Abstract
The metabolically connected triad of obesity, diabetes, and cardiovascular diseases is a major public health threat, and is expected to worsen due to the global shift toward energy-rich and sedentary living. Despite decades of intense research, a large part of the molecular pathogenesis behind complex metabolic diseases remains unknown. Recent advances in genetics, epigenomics, transcriptomics, proteomics and metabolomics enable us to obtain large-scale snapshots of the etiological processes in multiple disease-related cells, tissues and organs. These datasets provide us with an opportunity to go beyond conventional reductionist approaches and to pinpoint the specific perturbations in critical biological processes. In this review, we summarize systems biology methodologies such as functional genomics, causality inference, data-driven biological network construction, and higher-level integrative analyses that can produce novel mechanistic insights, identify disease biomarkers, and uncover potential therapeutic targets from a combination of omics datasets. Importantly, we also demonstrate the power of these approaches by application examples in obesity, diabetes, and cardiovascular diseases.
Highlights
Common metabolically connected diseases (MetDs) such as cardiovascular disease (CVD), type 2 diabetes (T2D), and obesity impose a substantial health burden worldwide, as demonstrated by the fact that both CVD and T2D are among the top ten leading causes of death in Europe and the United States
We systematically review the principles, advantages, and limitations of various systems biology approaches available and how these approaches have been applied to MetDs
Higher level integrative approaches that take advantage of multiple methodologies are used to derive key regulatory genes and subnetworks underlying disease development in a tissue-specific fashion we mainly focus on gene expression profiling and genetic association studies due to their ubiquitous applications in biomedicine to illustrate the power and limitations of the traditional approaches
Summary
Common metabolically connected diseases (MetDs) such as cardiovascular disease (CVD), type 2 diabetes (T2D), and obesity impose a substantial health burden worldwide, as demonstrated by the fact that both CVD and T2D are among the top ten leading causes of death in Europe and the United States. In two studies on a Finnish cohort, Inouye et al constructed co-expression networks using blood transcriptomic data and identified a lipid-leukocyte module that was highly enriched for inflammatory genes and significantly linked to over 80 serum metabolites including lipoprotein subclasses, lipids, and amino acids, thereby playing an important role in connecting inflammation, metabolism, adiposity, and atherogenesis [71, 72] All these examples substantiate the power of WGCNA in identifying novel genes and mechanisms that contribute to MetDs. WGCNA is highly informative for deriving the overall organization of genes or other IMPs and for linking particular co-expression modules to disease phenotypes, the detailed relationships among genes within a module or between modules can be less descriptive. By leveraging tissue-specific gene expression with PPI networks, they identified an inflammation- and
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