The implications of human metabolic network topology for disease comorbidity

Abstract

Most diseases are the consequence of the breakdown of cellular processes, but the relationships among genetic/epigenetic defects, the molecular interaction networks underlying them, and the disease phenotypes remain poorly understood. To gain insights into such relationships, here we constructed a bipartite human disease association network in which nodes are diseases and two diseases are linked if mutated enzymes associated with them catalyze adjacent metabolic reactions. We find that connected disease pairs display higher correlated reaction flux rate, corresponding enzyme-encoding gene coexpression, and higher comorbidity than those that have no metabolic link between them. Furthermore, the more connected a disease is to other diseases, the higher is its prevalence and associated mortality rate. The network topology-based approach also helps to uncover potential mechanisms that contribute to their shared pathophysiology. Thus, the structure and modeled function of the human metabolic network can provide insights into disease comorbidity, with potentially important consequences for disease diagnosis and prevention.

Fig. 1.

MDN. (a) Construction of the MDN. (Upper) A local region of the glycolysis, where the catalytic enzymes are shown with red background and their corresponding genes are shown with orange background. (Lower) A local neighborhood of the metabolic diseases (blue) associated with the shown reactions. The gene ENO3 encodes the enzyme catalyzing the conversion between phosphoenolpyruvate and glycerate-2P, and its mutation is involved in the development of enolase-β deficiency. The gene products of PGAM2 and BPGM, catalyzing the reaction involving glycerate-2P and glycerate-3P, are connected to myopathy and hemolytic anemia. Then the two diseases are not only connected with each other but also linked to enolase-β deficiency due to the adjacency of their associated reactions. (b) In the network representation, 308 nonisolated diseases (nodes) are connected by 878 metabolic links combining the potential links predicted by KEGG and OMIM reconstructions. The color of the nodes indicates the disease class (see SI Text and Dataset S1), and node size is proportional to the prevalence of each disease in the Medicare dataset. The width of the link between diseases is proportional to the comorbidity C of the two connected diseases. We show with red the links with significant (P < 0.01) comorbidity. Clusters of diseases associated with purine metabolism (blue shading), fatty acid metabolism (red shading), and porphyrin metabolism (green shading) are shown.

Fig. 2.

Flux coupling and coexpression of metabolic genes. (a) To illustrate the use of flux-coupling analysis, we show the reactions that display directional coupling (DC) with the reaction converting propanoyl–CoA to (S)-methylmalonyl–CoA. In blue, we indicate the genes encoding the corresponding enzymes, and in red, we indicate the associated diseases. The production (consumption) of pentadecanoyl–CoA is performed by a single reaction, catalyzed by CPT2 (ACADM, ACADS), and therefore the ratio of their fluxes should be a constant (full coupling; FC). On the contrary, propanoyl–CoA may be produced by four reactions and is consumed by only one reaction. Therefore a nonzero flux of any of those four reactions implies a nonzero flux of the reaction consuming propanoyl–CoA, but the opposite is not the case, which is DC. Because of the FC between the reactions producing and consuming pentadecanoyl–CoA, the reaction (CPT2) has DC also with the reaction (PCCA, PCCB). (b) Distribution of the PCC for all pairs of metabolism-related genes and for the pairs of genes connected by metabolic links based on the KEGG database. (c) Average PCC for all pairs of genes, all pairs of metabolism-related genes, genes connected by metabolic links, and genes associated with flux-coupled reactions displaying DC or FC. The coexpression is stronger for connected genes and significantly higher for flux-coupled genes.

Fig. 3.

Comorbidity and the human MDN. (a) Comorbidity distributions for all pairs of metabolism-related diseases and for connected diseases. (Inset) The average comorbidities. (b) Distribution of the prevalence of metabolism-related diseases, well approximated by a power–law with exponent −2.03 ± 0.05 (see red line). (c) Prevalence as a function of the degree of the disease in the MDN. The prevalence increases with the degree with the PCC 0.333 for KEGG database and 0.092 for BiGG database with P values <10⁻⁷ and ≈0.07, respectively. (d) Comorbidity as a function of the distance between diseases in the MDN, decreasing as the distance increases. The PCCs are −0.06233 and −0.12511 for the KEGG and BiGG databases, respectively, and the P values are <10⁻⁸ for KEGG and ≈0.0002 for BiGG database. (e) Mortality as a function of disease degree in the MDN. The mortality increases with the degree with the PCC 0.162 for KEGG database and 0.0693 for BiGG database with P values 0.044 and 0.22, respectively. (f) Correlation of potential disease comorbidity factors with disease comorbidity. PCCs between the presence of common associated genes, of metabolic links, and of flux-coupled links, with disease comorbidity are presented for metabolism-related diseases and classical metabolic diseases.