(1147) Transcriptional Meta-Analysis of Preeclampsia Compared to Placenta Accreta: Implications for Placentation

Friday, January 31, 2025

4:00 PM – 6:00 PM

Submitting Author and Presenting Author(s)

Ophelia Yin, MD

Maternal-Fetal Medicine Fellow
Division of Maternal Fetal Medicine and Reproductive Genetics, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Francisco
San Francisco, CA, United States

Coauthor(s)

LA

Laura Almonte, BS

Bakar Computational Health Sciences Institute, University of California (UCSF), San Francisco
San Francisco, California, United States
RA

Romina Appierdo, MS

Program in Cellular and Molecular Biology, Department of Biology, University of Rome “Tor Vergata”
San Francisco, California, United States
MY

Monica Yang, MD

Division of Rheumatology, Department of Medicine, University of California (UCSF)
San Francisco, California, United States
TO

Tomiko Oskotsky, MD

Bakar Computational Health Sciences Institute, University of California (UCSF)
San Francisco, California, United States
Yalda Afshar, MD, PhD (she/her/hers)

University of California, Los Angeles
Los Angeles, California, United States
MS

Marina Sirota, PhD

Bakar Computational Health Sciences Institute, University of California (UCSF)
San Francisco, California, United States

Objective: To compare the transcriptomic signature of early onset preeclampsia with placenta accreta to gain insight into genes and pathways relevant to placentation at the maternal-fetal interface.

Study Design: The Gene Expression Omnibus (GEO) was queried for preeclampsia and placenta accreta transcriptional datasets derived from human placental tissue from 2010 onwards. Microarray analysis was conducted in R and performed with crossmeta or oligo and batch corrected with ComBat. Differential gene expression using Limma was adjusted for cell type. Processing of single cell data was performed using Seurat with MAST. Gene hyper enrichment, correlation testing, and gene set enrichment analysis with clusterprofiler was performed for pathway analysis.

Results: We performed a gene expression meta-analysis of placental samples from 86 patients with early onset preeclampsia and 65 preterm controls from 4 publicly available microarray datasets. This preeclampsia gene signature was compared to 1 placenta accreta microarray with 6 accreta and 3 preterm controls and 1 placenta accreta single-cell dataset with 3 accreta and 3 controls. Comparing the 4,903 differentially expressed genes in the early onset preeclampsia meta-analysis to 416 differentially expressed genes in placenta accreta microarray, we found 120 overlapping genes with significant negative correlation in expression (r = -0.39, p = 8.06e-6). Key genes included ANKRD37, AOX1, CP, GBP3, IFIT1, IGFBP6, NEK11, OPRK1, and SERPINA3 (Figure 1). Comparing to single-cell placenta accreta, we found that preeclampsia genes were enriched in all cell types and negatively correlated for decidua type, endothelial, and extravillous trophoblasts (Table 1). Shared pathways included hypoxia, glycolysis, oxidative phosphorylation, and early estrogen response.

Conclusion:

We identified early onset preeclampsia genes that are oppositely regulated in placenta accreta, both in bulk tissue and in specific cell types at the maternal-fetal interface. We hypothesize that these candidate genes underly the biology of placental attachment and spiral artery remodeling for both disorders.