Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production
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 reacti...
| Main Authors: | , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Public Library of Science
2010
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| Online Access: | http://hdl.handle.net/1721.1/52472 |
| _version_ | 1811088925375070208 |
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| author | Murray, Megan B. Moody, D. Branch Cheng, Tan-Yun Farhat, Maha R. Galagan, James E. Weiner, Brian Lun, Desmond S. Brandes, Aaron Colijn, Caroline Zucker, Jeremy |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Murray, Megan B. Moody, D. Branch Cheng, Tan-Yun Farhat, Maha R. Galagan, James E. Weiner, Brian Lun, Desmond S. Brandes, Aaron Colijn, Caroline Zucker, Jeremy |
| author_sort | Murray, Megan B. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:10:06Z |
| format | Article |
| id | mit-1721.1/52472 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:10:06Z |
| publishDate | 2010 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/524722022-09-28T18:58:35Z Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production Murray, Megan B. Moody, D. Branch Cheng, Tan-Yun Farhat, Maha R. Galagan, James E. Weiner, Brian Lun, Desmond S. Brandes, Aaron Colijn, Caroline Zucker, Jeremy Massachusetts Institute of Technology. Department of Biology Zucker, Jeremy Zucker, Jeremy 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. Burroughs Wellcome Fund Ellison Medical Foundation (ID-SS-0693-04) Dedicated Tuberculosis Gene Expression Database Bill & Melinda Gates Foundation National Institutes of Health. NIH/NIAID Network for Large-Scale Sequencing of Microbial Genomes (014334-001) National Institutes of Health (HHSN 26620040000IC) National Institute of Allergy and Infectious Diseases (R01 071155) National Institute of Allergy and Infectious Diseases (1U19AI076217) National Institutes of Health. Department of Health and Human Services (Contract No. HHSN266200400001C) National Institute of Allergy and Infectious Disease 2010-03-10T18:17:40Z 2010-03-10T18:17:40Z 2009-08 2009-03 Article http://purl.org/eprint/type/JournalArticle 1553-734X 1553-7358 http://hdl.handle.net/1721.1/52472 Colijn, Caroline et al. “Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production.” PLoS Comput Biol 5.8 (2009): e1000489. en_US http://dx.doi.org/10.1371/journal.pcbi.1000489 PLoS Computational Biology Creative Commons Attribution http://creativecommons.org/licenses/by/2.5/ application/pdf Public Library of Science PLoS |
| spellingShingle | Murray, Megan B. Moody, D. Branch Cheng, Tan-Yun Farhat, Maha R. Galagan, James E. Weiner, Brian Lun, Desmond S. Brandes, Aaron Colijn, Caroline Zucker, Jeremy Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production |
| title | Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production |
| title_full | Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production |
| title_fullStr | Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production |
| title_full_unstemmed | Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production |
| title_short | Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production |
| title_sort | interpreting expression data with metabolic flux models predicting mycobacterium tuberculosis mycolic acid production |
| url | http://hdl.handle.net/1721.1/52472 |
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