Authors

Mohammad Sajjad Ghaemi, Stanford University School of Medicine
Daniel B. DiGiulio, Stanford University School of Medicine
Kévin Contrepois, Stanford University School of Medicine
Benjamin Callahan, Stanford University School of Medicine
Thuy T.M. Ngo, Stanford University
Brittany Lee-Mcmullen, Stanford University School of Medicine
Benoit Lehallier, Stanford University School of Medicine
Anna Robaczewska, Stanford University School of Medicine
David McIlwain, Stanford University
Yael Rosenberg-Hasson, Human Immune Monitoring Center
Ronald J. Wong, Stanford University School of Medicine
Cecele Quaintance, Stanford University School of Medicine
Anthony Culos, Stanford University School of Medicine
Natalie Stanley, Stanford University School of Medicine
Athena Tanada, Stanford University School of Medicine
Amy Tsai, Stanford University School of Medicine
Dyani Gaudilliere, Stanford University School of Medicine
Edward Ganio, Stanford University School of Medicine
Xiaoyuan Han, Stanford University School of Medicine
Kazuo Ando, Stanford University School of Medicine
Leslie McNeil, Stanford University School of Medicine
Martha Tingle, Stanford University School of Medicine
Paul Wise, Stanford University School of Medicine
Ivana Maric, Stanford University School of Medicine
Marina Sirota, University of California, San Francisco
Tony Wyss-Coray, Stanford University School of Medicine
Virginia D. Winn, Stanford University School of Medicine
Maurice L. Druzin, Stanford University School of Medicine
Ronald S. Gibbs, Stanford University School of Medicine

Document Type

Article

Publication Title

Bioinformatics

ISSN

1367-4803

Volume

35

Issue

1

DOI

10.1093/bioinformatics/bty537

First Page

95

Last Page

103

Publication Date

1-1-2019

Abstract

Motivation Multiple 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. Results We 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 implementation Datasets and scripts for reproduction of results are available through: Https://nalab.stanford.edu/multiomics-pregnancy/.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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