is built. Two Gaussian distributions are fitted to the histogram, assuming that the probes within each bin will have a bimodal distribution. One of the modes (the left one) corresponds to the unmethylated probes and the other (the right) to the

methylated probes. For each log2 ratio within each bin, Z scores (distance between the log2 ratio value and the mean measured in standards deviations) are calculated for each of the fitted Gaussian curves. Those Z scores will have negative values if the log2 ratio is at the right of the curve and positive, if at the left. Finally the 2 Z scores are added and the resulting value is the output of each probe. The higher the Z score the higher the probability of being methylated. Thus, the value that was used in the comparative analysis with BC-pyro data after this correction was the combined Z score.

8.4 Results

Methylation values from the two microarray platforms were first compared to the corresponding average percent methylation of BC-pyro assays for effectiveness to predict region methylation level and differences across samples (Figure 8.1).

8.4.1 BC-arrays versus BC-pyro

Methylation ratio beta-values were obtained for 5 CpG sites of 27 samples, each CpG site overlapping one BC-pyro assay, generating 95 comparison data points (not all 27 samples were used for each BC-pyro assay). Plotting the BC-Array against BC-pyro values produced a Pearson correlation of 0.87 (Figure 8.1A). This suggested that the methylation level at one CpG site is a good predictor of regional DNA methylation of surrounding CpG sites in close proximity, as suggested in previous studies (Eckhardt et al., 2006). The regression line was close to the expected one-to-one correlation (slope = 0.87). The deviation of the Pearson correlation and slope can be explained by several factors affecting both pyrosequencing and BC-arrays. PCR bias due to CpG content or thymine overrepresentation may impact pyrosequencing results. A recent study investigating promoter methylation of the ABCB1 gene found that pyrosequencing underestimated methylation greater than 50% for the CpG sites analyzed (Reed et al., 2010; Shen

221 et al., 2007). We have found that this variation is dependent on the locus being analyzed, which may in part explain why the correlation was less than one. Whole genome amplification and hybridization efficiency variation between the probes on the BC-array are other possible explanations for the correlation level.

8.4.2 MeDIP-CGI-arrays versus BC-pyro

Quantile normalized log2 ratios were extracted for 20 probes on the MeDIP-array spanning 12 CGI genomic regions from 29 samples and compared to data from 12 BC-pyro assays generating 86 comparisons data points. All pyrosequencing assays were initially designed to validate the difference between individual samples showing 3-fold or higher differences between the enriched and the non-enriched fractions hybridized to the Agilent® microarrays (Grafodatskaya et al., 2010), and the direction of change was validated in all cases. Plotting the quantile normalized log2 ratios against the BC-pyro values resulted in a Pearson correlation of 0.79

(Figure 8.1B). The quantile normalized log2 ratios when less than -1 appeared to predict low absolute methylation levels (<20%) and greater than 2 appeared to predict high absolute methylation levels (>70%). For log2 ratios between -1 and 2, absolute methylation was too variable to be useful in predicting relative methylation differences. The Pearson correlation of the MeDIP-CGI array intermediate values, represented by interquartile range (IQR), was 0.56 suggesting that MeDIP-CGI arrays are reliable at distinguishing highly methylated and unmethylated regions, but less accurate at quantifying intermediate methylation levels. Given its methodological nature, MeDIP-CGI-arrays data do not represent absolute methylation values. Although each probe targets only 50 bp, the DNA fragments that will hybridize to each probe, obtained by sonication of genomic DNA, measure between 100 and 500 bp in size. It is assumed thus, that each probe ratio is a measure of enrichment in methylated DNA for the bound DNA fragment and thus it will reflect the methylation level in the region represented by the probe sequence. As such, higher values would correspond to increased methylation than lower values. However the MeDIP-CGI array method is prone to certain technical biases potentially decreasing the linear correlation with absolute BC-pyro methylation levels.

I thus hypothesized that correcting for specific technical biases could increase the correlation of MeDIP-CGI data with BC-pyro data and improve the predictability of intermediate DNA

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Figure 8.1: Correlation between BC-pyro and the two array platforms. A) Correlation between BC-array average methylation percentage (normalized Beta output value) and the average percent methylation of the included and surrounding CpG sites (r = 0.87). The slope of the regression line was 0.87. Samples were analyzed at 5 loci with variable methylation levels using BC-pyrosequencing of the region, and the BC-array value of the CpG site within this region was obtained (95 data points). The number of CpG sites analyzed by each BC-pyrosequencing assay ranged from 2 to 4. B) Correlation between MeDIP-CGI-array

quantile normalized log2 ratios and the average percent methylation of the CpGs within or adjacent to the probe (r = 0.79). The log2 ratios were extracted from 20 probes on the MeDIP- CGI-array spanning 12 regions of the genome (86 data points). The number of CpG sites analyzed by each BC-pyrosequencing assay ranged from 2 to 8.

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Figure 8.1: Correlation between BC-pyro and the two array platforms.

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methylation level. I chose to correct for the following potential technical biases: batch effect, differences in density of CpG sites across CpG islands and differences in the melting temperatures of microarray probes, resulting in variation of hybridization efficiency. Three

transformations of the MeDIP-CGI-arrays log2 ratios were applied to assess if compensation for the aforementioned putative technical biases could improve prediction of regional methylation, as detailed in the “Methods” section.

Batch effects have been described as a confounding factor in microarray experiments for which several batches of microarrays are processed at different points in time within the same experiment (Johnson et al., 2007; Leek et al., 2010; Sharma et al., 2005). Since the data I used for this study was originated from arrays that had been processed at different times, the quantile

normalized log2 ratios were corrected to address the batch effects in Partek Genome Suite using an ANOVA based process (Johnson et al., 2007; Sharma et al., 2005). Plotting the BC-pyro values against the batch corrected ratios reduced Pearson correlation coefficient to 0.69 (Figure 8.2A).

The MeDIP enrichment efficiency is proportional to the number of CpG sites present in a given

sonicated DNA fragment; thus, log2 ratios of regions with different CpG densities provide different results even when methylation levels are the same (Rakyan et al., 2008). A Bayesian Tool for Methylation Analysis (Batman), developed by Down and colleagues aiming to correct the CpG density bias, employs a Gaussian probability density function in order to generate quantitative methylation values based on microarray signal intensity, MeDIP fragment length, and surrounding CpG content (Down et al., 2008). After processing the log2 ratios with the Batman algorithm in order to reduce putative bias due to variable CpG density, these values were also correlated against the corresponding BC-pyro values, and the result was a reduction in correlation (R=0.20) compared to quantile normalized log2 ratios (Figure 8.2B).

Log2 ratios from probes that share similar Tm should display a bimodal distribution. Each of the 2 modes corresponds to the non-methylated or methylated set of probes. This bimodal distribution can be fit to a set of two Gaussian curves (Agilent, 2009). In order to reduce the effects of differing Tm of microarray probes I used the Agilent® Genomic Workbench analytical software, version 5.0, which transforms the output from log2 ratios to “combined Z scores” on each Tm bin. The two Z scores, number of standard deviations, calculated for each probe from

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Figure 8.2: Correlation between BC-pyro and the transformed MeDIP-CGI-arrays output.

A) MeDIP-CGI-array log2 ratios were quantile normalized and corrected for batch affects (r = 0.69). B) MeDIP-CGI-array log2 ratios were transformed using the Batman algorithm (r = 0.20). C) Combined Z scores of the MeDIP-CGI-array log2 ratios were used (r = 0.60).

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Figure 8.2: Correlation between BC-pyro and the transformed MeDIP-CGI-arrays output.

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the two Gaussian curves, corresponding to the methylated and unmethylated log ratios were combined and then correlated to the BC-pyro values. Pearson correlation of the output values was 0.60 (Figure 8.2C).

From these results I concluded that batch correction, Batman transformation of MeDIP-CGI-

arrays log2 ratios and the combined Z scores did not improve the predictability of the methylation values generated across several samples for several regions.

8.4.3 MeDIP-CGI-arrays versus BC-arrays

I then wanted to test the accuracy of the MeDIP-CGI-arrays method and the Batman and Z score correction algorithms in distinguishing genomic regions of high versus low methylation within the same sample. In order to increase the number of data points for this comparison I used BC- Array data from the same sample, as a high Pearson correlation was observed for the two sodium bisulfite based methods (Figure 8.1A). Overlapping probes from both the BC-Array and MeDIP- CGI microarrays were selected for this analysis (4666 data points). Correlating the quantile normalized log2 ratios from the MeDIP-CGI-array platform with the background corrected beta values from the BC-array for one sample (Figure 8.3A) resulted in a Pearson correlation

coefficient of 0.26. The majority of the MeDIP-CGI log2 ratios corresponded to low CpG site methylation (<0.2). While this was expected since most CpG islands are not methylated, it was

unexpected that such a large range of log2 ratios would correlate to low methylation. Combined Z scores from the MeDIP-CGI microarray output were also correlated with the BC-array methylation values for the same sample producing a Pearson correlation of 0.31 (Figure 8.3B). Batman transformed MeDIP-CGI methylation values were correlated against BC-Array beta values producing a reduced 0.19 Pearson correlation coefficient (Figure 8.3C). Again, a high range of data points on the MeDIP-CGI platform were seen to correlate with low methylation levels on the BC-Array. Thus, none of the correction methods appeared to improve the predictability of intermediate methylation values.

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Figure 8.3: Correlation between the two array platforms for the same sample. BC-array beta values (beta value = C/(T+C)) correlated to different transformation of MeDIP- CGI-array for one sample. 4666 BC-array probes were matched to MeDIP-CGI-array probes. A)

Correlation of BC-array beta values and quantile normalized MeDIP-CGI-array log2 ratios (r = 0.26). B) Correlation of BC-array beta values and combined Z scores (r = 0.31). C) Correlation of BC-array beta values and Batman transformed MeDIP-CGI-array (r = 0.19). Similar results were obtained for other samples (data not shown).

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Figure 8.3: Correlation between the two array platforms for the same sample.

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8.5 Discussion

In DNA methylation studies microarrays are frequently used as a tool for identifying target genomic locations for further quantitative analysis. From the data here presented, BC-array methylation assessment of single CpGs demonstrates reasonable predictability of the methylation level of the 11 to 95bp (2 to 8 CpGs) genomic region surrounding the CpG site analyzed. MeDIP-CGI-arrays are relatively reliable in identifying high DNA methylation differences within the same region between different samples, as was expected (Cheung et al., 2010). However it performs less reliably in distinguishing intermediate methylation differences between samples (r=0.56 for IQR, Figure 1B) and in classifying different CGIs in the same sample according to their methylation levels. Biases expected to occur, some specific to this method, as the CpG enrichment bias and the related Tm bias, and some more global, such as the batch effect, could explain this lack of accuracy. Several strategies have been developed aiming at the correction of these and other biases, in order to increase its prediction capabilities (Agilent, 2009; Down et al., 2008; Pelizzola et al., 2008; Yamashita et al., 2009). I applied two of the specific bias correction methods and one of the global biases. However, none of the three corrections employed improved the accuracy of the prediction of intermediate methylation data. The Batman correction model was developed from data obtained from an array platform that targeted promoter regions which were both CpG poor and CpG rich. In their study this correction did improve the correlation with sodium bisulfite sequencing data obtained from the same samples run on the microarrays and with data obtained in different experiments (Rakyan et al., 2008). It is probable that the effectiveness of the algorithm depends on the presence of a minimum number of probes from low CpG content regions. While ~50% of promoters include a CpG island, a number of regions targeted by those microarrays are poor in CpG content (Fan and Zhang, 2009). Thus the lack of data in the low CpG density regions in MeDIP-CGI array likely affected downstream estimates of methylation values and resulted in the failure of Batman algorithm to improve the correlation of MeDIP-CGI output vs. BC-pyro data in our data set. This could also explain the lack of improvement of the related Tm bias correction. Finally, since the batch effect occurs for specific probes (Leek et al., 2010), it may not have an overall effect on the accuracy of the DNA methylation predictions that I was trying to assess.

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None of the three correction methods improved the results. In fact, they actually decreased the correlation obtained with the non-transformed quantile normalized data, suggesting that quantile normalized data is the most accurate in predicting DNA methylation from the MeDIP-CGI arrays. One potential caveat of our study in regard to the low predictability for intermediate ranges of DNA methylation, is the difference in the sizes of the regions covered by BC-pyro assays (<100 bp) and MeDIP-based enrichment (~500 bp). Variation of methylation across 500 bp genomic regions may be potential sources of discrepancy between methylation levels predicted by BC-pyro assays and MeDIP-CGI arrays.

There are other potential correction techniques beyond the ones I used, which potentially could improve the accuracy of MeDIP-CGI arrays. Pelizzola and colleagues have suggested a correction algorithm based on using an in vitro completely methylated sample as a calibrator for each specific region. A new transformed value – the MEDME – would be used in downstream comparisons (Pelizzola et al., 2008). A mathematical algorithm has also been developed by Yamashita and colleagues that is reported to improve accuracy for the detection of either fully methylated or unmethylated CGIs as well as for the intermediate values (Yamashita et al., 2009). Both these algorithms use smoothing of the results based on the values of nearby probes. The last

method uses the position of the smoothed log2 ratio in its distribution across the entire genome of a given sample. This is used to calculate a probability that is incorporated into an algorithm that will generate a new transformed value – the ME value. This new value is then used for downstream comparisons.

In spite of the possibility that these other alternative methods could improve the accuracy of MeDIP-CGI based array methods for intermediate values, they will require complex transformative methods. In this study, the transformation of the values did not improve the results and actually decreased the correlation I got with the non-transformed quantile normalized data. Studies comparing the different methods are needed but they may offer conflicting results depending on the platform used or the objectives and specificity of each application.

In conclusion, the results of this study suggest that specific array choice depends on the type of study to be carried out. MeDIP-CGI arrays should be used if the experimental objective is to find big differences in methylation across genomic regions of 500 bp or larger among samples. Complex transformations of the output should be assessed for each specific application. To

232 identify small differences in methylation at single CpG resolution, methods based on bisulfite conversion of DNA followed by hybridization to arrays targeting specific CpGs appear to be more appropriate. The high throughput, single CpG resolution, accuracy and affordable cost of BC arrays suggest they might be the more commonly used technique in the future. This will be especially true when the coverage of this type of array is expanded to an increased number of CpGs per locus, and a larger number of loci.

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Chapter 9: Novel Global Genomic Comparative Analysis of Methylation of Placenta DNA Between Intrauterine Growth Restriction Cases and Controls Using Bisulfite Based CpG Arrays

The following chapter will be submitted for publication after validation studies, currently underway, are completed.

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9.1 Summary

Data previously described, in Chapter 7, give support to my hypothesis that epigenetic variation is biologically important with respect to the mechanisms controlling normal fetal growth. In effect, I found that an epigenetic variant in placenta is associated with low birthweight percentile. This epigenetic variant results from increased DNA methylation at the promoter of the WNT2 gene. In mouse, mutations in this gene disrupt placental and fetal development. Despite this very interesting result I then showed that the methods used to screen for DNA methylation differences were suboptimal in identifying small methylation differences that may still have biologic effects. Thus I hypothesized that different technologies could be used to better identify other DNA methylation variants with improved accuracy. Here I report the results of a screen for DNA methylation differences using a different microarray technology. Bisulfite converted DNA was hybridized onto bead arrays targeting CpGs mapped to gene promoters (Illumina®) in placenta DNA samples from 12 small for gestational age newborns with placental lesions (cases) and 12 controls. I compared the methylation analysis results with expression data. The expression data were obtained from expression arrays hybridized with placental cDNA obtained from most of the placenta samples run on the methylation arrays.

From this experiment I have identified 59 candidate genes with statistically significant differences in promoter methylation between the cases and controls. From those, I prioritized 5 for follow-up investigation because of a significant correlation between methylation and expression, statistically significant differences in expression between cases and controls, high methylation differences and / or information suggesting a role for the candidate in placental development - GPX3, PAPPA2, INHA, CYP11A1, ACADL.

The results of this analysis provide further and more reliable support for the hypothesis of this thesis.

9.2 Introduction

It is my objective to test the hypothesis that variation or aberrations in placental DNA methylation are involved in placental development affecting fetal growth. To this end I have been following a methodological strategy that includes a screening step using array based DNA

235 methylation analysis technology applied to a placental DNA sample cohort of newborns homogeneously defined as Intrauterine Growth Restriction (IUGR) and gestational aged matched controls, followed by a validation step.

Previous analysis of methylation differences between placental DNA obtained from 8 newborns classified as IUGR and from 8 controls, using data from the Agilent® CpG Island 244K arrays (Chapter 7 of this thesis), has allowed the identification of a DNA methylation variant in the promoter of the WNT2 gene that was present in 3 cases and none of the controls. Targeted methylation analysis of this region and expression studies of this gene in an extended cohort demonstrated an association between the high WNT2 Promoter methylation (WNT2PrMe) variant and low WNT2 expression. An association was also demonstrated between high WNT2PrMe in the placenta and low birthweight percentile in the neonate. This finding supports the hypothesis that is the object of my thesis i.e. epigenetic mechanisms are involved in placental development and fetal growth.

After this analysis was completed, Agilent® issued an annotation file that excluded many of the targets of their array - ~40,000 of the original ~244,000 oligonucleotides – apparently due to poor reliability and reproducibility of those targets. In addition, I used pyrosequencing of bisulfite converted DNA as the target validation technique, to study the accuracy of the Agilent® methylation platform to predict methylation. Another platform that had become available – Illumina® Infinium HumanMethylation27 BeadChip - was similarly assessed (Chapter 8 of this thesis). Comparisons between those two platforms, addressing their relative weaknesses and strengths, were also performed. The results of this study showed that the Agilent® arrays were useful in the identification of differences between non methylated versus fully methylated genomic regions. This platform has the advantage of extended and high resolution coverage of its genomic targets – CpG Islands. Thus, any differences are likely to span extended regions of the genome (up to 2 Mb, the extension of the biggest CpG Islands). Conversely, the Illumina® arrays were better at detecting differences across all levels of methylation. However, given its coverage of 1-2 representative CpGs of gene promoter regions, the discrimination of differences with this platform is expected to span much smaller genomic region (up to 200 bp, corresponding to the extension of the pyrosequencing assays targeting the genomic region represented by the probe) (see sections 3.8 and 3.9 for technical details of these two platforms). Thus I expected that

236 an analysis of placental DNA using the Illumina® methylation array platform would complement the information provided by the previous study using Agilent® arrays.

Therefore I decided to test my thesis hypothesis using the Illumina® methylation array platform to screen for differences in placenta DNA methylation in gene promoters using a similar cohort of cases and gestational age matched controls. Also, given its lower cost, it was possible to extend the number of cases and controls to 12.

The analysis of the methylation differences between cases and controls using the Illumina® methylation arrays data was extended to include data from Illumina® expression arrays (which targets genes parallel to the methylation arrays) for a comparison of methylation and expression differences between cases and controls.

The results of this analysis demonstrated a higher number of methylation aberrations in IUGR versus control samples. Furthermore, the analysis of each individual probed region has identified some genes with variation in CpGs mapping to their promoters that are likely to be associated with IUGR. Further evidence of such association for some of those genes – PAPPA2, INHA, GPX3, CYP11A1 and ACADL – is provided by the known or predicted functional roles in placenta development and, for PAPPA2, INHA, GPX3 and CYP11A1, by corresponding correlation with gene expression. Finally expression data for PAPPA2, INHA, CYP11A1 and GPX3, from this and other studies, is also suggestive of the occurrence of differences in expression between cases and controls. PAPPA2, INHA and GPX3 code for secreted proteins that thus can be thought of as potential clinical useful markers, pending appropriate characterization of the clinically relevant associated phenotypes.

9.3 Materials and Methods

The strategy used in this study to screen for candidate genomic regions with methylation variation associated with abnormal placental development and poor fetal growth was similar to the general analytical model used previously and summarized in section 3.1.

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9.3.1 Sample selection

The selection of cases and controls followed the same previously described criteria (Chapters 5 and 7). The samples and arrays used in this study are the same as the ones used in the study described in Chapter 5. The placenta samples for this study were collected, processed and characterized as described in section 3.2. DNA and RNA was extracted from placenta and assessed as described in sections 3.4 and 3.5.

Among placenta DNA samples obtained from singletons, I selected 12 cases with birthweight percentile less than the 10th (small for gestational age or SGA) and with records of any of the placental lesions described in Table 3.2 considered to be associated with low birthweight (Redline, 2008). Twelve gestational age matched control samples were selected from newborns with birthweight percentile higher than 10th and without placental lesions. Exclusion criteria, outlined in Table 3.1, were the same as in Chapter 5, including preeclampsia. DNA from the above described samples was hybridized to Illumina® methylation arrays. As stated and justified in section 3.14, I decided to use the same samples used in the Agilent® arrays and added 8 more samples. In the process of selecting those 8 additional samples the WNT2PrMe status was known and entered into the selection process – one of the controls and 4 of the cases was selected among the high WNT2PrMe samples. Among the above described samples, good quality RNA for hybridization to Illumina® expression arrays was obtained from 9 of the controls and 10 of the cases. Two additional good quality RNA samples not used in any of the methylation arrays (one case and one control) were added to the Illumina® expression cohort for the differential expression analysis. Two biological replicates were hybridized into distinct expression array slides arrays for reproducibility assessment.

9.3.2 DNA methylation analysis by microarray

DNA hybridization protocol to Illumina® Infinium HumanMethylation27, BeadChip silica slides and data output was described in Chapter 5 (5.3.4) with technical details provided in sections 3.7 and 3.9.

9.3.3 RNA expression analysis by microarray

RNA hybridization protocol to Illumina® single channel HumanHT-12 v3 Expression BeadChip and data output was described in section 5.3.5 with technical details in section 3.10.

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9.3.4 Data analysis

9.3.4.1 Methylation array data analysis

The methylation array data analysis included 1) global comparisons between cases and controls using cluster analysis, 2) a categorical approach similar to the one used for the study of the imprinting centers, described in Chapter 5 and 3) a CpG specific quantitative statistical analysis.

Global comparisons were performed between all the samples by non-hierarchical Euclidean cluster analysis, using Partek Genomic Suite version 6.5.

For the categorical approach, methylation results were categorized, based on their position in boxplots created for controls, as normal, outliers (positive and negative) and far outliers (positive – hypermethylated - and negative – hypomethylated) (see section 5.3.1.2 and Figure 5.1 for details). For this categorical approach a “Sample specific number of abnormal CpG sites analysis” and a “Global CpG Categorical Analysis” were also performed. The results of the latter analysis were already reported in Chapter 5 in comparison with the CpGs mapping to Imprinting Centers. A “CpG specific categorical analysis” was also performed for the probes identified through the quantitative analysis (see below) to have differences in methylation between cases and controls.

For the CpG specific quantitative statistical analysis, methylation of each CpG site was treated as a quantitative variable and the clinical definition of each sample as case or control was treated as a categorical variable. CpG sites were classified as differentially methylated between cases and controls if 1) the Mann-Whitney U test determined the p value to be less than 0.05, without correction for multiple testing; and 2) a difference in the averages of methylation between cases and controls was equal to or above 10%.

I have excluded sex chromosomes from the analysis since the cases and controls were not completely controlled for sex (Table 3.5).

9.3.4.2 Methylation and expression array data analysis

For genes represented in both types of arrays I analyzed the correlation between each CpG Illumina® methylation array value and the expression of the related gene across all samples from both, cases and controls. For the expression values I used the Illumina® expression array data

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corresponding to the samples for which methylation had been analyzed. Methylation and expression were treated as quantitative variables. Probes were considered to demonstrate positive correlation if there was valid expression data available and the correlation between methylation and expression, measured in Spearman R2 was above the 90th percentile for all the correlations. Expression data for a given gene was considered valid if, at least, 3/4 of the possible expression data points (represented on the expression arrays) had a reliable reading (significantly higher than background, i.e. detection p value <0.05).

I also assessed, for the genes for which there was correlation between methylation and expression, if there were differences in expression between cases and controls. For this, I added expression array data from two more samples for which methylation arrays were not available. Expression data was treated as a quantitative dependent variable and cases versus controls was treated as a categorical variable. I used the Mann-Whitney U test to determine a p value < 0.05, without correction for multiple testing, as a criterion to demonstrate whether a gene demonstrated differential expression between cases and controls.

9.4 Results

9.4.1 Differential methylation between IUGR and controls

To identify DNA methylation variation associated with fetal growth I analyzed the differences in methylation detected by the Illumina® Infinium HumanMethylation27 BeadChip arrays between the DNA placenta samples from a group of 12 SGA singleton newborns with placental pathology lesions suggestive of insufficiency and a group of 12 controls.

The description of the samples analyzed in this study has already been presented in Chapter 5 of this thesis (section 5.4.3). As explained, details of each of the samples are presented in Table 3.3 (identifiable by “9” under the heading “Methyl. Illumina”) and a descriptive summary of the 24 samples analyzed with the Illumina® methylation arrays are presented in Table 3.5.

All methylation arrays produced for this study passed quality control criteria (detailed in section 3.9). Reproducibility of the methylation arrays, assessed by the correlation between 3 pairs of

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technical replicates hybridized into 2 different slides (>0.99) has already been reported in section 6.4.2.

Three different analytical approaches to the differences in methylation between cases and controls were used, as detailed in Methods. One approach – cluster analysis - sought to verify if globally there were differences in methylation between cases and controls and if there were batch effects in the data (Leek et al., 2010); the second approach sought to assess the frequency of aberrant methylated CpGs in cases and controls and to verify if there were differences in those frequencies, as was done for the analysis of CpGs mapping to imprinting centers (Chapter 5); finally the third sought to identify specific differentially methylated CpGs between cases and controls.

9.4.1.1 Cluster analysis of methylation in cases and controls

Euclidean non-hierarchical cluster analysis of the methylation arrays was performed to check for clustering by type of sample (cases versus controls) and for batch. It does not demonstrate clustering by any of those two variables although, after batch correction using the same method described in section 8.3.1, it shows a partial clustering of cases and controls (Figure 9.1). Since there is no clustering by batch, I used the non-batch corrected data for the downstream analysis of individual CpG sites.

9.4.1.2 Frequency of aberrant methylation frequency in cases and controls

For this analysis I categorized the methylation values into normal, outlier and far outlier, as explained in “Methods”, and compared, for all the probes in the array, the number of outlier and far outlier probes present in each of the cases with the same number in each of the controls.

As shown in the plots of Figure 9.2 cases have more outlier or far outlier probes than controls, both positive and negative (Mann-Whitney U test p value < 0.05). The data on CpGs mapping to imprinting centers (ICs) have demonstrated differences between cases and controls only for the frequency of hypermethylated probes.

As already reported in Chapter 5, and presented in Table 5.2, I compared the number of outliers or far outliers in all case samples with the same number in all controls. Again, cases had more

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Figure 9.1: Euclidean non-hierarchical cluster analysis of the methylation arrays. Cluster analysis by batch and sample type, prior (A) and after (B) batch correction, using an ANOVA based method. It shows that there is no clustering by batch but after correction for this variable there are 2 main big clusters, with imperfect clustering by sample type.

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Figure 9.1: Euclidean non-hierarchical cluster analysis.

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Figure 9.2: Comparison between the number of outliers and far outliers in each of the cases and in each of the controls. Barplots of the number of outliers and far outliers, positive (hypermethylation) and negative (hypomethylation) for each case and each control. Cases are color labeled in red and controls in green. Cases have more outliers and far outliers than controls. The strongest association (lower p value) is with negative far outliers.

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Figure 9.2: Comparison between the number of outliers and far outliers in each of the cases and in each of the controls.

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aberrantly methylated probes than controls. The highest odds ratio was for negative far outliers suggesting a higher difference between cases and controls for hypomethylated probes. This contrasts with the probes mapping to imprinting centers in which the biggest difference is in the hypermethylated group.

The much higher number of all the CpGs in the array, having more power to detect small differences, may underlie the fact that both, hyper and hypomethylated probes, were more frequent in cases than controls, whereas for the probes mapping to ICs only the hypermethylated demonstrated statistically significant differences. Still, the fact that the odds ratio (a measure of effect size) was highest for the frequency of all the hypomethylated CpGs among all probes from cases and highest for the frequency of all the hypermethylated CpGs mapping to ICs suggests a specific methylation characteristic in the probes mapping to ICs.

9.4.1.3 Identification of CpGs with differences in methylation between cases and controls

I first identified probes that had a likely biological meaningful difference in methylation levels between the two group categories – IUGR and controls. The methylation was assessed as a quantitative outcome variable. Probes were selected if the methylation difference between the averages of the 2 groups was at least 10% and the Mann-Whitney U test p value was less than 0.05. The 10% difference requirement was used to filter out probes with small differences that would be more likely to be false positives. As such, this criterion was used as a “replacement” of correction for multiple testing, since such correction would exclude all probes.

Among the 27,578 probes in the array, 1256 probes corresponding to 1202 genes had a p value less than 0.05. Of those, only 64 probes, corresponding to 59 genes had a difference in methylation averages between both groups of at least 10% and valid data. It is noteworthy that the range of methylation values in all these probes is relatively high (>0.29). Four of those probes, corresponding to four genes, mapped to chromosome X and were excluded. One gene – SNRPN - was also excluded because only an intragenic probe out of 13 probes in the array showed differences. The remaining 54 genes, considered the selected genes whose differences in promoter methylation are more likely to have a biologic effect from this analysis, are presented in Table 9.1.

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Table 9.1: Genes selected with differences in methylation between cases and controls

Methyl. Methyl. R2 Spearman Probe Location Distan Mapped to Probes in the MW p Symbol Average in Average in Correlation Chr Genomic relative to ce to CpG array value Controls Cases Meth/Exp coordinate TSS TSS Island? 1 probe SLC45A1 0.62 0.50 0.008 NA 1 8195939 upstream 1440 YES different 2 probes, 1 CLDN19 0.74 0.61 0.024 0.15 1 42978210 downstream 302 NO different 2 probes, 1 TAL1 0.51 0.61 0.007 NA 1 47468062 upstream 32 YES different 2 probes, 1 CHI3L2 0.76 0.64 0.012 NA 1 111571814 downstream 10 NO different 1 probe PAPPA2 0.63 0.51 0.001 0.48 1 174699121 downstream 191 NO different 2 probes, 1 ASTN 0.32 0.44 0.008 NA 1 175400642 downstream 5 YES different 2 probes, 1 GPR25 0.61 0.71 0.004 NA 1 199109052 downstream 263 YES different 2 probes, 1 PKP1 0.28 0.38 0.039 NA 1 199519095 upstream 108 YES different 2 probes, 1 SFT2D3 0.81 0.71 0.039 0.33 2 128174869 upstream 198 YES different 5 probes, 1 HOXD4 0.37 0.49 0.039 NA 2 176722931 upstream 1428 YES different 2 probes, both ACADL 0.11 0.35 0.033 NA 2 210798060 downstream 332 YES different 2 probes, 1 INHA 0.53 0.43 0.007 0.27 2 220145681 downstream 483 NO different 2 probes, 1 GBX2 0.33 0.43 0.012 NA 2 236742443 upstream 1052 YES different 2 probes, 1 LSAMP 0.49 0.62 0.017 NA 3 117646384 downstream 684 YES different 2 probes, 1 TLL1 0.04 0.16 0.010 NA 4 167013921 downstream 61 YES different 2 probes, 1 CDH6 0.13 0.24 0.012 NA 5 31230167 downstream 614 YES different 2 probes, 1 IL6ST 0.19 0.29 0.024 0.07 5 55326503 downstream 17 YES different 1 probe BHMT2 0.19 0.30 0.012 NA 5 78401557 downstream 218 YES different 7 probes, 1 LOX 0.09 0.22 0.012 NA 5 121441696 downstream 157 YES different 2 probes, 1 PCDHB15 0.34 0.44 0.039 NA 5 140605243 upstream 88 YES different 2 probes, 1 PCDHGB7 0.42 0.58 0.014 NA 5 140777470 downstream 4 YES different 5 probes, 3 GPX3 0.13 0.28 0.010 0.04 5 150380194 downstream 82 YES different 2 probes, 1 CRISP2 0.18 0.30 0.028 NA 6 49789192 downstream 41 YES different 2 probes, both COL21A1 0.44 0.31 0.024 NA 6 56220250 downstream 51 YES different 2 probes, 1 MGC9712 0.61 0.49 0.007 NA 7 1562576 upstream 303 NO different 2 probes, 1 GIMAP7 0.45 0.56 0.004 0.01 7 149842112 upstream 766 YES different 2 probes, 1 DMRT3 0.51 0.64 0.017 NA 9 965856 upstream 1108 YES different 2 probes, 1 OR1N1 0.63 0.50 0.002 NA 9 124329941 upstream 548 NO different 2 probes, 1 TUBB8 0.64 0.74 0.010 NA 10 85127 downstream 51 YES different 2 probes, 1 IRXL1 0.30 0.41 0.020 NA 10 28075637 upstream 884 YES different 2 probes, 1 SORCS3 0.24 0.40 0.008 NA 10 106389947 upstream 902 YES different 1 probe OR10A5 0.40 0.50 0.012 NA 11 6823856 downstream 366 NO different

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Methyl. Methyl. R2 Spearman Probe Location Distan Mapped to Probes in the MW p Symbol Average in Average in Correlation Chr Genomic relative to ce to CpG array value Controls Cases Meth/Exp coordinate TSS TSS Island? 7 probes, 1 CD44 0.20 0.32 0.045 0.04 11 35117038 downstream 45 YES different 2 probes, 1 LTBP3 0.47 0.36 0.008 NA 11 65081734 downstream 272 YES different 2 probes, 1 KCTD14 0.31 0.49 0.002 0.05 11 77411928 downstream 6 YES different 2 probes, 1 FLI1 0.06 0.23 0.017 NA 11 128069390 downstream 191 YES different 2 probes, 1 LOC89944 0.12 0.22 0.039 NA 11 133707546 downstream 530 YES different 2 probes, 1 KCNJ8 0.25 0.14 0.033 0.07 12 21818879 downstream 135 YES different 2 probes, 1 BIN2 0.71 0.58 0.039 NA 12 50003941 downstream 264 YES different 2 probes, 1 EFS 0.39 0.26 0.008 0.02 14 22905852 upstream 1170 YES different 2 probes, 1 CYP11A1 0.60 0.49 0.008 0.23 15 72447318 upstream 298 NO different 6 probes, 1 CRABP1 0.20 0.41 0.045 NA 15 76419916 downstream 166 YES different 2 probes, 1 NOL3 0.37 0.60 0.039 0.02 16 65765748 downstream 377 YES different 2 probes, both PYY 0.58 0.69 0.012 NA 17 39438719 upstream 1356 YES different 2 probes, 1 MPO 0.55 0.68 0.033 NA 17 53714577 upstream 1282 NO different 2 probes, 1 ONECUT2 0.73 0.83 0.045 NA 18 53254351 downstream 436 YES different 2 probes, 1 C18orf22 0.34 0.46 0.045 0.03 18 75894170 upstream 1176 YES different 2 probes, 1 TLE6 0.59 0.49 0.001 NA 19 2928843 downstream 281 NO different 2 probes, 1 DAPK3 0.34 0.22 0.000 0.02 19 3920736 downstream 90 NO different 2 probes, 1 ZNF266 0.53 0.64 0.020 0.12 19 9407752 upstream 518 YES different 2 probes, 1 SHKBP1 0.34 0.47 0.028 0.00 19 45774154 upstream 476 YES different 2 probes, 1 ZNF135 0.19 0.34 0.045 NA 19 63262280 upstream 144 YES different 2 probes, 1 TRIB3 0.67 0.57 0.024 0.09 20 308745 upstream 563 NO different 2 probes, 1 PLCB1 0.09 0.21 0.010 0.04 20 8060754 upstream 542 YES different Genes were selected if they had at least one probe with differences in methylation between IUGR cases and controls. In red are the genes with higher correlation between methylation and expression (underlined, the ones for which there is a difference in expression between cases and controls); GPX3 has a high power correlation with expression, not linear. In blue are the genes for which there is a low correlation between methylation and expression. In black are the genes for which there is no valid expression data.

