epigenetic regulation of plac8 contributes to altered

Epigenetic regulation of PLAC8 contributes to altered function of endothelial colony 1

forming cells exposed to intrauterine gestational diabetes mellitus 2

3

4

Running Title: Increased PLAC8 in GDM-exposed neonatal ECFCs 5

6

7

Emily K. Blue1,2*, BreAnn M. Sheehan

1,2*, Zia V. Nuss

1,2, Frances A. Boyle

1,2, Caleb M. 8

Hocutt1,2, Cassandra R. Gohn

3, Kaela M. Varberg

3, Jeanette N. McClintick

4, and 9

Laura S. Haneline1,2,3,5,6

10

11

12

*Both authors made equal contributions to the manuscript. 13

14

15

Authors’ institutional affiliations: 1Department of Pediatrics, Indiana University School of 16

Medicine, Indianapolis, IN, the 2Herman B Wells Center for Pediatric Research,

3Department of 17

Cellular & Integrative Physiology, Indiana University School of Medicine, Indianapolis, IN, 18 4Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 19

Indianapolis, IN, 5Department of Microbiology & Immunology, Indiana University School of 20

Medicine, Indianapolis, IN, and the 6Indiana University Simon Cancer Center, Indiana 21

University School of Medicine, Indianapolis, IN, USA 22

23

24

Corresponding Author: 25

Laura S. Haneline 26

699 Riley Hospital Dr. RR 208 27

Indianapolis, IN 46202 28

Phone: 317-274-8916 29

Fax: 317-274-2065 30

Email: [email protected] 31

32

33

Word count: 3,996 34

Number of Figures and Tables: 5 35

36

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Diabetes Publish Ahead of Print, published online February 26, 2015

ABSTRACT 37

Intrauterine exposure to gestational diabetes mellitus (GDM) is linked to development of 38

hypertension, obesity, and type 2 diabetes in children. Our previous studies determined that 39

endothelial colony forming cells (ECFCs) from neonates exposed to GDM exhibit impaired 40

function. The current goals were to identify aberrantly expressed genes that contribute to 41

impaired function of GDM-exposed ECFCs and to evaluate for evidence of altered epigenetic 42

regulation of gene expression. Genome-wide mRNA expression analysis was conducted on 43

ECFCs from control and GDM pregnancies. Candidate genes were validated by qRT-PCR and 44

western blotting. Bisulfite sequencing evaluated DNA methylation of PLAC8. Proliferation and 45

senescence assays of ECFCs transfected with siRNA to knockdown PLAC8 were performed to 46

determine functional impact. Thirty-eight genes were differentially expressed between control 47

and GDM-exposed ECFCs. PLAC8 was highly expressed in GDM-exposed ECFCs, and PLAC8 48

expression correlated with maternal hyperglycemia. Methylation status of 17 CpG sites in 49

PLAC8 negatively correlated with mRNA expression. Knockdown of PLAC8 in GDM-exposed 50

ECFCs improved proliferation and senescence defects. This study provides strong evidence in 51

neonatal endothelial progenitor cells that GDM exposure in utero leads to altered gene 52

expression and DNA methylation, suggesting the possibility of altered epigenetic regulation. 53

54

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The Barker hypothesis postulates that alterations in the intrauterine environment and in fetal and 55

infant nutrition correlate with development of adult diseases (1). This concept of a 56

developmental origin of adult disease was based upon a landmark study linking low birth weight 57

with increased risk of death from ischemic heart disease (2). Several subsequent studies have 58

confirmed that infants born small at birth are at increased risk of developing hypertension, 59

stroke, type 2 diabetes, and obesity (1). Collectively, these observations infer that permanent 60

changes occur during fetal development allowing adaptation and survival in a suboptimal 61

intrauterine environment and that in a postnatal setting these developmental adaptations 62

mechanistically contribute to the pathogenesis of multiple chronic diseases. Similarly, infants 63

born to women with pre-gestational diabetes mellitus (PGDM) and gestational diabetes mellitus 64

(GDM) have an increased risk for developing chronic diseases including hypertension, type 2 65

diabetes, and obesity (3-7). Numerous animal studies show long-term, harmful effects of fetal 66

overnutrition (8,9). Thus, fetal intrauterine exposure to either undernourishment or diabetes 67

increases disease risk later in life. 68

69

Because of the long-lasting nature of an individual’s response to adverse intrauterine 70

environment exposure, dysfunctional stem and progenitor cells are hypothesized to participate in 71

disease pathogenesis. Our previous work evaluating the function of endothelial progenitor cells 72

supports this supposition. Using cord blood endothelial colony forming cells (ECFCs), a highly 73

proliferative and self-renewing endothelial progenitor population, we identified numerous 74

functional deficits of ECFCs exposed to GDM in utero (10). Importantly, fetal GDM exposure 75

resulted in increased proliferation, reduced vasculogenesis, and resistance to hyperglycemia-76

induced senescence of ECFCs (10). 77

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78

A potential mechanism for these fetal adaptations includes epigenetic modifications that lead to 79

aberrant gene expression and subsequent cellular dysfunction (11,12). Epigenetic changes, such 80

as alterations in DNA methylation and histone acetylation, have been reported in animal models 81

of intrauterine growth restriction and diabetes (13,14). However, few studies have been 82

conducted in humans to solidify whether epigenetic changes alter the functional capacity of cells 83

from infants born small for gestational age or to women with GDM. Furthermore, the majority 84

of published data do not address whether molecular adaptations occur in stem and/or progenitor 85

cells. We hypothesized that epigenetic changes are induced in ECFCs during fetal exposure to 86

GDM, resulting in abnormal gene expression and cellular dysfunction. The goals of the current 87

study were to identify candidate genes with altered expression that contribute to the aberrant 88

function of GDM-exposed ECFCs and to determine whether impaired DNA methylation 89

promotes aberrant expression of a candidate gene. 90

91

RESEARCH DESIGN AND METHODS 92

Umbilical Cord Blood Collection. Umbilical cord blood samples were collected from healthy, 93

control pregnancies and pregnancies complicated by GDM following informed consent from the 94

mothers. GDM was defined according to the guidelines of the American College of Obstetrics 95

and Gynecology (15). All pregnancies were singleton gestations. Women with preeclampsia or 96

hypertension, women with other illnesses known to affect glucose metabolism, and women 97

taking medications known to affect glucose metabolism were excluded. In addition, infants with 98

known chromosomal abnormalities were excluded. The Institutional Review Board at the Indiana 99

University School of Medicine approved this protocol. GDM samples were separated into two 100

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groups for initial analyses: conservatively-managed (diet and exercise) and insulin-treated. 101

Clinical data for mothers (Supplemental Table 1) and infants (Supplemental Table 2) is included 102

for cohorts 1 and 2. Glucose values were obtained for all women from the 50-gram, 1-hour 103

glucose screening test that is performed between 24 and 28 weeks of gestation during routine 104

obstetric care. 105

106

Cell culture. ECFCs were cultured from umbilical cord blood samples by the Indiana University 107

Simon Cancer Center Angio BioCore (ABC, formerly the Angiogenesis, Endothelial and Pro-108

Angiogenic Cell Core) as previously described (10). HEK/293 cells (American Type Culture 109

Collection, Manassas, VA) were cultured in Dulbecco’s Modified Eagle Medium (Mediatech, 110

Corning Cellgro, Manassas, VA) containing 10% fetal calf serum (FCS, Atlanta Biologicals, 111

Flowery Branch, GA) and antibiotic-antimycotic solution (Mediatech). Jurkat cells were the kind 112

gift of Helmut Hanenberg (Indiana University School of Medicine) and were cultured in Roswell 113

Park Memorial Institute-1640 (Invitrogen, Grand Island, NY) containing 10% FCS and 114

antibiotic-antimycotic solution. 115

116

RNA and DNA isolation. Total RNA and genomic DNA were isolated from ECFCs during log 117

phase growth at passage 3 or 4. RNA was extracted using an miRNeasy kit (Qiagen, Valencia, 118

CA). RNA concentration was determined by Nanodrop (Wilmington, DE), and RNA quality 119

was examined by either electrophoresis or Bioanalyzer (Agilent Technologies, Santa Clara, CA). 120

DNA was isolated using a QIAamp DNA Mini Kit (Qiagen) per manufacturer’s instructions. 121

122

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Affymetrix Microarray. The Center for Medical Genomics (Indiana University School of 123

Medicine, Indianapolis, IN) conducted these studies. Total RNA samples were labeled using the 124

standard protocol for the Ambion WT Expression kit (Life Technologies, Grand Island, NY) 125

combined with the Affymetrix GeneChip® WT Terminal Labeling and Controls Kit 126

(Affymetrix, Santa Clara, CA). Individual labeled samples were hybridized to the Human Gene 127

1.0 ST GeneChips® for 17 hours then washed, stained, and scanned with the standard protocol 128

using Affymetrix GeneChip® Command Console Software to generate data (CEL files). Arrays 129

were visually scanned for abnormalities or defects. CEL files were imported into Partek 130

Genomics Suite (Partek, Inc., St. Louis, Mo). Robust Multi-Array Average (RMA) signals were 131

generated for the core probe sets using the RMA background correction, Quantile normalization 132

and summarization by Median Polish. Summarized signals for each probe set were log2 133

transformed. These log transformed signals were used for Principal Components Analysis, 134

hierarchical clustering and signal histograms to determine if there were any outlier arrays; none 135

were found. Untransformed RMA signals were used for fold change calculations. Data were 136

analyzed using a 1-way analysis of variance (ANOVA) using log2 transformed signals with 137

phenotype (control, GDM-conservatively managed, GDM-insulin treated) as a factor and all 138

possible contrasts made. Fold changes were calculated using the untransformed RMA 139

signals. Probe sets whose expression level was < 4.0 for all phenotypes were removed. False 140

Discovery Rates were calculated using the Qvalue program in R. 141

142

qRT-PCR. RNA was reverse transcribed using Transcriptor Universal Master cDNA Kit 143

(Roche, Indianapolis, IN). qRT-PCR was performed using Lightcycler 480 SYBR Green I 144

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Master Mix (Roche) and gene specific intron-spanning primers (Supplemental Table 3) as 145

previously described (10). 146

147

Rapid Amplification of cDNA Ends (RACE). cDNA from control ECFCs was amplified by 148

PCR using the 5’/3’ RACE kit, 2nd generation (Roche). Reverse primers complementary to 149

PLAC8 exon 3 (Supplemental Table 3) were used to amplify the PLAC8 5’ end. PCR products 150

were cloned into the pCR4-TOPO vector, and transformed into One Shot TOP10 E. coli using 151

the TOPO TA Cloning Kit (Life Technologies). DNA was isolated using PureYield Plasmid 152

Miniprep System (Promega, Madison, WI), sequenced by the DNA Sequencing Core (Indiana 153

University School of Medicine, Indianapolis, IN) and ProteinCT Biotechnologies (Madison, WI), 154

and analyzed using MacVector software (Cary, NC). 155

156

Bisulfite sequencing. DNA was bisulfite treated using EZ DNA Methylation-Direct Kit (Zymo 157

Research, Irvine, CA) per manufacturer’s instructions. Bisulfite-treated DNA was amplified by 158

PCR using ZymoTaq DNA Polymerase and primers listed in Supplemental Table 3. PCR 159

products were cloned as described above. DNA was isolated from 8-12 clones and analyzed by 160

sequencing. The nonconversion rate of cytosines to uracils was determined to be 0.5% (6/1277 161

cytosines) using individual non-CpG cytosines. This nonconversion rate was representative of 162

21 clones from 10 different bisulfite conversions. 163

164

Western blotting. Cells were lysed in radio-immunoprecipitation assay buffer containing 165

mammalian protease inhibitor cocktail (Sigma). Equal protein amounts were separated by gel 166

electrophoresis on precast gels (Life Technologies), transferred to nitrocellulose, and 167

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immunoblotted with antibodies to PLAC8 (ab122652, Abcam, Cambridge, MA and HPA040465, 168

Sigma), ALX1 (ab181101, Abcam) NOS3 (610296, BD Biosciences, San Jose, CA), or vinculin 169

(VIN11-5, Sigma). Secondary antibodies conjugated to horseradish peroxidase (HRP) were from 170

Biorad (Hercules, CA). Blots were developed with Pierce Supersignal West Pico (ThermoFisher, 171

Hanover Park, IL), exposed to film, scanned and compiled in Photoshop CS5.1 (Adobe, San 172

Jose, CA). 173

174

siRNA Transfection. Low passage GDM-exposed ECFCs were transfected with short-175

interfering RNAs (siRNA) using Lipofectamine RNAiMAX reagent (Life Technologies) 176

following manufacturer’s instructions. Cells were transfected with either a non-targeting smart-177

pool siRNA (siControl; ON-TARGETplus, #D-001810-10-05) or human PLAC8 siRNA 178

(siPLAC8, ON-TARGETplus, #J-020311-10). All siRNAs were purchased from GE Dharmacon 179

