CoryneRegNet 3.0—An interactive systems biology platform for the analysis of gene regulatory networks in corynebacteria and Escherichia coli

CoryneRegNet 7, the reference database and analysis platform for corynebacterial gene regulatory networks

We present the newest version of CoryneRegNet, the reference database for corynebacterial regulatory interactions, available at www.exbio.wzw.tum.de/coryneregnet/. The exponential growth of next-generation sequencing data in recent years has allowed a better understanding of bacterial molecular mechanisms. Transcriptional regulation is one of the most important mechanisms for bacterial adaptation and survival. These mechanisms may be understood via an organism's network of regulatory interactions. Although the Corynebacterium genus is important in medical, veterinary and biotechnological research, little is known concerning the transcriptional regulation of these bacteria. Here, we unravel transcriptional regulatory networks (TRNs) for 224 corynebacterial strains by utilizing genome-scale transfer of TRNs from four model organisms and assigning statistical significance values to all predicted regulations. As a result, the number of corynebacterial strains with TRNs increased twenty times and the back-end and front-end were reimplemented to support new features as well as future database growth. CoryneRegNet 7 is the largest TRN database for the Corynebacterium genus and aids in elucidating transcriptional mechanisms enabling adaptation, survival and infection.

Advantages of mixing bioinformatics and visualization approaches for analyzing sRNA-mediated regulatory bacterial networks

The revolution in high-throughput sequencing technologies has enabled the acquisition of gigabytes of RNA sequences in many different conditions and has highlighted an unexpected number of small RNAs (sRNAs) in bacteria. Ongoing exploitation of these data enables numerous applications for investigating bacterial transacting sRNA-mediated regulation networks. Focusing on sRNAs that regulate mRNA translation in trans, recent works have noted several sRNA-based regulatory pathways that are essential for key cellular processes. Although the number of known bacterial sRNAs is increasing, the experimental validation of their interactions with mRNA targets remains challenging and involves expensive and time-consuming experimental strategies. Hence, bioinformatics is crucial for selecting and prioritizing candidates before designing any experimental work. However, current software for target prediction produces a prohibitive number of candidates because of the lack of biological knowledge regarding the rules governing sRNA-mRNA interactions. Therefore, there is a real need to develop new approaches to help biologists focus on the most promising predicted sRNA-mRNA interactions. In this perspective, this review aims at presenting the advantages of mixing bioinformatics and visualization approaches for analyzing predicted sRNA-mediated regulatory bacterial networks.

CMRegNet-An interspecies reference database for corynebacterial and mycobacterial regulatory networks

Background

Organisms utilize a multitude of mechanisms for responding to changing environmental conditions, maintaining their functional homeostasis and to overcome stress situations. One of the most important mechanisms is transcriptional gene regulation. In-depth study of the transcriptional gene regulatory network can lead to various practical applications, creating a greater understanding of how organisms control their cellular behavior.

Description

In this work, we present a new database, CMRegNet for the gene regulatory networks of Corynebacterium glutamicum ATCC 13032 and Mycobacterium tuberculosis H37Rv. We furthermore transferred the known networks of these model organisms to 18 other non-model but phylogenetically close species (target organisms) of the CMNR group. In comparison to other network transfers, for the first time we utilized two model organisms resulting into a more diverse and complete network of the target organisms.

Conclusion

CMRegNet provides easy access to a total of 3,103 known regulations in C. glutamicum ATCC 13032 and M. tuberculosis H37Rv and to 38,940 evolutionary conserved interactions for 18 non-model species of the CMNR group. This makes CMRegNet to date the most comprehensive database of regulatory interactions of CMNR bacteria. The content of CMRegNet is publicly available online via a web interface found at http://lgcm.icb.ufmg.br/cmregnet.

Transcriptional control of the F0F1-ATP synthase operon of Corynebacterium glutamicum: SigmaH factor binds to its promoter and regulates its expression at different pH values

Corynebacterium glutamicum used in the amino acid fermentation industries is an alkaliphilic microorganism. Its F₀F₁-ATPase operon (atpBEFHAGDC) is expressed optimally at pH 9.0 forming a polycistronic (7.5 kb) and a monocistronic (1.2 kb) transcripts both starting upstream of the atpB gene. Expression of this operon is controlled by the SigmaH factor. The sigmaH gene (sigH) was cloned and shown to be co-transcribed with a small gene, cg0877, encoding a putative anti-sigma factor. A mutant deleted in the sigH gene expressed the atpBEFHAGDC operon optimally at pH 7.0 at difference of the wild-type strain (optimal expression at pH 9.0). These results suggested that the SigmaH factor is involved in pH control of expression of the F₀F₁ ATPase operon. The SigmaH protein was expressed in Escherichia coli fused to the GST (glutathione-S-transferase) and purified to homogeneity by affinity chromatography on a GSTrap HP column. The fused protein was identified by immunodetection with anti-GST antibodies. DNA-binding studies by electrophoretic mobility shift assays showed that the SigH protein binds to a region of the atpB promoter containing the sigmaH recognition sequence (−35)TTGGAT…18nt…GTTA(−10). SigmaH plays an important role in the cascade of control of pH stress in Corynebacterium.

