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    Keywords: CELLS ; EXPRESSION ; Germany ; SYSTEMS ; GENE ; GENE-EXPRESSION ; GENES ; microarray ; SACCHAROMYCES-CEREVISIAE ; METABOLISM ; COMPLEX ; COMPLEXES ; SEQUENCE ; METABOLITES ; gene expression ; ESCHERICHIA-COLI ; DATABASE ; OXYGEN ; CLUSTER ; MATRIX ; SYNTHETASE ; EXTRACTION ; LEVEL ; ENZYME ; TECHNOLOGY ; EXPRESSION PATTERNS ; CHAIN AMINO-ACIDS ; K-12
    Abstract: Background: Microarray technology produces gene expression data on a genomic scale for an endless variety of organisms and conditions. However, this vast amount of information needs to be extracted in a reasonable way and funneled into manageable and functionally meaningful patterns. Genes may be reasonably combined using knowledge about their interaction behaviour. On a proteomic level, biochemical research has elucidated an increasingly complete image of the metabolic architecture, especially for less complex organisms like the well studied bacterium Escherichia coli. Results: We sought to discover central components of the metabolic network, regulated by the expression of associated genes under changing conditions. We mapped gene expression data from E. coli under aerobic and anaerobic conditions onto the enzymatic reaction nodes of its metabolic network. An adjacency matrix of the metabolites was created from this graph. A consecutive ones clustering method was used to obtain network clusters in the matrix. The wavelet method was applied on the adjacency matrices of these clusters to collect features for the classifier. With a feature extraction method the most discriminating features were selected. We yielded network sub-graphs from these top ranking features representing formate fermentation, in good agreement with the anaerobic response of heterofermentative bacteria. Furthermore, we found a switch in the starting point for NAD biosynthesis, and an adaptation of the l-aspartate metabolism, in accordance with its higher abundance under anaerobic conditions. Conclusion: We developed and tested a novel method, based on a combination of rationally chosen machine learning methods, to analyse gene expression data on the basis of interaction data, using a metabolic network of enzymes. As a case study, we applied our method to E. coli under oxygen deprived conditions and extracted physiologically relevant patterns that represent an adaptation of the cells to changing environmental conditions. In general, our concept may be transferred to network analyses on biological interaction data, when data for two comparable states of the associated nodes are made available
    Type of Publication: Journal article published
    PubMed ID: 16524469
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  • 3
    Keywords: ENVIRONMENT ; SPECTRA ; CELLS ; EXPRESSION ; GROWTH ; CELL ; Germany ; PATHWAY ; PATHWAYS ; INFORMATION ; SYSTEM ; SYSTEMS ; GENE ; GENE-EXPRESSION ; GENOME ; microarray ; SACCHAROMYCES-CEREVISIAE ; METABOLISM ; DOWN-REGULATION ; treatment ; culture ; PATTERNS ; gene expression ; MICROARRAY DATA ; ESCHERICHIA-COLI ; UP-REGULATION ; OXYGEN ; CLUSTERS ; TRANSCRIPTIONAL REGULATION ; CLUSTER ; RE ; PRODUCTS ; HYDROGEN-PEROXIDE ; EXCRETION ; LEVEL ; methods ; PROFILES ; EXPRESSION PROFILES ; technique ; uptake ; E ; SPECTRUM ; microbiology ; image processing ; TOPOLOGY ; METABOLIC PATHWAYS ; SALMONELLA-TYPHIMURIUM ; ADAPTIVE RESPONSE ; ANAEROBIC RESPIRATION ; DEOXYRIBONUCLEOTIDE SYNTHESIS ; FUMARATE REDUCTASE ; MULTIORGANISM DATABASE
    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
    Type of Publication: Journal article published
    PubMed ID: 17488495
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  • 4
    Keywords: CANCER ; EXPRESSION ; GROWTH ; tumor ; Germany ; PATHWAY ; PATHWAYS ; THERAPY ; CLASSIFICATION ; NETWORK ; NETWORKS ; GENE ; GENE-EXPRESSION ; GENES ; GENOME ; microarray ; DRUG ; METABOLISM ; TUMORS ; validation ; BIOLOGY ; RECOGNITION ; PATTERNS ; gene expression ; microarrays ; neuroblastoma ; INHIBITORS ; REGRESSION ; PATTERN ; SCIENCE ; methods ; DRUGS ; glutamine ; outcome ; PROBABILITIES ; SHIFT ; motivation
