Dynamic modeling of gene expression in prokaryotes: application to glucose-lactose diauxie in Escherichia coli

Coexpression of genes or, more generally, similarity in the expression profiles poses an unsurmountable obstacle to inferring the gene regulatory network (GRN) based solely on data from DNA microarray time series. Clustering of genes with similar expression profiles allows for a course-grained view of the GRN and a probabilistic determination of the connectivity among the clusters. We present a model for the temporal evolution of a gene cluster network which takes into account interactions of gene products with genes and, through a non-constant degradation rate, with other gene products. The number of model parameters is reduced by using polynomial functions to interpolate temporal data points. In this manner, the task of parameter estimation is reduced to a system of linear algebraic equations, thus making the computation time shorter by orders of magnitude. To eliminate irrelevant networks, we test each GRN for stability with respect to parameter variations, and impose restrictions on its behavior near the steady state. We apply our model and methods to DNA microarray time series' data collected on Escherichia coli during glucose-lactose diauxie and infer the most probable cluster network for different phases of the experiment.Comment: 20 pages, 4 figures; Systems and Synthetic Biology 5 (2011

Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect coregulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1 was not uploaded but is available by contacting the author. 27 pages, 5 figures, 15 supplementary file

A Transcriptional “Scream” Early Response of E. coli Prey to Predatory Invasion by Bdellovibrio

We have transcriptionally profiled the genes differentially expressed in E. coli prey cells when predatorily attacked by Bdellovibrio bacteriovorus just prior to prey cell killing. This is a brief, approximately 20–25 min period when the prey cell is still alive but contains a Bdellovibrio cell in its periplasm or attached to and penetrating its outer membrane. Total RNA was harvested and labelled 15 min after initiating a semi-synchronous infection with an excess of Bdellovibrio preying upon E. coli and hybridised to a macroarray spotted with all predicted ORFs of E. coli. SAM analysis and t-tests were performed on the resulting data and 126 E. coli genes were found to be significantly differentially regulated by the prey upon attack by Bdellovibrio. The results were confirmed by QRT-PCR. Amongst the prey genes upregulated were a variety of general stress response genes, potentially “selfish” genes within or near prophages and transposable elements, and genes responding to damage in the periplasm and osmotic stress. Essentially, the presence of the invading Bdellovibrio and the resulting damage to the prey cell elicited a small “transcriptional scream”, but seemingly no specific defensive mechanism with which to counter the Bdellovibrio attack. This supports other studies which do not find Bdellovibrio resistance responses in prey, and bodes well for its use as a “living antibiotic”

Stability of Metabolic Correlations under Changing Environmental Conditions in Escherichia coli – A Systems Approach

Background: Biological systems adapt to changing environments by reorganizing their cellular and physiological program with metabolites representing one important response level. Different stresses lead to both conserved and specific responses on the metabolite level which should be reflected in the underlying metabolic network. Methodology/Principal Findings: Starting from experimental data obtained by a GC-MS based high-throughput metabolic profiling technology we here develop an approach that: (1) extracts network representations from metabolic condition-dependent data by using pairwise correlations, (2) determines the sets of stable and condition-dependent correlations based on a combination of statistical significance and homogeneity tests, and (3) can identify metabolites related to the stress response, which goes beyond simple observations about the changes of metabolic concentrations. The approach was tested with Escherichia coli as a model organism observed under four different environmental stress conditions (cold stress, heat stress, oxidative stress, lactose diauxie) and control unperturbed conditions. By constructing the stable network component, which displays a scale free topology and small-world characteristics, we demonstrated that: (1) metabolite hubs in this reconstructed correlation networks are significantly enriched for those contained in biochemical networks such as EcoCyc, (2) particular components of the stable network are enriched for functionally related biochemical pathways, and (3) independently of the response scale, based on their importance in the reorganization of the correlation network a set of metabolites can be identified which represent hypothetical candidates for adjusting to a stress-specific response. Conclusions/Significance: Network-based tools allowed the identification of stress-dependent and general metabolic correlation networks. This correlation-network-based approach does not rely on major changes in concentration to identify metabolites important for stress adaptation, but rather on the changes in network properties with respect to metabolites. This should represent a useful complementary technique in addition to more classical approaches

Stringent response of Escherichia coli: revisiting the bibliome using literature mining

Understanding the mechanisms responsible for cellular responses depends on the systematic collection and analysis of information on the main biological concepts involved. Indeed, the identification of biologically relevant concepts in free text, namely genes, tRNAs, mRNAs, gene products and small molecules, is crucial to capture the structure and functioning of different responses. Results In this work, we review literature reports on the study of the stringent response in Escherichia coli. Rather than undertaking the development of a highly specialised literature mining approach, we investigate the suitability of concept recognition and statistical analysis of concept occurrence as means to highlight the concepts that are most likely to be biologically engaged during this response. The co-occurrence analysis of core concepts in this stringent response, i.e. the (p)ppGpp nucleotides with gene products was also inspected and suggest that besides the enzymes RelA and SpoT that control the basal levels of (p)ppGpp nucleotides, many other proteins have a key role in this response. Functional enrichment analysis revealed that basic cellular processes such as metabolism, transcriptional and translational regulation are central, but other stress-associated responses might be elicited during the stringent response. In addition, the identification of less annotated concepts revealed that some (p)ppGpp-induced functional activities are still overlooked in most reviews. Conclusions In this paper we applied a literature mining approach that offers a more comprehensive analysis of the stringent response in E. coli. The compilation of relevant biological entities to this stress response and the assessment of their functional roles provided a more systematic understanding of this cellular response. Overlooked regulatory entities, such as transcriptional regulators, were found to play a role in this stress response. Moreover, the involvement of other stress-associated concepts demonstrates the complexity of this cellular response

Comparative genomics reveals phylogenetic distribution patterns of secondary metabolites in Amycolatopsis species

Background Genome mining tools have enabled us to predict biosynthetic gene clusters that might encode compounds with valuable functions for industrial and medical applications. With the continuously increasing number of genomes sequenced, we are confronted with an overwhelming number of predicted clusters. In order to guide the effective prioritization of biosynthetic gene clusters towards finding the most promising compounds, knowledge about diversity, phylogenetic relationships and distribution patterns of biosynthetic gene clusters is necessary. Results Here, we provide a comprehensive analysis of the model actinobacterial genus Amycolatopsis and its potential for the production of secondary metabolites. A phylogenetic characterization, together with a pan-genome analysis showed that within this highly diverse genus, four major lineages could be distinguished which differed in their potential to produce secondary metabolites. Furthermore, we were able to distinguish gene cluster families whose distribution correlated with phylogeny, indicating that vertical gene transfer plays a major role in the evolution of secondary metabolite gene clusters. Still, the vast majority of the diverse biosynthetic gene clusters were derived from clusters unique to the genus, and also unique in comparison to a database of known compounds. Our study on the locations of biosynthetic gene clusters in the genomes of Amycolatopsis’ strains showed that clusters acquired by horizontal gene transfer tend to be incorporated into non-conserved regions of the genome thereby allowing us to distinguish core and hypervariable regions in Amycolatopsis genomes. Conclusions Using a comparative genomics approach, it was possible to determine the potential of the genus Amycolatopsis to produce a huge diversity of secondary metabolites. Furthermore, the analysis demonstrates that horizontal and vertical gene transfer play an important role in the acquisition and maintenance of valuable secondary metabolites. Our results cast light on the interconnections between secondary metabolite gene clusters and provide a way to prioritize biosynthetic pathways in the search and discovery of novel compounds