RNA profiles reveal signatures of future health and disease in pregnancy

Morten Rasmussen*, Mitsu Reddy, Rory Nolan, Joan Camunas-Soler, Arkady Khodursky, Nikolai M. Scheller, David E. Cantonwine, Line Engelbrechtsen, Jia Dai Mi, Arup Dutta, Tiffany Brundage, Farooq Siddiqui, Mainou Thao, Elaine P.S. Gee, Johnny La, Courtney Baruch-Gravett, Mark K. Santillan, Saikat Deb, Shaali M. Ame, Said M. AliMelanie Adkins, Mark A. DePristo, Manfred Lee, Eugeni Namsaraev, Dorte Jensen Gybel-Brask, Lillian Skibsted, James A. Litch, Donna A. Santillan, Sunil Sazawal, Rachel M. Tribe, James M. Roberts, Maneesh Jain, Estrid Høgdall, Claudia Holzman, Stephen R. Quake, Michal A. Elovitz, Thomas F. McElrath

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

86 Citations (Scopus)


Maternal morbidity and mortality continue to rise, and pre-eclampsia is a major driver of this burden1. Yet the ability to assess underlying pathophysiology before clinical presentation to enable identification of pregnancies at risk remains elusive. Here we demonstrate the ability of plasma cell-free RNA (cfRNA) to reveal patterns of normal pregnancy progression and determine the risk of developing pre-eclampsia months before clinical presentation. Our results centre on comprehensive transcriptome data from eight independent prospectively collected cohorts comprising 1,840 racially diverse pregnancies and retrospective analysis of 2,539 banked plasma samples. The pre-eclampsia data include 524 samples (72 cases and 452 non-cases) from two diverse independent cohorts collected 14.5 weeks (s.d., 4.5 weeks) before delivery. We show that cfRNA signatures from a single blood draw can track pregnancy progression at the placental, maternal and fetal levels and can robustly predict pre-eclampsia, with a sensitivity of 75% and a positive predictive value of 32.3% (s.d., 3%), which is superior to the state-of-the-art method2. cfRNA signatures of normal pregnancy progression and pre-eclampsia are independent of clinical factors, such as maternal age, body mass index and race, which cumulatively account for less than 1% of model variance. Further, the cfRNA signature for pre-eclampsia contains gene features linked to biological processes implicated in the underlying pathophysiology of pre-eclampsia.

Original languageEnglish
Pages (from-to)422-427
Number of pages6
Issue number7893
Early online date5 Jan 2022
Publication statusPublished - 20 Jan 2022


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