# Distinct DNA methylation signatures in maternal blood reveal unique immune cell shifts in preeclampsia and the pregnancy-postpartum transition

## TL;DR

This study uses DNA methylation to explore immune cell changes in preeclampsia and the postpartum period, finding significant shifts after childbirth.

## Contribution

The study introduces a method to track immune cell dynamics during pregnancy using DNA methylation data, revealing postpartum immune remodeling.

## Key findings

- No significant immune cell differences were found between preeclampsia and healthy pregnancies.
- Postpartum immune remodeling was marked by decreased monocytes and granulocytes, and increased natural killer cells, B cells, and T cells.
- Longitudinal trends were consistent across datasets, highlighting the role of gestational age in immune dynamics.

## Abstract

Preeclampsia (PE) is a hypertensive disorder of pregnancy characterized by immune dysregulation and significant risks to maternal and fetal health. While current management relies on high-risk patient monitoring and early diagnosis, these methods are costly and burdensome, especially for low-risk pregnancies. DNA methylation (DNAm) is a type of chemical modification that influences gene expression and has been associated with immune cell dynamics and PE pathogenesis. This study explores whether DNAm-based immune cell composition profiling can provide insights into immune dysregulation associated with PE. By also examining changes in immune cell composition across gestational timepoints and into the postpartum period, we aimed to establish a baseline of healthy immune adaptation during pregnancy, against which PE-related disruptions can be better understood. We conducted a search in the Gene Expression Omnibus (GEO) for DNAm datasets using Illumina 27K, 450K, and EPIC arrays from maternal blood in both healthy and PE pregnancies. We found two studies (GSE37722 and GSE192918) that met our criteria, involving a total of 24 healthy pregnancies and 14 with PE. To estimate immune cell composition (CD8 + T cells, CD4 + T cells, monocytes, granulocytes, natural killer cells, and B cells) from DNAm data, we applied the deconvolution algorithm developed by Houseman et al (2012). A linear model was used to assess statistical differences in immune cell proportions between PE cases and controls. Longitudinal analyses were also conducted to examine immune cell shifts during pregnancy and postpartum. No significant differences were observed between PE and control groups in any immune cell type. However, longitudinal analyses revealed substantial immune remodeling in the postpartum period, characterized by decreased monocytes and granulocytes, and increased natural killer cells, B cells, and T cells. While subgroup analyses showed some variability in significance, particularly in GSE192918, the overall trends were consistent across datasets, emphasizing the importance of gestational age in immune dynamics. These findings support the use of DNAm profiling as a valuable tool for characterizing immune cell dynamics during pregnancy. Although immune differences between PE cases and controls were not observed with the Houseman method, longitudinal shifts were consistently captured and provide additional insights into the evolution of immune changes from pregnancy to postpartum, supporting the potential of DNAm-based profiling for developing predictive and monitoring tools for pregnancy and pregnancy-related pathology. It is important to note that these analyses were based on a single deconvolution approach applied to a cohort with well-matched clinical criteria; and that differences in study design, timing of sample collection, and cohort characteristics may limit broader generalizability. Future studies leveraging pregnancy-included reference matrices in deconvolution methods and larger, more diverse cohorts are essential to refine the application of DNAm-based immune profiling in pregnancy and pregnancy complications.