Circulating transcripts in maternal blood reflect a molecular signature of early-onset preeclampsia
Sarah Munchel1, Suzanne Rohrback1, Carlo Randise-Hinchliff1, Sarah Kinnings1, Shweta Deshmukh1, Nagesh Alla1, Catherine Tan1, Amirali Kia1, Grainger Greene1, Linda Leety1, Matthew Rhoa1, Scott Yeats1, Matthew Saul1, Julia Chou1, Kimberley Bianco1, Kevin O'Shea1, Emmanuel Bujold2, Errol Norwitz3, Ronald Wapner4, George Saade5, Fiona Kaper6
- Illumina Inc., San Diego, CA 92122, USA.
- Department of Obstetrics and Gynecology and Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, Quebec G1V 086, Canada.
- Department of Obstetrics and Gynecology and the Mother Infant Research Institute, Tufts University School of Medicine, Boston, MA 02111, USA.
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY 10032, USA.
- Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, TX 77555, USA.
- Illumina Inc., San Diego, CA 92122, USA. fkaper@illumina.com.
Abstract
Circulating RNA (C-RNA) is continually released into the bloodstream from tissues throughout the body, offering an opportunity to noninvasively monitor all aspects of pregnancy health from conception to birth. We asked whether C-RNA analysis could robustly detect aberrations in patients diagnosed with preeclampsia (PE), a prevalent and potentially fatal pregnancy complication. As an initial examination, we sequenced the circulating transcriptome from 40 pregnancies at the time of severe, early-onset PE diagnosis and 73 gestational age-matched controls. Differential expression analysis identified 30 transcripts with gene ontology annotations and tissue expression patterns consistent with the placental dysfunction, impaired fetal development, and maternal immune and cardiovascular system dysregulation characteristic of PE. Furthermore, machine learning identified combinations of 49 C-RNA transcripts that classified an independent cohort of patients (early-onset PE, n = 12; control, n = 12) with 85 to 89% accuracy. C-RNA may thus hold promise for improving the diagnosis and identification of at-risk pregnancies.
Presented By Suzanne Rohrback | ORCID iD