Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Hypertens Pregnancy. Author manuscript; available in PMC 2017 August 1.
Published in final edited form as:
PMCID: PMC5016246

Preeclampsia/eclampsia Candidate Genes Show Altered Methylation in Maternal Leukocytes of Preeclamptic Women at the Time of Delivery



To analyze methylation profiles of known preeclampsia/eclampsia (PE) candidate genes in normal (NL) and preeclamptic (PE) women at delivery.


A matched case-control study comparing methylation in 79 CpG sites/33 genes from an independent gene set in maternal leukocyte DNA in PE and NL (n=14 each) on an Illumina beadchip platform. Replication performed on second cohort (PE=12; NL=32).


PE demonstrates differential methylation in POMC, AGT, CALCA, and DDAH1 compared with NL.


Differential methylation in 4 genes associated with PE may represent a potential biomarker or an epigenetic pathophysiologic mechanism altering gene transcription.

Keywords: AGT, CALCA, candidate gene, DDAH1, DNA methylation, leukocyte, POMC, preeclampsia, pregnancy


Preeclampsia can be diagnosed when new onset hypertension occurs in pregnancy after 20 weeks gestation in combination with either proteinuria >300 mg in 24 hours or other evidence of end organ dysfunction manifested by a strictly defined set of symptoms or other laboratory abnormalities. Of the 5%-8% of pregnancies meeting criteria for preeclampsia, a subset of these patients develop seizure activity leading to a diagnosis of eclampsia. Maternal and fetal morbidity and mortality related to preeclampsia/eclampsia (PE) is high.

Alterations in the levels of many plasma or serum proteins have been associated with PE; however, it is unclear what mediates these changes.1-4 These proteins serve as agents of metabolic change, endothelial dysfunction, hypercoagulability, and inflammatory response, all of which are present to some degree in normal pregnancy, but are further accentuated in preeclampsia.5 The regulation of the differential expression of these proteins and the potential role of epigenetic factors are not understood. 6 DNA methylation is an epigenetic mechanism that could explain changes in gene transcription that contribute to the PE phenotype.7

Epigenetic changes affect patterns of gene transcription without altering the primary DNA sequence. One example is DNA methylation, where the addition of a methyl group to the DNA molecule at the site of cytosine – guanine residue combinations called CpG sites, may result in an altered structure which blocks or enhances transcription. Methylation can result in gene silencing or downregulation if present in a promoter, and is associated with increased gene expression if present in a gene body.8

We have previously demonstrated that normal early pregnancy is associated with a transient state of altered methylation in maternal leukocyte DNA.9 We have also shown that maternal leukocyte DNA in preeclampsia cases at the time of delivery has an altered genome-wide methylation profile and that performing pathway analysis on these differentially-methylated genes independently identifies the PE disease state with high statistical significance.10 In the current study, we sought to characterize the methylation patterns of 33 candidate genes in maternal leukocyte DNA in normal and preeclamptic pregnancies at delivery.


Ethical Approval

This study was approved by the Mayo Clinic Institutional Review Board (#2104-05), and informed consent was obtained prior to enrollment.

Sources for Original Cohort Case and Control DNA

This subanalysis was part of a larger experimental design, planned from the outset to study methylation changes in normal and preeclamptic pregnancies at various times in gestation from an agnostic (previously published for both normal and preeclamptics at delivery) and candidate gene set approach.

Preeclampsia was defined as the presence of new onset hypertension (>140/90 mm Hg), as documented by at least two readings 6 hours apart, accompanied by proteinuria, as defined by a 24-hour urine protein >300 mg or the equivalent protein/creatinine ratio. Preeclamptic patients (n=14) and normotensive controls (n=14) were matched by age (age±5 years) and body mass index (BMI [in kg/m2±5]) by design. Electronic medical records were abstracted for data regarding gravidity (primigravid being defined as no prior pregnancies), absence of comorbidities (such as chronic hypertension or chronic kidney disease), BMI, ethnicity, intrapartum magnesium exposure, blood pressures, proteinuria, and gestational age at time of delivery. Selection criteria for control and preeclamptic patients included European ancestry, first pregnancy, and non-smoking status. The discovery cohort of 28 samples plus technical replicates were selected from a larger study (total samples n=96) of normotensive and preeclamptic women sampled at different time points during their gestations; we selected all the samples at the delivery time point for inclusion in this study.

