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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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Khan S, Ince-Dunn G, Suomalainen A, Elo LL. Integrative omics approaches provide biological and clinical insights: examples from mitochondrial diseases. J Clin Invest 2020; 130:20-28. [PMID: 31895050 PMCID: PMC6934214 DOI: 10.1172/jci129202] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
High-throughput technologies for genomics, transcriptomics, proteomics, and metabolomics, and integrative analysis of these data, enable new, systems-level insights into disease pathogenesis. Mitochondrial diseases are an excellent target for hypothesis-generating omics approaches, as the disease group is mechanistically exceptionally complex. Although the genetic background in mitochondrial diseases is in either the nuclear or the mitochondrial genome, the typical downstream effect is dysfunction of the mitochondrial respiratory chain. However, the clinical manifestations show unprecedented variability, including either systemic or tissue-specific effects across multiple organ systems, with mild to severe symptoms, and occurring at any age. So far, the omics approaches have provided mechanistic understanding of tissue-specificity and potential treatment options for mitochondrial diseases, such as metabolome remodeling. However, no curative treatments exist, suggesting that novel approaches are needed. In this Review, we discuss omics approaches and discoveries with the potential to elucidate mechanisms of and therapies for mitochondrial diseases.
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Affiliation(s)
- Sofia Khan
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Gulayse Ince-Dunn
- Research Programs Unit, Stem Cells and Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Anu Suomalainen
- Research Programs Unit, Stem Cells and Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Neuroscience Center, HiLife, University of Helsinki, Helsinki, Finland
- Helsinki University Hospital, HUSlab, Helsinki, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
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Fukushima A, Sugimoto M, Hiwa S, Hiroyasu T. Elastic net-based prediction of IFN-β treatment response of patients with multiple sclerosis using time series microarray gene expression profiles. Sci Rep 2019; 9:1822. [PMID: 30755676 PMCID: PMC6372673 DOI: 10.1038/s41598-018-38441-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 12/14/2018] [Indexed: 01/08/2023] Open
Abstract
INF-β has been widely used to treat patients with multiple sclerosis (MS) in relapse. Accurate prediction of treatment response is important for effective personalization of treatment. Microarray data have been frequently used to discover new genes and to predict treatment responses. However, conventional analytical methods suffer from three difficulties: high-dimensionality of datasets; high degree of multi-collinearity; and achieving gene identification in time-course data. The use of Elastic net, a sparse modelling method, would decrease the first two issues; however, Elastic net is currently unable to solve these three issues simultaneously. Here, we improved Elastic net to accommodate time-course data analyses. Numerical experiments were conducted using two time-course microarray datasets derived from peripheral blood mononuclear cells collected from patients with MS. The proposed methods successfully identified genes showing a high predictive ability for INF-β treatment response. Bootstrap sampling resulted in an 81% and 78% accuracy for each dataset, which was significantly higher than the 71% and 73% accuracy obtained using conventional methods. Our methods selected genes showing consistent differentiation throughout all time-courses. These genes are expected to provide new predictive biomarkers that can influence INF-β treatment for MS patients.
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Affiliation(s)
- Arika Fukushima
- Doshisha University, Graduate School of Life and Medical Sciences, Kyoto, Japan
| | - Masahiro Sugimoto
- Research and Development Center for Minimally Invasive Therapies Health Promotion and Preemptive Medicine, Tokyo Medical University, Shinjuku, Tokyo, 160-8402, Japan.,Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan.,University of Tsukuba, Research and Development Center for Precision Medicine, Tukuba, Ibaraki, 305-8550, Japan
| | - Satoru Hiwa
- Doshisha University, Graduate School of Life and Medical Sciences, Kyoto, Japan
| | - Tomoyuki Hiroyasu
- Doshisha University, Graduate School of Life and Medical Sciences, Kyoto, Japan.