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Figure 9.3 shows a color coding table similar to the one presented in the analysis of the CpGs mapping to ICs (Chapter 5). For this table I selected the genes that had been identified through the quantitative analysis. It shows, as expected for the selected genes, that there are more outliers and far outliers in cases than controls.

9.4.1.4 Cross validation of the Illumina® methylation data using WNT2 methylation data of Chapter 7

In the analysis of the Agilent® methylation arrays (Chapter 7 of this thesis) a candidate gene – WNT2 - had been identified by the selection criteria and subsequently validated as having a differential promoter methylation between cases and controls. In the Agilent® arrays, 3 samples

from IUGR cases had methylation values (measured in log2 ratios) from probes mapping to WNT2 promoter suggesting a big difference in methylation with the remaining 13 samples (5 cases and the 8 controls). There are 2 probes in the Illumina® methylation arrays for WNT2, one 459 bp prior to the TSS (probe 1) and the other 51 bp after the TSS (probe 2). They both map to the region that was identified by the Agilent® array analysis. Probe 2 maps to the region targeted by the WNT2 promoter targeted pyrosequencing assay. There were 4 more cases and 4 more controls in the Illumina® methylation arrays cohort than in the Agilent® arrays cohort. When selecting the additional samples for the Illumina® methylation arrays, I have selected one control and 2 more cases with high WNT2 promoter methylation (WNT2PrMe). Thus, there were 5 of 12 case samples and 1 of 12 control samples with high WNT2PrMe in the Illumina® methylation arrays cohort. Therefore I can use the data from the pyrosequencing assay of WNT2 (details of the assay are described in Chapter 7) to validate the readings provided by those 2 CpGs. The Pearson correlations (R) between the methylation values provided by both probes and the methylation values provided by the pyrosequencing assay were both 0.94, with a slope for the targeted CpG of 1.18 and a slope for the other CpG of 0.87 (n=24).

9.4.2 Gene expression analysis for the differentially methylated genes

Results of the methylation differences were compared to expression data obtained from 19 matching Illumina® single channel HumanHT-12 v3 Expression BeadChip arrays (9 controls and 10 cases).

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Figure 9.3: Detailed categorical results of all the 60 CpGs selected as significantly different between cases and controls, excluding the X chr. mapping probes. The table in this figure details the results of the analysis of all CpGs that were identified by quantitative analysis as being differentially methylated between cases and controls. Each line of the table corresponds to one of the CpG sites. The 1st column corresponds to the gene symbol, the 2nd to the CpG probe ID (provided by the Illumina® annotation). The columns of cells colored in white correspond to the data analysis results for each of the CpG sites. It details the sample categorization of each CpG site. The numbers correspond to the category of the probe for the sample: 0 is normal, ±1 is positive or negative outlier and ±2 is positive or negative far outlier. For easier visualization, color coding was added as follows:

2 Positive far outliers with a high difference in methylation from the controls 2 Positive far outliers with a low difference in methylation from the controls 1 Positive outliers with a high difference in methylation from the controls ‐1 Negative outliers with a high difference in methylation from the controls ‐2 Negative far outliers with a low difference in methylation from the controls ‐2 Negative far outliers with a high difference in methylation from the controls High differences correspond to more than 10% methylation above or below the 3rd and 1st quartile of the controls, respectively. The last rows correspond to sums of the values above. It shows that the number of far outliers in each sample from cases is higher than in samples from controls.

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SYMBOL TargetID C1227 C1243 C1256 C3010 C3040 C3086 C4196 C7633 C8331 C8539 C9363 C9375 F134 F620 F7264 F7285 F7740 F8174 F8177 F8298 F8666 F8678 F8811 F9157 SLC45A1 cg11283860 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 -1 0 0 0 0 0 0 0 0 CLDN19 cg11172423 0 1 0 0 0 0 0 2 0 -2 0 -1 -2 -2 -2 0 -2 0 -1 -2 -2 -1 -1 0 TAL1 cg19797376 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CHI3L2 cg10045881 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 PAPPA2 cg10994126 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 -2 0 0 -1 0 0 0 -2 -1 ASTN cg14659404 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 GPR25 cg21870884 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 PKP1 cg19570545 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SFT2D3 cg11206634 -2 0 0 0 0 -1 0 1 0 0 0 0 0 0 -2 -2 0 0 0 0 -2 0 -2 0 HOXD4 cg12127282 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 1 0 110 0 ACADL cg14795968 0 0 0 0 0 0 0 1 0 0 0 0 120 1 0 1110 0 0 2 ACADL cg09068528 0 0 0 0 0 0 0 2 0 0 0 0 220 2 0 2220 0 0 2 INHA cg25928444 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 -1 0 GBX2 cg23095584 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 110 1 0 0 0 0 1 LSAMP cg14294758 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 TLL1 cg08570521 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 2 0 0 0 0 0 2 CDH6 cg05822100 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 110 110 0 0 1 IL6ST cg21950518 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 BHMT2 cg03400060 0 0 0 0 1 0 0 0 0 0 0 0 1120 0 120 0 0 0 0 LOX cg01429321 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 220 0 0 0 2 PCDHB15 cg11368643 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PCDHGB7 cg23563234 0 0 0 0 0 0 0 0 0 0 0 0 110 0 0 0 0 0 0 0 0 0 GPX3 cg21504918 2 0 0 0 2 0 0 0 0 0 0 0 220 2220 0 220 0 GPX3 cg21516478 2 0 0 0 2 0 0 0 0 0 0 0 220 2220 0 220 0 GPX3 cg17820459 2 0 0 0 2 0 0 0 0 0 0 0 220 2220 0 220 0 CRISP2 cg04595372 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 0 COL21A1 cg13830624 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 COL21A1 cg05157725 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 MGC9712 cg00411097 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 GIM A P7 cg01227741 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 DMRT3 cg26489108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 OR1N1 cg16473288 0 0 0 0 0 0 -1 0 0 0 0 -1 0 -2 0 -2 0 0 -1 0 -2 -1 TUBB8 cg01036012 0 0 0 0 0 0 0 0 0 0 0 0 2 0 1110 1 0 1 0 0 1 IRXL1 cg02161900 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SORCS3 cg09551147 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 OR10A5 cg22951794 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 CD44 cg17640322 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 LTBP3 cg08965235 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 KCTD14 cg17272843 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 FLI1 cg17872757 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 222120 0 LOC89944 cg11012046 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 KCNJ8 cg01226811 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 BIN2 cg10590292 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 EFS cg07197059 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 -1 0 -1 0 0 0 0 0 0 -1 SNRPN cg27644292 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 110 0 0 0 0 0 0 CYP11A1 cg17790333 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 -2 0 0 0 0 -1 0 -2 0 CRABP1 cg17133183 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 NOL3 cg00332745 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PYY cg09467501 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 PYY cg26796190 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 MPO cg04988978 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ONECUT2 cg02250594 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C18orf22 cg04727522 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 TLE6 cg08794763 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 DAPK3 cg03752885 0 0 0 0 0 0 0 0 0 0 0 0 -1 -1 -2 -1 0 0 0 0 -1 -1 ZNF266 cg21116314 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SHKBP1 cg09381003 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 ZNF135 cg16638540 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 TRIB3 cg05067973 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 -1 0 0 0 0 0 0 0 PLCB1 cg23657409 0 0 0 0 0 0 0 0 0 0 0 0 0 0 220 0 1 0 0 0 0 2 Hypermethylated 3 0 0 0 3 0 0 2 0 0 0 0 9628375234 0 5 Hypomethylated 1 0 0 0 0 0 0 0 0 1 0 0 1125110 120 4 0 Figure 9.3: Detailed categorical results of all the 60 CpGs selected as significantly different between cases and controls, excluding the X chr. mapping probes.

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Furthermore, expression differences between cases and controls were assessed using expression data from the same arrays and two additional arrays obtained from two samples for which no methylation array data was available. The assumption underlying these expression correlation analyses is that, at least for some of the genes with differences in methylation between cases and controls, an effect could still be appreciated in the expression of related genes, at the time of delivery. However this was not expected to be valid for all genes.

The detailed description of each of the 21 (19 + 2) samples here analyzed are presented in Table 3.3 (identifiable by “9” under the heading “Express Illumina”).

All 21 expression arrays hybridized with good quality RNA samples passed quality control criteria (detailed in 3.10). Reproducibility of the expression arrays, assessed by the correlation between 2 pairs of technical replicates hybridized into 2 different slides has already been reported in Chapter 5 (5.4.4).

The expression arrays assess 25,200 annotated genes. Each of those had, in the 21 expression arrays selected for this study, a maximum of 21 expression data points. Only 10,687 of those genes, (9,790 mapping to autosomes), had valid data, i.e., at least 16 detected data points out of the 21 possible (3/4).

9.4.2.1 Correlation between methylation and expression for the differentially methylated genes

The methylation arrays have 27,578 probes. From all the probes in the methylation array, 27,321 map to 14,353 of the 25,200 genes represented in the expression arrays, but only 13,531 map to 6,964 of the 9,790 autosomes with valid expression data. The 90th percentile for the correlations between methylation and expression, measured by Spearman R2 for these probes, was 0.22 (R=0.47). This was arbitrarily selected as the cut-off level above which I would consider methylation to be correlated with expression.

Twenty one of the 64 probes selected by the quantitative methylation analysis, corresponding to 19 genes (presented in blue or red font in Table 9.1), had valid expression data for the corresponding gene, whereas the remaining 39 probes, corresponding to 36 autosome genes, have differences in methylation but without valid expression information.

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From the 21 probes with valid corresponding expression data, only 7, corresponding to 5 genes, have a Spearman correlation with an R2 above the 90th percentile - CYP11A1, PAPPA2, INHA GPX3 and SFT2D3 (presented in red font in Table 9.1).

One of the genes for which there was no valid expression data available – ACADL – was notable because 1) it had 2 probes present in the array and both showed an association between variation in methylation and the SGA placental lesions phenotype, 2) had the highest differences between cases and controls and 3) it was also selected in more than one of the analyses performed in the data generated by the Agilent® arrays study (Chapter 7, Table 7.3).

9.4.2.2 Identification of differences in expression between cases and controls for the differentially methylated genes that correlated with expression

I then assessed the genes with valid expression data for differences in expression between the cases and the controls, using the data from the 21 Illumina® expression arrays, and compared results with the list of genes for which there was a correlation between methylation and expression. It is interesting to note that of the 5 genes for which changes in methylation were considered to show an association with expression, 3 of them – CYP11A1, PAPPA2 and INHA (underlined in Table 9.1) - also had a statistically significant difference in expression between cases and controls (non-multiple testing corrected Mann-Whitney U test p value < 0.05). Furthermore, 2 of those - PAPPA2 and INHA – plus a third one – GPX3 - have known roles in placenta development and / or disease – see “Discussion” for details about each one of these genes. A fifth gene with correlation between methylation and expression - SFT2D3 (vesicle transport protein SFT2C) – is not known to be associated with placenta development and its expression is not statistically significantly different between cases and controls.

I thus decided to select PAPPA2, INHA, CYP11A1, GPX3 and ACADL as having the highest priority for validation and follow-up analysis using the model previously applied in the follow- up analysis of WNT2 (Chapter 7). Details of methylation and expression for these genes in relation to their association with the growth restriction phenotype used in this study are presented in Figure 9.4 and Table 9.2.

Given its singularity, further presentation of results for GPX3 is warranted. The distribution of the methylation, seemingly trimodal (Figure 9.4, top plot of column C), suggests a genetic

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Figure 9.4: Details of the genes selected for further follow-up: A: GPX3, PAPPA2, CYP11A1, INHA. Dot plots on column A show correlation between methylation and expression. Cases are represented by solid circles and controls by open circles. Dot plots on column B show methylation levels in cases and controls. Dot plots in column C show expression levels in cases and controls except for GPX3, which shows, in this column, the distribution of GPX3 methylation. GPX3 does not show an association between expression and the type of sample.

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Figure 9.4: Details of the genes elected for further follow-up: A: GPX3, PAPPA2, CYP11A1, INHA.

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Figure 9.4: Details of the genes elected for further follow-up: B: ACADL. Dot plot of ACADL methylation versus type of sample. ACADL does not have valid expression data and thus it is not possible to verify the association between methylation and expression neither the association of expression with the condition

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Figure 9.4: Details of the genes elected for further follow-up: B: ACADL.

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Table 9.2: Details of differentially methylated candidate genes proposed for validation # of Statistically Probe probes Methylation in significant location Correlation between in the Cases versus association Genomic relative to methylation and Symbol Gene name array / Controls (p between Comments location TSS expression (p value # of value and expression and (selected in and effect size**) probes effect size*) IUGR (p value bold) selected and effect size*) pappalysin 2 Less methylated Inverse correlation Higher expression PAPPA2 1q25 1/1 191 bp ds isoform 1 0.002, 0.65 <0.001, -0.70 0.009, 0.62 cytochrome P450; 298 bp us Less methylated Inverse correlation Higher expression CYP11A1 subfamily XIA 15q23-q24 2/1 66 bp ds 0.009, 0.54 0.02, -0.47 0.01, 0.6 precursor acyl-Coenzyme A No correlation More dehydrogenase, 24 bp us (expression too low to ACADL 2q34-q35 2/2 methylated Unknown long chain, a.k.a. 332 bp ds be measurable by 0.01, 0.52 LCAD array) inhibin alpha 1116 bp us Less methylated Inverse correlation Higher expression INHA 2q33-q36 2/1 subunit precursor 483 bp ds 0.008, 0.55 0.007, -0.54 0.03, 0.5 More 138 bp us methylated (all Inverse power (non- The last 4 probes map to one CGI. plasma glutathione 82 bp ds probes, only 3 linear) correlation Not associated The distribution of the methylation, GPX3 peroxidase 3 15q23-q24 5/3 220 bp ds pass selection (0.047, -0.41) 0.2, 0.29 seemingly tri-modal, suggests a precursor 409 bp ds criteria) genetic polymorphism association. 612 bp ds 0.007, 0.57 Us: upstream of TSS; ds: downstream of TSS; TSS – transcription start site; IUGR – intrauterine growth restriction; CGI – CpG island * Mann-Whitney U test and Spearman correlation, **Regression p value and R GPX3 - For power correlation P value – 0.047, R – 0.41, R2 – 0.17, Adjusted R2 – 0.13 (for linear and logarithmic correlations - P value – 0.072, R – 0.37, R2 – 0.14, Adjusted R2 – 0.10; for logarithmic correlation - P value– 0.072, R – 0.37, R2 – 0.14, Adjusted R2 – 0.10), Spearman -0.35, p 0.09 PAPPA2 – For linear correlation - P value– <0.001, R – 0.70, R2 – 0.50, Adjusted R2 – 0.45, Spearman -0.68, p <0.001 CYP11A1 - For linear correlation - P value– 0.022, R – 0.47, R2 – 0.22, Adjusted R2 – 0.16, Spearman -0.48, p 0.02 INHA - For linear correlation - P value– 0.007, R – 0.54, R2 – 0.29, Adjusted R2 – 0.24, Spearman -0.51, p 0.01

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polymorphism association. Methylation expression correlation plots show that, for the selected 4 genes with a high correlation between methylation and expression, there is an inverse correlation. However, while for GPX3 the only correlation that is statistically significant is a power correlation, given by the regression formula provided in the figure, for the other 3 genes the correlation is linear. Both, the power correlation between methylation and expression and the tri-modal distribution of the methylation values raise the hypothesis that it may be associated with a single nucleotide polymorphism (SNP). For the other genes, both the linear methylation expression correlations and the methylation distributions are not as suggestive of SNP associations and are more likely to be stochastic. Furthermore, whereas for 3 of the genes – PAPPA2, INHA and CYP11A1 - cases are associated with lower methylation and higher expression, for GPX3, cases are associated with higher methylation but not with expression, at delivery.

9.4.2.3 Cross validation of the Illumina® expression data using WNT2 expression data of Chapter 7

Similar to the methylation data arrays, I also used data obtained for WNT2 in the study reported in Chapter 7, for the same samples, to partially validate the results of the Illumina® expression arrays. Expression data was obtained using a targeted technique - quantitative reverse transcriptase PCR - and could thus be used for this purpose. Correlation between the array data and expression, measured with Pearson R, was 0.67 (n=21).

In summary, the analysis of the data provided by the Illumina® methylation arrays has demonstrated 59 genes for which variation in promoter methylation is more likely to have a biologically meaningful association with fetal growth, with the presence of placental lesions or with both. Of those, 4 genes demonstrated a high correlation between methylation and expression when adding the data provided by the expression arrays and have a known role in placenta development. Those are the most interesting to pursue follow-up studies. From the remaining ones, 18 have a low correlation between methylation and expression and for 36 the expression arrays did not detect valid expression values. As expression was measured after delivery and as the sensitivity of the expression arrays may be too low to detect low gene expression levels, it is not possible to rule out an effect on gene expression that may have occurred earlier in development and/or that is not measurable by the use of this technology.

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9.5 Discussion

The data presented here allow for the generation of hypotheses of associations between variation in methylation in the promoter of several genes and growth restriction. Such hypotheses need to be tested in extended sample sets as previously modeled with the WNT2 promoter methylation variant. The confirmation of some of these associations will provide further evidence to support the hypothesis of a role for epigenetic mechanisms, specifically DNA methylation of gene promoters, in placental dysfunction associated with poor fetal growth.

Some of the genes identified in this analysis as candidates are known to have a role in placental development and disease and show differences in methylation of their promoters associated with differences in gene expression. However it is interesting to note that for the majority of genes for which promoter methylation differences was found between cases and controls (51 genes) no corresponding variation in expression was demonstrated. Among those, the majority (34) did not have valid expression data in the expression arrays. This lack of data means that the expression array signal is not high enough to be reliably distinguished from background. Explanations other than absence of expression for that particular gene in the placenta at the time the sample was collected are possible. It could be that the expression is too low to be reliably captured by the array. Other methods of expression assessment should be sought for these genes. In the case of the 17 genes for which valid expression is available but there was no correlation between methylation and expression, a possible explanation for the association between methylation variation and the abnormal growth phenotype is not clear. However it is important to note that the expression and methylation measurements were taken after delivery. Thus, it is possible that the biologic effect of the methylation variation occurred earlier in pregnancy. Finally, one has to consider the possibility that some of them may actually be false positives that were not filtered out by the lack of correction for multiple testing.

For future validation and biologic characterization of the array findings it makes sense to start with the genes for which a correlation between expression and methylation was demonstrated, especially if an association between the growth restriction phenotype and expression was also identified and / or if there is a known role in placenta development and / or disease. This was the case for PAPPA2, GPX3, INHA and CYP11A1. I also selected ACADL as a priority candidate for

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further downstream analysis in spite of a lack of valid expression array data because both of the probes represented in the array showed the highest differential methylation between cases and controls. Further, this gene was also selected as a candidate in the analyses done in the Agilent® arrays. Finally fatty acid oxidation defects are associated with HELLP (Hemolysis, Elevated Liver enzymes and Low Platelets) syndrome, a preeclampsia related condition, in the mothers of affected fetuses (Modre-Osprian et al., 2009). Details on each of the highest priority candidate genes follow.

GPX3

GPX3 is one of 8 glutathione peroxidases. These enzymes catalyze the reduction of hydrogen peroxide, organic hydroperoxide, and lipid peroxides by reduced glutathione (Whalen and Boyer, 1998) and thus protect the cells against oxidative damage. They reduce reactive oxygen species (ROS) in the vasculature and maintain the bioavailability of NO, thereby preserving normal endothelium function and an antithrombotic vascular environment (Miyamoto et al., 2003). During normal pregnancy an increased antioxidant capacity is associated with increasing gestational age. Oxidative stress is increased in pregnancies complicated by preeclampsia and IUGR (Burton and Jauniaux, 2004; Burton et al., 2009; Hung et al., 2001; Wang et al., 1992). A recent published work reports reductions in proteins GPx1, 3 and 4 expression and in GPx overall activity in placentas from women with preeclampsia (Mistry et al., 2010).

Interestingly, compromise of oxidative stress defense mechanisms in general and of GPX3 in particular appear to be also associated with vascular diseases such as coronary artery disease (Dogru-Abbasoglu et al., 1999; Porter et al., 1992) and familial arterial ischemic stroke (Kenet et al., 1999). If the association suggested by my data is identifiable not only in the placenta but also in other embryo derived tissues, this could be a potential link between low birthweight percentile and late onset adult vascular disorders. Furthermore, recently, an association between a GPX3 promoter H2 haplotype with decreased transcriptional activity and arterial ischemic stroke in young individuals has been reported (Voetsch et al., 2007). It is possible that the variation in methylation detected in the placenta is driven by genetic polymorphism(s), as suggested by the distribution in methylation. Therefore promoter methylation of GPX3 could drive this association.

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GPX3 is included in the Gene Ontology Antioxidant “Molecular activity” gene family. Interestingly, probes mapping to 2 out of 30 other genes belonging to this family present in the Illumina® methylation array such as GPX7 (2 probes in the array) and SOD3 (1 probe in the array), also show variation in methylation among samples, variation through pregnancy and an inverse correlation between methylation and expression, although there was no correlation of the individual methylation status of these genes with the placental related fetal growth restriction phenotype (data not shown). All these data suggest that an important antioxidant pathway in placenta is epigenetically regulated.

PAPPA2

PAPPA2 (a.k.a. PAPP-E) codes for PAPP-A2 which is a member of a family of metalloproteinases. Another protein in this family, pregnancy-associated plasma protein-A (PAPP-A), has been the subject of intensive study. This is due to the well accepted association between low maternal serum levels of PAPP-A and Down syndrome as recently reviewed (Kirkegaard et al., 2010). PAPP-A2 has a structure similar to PAPP-A, with which it shares 45% of its residues (Farr et al., 2000; Page et al., 2001). These two proteins have similar functions. Whereas PAPP-A cleaves insulin-like growth factor-binding protein-4 (IGFBP-4), PAPP-A2 cleaves IGFBP-5 specifically at one site, between Ser-143 and Lys-144, releasing IGF1, (Overgaard et al., 2001).

Pappa2 is highly expressed in mouse placenta, as is the case in humans. Specifically, it is expressed at the interface of the maternal and fetal layers of the mouse placenta at all gestational stages studied (10.5-16.5 days post coitum). Similarly, PAPPA2 is expressed in the syncytiotrophoblast layer of human placental villi and is also detected in some invasive extravillous trophoblasts in the first trimester. Thus, both PAPP-A and PAPP-A2 likely act as local regulators of IGF bioavailability through the cleavage of IGFBPs (Boldt and Conover, 2007). IGF1 signals are transduced to a complex network of intracellular lipid and serine/threonine kinases through multiple tyrosine phosphorylation reactions. The end result is cell proliferation, modulation of tissue differentiation, and protection from apoptosis (Cianfarani et al., 2007). These results are consistent with a model whereby PAPP-A and PAPP-A2 cleave IGFBPs produced in the maternal decidua to promote feto-placental growth.

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PAPP-A and PAPP-A2 may play parallel roles in human and mouse placenta. PAPP-A2 protein is detectable in the circulation of pregnant mice and humans during the first trimester and at term (Wang et al., 2009a). PAPPA2 mRNA and protein, both in placenta tissue (in invasive cytotrophoblasts and syncytiotrophoblasts) and maternal serum, has been reported to be increased in preeclampsia (Winn et al., 2009) and HELLP syndrome (Buimer et al., 2008; Nishizawa et al., 2008). IGFBP5 mRNA, but not IGFBP5 protein, was also found to be increased in preeclampsia (Nishizawa et al., 2008). All this, together with the known association between low PAPP-A and placental disease (Kirkegaard et al., 2010) are consistent with my preliminary data suggesting that increase of PAPP-A2 production is a type of compensatory mechanism for the decrease in PAPP-A. However, in my cohort, no association was found between the growth restriction phenotype and expression or methylation of PAPP-A. It is again important to remember that the placenta sampling in my study was performed late in pregnancy whereas the association between low maternal serum PAPP-A and placenta insufficiency is described for serum determinations occurring during the late first trimester.

Thus I predict that methylation I identified in the promoter of PAPPA2 in the low birthweight cases is an epigenetic mechanism that upregulates the production of this protein in certain placental disorders.

Furthermore, the IGF1 axis, in which PAPPA and PAPPA2 are involved, seems to sometimes be dysregulated in IUGR, and it has been suggested to be involved in the later development of insulin resistance and diabetes in adults born as IUGR (Setia and Sridhar, 2009).

INHA

INHA codes for the inhibin alpha subunit precursor. The inhibin alpha subunit dimerizes with inhibin beta A or beta B to produce Inhibin A or Inhibin B, respectively. High levels of maternal Inhibin A have long been known to be associated with various adverse obstetrical outcomes, including fetal trisomy 21, IUGR, preeclampsia (Gagnon et al., 2008). Serum determinations of Inhibin A in the second trimester, together with other analytes, are currently used clinically to identify patients at increased risk for adverse outcomes in spite of the fact that, at present, no specific treatment protocol is available.

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Inhibin A was found by Morris and colleagues to be the best maternal serum marker for the prediction of preeclampsia (Morris et al., 2008). Maternal blood circulating mRNA for INHA was also found to be increased in preeclampsia (Farina et al., 2006).

Also for pregnancies with IUGR, higher levels of maternal serum inhibin A and B than in controls have been found (Morpurgo et al., 2004). Interestingly, significantly lower expression of the inhibin-alpha subunit in extravillous trophoblast was observed in IUGR compared to normal pregnancies, while the inhibin-alpha immunostaining was significantly upregulated in syncytiotrophoblast (Mylonas et al., 2006).

My preliminary data suggest that epigenetic mechanisms are likely to be involved in the upregulation of inhibin A found in association with the most common clinical expressions of poor placenta development, i.e. preeclampsia and IUGR.

CYP11A1

CYP11A1 encodes cytochrome P450, family 11, subfamily A, polypeptide 1 which is “a member of the cytochrome P450 superfamily of enzymes. The cytochrome P450 proteins are monooxygenases which catalyze many reactions involved in drug metabolism and the synthesis of cholesterol, steroids and other lipids. This protein localizes to the mitochondrial inner membrane and catalyzes the conversion of cholesterol to pregnenolone, the first and rate-limiting step in the synthesis of steroid hormones. Two transcript variants encoding different isoforms have been found for this gene. The cellular location of the smaller isoform is unclear since it lacks the mitochondrial-targeting transit peptide” (from RefSeq (Pruitt et al., 2007)).

A role for cytochrome P450 family members has been described in animal models of IUGR. Reduced serum corticosterone concentrations in nutritionally deprived rat fetuses were interpreted to result from decreased expression of genes involved in steroidogenesis such as cytochrome P450 11beta-hydroxylase (Bibeau et al., 2010).

Since cytochrome P450 family members are involved in detoxification it is not surprising that several groups have been studying the consequences of exposure to environmental substances in association with altered activities or functional polymorphisms in cytochrome P450 genes. For instance, Delpisheh and colleagues report an increased risk of fetal growth restriction in mothers

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who carried a CYP1A1 allelic SNP variant and smoked during pregnancy (Delpisheh et al., 2009). In a study reported by Wang and colleagues, prenatal nicotine exposure resulted in a significant inhibition of fetal growth. CYP2E1 and CYP1A1 gene expression was induced in maternal liver and placenta by exposure to nicotine. The resulting increase in lipid peroxidation was suggested to increase oxidative stress, a mechanism suspected to be involved in placental dysfunction (Wang et al., 2009b).

Ethanol exposure, another known risk factor for IUGR, was also found to have an increase in its effect on growth restriction in association with a specific A1A1 CYP17 genotype (Delpisheh et al., 2008). Furthermore it was found to inhibit fetal HPA axis activity. This inhibition was thought to result from ethanol-induced maternal HPA axis activation with resulting high glucocorticoid levels. This would lead to fetal overexposure to maternal glucocorticoid eventually resulting in the inhibition of the fetal HPA axis (Liang et al., 2010). Finally high exposure to particulate matter less than 10 microm during the 1st trimester increased the risk for reduced birth weight in concert with certain CYP1A1 polymorphic genotypes in Korean women (Suh et al., 2007).

Taken together the above studies support the hypothesis of environment and/or genotype mediated changes in promoter methylation of cytochrome P450 family members as the expression modulating mechanism associated with growth restriction. This also enhances the concept of an epigenetically mediated long term effect via modifications of corticosteroid and detoxification pathways.

ACADL

Again from RefSeq database (Pruitt et al., 2007), “the protein encoded by this gene belongs to the acyl-CoA dehydrogenase family, which is a family of mitochondrial flavoenzymes involved in fatty acid and branched chain amino-acid metabolism. This protein is one of the four enzymes that catalyze the initial step of mitochondrial beta-oxidation of straight-chain fatty acid. Defects in this gene are the cause of long-chain acyl-CoA dehydrogenase (LCAD) deficiency, leading to nonketotic hypoglycemia.” Most cases of fatty acid oxidative defects (FAOD) have an autosomal recessive mode of inheritance. FAOD, in fetuses represent a very high risk factor for maternal liver complications in the carrier mother during pregnancy. These include the HELLP syndrome, acute fatty liver of pregnancy, and preeclampsia. Higher maternal levels of acyl-carnitines and

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long chain fatty acids, coming from a non-metabolizer fetus, are thought to be toxic to the maternal liver (Shekhawat et al., 2005). In a recent study of 33 preeclamptic mothers, the maternal plasma levels of long and very-long chain acylcarnitines were significantly higher than controls (Thiele et al., 2004). The first reference to the association of FAOD and pregnancy complications was reported by Wilcken and colleagues, who described it specifically for LCHAD deficiency (Wilcken et al., 1993). Since then such association have been reported for other types of FAOD (Shekhawat et al., 2005). However ACADL (a.k.a LCAD) deficiency has not been specifically reported in the context of a maternal pregnancy complication. One possible reason for this is that a severe phenotype of such deficiency would be an embryonic lethal (Modre-Osprian et al., 2009). It is thus possible that a milder decrease in LCAD expression caused by variation in its promoter methylation could result in a placental disorder phenotype such as IUGR. Since fetal energy requirements rely on lipid metabolism because of the lack of glycogen reserves (Shekhawat et al., 2005), dysregulation of this process by low expression of LCAD could be one of several factors interfering with normal placental development and/or fetal growth.

Since I did not have valid expression data for ACADL, other more sensitive techniques for the assessment of its expression in placenta, such as qRT-PCR, could be used to test for the hypothesis of an association between increased promoter methylation and lower expression.

In conclusion, the results of my study give further support to the hypothesis that epigenetic mechanisms are part of the complex molecular dysregulation underlying the abnormal development of the placenta that results in poor fetal growth. To better and more precisely understand the role of each of the methylation variants I have identified in this study, association studies with targeted analysis of methylation and expression of each of the candidates will be required. The data are more consistent with the concept of multiple additive small effect factors (“hits”) underlying the etiopathogeny of complex disorders, such as placental insufficiency, rather than few high effect factors. However it seems that multiple “hits” are probably required in order to have a final phenotype. Thus, each of the factors, when analyzed in isolation, is likely to have small effects, requiring large cohorts to prove associations. Finally, to prove causal relationships, development of models in which methylation can be experimentally modified will be required.

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Chapter 10: Summary and Conclusions, General Discussion and Future Directions

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10.1 Summary and Conclusions

My hypothesis that epigenetic mechanisms are involved in placental development and that its alterations lead to downstream impact on fetal development and growth was supported by my findings.

10.1.1 Candidate gene promoter regions with methylation changes in association with IUGR

There were several specific genomic locations whose variation in methylation was found to be associated with poor fetal growth (e.g. WNT2) or likely to be associated with poor fetal growth, placental lesions or both, as in IUGR. Those regions do not map to known imprinting centers. Instead they map to promoters of genes with roles in placental development. Among these, PAPPA2, INHA, CYP1A1, GPX3 and ACADL were the best candidates for follow-up because:

1) The methylation of the promoters for PAPPA2, INHA, CYP1A1 and GPX3 correlate inversely with expression;

2) PAPPA2, INHA, CYP1A1 are candidates for differential expression between IUGR cases and controls;

3) Previously published data have demonstrated an association between variation of expression of PAPPA2, INHA, GPX3 and placental dysfunction;

4) ACADL had the strongest and most consistent (across more than one experimental platform) differences in methylation between cases and controls.

It was common for cases to have DNA methylation changes at multiple loci suggesting that a combination of several defects lead to adverse pregnancy outcomes. Although, for most of the identified genes, validation steps are still required, the concordant results of changes in methylation and expression for specific genes, as well as previously published independent reports linking these genes to placental disease and IUGR support their involvement in poor fetal growth.

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10.1.2 No consistent role for DNA methylation alterations of imprinting centers in IUGR

It was known that an epimutation in blood DNA in an imprinting center – H19 DMR – causes a growth restriction phenotype that, in the neonatal period is not distinguishable from other common forms of intra-uterine growth restriction (IUGR), such as that secondary to placental insufficiency (Horike et al., 2009). In spite of this, neither this imprinting center nor any of the CpGs mapping to other known imprinting centers on the arrays used demonstrated aberrations in methylation of placental DNA in strong association with IUGR. However there was a higher number of these methylation aberrations, in various loci, in the IUGR cases than in the controls. There were more hypermethylated CpGs mapping to imprinting centers and more hypomethylated CpGs mapping to gene promoters. This suggests that methylation aberrations are relatively common but it may be that there is a quantitative or qualitative threshold above which a deleterious effect on fetal growth may occur.