(Lafayette, CO). Media was changed after 18-24 hours, and cells were passaged 24 hours later 180

for proliferation, apoptosis, and senescence assays. PLAC8 expression was examined three days 181

after transfection to confirm knockdown by western blotting. 182

183

BrDU and 7-AAD Proliferation Assays. GDM-exposed ECFCs that were previously 184

transfected with siControl or siPLAC8 were incubated with BrDU labeling reagent (Invitrogen) 185

for 1 hour. Cells were then trypsinized, and stained using standard ethanol fixation and acid-186

denaturation protocols for BrDU (anti-BrDU mouse monoclonal conjugated with Alexa Fluor 187

488; Invitrogen) and 7-AAD (Life Technologies). Samples were analyzed using flow cytometry 188

on an LSRII (Becton Dickinson, San Jose, CA) and FlowJo software (TreeStar, Inc., Ashland, 189

OR). At least 10,000 events were collected per sample. 190

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191

Senescence Assays. Transfected, GDM-exposed ECFCs were plated at a density of 10,000 cells 192

per well of a 6-well plate. After 3 days of culture, staining for senescence associate beta-193

galactosidase (SA-β-gal) was performed to assess senescence as described (10). At least 100 194

total cells per well were scored, and the percentage of SA-β-gal positive cells was calculated. 195

Senescence was quantified in five independent transfection experiments using two different 196

GDM samples. 197

198

Apoptosis assay. ECFCs were treated with or without 84 µM etoposide (Cayman Chemical, Ann 199

Arbor, MI) for 24 hours to induce apoptosis. Adherent ECFCs were collected by trypsinization 200

and combined with non-adherent cells. Apoptosis was assayed using FITC-Annexin-201

V/Propidium Iodide Kit following the manufacturer’s instructions (Biolegend, San Diego, CA). 202

Cells were assayed on an LSRII Flow Cytometer and analyzed using FlowJo software. 203

204

Statistical analyses. Data illustrated in graphs are mean +/- standard error of the mean (SEM). 205

Statistical analyses used are described in the Figure Legends. Pearson and Spearman correlation 206

analyses were performed on normal and non-normal distributions, respectively. Prism 6 207

(GraphPad Software, La Jolla, CA) was used for all statistical analyses. Significance was noted 208

when p<0.05. For DNA methylation and RNA expression correlations, p values were corrected 209

for multiple comparisons by the Benjamini-Hochberg method (16). 210

211

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RESULTS 212

PLAC8 is increased in ECFCs from GDM Pregnancies. To identify genes in ECFCs that have 213

altered expression following intrauterine exposure to GDM, we performed a microarray analysis. 214

ECFC samples from both conservatively-managed GDM patients (treated with diet and exercise) 215

and insulin-treated GDM patients were included. Of the 28,000 genetic loci tested, 596 mRNAs 216

were altered between control and GDM ECFCs (p<0.01). More stringent criteria identified genes 217

for further investigation by limiting analysis to genes that exhibited increased or decreased 218

expression by at least 50%, with a p<0.01. Figure 1 illustrates a hierarchical clustering analysis 219

of the 38 genes that fulfilled the criteria, comparing control ECFCs to the two GDM groups 220

independently. Using this analysis strategy, 26 genes were differentially expressed between 221

conservatively-managed GDM and control ECFCs (7 increased and 19 decreased; Table 1), and 222

15 genes were differentially expressed between GDM insulin-treated and control ECFCs (4 223

increased and 11 decreased; Table 1). While there may be subtle differences in microarray data 224

between the conservatively-managed and insulin-treated GDM groups, no differences in ECFC 225

function have been detected between these two groups (data not shown). Given this observation, 226

we focused follow-up studies on gene products that were differentially expressed between 227

combined GDM data and controls. Quantitative RT-PCR (qRT-PCR) was used to validate the 228

microarray results using gene-specific primers for 18 genes (Supplemental Table 4). Of the 229

genes tested by qRT-PCR, 72% (13/18) had significantly altered mRNA expression 230

(Supplemental Table 4). One highly upregulated gene was placenta-specific 8 (PLAC8), and one 231

highly downregulated gene was ALX homeobox 1 (ALX1); both were confirmed by qRT-PCR 232

(Fig. 2A). Endothelial nitric oxide synthase (NOS3) mRNA was also reduced in ECFCs from 233

GDM pregnancies (Fig. 2A). Western blot analyses confirmed increased PLAC8 expression in 234

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GDM-exposed ECFCs with no detectable PLAC8 in control ECFCs (Fig. 2B). ALX1 and NOS3 235

were modestly decreased overall in GDM ECFC samples (Fig. 2B). Interestingly, PLAC8 236

mRNA levels positively correlated with maternal glucose levels during the screening glucose 237

tolerance test (Fig. 2C, r=0.83, p=0.0001). 238

239

PLAC8 expression correlates with CpG methylation. After confirming that PLAC8 was 240

significantly upregulated in GDM-exposed ECFCs, we next evaluated the mechanism by which 241

PLAC8 expression is regulated. We speculated that an epigenetic mechanism was mediating the 242

long-term changes in gene expression of ECFCs from GDM pregnancies. Therefore, our next 243

studies focused on identifying alterations in DNA methylation. Examination of the PLAC8 gene 244

revealed two putative transcriptional start sites, denoted as exon 1A and exon 1B (E1A and E1B 245

in Fig. 3A). To determine PLAC8 isoforms present in ECFCs, 5’-rapid amplification of cDNA 246

ends (RACE) analysis was conducted. Amplification of cDNA using a reverse primer 247

complementary to the sequence in the shared exon 3 of PLAC8 revealed two transcript types; one 248

that contained E1A and another with E1B (data not shown). RT-PCR analysis was conducted to 249

determine whether the levels of the two types of transcripts differed in control and GDM ECFCs. 250

In control cells, there were approximately equal quantities of transcripts containing E1A and 251

E1B (Fig. 3B). However, in GDM ECFCs, E1A transcripts were elevated compared to E1B (Fig. 252

3B), suggesting an increase in promoter activity for E1A. Given our hypothesis that alterations in 253

DNA methylation were responsible for increased PLAC8 expression in ECFCs from GDM 254

pregnancies, the CpG density of a 9-kilobase region beginning 2000 nucleotides upstream of 255

E1A (-2000) and ending in intron 1 (+7000) was evaluated for CpG-rich areas that could serve a 256

regulatory function (Fig. 3A). Based on the CpG densities, a targeted approach was used to 257

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examine the regions surrounding PLAC8 E1A and E1B with higher than average CpG density 258

(>10 sites per 1000 base pairs or 1.0%) (17). Using this strategy, three areas were interrogated 259

(see shaded areas in Fig. 3A). First, a CpG island was identified in a region encompassing E1B 260

(+4457 to +4909). Bisulfite sequencing demonstrated that the CpG island was unmethylated at 261

35 CpG sites in both control and GDM ECFCs (data not shown). As a control, genomic DNA 262

from 293/HEK cells, which have minimal detectable PLAC8 by western blotting, was assessed. 263

In these cells, the PLAC8 CpG island was 90-100% methylated at all 35 CpG sites tested (data 264

not shown). Together these data suggest that methylation of the CpG island is not involved in 265

upregulated PLAC8 expression in ECFCs, though it may be important in other cell types. Next, 266

the methylation status of two CpG dense regions surrounding the E1A and E1B start sites was 267

evaluated by bisulfite sequencing (Fig. 3A, +1357 to +1617 and +5791 to +6072). Control 268

ECFCs had consistently higher CpG methylation frequencies across the region spanning from 269

+1357 to +1617, while GDM ECFCs were hypomethylated (Fig. 3C). Similarly, the CpG-rich 270

region in intron 1 (+5791 to +6072 was hypomethylated in GDM-exposed ECFCs compared to 271

control cells (Fig. 3D). Together these data are consistent with the hypothesis that decreased 272

DNA methylation in putative regulatory regions of PLAC8 facilitate increased expression in 273

ECFCs from GDM pregnancies. To more directly assess this hypothesis, we examined whether 274

DNA methylation frequency at specific CpG sites inversely correlated with PLAC8 mRNA 275

expression. These analyses demonstrated a negative correlation between DNA methylation 276

frequency and PLAC8 mRNA expression at 17 CpG sites (Fig. 3E and Supplemental Table 5). 277

To validate our findings, a second cohort of control and GDM ECFCs was interrogated for 278

PLAC8 mRNA expression and CpG methylation. Consistent with our original cohort, PLAC8 279

mRNA was upregulated in GDM-exposed ECFCs (control 1.0 +/- 0.6; GDM 4.9 +/- 1.2; 280

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p=0.009). To test the correlation between PLAC8 mRNA levels and DNA methylation, we 281

performed bisulfite sequencing of the region from +1357 to +1617. This region contained 12 of 282

the 17 CpG sites whose methylation inversely correlated with PLAC8 mRNA levels in cohort 283

1. The results of this experiment verify a significant negative correlation between mRNA 284

expression and methylation of 11 of 12 CpG sites (Supplementary Table 6). Thus, DNA 285

methylation of a region in intron 1 of PLAC8 negatively correlates with mRNA expression, 286

suggesting a possible regulatory function. 287

288

289

Previous studies identified several methylated CpG sites in the NOS3 promoter that regulate 290

NOS3 expression (18). Thus, after observing differential methylation of PLAC8, we questioned 291

whether altered DNA methylation was responsible for decreased NOS3 expression in ECFCs 292

exposed to GDM in utero. Bisulfite sequencing of -209 to -51 base pairs upstream of the NOS3 293

transcription start site revealed that this region contained very low methylation frequencies in 294

both control and GDM-exposed ECFCs (0-20%). Moreover, there was no correlation between 295

CpG methylation and mRNA expression. In contrast, Jurkat cells, which have undetectable 296

NOS3 mRNA, were 100% methylated at six CpG sites, and 40-80% methylated at the other two 297

sites in this region. These data indicate that while this region is important in regulating NOS3 298

expression between endothelial cells and other cell types, it is not responsible for the change in 299

mRNA levels between control and GDM-exposed ECFCs. 300

301

Depletion of PLAC8 in GDM ECFCs results in decreased proliferation and increased 302

senescence. Previous studies showed that PLAC8 overexpression induces loss of cell cycle 303

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control, increased proliferation, and resistance to apoptosis (19). GDM-exposed ECFCs exhibit 304

increased proliferation and resistance to senescence compared to control ECFCs (10). Therefore, 305

we hypothesized that increased PLAC8 expression in GDM-exposed ECFCs may contribute to 306

the aberrant proliferation and senescence observed in these cells. To test this hypothesis, we 307

depleted PLAC8 from GDM-exposed ECFCs and evaluated the effect on proliferation, 308

apoptosis, and senescence. GDM-exposed ECFCs were transfected with non-targeting control 309

siRNA pool (siControl) or PLAC8-specific siRNA (siPLAC8). Western blot analysis confirmed 310

that the siControl-transfected ECFCs had no change in PLAC8 expression compared to 311

untransfected controls and that the siPLAC8-transfected cells had effective depletion of PLAC8 312

(Fig. 4A). A one-hour BrdU pulse of actively proliferating GDM-exposed ECFCs revealed a 313

significant decrease in S phase ECFCs that were transfected with siPLAC8 compared to 314

siControl (Fig. 4B and 4C). These data suggest that PLAC8 overexpression may contribute to 315

the hyperproliferative phenotype detected in ECFCs from GDM pregnancies. Since PLAC8 316

overexpression inhibits apoptosis (19), we next examined whether apoptosis was affected by 317

reducing PLAC8 expression. These studies detected no differences in baseline or induced 318

apoptosis in GDM-exposed ECFCs transfected with siPLAC8 or siControl (data not shown). 319

However, ECFCs tend to undergo senescence rather than apoptosis in response to stress stimuli 320

(20). Therefore, we speculated that increased PLAC8 expression in GDM-exposed ECFCs may 321

participate in the resistance to senescence observed previously (10). GDM-exposed ECFCs 322

transfected with siPLAC8 exhibited a dramatic increase in basal senescence compared to 323

siControl-transfected cells (Fig. 4D and 4E). Together these data suggest that PLAC8 324

overexpression in GDM-exposed ECFCs contributes to their abnormal phenotype by increasing 325

proliferation and protecting from senescence. 326

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DISCUSSION 327

Developmental origins of cardiovascular disease are well established in humans and animal 328

models (3-9,13,14,21). Elucidation of the mechanisms underlying disease predisposition is the 329

current challenge for scientists and clinicians to develop innovative prevention and treatment 330

strategies. This study provides strong evidence in neonatal endothelial progenitor cells that 331

GDM exposure in utero leads to altered gene expression and that disrupted epigenetic regulation 332

may contribute to aberrant expression of PLAC8. 333

334

Previous studies have identified global changes in DNA methylation of placenta and 335

unfractionated cord blood cells following GDM exposure (22,23). Other studies examined these 336

same tissues from GDM pregnancies for alterations in CpG methylation using a targeted gene 337

approach (23-25) or unbiased screening (26-29). El Hajj et al. examined the methylation status of 338

14 candidate genes and found reduced CpG methylation in GDM samples in regulatory regions 339

of MEST (mesoderm specific transcript) and NR3C1, which encodes a glucocorticoid receptor 340

