- Open Access
CoryneRegNet: An ontology-based data warehouse of corynebacterial transcription factors and regulatory networks
© Baumbach et al; licensee BioMed Central Ltd. 2006
- Received: 11 November 2005
- Accepted: 14 February 2006
- Published: 14 February 2006
The application of DNA microarray technology in post-genomic analysis of bacterial genome sequences has allowed the generation of huge amounts of data related to regulatory networks. This data along with literature-derived knowledge on regulation of gene expression has opened the way for genome-wide reconstruction of transcriptional regulatory networks. These large-scale reconstructions can be converted into in silico models of bacterial cells that allow a systematic analysis of network behavior in response to changing environmental conditions.
CoryneRegNet was designed to facilitate the genome-wide reconstruction of transcriptional regulatory networks of corynebacteria relevant in biotechnology and human medicine. During the import and integration process of data derived from experimental studies or literature knowledge CoryneRegNet generates links to genome annotations, to identified transcription factors and to the corresponding cis-regulatory elements. CoryneRegNet is based on a multi-layered, hierarchical and modular concept of transcriptional regulation and was implemented by using the relational database management system MySQL and an ontology-based data structure. Reconstructed regulatory networks can be visualized by using the yFiles JAVA graph library. As an application example of CoryneRegNet, we have reconstructed the global transcriptional regulation of a cellular module involved in SOS and stress response of corynebacteria.
CoryneRegNet is an ontology-based data warehouse that allows a pertinent data management of regulatory interactions along with the genome-scale reconstruction of transcriptional regulatory networks. These models can further be combined with metabolic networks to build integrated models of cellular function including both metabolism and its transcriptional regulation.
- Regulatory Network
- Sigma Factor
- Transcriptional Regulatory Network
- Transcription Factor Module
- Transcriptional Regulatory Interaction
Microorganisms continuously have to handle changing environmental conditions to maintain their functional homeostasis and to overcome stress situations with detrimental consequences for growth and survival . Therefore, they evolved mechanisms to sense alterations within their environmental surroundings and developed molecular strategies co-ordinated by complex transcriptional regulatory networks to manage unfavourable conditions. The complexity of such regulatory networks results from the interaction of numerous transcription units consisting of a transcription factor and a defined set of regulated target genes . The most important components of these units are apparently the DNA-binding transcription factors. They are responsible for sensing environmental and intracellular signals to control cellular reproduction and growth , and they include a DNA-binding domain that possesses a secondary structure to recognize the operator sequences of regulated genes . Depending on the growth conditions of a bacterial cell certain fractions of the total set of transcription factors are operating . Some of them only control the expression of a single gene whereas others organize the activation or repression of numerous target genes .
The availability of whole genome sequences provides the opportunity to define the total set of DNA-binding transcription factors of an organism [6, 7]. This is a first step not only in understanding the regulatory complexity of a certain bacterial cell but also for reconstructing the global connectivity of a regulatory network to theoretically describe and deduce gene expression pattern of a microorganism . From a set of complete genome sequences it has been deduced that large genomes include more transcription factors per gene than small genomes . The increase of genomic complexity is thus associated with a more complex regulation of gene expression since the additional genetic information has to be integrated into the existing regulatory network basically operating in a bacterial cell. The transcriptional regulatory network of Escherichia coli so far is one of the best characterized regulatory systems of a single cell. The total number of about 320 transcriptional regulators of E. coli K-12 were classified into eight distinct regulatory modules with defined physiological functions . Additional bioinformatics studies suggested a hierarchical and modular structure of the regulatory network, excluding circular feedback loops on transcriptional level for this organism .
The genus Corynebacterium comprises a number of human pathogens, like Corynebacterium diphtheriae and Corynebacterium jeikeium, as well as the non-pathogenic soil bacteria Corynebacterium glutamicum and Corynebacterium efficiens that are widely used in biotechnological production processes of food and feed additives [11, 12]. Because of their relevance in biotechnology and medicine the genome sequences of C. glutamicum ATCC 13032, C. efficiens YS-314, C. diphtheriae NCTC 13129, and C. jeikeium K411 have recently been determined [13–16]. First comparative analysis revealed a high-level conservation of orthologous genes in these genome sequences, indicating that the corynebacterial species have rarely undergone genome rearrangements and thus largely retained their ancestral genome structure . An initial step in understanding the transcriptional regulatory machinery of corynebacteria was the bioinformatics identification of the encoded transcription factors . A collection of 127 DNA-binding transcription factors was detected in the genome sequence of C. glutamicum, whereas 103 regulators were identified in C. efficiens, 63 in C. diphtheriae and 55 in C. jeikeium. The relation between these numbers agrees well with the assumption that the quantity of transcription factors of an organism is correlated to the genome size and the environmental surrounding a bacterial cell is exposed to . Accordingly, the physiological versatility of C. glutamicum results in a considerably higher number of transcriptional regulators, and in consequence in a more complex regulatory network by integrating and co-ordinating additional regulatory subnetworks. According to amino acid comparisons and protein structure predictions the repertoire of DNA-binding transcription factors of C. glutamicum, C. efficiens, C. diphtheriae, and C. jeikeium were further on divided into 25 families of regulatory proteins. A common set of only 28 regulators was encoded by all of the four genome sequences and thus presumably includes the core set of DNA-binding transcription factors of these bacteria . Despite the progress in bioinformatics prediction of transcription factors, the reconstruction of regulatory networks is generally hindered by the relatively low level of evolutionary conservation of other molecular network components, for instance of the cognate operator sequence of a DNA-binding transcription factor. However, developments in DNA microarray technology have allowed the generation of genome-wide data sets characterizing experimentally the regulatory networks of corynebacteria [18–20].
The ambition of our current post-genomic approaches is to decipher and reconstruct the transcriptional regulatory network of C. glutamicum. Here, we propose a hierarchical and modular scheme of the regulatory network, separating the repertoire of DNA-binding transcription factors into five well-defined and functionally distinct modules with respect to the physiological role of the regulated target genes. This biological concept was applied to design and implement the ontology-based data warehouse CoryneRegNet that provides a solid basis for further regulatory network studies in the field of systems biology. As an application example of CoryneRegNet we reconstructed and visualized the functional module "SOS and stress response" of C. glutamicum, revealing a multi-layered, hierarchical and modular structure of the respective transcriptional regulatory interactions.
The biological concept of CoryneRegNet
The database concept of CoryneRegNet
For the reconstruction of corynebacterial regulatory networks, the complete genome sequence of C. glutamicum along with the genome annotation  was downloaded from NCBI  in GenBank format and imported into CoryneRegNet. Subsequently, the gene identifiers were mapped to a second C. glutamicum genome sequence and annotation  to enable scientists working with either of the two annotations the efficient usage of CoryneRegNet. Furthermore, biological data relevant to transcriptional regulations were imported into the database as derived from literature knowledge (included in the database as PubMed link) , computer predictions  or experimental studies [19, 20] (Figure 3B). The data import process was realized by running a parser that was implemented in Java. The parser software additionally integrates the imported data into a single ontology-based data structure and converts it into a relational data model (Figure 2). The output are tab-delimited flat-files that in turn are input files for the MySQL built-in import procedure and finally used to fill the CoryneRegNet database (Figure 3B).
