Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models
Background: Bacteria have evolved the ability to efficiently and resourcefully adapt to changing environments. A key means by which they optimize their use of available nutrients is through adjustments in gene expression with consequent changes in enzyme activity. We report a new method for drawi...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Public Library of Science
2012
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| Online Access: | http://hdl.handle.net/1721.1/71733 |
| _version_ | 1826198412981174272 |
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| author | Brandes, Aaron Lun, Desmond S. Ip, Kuhn Zucker, Jeremy Colijn, Caroline Weiner, Brian Galagan, James E. |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Brandes, Aaron Lun, Desmond S. Ip, Kuhn Zucker, Jeremy Colijn, Caroline Weiner, Brian Galagan, James E. |
| author_sort | Brandes, Aaron |
| collection | MIT |
| description | Background:
Bacteria have evolved the ability to efficiently and resourcefully adapt to changing environments. A key means by which they optimize their use of available nutrients is through adjustments in gene expression with consequent changes in enzyme activity. We report a new method for drawing environmental inferences from gene expression data. Our method prioritizes a list of candidate carbon sources for their compatibility with a gene expression profile using the framework of flux balance analysis to model the organism’s metabolic network.
Principal Findings:
For each of six gene expression profiles for Escherichia coli grown under differing nutrient conditions, we applied our method to prioritize a set of eighteen different candidate carbon sources. Our method ranked the correct carbon source as one of the top three candidates for five of the six expression sets when used with a genome-scale model. The correct candidate ranked fifth in the remaining case. Additional analyses show that these rankings are robust with respect to biological and measurement variation, and depend on specific gene expression, rather than general expression level. The gene expression profiles are highly adaptive: simulated production of biomass averaged 94.84% of maximum when the in silico carbon source matched the in vitro source of the expression profile, and 65.97% when it did not.
Conclusions:
Inferences about a microorganism’s nutrient environment can be made by integrating gene expression data into a metabolic framework. This work demonstrates that reaction flux limits for a model can be computed which are realistic in the sense that they affect in silico growth in a manner analogous to that in which a microorganism’s alteration of gene expression is adaptive to its nutrient environment. |
| first_indexed | 2024-09-23T11:04:29Z |
| format | Article |
| id | mit-1721.1/71733 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:04:29Z |
| publishDate | 2012 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/717332022-10-01T00:59:07Z Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models Brandes, Aaron Lun, Desmond S. Ip, Kuhn Zucker, Jeremy Colijn, Caroline Weiner, Brian Galagan, James E. Massachusetts Institute of Technology. Department of Biology Zucker, Jeremy Zucker, Jeremy Background: Bacteria have evolved the ability to efficiently and resourcefully adapt to changing environments. A key means by which they optimize their use of available nutrients is through adjustments in gene expression with consequent changes in enzyme activity. We report a new method for drawing environmental inferences from gene expression data. Our method prioritizes a list of candidate carbon sources for their compatibility with a gene expression profile using the framework of flux balance analysis to model the organism’s metabolic network. Principal Findings: For each of six gene expression profiles for Escherichia coli grown under differing nutrient conditions, we applied our method to prioritize a set of eighteen different candidate carbon sources. Our method ranked the correct carbon source as one of the top three candidates for five of the six expression sets when used with a genome-scale model. The correct candidate ranked fifth in the remaining case. Additional analyses show that these rankings are robust with respect to biological and measurement variation, and depend on specific gene expression, rather than general expression level. The gene expression profiles are highly adaptive: simulated production of biomass averaged 94.84% of maximum when the in silico carbon source matched the in vitro source of the expression profile, and 65.97% when it did not. Conclusions: Inferences about a microorganism’s nutrient environment can be made by integrating gene expression data into a metabolic framework. This work demonstrates that reaction flux limits for a model can be computed which are realistic in the sense that they affect in silico growth in a manner analogous to that in which a microorganism’s alteration of gene expression is adaptive to its nutrient environment. National Institute of Allergy and Infectious Diseases (U.S.) (grant HHSN 2722008000059C) National Institute of Allergy and Infectious Diseases (U.S.) (grant HHSN 26620040000IC) Bill & Melinda Gates Foundation (grant 18651010-37352-A) 2012-07-20T18:05:34Z 2012-07-20T18:05:34Z 2012-05 2011-06 Article http://purl.org/eprint/type/JournalArticle 1932-6203 http://hdl.handle.net/1721.1/71733 Brandes, Aaron et al. “Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models.” Ed. Mukund Thattai. PLoS ONE 7.5 (2012): e36947. en_US http://dx.doi.org/10.1371/journal.pone.0036947 PLoS ONE Creative Commons Attribution http://creativecommons.org/licenses/by/2.5/ application/pdf Public Library of Science PLoS |
| spellingShingle | Brandes, Aaron Lun, Desmond S. Ip, Kuhn Zucker, Jeremy Colijn, Caroline Weiner, Brian Galagan, James E. Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models |
| title | Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models |
| title_full | Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models |
| title_fullStr | Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models |
| title_full_unstemmed | Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models |
| title_short | Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models |
| title_sort | inferring carbon sources from gene expression profiles using metabolic flux models |
| url | http://hdl.handle.net/1721.1/71733 |
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