Systems biology approaches for the identification of key regulators and underlying mechanisms of metabolic diseases

Abstract

Metabolic diseases such as obesity, diabetes, and cardiovascular disease involve complex genetic and molecular alterations. Despite of numerous efforts, only a small fraction of the molecular component and pathogenesis is known. Recent advances in high-throughput technologies and genome-scale molecular data enable large-scale quantitative measurement of biomolecules in cells, tissues and organs. Systems biology attempts to understand disease mechanisms and identify biomarkers and potential therapeutic targets by using high-throughput biological data. In this thesis, we introduced systems biology approaches to identify disease mechanisms and molecular signatures of metabolic diseases.First, we identified retrograde signaling pathways that contributed to the alteration of cellular processes related to mitochondrial diseases. We performed gene expression profiling of engineered cells that had mitochondria containing a disease-associated mutation (mt3243 mutation), which induce mitochondrial dysfunction. By analyzing the gene expression profiles and the transcription factors (TFs) that regulate the differentially expressed genes, we identified 72 TFs that are potentially involved in mitochondrial retrograde signaling pathway. We experimentally validated that the mt3243 mutation induces a retrograde signaling pathway involving retinoid X receptor alpha (RXRA), reactive oxygen species (ROS), JUN N-terminal kinase (JNK) and peroxisome proliferator-activated receptor gamma, coactivator 1 alpha (PGC1). This RXRA pathway contributed to the decrease of oxidative phosphorylation enzymes, thereby aggravating the mitochondrial dysfunction.Second, we identified a protein profile that can represent type 2 diabetes mellitus (T2DM) pathophysiology of visceral adipose tissue (VAT). We performed comprehensive proteomic analysis of VAT in drug naïve, early T2DM patients and subjects with normal glucose tolerance (NGT). A total of 4,707 proteins were identified by LC-MS/MS experiments. Among them, 444 proteins increased in their abundances in T2DM, compared to NGT, whereas 328 decreased in T2DM. They were involved in T2DM-related processes including inflammatory responses, peroxisome proliferator-activated receptor signaling, oxidative phosphorylation, fatty acid oxidation, and glucose metabolism. Of these proteins, we selected 11 VAT proteins that can represent alteration in early T2DM patients. Among them, up-regulation of FABP4, C1QA, S100A8, and SORBS1 and down-regulation of ACADL and PLIN4 were confirmed using western blot in VAT samples of independent early T2DM patients.Third, we identified novel biomarker candidates predictive of type 1 diabetes (T1DM). We systematically explored auto-antibodies from serum samples of 16 T1DM, 16 T2DM patients, and 27 healthy controls with NGT using protein microarrays. Among 9,480 proteins on the array, two novel auto-antibody candidates (EEF1A1-AAb and UBE2L3-AAb) were identified by M-test coupled with PLS-DA and immunofluorescence staining. The increase in abundances of the selected novel auto-antibodies in T1DM were confirmed by ELISA in two independent cohorts.In summary, we provided a comprehensive basis to understand the pathogenesis and a novel dimension of the information for diagnosis, classification, and therapy of metabolic diseases using systems biology approaches. Furthermore, these results are expected to help discover strategies for preventing the metabolic diseases for which there are currently no therapeutic options.