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Yousefi B, Melograna F, Galazzo G, van Best N, Mommers M, Penders J, Schwikowski B, Van Steen K. Capturing the dynamics of microbial interactions through individual-specific networks. Front Microbiol 2023; 14:1170391. [PMID: 37256048 PMCID: PMC10225591 DOI: 10.3389/fmicb.2023.1170391] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/21/2023] [Indexed: 06/01/2023] Open
Abstract
Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on 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 handling microbial interactions in temporal analyses. This study proposes a novel data analysis framework, MNDA, that combines representation learning and individual-specific microbial co-occurrence networks to uncover taxon neighborhood dynamics. As a use case, we consider a cohort of newborns with microbiomes available at 6 and 9 months after birth, and extraneous data available on the mode of delivery and diet changes between the considered time points. Our results show that prediction models for these extraneous outcomes based on an MNDA measure of local neighborhood dynamics for each taxon outperform traditional prediction models solely based on individual-specific microbial abundances. Furthermore, our results show that unsupervised similarity analysis of newborns in the study, again using the notion of a taxon's dynamic neighborhood derived from time-matched individual-specific microbial networks, can reveal different subpopulations of individuals, compared to standard microbiome-based clustering, with potential relevance to clinical practice. This study highlights the complementarity of microbial interactions and abundances in downstream analyses and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.
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Affiliation(s)
- Behnam Yousefi
- Computational Systems Biomedicine Lab, Institut Pasteur, University Paris City, Paris, France
- École Doctorale Complexite du vivant, Sorbonne University, Paris, France
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Federico Melograna
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Gianluca Galazzo
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Niels van Best
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
- Institute of Medical Microbiology, Rhine-Westphalia Technical University of Aachen, RWTH University, Aachen, Germany
| | - Monique Mommers
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, Netherlands
| | - John Penders
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, Netherlands
- Department of Medical Microbiology, Infectious Diseases and Infection Prevention, Care and Public Health Research Institute (CAPHRI), Maastricht University Medical Center+, Maastricht, Netherlands
| | - Benno Schwikowski
- Computational Systems Biomedicine Lab, Institut Pasteur, University Paris City, Paris, France
| | - Kristel Van Steen
- BIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
- BIO3—Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Lièvzge, Liège, Belgium
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Melograna F, Li Z, Galazzo G, van Best N, Mommers M, Penders J, Stella F, Van Steen K. Edge and modular significance assessment in individual-specific networks. Sci Rep 2023; 13:7868. [PMID: 37188794 PMCID: PMC10185658 DOI: 10.1038/s41598-023-34759-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 05/07/2023] [Indexed: 05/17/2023] Open
Abstract
Individual-specific networks, defined as networks of nodes and connecting edges that are specific to an individual, are promising tools for precision medicine. When such networks are biological, interpretation of functional modules at an individual level becomes possible. An under-investigated problem is relevance or "significance" assessment of each individual-specific network. This paper proposes novel edge and module significance assessment procedures for weighted and unweighted individual-specific networks. Specifically, we propose a modular Cook's distance using a method that involves iterative modeling of one edge versus all the others within a module. Two procedures assessing changes between using all individuals and using all individuals but leaving one individual out (LOO) are proposed as well (LOO-ISN, MultiLOO-ISN), relying on empirically derived edges. We compare our proposals to competitors, including adaptions of OPTICS, kNN, and Spoutlier methods, by an extensive simulation study, templated on real-life scenarios for gene co-expression and microbial interaction networks. Results show the advantages of performing modular versus edge-wise significance assessments for individual-specific networks. Furthermore, modular Cook's distance is among the top performers across all considered simulation settings. Finally, the identification of outlying individuals regarding their individual-specific networks, is meaningful for precision medicine purposes, as confirmed by network analysis of microbiome abundance profiles.
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Affiliation(s)
- Federico Melograna
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium.
| | - Zuqi Li
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Gianluca Galazzo
- School of Nutrition and Translational Research in Metabolism (NUTRIM), Department of Medical Microbiology Infectious Diseases and Infection Prevention, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Niels van Best
- Institute of Medical Microbiology, RWTH University Hospital Aachen, RWTH University, Aachen, Germany
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Monique Mommers
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - John Penders
- School of Nutrition and Translational Research in Metabolism (NUTRIM), Department of Medical Microbiology Infectious Diseases and Infection Prevention, Maastricht University Medical Center+, Maastricht, The Netherlands
- Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Fabio Stella
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126, Milan, Italy
| | - Kristel Van Steen
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
- BIO3 - Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
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Li Z, Melograna F, Hoskens H, Duroux D, Marazita ML, Walsh S, Weinberg SM, Shriver MD, Müller-Myhsok B, Claes P, Van Steen K. netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.04.539350. [PMID: 37205363 PMCID: PMC10187283 DOI: 10.1101/2023.05.04.539350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these classes. NetMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.
