Metabolomics in Systems Medicine
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COISB239_proof ■ 15 April 2019 ■ 1/9 Available online at www.sciencedirect.com Current Opinion in ScienceDirect Systems Biology 59 60 61 62 63 Q1 Metabolomics in systems medicine: an overview of 1 64 2 Q11 methods and applications 65 3 1,2,† 1,3,† 66 4 Q10 Effrosyni Karakitsou , Carles Foguet , 67 5 Pedro de Atauri1,3, Kim Kultima4, Payam Emami Khoonsari4, 68 6 5,6 5 69 7 Vitor A. P. Martins dos Santos , Edoardo Saccenti , 70 8 7 1,3 71 9 Antonio Rosato and Marta Cascante 72 10 73 11 The brave new world of systems medicine 74 12 Abstract 75 13 Patient-derived metabolomics offers valuable insights into the Systems biology treats biological systems as ensembles 76 14 metabolic phenotype underlying diseases with a strong meta- of networks at multiple levels, starting from the mo- 77 15 bolic component. Thus, these data sets will be pivotal to the lecular level and from there gradually addressing more 78 16 implementation of personalized medicine strategies in health complex systems such as cells, tissues, organs, whole 79 17 and disease. However, to take full advantage of such data sets, organisms, and finally analyzing population dynamics. 80 18 they must be integrated with other omics within a coherent Systems biology aims to describe and predict the 81 19 pathophysiological framework to enable improved diagnostics, behavior of groups of interacting components. To do so, 82 20 to identify therapeutic interventions, and to accurately stratify it uses mathematical and computational tools to analyze 83 21 Q2 patients. Herein, we provide an overview of the state-of-the-art measurements collected by systematic high-throughput 84 22 for different data analysis and modeling approaches applicable technologies such as (post)genomics, metabolomics, or 85 23 to metabolomics data and of their potential for systems proteomics among others. The goal of systems ap- 86 24 medicine. proaches is to boost our understanding of biology by 87 25 overcoming the limitations of reductive science, which 88 26 Addresses addresses individual genes, proteins, metabolites, 89 1 27 Department of Biochemistry and Molecular Biomedicine and Institute pathways, or cells, and thus does not account for the 90 28 of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona properties emerging from their interactions [1,2]. 91 29 (UB), Barcelona, Spain 2 92 30 Institute of Cancer and Genomic Sciences and Centre for Compu- tational Biology, University of Birmingham, B15 2TT, Birmingham, UK Current medical science is mostly conducted using the 93 31 3 Centro de Investigación Biomédica en Red de Enfermedades He- 94 32 reductionist approach [3,4]. This limits our ability to páticas y Digestivas (CIBEREHD), Metabolomics Node at INB- grasp how multiple variables interact with one another 95 33 Bioinformatics Platform, Instituto de Salud Carlos III (ISCIII), Madrid, 96 to create emergent effects [3] and hampers our under- 34 Spain 97 4 standing of diseases, as well as our capability of deliv- 35 Department of Medical Sciences, Clinical Chemistry, Uppsala Uni- 98 36 versity, Uppsala, Sweden ering better treatments. Systems medicine can be 5 Laboratory of Systems and Synthetic Biology, Wageningen University 99 37 regarded as the application of systems biology to human 100 & Research, Stippeneng 4, 6708WE, Wageningen, the Netherlands physiology in a clinical context [5,6]. It addresses the 38 6 LifeGlimmer GmbH, Markelstraße 38, 12163 Berlin, Germany 101 39 7 Department of Chemistry and Magnetic Resonance Center, Univer- aforementioned issues by applying iterative and recip- 102 40 sity of Florence, Italy rocal feedback between clinical research and practice 103 41 through computational, statistical, and mathematical 104 Corresponding authors: Saccenti, Edoardo (edoardo.saccenti@wur. 42 multiscale analysis. This includes modeling of disease 105 43 nl); Rosato, Antonio ([email protected]); Cascante, Marta (marta- [email protected]) progression and remission, treatment responses, and 106 44 † These authors contributed equally to this work. adverse events both at the epidemiological and patient 107 45 level. This new paradigm of systems science and med- 108 46 icine strongly complements the traditional reductionist 109 47 Current Opinion in Systems Biology xxxx, xxx:xxx approach (Figure 1). 110 48 Edited by Shinya Kuroda and Mariko Okada 111 49 This reviews comes from a themed issue on Gene regulation The functioning of the human body is regulated by the 112 50 interaction and interdependencies of biological mole- 113 51 For a complete overview see the Issue and the Editorial cules at multiple levels (proteineprotein, proteine 114 52 Available online xxx RNA, and proteineDNA networks and metabolic net- 115 53 https://doi.org/10.1016/j.coisb.2019.03.009 116 54 works) [8]. Therefore, it can only be efficiently analyzed 2452-3100/© 2019 Published by Elsevier Ltd. 117 55 by examining various omics concurrently. Systems medicine provides the appropriate framework to achieve 118 56 119 57 120 58 www.sciencedirect.com Current Opinion in Systems Biology xxxx, xxx:xxx 121 COISB239_proof ■ 15 April 2019 ■ 2/9 2 Gene regulation 1 64 2 Figure 1 65 3 66 4 67 5 68 6 69 7 70 8 71 9 72 10 73 11 74 12 75 13 76 14 77 15 78 16 79 17 80 18 81 19 82 20 83 21 84 22 85 23 86 24 87 25 88 26 89 27 90 28 91 29 Overview of the core differences between reductionism and systems science, when analyzing the properties of a system; figure initially published in 92 30 Tillmann et al., 2015 [7] under the terms of Creative Commons Attribution 2.0 license. 