Sci Transl Med. 2020 Jul 1;12(550):eaaz0131. doi: 10.1126/scitranslmed.aaz0131. | PubMed

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

1. Illumina Inc., San Diego, CA 92122, USA.
2. Department of Obstetrics and Gynecology and Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, Quebec G1V 086, Canada.
3. Department of Obstetrics and Gynecology and the Mother Infant Research Institute, Tufts University School of Medicine, Boston, MA 02111, USA.
4. Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY 10032, USA.
5. Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, TX 77555, USA.
6. 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