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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Alcohol. Author manuscript; available in PMC 2013 May 15.
Published in final edited form as:
PMCID: PMC3654802

Effects of moderate drinking during pregnancy on placental gene expression


Many children adversely affected by maternal drinking during pregnancy cannot be identified early in life using current diagnostic criteria for fetal alcohol spectrum disorder (FASD). We conducted a preliminary investigation to determine whether ethanol-induced alterations in placental gene expression may have some utility as a diagnostic indicator of maternal drinking during pregnancy and as a prognostic indicator of risk for adverse neurobehavioral outcomes in affected offspring. Pregnant Long-Evans rats voluntarily consumed either a 0 or 5% ethanol solution 4 h each day throughout gestation. Ethanol consumption produced a mean maternal daily intermittent peak serum ethanol concentration of 84 mg/dL. Placentas were harvested on gestational day 20 for gene expression studies. Microarray analysis of more than 28,000 genes revealed that the expression of 304 known genes was altered twofold or greater in placenta from ethanol-consuming dams compared with controls. About 76% of these genes were repressed in ethanol-exposed placentas. Gene expression changes involved proteins associated with central nervous system development; organ morphogenesis; immunological responses; endocrine function; ion homeostasis; and skeletal, cardiovascular, and cartilage development. To date, quantitative real-time polymerase chain reaction analysis has confirmed significant alterations in gene expression for 22 genes, including genes encoding for three calcium binding proteins, two matrix metalloproteinases, the cannabinoid 1, galanin 2 and toll-like receptor 4, iodothyronine deiodinase 2, 11-β hydroxysteroid dehydrogenase 2, placental growth factor, transforming growth factor alpha, gremlin 1, and epithelial growth factor (EGF)-containing extracellular matrix protein. These results suggest that the expression of a sufficiently large number of placental mRNAs is altered after moderate drinking during pregnancy to warrant more detailed investigation of the placenta as a biomarker system for maternal drinking during pregnancy and as an early indicator of FASD. Furthermore, these results provide new insights into novel mechanisms on how ethanol may directly or indirectly mediate its teratogenic effects through alterations in placental function during pregnancy.

Keywords: Fetal alcohol spectrum disorder, Ethanol, Placenta, Microarray, qRT-PCR, Biomarker


Heavy or binge patterns of drinking during pregnancy can cause profound morphological and neurological aberrations in offspring called fetal alcohol syndrome (FAS; Clarren and Smith, 1978; Jones et al., 1973; Jones and Smith, 1973; Lemoine et al., 1968). Increasing evidence indicates that moderate drinking during pregnancy can cause subtle, long-term behavioral and cognitive impairments in the absence of the birth defects associated with FAS (Abel, 1995; Hanson et al., 1978; Shaywitz et al., 1980). These behavioral deficits may not become apparent until the educational years (Conry 1990; Hamilton et al., 2003; Jacobson et al., 1998; Streissguth et al., 1990) and may increase in severity as the child matures (Jacobson et al., 2004; Streissguth et al., 1991, 1994). Collectively, these observations led to an expansion of the diagnostic classification of prenatal alcohol-related effects to include alcohol-related neurodevelopmental disorder (Stratton et al., 1996) and, subsequently, fetal alcohol spectrum disorder (FASD) to encompass the entire range of fetal ethanol-affected children.

Current estimates suggest that at least 1% of the pediatric population has FASD (May et al., 2007, 2008; Sampson et al., 1997), and that a large majority of this group may have no physical evidence of prenatal alcohol effects at birth. In such cases, adverse neurobehavioral consequences may not be diagnosed for years, diminishing the beneficial prospect of earlier interventional opportunities. Thus, one of the critical challenges for the fetal alcohol research community is to develop more sensitive and reliable means to detect moderate drinking during pregnancy. Ideally, a reliable indicator of prenatal ethanol exposure may also serve as a predictor of functional damage to newborns, allowing earlier identification of children at risk for longer-term adverse neurobehavioral outcomes.

One approach to this clinical challenge has focused on the identification of biomarkers of alcohol consumption as a means to confirm maternal drinking during pregnancy. A relatively small number of studies on biomarkers of drinking during pregnancy have been reported (see review by Bearer, 2001a). Most of these efforts have been clinical studies of serum biomarkers and have focused either on measurements of ethanol, ethanol metabolites, compounds that chemically interact with ethanol, or a variety of proteins either directly involved in ethanol metabolism or impacted indirectly as a consequence of ethanol metabolism. A recurring theme in most of these clinical studies is that the sensitivity of these biomarkers is generally limited to heavy drinking, these ethanol-induced changes are often short-lived with abstinence, and specificity is impacted by confounding variables present in subject populations (Bearer, 2001).

Another biomarker that appears to have greater sensitivity and specificity for detecting drinking during pregnancy are the fatty acid ethyl esters (FAEEs) ethyl linolate, ethyl oleate, and ethyl arachidonate, which accumulate in maternal liver, placenta, fetal tissues, meconium, and hair after ethanol consumption (Bearer et al., 1999, 2003a; Bearer, 2001a; Kulaga et al., 2006). FAEEs have a half-life of approximately 7 days in mouse placenta (Bearer et al., 1992) and have been detected in umbilical cord blood and meconium from newborns of alcoholic mothers (Bearer et al., 1999, 2003b, 2005). However, FAEEs have also been found in some abstinent groups (Bearer, 2001a) and their sensitivity for moderate drinking during pregnancy is not firmly established at present, possibly indicating a need for more sensitive analytical approaches for detecting these compounds in clinical samples (Pichini et al., 2008).

One alternative strategy to the challenge of diagnosing drinking during pregnancy is to use a bottom-up approach, where biomarkers are first identified and validated in animal models of drinking during pregnancy and then, based on this information, pursue parallel human studies to assess clinical utility. This approach has four distinct advantages. First, it increases the prospects of identifying novel markers without the confounding variables associated with patient populations. Second, biomarker validation can proceed in a more systematic and controlled fashion over a shorter period of time and in a more cost-effective manner. Third, an animal model system provides an opportunity to assess more directly how a biomarker signature may change as a function of ethanol dosing, patterns of ethanol exposure, and the influence of other interacting risk factors during pregnancy, such as concomitant exposure to nicotine, other drugs of abuse, stress, malnutrition, or heavy metals. How a biosignature pattern is altered by concurrent exposure to other risk factors would be critically important to the interpretation of data from clinical studies. Finally, an animal model system allows for direct correlation of biomarker patterns with markers of functional damage to the fetus, longer-term adverse neurobehavioral outcomes and, in the best-case scenario, provide insights about the mechanistic basis for the teratogenic damage—assessments that would be more difficult, if not impossible, to examine in a clinical study.

In the present study, we used a recently developed rat model of voluntary drinking during pregnancy that produces offspring with deficits in hippocampal synaptic plasticity and learning to study the effects of moderate drinking during pregnancy on placental gene expression. From a clinical standpoint, placenta has a number of advantages in consideration as a biomarker tissue given that relatively large quantities are readily obtainable by minimally invasive and inexpensive procedures that are generally ethically acceptable to both the mother and in clinical practice. To date, we have confirmed that moderate drinking during pregnancy significantly altered the expression of nearly two dozen genes, of which the protein products play important roles in placental function and fetal development.

Materials and methods


All reagents were acquired from Sigma Chemical Company unless indicated otherwise in parenthetical text.

Voluntary drinking paradigm

All procedures involving the use of live rats were approved by the University of New Mexico Health Sciences Center Institutional Animal Care and Use Committee. Four-month-old Long-Evans rat breeders (Harlan Industries, Indianapolis, IN) were single housed in plastic cages at 22°C and kept on a “reverse” 12-h dark/12-h light schedule (lights on from 2100–0900 h) with Harlan Teklad rodent chow and tap water ad libitum. After 1 week of acclimation to the animal facility, all female rats were provided 0.066% saccharin in tap water for 4 h each day from 1000 to 1400 h. The saccharin water contained 0% ethanol on the first and second day, 2.5% ethanol (vol/vol) on the third and fourth day, and 5% ethanol on the fifth day and thereafter. Daily 4-h consumption of ethanol was monitored for at least 2 weeks, and then, the mean daily ethanol consumption was determined for each female breeder. At the end of 2 weeks of daily ethanol consumption, females that drank less than 1 standard deviation below the mean of the entire group were removed from the study. The remainder of the females were assigned to either a saccharin control or 5% ethanol-drinking group and matched such that the mean prepregnancy ethanol consumption by each group was similar.

Subsequently, females were placed with proven male breeders until pregnant, as indicated by the presence of a vaginal plug. Female rats did not consume ethanol during the breeding procedure. Beginning on gestational day 1, rat dams were provided saccharin water containing either 0 or 5% ethanol for 4 h a day. The volume of 0% ethanol saccharin water provided to the controls was matched with the mean volume of saccharin water consumed by the 5% ethanol-drinking group. Daily 4-h ethanol consumption was recorded for each dam.

Maternal serum ethanol levels

A separate set of 12 rat dams was used to determine serum ethanol concentrations. These dams were run through the same voluntary drinking paradigm as described earlier, except that at the end of the 4-h ethanol consumption episode on each of three alternate days during the third week of gestation, each rat dam was briefly anesthetized with isoflurane. One hundred microlitres of whole blood was collected from the tail vein and immediately mixed with 0.2 mL of 6.6% perchloric acid, was frozen, and was stored at –20°C until assayed. Serum ethanol standards were created by mixing rat whole blood from untreated rats with known amounts of ethanol ranging from 0 to 240 mg ethanol/dL and then mixing 100-μL aliquots of each standard with perchloric acid and storing the standards frozen with the samples. Serum ethanol samples were assayed using a modification of the method of Lundquist (1959).

Tissue harvesting and RNA preparative procedures

On gestational day 20, rat dams were sacrificed, Caesarian sections were performed, and placental tissue was harvested rapidly. The position of each placenta within the uterine horn and the gender of the associated fetus were noted. The placenta was perfused with ice-cold saline to remove blood, frozen in liquid nitrogen and stored at –80°C. Total RNA was isolated from the frozen tissue using the RNeasy kit following the manufacturer's instructions (Qiagen, Valencia, CA), and the yield was determined by spectrophotometry (Nanodrop, Wilmington, DE). Total RNA was assessed with an Agilent 2001 Bioanalyzer using RNA 6000 nanochips (Agilent Technologies, Santa Clara, CA). All samples had a RNA integrity number of 9.8 or higher, indicating high quality RNA (Schroeder et al., 2006).

Microarray analysis

RNA samples from individual placenta were labeled and analyzed separately on GeneChip Rat Genome 230 2.0 Arrays (Affymetrix Inc., Santa Clara, CA). Equal amounts of total RNA (5 μg) were converted into double-stranded cDNA using Superscript II (Invitrogen, Carlsbad, CA). The resulting cDNA was used for the in vitro synthesis of biotin-labeled cRNA using the ENZO Bioarray High Yield RNA Transcript Labeling Kit T7 (Enzo Diagnostics Inc., Farmingdale, NY). After a cleanup step, 15 μg of the antisense cRNA was fragmented for 35 min at 94°C and then used as a probe on the microarray. Immediately following incubation for 16 h at 45°C, the chips were washed and stained with streptavidin–phycoerythrin using a GeneChip Fluidics Station 400 (Affymetrix Inc.). Washing, staining, and scanning were carried out according to the standard Affymetrix protocol.

The raw data were analyzed with the Affymetrix Microarray Analysis Suite (MAS 5.0) and GeneSpring GX 7.3 software (Agilent Technologies, Santa Clara, CA), starting with a per-chip normalization. The microarray data are available from the National Center for Biotechnology Information's Gene Expression Omnibus at (Barrett et al., 2005; Edgar et al., 2002) under series accession number GSE18162. All samples had a scaling factor of less than 20 to achieve the same overall intensity (500 RFU). Raw data were adjusted using a pergene normalization step to the median to compare the relative expression profiles of genes that might be expressed at very different absolute levels. Next, samples from the ethanol-exposed group were normalized to the saccharin controls. Normalized data was prefiltered by expression level (> 100 RFU). In addition, only genes that were called “present” (i.e., by intensity of signal and specificity of hybridization to all of the corresponding oligonucleotide probes per set of each gene on the chip) in at least three of the seven samples were analyzed, thereby reducing false-positive calls and removing genes that were not reliably detected (McClintick and Edenberg, 2006). A principal component analysis of the samples demonstrated that all of them passed this quality control step (data not shown). Significant changes in gene expression were defined using two filters: first by fold change of more than 2 and then by a Student's t-test multiple testing correction with a threshold of P < .05 for false discovery rate (Benjamini and Hochberg, 1995).

A global characterization of significant genes in gene ontology (GO) categories of biological processes, molecular function, and cellular compartment (Ashburner, Ball et al., 2000; Harris, Clark et al., 2004) was performed using the Gene Ontology Tree Machine tool of Vanderbilt University in Nashville, TN ( Briefly, a list of differentially expressed genes was compared with a list of all genes represented on the Rat Genome 230 2.0 Array. Relatively enriched genes were identified using the GO hypergeometric distribution analysis. Categories were considered significant at P < .01.

Real-time quantitative polymerase chain reaction analysis

Total RNA was isolated and quantified as described earlier and stored in aliquots at –80°C until use. First-strand cDNA synthesis from 1 μg of total RNA was performed using Superscript II reverse transcriptase and oligo(dT) primer (Invitrogen, Carlsbad, CA). Gene expression levels in all samples were examined by quantitative real-time polymerase chain reaction (qRT-PCR) reactions using SYBR® green Supermix (BioRad, Hercules, CA) on an ABI 7300 system (Applied Biosystems, Foster City, CA). Using Primer 3 software (Rozen and Skaletsky, 2000), the primer pairs were designed to be exon-spanning if possible to ensure that no product was amplified from genomic DNA and were created to be specific for each gene (as verified by a BLAST search) to a region different from the one used by the oligonucleotides on the Affymetrix chip. Table A1 provides detailed information of the primer sets used in the qRT-PCR studies. In preliminary studies, the optimal concentration for each primer set was determined using 5 ng of template per reaction, and a dissociation curve analysis was performed to ensure that specific amplification was achieved. The amplification conditions consisted of an initial step at 50°C for 2 min, denaturation at 95°C for 10 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. Controls included analysis of template-free reactions (both in the reverse transcription and in the PCR reaction), RNA not being reverse transcribed (to detect contamination with DNA in the RNA preparation) and samples treated with RNase A before reverse transcription reaction.

RNA samples were run in triplicate for the genes of interest and for the reference gene within the same experiment. Each experiment was performed three times. Triplicate cycle thresholds (Ct's) of all the experiments were averaged for each sample. The size of the amplicons and specificity of the primer set was verified on a 2% agarose Tris-acetate-EDTA (TAE) gel.

All data were normalized against β-actin as a reference gene. The expression of β-actin was similar in the saccharin and ethanol-exposed groups both in the microarray data and the qRT-PCR experiments. The mean Ct values for all samples were similar, making β-actin an appropriate control. Relative quantification of gene expression, that is, the relative amount of target RNA, was determined using the 2–ΔΔCt method (Livak and Schmittgen, 2001).


Voluntary drinking paradigm

Rat dams stably consumed an average of 2.82 ± 0.13 g of ethanol/kg body weight over the 4-h interval each day (approximately 16 mL of 5% ethanol in 0.066% saccharin water). This pattern and level of ethanol consumption produced a mean maternal serum ethanol concentration of 84.0 ± 5.5 mg/dL during the third week of gestation. Ethanol consumption did not affect maternal weight gain during pregnancy (Table 1). Litter sizes and placental wet weights at harvesting were not different between the two experimental groups.

Table 1
Effects of the voluntary ethanol consumption paradigm

Microarray analysis of placental gene expression

The Rat Genome 230 2.0 Expression Array contains more than 31,000 probe sets (30,000 transcripts and variants) from more than 28,000 rat genes. Of those, about 53% were detected as being expressed in placental tissue, that is, “present” in at least three of seven samples. After applying our criteria of a minimum twofold change in expression and statistical significance (P < .05) based on a Student's t-test, 649 genes were identified as significantly altered in the placenta of ethanol-consuming dams compared with the saccharin controls. The whisker box plots shown in Fig. 1A illustrate that the distribution of signal intensities of altered genes among the placental samples within each of the two experimental groups was similar, with the alcohol-exposed samples showing an overall reduction in expression levels. Figure 1B shows an unsupervised hierarchical cluster analysis of expression profile similarity between the two experimental conditions for the 649 identified genes, indicating that the two groups can be clearly distinguished. After excluding expressed sequence tags and unidentified genes, 304 of the identified genes remained. A compilation of the 304 identified genes is available in Table A2.

Fig. 1
A. Box whisker plots of placental gene expression in control and ethanol-exposed placentas representing the minimum (end of the bottom whisker), the first quartile (bottom border of the box), the median (line through the box), the third quartile (top ...

In general, ethanol consumption repressed placental gene expression. About 76% of the 304 identified genes were downregulated in the placentas harvested from ethanol-consuming dams compared with the controls. Of the 304 selected genes altered by ethanol consumption, 147 were differentially regulated between two- to threefold; 115 genes displayed a three- to fivefold difference in expression; and 40 genes showed differences in expression greater than fivefold (Table 2), including genes encoding proteins involved in a wide array of biological processes associated with placental and fetal development.

Table 2
Differentially regulated placental genes, filtered by statistical significance and fold change of more than five

Gene ontology analyses

Of 15,389 GO categories surveyed by GO analyses, 77 were significantly enriched in altered placental genes, that is, the odds ratio of experimentally observed differentially transcribed genes to expected genes in a given GO category was greater than 1 with P values less than .01. Those enriched categories were divided into three main groups (GO level 1): biological processes, molecular function, and cellular component (localization). The vast majority of enriched gene categories were found within the biological processes component. After applying an exclusion criterion of at least four ethanol-altered genes per category, several categories were significantly overpopulated with alcohol-altered gene products, including nervous system development; organ morphogenesis; immunological responses; ion homeostasis; and skeletal, cardiovascular, and cartilage development (Fig. 2). A prominent category of particular interest to us, within the context of biomarkers for fetal alcohol-related synaptic plasticity and learning deficits, was the nervous system development category, where 31 genes were significantly altered greater than twofold (Table 3) with an enrichment factor of 1.8 (P < .002).

Fig. 2
Selected enriched “biological processes” gene ontology (GO) level 5 categories. Data were analyzed using Gene Ontology Tree Machine GOTM software (Zhang et al., 2000). The distribution of differentially regulated genes (gray bars) in each ...
Table 3
Thirty-one differentially regulated genes in the “Nervous System Development Ontology Category”

Within the molecular function GO categories, five were overpopulated by alcohol-sensitive placenta genes after application of our exclusion criterion (Fig. 3). Most significant among these were the hormone activity category (enrichment factor of 3.9) and the calcium ion–binding category (enrichment factor of 2.1). It is worth mentioning some categories that failed to pass the exclusion criteria of more than four genes/term. Several functional categories consisting of only two to three members have the common theme of binding to protein families dealing with attachment, migration, and organization of cells (i.e., laminin, collagen, actinin). GO cellular component analyses revealed that altered genes coded for proteins at various cellular locations. Most of the proteins in significantly enriched GO categories are in the extracellular region (95 observed genes, 53.12 expected genes, P > .0005).

Fig. 3
Selected enriched “molecular function” gene ontology (GO) categories. Numbers in parenthesis after the molecular function denote the GO category level. Data were analyzed using Gene Ontology Tree Machine GOTM software (Zhang et al., 2000 ...

Real-time polymerase chain reaction confirmation of differential gene expression

Quantitative reverse-transcription (RT-PCR) measurements were conducted to verify placental gene alterations observed in the microarray studies. Thus far, 38 placental genes have been evaluated using qRT-PCR. These genes were selected based primarily on either having a relatively high microarray fold change and/or specific interest related to known placental function or putative teratologic mechanisms of ethanol action, based on the literature. Table 4 summarizes the qRT-PCR results for these genes organized by the cellular location of action of their protein product. Within cellular location, genes are listed from most to least statistically significant. Overall, the mRNA expression values obtained for each gene using qRT-PCR qualitatively mirrored the directional change in the microarray data, but the quantitative differences varied considerably and, in general, were of smaller magnitude compared with the microarray fold-change data. These differences likely reflect technical differences between the two analytical platforms.

Table 4
Relative quantification of mRNA using the comparative cycle threshold (Ct) method

In general, qRT-PCR expression values less than 0.2 or greater than 5.0 correlated with statistically significant alterations in gene expression. Six exceptions to this observation were the genes for thyrotropin-release hormone (TRH), orosomucoid 1, matrix metalloproteinase 3 (MMP-3), secreted frizzled-related protein 5, small X-linked muscle protein, and the progesterone receptor. In each of these six cases, the qRT-PCR value was in the same direction as the microarray fold-change data, but greater variability among individual samples in one or both experimental groups and the small group sample sizes resulted in P values greater than .05 but less than .10.

Thus far, 22 of the genes examined were confirmed as significantly altered (P < .05) based on qRT-PCR analysis (Table 4). This group includes genes encoding for three isoforms of S100 calcium binding proteins (A4, B, and G), two isoforms of hemoglobin (ε1 and γA), galanin and the galanin 2 receptor, the cannabinoid 1 (CB1) and toll-like 4 receptors, iodothyronine deiodinase 2, 11-β hydroxysteroid dehydrogenase 2 (HSD2), placental growth factor, transforming growth factor (TGF)-α, gremlin 1, EGF-containing extracellular matrix protein, and MMP-2.


The salient observation from this study is that intermittent consumption of moderate quantities of ethanol during pregnancy alters the expression of at least 22 placental genes. These alterations occur in the absence of any gross observable effects of ethanol on the mother's weight gain during pregnancy, the placenta at term, fetal litter size, or pup weight (Table 1). Nevertheless, adult offspring of this moderate prenatal ethanol exposure paradigm exhibits hippocampal synaptic plasticity deficits and performance deficits in learning paradigms (unpublished observations), indicating long-lasting functional brain damage in the absence of physical defects at birth. Taken together, these results suggest that placental gene expression may be a more sensitive indicator of moderate ethanol consumption than most current ethanol biomarker systems.

Although we are encouraged by the number of placental gene alterations confirmed to date, we have also identified at least five factors that may have contributed to variability in the results of this initial study that precluded the likely identification of a larger number of ethanol-induced placental gene changes. One factor is the impact of individual genetic variation in an outbred rat stock. However, the ability to identify altered genes in an outbred stock should be considered a strength of this paradigm, as it more accurately models the human condition and promises that gene alterations that stand out will be a reliable tool for the detection of maternal drinking. A second putative factor contributing to variability may be the voluntary drinking paradigm itself. We endeavored to minimize this by screening female drinking behavior during the prepregnancy period and removing females from the study whose ethanol consumption was greater than 1 standard deviation below the mean of the entire group. Furthermore, although there were some small day-to-day variations in ethanol consumption by individual rat dams, the ethanol-exposed placentas used in this study were harvested from four ethanol-consuming dams whose mean daily ethanol consumption was within the standard error of the mean range shown in Table 1. A third factor relates to intrauterine variability in ethanol's effects (Mitchell et al., 2002). We strove to minimize this factor in our initial study by only selecting placentas attached to female fetuses, from litters with greater than nine pups (average litter size was 13), where at least one of the adjacent fetuses was male and the location of the selected fetal–placental unit was a least one position away from either the proximal or distal end of a uterine horn. Even with the incorporation of these selection criteria, it is likely that ethanol has variable effects within a litter and that this putative effect on gene expression will require more detailed investigation.

A fourth issue relates to the fact that we opted to examine whole placenta in this initial study. The placenta contains multiple cell types and, in some cases, it is clear that some genes are primarily expressed in more discreet regions within the placenta (Sood et al., 2006). Thus, sampling from whole placenta diminished our “signal to noise ratio” for detecting effects of ethanol on gene expression. For example, placental gene and protein expression of the CB1 receptor is primarily located in the syncytiotrophoblast layer near the surface facing the maternal boundary (Park et al., 2003). A similar distribution has been observed for 11β-HSD2 in preliminary in situ hybridization studies (unpublished observations). Although we were able to confirm ethanol-induced gene repression of both the CB1 receptor and HSD2 in whole placenta (Table 4), the question remains as to how many genes whose expression is heterogeneously distributed across placenta were missed in an analysis of the effects of ethanol on whole placenta. Subsequent histological approaches using in situ hybridization to examine gene expression and immuno- and radiohistochemical approaches for quantitating protein will be required to better address this question. Finally, another factor contributing to variability is that we were limited in our ability to analyze a larger number of samples in a preliminary microarray analyses. It is likely that larger sample sizes would have resulted in more significantly altered genes based on the qRT-PCR analysis. Subsequent studies will use larger sample sizes to better address this point.

Considerable work remains to confirm the utility of placental gene alterations as a biomarker system, both for detecting ethanol consumption and a prognostic indicator of adverse neurobehavioral outcomes in the absence of morphological alterations. Systematic examination of altered gene expression as a function of different levels and patterns of ethanol consumption as well as the persistence of gene alterations after the last drinking episode are critical translational research questions to address. Further, how the presence of other common pregnancy risk factors impacts ethanol-induced alterations in placental gene expression patterns needs to be determined. For example, how will concomitant exposure to such factors as nicotine, other drugs of abuse, stress, malnutrition, or heavy metals modify a biomarker signature pattern? Data from such studies would be critical for interpreting altered patterns of placental gene expression in clinical studies.

The results of this initial study also provide intriguing insights into the implications of maternal drinking on placental function and putative mechanisms of ethanol teratogenesis. At a more global level, the GO analyses of biological processes (Fig. 2) indicated that ethanol has significant effects on genes associated with organ morphogenesis as well as nervous, endocrine, and immune system development and function. This observation is consistent with a wealth of literature indicating that prenatal ethanol exposure affects organ development (Weinberg, 1994, Byrnes et al., 2003; Qiang et al., 2002; Taylor et al., 1999), particularly the development of these three highly susceptible and critically interactive organ systems. Altered expression of genes associated with vascular, skeletal, and cartilage development, although less investigated in the fetal alcohol research field to date, clearly merit additional study.

Of particular interest was the observation that a number of placental genes altered by moderate ethanol exposure are known to play critical roles in pattern formation during nervous system development. For example, interactions of the members of the TGFβ family, such as bone morphogenic protein (BMP)4 and the BMP4 antagonist chordin, help regulate polarity (i.e., back to front patterning) of the developing embryo (Chesnutt et al., 2004; Millet et al., 2001). Likewise, the products of the WnT and Notch signaling–related genes ASCL2, HeYL, SFRP4, SFRP5, and WnT1 are known to regulate cell fate during the induction of both the central and peripheral nervous system (Ciani and Salinas, 2005; Nakagawa et al., 2001). Further, both the products of SEMA3B, semaphorin 3B (Falk et al., 2005) and of THBS4, thrombospondin (Arber and Caroni, 1996) are important for axonal growth and guidance. It is also important to note that similar levels of these genes occur in both placenta and fetal brain (Genomics Institute of the Novartis Research Foundation website Taken together, these observations suggest that some changes in placental gene expression may be predictive of similar changes in gene expression in fetal brain, and that the placenta could serve as a window on brain development. Follow-up studies examining both placental and fetal brain gene expression in the same placental–fetal brain unit will directly address this supposition and, in the process, could strengthen the prospects of establishing meaningful cause–effect relationships that will further our understanding of ethanol's impact on early developmental processes.

GO analyses of molecular processes also suggested important effects of maternal ethanol consumption on endocrine mechanisms (Fig. 3). Of particular note are the systems that regulate corticosterone and thyroid hormone. The expression of 11β-HSD2 mRNA was significantly reduced by maternal ethanol consumption (Table 4). Placental HSD2 inactivates corticosterone, and the enzyme plays a critical role in regulating the levels of maternal corticosterone that cross the placenta and enter fetal circulation (Michael et al., 2003; Waddel et al., 1998). If reduced gene expression results in diminished HSD2 protein or enzymatic activity, the fetus may be exposed to abnormally high levels of corticosterone, which has been shown to have deleterious effects on brain development (Holmes et al., 2006; Welberg et al., 2000; Weinstock, 2007) and longer-term consequences (see review by Seckl and Holmes, 2007). In contrast to the neuroprotective effects of placental HSD2 during development, placental iodothyronine deiodinase 2 (DIO2) is responsible for the conversion of maternal thyroxine (T4) to the triiodothyronine (T3), the physiologically active form of thyroid hormone. Placental conversion of maternal T4 to T3 provides the only source of active thyroid hormone to the fetus through most of gestation in rodents (Morreale de Escobar et al., 1987). T3 regulates the expression of a large number of molecules important in fetal development, including neurotropic factors (Alvarez-Dolado et al., 1994), cytoskeletal elements (Silva and Rudas, 1990), and extracellular matrix molecules, such as L1 (Alvarez-Dolado et al., 2000), which is also affected by relatively low levels of ethanol exposure (see review by Bearer, 2001b). Thus, if diminished DIO2 protein or activity follows from an ethanol-induced reduction in placental DIO2 gene expression (Table 4), the fetus may be subject to a broad array of immediate and prolonged neurodevelopmental consequences as a function of a hypothyroid environment during most of the prenatal period.

GO analysis of molecular processes also suggested important effects of ethanol exposure on calcium ion binding and various types of protein- and carbohydrate-binding interactions in placenta (Fig. 3). Of particular note was ethanol's impact on three of the S100 calcium binding proteins (Table 4). The members of the S100 family are multifunctional signaling proteins that influence with many cellular events. S100B, S100A4, and S100G appear to be involved in neurotrophic and/or neuroprotective processes (Donato 2007; Druse et al., 2007; Santamaria-Kisiel et al., 2006). The literature on the function of these proteins in placental tissue is sparse. However, S100B is highly abundant in the nervous system, predominantly in astroglia, exhibiting temporal and spatial concentration patterns during brain maturation. Although the mechanisms of action of these proteins are not completely understood, they have protective neurotrophic effects during brain development, and alterations may serve as early, quantitative indicators of fetal brain damage in some biological fluids, for example, cord blood (Michetti and Gazzolo, 2002). Of particular note is the observation that S100B acts as a trophic factor for the development of the brainstem serotonergic system, which is adversely affected by prenatal ethanol exposure (Druse et al., 1991; Zhou et al., 2001, 2005).

