σE of Streptomyces coelicolor can function both as a direct activator or repressor of transcription.

σ factors are considered as positive regulators of gene expression. Here we reveal the opposite, inhibitory role of these proteins. We used a combination of molecular biology methods and computational modeling to analyze the regulatory activity of the extracytoplasmic σE factor from Streptomyces coelicolor. The direct activator/repressor function of σE was then explored by experimental analysis of selected promoter regions in vivo. Additionally, the σE interactome was defined. Taken together, the results characterize σE, its regulation, regulon, and suggest its direct inhibitory function (as a repressor) in gene expression, a phenomenon that may be common also to other σ factors and organisms.

Quantitative Aspect of Bacillus subtilis σB Regulatory Network-A Computational Simulation.

Bacillus subtilis is a model organism used to study molecular processes in prokaryotic cells. Sigma factor B, which associates with RNA polymerase, is one of the transcriptional regulators involved in the cell's response to environmental stress. This study addresses the key question of how the levels of free SigB, which acts as the actual regulator of gene expression, are controlled. A set of chemical equations describing the network controlling the levels of free SigB was designed, leading to a set of differential equations quantifying the dynamics of the network. Utilizing a microarray-measured gene expression time series then allowed the simulation of the kinetic behavior of the network in real conditions and investigation of the role of phosphatases RsbU/RsbP transmitting the environmental signal and controlling the amounts of free SigB. Moreover, the role of kinetic constants controlling the formation of the molecular complexes, which consequently influence the amount of free SigB, was investigated. The simulation showed that although the total amount of sigma B is relatively high in the unstressed population, the amount of free SigB, which actually controls its regulon, is quite low. The simulation also allowed determination of the proportion of all the network members that were free or bound in complexes. While previously the qualitative features of B. subtilis SigB have been studied in detail, the kinetics of the network have mostly been ignored. In summary, the computational results based on experimental data provide a quantitative insight into the functioning of the SigB-dependent circuit and provide a roadmap for its further exploration in this industrially important bacterium.

rboAnalyzer webserver: web service for non-coding RNA characterization from NCBI BLAST output.

SUMMARY: We present a web service for improving characterization of non-coding RNAs (ncRNAs) from NCBI BLAST outputs, based on a command-line application rboAnalyzer. Briefly, the application extends subject sequences of selected high scoring pairs (HSPs) in BLAST output to their plausible full length, and predicts their homology and secondary structures. The aim of the application is to aid to characterize subject RNAs in HSPs that come uncharacterized in BLAST output. The main advantages of the web-server are easy use and interactive analysis with search, filtering and data export options. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at rboanalyzer.elixir-czech.cz. The website frontend is implemented in Elm, while backend is implemented in Python and served by Apache.

Kinetic Modeling and Meta-Analysis of the Bacillus subtilis SigB Regulon during Spore Germination and Outgrowth.

The exponential increase in the number of conducted studies combined with the development of sequencing methods have led to an enormous accumulation of partially processed experimental data in the past two decades. Here, we present an approach using literature-mined data complemented with gene expression kinetic modeling and promoter sequence analysis. This approach allowed us to identify the regulon of Bacillus subtilis sigma factor SigB of RNA polymerase (RNAP) specifically expressed during germination and outgrowth. SigB is critical for the cell's response to general stress but is also expressed during spore germination and outgrowth, and this specific regulon is not known. This approach allowed us to (i) define a subset of the known SigB regulon controlled by SigB specifically during spore germination and outgrowth, (ii) identify the influence of the promoter sequence binding motif organization on the expression of the SigB-regulated genes, and (iii) suggest additional sigma factors co-controlling other SigB-dependent genes. Experiments then validated promoter sequence characteristics necessary for direct RNAP-SigB binding. In summary, this work documents the potential of computational approaches to unravel new information even for a well-studied system; moreover, the study specifically identifies the subset of the SigB regulon, which is activated during germination and outgrowth.

Recommendations for connecting molecular sequence and biodiversity research infrastructures through ELIXIR.

