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Open Access Highly Accessed Methodology article

A network-based approach for predicting key enzymes explaining metabolite abundance alterations in a disease phenotype

Jon Pey1, Luis Tobalina1, Joaquín Prada J de Cisneros12 and Francisco J Planes1*

  • * Corresponding author: Francisco J Planes fplanes@ceit.es

  • † Equal contributors

Author Affiliations

1 CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, San Sebastian 20018, Spain

2 Institute of Infection, Immunity and Inflammation, University of Glasgow, Garscube Campus, Bearsden Road, Glasgow G61 1QH, Scotland

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BMC Systems Biology 2013, 7:62  doi:10.1186/1752-0509-7-62

Published: 19 July 2013

Abstract

Background

The study of metabolism has attracted much attention during the last years due to its relevance in various diseases. The advance in metabolomics platforms allows us to detect an increasing number of metabolites in abnormal high/low concentration in a disease phenotype. Finding a mechanistic interpretation for these alterations is important to understand pathophysiological processes, however it is not an easy task. The availability of genome scale metabolic networks and Systems Biology techniques open new avenues to address this question.

Results

In this article we present a novel mathematical framework to find enzymes whose malfunction explains the accumulation/depletion of a given metabolite in a disease phenotype. Our approach is based on a recently introduced pathway concept termed Carbon Flux Paths (CFPs), which extends classical topological definition by including network stoichiometry. Using CFPs, we determine the Connectivity Curve of an altered metabolite, which allows us to quantify changes in its pathway structure when a certain enzyme is removed. The influence of enzyme removal is then ranked and used to explain the accumulation/depletion of such metabolite. For illustration, we center our study in the accumulation of two metabolites (L-Cystine and Homocysteine) found in high concentration in the brain of patients with mental disorders. Our results were discussed based on literature and found a good agreement with previously reported mechanisms. In addition, we hypothesize a novel role of several enzymes for the accumulation of these metabolites, which opens new strategies to understand the metabolic processes underlying these diseases.

Conclusions

With personalized medicine on the horizon, metabolomic platforms are providing us with a vast amount of experimental data for a number of complex diseases. Our approach provides a novel apparatus to rationally investigate and understand metabolite alterations under disease phenotypes. This work contributes to the development of Systems Medicine, whose objective is to answer clinical questions based on theoretical methods and high-throughput “omics” data.