A Genome-Scale Modeling Approach to Study Inborn Errors of Liver Metabolism: Toward an In Silico Patient

Abstract

Inborn errors of metabolism (IEM) are genetic diseases caused by mutations in enzymes or transporters affecting specific metabolic reactions that cause a block in the physiological metabolic fluxes. Therapeutic treatment can be achieved either by decreasing the metabolic flux upstream of the block or by increasing the flux downstream of the block. The identification of upstream and downstream fluxes however is not trivial, since metabolic reactions are intertwined in a complex network. To overcome this problem, we propose an innovative computational workflow to model the alteration of metabolism caused by IEM and predict the metabolites and reactions that are affected by the mutation. Our workflow exploits a recent genome-scale metabolic network model of hepatocyte metabolism to identify metabolites accumulating in hepatocytes due to single gene mutations in IEM via an innovative ''differential flux analysis.'' We simulated 38 IEMs in the liver, and in about half of the cases, our workflow correctly identified the metabolites known to accumulate in the blood and urine of IEM patients.

Fibroblast-specific genome-scale modelling predicts an imbalance in amino acid metabolism in Refsum disease

“…Using the curated model, we aimed to get insight into metabolic changes that may provide leads for pathophysiology and biomarkers. Genomescale metabolic-models have been described to be useful tools for these aims [8][9][10]14,[19][20][21][22]35]. In our fibroblast-specific model resembling Refsum disease, the flux of phytanic acid uptake was reduced, reflecting the accumulation of phytanic acid in the body, a known biomarker for Refsum disease [3].…”

“…Using transcriptomics and proteomics data, we developed a cell-specific metabolic network based on Recon 3D_FAD [20]. Cell-typespecific metabolic models have been reported earlier [13,14,17,21,22,35], and are essential tools to study specific research questions. We studied the effect of phytanic acid loading on the metabolic fluxes in a fibroblast-specific model for Refsum disease, which is characterised by a defect in α -oxidation.…”

“…Today, IEMs are no longer viewed according to the "one gene, one disease" paradigm as proposed more than 100 years ago, but recognised to be complex diseases [16]. However, only few studies using systems biology and multi-omics approaches that are widely used for complex diseases have been published for IEMs [8,9,14,[17][18][19][20][21][22].…”

A Next Generation Multiscale View of Inborn Errors of Metabolism

“…For example, a human disease network revealed that most disease genes are nonessential and do not have a tendency to encode hub proteins (Goh et al, 2007). Genome-scale networks were used to predict the phenotypic consequences of SNPs (Jamshidi and Palsson, 2006) and reveal known and novel biomarkers of IEM (Pagliarini and di Bernardo, 2013; Shlomi et al, 2009; Thiele et al, 2013). …”

Modeling the effects of commonly used drugs on human metabolism

“…the small intestine, kidney, lung and heart) [12]. Both the current human metabolic reconstruction and its predecessor have been used to investigate the role of IEMs [10,13,14], off-target drug effects [15] and to predict novel drug targets [16][17][18][19][20][21][22][23]. Furthermore, the liver-specific metabolic network, HepatoNet1 [24], has been combined with a whole-body physiology-based pharmacokinetic model to study the toxic effects of drugs [25].…”