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Identification of crosstalk between phosphoprotein signaling pathways in RAW 264.7 macrophage cells.

Gupta S, Maurya MR, Subramaniam S - PLoS Comput. Biol. (2010)

Bottom Line: Signaling pathways mediate the effect of external stimuli on gene expression in cells.Our novel approach captures the temporal causality and directionality in intracellular signaling pathways.Further, case specific analysis of the phosphoproteins in the network has led us to propose hypothesis about inhibition (phosphorylation) of GSKalpha/beta via P38.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America.

ABSTRACT
Signaling pathways mediate the effect of external stimuli on gene expression in cells. The signaling proteins in these pathways interact with each other and their phosphorylation levels often serve as indicators for the activity of signaling pathways. Several signaling pathways have been identified in mammalian cells but the crosstalk between them is not well understood. Alliance for Cellular Signaling (AfCS) has measured time-course data in RAW 264.7 macrophage cells on important phosphoproteins, such as the mitogen-activated protein kinases (MAPKs) and signal transducer and activator of transcription (STATs), in single- and double-ligand stimulation experiments for 22 ligands. In the present work, we have used a data-driven approach to analyze the AfCS data to decipher the interactions and crosstalk between signaling pathways in stimulated macrophage cells. We have used dynamic mapping to develop a predictive model using a partial least squares approach. Significant interactions were selected through statistical hypothesis testing and were used to reconstruct the phosphoprotein signaling network. The proposed data-driven approach is able to identify most of the known signaling interactions such as protein kinase B (Akt) --> glycogen synthase kinase 3alpha/beta (GSKalpha/beta) etc., and predicts potential novel interactions such as P38 --> RSK and GSK --> ezrin/radixin/moesin. We have also shown that the model has good predictive power for extrapolation. Our novel approach captures the temporal causality and directionality in intracellular signaling pathways. Further, case specific analysis of the phosphoproteins in the network has led us to propose hypothesis about inhibition (phosphorylation) of GSKalpha/beta via P38.

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Related in: MedlinePlus

Heat-map of the correlation matrix between the input and the output variables.The rows and columns correspond to inputs and outputs, respectively. Negative values of the correlation are small in magnitude (e.g., AKT to P40) compared to positive values of the correlation. Hence, to enhance the visualization, asymmetric color-scale is used.
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pcbi-1000654-g001: Heat-map of the correlation matrix between the input and the output variables.The rows and columns correspond to inputs and outputs, respectively. Negative values of the correlation are small in magnitude (e.g., AKT to P40) compared to positive values of the correlation. Hence, to enhance the visualization, asymmetric color-scale is used.

Mentions: The potential relationships were inferred using the correlation between the input (predictor) variables and the output (response) variables. The correlation matrix between the input and output data is visualized using heat-map in Figure 1. The rows and the columns represent the inputs (PPs at tk−1) and outputs (PPs at tk), respectively. Please see Table 1 for the names of the PPs. High correlation was observed along the diagonal in Figure 1 with the indication that most of the phosphoproteins were highly self-regulated. The high self-correlation of PPs can be explained from the fact that most of the chosen PPs are from independent signaling pathways in this study and majorly activated by their upstream signaling molecule rather than by interaction/cross-talk between pathways. Thus in our modeling approach, we have also allowed this possibility via self activation of its phosphorylation. PPs in the same pathways showed high correlation. For example, ERK1/2 and RSK are the part of classical map kinase pathway and showed high correlation with each other. Variants of the same PP (i.e. GSKα/β and ST1A/B) and the member of the same family (EZR and MOE: part of ERM family) also show high correlation. The PPs belonging to independent pathway (e.g. P40 and ST1A/B) showed no correlation with most of other PPs. In this dynamic correlation matrix, high correlation was also observed from P38 to ERK1/2 and its downstream target RSK. We did not observe good correlation from ERK1/2 to P38, which suggested that there is a directed edge from P38 to ERK1/2 but not vice versa. The isoforms of protein kinase C (PKCD and PKCM) also did not show good correlation with each other indicating that they are regulated differently.


Identification of crosstalk between phosphoprotein signaling pathways in RAW 264.7 macrophage cells.

Gupta S, Maurya MR, Subramaniam S - PLoS Comput. Biol. (2010)

Heat-map of the correlation matrix between the input and the output variables.The rows and columns correspond to inputs and outputs, respectively. Negative values of the correlation are small in magnitude (e.g., AKT to P40) compared to positive values of the correlation. Hence, to enhance the visualization, asymmetric color-scale is used.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2813256&req=5

pcbi-1000654-g001: Heat-map of the correlation matrix between the input and the output variables.The rows and columns correspond to inputs and outputs, respectively. Negative values of the correlation are small in magnitude (e.g., AKT to P40) compared to positive values of the correlation. Hence, to enhance the visualization, asymmetric color-scale is used.
Mentions: The potential relationships were inferred using the correlation between the input (predictor) variables and the output (response) variables. The correlation matrix between the input and output data is visualized using heat-map in Figure 1. The rows and the columns represent the inputs (PPs at tk−1) and outputs (PPs at tk), respectively. Please see Table 1 for the names of the PPs. High correlation was observed along the diagonal in Figure 1 with the indication that most of the phosphoproteins were highly self-regulated. The high self-correlation of PPs can be explained from the fact that most of the chosen PPs are from independent signaling pathways in this study and majorly activated by their upstream signaling molecule rather than by interaction/cross-talk between pathways. Thus in our modeling approach, we have also allowed this possibility via self activation of its phosphorylation. PPs in the same pathways showed high correlation. For example, ERK1/2 and RSK are the part of classical map kinase pathway and showed high correlation with each other. Variants of the same PP (i.e. GSKα/β and ST1A/B) and the member of the same family (EZR and MOE: part of ERM family) also show high correlation. The PPs belonging to independent pathway (e.g. P40 and ST1A/B) showed no correlation with most of other PPs. In this dynamic correlation matrix, high correlation was also observed from P38 to ERK1/2 and its downstream target RSK. We did not observe good correlation from ERK1/2 to P38, which suggested that there is a directed edge from P38 to ERK1/2 but not vice versa. The isoforms of protein kinase C (PKCD and PKCM) also did not show good correlation with each other indicating that they are regulated differently.

Bottom Line: Signaling pathways mediate the effect of external stimuli on gene expression in cells.Our novel approach captures the temporal causality and directionality in intracellular signaling pathways.Further, case specific analysis of the phosphoproteins in the network has led us to propose hypothesis about inhibition (phosphorylation) of GSKalpha/beta via P38.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America.

ABSTRACT
Signaling pathways mediate the effect of external stimuli on gene expression in cells. The signaling proteins in these pathways interact with each other and their phosphorylation levels often serve as indicators for the activity of signaling pathways. Several signaling pathways have been identified in mammalian cells but the crosstalk between them is not well understood. Alliance for Cellular Signaling (AfCS) has measured time-course data in RAW 264.7 macrophage cells on important phosphoproteins, such as the mitogen-activated protein kinases (MAPKs) and signal transducer and activator of transcription (STATs), in single- and double-ligand stimulation experiments for 22 ligands. In the present work, we have used a data-driven approach to analyze the AfCS data to decipher the interactions and crosstalk between signaling pathways in stimulated macrophage cells. We have used dynamic mapping to develop a predictive model using a partial least squares approach. Significant interactions were selected through statistical hypothesis testing and were used to reconstruct the phosphoprotein signaling network. The proposed data-driven approach is able to identify most of the known signaling interactions such as protein kinase B (Akt) --> glycogen synthase kinase 3alpha/beta (GSKalpha/beta) etc., and predicts potential novel interactions such as P38 --> RSK and GSK --> ezrin/radixin/moesin. We have also shown that the model has good predictive power for extrapolation. Our novel approach captures the temporal causality and directionality in intracellular signaling pathways. Further, case specific analysis of the phosphoproteins in the network has led us to propose hypothesis about inhibition (phosphorylation) of GSKalpha/beta via P38.

Show MeSH
Related in: MedlinePlus