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
Page 1 of 42 Diabetes
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
Page 2 of 42Diabetes
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
Page 3 of 42 Diabetes
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
Page 4 of 42Diabetes
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
Page 5 of 42 Diabetes
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
Page 6 of 42Diabetes
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
Page 7 of 42 Diabetes
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
Page 8 of 42Diabetes
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
Page 9 of 42 Diabetes
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
Page 10 of 42Diabetes
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
Page 11 of 42 Diabetes
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
Page 12 of 42Diabetes
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
Page 13 of 42 Diabetes
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
Page 14 of 42Diabetes
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
Page 15 of 42 Diabetes
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
Page 16 of 42Diabetes
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
Page 17 of 42 Diabetes
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
Page 18 of 42Diabetes
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
Page 19 of 42 Diabetes
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
Page 20 of 42Diabetes
REFERENCES
1. Calkins K, Devaskar SU: Fetal origins of adult disease. Curr Probl Pediatr Adolesc Health 454
Care 2011;41:158-176 455
2. Barker DJ, Winter PD, Osmond C, Margetts B, Simmonds SJ: Weight in infancy and death 456
from ischaemic heart disease. Lancet 1989;2:577-580 457
3. Cho NH, Silverman BL, Rizzo TA, Metzger BE: Correlations between the intrauterine 458
metabolic environment and blood pressure in adolescent offspring of diabetic mothers. J Pediatr 459
2000;136:587-592 460
4. Boney CM, Verma A, Tucker R, Vohr BR: Metabolic syndrome in childhood: association 461
with birth weight, maternal obesity, and gestational diabetes mellitus. Pediatrics 2005;115:e290-462
296 463
5. Bunt JC, Tataranni PA, Salbe AD: Intrauterine exposure to diabetes is a determinant of 464
hemoglobin A(1)c and systolic blood pressure in pima Indian children. J Clin Endocrinol Metab 465
2005;90:3225-3229 466
6. Lawlor DA, Lichtenstein P, Langstrom N: Association of maternal diabetes mellitus in 467
pregnancy with offspring adiposity into early adulthood: sibling study in a prospective cohort of 468
280,866 men from 248,293 families. Circulation 2011;123:258-265 469
7. Crume TL, Ogden L, Daniels S, Hamman RF, Norris JM, Dabelea D: The impact of in utero 470
exposure to diabetes on childhood body mass index growth trajectories: the EPOCH study. J 471
Pediatr 2011;158:941-946 472
8. Li M, Sloboda DM, Vickers MH: Maternal obesity and developmental programming of 473
metabolic disorders in offspring: evidence from animal models. Exp Diabetes Res 474
2011;2011:592408 475
9. Jawerbaum A, White V: Animal models in diabetes and pregnancy. Endocr Rev 2010;31:680-476
701 477
10. Blue EK, DiGiuseppe R, Derr-Yellin E, Acosta JC, Pay SL, Hanenberg H, Schellinger MM, 478
Quinney SK, Mund JA, Case J, Haneline LS: Gestational diabetes induces alterations in the 479
function of neonatal endothelial colony-forming cells. Pediatr Res 2014;75:266-272 480
11. Osborne-Majnik A, Fu Q, Lane RH: Epigenetic mechanisms in fetal origins of health and 481
disease. Clin Obstet Gynecol 2013;56:622-632 482
12. Pinney SE, Simmons RA: Metabolic programming, epigenetics, and gestational diabetes 483
mellitus. Curr Diab Rep 2012;12:67-74 484
