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Capturing the dynamics of microbiomes using individual-specific networks

View ORCID ProfileBehnam Yousefi, View ORCID ProfileFederico Melograna, Gianluca Galazzo, Niels van Best, Monique Mommers, View ORCID ProfileJohn Penders, View ORCID ProfileBenno Schwikowski, View ORCID ProfileKristel van Steen
doi: https://doi.org/10.1101/2023.01.22.525058
Behnam Yousefi
1Computational Systems Biomedicine Lab, Institut Pasteur, Université Paris Cité, Paris, France
2École Doctorale Complexite du vivant, Sorbonne Universite, 75005 Paris, France
3BIO3 – Laboratory for Systems Medicine, KU Leuven, Leuven, Belgium
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  • ORCID record for Behnam Yousefi
Federico Melograna
3BIO3 – Laboratory for Systems Medicine, KU Leuven, Leuven, Belgium
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  • ORCID record for Federico Melograna
Gianluca Galazzo
4School of Nutrition and Translational Research in Metabolism (NUTRIM), Department of Medical Microbiology, Infectious Diseases and Infection Prevention, Maastricht University Medical Center+, Maastricht, The Netherlands
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Niels van Best
4School of Nutrition and Translational Research in Metabolism (NUTRIM), Department of Medical Microbiology, Infectious Diseases and Infection Prevention, Maastricht University Medical Center+, Maastricht, The Netherlands
5Institute of Medical Microbiology, RWTH University Hospital Aachen, RWTH University, Aachen, Germany
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Monique Mommers
6Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
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John Penders
6Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
7Care and Public Health Research Institute (CAPHRI), Department of Medical Microbiology, Infectious Diseases and Infection Prevention, Maastricht University Medical Center+, Maastricht, The Netherlands
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Benno Schwikowski
1Computational Systems Biomedicine Lab, Institut Pasteur, Université Paris Cité, Paris, France
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Kristel van Steen
3BIO3 – Laboratory for Systems Medicine, KU Leuven, Leuven, Belgium
8BIO3 – Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
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  • For correspondence: kristel.vansteen@uliege.be
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Abstract

Background Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains a daunting task. The vast majority of statistical tools and methods available to study the microbiota are based upon cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on how to incorporate individual-specific microbial associations in temporal analyses when the focus lies on microbial interactions.

Results Here, we propose a novel data analysis framework, called MNDA, to uncover taxon neighbourhood dynamics that combines representation learning and individual-specific microbiome co-occurrence networks. We show that tracking local neighbourhood dynamics in microbiome interaction or co-occurrence networks can yield complementary information to standard approaches that only use microbial abundances or pairwise microbial interactions. We use cohort data on infants for whom microbiome data was available at 6 and 9 months after birth, as well as information on mode of delivery and diet changes over time. In particular, MNDA-based prediction models outperform traditional prediction models based on individual-specific abundances, and enable the detection of microbes whose neighbourhood dynamics are informative of clinical variables. We further show that similarity analyses of individuals based on microbial neighbourhood dynamics can be used to find subpopulations of individuals with potential relevance to clinical practice. The annotated source code for the MNDA framework can be downloaded from: https://github.com/H2020TranSYS/microbiome_dynamics

Conclusions MNDA extracts information from matched microbiome profiles and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 22, 2023.
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Capturing the dynamics of microbiomes using individual-specific networks
Behnam Yousefi, Federico Melograna, Gianluca Galazzo, Niels van Best, Monique Mommers, John Penders, Benno Schwikowski, Kristel van Steen
bioRxiv 2023.01.22.525058; doi: https://doi.org/10.1101/2023.01.22.525058
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Capturing the dynamics of microbiomes using individual-specific networks
Behnam Yousefi, Federico Melograna, Gianluca Galazzo, Niels van Best, Monique Mommers, John Penders, Benno Schwikowski, Kristel van Steen
bioRxiv 2023.01.22.525058; doi: https://doi.org/10.1101/2023.01.22.525058

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