DNA Extraction and Processing

Blood was drawn within a 24-hour window of delivery and was collected in a 10 mL EDTA tube, separated into a buffy coat, and stored at −80 degrees C until processed. Genomic DNA was extracted from the thawed buffy coat using the Qiagen FlexiGene DNA kit, purified using an AutoGen Flex DNA purification kit, quantified using a NanoDrop spectrophotometer (Thermo Fisher Scientific), normalized with standard PicoGreen methodology, and plated in 1,000 ng aliquots. Bisulfite modification was performed using the EZ DNA Methylation Kit (Zymo Research).

Methylation Assay

Plate maps were generated to determine the random location for each sample on the plate, as well as the samples that were run in duplicate. All samples were run in a single batch. We used the Illumina Human Methylation-27 Assay - a platform utilizing bead chip technology- to evaluate the methylation status of over 27,000 CpG sites in 14,495 genes.

Quality Control

The raw data were processed using the BeadArray Reader from Illumina GenomeStudio (version 2010.2), with methylation module (version 1.7) Quality assessment of the array was conducted using the “Control Dashboard” in the software package, which includes a graphical inspection of the 8 types of embedded control probes: staining, hybridization, target removal, extension, bisulfite conversion, G/T mismatch, negative control and non-polymorphic controls.

Overall sample performance was determined by the total number of detected CpGs, the average detection P value across all CpG sites, and the distribution of average beta values for all CpGs. Call rates for each CpG site and sample were determined. Methylation sites and samples were excluded if the unreliable call rate (detection P value) was greater than 5%. Technical replicate reproducibility was estimated by the Pearson correlation coefficient.

All samples were bisulfite modified, plated, and run concurrently to avoid batch effect. However, different BeadChips, even when processed at the same time, can have variations in assay integrity leading to the “chip” effect. Data were examined using principal components analysis and unsupervised hierarchical clustering.11

Statistical Analysis

The average beta value between 0 and 1 was generated for each CpG site, representing the ratio of methylated cytosine residues (methylated signal intensity) to the total number of cytosine residues (the sum of methylated and unmethylated signal intensity) at that site for each sample. Using GeneGo MetaCore version July 2010 (Thomson Reuters), a candidate gene set for preeclampsia/eclampsia was selected. For all CpG sites in each of the candidate genes on our platform, a Student’s group t-test was used to compare the mean methylation levels between the preeclampsia and normotensive pregnant groups. A significant difference in methylation between the two groups was defined as an absolute difference between beta values ≥ 0.02 and a P value <.05. A correction for multiple comparisons was not performed, as the comparison was confined to a predetermined gene set.

Replication Dataset

The replication cohort included 12 independent preeclampsia subjects and 32 normotensive controls; DNA was extracted and processed in the same way as the discovery cohort. The subjects’ case or control status was confirmed by chart review, but they were not matched based on any other characteristics such as parity or smoking. These samples were run on an Illumina HumanMethylation450 BeadChip Kit. The raw data were processed using Illumina GenomeStudio (version 2011.1), with methylation module (version 1.9) to get the average beta value. The probe II bias was adjusted to be more similar to probe I using the BMIQ algorithm.12 Potential batch effects were evaluated using principal components analysis. Some minor batch effect was observed between two plates of samples and subsequently corrected using empirical Bayes method.11, 13 After the data processing, all CpG sites (n=140) in the six genes previously identified (from the discovery dataset) as differentially methylated were extracted from the whole dataset for analysis, i.e., the beta values were compared between the cases and controls with a t-test, and a P value <.05 was considered significant. Differential methylation as a potential epigenetic regulatory mechanism was confirmed if differential methylation occurred at any CpG site in any of these six genes in the replication cohort.


