Project description:Hypertension in pregnancy is the leading cause of morbidity and mortality in pregnancy, affecting up to 10% of all gestations1. Hypertensive disorders of pregnancy include chronic hypertension, gestational hypertension, eclampsia and pre-eclampsia, all of which increase the risk of complications in both mothers and babies during gestation. Preeclampsia, in particular, is characterised by the new-onset of gestational hypertension in the presence of proteinuria or other organ damage. It affects 5-7% of pregnancies and causes approximately 76,000 maternal deaths and 500,000 foetal deaths worldwide each year2. The classification of preeclampsia has been evolving over the last decade. In 2013, American College of Obstetricians and Gynecologists (ACOG) and International Society for the Study of Hypertension in Pregnancy (ISSHP) in the absence of proteinuria included other symptoms/features including liver dysfunction, thrombocytopenia, cerebrovascular events or foetal growth restriction (FGR) in the diagnosis of preeclampsia 1,3,4. Despite the fact that preeclampsia is a multifactorial and heterogeneous disorder, it is now widely accepted that preeclampsia is stratified depending on the time of onset into: i) early-onset PE (EOPE) manifested before 34 weeks of gestation, and ii) late-onset PE (LOPE) manifested from 34 weeks of gestation. Although EOPE and LOPE share the same diagnostic criteria, these two phenotypes of preeclampsia lead to different outcomes. EOPE is commonly associated with FGR, abnormal uterine artery Doppler often leading to preterm birth and higher risk of post-pregnancy morbidities5,6. On the other hand, LOPE appears to be a less severe disorder, often with normal or slightly increased uterine resistance index and a low rate of FGR6,7. Distinct delineation between EOPE and LOPE is still not well understood, with most patients with preeclampsia presenting elements of both pathologies proposing a clinical spectrum for preeclampsia. The lack of untargeted discovery studies involving ‘omics’ analyses has impeded understanding of the molecular differences between these two phenotypes of preeclampsia. Notably, a study from 20058 utilised a proteomics approach using urine samples from a cohort of pregnant women with EOPE and healthy controls, however the differences between EOPE and LOPE were not elucidated. Another more recent bioinformatics study, identified overlapping pathogenic mechanisms between preeclampsia, hypertension and heart disease but could not stratify between EOPE and LOPE due to underreported of data related to individual phenotypes9. In this study, we conducted an unbiased, comprehensive proteomics investigation using plasma samples collected from patients with EOPE (n=17) and LOPE (n=11), compared with age- and BMI-matched normotensive controls (n=18). The use of plasma samples is able to better reflect the pathogenesis of EOPE and LOPE as systemic conditions centred by widespread endothelial dysfunction.

Project description:Background: Early-onset preeclampsia (EOPE) and late-onset preeclampsia (LOPE) has been regarded as two different phenotypes with heterogeneous manifestation. The underlying mechanisms remain elusive. Aim to gain insight into the pathogenesis of the two traits, we analyzed the placental gene expression profiles in preeclampsia placentas. Methods: Whole genome-wide microarray was used to describe the gene expression profiles in the placenta tissues from patients with early-(n=7; <34 weeks), late-onset(n=8; >36 weeks) PE and their controls who delivered preterm (n=5;<34 weeks) or at term(n=5; >36 weeks) Genes were selected as differentially expressed upon a fold-changeâ?¥2 and q-value<0.05. qRT-PCR was undertaken to verify the results. Western blot was further performed to verify secreted genes at the protein level. Results: A total of 627 genes were differentially expressed in early-compared with late-onset PE. Of these, 177 genes were up-regulated and 450 genes down-regulated in early-onset PE. Go analysis showed significant alteration in several biological processes, in addition to the processes which have been found before, such as immune and inflammatory response, cell adhension, female pregnancy and blood vessel development. We also found alteration in G-protein coupled receptor protein signaling pathway, G protein-coupled receptor 124 (GPR124) (P=0.0064) and MAS-related GPR, member F (MRGPRF)(P=0.0155 ) were both down-regulated obviously in early-onset PE. Conclusion: The different gene expression profiles suggested early- and late-onset PE are separate disease entities. Moreover, G-protein coupled receptor protein signaling pathway may contribute to the mechanism underlying early- and late-onset preeclampsia. Whole genome-wide microarray was used to describe the gene expression profiles in the placenta tissues from patients with early-(n=7; <34 weeks), late-onset (n=8; >36 weeks) PE and their controls who delivered preterm(n=5;<34 weeks) or at term(n=5; >36 weeks). Pooled controls who delivered at term were labled with cy5.