    Abstract: Motivation: Gene expression pro. ling by microarrays or transcript sequencing enables observing the pathogenic function of tumors on a mesoscopic level. Results: We investigated neuroblastoma tumors that clinically exhibit a very heterogeneous course ranging from rapid growth with fatal outcome to spontaneous regression and detected regulatory oncogenetic shifts in their metabolic networks. In contrast to common enrichment tests, we took network topology into account by applying adjusted wavelet transforms on an elaborated and new 2D grid representation of curated pathway maps from the Kyoto Enzyclopedia of Genes and Genomes. The aggressive form of the tumors showed regulatory shifts for purine and pyrimidine biosynthesis as well as folate-mediated metabolism of the one-carbon pool in respect to increased nucleotide production. We spotted an oncogentic regulatory switch in glutamate metabolism for which we provided experimental validation, being the first steps towards new possible drug therapy. The pattern recognition method we used complements normal enrichment tests to detect such functionally related regulation patterns
    Type of Publication: Journal article published
    PubMed ID: 20335275
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  • 5
    Keywords: APOPTOSIS ; CELLS ; CELL ; CLASSIFICATION ; GENE ; GENE-EXPRESSION ; PROTEINS ; BIOLOGY ; chromosome ; BREAST-CANCER ; IDENTIFICATION ; PROGRESSION ; GENE-REGULATION ; COMMUNICATION ; MATHEMATICAL-THEORY ; PROTEIN REFERENCE DATABASE
    Abstract: Background: Formation of cellular malignancy results from the disruption of fine tuned signaling homeostasis for proliferation, accompanied by mal-functional signals for differentiation, cell cycle and apoptosis. We wanted to observe central signaling characteristics on a global view of malignant cells which have evolved to selfishness and independence in comparison to their non-malignant counterparts that fulfill well defined tasks in their sample. Results: We investigated the regulation of signaling networks with twenty microarray datasets from eleven different tumor types and their corresponding non-malignant tissue samples. Proteins were represented by their coding genes and regulatory distances were defined by correlating the gene-regulation between neighboring proteins in the network (high correlation = small distance). In cancer cells we observed shorter pathways, larger extension of the networks, a lower signaling frequency of central proteins and links and a higher information content of the network. Proteins of high signaling frequency were enriched with cancer mutations. These proteins showed motifs of regulatory integration in normal cells which was disrupted in tumor cells. Conclusion: Our global analysis revealed a distinct formation of signaling-regulation in cancer cells when compared to cells of normal samples. From these cancer-specific regulation patterns novel signaling motifs are proposed
    Type of Publication: Journal article published
    PubMed ID: 21110851
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  • 6
    Abstract: BACKGROUND AND PURPOSE: To provide a systematic measure of changes of brain perfusion in healthy tissue following a fractionated radiotherapy of brain tumors. MATERIALS AND METHODS: Perfusion was assessed before and after radiochemotherapy using arterial spin labeling in a group of 24 patients (mean age 54.3+/-14.1years) with glioblastoma multiforme. Mean relative perfusion change in gray matter in the hemisphere contralateral to the tumor was obtained for the whole hemisphere and also for six regions created by thresholding the individual dose maps at 10Gy steps. RESULTS: A significant decrease of perfusion of -9.8+/-20.9% (p=0.032) compared to the pre-treatment baseline was observed 3months after the end of radiotherapy. The decrease was more pronounced for high-dose regions above 50Gy (-16.8+/-21.0%, p=0.0014) than for low-dose regions below 10Gy (-2.3+/-20.0%, p=0.54). No further significant decrease compared to the post-treatment baseline was observed 6months (-0.4+/-18.4%, p=0.94) and 9months (2.0+/-15.4%, p=0.74) after the end of radiotherapy. CONCLUSIONS: Perfusion decreased significantly during the course of radiochemotherapy. The decrease was higher in regions receiving a higher dose of radiation. This suggests that the perfusion decrease is at least partly caused by radiotherapy. Our results suggest that the detrimental effects of radiochemotherapy on perfusion occur early rather than later.
