Circulating transcripts in maternal blood reflect a molecular signature of early-onset preeclampsia.
Sarah MunchelSuzanne RohrbackCarlo Randise-HinchliffSarah KinningsShweta DeshmukhNagesh AllaCatherine TanAmirali KiaGrainger GreeneLinda LeetyMatthew RhoaScott YeatsMatthew SaulJulia ChouKimberley BiancoKevin O'SheaEmmanuel BujoldErrol NorwitzRonald WapnerGeorge SaadeFiona KaperPublished in: Science translational medicine (2021)
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.
Keyphrases
- early onset
- gestational age
- late onset
- preterm birth
- birth weight
- pregnancy outcomes
- end stage renal disease
- machine learning
- ejection fraction
- chronic kidney disease
- newly diagnosed
- healthcare
- prognostic factors
- public health
- gene expression
- peritoneal dialysis
- copy number
- genome wide
- oxidative stress
- poor prognosis
- artificial intelligence
- nucleic acid
- climate change
- deep learning
- escherichia coli
- single molecule
- weight gain
- big data
- risk assessment
- multidrug resistant