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Perceiving molecular evolution processes in Escherichia coli by comprehensive metabolite and gene expression profiling.

Vijayendran C, Barsch A, Friehs K, Niehaus K, Becker A, Flaschel E - Genome Biol. (2008)

Bottom Line: Excess nutrient adapted lines were found to exhibit greater degrees of positive correlation, indicating parallelism between ancestor and evolved lines, when compared with prolonged stationary phase adapted lines.Gene-metabolite correlation network analysis revealed over-representation of membrane-associated functional categories.GltB, LamB and YaeT proteins in excess nutrient lines, and FepA, CirA, OmpC and OmpA in prolonged stationary phase lines were found to be differentially over-expressed.

View Article: PubMed Central - HTML - PubMed

Affiliation: International NRW Graduate School in Bioinformatics and Genome Research, Bielefeld University, D-33594 Bielefeld, Germany. cvijayen@cebitec.uni-bielefeld.de

ABSTRACT

Background: Evolutionary changes that are due to different environmental conditions can be examined based on the various molecular aspects that constitute a cell, namely transcript, protein, or metabolite abundance. We analyzed changes in transcript and metabolite abundance in evolved and ancestor strains in three different evolutionary conditions - excess nutrient adaptation, prolonged stationary phase adaptation, and adaptation because of environmental shift - in two different strains of bacterium Escherichia coli K-12 (MG1655 and DH10B).

Results: Metabolite profiling of 84 identified metabolites revealed that most of the metabolites involved in the tricarboxylic acid cycle and nucleotide metabolism were altered in both of the excess nutrient evolved lines. Gene expression profiling using whole genome microarray with 4,288 open reading frames revealed over-representation of the transport functional category in all evolved lines. Excess nutrient adapted lines were found to exhibit greater degrees of positive correlation, indicating parallelism between ancestor and evolved lines, when compared with prolonged stationary phase adapted lines. Gene-metabolite correlation network analysis revealed over-representation of membrane-associated functional categories. Proteome analysis revealed the major role played by outer membrane proteins in adaptive evolution. GltB, LamB and YaeT proteins in excess nutrient lines, and FepA, CirA, OmpC and OmpA in prolonged stationary phase lines were found to be differentially over-expressed.

Conclusion: In summary, we report the vital involvement of energy metabolism and membrane-associated functional categories in all of the evolutionary conditions examined in this study within the context of transcript, outer membrane protein, and metabolite levels. These initial data obtained may help to enhance our understanding of the evolutionary process from a systems biology perspective.

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Gene-to-metabolite correlation network analyses. (a) Substructure extracted from Adp correlation network with MCODE algorithm, showing preferentially linked functionally related metabolites. The m/z values of selective ions used for quantification are shown in parentheses for each metabolite. In the box and whisker plots of the metabolites 1 and 3 represent MG and DH lines (ancestors), and 2 and 4 represent MGAdp and DHAdp lines (evolved). (b-g) Topologic properties of all evolution-specific coexpression networks. Panel b shows the degree distribution of the clustering coefficients of all of the evolution-specific network entities. The average clustering coefficient of all the nodes was plotted against the number of neighbours. Panel c shows the degree distribution of the networks; the number of nodes with a given degree (k) in the networks approximates a power law (P [k] about kγ ; Adp γ = 1.70, AdpGal γ = 1.76, and Stat γ = 1.32). Distribution of the shortest path between pairs of nodes in the evolution specific (panels d and e) and intersection (panels f and g) networks; constructed with principal components analysis thresholds of 0.8 (panels d and f) and 0.9 (panels e and g).
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Figure 6: Gene-to-metabolite correlation network analyses. (a) Substructure extracted from Adp correlation network with MCODE algorithm, showing preferentially linked functionally related metabolites. The m/z values of selective ions used for quantification are shown in parentheses for each metabolite. In the box and whisker plots of the metabolites 1 and 3 represent MG and DH lines (ancestors), and 2 and 4 represent MGAdp and DHAdp lines (evolved). (b-g) Topologic properties of all evolution-specific coexpression networks. Panel b shows the degree distribution of the clustering coefficients of all of the evolution-specific network entities. The average clustering coefficient of all the nodes was plotted against the number of neighbours. Panel c shows the degree distribution of the networks; the number of nodes with a given degree (k) in the networks approximates a power law (P [k] about kγ ; Adp γ = 1.70, AdpGal γ = 1.76, and Stat γ = 1.32). Distribution of the shortest path between pairs of nodes in the evolution specific (panels d and e) and intersection (panels f and g) networks; constructed with principal components analysis thresholds of 0.8 (panels d and f) and 0.9 (panels e and g).

Mentions: It has been demonstrated that functionally related genes are preferentially linked in co-expression networks [21]. By integrating and comparing the gene expression and metabolite profile patterns, we were able to explore the connections between the gene-gene and gene-metabolite links and associated functions (Figure 6a) by assuming that the more similar the expression pattern is, the shorter is the distance between genes and/or metabolites in the co-expression network. Relative transcript amounts of all genes and relative concentrations of all nonredundant metabolites were combined to form distance matrices, which were calculated by using the PCC to build co-expression networks. In many cases there were striking relationships between network substructure, gene, or metabolite function and co-expression (Figure 6a). The co-expression network analysis provides a possibility to use it as a quantifiable and analytical tool to unravel the relationships among cellular entities that govern the cellular functions [22].