Other proteins whose gene expression was altered (Table 4) suggest that a number of additional placental functions important for fetal development may be compromised by maternal drinking during pregnancy. However, confirmation of this speculation will require quantitation of protein levels and function in placental tissues. Such studies will be challenging for a number of reasons, including the relative paucity of tools for quantitating these proteins by standard means. Many of these proteins are membrane associated and likely to be scarce enough to be difficult to detect by proteomic approaches. Further, a number of these proteins have not been studied in placental preparations and, in some cases, the function of these proteins is not well understood in any tissue type.

Nevertheless, these challenges do not diminish the diagnostic potential of altered placental gene expression as a biomarker of fetal alcohol exposure and fetal alcohol effects. Even in this preliminary report, a sufficiently large enough number of placental genes were altered by ethanol exposure to warrant more detailed investigation of placenta as a biomarker system. Given that these genes are also expressed in human placenta, it is reasonable to expect that these findings could translate into human studies of drinking during pregnancy. Further, the clinical relevance of our findings is underscored by the fact that these gene changes occur after moderate intermittent ethanol consumption during pregnancy, a level that causes functional brain damage and learning deficits in the absence of any observable dysmorphologic effects in rat offspring. With the growing realization that most of the children with FASD exhibit neurobehavioral deficits in the absence of dysmorphologic features, the discovery of more sensitive biomarkers of fetal alcohol effects becomes an increasingly important objective for earlier diagnosis and treatment of FASD.


The authors thank the technical support of Marilee Morgan and Gavin Pickett at the Keck UNM Genomics Shared Resource, which is supported by the UNM Cancer Research and TreatmentCenter, the WM Keck Foundation,and the State of New Mexico. We also thank Ms. Denise Cordaro and Ms. Christie Wilcox for their outstanding animal care support for this project. This work was supported by AA15420, AA16619, AA17068, MH19101, and Dedicated Research Funds from the UNM Health Sciences Center.


Table A1

Primer sets used in quantitative real-time polymerase chain reaction validation of candidate placental genes listed in Table 4

SymbolGene nameAffymetrix IDForward primerReverse primerAmplicon bp sizePositiona
CNRNA7Nicotinic cholinergic receptor α71387419_atTATCACCACCATGACCCTGACAGAAACCATGCACACCAGT81121903
EFEMP1EGF-containing fibulin-like extracellular matrix protein 11390112_atCCGGGTTCCTTTTACTGTCACCACTTGGTAACCCTGAGGA28674674
FMO3Flavin-containing monooxygenase 31368304_atGAGAAACCAACCATGGCAGTCTGGGGTCCTTGAGAAACAG28415144
Hand2Heart and neural crest derivatives expressed 21369818_atCAAGGCGGAGATCAAGAAGATGGTTTTCTTGTCGTTGCTG8194561
HSD11B211β-hydroxysteroid dehydrogenase type 21368102_atGCTATTGCACTGCTCATGGAGCAATGCCATTCTGAGTGAA2354060
IGFBP6Insulin growth factor binding protein 61387625_atCAGAGACCGGCAAAAGAATCCTGCTTGCGGTAGAAACCTC1932779
LBPLippopolysaccharide binding protein1387868_atAAGGCGCAAGTGAGACTGATAGTCGAGGTCGTGGAGCTTA172216
MMP10Matrix metalloproteinase protein 10 (Stromolysin2)1368713_atGGATAAAGGCTTCCCGAGACTGTGATGATCCACGGAAGAA1116274
MMP2Matrix metalloproteinase protein 2 (gelatinase A)1370301_atGGATACAGGTGTGCCAAGGTTCGGTGAGAAAAATGCAGTG14137
MMP3Matrix metalloproteinase protein 31368657_atGATCGATGCAGCCATTTCTTCACTTTCCCTGCATTTGGAT2359908
S1004AS100 calcium binding protein A41367846_atCAACGAGGGTGACAAGTTCATGCAGGACAGGAAGACACAG1821281
S100BS100 calcium binding protein G1386903_atGGTGACAAGCACAAGCTGAATGGAGACGAAGGCCATAAAC17228294
S100GS100 calcium binding protein B1368339_atCTCTGGCAGCACTCACTGACGCTGGGGAACTCTGACTGAA16424
SFRP4Secreted frizzled-related protein 41368394_atTATGACCGTGGAGTGTGCATCGATCAGGGCTCAGATGTTT140485
SFRP5Secreted frizzled-related protein 51393069_atTCCTCTGGACAACGACCTCTCTTAATGCGCATCTTGACCA163484
SMPXSmall muscle protein, X-linked1370165_atAGCCTCCCAGAAGGAAAGAGCCATTGAGAAAGCACGTCAA21215686
SPINK5Serine peptidase inhibitor, Kazal type 51398688_atTTAGAGCACCAGCTGAGCAAGCCTTGTGGACATGACAGTG20230
TGFATransforming growth factor alpha1387450_atGGTTTTTGGTGCAGGAAGAGGGCACCACTCACAGTGCTT21969269
TRHThyrotropin releasing hormone1368912_atCAGAACGTCGATTCTTGTGGTTCTCCCAAGTCTCCCCTCT1521515
TRPC4Transient receptor potential cation channel C41369164_a_atGATGGCGGACTTCAGGATTACAGGTGAGAATTGGCAGTGA240100275
aPosition number refers to the first base of the target sequence from transcription start.

Table A2

Differentially regulated genes (304) in microarray experiment filtered by statistical significance and fold change greater than two, excluding expressed sequence tags (ESTs) and not annotated genes