Clinical Characteristics of Discovery and Replication Cohort

Demographic and clinical parameters were similar between both preeclamptic and normal pregnancy groups of the discovery cohort by design (age, BMI, tobacco use, comorbidities, parity), with the exception of gestational age at delivery (normal mean=40 weeks; preeclampsia mean=37 weeks) (Table 1). Magnesium prophylaxis was used in 9 of 14 preeclamptic cases who were considered at risk for eclamptic seizures; none of the normal pregnancy controls were exposed to magnesium.

Table 1
Subject Characteristics in Discovery Cohort

Age, BMI and gestational age at delivery were also similar between both preeclamptic (n = 12) and normal pregnancy (n = 32) groups of the Replication Cohort (Table 2).

Table 2
Subject Characteristics in Replication Cohort

The mean systolic and diastolic blood pressures at admission for delivery for cases and controls were different in both the Discovery and Replication cohorts as expected.

Discovery Dataset from the Illumina 27K Platform

Of a total of 27,578 sites, 27,104 CpGs and all of the 28 samples passed our quality assurance steps. Technical replicates were highly reproducible (r2 >0.994).

The candidate gene set implicated in the preeclampsia/eclampsia disease state was predefined in GeneGo MetaCore ( as of July 2010), which consisted of 39 genes. Of these, 33 were present on the Illumina 27K platform with a total of 73 CpG sites (Table 3). Six of these genes were differentially methylated in our discovery comparison. Table 4 summarizes these genes, mean beta methylation levels, and the absolute methylation difference in the discovery experiment.

Table 3
Discovery Cohort Candidate Gene Set
Table 4
Differentially-Methylated Candidate Genes in Discovery Cohort

Validation of the Six Genes in an Independent Cohort with the Illumina 450k Platform

Of these six genes, POMC (proopiomelanocortin), AGT (angiotensinogen), CALCA (calcitonin-related polypeptide alpha), and DDHA1 (dimethylarginine dimethylaminohydrolase 1) had two or more CpG sites within or proximate to the gene region with differential methylation in our replication experiment. Details about the absolute level of change, the location of these CpG sites within the gene, association with known single nucleotide polymorphisms (SNPs), and the Illumina 27k results are presented in Table 5.

Table 5
List of Differentially-Methylated CpG Sites in Four Genes in the Replication Cohort on the Illumina 450K Platform


In our previous work, we demonstrated that normal early pregnancy is associated with a relative shift to hypomethylation in many genes across the genome and that this shift reverts back to a baseline non-pregnant state, similar to the nulligravid population, by 6 weeks postpartum.9 We also demonstrated that, compared to normotensive controls, preeclamptic pregnancies are associated with an incomplete shift in normal hypomethylation resulting in a relative hypermethylation of much of the genome when measured at the time of delivery.10 Pathway analysis of these differentially-methylated genes independently implicated the preeclampsia/eclampsia disease process with a P<9.97 × 10−20.10

In the current study, we characterized the methylation profiles of 33 candidate genes from an independently-defined gene set known to be involved in PE. These candidate genes have been characterized by others at the transcriptional or protein levels, and differences have been associated with the PE clinical state. We found evidence of differential methylation in 6 genes, AGT, CALCA , DDAH1, MTHFR, POMC, and PTGS2, in our initial cohort and replicated this finding in 4 (AGT, CALCA, DDAH1, POMC) in a second independent cohort.

DNA methylation changes in preeclampsia have not been widely studied. When such epigenetic changes were studied, most researchers have focused on candidate genes or genome-wide approaches in DNA originating from fetal derived tissues, mainly placental14,15,16,17 or free fetal DNA in maternal plasma18, but not from maternal DNA.