10.1.3 Cell type specificity of DNA methylation in the placenta

Even with the limited number of meaningful differences in DNA methylation between two of the main placental cell types – cytotrophoblasts and fibroblasts – it was apparent that placental DNA methylation correlated more with cytotrophoblast DNA methylation than with fibroblasts. The differences in DNA methylation between those two cell types, likely reflecting epigenetically regulated cell specific differences in gene expression, may be targets for potential epigenetic dysregulation associated with cell dysfunction. Interestingly, among the genes more methylated in cytotrophoblasts than fibroblasts, there was enrichment for a set of genes that have been found in cancer studies to function as tumor suppressor genes. Further, cytotrophoblasts are often described has having a tumor-like biologic behavior given their proliferative and invasive characteristics. DNA methylation of the promoter of tumor suppressor genes has been demonstrated to be one of the mechanisms important for silencing genes during the development of cancer (Herman and Baylin, 2003; Herranz and Esteller, 2007). My data suggest that the parallel behaviors between cancer cells and cytotrophoblasts is, at least in part, epigenetically determined.

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10.1.4 Evaluation of techniques used for DNA methylation assessment

Several microarray methods are currently available for the analysis of DNA methylation in a non-biased whole genomic approach. My comparative analysis of methylated DNA using immunoprecipitation coupled with CpG island microarrays (MeDIP-CGI-array) and sodium bisulfite conversion based microarray (BC-array) platforms will help other researchers choose the technology that is best able to address their specific hypotheses. I found that BC-arrays were more accurate for quantitation of DNA methylation and were easier to analyze, although the array itself is less comprehensive and more limited in the regional assessment of DNA methylation compared to the MeDIP-CGI-arrays. My evaluation was based on measurements by pyrosequencing of bisulfite converted DNA (BC-pyrosequencing). BC-pyrosequencing measures methylation at the nucleotide level and is one accurate and efficient method currently available to assess methylation at a specified locus.

10.2 General Discussion

I had expected to find larger effect sizes with DNA methylation alterations driving deregulation of placental development and fetal growth. However, my data suggest that these adverse pregnancy outcomes are associated with multiple small effects of different epigenetic factors underlying the phenotypes I have studied. These may be part of the primary cause or a secondary adaptation. The etiological heterogeneity of IUGR may explain the small effect sizes. Another possible explanation is that, at least for some of the epigenetic factors, their disturbance may be biologically so severe, that there is embryonic or fetal loss instead of growth compromise. I propose this to be more likely for the imprinting centers and for WNT2. Severe disruption of imprinting in animal models has embryonic lethal effects (Howell et al., 2001; Li et al., 1992). The same was observed in mice with a silencing engineered mutation of Wnt2 (Monkley et al., 1996). Furthermore none of the placental DNA samples I analyzed has shown either full methylation or complete loss of expression of WNT2, suggesting that such alterations would not be compatible with fetal survival.

The sample size I used in this thesis for the array screening step compares favorably with the current practices in the field. Still it may be underpowered to detect small effect sizes. This may

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explain why, for the array based lists of identified candidate regions, none remained statistically significant after correction for multiple testing. Consequently I added, as a second criterion for selection of candidate genomic regions, a requirement that the difference in methylation between the two compared groups be above a certain cut-off value. This second criterion limits the number of final calls to regions whose difference in methylation between the groups are more likely to be biologically significant and had the additional compensatory benefit of limiting the number of false positives. However, I do not consider this an important limitation of my methodology since my microarray analyses were the first of a two-step approach and false positives could be tolerated. I used the arrays as a screening step through which the data generated could provide candidates to be validated using a second independent technology. The selection criteria I chose also included biological information relevant to my main hypothesis. I was thus able to obtain a reasonable number of candidates for follow-up. A more comprehensive statistical analysis of the array data, complete with multiple testing evaluation, was not possible given the sample sizes available.

Driven by rising interest in the field, techniques for the study of epigenetics in general and DNA methylation in particular, have been the subject of rapid development in recent years. In section 1.4.1.1, I refer to many techniques currently used for the study of DNA methylation. However the ability to interpret the data generated by the use of the high throughput technologies, that is, the analytical components, has lagged behind the development of the data acquisition tools (Laird, 2010). These issues resulted in my use of 1) multiple platforms and 2) the investigation of several analytical approaches. I had to rely not only on recently published methods but also had to identify technical and statistical analyses limitations.

In summary, I provide in this thesis, data to support my hypothesis that epigenetic mechanisms and their dysregulation play an important role in the development of fetal growth compromise. Such mechanisms also serve as important candidates to explain the relationship between fetal growth restriction and adult onset diseases e.g. of the cardiovascular system.

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10.3 Future Directions

Considering future directions of my studies I will first propose what needs to be done as direct follow-up of my results. Secondly, I will propose studies to assess the role of my findings for the longer range in the context of my hypothesis that epigenetic mechanisms are mediators of the association between low birthweight and late onset adult disorders. Thirdly I will propose additional studies aimed at assessing the role of epigenetic variation in placenta and placental disease. Finally, I will propose how to address unanswered questions regarding the biological relevance of variation in DNA methylation.

10.3.1 Follow-up studies to validate the most promising candidates identified in my studies

As per the general approach I have been following, the first and immediate step to consider will be the confirmation of the methylation differences found in the candidate regions generated in Chapters 5 and 9 using a specific targeted methylation assessment technique. Pyrosequencing of bisulfite converted DNA (for the samples used on the array) is the validation technique that I would elect to use given its reproducibility and proven accuracy (Tost and Gut, 2007a). Given the consistent high correlation between Illumina® methylation arrays and pyrosequencing data generated to date in our laboratory, I foresee that the results identified in the array screening will be confirmed. As referred to in section 10.1, the candidates were prioritized based on their known biology and, secondarily, on the correlation between the methylation values and expression.

For the newly generated candidates, hypothesized associations with poor growth and placental lesions should be explored. For this goal, I suggest targeted methylation and expression studies of a larger cohort of samples, as modeled by the approach used for the WNT2 candidate region (as described in Chapter 7).

For candidates with confirmed association with poor growth, or other poor outcome phenotypes, consideration should be given to their assessment as early markers for the associated phenotype. Identification of early clinical makers for placental dysfunction phenotypes is currently an active research area, in spite of the lack of effective preventive intervention strategies (Gagnon et al., 2008; Hourrier et al., 2010; Proctor et al., 2009). It is possible that some of the currently

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proposed preventive strategies will be effective only in subtypes that could be defined by one or more early pregnancy molecular markers. Such a strategy has previously been successfully developed for the prenatal identification of patients to be submitted to preventive interventions. For instance, biochemical and ultrasound markers are currently employed to identify patients with increased risks for fetal aneuploidy thereby justifying the use of targeted invasive prenatal diagnostic interventions. This targeted intervention has a more favorable risk benefit ratio than if it were applied to the whole population (Hourrier et al., 2010). To achieve a parallel situation for IUGR, methylation and expression of the potential candidates would have to initially be assessed in normal late first trimester and/or early second trimester placenta, in order to confirm that the gene is expressed at that time of pregnancy. Sources for these types of samples could be voluntary terminations of pregnancy and/or discarded chorionic villi taken for prenatal diagnosis. Finally, prospective studies of secreted candidate products detectable in maternal serum could follow to achieve easily measured and reliable associations with poor outcomes. In this regard, WNT2, GPX3 and PAPPA2 are secreted proteins and are thus the most favorable candidates for this type of assessment. Furthermore, for both GPX3 and PAPPA2, ELISA-based clinical commercial tests are already currently available. Alternatively, direct methylation measurements of candidates in maternal plasma DNA could be considered even if the gene product is not a secreted protein. Plasma DNA bears the methylation marks of the tissue from which it originates. The use of such marks to identify fetal specific patterns in maternal plasma DNA has been suggested for prenatal diagnosis or screening of other conditions, primarily of severe genetic fetal diseases such as aneuploidies. Fetal specific methylation marks have been proposed as a method of enrichment for the fetal DNA fraction in maternal plasma. This would allow a more accurate measurement of the ratios of single nucleotide polymorphic alleles as a method to detect fetal chromosomal imbalances (Chan et al., 2006a; Dennis Lo, 2006). Abnormal methylation of placenta DNA, if identifiable in maternal plasma DNA, can thus be a potential alternative method to protein gene products to be explored for screening or diagnosis of associated placental or fetal diseases (Bellido et al., 2010).

In addition to follow-up studies to confirm the hypothesis of association between DNA methylation variation and placental insufficiency or fetal growth related phenotypes, other biological questions will need to be answered. One of the hypotheses generated based on the observed distribution of DNA methylation related to WNT2 and GPX3 is the possibility that

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single nucleotide polymorphisms (SNPs) may be driving the differences detected. Such allele specific methylation has been previously reported for other genes (Kerkel et al., 2008; Schalkwyk et al., 2010; Shoemaker et al., 2010). This hypothesis will need to be tested by association studies between the levels of methylation and SNPs in the region. Elevated numbers of SNPs in a given region and recognition that relevant SNPs may act over some distance may require high throughput techniques, such as arrays, to assess such associations.

Another type of genetic polymorphism that can underlie differences in methylation is copy number variation (CNVs). This type of polymorphism needs to be considered using targeted quantitative PCR assessment.

Related to allele specific methylation is parent of origin specific methylation and parent of origin allelic expression (imprinting). Yuen and colleagues identified the high WNT2 promoter methylation (WNT2PrMe) variant I described in section 7.4.3 (Yuen et al., 2009). They tested the two variants for allelic expression and found that the WNT2PrMe variant with increased methylation was indeed monoallelically expressed, whereas the less methylated variant was biallelic. Preliminary data from our placental samples (not shown) are in agreement with this report. However, more investigation is required to determine if the monoallelic expression is parent of origin -specific. The monoallelic expression found in highly methylated WNT2 promoter samples raises the possibility of an imprinting variant. Parental saliva samples have been collected for recently obtained placentas and will be used to test this hypothesis in informative family trios.

The identification of differences in methylation between cell populations in the placenta was suggested as another method to identify genes whose function is likely to be epigenetically regulated and, as such, to be additional targets for epigenetically related dysfunction. To enhance the identification of such genes, the ability to separate distinct cell populations is of critical importance. I have shown that the cell separation procedures are still suboptimal and there is room for improvement. The efficiency of available procedures also clearly decreases in more advanced gestational ages and, research toward increased efficiency in cell separation procedures should continue. Advances in our understanding of DNA methylation and other epigenetic marks as well, at the cellular level, will allow a better appreciation of the role of epigenetic mechanisms in placental development and dysfunction.

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Finally, relationships between the methylation variants and other epigenetic mechanisms such as histone modifications should also be assessed. The most frequently used method for histone modification detection is chromatin immunoprecipitation (ChIP) (Collas, 2009, 2010). A targeted approach using such technology would be appropriate, and should be followed by correlation to methylation and gene expression.

10.3.2 Are the methylation alterations identified in the placenta of low birthweight percentile neonates relevant to the occurrence of adult onset disorders?

Research aimed at characterizing the epigenome of the placenta/fetus and defining epigenetic marks that are associated with poor fetal growth could be extended to ask whether such epigenetic marks are associated with an increased susceptibility to adult-onset diseases, e.g. cardiovascular disorders. Such data could generate an opportunity to develop tests that could define risks for adult onset cardiovascular disease even as early as the neonatal period. Earlier risk identification allows for potentially more effective interventions. Furthermore, understanding the patterns of human developmental plasticity in response to early nutrition and other environmental factors will play a significant role in developing new and directed treatment and prevention paradigms.

Prior to embarking on costly long term epidemiologic follow-up studies, it is important to address some specific aspects of the confirmed placental epigenetic associations. First, following the WNT2 model, it will be important to test for the presence of DNA methylation variation in tissues of embryonic origin, such as umbilical cord tissue or cord blood. If equivalent changes are found in such tissues, it favors the hypothesis that the epigenetic variant occurred earlier in development, prior to the first embryonic cell differentiation event. A broadly occurring anomaly is more likely to be associated with late onset disorders. If the same methylation changes are not identified in other tissues, as with WNT2, testing for the hypothesis that such epigenetic changes are mediators of long term effects will require more elaborate approaches at least to better establish that very long term epidemiologic surveillance studies are warranted. Methodologies similar to the ones I used to identify associations between placental DNA methylation variants and birthweight percentile or IUGR phenotypes can be used to screen for associations between epigenetic variants in the neonate and DNA methylation variants found in placental DNA. For

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example, white blood cells DNA could be screened for differences in methylation between newborns with high and low WNT2 promoter methylation.

Other approaches may involve the use of model organisms. They would more readily allow for testing for epigenetic differences in tissues relevant to long term adult onset outcomes e.g., arterial walls (muscle, endothelium). Currently there are no simple approaches available for modeling specific DNA methylation marks in model organisms. This technical limitation affects the possibility of determining causality between epigenetic variation and outcomes. However, as long as an association between methylation and expression can be demonstrated, expression modulation could be used as a surrogate for the epigenetic variants. Experimental models with changes in gene expression that reflect methylation variants and that can be induced specifically in the placenta, could be used to address the relationship between placenta dysregulation and epigenetic consequences in embryonic derived tissues. Experimental models with placenta specific gene manipulation can be obtained from transduction of blastocysts with lentiviral vectors. Such procedures result in trophoblast cell-specific gene transfer (Georgiades et al., 2007; Okada et al., 2007). Models with either disruption or over expression of specific genes, only in placenta, can also be induced using such vectors in association with Cre/LoxP systems (Furuta and Behringer, 2005; Nagy, 2000; Wenzel and Leone, 2007). Using an integrase defective variant of this virus carrying a Cre Recombinase gene, which enables transient Cre recombinase expression without genomic integration (Nightingale et al., 2006; Philippe et al., 2006), and several types of LoxP mice embryos, Morioka and colleagues successfully created mousee models overexpressing or underexpressing different genes specifically in the placenta (Morioka et al., 2009). Alternatively, cells from the pluripotent inner cell mass of wild type embryos could be placed into engineered mouse embryos carrying a silencing mutation or into transgenic mice engineered for overexpression of a specific gene, thereby generating hybrid mice with placenta- specific features (Rossant et al., 1983; Zheng et al., 2005). Global epigenetic comparisons of DNA from several organs or tissues could then be made between mice with wild type and mutated placentas.

Considering expression modulation as a surrogate for epigenetic variation one could also consider interference RNA experiments to model the effect of methylation variation at the cell or tissue level using either trophoblastic cell cultures or villous cultures (Drewlo et al., 2008; Forbes et al., 2009). Since most methylation changes identified do not completely abolish gene

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expression, this type of expression modeling is closer to the effects of epigenetic variation in expression than model systems with constitutionally abrogated gene expression. Although such models would not be useful for assessment of long term late onset effects, they could be used to assess how changes in a cell type, tissue or organ can affect neighboring tissues or organs.

10.3.3 Other issues that need to be addressed to clarify the role of epigenetic variation in placenta and placental disease

In general, very little is known about gestational age and DNA methylation. DNA methylation at specific genomic sites is known to be age dependent (Liu, 2003; Maegawa et al., 2010), so it is reasonable to hypothesize that this may also be the case for gestational age. The only evidence supporting such changes is a study by Fuke and colleagues reporting an increase in 5- methylcytosine content in placental DNA with gestational age (Fuke et al., 2004). Therefore, in the methods I used to screen for differences in placental DNA methylation in association with growth restriction I assumed there would be consequences and aimed to control for gestational age. A preliminary analysis using my array data supported this, although directly designed investigations are needed to appropriately address this issue and to better understand the natural history of DNA methylation in normal placental development and in disease.

In the study described in Chapter 6 it was found that there was enrichment for CpGs in the promoter regions of tumor suppressor genes, among the differentially more methylated CpGs in cytotrophoblasts than fibroblasts. It was proposed that this could be one of the molecular signatures responsible for the tumor-like behavior of cytotrophoblasts, since in tumors this type of epigenetic mark is frequently reported. Accordingly, modulation of DNA methylation in the promoter of these genes could be a regulating mechanism of the tumor-like behavior of cytotrophoblasts. If this hypothesis is correct, it would be expected that the difference in methylation of these genes between these two cell types would be high in the first trimester of pregnancy, since cytotrophoblast invasion is an early event in placental development. Thus, a study of these differences across several gestational ages could provide support for this hypothesis. Samples from earlier stages of pregnancy could be obtained from voluntary terminations of pregnancy and samples from later stages could be obtained using the strategy used to collect samples for my other studies – see section 3.1.

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The range of inter-individual variation in placenta DNA methylation, as measured across all of the CpGs targeted by the Illumina® methylation arrays, was high (data not shown). Some questions arise from this observation: what determines the range of variation in DNA methylation? what causes the variation? is it tissue/cell specific? is it genetic/subject specific? Comparison of genetic characteristics between the loci with a high range of variation and the loci with a lower range of variation could provide the answers to these questions. A more frequent presence of SNPs or other genetic variation in close proximity to CpGs of high inter-individual variation would give support to the genetic determination hypothesis as would consistency across tissues. Inter tissue variation would argue against a genetic hypothesis and would favor a random or tissue/cell specific determination of variation in DNA methylation.

At the end of section 10.3.1, I mentioned that it is important to know how the identified changes in methylation correlate with other epigenetic mechanisms. Since my more general hypothesis is that epigenetic mechanisms are involved in placental development and its dysregulation will have a secondary impact on fetal development and growth, a more global and non-targeted approach to such mechanisms is required to fully test the general hypothesis. Histone modifications using global chromatin immunoprecipitation studies (array hybridization based or sequencing based) would be required to assess variation in histone modifications in association with the fetal growth phenotypes under study. A strategy similar to the one I used for DNA methylation could be undertaken. Similarly, association studies with non-coding RNA expression, another epigenetic mechanism, should also be addressed to assess if and how these different epigenetic determinants interact. Arrays are currently available, for instance, for global non-biased assessment of microRNA expression (Zhu et al., 2009).

In section 10.1, I hypothesized that some DNA methylation aberrations, especially in imprinting centers, may be associated with severe biological phenotypes, resulting in lethality. To test this hypothesis a methodology similar to the one described in this thesis could be used to screen for differences in methylation between placenta DNA samples obtained from miscarriages versus voluntary terminations of pregnancy from embryos matched for gestational age.

As stated in Chapter 1, epigenetic mechanisms are hypothesized to be the mediators between environmental factors and the long term molecular consequences, directly or via allelic specific variation. To test this hypothesis more directly, trophoblastic cell cultures or floating villi explant

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model systems could be used to assess the effects of environmental exposures on DNA methylation or other epigenetic marks. Factors that would be important to test are those known to be associated with poor fetal growth, such as hypoxia or nutrient deprivation. Candidate or global comparisons could be done between the exposed and non-exposed model systems. Expression variation has previously been investigated using these approaches and showed increased expression of several VEGR family factor genes under hypoxic conditions - VEGF, VEGFR-1, sVEGFR-1 and VEGFR-2 (Munaut et al., 2008; Padavala et al., 2006). Notwithstanding the known mediation role of the HIF-1 transcription factor between hypoxia and the increase in sVEGFR-1 (Nevo et al., 2006), it would be interesting to verify if some of the expression changes could be mediated by epigenetic mechanisms, potentially acting directly or indirectly on other upstream regulator genes. This type of experiment would also allow testing of other hypothetical environmental determinants of DNA methylation such as exposure to folic acid.

Another approach to identify epigenetic variants that may be associated with abnormal growth would be to compare, with global genomic approaches, placenta DNA collected from monozygotic dichorionic twins with growth discordance. Non-discordant twins could be used as controls. Candidate DNA methylation genomic regions could be selected among the differences in methylation that would be potentially specific for the discordant growth.

Another interesting hypothesis that could be explored using differences in methylation in twin placenta DNA is the possibility of epigenetic mechanisms being involved in twinning. Evidence supporting this hypothesis is limited to date. However this possibility could explain why the molecular alteration underlying Beckwith-Wiedemann syndrome (BWS) in phenotypically discordant monozygotic twins ususally involves loss of methylation at the imprinting center regulating the maternal expression of KCNQ1OT1 on 11p15. In contrast, dizygotic twins and singletons only demonstrate this defect in ~50% of the cases (Weksberg et al., 2002). To test the hypothesis that DNA methylation could be involved in the twinning process, differences could be sought in monozygotic dichorionic versus dizygotic, which are (always) dichorionic twin pairs. Also comparisons could be made between monozygotic dichorionic and monozygotic monochorionic twins. Differences specific to monozygosity and quantitatively bigger differences in methylation in dichorionic versus monochorionic twins (dichorionicity being an earlier event

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and thus likely resulting from a more “severe molecular anomaly”) would support an epigenetic mechanism associated with the twinning process.

10.3.4 Other more general epigenetics / DNA methylation unanswered questions

In spite of the advances and the increasingly vast body of literature about epigenetics, some basic questions remain unanswered.

It is widely believed that methylation of promoter genes is, in general, associated with gene silencing (Klose and Bird, 2006). Most of the regions I have identified indeed seem to support this concept. However, systematic approaches aiming to correlate, in a non-biased way, methylation and expression, are lacking. In that regard, a preliminary assessment of such correlation, using my placenta methylation and corresponding expression arrays, suggests that this general rule may not stand up very well. In fact, the brief preliminary analysis I have carried out showed that there are as many methylated promoter sites showing positive as negative correlations with expression. Studies specifically designed to address this type of question, ideally using DNA and RNA extracted from the same sample, are needed. It is likely that tissue specific variation will need to be addressed as well.

Another related unanswered question is the degree of the DNA methylation difference required to show an impact in expression. Also, is the methylation difference effect continuous or is there a threshold below or above which the effect in expression is noticeable? I have assumed, in my analyses, that differences of methylation lower than 10% would be too small to show a detectable expression effect. But if this assumption is incorrect important candidate differences will be missed. Some researchers report statistically significant, yet small differences in methylation (Bourque et al., 2010; Lambertini et al., 2008). Are those small differences in methylation biologically meaningful? To address this, studies will be needed using model systems in which methylation levels of specific gene promoters can be manipulated and the effects of that manipulation assessed with quantitative expression data. Although technically challenging, in vitro cell culture model systems have been reported allowing such manipulation (DiNardo et al., 2001). To complicate things still further it is possible that conclusions drawn for a particular gene may not be generalizable to other genes. Models allowing fine tuning of specific CpGs will also be required to determine if single CpG methylation variation may be

280 sufficient to impact gene expression or if a whole region needs to be modified for that effect to take place. Some reports indeed show associations of single CpG sites with phenotypes (Kucharski et al., 2008). In Russell-Silver syndrome, it seems that there is a minimal critical hypomethylated region that is necessary and sufficient to induce the clinical phenotype (Horike et al., 2009). But this may also be not a general rule and it may vary with genomic characteristics.

Finally other questions that remain to be answered are: what does a methylation level mean? what distinguishes 20% methylation from 50% methylation? what is the biologic impact of only a percentage of cells in a tissue cell population being fully methylated or having allele specific methylation?

In comparison with genomic studies, epigenetic studies are more complex given the existence not only of inter-individual variation (as in genetic marks) but also of intra-individual variation. In effect, epigenetic marks, within the same individual vary among tissues (Ghosh et al., 2010; Rakyan et al., 2008), cell types (Bloushtain-Qimron et al., 2008; Rakyan et al., 2008; Sakamoto et al., 2007), developmental stages (Bogdanovic and Veenstra, 2009; Hawkins et al., 2010) and even age (Bjornsson et al., 2008; Gronniger et al., 2010). Thus, the study of the epigenome and of the association of its variation to common disease requires even more effort and more complex analytical methodologies than the study of genomic variation. Furthermore, for some diseases, the tissue of interest may not be accessible. Stem cell research may, in the near future, allow the generation of several types of cells from one pluripotent cell type – e.g. induced pluripotent stem cells (iPSCs) from skin fibroblasts. Such cells could be differentiated into other cells thus making accessible the epigenome of some of those more inaccessible disease related cell types. Still, preliminary data suggests that epigenetic marks retain some specificity reflecting the original differentiated cell source (Polo et al., 2010). All these difficulties may be the reason why genome-wide epigenetic association studies have not yet followed the genome-wide genetic association studies (GWAS), in spite of the likely role of epigenetic variation in complex disorders (Petronis, 2010). In spite of all these difficulties, efforts such as the Human Epigenome Project are underway to characterize normal methylation patterns and variation. This is a basic requirement for the identification of what is abnormal.

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When the Human Genome Project began it seemed an insurmountable effort. Eventually it reached its goals. In spite of the formidable effort that the characterization of the epigenome seems to represent with the current technologies it may reach its goals sooner than expected as well. Technological development is usually driven by data generating technology but analytical tools eventually catch-up. Improvements in computer power will allow the development of the algorithms required for the analysis of these complex data. Such developments will allow the exploration of epigenetic variants in concert with genetic variants that are believed to underlie most common disorders.

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References

ACOG practice bulletin. Diagnosis and management of preeclampsia and eclampsia. Number 33, January 2002. Obstet Gynecol 99, 159-167.

Aagaard-Tillery, K.M., Grove, K., Bishop, J., Ke, X., Fu, Q., McKnight, R., and Lane, R.H. (2008). Developmental origins of disease and determinants of chromatin structure: maternal diet modifies the primate fetal epigenome. J Mol Endocrinol 41, 91-102.

Abu-Amero, S., Monk, D., Frost, J., Preece, M., Stanier, P., and Moore, G.E. (2008). The genetic aetiology of Silver-Russell syndrome. J Med Genet 45, 193-199.

Aerts, S., Thijs, G., Dabrowski, M., Moreau, Y., and De Moor, B. (2004). Comprehensive analysis of the base composition around the transcription start site in Metazoa. BMC Genomics 5, 34.

Agilent (2009). Methylation (CH3) Analysis. In Agilent Genomic Workbench 50 User Guide (Agilent Technologies, Inc.).

Agilent (2010). Human CpG Island Microarray Kit (Agilent Technologies).

Akama, T.O., Okazaki, Y., Ito, M., Okuizumi, H., Konno, H., Muramatsu, M., Plass, C., Held, W.A., and Hayashizaki, Y. (1997). Restriction landmark genomic scanning (RLGS-M)-based genome-wide scanning of mouse liver tumors for alterations in DNA methylation status. Cancer Res 57, 3294-3299.

Al-Fifi, S., Teebi, A.S., and Shevell, M. (1996). Autosomal dominant Russell-Silver syndrome. Am J Med Genet 61, 96-97.

Antoniades, L., MacGregor, A.J., Andrew, T., and Spector, T.D. (2003). Association of birth weight with osteoporosis and osteoarthritis in adult twins. Rheumatology (Oxford) 42, 791-796.

Aplin, J.D. (2010). Developmental cell biology of human villous trophoblast: current research problems. Int J Dev Biol 54, 323-329.

Apostolidou, S., Abu-Amero, S., O’Donoghue, K., Frost, J., Olafsdottir, O., Chavele, K., Whittaker, J., Loughna, P., Stanier, P., and Moore, G. (2007). Elevated placental expression of the imprinted PHLDA2 gene is associated with low birth weight. Journal of Molecular Medicine 85, 379-387.

Arima, T., Yamasaki, K., John, R.M., Kato, K., Sakumi, K., Nakabeppu, Y., Wake, N., and Kono, T. (2006). The human HYMAI/PLAGL1 differentially methylated region acts as an imprint control region in mice. Genomics 88, 650-658.

Arnaud, P., and Feil, R. (2005). Epigenetic deregulation of genomic imprinting in human disorders and following assisted reproduction. Birth Defects Res C Embryo Today 75, 81-97.

283

Arnaud, P., Monk, D., Hitchins, M., Gordon, E., Dean, W., Beechey, C.V., Peters, J., Craigen, W., Preece, M., Stanier, P., et al. (2003). Conserved methylation imprints in the human and mouse GRB10 genes with divergent allelic expression suggests differential reading of the same mark. Hum Mol Genet 12, 1005-1019.

Astuti, D., Latif, F., Wagner, K., Gentle, D., Cooper, W.N., Catchpoole, D., Grundy, R., Ferguson-Smith, A.C., and Maher, E.R. (2005). Epigenetic alteration at the DLK1-GTL2 imprinted domain in human neoplasia: analysis of neuroblastoma, phaeochromocytoma and Wilms' tumour. Br J Cancer 92, 1574-1580.

Azzi, S., Rossignol, S., Steunou, V., Sas, T., Thibaud, N., Danton, F., Le Jule, M., Heinrichs, C., Cabrol, S., Gicquel, C., et al. (2009). Multilocus methylation analysis in a large cohort of 11p15- related foetal growth disorders (Russell Silver and Beckwith Wiedemann syndromes) reveals simultaneous loss of methylation at paternal and maternal imprinted loci. Hum Mol Genet 18, 4724-4733.

Baczyk, D., Drewlo, S., Proctor, L., Dunk, C., Lye, S., and Kingdom, J. (2009). Glial cell missing-1 transcription factor is required for the differentiation of the human trophoblast. Cell Death Differ 16, 719-727.

Ball, M.P., Li, J.B., Gao, Y., Lee, J.H., LeProust, E.M., Park, I.H., Xie, B., Daley, G.Q., and Church, G.M. (2009). Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat Biotechnol 27, 361-368.

Barker, D.J. (2004). The developmental origins of chronic adult disease. Acta Paediatr Suppl 93, 26-33.

Barker, D.J. (2006). Adult consequences of fetal growth restriction. Clin Obstet Gynecol 49, 270-283.

Barker, D.J., Eriksson, J.G., Forsen, T., and Osmond, C. (2002). Fetal origins of adult disease: strength of effects and biological basis. Int J Epidemiol 31, 1235-1239.

Barker, D.J., Hales, C.N., Fall, C.H., Osmond, C., Phipps, K., and Clark, P.M. (1993). Type 2 (non-insulin-dependent) diabetes mellitus, hypertension and hyperlipidaemia (syndrome X): relation to reduced fetal growth. Diabetologia 36, 62-67.

Barker, D.J., Osmond, C., Forsen, T.J., Kajantie, E., and Eriksson, J.G. (2005). Trajectories of growth among children who have coronary events as adults. N Engl J Med 353, 1802-1809.

Barker, D.J., Osmond, C., Forsen, T.J., Kajantie, E., and Eriksson, J.G. (2007). Maternal and social origins of hypertension. Hypertension 50, 565-571.

Barker, D.J., Winter, P.D., Osmond, C., Margetts, B., and Simmonds, S.J. (1989). Weight in infancy and death from ischaemic heart disease. Lancet 2, 577-580.

Bartholdi, D., Krajewska-Walasek, M., Ounap, K., Gaspar, H., Chrzanowska, K.H., Ilyana, H., Kayserili, H., Lurie, I.W., Schinzel, A., and Baumer, A. (2009). Epigenetic mutations of the

284

imprinted IGF2-H19 domain in Silver-Russell syndrome (SRS): results from a large cohort of patients with SRS and SRS-like phenotypes. J Med Genet 46, 192-197.

Baschat, A.A., Gembruch, U., Reiss, I., Gortner, L., Weiner, C.P., and Harman, C.R. (2000). Relationship between arterial and venous Doppler and perinatal outcome in fetal growth restriction. Ultrasound Obstet Gynecol 16, 407-413.

Bastepe, M., Pincus, J.E., Sugimoto, T., Tojo, K., Kanatani, M., Azuma, Y., Kruse, K., Rosenbloom, A.L., Koshiyama, H., and Juppner, H. (2001). Positional dissociation between the genetic mutation responsible for pseudohypoparathyroidism type Ib and the associated methylation defect at exon A/B: evidence for a long-range regulatory element within the imprinted GNAS1 locus. Hum Mol Genet 10, 1231-1241.

Beatty, L., Weksberg, R., and Sadowski, P.D. (2006). Detailed analysis of the methylation patterns of the KvDMR1 imprinting control region of human chromosome 11. Genomics 87, 46- 56.

Bell, A.C., and Felsenfeld, G. (2000). Methylation of a CTCF-dependent boundary controls imprinted expression of the Igf2 gene. Nature 405, 482-485.

Bellido, M.L., Radpour, R., Lapaire, O., De Bie, I., Hosli, I., Bitzer, J., Hmadcha, A., Zhong, X.Y., and Holzgreve, W. (2010). MALDI-TOF mass array analysis of RASSF1A and SERPINB5 methylation patterns in human placenta and plasma. Biol Reprod 82, 745-750.

Bernard, L.E., Penaherrera, M.S., Van Allen, M.I., Wang, M.S., Yong, S.L., Gareis, F., Langlois, S., and Robinson, W.P. (1999). Clinical and molecular findings in two patients with russell-silver syndrome and UPD7: comparison with non-UPD7 cases. Am J Med Genet 87, 230-236.

Bian, Y.S., Yan, P., Osterheld, M.C., Fontolliet, C., and Benhattar, J. (2001). Promoter methylation analysis on microdissected paraffin-embedded tissues using bisulfite treatment and PCR-SSCP. Biotechniques 30, 66-72.

Bibeau, K., Battista, M.C., Houde, V., and Brochu, M. (2010). Fetal adrenal gland alterations in a rat model of adverse intrauterine environment. Am J Physiol Regul Integr Comp Physiol 298, R899-911.

Bibikova, M., Lin, Z., Zhou, L., Chudin, E., Garcia, E.W., Wu, B., Doucet, D., Thomas, N.J., Wang, Y., Vollmer, E., et al. (2006). High-throughput DNA methylation profiling using universal bead arrays. Genome Res 16, 383-393.

Binder, G., Seidel, A.-K., Martin, D.D., Schweizer, R., Schwarze, C.P., Wollmann, H.A., Eggermann, T., and Ranke, M.B. (2008). The Endocrine Phenotype in Silver-Russell Syndrome Is Defined by the Underlying Epigenetic Alteration. J Clin Endocrinol Metab 93, 1402-1407.

Binder, G., Seidel, A.K., Weber, K., Haase, M., Wollmann, H.A., Ranke, M.B., and Eggermann, T. (2006). IGF-II serum levels are normal in children with Silver-Russell syndrome who frequently carry epimutations at the IGF2 locus. J Clin Endocrinol Metab 91, 4709-4712.

Bird, A. (2002). DNA methylation patterns and epigenetic memory. Genes Dev 16, 6-21.

285

Bird, A.P. (1986). CpG-rich islands and the function of DNA methylation. Nature 321, 209-213.

Biswas, S., Ghosh, S.K., and Chhabra, S. (2008). Surface area of chorionic villi of placentas: an index of intrauterine growth restriction of fetuses. J Obstet Gynaecol Res 34, 487-493.

Bjornsson, H.T., Sigurdsson, M.I., Fallin, M.D., Irizarry, R.A., Aspelund, T., Cui, H., Yu, W., Rongione, M.A., Ekstrom, T.J., Harris, T.B., et al. (2008). Intra-individual change over time in DNA methylation with familial clustering. Jama 299, 2877-2883.

