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

Mohammad Sajjad Ghaemi, Daniel B. DiGiulio, Kévin Contrepois, Benjamin Callahan, Thuy Ngo, Brittany Lee-Mcmullen, Benoit Lehallier, Anna Robaczewska, David McIlwain, Yael Rosenberg-Hasson, Ronald J. Wong, Cecele Quaintance, Anthony Culos, Natalie Stanley, Athena Tanada, Amy Tsai, Dyani Gaudilliere, Edward Ganio, Xiaoyuan Han, Kazuo AndoLeslie McNeil, Martha Tingle, Paul Wise, Ivana Maric, Marina Sirota, Tony Wyss-Coray, Virginia D. Winn, Maurice L. Druzin, Ronald Gibbs, Gary L. Darmstadt, David B. Lewis, Vahid Partovi Nia, Bruno Agard, Robert Tibshirani, Garry Nolan, Michael P. Snyder, David A. Relman, Stephen R. Quake, Gary M. Shaw, David K. Stevenson, Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour

Research output: Contribution to journalArticle

17 Scopus citations

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/.

Original languageEnglish (US)
Pages (from-to)95-103
Number of pages9
JournalBioinformatics
Volume35
Issue number1
DOIs
StatePublished - Jan 1 2019

    Fingerprint

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Ghaemi, M. S., DiGiulio, D. B., Contrepois, K., Callahan, B., Ngo, 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., ... Aghaeepour, N. (2019). Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics, 35(1), 95-103. https://doi.org/10.1093/bioinformatics/bty537