A genome scale transcriptional regulatory model of the human placenta

作者：Alison G. Paquette, Kylia Ahuna, Yeon Mi Hwang, Jocelynn R. Pearl, Hanna Liao, Paul Shannon, Leena Kadam, Samantha Lapehn, Matthew Bucher, Ryan Roper, Cory C. Funk, James W. MacDonald, Theo K. Bammler, Priyanka Baloni, Heather Brockway, W. Alex Mason, Nicole R. Bush, Kaja Z. LeWinn, Catherine J. Karr, J Stamatoyannopoulos, Louis J. Muglia, Helen Jones, Yoel Sadovsky, Leslie Myatt, Sheela Sathyanarayana, Nathan D. Price · 发表于：Science Advances · 年份：2024 · DOI：10.1126/sciadv.adf3411 · 被引用次数：19 · 研究领域：Epigenetics and DNA Methylation、Prenatal Screening and Diagnostics、Pregnancy and preeclampsia studies

Gene regulation is essential to placental function and fetal development. We built a genome-scale transcriptional regulatory network (TRN) of the human placenta using digital genomic footprinting and transcriptomic data. We integrated 475 transcriptomes and 12 DNase hypersensitivity datasets from placental samples to globally and quantitatively map transcription factor (TF)–target gene interactions. In an independent dataset, the TRN model predicted target gene expression with an out-of-sample R 2 greater than 0.25 for 73% of target genes. We performed siRNA knockdowns of four TFs and achieved concordance between the predicted gene targets in our TRN and differences in expression of knockdowns with an accuracy of >0.7 for three of the four TFs. Our final model contained 113,158 interactions across 391 TFs and 7712 target genes and is publicly available. We identified 29 TFs which were significantly enriched as regulators for genes previously associated with preterm birth, and eight of these TFs were decreased in preterm placentas.