Search

Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95% confidence interval (CI) [0.79 to 0.89], P = 1.2 × 10-40, N = 53, training set; R = 0.81, 95% CI [0.61 to 0.91], P = 3.9 × 10-7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies.

Residence at high altitude (> 2500 m) has been associated with an increased frequency of preeclampsia. Pappalysin-2 (PAPP-A2) is an insulin-like growth factor binding protein-5 (IGFBP-5) protease that is elevated in preeclampsia, and up-regulated by hypoxia in placental explants. The relationships between PAPP-A2, altitude, and indices of uteroplacental ischemia are unknown. We aimed to evaluate the association of altitude, preeclampsia, and uterine artery flow or vascular resistance with PAPP-A2 levels. PAPP-A2, uterine artery diameter, volumetric blood flow, and pulsatility indices were measured longitudinally in normotensive Andean women residing at low or high altitudes in Bolivia and in a separate Andean high-altitude cohort with or without preeclampsia. PAPP-A2 levels increased with advancing gestation, with the rise tending to be greater at high compared to low altitude, and higher in early-onset preeclamptic compared to normotensive women at high altitude. Uterine artery blood flow was markedly lower and pulsatility index higher in early-onset preeclamptic normotensive women compared to normotensive women. PAPP-A2 was unrelated to uterine artery pulsatility index in normotensive women but positively correlated in the early-onset preeclampsia cases. We concluded that PAPP-A2 is elevated at high altitude and especially in cases of early-onset preeclampsia with Doppler indices of uteroplacental ischemia.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online.

Background: The prevalence of atopic diseases has increased with atopic dermatitis (AD) as the earliest manifestation. We assessed if molecular risk factors in atopic mothers influence their infants' susceptibility to an atopic disease. Methods: Pregnant women and their infants with (n = 174, high‐risk) or without (n = 126, low‐risk) parental atopy were enrolled in a prospective birth cohort. Global differentially methylated regions (DMRs) were determined in atopic (n = 92) and non‐atopic (n = 82) mothers. Principal component analysis was used to predict atopy risk in children dependent on maternal atopy. Genome‐wide transcriptomic analyses were performed in paired atopic (n = 20) and non‐atopic (n = 15) mothers and cord blood. Integrative genomic analyses were conducted to define methylation–gene expression relationships. Results: Atopic dermatitis was more prevalent in high‐risk compared to low‐risk children by age 2. Differential methylation analyses identified 165 DMRs distinguishing atopic from non‐atopic mothers. Inclusion of DMRs in addition to maternal atopy significantly increased the odds ratio to develop AD in children from 2.56 to 4.26. In atopic compared to non‐atopic mothers, 139 differentially expressed genes (DEGs) were identified significantly enriched of genes within the interferon signaling pathway. Expression quantitative trait methylation analyses dependent on maternal atopy identified 29 DEGs controlled by 136 trans‐acting methylation marks, some located near transcription factors. Differential expression for the same nine genes, including MX1 and IFI6 within the interferon pathway, was identified in atopic and non‐atopic mothers and high‐risk and low‐risk children. Conclusion: These data suggest that in utero epigenetic and transcriptomic mechanisms predominantly involving the interferon pathway may impact and predict the development of infant atopy. [ABSTRACT FROM AUTHOR]

Background: Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular approaches used for risk prediction. However, derivation of protein signatures of sufficient predictive power has been challenging. The recent availability of platforms simultaneously assessing over 1000 plasma proteins offers broad examinations of the plasma proteome, which may enable the extraction of proteomic signatures with improved prognostic performance in prenatal care. Objective: The primary aim of this study was to examine the generalizability of proteomic signatures predictive of PE in two cohorts of pregnant women whose plasma proteome was interrogated with the same highly multiplexed platform. Establishing generalizability, or lack thereof, is critical to devise strategies facilitating the development of clinically useful predictive tests. A second aim was to examine the generalizability of protein signatures predictive of gestational age (GA) in uncomplicated pregnancies in the same cohorts to contrast physiological and pathological pregnancy outcomes. Study design: Serial blood samples were collected during the first, second, and third trimesters in 18 women who developed PE and 18 women with uncomplicated pregnancies (Stanford cohort). The second cohort (Detroit), used for comparative analysis, consisted of 76 women with PE and 90 women with uncomplicated pregnancies. Multivariate analyses were applied to infer predictive and cohort-specific proteomic models, which were then tested in the alternate cohort. Gene ontology (GO) analysis was performed to identify biological processes that were over-represented among top-ranked proteins associated with PE. Results: The model derived in the Stanford cohort was highly significant (p = 3.9E–15) and predictive (AUC = 0.96), but failed validation in the Detroit cohort (p = 9.7E–01, AUC = 0.50). Similarly, the model derived in the Detroit cohort was highly significant (p = 1.0E–21, AUC = 0.73), but failed validation in the Stanford cohort (p = 7.3E–02, AUC = 0.60). By contrast, proteomic models predicting GA were readily validated across the Stanford (p = 1.1E–454, R = 0.92) and Detroit cohorts (p = 1.1.E–92, R = 0.92) indicating that the proteomic assay performed well enough to infer a generalizable model across studied cohorts, which makes it less likely that technical aspects of the assay, including batch effects, accounted for observed differences. Conclusions: Results point to a broader issue relevant for proteomic and other omic discovery studies in patient cohorts suffering from a clinical syndrome, such as PE, driven by heterogeneous pathophysiologies. While novel technologies including highly multiplex proteomic arrays and adapted computational algorithms allow for novel discoveries for a particular study cohort, they may not readily generalize across cohorts. A likely reason is that the prevalence of pathophysiologic processes leading up to the “same” clinical syndrome can be distributed differently in different and smaller-sized cohorts. Signatures derived in individual cohorts may simply capture different facets of the spectrum of pathophysiologic processes driving a syndrome. Our findings have important implications for the design of omic studies of a syndrome like PE. They highlight the need for performing such studies in diverse and well-phenotyped patient populations that are large enough to characterize subsets of patients with shared pathophysiologies to then derive subset-specific signatures of sufficient predictive power. [ABSTRACT FROM AUTHOR]