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      Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production

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          Abstract

          Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression), extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB). Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.

          Author Summary

          The ability of cells to survive and grow depends on their ability to metabolize nutrients and create products vital for cell function. This is done through a complex network of reactions controlled by many genes. Changes in cellular metabolism play a role in a wide variety of diseases. However, despite the availability of genome sequences and of genome-scale expression data, which give information about which genes are present and how active they are, our ability to use these data to understand changes in cellular metabolism has been limited. We present a new approach to this problem, linking gene expression data with models of cellular metabolism. We apply the method to predict the effects of drugs and agents on Mycobacterium tuberculosis (M. tb). Virulence, growth in human hosts, and drug resistance are all related to changes in M. tb's metabolism. We predict the effects of a variety of conditions on the production of mycolic acids, essential cell wall components. Our method successfully identifies seven of the eight known mycolic acid inhibitors in a compendium of 235 conditions, and identifies the top anti-TB drugs in this dataset. We anticipate that the method will have a range of applications in metabolic engineering, the characterization of disease states, and drug discovery.

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          Most cited references45

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          Cancer's molecular sweet tooth and the Warburg effect.

          More than 80 years ago, the renowned biochemist Otto Warburg described how cancer cells avidly consume glucose and produce lactic acid under aerobic conditions. Recent studies arguing that cancer cells benefit from this phenomenon, termed the Warburg effect, have renewed discussions about its exact role as cause, correlate, or facilitator of cancer. Molecular advances in this area may reveal tactics to exploit the cancer cell's "sweet tooth" for cancer therapy.
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            An in vitro model for sequential study of shiftdown of Mycobacterium tuberculosis through two stages of nonreplicating persistence.

            It was demonstrated previously that abrupt transfer of vigorously aerated cultures of Mycobacterium tuberculosis to anaerobic conditions resulted in their rapid death, but gradual depletion of available O2 permitted expression of increased tolerance to anaerobiosis. Those studies used a model based on adaptation of unagitated bacilli as they settled through a self-generated O2 gradient, but the model did not permit examination of homogeneous populations of bacilli during discrete stages in that adaptation. The present report describes a model based on culture of tubercle bacilli in deep liquid medium with very gentle stirring that keeps them in uniform dispersion while controlling the rate at which O2 is depleted. In this model, at least two stages of nonreplicating persistence were seen. The shift into first stage, designated NRP stage 1, occurred abruptly at a point when the declining dissolved O2 level approached 1% saturation. This microaerophilic stage was characterized by a slow rate of increase in turbidity without a corresponding increase in numbers of CFU or synthesis of DNA. However, a high rate of production of glycine dehydrogenase was initiated and sustained while the bacilli were in this state, and a steady ATP concentration was maintained. When the dissolved O2 content of the culture dropped below about 0.06% saturation, the bacilli shifted down abruptly to an anaerobic stage, designated NRP stage 2, in which no further increase in turbidity was seen and the concentration of glycine dehydrogenase declined markedly. The ability of bacilli in NRP stage 2 to survive anaerobically was dependent in part on having spent sufficient transit time in NRP stage 1. The effects of four antimicrobial agents on the bacilli depended on which of the different physiologic stages the bacilli occupied at a given time and reflected the recognized modes of action of these agents. It is suggested that the ability to shift down into one or both of the two nonreplicating stages, corresponding to microaerophilic and anaerobic persistence, is responsible for the ability of tubercle bacilli to lie dormant in the host for long periods of time, with the capacity to revive and activate disease at a later time. The model described here holds promise as a tool to help clarify events at the molecular level that permit the bacilli to persist under adverse conditions and to resume growth when conditions become favorable. The culture model presented here is also useful for screening drugs for the ability to kill tubercle bacilli in their different stages of nonreplicating persistence.
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              Network-based prediction of human tissue-specific metabolism.

              Direct in vivo investigation of mammalian metabolism is complicated by the distinct metabolic functions of different tissues. We present a computational method that successfully describes the tissue specificity of human metabolism on a large scale. By integrating tissue-specific gene- and protein-expression data with an existing comprehensive reconstruction of the global human metabolic network, we predict tissue-specific metabolic activity in ten human tissues. This reveals a central role for post-transcriptional regulation in shaping tissue-specific metabolic activity profiles. The predicted tissue specificity of genes responsible for metabolic diseases and tissue-specific differences in metabolite exchange with biofluids extend markedly beyond tissue-specific differences manifest in enzyme-expression data, and are validated by large-scale mining of tissue-specificity data. Our results establish a computational basis for the genome-wide study of normal and abnormal human metabolism in a tissue-specific manner.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                August 2009
                August 2009
                28 August 2009
                : 5
                : 8
                : e1000489
                Affiliations
                [1 ]Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
                [2 ]Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
                [3 ]Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
                [4 ]Department of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
                [5 ]Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom
                [6 ]Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
                [7 ]Phenomics and Bioinformatics Research Centre, School of Mathematics and Statistics, and Australian Centre for Plant Functional Genomics, University of South Australia, Mawson Lakes, South Australia, Australia
                [8 ]Department of Biomedical Engineering and Department of Microbiology, Boston University, Boston, Massachusetts, United States of America
                University of Virginia, United States of America
                Author notes

                Conceived and designed the experiments: CC AB JZ JEG. Performed the experiments: CC AB JZ TYC DBM. Analyzed the data: CC AB JZ DSL BW MRF MM JEG. Wrote the paper: CC DSL BW JEG.

                Article
                09-PLCB-RA-0241R2
                10.1371/journal.pcbi.1000489
                2726785
                19714220
                4ea310af-fadb-46a3-b01a-c67cdaf2d7ad
                Colijn et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 6 March 2009
                : 27 July 2009
                Page count
                Pages: 14
                Categories
                Research Article
                Computational Biology/Metabolic Networks
                Genetics and Genomics/Gene Expression

                Quantitative & Systems biology
                Quantitative & Systems biology

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