Affymetrix IDSymbolRefSeqDownregulated gene nameFold Δ
1369113_atGREM1NM_019282Gremlin 1, cysteine knot SuperFamily–25.3
1385568_atDIO2NM_031720Deiodinase, iodothyronine type II–25.2
1368731_atORM1NM_053288Orosomucoid 1–17.0
1388269_atHBG1NM_172093Hemoglobin, gamma A–16.2
1390112_atEFEMP1NM_001012039EGF-containing fibulin-like extracellular matrix protein 1–15.8
1369677_atCNR1NM_012784Cannabinoid receptor 1–15.5
1368304_atFMO3NM_053433Flavin-containing monooxygenase 3–14.2
1368394_atSFRP4NM_053544Secreted frizzled-related protein 4–13.9
1368912_atTRHNM_013046Thyrotropin-releasing hormone–13.3
1398688_atSPINK5XM_341607Serine peptidase inhibitor, Kazal type 5–10.7
1374558_atICOSLGXM_574731Inducible T-cell co-stimulator ligand–9.31
1387563_atPGRNM_022847Progesterone receptor–9.21
1369625_atAQP1NM_012778Aquaporin 1–9.15
1367846_atS100A4NM_012618S100 calcium binding protein A4–8.78
1367627_atGATMNM_031031Glycine amidinotransferase–8.26
1370843_atGNG8NM_139185Guanine nucleotide binding protein, gamma 8–8.25
1369695_atWT1NM_031534Wilms tumor 1–8.14
1393069_atSFRP5XM_219887Secreted frizzled-related protein 5–7.86
1369164_a_atTRPC4NM_080396Transient receptor potential cation channel C4–7.63
1387450_atTGFANM_012671Transforming growth factor alpha–7.62
1371102_x_atHBDNM_033234Hemoglobin, delta–7.35
1369817_atHAND2NM_022696Heart and neural crest derivatives expressed 2–6.97
1388270_atHBE1NM_001008890Hemoglobin, epsilon 1–6.91
1368919_atPGFNM_053595Placental growth factor, VEGF-related protein–6.46
1396407_atGAS8NM_001039030Growth arrest-specific protein 8–6.43
1380206_atKIF5CXM_221307Kinesin family member 5C–6.36
1388138_atTHBS4XM_342172Thrombospondin 4–5.70
1367794_atA2MNM_012488Alpha2 macroglobulin–5.62
1387656_atSLC4A1NM_012651Solute carrier family 4, anion exchanger 1–5.60
1368713_atMMP10NM_133514Matrix metalloproteinase 10 (stromelysin 2)–5.49
1368342_atAMPD3NM_031544Adenosine monophosphate deaminase E–5.18
1386903_atS100BNM_013191S100 calcium binding protein B–4.93
1370301_atMMP2NM_031054Matrix metalloproteinase 2–4.87
1387295_atSLC6A12NM_017335Solute carrier family 6, betaine/GABA, member 12–4.69
1387868_atLBPNM_017208Lipopolysaccharide binding protein–4.66
1382757_atFOXL2XM_345975Forkhead box l2–4.62
1368362_a_atASGR2NM_017189Asialoglycoprotein receptor 2–4.49
1380432_atCMAHXM_341876Cytidine monophosphate-n-acetylneuraminic acid hydroxylase–4.46
1385182_atPKP1XM_222666Plakophilin 1–4.41
1387659_atGDANM_031776Guanine deaminase–4.33
1387625_atIGFBP6NM_013104Insulin-like growth factor binding protein 6–4.26
1383792_atSYTL1NM_001025651Synaptotagmin-like 1–4.19
1389670_atHOXA11XM_575479Homeobox A11–4.19
1386921_atCPENM_013128Carboxypeptidase E–4.17
1377311_atEMX2XM_574698Empty spiracles homolog 2–4.11
1388292_atKCNJ3NM_031610Potassium inwardly-rectifying channel J3–4.08
1387025_atDYNC1I1NM_019234Dynein, cytoplasmic 1, intermediate chain 1–4.04
1373032_atMUSTN1NM_181368Musculoskeletal embryonic nuclear protein 1–4.02
1372254_atSERPING1NM_199093Serpin peptidase inhibitor, Clade G (C1 inhibitor)–3.95
1368413_atABP1NM_022935Amiloride binding protein 1–3.93
1368914_atRUNX1NM_017325Runt-related transcription factor 1–3.93
1372065_atART3NM_001012034ADP–ribosyltransferase 3–3.92
1387004_atNBL1NM_031609Neuroblastoma, suppression of tumorigenicity 1–3.90
1388608_x_atHBA2NM_001013853Hemoglobin, alpha 2–3.90
1387419_atCHRNA7NM_012832Nicotinic cholinergic receptor alpha 7–3.89
1367992_atSCG5NM_013175Secretogranin V (7B2 protein)–3.88
1369773_atBMP3NM_017105Bone morphogenetic protein 3–3.88
1376198_atASAMNM_173154Adipocyte-specific adhesion molecule–3.87
1389160_atERAFXM_215059Erythroid-associated factor–3.86
1387200_atOLIG1NM_021770Oligodendrocyte transcription factor 1–3.84
1369430_atBCMO1NM_022862Beta-carotene 15, 15’-monooxygenase 1–3.74
1378745_atPER3NM_023978Period homolog 3–3.73
1368081_atABCA2NM_024396ATP-binding cassette, sub-family A (ABC1), member 2–3.71
1369735_atGAS6NM_057100Growth arrest-specific 6–3.69
1391534_atELOVL2XM_574001Elongation of very long chain fatty acids-like 2–3.64
1377867_atQPCTXM_233812Glutaminyl-peptide cyclotransferase–3.61
1367985_atALAS2NM_013197Delta-aminolevulinate, synthase 2–3.60
1372649_atHSPB7XM_342966Heat shock 27 kDa protein family member 7–3.60
1369572_atMCPT1NM_017145Mast cell protease 1–3.58
1369464_atZP1NM_133569Zona pellucida glycoprotein 1–3.57
1388569_atSERPINF1NM_177927Serpin peptidase I, Clade F (alpha-2 antiplasmin)–3.52
1393588_atCLDN14NM_001013429Claudin 14–3.51
1377643_atHOXD10XM_221510Homeobox D10–3.47
1367566_atSCGB1A1NM_013051Secretoglobin 1A1–3.46
1378898_atDDX19ANM_001005381Dead box polypeptide 19A–3.44
1368583_a_atHRGNM_133428Histidine-rich glycoprotein–3.44
1394316_a_atTSPAN5NM_001004090Tetraspanin 5–3.42
1391018_atMYO5CXM_236411Myosin VC–3.41
1372335_atPCGF1NM_001007000Polycomb group ring finger 1–3.38
1369926_atGPX3NM_022525Glutathione peroxidase 3–3.35
1369520_a_atBCAT1NM_017253Branched-chain aminotransferase 1–3.33
1377336_atSEMA3BXM_343479Semaphorin 3B–3.31
1388170_atKCTD1XM_214617Potassium channel tetramerisation domain-containing 1–3.32
1367847_atNUPR1NM_053611Nuclear protein 1–3.27
1368102_atHSD11B2NM_017081Hydroxysteroid (11-beta) dehydrogenase 2–3.22
1387982_atTLR4NM_019178Toll-like receptor 4–3.21
1379039_atCMKLR1NM_022218Chemokine-like receptor 1–3.21
1388204_atMMP13XM_343345Matrix metalloproteinase 13 (collagenase 3)–3.21
1378673_atMITFXM_001065525Microphthalmia-associated transcription Factor–3.19
1368167_atCTSENM_012938Cathepsin E–3.16
1386637_atFGL2NM_053455Fibrinogen-like 2–3.14
1368338_atCD52NM_053983CD52 molecule–3.12
1397516_atALG2XM_232987Asparagine-linked glycosylation 2 homolog–3.08
1382612_atHOXA9XM_001057018Homeobox A9–3.06
1393219_atC2NM_172222Complement component 2–3.06
1398398_atHOXA10XM_347220Homeobox A10–3.03
1377729_atELOVL4XM_236476Elongation of very long chain fatty acids-like 4–3.03
1389408_atRRM2NM_001025740Ribonucleotide reductase M2 polypeptide–3.02