Bliek, J., Terhal, P., van den Bogaard, M.J., Maas, S., Hamel, B., Salieb-Beugelaar, G., Simon, M., Letteboer, T., van der Smagt, J., Kroes, H., et al. (2006). Hypomethylation of the H19 gene causes not only Silver-Russell syndrome (SRS) but also isolated asymmetry or an SRS-like phenotype. Am J Hum Genet 78, 604-614.

Bliek, J., Verde, G., Callaway, J., Maas, S.M., De Crescenzo, A., Sparago, A., Cerrato, F., Russo, S., Ferraiuolo, S., Rinaldi, M.M., et al. (2009). Hypomethylation at multiple maternally methylated imprinted regions including PLAGL1 and GNAS loci in Beckwith-Wiedemann syndrome. Eur J Hum Genet 17, 611-619.

Bloushtain-Qimron, N., Yao, J., Shipitsin, M., Maruyama, R., and Polyak, K. (2009). Epigenetic patterns of embryonic and adult stem cells. Cell Cycle 8, 809-817.

Bloushtain-Qimron, N., Yao, J., Snyder, E.L., Shipitsin, M., Campbell, L.L., Mani, S.A., Hu, M., Chen, H., Ustyansky, V., Antosiewicz, J.E., et al. (2008). Cell type-specific DNA methylation patterns in the human breast. Proc Natl Acad Sci U S A 105, 14076-14081.

Bloxam, D.L., Bax, C.M., and Bax, B.E. (1997). Culture of syncytiotrophoblast for the study of human placental transfer. Part I: Isolation and purification of cytotrophoblast. Placenta 18, 93-98.

Bogdanovic, O., and Veenstra, G.J. (2009). DNA methylation and methyl-CpG binding proteins: developmental requirements and function. Chromosoma 118, 549-565.

Boldt, H.B., and Conover, C.A. (2007). Pregnancy-associated plasma protein-A (PAPP-A): a local regulator of IGF bioavailability through cleavage of IGFBPs. Growth Horm IGF Res 17, 10-18.

Bossenmeyer-Pourie, C., Blaise, S., Pourie, G., Tomasetto, C., Audonnet, S., Ortiou, S., Koziel, V., Rio, M.C., Daval, J.L., Gueant, J.L., et al. (2010). Methyl donor deficiency affects fetal programming of gastric ghrelin cell organization and function in the rat. Am J Pathol 176, 270- 277.

Bourque, D.K., Avila, L., Penaherrera, M., von Dadelszen, P., and Robinson, W.P. (2010). Decreased placental methylation at the H19/IGF2 imprinting control region is associated with normotensive intrauterine growth restriction but not preeclampsia. Placenta 31, 197-202.

Brena, R.M., and Costello, J.F. (2007). Genome-epigenome interactions in cancer. Hum Mol Genet 16 Spec No 1, R96-105.

286

Brodszki, J., Lanne, T., Marsal, K., and Ley, D. (2005). Impaired vascular growth in late adolescence after intrauterine growth restriction. Circulation 111, 2623-2628.

Brunner, A.L., Johnson, D.S., Kim, S.W., Valouev, A., Reddy, T.E., Neff, N.F., Anton, E., Medina, C., Nguyen, L., Chiao, E., et al. (2009). Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver. Genome Res 19, 1044-1056.

Buimer, M., Keijser, R., Jebbink, J.M., Wehkamp, D., van Kampen, A.H., Boer, K., van der Post, J.A., and Ris-Stalpers, C. (2008). Seven placental transcripts characterize HELLP- syndrome. Placenta 29, 444-453.

Buiting, K., Saitoh, S., Gross, S., Dittrich, B., Schwartz, S., Nicholls, R.D., and Horsthemke, B. (1995). Inherited microdeletions in the Angelman and Prader-Willi syndromes define an imprinting centre on human chromosome 15. Nat Genet 9, 395-400.

Burton, G.J., and Jauniaux, E. (2004). Placental oxidative stress: from miscarriage to preeclampsia. J Soc Gynecol Investig 11, 342-352.

Burton, G.J., Yung, H.W., Cindrova-Davies, T., and Charnock-Jones, D.S. (2009). Placental endoplasmic reticulum stress and oxidative stress in the pathophysiology of unexplained intrauterine growth restriction and early onset preeclampsia. Placenta 30 Suppl A, S43-48.

Byun, H.M., Wong, H.L., Birnstein, E.A., Wolff, E.M., Liang, G., and Yang, A.S. (2007). Examination of IGF2 and H19 loss of imprinting in bladder cancer. Cancer Res 67, 10753- 10758.

Carter, A.M., Hills, F., O'Gorman, D.B., Roberts, C.T., Sooranna, S.R., Watson, C.S., and Westwood, M. (2004). The insulin-like growth factor system in mammalian pregnancy--a workshop report. Placenta 25 Suppl A, S53-56.

Caspary, T., Cleary, M.A., Perlman, E.J., Zhang, P., Elledge, S.J., and Tilghman, S.M. (1999). Oppositely imprinted genes p57(Kip2) and igf2 interact in a mouse model for Beckwith- Wiedemann syndrome. Genes Dev 13, 3115-3124.

Cedar, H., Solage, A., Glaser, G., and Razin, A. (1979). Direct detection of methylated cytosine in DNA by use of the restriction enzyme MspI. Nucleic Acids Res 6, 2125-2132.

Chaddha, V., Viero, S., Huppertz, B., and Kingdom, J. (2004). Developmental biology of the placenta and the origins of placental insufficiency. Semin Fetal Neonatal Med 9, 357-369.

Chan, K.C., Ding, C., Gerovassili, A., Yeung, S.W., Chiu, R.W., Leung, T.N., Lau, T.K., Chim, S.S., Chung, G.T., Nicolaides, K.H., et al. (2006a). Hypermethylated RASSF1A in maternal plasma: A universal fetal DNA marker that improves the reliability of noninvasive prenatal diagnosis. Clin Chem 52, 2211-2218.

Chan, T.L., Yuen, S.T., Kong, C.K., Chan, Y.W., Chan, A.S.Y., Ng, W.F., Tsui, W.Y., Lo, M.W.S., Tam, W.Y., Li, V.S.W., et al. (2006b). Heritable germline epimutation of MSH2 in a family with hereditary nonpolyposis colorectal cancer. Nature Genetics 38, 1178-1183.

287

Chawla, R.K., Watson, W.H., and Jones, D.P. (1996). Effect of hypoxia on hepatic DNA methylation and tRNA methyltransferase in rat: similarities to effects of methyl-deficient diets. J Cell Biochem 61, 72-80.

Cheung, H.H., Lee, T.L., Davis, A.J., Taft, D.H., Rennert, O.M., and Chan, W.Y. (2010). Genome-wide DNA methylation profiling reveals novel epigenetically regulated genes and non- coding RNAs in human testicular cancer. Br J Cancer 102, 419-427.

Chiu, R.W., Chim, S.S., Wong, I.H., Wong, C.S., Lee, W.S., To, K.F., Tong, J.H., Yuen, R.K., Shum, A.S., Chan, J.K., et al. (2007). Hypermethylation of RASSF1A in human and rhesus placentas. Am J Pathol 170, 941-950.

Choufani, S., Shapiro, J.S., Susiarjo, M., Butcher, D.T., Grafodatskaya, D., Lou, Y., Ferreira, J.C., Pinto, D., Scherer, S.W., Schaffer, L.G., et al. (2011). A Novel Approach Identifies New Differentially Methylated Regions (DMRs) Associated with Imprinted Genes. Genome Res 21.

Chuang, J.C., and Jones, P.A. (2007). Epigenetics and microRNAs. Pediatr Res 61, 24R-29R.

Cianfarani, S., Geremia, C., Puglianiello, A., Maiorana, A., and Germani, D. (2007). Late effects of disturbed IGF signaling in congenital diseases. Endocr Dev 11, 16-27.

Clark, S.J., Harrison, J., Paul, C.L., and Frommer, M. (1994). High sensitivity mapping of methylated cytosines. Nucleic Acids Res 22, 2990-2997.

Clarke, J., Wu, H.C., Jayasinghe, L., Patel, A., Reid, S., and Bayley, H. (2009). Continuous base identification for single-molecule nanopore DNA sequencing. Nat Nanotechnol 4, 265-270.

Cnossen, J.S., Morris, R.K., ter Riet, G., Mol, B.W., van der Post, J.A., Coomarasamy, A., Zwinderman, A.H., Robson, S.C., Bindels, P.J., Kleijnen, J., et al. (2008). Use of uterine artery Doppler ultrasonography to predict pre-eclampsia and intrauterine growth restriction: a systematic review and bivariable meta-analysis. CMAJ 178, 701-711.

Coan, P.M., Burton, G.J., and Ferguson-Smith, A.C. (2005). Imprinted genes in the placenta--a review. Placenta 26 Suppl A, S10-20.

Cokus, S.J., Feng, S., Zhang, X., Chen, Z., Merriman, B., Haudenschild, C.D., Pradhan, S., Nelson, S.F., Pellegrini, M., and Jacobsen, S.E. (2008). Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 452, 215-219.

Coleman, M.A., McCowan, L.M., and North, R.A. (2000). Mid-trimester uterine artery Doppler screening as a predictor of adverse pregnancy outcome in high-risk women. Ultrasound Obstet Gynecol 15, 7-12.

Collas, P. (2009). The state-of-the-art of chromatin immunoprecipitation. Methods Mol Biol 567, 1-25.

Collas, P. (2010). The current state of chromatin immunoprecipitation. Mol Biotechnol 45, 87- 100.

288

Collin, S.M., Metcalfe, C., Zuccolo, L., Lewis, S.J., Chen, L., Cox, A., Davis, M., Lane, J.A., Donovan, J., Smith, G.D., et al. (2009). Association of folate-pathway gene polymorphisms with the risk of prostate cancer: a population-based nested case-control study, systematic review, and meta-analysis. Cancer Epidemiol Biomarkers Prev 18, 2528-2539.

Constancia, M., Hemberger, M., Hughes, J., Dean, W., Ferguson-Smith, A., Fundele, R., Stewart, F., Kelsey, G., Fowden, A., Sibley, C., et al. (2002). Placental-specific IGF-II is a major modulator of placental and fetal growth. Nature 417, 945-948.

Constancia, M., Pickard, B., Kelsey, G., and Reik, W. (1998). Imprinting mechanisms. Genome Research 8, 881-900.

Costello, J.F., and Plass, C. (2001). Methylation matters. J Med Genet 38, 285-303.

Costello, J.F., and Vertino, P.M. (2002). Methylation matters: a new spin on maspin. Nat Genet 31, 123-124.

Cross, S.H., Charlton, J.A., Nan, X., and Bird, A.P. (1994). Purification of CpG islands using a methylated DNA binding column. Nat Genet 6, 236-244.

Curhan, G.C., Chertow, G.M., Willett, W.C., Spiegelman, D., Colditz, G.A., Manson, J.E., Speizer, F.E., and Stampfer, M.J. (1996a). Birth weight and adult hypertension and obesity in women. Circulation 94, 1310-1315.

Curhan, G.C., Willett, W.C., Rimm, E.B., Spiegelman, D., Ascherio, A.L., and Stampfer, M.J. (1996b). Birth weight and adult hypertension, diabetes mellitus, and obesity in US men. Circulation 94, 3246-3250.

Dasoula, A., Georgiou, I., Kontogianni, E., Sofikitis, N., and Syrrou, M. (2007). Methylation status of the SNRPN and HUMARA genes in testicular biopsy samples. Fertil Steril 87, 805-809.

De Carvalho, D.D., You, J.S., and Jones, P.A. (2010). DNA methylation and cellular reprogramming. Trends Cell Biol.

Delpisheh, A., Brabin, L., Topping, J., Reyad, M., Tang, A.W., and Brabin, B.J. (2009). A case- control study of CYP1A1, GSTT1 and GSTM1 gene polymorphisms, pregnancy smoking and fetal growth restriction. Eur J Obstet Gynecol Reprod Biol 143, 38-42.

Delpisheh, A., Topping, J., Reyad, M., Tang, A., and Brabin, B.J. (2008). Prenatal alcohol exposure, CYP17 gene polymorphisms and fetal growth restriction. Eur J Obstet Gynecol Reprod Biol 138, 49-53.

Deng, J., Shoemaker, R., Xie, B., Gore, A., LeProust, E.M., Antosiewicz-Bourget, J., Egli, D., Maherali, N., Park, I.H., Yu, J., et al. (2009). Targeted bisulfite sequencing reveals changes in DNA methylation associated with nuclear reprogramming. Nat Biotechnol 27, 353-360.

Dennis Lo, Y. (2006). Fetal DNA in Maternal Plasma. Annals of the New York Academy of Sciences 1075, 74-80.

289

Dennison, E.M., Syddall, H.E., Sayer, A.A., Gilbody, H.J., and Cooper, C. (2005). Birth weight and weight at 1 year are independent determinants of bone mass in the seventh decade: the Hertfordshire cohort study. Pediatr Res 57, 582-586.

Deveault, C., Qian, J.H., Chebaro, W., Ao, A., Gilbert, L., Mehio, A., Khan, R., Tan, S.L., Wischmeijer, A., Coullin, P., et al. (2009). NLRP7 mutations in women with diploid androgenetic and triploid moles: a proposed mechanism for mole formation. Hum Mol Genet 18, 888-897.

DiNardo, D.N., Butcher, D.T., Robinson, D.P., Archer, T.K., and Rodenhiser, D.I. (2001). Functional analysis of CpG methylation in the BRCA1 promoter region. Oncogene 20, 5331- 5340.

Diplas, A.I., Lambertini, L., Lee, M.J., Sperling, R., Lee, Y.L., Wetmur, J., and Chen, J. (2009). Differential expression of imprinted genes in normal and IUGR human placentas. Epigenetics 4.

Dogru-Abbasoglu, S., Kanbagli, O., Bulur, H., Babalik, E., Ozturk, S., Aykac-Toker, G., and Uysal, M. (1999). Lipid peroxides and antioxidant status in serum of patients with angiographically defined coronary atherosclerosis. Clin Biochem 32, 671-672.

Dokras, A. (2006). Epigenetic regulation of maspin expression in the human placenta. Molecular Human Reproduction 12, 611-617.

Dokras, A., Coffin, J., Field, L., Frakes, A., Lee, H., Madan, A., Nelson, T., Ryu, G.Y., and Yoon, J.G. (2006). Epigenetic regulation of maspin expression in the human placenta. Mol Hum Reprod 12, 611-617.

Donker, R.B., Mouillet, J.F., Nelson, D.M., and Sadovsky, Y. (2007). The expression of Argonaute2 and related microRNA biogenesis proteins in normal and hypoxic trophoblasts. Mol Hum Reprod 13, 273-279.

Down, T.A., Rakyan, V.K., Turner, D.J., Flicek, P., Li, H., Kulesha, E., Graf, S., Johnson, N., Herrero, J., Tomazou, E.M., et al. (2008). A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat Biotechnol 26, 779-785.

Drewlo, S., Baczyk, D., Dunk, C., and Kingdom, J. (2008). Fusion assays and models for the trophoblast. Methods Mol Biol 475, 363-382.

Duncan, P.A., Hall, J.G., Shapiro, L.R., and Vibert, B.K. (1990). Three-generation dominant transmission of the Silver-Russell syndrome. Am J Med Genet 35, 245-250.

Dunn, J.J., McCorkle, S.R., Everett, L., and Anderson, C.W. (2007). Paired-end genomic signature tags: a method for the functional analysis of genomes and epigenomes. Genet Eng (N Y) 28, 159-173.

Duvic, M., and Vu, J. (2007). Vorinostat in cutaneous T-cell lymphoma. Drugs Today (Barc) 43, 585-599.

290

Eads, C.A., Danenberg, K.D., Kawakami, K., Saltz, L.B., Blake, C., Shibata, D., Danenberg, P.V., and Laird, P.W. (2000). MethyLight: a high-throughput assay to measure DNA methylation. Nucleic Acids Res 28, E32.

Eckhardt, F., Lewin, J., Cortese, R., Rakyan, V.K., Attwood, J., Burger, M., Burton, J., Cox, T.V., Davies, R., Down, T.A., et al. (2006). DNA methylation profiling of human chromosomes 6, 20 and 22. Nature Genetics 38, 1378-1385.

Edwards, A., Megens, A., Peek, M., and Wallace, E.M. (2000). Sexual origins of placental dysfunction. Lancet 355, 203-204.

Edwards, C.A., and Ferguson-Smith, A.C. (2007). Mechanisms regulating imprinted genes in clusters. Curr Opin Cell Biol 19, 281-289.

Edwards, J.R., O'Donnell, A.H., Rollins, R.A., Peckham, H.E., Lee, C., Milekic, M.H., Chanrion, B., Fu, Y., Su, T., Hibshoosh, H., et al. (2010). Chromatin and sequence features that define the fine and gross structure of genomic methylation patterns. Genome Res 20, 972-980.

Eggerding, F.A., Schonberg, S.A., Chehab, F.F., Norton, M.E., Cox, V.A., and Epstein, C.J. (1994). Uniparental isodisomy for paternal 7p and maternal 7q in a child with growth retardation. Am J Hum Genet 55, 253-265.

Eggermann, T., Meyer, E., Caglayan, A.O., Dundar, M., and Schonherr, N. (2008a). ICR1 epimutations in llp15 are restricted to patients with Silver-Russell syndrome features. J Pediatr Endocrinol Metab 21, 59-62.

Eggermann, T., Meyer, E., Obermann, C., Heil, I., Schuler, H., Ranke, M.B., Eggermann, K., and Wollmann, H.A. (2005). Is maternal duplication of 11p15 associated with Silver-Russell syndrome? Journal of Medical Genetics 42, e26-e26.

Eggermann, T., Schonherr, N., Eggermann, K., Buiting, K., Ranke, M.B., Wollmann, H.A., and Binder, G. (2008b). Use of multiplex ligation-dependent probe amplification increases the detection rate for 11p15 epigenetic alterations in Silver-Russell syndrome. Clin Genet 73, 79-84.

Eggermann, T., Schonherr, N., Meyer, E., Obermann, C., Mavany, M., Eggermann, K., Ranke, M.B., and Wollmann, H.A. (2006). Epigenetic mutations in 11p15 in Silver-Russell syndrome are restricted to the telomeric imprinting domain. Journal of Medical Genetics 43, 615-616.

Eggermann, T., Wollmann, H.A., Kuner, R., Eggermann, K., Enders, H., Kaiser, P., and Ranke, M.B. (1997). Molecular studies in 37 Silver-Russell syndrome patients: frequency and etiology of uniparental disomy. Hum Genet 100, 415-419.

Ehrlich, M., Gama-Sosa, M.A., Huang, L.H., Midgett, R.M., Kuo, K.C., McCune, R.A., and Gehrke, C. (1982). Amount and distribution of 5-methylcytosine in human DNA from different types of tissues of cells. Nucleic Acids Res 10, 2709-2721.

Eich, G.F., Silver, M.M., Weksberg, R., Daneman, A., and Costa, T. (1991). Marshall-Smith syndrome: new radiographic, clinical, and pathologic observations. Radiology 181, 183-188.

291

Emerman, M., and Temin, H.M. (1984). Genes with promoters in retrovirus vectors can be independently suppressed by an epigenetic mechanism. Cell 39, 449-467.

Eriksson, H. (2000). [A case report. H2-antagonist efficient against drug-related weight gain]. Lakartidningen 97, 2869.

Eriksson, J.G., Forsen, T., Tuomilehto, J., Osmond, C., and Barker, D.J. (2001). Early growth and coronary heart disease in later life: longitudinal study. Bmj 322, 949-953.

Estecio, M.R., Yan, P.S., Ibrahim, A.E., Tellez, C.S., Shen, L., Huang, T.H., and Issa, J.P. (2007). High-throughput methylation profiling by MCA coupled to CpG island microarray. Genome Res 17, 1529-1536.

Esteller, M. (2007). Cancer epigenomics: DNA methylomes and histone-modification maps. Nat Rev Genet 8, 286-298.

Fan, S., and Zhang, X. (2009). CpG island methylation pattern in different human tissues and its correlation with gene expression. Biochem Biophys Res Commun 383, 421-425.

Farina, A., Sekizawa, A., Purwosunu, Y., Rizzo, N., Banzola, I., Concu, M., Morano, D., Giommi, F., Bevini, M., Mabrook, M., et al. (2006). Quantitative distribution of a panel of circulating mRNA in preeclampsia versus controls. Prenat Diagn 26, 1115-1120.

Farr, M., Strube, J., Geppert, H.G., Kocourek, A., Mahne, M., and Tschesche, H. (2000). Pregnancy-associated plasma protein-E (PAPP-E). Biochim Biophys Acta 1493, 356-362.

Feinberg, A.P. (2007). Phenotypic plasticity and the epigenetics of human disease. Nature 447, 433-440.

Feng, S., Cokus, S.J., Zhang, X., Chen, P.Y., Bostick, M., Goll, M.G., Hetzel, J., Jain, J., Strauss, S.H., Halpern, M.E., et al. (2010). Conservation and divergence of methylation patterning in plants and animals. Proc Natl Acad Sci U S A 107, 8689-8694.

Ferguson-Smith, A.C., and Surani, M.A. (2001). Imprinting and the epigenetic asymmetry between parental genomes. Science 293, 1086-1089.

Ferreira, J.C., Choufani, S., Grafodatskaya, D., Butcher, D.T., Zhao, C., Chitayat, D., Shuman, C., Kingdom, J., Keating, S., and Weksberg, R. (2011). WNT2 promoter methylation in human placenta is associated with low birthweight percentile in the neonate. Epigenetics 6.

Ferretti, C., Bruni, L., Dangles-Marie, V., Pecking, A.P., and Bellet, D. (2007). Molecular circuits shared by placental and cancer cells, and their implications in the proliferative, invasive and migratory capacities of trophoblasts. Hum Reprod Update 13, 121-141.

Figueroa, R., and Maulik, D. (2006). Prenatal therapy for fetal growth restriction. Clin Obstet Gynecol 49, 308-319.

292

Fisher, A.M., Thomas, N.S., Cockwell, A., Stecko, O., Kerr, B., Temple, I.K., and Clayton, P. (2002). Duplications of chromosome 11p15 of maternal origin result in a phenotype that includes growth retardation. Hum Genet 111, 290-296.

Fisher, S.J., and Laine, R.A. (1983). High alpha-amylase activity in the syncytiotrophoblastic cells of first-trimester human placentas. J Cell Biochem 22, 47-54.

Flusberg, B.A., Webster, D.R., Lee, J.H., Travers, K.J., Olivares, E.C., Clark, T.A., Korlach, J., and Turner, S.W. (2010). Direct detection of DNA methylation during single-molecule, real-time sequencing. Nat Methods 7, 461-465.

Forbes, K., Desforges, M., Garside, R., Aplin, J.D., and Westwood, M. (2009). Methods for siRNA-mediated reduction of mRNA and protein expression in human placental explants, isolated primary cells and cell lines. Placenta 30, 124-129.

Forsen, T., Eriksson, J., Tuomilehto, J., Reunanen, A., Osmond, C., and Barker, D. (2000). The fetal and childhood growth of persons who develop type 2 diabetes. Ann Intern Med 133, 176- 182.

Forsen, T., Eriksson, J.G., Tuomilehto, J., Teramo, K., Osmond, C., and Barker, D.J. (1997). Mother's weight in pregnancy and coronary heart disease in a cohort of Finnish men: follow up study. Bmj 315, 837-840.

Frankel, S., Elwood, P., Sweetnam, P., Yarnell, J., and Smith, G.D. (1996). Birthweight, body- mass index in middle age, and incident coronary heart disease. Lancet 348, 1478-1480.

Frevel, M.A., Sowerby, S.J., Petersen, G.B., and Reeve, A.E. (1999). Methylation sequencing analysis refines the region of H19 epimutation in Wilms tumor. J Biol Chem 274, 29331-29340.

Frigola, J., Ribas, M., Risques, R.A., and Peinado, M.A. (2002). Methylome profiling of cancer cells by amplification of inter-methylated sites (AIMS). Nucleic Acids Res 30, e28.

Frommer, M., McDonald, L.E., Millar, D.S., Collis, C.M., Watt, F., Grigg, G.W., Molloy, P.L., and Paul, C.L. (1992). A genomic sequencing protocol that yields a positive display of 5- methylcytosine residues in individual DNA strands. Proc Natl Acad Sci U S A 89, 1827-1831.

Fu, Q., McKnight, R.A., Yu, X., Callaway, C.W., and Lane, R.H. (2006). Growth retardation alters the epigenetic characteristics of hepatic dual specificity phosphatase 5. FASEB J 20, 2127- 2129.

Fu, Q., McKnight, R.A., Yu, X., Wang, L., Callaway, C.W., and Lane, R.H. (2004). Uteroplacental insufficiency induces site-specific changes in histone H3 covalent modifications and affects DNA-histone H3 positioning in day 0 IUGR rat liver. Physiol Genomics 20, 108-116.

Fu, Q., Yu, X., Callaway, C.W., Lane, R.H., and McKnight, R.A. (2009). Epigenetics: intrauterine growth retardation (IUGR) modifies the histone code along the rat hepatic IGF-1 gene. FASEB J 23, 2438-2449.

293

Fuke, C., Shimabukuro, M., Petronis, A., Sugimoto, J., Oda, T., Miura, K., Miyazaki, T., Ogura, C., Okazaki, Y., and Jinno, Y. (2004). Age related changes in 5-methylcytosine content in human peripheral leukocytes and placentas: an HPLC-based study. Ann Hum Genet 68, 196-204.

Furuta, Y., and Behringer, R.R. (2005). Recent innovations in tissue-specific gene modifications in the mouse. Birth Defects Res C Embryo Today 75, 43-57.

Gagnon, A., Wilson, R.D., Audibert, F., Allen, V.M., Blight, C., Brock, J.A., Desilets, V.A., Johnson, J.A., Langlois, S., Summers, A., et al. (2008). Obstetrical complications associated with abnormal maternal serum markers analytes. J Obstet Gynaecol Can 30, 918-949.

Gardiner-Garden, M., and Frommer, M. (1987). CpG islands in vertebrate genomes. J Mol Biol 196, 261-282.

Georgiades, P., Cox, B., Gertsenstein, M., Chawengsaksophak, K., and Rossant, J. (2007). Trophoblast-specific gene manipulation using lentivirus-based vectors. Biotechniques 42, 317- 318, 320, 322-315.

Geva, R., Eshel, R., Leitner, Y., Valevski, A.F., and Harel, S. (2006). Neuropsychological outcome of children with intrauterine growth restriction: a 9-year prospective study. Pediatrics 118, 91-100.

Ghosh, G., Breborowicz, A., Brazert, M., Maczkiewicz, M., Kobelski, M., Dubiel, M., and Gudmundsson, S. (2006). Evaluation of third trimester uterine artery flow velocity indices in relationship to perinatal complications. J Matern Fetal Neonatal Med 19, 551-555.

Ghosh, S., Yates, A.J., Fruhwald, M.C., Miecznikowski, J.C., Plass, C., and Smiraglia, D.J. (2010). Tissue specific DNA methylation of CpG islands in normal human adult somatic tissues distinguishes neural from non-neural tissues. Epigenetics 5.

Gicquel, C., Gaston, V., Mandelbaum, J., Siffroi, J.P., Flahault, A., and Le Bouc, Y. (2003). In vitro fertilization may increase the risk of Beckwith-Wiedemann syndrome related to the abnormal imprinting of the KCN1OT gene. Am J Hum Genet 72, 1338-1341.

Gicquel, C., Rossignol, S., Cabrol, S., Houang, M., Steunou, V., Barbu, V., Danton, F., Thibaud, N., Merrer, M.L., Burglen, L., et al. (2005). Epimutation of the telomeric imprinting center region on chromosome 11p15 in Silver-Russell syndrome. Nature Genetics 37, 1003-1007.

Gilbody, S., Lewis, S., and Lightfoot, T. (2007). Methylenetetrahydrofolate reductase (MTHFR) genetic polymorphisms and psychiatric disorders: a HuGE review. Am J Epidemiol 165, 1-13.

Gitan, R.S., Shi, H., Chen, C.M., Yan, P.S., and Huang, T.H. (2002). Methylation-specific oligonucleotide microarray: a new potential for high-throughput methylation analysis. Genome Res 12, 158-164.

Godfrey, K.M., and Barker, D.J. (2000). Fetal nutrition and adult disease. Am J Clin Nutr 71, 1344S-1352S.

294

Gonzalgo, M.L., and Jones, P.A. (1997). Rapid quantitation of methylation differences at specific sites using methylation-sensitive single nucleotide primer extension (Ms-SNuPE). Nucleic Acids Res 25, 2529-2531.

Grafodatskaya, D., Choufani, S., Ferreira, J.C., Butcher, D.T., Lou, Y., Zhao, C., Scherer, S.W., and Weksberg, R. (2010). EBV transformation and cell culturing destabilizes DNA methylation in human lymphoblastoid cell lines. Genomics 95, 73-83.

Griffiths-Jones, S., Saini, H.K., van Dongen, S., and Enright, A.J. (2008). miRBase: tools for microRNA genomics. Nucleic Acids Res 36, D154-158.

Grigoriu, A., Ferreira, J.C., Choufani, S., Baczyk, D., Kingdom, J., and Weksberg, R. (2011). Cell specific patterns of methylation in the human placenta. Epigenetics 6, 368-379.

Gronniger, E., Weber, B., Heil, O., Peters, N., Stab, F., Wenck, H., Korn, B., Winnefeld, M., and Lyko, F. (2010). Aging and chronic sun exposure cause distinct epigenetic changes in human skin. PLoS Genet 6, e1000971.

Guo, L., Choufani, S., Ferreira, J., Smith, A., Chitayat, D., Shuman, C., Uxa, R., Keating, S., Kingdom, J., and Weksberg, R. (2008). Altered gene expression and methylation of the human chromosome 11 imprinted region in small for gestational age (SGA) placentae. Developmental Biology 320, 79-91.

Hack, M., Schluchter, M., Cartar, L., Rahman, M., Cuttler, L., and Borawski, E. (2003). Growth of very low birth weight infants to age 20 years. Pediatrics 112, e30-38.

Hales, C.N., Barker, D.J., Clark, P.M., Cox, L.J., Fall, C., Osmond, C., and Winter, P.D. (1991). Fetal and infant growth and impaired glucose tolerance at age 64. Bmj 303, 1019-1022.

Hallan, S., Euser, A.M., Irgens, L.M., Finken, M.J., Holmen, J., and Dekker, F.W. (2008). Effect of intrauterine growth restriction on kidney function at young adult age: the Nord Trondelag Health (HUNT 2) Study. Am J Kidney Dis 51, 10-20.

Hannula, K., Kere, J., Pirinen, S., Holmberg, C., and Lipsanen-Nyman, M. (2001a). Do patients with maternal uniparental disomy for chromosome 7 have a distinct mild Silver-Russell phenotype? J Med Genet 38, 273-278.

Hannula, K., Lipsanen-Nyman, M., Kontiokari, T., and Kere, J. (2001b). A narrow segment of maternal uniparental disomy of chromosome 7q31-qter in Silver-Russell syndrome delimits a candidate gene region. Am J Hum Genet 68, 247-253.

Hark, A.T., Schoenherr, C.J., Katz, D.J., Ingram, R.S., Levorse, J.M., and Tilghman, S.M. (2000). CTCF mediates methylation-sensitive enhancer-blocking activity at the H19/Igf2 locus. Nature 405, 486-489.

Hatada, I., Hayashizaki, Y., Hirotsune, S., Komatsubara, H., and Mukai, T. (1991). A genomic scanning method for higher organisms using restriction sites as landmarks. Proc Natl Acad Sci U S A 88, 9523-9527.

295

Hawkins, R.D., Hon, G.C., Lee, L.K., Ngo, Q., Lister, R., Pelizzola, M., Edsall, L.E., Kuan, S., Luu, Y., Klugman, S., et al. (2010). Distinct epigenomic landscapes of pluripotent and lineage- committed human cells. Cell Stem Cell 6, 479-491.

Hayashizaki, Y., Hirotsune, S., Okazaki, Y., Hatada, I., Shibata, H., Kawai, J., Hirose, K., Watanabe, S., Fushiki, S., Wada, S., et al. (1993). Restriction landmark genomic scanning method and its various applications. Electrophoresis 14, 251-258.

Hayatsu, H., Shiraishi, M., and Negishi, K. (2008). Bisulfite modification for analysis of DNA methylation. Curr Protoc Nucleic Acid Chem Chapter 6, Unit 6 10.

Haycock, P.C., and Ramsay, M. (2009). Exposure of mouse embryos to ethanol during preimplantation development: effect on DNA methylation in the h19 imprinting control region. Biol Reprod 81, 618-627.

Heijmans, B.T., Tobi, E.W., Stein, A.D., Putter, H., Blauw, G.J., Susser, E.S., Slagboom, P.E., and Lumey, L.H. (2008). Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc Natl Acad Sci U S A 105, 17046-17049.

Helmerhorst, F.M., Perquin, D.A., Donker, D., and Keirse, M.J. (2004). Perinatal outcome of singletons and twins after assisted conception: a systematic review of controlled studies. Bmj 328, 261.

Henke, A., and Gromoll, J. (2008). New insights into the evolution of chorionic gonadotrophin. Mol Cell Endocrinol 291, 11-19.

Henriksen, T., and Clausen, T. (2002). The fetal origins hypothesis: placental insufficiency and inheritance versus maternal malnutrition in well-nourished populations. Acta Obstet Gynecol Scand 81, 112-114.

Herman, J.G., and Baylin, S.B. (2003). Gene silencing in cancer in association with promoter hypermethylation. N Engl J Med 349, 2042-2054.

Herman, J.G., Graff, J.R., Myohanen, S., Nelkin, B.D., and Baylin, S.B. (1996). Methylation- specific PCR: a novel PCR assay for methylation status of CpG islands. Proceedings of the National Academy of Sciences of the United States of America 93, 9821-9826.

Herranz, M., and Esteller, M. (2007). DNA methylation and histone modifications in patients with cancer: potential prognostic and therapeutic targets. Methods Mol Biol 361, 25-62.

Hiby, S.E., Lough, M., Keverne, E.B., Surani, M.A., Loke, Y.W., and King, A. (2001). Paternal monoallelic expression of PEG3 in the human placenta. Hum Mol Genet 10, 1093-1100.

Ho, S.M., and Tang, W.Y. (2007). Techniques used in studies of epigenome dysregulation due to aberrant DNA methylation: an emphasis on fetal-based adult diseases. Reprod Toxicol 23, 267- 282.

Hodges, E., Smith, A.D., Kendall, J., Xuan, Z., Ravi, K., Rooks, M., Zhang, M.Q., Ye, K., Bhattacharjee, A., Brizuela, L., et al. (2009). High definition profiling of mammalian DNA

296 methylation by array capture and single molecule bisulfite sequencing. Genome Res 19, 1593- 1605.

Horike, S., Ferreira, J.C., Meguro-Horike, M., Choufani, S., Smith, A.C., Shuman, C., Meschino, W., Chitayat, D., Zackai, E., Scherer, S.W., et al. (2009). Screening of DNA methylation at the H19 promoter or the distal region of its ICR1 ensures efficient detection of chromosome 11p15 epimutations in Russell-Silver syndrome. Am J Med Genet A 149A, 2415-2423.

Horsthemke, B., and Buiting, K. (2008). Genomic imprinting and imprinting defects in humans. Adv Genet 61, 225-246.

Hou, P., Shen, J.Y., Ji, M.J., He, N.Y., and Lu, Z.H. (2004). Microarray-based method for detecting methylation changes of p16(Ink4a) gene 5'-CpG islands in gastric carcinomas. World J Gastroenterol 10, 3553-3558.

Hourrier, S., Salomon, L.J., Dreux, S., and Muller, F. (2010). Screening for adverse pregnancy outcome at early gestational age. Clin Chim Acta 411, 1547-1552.

Howell, C.Y., Bestor, T.H., Ding, F., Latham, K.E., Mertineit, C., Trasler, J.M., and Chaillet, J.R. (2001). Genomic Imprinting Disrupted by a Maternal Effect Mutation in the Dnmt1 Gene. Cell 104, 829-838.

Hu, J.F., Ulaner, G.A., Oruganti, H., Ivaturi, R.D., Balagura, K.A., Pham, J., Vu, T.H., and Hoffman, A.R. (2000). Allelic expression of the putative tumor suppressor gene p73 in human fetal tissues and tumor specimens. Biochim Biophys Acta 1491, 49-56.

Hu, M., Yao, J., Cai, L., Bachman, K.E., van den Brule, F., Velculescu, V., and Polyak, K. (2005). Distinct epigenetic changes in the stromal cells of breast cancers. Nat Genet 37, 899-905.

Huang, T.H., Laux, D.E., Hamlin, B.C., Tran, P., Tran, H., and Lubahn, D.B. (1997). Identification of DNA methylation markers for human breast carcinomas using the methylation- sensitive restriction fingerprinting technique. Cancer Res 57, 1030-1034.

Huang, T.H., Perry, M.R., and Laux, D.E. (1999). Methylation profiling of CpG islands in human breast cancer cells. Hum Mol Genet 8, 459-470.

Hung, T.H., Skepper, J.N., and Burton, G.J. (2001). In vitro ischemia-reperfusion injury in term human placenta as a model for oxidative stress in pathological pregnancies. Am J Pathol 159, 1031-1043.

Iacobuzio-Donahue, C.A. (2009). Epigenetic changes in cancer. Annu Rev Pathol 4, 229-249.

Ibrahim, A.E., Thorne, N.P., Baird, K., Barbosa-Morais, N.L., Tavare, S., Collins, V.P., Wyllie, A.H., Arends, M.J., and Brenton, J.D. (2006). MMASS: an optimized array-based method for assessing CpG island methylation. Nucleic Acids Res 34, e136.

Ichimiya, S., Nimura, Y., Kageyama, H., Takada, N., Sunahara, M., Shishikura, T., Nakamura, Y., Sakiyama, S., Seki, N., Ohira, M., et al. (1999). p73 at chromosome 1p36.3 is lost in advanced stage neuroblastoma but its mutation is infrequent. Oncogene 18, 1061-1066.

297

Illumina (2010). Infinium HumanMethylation27, RevB BeadChip Kits (Illumina Inc.).

Imbeaud, S., Graudens, E., Boulanger, V., Barlet, X., Zaborski, P., Eveno, E., Mueller, O., Schroeder, A., and Auffray, C. (2005). Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces. Nucleic Acids Res 33, e56.

Ioshikhes, I.P., and Zhang, M.Q. (2000). Large-scale human promoter mapping using CpG islands. Nat Genet 26, 61-63.

Irizarry, R.A., Ladd-Acosta, C., Carvalho, B., Wu, H., Brandenburg, S.A., Jeddeloh, J.A., Wen, B., and Feinberg, A.P. (2008). Comprehensive high-throughput arrays for relative methylation (CHARM). Genome Res 18, 780-790.

Ishikawa, T., Tamai, Y., Zorn, A.M., Yoshida, H., Seldin, M.F., Nishikawa, S., and Taketo, M.M. (2001). Mouse Wnt receptor gene Fzd5 is essential for yolk sac and placental angiogenesis. Development 128, 25-33.

Jackson, R.A., Gibson, K.A., Wu, Y.W., and Croughan, M.S. (2004). Perinatal outcomes in singletons following in vitro fertilization: a meta-analysis. Obstet Gynecol 103, 551-563.

Jaenisch, R., Beard, C., Lee, J., Marahrens, Y., and Panning, B. (1998). Mammalian X chromosome inactivation. Novartis Foundation Symposium 214, 200-209; discussion 209-213, 228-232.

Jelliffe-Pawlowski, L.L., and Hansen, R.L. (2004). Neurodevelopmental outcome at 8 months and 4 years among infants born full-term small-for-gestational-age. J Perinatol 24, 505-514.

Jinno, Y., Ikeda, Y., Yun, K., Maw, M., Masuzaki, H., Fukuda, H., Inuzuka, K., Fujishita, A., Ohtani, Y., Okimoto, T., et al. (1995). Establishment of functional imprinting of the H19 gene in human developing placentae. Nature Genetics 10, 318-324.

Jirtle, R.L. (2008). Interview. Epigenomics, imprinting and disease susceptibility. Pharmacogenomics 9, 1791-1795.

Johnson, W.E., Li, C., and Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118-127.

Joss-Moore, L.A., and Lane, R.H. (2009). The developmental origins of adult disease. Current Opinion in Pediatrics 21, 230-234.

Joyce, C.A., Sharp, A., Walker, J.M., Bullman, H., and Temple, I.K. (1999). Duplication of 7p12.1-p13, including GRB10 and IGFBP1, in a mother and daughter with features of Silver- Russell syndrome. Hum Genet 105, 273-280.

Kacem, S., and Feil, R. (2009). Chromatin mechanisms in genomic imprinting. Mamm Genome 20, 544-556.

298

Kadyrov, M., Kingdom, J.C., and Huppertz, B. (2006). Divergent trophoblast invasion and apoptosis in placental bed spiral arteries from pregnancies complicated by maternal anemia and early-onset preeclampsia/intrauterine growth restriction. Am J Obstet Gynecol 194, 557-563.

Kagami, M., Nagai, T., Fukami, M., Yamazawa, K., and Ogata, T. (2007). Silver-Russell syndrome in a girl born after in vitro fertilization: partial hypermethylation at the differentially methylated region of PEG1 / MEST. Journal of Assisted Reproduction and Genetics 24, 131- 136.

Kagami, M., O'Sullivan, M.J., Green, A.J., Watabe, Y., Arisaka, O., Masawa, N., Matsuoka, K., Fukami, M., Matsubara, K., Kato, F., et al. (2010). The IG-DMR and the MEG3-DMR at human chromosome 14q32.2: hierarchical interaction and distinct functional properties as imprinting control centers. PLoS Genet 6, e1000992.

Kagami, M., Sekita, Y., Nishimura, G., Irie, M., Kato, F., Okada, M., Yamamori, S., Kishimoto, H., Nakayama, M., Tanaka, Y., et al. (2008). Deletions and epimutations affecting the human 14q32.2 imprinted region in individuals with paternal and maternal upd(14)-like phenotypes. Nature Genetics 40, 237-242.

Kaghad, M., Bonnet, H., Yang, A., Creancier, L., Biscan, J.C., Valent, A., Minty, A., Chalon, P., Lelias, J.M., Dumont, X., et al. (1997). Monoallelically expressed gene related to p53 at 1p36, a region frequently deleted in neuroblastoma and other human cancers. Cell 90, 809-819.

Kaijser, M., Bonamy, A.K., Akre, O., Cnattingius, S., Granath, F., Norman, M., and Ekbom, A. (2008). Perinatal risk factors for ischemic heart disease: disentangling the roles of birth weight and preterm birth. Circulation 117, 405-410.

Kainz, B., Shehata, M., Bilban, M., Kienle, D., Heintel, D., Kromer-Holzinger, E., Le, T., Krober, A., Heller, G., Schwarzinger, I., et al. (2007). Overexpression of the paternally expressed gene 10 (PEG10) from the imprinted locus on chromosome 7q21 in high-risk B-cell chronic lymphocytic leukemia. Int J Cancer 121, 1984-1993.

Kamiya, M., Judson, H., Okazaki, Y., Kusakabe, M., Muramatsu, M., Takada, S., Takagi, N., Arima, T., Wake, N., Kamimura, K., et al. (2000). The cell cycle control gene ZAC/PLAGL1 is imprinted--a strong candidate gene for transient neonatal diabetes. Hum Mol Genet 9, 453-460.

Karimi, M., Johansson, S., Stach, D., Corcoran, M., Grander, D., Schalling, M., Bakalkin, G., Lyko, F., Larsson, C., and Ekstrom, T.J. (2006). LUMA (LUminometric Methylation Assay)--a high throughput method to the analysis of genomic DNA methylation. Exp Cell Res 312, 1989- 1995.

Ke, X., Lei, Q., James, S.J., Kelleher, S.L., Melnyk, S., Jernigan, S., Yu, X., Wang, L., Callaway, C.W., Gill, G., et al. (2006). Uteroplacental insufficiency affects epigenetic determinants of chromatin structure in brains of neonatal and juvenile IUGR rats. Physiol Genomics 25, 16-28.

Keating, E., Goncalves, P., Costa, F., Campos, I., Pinho, M.J., Azevedo, I., and Martel, F. (2009). Comparison of the transport characteristics of bioactive substances in IUGR and normal placentas. Pediatr Res 66, 495-500.

299

Kenet, G., Freedman, J., Shenkman, B., Regina, E., Brok-Simoni, F., Holzman, F., Vavva, F., Brand, N., Michelson, A., Trolliet, M., et al. (1999). Plasma glutathione peroxidase deficiency and platelet insensitivity to nitric oxide in children with familial stroke. Arterioscler Thromb Vasc Biol 19, 2017-2023.

Kerkel, K., Spadola, A., Yuan, E., Kosek, J., Jiang, L., Hod, E., Li, K., Murty, V.V., Schupf, N., Vilain, E., et al. (2008). Genomic surveys by methylation-sensitive SNP analysis identify sequence-dependent allele-specific DNA methylation. Nat Genet 40, 904-908.

Khulan, B., Thompson, R.F., Ye, K., Fazzari, M.J., Suzuki, M., Stasiek, E., Figueroa, M.E., Glass, J.L., Chen, Q., Montagna, C., et al. (2006). Comparative isoschizomer profiling of cytosine methylation: the HELP assay. Genome Res 16, 1046-1055.

Kim, J., Ashworth, L., Branscomb, E., and Stubbs, L. (1997). The human homolog of a mouse- imprinted gene, Peg3, maps to a zinc finger gene-rich region of human chromosome 19q13.4. Genome Res 7, 532-540.

Kim, K., Doi, A., Wen, B., Ng, K., Zhao, R., Cahan, P., Kim, J., Aryee, M.J., Ji, H., Ehrlich, L.I., et al. (2010). Epigenetic memory in induced pluripotent stem cells. Nature.

Kingdom, J., Huppertz, B., Seaward, G., and Kaufmann, P. (2000). Development of the placental villous tree and its consequences for fetal growth. Eur J Obstet Gynecol Reprod Biol 92, 35-43.

Kirkegaard, I., Uldbjerg, N., and Oxvig, C. (2010). Biology of pregnancy-associated plasma protein-A in relation to prenatal diagnostics: an overview. Acta Obstet Gynecol Scand 89, 1118- 1125.

Klose, R.J., and Bird, A.P. (2006). Genomic DNA methylation: the mark and its mediators. Trends Biochem Sci 31, 89-97.

Knofler, M. (2010). Critical growth factors and signalling pathways controlling human trophoblast invasion. Int J Dev Biol 54, 269-280.

Kobayashi, S., Uemura, H., Kohda, T., Nagai, T., Chinen, Y., Naritomi, K., Kinoshita, E.I., Ohashi, H., Imaizumi, K., Tsukahara, M., et al. (2001). No evidence of PEG1/MEST gene mutations in Silver-Russell syndrome patients. Am J Med Genet 104, 225-231.

Kohda, T., Asai, A., Kuroiwa, Y., Kobayashi, S., Aisaka, K., Nagashima, G., Yoshida, M.C., Kondo, Y., Kagiyama, N., Kirino, T., et al. (2001). Tumour suppressor activity of human imprinted gene PEG3 in a glioma cell line. Genes Cells 6, 237-247.

Kojima, M., and Kangawa, K. (2005). Ghrelin: structure and function. Physiol Rev 85, 495-522.

Kondo, Y., Shen, L., Cheng, A.S., Ahmed, S., Boumber, Y., Charo, C., Yamochi, T., Urano, T., Furukawa, K., Kwabi-Addo, B., et al. (2008). Gene silencing in cancer by histone H3 lysine 27 trimethylation independent of promoter DNA methylation. Nat Genet 40, 741-750.

300

Kondo, Y., Shen, L., Yan, P.S., Huang, T.H., and Issa, J.P. (2004). Chromatin immunoprecipitation microarrays for identification of genes silenced by histone H3 lysine 9 methylation. Proc Natl Acad Sci U S A 101, 7398-7403.

Kondrashov, A.S. (2003). Direct estimates of human per nucleotide mutation rates at 20 loci causing Mendelian diseases. Hum Mutat 21, 12-27.

Kotzot, D. (1999). Abnormal phenotypes in uniparental disomy (UPD): fundamental aspects and a critical review with bibliography of UPD other than 15. American Journal of Medical Genetics 82, 265-274.

Kotzot, D., Balmer, D., Baumer, A., Chrzanowska, K., Hamel, B.C., Ilyina, H., Krajewska- Walasek, M., Lurie, I.W., Otten, B.J., Schoenle, E., et al. (2000). Maternal uniparental disomy 7- -review and further delineation of the phenotype. Eur J Pediatr 159, 247-256.

Kotzot, D., Holland, H., Keller, E., and Froster, U.G. (2001). Maternal isochromosome 7q and paternal isochromosome 7p in a boy with growth retardation. Am J Med Genet 102, 169-172.

Kraus FT, R.I.W., Gersell DJ; Nelson DM, Dicke ,IM (2004). Placental Pathology. In Placental Pathology (Washington DC, American Registry of Pathology), pp. 312-314.

Krueger, C., and Morison, I.M. (2008). Random monoallelic expression: making a choice. Trends Genet 24, 257-259.

Kucharski, R., Maleszka, J., Foret, S., and Maleszka, R. (2008). Nutritional control of reproductive status in honeybees via DNA methylation. Science 319, 1827-1830.

Ladd-Acosta, C., M, J.A., Ordway, J.M., and Feinberg, A.P. (2010). Comprehensive high- throughput arrays for relative methylation (CHARM). Curr Protoc Hum Genet Chapter 20, Unit 20 21 21-19.

Laird, P.W. (2010). Principles and challenges of genome-wide DNA methylation analysis. Nat Rev Genet 11, 191-203.

Lambertini, L., Diplas, A.I., Lee, M.J., Sperling, R., Chen, J., and Wetmur, J. (2008). A sensitive functional assay reveals frequent loss of genomic imprinting in human placenta. Epigenetics 3, 261-269.

Lane, R.H., Kelley, D.E., Gruetzmacher, E.M., and Devaskar, S.U. (2001). Uteroplacental insufficiency alters hepatic fatty acid-metabolizing enzymes in juvenile and adult rats. Am J Physiol Regul Integr Comp Physiol 280, R183-190.

Lang, U., Baker, R.S., Braems, G., Zygmunt, M., Kunzel, W., and Clark, K.E. (2003). Uterine blood flow--a determinant of fetal growth. Eur J Obstet Gynecol Reprod Biol 110 Suppl 1, S55- 61.

Langlois, S., Yong, S.L., Wilson, R.D., Kwong, L.C., and Kalousek, D.K. (1995). Prenatal and postnatal growth failure associated with maternal heterodisomy for chromosome 7. J Med Genet 32, 871-875.

301

Langston, C., Kaplan, C., Macpherson, T., Manci, E., Peevy, K., Clark, B., Murtagh, C., Cox, S., and Glenn, G. (1997). Practice guideline for examination of the placenta: developed by the Placental Pathology Practice Guideline Development Task Force of the College of American Pathologists. Arch Pathol Lab Med 121, 449-476.

Laurent, L., Wong, E., Li, G., Huynh, T., Tsirigos, A., Ong, C.T., Low, H.M., Kin Sung, K.W., Rigoutsos, I., Loring, J., et al. (2010). Dynamic changes in the human methylome during differentiation. Genome Res 20, 320-331.

Lee, J.H., Byun, D.S., Lee, M.G., Ryu, B.K., Kang, M.J., Chae, K.S., Lee, K.Y., Kim, H.J., Park, H., and Chi, S.G. (2008). Frequent epigenetic inactivation of hSRBC in gastric cancer and its implication in attenuated p53 response to stresses. Int J Cancer 122, 1573-1584.

Lee, M.H., Jeon, Y.J., Lee, S.M., Park, M.H., Jung, S.C., and Kim, Y.J. (2010). Placental gene expression is related to glucose metabolism and fetal cord blood levels of insulin and insulin-like growth factors in intrauterine growth restriction. Early Hum Dev 86, 45-50.

Leek, J.T., Scharpf, R.B., Bravo, H.C., Simcha, D., Langmead, B., Johnson, W.E., Geman, D., Baggerly, K., and Irizarry, R.A. (2010). Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11, 733-739.

Lefebvre, L., Viville, S., Barton, S.C., Ishino, F., Keverne, E.B., and Surani, M.A. (1998). Abnormal maternal behaviour and growth retardation associated with loss of the imprinted gene Mest [see comments]. Nature Genetics 20, 163-169.

Leon, D.A., Lithell, H.O., Vagero, D., Koupilova, I., Mohsen, R., Berglund, L., Lithell, U.B., and McKeigue, P.M. (1998). Reduced fetal growth rate and increased risk of death from ischaemic heart disease: cohort study of 15 000 Swedish men and women born 1915-29. Bmj 317, 241-245.

Li, E. (2002). Chromatin modification and epigenetic reprogramming in mammalian development. Nat Rev Genet 3, 662-673.

Li, E., Bestor, T.H., and Jaenisch, R. (1992). Targeted mutation of the DNA methyltransferase gene results in embryonic lethality. Cell 69, 915-926.

Li, F., Chong, Z.Z., and Maiese, K. (2006). Winding through the WNT pathway during cellular development and demise. Histol Histopathol 21, 103-124.

Li, J.B., Gao, Y., Aach, J., Zhang, K., Kryukov, G.V., Xie, B., Ahlford, A., Yoon, J.K., Rosenbaum, A.M., Zaranek, A.W., et al. (2009). Multiplex padlock targeted sequencing reveals human hypermutable CpG variations. Genome Res 19, 1606-1615.

Li, N., Ye, M., Li, Y., Yan, Z., Butcher, L.M., Sun, J., Han, X., Chen, Q., Zhang, X., and Wang, J. (2010a). Whole genome DNA methylation analysis based on high throughput sequencing technology. Methods 52, 203-212.

302

Li, T., Vu, T.H., Lee, K.O., Yang, Y., Nguyen, C.V., Bui, H.Q., Zeng, Z.L., Nguyen, B.T., Hu, J.F., Murphy, S.K., et al. (2002). An imprinted PEG1/MEST antisense expressed predominantly in human testis and in mature spermatozoa. J Biol Chem 277, 13518-13527.

Li, Y., Zhu, J., Tian, G., Li, N., Li, Q., Ye, M., Zheng, H., Yu, J., Wu, H., Sun, J., et al. (2010b). The DNA methylome of human peripheral blood mononuclear cells. PLoS Biol 8, e1000533.

Lian, I.A., Toft, J.H., Olsen, G.D., Langaas, M., Bjorge, L., Eide, I.P., Bordahl, P.E., and Austgulen, R. (2010). Matrix metalloproteinase 1 in pre-eclampsia and fetal growth restriction: reduced gene expression in decidual tissue and protein expression in extravillous trophoblasts. Placenta 31, 615-620.

Liang, G., Chen, M., Pan, X.L., Zheng, J., and Wang, H. (2010). Ethanol-induced inhibition of fetal hypothalamic-pituitary-adrenal axis due to prenatal overexposure to maternal glucocorticoid in mice. Exp Toxicol Pathol.

Liang, G., Gonzalgo, M.L., Salem, C., and Jones, P.A. (2002). Identification of DNA methylation differences during tumorigenesis by methylation-sensitive arbitrarily primed polymerase chain reaction. Methods 27, 150-155.

Liang, Y., Ridzon, D., Wong, L., and Chen, C. (2007). Characterization of microRNA expression profiles in normal human tissues. BMC Genomics 8, 166.

Ligtenberg, M.J.L., Kuiper, R.P., Chan, T.L., Goossens, M., Hebeda, K.M., Voorendt, M., Lee, T.Y.H., Bodmer, D., Hoenselaar, E., Hendriks-Cornelissen, S.J.B., et al. (2008). Heritable somatic methylation and inactivation of MSH2 in families with Lynch syndrome due to deletion of the 3′ exons of TACSTD1. Nature Genetics 41, 112-117.

Lima, S.C., Hernandez-Vargas, H., and Herceg, Z. (2010). Epigenetic signatures in cancer: Implications for the control of cancer in the clinic. Curr Opin Mol Ther 12, 316-324.

Lissowska, J., Gaudet, M.M., Brinton, L.A., Chanock, S.J., Peplonska, B., Welch, R., Zatonski, W., Szeszenia-Dabrowska, N., Park, S., Sherman, M., et al. (2007). Genetic polymorphisms in the one-carbon metabolism pathway and breast cancer risk: a population-based case-control study and meta-analyses. Int J Cancer 120, 2696-2703.

Lister, R., and Ecker, J.R. (2009). Finding the fifth base: genome-wide sequencing of cytosine methylation. Genome Res 19, 959-966.

Lister, R., O'Malley, R.C., Tonti-Filippini, J., Gregory, B.D., Berry, C.C., Millar, A.H., and Ecker, J.R. (2008). Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523-536.

Lister, R., Pelizzola, M., Dowen, R.H., Hawkins, R.D., Hon, G., Tonti-Filippini, J., Nery, J.R., Lee, L., Ye, Z., Ngo, Q.M., et al. (2009). Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315-322.

303

Lithell, H.O., McKeigue, P.M., Berglund, L., Mohsen, R., Lithell, U.B., and Leon, D.A. (1996). Relation of size at birth to non-insulin dependent diabetes and insulin concentrations in men aged 50-60 years. Bmj 312, 406-410.

Liu, J., Litman, D., Rosenberg, M.J., Yu, S., Biesecker, L.G., and Weinstein, L.S. (2000). A GNAS1 imprinting defect in pseudohypoparathyroidism type IB. J Clin Invest 106, 1167-1174.

Liu, L. (2003). Aging, cancer and nutrition: the DNA methylation connection. Mechanisms of Ageing and Development 124, 989-998.

Luedi, P.P., Dietrich, F.S., Weidman, J.R., Bosko, J.M., Jirtle, R.L., and Hartemink, A.J. (2007). Computational and experimental identification of novel human imprinted genes. Genome Res 17, 1723-1730.

Lumey, L.H., and Stein, A.D. (1997). Offspring birth weights after maternal intrauterine undernutrition: a comparison within sibships. Am J Epidemiol 146, 810-819.

Luo, S.S., Ishibashi, O., Ishikawa, G., Ishikawa, T., Katayama, A., Mishima, T., Takizawa, T., Shigihara, T., Goto, T., Izumi, A., et al. (2009). Human villous trophoblasts express and secrete placenta-specific microRNAs into maternal circulation via exosomes. Biol Reprod 81, 717-729.

Maccani, M.A., and Marsit, C.J. (2009). Epigenetics in the placenta. Am J Reprod Immunol 62, 78-89.

Mackay, D., Boonen, S., Clayton-Smith, J., Goodship, J., Hahnemann, J., Kant, S., Njølstad, P., Robin, N., Robinson, D., Siebert, R., et al. (2006a). A maternal hypomethylation syndrome presenting as transient neonatal diabetes mellitus. Human Genetics 120, 262-269.

Mackay, D.J., Hahnemann, J.M., Boonen, S.E., Poerksen, S., Bunyan, D.J., White, H.E., Durston, V.J., Thomas, N.S., Robinson, D.O., Shield, J.P., et al. (2006b). Epimutation of the TNDM locus and the Beckwith-Wiedemann syndrome centromeric locus in individuals with transient neonatal diabetes mellitus. Hum Genet 119, 179-184.

Mackay, D.J., Temple, I.K., Shield, J.P., and Robinson, D.O. (2005). Bisulphite sequencing of the transient neonatal diabetes mellitus DMR facilitates a novel diagnostic test but reveals no methylation anomalies in patients of unknown aetiology. Hum Genet 116, 255-261.

MacLennan, N.K., James, S.J., Melnyk, S., Piroozi, A., Jernigan, S., Hsu, J.L., Janke, S.M., Pham, T.D., and Lane, R.H. (2004). Uteroplacental insufficiency alters DNA methylation, one- carbon metabolism, and histone acetylation in IUGR rats. Physiol Genomics 18, 43-50.

Madazli, R., Somunkiran, A., Calay, Z., Ilvan, S., and Aksu, M.F. (2003). Histomorphology of the placenta and the placental bed of growth restricted foetuses and correlation with the Doppler velocimetries of the uterine and umbilical arteries. Placenta 24, 510-516.

Maegawa, S., Hinkal, G., Kim, H.S., Shen, L., Zhang, L., Zhang, J., Zhang, N., Liang, S., Donehower, L.A., and Issa, J.P. (2010). Widespread and tissue specific age-related DNA methylation changes in mice. Genome Res 20, 332-340.

304

Maldonado-Estrada, J., Menu, E., Roques, P., Barre-Sinoussi, F., and Chaouat, G. (2004). Evaluation of Cytokeratin 7 as an accurate intracellular marker with which to assess the purity of human placental villous trophoblast cells by flow cytometry. J Immunol Methods 286, 21-34.

Manning, J., and Vehaskari, V.M. (2001). Low birth weight-associated adult hypertension in the rat. Pediatr Nephrol 16, 417-422.

Marsit, C.J., Eddy, K., and Kelsey, K.T. (2006). MicroRNA responses to cellular stress. Cancer Res 66, 10843-10848.

Martin-Subero, J.I., Bibikova, M., Mackay, D., Wickham-Garcia, E., Sellami, N., Richter, J., Santer, R., Caliebe, A., Fan, J.B., Temple, I.K., et al. (2008). Microarray-based DNA methylation analysis of imprinted loci in a patient with transient neonatal diabetes mellitus. Am J Med Genet A 146A, 3227-3229.

Martinez-Delgado, B., Melendez, B., Cuadros, M., Jose Garcia, M., Nomdedeu, J., Rivas, C., Fernandez-Piqueras, J., and Benitez, J. (2002). Frequent inactivation of the p73 gene by abnormal methylation or LOH in non-Hodgkin's lymphomas. Int J Cancer 102, 15-19.

Mathers, J.C. (2007). Early nutrition: impact on epigenetics. Forum Nutr 60, 42-48.

Maulik, D. (2006a). Fetal growth restriction: the etiology. Clin Obstet Gynecol 49, 228-235.

Maulik, D. (2006b). Management of fetal growth restriction: an evidence-based approach. Clin Obstet Gynecol 49, 320-334.

Mayhew, T.M., and Desoye, G. (2004). A simple method for comparing immunogold distributions in two or more experimental groups illustrated using GLUT1 labelling of isolated trophoblast cells. Placenta 25, 580-584.

Mayhew, T.M., Manwani, R., Ohadike, C., Wijesekara, J., and Baker, P.N. (2007). The placenta in pre-eclampsia and intrauterine growth restriction: studies on exchange surface areas, diffusion distances and villous membrane diffusive conductances. Placenta 28, 233-238.

McCarthy, C., Cotter, F.E., McElwaine, S., Twomey, A., Mooney, E.E., Ryan, F., and Vaughan, J. (2007). Altered gene expression patterns in intrauterine growth restriction: potential role of hypoxia. Am J Obstet Gynecol 196, 70 e71-76.

McDonald, S.D., Murphy, K., Beyene, J., and Ohlsson, A. (2005). Perinatal outcomes of singleton pregnancies achieved by in vitro fertilization: a systematic review and meta-analysis. J Obstet Gynaecol Can 27, 449-459.

McMinn, J., Wei, M., Schupf, N., Cusmai, J., Johnson, E.B., Smith, A.C., Weksberg, R., Thaker, H.M., and Tycko, B. (2006). Unbalanced Placental Expression of Imprinted Genes in Human Intrauterine Growth Restriction. Placenta 27, 540-549.

Meekins, J.W., Pijnenborg, R., Hanssens, M., McFadyen, I.R., and van Asshe, A. (1994). A study of placental bed spiral arteries and trophoblast invasion in normal and severe pre-eclamptic pregnancies. Br J Obstet Gynaecol 101, 669-674.

305

Meissner, A., Gnirke, A., Bell, G.W., Ramsahoye, B., Lander, E.S., and Jaenisch, R. (2005). Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res 33, 5868-5877.

Meissner, A., Mikkelsen, T.S., Gu, H., Wernig, M., Hanna, J., Sivachenko, A., Zhang, X., Bernstein, B.E., Nusbaum, C., Jaffe, D.B., et al. (2008). Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 454, 766-770.

Mendell, J.T. (2008). miRiad roles for the miR-17-92 cluster in development and disease. Cell 133, 217-222.

Meschia, G. (1985). Safety margin of fetal oxygenation. J Reprod Med 30, 308-311.

Minard, M.E., Jain, A.K., and Barton, M.C. (2009). Analysis of epigenetic alterations to chromatin during development. Genesis 47, 559-572.

Mistry, H.D., Kurlak, L.O., Williams, P.J., Ramsay, M.M., Symonds, M.E., and Pipkin, F.B. (2010). Differential expression and distribution of placental glutathione peroxidases 1, 3 and 4 in normal and preeclamptic pregnancy. Placenta 31, 401-408.

Miyamoto, Y., Koh, Y.H., Park, Y.S., Fujiwara, N., Sakiyama, H., Misonou, Y., Ookawara, T., Suzuki, K., Honke, K., and Taniguchi, N. (2003). Oxidative stress caused by inactivation of glutathione peroxidase and adaptive responses. Biol Chem 384, 567-574.

Modre-Osprian, R., Osprian, I., Tilg, B., Schreier, G., Weinberger, K.M., and Graber, A. (2009). Dynamic simulations on the mitochondrial fatty acid beta-oxidation network. BMC Syst Biol 3, 2.

Mohammad, N.S., Jain, J.M., Chintakindi, K.P., Singh, R.P., Naik, U., and Akella, R.R. (2009). Aberrations in folate metabolic pathway and altered susceptibility to autism. Psychiatr Genet 19, 171-176.

Monk, D., Arnaud, P., Apostolidou, S., Hills, F.A., Kelsey, G., Stanier, P., Feil, R., and Moore, G.E. (2006a). Limited evolutionary conservation of imprinting in the human placenta. Proc Natl Acad Sci U S A 103, 6623-6628.

Monk, D., Bentley, L., Hitchins, M., Myler, R.A., Clayton-Smith, J., Ismail, S., Price, S.M., Preece, M.A., Stanier, P., and Moore, G.E. (2002). Chromosome 7p disruptions in Silver Russell syndrome: delineating an imprinted candidate gene region. Hum Genet 111, 376-387.

Monk, D., Sanches, R., Arnaud, P., Apostolidou, S., Hills, F.A., Abu-Amero, S., Murrell, A., Friess, H., Reik, W., Stanier, P., et al. (2006b). Imprinting of IGF2 P0 transcript and novel alternatively spliced INS-IGF2 isoforms show differences between mouse and human. Hum Mol Genet 15, 1259-1269.

Monk, D., Wagschal, A., Arnaud, P., Muller, P.S., Parker-Katiraee, L., Bourc'his, D., Scherer, S.W., Feil, R., Stanier, P., and Moore, G.E. (2008). Comparative analysis of human chromosome 7q21 and mouse proximal chromosome 6 reveals a placental-specific imprinted gene, TFPI2/Tfpi2, which requires EHMT2 and EED for allelic-silencing. Genome Res 18, 1270-1281.

306

Monkley, S.J., Delaney, S.J., Pennisi, D.J., Christiansen, J.H., and Wainwright, B.J. (1996). Targeted disruption of the Wnt2 gene results in placentation defects. Development 122, 3343- 3353.

Moore, T., and Haig, D. (1991). Genomic imprinting in mammalian development: a parental tug- of-war. Trends in Genetics 7, 45-49.

Morioka, Y., Isotani, A., Oshima, R.G., Okabe, M., and Ikawa, M. (2009). Placenta-specific gene activation and inactivation using integrase-defective lentiviral vectors with the Cre/LoxP system. Genesis 47, 793-798.

Morison, I.M., and Reeve, A.E. (1998). A catalogue of imprinted genes and parent-of-origin effects in humans and animals. Human Molecular Genetics 7, 1599-1609.

Morpurgo, P.S., Cetin, I., Borgato, S., Cortelazzi, D., Nobile-Desantis, M.S., Vaghi, I., and Beck-Peccoz, P. (2004). Circulating levels of inhibin A, inhibin B and activin A in normal and intrauterine growth restricted (IUGR) fetuses. Eur J Obstet Gynecol Reprod Biol 117, 38-44.

Morris, R.K., Cnossen, J.S., Langejans, M., Robson, S.C., Kleijnen, J., Ter Riet, G., Mol, B.W., van der Post, J.A., and Khan, K.S. (2008). Serum screening with Down's syndrome markers to predict pre-eclampsia and small for gestational age: systematic review and meta-analysis. BMC Pregnancy Childbirth 8, 33.

Munaut, C., Lorquet, S., Pequeux, C., Blacher, S., Berndt, S., Frankenne, F., and Foidart, J.M. (2008). Hypoxia is responsible for soluble vascular endothelial growth factor receptor-1 (VEGFR-1) but not for soluble endoglin induction in villous trophoblast. Hum Reprod 23, 1407- 1415.

Munshi, A., Shafi, G., Aliya, N., and Jyothy, A. (2009). Histone modifications dictate specific biological readouts. J Genet Genomics 36, 75-88.

Murphy, S.K., Wylie, A.A., and Jirtle, R.L. (2001). Imprinting of PEG3, the human homologue of a mouse gene involved in nurturing behavior. Genomics 71, 110-117.

Murrell, A., Heeson, S., and Reik, W. (2004). Interaction between differentially methylated regions partitions the imprinted genes Igf2 and H19 into parent-specific chromatin loops. Nat Genet 36, 889-893.

Mylonas, I., Schiessl, B., Jeschke, U., Vogl, J., Makrigiannakis, A., Kuhn, C., Kunze, S., Schulze, S., Kainer, F., and Friese, K. (2006). Expression of inhibin/activin subunits alpha (- alpha), beta A (-beta (A)) and beta B (-beta (B)) in placental tissue of normal and intrauterine growth restricted (IUGR) pregnancies. J Mol Histol 37, 43-52.

Nagy, A. (2000). Cre recombinase: the universal reagent for genome tailoring. Genesis 26, 99- 109.

Nakabayashi, K., Fernandez, B.A., Teshima, I., Shuman, C., Proud, V.K., Curry, C.J., Chitayat, D., Grebe, T., Ming, J., Oshimura, M., et al. (2002). Molecular genetic studies of human chromosome 7 in Russell-Silver syndrome. Genomics 79, 186-196.

307

Netchine, I., Rossignol, S., Dufourg, M.N., Azzi, S., Rousseau, A., Perin, L., Houang, M., Steunou, V., Esteva, B., Thibaud, N., et al. (2007). 11p15 imprinting center region 1 loss of methylation is a common and specific cause of typical Russell-Silver syndrome: clinical scoring system and epigenetic-phenotypic correlations. J Clin Endocrinol Metab 92, 3148-3154.

Nevo, O., Soleymanlou, N., Wu, Y., Xu, J., Kingdom, J., Many, A., Zamudio, S., and Caniggia, I. (2006). Increased expression of sFlt-1 in in vivo and in vitro models of human placental hypoxia is mediated by HIF-1. Am J Physiol Regul Integr Comp Physiol 291, R1085-1093.

Nightingale, S.J., Hollis, R.P., Pepper, K.A., Petersen, D., Yu, X.J., Yang, C., Bahner, I., and Kohn, D.B. (2006). Transient gene expression by nonintegrating lentiviral vectors. Mol Ther 13, 1121-1132.

Nishizawa, H., Pryor-Koishi, K., Suzuki, M., Kato, T., Kogo, H., Sekiya, T., Kurahashi, H., and Udagawa, Y. (2008). Increased levels of pregnancy-associated plasma protein-A2 in the serum of pre-eclamptic patients. Mol Hum Reprod 14, 595-602.

Novakovic, B., Rakyan, V., Ng, H.K., Manuelpillai, U., Dewi, C., Wong, N.C., Morley, R., Down, T., Beck, S., Craig, J.M., et al. (2008). Specific tumour-associated methylation in normal human term placenta and first-trimester cytotrophoblasts. Mol Hum Reprod 14, 547-554.

Novakovic, B., Sibson, M., Ng, H.K., Manuelpillai, U., Rakyan, V., Down, T., Beck, S., Fournier, T., Evain-Brion, D., Dimitriadis, E., et al. (2009). Placenta-specific methylation of the vitamin D 24-hydroxylase gene: implications for feedback autoregulation of active vitamin D levels at the fetomaternal interface. J Biol Chem 284, 14838-14848.

Oda, M., Glass, J.L., Thompson, R.F., Mo, Y., Olivier, E.N., Figueroa, M.E., Selzer, R.R., Richmond, T.A., Zhang, X., Dannenberg, L., et al. (2009). High-resolution genome-wide cytosine methylation profiling with simultaneous copy number analysis and optimization for limited cell numbers. Nucleic Acids Res 37, 3829-3839.

Okada, Y., Ueshin, Y., Isotani, A., Saito-Fujita, T., Nakashima, H., Kimura, K., Mizoguchi, A., Oh-Hora, M., Mori, Y., Ogata, M., et al. (2007). Complementation of placental defects and embryonic lethality by trophoblast-specific lentiviral gene transfer. Nat Biotechnol 25, 233-237.

Okamoto, A., Endo, H., Kalionis, B., Shinya, M., Saito, M., Nikaido, T., and Tanaka, T. (2006). IGFBP1 and Follistatin-like 3 genes are significantly up-regulated in expression profiles of the IUGR placenta. Placenta 27, 317-321.

Oken, E., Kleinman, K.P., Rich-Edwards, J., and Gillman, M.W. (2003). A nearly continuous measure of birth weight for gestational age using a United States national reference. BMC Pediatr 3, 6.

Ordway, J.M., Bedell, J.A., Citek, R.W., Nunberg, A., Garrido, A., Kendall, R., Stevens, J.R., Cao, D., Doerge, R.W., Korshunova, Y., et al. (2006). Comprehensive DNA methylation profiling in a human cancer genome identifies novel epigenetic targets. Carcinogenesis 27, 2409- 2423.

308

Osmond, C., Barker, D.J., Winter, P.D., Fall, C.H., and Simmonds, S.J. (1993). Early growth and death from cardiovascular disease in women. Bmj 307, 1519-1524.

Overgaard, M.T., Boldt, H.B., Laursen, L.S., Sottrup-Jensen, L., Conover, C.A., and Oxvig, C. (2001). Pregnancy-associated plasma protein-A2 (PAPP-A2), a novel insulin-like growth factor- binding protein-5 proteinase. J Biol Chem 276, 21849-21853.

Padavala, S., Pope, N., Baker, P., and Crocker, I. (2006). An imbalance between vascular endothelial growth factor and its soluble receptor in placental villous explants of intrauterine growth-restricted pregnancies. J Soc Gynecol Investig 13, 40-47.

Page, N.M., Butlin, D.J., Lomthaisong, K., and Lowry, P.J. (2001). The characterization of pregnancy associated plasma protein-E and the identification of an alternative splice variant. Placenta 22, 681-687.

Pallotto, E.K., and Kilbride, H.W. (2006). Perinatal outcome and later implications of intrauterine growth restriction. Clin Obstet Gynecol 49, 257-269.

Park, B.H., Kim, Y.J., Park, J.S., Lee, H.Y., Ha, E.H., Min, J.W., and Park, H.S. (2005). [Folate and homocysteine levels during pregnancy affect DNA methylation in human placenta]. J Prev Med Public Health 38, 437-442.

Park, J.H., Stoffers, D.A., Nicholls, R.D., and Simmons, R.A. (2008). Development of type 2 diabetes following intrauterine growth retardation in rats is associated with progressive epigenetic silencing of Pdx1. J Clin Invest 118, 2316-2324.

Pastinen, T. (2010). Genome-wide allele-specific analysis: insights into regulatory variation. Nat Rev Genet 11, 533-538.

Pavri, R., Lewis, B., Kim, T.K., Dilworth, F.J., Erdjument-Bromage, H., Tempst, P., de Murcia, G., Evans, R., Chambon, P., and Reinberg, D. (2005). PARP-1 determines specificity in a retinoid signaling pathway via direct modulation of mediator. Mol Cell 18, 83-96.

Pelizzola, M., Koga, Y., Urban, A.E., Krauthammer, M., Weissman, S., Halaban, R., and Molinaro, A.M. (2008). MEDME: an experimental and analytical methodology for the estimation of DNA methylation levels based on microarray derived MeDIP-enrichment. Genome Res 18, 1652-1659.

Persson, J., and Ekwall, K. (2010). Chd1 remodelers maintain open chromatin and regulate the epigenetics of differentiation. Exp Cell Res 316, 1316-1323.

Petronis, A. (2010). Epigenetics as a unifying principle in the aetiology of complex traits and diseases. Nature 465, 721-727.

Philippe, S., Sarkis, C., Barkats, M., Mammeri, H., Ladroue, C., Petit, C., Mallet, J., and Serguera, C. (2006). Lentiviral vectors with a defective integrase allow efficient and sustained transgene expression in vitro and in vivo. Proc Natl Acad Sci U S A 103, 17684-17689.

309

Pierce, J.G., and Parsons, T.F. (1981). Glycoprotein hormones: structure and function. Annu Rev Biochem 50, 465-495.

Pijnenborg, R. (2000). Uterine haemodynamics as a possible driving force for endovascular trophoblast migration in the placental bed. Med Hypotheses 55, 114-118.

Pineles, B.L., Romero, R., Montenegro, D., Tarca, A.L., Han, Y.M., Kim, Y.M., Draghici, S., Espinoza, J., Kusanovic, J.P., Mittal, P., et al. (2007). Distinct subsets of microRNAs are expressed differentially in the human placentas of patients with preeclampsia. American Journal of Obstetrics and Gynecology 196, 261.e261-261.e266.

Plouin, P.F., Breart, G., Rabarison, Y., Rumeau-Rouquette, C., Sureau, C., and Menard, J. (1983). Fetal growth retardation in gestational hypertension: relationships with blood pressure levels and the time of onset of hypertension. Eur J Obstet Gynecol Reprod Biol 16, 253-262.

Polo, J.M., Liu, S., Figueroa, M.E., Kulalert, W., Eminli, S., Tan, K.Y., Apostolou, E., Stadtfeld, M., Li, Y., Shioda, T., et al. (2010). Cell type of origin influences the molecular and functional properties of mouse induced pluripotent stem cells. Nat Biotechnol 28, 848-855.

Polo Perucchin, P., Traggiai, C., Calevo, M.G., Gastaldi, R., Di Battista, E., Amisano, A., and Lorini, R. (2011). Auxological and metabolic study in small for gestational age children during 2 years follow-up. J Matern Fetal Neonatal Med 24, 381-387.

Porter, M., Pearson, D.J., Suarez-Mendez, V.J., and Blann, A.D. (1992). Plasma, platelet and erythrocyte glutathione peroxidases as risk factors in ischaemic heart disease in man. Clin Sci (Lond) 83, 343-345.

Pozharny, Y., Lambertini, L., Ma, Y., Ferrara, L., Litton, C.G., Diplas, A., Jacobs, A.R., Chen, J., Stone, J.L., Wetmur, J., et al. (2010). Genomic loss of imprinting in first-trimester human placenta. Am J Obstet Gynecol 202, 391 e391-398.

Preece, M.A., Price, S.M., Davies, V., Clough, L., Stanier, P., Trembath, R.C., and Moore, G.E. (1997). Maternal uniparental disomy 7 in Silver-Russell syndrome. J Med Genet 34, 6-9.

Procter, M., Chou, L.S., Tang, W., Jama, M., and Mao, R. (2006). Molecular diagnosis of Prader-Willi and Angelman syndromes by methylation-specific melting analysis and methylation-specific multiplex ligation-dependent probe amplification. Clin Chem 52, 1276- 1283.

Proctor, L.K., Toal, M., Keating, S., Chitayat, D., Okun, N., Windrim, R.C., Smith, G.C., and Kingdom, J.C. (2009). Placental size and the prediction of severe early-onset intrauterine growth restriction in women with low pregnancy-associated plasma protein-A. Ultrasound Obstet Gynecol 34, 274-282.

Pruitt, K.D., Tatusova, T., and Maglott, D.R. (2007). NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 35, D61-65.

310

Rajendram, R., Ferreira, J.C., Grafodatskaya, D., Choufani, S., Chiang, T., Pu, S., Butcher, D.T., Wodak, S.J., and Weksberg, R. (2011). Assessment of methylation level prediction accuracy in methyl-DNA immunoprecipitation and sodium bisulfite based microarray platforms. Epigenetics 6, 410-415.

Rakyan, V.K., Down, T.A., Thorne, N.P., Flicek, P., Kulesha, E., Graf, S., Tomazou, E.M., Backdahl, L., Johnson, N., Herberth, M., et al. (2008). An integrated resource for genome-wide identification and analysis of human tissue-specific differentially methylated regions (tDMRs). Genome Res 18, 1518-1529.

Ramsahoye, B.H. (2002). Measurement of genome wide DNA methylation by reversed-phase high-performance liquid chromatography. Methods 27, 156-161.

Ranade, S.S., Chung, C.B., Zon, G., and Boyd, V.L. (2009). Preparation of genome-wide DNA fragment libraries using bisulfite in polyacrylamide gel electrophoresis slices with formamide denaturation and quality control for massively parallel sequencing by oligonucleotide ligation and detection. Anal Biochem 390, 126-135.

Randhawa, R.S. (2008). The insulin-like growth factor system and fetal growth restrictionn. Pediatr Endocrinol Rev 6, 235-240.

Rauch, T., and Pfeifer, G.P. (2005). Methylated-CpG island recovery assay: a new technique for the rapid detection of methylated-CpG islands in cancer. Lab Invest 85, 1172-1180.

Raychaudhuri, N., Raychaudhuri, S., Thamotharan, M., and Devaskar, S.U. (2008). Histone code modifications repress glucose transporter 4 expression in the intrauterine growth-restricted offspring. J Biol Chem 283, 13611-13626.

Recillas-Targa, F. (2002). DNA methylation, chromatin boundaries, and mechanisms of genomic imprinting. Arch Med Res 33, 428-438.

Redline, R.W. (2008). Placental pathology: a systematic approach with clinical correlations. Placenta 29 Suppl A, S86-91.

Redline, R.W., Boyd, T., Campbell, V., Hyde, S., Kaplan, C., Khong, T.Y., Prashner, H.R., and Waters, B.L. (2004). Maternal vascular underperfusion: nosology and reproducibility of placental reaction patterns. Pediatr Dev Pathol 7, 237-249.

Redline, R.W., Heller, D., Keating, S., and Kingdom, J. (2005). Placental diagnostic criteria and clinical correlation--a workshop report. Placenta 26 Suppl A, S114-117.

Reed, K., Poulin, M.L., Yan, L., and Parissenti, A.M. (2010). Comparison of bisulfite sequencing PCR with pyrosequencing for measuring differences in DNA methylation. Anal Biochem 397, 96-106.

Reik, W., Constancia, M., Fowden, A., Anderson, N., Dean, W., Ferguson-Smith, A., Tycko, B., and Sibley, C. (2003). Regulation of supply and demand for maternal nutrients in mammals by imprinted genes. J Physiol 547, 35-44.

311

Reik, W., Dean, W., and Walter, J. (2001). Epigenetic reprogramming in mammalian development. Science 293, 1089-1093.

Reinders, J., Delucinge Vivier, C., Theiler, G., Chollet, D., Descombes, P., and Paszkowski, J. (2008). Genome-wide, high-resolution DNA methylation profiling using bisulfite-mediated cytosine conversion. Genome Res 18, 469-476.

Reiss, D., Zhang, Y., Rouhi, A., Reuter, M., and Mager, D.L. (2010). Variable DNA methylation of transposable elements: the case study of mouse Early Transposons. Epigenetics 5, 68-79.

Relaix, F., Wei, X.J., Wu, X., and Sassoon, D.A. (1998). Peg3/Pw1 is an imprinted gene involved in the TNF-NFkappaB signal transduction pathway. Nat Genet 18, 287-291.

Rich-Edwards, J.W., Colditz, G.A., Stampfer, M.J., Willett, W.C., Gillman, M.W., Hennekens, C.H., Speizer, F.E., and Manson, J.E. (1999). Birthweight and the risk for type 2 diabetes mellitus in adult women. Ann Intern Med 130, 278-284.

Rich-Edwards, J.W., Stampfer, M.J., Manson, J.E., Rosner, B., Hankinson, S.E., Colditz, G.A., Willett, W.C., and Hennekens, C.H. (1997). Birth weight and risk of cardiovascular disease in a cohort of women followed up since 1976. Bmj 315, 396-400.

Richter, A.M., Pfeifer, G.P., and Dammann, R.H. (2009). The RASSF proteins in cancer; from epigenetic silencing to functional characterization. Biochim Biophys Acta 1796, 114-128.

Riemenschneider, M.J., Reifenberger, J., and Reifenberger, G. (2008). Frequent biallelic inactivation and transcriptional silencing of the DIRAS3 gene at 1p31 in oligodendroglial tumors with 1p loss. Int J Cancer 122, 2503-2510.

Riesewijk, A.M., Blagitko, N., Schinzel, A.A., Hu, L., Schulz, U., Hamel, B.C., Ropers, H.H., and Kalscheuer, V.M. (1998). Evidence against a major role of PEG1/MEST in Silver-Russell syndrome. European Journal of Human Genetics 6, 114-120.

Riesewijk, A.M., Hu, L., Schulz, U., Tariverdian, G., Hoglund, P., Kere, J., Ropers, H.H., and Kalscheuer, V.M. (1997). Monoallelic expression of human PEG1/MEST is paralleled by parent- specific methylation in fetuses. Genomics 42, 236-244.

Robichaux, V., Fraikor, A., Favara, B., and Richer, M. (1981). Silver-Russell syndrome. A family with symmetric and asymmetric siblings. Arch Pathol Lab Med 105, 157-159.

Roh, C.R., Budhraja, V., Kim, H.S., Nelson, D.M., and Sadovsky, Y. (2005). Microarray-based identification of differentially expressed genes in hypoxic term human trophoblasts and in placental villi of pregnancies with growth restricted fetuses. Placenta 26, 319-328.

Rollins, R.A., Haghighi, F., Edwards, J.R., Das, R., Zhang, M.Q., Ju, J., and Bestor, T.H. (2006). Large-scale structure of genomic methylation patterns. Genome Res 16, 157-163.

Romanov, G.A., and Vaniushin, B.F. (1980). [Intragenomic specificity of DNA methylation in animals. Qualitative differences in tissues and changes in methylation of repeating sequences during aging, carcinogenesis and hormonal induction]. Mol Biol (Mosk) 14, 357-368.

312

Rosa, A.L., Wu, Y.Q., Kwabi-Addo, B., Coveler, K.J., Reid Sutton, V., and Shaffer, L.G. (2005). Allele-specific methylation of a functional CTCF binding site upstream of MEG3 in the human imprinted domain of 14q32. Chromosome Res 13, 809-818.

Roseboom, T.J., van der Meulen, J.H., Ravelli, A.C., Osmond, C., Barker, D.J., and Bleker, O.P. (2001). Effects of prenatal exposure to the Dutch famine on adult disease in later life: an overview. Twin Res 4, 293-298.

Rossant, J., Croy, B.A., Clark, D.A., and Chapman, V.M. (1983). Interspecific hybrids and chimeras in mice. J Exp Zool 228, 223-233.

Royo, H., and Cavaille, J. (2008). Non-coding RNAs in imprinted gene clusters. Biol Cell 100, 149-166.

Royo, J.L., Hidalgo, M., and Ruiz, A. (2007). Pyrosequencing protocol using a universal biotinylated primer for mutation detection and SNP genotyping. Nat Protoc 2, 1734-1739.

Royo, J.L., Pascual, M.H., Salinas, A., Tello, F.J., Rivero Mdel, C., Herrero, E.F., Real, L.M., and Ruiz, A. (2006). Pyrosequencing protocol requiring a unique biotinylated primer. Clin Chem Lab Med 44, 435-441.

Ruike, Y., Imanaka, Y., Sato, F., Shimizu, K., and Tsujimoto, G. (2010). Genome-wide analysis of aberrant methylation in human breast cancer cells using methyl-DNA immunoprecipitation combined with high-throughput sequencing. BMC Genomics 11, 137.

Russell, A. (1954). A syndrome of intra-uterine dwarfism recognizable at birth with cranio-facial dysostosis, disproportionately short arms, and other anomalies (5 examples). Proc R Soc Med 47, 1040-1044.

Rutledge, R.G., and Cote, C. (2003). Mathematics of quantitative kinetic PCR and the application of standard curves. Nucleic Acids Res 31, e93.

Sadikovic, B., Yoshimoto, M., Al-Romaih, K., Maire, G., Zielenska, M., and Squire, J.A. (2008). In vitro analysis of integrated global high-resolution DNA methylation profiling with genomic imbalance and gene expression in osteosarcoma. PLoS One 3, e2834.

Saidi, S. (2003). LOESS-based Normalisation for Gene Array Data (University of Cambridge).

Sakamoto, H., Suzuki, M., Abe, T., Hosoyama, T., Himeno, E., Tanaka, S., Greally, J.M., Hattori, N., Yagi, S., and Shiota, K. (2007). Cell type-specific methylation profiles occurring disproportionately in CpG-less regions that delineate developmental similarity. Genes Cells 12, 1123-1132.

Salas, M., John, R., Saxena, A., Barton, S., Frank, D., Fitzpatrick, G., Higgins, M.J., and Tycko, B. (2004). Placental growth retardation due to loss of imprinting of Phlda2. Mech Dev 121, 1199-1210.

313

Sapin, V., Blanchon, L., Serre, A.F., Lemery, D., Dastugue, B., and Ward, S.J. (2001). Use of transgenic mice model for understanding the placentation: towards clinical applications in human obstetrical pathologies? Transgenic Res 10, 377-398.

Schalkwyk, L.C., Meaburn, E.L., Smith, R., Dempster, E.L., Jeffries, A.R., Davies, M.N., Plomin, R., and Mill, J. (2010). Allelic Skewing of DNA Methylation Is Widespread across the Genome. The American Journal of Human Genetics 86, 196-212.

Schatz, P., Dietrich, D., and Schuster, M. (2004). Rapid analysis of CpG methylation patterns using RNase T1 cleavage and MALDI-TOF. Nucleic Acids Res 32, e167.

Schöherr, N., Jäger, S., Ranke, M.B., Wollmann, H.A., Binder, G., and Eggermann, T. (2008). No evidence for isolated imprinting mutations in the PEG1/MEST locus in Silver-Russell patients. European Journal of Medical Genetics 51, 322-324.

Schonherr, N., Meyer, E., Binder, G., Wollmann, H.A., and Eggermann, T. (2007a). No evidence for additional imprinting defects in silver-russell syndrome patients with maternal uniparental disomy 7 or 11p15 epimutation. J Pediatr Endocrinol Metab 20, 1329-1331.

Schonherr, N., Meyer, E., Eggermann, K., Ranke, M.B., Wollmann, H.A., and Eggermann, T. (2006). (Epi)mutations in 11p15 significantly contribute to Silver-Russell syndrome: but are they generally involved in growth retardation? Eur J Med Genet 49, 414-418.

Schonherr, N., Meyer, E., Roos, A., Schmidt, A., Wollmann, H.A., and Eggermann, T. (2007b). The centromeric 11p15 imprinting centre is also involved in Silver-Russell syndrome. J Med Genet 44, 59-63.

Schroeder, A., Mueller, O., Stocker, S., Salowsky, R., Leiber, M., Gassmann, M., Lightfoot, S., Menzel, W., Granzow, M., and Ragg, T. (2006). The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol 7, 3.

Selling, K.E., Carstensen, J., Finnstrom, O., and Sydsjo, G. (2006). Intergenerational effects of preterm birth and reduced intrauterine growth: a population-based study of Swedish mother- offspring pairs. Bjog 113, 430-440.

Setia, S., and Sridhar, M.G. (2009). Changes in GH/IGF-1 axis in intrauterine growth retardation: consequences of fetal programming? Horm Metab Res 41, 791-798.

Sharma, P., Sahni, N.S., Tibshirani, R., Skaane, P., Urdal, P., Berghagen, H., Jensen, M., Kristiansen, L., Moen, C., Zaka, A., et al. (2005). Early detection of breast cancer based on gene- expression patterns in peripheral blood cells. Breast Cancer Res 7, R634-644.

Sharma, S., Kelly, T.K., and Jones, P.A. (2010). Epigenetics in cancer. Carcinogenesis 31, 27- 36.

Sharp, L., and Little, J. (2004). Polymorphisms in genes involved in folate metabolism and colorectal neoplasia: a HuGE review. Am J Epidemiol 159, 423-443.

314

Shekhawat, P.S., Matern, D., and Strauss, A.W. (2005). Fetal fatty acid oxidation disorders, their effect on maternal health and neonatal outcome: impact of expanded newborn screening on their diagnosis and management. Pediatr Res 57, 78R-86R.

Shen, L., Guo, Y., Chen, X., Ahmed, S., and Issa, J.P. (2007). Optimizing annealing temperature overcomes bias in bisulfite PCR methylation analysis. Biotechniques 42, 48-58.

Shiura, H., Nakamura, K., Hikichi, T., Hino, T., Oda, K., Suzuki-Migishima, R., Kohda, T., Kaneko-Ishino, T., and Ishino, F. (2009). Paternal deletion of Meg1/Grb10 DMR causes maternalization of the Meg1/Grb10 cluster in mouse proximal Chromosome 11 leading to severe pre- and postnatal growth retardation. Hum Mol Genet 18, 1424-1438.

Shoemaker, R., Deng, J., Wang, W., and Zhang, K. (2010). Allele-specific methylation is prevalent and is contributed by CpG-SNPs in the human genome. Genome Res 20, 883-889.

Sibai, B.M. (2002). Chronic hypertension in pregnancy. Obstet Gynecol 100, 369-377.

Silver, H.K., Kiyasu, W., George, J., and Deamer, W.C. (1953). Syndrome of congenital hemihypertrophy, shortness of stature, and elevated urinary gonadotropins. Pediatrics 12, 368- 376.

Simmons, R.A., Templeton, L.J., and Gertz, S.J. (2001). Intrauterine growth retardation leads to the development of type 2 diabetes in the rat. Diabetes 50, 2279-2286.

Singer-Sam, J., LeBon, J.M., Tanguay, R.L., and Riggs, A.D. (1990). A quantitative HpaII-PCR assay to measure methylation of DNA from a small number of cells. Nucleic Acids Res 18, 687.

Singhal, A., Cole, T.J., Fewtrell, M., Kennedy, K., Stephenson, T., Elias-Jones, A., and Lucas, A. (2007). Promotion of faster weight gain in infants born small for gestational age: is there an adverse effect on later blood pressure? Circulation 115, 213-220.

Singhal, A., and Lucas, A. (2004). Early origins of cardiovascular disease: is there a unifying hypothesis? Lancet 363, 1642-1645.

Sitras, V., Paulssen, R., Leirvik, J., Vartun, A., and Acharya, G. (2009). Placental gene expression profile in intrauterine growth restriction due to placental insufficiency. Reproductive Sciences 16, 701-711.

Sleutels, F., Zwart, R., and Barlow, D.P. (2002). The non-coding Air RNA is required for silencing autosomal imprinted genes. Nature 415, 810-813.

Sonderegger, S., Husslein, H., Leisser, C., and Knöfler, M. (2007). Complex Expression Pattern of Wnt Ligands and Frizzled Receptors in Human Placenta and its Trophoblast Subtypes. Placenta 28, S97-S102.

Soundararajan, R., and Rao, A.J. (2004). Trophoblast 'pseudo-tumorigenesis': significance and contributory factors. Reprod Biol Endocrinol 2, 15.

315

Spence, J.E., Perciaccante, R.G., Greig, G.M., Willard, H.F., Ledbetter, D.H., Hejtmancik, J.F., Pollack, M.S., O'Brien, W.E., and Beaudet, A.L. (1988). Uniparental disomy as a mechanism for human genetic disease. Am J Hum Genet 42, 217-226.

Spotila, L.D., Sereda, L., and Prockop, D.J. (1992). Partial isodisomy for maternal chromosome 7 and short stature in an individual with a mutation at the COL1A2 locus. Am J Hum Genet 51, 1396-1405.

Squire, J.A., Li, M., Perlikowski, S., Fei, Y.L., Bayani, J., Zhang, Z.M., and Weksberg, R. (2000). Alterations of H19 imprinting and IGF2 replication timing are infrequent in Beckwith- Wiedemann syndrome. Genomics 65, 234-242.

Steemers, F.J., and Gunderson, K.L. (2007). Whole genome genotyping technologies on the BeadArray platform. Biotechnol J 2, 41-49.

Stefani, G., and Slack, F.J. (2008). Small non-coding RNAs in animal development. Nat Rev Mol Cell Biol 9, 219-230.

Strain, L., Warner, J.P., Johnston, T., and Bonthron, D.T. (1995). A human parthenogenetic chimaera. Nat Genet 11, 164-169.

Strauss, R.S. (2000). Adult functional outcome of those born small for gestational age: twenty- six-year follow-up of the 1970 British Birth Cohort. Jama 283, 625-632.

Struwe, E., Berzl, G., Schild, R., Blessing, H., Drexel, L., Hauck, B., Tzschoppe, A., Weidinger, M., Sachs, M., Scheler, C., et al. (2010). Microarray analysis of placental tissue in intrauterine growth restriction. Clin Endocrinol (Oxf) 72, 241-247.

Su, A.I., Wiltshire, T., Batalov, S., Lapp, H., Ching, K.A., Block, D., Zhang, J., Soden, R., Hayakawa, M., Kreiman, G., et al. (2004). A gene atlas of the mouse and human protein- encoding transcriptomes. Proc Natl Acad Sci U S A 101, 6062-6067.

Suh, Y.J., Kim, B.M., Park, B.H., Park, H., Kim, Y.J., Kim, H., Hong, Y.C., and Ha, E.H. (2007). Cytochrome P450IA1 polymorphisms along with PM(10) exposure contribute to the risk of birth weight reduction. Reprod Toxicol 24, 281-288.

Suzuki, M.M., and Bird, A. (2008). DNA methylation landscapes: provocative insights from epigenomics. Nat Rev Genet 9, 465-476.

Szyf, M. (2009). Epigenetics, DNA methylation, and chromatin modifying drugs. Annu Rev Pharmacol Toxicol 49, 243-263.

Takada, S., Paulsen, M., Tevendale, M., Tsai, C.E., Kelsey, G., Cattanach, B.M., and Ferguson- Smith, A.C. (2002). Epigenetic analysis of the Dlk1-Gtl2 imprinted domain on mouse chromosome 12: implications for imprinting control from comparison with Igf2-H19. Hum Mol Genet 11, 77-86.

Takagi, N. (2003). Imprinted X-chromosome inactivation: enlightenment from embryos in vivo. Semin Cell Dev Biol 14, 319-329.

316

Takai, D., and Jones, P.A. (2002). Comprehensive analysis of CpG islands in human chromosomes 21 and 22. Proceedings of the National Academy of Sciences 99, 3740-3745.

Tamakoshi, K., Yatsuya, H., Wada, K., Matsushita, K., Otsuka, R., Yang, P.O., Sugiura, K., Hotta, Y., Mitsuhashi, H., Kondo, T., et al. (2006). Birth weight and adult hypertension: cross- sectional study in a Japanese workplace population. Circ J 70, 262-267.

Teebi, A.S. (1992). Autosomal recessive Silver-Russell syndrome. Clin Dysmorphol 1, 151-156.

Thiele, I.G., Niezen-Koning, K.E., van Gennip, A.H., and Aarnoudse, J.G. (2004). Increased plasma carnitine concentrations in preeclampsia. Obstet Gynecol 103, 876-880.

Tilghman, S.M. (1999). The sins of the fathers and mothers: genomic imprinting in mammalian development. Cell 96, 185-193.

Toal, M., Chan, C., Fallah, S., Alkazaleh, F., Chaddha, V., Windrim, R.C., and Kingdom, J.C. (2007). Usefulness of a placental profile in high-risk pregnancies. Am J Obstet Gynecol 196, 363 e361-367.

Todros, T., Sciarrone, A., Piccoli, E., Guiot, C., Kaufmann, P., and Kingdom, J. (1999). Umbilical Doppler waveforms and placental villous angiogenesis in pregnancies complicated by fetal growth restriction. Obstet Gynecol 93, 499-503.

Toft, J.H., Lian, I.A., Tarca, A.L., Erez, O., Espinoza, J., Eide, I.P., Bjorge, L., Draghici, S., Romero, R., and Austgulen, R. (2008). Whole-genome microarray and targeted analysis of angiogenesis-regulating gene expression (ENG, FLT1, VEGF, PlGF) in placentas from preeclamptic and small-for-gestational-age pregnancies. J Matern Fetal Neonatal Med 21, 267-273.

Torsello, A., Scibona, B., Leo, G., Bresciani, E., Avallone, R., Bulgarelli, I., Luoni, M., Zoli, M., Rindi, G., Cocchi, D., et al. (2003). Ontogeny and tissue-specific regulation of ghrelin mRNA expression suggest that ghrelin is primarily involved in the control of extraendocrine functions in the rat. Neuroendocrinology 77, 91-99.

Tost, J., El abdalaoui, H., and Gut, I.G. (2006). Serial pyrosequencing for quantitative DNA methylation analysis. Biotechniques 40, 721-722, 724, 726.

Tost, J., and Gut, I.G. (2007a). Analysis of gene-specific DNA methylation patterns by pyrosequencing technology. Methods Mol Biol 373, 89-102.

Tost, J., and Gut, I.G. (2007b). DNA methylation analysis by pyrosequencing. Nat Protoc 2, 2265-2275.

Toumba, M., Hadjidemetriou, A., Topouzi, M., Savva, S.C., Demetriadou, R., Kanaris, C., and Skordis, N. (2005). Evaluation of the auxological and metabolic status in prepubertal children born small for gestational age. J Pediatr Endocrinol Metab 18, 677-688.

Toyota, M., Ho, C., Ahuja, N., Jair, K.W., Li, Q., Ohe-Toyota, M., Baylin, S.B., and Issa, J.P. (1999). Identification of differentially methylated sequences in colorectal cancer by methylated CpG island amplification. Cancer Res 59, 2307-2312.

317

Turner, C.L., Mackay, D.M., Callaway, J.L., Docherty, L.E., Poole, R.L., Bullman, H., Lever, M., Castle, B.M., Kivuva, E.C., Turnpenny, P.D., et al. (2010). Methylation analysis of 79 patients with growth restriction reveals novel patterns of methylation change at imprinted loci. Eur J Hum Genet 18, 648-655.

Tycko, B., and Morison, I.M. (2002). Physiological functions of imprinted genes. J Cell Physiol 192, 245-258.

Valleley, E.M., Cordery, S.F., Carr, I.M., MacLennan, K.A., and Bonthron, D.T. (2010). Loss of expression of ZAC/PLAGL1 in diffuse large B-cell lymphoma is independent of promoter hypermethylation. Genes Chromosomes Cancer 49, 480-486.

Valley, C.M., and Willard, H.F. (2006). Genomic and epigenomic approaches to the study of X chromosome inactivation. Curr Opin Genet Dev 16, 240-245.

Van den Veyver, I.B., Norman, B., Tran, C.Q., Bourjac, J., and Slim, R. (2001). The human homologue (PEG3) of the mouse paternally expressed gene 3 (Peg3) is maternally imprinted but not mutated in women with familial recurrent hydatidiform molar pregnancies. J Soc Gynecol Investig 8, 305-313.

Varmuza, S., and Mann, M. (1994). Genomic imprinting--defusing the ovarian time bomb. Trends Genet 10, 118-123.

Varrault, A., Ciani, E., Apiou, F., Bilanges, B., Hoffmann, A., Pantaloni, C., Bockaert, J., Spengler, D., and Journot, L. (1998). hZAC encodes a zinc finger protein with antiproliferative properties and maps to a chromosomal region frequently lost in cancer. Proc Natl Acad Sci U S A 95, 8835-8840.

Viggedal, G., Lundalv, E., Carlsson, G., and Kjellmer, I. (2004). Neuropsychological follow-up into young adulthood of term infants born small for gestational age. Med Sci Monit 10, CR8-16.

Voetsch, B., Jin, R.C., Bierl, C., Benke, K.S., Kenet, G., Simioni, P., Ottaviano, F., Damasceno, B.P., Annichino-Bizacchi, J.M., Handy, D.E., et al. (2007). Promoter polymorphisms in the plasma glutathione peroxidase (GPx-3) gene: a novel risk factor for arterial ischemic stroke among young adults and children. Stroke 38, 41-49.

Voss, R., Ben-Simon, E., Avital, A., Godfrey, S., Zlotogora, J., Dagan, J., Tikochinski, Y., and Hillel, J. (1989). Isodisomy of chromosome 7 in a patient with cystic fibrosis: could uniparental disomy be common in humans? Am J Hum Genet 45, 373-380.

Vu, T.H., Li, T., Nguyen, D., Nguyen, B.T., Yao, X.M., Hu, J.F., and Hoffman, A.R. (2000). Symmetric and Asymmetric DNA Methylation in the Human IGF2-H19 Imprinted Region. Genomics 64, 132-143.

Wagschal, A., and Feil, R. (2006). Genomic imprinting in the placenta. Cytogenet Genome Res 113, 90-98.

318

Wang, J., Qiu, Q., Haider, M., Bell, M., Gruslin, A., and Christians, J.K. (2009a). Expression of pregnancy-associated plasma protein A2 during pregnancy in human and mouse. J Endocrinol 202, 337-345.

Wang, T., Chen, M., Yan, Y.E., Xiao, F.Q., Pan, X.L., and Wang, H. (2009b). Growth retardation of fetal rats exposed to nicotine in utero: possible involvement of CYP1A1, CYP2E1, and P-glycoprotein. Environ Toxicol 24, 33-42.

Wang, X., Tomso, D.J., Liu, X., and Bell, D.A. (2005). Single nucleotide polymorphism in transcriptional regulatory regions and expression of environmentally responsive genes. Toxicol Appl Pharmacol 207, 84-90.

Wang, Y., Walsh, S.W., and Kay, H.H. (1992). Placental lipid peroxides and thromboxane are increased and prostacyclin is decreased in women with preeclampsia. Am J Obstet Gynecol 167, 946-949.

Weber, M., Davies, J.J., Wittig, D., Oakeley, E.J., Haase, M., Lam, W.L., and Schubeler, D. (2005a). Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet 37, 853-862.

Weber, M., Davies, J.J., Wittig, D., Oakeley, E.J., Haase, M., Lam, W.L., and Schubeler, D. (2005b). Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet 37, 853-862.

Weber, M., and Schubeler, D. (2007). Genomic patterns of DNA methylation: targets and function of an epigenetic mark. Curr Opin Cell Biol 19, 273-280.

Weksberg, R., Shen, D.R., Fei, Y.L., Song, Q.L., and Squire, J. (1993). Disruption of insulin-like growth factor 2 imprinting in Beckwith-Wiedemann syndrome. Nature Genetics 5, 143-150.

Weksberg, R., Shuman, C., Caluseriu, O., Smith, A.C., Fei, Y.L., Nishikawa, J., Stockley, T.L., Best, L., Chitayat, D., Olney, A., et al. (2002). Discordant KCNQ1OT1 imprinting in sets of monozygotic twins discordant for Beckwith-Wiedemann syndrome. Hum Mol Genet 11, 1317- 1325.

Weksberg, R., Shuman, C., and Smith, A.C. (2005). Beckwith-Wiedemann syndrome. Am J Med Genet C Semin Med Genet 137, 12-23.

Weksberg, R., Smith, A.C., Squire, J., and Sadowski, P. (2003). Beckwith-Wiedemann syndrome demonstrates a role for epigenetic control of normal development. Hum Mol Genet 12 Spec No 1, R61-68.

Wellmann, S., Bettkober, M., Zelmer, A., Seeger, K., Faigle, M., Eltzschig, H.K., and Buhrer, C. (2008). Hypoxia upregulates the histone demethylase JMJD1A via HIF-1. Biochem Biophys Res Commun 372, 892-897.

Wen, S.W., Zhou, J., Yang, Q., Fraser, W., Olatunbosun, O., and Walker, M. (2008). Maternal exposure to folic acid antagonists and placenta-mediated adverse pregnancy outcomes. CMAJ 179, 1263-1268.

319

Wenzel, P.L., and Leone, G. (2007). Expression of Cre recombinase in early diploid trophoblast cells of the mouse placenta. Genesis 45, 129-134.

Whalen, R., and Boyer, T.D. (1998). Human glutathione S-transferases. Semin Liver Dis 18, 345-358.

Whitelaw, E., and Martin, D.I. (2001). Retrotransposons as epigenetic mediators of phenotypic variation in mammals. Nat Genet 27, 361-365.

Wilcken, B., Leung, K.C., Hammond, J., Kamath, R., and Leonard, J.V. (1993). Pregnancy and fetal long-chain 3-hydroxyacyl coenzyme A dehydrogenase deficiency. Lancet 341, 407-408.

Wilkening, R.B., and Meschia, G. (1983). Fetal oxygen uptake, oxygenation, and acid-base balance as a function of uterine blood flow. Am J Physiol 244, H749-755.

Wilson, V.L., Smith, R.A., Ma, S., and Cutler, R.G. (1987). Genomic 5-methyldeoxycytidine decreases with age. J Biol Chem 262, 9948-9951.

Winn, V.D., Gormley, M., Paquet, A.C., Kjaer-Sorensen, K., Kramer, A., Rumer, K.K., Haimov- Kochman, R., Yeh, R.F., Overgaard, M.T., Varki, A., et al. (2009). Severe preeclampsia-related changes in gene expression at the maternal-fetal interface include sialic acid-binding immunoglobulin-like lectin-6 and pappalysin-2. Endocrinology 150, 452-462.

Wong, N.C., Novakovic, B., Weinrich, B., Dewi, C., Andronikos, R., Sibson, M., Macrae, F., Morley, R., Pertile, M.D., Craig, J.M., et al. (2008). Methylation of the adenomatous polyposis coli (APC) gene in human placenta and hypermethylation in choriocarcinoma cells. Cancer Lett 268, 56-62.

Worm, J., Aggerholm, A., and Guldberg, P. (2001). In-tube DNA methylation profiling by fluorescence melting curve analysis. Clin Chem 47, 1183-1189.

Wu, C., Orozco, C., Boyer, J., Leglise, M., Goodale, J., Batalov, S., Hodge, C.L., Haase, J., Janes, J., Huss, J.W., 3rd, et al. (2009). BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol 10, R130.

Xiang, H., Zhu, J., Chen, Q., Dai, F., Li, X., Li, M., Zhang, H., Zhang, G., Li, D., Dong, Y., et al. (2010). Single base-resolution methylome of the silkworm reveals a sparse epigenomic map. Nat Biotechnol 28, 516-520.

Xiong, Z., and Laird, P.W. (1997). COBRA: a sensitive and quantitative DNA methylation assay. Nucleic Acids Res 25, 2532-2534.

Yamashita, S., Hosoya, K., Gyobu, K., Takeshima, H., and Ushijima, T. (2009). Development of a novel output value for quantitative assessment in methylated DNA immunoprecipitation-CpG island microarray analysis. DNA Res 16, 275-286.

Yamazawa, K., Kagami, M., Nagai, T., Kondoh, T., Onigata, K., Maeyama, K., Hasegawa, T., Hasegawa, Y., Yamazaki, T., Mizuno, S., et al. (2008). Molecular and clinical findings and their correlations in Silver-Russell syndrome: implications for a positive role of IGF2 in growth

320

determination and differential imprinting regulation of the IGF2-H19 domain in bodies and placentas. J Mol Med 86, 1171-1181.

Yang, J., Ledaki, I., Turley, H., Gatter, K.C., Montero, J.C., Li, J.L., and Harris, A.L. (2009). Role of hypoxia-inducible factors in epigenetic regulation via histone demethylases. Ann N Y Acad Sci 1177, 185-197.

Yarbrough, D.E., Barrett-Connor, E., and Morton, D.J. (2000). Birth weight as a predictor of adult bone mass in postmenopausal women: the Rancho Bernardo Study. Osteoporos Int 11, 626- 630.

Yu, L., Chen, M., Zhao, D., Yi, P., Lu, L., Han, J., Zheng, X., Zhou, Y., and Li, L. (2009). The H19 Gene Imprinting in Normal Pregnancy and Pre-eclampsia. Placenta 30, 443-447.

Yu, Y., Xu, F., Peng, H., Fang, X., Zhao, S., Li, Y., Cuevas, B., Kuo, W.L., Gray, J.W., Siciliano, M., et al. (1999). NOEY2 (ARHI), an imprinted putative tumor suppressor gene in ovarian and breast carcinomas. Proc Natl Acad Sci U S A 96, 214-219.

Yuan, J., Luo, R.Z., Fujii, S., Wang, L., Hu, W., Andreeff, M., Pan, Y., Kadota, M., Oshimura, M., Sahin, A.A., et al. (2003). Aberrant Methylation and Silencing of ARHI, an Imprinted Tumor Suppressor Gene in which the Function Is Lost in Breast Cancers. Cancer Res 63, 4174- 4180.

Yuen, R.K., Avila, L., Penaherrera, M.S., von Dadelszen, P., Lefebvre, L., Kobor, M.S., and Robinson, W.P. (2009). Human placental-specific epipolymorphism and its association with adverse pregnancy outcomes. PLoS ONE 4, e7389.

Zeng, S.M., and Yankowitz, J. (2003). X-inactivation patterns in human embryonic and extra- embryonic tissues. Placenta 24, 270-275.

Zeschnigk, M., Albrecht, B., Buiting, K., Kanber, D., Eggermann, T., Binder, G., Gromoll, J., Prott, E.-C., Seland, S., and Horsthemke, B. (2008). IGF2//H19 hypomethylation in Silver- Russell syndrome and isolated hemihypoplasia. Eur J Hum Genet 16, 328-334.

Zeschnigk, M., Schmitz, B., Dittrich, B., Buiting, K., Horsthemke, B., and Doerfler, W. (1997). Imprinted segments in the human genome: different DNA methylation patterns in the Prader- Willi/Angelman syndrome region as determined by the genomic sequencing method. Hum Mol Genet 6, 387-395.

Zheng, Y.L., Jiang, M.X., OuYang, Y.C., Sun, Q.Y., and Chen, D.Y. (2005). Production of mouse by inter-strain inner cell mass replacement. Zygote 13, 73-77.

Zhu, X.M., Han, T., Sargent, I.L., Yin, G.W., and Yao, Y.Q. (2009). Differential expression profile of microRNAs in human placentas from preeclamptic pregnancies vs normal pregnancies. Am J Obstet Gynecol 200, 661 e661-667.

Zogel, C., Bohringer, S., Gross, S., Varon, R., Buiting, K., and Horsthemke, B. (2006). Identification of cis- and trans-acting factors possibly modifying the risk of epimutations on chromosome 15. Eur J Hum Genet 14, 752-758.

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Appendices

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Appendix 1 – List of genes considered to have biologically meaningful differences in DNA methylation between cytotrophoblasts and fibroblasts (Chapter 6) Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F A1BG 19 alpha 1B-glycoprotein 1 probe Down after TSS, less than 500bp 0.68 -0.15 -0.29 2 probes AATK 17 apoptosis-associated tyrosine kinase Down one probe on each side of TSS 0.62 -0.03 -0.10 discordant two probes on each side of TSS ATP-binding cassette; subfamily B; ABCB4 7 3 probes Down down, one downstream not 0.27 -0.03 -0.18 member 4 isoform B different 2 probes ACR 22 acrosin precursor Down one probe on each side of TSS 0.33 -0.11 -0.21 discordant 2 probes ACVR1 2 activin A type I receptor precursor Up one probe on each side of TSS 0.46 0.18 0.09 discordant 2 probes ADCY4 14 adenylate cyclase 4 Up one probe on each side of TSS 0.31 0.13 0.13 discordant 2 probes AIFL 22 apoptosis-inducing factor like isoform 1 Up one probe on each side of TSS 0.37 0.08 0.12 discordant 2 probes ALOX12 17 arachidonate 12-lipoxygenase Down one probe on each side of TSS 0.13 0.00 -0.11 discordant AMH 19 anti-Mullerian hormone 1 probe Down after TSS, less than 500bp 0.32 -0.09 -0.21 ANGPTL5 11 angiopoietin-like 5 1 probe Up after TSS, more than 500 bp 0.49 0.12 0.20 2 probes ANKDD1A 15 hypothetical protein LOC348094 Down one probe on each side of TSS 0.30 -0.13 -0.23 concordant 2 probes ANXA8 10 annexin A8 Up one probe on each side of TSS 0.74 0.06 0.00 discordant five probes up, one filled only APC 5 adenomatosis polyposis coli 6 probes Up 0.55 0.19 0.21 one of the criteria apolipoprotein B mRNA editing enzyme; APOBEC3C 22 1 probe Up after TSS, less than 500bp 0.48 0.18 0.24 catalytic polypeptide-like 3C Rho-specific guanine nucleotide exchange 2 probes ARHGEF18 19 Down one probe on each side of TSS 0.48 -0.15 -0.30 factor p114 discordant Rho guanine nucleotide exchange factor ARHGEF19 1 1 probe Down after TSS, less than 500bp 0.33 -0.13 -0.23 (GEF) 19 2 probes ASAH3 19 N-acylsphingosine amidohydrolase 3 Down one probe on each side of TSS 0.37 -0.08 -0.22 concordant 2 probes ASCL2 11 achaete-scute complex homolog-like 2 Down one probe on each side of TSS 0.13 -0.06 -0.04 discordant 2 probes ATN1 12 atrophin-1 Down one probe on each side of TSS 0.06 -0.01 -0.01 discordant plasma membrane calcium ATPase 2 2 probes ATP2B2 3 Down one probe on each side of TSS 0.55 -0.06 -0.20 isoform b discordant ATP4B 13 ATPase; H+/K+ exchanging; beta 2 probes Down one probe on each side of TSS 0.52 -0.08 -0.17

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Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F polypeptide discordant ATP synthase; H+ transporting; 2 probes ATP5G2 12 mitochondrial F0 complex; subunit c Down one probe on each side of TSS 0.15 -0.01 -0.04 discordant isoform 2b precursor ATPase; H+ transporting; lysosomal 2 probes ATP6V1B1 2 Up one probe on each side of TSS 0.82 0.05 0.01 56/58kD; V1 subunit B; isoform 1 discordant brain-specific angiogenesis inhibitor 1 2 probes BAI1 8 Down one probe on each side of TSS 0.65 -0.14 -0.27 precursor discordant 2 probes BIN2 12 bridging integrator 2 Down one probe on each side of TSS 0.43 -0.05 -0.08 discordant BTBD2 19 BTB (POZ) domain containing 2 1 probe Down prior to TSS 0.40 -0.15 -0.35 2 probes C10orf116 10 adipose specific 2 Down one probe on each side of TSS 0.32 -0.14 -0.24 discordant C10orf65 10 hypothetical protein LOC112817 1 probe Down after TSS, more than 500 bp 0.37 -0.09 -0.23 2 probes both probes on promoter, less C10orf92 10 hypothetical protein LOC54777 Down 0.53 -0.15 -0.30 discordant than 300bp from each other 2 probes C11orf16 11 hypothetical protein LOC56673 Down one probe on each side of TSS 0.47 0.13 -0.27 discordant 2 probes C11orf39 11 hypothetical protein LOC399980 Down one probe on each side of TSS 0.55 -0.04 -0.04 discordant 2 probes C14orf49 14 hypothetical protein LOC161176 Down one probe on each side of TSS 0.28 -0.12 -0.22 concordant conserved gene telomeric to alpha globin C16orf35 16 1 probe Down prior to TSS 0.62 -0.03 -0.23 cluster 2 probes C19orf18 19 hypothetical protein LOC147685 Up one probe on each side of TSS 0.49 0.15 0.08 discordant 2 probes C1orf177 1 hypothetical protein LOC163747 Down one probe on each side of TSS 0.60 -0.14 -0.28 discordant 2 probes C1orf59 1 hypothetical protein LOC113802 Down one probe on each side of TSS 0.17 -0.08 0.00 discordant complement component 1; r subcomponent- 2 probes C1RL 12 Up one probe on each side of TSS 0.61 0.07 0.23 like precursor discordant 2 probes C20orf151 20 hypothetical protein LOC140893 Down one probe on each side of TSS 0.46 -0.13 -0.30 concordant 2 probes C20orf186 20 antimicrobial peptide RY2G5 Down one probe on each side of TSS 0.36 -0.02 -0.02 discordant 2 probes C21orf123 21 hypothetical protein LOC378832 Down one probe on each side of TSS 0.60 -0.06 -0.13 discordant 2 probes C21orf129 21 hypothetical protein LOC150135 Down one probe on each side of TSS 0.39 -0.11 -0.33 discordant 2 probes C21orf77 21 hypothetical protein LOC55264 Down one probe on each side of TSS 0.46 -0.15 -0.25 discordant

324

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F 2 probes C21orf84 21 hypothetical protein LOC114038 Down one probe on each side of TSS 0.38 -0.17 -0.30 discordant 2 probes C3orf32 3 fls485 Down one probe on each side of TSS 0.41 0.02 -0.02 discordant 2 probes C6orf32 6 hypothetical protein LOC9750 Down one probe on each side of TSS 0.37 -0.15 -0.28 discordant C6orf71 6 chromosome 6 open reading frame 71 1 probe Down after TSS, less than 500bp 0.47 -0.11 -0.20 complement component 8; gamma C8G 9 1 probe Down after TSS, more than 500 bp 0.39 -0.08 -0.24 polypeptide 2 probes CALN1 7 calneuron 1 Down one probe on each side of TSS 0.55 -0.15 -0.22 discordant 2 probes CARD10 22 caspase recruitment domain protein 10 Down one probe on each side of TSS 0.48 -0.13 -0.23 discordant Cas-Br-M (murine) ecotropic retroviral 2 probes CBLC 19 Down one probe on each side of TSS 0.39 -0.14 -0.28 transforming sequence c discordant 2 probes CCBP2 3 chemokine binding protein 2 Down one probe on each side of TSS 0.37 -0.14 -0.20 concordant CCL11 17 small inducible cytokine A11 precursor 1 probe Down prior to TSS 0.44 -0.13 -0.22 2 probes CD248 11 tumor endothelial marker 1 precursor Down one probe on each side of TSS 0.54 0.01 -0.01 discordant CD8 antigen beta polypeptide 1 isoform 5 2 probes CD8B1 2 Up one probe on each side of TSS 0.58 0.15 0.23 precursor discordant 2 probes CDH11 16 cadherin 11; type 2 preproprotein Up one probe on each side of TSS 0.17 0.05 0.15 discordant 2 probes CDK3 17 cyclin-dependent kinase 3 Down one probe on each side of TSS 0.44 -0.08 -0.32 discordant 2 probes CETN1 18 centrin 1 Down one probe on each side of TSS 0.48 -0.21 -0.34 concordant chorionic gonadotropin beta 3 subunit CGB 19 1 probe Down after TSS, less than 500bp 0.56 -0.14 -0.30 precursor 2 probes CGB1 19 chorionic gonadotropin; beta polypeptide 1 Down one probe on each side of TSS 0.56 -0.05 -0.14 discordant chorionic gonadotropin; beta polypeptide 2 probes CGB5 19 Down one probe on each side of TSS 0.28 -0.06 -0.22 5 precursor concordant chorionic gonadotropin; beta polypeptide 8 CGB8 19 1 probe Down prior to TSS 0.29 -0.07 -0.23 recursor 2 probes CHAD 17 chondroadherin precursor Up one probe on each side of TSS 0.72 0.13 0.20 concordant chondroitin beta1;4 N- 2 probes ChGn 8 Down one probe on each side of TSS 0.37 -0.09 -0.03 acetylgalactosaminyltransferase discordant 2 probes CHRD 3 chordin isoform b Down one probe on each side of TSS 0.37 -0.18 -0.34 discordant CIAS1 1 cryopyrin isoform b 2 probes Up one probe on each side of TSS 0.50 0.22 0.25

325

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F discordant 2 probes both probes on same side of CIZ1 9 Cip1-interacting zinc finger protein Down 0.49 -0.10 -0.24 concordant TSS, less than 500bp 2 probes CLDN16 3 claudin 16 Down one probe on each side of TSS 0.36 -0.11 -0.22 discordant 2 probes CLDN19 1 claudin 19 Down one probe on each side of TSS 0.65 -0.13 -0.14 discordant CLDN4 7 claudin 4 1 probe Down after TSS, less than 500bp 0.39 -0.10 -0.32 CLIC1 6 chloride intracellular channel 1 1 probe Up prior to TSS 0.62 0.17 0.27 alpha 1 type XI collagen isoform A 2 probes COL11A1 1 Up one probe on each side of TSS 0.48 0.21 0.22 preproprotein discordant 2 probes COL1A2 7 alpha 2 type I collagen Up one probe on each side of TSS 0.31 0.12 0.25 concordant 2 probes COL7A1 3 alpha 1 type VII collagen precursor Up one probe on each side of TSS 0.60 0.18 0.31 discordant cysteine-rich secretory protein LCCL 2 probes CRISPLD2 16 Down one probe on each side of TSS 0.27 -0.14 -0.23 domain containing 2 discordant 2 probes CRYAA 21 crystallin; alpha A Down one probe on each side of TSS 0.30 -0.09 -0.16 discordant 2 probes CRYGN 7 gammaN-crystallin variant Up one probe on each side of TSS 0.56 0.16 0.22 discordant chorionic somatomammotropin hormone 1 2 probes CSH1 17 Down one probe on each side of TSS 0.69 -0.06 -0.11 isoform 1 discordant cutaneous T-cell lymphoma-associated 2 probes CTAGE1 18 Down one probe on each side of TSS 0.37 -0.18 -0.29 antigen 1 isoform 2 concordant 2 probes CYP19A1 15 cytochrome P450; family 19 Down one probe on each side of TSS 0.42 -0.08 -0.07 discordant cytochrome P450; family 2; subfamily E; 2 probes CYP2E1 10 Down one probe on each side of TSS 0.35 -0.14 -0.22 polypeptide 1 discordant the three probes upstream of the DAB2IP 9 DAB2 interacting protein isoform 2 9 probes Up TSS are up, the remaining six, 0.66 0.08 0.14 downstream, are not different discoidin domain receptor family; member 2 probes DDR1 6 Down one probe on each side of TSS 0.35 -0.16 -0.29 1 isoform b discordant DEAD (Asp-Glu-Ala-As) box polypeptide 2 probes DDX19B 16 Up one probe on each side of TSS 0.53 0.15 0.25 19 isoform 1 discordant DEAD (Asp-Glu-Ala-Asp) box polypeptide 2 probes DDX43 6 Down one probe on each side of TSS 0.27 -0.12 -0.22 43 concordant ATP-dependent RNA helicase ROK1 2 probes DDX52 17 Down one probe on each side of TSS 0.13 -0.06 -0.03 isoform b discordant DEFB119 20 defensin; beta 119 isoform a precursor 1 probe Down after TSS, less than 500bp 0.36 -0.15 -0.21

326

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F 2 probes DNAJB8 3 DnaJ homolog; subfamily B; member 8 Down one probe on each side of TSS 0.47 -0.10 -0.23 discordant cytosine-5-methyltransferase 3-like protein DNMT3L 21 1 probe Down prior to TSS 0.40 -0.17 -0.33 isoform 1 downregulated in ovarian cancer 1 isoform 2 probes DOC1 3 Down one probe on each side of TSS 0.45 -0.11 -0.27 1 discordant 2 probes DPYS 8 dihydropyrimidinase Up one probe on each side of TSS 0.20 0.11 0.23 discordant ECAT11 1 hypothetical protein LOC54596 1 probe Down prior to TSS 0.46 -0.06 -0.22 2 probes ELA2 19 elastase 2; neutrophil preproprotein Down one probe on each side of TSS 0.39 -0.16 -0.22 discordant 2 probes ELF5 11 E74-like factor 5 ESE-2b Down one probe on each side of TSS 0.34 -0.17 -0.27 discordant 2 probes ELMO3 16 engulfment and cell motility 3 Down one probe on each side of TSS 0.56 -0.19 -0.49 concordant erythrocyte membrane protein band 4.1-like 2 probes EPB41L1 20 Down one probe on each side of TSS 0.29 -0.14 -0.24 1 isoform a discordant 2 probes FAM111A 11 hypothetical protein LOC63901 Up one probe on each side of TSS 0.38 0.14 0.28 concordant 2 probes FCGBP 19 Fc fragment of IgG binding protein Down one probe on each side of TSS 0.45 -0.14 -0.27 discordant low affinity immunoglobulin gamma Fc 2 probes FCGR3B 1 Down one probe on each side of TSS 0.48 -0.13 -0.21 region receptor III-B precursor discordant Fc fragment of IgG; receptor; transporter; 2 probes FCGRT 19 Up one probe on each side of TSS 0.54 0.13 0.24 alpha discordant 2 probes FCN1 9 ficolin 1 precursor Down one probe on each side of TSS 0.40 -0.04 -0.11 discordant 2 probes FCN2 9 ficolin 2 isoform a precursor Down one probe on each side of TSS 0.45 -0.11 -0.18 discordant 2 probes FFAR1 19 G protein-coupled receptor 40 Down one probe on each side of TSS 0.63 -0.09 -0.27 concordant 2 probes FFAR2 19 G protein-coupled receptor 43 Up one probe on each side of TSS 0.28 0.00 0.01 discordant 2 probes FGF6 12 fibroblast growth factor 6 precursor Down one probe on each side of TSS 0.74 -0.12 -0.16 discordant 2 probes FLJ13391 2 hypothetical protein LOC84141 Down one probe on each side of TSS 0.30 -0.10 -0.27 concordant 2 probes FLJ13841 17 hypothetical protein LOC79755 Down one probe on each side of TSS 0.44 -0.15 -0.28 concordant 2 probes FLJ25421 22 hypothetical protein LOC150350 Down one probe on each side of TSS 0.52 -0.12 -0.24 discordant 2 probes FLJ26443 13 hypothetical protein LOC400165 Down one probe on each side of TSS 0.50 -0.15 -0.23 discordant

327

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F 2 probes FLJ31568 22 hypothetical protein LOC150244 Down one probe on each side of TSS 0.40 -0.16 -0.30 discordant 2 probes FLJ31951 5 hypothetical protein LOC153830 Up one probe on each side of TSS 0.45 0.14 0.25 discordant 2 probes FLJ32447 2 hypothetical protein LOC151278 Up one probe on each side of TSS 0.39 0.12 0.18 discordant 2 probes FLJ32569 1 hypothetical protein LOC148811 Up one probe on each side of TSS 0.55 0.08 0.21 discordant 2 probes FLJ36445 19 hypothetical protein LOC163183 Down one probe on each side of TSS 0.47 -0.15 -0.40 discordant cytochrome P450; family 2; subfamily E; FLJ39501 19 1 probe Down prior to TSS 0.37 -0.18 -0.30 polypeptide 2 homolog 2 probes FLJ43339 15 hypothetical protein LOC388115 Up one probe on each side of TSS 0.05 -0.01 0.01 discordant 2 probes FLJ46072 8 hypothetical protein LOC286077 Up one probe on each side of TSS 0.75 0.12 0.23 discordant 2 probes FLJ90586 7 hypothetical protein LOC135932 Down one probe on each side of TSS 0.40 -0.17 -0.36 concordant fibronectin leucine rich transmembrane 2 probes FLRT2 14 Up one probe on each side of TSS 0.28 0.17 0.11 protein 2 discordant 2 probes FOXE1 9 forkhead box E1 Up one probe on each side of TSS 0.28 0.10 0.20 discordant 2 probes FOXI1 5 forkhead box I1 isoform a Down one probe on each side of TSS 0.32 -0.18 -0.23 discordant FXYD domain containing ion transport 2 probes FXYD4 10 Down one probe on each side of TSS 0.46 -0.19 -0.38 regulator 4 discordant five probes are down, all intronic, three probes, including GATA5 20 GATA binding protein 5 8 probes Down 0.51 -0.13 -0.42 two in the promoter, are not different growth hormone releasing hormone 2 probes GHRH 20 Up one probe on each side of TSS 0.72 0.08 0.09 preproprotein discordant guanine nucleotide binding protein (G GNA15 19 1 probe Up after TSS, less than 500bp 0.49 0.17 0.24 protein); alpha 15 (Gq class) guanine nucleotide binding protein; alpha three probes, intronic are GNAS 20 30 probes Up 0.63 0.07 0.04 stimulating activity polypeptide 1 isoform a different one probe, upstream of TSS is GNMT 6 glycine N-methyltransferase 7 probes Down 0.31 -0.14 -0.17 different 2 probes GP1BB 22 glycoprotein Ib beta polypeptide precursor Down one probe on each side of TSS 0.44 -0.15 -0.40 discordant 2 probes GPR142 17 G protein-coupled receptor 142 Down one probe on each side of TSS 0.39 -0.12 -0.23 discordant 2 probes GPR21 9 G protein-coupled receptor 21 Up one probe on each side of TSS 0.58 0.20 0.20 discordant

328

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F 2 probes GPR35 2 G protein-coupled receptor 35 Down one probe on each side of TSS 0.32 -0.14 -0.23 discordant 2 probes GPR42 19 G protein-coupled receptor 42 Down one probe on each side of TSS 0.24 0.02 -0.04 discordant 2 probes GPR55 2 G protein-coupled receptor 55 Down one probe on each side of TSS 0.33 -0.02 0.00 discordant 2 probes GPR56 16 G protein-coupled receptor 56 isoform b Down one probe on each side of TSS 0.62 -0.05 -0.19 discordant G-protein signalling modulator 1 (AGS3- 2 probes GPSM1 9 Down one probe on each side of TSS 0.39 -0.20 -0.25 like; C. elegans) discordant glutamic-pyruvate transaminase (alanine 2 probes GPT 8 Down one probe on each side of TSS 0.71 -0.05 -0.04 aminotransferase) discordant glutamate receptor interacting protein 1 2 probes GRIP1 12 Down one probe on each side of TSS 0.37 -0.10 -0.25 isoform 2 discordant glutamate receptor; metabotropic 2 2 probes GRM2 3 Down one probe on each side of TSS 0.43 -0.05 -0.11 precursor discordant 2 probes GRM4 6 glutamate receptor; metabotropic 4 Down one probe on each side of TSS 0.38 -0.16 -0.31 concordant glutamate receptor; metabotropic 6 2 probes GRM6 5 Up one probe on each side of TSS 0.46 0.11 0.10 precursor discordant guanylate cyclase 2D; membrane (retina- 2 probes GUCY2D 17 Up one probe on each side of TSS 0.15 0.06 0.08 specific) discordant 2 probes GUP1 3 hypothetical protein LOC57467 Down one probe on each side of TSS 0.15 -0.07 -0.08 discordant 2 probes GZMM 19 granzyme M precursor Down one probe on each side of TSS 0.57 -0.07 -0.11 discordant basic helix-loop-helix transcription factor 2 probes HAND2 4 Up one probe on each side of TSS 0.50 0.09 0.21 HAND2 discordant 2 probes both probes on same side of HAS1 19 hyaluronan synthase 1 Down 0.41 -0.20 -0.34 concordant TSS, less than 500bp hyperpolarization activated cyclic 2 probes HCN4 15 Down one probe on each side of TSS 0.20 -0.02 -0.01 nucleotide-gated potassium channel 4 discordant HERV-FRD provirus ancestral Env 2 probes HERV-FRD 6 Down one probe on each side of TSS 0.41 -0.13 -0.27 polyprotein discordant both probes on promoter far 2 probes HEXIM1 17 HMBA-inducible Down from each other (more than 300 0.26 -0.13 -0.20 discordant bp) HIST1H2AC 6 H2A histone family; member L 1 probe Down after TSS, less than 500bp 0.38 -0.12 -0.24 2 probes HLF 17 hepatic leukemia factor Up one probe on each side of TSS 0.15 0.02 0.02 discordant 2 probes HMG20B 19 high-mobility group 20B Down one probe on each side of TSS 0.03 -0.01 -0.01 discordant 3-hydroxymethyl-3-methylglutaryl- 2 probes HMGCL 1 Down one probe on each side of TSS 0.09 -0.04 -0.02 Coenzyme A lyase discordant

329

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F (hydroxymethylglutaricaciduria) 2 probes HNF4A 20 hepatocyte nuclear factor 4 alpha isoform b Down one probe on each side of TSS 0.48 -0.15 -0.23 discordant 2 probes HOXA7 7 homeobox protein A7 Down one probe on each side of TSS 0.10 -0.04 0.00 discordant 2 probes HOXD12 2 homeobox D12 Up one probe on each side of TSS 0.25 0.13 0.19 discordant 2 probes HOXD13 2 homeobox D13 Up one probe on each side of TSS 0.44 0.20 0.22 discordant 3 beta-hydroxy-delta 5-C27-steroid 2 probes HSD3B7 16 Down one probe on each side of TSS 0.90 -0.04 -0.10 oxidoreductase discordant heat shock protein; alpha-crystallin-related; 2 probes HSPB6 19 Up one probe on each side of TSS 0.48 0.17 0.36 B6 concordant heat shock protein; alpha-crystallin-related; 2 probes both probes on same side of HSPB9 17 Down 0.46 -0.19 -0.40 B9 concordant TSS, less than 500bp 2 probes HYAL2 3 hyaluronoglucosaminidase 2 Up one probe on each side of TSS 0.67 0.15 0.23 discordant intercellular adhesion molecule 4 isoform 1 2 probes ICAM4 19 Down one probe on each side of TSS 0.23 -0.06 -0.04 precursor discordant 2 probes IDUA 4 alpha-L-iduronidase precursor Up one probe on each side of TSS 0.25 -0.01 0.08 discordant interleukin 18 binding protein isoform C 2 probes IL18BP 11 Up one probe on each side of TSS 0.66 0.13 0.19 precursor discordant three probes are different, INS 11 proinsulin precursor 4 probes Down including two upstream and one 0.35 -0.17 -0.23 downstream of TSS 2 probes INT1 7 integrator complex subunit 1 isoform 3 Down one probe on each side of TSS 0.04 0.00 0.00 discordant 2 probes IRF7 11 interferon regulatory factor 7 isoform a Up one probe on each side of TSS 0.17 0.07 0.10 discordant potassium voltage-gated channel; subfamily 2 probes KCNH5 14 Down one probe on each side of TSS 0.36 -0.13 -0.09 H; member 5 isoform 1 discordant 2 probes KDELR2 7 KDEL receptor 2 Down one probe on each side of TSS 0.03 -0.01 0.00 discordant 2 probes KIF3B 20 kinesin family member 3B Down one probe on each side of TSS 0.41 -0.14 -0.30 discordant 2 probes KIF5A 12 kinesin family member 5A Down one probe on each side of TSS 0.33 -0.13 -0.20 discordant 2 probes KLHDC7A 1 hypothetical protein LOC127707 Down one probe on each side of TSS 0.36 -0.17 -0.30 discordant 2 probes KLK15 19 kallikrein 15 isoform 1 preproprotein Down one probe on each side of TSS 0.39 -0.11 -0.27 discordant 2 probes KLK6 19 kallikrein 6 isoform A preproprotein Down one probe on each side of TSS 0.41 -0.06 -0.22 discordant

330

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F both probes on promoter far 2 probes KLK9 19 kallikrein 9 Down from each other (more than 300 0.32 -0.07 -0.03 discordant bp) 2 probes KRT9 17 keratin 9 Down one probe on each side of TSS 0.19 0.00 -0.06 discordant both probes on promoter far 2 probes KRTAP5-9 11 keratin associated protein 5-9 Down from each other (more than 300 0.50 -0.11 -0.23 discordant bp) KRTCAP3 2 keratinocyte associated protein 3 1 probe Down after TSS, less than 500bp 0.44 -0.13 -0.41 2 probes KRTHB2 12 keratin; hair; basic; 2 Down one probe on each side of TSS 0.49 -0.16 -0.26 discordant 2 probes LAMA4 6 laminin; alpha 4 precursor Up one probe on each side of TSS 0.29 0.06 0.14 discordant 2 probes LAMB3 1 laminin subunit beta 3 precursor Down one probe on each side of TSS 0.35 -0.12 -0.32 concordant 2 probes LARP1 5 la related protein isoform 2 Down one probe on each side of TSS 0.32 -0.16 -0.22 discordant 2 probes LCN1 9 lipocalin 1 precursor Down one probe on each side of TSS 0.41 -0.18 -0.30 discordant 2 probes LEPREL2 12 leprecan-like 2 Down one probe on each side of TSS 0.63 -0.16 -0.20 discordant leukocyte immunoglobulin-like receptor; 2 probes LILRB5 19 subfamily B (with TM and ITIM domains); Down one probe on each side of TSS 0.50 -0.16 -0.29 discordant member 5 LOC340061 5 hypothetical protein LOC340061 1 probe Up prior to TSS 0.53 0.14 0.21 2 probes LOC349236 9 hypothetical protein LOC349236 Up one probe on each side of TSS 0.88 0.01 0.02 discordant hepatocellular carcinoma-associated gene 2 probes LOC55908 19 Down one probe on each side of TSS 0.51 -0.14 -0.25 TD26 discordant 2 probes LRMP 12 lymphoid-restricted membrane protein Down one probe on each side of TSS 0.33 -0.12 -0.07 discordant 2 probes LRRC4 7 netrin-G1 ligand Up one probe on each side of TSS 0.82 0.06 0.09 discordant 2 probes LTC4S 5 leukotriene C4 synthase isoform 1 Up one probe on each side of TSS 0.56 0.16 0.21 discordant 2 probes LY6K 8 lymphocyte antigen 6 complex; locus K Down one probe on each side of TSS 0.08 -0.01 -0.02 discordant 2 probes LY86 6 MD-1; RP105-associated Up one probe on each side of TSS 0.44 0.21 0.08 discordant 2 probes LYPD4 19 LY6/PLAUR domain containing 4 Down one probe on each side of TSS 0.72 -0.08 -0.08 discordant myelin associated glycoprotein isoform a 2 probes MAG 19 Down one probe on each side of TSS 0.38 -0.07 -0.17 precursor discordant

331

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F mitogen-activated protein kinase kinase 2 probes MAP4K1 19 Up one probe on each side of TSS 0.19 0.05 0.07 kinase kinase 1 discordant microtubule-associated protein; RP/EB 2 probes MAPRE3 2 Up one probe on each side of TSS 0.59 0.10 0.01 family; member 3 discordant 2 probes MBD3 19 methyl-CpG binding domain protein 3 Down one probe on each side of TSS 0.06 -0.02 0.00 discordant 2 probes MFAP5 12 microfibrillar associated protein 5 Down one probe on each side of TSS 0.36 -0.14 -0.25 discordant 2 probes MFRP 11 membrane frizzled-related protein Down one probe on each side of TSS 0.39 -0.19 -0.28 discordant 2 probes MGC3101 16 hypothetical protein LOC79007 Down one probe on each side of TSS 0.68 -0.05 -0.09 discordant 2 probes MGC34647 16 hypothetical protein LOC146433 Down one probe on each side of TSS 0.52 -0.20 -0.30 concordant 2 probes MGC35048 16 hypothetical protein LOC124152 Up one probe on each side of TSS 0.83 -0.14 0.01 discordant 2 probes MKL1 22 megakaryoblastic leukemia 1 protein Up one probe on each side of TSS 0.02 0.00 0.00 discordant 2 probes MLCK 16 MLCK protein Down one probe on each side of TSS 0.33 -0.14 -0.26 discordant transcription factor-like protein 4 isoform 2 probes MLX 17 Down one probe on each side of TSS 0.34 -0.05 -0.21 gamma discordant both probes on promoter far 2 probes MORF4L1 15 MORF-related gene 15 isoform 2 Up from each other (more than 300 0.63 0.12 0.06 discordant bp) MOCO sulphurase C-terminal domain 2 probes MOSC1 1 Up one probe on each side of TSS 0.26 0.01 0.04 containing 1 discordant MOCO sulphurase C-terminal domain 2 probes MOSC2 1 Down one probe on each side of TSS 0.45 -0.09 -0.02 containing 2 discordant N-methylpurine-DNA glycosylase isoform 2 probes MPG 16 Up one probe on each side of TSS 0.15 0.01 0.08 b discordant 2 probes MSH4 1 mutS homolog 4 Down one probe on each side of TSS 0.35 -0.13 -0.15 discordant 2 probes MSLN 16 mesothelin isoform 1 preproprotein Down one probe on each side of TSS 0.53 -0.12 -0.28 concordant 2 probes MUC15 11 mucin 15 Down one probe on each side of TSS 0.35 -0.15 -0.23 concordant 2 probes MUC5B 11 mucin 5; subtype B; tracheobronchial Down one probe on each side of TSS 0.56 -0.12 -0.17 discordant 2 probes MX1 21 myxovirus resistance protein 1 Up one probe on each side of TSS 0.36 0.01 0.20 concordant 2 probes MYBPC2 19 myosin binding protein C; fast type Down one probe on each side of TSS 0.06 -0.03 -0.01 discordant MYF6 12 myogenic factor 6 (herculin) 1 probe Up after TSS, less than 500bp 0.41 0.16 0.22

332

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F 2 probes MYO1C 17 myosin IC Down one probe on each side of TSS 0.29 -0.12 -0.22 discordant NACHT; leucine rich repeat and PYD 2 probes NALP2 19 Down one probe on each side of TSS 0.32 -0.15 -0.22 containing 2 discordant 2 probes NAV1 1 neuron navigator 1 Up one probe on each side of TSS 0.44 0.02 0.05 discordant 2 probes NCOR2 12 nuclear receptor co-repressor 2 Up one probe on each side of TSS 0.75 0.14 0.13 discordant 2 probes NIPSNAP1 22 nipsnap homolog 1 Down one probe on each side of TSS 0.04 -0.02 0.00 discordant 2 probes NOC2L 1 nucleolar complex associated 2 homolog Down one probe on each side of TSS 0.05 -0.02 -0.01 discordant nucleolar protein family A; member 2 2 probes NOLA2 5 Down one probe on each side of TSS 0.02 -0.01 0.00 isoform a discordant 2 probes NOS3 7 nitric oxide synthase 3 (endothelial cell) Down one probe on each side of TSS 0.54 -0.15 -0.09 discordant 2 probes NOX4 11 NADPH oxidase 4 Up one probe on each side of TSS 0.63 0.08 0.12 discordant 2 probes NPBWR2 20 neuropeptides B/W receptor 2 Down one probe on each side of TSS 0.50 -0.16 -0.32 discordant photoreceptor-specific nuclear receptor 2 probes NR2E3 15 Up one probe on each side of TSS 0.82 0.02 0.05 isoform b discordant 2 probes NTSR1 20 neurotensin receptor 1 Up one probe on each side of TSS 0.35 0.09 0.15 discordant NUAK1 12 AMPK-related protein kinase 5 1 probe Up prior to TSS 0.63 0.22 0.29 NUMA1 11 nuclear mitotic apparatus protein 1 1 probe Down prior to TSS 0.32 -0.12 -0.21 2 probes OBP2B 9 odorant binding protein 2B Down one probe on each side of TSS 0.54 -0.06 -0.17 discordant obscurin; cytoskeletal calmodulin and titin- 2 probes OBSCN 1 Down one probe on each side of TSS 0.58 -0.08 -0.12 interacting RhoGEF discordant 2 probes OCIAD2 4 OCIA domain containing 2 isoform 2 Up one probe on each side of TSS 0.58 0.20 0.34 concordant 2 probes ODF2 9 outer dense fiber of sperm tails 2 isoform 1 Down one probe on each side of TSS 0.48 -0.15 -0.30 discordant 2 probes ODF3 11 sperm tail protein SHIPPO1 Down one probe on each side of TSS 0.28 -0.04 -0.11 discordant olfactory receptor; family 2; subfamily A; 2 probes OR2A4 6 Down one probe on each side of TSS 0.51 -0.13 -0.20 member 4 discordant 2 probes PADI3 1 peptidylarginine deiminase type III Down one probe on each side of TSS 0.49 -0.12 -0.21 discordant progestin and adipoQ receptor family 2 probes PAQR6 1 Down one probe on each side of TSS 0.44 -0.03 -0.08 member VI isoform 1 discordant PAX4 7 paired box gene 4 2 probes Down one probe on each side of TSS 0.31 -0.10 -0.26

333

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F discordant 2 probes PAX7 1 paired box gene 7 isoform 1 Up one probe on each side of TSS 0.37 0.10 0.20 discordant 2 probes both probes on promoter, less PAX9 14 paired box gene 9 Up 0.51 0.18 0.17 discordant than 300bp from each other phosphodiesterase 6B; cGMP-specific; rod; 2 probes both probes on promoter, less PDE6B 4 Down 0.25 -0.10 -0.16 beta discordant than 300bp from each other platelet-derived growth factor receptor 2 probes PDGFRA 4 Down one probe on each side of TSS 0.05 -0.02 -0.01 alpha precursor discordant 2 probes PKD2L1 10 polycystic kidney disease 2-like 1 Down one probe on each side of TSS 0.52 -0.08 -0.21 discordant 2 probes PKP3 11 plakophilin 3 Down one probe on each side of TSS 0.59 -0.27 -0.54 concordant 2 probes PLA2G3 22 phospholipase A2; group III precursor Down one probe on each side of TSS 0.44 -0.15 -0.26 discordant 2 probes PLCB2 15 phospholipase C; beta 2 Up one probe on each side of TSS 0.60 0.14 0.21 concordant 2 probes PLEK 2 pleckstrin Up one probe on each side of TSS 0.53 0.12 0.23 discordant phosphatidic acid phosphatase type 2C 2 probes both probes on promoter, less PPAP2C 19 Down 0.39 0.00 -0.10 isoform 1 discordant than 300bp from each other protein phosphatase 1 regulatory inhibitor 2 probes PPP1R16B 20 Up one probe on each side of TSS 0.69 0.15 0.20 subunit 16B discordant alpha isoform of regulatory subunit B55; 2 probes PPP2R2A 8 Down one probe on each side of TSS 0.58 -0.16 -0.22 protein phosphatase 2 discordant 2 probes PRAC 17 small nuclear protein PRAC Up one probe on each side of TSS 0.66 0.16 0.17 discordant preferentially expressed antigen in PRAME 22 1 probe Down after TSS, less than 500bp 0.31 -0.05 -0.21 melanoma one prbe upstream of TSS is PRKCDBP 11 protein kinase C; delta binding protein 4 probes Up 0.12 0.09 0.03 different 2 probes PRLH 2 prolactin releasing hormone Down one probe on each side of TSS 0.54 -0.24 -0.25 discordant 2 probes PROM2 2 prominin 2 Down one probe on each side of TSS 0.81 0.01 -0.05 discordant protein Z; vitamin K-dependent plasma 2 probes PROZ 13 Down one probe on each side of TSS 0.83 -0.03 -0.02 glycoprotein discordant 2 probes PRP2 5 proline-rich protein PRP2 Up one probe on each side of TSS 0.61 0.16 0.26 discordant 2 probes PRRT1 6 NG5 protein Up one probe on each side of TSS 0.52 0.18 0.15 discordant 2 probes PRSS27 16 marapsin Down one probe on each side of TSS 0.40 -0.09 -0.29 discordant

334

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F PRSS8 16 prostasin preproprotein 1 probe Down after TSS, less than 500bp 0.41 -0.20 -0.31 2 probes PSCA 8 prostate stem cell antigen Down one probe on each side of TSS 0.46 -0.17 -0.30 discordant 2 probes PSORS1C2 6 SPR1 protein Down one probe on each side of TSS 0.35 -0.15 -0.30 discordant five probes in the promoter are receptor-type protein tyrosine phosphatase PTPRO 12 9 probes Up different, two only pass one of 0.62 0.22 0.18 O isoform b precursor the criteria, two are not different protein tyrosine phosphatase; receptor type; 2 probes PTPRS 19 Down one probe on each side of TSS 0.33 -0.14 -0.26 sigma isoform 1 precursor discordant 2 probes PVRL4 1 poliovirus receptor-related 4 Down one probe on each side of TSS 0.37 -0.13 -0.34 discordant similar to Peroxisomal membrane protein 2 PXMP2 12 1 probe Down prior to TSS 0.41 -0.13 -0.30 (22 kDa peroxisomal membrane protein) 2 probes both probes on promoter, less PYY 17 peptide YY Up 0.53 0.12 0.18 discordant than 300bp from each other 2 probes RAB25 1 RAB25 Down one probe on each side of TSS 0.31 -0.12 -0.21 concordant 2 probes RAB8A 19 mel transforming oncogene Down one probe on each side of TSS 0.33 -0.05 -0.21 discordant RAD1 5 RAD1 homolog isoform 4 1 probe Down prior to TSS 0.45 -0.11 -0.31 three probes , in the promoter, RASSF1 3 Ras association domain family 1 isoform A 9 probes Down are different; six probes 0.10 -0.03 -0.02 intragenic are not one probe upstream is different; Ras association (RalGDS/AF-6) domain RASSF5 1 9 probes Up the remaining eight, five of 0.50 0.12 0.22 family 5 isoform B which intragenic, are not Ras association (RalGDS/AF-6) domain RASSF7 11 1 probe Down after TSS, more than 500 bp 0.57 -0.15 -0.34 family 7 2 probes REGL 2 Down one probe on each side of TSS 0.57 -0.15 -0.21 discordant colon and small intestine-specific cysteine- 2 probes RETNLB 3 Down one probe on each side of TSS 0.46 -0.23 -0.23 rich protein precursor discordant both probes on promoter far 2 probes RHEB 7 Ras homolog enriched in brain Up from each other (more than 300 0.28 0.03 0.12 discordant bp) 2 probes RHOBTB3 5 rho-related BTB domain containing 3 Up one probe on each side of TSS 0.45 0.15 0.26 discordant 2 probes RHOC 1 ras homolog gene family; member C Down one probe on each side of TSS 0.03 -0.01 -0.01 discordant 2 probes RHOH 4 ras homolog gene family; member H Up one probe on each side of TSS 0.59 0.16 0.20 discordant RPS6KA2 6 ribosomal protein S6 kinase; 90kDa; 2 probes Down one probe on each side of TSS 0.39 -0.05 -0.03

335

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F polypeptide 2 isoform b discordant one probe in the body of the RUNX3 1 runt-related transcription factor 3 isoform 2 19 probes Down 0.80 -0.06 -0.10 gene is different 2 probes S100A4 1 S100 calcium-binding protein A4 Down one probe on each side of TSS 0.04 0.04 -0.01 discordant 2 probes S100P 4 S100 calcium binding protein P Down one probe on each side of TSS 0.28 -0.07 -0.23 discordant 2 probes SARDH 9 sarcosine dehydrogenase Down one probe on each side of TSS 0.38 -0.15 -0.21 discordant 2 probes SBSN 19 suprabasin Down one probe on each side of TSS 0.69 -0.10 -0.13 discordant 2 probes SCGB1D1 11 lipophilin A precursor Down one probe on each side of TSS 0.41 -0.18 -0.21 concordant 2 probes SCGB2A1 11 secretoglobin; family 2A; member 1 Down one probe on each side of TSS 0.41 -0.15 -0.22 concordant 2 probes SCNN1D 1 sodium channel; nonvoltage-gated 1; delta Down one probe on each side of TSS 0.56 -0.12 -0.21 discordant 2 probes SELPLG 12 selectin P ligand Up one probe on each side of TSS 0.54 0.14 0.21 concordant SEMA4A 1 semaphorin B 1 probe Down after TSS, less than 500bp 0.35 -0.16 -0.25 2 probes SEMA6B 19 semaphorin 6B isoform 1 precursor Down one probe on each side of TSS 0.35 -0.10 -0.29 discordant serine (or cysteine) proteinase inhibitor; 2 probes SERPINA12 14 clade A (alpha-1 antiproteinase; Down one probe on each side of TSS 0.45 -0.21 -0.26 discordant antitrypsin); member 12 serpin peptidase inhibitor; clade A (alpha-1 2 probes SERPINA13 14 Down one probe on each side of TSS 0.59 -0.10 -0.20 antiproteinase; antitrypsin); member 13 discordant three probes, 2 in the promoter serine (or cysteine) proteinase inhibitor; SERPINB5 18 7 probes Down and one in the body of the gene, 0.33 -0.15 -0.23 clade B (ovalbumin); member 5 are different both probes on promoter far serine (or cysteine) proteinase inhibitor; 2 probes SERPINC1 1 Down from each other (more than 300 0.71 0.06 -0.06 clade C (antithrombin); member 1 discordant bp) serine (or cysteine) proteinase inhibitor; 2 probes both probes on promoter, less SERPINF1 17 clade F (alpha-2 antiplasmin; pigment Down 0.34 -0.09 -0.27 discordant than 300bp from each other epithelium derived factor); member 1 2 probes both probes on same side of SH2D3C 9 SH2 domain containing 3C isoform 2 Up 0.67 0.13 0.20 concordant TSS, less than 500bp SH3 and multiple ankyrin repeat domains 2 2 probes SHANK2 11 Up one probe on each side of TSS 0.77 0.10 0.16 isoform 1 discordant B lymphocyte activator macrophage 2 probes SLAMF8 1 Up one probe on each side of TSS 0.60 0.17 0.25 expressed discordant SLC13A4 7 solute carrier family 13 (sodium/sulfate 1 probe Down after TSS, less than 500bp 0.39 -0.13 -0.21

336

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F symporters); member 4 2 probes SLC15A3 11 solute carrier family 15; member 3 Up one probe on each side of TSS 0.62 0.19 0.33 discordant 2 probes SLC16A11 17 solute carrier family 16; member 11 Up one probe on each side of TSS 0.50 0.20 0.36 concordant 2 probes SLC16A8 22 solute carrier family 16; member 8 Up one probe on each side of TSS 0.18 0.03 0.03 discordant 2 probes SLC22A11 11 solute carrier family 22 member 11 Down one probe on each side of TSS 0.34 -0.06 -0.09 discordant 2 probes SLC35C1 11 solute carrier family 35; member C1 Up one probe on each side of TSS 0.03 0.00 0.00 discordant 2 probes SLC44A2 19 CTL2 protein Down one probe on each side of TSS 0.32 -0.16 -0.30 concordant 2 probes SLC4A11 20 solute carrier family 4 member 11 Down one probe on each side of TSS 0.47 -0.20 -0.32 discordant solute carrier family 6 (neurotransmitter 2 probes SLC6A13 12 Down one probe on each side of TSS 0.30 -0.14 -0.20 transporter; GABA); member 13 discordant solute carrier family 7; (cationic amino acid 2 probes SLC7A11 4 Down one probe on each side of TSS 0.32 -0.13 -0.20 transporter; y+ system) member 11 discordant 2 probes SLITL2 16 slit-like 2 Down one probe on each side of TSS 0.26 -0.09 -0.21 discordant both probes on promoter far Smith-Magenis syndrome chromosome 2 probes SMCR7 17 Down from each other (more than 300 0.38 -0.18 -0.24 region; candidate 7 discordant bp) 2 probes SMOC2 6 secreted modular calcium-binding protein 2 Up one probe on each side of TSS 0.57 0.07 0.22 concordant 2 probes SORD 15 sorbitol dehydrogenase Up one probe on each side of TSS 0.31 0.08 0.03 discordant 2 probes SOX1 13 SRY (sex determining region Y)-box 1 Up one probe on each side of TSS 0.41 0.14 0.22 discordant 2 probes SOX7 8 SRY-box 7 Up one probe on each side of TSS 0.03 -0.01 0.00 discordant sperm associated antigen 11 isoform A 2 probes SPAG11 8 Down one probe on each side of TSS 0.34 -0.14 -0.21 precursor discordant meiotic recombination protein SPO11 2 probes SPO11 20 Down one probe on each side of TSS 0.58 -0.15 -0.36 isoform a concordant 2 probes SSTR3 22 somatostatin receptor 3 Down one probe on each side of TSS 0.42 0.01 -0.10 discordant 2 probes SSTR5 16 somatostatin receptor 5 Down one probe on each side of TSS 0.37 -0.21 -0.31 discordant signal transducer and activator of 2 probes STAT5A 17 Up one probe on each side of TSS 0.60 0.18 0.32 transcription 5A discordant 2 probes SUSD2 22 sushi domain containing 2 Down one probe on each side of TSS 0.34 -0.07 -0.29 concordant

337

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F 2 probes SYT8 11 synaptotagmin VIII Down one probe on each side of TSS 0.34 -0.15 -0.30 discordant TAAR5 6 putative neurotransmitter receptor 1 probe Down after TSS, less than 500bp 0.36 -0.17 -0.25 2 probes TACR2 10 tachykinin receptor 2 Up one probe on each side of TSS 0.85 0.02 0.02 discordant 2 probes TAL1 1 T-cell acute lymphocytic leukemia 1 Up one probe on each side of TSS 0.28 0.02 0.05 discordant 2 probes TBX4 17 T-box 4 Up one probe on each side of TSS 0.46 0.13 0.19 discordant 2 probes TBX6 16 T-box 6 isoform 2 Up one probe on each side of TSS 0.04 0.00 0.00 discordant transcription elongation factor B one probe, in the body of the TCEB3C 18 4 probes Down 0.32 -0.09 -0.14 polypeptide 3C gene, is different 2 probes TCL1A 14 T-cell lymphoma-1 Down one probe on each side of TSS 0.69 -0.10 -0.14 discordant 2 probes TCL1B 14 T-cell leukemia/lymphoma 1B Up one probe on each side of TSS 0.76 0.05 0.05 discordant 2 probes TCP10 6 t-complex 10 Down one probe on each side of TSS 0.36 -0.15 -0.09 discordant 2 probes TEB1 13 hypothetical protein LOC54937 Down one probe on each side of TSS 0.33 -0.09 -0.23 discordant tensin like C1 domain containing 2 probes TENC1 12 Down one probe on each side of TSS 0.05 -0.02 -0.01 phosphatase isoform 1 discordant transcription factor AP-2 epsilon (activating 2 probes TFAP2E 1 Up one probe on each side of TSS 0.61 0.06 0.10 enhancer binding protein 2 epsilon) discordant transglutaminase 1 (K polypeptide 2 probes TGM1 14 epidermal type I; protein-glutamine- Up one probe on each side of TSS 0.78 0.07 0.03 discordant gamma-glutamyltransferase) 2 probes THY1 11 Thy-1 cell surface antigen Up one probe on each side of TSS 0.24 0.14 0.19 discordant 2 probes both probes on promoter, less TIGD5 8 tigger transposable element derived 5 Down 0.27 -0.09 -0.21 discordant than 300bp from each other TMC1 9 transmembrane channel-like 1 1 probe Down prior to TSS 0.46 -0.14 -0.25 2 probes TMEM17 2 transmembrane protein 17 Up one probe on each side of TSS 0.54 0.15 0.20 concordant 2 probes TMEM40 3 transmembrane protein 40 Down one probe on each side of TSS 0.41 -0.13 -0.21 concordant 2 probes TMPRSS6 22 transmembrane protease; serine 6 Down one probe on each side of TSS 0.31 -0.05 -0.13 discordant tumor necrosis factor; alpha-induced protein 2 probes TNFAIP8 5 Down one probe on each side of TSS 0.32 -0.12 -0.23 8 discordant tumor necrosis factor ligand superfamily; TNFSF13 17 1 probe Down prior to TSS 0.53 -0.10 -0.26 member 13 isoform alpha proprotein

338

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F 2 probes TNNC1 3 troponin C; slow Up one probe on each side of TSS 0.68 0.06 0.00 discordant 2 probes TNS1 2 tensin Down one probe on each side of TSS 0.20 0.10 -0.10 discordant 2 probes TNS4 17 C-terminal tensin-like Down one probe on each side of TSS 0.46 -0.15 -0.28 concordant of the six probes in the promoter, one is up and the TP73 1 tumor protein p73 12 probes Up other passed one of the 0.53 0.20 0.25 criteria; three probes in the body, are down 2 probes TPSAB1 16 tryptase alpha/beta 1 precursor Down one probe on each side of TSS 0.51 -0.17 -0.26 concordant TPSB2 16 tryptase beta 2 precursor 1 probe Down prior to TSS 0.51 -0.17 -0.28 2 probes TPSD1 16 tryptase delta 1 Down one probe on each side of TSS 0.30 -0.10 -0.14 discordant transmembrane phosphatase with tensin 2 probes TPTE 21 Down one probe on each side of TSS 0.32 -0.06 -0.20 homology isoform alpha discordant tripartite motif protein TRIM29 isoform 2 probes TRIM29 11 Down one probe on each side of TSS 0.58 -0.19 -0.42 beta discordant 2 probes TRIM60 4 ring finger protein 129 Down one probe on each side of TSS 0.36 -0.13 -0.24 discordant 2 probes TSP50 3 testes-specific protease 50 Down one probe on each side of TSS 0.52 -0.14 -0.30 discordant 2 probes TUB 11 tubby isoform b Down one probe on each side of TSS 0.42 -0.12 -0.22 discordant tubulin; gamma complex associated protein 2 probes TUBGCP2 10 Down one probe on each side of TSS 0.03 0.00 0.00 2 discordant 2 probes TULP2 19 tubby like protein 2 Up one probe on each side of TSS 0.68 0.10 0.09 discordant one probe, in the promoter, is TWIST1 7 twist 7 probes Up 0.07 0.00 0.02 different 2 probes TXNDC3 7 NM23-H8 Down one probe on each side of TSS 0.48 -0.10 -0.21 discordant 2 probes both probes on same side of UBTD1 10 ubiquitin domain containing 1 Up 0.53 0.18 0.27 concordant TSS, less than 500bp 2 probes UCN3 10 urocortin 3 preproprotein Down one probe on each side of TSS 0.34 -0.15 -0.23 discordant UDP glycosyltransferase 1 family; 2 probes UGT1A3 2 Down one probe on each side of TSS 0.37 -0.18 -0.22 polypeptide A3 precursor discordant ubiquitin-like; containing PHD and RING 2 probes UHRF1 19 Down one probe on each side of TSS 0.07 -0.01 -0.01 finger domains; 1 discordant 2 probes ULK1 12 unc-51-like kinase 1 Down one probe on each side of TSS 0.43 -0.11 -0.24 discordant

339

Methyl. Methyl. Number of Methyl in difference difference SYMBOL Chr PRODUCT Probes position F methyl. probes C Vs F between between C and placenta and F F 2 probes UNQ9391 8 hypothetical protein LOC203074 Down one probe on each side of TSS 0.35 -0.13 -0.20 discordant 2 probes URP2 11 UNC-112 related protein 2 short form Up one probe on each side of TSS 0.76 0.11 0.14 discordant vesicle-associated membrane protein 5 2 probes VAMP5 2 Up one probe on each side of TSS 0.62 0.18 0.31 (myobrevin) concordant WFDC9 20 protease inhibitor WAP9 1 probe Down prior to TSS 0.55 -0.13 -0.22 WNT1 inducible signaling pathway protein 2 probes WISP1 8 Down one probe on each side of TSS 0.38 -0.20 -0.32 1 isoform 1 precursor discordant one probe, in the body of the WT1 11 Wilms tumor 1 isoform C 20 probes Up 0.09 0.06 0.08 gene, is different protein 7 transactivated by hepatitis B virus 2 probes both probes on same side of XTP7 19 Down 0.69 -0.09 -0.35 X antigen concordant TSS, less than 500bp 2 probes ZDHHC11 5 zinc finger; DHHC domain containing 11 Down one probe on each side of TSS 0.36 -0.18 -0.22 concordant 2 probes ZNF177 19 zinc finger protein 177 Up one probe on each side of TSS 0.39 -0.04 0.00 discordant 2 probes ZNF544 19 zinc finger protein 544 Down one probe on each side of TSS 0.03 -0.01 -0.01 discordant 2 probes ZNF710 15 zinc finger protein 710 Up one probe on each side of TSS 0.68 0.16 0.24 discordant 2 probes discordant mean that there are two probes mapping to the gene in the array and only one is differentially methylated. 2 probes concordant mean that both are differentially methylated; TSS - transcription start site; C - cytotrophoblasts; F – fibroblasts; Methyl. – Methylation.

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Appendix 2 - List of publications Publications used in the thesis Epigenetic Programming and Fetal Growth Restriction. Jose Carlos Ferreira, Sanaa Choufani, John Kingdom, Rosanna Weksberg Fetal and Maternal Medicine Review, 2010; 21:204-224. http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=7873789 Used as the basis for Chapter 1 of this thesis

Screening of DNA methylation at the H19 promoter or the distal region of its ICR1 ensures efficient detection of chromosome 11p15 epimutations in Russell-Silver syndrome. Shin-Itchi Horike S*, Jose Carlos Ferreira*, Makiko Meguro-Horike, Sanaa Choufani, Adam C Smith, Cheryl Shuman, Wendy Meschino, David Chitaya, E Zackai, Steve W Scherer, Rosanna Weksberg. Am J Med Genet A. 2009; Nov 149A:2415-23. http://onlinelibrary.wiley.com/doi/10.1002/ajmg.a.33065/full#fn1 Used as the basis for Chapter 4 of this thesis

Cell Specific Patterns of Methylation in the Human Placenta. Ariadna Grigoriu*, Jose Carlos Ferreira*, Sanaa Choufani, Dora Baczyk, John Kingdom, Rosanna Weksberg Epigenetics, 2011; Mar 6:368-379. http://www.landesbioscience.com/journals/epigenetics/article/14196/ Used as the basis for Chapter 6 of this thesis

WNT2 promoter methylation in human placenta is associated with low birthweight percentile in the neonate. Jose Carlos Ferreira, Sanaa Choufani, Daria Grafodatskaya, Darci T. Butcher, Chunhua Zhao, David Chitayat, Cheryl Shuman, John Kingdom, Sarah Keating, Rosanna Weksberg. Epigenetics, 2011; April 6:440-449. http://www.landesbioscience.com/journals/epigenetics/article/14554/ Used as the basis for Chapter 7 of this thesis

Assessment of methylation level prediction accuracy in Methyl-DNA immunoprecipitation and sodium bisulfite based microarray platforms. Rageen Rajendram*, Jose C Ferreira*, Daria Grafodatskaya, Sanaa Choufani, Theodore Chiang, Shuye Pu, Darci T. Butcher, Shoshana Wodak, Rosanna Weksberg .

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Epigenetics, 2011; April 6:410-415. http://www.landesbioscience.com/journals/epigenetics/article/14763/ Used as the basis for Chapter 8 of this thesis.

Other publications related to my PhD project, not part of the thesis: Growth regulation, imprinted genes, and chromosome 11p15.5 Adam C Smith, Sanaa Choufani, Jose Carlos Ferreira, Rosanna Weksberg Pediatr Res. 2007; May 61:43R-7R http://journals.lww.com/pedresearch/Fulltext/2007/05001/Growth_Regulation,_Imprinted_Genes ,_and_Chromosome.8.aspx

Altered gene expression and methylation of the human chromosome 11 imprinted region in small for gestational age (SGA) placentae. Lin Guo, Sanaa Choufani, Jose Carlos Ferreira, Adam Smith, David Chitayat, Cheryl Shuman, Ruchita Uxa, Sarah Keating, John Kingdom, Rosanna Weksberg Dev Biol. 2008; Aug 320:79-91. http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WDG-4SCKRW6- 1&_user=10&_coverDate=08/01/2008&_rdoc=1&_fmt=high&_orig=search&_origin=search&_ sort=d&_docanchor=&view=c&_searchStrId=1548748023&_rerunOrigin=google&_acct=C000 050221&_version=1&_urlVersion=0&_userid=10&md5=66cc732d38be329f7c3a970f6c5ddce2 &searchtype=a

EBV transformation and cell culturing destabilizes DNA methylation in human lymphoblastoid cell lines. Daria Grafodatskaya, Sanaa Choufani, Jose Carlos Ferreira, Darci T Butcher, Youliang Lou, Chunhua Zhao, Steve W Scherer, Rosanna Weksberg. Genomics. 2009; 95:73-83. http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WG1-4XY9DH2- 1&_user=10&_coverDate=02/28/2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_ sort=d&_docanchor=&view=c&_searchStrId=1548745163&_rerunOrigin=google&_acct=C000 050221&_version=1&_urlVersion=0&_userid=10&md5=ab09c4ebb5a823bfa28e9665c2805a92 &searchtype=a

Sequence overlap between autosomal and sex-linked probes on the Illumina HumanMethylation27 microarray. Yi-an Chen, Sanaa Choufani, Jose Carlos Ferreira, Daria Grafodatskaya, Darci T Butcher, Rosanna Weksberg Genomics. 2011; Jan 97:214-222

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http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WG1-51W056C- 1&_user=10&_coverDate=01/04/2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_ sort=d&_docanchor=&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10& md5=db5bc5ab39d249c9ba95b4f2928fcc4e&searchtype=a

A Novel Approach Identifies New Differentially Methylated Regions (DMRs) Associated with Imprinted Genes. Sanaa Choufani, Jonathan S. Shapiro, Martha Susiarjo, Darci T. Butcher, Daria Grafodatskaya, Youliang Lou, Jose C. Ferreira, Dalila Pinto, Stephen W. Scherer, Lisa G. Shaffer, Philippe Coullin, Isabella Caniggia, Joseph Beyene, Rima Slim, Marisa S. Bartolomei, Rosanna Weksberg Genome Res. 2011; Mar 21:465-76. http://genome.cshlp.org/content/early/2011/02/07/gr.111922.110.abstract