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Reconstruction of the temporal signaling network in Salmonella-infected human cells.

Budak G, Eren Ozsoy O, Aydin Son Y, Can T, Tuncbag N - Front Microbiol (2015)

Bottom Line: The resulting network conserves the information on temporality, direction of interactions, while revealing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways.Targets of the Salmonella effectors in the host cells such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were included in the reconstructed signaling network although they were not present in the initial phosphoproteomic data.We believe that integrated approaches, such as the one presented here, have a high potential for the identification of clinical targets in infectious diseases, especially in the Salmonella infections.

View Article: PubMed Central - PubMed

Affiliation: Department of Health Informatics, Graduate School of Informatics, Middle East Technical University Ankara, Turkey.

ABSTRACT
Salmonella enterica is a bacterial pathogen that usually infects its host through food sources. Translocation of the pathogen proteins into the host cells leads to changes in the signaling mechanism either by activating or inhibiting the host proteins. Given that the bacterial infection modifies the response network of the host, a more coherent view of the underlying biological processes and the signaling networks can be obtained by using a network modeling approach based on the reverse engineering principles. In this work, we have used a published temporal phosphoproteomic dataset of Salmonella-infected human cells and reconstructed the temporal signaling network of the human host by integrating the interactome and the phosphoproteomic dataset. We have combined two well-established network modeling frameworks, the Prize-collecting Steiner Forest (PCSF) approach and the Integer Linear Programming (ILP) based edge inference approach. The resulting network conserves the information on temporality, direction of interactions, while revealing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways. Targets of the Salmonella effectors in the host cells such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were included in the reconstructed signaling network although they were not present in the initial phosphoproteomic data. We believe that integrated approaches, such as the one presented here, have a high potential for the identification of clinical targets in infectious diseases, especially in the Salmonella infections.

No MeSH data available.


Related in: MedlinePlus

The flowchart of complete analysis. The dataset which includes temporal fold changes of phosphopeptides at four different time points (t1 = 2 min, t2 = 5 min, t3 = 10 min, t4 = 20 min) and at three different locations (nucleus, cytoplasm, and membrane) was split and converted into temporal fold changes datasets of the corresponding phosphoproteins by taking the maximum fold change among phosphopeptides that were observed at different locations and mapped to the same phosphoprotein. Next, we applied PCSF approach for each fold changes dataset by integrating human interactome in order to discover hidden intermediate proteins. The resulting networks (F1, F2, F3, F4) are then used to form a binary matrix where the rows are time points and columns are phosphoproteins. Each corresponding cell of the binary matrix represents a significant change (p < 0.05 and variance <15%) in the phosphoprotein at the time point. Finally, we applied an ILP-based edge inference approach by integrating human interactome in order to validate and determine edges and edge directions.
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Figure 1: The flowchart of complete analysis. The dataset which includes temporal fold changes of phosphopeptides at four different time points (t1 = 2 min, t2 = 5 min, t3 = 10 min, t4 = 20 min) and at three different locations (nucleus, cytoplasm, and membrane) was split and converted into temporal fold changes datasets of the corresponding phosphoproteins by taking the maximum fold change among phosphopeptides that were observed at different locations and mapped to the same phosphoprotein. Next, we applied PCSF approach for each fold changes dataset by integrating human interactome in order to discover hidden intermediate proteins. The resulting networks (F1, F2, F3, F4) are then used to form a binary matrix where the rows are time points and columns are phosphoproteins. Each corresponding cell of the binary matrix represents a significant change (p < 0.05 and variance <15%) in the phosphoprotein at the time point. Finally, we applied an ILP-based edge inference approach by integrating human interactome in order to validate and determine edges and edge directions.

Mentions: The network modeling procedure is composed of two stages; (i) network construction using the Prize-collecting Steiner Forest (PCSF) approach, and (ii) network reconstruction using the ILP based edge inference approach. These two approaches complement each other as the PCSF approach reveals the hidden components in signaling by finding the high confidence regions in the interactome, and the ILP-based edge inference approach reconstructs interactions and their directionality by using temporal data as constraints. In Figure 1, the flowchart of our integrated approach is given.


