| Literature DB >> 27293492 |
Masako Suzuki1, Ryo Maekawa2, Nicole E Patterson1, David M Reynolds1, Brent R Calder1, Sandra E Reznik3, Hye J Heo4, Francine Hughes Einstein4, John M Greally1.
Abstract
BACKGROUND: Preeclampsia, traditionally characterized by high blood pressure and proteinuria, is a common pregnancy complication, which affects 2-8 % of all pregnancies. Although children born to women with preeclampsia have a higher risk of hypertension in later life, the mechanism of this increased risk is unknown. DNA methylation is an epigenetic modification that has been studied as a mediator of cellular memory of adverse exposures in utero. Since each cell type in the body has a unique DNA profile, cell subtype composition is a major confounding factor in studies of tissues with heterogeneous cell types. The best way to avoid this confounding effect is by using purified cell types. However, using purified cell types in large cohort translational studies is difficult. The amnion, the inner layer of the fetal membranes of the placenta, is derived from the epiblast and consists of two cell types, which are easy to isolate from the delivered placenta. In this study, we demonstrate the value of using amnion samples for DNA methylation studies, revealing distinctive patterns between fetuses exposed to proteinuria or hypertension and fetuses from normal pregnancies.Entities:
Keywords: Amnion; DNA methylation; Genome-wide; HELP-tagging; Hypertension; Preeclampsia; Pregnancy; Sodium bisulfite
Mesh:
Year: 2016 PMID: 27293492 PMCID: PMC4902972 DOI: 10.1186/s13148-016-0234-1
Source DB: PubMed Journal: Clin Epigenetics ISSN: 1868-7075 Impact factor: 6.551
Sample characteristics
| Without complication (control) | With complication | |||||||
|---|---|---|---|---|---|---|---|---|
| Hypertension | PE |
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| Diagnostic criteria | Systolic blood pressure (max) | 130 | IQR 122–134 | 160 | IQR 148–170 | 175.5 | IQR 157–183 | |
| Diastolic blood pressure (max) | 82 | IQR 77–85 | 98 | IQR 93–108 | 103 | IQR 98–115.5 | ||
| Proteinuria | ||||||||
| Negative/trace | 15 | 11 | 6 | |||||
| 1+ | 0 | 0 | 13 | |||||
| 2+ | 0 | 0 | 8 | |||||
| >3+ | 0 | 0 | 9 | |||||
| Characteristics | Maternal age (year, mean ± SD) | 27.3 ± 3.3 | 26 ± 6.0 | 26.6 ± 6.1 | 0.8413 | |||
| (Mother and fetus) | Week of gestation (median) | 39.3 | IQR 39.1–40.2 | 39.2 | IQR 37.4–39.5 | 39 | IQR 37.4–39.4 | 0.0191 |
| Weight at birth (g, mean ± SD) | 3454.3 ± .96 | 3097.3 ± 606.8 | 2931.1 ± 767.3 | 0.0566 | ||||
| Race | 0.58 | |||||||
| Black | 4 | 2 | 11 | |||||
| Hispanic | 5 | 6 | 19 | |||||
| White | 1 | 1 | 4 | |||||
| Other | 3 | 2 | 1 | |||||
| Declined | 2 | 0 | 1 | |||||
| Sex (male, | 5 | 8 | 15 | 0.120 | ||||
| NSVD ( | 5 | 8 | 15 | 0.120 | ||||
| Primiparous ( | 3 | 6 | 24 | 0.016 | ||||
| Smoked ( | 0 | 0 | 2 | 0.999 | ||||
| History of PE ( | 0 | 2 | 5 | 0.242 | ||||
| Comorbidities ( | Anemia | 4 | 1 | 5 | 0.502 | |||
| (Mother) | Asthma | 1 | 3 | 5 | 0.419 | |||
| Obese (BMI > 30) | 4 | 4 | 17 | 0.401 | ||||
| GDM | 1 | 0 | 7 | 0.289 | ||||
| Chronic hypertension | 0 | 1 | 4 | 0.453 | ||||
| Migraines | 1 | 2 | 3 | 0.607 | ||||
| Medication ( | Hypertensive drug prescribed | 0 | 1 | 9 | 0.006 | |||
| (Mother) | MgSO4 administration | 0 | 6 | 25 | <0.0001 | |||
IQR interquartile range
Fig. 1Biological and technical confounders contribute to DNA methylation value variations. The heatmap displays significant correlations for each covariate. The –log10 p values of the linear regressions of the top ten principal components onto each known covariate are shown. The color key shows corresponding numeric values, with darker color indicating increased significance. Proteinuria, maximum systolic blood pressure, and magnesium sulfate treatment are contributing to the DNA methylation variability
Fig. 2Differentially methylated HpaII (DM-HpaII) sites. a DM-HpaIIs common in between models, a Venn diagram showing the overlapping DM-HpaIIs between models. b Exposures of maternal hypertension and proteinuria showed different target genes and biological process. The heatmap displays the –log10 p values of gene ontology enrichment status (Enrichr, GO biological process). Distinct patterns of enrichment for gene ontology biological processes between models were observed