13. Park JH, Stoffers DA, Nicholls RD, Simmons RA: Development of type 2 diabetes following 485
intrauterine growth retardation in rats is associated with progressive epigenetic silencing of 486
Pdx1. J Clin Invest 2008;118:2316-2324 487
14. Zinkhan EK, Fu Q, Wang Y, Yu X, Callaway CW, Segar JL, Scholz TD, McKnight RA, 488
Joss-Moore L, Lane RH: Maternal Hyperglycemia Disrupts Histone 3 Lysine 36 Trimethylation 489
of the IGF-1 Gene. J Nutr Metab 2012;2012:930364 490
15. ACOG Practice Bulletin. Clinical management guidelines for obstetrician-gynecologists. 491
Number 30, September 2001 (replaces Technical Bulletin Number 200, December 1994). 492
Gestational diabetes. Obstet Gynecol 2001;98:525-538 493
16. Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: A Practical and Powerful 494
Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological) 495
1995;57:289-300 496
17. Jabbari K, Caccio S, Pais de Barros JP, Desgres J, Bernardi G: Evolutionary changes in CpG 497
and methylation levels in the genome of vertebrates. Gene 1997;205:109-118 498
Page 21 of 42 Diabetes
18. Chan Y, Fish JE, D'Abreo C, Lin S, Robb GB, Teichert AM, Karantzoulis-Fegaras F, 499
Keightley A, Steer BM, Marsden PA: The cell-specific expression of endothelial nitric-oxide 500
synthase: a role for DNA methylation. J Biol Chem 2004;279:35087-35100 501
19. Rogulski K, Li Y, Rothermund K, Pu L, Watkins S, Yi F, Prochownik EV: Onzin, a c-Myc-502
repressed target, promotes survival and transformation by modulating the Akt-Mdm2-p53 503
pathway. Oncogene 2005;24:7524-7541 504
20. Zhang Y, Herbert BS, Rajashekhar G, Ingram DA, Yoder MC, Clauss M, Rehman J: 505
Premature senescence of highly proliferative endothelial progenitor cells is induced by tumor 506
necrosis factor-alpha via the p38 mitogen-activated protein kinase pathway. FASEB J 507
2009;23:1358-1365 508
21. McDonnold M, Tamayo E, Kechichian T, Gamble P, Longo M, Hankins GD, Saade GR, 509
Costantine MM: The effect of prenatal pravastatin treatment on altered fetal programming of 510
postnatal growth and metabolic function in a preeclampsia-like murine model. Am J Obstet 511
Gynecol 2014;210:542 e541-547 512
22. Nomura Y, Lambertini L, Rialdi A, Lee M, Mystal EY, Grabie M, Manaster I, Huynh N, 513
Finik J, Davey M, Davey K, Ly J, Stone J, Loudon H, Eglinton G, Hurd Y, Newcorn JH, Chen J: 514
Global methylation in the placenta and umbilical cord blood from pregnancies with maternal 515
gestational diabetes, preeclampsia, and obesity. Reprod Sci 2014;21:131-137 516
23. El Hajj N, Pliushch G, Schneider E, Dittrich M, Muller T, Korenkov M, Aretz M, Zechner U, 517
Lehnen H, Haaf T: Metabolic programming of MEST DNA methylation by intrauterine exposure 518
to gestational diabetes mellitus. Diabetes 2013;62:1320-1328 519
24. Bouchard L, Hivert MF, Guay SP, St-Pierre J, Perron P, Brisson D: Placental adiponectin 520
gene DNA methylation levels are associated with mothers' blood glucose concentration. Diabetes 521
2012;61:1272-1280 522
25. Houde AA, St-Pierre J, Hivert MF, Baillargeon JP, Perron P, Gaudet D, Brisson D, Bouchard 523
L: Placental lipoprotein lipase DNA methylation levels are associated with gestational diabetes 524
mellitus and maternal and cord blood lipid profiles. J Dev Orig Health Dis 2014;5:132-141 525
26. West NA, Kechris K, Dabelea D: Exposure to Maternal Diabetes in Utero and DNA 526
Methylation Patterns in the Offspring. Immunometabolism 2013;1:1-9 527
27. Quilter CR, Cooper WN, Cliffe KM, Skinner BM, Prentice PM, Nelson L, Bauer J, Ong KK, 528
Constancia M, Lowe WL, Affara NA, Dunger DB: Impact on offspring methylation patterns of 529
maternal gestational diabetes mellitus and intrauterine growth restraint suggest common genes 530
