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Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of Escherichia coli.

Schramm G, Zapatka M, Eils R, König R - BMC Bioinformatics (2007)

Bottom Line: In the study reported here, one-dimensional enzyme-enzyme pairs were tracked to reveal sub-graphs of a biological interaction network which showed significant adaptations to a changing environment.Furthermore, our approach revealed a down-regulation in iron processing as well as the up-regulation of the histidine biosynthesis pathway.Based on microarray expression profiling data of prokaryotic cells exposed to fundamental treatment changes, our novel technique proved to extract system changes for a rather broad spectrum of the biochemical network.

View Article: PubMed Central - HTML - PubMed

Affiliation: Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany. g.schramm@dkfz.de <g.schramm@dkfz.de>

ABSTRACT

Background: Biochemical investigations over the last decades have elucidated an increasingly complete image of the cellular metabolism. To derive a systems view for the regulation of the metabolism when cells adapt to environmental changes, whole genome gene expression profiles can be analysed. Moreover, utilising a network topology based on gene relationships may facilitate interpreting this vast amount of information, and extracting significant patterns within the networks.

Results: Interpreting expression levels as pixels with grey value intensities and network topology as relationships between pixels, allows for an image-like representation of cellular metabolism. While the topology of a regular image is a lattice grid, biological networks demonstrate scale-free architecture and thus advanced image processing methods such as wavelet transforms cannot directly be applied. In the study reported here, one-dimensional enzyme-enzyme pairs were tracked to reveal sub-graphs of a biological interaction network which showed significant adaptations to a changing environment. As a case study, the response of the hetero-fermentative bacterium E. coli to oxygen deprivation was investigated. With our novel method, we detected, as expected, an up-regulation in the pathways of hexose nutrients up-take and metabolism and formate fermentation. Furthermore, our approach revealed a down-regulation in iron processing as well as the up-regulation of the histidine biosynthesis pathway. The latter may reflect an adaptive response of E. coli against an increasingly acidic environment due to the excretion of acidic products during anaerobic growth in a batch culture.

Conclusion: Based on microarray expression profiling data of prokaryotic cells exposed to fundamental treatment changes, our novel technique proved to extract system changes for a rather broad spectrum of the biochemical network.

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Related in: MedlinePlus

General workflow of the method. The metabolic network of E. coli was put up using the EcoCyc database. Gene expression data was mapped onto the reactions of the network resulting in an image like representation (red boxes). Features were generated by using the Haar wavelet transformation on every connected reaction pair. The most discriminative features were identified by a t-test. Sub-graphs were built by connecting significant reaction pairs. Regions with identical regulation of more than four reactions were extracted (clusters). Reaction pairs with opposite regulation were identified as switches and were also extracted. The resulting pathways were analysed by literature scanning in-depth. Assembling the found pathways yielded an overall picture of the metabolic processes.
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Figure 2: General workflow of the method. The metabolic network of E. coli was put up using the EcoCyc database. Gene expression data was mapped onto the reactions of the network resulting in an image like representation (red boxes). Features were generated by using the Haar wavelet transformation on every connected reaction pair. The most discriminative features were identified by a t-test. Sub-graphs were built by connecting significant reaction pairs. Regions with identical regulation of more than four reactions were extracted (clusters). Reaction pairs with opposite regulation were identified as switches and were also extracted. The resulting pathways were analysed by literature scanning in-depth. Assembling the found pathways yielded an overall picture of the metabolic processes.

Mentions: To test our method with simulated data on a simplified model network, we constructed a regular grid of 30 × 40 artificial reactions (workflow see Figure 2). On this simulated image-like metabolic network we randomly selected pathways of connected reactions with lengths 7, 10 and 24. These lengths corresponded to an expected length of a biological pathway (7, 10) and to the most frequent path length of the shortest paths between all pairs of nodes in the regular grid, respectively. 100 runs were performed generating 44 experiments of simulated expression data with a ground level of 6, in rather good agreement with our normalised gene expression data. To this, a Gaussian noise of mean 0 and standard deviation 1 was added. Two classes were formed with 22 experiments each. In one class the reactions of the randomly chosen pathways were up-regulated by adding a constant level Δ to the random expression levels.


Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of Escherichia coli.

Schramm G, Zapatka M, Eils R, König R - BMC Bioinformatics (2007)

General workflow of the method. The metabolic network of E. coli was put up using the EcoCyc database. Gene expression data was mapped onto the reactions of the network resulting in an image like representation (red boxes). Features were generated by using the Haar wavelet transformation on every connected reaction pair. The most discriminative features were identified by a t-test. Sub-graphs were built by connecting significant reaction pairs. Regions with identical regulation of more than four reactions were extracted (clusters). Reaction pairs with opposite regulation were identified as switches and were also extracted. The resulting pathways were analysed by literature scanning in-depth. Assembling the found pathways yielded an overall picture of the metabolic processes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1884177&req=5

Figure 2: General workflow of the method. The metabolic network of E. coli was put up using the EcoCyc database. Gene expression data was mapped onto the reactions of the network resulting in an image like representation (red boxes). Features were generated by using the Haar wavelet transformation on every connected reaction pair. The most discriminative features were identified by a t-test. Sub-graphs were built by connecting significant reaction pairs. Regions with identical regulation of more than four reactions were extracted (clusters). Reaction pairs with opposite regulation were identified as switches and were also extracted. The resulting pathways were analysed by literature scanning in-depth. Assembling the found pathways yielded an overall picture of the metabolic processes.
Mentions: To test our method with simulated data on a simplified model network, we constructed a regular grid of 30 × 40 artificial reactions (workflow see Figure 2). On this simulated image-like metabolic network we randomly selected pathways of connected reactions with lengths 7, 10 and 24. These lengths corresponded to an expected length of a biological pathway (7, 10) and to the most frequent path length of the shortest paths between all pairs of nodes in the regular grid, respectively. 100 runs were performed generating 44 experiments of simulated expression data with a ground level of 6, in rather good agreement with our normalised gene expression data. To this, a Gaussian noise of mean 0 and standard deviation 1 was added. Two classes were formed with 22 experiments each. In one class the reactions of the randomly chosen pathways were up-regulated by adding a constant level Δ to the random expression levels.

Bottom Line: In the study reported here, one-dimensional enzyme-enzyme pairs were tracked to reveal sub-graphs of a biological interaction network which showed significant adaptations to a changing environment.Furthermore, our approach revealed a down-regulation in iron processing as well as the up-regulation of the histidine biosynthesis pathway.Based on microarray expression profiling data of prokaryotic cells exposed to fundamental treatment changes, our novel technique proved to extract system changes for a rather broad spectrum of the biochemical network.

View Article: PubMed Central - HTML - PubMed

Affiliation: Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany. g.schramm@dkfz.de <g.schramm@dkfz.de>

ABSTRACT

Background: Biochemical investigations over the last decades have elucidated an increasingly complete image of the cellular metabolism. To derive a systems view for the regulation of the metabolism when cells adapt to environmental changes, whole genome gene expression profiles can be analysed. Moreover, utilising a network topology based on gene relationships may facilitate interpreting this vast amount of information, and extracting significant patterns within the networks.

Results: Interpreting expression levels as pixels with grey value intensities and network topology as relationships between pixels, allows for an image-like representation of cellular metabolism. While the topology of a regular image is a lattice grid, biological networks demonstrate scale-free architecture and thus advanced image processing methods such as wavelet transforms cannot directly be applied. In the study reported here, one-dimensional enzyme-enzyme pairs were tracked to reveal sub-graphs of a biological interaction network which showed significant adaptations to a changing environment. As a case study, the response of the hetero-fermentative bacterium E. coli to oxygen deprivation was investigated. With our novel method, we detected, as expected, an up-regulation in the pathways of hexose nutrients up-take and metabolism and formate fermentation. Furthermore, our approach revealed a down-regulation in iron processing as well as the up-regulation of the histidine biosynthesis pathway. The latter may reflect an adaptive response of E. coli against an increasingly acidic environment due to the excretion of acidic products during anaerobic growth in a batch culture.

Conclusion: Based on microarray expression profiling data of prokaryotic cells exposed to fundamental treatment changes, our novel technique proved to extract system changes for a rather broad spectrum of the biochemical network.

Show MeSH
Related in: MedlinePlus