Networking in metabolism and human disease

A nexus of many complex human diseases and conditions, including type 2 diabetes (T2D), obesity, and cancer, is an altered cellular metabolism. Deviations in metabolism from a healthy phenotype often influence the metabolic network on a global level, rather than exclusively affecting specific pathways. The apparent complexity makes it challenging to study these diseases and require a combination of genome-wide data and innovative holistic analysis approaches [1]. Metabolites are connected to each other through the chemical reaction network and the reactions are connected to their corresponding enzymes, providing a bridge between metabolism and the genome. This structure allows for constructing computer models of metabolism, enabling study of human disease and metabolism at the global level. These models are termed genome-scale metabolic models (GEMs) and can be used for high throughput simulation, contextual data analysis and interpretation, as well as network based analysis and comparison, and are fundamental for the study of metabolism in the area of systems biology [2]. Besides being comprehensive network representations of metabolism, GEMs also contain the stoichiometric information about each reaction so that the system is mass-balanced. Several methods have been developed for using GEMs to simulate and quantify reaction fluxes under different conditions. Furthermore, the inherent metabolite-reaction-gene topology makes these models optimal for integrative analysis of gene expression data, in the context of transcriptional regulation of metabolism [3, 4]. T2D, as a complex metabolic disease, has been studied using GEMs [5]. A central feature of T2D is the development of insulin resistance in several tissues, including liver, adipose and skeletal muscle, thus leading to high glucose levels in the blood. Muscle in particular is important in this context since it is the major site for glucose disposal. Insights into the transcriptional and metabolic changes in diabetic skeletal myocytes are thus important in order to fully understand the pathology of T2D. However, until now there was no available comprehensive myocyte GEM to allow for analysis and contextualization of diabetic muscle transcription data. In a recent study, published in Cell Reports, we therefore set out to reconstruct the skeletal myocyte GEM [6]. By generating and integrating genome wide expression data at both the transcript and protein level we were able to determine, for each enzyme, if it is present or absent in skeletal myocytes and thus infer the presence of each corresponding metabolic reaction. This information could then be translated into a GEM, representing the metabolic capability …