and pathways linked to subsequent type 2 diabetes risk. FASEB J 2014; 531
28. Ruchat SM, Houde AA, Voisin G, St-Pierre J, Perron P, Baillargeon JP, Gaudet D, Hivert 532
MF, Brisson D, Bouchard L: Gestational diabetes mellitus epigenetically affects genes 533
predominantly involved in metabolic diseases. Epigenetics 2013;8:935-943 534
29. Cheng X, Chapple SJ, Patel B, Puszyk W, Sugden D, Yin X, Mayr M, Siow RC, Mann GE: 535
Gestational diabetes mellitus impairs Nrf2-mediated adaptive antioxidant defenses and redox 536
signaling in fetal endothelial cells in utero. Diabetes 2013;62:4088-4097 537
30. Zilbauer M, Rayner TF, Clark C, Coffey AJ, Joyce CJ, Palta P, Palotie A, Lyons PA, Smith 538
KG: Genome-wide methylation analyses of primary human leukocyte subsets identifies 539
functionally important cell-type-specific hypomethylated regions. Blood 2013;122:e52-60 540
31. Yeruchimovich M, Mimouni FB, Green DW, Dollberg S: Nucleated red blood cells in 541
healthy infants of women with gestational diabetes. Obstet Gynecol 2000;95:84-86 542
Page 22 of 42Diabetes
32. Ingram DA, Mead LE, Moore DB, Woodard W, Fenoglio A, Yoder MC: Vessel wall-derived 543
endothelial cells rapidly proliferate because they contain a complete hierarchy of endothelial 544
progenitor cells. Blood 2005;105:2783-2786 545
33. Guven H, Shepherd RM, Bach RG, Capoccia BJ, Link DC: The number of endothelial 546
progenitor cell colonies in the blood is increased in patients with angiographically significant 547
coronary artery disease. J Am Coll Cardiol 2006;48:1579-1587 548
34. Tan K, Lessieur E, Cutler A, Nerone P, Vasanji A, Asosingh K, Erzurum S, Anand-Apte B: 549
Impaired function of circulating CD34(+) CD45(-) cells in patients with proliferative diabetic 550
retinopathy. Exp Eye Res 2010;91:229-237 551
35. Meneveau N, Deschaseaux F, Seronde MF, Chopard R, Schiele F, Jehl J, Tiberghien P, 552
Bassand JP, Kantelip JP, Davani S: Presence of endothelial colony-forming cells is associated 553
with reduced microvascular obstruction limiting infarct size and left ventricular remodelling in 554
patients with acute myocardial infarction. Basic Res Cardiol 2011;106:1397-1410 555
36. DiMeglio LA, Tosh A, Saha C, Estes M, Mund J, Mead LE, Lien I, Ingram DA, Haneline 556
LS: Endothelial abnormalities in adolescents with type 1 diabetes: a biomarker for vascular 557
sequelae? J Pediatr 2010;157:540-546 558
37. Duong HT, Comhair SA, Aldred MA, Mavrakis L, Savasky BM, Erzurum SC, Asosingh K: 559
Pulmonary artery endothelium resident endothelial colony-forming cells in pulmonary arterial 560
hypertension. Pulm Circ 2011;1:475-486 561
38. Sen S, McDonald SP, Coates PT, Bonder CS: Endothelial progenitor cells: novel biomarker 562
and promising cell therapy for cardiovascular disease. Clin Sci (Lond) 2011;120:263-283 563
39. Ingram DA, Lien IZ, Mead LE, Estes M, Prater DN, Derr-Yellin E, DiMeglio LA, Haneline 564
LS: In vitro hyperglycemia or a diabetic intrauterine environment reduces neonatal endothelial 565
colony-forming cell numbers and function. Diabetes 2008;57:724-731 566
40. Sipos PI, Bourque SL, Hubel CA, Baker PN, Sibley CP, Davidge ST, Crocker IP: 567
Endothelial colony-forming cells derived from pregnancies complicated by intrauterine growth 568
restriction are fewer and have reduced vasculogenic capacity. J Clin Endocrinol Metab 569
2013;98:4953-4960 570
41. Munoz-Hernandez R, Miranda ML, Stiefel P, Lin RZ, Praena-Fernandez JM, Dominguez-571
Simeon MJ, Villar J, Moreno-Luna R, Melero-Martin JM: Decreased level of cord blood 572
circulating endothelial colony-forming cells in preeclampsia. Hypertension 2014;64:165-171 573
42. Moreno-Luna R, Munoz-Hernandez R, Lin RZ, Miranda ML, Vallejo-Vaz AJ, Stiefel P, 574