CoryneRegNet 6.0--Updated database content, new analysis methods and novel features focusing on community demands

Post-genomic analysis techniques such as next-generation sequencing have produced vast amounts of data about micro organisms including genetic sequences, their functional annotations and gene regulatory interactions. The latter are genetic mechanisms that control a cell's characteristics, for instance, pathogenicity as well as survival and reproduction strategies. CoryneRegNet is the reference database and analysis platform for corynebacterial gene regulatory networks. In this article we introduce the updated version 6.0 of CoryneRegNet and describe the updated database content which includes, 6352 corynebacterial regulatory interactions compared with 4928 interactions in release 5.0 and 3235 regulations in release 4.0, respectively. We also demonstrate how we support the community by integrating analysis and visualization features for transiently imported custom data, such as gene regulatory interactions. Furthermore, with release 6.0, we provide easy-to-use functions that allow the user to submit data for persistent storage with the CoryneRegNet database. Thus, it offers important options to its users in terms of community demands. CoryneRegNet is publicly available at http://www.coryneregnet.de.

On the power and limits of evolutionary conservation--unraveling bacterial gene regulatory networks

The National Center for Biotechnology Information (NCBI) recently announced ‘1000 prokaryotic genomes are now completed and available in the Genome database’. The increasing trend will provide us with thousands of sequenced microbial organisms over the next years. However, this is only the first step in understanding how cells survive, reproduce and adapt their behavior while being exposed to changing environmental conditions. One major control mechanism is transcriptional gene regulation. Here, striking is the direct juxtaposition of the handful of bacterial model organisms to the 1000 prokaryotic genomes. Next-generation sequencing technologies will further widen this gap drastically. However, several computational approaches have proven to be helpful. The main idea is to use the known transcriptional regulatory network of reference organisms as template in order to unravel evolutionarily conserved gene regulations in newly sequenced species. This transfer essentially depends on the reliable identification of several types of conserved DNA sequences. We decompose this problem into three computational processes, review the state of the art and illustrate future perspectives.

Genetic makeup of the Corynebacterium glutamicum LexA regulon deduced from comparative transcriptomics and in vitro DNA band shift assays

The lexA gene of Corynebacterium glutamicum ATCC 13032 was deleted to create the mutant strain C. glutamicum NJ2114, which has an elongated cell morphology and an increased doubling time. To characterize the SOS regulon in C. glutamicum, the transcriptomes of NJ2114 and a DNA-damage-induced wild-type strain were compared with that of a wild-type control using DNA microarray hybridization. The expression data were combined with bioinformatic pattern searches for LexA binding sites, leading to the detection of 46 potential SOS boxes located upstream of differentially expressed transcription units. Binding of a hexahistidyl-tagged LexA protein to 40 double-stranded oligonucleotides containing the potential SOS boxes was demonstrated in vitro by DNA band shift assays. It turned out that LexA binds not only to SOS boxes in the promoter-operator region of upregulated genes, but also to SOS boxes detected upstream of downregulated genes. These results demonstrated that LexA controls directly the expression of at least 48 SOS genes organized in 36 transcription units. The deduced genes encode a variety of physiological functions, many of them involved in DNA repair and survival after DNA damage, but nearly half of them have hitherto unknown functions. Alignment of the LexA binding sites allowed the corynebacterial SOS box consensus sequence TcGAA(a/c)AnnTGTtCGA to be deduced. Furthermore, the common intergenic region of lexA and the differentially expressed divS-nrdR operon, encoding a cell division suppressor and a regulator of deoxyribonucleotide biosynthesis, was characterized in detail. Promoter mapping revealed differences in divS-nrdR expression during SOS response and normal growth conditions. One of the four LexA binding sites detected in the intergenic region is involved in regulating divS-nrdR transcription, whereas the other sites are apparently used for negative autoregulation of lexA expression.

Regulation of ldh expression during biotin-limited growth of Corynebacterium glutamicum

Corynebacterium glutamicum is a biotin-auxotrophic bacterium and some strains efficiently produce glutamic acid under biotin-limiting conditions. In an effort to understand C. glutamicum metabolism under biotin limitation, growth of the type strain ATCC 13032 was investigated in batch cultures and a time-course analysis was performed. A transient excretion of organic acids was observed and we focused our attention on lactate synthesis. Lactate synthesis was due to the ldh-encoded l-lactate dehydrogenase (Ldh). Features of Ldh activity and ldh transcription were analysed. The ldh gene was shown to be regulated at the transcriptional level by SugR, a pleiotropic transcriptional repressor also acting on most phosphotransferase system (PTS) genes. Electrophoretic mobility shift assays (EMSAs) and site-directed mutagenesis allowed the identification of the SugR-binding site. Effector studies using EMSAs and analysis of ldh expression in a ptsF mutant revealed fructose 1-phosphate as a highly efficient negative effector of SugR. Fructose 1,6-bisphosphate also affected SugR binding.

Reliable transfer of transcriptional gene regulatory networks between taxonomically related organisms

BACKGROUND

Transcriptional regulation of gene activity is essential for any living organism. Transcription factors therefore recognize specific binding sites within the DNA to regulate the expression of particular target genes. The genome-scale reconstruction of the emerging regulatory networks is important for biotechnology and human medicine but cost-intensive, time-consuming, and impossible to perform for any species separately. By using bioinformatics methods one can partially transfer networks from well-studied model organisms to closely related species. However, the prediction quality is limited by the low level of evolutionary conservation of the transcription factor binding sites, even within organisms of the same genus.

RESULTS

Here we present an integrated bioinformatics workflow that assures the reliability of transferred gene regulatory networks. Our approach combines three methods that can be applied on a large-scale: re-assessment of annotated binding sites, subsequent binding site prediction, and homology detection. A gene regulatory interaction is considered to be conserved if (1) the transcription factor, (2) the adjusted binding site, and (3) the target gene are conserved. The power of the approach is demonstrated by transferring gene regulations from the model organism Corynebacterium glutamicum to the human pathogens C. diphtheriae, C. jeikeium, and the biotechnologically relevant C. efficiens. For these three organisms we identified reliable transcriptional regulations for ~40% of the common transcription factors, compared to ~5% for which knowledge was available before.

CONCLUSION

Our results suggest that trustworthy genome-scale transfer of gene regulatory networks between organisms is feasible in general but still limited by the level of evolutionary conservation.

Methods to reconstruct and compare transcriptional regulatory networks

The availability of completely sequenced genomes and the wealth of literature on gene regulation have enabled researchers to model the transcriptional regulation system of some organisms in the form of a network. In order to reconstruct such networks in non-model organisms, three principal approaches have been taken. First, one can transfer the interactions between homologous components from a model organism to the organism of interest. Second, microarray experiments can be used to detect patterns in gene expression that stem from regulatory interactions. Finally, knowledge of experimentally characterized transcription factor binding sites can be used to analyze the promoter sequences in a genome in order to identify potential binding sites. In this chapter, we will focus in detail on the first approach and describe methods to reconstruct and analyze the transcriptional regulatory networks of uncharacterized organisms by using a known regulatory network as a template.

Towards the integrated analysis, visualization and reconstruction of microbial gene regulatory networks

To handle changing environmental surroundings and to manage unfavorable conditions, microbial organisms have evolved complex transcriptional regulatory networks. To comprehensively analyze these gene regulatory networks, several online available databases and analysis platforms have been implemented and established. In this article, we address the typical cycle of scientific knowledge exploration and integration in the area of procaryotic transcriptional gene regulation. We briefly review five popular, publicly available systems that support (i) the integration of existing knowledge, (ii) visualization capabilities and (iii) computer analysis to predict promising wet lab targets. We exemplify the benefits of such integrated data analysis platforms by means of four application cases exemplarily performed with the corynebacterial reference database CoryneRegNet.

The dual transcriptional regulator CysR in Corynebacterium glutamicum ATCC 13032 controls a subset of genes of the McbR regulon in response to the availability of sulphide acceptor molecules

BACKGROUND

Regulation of sulphur metabolism in Corynebacterium glutamicum ATCC 13032 has been studied intensively in the last few years, due to its industrial as well as scientific importance. Previously, the gene cg0156 was shown to belong to the regulon of McbR, a global transcriptional repressor of sulphur metabolism in C. glutamicum. This gene encodes a putative ROK-type regulator, a paralogue of the activator of sulphonate utilisation, SsuR. Therefore, it is an interesting candidate for study to further the understanding of the regulation of sulphur metabolism in C. glutamicum.

RESULTS

Deletion of cg0156, now designated cysR, results in the inability of the mutant to utilise sulphate and aliphatic sulphonates. DNA microarray hybridisations revealed 49 genes with significantly increased and 48 with decreased transcript levels in presence of the native CysR compared to a cysR deletion mutant. Among the genes positively controlled by CysR were the gene cluster involved in sulphate reduction, fpr2 cysIXHDNYZ, and ssuR. Gel retardation experiments demonstrated that binding of CysR to DNA depends in vitro on the presence of either O-acetyl-L-serine or O-acetyl-L-homoserine. Mapping of the transcription start points of five transcription units helped to identify a 10 bp inverted repeat as the possible CysR binding site. Subsequent in vivo tests proved this motif to be necessary for CysR-dependent transcriptional regulation.

CONCLUSION

CysR acts as the functional analogue of the unrelated LysR-type regulator CysB from Escherichia coli, controlling sulphide production in response to acceptor availability. In both bacteria, gene duplication events seem to have taken place which resulted in the evolution of dedicated regulators for the control of sulphonate utilisation. The striking convergent evolution of network topology indicates the strong selective pressure to control the metabolism of the essential but often toxic sulphur-containing (bio-)molecules.

Linking Cytoscape and the corynebacterial reference database CoryneRegNet

Background

Recently, the research community has seen an influx of data relating to transcriptional regulatory interactions of Corynebacteria, organisms that are highly relevant to fields of systems biology, biotechnology, and human medicine. Information derived from DNA microarray experiments, computational predictions, and literature has opened the way for the graph-based analysis, visualization, and reconstruction of transcriptional regulatory networks across entire organisms. The reference database for corynebacterial gene regulatory networks CoryneRegNet provides methods for data storage and data exchange in a well-structured manner. Additional information on the model organism Escherichia coli K12 obtained from RegulonDB has been integrated. Generally, gene regulatory networks can be visualized as graphs by drawing directed edges between nodes, where a node represents a gene and an edge corresponds to a typed regulatory interaction. Cytoscape is an open-source software project whose aim is to provide graph-based visualization and analysis for biological networks. Its architecture allows the development and integration of user-made plugins to enhance core functionalities.

Results

We introduce two novel plugins for the Cytoscape environment designed to enhance in silico studies of procaryotic transcriptional regulatory networks. Our plugins leverage the information from the cornyebacterial reference database CoryneRegNet with the graph analysis capabilities of Cytoscape. CoryneRegNetLoader queries the CoryneRegNet database to extract a gene regulatory network represented as a directed graph. Additional information is stored as node/edge attributes within the graph. COMA facilitates consistency checks for gene expression studies given a gene regulatory network. COMA tests whether all gene expression levels correlate properly with the given network topology.

Conclusion

The plugins facilitate in silico studies of procaryotic transcriptional gene regulation, particularly in Corynebacteria and E. coli, by combining the knowledge from the corynebacterial reference database with the graph analysis capabilities of Cytoscape, which is one of the most-widely used tools for biological network analyses.

CoryneCenter - an online resource for the integrated analysis of corynebacterial genome and transcriptome data

BACKGROUND

The introduction of high-throughput genome sequencing and post-genome analysis technologies, e.g. DNA microarray approaches, has created the potential to unravel and scrutinize complex gene-regulatory networks on a large scale. The discovery of transcriptional regulatory interactions has become a major topic in modern functional genomics.

RESULTS

To facilitate the analysis of gene-regulatory networks, we have developed CoryneCenter, a web-based resource for the systematic integration and analysis of genome, transcriptome, and gene regulatory information for prokaryotes, especially corynebacteria. For this purpose, we extended and combined the following systems into a common platform: (1) GenDB, an open source genome annotation system, (2) EMMA, a MAGE compliant application for high-throughput transcriptome data storage and analysis, and (3) CoryneRegNet, an ontology-based data warehouse designed to facilitate the reconstruction and analysis of gene regulatory interactions. We demonstrate the potential of CoryneCenter by means of an application example. Using microarray hybridization data, we compare the gene expression of Corynebacterium glutamicum under acetate and glucose feeding conditions: Known regulatory networks are confirmed, but moreover CoryneCenter points out additional regulatory interactions.

CONCLUSION

CoryneCenter provides more than the sum of its parts. Its novel analysis and visualization features significantly simplify the process of obtaining new biological insights into complex regulatory systems. Although the platform currently focusses on corynebacteria, the integrated tools are by no means restricted to these species, and the presented approach offers a general strategy for the analysis and verification of gene regulatory networks. CoryneCenter provides freely accessible projects with the underlying genome annotation, gene expression, and gene regulation data. The system is publicly available at http://www.CoryneCenter.de.

CoryneRegNet 4.0 - A reference database for corynebacterial gene regulatory networks

Background

Detailed information on DNA-binding transcription factors (the key players in the regulation of gene expression) and on transcriptional regulatory interactions of microorganisms deduced from literature-derived knowledge, computer predictions and global DNA microarray hybridization experiments, has opened the way for the genome-wide analysis of transcriptional regulatory networks. The large-scale reconstruction of these networks allows the in silico analysis of cell behavior in response to changing environmental conditions. We previously published CoryneRegNet, an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. Initially, it was designed to provide methods for the analysis and visualization of the gene regulatory network of Corynebacterium glutamicum.

Results

Now we introduce CoryneRegNet release 4.0, which integrates data on the gene regulatory networks of 4 corynebacteria, 2 mycobacteria and the model organism Escherichia coli K12. As the previous versions, CoryneRegNet provides a web-based user interface to access the database content, to allow various queries, and to support the reconstruction, analysis and visualization of regulatory networks at different hierarchical levels. In this article, we present the further improved database content of CoryneRegNet along with novel analysis features. The network visualization feature GraphVis now allows the inter-species comparisons of reconstructed gene regulatory networks and the projection of gene expression levels onto that networks. Therefore, we added stimulon data directly into the database, but also provide Web Service access to the DNA microarray analysis platform EMMA. Additionally, CoryneRegNet now provides a SOAP based Web Service server, which can easily be consumed by other bioinformatics software systems. Stimulons (imported from the database, or uploaded by the user) can be analyzed in the context of known transcriptional regulatory networks to predict putative contradictions or further gene regulatory interactions. Furthermore, it integrates protein clusters by means of heuristically solving the weighted graph cluster editing problem. In addition, it provides Web Service based access to up to date gene annotation data from GenDB.

Conclusion

The release 4.0 of CoryneRegNet is a comprehensive system for the integrated analysis of procaryotic gene regulatory networks. It is a versatile systems biology platform to support the efficient and large-scale analysis of transcriptional regulation of gene expression in microorganisms. It is publicly available at .

Large scale clustering of protein sequences with FORCE -A layout based heuristic for weighted cluster editing

Background

Detecting groups of functionally related proteins from their amino acid sequence alone has been a long-standing challenge in computational genome research. Several clustering approaches, following different strategies, have been published to attack this problem. Today, new sequencing technologies provide huge amounts of sequence data that has to be efficiently clustered with constant or increased accuracy, at increased speed.

Results

We advocate that the model of weighted cluster editing, also known as transitive graph projection is well-suited to protein clustering. We present the FORCE heuristic that is based on transitive graph projection and clusters arbitrary sets of objects, given pairwise similarity measures. In particular, we apply FORCE to the problem of protein clustering and show that it outperforms the most popular existing clustering tools (Spectral clustering, TribeMCL, GeneRAGE, Hierarchical clustering, and Affinity Propagation). Furthermore, we show that FORCE is able to handle huge datasets by calculating clusters for all 192 187 prokaryotic protein sequences (66 organisms) obtained from the COG database. Finally, FORCE is integrated into the corynebacterial reference database CoryneRegNet.

Conclusion

FORCE is an applicable alternative to existing clustering algorithms. Its theoretical foundation, weighted cluster editing, can outperform other clustering paradigms on protein homology clustering. FORCE is open source and implemented in Java. The software, including the source code, the clustering results for COG and CoryneRegNet, and all evaluation datasets are available at .

Insights into the biology of Escherichia coli through structural proteomics

Escherichia coli has historically been an important organism for understanding a multitude of biological processes, and represents a model system as we attempt to simulate the workings of living cells. Many E. coli strains are also important human and animal pathogens for which new therapeutic strategies are required. For both reasons, a more complete and comprehensive understanding of the protein structure complement of E. coli is needed at the genome level. Here, we provide examples of insights into the mechanism and function of bacterial proteins that we have gained through the Bacterial Structural Genomics Initiative (BSGI), focused on medium-throughput structure determination of proteins from E. coli. We describe the structural characterization of several enzymes from the histidine biosynthetic pathway, the structures of three pseudouridine synthases, enzymes that synthesize one of the most abundant modified bases in RNA, as well as the combined use of protein structure and focused functional analysis to decipher functions for hypothetical proteins. Together, these results illustrate the power of structural genomics to contribute to a deeper biological understanding of bacterial processes.