Systems Biology of Type 2 Diabetes in Skeletal Muscle

Type 2 diabetes (T2D) is a heterogeneous and complex disease that currently affects more than 350 million people worldwide. A wide range of risk factors influence the pathogenesis of T2D, including genetic and epigenetic components, as well as controllable factors such as diet, obesity, and sedentary lifestyle. T2D is characterized by abnormally high blood glucose levels as a consequence of the development of insulin resistance in multiple tissues (primarily skeletal muscle, liver, and adipose tissue) in combination with impaired insulin secretion in the pancreas. Skeletal muscle accounts for around 75-80% of the insulin-stimulated glucose uptake from the blood. Consequently, deficiency in glucose uptake mediated by insulin resistance in skeletal myocytes is an important factor for the disrupted glucose homeostasis associated with T2D. In fact, skeletal muscle insulin resistance can appear long before the onset of the disease itself, making it one of the primary defects preceding the development of T2D. The pathophysiology of T2D and the mechanisms underlying the development of insulin resistance in skeletal muscle are not yet fully understood. In light of the multifactorial complexity of T2D we have adopted a systems biology approach to study skeletal muscle in response to this disease, using network modeling of metabolism and analysis of genome-wide data from human subjects. We developed three tools for analyzing gene expression data and facilitating its interpretation. The R package piano enables functional characterization and interpretation of gene expression profiles (and other omics data), through so called gene-set analysis (GSA). The skeletal myocyte genome-scale metabolic model (GEM), that we reconstructed based on transcriptome and proteome data, constitutes a comprehensive map of the myocyte metabolic network that can be used for simulation and integration of genome-wide data. The Python tool Kiwi visualizes the output from GSA using metabolite gene-sets and the topology of a GEM so that significant metabolite subnetworks affected by gene expression changes can be identified. Leveraged by these tools, we performed two studies of T2D. In the first study, we carried out a meta-analysis of muscle tissue transcriptome data from 6 published datasets, providing a holistic insight into the metabolic state of T2D muscle. In particular, we identified a metabolic signature that has the power to predict T2D in individual subjects, highlighting its potential use for biomarkers or drug targets. In the second study, we analyzed transcriptome data from primary differentiated myocytes to explore inherent properties associated with T2D and obesity. We found a remarkable similarity between the transcriptional profiles in response to T2D and obesity, independent of each other, and identified a possible epigenetic mechanism behind these patterns. We performed a systematic characterization of the individual intrinsic effects of T2D and obesity, which are hardwired in the myocytes rather than attributable to a diabetic or obese extracellular environment. In summary, this thesis provides novel methods for analysis of genome-wide data and contributes to disentangling the complexity of T2D.