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Construction and verification of the transcriptional regulatory response network of Streptococcus mutans upon treatment with the biofilm inhibitor carolacton.

Sudhakar P, Reck M, Wang W, He FQ, Wagner-Döbler I, Dobler IW, Zeng AP - BMC Genomics (2014)

Bottom Line: To unravel key regulators mediating these effects, the transcriptional regulatory response network of S. mutans biofilms upon carolacton treatment was constructed and analyzed.These sub-networks were significantly enriched with genes sharing common functions.Deletion of cysR, the node having the highest connectivity among the regulators chosen from the regulatory network, resulted in a mutant which was insensitive to carolacton thus demonstrating not only the essentiality of cysR for the response of S. mutans biofilms to carolacton but also the relevance of the predicted network.

View Article: PubMed Central - PubMed

Affiliation: Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, 21073 Hamburg, Germany. iwd@helmholtz-hzi.de.

ABSTRACT

Background: Carolacton is a newly identified secondary metabolite causing altered cell morphology and death of Streptococcus mutans biofilm cells. To unravel key regulators mediating these effects, the transcriptional regulatory response network of S. mutans biofilms upon carolacton treatment was constructed and analyzed. A systems biological approach integrating time-resolved transcriptomic data, reverse engineering, transcription factor binding sites, and experimental validation was carried out.

Results: The co-expression response network constructed from transcriptomic data using the reverse engineering algorithm called the Trend Correlation method consisted of 8284 gene pairs. The regulatory response network inferred by superimposing transcription factor binding site information into the co-expression network comprised 329 putative transcriptional regulatory interactions and could be classified into 27 sub-networks each co-regulated by a transcription factor. These sub-networks were significantly enriched with genes sharing common functions. The regulatory response network displayed global hierarchy and network motifs as observed in model organisms. The sub-networks modulated by the pyrimidine biosynthesis regulator PyrR, the glutamine synthetase repressor GlnR, the cysteine metabolism regulator CysR, global regulators CcpA and CodY and the two component system response regulators VicR and MbrC among others could putatively be related to the physiological effect of carolacton. The predicted interactions from the regulatory network between MbrC, known to be involved in cell envelope stress response, and the murMN-SMU_718c genes encoding peptidoglycan biosynthetic enzymes were experimentally confirmed using Electro Mobility Shift Assays. Furthermore, gene deletion mutants of five predicted key regulators from the response networks were constructed and their sensitivities towards carolacton were investigated. Deletion of cysR, the node having the highest connectivity among the regulators chosen from the regulatory network, resulted in a mutant which was insensitive to carolacton thus demonstrating not only the essentiality of cysR for the response of S. mutans biofilms to carolacton but also the relevance of the predicted network.

Conclusion: The network approach used in this study revealed important regulators and interactions as part of the response mechanisms of S. mutans biofilm cells to carolacton. It also opens a door for further studies into novel drug targets against streptococci.

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Categorical enrichment within the sub-networks comprising theS. mutansbiofilms TRRN upon carolacton treatment. The co-regulated groups or sub-networks within the TRRN were found to be enriched with functional categories such as KEGG metabolic pathways, biological functional classes and gene ontology terms thus lending biological meaning to the inferred regulatory response network. Co-regulated groups denote sets of genes predicted to be commonly modulated by a transcription factor (indicated by filled pink rectangles). The regulatory response network also includes other transcriptional factors with only incoming connections (black rectangles in bold) and found to be among the co-regulated groups. Some of the co-regulated groups were found to be enriched with functional categories such as KEGG metabolic pathways (octagons), biological functional classes (circles) and gene ontology terms (triangles) whereas others (black circles filled in with letters ‘UK’) did not display any enrichment. Black edges with ellipsoid target edges represent relationships with dual regulation (some of the transcriptional regulatory relationships within the sub-network are characterized by positive (activational) expression patterns and others by inverted (repressive) expression patterns between the regulator and the target gene). The meanings of all the other arrow symbols are as described in the legend of Figure 2.
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Fig3: Categorical enrichment within the sub-networks comprising theS. mutansbiofilms TRRN upon carolacton treatment. The co-regulated groups or sub-networks within the TRRN were found to be enriched with functional categories such as KEGG metabolic pathways, biological functional classes and gene ontology terms thus lending biological meaning to the inferred regulatory response network. Co-regulated groups denote sets of genes predicted to be commonly modulated by a transcription factor (indicated by filled pink rectangles). The regulatory response network also includes other transcriptional factors with only incoming connections (black rectangles in bold) and found to be among the co-regulated groups. Some of the co-regulated groups were found to be enriched with functional categories such as KEGG metabolic pathways (octagons), biological functional classes (circles) and gene ontology terms (triangles) whereas others (black circles filled in with letters ‘UK’) did not display any enrichment. Black edges with ellipsoid target edges represent relationships with dual regulation (some of the transcriptional regulatory relationships within the sub-network are characterized by positive (activational) expression patterns and others by inverted (repressive) expression patterns between the regulator and the target gene). The meanings of all the other arrow symbols are as described in the legend of Figure 2.

