Comparative profiling of prenatal cortisol and DHEA-S among pregnant women with poor birth outcome and pregnant women with normal birth outcome

Maternal exposure to ambient PM2.5 perturbs the metabolic homeostasis of maternal serum and placenta in mice

Epidemiological and animal studies have shown that maternal fine particulate matters (PM_2.5) exposure correlates with various adverse pregnancy outcomes such as low birth weight (LBW) of offspring. However, the underlying biological mechanisms have not been fully understood. In this study, female C57Bl/6 J mice were exposed to filtered air (FA) or concentrated ambient PM_2.5 (CAP) during pregestational and gestational periods, and metabolomics was performed to analyze the metabolic features in maternal serum and placenta by liquid chromatography-mass spectrometry (LC-MS). The partial least squares discriminate analysis (PLS-DA) displayed evident clustering of FA- and CAP-exposed samples for both maternal serum and placenta. In addition, pathway analysis identified that vitamin digestion and absorption was perturbed in maternal serum, while metabolic pathways including arachidonic acid metabolism, serotonergic synapse, 2-oxocarboxylic acid metabolism and cAMP signaling pathway were perturbed in placenta. Further analysis indicated that CAP exposure influenced the nutrient transportation capacity of placenta, by not only changing the ratios of some critical metabolites in placenta to maternal serum but also significantly altering the expressions of nutrition transporters in placenta. These findings reaffirm the importance of protecting women from PM_2.5 exposure, and also advance our understanding of the toxic actions of ambient PM_2.5.

Gestational Dating by Urine Metabolic Profile at High Resolution Weekly Sampling Timepoints: Discovery and Validation

Background: Pregnancy triggers longitudinal metabolic alterations in women to allow precisely-programmed fetal growth. Comprehensive characterization of such a "metabolic clock" of pregnancy may provide a molecular reference in relation to studies of adverse pregnancy outcomes. However, a high-resolution temporal profile of metabolites along a healthy pregnancy remains to be defined. Methods: Two independent, normal pregnancy cohorts with high-density weekly urine sampling (discovery: 478 samples from 19 subjects at California; validation: 171 samples from 10 subjects at Alabama) were studied. Urine samples were profiled by liquid chromatography-mass spectrometry (LC-MS) for untargeted metabolomics, which was applied for gestational age dating and prediction of time to delivery. Results: 5,473 urinary metabolic features were identified. Partial least-squares discriminant analysis on features with robust signals (n = 1,716) revealed that the samples were distributed on the basis of the first two principal components according to their gestational age. Pathways of bile secretion, steroid hormone biosynthesis, pantohenate, and CoA biosynthesis, benzoate degradation, and phenylpropanoid biosynthesis were significantly regulated, which was collectively applied to discover and validate a predictive model that accurately captures the chronology of pregnancy. With six urine metabolites (acetylcholine, estriol-3-glucuronide, dehydroepiandrosterone sulfate, α-lactose, hydroxyexanoy-carnitine, and l-carnitine), models were constructed based on gradient-boosting decision trees to date gestational age in high accordance with ultrasound results, and to accurately predict time to delivery. Conclusion: Our study characterizes the weekly baseline profile of the human pregnancy metabolome, which provides a high-resolution molecular reference for future studies of adverse pregnancy outcomes.