Integration of metabolomic and transcriptomic networks in pregnant women reveals biological pathways and predictive signatures associated with preeclampsia
Integration of metabolomic and transcriptomic networks in pregnant women reveals biological pathways and predictive signatures associated with preeclampsia
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DOI：
10.1007/s11306-016-1149-8
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发表时间：
2017-01-01
期刊：
影响因子：
3.6
通讯作者：
Lasky-Su, Jessica A.
中科院分区：
文献类型：
--
作者：
Kelly, Rachel S.;Croteau-Chonka, Damien C.;Lasky-Su, Jessica A.
先兆子痫是全球孕产妇和胎儿死亡的主要原因，但其确切的发病机制仍然难以捉摸。本研究是维生素D产前哮喘减少试验（VDAART）的一部分，旨在建立子痫前期的综合组学模型，用于预测和阐明潜在的生物学机制。方法采用液相色谱-串联质谱法对47例VDAART后发生子痫前期的孕妇和62例健康妊娠的对照孕妇的妊娠早期血浆样本进行代谢组学分析。代谢组学特征是基于逻辑回归模型生成的，并使用接收算子特征曲线分析进行评估。将这些资料与使用妊娠晚期样本生成的资料进行比较。然后使用网络方法将前三个月代谢物谱与预先存在的转录组谱整合。结果调整母亲年龄、种族、胎龄后，72例（0.9%）代谢物特征与子痫前期相关（p < 0.01）。这些特征具有中等至良好的区分能力；在ROC曲线分析中，基于这些特征的汇总评分显示曲线下面积（AUC）为0.794 （95% CI 0.700, 0.888）。该特征保留了在妊娠晚期区分子痫前期和健康妊娠的能力[AUC: 0.762 (95% CI: 0.663, 0.860)]。此外，代谢物集富集分析确定了两个时间点的共同途径，包括甘油磷脂代谢。结合转录组学特征，这些结果表明脂质失衡、免疫功能和循环系统具有特殊作用。结论：这些研究结果表明，有可能建立一种预测子痫前期代谢组学特征。这种情况的特点是脂质和氨基酸代谢的变化以及免疫反应的失调，可以通过与转录组学数据的相互作用来完善。然而，需要在更大、更多样化的人群中进行验证。
Introduction Preeclampsia is a leading cause of maternal and fetal mortality worldwide, yet its exact pathogenesis remains elusive.Objectives This study, nested within the Vitamin D Antenatal Asthma Reduction Trial (VDAART), aimed to develop integrated omics models of preeclampsia that have utility in both prediction and in the elucidation of underlying biological mechanisms.Methods Metabolomic profiling was performed on first trimester plasma samples of 47 pregnant women from VDAART who subsequently developed preeclampsia and 62 controls with healthy pregnancies, using liquid-chromatography tandem mass-spectrometry. Metabolomic profiles were generated based on logistic regression models and assessed using Received Operator Characteristic Curve analysis. These profiles were compared to profiles from generated using third trimester samples. The first trimester metabolite profile was then integrated with a pre-existing transcriptomic profile using network methods.Results In total, 72 (0.9%) metabolite features were associated (p < 0.01) with preeclampsia after adjustment for maternal age, race, and gestational age. These features had moderate to good discriminatory ability; in ROC curve analyses a summary score based on these features displayed an area under the curve (AUC) of 0.794 (95% CI 0.700, 0.888). This profile retained the ability to distinguish preeclamptic from healthy pregnancies in the third trimester [AUC: 0.762 (95% CI 0.663, 0.860)]. Additionally, metabolite set enrichment analysis identified common pathways, including glycerophospholipid metabolism, at the two time-points. Integration with the transcriptomic signature refined these results suggesting a particular role for lipid imbalance, immune function and the circulatory system.Conclusions These findings suggest it is possible to develop a predictive metabolomic profile of preeclampsia. This profile is characterized by changes in lipid and amino acid metabolism and dysregulation of immune response and can be refined through interaction with transcriptomic data. However validation in larger and more diverse populations is required.