Mentions: The 27 co-regulated groups or sub-networks contained statistically significant over-represented (significance score > =0) functional categories such as biological functional classes, KEGG metabolic pathways and gene ontology terms (see Additional file 7). Figure 3 visualizes the TRRN enrichment data shown in Additional file 7. Put together, 22 enrichment events involving 6 KEGG metabolic pathways, 7 biological functional classes and 6 gene ontology terms were collectively identified within the 27 sub-networks. At least 7 cases were found in which the transcription factor or regulator belonged to the same functional category as that found to be over-represented in its target gene group, confirming that the constructed regulatory network is biologically meaningful. For instance, the sub-network co-regulated by the glutamine synthetase repressor GlnR known to control glutamine metabolism [49] in S. mutans was enriched with genes belonging to the functional categories “amino acid biosynthesis” and “nitrogen metabolism”. On closer observation, most of the genes within the GlnR co-regulated sub-network were related to glutamine metabolism. Genes belonging to the biological functional class of cell envelope metabolism were over-represented in the sub-network co-regulated by the TCS RR MbrC associated with cell envelope stress response [50]. Similarly, the genes putatively modulated by the pyrimidine regulatory protein PyrR contained an over-representation of pyrimidine metabolism genes. Several instances were also observed in which the functional category to which the regulator belonged to was different from that found to be over represented within the given sub-network (Figure 3, Additional file 7). Besides the above described intra- and inter categorical relationships between regulators and their sub-networks, the TRRN also contained regulatory relationships which were already verified by others (see Additional file 5).Figure 3


Construction and verification of the transcriptional regulatory response network of Streptococcus mutans upon treatment with the biofilm inhibitor carolacton.

Sudhakar P, Reck M, Wang W, He FQ, Wagner-Döbler I, Dobler IW, Zeng AP - BMC Genomics (2014)

Categorical enrichment within the sub-networks comprising theS. mutansbiofilms TRRN upon carolacton treatment. The co-regulated groups or sub-networks within the TRRN were found to be enriched with functional categories such as KEGG metabolic pathways, biological functional classes and gene ontology terms thus lending biological meaning to the inferred regulatory response network. Co-regulated groups denote sets of genes predicted to be commonly modulated by a transcription factor (indicated by filled pink rectangles). The regulatory response network also includes other transcriptional factors with only incoming connections (black rectangles in bold) and found to be among the co-regulated groups. Some of the co-regulated groups were found to be enriched with functional categories such as KEGG metabolic pathways (octagons), biological functional classes (circles) and gene ontology terms (triangles) whereas others (black circles filled in with letters ‘UK’) did not display any enrichment. Black edges with ellipsoid target edges represent relationships with dual regulation (some of the transcriptional regulatory relationships within the sub-network are characterized by positive (activational) expression patterns and others by inverted (repressive) expression patterns between the regulator and the target gene). The meanings of all the other arrow symbols are as described in the legend of Figure 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4048456&req=5