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Affiliation(s)
- Zuqi Li
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
| | | | - Hanne Hoskens
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
| | - Diane Duroux
- GIGA-R Medical Genomics, University of Liège, Liège, Belgium
| | - Mary L. Marazita
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Susan Walsh
- Department of Biology, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
| | - Seth M. Weinberg
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Mark D. Shriver
- Department of Anthropology, Pennsylvania State University, State College, PA 16801, USA
| | | | - Peter Claes
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
- Murdoch Children’s Research Institute, Melbourne, Victoria, Australia
| | - Kristel Van Steen
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- GIGA-R Medical Genomics, University of Liège, Liège, Belgium
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Kuchroo M, DiStasio M, Song E, Calapkulu E, Zhang L, Ige M, Sheth AH, Majdoubi A, Menon M, Tong A, Godavarthi A, Xing Y, Gigante S, Steach H, Huang J, Huguet G, Narain J, You K, Mourgkos G, Dhodapkar RM, Hirn MJ, Rieck B, Wolf G, Krishnaswamy S, Hafler BP. Single-cell analysis reveals inflammatory interactions driving macular degeneration. Nat Commun 2023; 14:2589. [PMID: 37147305 PMCID: PMC10162998 DOI: 10.1038/s41467-023-37025-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 02/27/2023] [Indexed: 05/07/2023] Open
Abstract
Due to commonalities in pathophysiology, age-related macular degeneration (AMD) represents a uniquely accessible model to investigate therapies for neurodegenerative diseases, leading us to examine whether pathways of disease progression are shared across neurodegenerative conditions. Here we use single-nucleus RNA sequencing to profile lesions from 11 postmortem human retinas with age-related macular degeneration and 6 control retinas with no history of retinal disease. We create a machine-learning pipeline based on recent advances in data geometry and topology and identify activated glial populations enriched in the early phase of disease. Examining single-cell data from Alzheimer's disease and progressive multiple sclerosis with our pipeline, we find a similar glial activation profile enriched in the early phase of these neurodegenerative diseases. In late-stage age-related macular degeneration, we identify a microglia-to-astrocyte signaling axis mediated by interleukin-1β which drives angiogenesis characteristic of disease pathogenesis. We validated this mechanism using in vitro and in vivo assays in mouse, identifying a possible new therapeutic target for AMD and possibly other neurodegenerative conditions. Thus, due to shared glial states, the retina provides a potential system for investigating therapeutic approaches in neurodegenerative diseases.
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Affiliation(s)
- Manik Kuchroo
- Department of Neuroscience, Yale University, New Haven, CT, USA
| | | | - Eric Song
- Department of Ophthalmology and Visual Science, Yale University, New Haven, CT, USA
| | - Eda Calapkulu
- Department of Ophthalmology and Visual Science, Yale University, New Haven, CT, USA
| | - Le Zhang
- Department of Neuroscience, Yale University, New Haven, CT, USA
- Department of Neurology, Yale University, New Haven, CT, USA
| | - Maryam Ige
- Yale School of Medicine, New Haven, CT, USA
| | | | - Abdelilah Majdoubi
- Department of Ophthalmology and Visual Science, Yale University, New Haven, CT, USA
| | - Madhvi Menon
- Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, UK
| | - Alexander Tong
- Department of Computer Science, Yale University, New Haven, CT, USA
| | | | - Yu Xing
- Department of Ophthalmology and Visual Science, Yale University, New Haven, CT, USA
| | - Scott Gigante
- Computational Biology, Bioinformatics Program, Yale University, New Haven, CT, USA
| | - Holly Steach
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Jessie Huang
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Guillaume Huguet
- Mila-Quebec AI institute, Montréal, QC, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
| | - Janhavi Narain
- Department of Computer Science, Rutgers University, New Brunswick, NJ, USA
| | - Kisung You
- Department of Genetics, Yale University, New Haven, CT, USA
| | - George Mourgkos
- Department of Ophthalmology and Visual Science, Yale University, New Haven, CT, USA
| | | | - Matthew J Hirn
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA
- Department of Mathematics, Michigan State University, East Lansing, MI, USA
| | - Bastian Rieck
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Guy Wolf
- Mila-Quebec AI institute, Montréal, QC, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
| | - Smita Krishnaswamy
- Department of Computer Science, Yale University, New Haven, CT, USA.
- Department of Genetics, Yale University, New Haven, CT, USA.
| | - Brian P Hafler
- Department of Pathology, Yale University, New Haven, CT, USA.
- Department of Ophthalmology and Visual Science, Yale University, New Haven, CT, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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