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Systems-level metabolic flux profiling identifies fatty acid synthesis as a target for antiviral therapy.

ABSTRACT: Viruses rely on the metabolic network of their cellular hosts to provide energy and building blocks for viral replication. We developed a flux measurement approach based on liquid chromatography-tandem mass spectrometry to quantify changes in metabolic activity induced by human cytomegalovirus (HCMV). This approach reliably elucidated fluxes in cultured mammalian cells by monitoring metabolome labeling kinetics after feeding cells (13)C-labeled forms of glucose and glutamine. Infection with HCMV markedly upregulated flux through much of the central carbon metabolism, including glycolysis. Particularly notable increases occurred in flux through the tricarboxylic acid cycle and its efflux to the fatty acid biosynthesis pathway. Pharmacological inhibition of fatty acid biosynthesis suppressed the replication of both HCMV and influenza A, another enveloped virus. These results show that fatty acid synthesis is essential for the replication of two divergent enveloped viruses and that systems-level metabolic flux profiling can identify metabolic targets for antiviral therapy.

SUBMITTER: Munger J

PROVIDER: S-EPMC2825756 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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Systems-level metabolic flux profiling identifies fatty acid synthesis as a target for antiviral therapy.

Munger Joshua J Bennett Bryson D BD Parikh Anuraag A Feng Xiao-Jiang XJ McArdle Jessica J Rabitz Herschel A HA Shenk Thomas T Rabinowitz Joshua D JD

Nature biotechnology 20080928 10

Viruses rely on the metabolic network of their cellular hosts to provide energy and building blocks for viral replication. We developed a flux measurement approach based on liquid chromatography-tandem mass spectrometry to quantify changes in metabolic activity induced by human cytomegalovirus (HCMV). This approach reliably elucidated fluxes in cultured mammalian cells by monitoring metabolome labeling kinetics after feeding cells (13)C-labeled forms of glucose and glutamine. Infection with HCMV ...[more]

PMID: 18820684

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Project description:BackgroundThe importance and power of isotope-based metabolic flux analysis and its contribution to understanding the metabolic network is increasingly recognized. Its application is, however, still limited partly due to computational inefficiency. 13C metabolic flux analysis aims to compute in vivo metabolic fluxes in terms of metabolite balancing extended by carbon isotopomer balances and involves a nonlinear least-squares problem. To solve the problem more efficiently, improved numerical optimization techniques are necessary.ResultsFor flux computation, we developed a gradient-based hybrid optimization algorithm. Here, independent flux variables were compactified into [0, 1)-ranged variables using a single transformation rule. The compactified parameters could be discriminated between non-identifiable and identifiable variables after model linearization. The developed hybrid algorithm was applied to the central metabolism of Bacillus subtilis with only succinate and glutamate as carbon sources. This creates difficulties caused by symmetry of succinate leading to limited introduction of 13C labeling information into the system. The algorithm was found to be superior to its parent algorithms and to global optimization methods both in accuracy and speed. The hybrid optimization with tolerance adjustment quickly converged to the minimum with close to zero deviation and exactly re-estimated flux variables. In the metabolic network studied, some fluxes were found to be either non-identifiable or nonlinearly correlated. The non-identifiable fluxes could correctly be predicted a priori using the model identification method applied, whereas the nonlinear flux correlation was revealed only by identification runs using different starting values a posteriori.ConclusionThis fast, robust and accurate optimization method is useful for high-throughput metabolic flux analysis, a posteriori identification of possible parameter correlations, and also for Monte Carlo simulations to obtain statistical qualities for flux estimates. In this way, it contributes to future quantitative studies of central metabolic networks in the framework of systems biology.

Dataset Information

Systems-level metabolic flux profiling identifies fatty acid synthesis as a target for antiviral therapy.

Publications

Systems-level metabolic flux profiling identifies fatty acid synthesis as a target for antiviral therapy.

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