In addition to our work, only a few other authors have focused on maternal tissues – omental arteries and leukocytes. Moussa et al. have studied maternal omental vessels, looking at methylation patterns in specific genes like the thromboxane synthase19 and collagen metabolism genes,20 as well as using a more genome-wide approach.21 Anderson et al.22 looked at methylation patterns in maternal leukocytes obtained during the first trimester in 6 PE and 6 NL cases genome wide using a 450K Illumina array and identified 207 CpG sites that had >0.20 difference in beta value between cases and controls. Their list of top differentially-methylated sites was generated without a correction for multiple comparisons despite >450,000 independent t-tests. Consequently, the resultant CpG sites demonstrated extreme heterogeneity in beta methylation values; no replication was performed to assess if these sites could predict preeclampsia cases from controls.

We have taken a novel approach, focusing on candidate genes expressed by maternal leukocytes, which are known to both alter immunomodulation (a potential cause of preeclampsia) and impact the inflammatory response (a potential effect from preeclampsia). We looked for altered methylation in maternal leukocytes at the time of acute preeclampsia in late pregnancy.

AGT produces angiotensinogen, which goes on to produce angiotensin I and angiotensin II (Ang II) after cleavage by renin and angiotensin converting enzyme, respectively. AGT is expressed in leukocytes,23 as well as in liver and placenta. A specific form of AGT, high molecular weight angiotensinogen, is formed from complexes between angiotensinogen, complement C3dg, and the pro-form of eosinophil major basic protein, and is elevated in the serum in pregnancy.24 Total AGT levels are raised 4-fold in normal pregnancy. Preeclampsia is associated with profound differences in the function of the renin-angiotensin system, with increased high molecular weight Ang levels in serum, and decreased Ang II and Ang (1-7) levels, despite overall unchanged, unbound levels of angiotensingen. In contrast, increased AGT transcription has been seen in PE placenta.25, 26

CALC, calcitonin- related polypeptide alpha, is a widely-expressed gene, including in leukocytes, placenta, uterus, and blood vessels, that, through alternative mRNA splicing, makes (pro)calcitonin and calcitonin gene-related peptide (CGRP), which are important in Ca++ regulation, pain and neurotransmission, inflammatory response, angiogenesis and vasodilatory adaptation in pregnancy. In normal pregnancy, maternal serum CGRP levels increase as gestation advances and then fall after delivery27; this same rise is not observed in untreated preeclamptic pregnancies, although levels do seem to increase acutely with magnesium exposure.4, 28

DDAH1, dimethylarginine dimethylamniohydrolase 1, is involved in nitric oxide generation via asymmetric dimethylarginine (ADMA) metabolism, the levels of which are known to be altered in preeclampsia29 and which have been implicated in hypertension and endothelial dysfunction. There are no studies of changes in DDAH1 expression associated with preeclampsia, although known SNPs in these genes have been associated with susceptibility to preeclampsia.30,31 Alterations in DDAH1 methylation have been shown to change mRNA expression in other diseases, such as idiopathic pulmonary fibrosis32; it is possible that changes in DDAH1 methylation regulate gene expression in preeclampsia as well.

POMC, proopiomelanocortin, produces a hormone precursor that can, after tissue-specific processing, lead to 10 different proteins, including beta-endorphin, alpha-melanocyte-stimulating hormone (MSH), and adrenocorticotropic hormone (ACTH). Serum levels of beta-endorphin and ACTH are known to be higher in the pregnant versus nonpregnant state. POMC is expressed in several white blood cell lineages, as well as in the pituitary and placenta.33 Leukocyte-derived proteins, especially alpha-MSH and ACTH, have been implicated in immune function and inflammation. Interestingly, intact POMC is not usually measurable in blood; however, it can be found in pregnancy early in the first trimester, and it disappears within days after delivery.34 There have not been specific studies of gene expression or protein levels in maternal leukocytes in the pregnant or preeclamptic state.

