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From Corynebacterium glutamicum to Mycobacterium tuberculosis--towards transfers of gene regulatory networks and integrated data analyses with MycoRegNet.

Krawczyk J, Kohl TA, Goesmann A, Kalinowski J, Baumbach J - Nucleic Acids Res. (2009)

Bottom Line: We designed a bioinformatics pipeline for the reliable transfer of gene regulations between taxonomically closely related organisms that incorporates (i) a prediction of orthologous genes and (ii) the prediction of transcription factor binding sites.Based on that, we designed a publicly available platform that aims to data integration, analysis, visualization and finally the reconstruction of mycobacterial transcriptional gene regulatory networks: MycoRegNet.It is a comprehensive database system and analysis platform that offers several methods for data exploration and the generation of novel hypotheses.

View Article: PubMed Central - PubMed

Affiliation: Computational Genomics, Center for Biotechnology, Bielefeld University, Bielefeld, Germany and International Computer Science Institute, Berkeley, CA, USA.

ABSTRACT
Year by year, approximately two million people die from tuberculosis, a disease caused by the bacterium Mycobacterium tuberculosis. There is a tremendous need for new anti-tuberculosis therapies (antituberculotica) and drugs to cope with the spread of tuberculosis. Despite many efforts to obtain a better understanding of M. tuberculosis' pathogenicity and its survival strategy in humans, many questions are still unresolved. Among other cellular processes in bacteria, pathogenicity is controlled by transcriptional regulation. Thus, various studies on M. tuberculosis concentrate on the analysis of transcriptional regulation in order to gain new insights on pathogenicity and other essential processes ensuring mycobacterial survival. We designed a bioinformatics pipeline for the reliable transfer of gene regulations between taxonomically closely related organisms that incorporates (i) a prediction of orthologous genes and (ii) the prediction of transcription factor binding sites. In total, 460 regulatory interactions were identified for M. tuberculosis using our comparative approach. Based on that, we designed a publicly available platform that aims to data integration, analysis, visualization and finally the reconstruction of mycobacterial transcriptional gene regulatory networks: MycoRegNet. It is a comprehensive database system and analysis platform that offers several methods for data exploration and the generation of novel hypotheses. MycoRegNet is publicly available at http://mycoregnet.cebitec.uni-bielefeld.de.

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Reconstructed network of the GlxR ortholog CrpMT. The network reconstruction of the CrpMT regulon is based on the 121 transcription units presented in Table 3. It was generated by the integrated network reconstruction tool GraphVis of MycoRegNet. Transcription units relying on binding site predictions/experimental verifications that were reported previously in (22–24,60) and correspond with our findings are colored according to the appropriate publication. Arrows and gene IDs (node labels) coloured in red indicate a repressive regulation of CrpMT, green arrows correspond to an activating regulation.
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Figure 4: Reconstructed network of the GlxR ortholog CrpMT. The network reconstruction of the CrpMT regulon is based on the 121 transcription units presented in Table 3. It was generated by the integrated network reconstruction tool GraphVis of MycoRegNet. Transcription units relying on binding site predictions/experimental verifications that were reported previously in (22–24,60) and correspond with our findings are colored according to the appropriate publication. Arrows and gene IDs (node labels) coloured in red indicate a repressive regulation of CrpMT, green arrows correspond to an activating regulation.

Mentions: In total, we identified 207 putative target genes of CrpMT, organized in 121 transcription units (see Table 3 and Figure 4). Of this set, 17 genes belong to the mycobacterial core regulon (62) and 41 were reported as essential for M. tuberculosis (63,64). Furthermore, at least 17 genes of the suggested regulon are connected to antibiotic resistance and virulence of M. tuberculosis (65–69). Based on annotation information for M. tuberculosis (69), knowledge about orthologous C. glutamicum genes and operon structures, we attributed individual target genes to distinct functional modules.Table 3.