Praena-Fernandez JM, Bernal-Bermejo J, Jimenez-Jimenez LM, Villar J, Melero-Martin JM: 575
Maternal body-mass index and cord blood circulating endothelial colony-forming cells. J Pediatr 576
2014;164:566-571 577
43. Ligi I, Simoncini S, Tellier E, Vassallo PF, Sabatier F, Guillet B, Lamy E, Sarlon G, 578
Quemener C, Bikfalvi A, Marcelli M, Pascal A, Dizier B, Simeoni U, Dignat-George F, Anfosso 579
F: A switch toward angiostatic gene expression impairs the angiogenic properties of endothelial 580
progenitor cells in low birth weight preterm infants. Blood 2011;118:1699-1709 581
44. von Versen-Hoynck F, Brodowski L, Dechend R, Myerski AC, Hubel CA: Vitamin D 582
antagonizes negative effects of preeclampsia on fetal endothelial colony forming cell number and 583
function. PLoS One 2014;9:e98990 584
45. Shivarov V, Bullinger L: Expression profiling of leukemia patients: key lessons and future 585
directions. Exp Hematol 2014;42:651-660 586
Page 23 of 42 Diabetes
46. Galaviz-Hernandez C, Stagg C, de Ridder G, Tanaka TS, Ko MS, Schlessinger D, Nagaraja 587
R: Plac8 and Plac9, novel placental-enriched genes identified through microarray analysis. Gene 588
2003;309:81-89 589
47. Kinsey C, Balakrishnan V, O'Dell MR, Huang JL, Newman L, Whitney-Miller CL, Hezel 590
AF, Land H: Plac8 links oncogenic mutations to regulation of autophagy and is critical to 591
pancreatic cancer progression. Cell Rep 2014;7:1143-1155 592
48. Li C, Ma H, Wang Y, Cao Z, Graves-Deal R, Powell AE, Starchenko A, Ayers GD, 593
Washington MK, Kamath V, Desai K, Gerdes MJ, Solnica-Krezel L, Coffey RJ: Excess PLAC8 594
promotes an unconventional ERK2-dependent EMT in colon cancer. J Clin Invest 595
2014;124:2172-2187 596
49. Jimenez-Preitner M, Berney X, Uldry M, Vitali A, Cinti S, Ledford JG, Thorens B: Plac8 is 597
an inducer of C/EBPbeta required for brown fat differentiation, thermoregulation, and control of 598
body weight. Cell Metab 2011;14:658-670 599
50. Ledford JG, Kovarova M, Koller BH: Impaired host defense in mice lacking ONZIN. J 600
Immunol 2007;178:5132-5143 601
51. Li Y, Rogulski K, Zhou Q, Sims PJ, Prochownik EV: The negative c-Myc target onzin 602
affects proliferation and apoptosis via its obligate interaction with phospholipid scramblase 1. 603
Mol Cell Biol 2006;26:3401-3413 604
52. McMurray HR, Sampson ER, Compitello G, Kinsey C, Newman L, Smith B, Chen SR, 605
Klebanov L, Salzman P, Yakovlev A, Land H: Synergistic response to oncogenic mutations 606
defines gene class critical to cancer phenotype. Nature 2008;453:1112-1116 607
53. Raney BJ, Cline MS, Rosenbloom KR, Dreszer TR, Learned K, Barber GP, Meyer LR, Sloan 608
CA, Malladi VS, Roskin KM, Suh BB, Hinrichs AS, Clawson H, Zweig AS, Kirkup V, Fujita 609
PA, Rhead B, Smith KE, Pohl A, Kuhn RM, Karolchik D, Haussler D, Kent WJ: ENCODE 610
whole-genome data in the UCSC genome browser (2011 update). Nucleic Acids Res 611
2011;39:D871-875 612
613
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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
Page 25 of 42 Diabetes
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
Page 26 of 42Diabetes
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
Page 27 of 42 Diabetes
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
Page 28 of 42Diabetes
PLAC8 depletion (n=5 experiments using 2 different GDM-exposed ECFC samples, *p<0.05 by 660
paired t test). 661
662
Page 29 of 42 Diabetes
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
Page 34 of 42Diabetes
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.
Page 35 of 42 Diabetes
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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