Our rationale for using leukocytes and our attempts to correct for known confounders of DNA methylation, such as age, BMI, and tobacco, have been detailed in previous papers. 9, 10 Potential uncontrolled confounders in this work include gestational age and exposure to magnesium. We are currently addressing the change in methylation across different time points in normal and pathological pregnancy to evaluate whether a gestational age difference of 3-4 weeks at the time of assessment is important. It is unknown if magnesium infusion impacts methylation.

Another limitation of this type of study is the possibility of SNP confounding effects. A common polymorphism, M235T, is associated with higher AGT levels, and a recent meta-analysis confirmed the relationship between this polymorphism and increased risk for PE.35 One known SNP in the AGT gene located approximately 35 base pairs from the CpG site was found to be differentially methylated in the original cohort; however, it is not clear if differences in gene frequencies between cases and controls impacted our methylation analysis. Other genes did not have known SNPs associated with them. Our replication cohort had 3 SNPs in the 10 CpGs, with differential methylation as noted in Table 4. Thus, the confounding SNP effects may explain some of the differential methylation that was observed.

Methylation as a mechanism of gene transcription control is incompletely understood. Unlike a single point mutation in a primary DNA sequence, methylation at a specific unique CpG does not have a direct and predictable correlation with changes in gene transcription.8 The methylation levels at specific CpG loci, outside of a CpG island (CGI), can be highly variable in terms of mean and standard deviation.36 It is difficult to predict the relationship between the direction of methylation change and transcriptional activity, as increased methylation in the areas of promotors is associated with decreased transcription, whereas methylation in a gene body and other untranslated regions is associated with increased transcription.8 Although this is suggestive of epigenetic regulation of gene transcription, in all studies where differential methylation is associated with a disease state, it is important to correlate these methylation changes with actual changes in RNA or protein, as well as explore the relationship with other epigenetic mechanisms such as histone modification.


We have shown in two independent cohorts that leukocyte DNA from women with preeclampsia at the time of delivery demonstrates differential methylation in CpG sites associated with 4 candidate genes: POMC, AGT, CALCA, and DDAH1. We postulate that the differential methylation we found in these four candidate gene may contribute to the alterations in gene expression and protein levels previously demonstrated by others in preeclampsia. The presence of any altered methylation may implicate epigenetic control as an explanation for why these genes act differently, and identifying which environmental factors influence methylation may allow us to better understand the pathophysiology of the disease. In future work, we plan to correlate gene transcription with altered methylation within the same individuals.


We thank the Mayo Clinic Center for Individualized Medicine for access to the Biobank samples.

Sources of Funding

1. Mayo Clinic: Mary Kathryn and Michael B. Panitch Career Development Award

2. K08 HD051714 (Vesna D. Garovic) from the Eunice Kennedy Shriver National Institute of Child Health & Human Development. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Eunice Kennedy Shriver National Institute of Child Health & Human Development or the National Institutes of Health.

3. This project was supported in part by funding from the Mayo Clinic Center for Individualized Medicine (CIM) Epigenomics Translational Program.


Declaration of interest: The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Authors’ Contributions: WMW designed the study, carried out the methylation experiments, and drafted the manuscript; ZS performed bioinformatics and statistical analysis; KSB/BCB/NPD/CHR participated in design and coordination of the study; VG conceived the study, participated in design and coordination, and helped in drafting the manuscript. All authors read and approved the final manuscript.