Reconstruction of the temporal signaling network in Salmonella-infected human cells.

Budak G, Eren Ozsoy O, Aydin Son Y, Can T, Tuncbag N - Front Microbiol (2015)

The flowchart of complete analysis. The dataset which includes temporal fold changes of phosphopeptides at four different time points (t1 = 2 min, t2 = 5 min, t3 = 10 min, t4 = 20 min) and at three different locations (nucleus, cytoplasm, and membrane) was split and converted into temporal fold changes datasets of the corresponding phosphoproteins by taking the maximum fold change among phosphopeptides that were observed at different locations and mapped to the same phosphoprotein. Next, we applied PCSF approach for each fold changes dataset by integrating human interactome in order to discover hidden intermediate proteins. The resulting networks (F1, F2, F3, F4) are then used to form a binary matrix where the rows are time points and columns are phosphoproteins. Each corresponding cell of the binary matrix represents a significant change (p < 0.05 and variance <15%) in the phosphoprotein at the time point. Finally, we applied an ILP-based edge inference approach by integrating human interactome in order to validate and determine edges and edge directions.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507143&req=5

Figure 1: The flowchart of complete analysis. The dataset which includes temporal fold changes of phosphopeptides at four different time points (t1 = 2 min, t2 = 5 min, t3 = 10 min, t4 = 20 min) and at three different locations (nucleus, cytoplasm, and membrane) was split and converted into temporal fold changes datasets of the corresponding phosphoproteins by taking the maximum fold change among phosphopeptides that were observed at different locations and mapped to the same phosphoprotein. Next, we applied PCSF approach for each fold changes dataset by integrating human interactome in order to discover hidden intermediate proteins. The resulting networks (F1, F2, F3, F4) are then used to form a binary matrix where the rows are time points and columns are phosphoproteins. Each corresponding cell of the binary matrix represents a significant change (p < 0.05 and variance <15%) in the phosphoprotein at the time point. Finally, we applied an ILP-based edge inference approach by integrating human interactome in order to validate and determine edges and edge directions.
Mentions: The network modeling procedure is composed of two stages; (i) network construction using the Prize-collecting Steiner Forest (PCSF) approach, and (ii) network reconstruction using the ILP based edge inference approach. These two approaches complement each other as the PCSF approach reveals the hidden components in signaling by finding the high confidence regions in the interactome, and the ILP-based edge inference approach reconstructs interactions and their directionality by using temporal data as constraints. In Figure 1, the flowchart of our integrated approach is given.

Bottom Line: The resulting network conserves the information on temporality, direction of interactions, while revealing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways.Targets of the Salmonella effectors in the host cells such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were included in the reconstructed signaling network although they were not present in the initial phosphoproteomic data.We believe that integrated approaches, such as the one presented here, have a high potential for the identification of clinical targets in infectious diseases, especially in the Salmonella infections.

View Article: PubMed Central - PubMed

Affiliation: Department of Health Informatics, Graduate School of Informatics, Middle East Technical University Ankara, Turkey.

ABSTRACT
Salmonella enterica is a bacterial pathogen that usually infects its host through food sources. Translocation of the pathogen proteins into the host cells leads to changes in the signaling mechanism either by activating or inhibiting the host proteins. Given that the bacterial infection modifies the response network of the host, a more coherent view of the underlying biological processes and the signaling networks can be obtained by using a network modeling approach based on the reverse engineering principles. In this work, we have used a published temporal phosphoproteomic dataset of Salmonella-infected human cells and reconstructed the temporal signaling network of the human host by integrating the interactome and the phosphoproteomic dataset. We have combined two well-established network modeling frameworks, the Prize-collecting Steiner Forest (PCSF) approach and the Integer Linear Programming (ILP) based edge inference approach. The resulting network conserves the information on temporality, direction of interactions, while revealing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways. Targets of the Salmonella effectors in the host cells such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were included in the reconstructed signaling network although they were not present in the initial phosphoproteomic data. We believe that integrated approaches, such as the one presented here, have a high potential for the identification of clinical targets in infectious diseases, especially in the Salmonella infections.

No MeSH data available.


Related in: MedlinePlus