(23). However, minimal differences were detected between GDM and control samples (4-7% in 341

MEST and 2% in NR3C1), making it difficult to extrapolate biologic significance in the absence 342

of functional data in placental or cord blood cells. A potential reason for small alterations in 343

DNA methylation in GDM samples may be that heterogeneous cell populations from cord blood 344

and placenta were used (23). Specific cell types have unique methylation patterns, in part to 345

determine cell fate (30). Therefore, analyses of heterogeneous cell populations dilute the ability 346

to detect meaningful changes in DNA methylation (23-28). Furthermore, if a disease state 347

changes the proportion of cell types in an input population then observed differences in 348

methylation may be indirect. For example, neonates from GDM pregnancies display increases in 349

nucleated red blood cells in their circulation (31). To circumvent this limitation, Cheng and 350

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colleagues utilized a more homogeneous cell population, human umbilical vein endothelial cells, 351

to determine whether GDM exposure impacts the proteome (29). In these studies, expression 352

changes were identified in several proteins involved in redox signaling, however epigenetic 353

alterations were not detected in the two gene promoters examined (29). While this important 354

study demonstrates a detrimental effect of GDM exposure on neonatal endothelial cells, it does 355

not provide evidence that an epigenetic mechanism is involved. 356

357

The identification of ECFCs as endothelial progenitor cells that circulate in human peripheral 358

blood and reside in the endothelium of vessel walls has expanded insight into vascular repair 359

processes as well as postnatal angiogenesis and vasculogenesis (32). Under a variety of disease 360

states ECFC function is disrupted, which further contributes to the pathogenesis of vascular 361

disease (33-37). The importance of ECFCs in maintenance of vascular health is highlighted by 362

numerous clinical trials assessing the therapeutic potential of infusing ECFCs for cardiovascular 363

diseases (38). Therefore, it is alarming that neonatal ECFCs exposed to GDM or PGDM in utero 364

have significant impairments in function (10,39). This is not unique to diabetes exposure as there 365

is increasing evidence that intrauterine exposure to pre-eclampsia, obesity, growth restriction, 366

and preterm delivery impair neonatal ECFC function and numbers as well (40-44). Therefore, it 367

is paramount to elucidate underlying molecular mechanisms that may be exploited for future 368

therapeutic benefit for these infants. 369

370

Our approach to understand the functional differences between control and GDM-exposed 371

ECFCs was to conduct an unbiased microarray screen followed by validation of selected gene 372

products and final functional assessment of PLAC8 in ECFCs. Control and GDM-exposed 373

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ECFCs exhibited modest differences in gene expression by microarray, which were subsequently 374

verified by independent methods. These data are intriguing and suggest the possibility of 375

defining a “molecular signature” that correlates with ECFC dysfunction; an approach that has 376

been successful in driving the discovery of the molecular underpinnings of acute leukemias and 377

exploited for prognostication and treatment decisions (45). Our finding that maternal 378

hyperglycemia at GDM diagnosis directly correlates with PLAC8 expression in neonatal ECFCs 379

provides rationale to pursue this ultimate goal. With this objective in mind, an important 380

limitation of the current study is the likelihood of underestimating the number of gene products 381

aberrantly expressed in GDM ECFCs due to a relatively low sample size. In addition, our study 382

populations in two independent cohorts had a high mean body mass index in both control and 383

GDM groups, which does not allow for the evaluation of a potential effect of maternal obesity on 384

the gene expression profile of ECFCs. Therefore, it will be important to expand upon this dataset 385

using an increased number of samples from healthy-normal weight, healthy-obese, and GDM 386

pregnancies to develop a robust molecular phenotype in ECFCs. However, the aberrantly 387

expressed genes that were validated in ECFCs from GDM pregnancies lend themselves to 388

exploration of the mechanisms responsible for altered expression and functional significance, 389

similar to studies conducted for PLAC8. 390

391

A novel discovery from our work was that PLAC8 expression is highly dysregulated in GDM 392

ECFCs, which was initially surprising since PLAC8 has not been reported to be expressed in 393

endothelial cells. PLAC8, which is also known as onzin, was originally identified as a placental-394

enriched protein (46). Subsequent studies showed that PLAC8 is expressed in epithelial cells, 395

adipocytes, and hematopoietic cells (19,47-50). Although the precise endogenous biochemical 396

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function of PLAC8 is unclear, data suggest a role in regulating adipocyte differentiation, innate 397

immune response, cell proliferation, and survival (19,49-51). Furthermore, recent studies 398

demonstrate an important role of PLAC8 in promoting tumorigenesis through mechanisms 399

involving proliferation, survival, autophagy, and epithelial-to-mesenchymal transition 400

(19,47,48,52). However, no studies report expression or function of PLAC8 in endothelial cells, 401

which may be because basal PLAC8 expression is negligible. Our data suggest that upregulated 402

PLAC8 expression in GDM-exposed ECFCs contributes to the hyperproliferative phenotype 403

previously reported (10), which is consistent with studies in other cell types (19). In addition, 404

our data support PLAC8 overexpression as a protective mechanism for GDM-exposed ECFCs to 405

avoid senescence. Given that hyperglycemia enhances ECFC senescence and impairs 406

vasculogenesis, these findings suggest an adaptive response of fetal ECFCs to circumvent the 407

untoward effects of a diabetic milieu. 408

409

To evaluate whether an epigenetic mechanism may be involved in the overexpression of PLAC8, 410

the methylation status of the PLAC8 gene was interrogated. In GenBank, three transcript 411

variants of PLAC8 are reported (variant 1: NM_001130716, v2: NM_016619, and v3: 412

NM_001130715), though no information regarding transcriptional regulation of PLAC8 is 413

available. The three PLAC8 isoforms differ only in the untranslated regions (UTR), thus the gene 414

products have identical amino acid sequences. Isoform 3 has a unique 3’-UTR and was 415

minimally expressed in ECFCs (~0.4 +/- 0.3% of total PLAC8 mRNA). Isoform 2, which 416

contains exon 1A, is highly upregulated in GDM ECFCs while isoform 1, which contains exon 417

1B, is not changed in GDM ECFCs. Interestingly, differential methylation was detected in 418

GDM-exposed ECFCs in the first intron of isoform 2. Moreover, the methylation status of 419

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several individual CpG sites in these regions negatively correlated with PLAC8 mRNA 420

expression suggesting a mechanistic link, possibly via altered transcription factor binding. 421

Interrogation of ChIP-seq data from the ENCODE project suggests that the 5-6 kB region 422

surrounding exons 1A and 1B of PLAC8 may have a role in regulating PLAC8 transcription 423

since an enrichment of transcription factor binding was observed in this region (53). Using these 424

publically accessible data, we found that several transcription factors bind near the PLAC8 425

transcription start sites including RUNX3, GATA3, EP300, TBP, RelA, MAX, PAX5, and 426

IKZF1. Future studies will investigate whether the altered methylation observed in GDM-427

exposed ECFCs directly impacts PLAC8 transcriptional regulation. Our data suggest that 428

intrauterine exposure to GDM may have induced epigenetic alterations in neonatal ECFCs that 429

modified PLAC8 expression and ultimately ECFC function. Collectively, these findings provide 430

the foundation for developing a molecular signature or a targeted biomarker to assess the impact 431

of intrauterine GDM exposure on ECFCs, so that novel interventions may be tested to prevent 432

future endothelial dysfunction in offspring of mothers with GDM. 433

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ACKNOWLEDGEMENTS 434

No potential conflicts of interest relevant to this article were reported. 435

E.K.B., B.M.S., Z.V.N., C.R.G., K.M.V., F.A.B., C.M.H., and J.N.M. conducted the experiments 436

and analyzed the data. E.K.B, B.M.S., and L.S.H. designed the studies and wrote the manuscript. 437

L.S.H. is the guarantor of this work and, as such, had access to all the data in the study and takes 438

responsibility for the integrity of the data and the accuracy of the data analysis. Funding for this 439

study came from the National Institutes of Health (Bethesda, MD, USA) R01 HL094725, U10 440

HD063094, and P30 DK090948 (L.S.H) and The Riley Children’s Foundation (Indianapolis, IN, 441

USA to L.S.H.). The microarray experiments were carried out using the facilities of the Center 442

for Medical Genomics at Indiana University School of Medicine (Indianapolis, IN, USA), which 443

was initially funded in part by a grant from the Indiana 21st Century Research and Technology 444

Fund and by the Indiana Genomics Initiative (INGEN). INGEN is supported in part by the Lilly 445

Endowment (Indianapolis, IN USA). The authors thank Dr. Jamie Case, Julie Mund, Matt 446

Repass, and Emily Sims of the Indiana University Simon Cancer Center Angio BioCore, Dr. 447

Paul Herring, Sarah Rust, and Cavya Chandra for excellent technical assistance (Indiana 448

University School of Medicine, Indianapolis, IN). We also thank Dr. Debbie Thurmond (Indiana 449

University School of Medicine, Indianapolis, IN) and Dr. David Skalnik (Purdue University 450

School of Science, Indianapolis, IN) for review and discussion of the manuscript, and Elizabeth 451

Rybak (Indiana University School of Medicine, Indianapolis, IN) for administrative support. 452

453

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Table 1: Genes with Altered Expression based on RNA

Microarray GDM Conservatively-Managed Compared to Control ECFCs

Gene symbol Fold

change

P value FDR Gene Name

PLAC8 +5.90 0.002 0.33 placenta specific 8

FST +4.68 0.004 0.33 follistatin

SNORA14A +3.05 0.003 0.33 small nucleolar RNA, H/ACA box 14A

ITM2A +2.71 0.008 0.34 integral membrane protein 2A

FAXC +2.37 0.010 0.34 failed axon connections homolog (Drosophila)

TMTC2 +1.73 0.009 0.34 transmembrane and tetratricopeptide repeat containing 2

RNY4P13 +1.59 0.007 0.34 RNA, Ro-associated Y4 pseudogene 13

PREX1 -1.50 0.010 0.34 phosphatidylinositol-3,4,5-trisphosphate-dependent Rac

exchange factor 1

SLC46A1 -1.54 0.001 0.33 solute carrier family 46 (folate transporter), member 1

NBEAL2 -1.57 0.003 0.33 neurobeachin-like 2

GCNT1 -1.61 0.005 0.33 glucosaminyl (N-acetyl) transferase 1, core 2

ABI3 -1.65 0.004 0.33 ABI family, member 3

GABRE -1.68 0.008 0.34 gamma-aminobutyric acid (GABA) A receptor, epsilon

HOXA-AS2 -1.69 0.010 0.34 HOXA cluster antisense RNA 2

PDE2A -1.71 0.009 0.34 phosphodiesterase 2A, cGMP-stimulated

IL18R1 -1.74 0.009 0.34 interleukin 18 receptor 1

B4GALT6 -1.81 0.003 0.33 beta-1,4-galactosyltransferase 6

MTSS1 -1.83 0.001 0.26 metastasis suppressor 1

TFEC -1.86 0.006 0.33 transcription factor EC

SLC7A8 -2.04 0.008 0.34 solute carrier family 7 (amino acid transporter light chain,

L system), member 8

GCKR -2.28 0.004 0.33 glucokinase (hexokinase 4) regulator

NOS3 -2.59 0.008 0.34 nitric oxide synthase 3 (endothelial cell)

DDR2* -2.66 0.002 0.33 discoidin domain-containing receptor 2

CLMP -2.66 0.001 0.32 CXADR-like membrane protein

KRT19 -2.98 0.001 0.33 keratin 19

ELMOD1 -3.23 0.002 0.34 ELMO/CED-12 domain containing 1

GDM Insulin-Treated Compared to Control ECFCs Gene symbol Fold

change

P value

FDR

Gene Name

PLAC8 +7.05 <0.001 0.84 placenta specific 8

SLCO2A1 +2.85 0.001 0.86 solute carrier organic anion transporter family, member

2A1

MPEG1 +1.86 0.004 0.87 macrophage expressed 1

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HAS2 +1.84 0.006 0.87 hyaluronan synthase 2

AP1S2 -1.56 0.001 0.84 adaptor-related protein complex 1, sigma 2 subunit

ARHGEF3 -1.62 0.006 0.87 Rho guanine nucleotide exchange factor (GEF) 3

KLHL6 -1.64 0.005 0.87 kelch-like family member 6

DPYSL4 -1.88 0.004 0.87 dihydropyrimidinase-like 4

ETS2 -2.05 0.009 0.87 v-ets avian erythroblastosis virus E26 oncogene homolog 2

DDR2* -2.46 0.002 0.87 discoidin domain-containing receptor 2

TFEC -2.69 <0.001 0.84 transcription factor EC

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FIGURE LEGENDS 614

Figure 1: Intrauterine exposure to GDM induces altered mRNA expression in neonatal 615

