A physiological state of insulin resistance is required to preferentially direct maternal nutrients toward the feto-placental unit, allowing adequate growth of the fetus. When women develop gestational diabetes mellitus (GDM), insulin resistance is more severe and disrupts the intrauterine milieu, resulting in accelerated fetal development with increased risk of macrosomia. As a natural interface between mother and fetus, the placenta is the obligatory target of such environmental changes. However, the molecular basis for the imbalance that leads to fetal, neonatal, and adult metabolic compromises is not well understood. We report that GDM elicits major changes in the expression profile of placental genes with a prominent increase in markers and mediators of inflammation. Within the 435 transcripts reproducibly modified, genes for stress-activated and inflammatory responses represented the largest functional cluster (18.5% of regulated genes). Upregulation of interleukins, leptin, and tumor necrosis factor-α receptors and their downstream molecular adaptors indicated an activation of pathways recruiting stress-activated protein/c-Jun NH2-terminal kinases. Transcriptional activation of extracellular matrix components and angiogenic activators pointed to a major structural reorganization of the placenta. Thus, placental transcriptome emerges as a primary target of the altered environment of diabetic pregnancy. The genes identified provide the basis to elucidate links between inflammatory pathways and GDM-associated insulin resistance.

Gestational diabetes mellitus (GDM) is a common metabolic disease of pregnancy that shares many features of type 2 diabetes, including glucose intolerance and insulin resistance (1,2). The maternal hormonal and metabolic alterations associated with GDM profoundly modify the in utero environment, leading to an abnormal pattern of fetal growth (3). Impaired fetal development has severe metabolic consequences with increased risk to develop glucose intolerance and obesity in adolescence and later life (4,5). The links between GDM-associated insulin resistance and altered fetal growth are not clearly understood. Insulin resistance and obesity are linked to aberrant whole-body homeostasis with a tight control through adipocyte-secreted factors, such as tumor necrosis factor-α (TNF-α) and leptin. We have shown that leptin and TNF-α are the strongest predictors of pregnancy-associated insulin resistance, far greater than previously suggested for gestational hormones, including human placental lactogen and steroids (6,7). Because the placenta is at the same time producer and natural target of both leptin and TNF-α, we asked whether molecular adaptations are elicited by GDM-induced insulin resistance. The remarkable structural diversity of the placenta resting on multiple cell types devoted to nutrient transport, and energy metabolism, as well as endocrine, immunological, and vascular functions, offers a unique opportunity to survey genes responsible for a large variety of biological processes (8).

The purpose of this study was to examine profiles of gene expression in human placenta obtained from normal and GDM pregnancies to determine whether insulin resistance modifies the pattern of placental transcriptome.

The protocol was approved by the institutional review board of Case Western Reserve University. Volunteers gave informed written consent in accordance with the MetroHealth Medical Center guidelines. GDM was defined as an abnormal glucose tolerance during the third trimester according to the criteria defined by Carpenter and Coustan (9). All GDM subjects required chronic insulin therapy for glucose control. Maternal and cord blood samples were obtained at delivery. Neonatal anthropometric measurements were performed within 48 h of delivery. Neonatal body composition was estimated using Total Body Electrical Conductivity (EM-Scan HP-2 TOBEC, Springfield, IL).

Analysis of glucose and insulin.

Plasma glucose was measured by the glucose oxidase method with a glucose analyser (Yellow Springs Instrument, Yellow Springs, OH). Plasma insulin was assayed by radioimmunoassay as described (1). We estimated insulin sensitivity using a prediction model based on a significant correlation between the 1-h glucose screen and insulin sensitivity measured with the hyperinsulinemic-euglycemic clamp (2).

RNA processing and microarrays.

Placental biopsies (∼1 cm3) were randomly obtained from six intact cotyledons after dissecting out the basal and the chorial plates and snap-freezing them in liquid nitrogen. Total RNA was prepared from whole-villous tissue using CsCl gradient (10). RNA samples were electrophoresed to verify integrity, pooled, and reversed transcribed using Superscript first-strand synthesis (Invitrogen, Carlsbad, CA). This pooling strategy was used to minimize variations in structural content of the biopsies due to placental heterogeneity. cDNA served as a template to generate biotinylated cRNA by in vitro transcription (ENZO kit; Affymetrix). The quality of each fragmented cRNA evaluated with test microarrays (Test-3array; Affymetrix) was considered satisfactory when bioC, bioD, and cre were present and the 3′/5′ ratio of the polyA control subjects was <3. The 3′/5′ ratios for β-actin and GAPDH were 1.4 ± 0.2 and 1.2 ± 0.2, respectively. Fragmented cRNAs were spotted onto eight human U133 Affymetrix microarrays. Hybridization was performed according to the manufacturer’s instructions. Signal scanning and analysis were performed with Affymetrix equipment (Fluidics station, HPgene array scanner, and MAS 5.0 microarray suite).

Data analysis.

To correct for hybridization efficiency, results were scaled to an average signal intensity of 1500. A serial four-step analysis was completed to select the significantly modified transcripts while minimizing false-positive genes. The first step excluded all probe sets with signal below the probe pair threshold (nc). Average background (67.8 ± 0.80) and scaled noise (3.7 ± 0.3) were calculated for each array and entered in the subsequent analysis. 1) Genes showing an absolute call of present (P-calls) according to MAS 5 algorithm. The average percentage of P-calls was 39.1 ± 2.8. 2) Genes showing a difference call of increased “I” or decreased “D.” 3) Genes with a difference in signal detection of at least 4.5 times the average background minus the scaled noise. The genes having satisfied these criteria were included in the fourth level of selection based on a fold change ≥2 or ≤−2 and consistent in at least two comparisons (Fig. 1). Functional clustering of the up- and downregulated genes was based on public databases according to the biological functions of their putative encoded proteins.

Quantitative real-time PCR.

RT-PCR analysis was performed using a fluorescence temperature cycler (Lightcycler; Roche Molecular Diagnosis, Indianapolis, IN). Specific primers were designed within the 3′ coding region of the genes (sequences available upon request). Real-time reactions were carried out in duplicate, and amplicons were analyzed by generating melting curves with continuous measurement of fluorescence. The PCR products were separated on 1.5% agarose gel. Results were calculated as relative differences in target Ct values normalized to β-actin.

Statistical analysis.

All data are presented as mean ± SE. Significance for statistical differences was calculated using a unpaired Student’s t test.

A total of 16 women with either normal glucose tolerance (control subjects) or GDM, who were recruited for the study, were matched for BMI and gestational age (Table 1). Insulin resistance was documented by a higher 1-h postchallenge blood and lower predicted insulin sensitivity compared with control subjects. This was observed despite satisfactory glycemic control with normal HbA1c (5.4 vs. 5.1%) and normal fasting blood glucose (90.7 ± 11 vs. 86.5 ± 4.5 mg/dl). Higher plasma insulin levels in the GDM women reflect chronic exogenous insulin therapy. Plasma leptin and TNF-α levels were higher in women with GDM. Birth weights were similar in the two groups; however, placental weights were increased with GDM (585 ± 64 vs. 434 ± 20 g, P < 0.001), and neonates of GDM mothers were fatter based on percent fat mass (15.1 ± 1.2 vs. 12.6 ± 1.3, P < 0.001) and ponderal index (2.9 ± 0.2 vs. 2.7 ± 0.1 g/cm3, P < 0.001). Plasma insulin levels were higher in neonates of GDM mothers (33.3 ± 9.6 vs. 12.5 ± 1.6 μU/ml, P < 0.01).

To characterize gene targets that determine altered placental functions, the global pattern of gene expression was analyzed in placenta from matched control subjects and GDM pregnancies. Of 22,823 gene sequences surveyed, 8,627 ± 172 were present in control subjects and 9,378 ± 165 in GDM, representing placental transcriptome. The consecutive screening filters that we applied allowed us to narrow these numbers to 2,682 genes eligible for analysis. The average fold change value was then calculated for all pairwise comparisons, resulting in the final selection of 435 genes significantly modified in GDM.

The genes included in the final selection represented 2.0% of the total gene sequences analyzed and 5.6% of the placental transcriptome. The 435 genes identified as significantly modified refer to genes whose transcriptional pattern was altered in GDM with 254 upregulated and 181 downregulated genes. Functional clustering of the 435 genes followed by quantitative ranking identified six major subgroups of genes (Fig. 2). They included genes associated with 1) stress-activated and inflammatory responses (n = 79), 2) endothelial structure and differentiation (n = 41), 3) substrate metabolism (n = 39), 4) transport and trafficking (n = 34), 5) translation (n = 33), and 6) signal transduction (n = 23). Genes related to inflammatory responses represented the largest cluster, accounting for up to 18% of the modified genes. Additional single clusters, each accounting for <5% of the modified genes, were those associated with transcription and early genes (n = 17), immunity and cell surface antigens (n = 19), hormones (n = 15), growth factors (n = 13), cell growth and maintenance (n = 9), and mitochondrial (n = 7). They represented 19% of the modified genes (Fig. 2, other clusters). The 103 genes with unknown functions were listed under nonclassified genes. The list of the two main clusters is given in Table 2, and the complete listing of the 435 significantly modified genes is available at http://www.ncbi.nlm.nih.gov/geo (accession no. GPL130). Modifications observed with microarrays were validated by real-time PCR analysis of the same RNA samples (Fig. 3). We analyzed mRNA expression for 22 genes of interest and were able to confirm 21 modifications with either increased or decreased expression (Fig. 3).

Expression profiling of the placenta revealed that genes regulating inflammatory responses and endothelial reorganization represent the two main functional clusters altered in GDM. With a total of 110 genes, they account for one-third of the modified genes, reflecting a state of chronic inflammation with signs of major vascular dysfunction. This is further supported by the findings that most of the genes were upregulated (75 of 110), as compared with a balanced ratio observed in the other gene categories.

The diabetic placentas analyzed in this study were obtained from women with marked glucose intolerance and insulin resistance (Table 1). Growing evidence in the literature suggests that insulin resistance is the result of an inflammatory milieu. Plasma levels of several markers of inflammation, C-reactive protein, lipopolysaccharide (LPS), interleukin (IL)-6, TNF-α, and leptin are elevated in individuals with obesity and type 2 diabetes (1114). TNF-α has been recognized as the most prominent factor contributing to insulin resistance in obesity and diabetes (1519). The changes in placental gene expression that we report herein further support our hypothesis that a panel of proinflammatory cytokines and cellular mediators act in concert with TNF-α to either relay or potentiate its action.

