Global DNA methylation in placental tissues from pregnant with preeclampsia: A systematic review and pathway analysis

Juliana de O Cruz1, Izabela M C A Conceição2, Jéssica A G Tosatti3

  • 1Graduate Program in Genetics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, 31270-901, Brazil.

Placenta
|September 17, 2020
PubMed

Pre-eclampsia (PE) is the major cause of fetal and maternal mortality and can be classified according to gestational age of onset into early-onset (EOPE, <34 weeks of gestation) and late- (LOPE, ≥34 weeks of gestation). DNA methylation (DNAm) may help to understand the abnormal placentation in PE. Therefore, we performed a systematic review to assess the role of global DNAm on pathophysiology of PE, focused on fetal and maternal tissues of placenta from pregnant with PE, including EOPE and LOPE. We searched the databases EMBASE, Medline/PubMed, Cochrane Central Register of Controlled Trials, Scopus, Lilacs, Scielo and Google Scholar, and followed the MOOSE guidelines. Moreover, we performed pathway analysis with the overlapping genes from the included studies. Twelve out of 24 included studies in the qualitative analysis considered the classification into EOPE and LOPE. We did not found heterogeneity in the criteria used for diagnosis of PE, and a few studies evaluated whether confounding factors would influence placental DNAm. Fourteen out of 24 included studies showed hypomethylation in placental tissue from pregnant with PE compared to controls. The differences in DNAm are specific to genes or differentially methylated regions, and more evident in EOPE and preterm PE compared to controls, rather than LOPE and term PE. The overlapping genes from included studies revealed pathways relevant to pathophysiology of PE. Our findings highlighted the heterogeneous results of the included studies, mainly focused on North America and China. Replication studies in different populations should use the same placental tissues, techniques to assess DNAm and pipelines for bioinformatic analysis.

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