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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 T. M. 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 Ando, Leslie 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 and Nima Aghaeepour

Article (2019)

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Terms of Use: Creative Commons Attribution .
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Cite this document: Ghaemi, M. S., DiGiulio, D. B., Contrepois, K., Callahan, B., Ngo, T. T. M., Lee-McMullen, B., ... Aghaeepour, N. (2019). Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics, 35(1), p. 95-103. doi:10.1093/bioinformatics/bty537
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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.

Uncontrolled Keywords

Computational Biology; Female; Humans; *Metabolome; *Microbiota; *Pregnancy; *Proteome; *Transcriptome

Open Access document in PolyPublie
Subjects: 1600 Génie industriel > 1600 Génie industriel
9000 Sciences de la santé > 9000 Sciences de la santé
Department: Département de mathématiques et de génie industriel
Research Center: CIRRELT - Centre interuniversitaire de recherche sur les réseaux d'entreprise, la logistique et le transport
GERAD - Groupe d'études et de recherche en analyse des décisions
Date Deposited: 05 Apr 2022 14:41
Last Modified: 06 Apr 2022 01:20
PolyPublie URL: https://publications.polymtl.ca/4882/
Document issued by the official publisher
Journal Title: Bioinformatics (vol. 35, no. 1)
Publisher: Oxford University Press
Official URL: https://doi.org/10.1093/bioinformatics/bty537


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