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
of myocytes, covering 5590 reactions, 2396 metabolites and 2419 genes.
With the aim to characterize the metabolic effects of T2D on skeletal muscle, we connected
the results from multiple studies by performing a meta-analysis of six published datasets
on T2D muscle gene expression. By integrating all of these condensed data with the
myocyte GEM, a metabolic subnetwork emerged that was significantly affected by transcriptional
regulation. Using the genes underlying this metabolic signature of T2D, we were able
to predict the disease state of individual samples from each separate study, confirming
the impact of these genes. In particular, the signature included down-regulation of
genes associated with pyruvate oxidation, tetrahydrofolate (THF) metabolism and branched-chain
amino acid (BCAA) catabolism.
The patterns for BCAA catabolism and pyruvate oxidation is in line with previous results,
but little has been reported about THF metabolism in connection to T2D. Our observation
of down-regulated THF metabolism coincided with the results from a pathway analysis,
showing transcriptional down-regulation of methionine and nucleotide metabolism, both
parts of the metabolism involving THF derivatives. Interestingly, in contrast, the
gene FTCD was up-regulated pointing to a flow from histidine catabolism to THF metabolism.
Histidine has been shown to have positive effects on T2D [7] and it is intriguing
to speculate whether increased histidine catabolism in myocytes is associated with
the negative effects seen in T2D.
The amount of available data and information in life science is growing and it is
essential to exploit and connect data from different sources in order to be able to
unravel the biology behind complex diseases. This includes connecting genome-wide
data from multiple levels (e.g. proteomics and transcriptomics), connecting analysis
results with available gene-level annotation and information (e.g. provided through
high-quality GEMs), and connecting and consolidating data from multiple studies. Furthermore,
GEMs have the ability to contextualize big data, which often can be hypothesis generating.
This is a natural part of systems biology where high-throughput large-scale analyses
can pinpoint likely targets of interest, worthwhile to study in more depth. It is
therefore also necessary to connect the output from systems biology research with
research in molecular biology. With good experimental design, proper data, and analysis
approaches that can connect multiple sources of information, successful studies can
result in increased mechanistic and molecular understanding of complex diseases, discrimination
between causes and effects, and identification of potential biomarkers and novel drug
targets.