Genome-wide maps of distal gene regulatory enhancers active in the human placenta

Abstract

Placental dysfunction is implicated in many pregnancy complications, including preeclampsia and preterm birth (PTB). While both these syndromes are influenced by environmental risk factors, they also have a substantial genetic component that is not well understood. Precisely controlled gene expression during development is crucial to proper placental function and often mediated through gene regulatory enhancers. However, we lack accurate maps of placental enhancer activity due to the challenges of assaying the placenta and the difficulty of comprehensively identifying enhancers. To address the gap in our knowledge of gene regulatory elements in the placenta, we used a two-step machine learning pipeline to synthesize existing functional genomics studies, transcription factor (TF) binding patterns, and evolutionary information to predict placental enhancers. The trained classifiers accurately distinguish enhancers from the genomic background and placental enhancers from enhancers active in other tissues. Genomic features collected from tissues and cell lines involved in pregnancy are the most predictive of placental regulatory activity. Applying the classifiers genome-wide enabled us to create a map of 33,010 predicted placental enhancers, including 4,562 high-confidence enhancer predictions. The genome-wide placental enhancers are significantly enriched nearby genes associated with placental development and birth disorders and for SNPs associated with gestational age. These genome-wide predicted placental enhancers provide candidate regions for further testing in vitro, will assist in guiding future studies of genetic associations with pregnancy phenotypes, and aid interpretation of potential mechanisms of action for variants found through genetic studies.

Analyzing a putative enhancer of optic disc morphology

“…Similarly, GFI1 plays a crucial role during embryogenesis of other tissues [ 33 ]. During embryogenesis the enhancers’ network is one of the expanded regulatory networks and often incorporates Transposable Elements (TE) including Alus , which later become repressed [ 34 – 36 ]. Currently, GeneHancer resource provides evidence (eQTL only) for the target region as an enhancer of GFI1 [ 11 ].…”

Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework

“…Therefore, the identification of active enhancers in placental tissue is extremely crucial. Here we designed an experiment for 4,562 placental enhancers [ 59 ] and then compared the experimental results of SMFM with other existing enhancer methods. To conduct a fair experiment in the placental enhancers task, we did not perform targeted parameter tuning for all methods used for comparison.…”

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

“…Concerning preterm birth, studies focus on cervix-related risk [ 102 , 103 ], classifying true preterm labor [ 104 , 105 ], determining neonate mortality and prognosis [ 106 , 107 ], predicting preterm birth risk [ 108–111 ], professional learning [ 112 ], estimating postnatal gestational age [ 113 ] and improving knowledge of gene regulatory elements in the placenta [ 114 ]. In 1982, Grignolio [ 106 ] presented a method to predict neonate mortality of premature newborns, using multiple regression.…”

“…Perinatal outcome was predicted with a DL model interpreting amniotic fluid metabolomics and proteomics in asymptomatic pregnant women with short CL [ 109 ]. SVM was applied to predict genome-wide placental enhancers, in order to further the understanding of placental dysfunction and implications of preterm birth and preeclampsia [ 114 ].…”