Dynamics of Uptake and Metabolism of Small Molecules in Cellular Response Systems

BACKGROUND: Proper cellular function requires uptake of small molecules from the environment. In response to changes in extracellular conditions cells alter the import and utilization of small molecules. For a wide variety of small molecules the cellular response is regulated by a network motif that combines two feedback loops, one which regulates the transport and the other which regulates the subsequent metabolism. RESULTS: We analyze the dynamic behavior of two widespread but logically distinct two-loop motifs. These motifs differ in the logic of the feedback loop regulating the uptake of the small molecule. Our aim is to examine the qualitative features of the dynamics of these two classes of feedback motifs. We find that the negative feedback to transport is accompanied by overshoot in the intracellular amount of small molecules, whereas a positive feedback to transport removes overshoot by boosting the final steady state level. On the other hand, the negative feedback allows for a rapid initial response, whereas the positive feedback is slower. We also illustrate how the dynamical deficiencies of one feedback motif can be mitigated by an additional loop, while maintaining the original steady-state properties. CONCLUSIONS: Our analysis emphasizes the core of the regulation found in many motifs at the interface between the metabolic network and the environment of the cell. By simplifying the regulation into uptake and the first metabolic step, we provide a basis for elaborate studies of more realistic network structures. Particularly, this theoretical analysis predicts that FeS cluster formation plays an important role in the dynamics of iron homeostasis

Invariant Distribution of Promoter Activities in Escherichia coli

Cells need to allocate their limited resources to express a wide range of genes. To understand how Escherichia coli partitions its transcriptional resources between its different promoters, we employ a robotic assay using a comprehensive reporter strain library for E. coli to measure promoter activity on a genomic scale at high-temporal resolution and accuracy. This allows continuous tracking of promoter activity as cells change their growth rate from exponential to stationary phase in different media. We find a heavy-tailed distribution of promoter activities, with promoter activities spanning several orders of magnitude. While the shape of the distribution is almost completely independent of the growth conditions, the identity of the promoters expressed at different levels does depend on them. Translation machinery genes, however, keep the same relative expression levels in the distribution across conditions, and their fractional promoter activity tracks growth rate tightly. We present a simple optimization model for resource allocation which suggests that the observed invariant distributions might maximize growth rate. These invariant features of the distribution of promoter activities may suggest design constraints that shape the allocation of transcriptional resources

Genetic changes that increase 5-hydroxymethyl furfural resistance in ethanol-producing Escherichia coli LY180

The ability of a biocatalyst to tolerate furan inhibitors present in hemicellulose hydrolysates is important for the production of renewable chemicals. This study shows EMFR9, a furfural-tolerant mutant of ethanologenic E. coli LY180, has also acquired tolerance to 5-hydroxymethyl furfural (5-HMF). The mechanism of action of 5-HMF and furfural appear similar. Furan tolerance results primarily from lower expression of yqhD and dkgA, two furan reductases with a low Km for NADPH. Furan tolerance was also increased by adding plasmids encoding a NADPH/NADH transhydrogenase (pntAB). Together, these results support the hypothesis that the NADPH-dependent reduction of furans by YqhD and DkgA inhibits growth by competing with biosynthesis for this limiting cofactor

Automatic reconstruction of a bacterial regulatory network using Natural Language Processing

<p>Abstract</p> <p>Background</p> <p>Manual curation of biological databases, an expensive and labor-intensive process, is essential for high quality integrated data. In this paper we report the implementation of a state-of-the-art Natural Language Processing system that creates computer-readable networks of regulatory interactions directly from different collections of abstracts and full-text papers. Our major aim is to understand how automatic annotation using Text-Mining techniques can complement manual curation of biological databases. We implemented a rule-based system to generate networks from different sets of documents dealing with regulation in <it>Escherichia coli </it>K-12.</p> <p>Results</p> <p>Performance evaluation is based on the most comprehensive transcriptional regulation database for any organism, the manually-curated RegulonDB, 45% of which we were able to recreate automatically. From our automated analysis we were also able to find some new interactions from papers not already curated, or that were missed in the manual filtering and review of the literature. We also put forward a novel Regulatory Interaction Markup Language better suited than SBML for simultaneously representing data of interest for biologists and text miners.</p> <p>Conclusion</p> <p>Manual curation of the output of automatic processing of text is a good way to complement a more detailed review of the literature, either for validating the results of what has been already annotated, or for discovering facts and information that might have been overlooked at the triage or curation stages.</p

Intuitive Visualization and Analysis of Multi-Omics Data and Application to Escherichia coli Carbon Metabolism

Combinations of ‘omics’ investigations (i.e, transcriptomic, proteomic, metabolomic and/or fluxomic) are increasingly applied to get comprehensive understanding of biological systems. Because the latter are organized as complex networks of molecular and functional interactions, the intuitive interpretation of multi-omics datasets is difficult. Here we describe a simple strategy to visualize and analyze multi-omics data. Graphical representations of complex biological networks can be generated using Cytoscape where all molecular and functional components could be explicitly represented using a set of dedicated symbols. This representation can be used i) to compile all biologically-relevant information regarding the network through web link association, and ii) to map the network components with multi-omics data. A Cytoscape plugin was developed to increase the possibilities of both multi-omic data representation and interpretation. This plugin allowed different adjustable colour scales to be applied to the various omics data and performed the automatic extraction and visualization of the most significant changes in the datasets. For illustration purpose, the approach was applied to the central carbon metabolism of Escherichia coli. The obtained network contained 774 components and 1232 interactions, highlighting the complexity of bacterial multi-level regulations. The structured representation of this network represents a valuable resource for systemic studies of E. coli, as illustrated from the application to multi-omics data. Some current issues in network representation are discussed on the basis of this work

The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks

We have compared a recently developed module-based algorithm LeMoNe for reverse-engineering transcriptional regulatory networks to a mutual information based direct algorithm CLR, using benchmark expression data and databases of known transcriptional regulatory interactions for Escherichia coli and Saccharomyces cerevisiae. A global comparison using recall versus precision curves hides the topologically distinct nature of the inferred networks and is not informative about the specific subtasks for which each method is most suited. Analysis of the degree distributions and a regulator specific comparison show that CLR is 'regulator-centric', making true predictions for a higher number of regulators, while LeMoNe is 'target-centric', recovering a higher number of known targets for fewer regulators, with limited overlap in the predicted interactions between both methods. Detailed biological examples in E. coli and S. cerevisiae are used to illustrate these differences and to prove that each method is able to infer parts of the network where the other fails. Biological validation of the inferred networks cautions against over-interpreting recall and precision values computed using incomplete reference networks.Comment: 13 pages, 1 table, 6 figures + 6 pages supplementary information (1 table, 5 figures

A fragile metabolic network adapted for cooperation in the symbiotic bacterium Buchnera aphidicola

<p>Abstract</p> <p>Background</p> <p><it>In silico </it>analyses provide valuable insight into the biology of obligately intracellular pathogens and symbionts with small genomes. There is a particular opportunity to apply systems-level tools developed for the model bacterium <it>Escherichia coli </it>to study the evolution and function of symbiotic bacteria which are metabolically specialised to overproduce specific nutrients for their host and, remarkably, have a gene complement that is a subset of the <it>E. coli </it>genome.</p> <p>Results</p> <p>We have reconstructed and analysed the metabolic network of the γ-proteobacterium <it>Buchnera aphidicola </it>(symbiont of the pea aphid) as a model for using systems-level approaches to discover key traits of symbionts with small genomes. The metabolic network is extremely fragile with > 90% of the reactions essential for viability <it>in silico</it>; and it is structured so that the bacterium cannot grow without producing the essential amino acid, histidine, which is released to the insect host. Further, the amount of essential amino acid produced by the bacterium <it>in silico </it>can be controlled by host supply of carbon and nitrogen substrates.</p> <p>Conclusion</p> <p>This systems-level analysis predicts that the fragility of the bacterial metabolic network renders the symbiotic bacterium intolerant of drastic environmental fluctuations, whilst the coupling of histidine production to growth prevents the bacterium from exploiting host nutrients without reciprocating. These metabolic traits underpin the sustained nutritional contribution of <it>B. aphidicola </it>to the host and, together with the impact of host-derived substrates on the profile of nutrients released from the bacteria, point to a dominant role of the host in controlling the symbiosis.</p