1. Molvarec A, Szarka A, Walentin S, et al. Serum leptin levels in relation to circulating cytokines, chemokines, adhesion molecules and angiogenic factors in normal pregnancy and preeclampsia. Reprod Biol Endocrinol. 2011;9:124. [PMC free article] [PubMed]
2. LaMarca BD, Ryan MJ, Gilbert JS, Murphy SR, Granger JP. Inflammatory cytokines in the pathophysiology of hypertension during preeclampsia. Curr Hypertens Rep. 2007;9:480–5. [PubMed]
3. Romero R, Nien JK, Espinoza J, et al. A longitudinal study of angiogenic (placental growth factor) and anti-angiogenic (soluble endoglin and soluble vascular endothelial growth factor receptor-1) factors in normal pregnancy and patients destined to develop preeclampsia and deliver a small for gestational age neonate. J Matern Fetal Neona. 2008;21:9–23. [PMC free article] [PubMed]
4. Halhali A, Wimalawansa SJ, Berentsen V, Avila E, Thota CS, Larrea F. Calcitonin gene- and parathyroid hormone-related peptides in preeclampsia: effects of magnesium sulfate. Obstet Gynecol. 2001;97:893–7. [PubMed]
5. Ahn H, Park J, Gilman-Sachs A, Kwak-Kim J. Immunologic characteristics of preeclampsia, a comprehensive review. Am J Reprod Immunol. 2011;65:377–94. [PubMed]
6. Chappell S, Morgan L. Searching for genetic clues to the causes of pre-eclampsia. Clin Sci (Lond) 2006;110:443–58. [PubMed]
7. Oudejans CB, van Dijk M, Oosterkamp M, Lachmeijer A, Blankenstein MA. Genetics of preeclampsia: paradigm shifts. Hum Genet. 2007;120:607–12. [PubMed]
8. Jones PA. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet. 2012;13:484–92. [PubMed]
9. White WM, Brost BC, Sun Z, et al. Normal early pregnancy: a transient state of epigenetic change favoring hypomethylation. Epigenetics. 2012;7:729–34. [PMC free article] [PubMed]
10. White WM, Brost B, Sun Z, et al. Genome-wide methylation profiling demonstrates hypermethylation in maternal leukocyte DNA in preeclamptic compared to normotensive pregnancies. Hypertens Pregnancy. 2013;32:257–69. [PMC free article] [PubMed]
11. Sun Z, Chai HS, Wu Y, et al. Batch effect correction for genome-wide methylation data with Illumina Infinium platform. BMC Med Genomics. 2011;4:84. [PMC free article] [PubMed]
12. Teschendorff AE, Marabita F, Lechner M, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics. 2013;29:189–96. [PMC free article] [PubMed]
13. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–27. [PubMed]
14. Avila L, Yuen RK, Diego-Alvarez D, Penaherrera MS, Jiang R, Robinson WP. Evaluating DNA methylation and gene expression variability in the human term placenta. Placenta. 2010;31:1070–7. [PubMed]
15. Katari S, Turan N, Bibikova M, et al. DNA methylation and gene expression differences in children conceived in vitro or in vivo. Hum Mol Genet. 2009;18:3769–78. [PMC free article] [PubMed]
16. Yuen RK, Penaherrera MS, von Dadelszen P, McFadden DE, Robinson WP. DNA methylation profiling of human placentas reveals promoter hypomethylation of multiple genes in early-onset preeclampsia. Eur J Hum Genet. 2010;18:1006–12. [PMC free article] [PubMed]
17. Zhao A, Cheng Y, Li X, et al. Promoter hypomethylation of COMT in human placenta is not associated with the development of pre-eclampsia. Mol Hum Reprod. 2011;17:199–206. [PubMed]
18. Bellido ML, Radpour R, Lapaire O, et al. MALDI-TOF mass array analysis of RASSF1A and SERPINB5 methylation patterns in human placenta and plasma. Biol Reprod. 2010;82:745–50. [PubMed]