IL-1 and TNF-α induce synergistic pleiotropic responses that profoundly affect production of extracellular matrix (ECM) proteins. Increased expression of fibronectin, laminin β-1, and metalloproteinases may induce a fibrotic response and disrupt the structural integrity of placental endothelial cells as in other cell types (2022). Increase in metalloproteinases may also influence angiogenesis by degrading matrix molecules, loosening the cellular network, and releasing growth factors sequestered in the ECM (23). Several other modified genes are directly modulated by IL-1 and TNF-α. For example, TNF-induced CG12 accompanies inflammatory reactions in atherosclerotic regions (24), and acute-phase reactants, such as LPS, pentaxin-related gene (PTX-3), calgranulin, and thrombospondin, are key components of impaired vascular function (2528). The increased expression of IL-8 receptor, IL-1 receptor, and the short form of leptin receptor points to an activation of the signaling pathways recruited by these cytokines. The activation of leptin gene expression in diabetic placenta is in keeping with its transcriptional regulation (29). The role of placental leptin has raised several hypotheses regarding its autocrine action within the placenta without reaching definite conclusions (30). The increase in short leptin receptor supports the view that this isoform transduces proinflammatory responses in the placenta.

Besides the metabolic control of adipose tissue homeostasis, leptin elicits inflammatory, immunological, and vascular responses (31,32). Administration of IL-6, LPS, and TNF-α to mice increases leptin production, suggesting that they induce concurrent effects in addition to the development of inflammation (33). The coordinated action of ILs, TNF-α, and leptin on placental genes may explain part of a fibrotic response via disruption of extracellular matrix components and vascular architecture (Fig. 4). These transcriptional changes are in line with the modifications of placental morphology and composition documented in diabetes with increased parenchymal tissue cellularity, alterations of surface expression of junctional proteins, and enhanced feto-placental angiogenesis (3436). The downstream effectors of TNF-α, ILs, and leptin activate p38 MAPK and JNK, which belong to the family of SAPK stress kinases abnormally activated in obesity and insulin resistance (37,38). MAPK and JNK/SAPK pathways cooperate to phosphorylate ras GTPases of the rho family, such as mig-6, a molecular adaptor triggering cellular hypertrophy in diabetic nephropathy (39,40). The upregulation of placental mig-6 expression, makes this gene a particularly attractive candidate as a mediator of placental overgrowth of diabetes.

The array of genes activated by TNF-α, leptin, and ILs also include several transcription factors, GATA, CREB-binding protein, CEBP-α, and AP-1, all of which are involved in the regulation of inflammatory processes (41). Upon activation, they are able to turn on genes involved in signal transduction related to stress and chronic inflammation (Fig. 5). Modifications of the expression of genes assigned to other functional clusters, signal transduction, growth factors, and cytoskeleton reorganization are also likely to contribute to global placental dysfunction. This may be particularly relevant for genes encoding mediators of the insulin signaling cascade, such as the insulin receptor itself, p 110 phosphatidylinositol-kinase, several small G proteins of the ras, rab, and rho families possibly recruited as a result of high insulin levels in maternal and fetal blood.

The best representation of subclasses of genes involved in inflammatory pathways represents the first characterization of an inflammatory response induced through a chronic diabetic insult. No such responses were reported in skeletal muscle of type 2 diabetic patients (42,43), and changes in diabetic mice were related to substrate and energy metabolism (44). These differences could result from cell type specificities or from a greater insulin resistance, as demonstrated by the need for insulin therapy to achieve adequate glucose control in GDM patients. Glucose intolerance and hyperinsulinemia are significant before conception in women who go on to develop GDM (45). A causal role of hyperglycemia has been suggested in the immune activation of diabetes (46). Therefore, the early onset of insulin resistance may initiate an endocrine feedback between mother and placenta with enhanced susceptibility to cytokine that culminate in the inflammation milieu that we describe.

This study provides the first molecular basis linking GDM to modifications of placental transcriptome. It suggests a postreceptor convergence of multiple signal transduction pathways mediated through TNF-α, IL-1, and leptin. Therefore, the fetus of diabetic mothers develops in an inflammatory milieu. We speculate that changes in expression of specific placental genes may be a leading cause to adverse fetal programming.

FIG. 1.

Stepwise selection analysis of placental gene profiling. The first three consecutive steps of the analysis identified a total of 2,682 genes that were eligible for further comparisons. The fourth filtering step of our analytical strategy allowed us to select 435 genes significantly regulated (black bars) and to reject 2,247 genes (gray bars). Bars to the right correspond to upregulated genes, and bars to the left represent the downregulated genes. The number of gene sequences analyzed is expressed as a function of their relative fold changes.

FIG. 1.

Stepwise selection analysis of placental gene profiling. The first three consecutive steps of the analysis identified a total of 2,682 genes that were eligible for further comparisons. The fourth filtering step of our analytical strategy allowed us to select 435 genes significantly regulated (black bars) and to reject 2,247 genes (gray bars). Bars to the right correspond to upregulated genes, and bars to the left represent the downregulated genes. The number of gene sequences analyzed is expressed as a function of their relative fold changes.

FIG. 2.