1388240_a_atITGA7NM_030842Integrin, alpha 7–3.01
1387011_atLCN2NM_130741Lipocalin 2 (oncogene 24p3)–3.01
1368464_atCLEC10ANM_022393C-type lectin domain 10A–2.99
1398253_atKAPNM_052802Kidney androgen-regulated protein–2.99
1379345_atCOL15A1XM_216399Collagen, type XV alpha 1–2.99
1367919_atNUP210NM_053322Nucleoporin 210 kDa–2.98
1370895_atCOL5A2XM_343564Collagen, type V, alpha 2–2.98
1367998_atSLPIXM_215940Secretory leukocyte peptidase inhibitor–2.97
1367960_atARL4ANM_019186ADP-ribosylation factor-like 4A–2.96
1370665_atHYOU1NM_001034028Hypoxia up-regulated 1–2.94
1367774_atGSTA1NM_031509Glutathione s-transferase A1–2.94
1391201_atWDHD1XM_223933WD repeat and HMG-box DNA binding protein 1–2.92
1386889_atSCD2NM_031841Stearoyl-coenzyme A desaturase 2–2.90
1368893_atCAP2NM_053874CAP, adenylate cyclase-associated protein 2–2.87
1377772_atTMEFF1NM_023020Transmembrane protein w/EGF-& follistatin-like domains 1–2.84
1368860_atPHLDA1NM_017180Pleckstrin homology-like domain A1–2.83
1398304_atFZD2NM_172035Frizzled homolog 2–2.82
1368490_atCD14NM_021744CD14 molecule–2.82
1383862_atCLEC2DXM_342769C-type lectin domain 2D–2.79
1385665_atADAM19XM_220328ADAM metallopeptidase domain 19 (Meltrin beta)–2.76
1386884_atHTRA1NM_031721HTRA serine peptidase 1–2.71
1375123_atSOX4XM_344594SRY (sex determining region y)-box 4–2.70
1370361_atCGREF1NM_139087Cell growth regulator with EF-hand domain 1–2.69
1368657_atMMP3NM_133523Matrix metalloproteinase 3 (stromelysin 1, progelatinase)–2.67
1368721_atASCL2NM_031503Achaete-scute complex homolog 2–2.67
1393808_atFA2HXM_001073350Fatty acid 2-hydroxylase–2.66
1371081_atRAPGEF4XM_215985RAP guanine nucleotide exchange F 4–2.64
1371087_a_atMAP6NM_017204Microtubule-associated protein 6–2.64
1382809_atCIRBPNM_031147Cold inducible RNA binding protein–2.60
1367564_atNPPANM_012612Natriuretic peptide precursor A–2.60
1370236_atPPT1NM_022502Palmitoyl-protein thioesterase 1–2.58
1370034_atCDC25BNM_133572Cell division cycle 25 homolog B–2.58
1388902_atLOXL1NM_001012125Lysyl oxidase-like 1–2.58
1370260_atADD3NM_031552Adducin 3, gamma–2.52
1368681_atPTHLHNM_012636Parathyroid hormone-like hormone–2.51
1368021_atADH1CNM_019286Alcohol dehydrogenase 1C gamma polypeptide–2.51
1377950_atIIGP1NM_001024884Interferon-inducible GTPase 1–2.50
1371989_atHMGN3NM_001007020High mobility group nucleosomal binding domain 3–2.47
1368970_atCDH23NM_053644Cadherin-like 23–2.44
1376973_atSDCBP2NM_001025692Syndecan binding protein 2–2.44
1392965_a_atSMOC2XM_214777SPARC-related modular calcium binding 2–2.43
1370384_a_atPRLRNM_001034111Prolactin receptor–2.41
1369977_atUCHL1NM_017237Ubiquitin carboxyl-terminal esterase 11 (Ubiquitin thiolesterase)–2.40
1384073_atADHFE1NM_001025423Alcohol dehydrogenase, iron-containing, 1–2.39
1387972_atMUCDHLNM_138525Mucin and cadherin-like protein–2.39
1369012_atINHBANM_017128Inhibin, beta A (activin A, activin AB alpha polypeptide)–2.38
1367816_atHOPNM_133621Homeodomain-only protein–2.37
1389746_atNAGLUXM_340905N-acetylglucosaminidase, alpha–2.35
1387873_atWFDC1NM_133581WAP four-disulfide core domain 1–2.34
1368503_atGCH1NM_024356GTP cyclohydrolase 1–2.33
1370633_atCXCL1NM_138522Chemokine ligand 1–2.33
1372042_atCMTM3XM_226200CKLF-like marvel transmembrane domain containing 3–2.33
1367705_atGLRXNM_022278Glutaredoxin (thioltransferase)–2.31
1374151_atTM6SF1NM_145785Transmembrane 6 SuperFamily, member 1–2.30
1397758_atGNPTABNM_001007750N-acetylglucosamine-1-phosphate Transferase, α & β subunits–2.29
1379075_atMBOAT2XM_234011Membrane bound O-acyltransferase domain containing 2–2.29
1367568_a_atMGPNM_012862Matrix GLA protein–2.28
1370714_a_atST6GAL1NM_147205ST6 beta-galactosamide alpha-2,6-sialyltranferase 1–2.27
1372715_atSFXN1NM_001012213Sideroflexin 1–2.27
1376697_atCHST12NM_001037775Carbohydrate (chondroitin 4) sulfotransferase 12–2.27
1368367_atCUZD1NM_054005Cub and zona pellucida-like domains 1–2.27
1385559_atDNHD3NM_001126292Dynein, axonemal, heavy chain 2–2.26
1383363_atDIRAS2XM_225214DIRAS family, GTP-binding RAS-like 2–2.25
1377573_atCA5BNM_001005551Carbonic anhydrase VB, mitochondrial–2.24
1370291_atPDLIM3NM_053650PDZ and LIM domain 3–2.24
1367722_atDPP7NM_031973Dipeptidyl-peptidase 7–2.23
1370026_atCRYABNM_012935Crystallin, Alpha B–2.23
1369724_atF13A1NM_021698Coagulation factor XIII, A1 polypeptide–2.22
1372301_atAEBP1XM_223583AE binding protein 1–2.22
1368005_atITPR3NM_013138Inositol 1,4,5-triphosphate receptor 3–2.22
1385013_atWNT1XM_235639Wingless-type MMTV integration site 1–2.22
1387588_atEHD3NM_138890EH-domain containing 3–2.21
1398311_a_atKIDINS220NM_053795Kinase D-interacting substance of 220 kDa–2.21
1370182_atPTPRN2NM_031600Protein tyrosine phosphatase receptor N2–2.21
1389423_atDDR2NP_113952Discoidin domain receptor family 2–2.21
1397164_atPOLA2NM_053480Polymerase (DNA-directed), alpha 2 (70 kDa Subunit)–2.20
1390233_atGLI2NM_001107169Gli-kruppel family member GLI2–2.20
1390846_atCOL16A1XM_345584Collagen, type XVI, alpha 1–2.20
1383630_atDOK3XM_225170Docking protein 3–2.20
1390882_atHEYLNM_001107977Hairy/enhancer-of-split related with VRPW motif-like–2.20
1367859_atTGFB3NM_013174Transforming growth factor beta 3–2.20
1387505_atGNAI1NM_013145G-Protein, alpha inhibiting activity polypeptide 1–2.19
1389651_atAPLNNM_031612Apelin, AGTRL1 ligand–2.18
1374863_atRBP7XM_575960Retinol binding protein 7–2.16
1367823_atTIMP2NM_021989TIMP metalloproteinase inhibitor 2–2.16
1368292_atDNM1NM_080689Dynamin 1–2.16
1375033_atCPT1CXM_218625Carnitine palmitoyltransferase 1C–2.15
1374849_atADAMTS7XM_236471ADAM metallopeptidase with thrombospondin type 1 motif, 7–2.15
1370342_atKCNK2NM_172041Potassium channel K2–2.15
1393245_atPHYHNM_053674Phytanoyl-CoA 2-hydroxylase–2.13
1371237_a_atMT1ENM_138826Metallothionein 1E–2.13
1369443_atANGPTL2NM_022926Angiopoietin-like 2–2.13
1389739_atNEURL2XM_230848Neuralized homolog 2–2.11
1369640_atGJA1NM_012567Gap junction protein, alpha 1, 43 kDa (Connexin 43)–2.11