Fig3: Categorical enrichment within the sub-networks comprising theS. mutansbiofilms TRRN upon carolacton treatment. The co-regulated groups or sub-networks within the TRRN were found to be enriched with functional categories such as KEGG metabolic pathways, biological functional classes and gene ontology terms thus lending biological meaning to the inferred regulatory response network. Co-regulated groups denote sets of genes predicted to be commonly modulated by a transcription factor (indicated by filled pink rectangles). The regulatory response network also includes other transcriptional factors with only incoming connections (black rectangles in bold) and found to be among the co-regulated groups. Some of the co-regulated groups were found to be enriched with functional categories such as KEGG metabolic pathways (octagons), biological functional classes (circles) and gene ontology terms (triangles) whereas others (black circles filled in with letters ‘UK’) did not display any enrichment. Black edges with ellipsoid target edges represent relationships with dual regulation (some of the transcriptional regulatory relationships within the sub-network are characterized by positive (activational) expression patterns and others by inverted (repressive) expression patterns between the regulator and the target gene). The meanings of all the other arrow symbols are as described in the legend of Figure 2.
Mentions: The 27 co-regulated groups or sub-networks contained statistically significant over-represented (significance score > =0) functional categories such as biological functional classes, KEGG metabolic pathways and gene ontology terms (see Additional file 7). Figure 3 visualizes the TRRN enrichment data shown in Additional file 7. Put together, 22 enrichment events involving 6 KEGG metabolic pathways, 7 biological functional classes and 6 gene ontology terms were collectively identified within the 27 sub-networks. At least 7 cases were found in which the transcription factor or regulator belonged to the same functional category as that found to be over-represented in its target gene group, confirming that the constructed regulatory network is biologically meaningful. For instance, the sub-network co-regulated by the glutamine synthetase repressor GlnR known to control glutamine metabolism [49] in S. mutans was enriched with genes belonging to the functional categories “amino acid biosynthesis” and “nitrogen metabolism”. On closer observation, most of the genes within the GlnR co-regulated sub-network were related to glutamine metabolism. Genes belonging to the biological functional class of cell envelope metabolism were over-represented in the sub-network co-regulated by the TCS RR MbrC associated with cell envelope stress response [50]. Similarly, the genes putatively modulated by the pyrimidine regulatory protein PyrR contained an over-representation of pyrimidine metabolism genes. Several instances were also observed in which the functional category to which the regulator belonged to was different from that found to be over represented within the given sub-network (Figure 3, Additional file 7). Besides the above described intra- and inter categorical relationships between regulators and their sub-networks, the TRRN also contained regulatory relationships which were already verified by others (see Additional file 5).Figure 3

Bottom Line: To unravel key regulators mediating these effects, the transcriptional regulatory response network of S. mutans biofilms upon carolacton treatment was constructed and analyzed.These sub-networks were significantly enriched with genes sharing common functions.Deletion of cysR, the node having the highest connectivity among the regulators chosen from the regulatory network, resulted in a mutant which was insensitive to carolacton thus demonstrating not only the essentiality of cysR for the response of S. mutans biofilms to carolacton but also the relevance of the predicted network.

View Article: PubMed Central - PubMed

Affiliation: Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, 21073 Hamburg, Germany. iwd@helmholtz-hzi.de.

ABSTRACT

Background: Carolacton is a newly identified secondary metabolite causing altered cell morphology and death of Streptococcus mutans biofilm cells. To unravel key regulators mediating these effects, the transcriptional regulatory response network of S. mutans biofilms upon carolacton treatment was constructed and analyzed. A systems biological approach integrating time-resolved transcriptomic data, reverse engineering, transcription factor binding sites, and experimental validation was carried out.

Results: The co-expression response network constructed from transcriptomic data using the reverse engineering algorithm called the Trend Correlation method consisted of 8284 gene pairs. The regulatory response network inferred by superimposing transcription factor binding site information into the co-expression network comprised 329 putative transcriptional regulatory interactions and could be classified into 27 sub-networks each co-regulated by a transcription factor. These sub-networks were significantly enriched with genes sharing common functions. The regulatory response network displayed global hierarchy and network motifs as observed in model organisms. The sub-networks modulated by the pyrimidine biosynthesis regulator PyrR, the glutamine synthetase repressor GlnR, the cysteine metabolism regulator CysR, global regulators CcpA and CodY and the two component system response regulators VicR and MbrC among others could putatively be related to the physiological effect of carolacton. The predicted interactions from the regulatory network between MbrC, known to be involved in cell envelope stress response, and the murMN-SMU_718c genes encoding peptidoglycan biosynthetic enzymes were experimentally confirmed using Electro Mobility Shift Assays. Furthermore, gene deletion mutants of five predicted key regulators from the response networks were constructed and their sensitivities towards carolacton were investigated. Deletion of cysR, the node having the highest connectivity among the regulators chosen from the regulatory network, resulted in a mutant which was insensitive to carolacton thus demonstrating not only the essentiality of cysR for the response of S. mutans biofilms to carolacton but also the relevance of the predicted network.

Conclusion: The network approach used in this study revealed important regulators and interactions as part of the response mechanisms of S. mutans biofilm cells to carolacton. It also opens a door for further studies into novel drug targets against streptococci.

Show MeSH
Related in: MedlinePlus