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Bioinformatics. 2019 Jan 1;35(1):95-103. doi: 10.1093/bioinformatics/bty537.

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

Author information

1
Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
2
Département de Mathématiques et de Génie Industriel, École Polytechnique de Montréal, QC, Canada.
3
Groupe d'Études et de Recherche en Analyse des Décision (GERAD), Montréal, QC, Canada.
4
Centre Interuniversitaire de Recherche sur les Réseaux d'Entreprise, la Logistique et le Transport (CIRRELT), Montréal, QC, Canada.
5
Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
6
Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
7
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
8
Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA.
9
Department of Bioengineering, Stanford University, Stanford, CA, USA.
10
Cancer Early Detection Advanced Research Center, Knight Cancer Institute and Department of Molecular and Medical Genetics, Oregon Health Sciences University, Portland, OR, USA.
11
Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
12
Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
13
Institute for Immunity, Transplantation and Infection, Human Immune Monitoring Center Stanford, CA, USA.
14
Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
15
Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, USA.
16
Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA.
17
Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA.
18
Departments of Biomedical Data Sciences and Statistics, Stanford University, Stanford, CA, USA.
19
Department of Statistics, Stanford University School of Medicine, Stanford, CA, USA.

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/.

Supplementary information:

Supplementary data are available at Bioinformatics online.

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