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Identification of crosstalk between phosphoprotein signaling pathways in RAW 264.7 macrophage cells
Shakti Gupta1, Mano Ram Maurya, Shankar Subramaniam
1Department 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.
Insights
This study analyzes cellular signaling pathways in macrophages using a data-driven model. It identifies known and predicts novel interactions, revealing temporal causality and directionality in cellular responses.
Area of Science:
- Cellular signaling and systems biology
- Mammalian cell signaling pathways
- Macrophage biology
Background:
- Cellular signaling pathways regulate gene expression in response to stimuli.
- Phosphorylation levels of signaling proteins indicate pathway activity.
- Crosstalk between mammalian cell signaling pathways is not fully understood.
Purpose of the Study:
- To analyze Alliance for Cellular Signaling (AfCS) data using a data-driven approach.
- To decipher interactions and crosstalk between signaling pathways in stimulated macrophage cells.
- To develop a predictive model for intracellular signaling.
Main Methods:
- Utilized a data-driven approach to analyze AfCS time-course phosphoprotein data.
- Employed dynamic mapping and partial least squares for predictive modeling.
- Applied statistical hypothesis testing to identify significant interactions and reconstruct signaling networks.
Main Results:
- Identified known signaling interactions, e.g., protein kinase B (Akt) to glycogen synthase kinase 3alpha/beta (GSKalpha/beta).
- Predicted novel interactions, including P38 to RSK and GSK to ezrin/radixin/moesin.
- Demonstrated good predictive power for model extrapolation and captured temporal causality.
Conclusions:
- The data-driven approach effectively models cellular signaling pathways.
- The study proposes novel hypotheses, such as P38-mediated inhibition of GSKalpha/beta.
- The developed model offers insights into the temporal dynamics and directionality of intracellular signaling.