Threats to global biodiversity are increasingly recognised by scientists and the public as a critical challenge. Molecular sequencing technologies offer means to catalogue, explore, and monitor the richness and biogeography of life on Earth. However, exploiting their full potential requires tools that connect biodiversity infrastructures and resources. As a research infrastructure developing services and technical solutions that help integrate and coordinate life science resources across Europe, ELIXIR is a key player. To identify opportunities, highlight priorities, and aid strategic thinking, here we survey approaches by which molecular technologies help inform understanding of biodiversity. We detail example use cases to highlight how DNA sequencing is: resolving taxonomic issues; Increasing knowledge of marine biodiversity; helping understand how agriculture and biodiversity are critically linked; and playing an essential role in ecological studies. Together with examples of national biodiversity programmes, the use cases show where progress is being made but also highlight common challenges and opportunities for future enhancement of underlying technologies and services that connect molecular and wider biodiversity domains. Based on emerging themes, we propose key recommendations to guide future funding for biodiversity research: biodiversity and bioinformatic infrastructures need to collaborate closely and strategically; taxonomic efforts need to be aligned and harmonised across domains; metadata needs to be standardised and common data management approaches widely adopted; current approaches need to be scaled up dramatically to address the anticipated explosion of molecular data; bioinformatics support for biodiversity research needs to be enabled and sustained; training for end users of biodiversity research infrastructures needs to be prioritised; and community initiatives need to be proactive and focused on enabling solutions. For sequencing data to deliver their full potential they must be connected to knowledge: together, molecular sequence data collection initiatives and biodiversity research infrastructures can advance global efforts to prevent further decline of Earth's biodiversity.

rboAnalyzer: A Software to Improve Characterization of Non-coding RNAs From Sequence Database Search Output.

Searching for similar sequences in a database via BLAST or a similar tool is one of the most common bioinformatics tasks applied in general, and to non-coding RNAs in particular. However, the results of the search might be difficult to interpret due to the presence of partial matches to the database subject sequences. Here, we present rboAnalyzer - a tool that helps with interpreting sequence search result by (1) extending partial matches into plausible full-length subject sequences, (2) predicting homology of RNAs represented by full-length subject sequences to the query RNA, (3) pooling information across homologous RNAs found in the search results and public databases such as Rfam to predict more reliable secondary structures for all matches, and (4) contextualizing the matches by providing the prediction results and other relevant information in a rich graphical output. Using predicted full-length matches improves secondary structure prediction and makes rboAnalyzer robust with regards to identification of homology. The output of the tool should help the user to reliably characterize non-coding RNAs in BLAST output. The usefulness of the rboAnalyzer and its ability to correctly extend partial matches to full-length is demonstrated on known homologous RNAs. To allow the user to use custom databases and search options, rboAnalyzer accepts any search results as a text file in the BLAST format. The main output is an interactive HTML page displaying the computed characteristics and other context of the matches. The output can also be exported in an appropriate sequence and/or secondary structure formats.

A Comparison of Protein and mRNA Expression during Development of the Soil Dwelling Prokaryote (S. coelicolor).

In this paper, correlation analysis of protein and mRNA levels in the soil dwelling bacteria Streptomyces coelicolor (S. coelicolor M145) is presented during development of the population as it grew in liquid medium using three biological and two technical replicates, measured during exponential growth, and its entry into the stationary phase. The proteome synthesis time series are compared with the gene expression time series measured previously under identical experimental conditions. Results reveal that about one third of protein/mRNA synthesis profiles are well correlated while another third are correlated negatively. Functional analysis of the highly correlated groups is presented. Based on numerical simulation, the negative correlation between protein and mRNA is shown to be caused by the difference between the rate of translation and protein degradation.

rPredictorDB: a predictive database of individual secondary structures of RNAs and their formatted plots.

Secondary data structure of RNA molecules provides insights into the identity and function of RNAs. With RNAs readily sequenced, the question of their structural characterization is increasingly important. However, RNA structure is difficult to acquire. Its experimental identification is extremely technically demanding, while computational prediction is not accurate enough, especially for large structures of long sequences. We address this difficult situation with rPredictorDB, a predictive database of RNA secondary structures that aims to form a middle ground between experimentally identified structures in PDB and predicted consensus secondary structures in Rfam. The database contains individual secondary structures predicted using a tool for template-based prediction of RNA secondary structure for the homologs of the RNA families with at least one homolog with experimentally solved structure. Experimentally identified structures are used as the structural templates and thus the prediction has higher reliability than de novo predictions in Rfam. The sequences are downloaded from public resources. So far rPredictorDB covers 7365 RNAs with their secondary structures. Plots of the secondary structures use the Traveler package for readable display of RNAs with long sequences and complex structures, such as ribosomal RNAs. The RNAs in the output of rPredictorDB are extensively annotated and can be viewed, browsed, searched and downloaded according to taxonomic, sequence and structure data. Additionally, structure of user-provided sequences can be predicted using the templates stored in rPredictorDB.

DNA mapping and kinetic modeling of the HrdB regulon in Streptomyces coelicolor.