The user interface of CoryneRegNet
Graphical visualization of regulatory interactions
Reconstruction of the SOS and stress response module
We used CoryneRegNet to reconstruct and visualize the transcriptional regulatory network of the SOS and stress response module of C. glutamicum (Figure 6). The module currently includes six DNA-binding transcription factors and 42 regulated genes. Since sigma factors play a key role in regulating gene expression when the cell is exposed to stress conditions and switches in part to the program "maintenance and survival" [21, 36], the regulatory network is apparently linked to components of the sigma factor competition module. Thus, the reconstructed network reveals a hierarchical scheme also including the top level regulator ppGpp, synthesized by the Rel protein and influencing expression of the sigma factors SigH and SigB [21, 22]. The reconstructed network allowed us to characterize the transcription factor module "SOS and stress response" in more detail: Several genes are under dual control by a DNA-binding transcription factor and by the alternative sigma factor SigH, whereas the groEL2 gene is co-regulated by two transcription factors. The network is additionally characterized by a number of autoregulatory loops (Figure 6) in which the transcription factor regulates its own expression. Regarding regulatory network motifs, the presence of feed-forward loops is apparent when considering the regulatory action on gene expression of both a transcriptional regulator (HspR or ClgR) and an alternative sigma factor (SigH). This is consistent with observations in E. coli that feed-forward loop motifs tend to be implemented within modules, whereas bi-fan motifs seem to be responsible for the connection between different physiological modules . Two types of feed-forward loops are present in the reconstructed network of the SOS and stress response module, namely the coherent type 1 and the incoherent type 1 motif . In a coherent type 1 feed-forward loop all the regulatory connetions are activating (SigH, ClgR, ClpP1-ClpP2), while in the incoherent type 1 motif one of the regulatory links represses the activity of the target node (SigH, HspR, DnaK). It is also apparent that the reconstructed regulatory network is composed of two distinct submodules reflecting different responses of the cell upon exposure to environmental stresses (Figure 6). The SOS response is induced by DNA damage and under control of the LexA protein, while the heat-shock and oxidative stress response is induced by denaturation and/or inactivation of proteins and is under SigH control . Accordingly, the reconstruction and visualization of the SOS and stress response module of C. glutamicum by CoryneRegNet reflects the hierarchical and modular scheme of the cell's transcriptional regulatory system.
With the recent progress made in large-scale postgenomic analysis of complete genome sequences a vast amount of novel data is becoming available. Comprehensive evaluation of postgenomic data asks for user-oriented databases supporting data management and data integration into existing knowledge. The CoryneRegNet database discloses detailed information on DNA-binding transcription factors, the key players in regulation of gene expression, and on transcriptional regulatory interactions of C. glutamicum deduced from literature-derived knowledge, computer predictions and global DNA microarray hybridization experiments. A web-based user interface provides access to the database content, allows various queries and supports the reconstruction and visualization of regulatory networks at different hierarchical levels. CoryneRegNet is moreover linked to the NCBI Entrez Gene database to provide direct access to corresponding genomic data. Although CoryneRegNet was developed as a data warehouse of transcriptional regulatory networks of C. glutamicum, its ontology-based design along with its programs and scripts is generally applicable to implement other species-specific databases. Consequently, CoryneRegNet is a versatile systems biology tool to support the large-scale analysis of transcriptional regulation of gene expression in microorganisms. The ultimate purpose of CoryneRegNet is to assist in reconstruction of transcriptional regulatory networks and to provide models that can be combined with metabolic networks of the cell to build integrated models including both cellular metabolism and transcriptional regulation. Since comparative computer analyses exploiting transcriptional regulatory data might be helpful to uncover hidden information on regulation of gene expression, transcriptional data of other sequenced corynebacterial species will be integrated into the next release of CoryneRegNet. For the future, we further plan to integrate existing and currently developing bioinformatics tools to perform for instance genome-wide searches for regulatory motifs specified by position weight matrices with sound statistical analysis  or to discover new potential motifs based on transcriptional profile analysis and comparative sequence analysis over several related genomes. We would also like to integrate algorithms and visualization techniques for comparing regulatory networks in several species. All of the above areas are active research fields with several new ideas being presented at every bioinformatics conference; therefore we are planning a flexible external tool plug-in concept for CoryneRegNet. Our long-term vision consists of CoryneRegNet proposing new regulatory hypotheses for wet-lab verification. While we expect that it will take some time for this vision to become reality, already now CoryneRegNet is a free open-source central repository and analysis tool for regulatory networks of microorganisms that is easy to extend because of its ontology-based design.
The CoryneRegNet database is freely accessible through the website https://www.cebitec.uni-bielefeld.de/groups/gi/software/coryneregnet/. Application of the yFiles JAVA graph library is restricted to academic users. Programs, scripts and information for setting up a species-specific database can be obtained from the authors upon request.
The authors thank Iris Brune, Olaf Brockmann-Gretza and Alfred Pühler for helpful discussions and Alexander Goesmann, Ralf Nolte and Peter Serocka for expert technical support.
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