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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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Validation of the method on a regular grid consisting of 40 × 30 reactions (pixel). Random gene expression data was generated and mapped onto the nodes of the grid. The 44 samples were divided into two classes differing only significantly in the reactions of three randomly chosen pathways (red arrows). Up-regulation of these reactions in one class was achieved by adding a constant value Δ to their expression levels. Our technique revealed significantly less false positives (FP) than the standard t-test for all chosen values of Δ. The last row shows the desired outcome after 100 runs (TP: true positives, FP: false positives, TN: true positives, FN: false negatives).
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Figure 3: Validation of the method on a regular grid consisting of 40 × 30 reactions (pixel). Random gene expression data was generated and mapped onto the nodes of the grid. The 44 samples were divided into two classes differing only significantly in the reactions of three randomly chosen pathways (red arrows). Up-regulation of these reactions in one class was achieved by adding a constant value Δ to their expression levels. Our technique revealed significantly less false positives (FP) than the standard t-test for all chosen values of Δ. The last row shows the desired outcome after 100 runs (TP: true positives, FP: false positives, TN: true positives, FN: false negatives).

Mentions: A standard Students t-test was applied on the simulated expression data without taking any network information into account. For both methods true positives, false positives, false negatives and true negatives were calculated. To investigate a broader spectrum for the precision and sensitivity of our technique, the validation was performed with a variety of added constants (Δ = 2, 4, 6). Our technique decreased the number of false positives significantly (Figure 3). In a step further we investigated how our technique performed on a biological network, choosing the metabolic network of E. coli, constructed as described in Methods. Out of this network we selected randomly pathways of lengths 5, 7, 10 and performed the same method as described above for different constants (Δ = 2, 4, 6). We obtained a similar superior performance of our approach. The number of false positives was reduced nearly threefold, while the detection power of true positives was identical (Results not shown).


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)

Validation of the method on a regular grid consisting of 40 × 30 reactions (pixel). Random gene expression data was generated and mapped onto the nodes of the grid. The 44 samples were divided into two classes differing only significantly in the reactions of three randomly chosen pathways (red arrows). Up-regulation of these reactions in one class was achieved by adding a constant value Δ to their expression levels. Our technique revealed significantly less false positives (FP) than the standard t-test for all chosen values of Δ. The last row shows the desired outcome after 100 runs (TP: true positives, FP: false positives, TN: true positives, FN: false negatives).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Validation of the method on a regular grid consisting of 40 × 30 reactions (pixel). Random gene expression data was generated and mapped onto the nodes of the grid. The 44 samples were divided into two classes differing only significantly in the reactions of three randomly chosen pathways (red arrows). Up-regulation of these reactions in one class was achieved by adding a constant value Δ to their expression levels. Our technique revealed significantly less false positives (FP) than the standard t-test for all chosen values of Δ. The last row shows the desired outcome after 100 runs (TP: true positives, FP: false positives, TN: true positives, FN: false negatives).
Mentions: A standard Students t-test was applied on the simulated expression data without taking any network information into account. For both methods true positives, false positives, false negatives and true negatives were calculated. To investigate a broader spectrum for the precision and sensitivity of our technique, the validation was performed with a variety of added constants (Δ = 2, 4, 6). Our technique decreased the number of false positives significantly (Figure 3). In a step further we investigated how our technique performed on a biological network, choosing the metabolic network of E. coli, constructed as described in Methods. Out of this network we selected randomly pathways of lengths 5, 7, 10 and performed the same method as described above for different constants (Δ = 2, 4, 6). We obtained a similar superior performance of our approach. The number of false positives was reduced nearly threefold, while the detection power of true positives was identical (Results not shown).

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