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      Quantitative Analysis of Protein Phosphorylations and Interactions by Multi-Colour IP-FCM as an Input for Kinetic Modelling of Signalling Networks

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          Abstract

          Background

          To understand complex biological signalling mechanisms, mathematical modelling of signal transduction pathways has been applied successfully in last few years. However, precise quantitative measurements of signal transduction events such as activation-dependent phosphorylation of proteins, remains one bottleneck to this success.

          Methodology/Principal Findings

          We use multi-colour immunoprecipitation measured by flow cytometry (IP-FCM) for studying signal transduction events to unrivalled precision. In this method, antibody-coupled latex beads capture the protein of interest from cellular lysates and are then stained with differently fluorescent-labelled antibodies to quantify the amount of the immunoprecipitated protein, of an interaction partner and of phosphorylation sites. The fluorescence signals are measured by FCM. Combining this procedure with beads containing defined amounts of a fluorophore allows retrieving absolute numbers of stained proteins, and not only relative values. Using IP-FCM we derived multidimensional data on the membrane-proximal T-cell antigen receptor (TCR-CD3) signalling network, including the recruitment of the kinase ZAP70 to the TCR-CD3 and subsequent ZAP70 activation by phosphorylation in the murine T-cell hybridoma and primary murine T cells. Counter-intuitively, these data showed that cell stimulation by pervanadate led to a transient decrease of the phospho-ZAP70/ZAP70 ratio at the TCR. A mechanistic mathematical model of the underlying processes demonstrated that an initial massive recruitment of non-phosphorylated ZAP70 was responsible for this behaviour. Further, the model predicted a temporal order of multisite phosphorylation of ZAP70 (with Y319 phosphorylation preceding phosphorylation at Y493) that we subsequently verified experimentally.

          Conclusions/Significance

          The quantitative data sets generated by IP-FCM are one order of magnitude more precise than Western blot data. This accuracy allowed us to gain unequalled insight into the dynamics of the TCR-CD3-ZAP70 signalling network.

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          Most cited references39

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          T cell receptor antagonist peptides induce positive selection.

          We have used organ culture of fetal thymic lobes from T cell receptor (TCR) transgenic beta 2M(-/-) mice to study the role of peptides in positive selection. The TCR used was from a CD8+ T cell specific for ovalbumin 257-264 in the context of Kb. Several peptides with the ability to induce positive selection were identified. These peptide-selected thymocytes have the same phenotype as mature CD8+ T cells and can respond to antigen. Those peptides with the ability to induce positive selection were all variants of the antigenic peptide and were identified as TCR antagonist peptides for this receptor. One peptide tested, E1, induced positive selection on the beta 2M(-/-) background but negative selection on the beta 2M(+/-) background. These results show that the process of positive selection is exquisitely peptide specific and sensitive to extremely low ligand density and support the notion that low efficacy ligands mediate positive selection.
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            Causal protein-signaling networks derived from multiparameter single-cell data.

            Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.
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              Signaling--2000 and beyond.

              T. Hunter (2000)
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                29 July 2011
                : 6
                : 7
                : e22928
                Affiliations
                [1 ]Max Planck Institute of Immunobiology and Epigenetics, and Faculty of Biology, Biology III, University of Freiburg, Freiburg, Germany
                [2 ]Spemann Graduate School of Biology and Medicine, Freiburg, Germany
                [3 ]Research Group Modeling of Biological Systems, German Cancer Research Center and BioQuant Center, Heidelberg, Germany
                [4 ]BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
                [5 ]Centre of Chronic Immunodeficiency (CCI), University Medical Center Freiburg, and University of Freiburg, Freiburg, Germany
                Memorial Sloan Kettering Cancer Center, United States of America
                Author notes

                Conceived and designed the experiments: SD WWAS. Performed the experiments: SD. Analyzed the data: SD AKS TH WWAS. Wrote the paper: SD AKS TH WWAS. Performed the mathematical modeling: AKS TH.

                Article
                PONE-D-11-04834
                10.1371/journal.pone.0022928
                3146539
                21829558
                c5bd6e82-339f-44be-8d8e-f57348748ed7
                Deswal et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 11 March 2011
                : 1 July 2011
                Page count
                Pages: 13
                Categories
                Research Article
                Biology
                Computational Biology
                Signaling Networks
                Systems Biology
                Molecular Cell Biology
                Cytometry
                Flow Cytometry
                Systems Biology

                Uncategorized
                Uncategorized

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