From Corynebacterium glutamicum to Mycobacterium tuberculosis--towards transfers of gene regulatory networks and integrated data analyses with MycoRegNet.

Krawczyk J, Kohl TA, Goesmann A, Kalinowski J, Baumbach J - Nucleic Acids Res. (2009)

Reconstructed network of the GlxR ortholog CrpMT. The network reconstruction of the CrpMT regulon is based on the 121 transcription units presented in Table 3. It was generated by the integrated network reconstruction tool GraphVis of MycoRegNet. Transcription units relying on binding site predictions/experimental verifications that were reported previously in (22–24,60) and correspond with our findings are colored according to the appropriate publication. Arrows and gene IDs (node labels) coloured in red indicate a repressive regulation of CrpMT, green arrows correspond to an activating regulation.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2724278&req=5

Figure 4: Reconstructed network of the GlxR ortholog CrpMT. The network reconstruction of the CrpMT regulon is based on the 121 transcription units presented in Table 3. It was generated by the integrated network reconstruction tool GraphVis of MycoRegNet. Transcription units relying on binding site predictions/experimental verifications that were reported previously in (22–24,60) and correspond with our findings are colored according to the appropriate publication. Arrows and gene IDs (node labels) coloured in red indicate a repressive regulation of CrpMT, green arrows correspond to an activating regulation.
Mentions: In total, we identified 207 putative target genes of CrpMT, organized in 121 transcription units (see Table 3 and Figure 4). Of this set, 17 genes belong to the mycobacterial core regulon (62) and 41 were reported as essential for M. tuberculosis (63,64). Furthermore, at least 17 genes of the suggested regulon are connected to antibiotic resistance and virulence of M. tuberculosis (65–69). Based on annotation information for M. tuberculosis (69), knowledge about orthologous C. glutamicum genes and operon structures, we attributed individual target genes to distinct functional modules.Table 3.

Bottom Line: We designed a bioinformatics pipeline for the reliable transfer of gene regulations between taxonomically closely related organisms that incorporates (i) a prediction of orthologous genes and (ii) the prediction of transcription factor binding sites.Based on that, we designed a publicly available platform that aims to data integration, analysis, visualization and finally the reconstruction of mycobacterial transcriptional gene regulatory networks: MycoRegNet.It is a comprehensive database system and analysis platform that offers several methods for data exploration and the generation of novel hypotheses.

View Article: PubMed Central - PubMed

Affiliation: Computational Genomics, Center for Biotechnology, Bielefeld University, Bielefeld, Germany and International Computer Science Institute, Berkeley, CA, USA.

ABSTRACT
Year by year, approximately two million people die from tuberculosis, a disease caused by the bacterium Mycobacterium tuberculosis. There is a tremendous need for new anti-tuberculosis therapies (antituberculotica) and drugs to cope with the spread of tuberculosis. Despite many efforts to obtain a better understanding of M. tuberculosis' pathogenicity and its survival strategy in humans, many questions are still unresolved. Among other cellular processes in bacteria, pathogenicity is controlled by transcriptional regulation. Thus, various studies on M. tuberculosis concentrate on the analysis of transcriptional regulation in order to gain new insights on pathogenicity and other essential processes ensuring mycobacterial survival. We designed a bioinformatics pipeline for the reliable transfer of gene regulations between taxonomically closely related organisms that incorporates (i) a prediction of orthologous genes and (ii) the prediction of transcription factor binding sites. In total, 460 regulatory interactions were identified for M. tuberculosis using our comparative approach. Based on that, we designed a publicly available platform that aims to data integration, analysis, visualization and finally the reconstruction of mycobacterial transcriptional gene regulatory networks: MycoRegNet. It is a comprehensive database system and analysis platform that offers several methods for data exploration and the generation of novel hypotheses. MycoRegNet is publicly available at http://mycoregnet.cebitec.uni-bielefeld.de.

Show MeSH
Related in: MedlinePlus