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

<p>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.</p> <p>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.</p> <p>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.</p&gt

Disruption of Intrinsic Motions as a Mechanism for Enzyme Inhibition

AbstractClostridium difficile (C. diff) is one of the most common and most severe hospital-acquired infections; its consequences range from lengthened hospital stay to outright lethality. C. diff causes cellular damage through the action of two large toxins TcdA and TcdB. Recently, there has been increased effort toward developing antitoxin therapies, rather than antibacterial treatments, in hopes of mitigating the acquisition of drug resistance. To date, no analysis of the recognition mechanism of TcdA or TcdB has been attempted. Here, we use small molecule flexible docking followed by unbiased molecular dynamics to obtain a more detailed perspective on how inhibitory peptides, exemplified by two species HQSPWHH and EGWHAHT function. Using principal component analysis and generalized masked Delaunay analysis, an examination of the conformational space of TcdB in its apo form as well as forms bound to the peptides and UDP-Glucose was performed. Although both species inhibit by binding in the active site, they do so in two very different ways. The simulations show that the conformational space occupied by TcdB bound to the two peptides are quite different and provide valuable insight for the future design of toxin inhibitors and other enzymes that interact with their substrates through conformational capture mechanisms and thus work by the disruption of the protein’s intrinsic motions