1377163_atINHBBXM_344130Inhibin, beta b (Activin AB beta polypeptide)–2.10
1388070_a_atAKAP1NM_053665A Kinase (PRKA) anchor protein 1–2.10
1381226_atNAV1XM_222662Neuron navigator 1–2.10
1370713_atCDC2L1NM_145766Cell division cycle 2-like 1 (PITSLRE proteins)–2.10
1375871_atSLC35B1NM_199081Solute carrier family 35, member B1–2.10
1378671_atCREBBPNM_133381CREB binding protein (Rubinstein-Taybi syndrome)–2.10
1398295_atSLC29A1NM_031684Solute carrier family 29 (nucleoside transporters), member 1–2.10
1369141_atCSH1NM_017363Chorionic somatomammotropin hormone 1–2.08
1396831_atMAML3XM_227165Mastermind-like 3–2.08
1375243_atBTBD2XM_576181BTB (POZ) domain-containing 2–2.08
1382511_atE2F1XM_230765E2F transcription factor 1–2.07
1368006_atLAPTM5NM_053538Lysosomal-associated multispanning membrane protein 5–2.07
1373970_atIL33NM_001014166Interleukin 33–2.07
1385587_atMCOLN2NM_001039005Mucolipin 2–2.07
1369194_a_atCDKN2ANM_053434Cyclin-dependent kinase inhibitor 2A–2.07
1379495_atPLXDC2NM_001108422Plexin domain-containing 2–2.07
1377699_atBACH1XM_221712BTB and CNC homology 1–2.06
1384667_x_atGALR2NM_019172Galanin receptor 2–2.05
1370202_atHRASLS3NM_017060HRAS-like suppressor 3–2.05
1387952_a_atCD44NM_012924CD44 molecule–2.04
1386953_atHSD11B1NM_017080Hydroxysteroid (11-beta) dehydrogenase 1–2.04
1368254_a_atSPHK1NM_133386Sphingosine kinase 1–2.04
1371441_atPEA15NM_001013231Phosphoprotein-enriched in astrocytes 15–2.04
1369431_atGALNT7NM_053648Polypeptide n-acetylgalactosaminyltransferase 7–2.03
1389533_atFBLN2XM_232197Fibulin 2–2.01
1367882_atMAP1ANM_030995Microtubule-associated protein 1A–2.01
1387296_atCYP2J2NM_023025Cytochrome P450 2J2–2.01
1394451_atANXA1NM_012904Annexin A1–2.01
Affymetrix IDSymbolRefSeqUpregulated gene nameFold Δ
1386980_atAPOMNM_019373Apolipoprotein M2.00
1370259_a_atPTHR1NM_020073Parathyroid hormone receptor 12.00
1369331_a_atUNC13BNM_031550unc-13 homolog B2.01
1391345_atBMPERNM_001135799BMP binding endothelial regulator2.02
1372690_atRTN1NM_181377Reticulon 12.02
1398383_atCYB561XM_221030Cytochrome B-5612.02
1375726_atLMO7NM_001001515LIM domain 72.02
1376799_a_atCRLF1XM_214312Cytokine receptor-like factor 12.02
1370420_atSRD5A1NM_017070Steroid-5 alpha-reductase2.02
1368785_a_atPITX2NM_019334Paired-like homeodomain transcription factor 22.03
1369756_a_atSLC4A4NM_053424Solute carrier family 4, sodium bicarbonate cotransporter 42.03
1386943_atPLLPNM_022533Transmembrane 4 SuperFamily, member 112.03
1385961_atKLF5NM_053394Kruppel-like factor 52.04
1374273_atCXADRNM_053570Coxsackie virus and adenovirus receptor2.04
1377304_atCDC26NM_001013240Cell division cycle 26 homolog2.05
1375849_atRGMANM_001107524Rgm domain family, member a2.05
1368247_atHSPA1BNM_212504Heat shock 70 kDa protein 1B2.06
1389735_atRPS6KA6XM_228473Ribosomal protein S6 kinase, 90 kDa, polypeptide 62.08
1387298_atPGA5NM_021753Pepsinogen 5, group I (Pepsinogen a)2.12
1387232_atBMP4NM_012827Bone morphogenetic protein 42.13
1392556_atSHROOM3XM_223229Shroom family member 32.16
1383981_atTRP53BP2XM_223012Tumor protein p53 binding protein 22.17
1394663_atPOLSXM_225072Polymerase (DNA-directed) Sigma2.18
1392773_atPCSK5XM_342032Proprotein convertase subtilisin/kexin type 52.20
1389276_atL3MBTL3XM_001062689L(3)MBT-like 32.20
1383747_atECT2XM_342220Epithelial cell transforming sequence 2 oncogene2.20
1387574_atCHRNA2NM_133420Nicotinic cholinergic receptor alpha 22.22
1389066_atDSCR1L1NM_175578Down syndrome critical region gene 1-like 12.22
1375729_atEPHA4XM_244186EPH receptor A42.25
1369727_atAPOA2NM_013112Apolipoprotein A-II2.29
1377379_atIRF6XM_344194Interferon regulatory factor 62.33
1368339_atS100 GNM_012521S100 calcium binding protein G2.35
1386396_atDUSP8XM_341963Dual specificity phosphatase 82.36
1367598_atTTRNM_012681Transthyretin (prealbumin, amyloidosis Type I)2.37
1371030_atSPP2NM_053577Secreted phosphoprotein 22.39
1386873_atTNNI1NM_017184Troponin I type 12.40
1393139_atAPOC2XM_214872Apolipoprotein C-II2.40
1389234_atVWFXM_342759Von Willebrand factor2.41
1371059_atPRKAR2ANM_019264CAMP-dependent protein kinase 2A2.44
1370463_x_atHLA-FNM_001008829Major histocompatibility complex, class I, F2.44
1389648_atRIPK4XM_221619Receptor-interacting serine-threonine kinase 42.44
1368280_atCTSCNM_017097Cathepsin C2.46
1384747_atGPR137BXM_237907G Protein-coupled receptor 137B2.51
1369837_atGULONM_022220Gulonolactone (L-) oxidase2.56
1387396_atHAMPNM_053469Hepcidin antimicrobial peptide2.59
1368278_atLGALS2NM_133599Lectin, galactoside-binding, soluble, 2 (galectin 2)2.59
1382965_atAMIGO3NM_178144Adhesion molecule with Ig-like domain 32.69
1375183_atID4NM_175582DNA binding inhibitor 4, dominant negative helix-loop-helix prot.2.80
1368442_atF2NM_022924Coagulation factor II (thrombin)2.85
1380621_atFESNM_001108488Feline sarcoma oncogene2.88
1379741_atATP6V0A4XM_231615ATPase, H+ transporting, lysosomal V0 subunit A42.92
1370086_atFGGNM_012559Fibrinogen, gamma chain2.92
1387414_atDUOX2NM_024141Dual oxidase 22.94
1368316_atAQP8NM_019158Aquaporin 82.97
1388190_atAPOBNM_019287Apolipoprotein B2.98
1378866_atABLIM1XM_217645Actin binding LIM protein 13.11
1368527_atPTGS2NM_017232Prostaglandin-endoperoxide synthase 23.16
1392703_atTBX4NM_001107034T-box 43.18
1387565_atTRPV6NM_053686Transient receptor potential cation channel V63.26
1370269_atCYP1A1NM_012540Cytochrome P450 1A13.33
1369663_atEPHX2NM_022936Epoxide hydrolase 24.10
1392948_atCLIC6NM_176078Chloride intracellular channel 64.33
1393891_atCOL8A1XM_221536Collagen, type VIII, alpha 14.40
1370310_atHMGCS2NM_1730943-Hydroxy-3-methylglutaryl-coenzyme A synthase 24.65
1368335_atAPOA1NM_012738Apolipoprotein A-14.88
1380134_atVTCN1NM_001024244V-set domain-containing T-cell activation inhibitor 15.08
1370594_atIGSF1NM_175763Immunoglobulin SuperFamily, member 15.99
1370165_atSMPXNM_053395Small muscle protein, X-linked13.7


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