Functional clustering of the regulated genes. Quantitative ranking of the six main functional categories of the 435 upregulated and downregulated genes revealed genes for inflammatory responses to represent the largest single cluster (18.5% of all the genes significantly modified in response to GDM). Genes assigned to clusters that accounted for <5% were included within “other clusters.” There were 103 miscellaneous genes not assigned to any of the functional clusters.

FIG. 2.

Functional clustering of the regulated genes. Quantitative ranking of the six main functional categories of the 435 upregulated and downregulated genes revealed genes for inflammatory responses to represent the largest single cluster (18.5% of all the genes significantly modified in response to GDM). Genes assigned to clusters that accounted for <5% were included within “other clusters.” There were 103 miscellaneous genes not assigned to any of the functional clusters.

FIG. 3.

Real-time PCR analysis of the modified genes. The PCR results expressed are given as the means ± SE of three to four comparisons in GDM versus control subjects (gray bars). Black bars represent the average fold change obtained with microarray analysis. All values are presented as relative fold changes with upregulation >1 and downregulation <1. TGF-beta 1-ind, TGF-β1–induced protein; mig-6, gene 33; PTX-3, pentaxin-related protein; Leptin-Rs, leptin receptor short isoform; IL1-R, IL-1 receptor; LPL, lipoprotein lipase; IGF-BP1, IGF binding protein 1.

FIG. 3.

Real-time PCR analysis of the modified genes. The PCR results expressed are given as the means ± SE of three to four comparisons in GDM versus control subjects (gray bars). Black bars represent the average fold change obtained with microarray analysis. All values are presented as relative fold changes with upregulation >1 and downregulation <1. TGF-beta 1-ind, TGF-β1–induced protein; mig-6, gene 33; PTX-3, pentaxin-related protein; Leptin-Rs, leptin receptor short isoform; IL1-R, IL-1 receptor; LPL, lipoprotein lipase; IGF-BP1, IGF binding protein 1.

FIG. 4.

Placental responses to the diabetic insult. The primary response to diabetes is the recruitment of TNF-α, IL-1, and leptin receptors present on the syncytiotrophoblast facing the maternal blood. Signal transduction through these receptors elicits a cascade of intracellular events leading to excess extracellular matrix components characteristic of a fibrotic response and a vascular reaction. The modifications of ECM gene expression also contribute to enhanced angiogenic activity and endothelial differentiation. The sum of these modifications points to a severe disorganization of placental structure. Lep-Rs, leptin receptor short isoform; IL-1R, IL-1 receptor.

FIG. 4.

Placental responses to the diabetic insult. The primary response to diabetes is the recruitment of TNF-α, IL-1, and leptin receptors present on the syncytiotrophoblast facing the maternal blood. Signal transduction through these receptors elicits a cascade of intracellular events leading to excess extracellular matrix components characteristic of a fibrotic response and a vascular reaction. The modifications of ECM gene expression also contribute to enhanced angiogenic activity and endothelial differentiation. The sum of these modifications points to a severe disorganization of placental structure. Lep-Rs, leptin receptor short isoform; IL-1R, IL-1 receptor.

FIG. 5.

Model for placental inflammatory pathways recruited in GDM. This scheme includes some of the placental genes participating in the diabetic response as an example of cooperation between proinflammatory cytokines. The array of genes induced in relation to inflammation suggests that concurrent signaling pathways are recruited through TNF-α, IL, and leptin stimulation. Stress kinases (SAPK/JNK) and NF-κB are key mediators of cross-talks linking inflammation to diabetes and insulin resistance. We propose that this cascade of events leads to cell hypertrophy and vasculosyncytial dysfunction. Details are given in the discussion section. Genes whose expression is modified in microarray analysis are boxed, and the asterisk represents genes for which changes in expression have been further validated by real-time PCR.

FIG. 5.

Model for placental inflammatory pathways recruited in GDM. This scheme includes some of the placental genes participating in the diabetic response as an example of cooperation between proinflammatory cytokines. The array of genes induced in relation to inflammation suggests that concurrent signaling pathways are recruited through TNF-α, IL, and leptin stimulation. Stress kinases (SAPK/JNK) and NF-κB are key mediators of cross-talks linking inflammation to diabetes and insulin resistance. We propose that this cascade of events leads to cell hypertrophy and vasculosyncytial dysfunction. Details are given in the discussion section. Genes whose expression is modified in microarray analysis are boxed, and the asterisk represents genes for which changes in expression have been further validated by real-time PCR.

TABLE 1

Clinical and metabolic characteristics of study subjects

SubjectsnGestational age (weeks)BMI (kg/m2)HbA1c (%)Fasting glucose (mg/dl)Insulin (μU/ml)1-h glucose (mg/dl)IS (mg · min−1 · kg−1)Leptin (ng/ml)TNF-α (pg/ml)
Control 38.9 ± 0.4 34.0 ± 3.3 5.3 ± 0.1 84.0 ± 2.9 36.9 ± 5.8 100 ± 10.1 6.0 ± 0.2 28.3 ± 5.5 1.5 ± 0.4 
GDM 38.5 ± 0.5 39.7 ± 2.6 5.7 ± 0.3 94.7 ± 8.2 58.7 ± 15.5* 173.2 ± 12.4 4.3 ± 0.7 45.0 ± 3.5* 2.3 ± 0.2* 
SubjectsnGestational age (weeks)BMI (kg/m2)HbA1c (%)Fasting glucose (mg/dl)Insulin (μU/ml)1-h glucose (mg/dl)IS (mg · min−1 · kg−1)Leptin (ng/ml)TNF-α (pg/ml)
Control 38.9 ± 0.4 34.0 ± 3.3 5.3 ± 0.1 84.0 ± 2.9 36.9 ± 5.8 100 ± 10.1 6.0 ± 0.2 28.3 ± 5.5 1.5 ± 0.4 
GDM 38.5 ± 0.5 39.7 ± 2.6 5.7 ± 0.3 94.7 ± 8.2 58.7 ± 15.5* 173.2 ± 12.4 4.3 ± 0.7 45.0 ± 3.5* 2.3 ± 0.2* 

Data are means ± SE.

