Regulation patterns in signaling networks of cancer

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>E. coli </it>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 <it>E. coli </it>against an increasingly acidic environment due to the excretion of acidic products during anaerobic growth in a batch culture.</p> <p>Conclusion</p> <p>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.</p

Discovering functional gene expression patterns in the metabolic network of Escherichia coli with wavelets transforms

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 hetero-fermentative 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

Effects of oral glucose-lowering drugs on long term outcomes in patients with diabetes mellitus following myocardial infarction not treated with emergent percutaneous coronary intervention - a retrospective nationwide cohort study

<p>Abstract</p> <p>Background</p> <p>The optimum oral pharmacological treatment of diabetes mellitus to reduce cardiovascular disease and mortality following myocardial infarction has not been established. We therefore set out to investigate the association between individual oral glucose-lowering drugs and cardiovascular outcomes following myocardial infarction in patients with diabetes mellitus not treated with emergent percutaneous coronary intervention.</p> <p>Materials and methods</p> <p>All patients aged 30 years or older receiving glucose-lowering drugs (GLDs) and admitted with myocardial infarction (MI) not treated with emergent percutaneous coronary intervention in Denmark during 1997-2006 were identified by individual-level linkage of nationwide registries of hospitalizations and drug dispensing from pharmacies. Multivariable Cox regression models adjusted for age, sex, calendar year, comorbidity, and concomitant pharmacotherapy were used to assess differences in the composite endpoint of non-fatal MI and cardiovascular mortality between individual GLDs, using metformin monotherapy as reference.</p> <p>Results</p> <p>The study comprised 9876 users of GLDs admitted with MI. The mean age was 72.3 years and 56.5% of patients were men. A total of 3649 received sulfonylureas and 711 received metformin at admission. The average length of follow-up was 2.2 (SD 2.6) years. A total of 6,171 patients experienced the composite study endpoint. The sulfonylureas glibenclamide, glimepiride, glipizide, and tolbutamide were associated with increased risk of cardiovascular mortality and/or nonfatal MI with hazard ratios [HRs] of 1.31 (95% confidence interval [CI] 1.17-1.46), 1.19 (1.06-1.32), 1.25 (1.11-1.42), and 1.18 (1.03-1.34), respectively, compared with metformin. Gliclazide was the only sulfonylurea not associated with increased risk compared with metformin (HR 1.03 [0.88-1.22]).</p> <p>Conclusions</p> <p>In patients with diabetes mellitus admitted with MI not treated with emergent percutaneous coronary intervention, monotherapy treatment with the sulfonylureas glibenclamide, glimepiride, glipizide, and tolbutamide was associated with increased cardiovascular risk compared with metformin monotherapy.</p

National Background is Associated with Disparities in Initiation and Persistence to Statin Treatment in Subjects with Diabetes in Denmark

Background: To investigate the effects of statin use over the last 10 years among diabetic patients who initiated glucose-lowering medications (GLMs) in Denmark. Methods: we identified all Danish citizens 30 years and older who claimed their first GLM between 1997 and 2006, with follow-up until 2007. Use of medications, national background, income, and hospitalizations were obtained by cross-linkage of national registries in Denmark. We analyzed factors related to initiation and interruption of statin treatment. The analyses included country of birth, citizenship and, as proxy for ethnic origin, we constructed variables based on both the subjects and on their parent's country of birth. Countries were grouped as Denmark, Western countries, Eastern countries, and Africa. Results: the cohort included 143,625 subjects. Compared with persons of Danish origin, the initiation of a statin medication during follow-up was significantly lower among patients of non-Danish origin: Odds ratio for subjects of Eastern origin 0.61 [CI 0.49–0.76] and 0.37 for subjects of African origin, [CI 0.24–0.59], both p < 0.001. The risk of interrupting statin treatment once it had been initiated was also higher in these groups (hazard ratio 2.03, [CI 1.91–2.17] for Eastern subjects and 1.94, [CI 1.63–2.32] for African subjects, both p < 0.0001). Combination of ethnic parameters to refine identification of the cohort led to the same conclusions as the analysis based only on country of birth or citizenship respectively. Conclusion: diabetes patients of African and Eastern origin in Denmark have less chance of being treated with a statin than those of western and Danish origin despite similar access to the Danish health care system

Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models

Proteomic and transcriptomic data from wild-type and laboratory-evolved strains of Escherichia coli are consistent with predicted pathway usage from optimal growth rate solutions.In laboratory-evolved strains, there is an upregulation of the pathways in the computed optimal growth states, and downregulation of non-functional pathways.Known regulatory mechanisms are only partially responsible for altered metabolic pathway activity