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Tareen SHK, Adriaens ME, Arts ICW, de Kok TM, Vink RG, Roumans NJT, van Baak MA, Mariman ECM, Evelo CT, Kutmon M. Profiling Cellular Processes in Adipose Tissue during Weight Loss Using Time Series Gene Expression. Genes (Basel) 2018; 9:E525. [PMID: 30380678 PMCID: PMC6266822 DOI: 10.3390/genes9110525] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 10/22/2018] [Accepted: 10/22/2018] [Indexed: 12/13/2022] Open
Abstract
Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans.
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Affiliation(s)
- Samar H K Tareen
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6211ER Maastricht, The Netherlands.
| | - Michiel E Adriaens
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6211ER Maastricht, The Netherlands.
| | - Ilja C W Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6211ER Maastricht, The Netherlands.
- Department of Epidemiology, CARIM School for Cardiovascular Diseases, Maastricht University, 6211ER Maastricht, The Netherlands.
| | - Theo M de Kok
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6211ER Maastricht, The Netherlands.
- Department of Toxicogenomics, GROW School of Oncology and Developmental Biology, Maastricht University, 6211ER Maastricht, The Netherlands.
| | - Roel G Vink
- Department of Human Biology, NUTRIM Research School, Maastricht University, 6211ER Maastricht, The Netherlands.
| | - Nadia J T Roumans
- Department of Human Biology, NUTRIM Research School, Maastricht University, 6211ER Maastricht, The Netherlands.
| | - Marleen A van Baak
- Department of Human Biology, NUTRIM Research School, Maastricht University, 6211ER Maastricht, The Netherlands.
| | - Edwin C M Mariman
- Department of Human Biology, NUTRIM Research School, Maastricht University, 6211ER Maastricht, The Netherlands.
| | - Chris T Evelo
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6211ER Maastricht, The Netherlands.
- Department of Bioinformatics-BiGCaT, NUTRIM Research School, Maastricht University, 6211ER Maastricht, The Netherlands.
| | - Martina Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6211ER Maastricht, The Netherlands.
- Department of Bioinformatics-BiGCaT, NUTRIM Research School, Maastricht University, 6211ER Maastricht, The Netherlands.
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Spies D, Ciaudo C. Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis. Comput Struct Biotechnol J 2015; 13:469-77. [PMID: 26430493 PMCID: PMC4564389 DOI: 10.1016/j.csbj.2015.08.004] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 08/05/2015] [Accepted: 08/07/2015] [Indexed: 12/17/2022] Open
Abstract
Analysis of gene expression has contributed to a plethora of biological and medical research studies. Microarrays have been intensively used for the profiling of gene expression during diverse developmental processes, treatments and diseases. New massively parallel sequencing methods, often named as RNA-sequencing (RNA-seq) are extensively improving our understanding of gene regulation and signaling networks. Computational methods developed originally for microarrays analysis can now be optimized and applied to genome-wide studies in order to have access to a better comprehension of the whole transcriptome. This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments. Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.
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Affiliation(s)
- Daniel Spies
- Swiss Federal Institute of Technology Zurich, Department of Biology, Institute of Molecular Health Sciences, Zurich, Otto-Stern Weg 7, 8093 Zurich, Switzerland
- Life Science Zurich Graduate School, Molecular Life Science Program, University of Zurich, Institute of Molecular Life Sciences, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Constance Ciaudo
- Swiss Federal Institute of Technology Zurich, Department of Biology, Institute of Molecular Health Sciences, Zurich, Otto-Stern Weg 7, 8093 Zurich, Switzerland
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Hendrickx DM, Jennen DGJ, Briedé JJ, Cavill R, de Kok TM, Kleinjans JCS. Pattern recognition methods to relate time profiles of gene expression with phenotypic data: a comparative study. Bioinformatics 2015; 31:2115-22. [DOI: 10.1093/bioinformatics/btv108] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2014] [Accepted: 02/16/2015] [Indexed: 12/13/2022] Open
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7
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Liu YF, Ai N, Keys A, Fan XH, Chen MJ. Network Pharmacology for Traditional Chinese Medicine Research: Methodologies and Applications. CHINESE HERBAL MEDICINES 2015. [DOI: 10.1016/s1674-6384(15)60015-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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