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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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Sequence logo of the predicted CrpMT binding sites (A) in comparison to the sequence logo of GlxR (B). The sequence logo models the binding site motif of CrpMT. It was deduced from the predicted binding sites in Table 3. The height of each letter within an individual stack represents the nucleotide's frequency relative to the particular motif position; thus, the degree of a nucleotide's conservation is indicated by the stack according to the respective position.
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Figure 3: Sequence logo of the predicted CrpMT binding sites (A) in comparison to the sequence logo of GlxR (B). The sequence logo models the binding site motif of CrpMT. It was deduced from the predicted binding sites in Table 3. The height of each letter within an individual stack represents the nucleotide's frequency relative to the particular motif position; thus, the degree of a nucleotide's conservation is indicated by the stack according to the respective position.

Mentions: In contrast to the TFBS searched within our pipeline, we created an adopted and optimized PWM for CrpMT from experimentally verified and predicted binding sites, and applied it for TFBS search. To detect the novel binding sites, we set the P-value threshold to 10−5 and performed a restrictive search on sequences upstream genes/operons concering the whole genome of MT. Again, we used PoSSuMsearch for binding site prediction scanning 580-bp long upstream sequence, ranging from +20 bp relative to the transcription start site. Using Weblogo (61), we generated a sequence logo from the detected binding sites of CrpMT and from the appropriate binding sites of GlxR that were used for PWM creation (see Methods section). The resulting sequence logos are shown in Figure 3.Figure 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)

Sequence logo of the predicted CrpMT binding sites (A) in comparison to the sequence logo of GlxR (B). The sequence logo models the binding site motif of CrpMT. It was deduced from the predicted binding sites in Table 3. The height of each letter within an individual stack represents the nucleotide's frequency relative to the particular motif position; thus, the degree of a nucleotide's conservation is indicated by the stack according to the respective position.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 3: Sequence logo of the predicted CrpMT binding sites (A) in comparison to the sequence logo of GlxR (B). The sequence logo models the binding site motif of CrpMT. It was deduced from the predicted binding sites in Table 3. The height of each letter within an individual stack represents the nucleotide's frequency relative to the particular motif position; thus, the degree of a nucleotide's conservation is indicated by the stack according to the respective position.
Mentions: In contrast to the TFBS searched within our pipeline, we created an adopted and optimized PWM for CrpMT from experimentally verified and predicted binding sites, and applied it for TFBS search. To detect the novel binding sites, we set the P-value threshold to 10−5 and performed a restrictive search on sequences upstream genes/operons concering the whole genome of MT. Again, we used PoSSuMsearch for binding site prediction scanning 580-bp long upstream sequence, ranging from +20 bp relative to the transcription start site. Using Weblogo (61), we generated a sequence logo from the detected binding sites of CrpMT and from the appropriate binding sites of GlxR that were used for PWM creation (see Methods section). The resulting sequence logos are shown in Figure 3.Figure 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