Project description:All subjects were recruited at Centennial Women?s Hospital and the Perinatal Research Center in Nashville, TN beginning in 2003. Pregnant women were enrolled during their first clinical visit after obtaining informed consent as described previously. Demographic and clinical data specific to the fetus was collected from clinical records. Gestational age of the neonate was determined by maternal reporting of the last menstrual period and corroboration by ultrasound dating. Accurate knowledge of gestational age (GA) is essential for proper monitoring and care of neonates. However, accurate GA measures are often not available. DNA methylation has previously been shown to associate with GA, and has been used to accurately predict chronological age in adults. In the current study, we examine whether DNA methylation in cord blood can be used to predict gestational age at birth. Results: We found that GA can be accurately predicted from DNA methylation of neonatal cord blood and blood spot samples (DNAm GA), using 148 CpG sites selected through elastic net regression in six training datasets (N=207). We evaluated predictive accuracy in six testing datasets (N=1,202), and found that the accuracy of DNAm GA meets or exceeds accuracy of gestational age estimates based on established methods. We also found an increased DNAm GA, relative to clinical GA, was associated with increased birthweight percentile (p=.00057), adjusting for GA, sex, and ancestry, suggesting that DNAm GA could represent developmental age more accurately than clinical estimates of GA. Conclusions: Further development of this predictor could provide a method of accurate neonatal estimation of GA for use in resource-limited populations, or in cases where GA cannot be estimated clinically. When clinical estimates are available, the predictor can be used to test hypotheses related to developmental age and other early life circumstances, and may provide increased accuracy beyond clinical estimates. 36 Umbilical cord blood samples were collected in EDTA tubes soon after placental delivery. Blood samples were centrifuged at 3,000 RPM to separate plasma, and buffy coats were aliquoted and stored at -80oC. DNA was extracted using the DNeasy Kit (Qiagen). DNA methylation was interrogated for each sample using the HumanMethylation450 BeadChip (Illumina).

Project description:All subjects were recruited at Centennial Women?s Hospital and the Perinatal Research Center in Nashville, TN beginning in 2003. Pregnant women were enrolled during their first clinical visit after obtaining informed consent as described previously. Demographic and clinical data specific to the fetus was collected from clinical records. Gestational age of the neonate was determined by maternal reporting of the last menstrual period and corroboration by ultrasound dating. Accurate knowledge of gestational age (GA) is essential for proper monitoring and care of neonates. However, accurate GA measures are often not available. DNA methylation has previously been shown to associate with GA, and has been used to accurately predict chronological age in adults. In the current study, we examine whether DNA methylation in cord blood can be used to predict gestational age at birth. Results: We found that GA can be accurately predicted from DNA methylation of neonatal cord blood and blood spot samples (DNAm GA), using 148 CpG sites selected through elastic net regression in six training datasets (N=207). We evaluated predictive accuracy in six testing datasets (N=1,202), and found that the accuracy of DNAm GA meets or exceeds accuracy of gestational age estimates based on established methods. We also found an increased DNAm GA, relative to clinical GA, was associated with increased birthweight percentile (p=.00057), adjusting for GA, sex, and ancestry, suggesting that DNAm GA could represent developmental age more accurately than clinical estimates of GA. Conclusions: Further development of this predictor could provide a method of accurate neonatal estimation of GA for use in resource-limited populations, or in cases where GA cannot be estimated clinically. When clinical estimates are available, the predictor can be used to test hypotheses related to developmental age and other early life circumstances, and may provide increased accuracy beyond clinical estimates.

Dataset Information

Utility and validation in placental DNA methylation profiling of preeclampsia and intrauterine growth restriction [discovery cohort]

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure