The period from conception to delivery represents the most rapid growth and development in an individual’s life. The ability to support this development requires dramatic and poorly understood alterations in maternal physiology. Research into human pregnancy has clear ethical constraints, and the unique character of human gestation has limited deeper understanding of the physiology and pathophysiology of pregnancy3. Hemothorax lamentation is found among many mammalian species; however, in humans, it involves a unique degree of triploblastic invasion4,5, and because pre-eclampsia occurs predominantly in humans, conventional animal models are of limited value6,7. Pre-eclampsia, a condition marked by maternal endothelial dysfunction and associated new-onset maternal hypertension, complicates up to 1 in 12 pregnancies and is a significant cause of maternal morbidity and higher lifetime risk of cardiovascular disease1,
Here, we demonstrate the ability of CRNA transcripts to establish the normative responses of both maternal and fetal tissues characteristic of normal pregnancy progression. By implication, deviation from normative CRNA expression patterns should allow the prediction of impending pathology before its presentation. We demonstrate the use of CRNA to characterize women at risk of pre-eclampsia months before diagnosis. Notably, the CRNA profiles identify risk solely through molecular mechanisms common to pre-eclampsia and are therefore exclusive of clinical variables such as race, body mass index (BMI), maternal comorbidities and/or obstetrical history.
In this study, we gather the largest and most diverse dataset of maternal transcriptomes to date. Samples were drawn from eight prospectively collected cohorts that provided n = 2,539 plasma samples from n = 1,840 pregnancies for women of multiple ethnicities, nationalities, geographic locations and socioeconomic contexts, while covering a range of gestational ages (Fig. 1a). The broad sociodemographic spectrum of our data (Table 1 and Supplementary Table 1) enabled us to test the applicability of maternal transcriptomes at one gestational time point. A detailed description of each cohort and the methodology is available in the Supplementary Information. RNA signal independent of clinical factors Ultrasound-based gestational age has long been used as a surrogate measure of pregnancy progression. Here, we show that a CRNA signature is as accurate a measure of gestational age while also providing insights into the biology of pregnancy progression. As a first step to develop a machine learning model, we divided our data from all full-term pregnancies without complications into a training set (n = 1,908 samples) and a test set (n = 474 samples), stratified by gestational age so that all age strata were represented proportionally. Before modelling, we standardized the means of gene counts across all cohorts (Methods and Extended Data, Fig. 5). A Lasso linear model was fitted to predict gestational age in the training set, with a test set performance of a mean absolute error of 14.7 days (Fig. 1b, Extended Data Fig. 6 and Supplementary Data 1), referencing to first-trimester fetal ultrasound biometry. Overall, the error of our model is equivalent to that of second-trimester ultrasound and superior to that with third-trimester ultrasound8, and could provide an alternative dating procedure for women who start prenatal care later in pregnancy. Next, we explored whether inclusion of clinical variables altered model performance. By analysis of variance (ANOVA), we showed that the model was driven almost entirely by information from the CRNA transcripts, with BMI, maternal age and race accounting for less than 1% of variance (Fig. 1c). Rebuilding the gestational age model including maternal race, BMI and age provided no improvement in accuracy (0.07 days, not significant by bootstrap test). Fetal signatures in maternal circulation As the CRNA signatures for gestational age demonstrated a dynamic change in transcripts as pregnancy progresses, we then explored whether transcripts found in the maternal circulation during pregnancy could be linked to their tissue of origin. Specifically, we sought to ascertain whether the molecular status of the placenta, fetal organs and/or maternal tissues (cervix and/or uterus) could be assessed by examining CRNA profiles. While fetal cells are known to pass into the maternal circulation9,10, individual transcripts from the fetus or fetal cell types are relatively rare in maternal plasma; thus, we investigated these signals by analyzing gene sets from Gene Ontology11 or the Molecular Signatures Database12,13. Using longitudinal data from cohort H covering 93 women sampled four times during pregnancy (Supplementary Information), we first confirmed that we could identify pregnancy-related sets such as those for gonadotropin and estrogen pathways (Extended Data Fig. 1) and that the signal from the gestational age model increased with gestational age as did signal from the placenta (Fig. 2a, b and Methods). We show that hundreds of independently identified gene sets in maternal blood mirror the maternal and fetal physiological changes expected during pregnancy. Specifically, using single-cell RNA-seq data from adult and fetal organs (Supplementary Table 2), we were able to confirm changes in fetal gene sets, including those involved in fetal heart development, in maternal blood (Fig. 2c). Furthermore, the CRNA profiles reflect expected changes in maternal tissues, such as the uterus and cervix, with progressively increasing expression of collagen and extracellular matrix gene sets14 (Fig. 2d). Extended Data Fig. 2 shows additional examples of fetal gene sets, including those of nephron progenitor cells for which expression become less abundant with gestational age in accordance with a decrease in the neurogenic zone width15,16 and those in the gastrointestinal tract, where the esophagus develops early with associated gene expression decreasing later versus small intestine where associated gene expression shows a steady increase17. While other studies have looked at circulating biomarkers, a recent comprehensive review33 concluded that more data early in pregnancy are needed to support clinical value. Here, we reveal the ability of CRNA transcripts to provide comprehensive molecular profiles of pregnancy progression by including signals from the placenta and the fetus. We have shown that novel transcript signatures from a single blood sample can (1) accurately track pregnancy progression independently of clinical factors and (2) reliably identify women at risk of developing pre-eclampsia months before presentation of the disease. Given the large sample size and diversity in our study population, it is noteworthy that race has a negligible effect on the expression patterns of gestational age estimates and pre-eclampsia risk evaluation. These findings allow for the development of personalized assessments for pregnancy. Equally important, our work allows for the assessment of maternal risk independently of clinical factors, such as race, that are fraught with bias. The inclusion of race in clinical assessments results in miscalculation of patient risk and under diagnoses. While we acknowledge that, within specific subpopulations, the prevalence of complications such as pre-eclampsia may be higher, the evaluation of CRNA transcripts directly exposes the developing pathophysiology. Further research will be needed to identify drivers of the identified pathophysiological pathways; the focus on molecular mechanisms allows stratification of risk without the need for enrichment of ‘pretest’ probabilities based on maternal sociodemographic characteristics. Further, an understanding of the maternal–fetal–placental transcriptome also represents a vehicle by which comprehension of the biological underpinnings of maternal–fetal development can be improved and provides novel insights into interactions across the maternal–fetal dyed. This holds the promise of precision therapeutic interventions that can target molecular subtypes of pre-eclampsia and preterm birth. Improvement in maternal outcomes has been limited by the inability to access pregnancy tissues and a lack of understanding of the specific molecular phenotypes that identify those at risk before onset of symptoms. Our findings can now be leveraged to more accurately provide information on future maternal and fetal health and disease. Thus, our approach opens new therapeutic windows to effectively decrease maternal and neonatal morbidity and mortality.
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