*

P < 0.01;

P < 0.001. IS, glucose disposal rate in mg · min−1 · kg fat-free mass−1

TABLE 2

Genes significantly modified in GDM

Accession no.Gene nameFold changeDescription
Inflammatory pathways    
NM 005345 HSPA1A 2.6 Heat shock 70kDa protein 1A 
NM 006948 STCH 2.3 Stress 70 protein chaperone, microsome-associated 60-kDa 
NM 006260 DNAJC3 −2 DnaJ (Hsp40) homolog, subfamily C, member 3 
NM 006644 HSP105B Heat shock 105kDa 
NM 005494 LOC136442 2.1 Heat shock protein J2 
NM 012328 DNAJB9 DnaJ (Hsp40) homolog, subfamily B, member 9 
NM 001423 EMP1 2.3 Epithelial membrane protein 1 
NM 005101 ISG15 −2.2 Interferon-stimulated protein, 15 kDa 
NM 002053 GBP1 3.5 Guanylate binding protein 1, interferon-inducible, 67kDa 
NM 000619 IFNG −2 Interferon, gamma 
NM 006084 ISGF3G −2.1 Interferon-stimulated transcription factor 3, gamma 48kDa 
NM 006332 IFI30 −3 Interferon, gamma-inducible protein 30 
NM 001144 AMFR 4.3 Autocrine motility factor receptor 
NM 004084 DEFA1 2.9 Defensin, alpha 1, myeloid-related sequence 
NM 003246 THBS1 3.3 Thrombospendin 1 
NM 004342 CALD1 2.3 Caldesmon 1 
NM 006350 FST −2.6 Follistatin 
NM 004079 CTSS 2.7 Cathepsin S 
NM 005860 FSTL3 −2 Follistatin-like 3 (secreted glycoprotein) 
NM 014795 ZFHX1B 2.3 Zinc finger homeobox 1b 
NM 000466 PEX1 Peroxisome biogenesis factor 1 
NM 015927 TGFB1I1 Transforming growth factor beta 1 (TGF beta) induced transcript 1 
NM 021073 BMP5 Bone morphogenetic protein 
NM 003743 NCOA1 6.1 Nuclear receptor coactivator 1 
NM 001901 CTGF 2.1 Connective tissue growth factor 
NM 005264 GFRA1 −3.7 GDNF family receptor alpha 1 
NM 004843 WSX1 2.1 Class I cytokine receptor 
NM 002303 LEPR 4.3 Leptin receptor, short isoform 
NM 000230 LEP 2.3 Leptin (obesity homolog, mouse) 
NM 001243 TNFRSF8 −2 Tumor necrosis factor receptor superfamily, member 8 
NM 016442 ARTS-1 18 Type 1 tumor necrosis factor receptor shedding aminopeptidase regulator 
NM 030817 DKFZP434F0318 3.2 TNF alpha induced protein, similar to CG-12 
NM 005903 MADH5 −2.3 MAD, mothers against decapentaplegic homolog 5 
NM 003743 NCOA1 6.1 Nuclear receptor coactivator 1 
NM 003489 NRIP1 2.2 Nuclear receptor interacting protein 1 
U 59863 TANK 2.3 TRAF family member-associated NFKB activator 
NM 018678 LSR68 3.3 Lipopolysaccharide specific response-68 protein 
NM 004887 CXCL 14 2.1 Chemokine (C-X-C motif) ligand 14 
NM 003856 IL1RL1 Interleukin 1 receptor-like 1 
NM 001557 IL8RB 5.3 Interleukin 8 receptor, beta 
NM 002852 PTX3 Pentaxin-related gene, rapidly induced by IL-1 beta 
NM 012294 GFR 2.1 Guanine nucleotide exchange factor for Rap1 
NM 021955 GNGT1 Guanine nucleotide binding protein (G protein) 
NM 021183 LOC57826 2.1 Protein similar to small G proteins, especially RAP-2A 
NM 012121 CDC42EP4 2.3 CDC42 effector protein (Rho GTPase binding) 4 
U 28936 YWHAE 2.5 Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, epsilon polypeptide 
AL 034417 MIG-6 Gene 33/Mig-6 
NM 004844 SH3BP5 2.3 SH3-domain binding protein 5 (BTK-associated) 
NM 32569 N-PAC −2.6 Cytokine-like nuclear factor n-pac 
NM 030751 TCF8 Transcription factor 8 (represses interleukin 2 expression) 
NM 001546 ID4 2.3 Inhibitor of DNA binding 4, dominant negative helix-loop-helix protein 
NM 004379 CREB1 2.1 cAMP responsive element binding protein 1 
NM 014335 CRI1 CREBBP/EP300 inhibitory protein 1 
NM 002943 RORA −2.1 RAR-related orphan receptor A 
NM 002051 GATA3 GATA binding protein 3 
NM 000929 PLA2G5 2.6 Phospholipase A2, group V 
NM 002415 MIF −2.1 Macrophage migration inhibitory factor 