HrdB in streptomycetes is a principal sigma factor whose deletion is lethal. This is also the reason why its regulon has not been investigated so far. To overcome experimental obstacles, for investigating the HrdB regulon, we constructed a strain whose HrdB protein was tagged by an HA epitope. ChIP-seq experiment, done in 3 repeats, identified 2137 protein-coding genes organized in 337 operons, 75 small RNAs, 62 tRNAs, 6 rRNAs and 3 miscellaneous RNAs. Subsequent kinetic modeling of regulation of protein-coding genes with HrdB alone and with a complex of HrdB and a transcriptional cofactor RbpA, using gene expression time series, identified 1694 genes that were under their direct control. When using the HrdB-RbpA complex in the model, an increase of the model fidelity was found for 322 genes. Functional analysis revealed that HrdB controls the majority of gene groups essential for the primary metabolism and the vegetative growth. Particularly, almost all ribosomal protein-coding genes were found in the HrdB regulon. Analysis of promoter binding sites revealed binding motif at the -10 region and suggested the possible role of mono- or di-nucleotides upstream of the -10 element.

Genexpi: a toolset for identifying regulons and validating gene regulatory networks using time-course expression data.

BACKGROUND: Identifying regulons of sigma factors is a vital subtask of gene network inference. Integrating multiple sources of data is essential for correct identification of regulons and complete gene regulatory networks. Time series of expression data measured with microarrays or RNA-seq combined with static binding experiments (e.g., ChIP-seq) or literature mining may be used for inference of sigma factor regulatory networks. RESULTS: We introduce Genexpi: a tool to identify sigma factors by combining candidates obtained from ChIP experiments or literature mining with time-course gene expression data. While Genexpi can be used to infer other types of regulatory interactions, it was designed and validated on real biological data from bacterial regulons. In this paper, we put primary focus on CyGenexpi: a plugin integrating Genexpi with the Cytoscape software for ease of use. As a part of this effort, a plugin for handling time series data in Cytoscape called CyDataseries has been developed and made available. Genexpi is also available as a standalone command line tool and an R package. CONCLUSIONS: Genexpi is a useful part of gene network inference toolbox. It provides meaningful information about the composition of regulons and delivers biologically interpretable results.

Lambda phage genetic switch as a system with critical behaviour.

Critical behaviour pervades scientific disciplines as diverse as geology, economy or sociology. The critical behaviour of cell control systems is an open issue whose role has not yet been fully explored. The control of the expression of lambda phage DNA in the host cell can be classified as a system with critical behaviour. Lambda phage is a virus that infects Escherichia coli. Its core genes maintain one of two states; lysogeny or lysis. Current knowledge of the lambda phage genetic network allows to build a computational model of transcriptional control of the genes involved in the lytic-lysogenic switch and to simulate the temporal changes of their expression. Here, we focused on the computational simulation of these gene expressions to demonstrate critical behaviour of the system.

Changes in activity of metabolic and regulatory pathways during germination of S. coelicolor.

BACKGROUND: Bacterial spore germination is a developmental process during which all required metabolic pathways are restored to transfer cells from their dormant state into vegetative growth. Streptomyces are soil dwelling filamentous bacteria with complex life cycle, studied mostly for they ability to synthesize secondary metabolites including antibiotics. RESULTS: Here, we present a systematic approach that analyzes gene expression data obtained from 13 time points taken over 5.5 h of Streptomyces germination. Genes whose expression was significantly enhanced/diminished during the time-course were identified, and classified to metabolic and regulatory pathways. The classification into metabolic pathways revealed timing of the activation of specific pathways during the course of germination. The analysis also identified remarkable changes in the expression of specific sigma factors over the course of germination. Based on our knowledge of the targets of these factors, we speculate on their possible roles during germination. Among the factors whose expression was enhanced during the initial part of germination, SigE is though to manage cell wall reconstruction, SigR controls protein re-aggregation, and others (SigH, SigB, SigI, SigJ) control osmotic and oxidative stress responses. CONCLUSIONS: From the results, we conclude that most of the metabolic pathway mRNAs required for the initial phases of germination were synthesized during the sporulation process and stably conserved in the spore. After rehydration in growth medium, the stored mRNAs are being degraded and resynthesized during first hour. From the analysis of sigma factors we conclude that conditions favoring germination evoke stress-like cell responses.

Inference of sigma factor controlled networks by using numerical modeling applied to microarray time series data of the germinating prokaryote.