19. Mousa AA, Strauss JF, 3rd, Walsh SW. Reduced methylation of the thromboxane synthase gene is correlated with its increased vascular expression in preeclampsia. Hypertension. 2012;59:1249–55. [PMC free article] [PubMed]
20. Mousa AA, Cappello RE, Estrada-Gutierrez G, et al. Preeclampsia is associated with alterations in DNA methylation of genes involved in collagen metabolism. Am J Pathol. 2012;181:1455–63. [PubMed]
21. Mousa AA, Archer KJ, Cappello R, et al. DNA methylation is altered in maternal blood vessels of women with preeclampsia. Reprod Sci. 2012;19:1332–42. [PMC free article] [PubMed]
22. Anderson CM, Ralph JL, Wright ML, Linggi B, Ohm JE. DNA methylation as a biomarker for preeclampsia. Biol Res Nurs. 2013 [PubMed]
23. Gomez RA, Norling LL, Wilfong N, et al. Leukocytes synthesize angiotensinogen. Hypertension. 1993;21:470–5. [PubMed]
24. Tewksbury DA. Quantitation of five forms of high molecular weight angiotensinogen from human placenta. Am J Hypertens. 1996;9:1029–34. [PubMed]
25. Anton L, Brosnihan KB. Systemic and uteroplacental renin--angiotensin system in normal and pre-eclamptic pregnancies. Ther Adv Cardiovasc Dis. 2008;2:349–62. [PMC free article] [PubMed]
26. Merrill DC, Karoly M, Chen K, Ferrario CM, Brosnihan KB. Angiotensin-(1-7) in normal and preeclamptic pregnancy. Endocrine. 2002;18:239–45. [PubMed]
27. Yallampalli C, Chauhan M, Thota CS, Kondapaka S, Wimalawansa SJ. Calcitonin gene-related peptide in pregnancy and its emerging receptor heterogeneity. Trends Endocrinol Metab. 2002;13:263–9. [PubMed]
28. Ariza AC, Bobadilla N, Diaz L, Avila E, Larrea F, Halhali A. Placental gene expression of calcitonin gene-related peptide and nitric oxide synthases in preeclampsia: effects of magnesium sulfate. Magnes Res. 2009;22:44–9. [PubMed]
29. Savvidou MD, Hingorani AD, Tsikas D, Frolich JC, Vallance P, Nicolaides KH. Endothelial dysfunction and raised plasma concentrations of asymmetric dimethylarginine in pregnant women who subsequently develop pre-eclampsia. Lancet. 2003;361:1511–7. [PubMed]
30. Akbar F, Heinonen S, Pirskanen M, Uimari P, Tuomainen TP, Salonen JT. Haplotypic association of DDAH1 with susceptibility to pre-eclampsia. Mol Hum Reprod. 2005;11:73–7. [PubMed]
31. Best LG, Nadeau M, Bercier S, et al. Genetic variants, endothelial function, and risk of preeclampsia among American Indians. Hypertens Pregnancy. 2012;31:1–10. [PubMed]
32. Sanders YY, Ambalavanan N, Halloran B, et al. Altered DNA methylation profile in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2012;186:525–35. [PMC free article] [PubMed]
33. Stephanou A, Fitzharris P, Knight RA, Lightman SL. Characteristics and kinetics of proopiomelanocortin mRNA expression by human leucocytes. Brain Behav Immun. 1991;5:319–27. [PubMed]
34. Raffin-Sanson ML, Ferre F, Coste J, Oliver C, Cabrol D, Bertagna X. Pro-opiomelanocortin in human pregnancy: evolution of maternal plasma levels, concentrations in cord blood, amniotic fluid and at the feto-maternal interface. Eur J Endocrinol. 2000;142:53–9. [PubMed]
35. Ni S, Zhang Y, Deng Y, et al. AGT M235T polymorphism contributes to risk of preeclampsia: evidence from a meta-analysis. J Renin Angiotensin Aldosterone Syst. 2012;13:379–86. [PubMed]
36. Wagner JR, Busche S, Ge B, Kwan T, Pastinen T, Blanchette M. The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts. Genome Biol. 2014;15:R37. [PMC free article] [PubMed]