NM 006039 ENDO180 2.1 Endocytic receptor (macrophage mannose receptor family) 
NM 002510 GPNMB 2.4 Glycoprotein (transmembrane) 
NM 000362 TIMP3 −2.6 Tissue inhibitor of metalloproteinase 3 (pseudoinflammatory) 
NM 002291 LAMB1 2.4 Laminin, beta 1 
NM 002727 PRG1 2.2 Proteoglycan 1, secretory granule 
NM 000088 COL1A1 2.3 Collagen, type I, alpha 1 
NM 004995 MMP14 −2.9 Matrix metalloproteinase 14 
NM 002421 MMP1 3.7 Matrix metalloproteinase 1 
NM 002426 MMP12 2.2 Matrix metalloproteinase 12 
NM 002213 ITGB5 4.8 Integrin, beta 5 
NM 004763 ICAP-1A 2.3 Integrin cytoplasmic domain-associated protein 1 
NM 000212 ITGB3 −2.5 Integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61) 
NM 004950 DSPG3 −5.1 Dermatan sulfate proteoglycan 3 
NM 001797 CDH11 −2.1 Cadherin 11, type 2, OB-cadherin 
NM 014265 ADAM28 −15 A disintegrin and metalloproteinase domain 28 
AL 576253 zizimin1 Zizimin 1 
NM 002026 FN1 −2.5 Fibronectin 1 
AI_189753 TM4SF1 Transmembrane 4 superfamily member 1 
NM 006691 XLKD1 2.1 Extracellular link domain containing 1 
NM 012338 NET-2 2.6 Transmembrane 4 superfamily member tetraspan NET-2 
NM 006665 HPSE −2.1 Heparanase 
NM 000091 COL4A3 −4 Collagen, type IV, alpha 3 
Endothelial differentiation, structural, and contractile proteins    
NM 004986 KTN1 2.1 Kinectin 1 (kinesin receptor) 
NM 003373 VCL Vinculin 
NM 004343 CALR 2.3 Calreticulin 
NM 006136 CAPZA2 2.5 Capping protein (actin filament) muscle Z-line 
NM 001839 CNN3 2.4 Calponin 3, acidic 
NM 016824 ADD3 2.3 Adducin 3 (gamma) 
BF 940043 NID1 2.2 Nidogen (entactin) 
NM 001615 ACTG2 2.1 Actin, gamma 2, smooth muscle, enteric 
NM 001613 ACTA2 2.4 Actin, alpha 2, smooth muscle, aorta 
NM 003072 SMARCA4 2.1 Matrix associated, actin dependent regulator of chromatin 
NM 001613 ACTA2 2.4 Actin, alpha 2, smooth muscle, aorta 
AI_382123 MYH10 −2 Human nonmuscle myosin heavy chain-B (MYH10) 
NM 002465 MYBPC1 −2.6 Myosin binding protein C, slow type 
NM 006097 MYL9 2.4 Myosin, light polypeptide 9, regulatory 
NM 004093 EFNB2 −2.5 Ephrin-B2 
NM 002964 S100A8 10.2 S100 calcium binding protein A8 (calgranulin A) 
NM 002961 S100A4 −2 S100 calcium binding protein A4 
NM 007269 STXBP3 2.1 Syntaxin binding protein 3 
NM 006322 TUBGCP3 4.3 Tubulin, gamma complex associated protein 3 
NM 032261 DKFZp434N0650 −2 Tubulin beta5 
NM 000227 LAMA3 −2.2 Laminin, alpha 3 
NM 000361 THBD Thrombomodulin 
NM 001888 CRYM −4.3 Crystallin, mu 
NM 001884 CRTL1 2.8 Cartilage linking protein 1 
NM 003186 TAGLN 2.1 Transgelin 
NM 002019 FLT1 Fms-related tyrosine kinase 1 (VEGF receptor) 
NM 003376 VEGF 2.5 Vascular endothelial growth factor 
NM 001147 ANGPT2 2.5 Angiopoietin 2 
NM 007351 MMRN 2.6 Multimerin 
NM 002658 PLAU −2.1 Plasminogen activator 
NM 000128 F11 −2.6 Coagulation factor XI 
NM 000300 PLA2G2A 2.4 Phospholipase A2, group IIA 
NM 001996 FBLN1 Fibulin 1 
NM 001102 ACTN1 2.2 Actinin, alpha 1 
NM 032261 DKFZp434N0650 −2 Tubulin beta5 
NM 005554 KRT6A −7.8 Keratin 6A 
NM 005555 KRT6B −2.5 Keratin 6B 
NM 002397 MEF2C MADS box transcription enhancer factor 2 
NM 001730 KLF5 2.5 Kruppel-like factor 5 
NM 005139 ANXA3 2.1 Annexin A3 
NM 000070 CAPN3 −2.6 Calpain 3, (p94) 
NM 003564 TAGLN2 −2.6 Transgelin 2 
M 28882 MCAM 2.1 Melanoma cell adhesion molecule 
NM 001999 FBN2 2.3 Fibrillin 2 
Accession no.Gene nameFold changeDescription
Inflammatory pathways    
NM 005345 HSPA1A 2.6 Heat shock 70kDa protein 1A 
NM 006948 STCH 2.3 Stress 70 protein chaperone, microsome-associated 60-kDa 
NM 006260 DNAJC3 −2 DnaJ (Hsp40) homolog, subfamily C, member 3 
NM 006644 HSP105B Heat shock 105kDa 
