Endocrinol Metab.  2020 Sep;35(3):507-514. 10.3803/EnM.2020.303.

Systems Biology: A Multi-Omics Integration Approach to Metabolism and the Microbiome

• Son JW
• Shoaie S
• Lee S

• KMID: 2507999
• DOI: http://doi.org/10.3803/EnM.2020.303

Abstract

The complex and dynamic nature of human physiology, as exemplified by metabolism, has often been overlooked due to the lack of quantitative and systems approaches. Recently, systems biology approaches have pushed the boundaries of our current understanding of complex biochemical, physiological, and environmental interactions, enabling proactive medicine in the near future. From this perspective, we review how state-of-the-art computational modelling of human metabolism, i.e., genome-scale metabolic modelling, could be used to identify the metabolic footprints of diseases, to guide the design of personalized treatments, and to estimate the microbiome contributions to host metabolism. These state-of-the-art models can serve as a scaffold for integrating multi-omics data, thereby enabling the identification of signatures of dysregulated metabolism by systems approaches. For example, increased plasma mannose levels due to decreased uptake in the liver have been identified as a potential biomarker of early insulin resistance by multi-omics approaches. In addition, we also review the emerging axis of human physiology and the human microbiome, discussing its contribution to host metabolism and quantitative approaches to study its variations in individuals.

Keyword

Systems biology; Metabolism; Gastrointestinal microbiome

Figure

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  Fig. 1 Quantitative and systems approaches in biology and medicine. (A) Dynamic and complex systems can be studied with computational modelling, such as genome-scale metabolic models, and network theory, such as network biology principles. Augmenting with multi-omics observations, we could interpret complex and dynamic biological/clinical problems through more understandable readouts. (B) Based on data-driven approaches and systems science, future medicine can be transformed from reactive medicine, interpreting the outcomes of an intervention, into proactive medicine, predicting outcomes from prior observations in healthy conditions. (C) For example, the progression from pre- to post-disease states can be investigated with multi-omics datasets and its signatures could help disease prevention. Likewise, outcomes of interventions, which reverse the states from post-disease to pre-disease, can be studied with multi-omics observations, enabling the prediction of treatment efficacy.

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  Fig. 2 Overview of genome-scale metabolic models (GEMs). (A) GEMs provide a computational “map” of all possible biochemical reactions within a living system, including bacteria, cells, and tissues. All biochemical reactions are composed of metabolites (e.g., metabolites from A to D) and reactions (e.g., reactions from R1 to R6) that catalyse the formation of those metabolites. In this computational map, reactions can be likened to “roads” that lead between “metabolite” destinies. Enzymes perform “traffic control” on each corresponding road, depending on cellular demands. After composing stoichiometric matrices of metabolites and reactions into GEMs, we can simulate and predict metabolic flux by linear optimization and statistical algorithms, including flux balance analysis (FBA), flux variability analysis (FVA), reporter metabolite analysis, and optimal knockout simulation. (B) A generic GEM of human metabolism has been constructed by integrating different sources of biochemical reaction knowledge, including Recon1, Edinburgh Human Metabolic Network (EHMN), HumanCyc, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome. The generic human GEM can be reconstructed into tissue-specific, disease-specific, or personalized GEMs based on contextual information about enzymes, including transcriptome and proteome findings. For example, liver tissue and hepatocellular carcinoma (HCC) GEMs were generated and used to identify metabolic alterations in HCC and other liver diseases and to guide the design of anti-metabolites for personalized treatments.

• 

  Fig. 3 Multi-omics integration approaches. (A) Based on the central dogma, information flows could affect different omics from the upstream level (the genome) to the downstream level (the metabolome). All possible changes in upstream omics can be read out into metabolomic changes. (B) In order to study possible abundance changes in different omics and changes in interactions in individuals with diseases, diverse biological networks can be integrated into genome-scale metabolic models and metabolomics changes can be interpreted by investigating integrated networks and multi-omics datasets. Co-regulation analysis could identify the densest modules that regulate specific metabolic pathways, as well as their dysregulation in disease conditions. (C) Based on a co-regulation analysis of integrated networks, and together with a reporter metabolite analysis, less consumption of mannose in liver tissue was identified among obese subjects. For example, the gene expression levels of 2 hexokinases were decreased, whereas glucokinase, the more efficient isozyme of hexokinase for glucose, showed increased gene expression among obese subjects. Therefore, we could conclude that metabolic adaptations led to increased uptake of glucose and decreased uptake of mannose in liver tissue. Interestingly, decreased levels of mannose, a major building block of glycosylation, can affect the glycosylation of hepatic insulin receptors, thereby leading to less clearance of free insulin and eventually to insulin resistance. HK, hexokinase; GCK, glucokinase; MPI, mannose phosphate isomerase.

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