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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Am J Obstet Gynecol. Author manuscript; available in PMC Jan 1, 2012.
Published in final edited form as:
PMCID: PMC3011026
NIHMSID: NIHMS234235

A transcriptional profile of the decidua in preeclampsia

Abstract

OBJECTIVE

To obtain insight into possible mechanisms underlying preeclampsia using genome-wide transcriptional profiling in decidua basalis.

STUDY DESIGN

Genome-wide transcriptional profiling was performed on decidua basalis tissue from preeclamptic (n = 37) and normal pregnancies (n = 58). Differentially expressed genes were identified and merged into canonical pathways and networks.

RESULTS

Of the 26,504 expressed transcripts detected, 455 were differentially expressed (P <0.05, FDR P <0.1). Both novel (ARL5B, SLITRK4) and previously reported preeclampsia-associated genes (PLA2G7, HMOX1) were identified. Pathway analysis revealed that ‘tryptophan metabolism’, ‘endoplasmic reticulum stress’, ‘linoleic acid metabolism’, ‘notch signaling’, ‘fatty acid metabolism’, ‘arachidonic acid metabolism’ and ‘NRF2-mediated oxidative stress response’ were overrepresented canonical pathways.

CONCLUSION

In the present study single genes, canonical pathways and gene-gene networks that are likely to play an important role in the pathogenesis of preeclampsia, have been identified. Future functional studies are needed to accomplish a greater understanding of the mechanisms involved.

Keywords: decidua, genome-wide gene expression, microarray, preeclampsia

INTRODUCTION

The etiology of preeclampsia is not fully understood, but a number of observations suggest that divergent abnormalities may be involved (immunological, inflammatory, vascular/ischemic).1 In a normal pregnancy extravillous trophoblasts (of fetal origin) invade decidua basalis and modify the spiral arteries. In preeclampsia, this pregnancy-associated adaption of spiral arteries may fail, with a hypoperfused placenta as a result. Oxidative stress is suggested to play a central role in the pathogenesis of preeclampsia,2 and may be generated in the decidua basalis.3, 4 Heritability of the disease has been estimated to be greater than 50%,5, 6 with both maternal and fetal (paternal) contributions.7

Microarray-based transcriptional profiling can be a powerful strategy for identification of disease-related genes and pathways,8 and this approach has been used for analysis of placental9 as well as decidual tissues6, 10, 11 from preeclamptic pregnancies. However, the data obtained have been inconsistent. In the case of the three decidual studies reported, the diverging results may be due to the relatively small number of samples analysed (≤12 preeclamptic samples included).6, 10, 11 In the current study we have applied genome-wide transcriptional profiling (measuring ≥48,000 transcripts from all known genes) on a large collection of decidual samples (from 37 preeclamptic and 58 normal pregnancies) to comprehensively investigate how gene expression at the maternal-fetal interface may be contributing to the pathogenesis of preeclampsia. We further aimed to identify the genetic canonical pathways and gene-gene interaction networks represented by the differently expressed genes using contemporary bioinformatic approaches.

MATERIALS AND METHODS

Human subjects

Women with pregnancies complicated by preeclampsia (n = 43) and women with normal pregnancies (n = 59) were recruited at St. Olavs’ University Hospital (Trondheim, Norway) and Haukeland University Hospital (Bergen, Norway) from 2002 to 2006. Preeclampsia was defined as persistent hypertension (blood pressure of ≥140/90 mmHg) plus proteinuria (≥0.3 g/L or ≥1+ by dipstick), developing after 20 weeks of pregnancy.12 Due to tissue sampling procedures, only pregnancies delivered by caesarean section were included. Women with preeclamptic pregnancies had caesarean section performed for medical indications, whereas women with normal pregnancies underwent caesarean section for reasons considered irrelevant to the aim of the study (e.g. breech presentation, cephalopelvic disproportion in previous delivery and fear of vaginal delivery). None of the included mothers were in labour prior to caesarean section. Exclusively healthy women with no history of preeclampsia were accepted in the normal pregnancy group. Multiple pregnancies, pregnancies with chromosomal aberrations, fetal and placental structural abnormalities or suspected perinatal infections were excluded from both study groups. The study was approved by the Norwegian Regional Committee for Medical Research Ethics. Informed consent was obtained from all participants prior to collection of decidual samples.

Decidual tissue collection

Samples of decidua basalis tissue were obtained by vacuum suction of the placental bed, a procedure which allows the collection of tissue from the whole placental bed.13 Collected samples were flushed with saline solution to remove excessive blood. The decidual tissue was immediately submerged in RNA-later (Ambion, Huntington, UK) and stored at −80°C.

Total RNA isolation

Total RNA was isolated using a trizol extraction protocol with chloroform interphase separation, isopropanol precipitation and ethanol wash steps. Precipitated total RNA was re-suspended in RNase free water and purified with an RNeasy Mini Kit using spin technology (Qiagen, Valencia, CA). Spectrophotometric determination of purified total RNA yield (μg) was performed using the NanoDrop ND-1000 (Wilmington, DE). Total RNA quality was measured using Agilent’s RNA 6000 Nano Series II Kit on a BioAnalyzer 2100 (Agilent Technologies, Germany). Ethical approval for total RNA processing and decidua expression analysis was obtained from the Institutional Review Board at The University of Texas Health Science Center in San Antonio.

Synthesis, amplification and purification of anti-sense RNA

Anti-sense RNA (aRNA) was synthesized, amplified and purified using the Illumina TotalPrep RNA Amplification Kit according to manufacturers’ instructions (Ambion, Austin TX). Synthesis of aRNA was performed using a T7 Oligo(dT) primer and the amplification underwent in vitro transcription with a T7 RNA polymerase to generate multiple copies of biotinylated aRNA from a double-stranded cDNA template. Purified aRNA yield was determined spectrophotometrically using the NanoDrop ND-1000.

Microarray data

Purified aRNA was hybridized to Illumina’s HumanWG-6 v2 Expression BeadChip (Illumina Inc. San Diego, CA). Washing, blocking and transcript signal detection (streptavidin-Cy3) was performed using Illumina’s 6×2 BeadChip protocol. Samples were scanned on the Illumina BeadArray 500GX Reader using Illumina BeadScan image data acquisition software (version 2.3.0.13). Illumina’s BeadStudio Gene Expression software module (version 3.2.7) was used to subtract background noise signals and generate an output file for statistical analysis.

Real-time RT-qPCR

We performed a technical replicate of the microarray experiment with quantitative real-time RT-PCR on six of the most differentially expressed transcripts using a 7900HT Fast Real-Time PCR instrument (Applied Biosystems, Foster City, CA). The six genes were prioritized for real-time RT-PCR based on beta values, FDR P-values and manual literature searches. Real-time RT-qPCR was run with 93 samples. Two of the total collection of 95 samples were excluded due to shortage of biological material. Pre-optimized TaqMan Gene Expression Assays (Applied Biosystems) were run, in triplicate, to measure mRNA expression levels relative to the reference genes, TBP and GAPDH. Reverse transcription and PCR amplification was performed in a two-step procedure, following Applied Biosystems High-Capacity cDNA ReverseTranscription Kit Protocol and TaqMan Gene Expression Master Mix Protocol. Negative controls were run, in triplicate, without RT enzyme or no cDNA template.

Statistical analysis

Transcript data for each sample was pre-processed and analysed using SOLAR14 as previously described.15 To evaluate the magnitude of differential gene expression the displacement of each detected transcript’s mean expression value was measured between the two groups. A standard regression analysis was performed on the preeclamptic group to test whether the mean transcription level differed from that of the normal pregnancy group.

The mRNA expression levels were calculated by the Comparative CT method as described elsewhere.16 For each target gene, the mean CT value for each sample was used for analysis, after exclusion of outliers. Outliers were determined as values more than 2SD from the mean. Delta CT (ΔCT) values were computed as the difference between the given mean value for a target gene and the mean of the CT values for the two reference genes.17 Fold change values were calculated, based on the differences in ΔCT values between tissue from preeclamptic women and normal pregnant women (2-ΔΔCT).16 A t-test statistic (SPSS 16.0) evaluated the difference between the ΔCT values of the preeclamptic pregnancies, as compared to the normal pregnancy group. Analysing for the two reference-genes separately did not change the results.

Canonical pathway and network identification

Differentially expressed transcripts in the preeclamptic group (P <0.05, false discovery rate (FDR)18 P <0.1) were imported into Ingenuity Pathways Analysis (IPA) v7.5 (www.ingenuity.com). Transcripts’ gene identifiers were mapped to their corresponding gene object in the Ingenuity Pathways Knowledge Base. IPA was used to bioinformatically identify canonical (i.e. cell signalling and metabolic) pathways and gene-gene interaction networks potentially involved in preeclampsia within our data set. IPA gene-gene networks were constructed from the published literature and they diagrammatically represent molecular relationships between gene-gene products.

Significant IPA pathways were further analyzed with Rotation Gene Set Enrichment Analysis (ROMER) pathway analysis, using the limma package, available via the Bioconductor Project (www.r-project.org).19

RESULTS

Human Subjects

The clinical information of women/pregnancies enrolled is presented in Table 1. Only those samples of sufficient RNA quality for gene expression analysis have been included. In the preeclamptic pregnancies, both mean gestational age and birth weight were lower than in the normal pregnancies (Table 1). As expected, the mean blood pressure was higher among preeclamptic than normal pregnancies (Table 1).

TABLE 1
Clinical characteristics of study groups

Decidual genome-wide transcriptional profiling

In total, 43 women with pregnancies complicated by preeclampsia and 59 women with normal pregnancies were included in the study. Six samples from preeclamptic pregnancies and one sample from a normal pregnancy were excluded from gene expression analyses due to low RNA quality. The 95 samples with good RNA quality were hybridized onto Illumina’s HumanWG-6 v2 genome-wide expression beadchip.

The non-normalized decidua basalis transcriptional profile data (n = 48,095) may be found at ArrayExpress (http://www.ebi.ac.uk/microarray-as/ae/) (accession code E-TABM-682). We detected 26,504 significantly expressed transcripts (55.1%), of which 455 were differentially expressed after FDR correction (P <0.05, FDR P <0.1); 285 were down-regulated and 170 were up-regulated. The significant differentially expressed transcripts are presented in Table 2 together with the corresponding P values (raw and FDR adjusted) and preeclampsia-correlated expression. The real-time RT-qPCR for the six genes (PLA2G7, ANGPTL2, MAN1A2, SLITRK4, FZD4 and ARL5B) tested showed a high grade of correlation with the microarray data (Table 3).

TABLE 2
Differentially expressed transcriptsa
TABLE 3
Results for the selected genes from microarray and RT-qPCR expression

Canonical pathways and network

The 455 differentially expressed transcripts were analyzed using IPA. The significant canonical pathways (P <0.01) are shown in Table 4, along with the included genes and P values. They included ‘tryptophan metabolism’, ‘endoplasmic reticulum stress’, ‘linoleic acid metabolism’, ‘notch signaling’, ‘fatty acid metabolism’, ‘arachidonic acid metabolism’ and ‘NRF2-mediated oxidative stress response’. All the canonical pathways identified in IPA were also found to be significant (P <0.01) using ROMER (Table 4), with the exception of the ‘NRF2-mediated oxidative stress response’ canonical pathway (IPA P = 0.009, ROMER P = 0.067).

TABLE 4
Canonical pathway analysis

Using network analysis in IPA, 59 of the preeclampsia associated genes could be connected into a single network of gene-gene product interactions (Figure 1). The genes in this network were among others involved in the function of endoplasmic reticulum (ER), oxidative stress, notch signaling and cell migration. The network included a cluster of 15 up-regulated genes (ATP2A2, TRAM1, FKBP2, HMOX1, SPCS2, ATF6, DNAJC3, EIF2AK3, PIGA, SEC23B, SEC24D, DNAJB9, SRPRB, DNAJB11 and XBP1) associated with ER stress and oxidative stress (Figure 1). All these genes were in a direct relationship to X-box binding protein 1 (XBP1). Epidermal growth factor receptor (EGFR) was another focus molecule with a direct relationship to seven other genes (PLCG1, NGF, MET, LRIG1, SLN, ATP2A2 and SHC2) in the network.

FIGURE I
The Ingenuity Pathway Analysis (Ingenuity Systems, www.ingenuity.com) generated gene/gene product interaction network of preeclampsia correlated genes. Genes or gene products are represented as nodes, and the biological relationship between two nodes ...

COMMENT

In this study, 455 differentially expressed transcripts were found when decidua basalis tissue from preeclamptic and normal pregnancies was compared. Some transcripts were novel findings (i.e., ARL5B and SLITRK4), whereas others, such as PLA2G720 and HMOX121, 22 have been reported to be associated to preeclampsia previously. Pathway analysis identified seven significant canonical pathways.