NM 005494 LOC136442 2.1 Heat shock protein J2 
NM 012328 DNAJB9 DnaJ (Hsp40) homolog, subfamily B, member 9 
NM 001423 EMP1 2.3 Epithelial membrane protein 1 
NM 005101 ISG15 −2.2 Interferon-stimulated protein, 15 kDa 
NM 002053 GBP1 3.5 Guanylate binding protein 1, interferon-inducible, 67kDa 
NM 000619 IFNG −2 Interferon, gamma 
NM 006084 ISGF3G −2.1 Interferon-stimulated transcription factor 3, gamma 48kDa 
NM 006332 IFI30 −3 Interferon, gamma-inducible protein 30 
NM 001144 AMFR 4.3 Autocrine motility factor receptor 
NM 004084 DEFA1 2.9 Defensin, alpha 1, myeloid-related sequence 
NM 003246 THBS1 3.3 Thrombospendin 1 
NM 004342 CALD1 2.3 Caldesmon 1 
NM 006350 FST −2.6 Follistatin 
NM 004079 CTSS 2.7 Cathepsin S 
NM 005860 FSTL3 −2 Follistatin-like 3 (secreted glycoprotein) 
NM 014795 ZFHX1B 2.3 Zinc finger homeobox 1b 
NM 000466 PEX1 Peroxisome biogenesis factor 1 
NM 015927 TGFB1I1 Transforming growth factor beta 1 (TGF beta) induced transcript 1 
NM 021073 BMP5 Bone morphogenetic protein 
NM 003743 NCOA1 6.1 Nuclear receptor coactivator 1 
NM 001901 CTGF 2.1 Connective tissue growth factor 
NM 005264 GFRA1 −3.7 GDNF family receptor alpha 1 
NM 004843 WSX1 2.1 Class I cytokine receptor 
NM 002303 LEPR 4.3 Leptin receptor, short isoform 
NM 000230 LEP 2.3 Leptin (obesity homolog, mouse) 
NM 001243 TNFRSF8 −2 Tumor necrosis factor receptor superfamily, member 8 
NM 016442 ARTS-1 18 Type 1 tumor necrosis factor receptor shedding aminopeptidase regulator 
NM 030817 DKFZP434F0318 3.2 TNF alpha induced protein, similar to CG-12 
NM 005903 MADH5 −2.3 MAD, mothers against decapentaplegic homolog 5 
NM 003743 NCOA1 6.1 Nuclear receptor coactivator 1 
NM 003489 NRIP1 2.2 Nuclear receptor interacting protein 1 
U 59863 TANK 2.3 TRAF family member-associated NFKB activator 
NM 018678 LSR68 3.3 Lipopolysaccharide specific response-68 protein 
NM 004887 CXCL 14 2.1 Chemokine (C-X-C motif) ligand 14 
NM 003856 IL1RL1 Interleukin 1 receptor-like 1 
NM 001557 IL8RB 5.3 Interleukin 8 receptor, beta 
NM 002852 PTX3 Pentaxin-related gene, rapidly induced by IL-1 beta 
NM 012294 GFR 2.1 Guanine nucleotide exchange factor for Rap1 
NM 021955 GNGT1 Guanine nucleotide binding protein (G protein) 
NM 021183 LOC57826 2.1 Protein similar to small G proteins, especially RAP-2A 
NM 012121 CDC42EP4 2.3 CDC42 effector protein (Rho GTPase binding) 4 
U 28936 YWHAE 2.5 Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, epsilon polypeptide 
AL 034417 MIG-6 Gene 33/Mig-6 
NM 004844 SH3BP5 2.3 SH3-domain binding protein 5 (BTK-associated) 
NM 32569 N-PAC −2.6 Cytokine-like nuclear factor n-pac 
NM 030751 TCF8 Transcription factor 8 (represses interleukin 2 expression) 
NM 001546 ID4 2.3 Inhibitor of DNA binding 4, dominant negative helix-loop-helix protein 
NM 004379 CREB1 2.1 cAMP responsive element binding protein 1 
NM 014335 CRI1 CREBBP/EP300 inhibitory protein 1 
NM 002943 RORA −2.1 RAR-related orphan receptor A 
NM 002051 GATA3 GATA binding protein 3 
NM 000929 PLA2G5 2.6 Phospholipase A2, group V 
NM 002415 MIF −2.1 Macrophage migration inhibitory factor 
NM 006039 ENDO180 2.1 Endocytic receptor (macrophage mannose receptor family) 
NM 002510 GPNMB 2.4 Glycoprotein (transmembrane) 
NM 000362 TIMP3 −2.6 Tissue inhibitor of metalloproteinase 3 (pseudoinflammatory) 
NM 002291 LAMB1 2.4 Laminin, beta 1 
NM 002727 PRG1 2.2 Proteoglycan 1, secretory granule 
NM 000088 COL1A1 2.3 Collagen, type I, alpha 1 
NM 004995 MMP14 −2.9 Matrix metalloproteinase 14 
NM 002421 MMP1 3.7 Matrix metalloproteinase 1 