    Type of Publication: Journal article published
    PubMed ID: 26747756
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  • 7
    Keywords: CELLS ; EXPRESSION ; INHIBITION ; CLASSIFICATION ; DISEASE ; GENE ; GENE-EXPRESSION ; GENES ; microarray ; gene expression ; AGE ; genetics ; leukemia ; genomics ; ACID-INDUCED APOPTOSIS ; GENDER ; SIGNATURE ; GLYCOLYSIS ; tumor therapy ; BILE-ACIDS
    Abstract: Background: Tumor therapy mainly attacks the metabolism to interfere the tumor's anabolism and signaling of proliferative second messengers. However, the metabolic demands of different cancers are very heterogeneous and depend on their origin of tissue, age, gender and other clinical parameters. We investigated tumor specific regulation in the metabolism of breast cancer. Methods: For this, we mapped gene expression data from microarrays onto the corresponding enzymes and their metabolic reaction network. We used Haar Wavelet transforms on optimally arranged grid representations of metabolic pathways as a pattern recognition method to detect orchestrated regulation of neighboring enzymes in the network. Significant combined expression patterns were used to select metabolic pathways showing shifted regulation of the aggressive tumors. Results: Besides up-regulation for energy production and nucleotide anabolism, we found an interesting cellular switch in the interplay of biosynthesis of steroids and bile acids. The biosynthesis of steroids was up-regulated for estrogen synthesis which is needed for proliferative signaling in breast cancer. In turn, the decomposition of steroid precursors was blocked by down-regulation of the bile acid pathway. Conclusion: We applied an intelligent pattern recognition method for analyzing the regulation of metabolism and elucidated substantial regulation of human breast cancer at the interplay of cholesterol biosynthesis and bile acid metabolism pointing to specific breast cancer treatment
    Type of Publication: Journal article published
    PubMed ID: 20831783
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  • 8
    Keywords: CEREBRAL BLOOD-FLOW ; ALZHEIMERS-DISEASE ; NERVOUS-SYSTEM ; GLUCOSE ; AMINO-ACIDS ; ABNORMALITIES ; RECONSTRUCTION ; RAT-BRAIN ; ALPHA-KETOGLUTARATE DEHYDROGENASE ; KETONE-BODIES
    Abstract: Metabolic interactions between multiple cell types are difficult to model using existing approaches. Here we present a workflow that integrates gene expression data, proteomics data and literature-based manual curation to model human metabolism within and between different types of cells. Transport reactions are used to account for the transfer of metabolites between models of different cell types via the interstitial fluid. We apply the method to create models of brain energy metabolism that recapitulate metabolic interactions between astrocytes and various neuron types relevant to Alzheimer's disease. Analysis of the models identifies genes and pathways that may explain observed experimental phenomena, including the differential effects of the disease on cell types and regions of the brain. Constraint-based modeling can thus contribute to the study and analysis of multicellular metabolic processes in the human tissue microenvironment and provide detailed mechanistic insight into high-throughput data analysis.
    Type of Publication: Journal article published
    PubMed ID: 21102456
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  • 9
    Keywords: DRUG ; DISEASE ; DISEASES ; resistance ; NETHERLANDS ; MALARIA PARASITE ; Plasmodium falciparum ; MALARIA ; Plasmodium
    Type of Publication: Meeting abstract published
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  • 10
    Keywords: CANCER ; Germany ; INHIBITION ; PATHWAY ; PATHWAYS ; COMMON ; TOOL ; DEATH ; DISEASE ; DISEASES ; GENOME ; DRUG ; METABOLISM ; TARGET ; resistance ; NETHERLANDS ; PREDICTION ; TARGETS ; glutathione-S-transferase ; PRODUCTS ; SYNTHASE ; DRUG DISCOVERY ; MALARIA PARASITE ; Plasmodium falciparum ; MALARIA ; STRAINS ; COMPOUND ; 3 ; ANTIMALARIAL-DRUGS ; Breadth first search ; DIHYDROOROTATE DEHYDROGENASE ; drug targets ; DRUG-TARGET ; ERYTHROCYTIC STAGES ; FATTY-ACID BIOSYNTHESIS ; ISOPRENYLCYSTEINE CARBOXYL METHYLTRANSFERASE ; Knock-out ; Metabolic network ; Plasmodium ; PYRIMIDINE BIOSYNTHESIS
    Abstract: Malaria is one of the world's most common and serious diseases causing death of about 3 million people each year. Its most severe occurrence is caused by the protozoan Plasmodium falciparum. Biomedical research could enable treating the disease by effectively and specifically targeting essential enzymes of this parasite. However, the parasite has developed resistance to existing drugs making it indispensable to discover new drugs. We have established a simple computational tool which analyses the topology of the metabolic network of P.falciparum to identify essential enzymes as possible drug targets. We investigated the essentiality of a reaction in the metabolic network by deleting (knocking-out) such a reaction in silico. The algorithm selected neighbouring compounds of the investigated reaction that had to be produced by alternative biochemical pathways. Using breadth first searches, we tested qualitatively if these products could be generated by reactions that serve as potential deviations of the metabolic flux. With this we identified 70 essential reactions. Our results were compared with a comprehensive list of 38 targets of approved malaria drugs. When combining our approach with an in silico analysis performed recently [Yeh, I., Hanekamp, T., Tsoka, S., Karp, P.D., Altman, R.B., 2004. Computational analysis of Plasmodium falciparum metabolism: organizing genomic information to facilitate drug discovery. Genome Res. 14, 917-924] we could improve the precision of the prediction results. Finally we present a refined list of 22 new potential candidate targets for P. falciparum, half of which have reasonable evidence to be valid targets against micro-organisms and cancer. (C) 2008 Elsevier B.V. All rights reserved
    Type of Publication: Journal article published
    PubMed ID: 18313365
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