ECFCs. Thirty-eight genes in GDM-exposed ECFCs exhibited either increased or decreased 616

expression by at least 50% compared to controls (p<0.01). A hierarchical clustering analysis of 617

the 38 genes is illustrated. 618

619

Figure 2: PLAC8 is increased in GDM ECFCs, while NOS3 and ALX1 are decreased. A. 620

qRT-PCR was performed to validate the results of the microarray analysis. Results were 621

normalized to hypoxanthine phosphoribosyltransferase (HPRT), and to the mean control 622

expression for each gene (n=6 control and 12 GDM, *p<0.05 by unpaired t test with Welch’s 623

correction). B. Western blot analysis showed that PLAC8 was increased in most GDM ECFCs 624

compared with controls. ALX1 and NOS3 were decreased in several GDM samples compared to 625

controls. Vinculin is the loading control. C. Maternal plasma glucose levels in the glucose 626

tolerance screen correlate with PLAC8 mRNA levels in neonatal ECFCs. (r=0.83 and p=0.0001 627

by Pearson correlation). 628

629

Figure 3: Several CpG sites in the PLAC8 promoter and 1st intron are differentially 630

hypomethylated in GDM-exposed ECFCs. A. The schematic shows the promoter and intron 1 631

of PLAC8, where there are two transcriptional start sites, exon 1A (E1A) and exon 1B (E1B). 632

Below the schematic is a graph illustrating the CpG frequency over each 1000 bp region. The 633

first start site E1A is denoted as “0” on the graph. PCR primers for bisulfite sequencing were 634

generated to amplify CpG-rich regions at +1357 to +1617, +4457 to +4909, and +5791 to +6072, 635

as shown by hash marks on the schematic. B: qRTPCR identified PLAC8 mRNA variants 636

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present in control and GDM-exposed ECFCs. Two primer sets differentiating E1A or E1B were 637

used to quantitate PLAC8 isoforms. Data were normalized to HPRT. n=4 control, n=7 GDM 638

ECFC samples, *p<0.001 by two-way ANOVA, followed by Sidak’s multiple comparisons. 639

C,D. Bisulfite sequencing was performed on regions amplified by the primer sets shown in panel 640

A and in Supplemental Table 1. Panels C (+1357 to +1617 region) and D (+5791 to +6072 641

region) illustrates bisulfite sequencing data from representative control and GDM-exposed ECFC 642

samples. Filled circles represent methylated CpGs, and open circles signify unmethylated CpGs. 643

Individual rows denote data from a single clone. The CpG site numbers are listed along the 644

bottom. E. A correlation between CpG methylation frequency and PLAC8 mRNA expression is 645

shown for 18 ECFC samples (n=6 control and n=12 GDM) by Pearson analysis. CpG 646

methylation at site +1557 was measured in 8-12 clones for each ECFC sample and is expressed 647

as percent methylation. RNA expression was measured by qRT-PCR on parallel samples. 648

649

Figure 4: Depletion of PLAC8 reduces proliferation and increases senescence. PLAC8-650

specific siRNA was used to deplete PLAC8 from GDM-exposed ECFCs, and functional assays 651

were performed. Transfection of a nontargeting control siRNA pool (siControl) was used as the 652

control. A: Western blotting confirms efficient PLAC8 protein knockdown. The western blot 653

shows that the control siRNA did not affect protein levels. Vinculin is the loading control. B,C: 654

Cell cycle analysis was conducted using flow cytometry. B: Dot plots from a representative 655

experiment are shown. C: Quantitation of cells in S phase shows increased proliferation with 656

PLAC8 depletion, (n=12 using 5 different GDM-exposed ECFC samples, *p<0.05 by paired t 657

test). D: Representative image of siControl and siPLAC8 transfected GDM ECFCs stained for 658

SA β-gal (blue cells). E: Quantitation of senescent cells demonstrates increased senescence with 659

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PLAC8 depletion (n=5 experiments using 2 different GDM-exposed ECFC samples, *p<0.05 by 660

paired t test). 661

662

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Figure 1

200x115mm (300 x 300 DPI)

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203x278mm (300 x 300 DPI)

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180x225mm (97 x 97 DPI)

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Figure 4

180x202mm (300 x 300 DPI)

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Supplemental Table 1: Maternal Data

COHORT 1 COHORT 2

Control

(n=7)

GDM

(n=13)

Control

(n=5)

GDM

(n=12)

GDM Treatment Groups

Conservatively-managed - 69% - 75%

Insulin-treated - 31% - 8%

Glyburide-treated 0% - 17%

Age (years) 31 +/- 6 31 +/- 6 27 +/- 6 30 +/- 6

Pre-pregnancy BMI 31.6 +/- 7.3 30.1 +/- 5.2 28.1 +/- 7.2 32.4 +/- 7.1

HgA1c ND 6.0 +/- 0.7 %

(42 +/- 7 mmol/mol)

ND 5.7 +/- 0.4%

(40 +/- 4 mmol/mol)

OGTT: 50g/1h (mg/dl) 116 +/- 24 197 +/- 58 * 124 +/- 19 172 +/- 31*

BMI = body mass index

HgA1c=glycosylated hemoglobin

ND=not done

Data are mean +/- standard deviation

* p<0.0001 by Mann-Whitney test

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Supplemental Table 2: Infant Data

COHORT 1 COHORT 2

Control

(n=7)

GDM

(n=13)

Control

(n=5)

GDM

(n=12)

Male Gender (%) 43 31 40 50

Gestational Age (wk) 39 +/- 1 39 +/- 2 39 +/- 1 38 +/- 1

Birth weight (kg) 3.5 +/- 0.5 3.6 +/- 0.5 3.4 +/- 0.3 3.5 +/- 0.4

Birth length (cm) 51.5 +/- 1.9 51.5 +/- 2.1 51.7 +/- 0.9 50.4 +/- 1.6 *

Ponderal index 2.6 +/- 0.3 2.7 +/- 0.3 2.6 +/- 0.2 2.7 +/- 0.2 *

Small-for-gestational age, n (%) 0 (0) 2 (15) 1 (20) 1 (8)

Large for gestational age, n (%) 1 (14) 3 (23) 0 (0) 1 (8)

Table shows mean +/- standard deviation as indicated.

* Infant length data was missing for 1 control infant and 2 infants exposed to GDM.

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Supplemental Table 3: PCR Primers

Forward (5’-3’) Reverse (5’-3’)

qRT-PCR

primers

ALX1 GAGAGGACCTCGCCCTGT GGCTGTCCTTCTACTTTAGTGATCC

B4GALT6 TTCTCCCTCTCTTCGTCCTG CCTCGAGCTTGTACCATAAAGAG

CLMP AGCCCTGCTGATTTTCCTCT GGAGCTTCAGCATCTTCTCG

DDR2 CTCCGAGCAGATGCCAAC TCCTTGAGCCGAGACATGA

EDA2R AAGCAGACCCCCACCTCT TCACCAGTGCAACAAGTGTG

ELMOD1 ACCCCGACGCTATTGAAAA GAAGGCAAGCCTGAAGAGAG

ETS2 CAGCGTCACCTACTGCTCTG AGTCGTGGTCTTTGGGAGTC

FST TGCCACCTGAGAAAGGCTAC TGGATATCTTCACAGGACTTTGC

HPRT CCTTGGTCAGGCAGTATAATCCA GGTCCTTTTCACCAGCAAGCT

IL18R1 TCTTGGACCAAAGCTTAACCA AAGCAGAGCAGTTGAGCCTTA

KRT19 GCCACTACTACACGACCATCC CAAACTTGGTTCGGAAGTCAT

MTSS1 CTTCTTGGACGCCTTTCAGA CATGCACATCCTGGTGAGAG

NOS3 GACCCTCACCGCTACAACAT CCGGGTATCCAGGTCCAT

PLAC8 (all isoforms) CGTCGCAATGAGGACTCTCT CTCTTGATTTGGCAAAGAGTACAA

PLAC8 E1A GGGGTGAGGGTTGATCGAAG GCTGCAACTTGACACCCAAG

PLAC8 E1B ATTCTCTCCCAGGCCACAA ATTTTCAGTGCAGGGCCTTA

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SLC7A8 CCACCTTCCCAGTGTGTTG AGCATCAGCAGGGTGGAG

SLCO2A1 GGGGTGCAGTTCTTGTTGAT CAATGGTGAGGCCATAGAGG

TFEC CGGTATGGAATCAAGTTTTAAAGAG TCACCGCTATACACATCCAAA

TMTC2 GCGGATTTCTGCTATGATGAC AATGTGCGTCCATGGAGTTT

USP32P1 AGAAACTCCCCTTGAACGTG GGGCCTGTCGTTTTCCTT

RACE primers

PLAC8 reverse

transcription primer

N/A CTCTTGATTTGGCAAAGAGTACAA

PLAC8 1° PCR primers Roche Oligo dT Anchor Primer CGGGTCCTGTAGAGAGTCCT

PLAC8 2° PCR primers Roche PCR Anchor Primer GCTGCAACTTGACACCCAAG

Bisulfite

sequencing

primers

PLAC8 +1357 to +1617 TGTTTAGAGATTGTGGTTTAGATAGAATGAG CRTAAATTACAAAATATACTCTACAAAACCACTAAC

PLAC8 +4457 to +4909 TTTTTTGTTGGGATTAGATTTGTTT ATACCCCAAAACTAAAATCCAAAAT

PLAC8 +5791 to +6456 TYGTGGGGTAAGGGAGTTGAAGAGTGTTG CTTCTTAATAAACTACCCCTACCACAC

NOS3 -209 to -51 TGTTTTAGTTTTTATGTTGTAGTTTTAG ACTACCTACTCCAACAAAACCCTAACC

Y=T/C

R=A/G

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Supplemental Table 4: qRT-PCR Confirmation of Microarray Results

Gene name

Control

GDM

Fold change

P value

SLCO2A1 1.00 +/- 0.78 5.95 +/- 4.59* +5.95 0.048

FST 1.00 +/- 0.72 3.39 +/- 1.58* +3.39 0.023

PLAC8 1.00 +/- 0.52 3.36 +/- 2.14* +3.25 0.024

TMTC2 1.00 +/- 0.35 1.95 +/- 0.47* +1.95 0.007

ETS2 1.00 +/- 0.33 0.87 +/- 0.29 -1.15 0.480

IL18R1 1.00 +/- 0.25 0.68 +/- 0.12* -1.47 0.028

B4GALT6 1.00 +/- 0.22 0.70 +/- 0.19* -1.49 0.028

NOS3 1.00 +/- 0.31 0.58 +/- 0.32* -1.72 0.018

MTSS1 1.00 +/- 0.30 0.57 +/- 0.14* -1.75 0.009

TFEC 1.00 +/- 0.28 0.50 +/- 0.20* -2.00 0.005

KRT19 1.00 +/- 0.32 0.44 +/- 0.25* -2.27 0.010

CLMP 1.00 +/- 0.25 0.42 +/- 0.33* -2.38 0.011

DDR2 1.00 +/- 0.33 0.38 +/- 0.21* -2.63 0.004

USP32P1 1.00 +/- 0.92 0.34 +/- 0.49 -2.94 0.150

ALX1 1.00 +/- 0.66 0.32 +/- 0.38* -3.14 0.013

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EDA2R 1.00 +/- 0.75 0.26 +/- 0.18 -3.85 0.141

SLC7A8 1.00 +/- 0.70 0.26 +/- 0.16 -3.85 0.120

ELMOD1 1.00 +/- 0.93 0.19 +/- 0.18 -5.26 0.150

Data was normalized to the average control ECFC RQ value for RNA expression. Data are mean +/- standard deviation.

*p < 0.05.

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Supplemental Table 5: Correlation of CpG methylation and RNA expression in Cohort 1

PCR primer set CpG site Pearson R p value

PLAC8

+1357 to +1617

+1357 -0.24 0.955

+1402 -0.64 0.011 *

+1441 -0.69 0.008 *

+1449 -0.58 0.019 *

+1474 -0.54 0.029 *

+1494 -0.52 0.032 *

+1502 -0.52 0.032 *

+1511 -0.58 0.019 *

+1523 -0.75 0.004 *

+1537 -0.54 0.029 *

+1557 -0.70 0.008 *

+1609 -0.66 0.011 *

+1617 -0.63 0.012 *

+5791 -0.61 0.014 *

+5802 -0.54 0.029 *

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PLAC8

+5791 to +6072

+5885 -0.77 0.004*

+5999 -0.65 0.011 *

+6020 -0.39 0.121

+6029 -0.08 0.801

+6072 -0.65 0.011 *

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Supplemental Table 6: Correlation of CpG methylation and RNA expression of Cohort 2

PCR primer set CpG site Pearson R p value

PLAC8

+1357 to +1617

+1357 0.1905 0.4797

+1402 -0.71 0.0065 *

+1441 -0.58 0.0250 *

+1449 -0.66 0.0133 *

+1474 -0.28 0.3070

+1494 -0.56 0.0266 *

+1502 -0.56 0.0267 *

+1511 -0.62 0.0192 *

+1523 -0.73 0.0052 *

+1537 -0.55 0.0274 *

+1557 -0.60 0.0201 *

+1609 -0.61 0.0193 *

+1617 -0.75 0.0052 *

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epigenetic regulation of plac8 contributes to altered

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