Dataset Information

Novel diagnostics of metabolic dysfunction detected in breath and plasma by selective isotope-assisted labeling.

ABSTRACT: Metabolomics is the study of a unique fingerprint of small molecules present in biological systems under healthy and disease conditions. One of the major challenges in metabolomics is validation of fingerprint molecules to identify specifically perturbed pathways in metabolic aberrations. This step is crucial to the understanding of budding metabolic pathologies and the ability to identify early indicators of common diseases such as obesity, type 2 diabetes mellitus, metabolic syndrome, polycystic ovary syndrome, and cancer. We present a novel approach to diagnosing aberrations in glucose utilization including metabolic pathway switching in a disease state. We used a well-defined prenatally exposed glucocorticoid mouse model that results in adult females with metabolic dysfunction. We applied the complementary technologies of nuclear magnetic resonance spectroscopy and cavity ring-down spectroscopy to analyze serial plasma samples and real-time breath measurements following selective (13)C-isotope-assisted labeling. These platforms allowed us to trace metabolic markers in whole animals and identify key metabolic pathway switching in prenatally glucocorticoid-treated animals. Total glucose flux is significantly proportionally increased through the major oxidative pathways of glycolysis and the pentose phosphate pathway in the prenatally glucocorticoid-treated animals relative to the control animals. This novel diagnostics approach is fast, noninvasive, and sensitive for determining specific pathway utilization, and provides a direct translational application in the health care field.

SUBMITTER: Haviland JA

PROVIDER: S-EPMC3346854 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Novel diagnostics of metabolic dysfunction detected in breath and plasma by selective isotope-assisted labeling.

Haviland Julia A JA Tonelli Marco M Haughey Dermot T DT Porter Warren P WP Assadi-Porter Fariba M FM

Metabolism: clinical and experimental 20120202 8

Metabolomics is the study of a unique fingerprint of small molecules present in biological systems under healthy and disease conditions. One of the major challenges in metabolomics is validation of fingerprint molecules to identify specifically perturbed pathways in metabolic aberrations. This step is crucial to the understanding of budding metabolic pathologies and the ability to identify early indicators of common diseases such as obesity, type 2 diabetes mellitus, metabolic syndrome, polycyst ...[more]

PMID: 22304834

Similar Datasets

Project description:BackgroundMetabolic Flux Analysis (MFA) based on isotope labeling experiments (ILEs) is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs) contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures.ResultsWith a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs) and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of E. coli comprising 10390 isotopomer pools.ConclusionExploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the E. coli example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global topological analysis of ILNs allows to comprehensively describe and understand the general patterns of label flow in complex networks. This is an invaluable tool for the structural design of new experiments and the interpretation of measured data.

Dataset Information

Novel diagnostics of metabolic dysfunction detected in breath and plasma by selective isotope-assisted labeling.

Publications

Novel diagnostics of metabolic dysfunction detected in breath and plasma by selective isotope-assisted labeling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets