A connection between stress and development in the multicelular prokaryote Streptomyces coelicolor

Morphological changes leading to aerial mycelium formation and sporulation in the mycelial bacterium Streptomyces coelicolor rely on establishing distinct patterns of gene expression in separate regions of the colony. sH was identified previously as one of three paralogous sigma factors associated with stress responses in S. coelicolor. Here, we show that sigH and the upstream gene prsH (encoding a putative antisigma factor of sH) form an operon transcribed from two developmentally regulated promoters, sigHp1 and sigHp2. While sigHp1 activity is confined to the early phase of growth, transcription of sigHp2 is dramatically induced at the time of aerial hyphae formation. Localization of sigHp2 activity using a transcriptional fusion to the green fluorescent protein reporter gene (sigHp2–egfp) showed that sigHp2 transcription is spatially restricted to sporulating aerial hyphae in wild-type S. coelicolor. However, analysis of mutants unable to form aerial hyphae (bld mutants) showed that sigHp2 transcription and sH protein levels are dramatically upregulated in a bldD mutant, and that the sigHp2–egfp fusion was expressed ectopically in the substrate mycelium in the bldD background. Finally, a protein possessing sigHp2 promoter-binding activity was purified to homogeneity from crude mycelial extracts of S. coelicolor and shown to be BldD. The BldD binding site in the sigHp2 promoter was defined by DNase I footprinting. These data show that expression of sH is subject to temporal and spatial regulation during colony development, that this tissue-specific regulation is mediated directly by the developmental transcription factor BldD and suggest that stress and developmental programmes may be intimately connected in Streptomyces morphogenesis

Broad spectrum thiopeptide recognition specificity of the Streptomyces lividans TipAL protein and its role in regulating gene expression.

Microbial metabolites isolated in screening programs for their ability to activate transcription of the tipA promoter (ptipA) in Streptomyces lividans define a class of cyclic thiopeptide antibiotics having dehydroalanine side chains ("tails"). Here we show that such compounds of heterogeneous primary structure (representatives tested: thiostrepton, nosiheptide, berninamycin, promothiocin) are all recognized by TipAS and TipAL, two in-frame translation products of the tipA gene. The N-terminal helix-turn-helix DNA binding motif of TipAL is homologous to the MerR family of transcriptional activators, while the C terminus forms a novel ligand-binding domain. ptipA inducers formed irreversible complexes in vitro and in vivo (presumably covalent) with TipAS by reacting with the second of the two C-terminal cysteine residues. Promothiocin and thiostrepton derivatives in which the dehydroalanine side chains were removed lost the ability to modify TipAS. They were able to induce expression of ptipA as well as the tipA gene, although with reduced activity. Thus, TipA required the thiopeptide ring structure for recognition, while the tail served either as a dispensable part of the recognition domain and/or locked thiopeptides onto TipA proteins, thus leading to an irreversible transcriptional activation. Construction and analysis of a disruption mutant showed that tipA was autogenously regulated and conferred thiopeptide resistance. Thiostrepton induced the synthesis of other proteins, some of which did not require tipA

Virtual Mutagenesis of the Yeast Cyclins Genetic Network Reveals Complex Dynamics of Transcriptional Control Networks

Study of genetic networks has moved from qualitative description of interactions between regulators and regulated genes to the analysis of the interaction dynamics. This paper focuses on the analysis of dynamics of one particular network – the yeast cyclins network. Using a dedicated mathematical model of gene expression and a procedure for computation of the parameters of the model from experimental data, a complete numerical model of the dynamics of the cyclins genetic network was attained. The model allowed for performing virtual experiments on the network and observing their influence on the expression dynamics of the genes downstream in the regulatory cascade. Results show that when the network structure is more complicated, and the regulatory interactions are indirect, results of gene deletion are highly unpredictable. As a consequence of quantitative behavior of the genes and their connections within the network, causal relationship between a regulator and target gene may not be discovered by gene deletion. Without including the dynamics of the system into the network, its functional properties cannot be studied and interpreted correctly

The suboptimal structures find the optimal RNAs: homology search for bacterial non-coding RNAs using suboptimal RNA structures

Non-coding RNAs (ncRNAs) are regulatory molecules encoded in the intergenic or intragenic regions of the genome. In prokaryotes, biocomputational identification of homologs of known ncRNAs in other species often fails due to weakly evolutionarily conserved sequences, structures, synteny and genome localization, except in the case of evolutionarily closely related species. To eliminate results from weak conservation, we focused on RNA structure, which is the most conserved ncRNA property. Analysis of the structure of one of the few well-studied bacterial ncRNAs, 6S RNA, demonstrated that unlike optimal and consensus structures, suboptimal structures are capable of capturing RNA homology even in divergent bacterial species. A computational procedure for the identification of homologous ncRNAs using suboptimal structures was created. The suggested procedure was applied to strongly divergent bacterial species and was capable of identifying homologous ncRNAs

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

The dpsA Gene of Streptomyces coelicolor: Induction of Expression from a Single Promoter in Response to Environmental Stress or during Development

The DpsA protein plays a dual role in Streptomyces coelicolor, both as part of the stress response and contributing to nucleoid condensation during sporulation. Promoter mapping experiments indicated that dpsA is transcribed from a single, sigB-like dependent promoter. Expression studies implicate SigH and SigB as the sigma factors responsible for dpsA expression while the contribution of other SigB-like factors is indirect by means of controlling sigH expression. The promoter is massively induced in response to osmotic stress, in part due to its sensitivity to changes in DNA supercoiling. In addition, we determined that WhiB is required for dpsA expression, particularly during development. Gel retardation experiments revealed direct interaction between apoWhiB and the dpsA promoter region, providing the first evidence for a direct WhiB target in S. coelicolor

Uncovering Genes with Divergent mRNA-Protein Dynamics in Streptomyces coelicolor

Many biological processes are intrinsically dynamic, incurring profound changes at both molecular and physiological levels. Systems analyses of such processes incorporating large-scale transcriptome or proteome profiling can be quite revealing. Although consistency between mRNA and proteins is often implicitly assumed in many studies, examples of divergent trends are frequently observed. Here, we present a comparative transcriptome and proteome analysis of growth and stationary phase adaptation in Streptomyces coelicolor, taking the time-dynamics of process into consideration. These processes are of immense interest in microbiology as they pertain to the physiological transformations eliciting biosynthesis of many naturally occurring therapeutic agents. A shotgun proteomics approach based on mass spectrometric analysis of isobaric stable isotope labeled peptides (iTRAQ™) enabled identification and rapid quantification of approximately 14% of the theoretical proteome of S. coelicolor. Independent principal component analyses of this and DNA microarray-derived transcriptome data revealed that the prominent patterns in both protein and mRNA domains are surprisingly well correlated. Despite this overall correlation, by employing a systematic concordance analysis, we estimated that over 30% of the analyzed genes likely exhibited significantly divergent patterns, of which nearly one-third displayed even opposing trends. Integrating this data with biological information, we discovered that certain groups of functionally related genes exhibit mRNA-protein discordance in a similar fashion. Our observations suggest that differences between mRNA and protein synthesis/degradation mechanisms are prominent in microbes while reaffirming the plausibility of such mechanisms acting in a concerted fashion at a protein complex or sub-pathway level

Network Topologies and Dynamics Leading to Endotoxin Tolerance and Priming in Innate Immune Cells

The innate immune system, acting as the first line of host defense, senses and adapts to foreign challenges through complex intracellular and intercellular signaling networks. Endotoxin tolerance and priming elicited by macrophages are classic examples of the complex adaptation of innate immune cells. Upon repetitive exposures to different doses of bacterial endotoxin (lipopolysaccharide) or other stimulants, macrophages show either suppressed or augmented inflammatory responses compared to a single exposure to the stimulant. Endotoxin tolerance and priming are critically involved in both immune homeostasis and the pathogenesis of diverse inflammatory diseases. However, the underlying molecular mechanisms are not well understood. By means of a computational search through the parameter space of a coarse-grained three-node network with a two-stage Metropolis sampling approach, we enumerated all the network topologies that can generate priming or tolerance. We discovered three major mechanisms for priming (pathway synergy, suppressor deactivation, activator induction) and one for tolerance (inhibitor persistence). These results not only explain existing experimental observations, but also reveal intriguing test scenarios for future experimental studies to clarify mechanisms of endotoxin priming and tolerance.Comment: 15 pages, 8 figures, submitte

Computing with bacterial constituents, cells and populations: from bioputing to bactoputing

The relevance of biological materials and processes to computing—aliasbioputing—has been explored for decades. These materials include DNA, RNA and proteins, while the processes include transcription, translation, signal transduction and regulation. Recently, the use of bacteria themselves as living computers has been explored but this use generally falls within the classical paradigm of computing. Computer scientists, however, have a variety of problems to which they seek solutions, while microbiologists are having new insights into the problems bacteria are solving and how they are solving them. Here, we envisage that bacteria might be used for new sorts of computing. These could be based on the capacity of bacteria to grow, move and adapt to a myriad different fickle environments both as individuals and as populations of bacteria plus bacteriophage. New principles might be based on the way that bacteria explore phenotype space via hyperstructure dynamics and the fundamental nature of the cell cycle. This computing might even extend to developing a high level language appropriate to using populations of bacteria and bacteriophage. Here, we offer a speculative tour of what we term bactoputing, namely the use of the natural behaviour of bacteria for calculating