Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy

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

Department

Biomedical Sciences

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/.

Recommended Citation

Ghaemi, M. S., DiGiulio, D. B., Contrepois, K., Callahan, B., Ngo, T. T., Lee-Mcmullen, B., Lehallier, B., Robaczewska, A., McIlwain, D., Rosenberg-Hasson, Y., Wong, R. J., Quaintance, C., Culos, A., Stanley, N., Tanada, A., Tsai, A., Gaudilliere, D., Ganio, E., Han, X., Ando, K., McNeil, L., Tingle, M., Wise, P., Maric, I., Sirota, M., Wyss-Coray, T., Winn, V. D., Druzin, M. L., & Gibbs, R. S. (2019). Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics, 35(1), 95–103. DOI: 10.1093/bioinformatics/bty537
https://scholarlycommons.pacific.edu/dugoni-facarticles/719

Creative Commons License

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