In our patient cohort, a lower gestational age was found in the preeclamptic group (average 32 weeks: range 28–36) as compared to the normal pregnancy group (39 weeks: range 38–40). This is not unexpected due to the need for early delivery in patients with severe preeclampsia. Since gene expression in uteroplacental tissues may be influenced by gestational age,23, 24 it cannot be excluded that some of the differences observed between the preeclamptic and normal pregnancy groups are in fact gestational age related. Winn et al compared global gene expression in basal plate (decidual) biopsies from normal pregnancies at mid-gestation (14 – 24 wk) and at term (37 – 40wk)23 and found that 418 genes (of 39,000 transcripts examined) changed expression throughout gestation. This provides a useful dataset for comparison with the data obtained in this current study, albeit different profiling platforms were used. Winn et al used the Affymetrix HG-U133 A&B chip for transcriptional profiling whereas we used the Illumina HumanWG-6 v2 Expression BeadChip. By this, the number of possible comparisons was restricted to the 16,799 genes shared in both systems. Of the 455 transcripts found to be differentially expressed in this current study, 368 genes demonstrate no gestational age influenced changes, according to the data of Winn et al.23 It is therefore tempting to speculate that the differential expression of these 368 genes may be related to disease mechanisms at play in preeclampsia. Seventeen of our differentially expressed genes (TEMEM97, KIAA1598, SULT2B1, EGFR, FHL1, PLA2G7, SHANK3, NOTCH4, UBASH3B, ROBO4, NRARP, GPR116, IL6ST, LDLR, ANGPTL2, SRPRB, KREMEN1) are reported to change expression with gestational age.23 For two of these genes (SULT2B1, EGFR), expression increases towards term.23 Thus, isolated gestational age related influences in the preeclampsia group would suggest a lower expression of SULT2B1and EGFR, but both were up-regulated in our dataset. Similarly, the ANGPTL2 gene is down-regulated towards term,23 but in contrast to what might be expected from gestational age related changes, expression was lower in the preeclampsia group than in the normal pregnancy group. Based on this, we conclude that the differential expression of these three genes may also be ascribed to disease related mechanisms. However, with regard to the remaining 14 genes in our dataset previously shown to exhibit gestational age dependent changes in expression, conclusions are hampered by the fact that gestational age may have contributed to the differences observed between preeclamptic and normal pregnancies. To illustrate; expression of FHL1, SHANK3, NOTCH4, ROBO4, NRARP and GPR116 increase towards term23 and were down-regulated in the preeclampsia group, whereas TMEM97, KIAA1598, PLA2G7, UBASH3B, IL6ST, LDLR, SRPRB and KREMEN1 expression decrease towards term23 and were up-regulated in the preeclampsia group.

In genome-wide transcriptional profiling, analysis of groups of genes is a strategy to increase power and reduce the dimensionality of the underlying statistical problem following multiple testing.25 Further, it may be advantageous to put focus on canonical pathways and networks instead of single genes when the aim is to obtain insight in the pathophysiology of complex diseases, such as preeclampsia. The high interconnectivity of focus genes with other correlated genes within a biological network may imply functional and biological importance of these genes.26, 27 To be able to assess this in a comprehensive manner, we increased the FDR cut off to 0.1 and consequently the number of genes included in the analysis. Using this approach, seven significant canonical pathways were found to be represented by the differentially expressed genes identified in this current study (Table 4).

The most significant canonical pathway detected was ‘tryptophan metabolism’. The metabolism of tryptophan, through the kynurenine pathway, has previously been suggested to be involved in preeclampsia pathogenesis,28, 29 and in accordance with this, the activity of the first enzyme of the kynurenine pathway, indoleamine 2,3 dioxygenase (IDO), has been reported to be reduced in placenta from preeclamptic pregnancies.28 We found no disease-associated changes in IDO expression, but the transcript encoding the enzyme kynureninase (KYNU) was up-regulated. KYNU metabolises L-kynurenine, which suppresses T-cell proliferation and natural killer cells and influences immunotolerance to foreign antigens.30 This implies that a consequence of KYNU up-regulation may be an increased inflammatory response (due to lack of L-kynurenine). An additional seven genes were assigned to this canonical pathway (Table 4).

The second most significant canonical pathway identified was the ‘endoplasmic reticulum stress pathway’. Three genes (EIF2AK3, ATF6 and XBP1) included in the unfolded protein response (UPR), a coordinated adaptive response to ER stress, were up-regulated. ER stress has previously been suggested as one of the main sources for generation of placental oxidative stress.31 Yung et al have reported similar associations of the UPR signalling pathways to preeclampsia in placental tissue32 but these findings are reported for the first time in decidual tissue. There is a close connection between oxidative stress and ER stress,31, 33 also indicated by the many direct relationships of the ER and oxidative stress related genes in the generated network (Figure 1). The canonical pathway ‘NRF2-mediated oxidative stress response’ was also among the significant pathways identified (Table 4). The nuclear factor NFR2 plays an essential role in the defence of oxidative stress by regulating the expression of antioxidant response elements (AREs).34 In case of excessive oxidative stress, activation by ROS, NO and pro-inflammatory cytokines, result in translocation of NRF2 to the nucleus. NRF2 binds to ARE sequences, leading to transcriptional activation of antioxidant genes (e.g. glutathione and HMOX1). ‘NRF2-mediated oxidative stress response’ included nine genes, of which three genes have previously been associated with preeclampsia; EIF2AK3,32 GSTA310 and HMOX1.21, 22 Several enzymes metabolize ROS to exportable compounds, and in this study the transcripts encoding the antioxidant enzymes GSTA3, HMOX1 and UBE2K were up-regulated.

Three of the remaining significant canonical pathways generated by IPA represented metabolism of fatty acids; ‘linoleic acid metabolism’, ‘fatty acid metabolism’ and ‘arachidonic acid metabolism’. The genes included in these pathways were partly overlapping, as shown in Table 4. Decidual arterioles of preeclamptic women show atherosclerotic-like lesions,35 suggesting an underlying atherogenic process of LDL lipid peroxidation.36 Lipid peroxidation contributes to the development of preeclampsia,37 and decidua basalis tissue from preeclamptic women has an increased content of lipid peroxides.4 The first enzyme of the fatty acid β-oxidation pathway, acyl-coenzyme A oxidase 1 (ACOX1)/palmitoyl-coA oxidase, donates electrons directly to molecular oxygen, thereby producing hydrogen peroxides. ACOX1 was found to be up-regulated, whereas acyl-coenzyme A oxidase 2 (ACOX2)/branched chain acyl-coA oxidase, which is involved in the degradation of long branched fatty acids and bile acid intermediates in peroxisomes, was found to be down-regulated. Two genes involved in elimination of lipid peroxidation products were also down-regulated in the material, alcohol dehydrogenase 1a (ADH1A), which metabolizes a wide variety of substrates including lipid peroxidation products, and aldehydedehydrogenase 3 family member A2 (ALDH3A2) isozymes, thought to play a major role in the detoxification of aldehydes generated by alcohol metabolism and lipid peroxidation. Increased generation or decreased elimination of lipid peroxidation products may be among the factors activating the maternal endothelium38 and triggering systemic inflammation in preeclampsia.

Finally, the pathway analysis suggested a role of ‘notch signalling’, with inclusion of four down-regulated genes; DTX3, HES1, NOTCH 3 and NOTCH 4. Notch signalling is known to be involved in cell differentiation, proliferation, apoptosis39 and blood vessel formation,40 processes neatly regulated in the placenta to maintain a normal pregnancy. Notch receptors are expressed on extravillous trophoblasts and are hypothesised to be involved in the differentiation and proliferation of both extravillous trophoblasts and endothelial cells.41 Placental villi from preeclamptic pregnancies show down-regulation of Notch pathway members.42 Notch signalling in placenta has been suggested to play a role in the development of preeclampsia,42, 43 and the altered expression of DTX and HES1 in tissue from preeclamptic pregnancies as compared to normal pregnancies, are presented for the first time.

In summary, we have provided a comprehensive transcriptional profile of the decidua in preeclampsia. Our network analysis has demonstrated extensive connectivity between the differently expressed genes. Alteration of the expression level of one gene may influence the transcription of others included in the network. Due to this, it is difficult to pin-point the genes having primary roles in perpetuating preeclampsia from our data set. Some of our findings confirm and elaborate the current knowledge on the pathophysiology of preeclampsia, while others are novel. Further studies are warranted to replicate findings and confirm involvement of specific genes that have been identified.

Acknowledgments

This work was supported by grants from The Norwegian University of Science and Technology (NTNU) (Mari Løset, Siv B. Mundal and Rigmor Austgulen), the Central Norway Regional Health Authority (Mona H. Fenstad), the Research Council of Norway (Mona H. Fenstad), the Fulbright Foundation for Educational Exchange (Mona H. Fenstad), National Institutes of Health grants R01 HD049847 (Eric K. Moses, John Blangero) and R01 MH059490 (John Blangero) and a grant from the Southwest Foundation Forum (Matthew P. Johnson). This investigation was conducted, in part, in facilities constructed with support for the Research Facilities Improvement Program grant C06 RR017515 from the National Center for Research Resources, National Institute of Health.

We thank all the delivering mothers whose participation made this work possible. We are grateful for the statistical expertise at Southwest Foundation for Biomedical Research, San Antonio, TX: Drs Jac C. Charlesworth and Thomas D. Dyer, and at the Norwegian University of Science and Technology, Trondheim, Norway: Dr Åsa Johansson and Jostein Johannesen at the FUGE bioinformatics platform. We thank Dr Linda Tømmerdal Roten for her valuable advice and comments made during the manuscript preparation.

Footnotes

Reprints: MD/PhD student Mari Løset, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Olav Kyrres gate 11, N-7006 Trondheim, Norway. maril/at/stud.ntnu.no

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