Predicting metabolic biomarkers of human inborn errors of metabolism

Abstract

Early diagnosis of inborn errors of metabolism is commonly performed through biofluid metabolomics, which detects specific metabolic biomarkers whose concentration is altered due to genomic mutations. The identification of new biomarkers is of major importance to biomedical research and is usually performed through data mining of metabolomic data. After the recent publication of the genome-scale network model of human metabolism, we present a novel computational approach for systematically predicting metabolic biomarkers in stochiometric metabolic models. Applying the method to predict biomarkers for disruptions of red-blood cell metabolism demonstrates a marked correlation with altered metabolic concentrations inferred through kinetic model simulations. Applying the method to the genome-scale human model reveals a set of 233 metabolites whose concentration is predicted to be either elevated or reduced as a result of 176 possible dysfunctional enzymes. The method's predictions are shown to significantly correlate with known disease biomarkers and to predict many novel potential biomarkers. Using this method to prioritize metabolite measurement experiments to identify new biomarkers can provide an order of a 10-fold increase in biomarker detection performance.

Figure 1

An illustrative example of the prediction of biomarker concentration changes. (A) Circular nodes represent metabolites, solid edges represent reactions. The disease causing reaction is marked with a red cross. Out of six boundary metabolites in this network, only four metabolites (M1, M2, M4, M6) are predicted to show a concentration change when the disease causing reaction is inactivated. (B) Concentration change predictions based on exchange interval comparisons: the healthy state and disease state exchange intervals are colored black and red, respectively. Positive flux values represent metabolite secretion, whereas negative values represent metabolite up-take. For example, the concentration of M2 (associated with V2) is predicted to be reduced with high confidence due to a substantial change in the exchange interval. Similarly, M1 is predicted to be elevated with high confidence. M4 is reduced in the disease state as it must be secreted in the healthy case but is only potentially secreted in the disease case. The concentration level of M5 and M7 is predicted to be unchanged between the healthy case and the disease case. (C) The distribution of the number of the predicted alterations among the 176 disorders analyzed. (D) The distribution of predicted biomarker alteration patterns that are jointly shared by a number of disorders. As shown, various disorders tend to have different sets of biomarkers (the histogram is skewed to the left).

Figure 2

Prediction of amino-acid biomarkers for a set of amino-acid metabolic disorders. Rows represent metabolic disorders and columns represent amino acids. The causative gene's name is indicated on the left. Blue and red entries represent biomarkers that are predicted by our method to be elevated or reduced, respectively. Table entries marked in ‘+' or ‘−' represent elevation or reduction in the metabolite's concentration in biofluids according to OMIM, respectively.

Figure 3

(A) A subnetwork that illustrates the effect of homocystinuria on the metabolism and transport of methionine. Circular nodes represent metabolites and edges represent biochemical reactions. For simplicity, only abbreviations of metabolite names and enzyme E.C (Enzyme Commission) numbers are specified (explicit names are given in Supplementary Table 1). Metabolites marked in green participate in other reactions that are not presented here for simplicity. Homocystinuria is caused by a dysfunctional CBS, and hypermethioninemia is caused by dysfunctional AHCY. (B) Prediction of concentration changes of the boundary metabolite methionine in homocystinuria and S-adenosylhomocysteine hydrolase deficiency based on interval comparison of its exchange reaction's flux. In both cases, inferring the elevated concentration of methionine simply based on the network topology is impossible, due to the cyclic methionine salvage pathway (involving reaction 4.1.1.50), which (though topologically plausible) cannot affect methionine concentration due to mass-balance constraints.