93 31 94 32 95 33 96 34 97 35 this goal. The complementary perspectives offered by applied to analyze the complexity of the interactions 98 36 different data sets allow the genotype of an individual to within a biological network and link a priori knowledge 99 37 be linked to its observed phenotype as a function of from the literature and databases [11]. The application 100 38 lifestyle and environmental conditions. Eventually, this of network analysis allows the identification of nodes 101 39 could lead to defining how any healthy state can tran- with a high degree of connectivity (‘hubs’) and groups of 102 40 sition into a pathological one and vice versa and pave the highly interconnected nodes (‘modules’), identifying 103 41 way for personalized medicine. molecules functionally related to a disease state [12e 104 42 14]. 105 43 Multi-omics data integration 106 44 107 The integration of multiple omics data (sometimes also It is possible to outline a general strategy to integrate 45 various omics data sets based on network representa- 108 46 called trans-omics) will further enhance the contribu- 109 tion of omics science to our understanding of biomedi- tions. First, the network scaffold is defined by defining 47 how the individual components are interconnected. The 110 48 cine [9]. The example in Figure 2 shows the 111 connections among genomics, transcriptomics, prote- structure of the network can be identified based on the 49 data or prior knowledge (i.e., database information). 112 50 omics, and metabolomics, thus providing an overview of 113 Subsequently, the network itself can be separated into 51 the system from its potential (encoded in DNA) to the 114 modules. Finally, all the information can be combined 52 actual outcome (monitored by metabolomics). 115 53 with computational models of the whole system to 116 54 It is commonly accepted that the relationships between simulate and predict how the network determines the 117 55 genes, gene products, and metabolites participate in observed phenotype. In practice, if two omics elements 118 56 complex, interconnected networks (Figure 2). Various share a common driver, or if one perturbs the other, they 119 57 biological molecules can be represented as nodes in a will exhibit correlation or association. Various specialized 120 58 network and the interactions connecting them as edges. statistical approaches can be applied to measure these 121 59 For example, in metabolomics, metabolites would be the correlations. For example, a linear model taking into 122 60 nodes, and the edges would represent the enzymatic account age, gender, body mass index, and white blood 123 61 reactions interconnecting them. Graph theory can be cell count was used to find correlations between DNA 124 62 125 63 Current Opinion in Systems Biology xxxx, xxx:xxx www.sciencedirect.com 126 COISB239_proof ■ 15 April 2019 ■ 3/9 Metabolomics in systems medicine Karakitsou et al. 3 1 64 2 Figure 2 the other hand, untargeted metabolomics typically fo- 65 3 cuses on capturing all the chemical compounds present 66 4 in a sample, including metabolites of unknown chemical 67 5 structure, thus generating notably large data sets. By 68 6 comparing the metabolome of the control and test 69 7 groups and focusing on the differences between their 70 8 metabolic profiles, the number of significant detected 71 9 signals becomes more manageable. Finally, the com- 72 10 pounds or metabolites identified are annotated using in 73 11 silico libraries when possible or by applying analytical 74 12 chemistry methods to explore the newly observed 75 13 structure [24]. 76 14 77 15 One of the technical challenges in connecting the 78 16 79 metabolome with other omics layers is matching the 17 80 identities of the same objects in different layers (ID 18 Multi-omics integration across different omics layers. Red arrows highlight 81 19 the top-down flow of interactions across layers: genes are transcribed, conversion). Various databases support this task: the 82 20 transcripts determine enzyme concentrations, and finally enzymes act on Kyoto Encyclopedia of Genes and Genomes (KEGG) 83 21 metabolites. Purple arrows highlight the bottom-up interactions, whereby integrates one computationally generated and fifteen 84 metabolite levels modulate enzyme activities, the DNA/RNA-binding af- 22 manually curated databases, allowing the users to link 85 finities of regulators or DNA methylation.