Limits...
Inferring carbon sources from gene expression profiles using metabolic flux models.

Brandes A, Lun DS, Ip K, Zucker J, Colijn C, Weiner B, Galagan JE - PLoS ONE (2012)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.

ABSTRACT

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.

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Related in: MedlinePlus

Priority ordering of candidate nutrients for each expression set.A panel for each of the six challenge expression sets presents the candidate nutrients ordered from top to bottom in order of decreasing relative biomass production. The length of the horizontal bars indicates the relative biomass production for the corresponding candidate nutrient for that challenge expression set. The bar for the in silico nutrient that corresponds to the in vitro carbon source for which expression was measured (the matching nutrient) is colored green. The other bars (for the non-matching nutrients) are blue. The order of candidate nutrients with equal relative biomass production is not meaningful. In particular this holds for the candidate nutrients with zero relative biomass production. Candidate nutrients with 100% relative biomass production are highlighted in bold.
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pone-0036947-g003: Priority ordering of candidate nutrients for each expression set.A panel for each of the six challenge expression sets presents the candidate nutrients ordered from top to bottom in order of decreasing relative biomass production. The length of the horizontal bars indicates the relative biomass production for the corresponding candidate nutrient for that challenge expression set. The bar for the in silico nutrient that corresponds to the in vitro carbon source for which expression was measured (the matching nutrient) is colored green. The other bars (for the non-matching nutrients) are blue. The order of candidate nutrients with equal relative biomass production is not meaningful. In particular this holds for the candidate nutrients with zero relative biomass production. Candidate nutrients with 100% relative biomass production are highlighted in bold.

Mentions: Each panel in Figure 3 presents results for one of the six challenge expression sets. The candidate nutrients are sorted with the best candidate (with the largest percent relative biomass production) at the top. For cases where the in silico nutrient corresponds to the in vitro carbon source for the expression in the panel, the bar has a distinguishing green color. We refer below to such correspondences as matching, and to other pairs of candidate nutrient and challenge expression set as non-matching.


Inferring carbon sources from gene expression profiles using metabolic flux models.

Brandes A, Lun DS, Ip K, Zucker J, Colijn C, Weiner B, Galagan JE - PLoS ONE (2012)

Priority ordering of candidate nutrients for each expression set.A panel for each of the six challenge expression sets presents the candidate nutrients ordered from top to bottom in order of decreasing relative biomass production. The length of the horizontal bars indicates the relative biomass production for the corresponding candidate nutrient for that challenge expression set. The bar for the in silico nutrient that corresponds to the in vitro carbon source for which expression was measured (the matching nutrient) is colored green. The other bars (for the non-matching nutrients) are blue. The order of candidate nutrients with equal relative biomass production is not meaningful. In particular this holds for the candidate nutrients with zero relative biomass production. Candidate nutrients with 100% relative biomass production are highlighted in bold.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3351459&req=5

pone-0036947-g003: Priority ordering of candidate nutrients for each expression set.A panel for each of the six challenge expression sets presents the candidate nutrients ordered from top to bottom in order of decreasing relative biomass production. The length of the horizontal bars indicates the relative biomass production for the corresponding candidate nutrient for that challenge expression set. The bar for the in silico nutrient that corresponds to the in vitro carbon source for which expression was measured (the matching nutrient) is colored green. The other bars (for the non-matching nutrients) are blue. The order of candidate nutrients with equal relative biomass production is not meaningful. In particular this holds for the candidate nutrients with zero relative biomass production. Candidate nutrients with 100% relative biomass production are highlighted in bold.
Mentions: Each panel in Figure 3 presents results for one of the six challenge expression sets. The candidate nutrients are sorted with the best candidate (with the largest percent relative biomass production) at the top. For cases where the in silico nutrient corresponds to the in vitro carbon source for the expression in the panel, the bar has a distinguishing green color. We refer below to such correspondences as matching, and to other pairs of candidate nutrient and challenge expression set as non-matching.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus