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

Machine learning based analyses on metabolic networks supports high-throughput knockout screens

Background: Computational identification of new drug targets is a major goal of pharmaceutical bioinformatics. Results: This paper presents a machine learning strategy to study and validate essential enzymes of a metabolic network. Each single enzyme was characterized by its local network topology, gene homologies and co-expression, and flux balance analyses. A machine learning system was trained to distinguish between essential and non-essential reactions. It was validated by a comprehensive experimental dataset, which consists of the phenotypic outcomes from single knockout mutants of Escherichia coli (KEIO collection). We yielded very reliable results with high accuracy (93%) and precision (90%). We show that topologic, genomic and transcriptomic features describing the network are sufficient for defining the essentiality of a reaction. These features do not substantially depend on specific media conditions and enabled us to apply our approach also for less specific media conditions, like the lysogeny broth rich medium. Conclusion: Our analysis is feasible to validate experimental knockout data of high throughput screens, can be used to improve flux balance analyses and supports experimental knockout screens to define drug targets

Co-Regulation of Metabolic Genes Is Better Explained by Flux Coupling Than by Network Distance

To what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools

Transcriptomic and metabolomic profiling of Zymomonas mobilis during aerobic and anaerobic fermentations

<p>Abstract</p> <p>Background</p> <p><it>Zymomonas mobilis </it>ZM4 (ZM4) produces near theoretical yields of ethanol with high specific productivity and recombinant strains are able to ferment both C-5 and C-6 sugars. <it>Z. mobilis </it>performs best under anaerobic conditions, but is an aerotolerant organism. However, the genetic and physiological basis of ZM4's response to various stresses is understood poorly.</p> <p>Results</p> <p>In this study, transcriptomic and metabolomic profiles for ZM4 aerobic and anaerobic fermentations were elucidated by microarray analysis and by high-performance liquid chromatography (HPLC), gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS) analyses. In the absence of oxygen, ZM4 consumed glucose more rapidly, had a higher growth rate, and ethanol was the major end-product. Greater amounts of other end-products such as acetate, lactate, and acetoin were detected under aerobic conditions and at 26 h there was only 1.7% of the amount of ethanol present aerobically as there was anaerobically. In the early exponential growth phase, significant differences in gene expression were not observed between aerobic and anaerobic conditions via microarray analysis. HPLC and GC analyses revealed minor differences in extracellular metabolite profiles at the corresponding early exponential phase time point.</p> <p>Differences in extracellular metabolite profiles between conditions became greater as the fermentations progressed. GC-MS analysis of stationary phase intracellular metabolites indicated that ZM4 contained lower levels of amino acids such as alanine, valine and lysine, and other metabolites like lactate, ribitol, and 4-hydroxybutanoate under anaerobic conditions relative to aerobic conditions. Stationary phase microarray analysis revealed that 166 genes were significantly differentially expressed by more than two-fold. Transcripts for Entner-Doudoroff (ED) pathway genes (<it>glk, zwf, pgl, pgk, and eno</it>) and gene <it>pdc</it>, encoding a key enzyme leading to ethanol production, were at least 30-fold more abundant under anaerobic conditions in the stationary phase based on quantitative-PCR results. We also identified differentially expressed ZM4 genes predicted by The Institute for Genomic Research (TIGR) that were not predicted in the primary annotation.</p> <p>Conclusion</p> <p>High oxygen concentrations present during <it>Z. mobilis </it>fermentations negatively influence fermentation performance. The maximum specific growth rates were not dramatically different between aerobic and anaerobic conditions, yet oxygen did affect the physiology of the cells leading to the buildup of metabolic byproducts that ultimately led to greater differences in transcriptomic profiles in stationary phase.</p

Enterohemorrhagic Escherichia coli O157∶H7 Gene Expression Profiling in Response to Growth in the Presence of Host Epithelia

BACKGROUND: The pathogenesis of enterohemorrhagic Escherichia coli (EHEC) O157:H7 infection is attributed to virulence factors encoded on multiple pathogenicity islands. Previous studies have shown that EHEC O157:H7 modulates host cell signal transduction cascades, independent of toxins and rearrangement of the cytoskeleton. However, the virulence factors and mechanisms responsible for EHEC-mediated subversion of signal transduction remain to be determined. Therefore, the purpose of this study was to first identify differentially regulated genes in response to EHEC O157:H7 grown in the presence of epithelial cells, compared to growth in the absence of epithelial cells (that is, growth in minimal essential tissue culture medium alone, minimal essential tissue culture medium in the presence of 5% CO(2), and Penassay broth alone) and, second, to identify EHEC virulence factors responsible for pathogen modulation of host cell signal transduction. METHODOLOGY/PRINCIPAL FINDINGS: Overnight cultures of EHEC O157:H7 were incubated for 6 hr at 37 degrees C in the presence or absence of confluent epithelial (HEp-2) cells. Total RNA was then extracted and used for microarray analyses (Affymetrix E. coli Genome 2.0 gene chips). Relative to bacteria grown in each of the other conditions, EHEC O157:H7 cultured in the presence of cultured epithelial cells displayed a distinct gene-expression profile. A 2.0-fold increase in the expression of 71 genes and a 2.0-fold decrease in expression of 60 other genes were identified in EHEC O157:H7 grown in the presence of epithelial cells, compared to bacteria grown in media alone. CONCLUSION/SIGNIFICANCE: Microarray analyses and gene deletion identified a protease on O-island 50, gene Z1787, as a potential virulence factor responsible for mediating EHEC inhibition of the interferon (IFN)-gamma-Jak1,2-STAT-1 signal transduction cascade. Up-regulated genes provide novel targets for use in developing strategies to interrupt the infectious process

