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

Effect of resveratrol on the biofilm formation and physiological properties of avian pathogenic Escherichia coli

Avian pathogenic Escherichia coli (APEC) is widely distributed, causing great economic losses to the poultry industry. The formation of APEC biofilms causes chronic, persistent, and repeated infections in the clinic, making treatment difficult. Resveratrol is a natural product, which has good health benefits including antimicrobial, anti-inflammatory, and cardiovascular activities. Resveratrol shows efficient inhibition of bacterial biofilm formation. However, a comprehensive understanding of the proteomic properties of APEC treated resveratrol is still lacking. In this study, APEC cells treated by resveratrol were investigated using a label-free differential proteomic method. Several proteins, including those related to a two-component system and chemotaxis, were found to be implicated in APEC biofilm formation. In addition, the physiological properties were significantly changed in terms of purine, pyruvate, and glyoxylate and dicarboxylate metabolism in APEC. Data are available via ProteomeXchange with the identifier PXD025706. We speculated that pyruvate dehydrogenase might be a potential target to inhibit Escherichia coli biofilm formation. Overall, our results indicated that resveratrol inhibits APEC biofilm formation by regulating the levels of proteins in two-component systems, especially chemotaxis proteins. The results showed that resveratrol had a potential application in inhibiting the biofilm formation of APEC. SIGNIFICANCE: This study elucidated the mechanism of resveratrol inhibiting biofilm formation of avian pathogenic Escherichia coli (APEC) based on a label-free differential proteomics. It was indicated that resveratrol inhibits APEC biofilm formation by regulating the levels of proteins in two component systems, especially chemotaxis proteins. Meanwhile, we speculated that pyruvate dehydrogenase might be a potential target to inhibit Escherichia coli biofilm formation. It shows that resveratrol has a potential application prospect in inhibiting the biofilm formation of APEC.

Biosynthesis of Histidine

The biosynthesis of histidine in Escherichia coli and Salmonella typhimurium has been an important model system for the study of relationships between the flow of intermediates through a biosynthetic pathway and the control of the genes encoding the enzymes that catalyze the steps in a pathway. This article provides a comprehensive review of the histidine biosynthetic pathway and enzymes, including regulation of the flow of intermediates through the pathway and mechanisms that regulate the amounts of the histidine biosynthetic enzymes. In addition, this article reviews the structure and regulation of the histidine (his) biosynthetic operon, including transcript processing, Rho-factor-dependent "classical" polarity, and the current model of his operon attenuation control. Emphasis is placed on areas of recent progress. Notably, most of the enzymes that catalyze histidine biosynthesis have recently been crystallized, and their structures have been determined. Many of the histidine biosynthetic intermediates are unstable, and the histidine biosynthetic enzymes catalyze some chemically unusual reactions. Therefore, these studies have led to considerable mechanistic insight into the pathway itself and have provided deep biochemical understanding of several fundamental processes, such as feedback control, allosteric interactions, and metabolite channeling. Considerable recent progress has also been made on aspects of his operon regulation, including the mechanism of pp(p)Gpp stimulation of his operon transcription, the molecular basis for transcriptional pausing by RNA polymerase, and pathway evolution. The progress in these areas will continue as sophisticated new genomic, metabolomic, proteomic, and structural approaches converge in studies of the histidine biosynthetic pathway and mechanisms of control of his biosynthetic genes in other bacterial species.

Diversity of metabolic shift in response to oxygen deprivation in Corynebacterium glutamicum and its close relatives

Oxygen-deprived Corynebacterium glutamicum R cells remain metabolically active, producing considerable amounts of organic acids even when not actively growing. We compared the proficiencies of C. glutamicum and close relatives grown under aerobic conditions to metabolize glucose when deprived of oxygen. Eight strains that readily consumed glucose without cell growth subsequently produced organic acids. Among these, the glucose consumption rates of the two C. glutamicum strains (>40 mM/h) and Corynebacterium efficiens (>12 mM/h) were an order of magnitude higher than those of the other five strains. The resultant organic acid yields of these three strains (>86%) consequently exceeded those of the other five (<60%). This difference is probably rooted in the comparatively inferior activities of glyceraldehyde-3-phosphate dehydrogenase, lactate dehydrogenase, and malate dehydrogenase observed in the five strains. Moreover, under oxygen deprivation, phosphoenolpyruvate carboxylase (PEPC) activity of C. efficiens was elevated tenfold, but its lack of fumarase activity meant that no succinic acid could be produced. The metabolic shift occasioned by addition of the PEPC substrate sodium bicarbonate resulted in a doubling of the glucose consumption rate of the two C. glutamicum strains but not that of the other six close relatives.

Biosynthesis of Histidine

The biosynthesis of histidine in Escherichia coli and Salmonella typhimurium has been an important model system for the study of relationships between the flow of intermediates through a biosynthetic pathway and the control of the genes encoding the enzymes that catalyze the steps in a pathway. This article provides a comprehensive review of the histidine biosynthetic pathway and enzymes, including regulation of the flow of intermediates through the pathway and mechanisms that regulate the amounts of the histidine biosynthetic enzymes. In addition, this article reviews the structure and regulation of the histidine (his) biosynthetic operon, including transcript processing, Rho-factor-dependent "classical" polarity, and the current model of his operon attenuation control. Emphasis is placed on areas of recent progress. Notably, most of the enzymes that catalyze histidine biosynthesis have recently been crystallized, and their structures have been determined. Many of the histidine biosynthetic intermediates are unstable, and the histidine biosynthetic enzymes catalyze some chemically unusual reactions. Therefore, these studies have led to considerable mechanistic insight into the pathway itself and have provided deep biochemical understanding of several fundamental processes, such as feedback control, allosteric interactions, and metabolite channeling. Considerable recent progress has also been made on aspects of his operon regulation, including the mechanism of pp(p)Gpp stimulation of his operon transcription, the molecular basis for transcriptional pausing by RNA polymerase, and pathway evolution. The progress in these areas will continue as sophisticated new genomic, metabolomic, proteomic, and structural approaches converge in studies of the histidine biosynthetic pathway and mechanisms of control of his biosynthetic genes in other bacterial species.

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% CO₂, 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°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)-γ-Jak1,2-STAT-1 signal transduction cascade. Up-regulated genes provide novel targets for use in developing strategies to interrupt the infectious process.

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

BACKGROUND

Zymomonas mobilis 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. Z. mobilis 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.

RESULTS

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. 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 (glk, zwf, pgl, pgk, and eno) and gene pdc, 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.

CONCLUSION

High oxygen concentrations present during Z. mobilis 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.

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.

Why do certain genes in a biological network show tight transcriptional co-regulation while others are more or less independently regulated? Prior studies showed that the degree of co-regulation between enzymatic genes decreases with their distance in the metabolic network. However, there are fundamental reasons to suspect that network distance is an incomplete descriptor of functional coherence (hence gene co-regulation), and other, biochemically more relevant measures, have been proposed to capture the functional dependencies between enzymes. We systematically examine whether flux coupling, a biochemically sound and computationally tractable measure of functional interaction between reactions, can better explain gene co-regulation than network distance in the metabolisms of Escherichia coli and Saccharomyces cerevisiae. After validating the flux coupling method using published experimental data on in vivo flux correlations (i.e., coherence of reaction usage), we demonstrate that it not only outperforms metabolic network distance in relation to in vivo flux correlations, but also in explaining transcriptional co-regulation and operonic organization. Future functional genomics studies could benefit from the concept of flux coupling by using it as a basis to test the reliability of computationally predicted functional associations.