NM 002426 MMP12 2.2 Matrix metalloproteinase 12 
NM 002213 ITGB5 4.8 Integrin, beta 5 
NM 004763 ICAP-1A 2.3 Integrin cytoplasmic domain-associated protein 1 
NM 000212 ITGB3 −2.5 Integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61) 
NM 004950 DSPG3 −5.1 Dermatan sulfate proteoglycan 3 
NM 001797 CDH11 −2.1 Cadherin 11, type 2, OB-cadherin 
NM 014265 ADAM28 −15 A disintegrin and metalloproteinase domain 28 
AL 576253 zizimin1 Zizimin 1 
NM 002026 FN1 −2.5 Fibronectin 1 
AI_189753 TM4SF1 Transmembrane 4 superfamily member 1 
NM 006691 XLKD1 2.1 Extracellular link domain containing 1 
NM 012338 NET-2 2.6 Transmembrane 4 superfamily member tetraspan NET-2 
NM 006665 HPSE −2.1 Heparanase 
NM 000091 COL4A3 −4 Collagen, type IV, alpha 3 
Endothelial differentiation, structural, and contractile proteins    
NM 004986 KTN1 2.1 Kinectin 1 (kinesin receptor) 
NM 003373 VCL Vinculin 
NM 004343 CALR 2.3 Calreticulin 
NM 006136 CAPZA2 2.5 Capping protein (actin filament) muscle Z-line 
NM 001839 CNN3 2.4 Calponin 3, acidic 
NM 016824 ADD3 2.3 Adducin 3 (gamma) 
BF 940043 NID1 2.2 Nidogen (entactin) 
NM 001615 ACTG2 2.1 Actin, gamma 2, smooth muscle, enteric 
NM 001613 ACTA2 2.4 Actin, alpha 2, smooth muscle, aorta 
NM 003072 SMARCA4 2.1 Matrix associated, actin dependent regulator of chromatin 
NM 001613 ACTA2 2.4 Actin, alpha 2, smooth muscle, aorta 
AI_382123 MYH10 −2 Human nonmuscle myosin heavy chain-B (MYH10) 
NM 002465 MYBPC1 −2.6 Myosin binding protein C, slow type 
NM 006097 MYL9 2.4 Myosin, light polypeptide 9, regulatory 
NM 004093 EFNB2 −2.5 Ephrin-B2 
NM 002964 S100A8 10.2 S100 calcium binding protein A8 (calgranulin A) 
NM 002961 S100A4 −2 S100 calcium binding protein A4 
NM 007269 STXBP3 2.1 Syntaxin binding protein 3 
NM 006322 TUBGCP3 4.3 Tubulin, gamma complex associated protein 3 
NM 032261 DKFZp434N0650 −2 Tubulin beta5 
NM 000227 LAMA3 −2.2 Laminin, alpha 3 
NM 000361 THBD Thrombomodulin 
NM 001888 CRYM −4.3 Crystallin, mu 
NM 001884 CRTL1 2.8 Cartilage linking protein 1 
NM 003186 TAGLN 2.1 Transgelin 
NM 002019 FLT1 Fms-related tyrosine kinase 1 (VEGF receptor) 
NM 003376 VEGF 2.5 Vascular endothelial growth factor 
NM 001147 ANGPT2 2.5 Angiopoietin 2 
NM 007351 MMRN 2.6 Multimerin 
NM 002658 PLAU −2.1 Plasminogen activator 
NM 000128 F11 −2.6 Coagulation factor XI 
NM 000300 PLA2G2A 2.4 Phospholipase A2, group IIA 
NM 001996 FBLN1 Fibulin 1 
NM 001102 ACTN1 2.2 Actinin, alpha 1 
NM 032261 DKFZp434N0650 −2 Tubulin beta5 
NM 005554 KRT6A −7.8 Keratin 6A 
NM 005555 KRT6B −2.5 Keratin 6B 
NM 002397 MEF2C MADS box transcription enhancer factor 2 
NM 001730 KLF5 2.5 Kruppel-like factor 5 
NM 005139 ANXA3 2.1 Annexin A3 
NM 000070 CAPN3 −2.6 Calpain 3, (p94) 
NM 003564 TAGLN2 −2.6 Transgelin 2 
M 28882 MCAM 2.1 Melanoma cell adhesion molecule 
NM 001999 FBN2 2.3 Fibrillin 2 

List of the two main functional clusters of up- and downregulated genes in the diabetic placentas. Data indicate the Genbank accession number, the name, the fold change compared with the control subjects (see research design and methods), and the description of each gene or its protein product.

T.R. is a recipient of a fetal diagnosis fellowship, University of Milan, Italy. This study is supported by grants from the National Institutes of Health (RO-1 HD22965 to P.C.) and Alfediam Roche Pharma (to S.H.-D.M).

We thank Patrick Leahy and the staff of the Gene Array facility at Case Western Reserve University for advice and excellent technical support.

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