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Integration of metabolomic and transcriptomic networks in pregnant women reveals biological pathways and predictive signatures associated with preeclampsia

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Abstract

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.

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Abbreviations

ACOG:

American congress of obstetricians and gynecologists

AUC:

Area under the curve

BMI:

Body mass index

FDR:

False discovery rate

GABA:

gamma-Aminobutyric acid

GO:

Gene Ontology

HILIC:

Hydrophilic interaction liquid chromatography

LCMS:

Liquid chromatography mass spectroscopy

m/z:

mass to charge ration

ROC:

Receiver operator characteristics

Rt:

Retention time

UPLC:

Ultra performance liquid chromatography

VDAART:

Vitamin D Antenatal Asthma Reduction trial

WGCNA:

Weighted Gene Co-expression Network Analysis

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Acknowledgements

We would like to thank Amy Deik, Kevin Bullock, Kerry Pierce, and Courtney Dennis for conducting LC-MS analyses at the Broad Institute. We also wish to thank the participants of VDAART for their participation and contribution to the trial.

Funding

This manuscript is supported by an R01 Grant from the National Heart, Lung, and Blood Institute of the National Institutes of Health, entitled Integrative metabolomics of asthma severity NHLBI 1R01HL123915-01 (JALS).

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Authors and Affiliations

  1. Channing Department of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, 02115, USA

    Rachel S. Kelly, Damien C. Croteau-Chonka, Amber Dahlin, Hooman Mirzakhani, Ann C. Wu, Emily S. Wan, Michael J. McGeachie, Weiliang Qiu, Joanne E. Sordillo, Amal Al-Garawi, Vincent J. Carey, Augusto A. Litonjua, Scott T. Weiss & Jessica A. Lasky-Su

  2. Children’s Hospital and Harvard Medical School, Boston, MA, 02115, USA

    Ann C. Wu

  3. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, 02115, USA

    Ann C. Wu

  4. Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Brigham and Women’s Hospital, Boston, MA, 02115, USA

    Kathryn J. Gray & Thomas F. McElrath

  5. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02141, USA

    Clary B. Clish

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  1. Rachel S. Kelly
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Corresponding author

Correspondence to Rachel S. Kelly.

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Conflict of interest

All authors declare that they have no conflicts of interest.

Ethical approval

The VDAART study was approved by the IRBs of the participating institutions (Washington University in St. Louis, Boston Medical Center, Kaiser Health Care San Diego) and Brigham and Women’s Hospital. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Written informed consent was obtained from all individual participants included in the study.

Additional information

The metabolomics dataset supporting the conclusions of this article will be made publically available at such a time as a suitable online repository that is approved by the metabolomics society becomes available. The generation of the transcriptomic dataset was funded by the ongoing VDAART grant and the data is currently being prepared for submission to the Gene Expression Omnibus (GEO) database, pending acceptance of the primary gene expression manuscript.

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Kelly, R.S., Croteau-Chonka, D.C., Dahlin, A. et al. Integration of metabolomic and transcriptomic networks in pregnant women reveals biological pathways and predictive signatures associated with preeclampsia. Metabolomics 13, 7 (2017). https://doi.org/10.1007/s11306-016-1149-8

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