Perceiving molecular evolution processes in Escherichia coli by comprehensive metabolite and gene expression profiling.

Vijayendran C, Barsch A, Friehs K, Niehaus K, Becker A, Flaschel E - Genome Biol. (2008)

Gene-to-metabolite correlation network analyses. (a) Substructure extracted from Adp correlation network with MCODE algorithm, showing preferentially linked functionally related metabolites. The m/z values of selective ions used for quantification are shown in parentheses for each metabolite. In the box and whisker plots of the metabolites 1 and 3 represent MG and DH lines (ancestors), and 2 and 4 represent MGAdp and DHAdp lines (evolved). (b-g) Topologic properties of all evolution-specific coexpression networks. Panel b shows the degree distribution of the clustering coefficients of all of the evolution-specific network entities. The average clustering coefficient of all the nodes was plotted against the number of neighbours. Panel c shows the degree distribution of the networks; the number of nodes with a given degree (k) in the networks approximates a power law (P [k] about kγ ; Adp γ = 1.70, AdpGal γ = 1.76, and Stat γ = 1.32). Distribution of the shortest path between pairs of nodes in the evolution specific (panels d and e) and intersection (panels f and g) networks; constructed with principal components analysis thresholds of 0.8 (panels d and f) and 0.9 (panels e and g).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2643943&req=5

Figure 6: Gene-to-metabolite correlation network analyses. (a) Substructure extracted from Adp correlation network with MCODE algorithm, showing preferentially linked functionally related metabolites. The m/z values of selective ions used for quantification are shown in parentheses for each metabolite. In the box and whisker plots of the metabolites 1 and 3 represent MG and DH lines (ancestors), and 2 and 4 represent MGAdp and DHAdp lines (evolved). (b-g) Topologic properties of all evolution-specific coexpression networks. Panel b shows the degree distribution of the clustering coefficients of all of the evolution-specific network entities. The average clustering coefficient of all the nodes was plotted against the number of neighbours. Panel c shows the degree distribution of the networks; the number of nodes with a given degree (k) in the networks approximates a power law (P [k] about kγ ; Adp γ = 1.70, AdpGal γ = 1.76, and Stat γ = 1.32). Distribution of the shortest path between pairs of nodes in the evolution specific (panels d and e) and intersection (panels f and g) networks; constructed with principal components analysis thresholds of 0.8 (panels d and f) and 0.9 (panels e and g).
Mentions: It has been demonstrated that functionally related genes are preferentially linked in co-expression networks [21]. By integrating and comparing the gene expression and metabolite profile patterns, we were able to explore the connections between the gene-gene and gene-metabolite links and associated functions (Figure 6a) by assuming that the more similar the expression pattern is, the shorter is the distance between genes and/or metabolites in the co-expression network. Relative transcript amounts of all genes and relative concentrations of all nonredundant metabolites were combined to form distance matrices, which were calculated by using the PCC to build co-expression networks. In many cases there were striking relationships between network substructure, gene, or metabolite function and co-expression (Figure 6a). The co-expression network analysis provides a possibility to use it as a quantifiable and analytical tool to unravel the relationships among cellular entities that govern the cellular functions [22].

Bottom Line: Excess nutrient adapted lines were found to exhibit greater degrees of positive correlation, indicating parallelism between ancestor and evolved lines, when compared with prolonged stationary phase adapted lines.Gene-metabolite correlation network analysis revealed over-representation of membrane-associated functional categories.GltB, LamB and YaeT proteins in excess nutrient lines, and FepA, CirA, OmpC and OmpA in prolonged stationary phase lines were found to be differentially over-expressed.

View Article: PubMed Central - HTML - PubMed

Affiliation: International NRW Graduate School in Bioinformatics and Genome Research, Bielefeld University, D-33594 Bielefeld, Germany. cvijayen@cebitec.uni-bielefeld.de

ABSTRACT

Background: Evolutionary changes that are due to different environmental conditions can be examined based on the various molecular aspects that constitute a cell, namely transcript, protein, or metabolite abundance. We analyzed changes in transcript and metabolite abundance in evolved and ancestor strains in three different evolutionary conditions - excess nutrient adaptation, prolonged stationary phase adaptation, and adaptation because of environmental shift - in two different strains of bacterium Escherichia coli K-12 (MG1655 and DH10B).

Results: Metabolite profiling of 84 identified metabolites revealed that most of the metabolites involved in the tricarboxylic acid cycle and nucleotide metabolism were altered in both of the excess nutrient evolved lines. Gene expression profiling using whole genome microarray with 4,288 open reading frames revealed over-representation of the transport functional category in all evolved lines. Excess nutrient adapted lines were found to exhibit greater degrees of positive correlation, indicating parallelism between ancestor and evolved lines, when compared with prolonged stationary phase adapted lines. Gene-metabolite correlation network analysis revealed over-representation of membrane-associated functional categories. Proteome analysis revealed the major role played by outer membrane proteins in adaptive evolution. GltB, LamB and YaeT proteins in excess nutrient lines, and FepA, CirA, OmpC and OmpA in prolonged stationary phase lines were found to be differentially over-expressed.

Conclusion: In summary, we report the vital involvement of energy metabolism and membrane-associated functional categories in all of the evolutionary conditions examined in this study within the context of transcript, outer membrane protein, and metabolite levels. These initial data obtained may help to enhance our understanding of the evolutionary process from a systems biology perspective.

Show MeSH
Related in: MedlinePlus