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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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Related in: MedlinePlus

Workflow to capture the network level effects of the biofilm inhibitor carolacton onS. mutansbiofilms. The directions of the arrow marks denote the flow of data processing and sequential steps. Shapes of boxes have no particular significance while the descriptions within the boxes represent the steps corresponding to data generation, algorithms, data processing, network and experimental analyses. *indicates the reference [32].
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Fig1: Workflow to capture the network level effects of the biofilm inhibitor carolacton onS. mutansbiofilms. The directions of the arrow marks denote the flow of data processing and sequential steps. Shapes of boxes have no particular significance while the descriptions within the boxes represent the steps corresponding to data generation, algorithms, data processing, network and experimental analyses. *indicates the reference [32].

Mentions: In order to identify co-expression relationships among genes immediately affected by carolacton, the co-expression network was confined to statistically significant optimal correlations which started either from 0, 5 or 20 min after carolacton treatment (see Methods). The contextual co-expression network inferred according to the workflow shown in Figure 1 consisted of 8284 gene-gene co-expression relationships (see Additional file 2). 5430 (65.5%) of the 8284 edges were characterized by time lagged co-expression relationships. 3959 (47.7%) of the total number of co-expression relationships could be described as being inverted (opposite change trend in expression patterns) whereas the remaining were described as being positive (showing similar change trends).Figure 1


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)

Workflow to capture the network level effects of the biofilm inhibitor carolacton onS. mutansbiofilms. The directions of the arrow marks denote the flow of data processing and sequential steps. Shapes of boxes have no particular significance while the descriptions within the boxes represent the steps corresponding to data generation, algorithms, data processing, network and experimental analyses. *indicates the reference [32].
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig1: Workflow to capture the network level effects of the biofilm inhibitor carolacton onS. mutansbiofilms. The directions of the arrow marks denote the flow of data processing and sequential steps. Shapes of boxes have no particular significance while the descriptions within the boxes represent the steps corresponding to data generation, algorithms, data processing, network and experimental analyses. *indicates the reference [32].
Mentions: In order to identify co-expression relationships among genes immediately affected by carolacton, the co-expression network was confined to statistically significant optimal correlations which started either from 0, 5 or 20 min after carolacton treatment (see Methods). The contextual co-expression network inferred according to the workflow shown in Figure 1 consisted of 8284 gene-gene co-expression relationships (see Additional file 2). 5430 (65.5%) of the 8284 edges were characterized by time lagged co-expression relationships. 3959 (47.7%) of the total number of co-expression relationships could be described as being inverted (opposite change trend in expression patterns) whereas the remaining were described as being positive (showing similar change trends).Figure 1

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