A computational model of gene expression was applied to a novel test set of microarray time series measurements to reveal regulatory interactions between transcriptional regulators represented by 45 sigma factors and the genes expressed during germination of a prokaryote Streptomyces coelicolor. Using microarrays, the first 5.5 h of the process was recorded in 13 time points, which provided a database of gene expression time series on genome-wide scale. The computational modeling of the kinetic relations between the sigma factors, individual genes and genes clustered according to the similarity of their expression kinetics identified kinetically plausible sigma factor-controlled networks. Using genome sequence annotations, functional groups of genes that were predominantly controlled by specific sigma factors were identified. Using external binding data complementing the modeling approach, specific genes involved in the control of the studied process were identified and their function suggested.

Global features of gene expression on the proteome and transcriptome levels in S. coelicolor during germination.

Streptomycetes have been studied mostly as producers of secondary metabolites, while the transition from dormant spores to an exponentially growing culture has largely been ignored. Here, we focus on a comparative analysis of fluorescently and radioactively labeled proteome and microarray acquired transcriptome expressed during the germination of Streptomyces coelicolor. The time-dynamics is considered, starting from dormant spores through 5.5 hours of growth with 13 time points. Time series of the gene expressions were analyzed using correlation, principal components analysis and an analysis of coding genes utilization. Principal component analysis was used to identify principal kinetic trends in gene expression and the corresponding genes driving S. coelicolor germination. In contrast with the correlation analysis, global trends in the gene/protein expression reflected by the first principal components showed that the prominent patterns in both the protein and the mRNA domains are surprisingly well correlated. Analysis of the number of expressed genes identified functional groups activated during different time intervals of the germination.

An evolutionary conserved pattern of 18S rRNA sequence complementarity to mRNA 5' UTRs and its implications for eukaryotic gene translation regulation.

There are several key mechanisms regulating eukaryotic gene expression at the level of protein synthesis. Interestingly, the least explored mechanisms of translational control are those that involve the translating ribosome per se, mediated for example via predicted interactions between the ribosomal RNAs (rRNAs) and mRNAs. Here, we took advantage of robustly growing large-scale data sets of mRNA sequences for numerous organisms, solved ribosomal structures and computational power to computationally explore the mRNA-rRNA complementarity that is statistically significant across the species. Our predictions reveal highly specific sequence complementarity of 18S rRNA sequences with mRNA 5' untranslated regions (UTRs) forming a well-defined 3D pattern on the rRNA sequence of the 40S subunit. Broader evolutionary conservation of this pattern may imply that 5' UTRs of eukaryotic mRNAs, which have already emerged from the mRNA-binding channel, may contact several complementary spots on 18S rRNA situated near the exit of the mRNA binding channel and on the middle-to-lower body of the solvent-exposed 40S ribosome including its left foot. We discuss physiological significance of this structurally conserved pattern and, in the context of previously published experimental results, propose that it modulates scanning of the 40S subunit through 5' UTRs of mRNAs.

Systems insight into the spore germination of Streptomyces coelicolor.

An example of bacterium, which undergoes a complex development, is the genus of Streptomyces whose importance lies in their wide capacity to produce secondary metabolites, including antibiotics. In this work, a proteomic approach was applied to the systems study of germination as a transition from dormancy to the metabolically active stage. The protein expression levels were examined throughout the germination time course, the kinetics of the accumulated and newly synthesized proteins were clustered, and proteins detected in each group were identified. Altogether, 104 2DE gel images at 13 time points, from dormant state until 5.5 h of growth, were analyzed. The mass spectrometry identified proteins were separated into functional groups and their potential roles during germination were further assessed. The results showed that the full competence of spores to effectively undergo active metabolism is derived from the sporulation step, which facilitates the rapid initiation of global protein expression during the first 10 min of cultivation. Within the first hour, the majority of proteins were synthesized. From this stage, the full capability of regulatory mechanisms to respond to environmental cues is presumed. The obtained results might also provide a data source for further investigations of the process of germination.

Stochastic simulation for the inference of transcriptional control network of yeast cyclins genes.

Cell cycle is controlled by the activity of protein family of cyclins and cyclin-dependent kinases that are periodically expressed during cell cycle and that are conserved among different species. Genome-wide location analysis found that cyclins are controlled by a small number of transcription factors that form closed network of genes controlling each other. To investigate gene expression dynamics of this network, we developed a general procedure for stochastic simulation of gene expression process. Using the binding data, we simulated gene expression of all genes of the network for all possible combinations of regulatory interactions and by statistical comparison with experimentally measured time series excluded those interactions that formed gene expression temporal profiles significantly different from the measured ones. These experiments led to a new definition of the cyclins regulatory network coherent with the binding experiments which are kinetically plausible. Level of influence of individual regulators in control of the regulated genes is defined. Simulation results indicate particular mechanism of regulatory activity of protein complexes involved in the control of cyclins.

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.