Pattern recognition of gene expression data on signalling networks of cancer

Krebs ist ein Ergebnis abweichender zellulärer Signalübertragungen. Das Verständnis der Eigenschaften dieser komplexen Netzwerke wird es ermöglichen, effiziente therapeutische Strategien zu entwickeln. Oft werden bei der Analyse von Tumoren nur einzelne Signalpfade berücksichtigt. Diese Art Analyse vernachlässigt das Prinzip zusammenhängender Signalproteine in einem Netzwerk. Die Analyse, die in dieser Dissertation beschrieben wird, verwendet einen auf Netzwerken basierenden Ansatz, um ein Verständnis der komplexen zellulären Signaltransduktionspfade (sog. Signalwege) zu ermöglichen. In dieser Dissertation wurden menschliche Tumor-Genexpressionsdaten in das menschliche Protein-Protein-Interaktionsnetzwerk eingebettet und Signalwege mittels eines auf der Graphentheorie basierenden Ansatzes vorausberechnet. Mehrere Eigenschaften von normalen und Tumorsignalnetzwerken wurden aus diesen berechneten Signalwege unter Verwendung von 10 Tumordatensätzen abgeleitet. Es wird gezeigt, dass die Signalwege der betrachteten Tumore verglichen mit denen in normalen Gewebe kürzere Kaskaden und stärker differenzierte Signalwege verwenden. Das Signalnetzwerk im Tumor ist allgemein differenzierter und stärker vernetzt als in normalen Zellen. Eine netzwerkbasierende Analyse wurde ausgeführt, um die verschiedenen Netzwerkeigenschaften zwischen normalen und Tumorzellen mittels mehrerer Tumorgenexpressions-Datensätzen zu vergleichen. Die Ergebnisse bestätigen ein Model weniger geordneter Signalwege in Tumoren, was in einer größeren Robustheit der Signalwege des Tumors resultiert. Mit den Erkenntnissen dieser Studie wird ein neues Signalübertragungsmotiv vorgeschlagen, das sich in hoher Anzahl in den analysierten Datensätzen findet

Use of Proteomics to Probe Dynamic Changes in Cyanobacteria

Cyanobacteria are unicellular photosynthetic microorganisms that capture and convert light energy to chemical energy, which is the precursor for feed, fuel, and food. These oxygenic phototrophs appear blue-green in color due to the blue bilin pigments in their phycobilisomes and green chlorophyll pigments in their photosystems. They also have diverse morphologies, and thrive in terrestrial, marine water, fresh water, as well as extreme environments. Cyanobacteria have developed a number of protective mechanisms and adaptive responses that allow the photosynthetic process to operate optimally under diverse and extreme conditions. Prolonged deprivation of essential nutrients, such as nitrogen and sulfur, commonly found in the natural environments cyanobacteria grow in, can disrupt crucial metabolic activities and promote the production of lethal reactive oxygen species. The dynamic remodeling of protein complexes and structures facilitates adaptation to environmental stresses, however, specific protein modifications are poorly understood. Synthetic and systems biology approaches have been used to study how photosynthetic microorganisms optimize their cellular metabolism in response to adverse environmental conditions. To gain insights on how cyanobacteria cope with environmental changes, we created a global proteomics map of redox-sensitive amino acid residues and examined the degradation of light harvesting apparatus in cyanobacteria. These studies offered significant insights into the broad redox regulation and protein degradation, advancing knowledge of how photosynthetic microbial cells dynamically rely on protective mechanisms to survive changing environmental conditions

Growth phase-specific promoters of cyanobacteria for synthetic biology applications

No abstract available

Functional Genomics and physiology of growth initiation in Salmonella

Abstract Lag phase is a period of bacterial adaptation that occurs prior to cell division. The aim of this project was to characterise the processes used by Salmonella enterica serovar Typhimurium to escape from lag phase, and determine whether these processes are dependent on the bacterial ‘physiological history’. The lag phase transcriptomic response at 25 °C of stationary phase cells that had been held for twelve days at 2 °C was compared with that of stationary phase cells not subjected to this cold storage treatment. Cold-stored cells showed significant changes in expression of 78 % genes during lag phase, with 875 genes altering their expression ≥2-fold within the first four minutes of inoculation into fresh medium. Functional categories of genes that were significantly up-regulated included those encoding systems involved with metal ion uptake, stress resistance, phosphate uptake, ribosome synthesis and cellular metabolism. Genes in the OxyR regulon were induced earlier in cold-stored cells, a response coupled with a delay in the expression of Fe2+ acquisition genes, and down-regulation of genes encoding central metabolic enzymes. Together, these findings with physiological tests demonstrated that Salmonella held in cold storage exhibited an increased sensitivity to oxidative stress in midlag phase, although the lag time was not increased. Despite an oxidative stress response at the transcriptomic level during lag phase under both experimental conditions, deletion of the OxyR and SoxRS systems did not lead to an increased lag time during aerobic growth at 25 °C. The intracellular concentration of metal ions was quantified using ICP-MS, and changes observed during lag phase confirmed the transcriptomic data. Metal ions specifically accumulated during lag phase included Mn2+, Fe2+, Cu2+ and Ca2+, with the latter being the most abundant metal ion. The intracellular concentration of Zn2+ and Mg2+ remained the same as for stationary phase cells, and Ni2+, Mo2+ and Co2+ were expelled from the cell during lag phase. Metal homeostasis was determined to be a critical process, highlighted by growth in the presence of a chelator causing an extended lag time. Overall, lag phase was found to be a robust and reproducible adaptation period which was not perturbed by the mutagenesis approaches utilised in this study